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WO2025096976A1 - Arid1a, marqueur pronostique pour la transformation agressive du lymphome et à vulnérabilité à l'inhibition pharmacologique de smarca4/2 - Google Patents

Arid1a, marqueur pronostique pour la transformation agressive du lymphome et à vulnérabilité à l'inhibition pharmacologique de smarca4/2 Download PDF

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WO2025096976A1
WO2025096976A1 PCT/US2024/054163 US2024054163W WO2025096976A1 WO 2025096976 A1 WO2025096976 A1 WO 2025096976A1 US 2024054163 W US2024054163 W US 2024054163W WO 2025096976 A1 WO2025096976 A1 WO 2025096976A1
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arid
loss
subject
cells
cell
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Darko BARISIC
Ari MELNICK
Christopher Chin
Cem MEYDAN
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Cornell University
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Cornell University
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    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6876—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
    • C12Q1/6883—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material
    • C12Q1/6886—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/106—Pharmacogenomics, i.e. genetic variability in individual responses to drugs and drug metabolism
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/156—Polymorphic or mutational markers

Definitions

  • Arid la is a prognostic marker for aggressive lymphoma transformation and presents a vulnerability to pharmacological inhibition of SMARCA4/2
  • the BAF chromatin remodeling complex (BRGl/BRM-associated factors) of the SWI/SNF (Switch/Sucrose Non-Fermentable) family of proteins govern the accessibility of DNA by positioning, ejecting, or reconfiguring nucleosomes, thereby regulating gene expression (Delmas et al., 1993; Kassabov et al., 2003; Kwon et al., 1994; Shen et al., 2000; Tamkun et al., 1992; Tsukiyama et al., 1995; Whitehouse et al., 1999).
  • the versatility of their function is derived from their composition, encompassing more than a dozen subunits that have several isoforms, with each isoform selectively integrated to create a variety of distinct complexes (He et al., 2020; Ho et al., 2009; Mashtalir et al., 2018, 2020; W. Wang et al., 1996).
  • This variation equips BAF complexes with an extraordinary ability to adapt and execute a wide range of biological functions (Raab et al., 2015).
  • even minor alterations in any subunit can significantly impact the complex function, and when dysregulated contribute to various types of diseases, including cancer (Centore et al., 2020; Kadoch et al., 2013).
  • BAF canonical BAF
  • PBAF polybromo-associated BAF
  • ncBAF non-canonical BAF
  • DLBCLs are a group of diverse and aggressive tumors that can be classified into several genetically-defined subtypes, each having a unique pattern of mutations (Chapuy et al., 2018; Schmitz et al., 2018; Wright et al., 2020).
  • DLBCLs in the EZB/C3 cluster are the most common and are specifically characterized by numerous somatic mutations in genes encoding chromatin regulatory proteins, such as KMTD2, CREBBP, and EZH2 (Mlynarczyk et al., 2019).
  • chromatin regulatory proteins such as KMTD2, CREBBP, and EZH2
  • the EZB/C3 cluster shows the highest enrichment for ARID1A mutations (Wright et al., 2020).
  • FLs typically begin as indolent tumors
  • patients are at a high risk for transformation into a more aggressive, and often untreatable type of DLBCL-like lymphoma. Our current understanding of the disease does not allow us to predict which patients will undergo this transformation, and the underlying mechanisms of transformation remain unclear.
  • FLs and EZB/C3 DLBCLs share similar genetic mutation patterns, particularly in genes responsible for chromatin regulation (Mlynarczyk et al., 2019). Both display GCB- like transcriptional characteristics and almost universally carry the t( 14; 18) translocation, resulting in abnormal BCL2 expression.
  • GCs germinal centers
  • GC B-cells experience extensive and rapid shifts in their chromatin and transcriptional programs as they cycle back and forth between proliferative bursting and T- cell directed selection and subsequent cell fate decisions (Mlynarczyk et al., 2019).
  • Proliferative bursting coincides with periods of immunoglobulin gene somatic hypermutation, which eventually leads to selection of B-cells encoding high affinity antibodies (Mesin et al., 2016).
  • this process also inadvertently introduces numerous off-target mutations, which can contribute to the development of malignancies (Mlynarczyk et al., 2019). These rapid and varied cellular transformations are dictated by constantly evolving chromatin patterns, likely overseen by specific transcription factors (TFs) (Mesin et al., 2016). Despite this understanding, our knowledge remains limited regarding the interplay between TFs and chromatin remodeling complexes in effecting the successive phases of this chromatin reorganization. We also lack insight into the exact roles of chromatin remodelers in instructing the diverse GC B-cell fates, and how they may contribute to the development of lymphomas.
  • TFs transcription factors
  • ARID 1 A a subunit of the canonical BAF nucleosome remodeling complex, is commonly mutated in lymphomas.
  • the present disclosure is based, at least in part, on the discovery that ARID 1 A orchestrates B-cell fate during the germinal center (GC) response, facilitating cooperative and sequential binding of PU.l and NF-kB at crucial genes for cytokine and CD40 signaling.
  • the absence of ARID 1 A tilts GC cell-fate towards immature IgM+CD80-PDL2- memory B-cells, known for their potential to re-enter new GCs.
  • AR1D1A haploin sufficiency hastens the progression of aggressive follicular lymphomas in mice.
  • follicular lymphoma patients with ARID JA-inactivating mutations preferentially display an immature memory B-cell-like state with increased transformation risk to aggressive disease.
  • the method comprises determining the presence or absence of a loss-of-function mutation of ARID 1 A in a biological sample from the subject.
  • the loss-of-function mutation of ARID 1 A results in a reduction or loss of ARID 1 A expression and/or activity.
  • the loss-of-function mutation of ARID 1 A is a homozygous mutation or a heterozygous mutation.
  • the loss-of-function mutation of ARID 1 A is a deletion, an insertion, or a substitution.
  • the loss-of-function mutation of ARID 1 A is a nonsense mutation or a missense mutation.
  • the loss-of-function mutation of ARID1A is Q474*.
  • the subject further comprises overexpression of translocation of BCL2.
  • the genomic DNA isolated from the biological sample is used for the determination of the presence of absence of the loss-of-function mutation in ARID 1 A.
  • the loss-of-function mutation of ARID 1 A is detected by a genotyping method.
  • the method comprises determining the level and/or activity of ARID 1 A in a biological sample from the subject and comparing it to a control.
  • control is a reference value.
  • control is a level and/or activity determined from a control sample.
  • control sample is a non-tumor sample obtained from the subject, a sample from a subject without FL, a sample from a subject without DLBCL, or a sample from a subject with FL but not at risk of transforming into DLBCL.
  • the level of ARID 1 A is detected by western blot or flow cytometry.
  • the western blot or flow cytometry uses a commercially available ARID 1 A antibody.
  • the biological sample is a tumor cell or tissue.
  • the method further comprises administering to the subject an inhibitor of SMARCA4 and/or SMARCA2 if the subject afflicted with FL is at risk for transforming into DLBCL.
  • the inhibitor of SMARCA4 and/or SMARCA2 is FHD-286 or AU-15330.
  • provided herein is a method of treating a subject afflicted with DLBLC and having a loss-of-function mutation of ARID 1 A, comprising administering to the subject a therapeutically effective amount of an inhibitor of SMARCA4 and/or SMARCA2.
  • provided herein is a method of preventing a subject afflicted with FL and having a loss-of-function mutation of ARID 1 A from transforming into DLBCL, comprising administering to the subject a therapeutically effective amount of an inhibitor of SMARCA4 and/or SMARCA2.
  • the loss-of-function mutation of ARID 1 A is a homozygous mutation or a heterozygous mutation.
  • the loss-of-function mutation of ARID 1 A is a deletion, an insertion, or a substitution.
  • the loss-of-function mutation of ARID 1 A is a nonsense mutation or a missense mutation.
  • the loss-of-function mutation of ARID1A is Q474*.
  • the subject further comprises overexpression of translocation of BCL2.
  • the inhibitor of SMARCA4 and/or SMARCA2 is FHD-286 or
  • the method further comprises identifying the subject afflicted with FL or DLBCL that has a loss-of-function mutation of ARID 1 A prior to the treatment.
  • the level and/or activity of ARID 1 A in a biological sample from the subject wherein the presence of a loss-of-function mutation of ARID 1 A in the biological sample, and/or a significantly decreased level and/or activity of ARID 1 A in the biological sample relative to the control identifies the FL or DLBCL as being more likely to respond to the inhibitor of SMARCA 4 and/or SMARCA2, and wherein the absence of a loss-of-function mutation of ARID 1 A in the biological sample, and/or a significantly increased level and/or activity of ARID 1 A in the biological sample relative to a control identifies the FL or DLBCL as being less likely to respond to the inhibitor of SMARCA 4 and/or SMARCA2.
  • the method comprises obtaining the biological sample from the subject for the determination step.
  • the method comprises determining the presence or absence of a loss-of-function mutation of ARID 1 A in a biological sample from the subject.
  • the loss-of-function mutation of ARID 1 A results in a reduction or loss of ARID 1 A expression and/or activity.
  • the loss-of-function mutation of ARID 1 A is a homozygous mutation or a heterozygous mutation.
  • the loss-of-function mutation of ARID 1 A is a deletion, an insertion, or a substitution.
  • the loss-of-function mutation of ARID 1 A is a nonsense mutation or a missense mutation.
  • the loss-of-function mutation of ARID1A is Q474*.
  • the subject further comprises overexpression of translocation of BCL2.
  • the genomic DNA isolated from the biological sample is used for the determination of the presence of absence of the loss-of-function mutation in ARID 1 A.
  • the loss-of-function mutation of ARID 1 A is detected by a genotyping method.
  • the method comprises determining the level and/or activity of ARID 1 A in a biological sample from the subject and comparing it to a control.
  • control is a reference value.
  • control is a level and/or activity determined from a control sample.
  • control sample is a non-tumor sample obtained from the subject, a sample from a subject without FL, a sample from a subject without DLBCL, a sample from a subject with FL but not at risk of transforming into DLBCL, or a sample from a subject with FL or DLBCL and a known outcome of the treatment of the inhibitor of SMARCA4 and/or SMARCA2.
  • the level of ARID 1 A is detected by western blot or flow cytometry.
  • the western blot or flow cytometry uses a commercially available ARID 1 A antibody.
  • the biological sample is a tumor cell or tissue.
  • the method further comprises administering to the subject the inhibitor of SMARCA4 and/or SMARCA2 if the subject afflicted with FL or DLBCL is likely to respond to the inhibitor of SMARCA4 and/or SMARCA2.
  • the inhibitor of SMARCA4 and/or SMARCA2 is FHD-286 or AU-15330.
  • the subject is a mammal.
  • the mammal is a mouse or a human.
  • the mammal is a human.
  • provided herein is a method of killing or inhibiting proliferation of a lymphoma cell having a loss-of-function mutation of ARID 1 A, comprising contacting the lymphoma cell with an inhibitor of SMARCA4 and/or SMARCA2.
  • the inhibitor of SMARCA4 and/or SMARCA2 is FHD-286 or AU-15330.
  • the method kills or inhibits proliferation of the lymphoma cell in vitro or in vivo.
  • the lymphoma cell is from a B-cell lymphoma.
  • the B-cell lymphoma is selected from FL, DLBCL, or Burkitt lymphoma.
  • FIG. 1A shows BAF complex mutations in FL and GCB-DLBCL ranked by frequency (Green et al., 2015; Ma et al., 2021; Schmitz et al., 2018; Wright et al., 2020).
  • FIG. IB shows mutational burden of AR1D1A in DLBCL patients. Majority of mutations are truncating mutations that deplete critical BAF interaction domain (BAF250_C- domain) (Ma et al., 2021).
  • FIG. 1C shows graphical representation of the germinal center dynamics and exit.
  • FIG. ID shows assay schematic, representative flow cytometry (FC) analysis and quantification of splenic B cells.
  • Graph represents pooled mice from three independent experiments, each with n>3 mice.
  • FIG. IE shows the same as FIG. ID for GC B cells.
  • FIG. 2A is a heatmap showing log2 fold change (log2FC) in mice heterozygous (cKO/WT) or Homozygous (cKO/cKO) for Aridla loss compared to WT/WT controls in Centroblasts (CB) or Centrocytes (CC). ATAC-seq peaks shown are union of WT/WT, cKO/WT and cKO/cKO for CB and CC, respectively. Peaks were ordered by log2 foldchange (log2FC), with individual peak log2FC summarized as a histogram on the right. llog2FCI>1.2 colored as orange or red for cKO/WT and cKO/cKO respectively.
  • FIG. 2B is a summary plot of all peaks in FIG. 2A showing pseudo-median reads shown as FPKM in CB and CC.
  • FIG. 2C shows the average log2FC of promoter (+/- 2kb TSS) and non-promoter peaks in cKO/WT or cKO/cKO mice compared to WT/WT in CB and CC.
  • Promoter peaks were compared with non-promoter peaks using a Wilcoxon rank sum test; all comparisons show p-values ⁇ l.Oe' 300 .
  • FIG. 2D is a schematic showing RIVA cell lines generated.
  • FIG. 2E is a heatmap showing log2FC in Q474*/WT or sh-induced (KD) cells compared to CRISPR-corrected WT/WT controls. ATAC-seq peaks shown are union of Q474*/WT, WT/WT and KD across all samples. Peaks were ordered based on log2FC, with individual peak log2FC summarized as a histogram on the right. llog2FCI>1.2 colored as orange or red for Q474*/WT and sh-KD respectively.
  • FIG. 2F is a summary plot of peaks in FIG. 2E showing Pseudo-median reads normalized by FPKM in Q474*/WT vs. corrected WT/WT.
  • FIG. 2G shows the average log2FC of promoter (+/- 2kb TSS) and non-promoter peaks in Q474*/WT cells compared to corrected WT/WT. Promoter peaks were compared to non-promoter peaks using Wilcoxon rank sum test.
  • FIG. 2H shows the electrophoresis of MNase digested nuclei DNA in Q474*/WT or WT/WT with either 3U or 5U enzyme.
  • FIG. 21 shows the normalized MNase Signal centered on AT AC peak summit.
  • FIG. 2J is a genome browser track showing Q474*/WT and WT/WT MNase tracks alongside AT AC peaks for different genotypes.
  • FIG. 2K is direct comparison of Q474*/WT vs. WT/WT nucleosome positions showing loci with significant fuzziness score (p.adj ⁇ 0.05).
  • Grey box indicates nucleosomes highly positioned in WT/WT that gain fuzziness in Q474*/WT.
  • FIG. 2L shows ranked loci based on Q474*/WT vs. WT/WT fuzziness log2FC.
  • FIG. 2M shows CUT&RUN for H3K27me3 in RIVA DLBCL cells.
  • Heatmaps show +/- lOkb of ATAC peak summits that are either significantly (q ⁇ 0.05, llog2FCI > 0) gaining (top) or losing (bottom) signal upon Aridla deletion. Summary plots below each heatmap show median reads normalized by FPKM.
  • FIG. 2N shows CUT&RUN for H3.3 in RIVA DLBCL cells.
  • Heatmaps show +/- lOkb of ATAC peak summits that are either significantly (q ⁇ 0.05, llog2FCI > 0) gaining (top) or losing (bottom). Summary plots below each heatmap show median reads normalized by FPKM.
  • FIG. 3A shows mouse Aridla WT/WT, cKO/WT and cKO/cKO ATAC-seq Manhattan Distance of sorted CB or CC cells based on peaks with a standard deviation (SD) > 0.5.
  • FIG. 3B shows PCA of mouse WT/WT, cKO/WT and cKO/cKO sorted CB or CC cells based on ATAC-seq peaks with SD > 0.5.
  • FIG. 3C shows ATAC-seq log2FC of peaks within lOkb of a gene body categorized by differential gene expression (q ⁇ 0.05, llog2FCI > 0) in cKO/WT vs. WT/WT CB (top) or CC (bottom). P-value for accessibility across groups calculated using ANOVA.
  • FIG. 3D shows the regulatory potential showing top transcriptional factors that have increased motif accessibility in WT/WT CC vs. CB ATAC-seq, colored by multivariate adjusted p-value (p-adj).
  • FIG. 3E shows the regulatory potential showing top transcriptional factors that have decreased motif accessibility in cKO/WT vs. WT/WT (multivariate with cell type regressed out), colored by multivariate p-adj.
  • FIG. 3F shows supervised k-means clustering of differentially accessible peaks between WT/WT, cKO/WT and cKO/cKO CC, normalized to WT/WT CB accessibility.
  • FIG. 3G shows peaks from each category shown in F were linked to the nearest gene within lOkb. PAGE analysis was then performed for enrichment of germinal center exit signaling pathways in these linked genes.
  • FIG. 3H shows Fisher’s exact test for presence of the indicated transcription factor motif enrichment in each differential accessibility cluster from FIG. 3E.
  • FIG. 31 shows TOBIAS footprint binding score analysis for PU.l (left) and NF-kB2 (right). P-values generated by TOBIAS BINDetect function.
  • FIG. 3J shows GSEA Normalized Enrichment Score (NES) of PU.l gene signatures in WT/WT vs. cKO/WT (left) or cKO/cKO (right) mouse CC cells.
  • NES GSEA Normalized Enrichment Score
  • FIG. 4A shows DLBCL cell line (RIVA/RI-1) Manhattan distance of ATAC-seq based on peaks with a SD > 0.5.
  • FIG. 4B shows ATAC-seq log2FC of peaks within lOkb to gene body grouped by differential gene expression (q ⁇ 0.01, llog2FCI > 0.58) between WT/WT and Q474*/WT direction.
  • FIG. 4C shows differentially (q ⁇ 0.01, llog2FCI > 0.58) opening or closing peaks were linked to the nearest gene body within lOkb. PAGE analysis was then performed for enrichment of germinal center exit signaling pathways in these linked genes (hypergeometric q ⁇ 0.05).
  • FIG. 4D shows regulatory potential of top transcription factor motifs closing in Q474*/WT vs. WT/WT (top) or KD vs. WT/WT (bottom), colored by multivariate p-adj.
  • FIG. 4E shows TOBIAS footprint binding score analysis of PU.l (top) or NF-kB2 (bottom) in Q474*/WT, WT/WT and KD cells. P-values generated by TOBIAS BINDetect function.
  • FIG. 4F shows overlap of ChlP-seq (PBRM1 and SMARCA4) and CUT&RUN (ARID1A and BRD9) peaks for BAF complex subunits in WT/WT RIVA/RI-1 cells.
  • FIG. 4G is a Venn diagram showing the overlap of ARID 1 A WT/WT CUT&RUN peaks and significantly (q ⁇ 0.05) opening and closing ATAC peaks in Q474*/WT vs.
  • FIG. 4H shows heatmaps showing log2FC for ARID1A, PRBM1 or IgG control at differentially closing AT AC peaks shown in order of decreasing ARID 1 A log2FC. Summary plots below each heatmap show mean FPKM of Q474*/WT and WT/WT binding signal.
  • FIG. 41 is a heatmap showing log2FC of CUT&RUN reads for PU.l at differentially closing AT AC peaks with SPI1 motif shown in the order of PU.l log2FC. Summary plot below show mean FPKM of Q474*/WT and WT/WT CUT&RUN signal.
  • FIG. 4J shows PAGE Analysis of genes closest to IgG or PU.l peaks for GC exit signatures.
  • FIG. 4K shows a genome browser track showing PU.l, ARID1A, H3K27me3 and H3.3 in WT/WT and Q474*/WT.
  • FIG. 4L shows unsupervised clustering of peaks based on WT/WT and Q474*/WT ATAC-seq, H3K27Ac and H3K27me3 signal.
  • WT/WT log2 FPKM of accessibility, H3K27Ac and H3K27me3 show in column one.
  • Difference in ATAC, H3K27Ac and H3K27me3 between Q474*/WT and WT/WT shown in in column two.
  • Difference in ATAC between KD and WT/WT show in column three. Delta indicates Q474*/WT vs. WT/WT unless stated otherwise.
  • FIG. 4M shows summaries of region size and peak number for each peak cluster.
  • FIG. 4N shows a change in RNA signal for genes closest to respective peaks in each cluster.
  • FIG. 40 shows a difference in log2FC between genotypes for the indicated BAF subunit and cluster.
  • FIG. 4P shows peak cluster annotation percentage for promoter, enhancer or superenhancer.
  • FIG. 4Q shows peak cluster percentage containing motifs from SPI1/SPIB, NF-kB or both.
  • FIG. 5 A is a Venn diagram showing the number of all ATAC peaks across all genotypes that have motifs for SPI1, NF-kB, or both within the ATAC dataset (top). GSEA enrichment scores for each of these categories (middle), based on ATAC log2FC ranking of Q474*/WT VS. WT/WT (bottom). All p-values ⁇ 0.001.
  • FIG. 5B shows enrichment of gene signatures based on hypergeometric analysis of genes within lOkb of peaks grouped by motif presence.
  • FIG. 5C shows NF-kB luciferase reporter assay in Raji cells WT/WT and Q474*/WT.
  • FIG. 5D shows NF-kB luciferase reporter assay in Raji cells with and without CRISPR targeted to the PU.l motif in the NF-kB reporter 75bp away. Luciferase was measured 48h post-CRISPR treatment.
  • FIG. 5E is a heatmap of ATAC-seq peaks binned by distance of TF motif (SPI1 and NF-kB) to the peak summit, colored by log2FC of AT AC signal. Only peaks that contain both motifs are shown.
  • FIG. 5F shows the log2FC of peaks in (F) divided into two bins: a motif present within 150bp of the AT AC summit, or a motif situated more than 150bp away from the summit. Wilcoxon rank sum was performed between different bins.
  • FIG. 5G is a proposed model of PU.l, cBAF and NF-kB chromatin remodeling.
  • FIG. 6A shows Uniform Manifold Approximation and Projection (UMAP) based on Multiome RNA and ATAC-seq with cell types labels transferred from previously published scRNA reference dataset based on RNA expression. Gene signatures used to confirm cell type assignment in Figure 16.
  • UMAP Uniform Manifold Approximation and Projection
  • FIG. 6B shows UMAP of cells split and colored by genotype.
  • FIG. 6C shows projection of indicated gene expression level Klf2, Cd38, Ccr6) onto the UMAP.
  • FIG. 6D shows cell type density along Slingshot pseudotime based on RNA expression for each cell type in the lineage, anchored at CB.
  • FIG. 6E shows difference in density between cKO/WT and WT/WT along pseudotime.
  • FIG. 6F shows expression of prememory genes (Laidlaw, GSE89897) across pseudotime. Grey area represents 99% confidence interval.
  • FIG. 6G shows difference in prememory gene expression across pseudotime between cKO/WT and WT/WT. Pseudotime values were broken into 10 deciles and Wilcoxon rank sum was performed between genotypes. Each significant decile is colored red with the minimum p ⁇ 1.82e' 45 .
  • FIG. 6H shows motif accessibility across pseudotime of the indicated transcription factor, with grey areas representing 95% confidence interval.
  • FIG. 61 shows difference in motif accessibility across pseudotime of the indicated transcription factor between genotypes (bottom). Pseudotime values were broken into 10 deciles and Wilcoxon rank sum was performed between cKO/WT and WT/WT. Each significant decile is colored red with minimum PU.l p ⁇ 3.83e' 4 , and minimum significant NF-KB p ⁇ 1.84e' 3 . Grey deciles are non-significant.
  • FIG. 6J shows TPM log2FC RNA-seq expression of Memory B cell genes for each replicate of RIVA/RI-1 cells Q474*/WT and WT/WT. Genes ranked on average log2FC. CXCR4 is not implied in MB regulation, it is shown to highlight similarity between mouse CB expansion phenotype with transcriptional regulation of human DLBCL cells upon ARID 1 A deletion.
  • FIG. 6K shows RT-PCR relative mRNA abundance of individual memory B cell genes normalized to GAPDH, each dot represents mean of three technical replicates, each bar represents a different CRISPR clone.
  • FIG. 6L shows TOBIAS motif footprinting score of KLF2 and BCL6 based on ATAC-seq from Q474*/WT, WT/WT and KD cells. P-values generated by TOBIAS BINDetect function.
  • FIG. 6M shows GSVA of Memory B cell signature in Q474*/WT or WT/WT cells.
  • FIG. 6N shows transcriptional regulation map of GC to MB fate.
  • FIG. 7A shows an experimental scheme and timeline for (FIG. 7C)-(FIG. 7E).
  • FIG. 7C shows relative GC B cell proportions compare to total naive B cells as defined in (FIG. 7B).
  • FIG. 7D shows relative MB cell proportions compare to total naive B cells as defined in (FIG. 7B).
  • FIG. 7E shows relative MB cell proportions compare to total GC B cells as defined in (FIG. 7B).
  • FIG. 7F shows experimental scheme and timeline for (FIG. 7G) and (FIG. 7H).
  • FIG. 7G shows time-course FC analysis and quantification of cell type composition of antigen-specific MB cells.
  • FIG. 7H shows day 50 FC analysis and quantification of cell type composition of antigen- specific MB cells.
  • FIG. 8D shows survival curves for CY-l-Cre;Aridla cKO/WT , CY-l-Cre;Aridla WT/WT VavP-Bcl2;CY-l-Cre;Aridla cKO/WT and VavP-Bcl2;CY-l-Cre;Aridla WT/WT mice.
  • FIG. 8E shows spleen size quantification at time of necropsy.
  • Graph shows spleen to body weight ratios for mice from the entire cohort.
  • FIG. 8F shows H&E of spleen sections from animals in (FIG. 8A) late time-point. Scale 100 pm (far left). Arrow indicates a mitotic figure in Bcl2;CY-l-Cre;Aridla cKO/WT animal.
  • FIG. 8G shows human ortholog GSVA scores of the mouse RNA signature comparing cKO/WT vs WT/WT for Aridla in patients from the NCI-DLBCL cohort. Patients stratified by ARID 1 A mutation status.
  • FIG. 8H shows dimensional reduction UMAP plots of CyTOF B cell data obtained from the combined analysis of 36 reactive lymph node (rLN) and 155 follicular lymphoma (FL) patient samples. Each dot in the plot represents a single cell, with cells colored according to their assigned cluster. The normal B cell populations in reactive lymph node (rLN) samples were manually identified and annotated based on the expression patterns of reference marker proteins as per (X. Wang et al., 2022). The contour lines on the plot represent the density of cells in the combined dataset.
  • FIG. 81 shows UMAP as in (I) but with cells colored by patients (only patients with ARID 1 A mutations shown).
  • FIG. 8J shows UMAP as in (I) but with cells colored by cluster (only memory-like B and D clusters are shown).
  • FIG. 8K shows number of cases with (ARID 1 A*) or without (WT) ARID1A mutations categorized and colored based on their cluster identity.
  • FIG. 8L shows Kaplan-Meier curve showing risk of DLBCL transformation for patients in clusters B or D.
  • FIG. 9B shows the same as in (FIG. 9A) except lOOnM FHD-286 was used.
  • FIG. 9C shows apoptosis analysis using Annexin V and 7-AAD for cells treated with lOnM FHD-286.
  • FIG. 9D shows DepMap Chronos score categorized by tissue and ranked by ARID1A dependency.
  • FIG. 9E shows the same as (FIG. 9A) but for cell lines with lymphoid tissue annotation, including RIVA/RI-1 and Raji cell lines used in this study.
  • FIG. 9F shows Q474*/WT and WT/WT (either DMSO or FHD-286 treated) ATAC- seq PCA based on peaks with a standard deviation (SD) > 0.5.
  • FIG. 9G shows the same as (FIG. 9G) but shown as Manhattan Distance.
  • FIG. 9H shows heatmaps showing log2FC of differentially closing ATAC-seq loci upon FHD-286 treatment (WT-FHD-286 and HET-FHD-286 vs. WT DMSO).
  • FIG. 91 shows psuedo-median FPKM ATAC-seq of ATAC-seq WT DMSO, WT- FHD-286 and HET-FHD-286.
  • FIG. 9J shows genome browser ATAC-seq track showing Q474*/WT and WT/WT both DMSO or FHD-286 treated, we well as non-treated PU.l CUT&RUN in Q474*/WT and WT/WT. IRF4 enhancer has been functionally verified previously (Wood et al., 2018).
  • FIG. 9K shows regulatory potential showing top transcriptional factors that show decreased motif accessibility in upon FHD-286 treatment compared to DMSO colored by multivariate p.adj. Top; WT/WT. Bottom: Q474*/WT.
  • FIG. 10 is a schematic summarizing the findings.
  • FIG. 11A show a heatmap showing log2FC of Nucleosome-free ATAC-seq Peaks (peaks ⁇ 120bp) in mice heterozygous (cKO/WT) or Homozygous (cKO/cKO) for Aridla loss compared to WT/WT controls in Centroblasts (CB) or Centrocytes (CC).
  • ATAC-seq peaks shown are union of WT/WT, cKO/WT and cKO/cKO for CB and CC, respectively. Peaks were ordered by log2FC of the Nucleosome-free ATAC-seq peaks.
  • FIG. 11B shows sanger-seq validation of RIVA/RI-1 CRISPR ARID1A Q747* mutation correction. Two separate clones are shown.
  • FIG. 11C shows genotyping of mouse GCB cells Aridla exon deletion post Cre- activation.
  • FIG. 11D shows genotyping of mouse tail Aridla exon deletion pre Cre-activation.
  • FIG. HE shows Representative FC analysis and quantification of GFP in sh- ARID1A (KD) cells of both KD-induced (left) and uninduced (right).
  • FIG. HF shows western blot of ARID1A protein Ctrl (uninduced) and KD-induced (sh-1 and sh-2).
  • FIG. 12A shows mouse TPM of Spil RNA in Aridla WT/WT, cKO/WT and cKO/cKO CB and CC.
  • FIG. 12 B shows mouse TPM of Nfkbl, Nfkb2, Rel and Rela RNA respectively in Aridla WT/WT, cKO/WT and cKO/cKO CB and CC.
  • FIG. 13A shows a full model with all 16 peak clusters. Clusters are ordered by hierarchical clustering.
  • FIG. 13B shows a fraction of PU.l CUT&RUN peaks with SPI1 or SPIB motifs for each replicate and the union of peaks.
  • FIG. 14A is an immunoblot showing Raji ARID 1 A CRISPR clone protein expression for ARID 1 A.
  • FIG. 14B shows a sanger-seq of example molecules in the CRISPR pool upon PU.l DNA motif CRISPR editing showing the mutations are constrained to the region with the PU.l motif.
  • FIG. 15A shows gene expression signatures used to identify and confirm populations present in the Multiome UMAP.
  • FIG. 15B shows an Emsa plot of pseudotime with Prememory cells removed (top) and difference in density plot of Aridla HET - WT (bottom). P-value calculated using Wilcoxon Rank Sum.
  • FIG. 16A shows an immunophenotyping assay schematic.
  • FIG. 16B shows representative FC analysis and quantification of MB cells in WT/WT (top) and cKO/WT mice.
  • FIG. 16C shows quantification of day 10 MB cells in relation to all B220+IgD- cells (top) and GC cells (bottom). P-value calculated using unpaired t-test.
  • FIG. 16D shows quantification of IgM+ MB cells. P-value calculated using unpaired t-test.
  • FIG. 16G shows assay schematic and representative FC analysis and quantification of MB cells upon NP-KLH immunization. Ratio of MB to GC cells at days 5, 11 and 50.
  • FIG. 16H shows bone marrow-derived long live plasm cells (LLPC) at day 50 post NP-KLH immunization.
  • FIG. 17 shows H&E of spleen sections from BCL2 and BCL2+HET animals at late time-point. Scale, 100 pm (left), 50 pm (center) or 20pm (right).
  • FIG. 18A shows representative FC analysis and quantification of apoptosis stage in lOnM FHD-286 treated WT/WT (left) and Q474*/WT (right) cells.
  • FIG. 18B shows representative FC analysis and quantification of GFP in sh-PU.l cells of both KD-induced (left) and uninduced (right).
  • FIG. 18C shows viability of PU.l-KD or non-targeting control cells after lOOnM FHD-286 treatment. 2- way ANOVA was used.
  • FIGs. 19A-19D show IC50 for both the FHD-286 inhibitor and AU-15330 degrader, showing vulnerability of ARID 1 A mutated lymphoma cells lines to both.
  • FIG. 20 shows in vivo data showing higher tumor growth inhibition of ARID 1 A mutated tumors compared to ARID 1 A WT tumors.
  • FIG. 21 shows in vivo data showing that animals with ARID 1 A mutation have a better prognosis after treatment with FHD-286 compared to ARID1A WT animals.
  • FIG. 22 shows drug treatment (FHD-286) of a different lymphoma cell line (Burkitt lymphoma), showing that the same vulnerability of ARID 1 A mutated tumor cells to treatment is observed in other tumor types.
  • FIGs. 23A-23C show BAF multi-subunit complex remodels nucleosomes (figures are adapted from Mashtalir et al. 2020 Cell).
  • FIGs. 24A-24B show BAF is highly mutated in cancer (figures are adapted from Kadoch et al. 2013 Nat Genetics; Wright et al. 2020 Cancer Cell).
  • FIG. 25 shows ARID1A loss increases H3K27me3 and decreases H3.3.
  • the present disclosure is based, at least in part, on a discovery of ARID 1 A mutation and/or loss as a novel biomarker for FL to DLBCL transformation and, in some aspects, provides a pharmacological way to target specifically ARID 1 A mutated cells.
  • FHD-286 (SMARCA4/2 inhibitor) used in this study is currently in clinical trials for AML (https://foghomtx.com/pipeline/). However, whether this is applicable to lymphomas is not known.
  • the results provided in the present disclosure indicate that this drug would target specifically ARIDlA-mutated cells in FL and DLBCL patients. More importantly, currently FL patients at diagnosis are not being treated because of the indolent nature of the disease. However, these patients are at high risk of transformation to DLBLC during their lifetime.
  • ARID 1 A can be a biomarker to determine if these FL patients should be treated to prevent the onset of transformation to DLBCL, something that is currently not done. It have also been demonstrated herein the same effect with another drug, AU-15330, a degrader of SMARCA4/2, further proving that the vulnerability of ARIDlA-mutated tumors is due to BAF complex loss.
  • the present discosure shows that: (1) ARIDlA-mutated patients are found within the FL group of patients with higher risk of transformation to DLBCL; (2) ARIDlA-deletion mice develop aggressive form of FL (DLBCL-like) and have shortened survival; (3) ARIDlA-loss leads to formation of immature memory B-cells that have the potential to undergo somatic hypermutation at a higher rate than WT cells; and (4) inhibition of SMARCA4/2 (BAF complex subunits) specifically kills ARIDlA-mutated but now WT cells.
  • an element means one element or more than one element.
  • administering is intended to include routes of administration which allow an agent (such as the compositions described herein) to perform its intended function.
  • routes of administration for treatment of a body which can be used include injection (subcutaneous, intravenous, parenterally, intraperitoneally, intrathecal, etc.), oral, inhalation, and transdermal routes.
  • the injection can be bolus injections or can be continuous infusion.
  • the agent can be coated with or disposed in a selected material to protect it from natural conditions which may detrimentally affect its ability to perform its intended function.
  • the agent may be administered alone, or in conjunction with a pharmaceutically acceptable carrier.
  • the agent also may be administered as a prodrug, which is converted to its active form in vivo.
  • the agent is orally administered.
  • the agent is administered through anal and/or colorectal route.
  • “About” and “approximately” shall generally mean an acceptable degree of error for the quantity measured given the nature or precision of the measurements. Typically, exemplary degrees of error are within 20%, preferably within 10%, and more preferably within 5% of a given value or range of values. Alternatively, and particularly in biological systems, the terms “about” and “approximately” may mean values that are within an order of magnitude, preferably within 5-fold and more preferably within 2-fold of a given value. Numerical quantities given herein are approximate unless stated otherwise, meaning that the term “about” or “approximately” can be inferred when not expressly stated.
  • the amount of a biomarker (e.g., one or more metabolites described herein) in a subject is “significantly” higher or lower than the normal amount of the biomarker, if the amount of the biomarker is greater or less, respectively, than the control level by an amount greater than the standard error of the assay employed to assess amount, and preferably at least 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 100%, 150%, 200%, 300%, 350%, 400%, 500%, 600%, 700%, 800%, 900%, 1000% or than that amount.
  • a biomarker e.g., one or more metabolites described herein
  • the amount of the biomarker in the subject can be considered “significantly” higher or lower than the control amount if the amount is at least about two, and preferably at least about three, four, or five times, higher or lower, respectively, than the control amount of the biomarker.
  • Such “significance” can also be applied to any other measured parameter described herein, such as for expression, inhibition, activity, and the like.
  • biomarker refers to a measurable parameter of the present disclosure that has been determined to be predictive of (1) a subject at risk for developing a specific condition (e.g., transforming into diffuse large B-cell lymphoma (DLBCL)), or (2) of the effects of an agent or therapy described herein, either alone or in combination with at least one other therapies, on a target disease or disorder (e.g., DLBLC or FL).
  • Biomarkers can include, without limitation, a loss-of-mutation of ARID1A, level and/or activity of ARID1A, and clinical characteristics of a subject, including those shown in the Tables, the Examples, the Figures, and otherwise described herein.
  • body fluid refers to fluids that are excreted or secreted from the body as well as fluids that are normally not (e.g. amniotic fluid, aqueous humor, bile, blood and blood plasma, cerebrospinal fluid, cerumen and earwax, cowper’s fluid or pre-ejaculatory fluid, chyle, chyme, stool, female ejaculate, interstitial fluid, intracellular fluid, lymph, menses, breast milk, mucus, pleural fluid, pus, saliva, sebum, semen, serum, sweat, synovial fluid, tears, urine, vaginal lubrication, vitreous humor, vomit).
  • any body fluid may be taken to detect and/or measure at least one biomarker described herein.
  • control refers to any reference standard suitable to provide a comparison to the biomarkers/products in the test sample.
  • the control comprises obtaining a “control sample” from which product or biomarker levels are detected and compared to the product or biomarker levels from the test sample.
  • a control sample may comprise any suitable sample, including but not limited to a sample from a control subject (can be stored sample or previous sample measurement) with a known outcome; cultured primary cells/tissues isolated from a control subject, a tissue or cell sample isolated from a control subject, or a primary cells/tissues obtained from a depository.
  • control may comprise a reference standard expression product or biomarker level from any suitable source, including but not limited to housekeeping genes, an expression product level range from normal tissue (or other previously analyzed control sample), a previously determined expression product level range within a test sample from a group of patients, or a set of patients with a certain outcome or receiving a certain treatment.
  • control samples and reference standard product or biomarker levels can be used in combination as controls in the methods of the present disclosure.
  • the specific product or biomarker level of each patient can be assigned to a percentile level of expression, or expressed as either higher or lower than the mean or average of the reference standard expression level.
  • the control may also comprise a measured value for example, average level of expression of a particular gene in a population compared to the level of expression of a housekeeping gene in the same population.
  • increased/decreased amount or “increased/decreased level” refers to increased or decreased absolute and/or relative amount and/or value of a biomarker (e.g., one or more metabolites described herein) in a subject, as compared to the amount and/or value of the same biomarker in the same subject in a prior time and/or in a normal and/or control subject, or a normal/control level representative of such subjects in general.
  • a biomarker e.g., one or more metabolites described herein
  • kits is any manufacture (e.g., a package or container) comprising at least one reagent, e.g. a probe or small molecule, for specifically detecting and/or affecting the expression of a marker of the present disclosure.
  • the kit may be promoted, distributed, or sold as a unit for performing the methods of the present disclosure.
  • the kit may comprise one or more reagents necessary to express a composition useful in the methods of the present disclosure.
  • the kit may further comprise a reference standard.
  • One skilled in the art can envision many such controls, including, but not limited to, common molecules.
  • Reagents in the kit may be provided in individual containers or as mixtures of two or more reagents in a single container.
  • instructional materials which describe the use of the compositions within the kit can be included.
  • an “over-expression” or “significantly higher level of expression” of a biomarker refers to an expression level in a test sample that is greater than the standard error of the assay employed to assess expression, and is preferably at least 10%, and more preferably 1.2, 1.3,
  • a “significantly lower level of expression” of a biomarker refers to an expression level in a test sample that is at least 10%, and more preferably 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 2.0, 2.1, 2.1, 2.2, 2.3, 2.4, 2.5, 2.6, 2.7, 2.8, 2.9, 3, 3.5, 4, 4.5, 5, 5.5, 6, 6.5, 7, 7.5, 8, 8.5, 9, 9.5, 10,
  • a “significantly higher level” or “significantly increased level” of a biomarker refers to an expression level, amount and/or activity level in a subject sample at one point in time that is greater than the standard error of the assay employed to assess the expression level, amount and/or activity level, and is preferably at least 10%, and more preferably 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 2.0, 2.1, 2.1, 2.2, 2.3, 2.4, 2.5, 2.6, 2.7, 2.8, 2.9, 3, 3.5, 4, 4.5, 5, 5.5, 6, 6.5, 7, 7.5, 8, 8.5, 9, 9.5, 10, 10.5, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 times or more higher than the expression level, amount
  • a “significantly lower level” or “significantly decreased level” of a biomarker refers to an expression level, amount and/or activity level in a subject sample at one point in time that is at least 10%, and more preferably 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 2.0, 2.1, 2.1, 2.2, 2.3, 2.4, 2.5, 2.6, 2.7, 2.8, 2.9, 3, 3.5, 4, 4.5, 5, 5.5, 6, 6.5, 7, 7.5, 8, 8.5, 9, 9.5, 10, 10.5, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 times or more lower than the expression level, amount or activity level of the biomarker in a subject sample at another point in time.
  • pre-determined biomarker amount and/or activity measurement(s) may be a biomarker amount and/or activity measurement(s) used to, by way of example only, evaluate a subject that may be selected for a particular treatment, evaluate a response to a treatment such as using a composition described herein, alone or in combination with other therapy.
  • a pre-determined biomarker amount and/or activity measurement(s) may be determined in populations of patients with or without a disease.
  • the pre-determined biomarker amount and/or activity measurement(s) can be a single number, equally applicable to every patient, or the pre-determined biomarker amount and/or activity measurement(s) can vary to reflect differences among specific subpopulations of patients.
  • Age, weight, height, and other factors of a subject may affect the pre-determined biomarker amount and/or activity measurement(s) of the individual.
  • the pre-determined biomarker amount and/or activity can be determined for each subject individually.
  • the amounts determined and/or compared in a method described herein are based on absolute measurements. In other embodiments, the amounts determined and/or compared in a method described herein are based on relative measurements, such as ratios (e.g., biomarker normalized to the expression of housekeeping or otherwise generally constant biomarker).
  • the pre-determined biomarker amount and/or activity measurement(s) can be any suitable standard.
  • the pre-determined biomarker amount and/or activity measurement(s) can be obtained from the same or a different subject for whom a subject selection is being assessed.
  • the pre-determined biomarker amount and/or activity measurement(s) can be obtained from a previous assessment of the same subject. In such a manner, the progress of the selection of the patient can be monitored over time.
  • the control can be obtained from an assessment of another subject or multiple subjects, e.g., selected groups of subjects.
  • the extent of the selection of the subject for whom selection is being assessed can be compared to suitable other subjects, e.g., other subjects who are in a similar situation to the human of interest, such as those suffering from similar or the same condition(s) and/or of the same ethnic group.
  • a therapeutic that “prevents” a condition refers to a composition that, when administered to a statistical sample prior to the onset of the disorder or condition, reduces the occurrence of the disorder or condition in the treated sample relative to an untreated control sample, or delays the onset or reduces the severity of one or more symptoms of the disorder or condition relative to the untreated control sample.
  • sample used for detecting or determining the presence or level of at least one biomarker is typically brain tissue, cerebrospinal fluid, whole blood, plasma, serum, saliva, urine, stool (e.g., feces), tears, and any other bodily fluid (e.g., as described above under the definition of “body fluids”), or a tissue sample (e.g., biopsy) such as a small intestine, colon sample, or surgical resection tissue.
  • the method of the present disclosure further comprises obtaining the sample from the individual prior to detecting or determining the presence or level of at least one biomarker in the sample.
  • subject refer to either a human or a non-human animal. This term includes mammals such as humans, primates, livestock animals (e.g., bovines, porcines), companion animals (e.g., canines, felines) and rodents (e.g., mice, rabbits and rats).
  • livestock animals e.g., bovines, porcines
  • companion animals e.g., canines, felines
  • rodents e.g., mice, rabbits and rats.
  • Treating” a disease in a subject or “treating” a subject having a disease refers to subjecting the subject to a pharmaceutical treatment, e.g., the administration of a drug, such that at least one symptom of the disease is decreased or prevented from worsening.
  • therapeutic effect refers to a local or systemic effect in animals, particularly mammals, and more particularly humans, caused by a pharmacologically active substance.
  • the term thus means any substance intended for use in the diagnosis, cure, mitigation, treatment or prevention of disease or in the enhancement of desirable physical or mental development and conditions in an animal or human.
  • therapeutically- effective amount means that amount of such a substance that produces some desired local or systemic effect at a reasonable benefit/risk ratio applicable to any treatment.
  • a therapeutically effective amount of a compound will depend on its therapeutic index, solubility, and the like.
  • certain compounds discovered by the methods of the present disclosure may be administered in a sufficient amount to produce a reasonable benefit/risk ratio applicable to such treatment.
  • the subject suitable for the compositions and methods disclosed herein is a mammal (e.g., mouse, rat, primate, non-human mammal, domestic animal, such as a dog, cat, cow, horse, and the like), and is preferably a human.
  • the subject is an animal model of FL or DLBCL.
  • the subject has not undergone treatment for FL or DLBCL. In still other embodiments, the subject has undergone treatment for FL or DLBCL.
  • the methods of the present disclosure can be used to treat FL or DLBCL such as those described herein, and/or determine the responsiveness to a composition described herein, alone or in combination with other therapies.
  • a variety of diagnostic, prognostic, and therapeutic methods are a variety of diagnostic, prognostic, and therapeutic methods.
  • all steps of the method can be performed by a single actor or, alternatively, by more than one actor.
  • diagnosis can be performed directly by the actor providing therapeutic treatment.
  • a person providing a therapeutic agent can request that a diagnostic assay be performed.
  • the diagnostician and/or the therapeutic interventionist can interpret the diagnostic assay results to determine a therapeutic strategy.
  • such alternative processes can apply to other assays, such as prognostic assays.
  • the present disclosure can pertain to the field of predictive medicine in which diagnostic assays, prognostic assays, and monitoring clinical trials are used for prognostic (predictive) purposes to thereby treat an individual prophy tactically. Accordingly, one aspect of the present disclosure relates to diagnostic assays for determining the amount and/or activity level of a biomarker described herein in the context of a biological sample (e.g., blood, serum, cells, stool, or tissue) to thereby determine whether an individual afflicted with follicular lymphoma (FL) is at risk for transforming into diffuse large B-cell lymphoma (DLBCL), or whether a FL or a DLBCL in a subject is likely to respond to an inhibitor of SMARCA 4 and/or SMARCA2.
  • a biological sample e.g., blood, serum, cells, stool, or tissue
  • Such assays can be used for prognostic or predictive purpose alone, or can be coupled with a therapeutic intervention to thereby prophylactically treat an individual prior to the onset or after recurrence of a disorder characterized by or associated with biomarker level or activity.
  • biomarkers described herein, such as those in the tables, figures, examples, and otherwise described in the specification.
  • the present disclosure provides, in part, methods, systems, and code for accurately classifying whether a biological sample (e.g., from a subject) or a subject afflicted with follicular lymphoma (FL) is at risk for transforming into diffuse large B-cell lymphoma (DLBCL).
  • a biological sample e.g., from a subject
  • FL follicular lymphoma
  • DLBCL diffuse large B-cell lymphoma
  • the present disclosure is useful for classifying a sample (e.g., from a subject afflicted with FL) or a subject afflicted with FL as at risk for transforming into DLBCL as disclosed herein using a statistical algorithm and/or empirical data (e.g., the amount or activity of a biomarker (e.g., ARID1A) described herein, such as in the tables, figures, examples, and otherwise described in the specification).
  • a biomarker e.g., ARID1A
  • An exemplary method for detecting the amount or activity of a biomarker described herein, and thus useful for classifying whether a sample or a subject afflicted with follicular lymphoma (FL) is at risk for transforming into diffuse large B-cell lymphoma (DLBCL) involves obtaining a biological sample from a test subject and contacting the biological sample with an agent, such as a protein-binding agent like an antibody or antigen-binding fragment thereof, or a nucleic acid-binding agent like an oligonucleotide, capable of detecting the amount or activity of the biomarker (e.g., ARID 1 A) in the biological sample.
  • an agent such as a protein-binding agent like an antibody or antigen-binding fragment thereof, or a nucleic acid-binding agent like an oligonucleotide, capable of detecting the amount or activity of the biomarker (e.g., ARID 1 A) in the biological sample.
  • At least one antibody or antigen-binding fragment thereof is used, wherein two, three, four, five, six, seven, eight, nine, ten, or more such antibodies or antibody fragments can be used in combination (e.g., in sandwich ELISAs) or in series.
  • the presence or absence of a loss-of-function mutation of the biomarker (e.g., ARID1A) in the biological sample is measured by standard methods used to detect a mutation, including but not limited to, sequencing.
  • the statistical algorithm is a single learning statistical classifier system.
  • a single learning statistical classifier system can be used to classify a sample as a based upon a prediction or probability value and the presence or level of the biomarker.
  • a single learning statistical classifier system typically classifies the sample as, for example, a likely therapy responder or progressor sample with a sensitivity, specificity, positive predictive value, negative predictive value, and/or overall accuracy of at least about 75%, 76%, 77%, 78%, 79%, 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99%.
  • learning statistical classifier systems include a machine learning algorithmic technique capable of adapting to complex data sets (e.g., panel of markers of interest) and making decisions based upon such data sets.
  • a single learning statistical classifier system such as a classification tree (e.g., random forest) is used.
  • a combination of 2, 3, 4, 5, 6, 7, 8, 9, 10, or more learning statistical classifier systems are used, preferably in tandem.
  • Examples of learning statistical classifier systems include, but are not limited to, those using inductive learning (e.g., decision/classification trees such as random forests, classification and regression trees (C&RT), boosted trees, etc.), Probably Approximately Correct (PAC) learning, connectionist learning (e.g., neural networks (NN), artificial neural networks (ANN), neuro fuzzy networks (NFN), network structures, perceptrons such as multi-layer perceptrons, multi-layer feed-forward networks, applications of neural networks, Bayesian learning in belief networks, etc.), reinforcement learning (e.g., passive learning in a known environment such as naive learning, adaptive dynamic learning, and temporal difference learning, passive learning in an unknown environment, active learning in an unknown environment, learning action- value functions, applications of reinforcement learning, etc.), and genetic algorithms and evolutionary programming.
  • inductive learning e.g., decision/classification trees such as random forests, classification and regression trees (C&RT), boosted trees, etc.
  • PAC Probably Approximately Correct
  • connectionist learning e.g., neural networks
  • the method of the present disclosure further comprises sending the sample classification results to a clinician, e.g., an oncologist.
  • a clinician e.g., an oncologist.
  • the diagnosis of a subject is followed by administering to the individual a therapeutically effective amount of a defined treatment based upon the diagnosis.
  • the methods further involve obtaining a control biological sample (e.g., biological sample from a subject who does not have FL or DLBCL, or from a subject who is afflicted with FL but is not at risk for transforming into DLBCL), a biological sample from the subject during remission, or a biological sample from the subject during treatment for developing DLBCL with FL progressing.
  • the diagnostic methods described herein can furthermore be utilized to identify subjects having FL or DLBCL or at risk of transforming into DLBCL that is likely or unlikely to be responsive to a composition as disclosed herein.
  • the assays described herein such as the preceding diagnostic assays or the following assays, can be utilized to identify a subject having or at risk of developing a disorder associated with a misregulation of the amount or activity of at least one biomarker (e.g., ARID1A) described herein.
  • the prognostic assays can be utilized to identify a subject having or at risk for developing a disorder associated with a misregulation of the at least one biomarker (e.g., ARID1A) described herein.
  • prognostic assays described herein can be used to determine whether a subject can be administered a composition as disclosed herein and/or an additional therapeutic regimen to treat a disease or disorder associated with the aberrant biomarker (e.g., ARID1A) expression or activity.
  • a disease or disorder associated with the aberrant biomarker e.g., ARID1A
  • an “isolated” or “purified” biomarker is substantially free of cellular material or other contaminating proteins from the cell or tissue source from which the protein is derived, or substantially free of chemical precursors or other chemicals when chemically synthesized.
  • the language “substantially free of cellular material” includes preparations of protein in which the protein is separated from cellular components of the cells from which it is isolated or recombinantly produced.
  • protein that is substantially free of cellular material includes preparations of protein having less than about 30%, 20%, 10%, or 5% (by dry weight) of heterologous protein (also referred to herein as a “contaminating protein”).
  • the protein or biologically active portion thereof is recombinantly produced, it is also preferably substantially free of culture medium, z.e., culture medium represents less than about 20%, 10%, or 5% of the volume of the protein preparation.
  • culture medium represents less than about 20%, 10%, or 5% of the volume of the protein preparation.
  • the protein is produced by chemical synthesis, it is preferably substantially free of chemical precursors or other chemicals, z.e., it is separated from chemical precursors or other chemicals which are involved in the synthesis of the protein. Accordingly, such preparations of the protein have
  • T1 less than about 30%, 20%, 10%, 5% (by dry weight) of chemical precursors or compounds other than the polypeptide of interest.
  • agents that specifically bind to a biomarker protein other than antibodies are used, such as peptides.
  • Peptides that specifically bind to a biomarker protein can be identified by any means known in the art. For example, specific peptide binders of a biomarker protein can be screened for using peptide phage display libraries.
  • biomarker amount and/or activity measurement(s) in a sample from a subject is compared to a predetermined control (standard) sample.
  • the control sample can be from the same subject or from a different subject.
  • the control sample is typically a normal, non-diseased sample.
  • the control sample can be from a diseased tissue.
  • the control sample can be a combination of samples from several different subjects.
  • the biomarker amount and/or activity measurement(s) from a subject is compared to a pre-determined level. This pre-determined level can be, for example, obtained from normal samples, or from control samples with known outcome.
  • a “pre-determined” biomarker amount and/or activity measurement(s) may be a biomarker amount and/or activity measurement(s) used to, by way of example only, evaluate a subject that may be selected for treatment, evaluate a response to a composition as disclosed herein, alone or in combination with one or more additional therapies.
  • a pre-determined biomarker amount and/or activity measurement(s) may be determined in populations of patients with or without FL or DLBCL, or with FL but not at risk of transforming into DLBCL.
  • the predetermined biomarker amount and/or activity measurement(s) can be a single number, equally applicable to every patient, or the pre-determined biomarker amount and/or activity measurement(s) can vary according to specific subpopulations of patients.
  • Age, weight, height, and other factors of a subject may affect the pre-determined biomarker amount and/or activity measurement(s) of the individual. Furthermore, the pre-determined biomarker amount and/or activity can be determined for each subject individually. In some embodiments, the amounts determined and/or compared in a method described herein are based on absolute measurements.
  • the amounts determined and/or compared in a method described herein are based on relative measurements, such as ratios (e.g., biomarker copy numbers, level, and/or activity before a treatment vs. after a treatment, such biomarker measurements relative to a spiked or man-made control, such biomarker measurements relative to the expression of a housekeeping gene, and the like).
  • the relative analysis can be based on the ratio of pre-treatment biomarker measurement as compared to post-treatment biomarker measurement.
  • Pre-treatment biomarker measurement can be made at any time prior to initiation of a treatment, e.g., a treatment with an inhibitor of SMARCA4 and/or SMARCA2.
  • Post- treatment biomarker measurement can be made at any time after initiation of the treatment.
  • post-treatment biomarker measurements are made 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 weeks or more after initiation of the treatment, and even longer toward indefinitely for continued monitoring.
  • Treatment can comprise, e.g., a therapeutic regimen comprising a composition as disclosed herein, or further in combination with other agents.
  • the pre-determined biomarker amount and/or activity measurement(s) can be any suitable standard.
  • the pre-determined biomarker amount and/or activity measurement(s) can be obtained from the same or a different human for whom a patient selection is being assessed.
  • the pre-determined biomarker amount and/or activity measurement(s) can be obtained from a previous assessment of the same patient. In such a manner, the progress of the selection of the patient can be monitored over time.
  • the control can be obtained from an assessment of another human or multiple humans, e.g., selected groups of humans, if the subject is a human.
  • the extent of the selection of the human for whom selection is being assessed can be compared to suitable other humans, e.g., other humans who are in a similar situation to the human of interest, such as those suffering from similar or the same condition(s) and/or of the same ethnic group.
  • the change of biomarker amount and/or activity measurement(s) from the pre-determined level is about 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5, or 5.0-fold or greater, or any range in between, inclusive.
  • cutoff values apply equally when the measurement is based on relative changes, such as based on the ratio of pre-treatment biomarker measurement as compared to post-treatment biomarker measurement.
  • Body fluids refer to fluids that are excreted or secreted from the body as well as fluids that are normally not (e.g., amniotic fluid, aqueous humor, bile, blood and blood plasma, cerebrospinal fluid, cerumen and earwax, cowper’s fluid or pre-ejaculatory fluid, chyle, chyme, stool, female ejaculate, interstitial fluid, intracellular fluid, lymph, menses, breast milk, mucus, pleural fluid, pus, saliva, sebum, semen, serum, sweat, synovial fluid, tears, urine, vaginal lubrication, vitreous humor, vomit).
  • the subject and/or control sample is selected from the group consisting of cells, cell lines, histological slides, paraffin embedded tissues, biopsies, whole blood, nipple aspirate, serum, plasma, buccal scrape, saliva, cerebrospinal fluid, urine, stool, and bone marrow.
  • the sample is serum, plasma, urine, or stool. In other embodiments, the sample is stool.
  • the samples can be collected from individuals repeatedly over a longitudinal period of time (e.g., once or more on the order of days, weeks, months, annually, biannually, etc.). Obtaining numerous samples from an individual over a period of time can be used to verify results from earlier detections and/or to identify an alteration in biological pattern as a result of, for example, disease progression, drug treatment, etc. For example, subject samples can be taken and monitored every month, every two months, or combinations of one, two, or three month intervals according to the present disclosure.
  • biomarker amount and/or activity measurements of the subject obtained over time can be conveniently compared with each other, as well as with those of controls during the monitoring period, thereby providing the subject’s own values, as an internal, or personal, control for long-term monitoring.
  • Sample preparation and separation can involve any of the procedures, depending on the type of sample collected and/or analysis of biomarker measurement(s).
  • Such procedures include, by way of example only, concentration, dilution, adjustment of pH, removal of high abundance polypeptides (e.g., albumin, gamma globulin, and transferrin, etc.), addition of preservatives and calibrants, addition of protease inhibitors, addition of denaturants, desalting of samples, concentration of sample proteins, extraction and purification of lipids.
  • the sample preparation can also isolate molecules that are bound in non-covalent complexes to other protein (e.g., carrier proteins).
  • carrier proteins e.g., albumin
  • This process may isolate those molecules bound to a specific carrier protein (e.g., albumin), or use a more general process, such as the release of bound molecules from all carrier proteins via protein denaturation, for example using an acid, followed by removal of the carrier proteins.
  • Removal of undesired proteins (e.g., high abundance, uninformative, or undetectable proteins) from a sample can be achieved using high affinity reagents, high molecular weight filters, ultracentrifugation and/or electrodialysis.
  • High affinity reagents include antibodies or other reagents (e.g., aptamers) that selectively bind to high abundance proteins.
  • Sample preparation could also include ion exchange chromatography, metal ion affinity chromatography, gel filtration, hydrophobic chromatography, chromatofocusing, adsorption chromatography, isoelectric focusing and related techniques.
  • Molecular weight filters include membranes that separate molecules on the basis of size and molecular weight. Such filters may further employ reverse osmosis, nanofiltration, ultrafiltration and microfiltration.
  • Ultracentrifugation is a method for removing undesired polypeptides from a sample. Ultracentrifugation is the centrifugation of a sample at about 15,000-60,000 rpm while monitoring with an optical system the sedimentation (or lack thereof) of particles. Electrodialysis is a procedure which uses an electro-membrane or semipermeable membrane in a process in which ions are transported through semi-permeable membranes from one solution to another under the influence of a potential gradient.
  • the membranes used in electrodialysis may have the ability to selectively transport ions having positive or negative charge, reject ions of the opposite charge, or to allow species to migrate through a semipermeable membrane based on size and charge, it renders electrodialysis useful for concentration, removal, or separation of electrolytes.
  • Electrophoresis is a method which can be used to separate ionic molecules under the influence of an electric field. Electrophoresis can be conducted in a gel, capillary, or in a microchannel on a chip. Examples of gels used for electrophoresis include starch, acrylamide, polyethylene oxides, agarose, or combinations thereof.
  • a gel can be modified by its cross-linking, addition of detergents, or denaturants, immobilization of enzymes or antibodies (affinity electrophoresis) or substrates (zymography) and incorporation of a pH gradient.
  • capillaries used for electrophoresis include capillaries that interface with an electrospray.
  • CE Capillary electrophoresis
  • CE technology can also be implemented on microfluidic chips.
  • CE can be further segmented into separation techniques such as capillary zone electrophoresis (CZE), capillary isoelectric focusing (CIEF), capillary isotachophoresis (cITP) and capillary electrochromatography (CEC).
  • CZE capillary zone electrophoresis
  • CIEF capillary isoelectric focusing
  • cITP capillary isotachophoresis
  • CEC capillary electrochromatography
  • CE techniques can be coupled to electrospray ionization through the use of volatile solutions, for example, aqueous mixtures containing a volatile acid and/or base and an organic such as an alcohol or acetonitrile.
  • Capillary isotachophoresis is a technique in which the analytes move through the capillary at a constant speed but are nevertheless separated by their respective mobilities.
  • Capillary zone electrophoresis also known as free-solution CE (FSCE)
  • FSCE free-solution CE
  • CIEF Capillary isoelectric focusing
  • CEC is a hybrid technique between traditional high performance liquid chromatography (HPLC) and CE.
  • Chromatography can be based on the differential adsorption and elution of certain analytes or partitioning of analytes between mobile and stationary phases.
  • Different examples of chromatography include, but not limited to, liquid chromatography (LC), gas chromatography (GC), high performance liquid chromatography (HPLC), etc.
  • provided herein are methods of treating a subject afflicted with DLBLC and having a loss-of-function mutation of ARID 1 A, comprising administering to the subject a therapeutically effective amount of an inhibitor of SMARCA4 and/or SMARCA2.
  • methods of preventing a subject afflicted with FL and having a loss-of-function mutation of ARID 1 A from transforming into DLBCL comprising administering to the subject a therapeutically effective amount of an inhibitor of SMARCA4 and/or SMARCA2.
  • a method of killing or inhibiting proliferation of a lymphoma cell having a loss-of-function mutation of ARID 1 A comprising contacting the lymphoma cell with an inhibitor of SMARCA4 and/or SMARCA2.
  • the inhibitor of SMARCA4 and/or SMARCA2 includes but is not limited to, for example, FHD-286 or AU-15330.
  • compositions The present disclosure provides pharmaceutically acceptable compositions of the agents disclosed herein.
  • the pharmaceutical compositions of the present disclosure may be specially formulated for administration in solid or liquid form, including those adapted for the following: (1) oral administration, for example, drenches (aqueous or non-aqueous solutions or suspensions), tablets, boluses, powders, granules, pastes; (2) parenteral administration, for example, by subcutaneous, intramuscular or intravenous injection as, for example, a sterile solution or suspension; (3) topical application, for example, as a cream, ointment or spray applied to the skin; (4) intravaginally or intrarectally, for example, as a pessary, cream or foam; or (5) aerosol, for example, as an aqueous aerosol, liposomal preparation or solid particles.
  • oral administration for example, drenches (aqueous or non-aqueous solutions or suspensions), tablets, boluses, powders, granules, pastes
  • phrases “pharmaceutically acceptable” is employed herein to refer to those agents, materials, compositions, and/or dosage forms which are, within the scope of sound medical judgment, suitable for use in contact with the tissues of human beings and animals without excessive toxicity, irritation, allergic response, or other problem or complication, commensurate with a reasonable benefit/risk ratio.
  • pharmaceutically-acceptable carrier means a pharmaceutically-acceptable material, composition or vehicle, such as a liquid or solid filler, diluent, excipient, solvent or encapsulating material, involved in carrying or transporting the subject chemical from one organ, or portion of the body, to another organ, or portion of the body.
  • a pharmaceutically-acceptable material such as a liquid or solid filler, diluent, excipient, solvent or encapsulating material, involved in carrying or transporting the subject chemical from one organ, or portion of the body, to another organ, or portion of the body.
  • Each carrier must be “acceptable” in the sense of being compatible with the other ingredients of the formulation and not injurious to the subject.
  • materials which can serve as pharmaceutically-acceptable carriers include: (1) sugars, such as lactose, glucose and sucrose; (2) starches, such as com starch and potato starch; (3) cellulose, and its derivatives, such as sodium carboxymethyl cellulose, ethyl cellulose and cellulose acetate; (4) powdered tragacanth; (5) malt; (6) gelatin; (7) talc; (8) excipients, such as cocoa butter and suppository waxes; (9) oils, such as peanut oil, cottonseed oil, safflower oil, sesame oil, olive oil, com oil and soybean oil; (10) glycols, such as propylene glycol; (11) polyols, such as glycerin, sorbitol, mannitol and polyethylene glycol; (12) esters, such as ethyl oleate and ethyl laurate; (13) agar; (14) buffering agents, such as magnesium hydroxide and aluminum, such
  • Formulations suitable for oral administration may be in the form of capsules, cachets, pills, tablets, lozenges (using a flavored basis, usually sucrose and acacia or tragacanth), powders, granules, or as a solution or a suspension in an aqueous or non-aqueous liquid, or as an oil-in-water or water-in-oil liquid emulsion, or as an elixir or syrup, or as pastilles (using an inert base, such as gelatin and glycerin, or sucrose and acacia) and/or as mouth washes and the like, each containing a predetermined amount of one or more bacterial strains as disclosed herein.
  • lozenges using a flavored basis, usually sucrose and acacia or tragacanth
  • kits for detecting and/or modulating biomarkers described herein may also include instructional materials disclosing or describing the use of the kit or an antibody of the disclosed invention in a method of the disclosed invention as provided herein.
  • a kit may also include additional components to facilitate the particular application for which the kit is designed.
  • a kit may additionally contain means of detecting the label (e.g., enzyme substrates for enzymatic labels, filter sets to detect fluorescent labels, appropriate secondary labels such as a sheep anti-mouse-HRP, etc.) and reagents necessary for controls (e.g., control biological samples or standards).
  • a kit may additionally include buffers and other reagents recognized for use in a method of the disclosed invention. Non-limiting examples include agents to reduce non-specific binding, such as a carrier protein or a detergent.
  • the Raji cells were cultured with Roswell Park Memorial Institute (RPMI) medium supplemented with 20% Fetal Bovine Serum (FBS), 2 mmol/E E-glutamine, and 10 mmol/E HEPES.
  • the RIVA (RL1) cell line was propagated in Iscove's Modified Dulbecco's medium that was supplemented with 20% FBS and 2 mmol/L L-glutamine.
  • DMEM Dulbecco's Modified Eagle Medium
  • Cell lines were grown under the presence of 1% penicillin G and streptomycin, maintained at 37 °C within a humidified environment comprising 5% carbon dioxide.
  • the RIVA cells were procured from Jose A. Martinez-Climent, (Universidad de Navarra, Pamplona, Spain). Routine examinations are performed within the laboratory to check for any potential mycoplasma contamination in the cell lines.
  • RNA concentration was determined using the Qubit RNA High Sensitivity Kit (Thermo Fisher Q32855) and Nanodrop (#FS-0057491) and RNA integrity was assessed using RNA 6000 Pico Kit (Agilent 5067-1513) on Agilent 2100 Bioanalyzer. Synthesis of cDNA was executed with consistent quantities of total RNA across all samples within each experiment, using the Verso cDNA Synthesis kit (Thermo Fisher AB1453B). Subsequent RT-qPCR was conducted on an equal volume of cDNA across all samples using a QuantStudio6 Flex Real-Time PCR System (#278860403), coupled with Fast SYBR Green Master Mix (Thermo Fisher 4385614).
  • the qPCR program has cycling stage; 95C for 30 sec, [95C for 3 sec, 60C for 30 sec] for 40 cycles and a melting curve stage; 95C for 3 sec, 60C for 30 sec, and 95C for 10 sec.
  • the qPCR signal for every gene was adjusted to align with those of Gapdh by employing the delta-Ct method. The results were then portrayed as fold expression in comparison to wild type, inclusive of the standard deviation calculated from 3-4 biological replicates.
  • HEK293T cells were plated and transfected with a suspension containing the packaging plasmids psPax2 (Addgene 12260) and psMD2.G (Addgene 12259), alongside with the NF-kB reporter plasmid (Xia et al., 2022). This was performed at a ratio of 4:3:1 in serum-free media.
  • the viral suspension was reconstituted with PBS incorporating 25 pmol/L HEPES, and subsequently introduced to RIVA cell lines for infection. A selection was conducted 1 day post-transfection, involving the addition of puromycin for a minimum period of 7 days.
  • lentiviruses were generated in 293T cells by cotransfection of short hairpin RNA (shRNA) sequences.
  • shRNAs were designed using the SplashRNA algorithm (Pelossof et al., 2017) and cloned into LT3GEPIR (Adgene: 111177) Tet-ON miR-E (miR-30 variant)-based RNAi vector.
  • knockdown was induced by the addition of doxycycline (1:1,000, Img/ml stock solution) for 24 hours and the GFP positive cells were sorted and transferred back to culture in media without doxycycline. In total four clones per cell line were kept based on their GFP positivity and frozen down to be used for further experiments.
  • the cells were electroporated with a ribonucleoprotein (RNP) complex, which contained the Alt-R recombinant S.p. HiFi Cas9 Nuclease (IDT 1081061), Alt-R CRISPR-Cas9 tracrRNA (IDT 1072534), and Alt-R CRISPR-Cas9 crRNA, the latter targeting the precise Q474* mutation region (GGACTTTACTGGTTGTAATA), as well as the WT DNA template (*A*C*AAGGCCCCAGCGGGTATGGTCAACAGGGCCAGACTCCATATTACAACCA GCAAAGTCCTCACCCTCAGCAGCAGCAGCCACCCTACTCCCAGCAA*C*A) (star sign indicates phosphorothioate nucleotides that were modified to ensure DNA stability).
  • RNP ribonucleoprotein
  • Electroporation was carried out in accordance with the manufacturer's protocol using the SF Cell Line 96-well Nucleofector Kit (V4SC-2096). Subsequently, single cells were seeded into 96-well plates by single-cell fluorescence-activated cell sorting. Clones were screened and verified using both Sanger sequencing of PCR amplicons and Western Blot. Aridla Q474* mutation was introduced in the Raji cell line by the same method, with the exception of using Alt-R CRISPR-Cas9 crRNA targeting WT allele (GGACTTTGCTGGTTGTAATA) and Q474* DNA template was used to introduce the mutation
  • Immunoblotting was conducted as follows: cells were lysed in RIPA buffer (20 mM HEPES KOH pH 7.5, 50 mM b-glycerophosphate, 1 mM EDTA pH 8.0, 1 mM EGTA pH 8.0, 0.5 mM Na3VO4, 100 mM KC1, 10% glycerol, and 1% Triton X-100, supplemented with complete protease inhibitor cocktail; Roche #05056489001) and the lysates were subjected to resolution through SDS-PAGE, then transferred to a PVDF membrane overnight. This membrane was subsequently blocked with 5% milk (Biorad 1706404) and probed using primary antibodies.
  • RIPA buffer 20 mM HEPES KOH pH 7.5, 50 mM b-glycerophosphate, 1 mM EDTA pH 8.0, 1 mM EGTA pH 8.0, 0.5 mM Na3VO4, 100 mM KC1, 10% glycerol, and 1%
  • the membrane was then incubated with a corresponding peroxidase- conjugated secondary antibody (1:10,000 dilution Cell Signaling Technology, 7074 & 7076), and detected with chemiluminescence (WBKLS0500, Millipore & VWR #PI34095, Thermo Scientific) on a ChemiDoc Touch imaging system, ChemiDoc MP Imaging System (BioRad). Densitometry values were subsequently gathered using Fiji software and the Image Lab software v6.1.0 (Bio-Rad).
  • Antibodies used Aridla:12354S (CST) 1:1,000 or Aridla:HPA005456 (Sigma Aldrich) 1:1,000, Tubulin: T0198-25UL (Sigma) 1:5,000, NF- kB2: 37359S (CST) 1:1,000, PU.l 2266S (CST), 1:100.
  • ATAC-seq was prepared from FACS-sorted cell suspensions as per the Omni- AT AC protocol (Ref).
  • 50,000 freshly sorted live cells were rinsed in cold PBS and lysed in 50 pL of cold lysis buffer (comprising 10 mM Tris-HCl at pH 7.5, 10 mM NaCl, 3 mM MgC12, 0.1% NP-40, 0.1% Tween-20, and 0.01% Digitonin) for 3 minutes on ice.
  • cold lysis buffer comprising 10 mM Tris-HCl at pH 7.5, 10 mM NaCl, 3 mM MgC12, and 0.1% Digitonin
  • 1 mF wash buffer comprising 10 mM Tris-HCl at pH 7.5, 10 mM NaCl, 3 mM MgC12, and 0.1% Tween-20
  • the nuclei were then resuspended in a transposition reaction mix that was prepared using the Illumina Tagment DNA TDE1 Enzyme and Buffer Kits (Illumina 20034197) and incubated at 37 °C for 30 minutes on a thermomixer set at 1,000 rpm.
  • Tagged DNA fragments were purified using the Clean & Concentrator-5 Kit (Zymo D4014) or AMPure XP beads (Beckman Coulter A63881) with a 2:1 bead-to-DNA ratio, and then subjected to PCR amplification to generate sequencing libraries.
  • PCR amplicons were purified using AMPure XP beads at a 2:1 bead-to- DNA ratio.
  • the size distribution and quality of the library were evaluated using the High Sensitivity DNA Kit (Agilent 5067-4626) and ran on the Agilent 2100 Bioanalyzer.
  • the libraries were sequenced on Illumina HiSeq X and NovaSeq 6000 platforms using the HiSeq X Ten Reagent Kit (FC-501-2501) and PE50 SP Kit (Illumina 20028401), respectively.
  • the cells were permeabilized via Triton-X (0.1%) in Wash Buffer (20 mM HEPES, pH 7.5, 150 mM NaCl, 0.5 mM Spermidine (#S25O1-1G), lx Roche Complete Protease Inhibitor-mini (CPI-mini) EDTA-free (0505648900). The samples were then transferred to individual eppendorf tubes and chilled.
  • wash Buffer and Concanavalin A beads (#NC 1526856) that were activated in Bead Activation Buffer (20 mM HEPES, pH 7.9 10 mM KC1, 1 mM CaC12,l mM MnC12), and this cell-bead mixture was chilled for 10 minutes.
  • Bead Activation Buffer (20 mM HEPES, pH 7.9 10 mM KC1, 1 mM CaC12,l mM MnC12
  • the beads were then incubated in the Antibody Buffer (Digitonin Buffer + 2 mM EDT) by adding the respective antibody in each tube (antibody added in 1:100 ratio), chilled, resuspended, and left to incubate overnight on a nutator. The next day, the supernatant was removed, and the beads were washed with Digitonin Buffer twice (Wash Buffer + 0.01% Digitonin 300410-250MG). Then, a pAG-MNase (SKU 15-1116) solution was introduced and samples were left at room temperature for 10 minutes.
  • the Antibody Buffer Digitonin Buffer + 2 mM EDT
  • the beads were washed again twice with Digitonin Buffer, then treated withlOOmM CaC12 (E506-100ML) in the same buffer. Following a 2-hour incubation at 4°C, a Stop Buffer (340 mM NaCl, 20 mM EDTA, 4 mM EGTA, 50 pg/ml RNase A, 50 pg/ml Glycogen) was added and samples were left at 37°C for 10 minutes. After placing the 8-strip tubes on the magnetic stand, the supernatant was transferred to fresh tubes, and crosslinks were reversed by the addition of 0.09% SDS (V6551) and Proteinase K (Life Technologies 25530049).
  • ChlP-seq was carried out as previously described (Weber et al., 2007) with slight modifications. Changes to the protocol were following; (1) crosslinking was performed at 37C for lOmin., (2) chromatin was sonicated for -30 cycles of 30 sec. using a Diagenode Bioruptor, with 45 sec. breaks in between cycles, (3) protein A/G magnetic Dynabeads Magnetic beads (Thermo Fisher Scientific) were used for both pre-clearing and IP, (4) DNA was purified using on-column purification instead of chloroform/phenol extraction (Qiagen PCR Purification Kit). Immunoprecipitated DNA and input DNA were submitted to library preparation (NEBNext Ultra II DNA Library Prep Kit, Illumina). Antibodies used; PBRM1 (A301-591A), IgG (ABIN101961), SMARCA4 (A300-813A), PU.l (2266S), H3K27ac (39133).
  • NEBNext Ultra II DNA Library Prep Kit for Illumina New England Biolabs #E7645L
  • the fragmented DNA was subjected to end repair reaction followed by adaptor ligation. Concerning the latter step the NEBNext Adaptor for Illumina was diluted using 1:25 ratio with nuclease-free water. Following the adaptor ligation reaction, 1.1X of AMpure beads (Thermo Fischer Scientific #NC9933872) was added to the ligated samples to be purified. Finally, DNA was eluted in 0.1X TE.
  • NEBNext Ultra II Q5 was used for PCR together with single (New England Biolabs #E6609S) or dual-indexes (New England Biolabs #E6440S).
  • CUT&RUN PCR reaction was run according to the Epicypher CUT & RUN-specific PCR cycling parameters; 45 sec at 98C, [15 sec at 98C & 10 sec at 60C] 14x, 1 min at 72C.
  • To purify PCR reaction products 1.1X Ampure beads was used.
  • Bone marrow cells were collected from the femur and tibia of donor mice aged between 8 and 12 weeks, followed by treatment with a red blood cell lysis solution (Qiagen 158904).
  • a red blood cell lysis solution Qiagen 158904
  • Lethally irradiated C57BL/6J host mice who received two doses of 450 RAD via the Rad Source Technologies RS 2000 Biological Research X-ray Irradiator, were then injected with one million of these cells through the retro-orbital sinus. These transplanted mice were incorporated into experiments 8 to 14 weeks post-transplantation. Excluding the mice euthanized at predetermined intervals, all rodents participating in lymphomagenesis research were observed until they exhibited one or more conditions warranting euthanasia. These conditions included significant lethargy and body weight loss as well as discernible splenomegaly. This procedure aligns with the guidelines of our animal protocol approved by the Weill Cornell Medicine Institutional Animal Care and Use Committee (protocol #
  • mice were immunized by intraperitoneal (i.p.) injection of 500 pL of 2% sheep red blood cells (SRBC) resuspended in sterile IX DPBS from a solution of sheep blood in Alsever's (Cocalico Biologicals 20- 1334A), or by i.p. injection of 80 pg NP(30-32)-KLH (Biosearch Technologies N-5060) absorbed in alum adjuvant (ThermoFisher 77161) at a 1:1 ratio of alum:immunogen prior to injection.
  • SRBC sheep red blood cells
  • Indirect immunohistochemistry was then performed using biotinylated peanut agglutinin (PNA, Vector Laboratories B1075) or anti-species-specific biotinylated secondary antibodies, followed by avidin-horseradish peroxidase and development with DAB substrate (Vector Laboratories). Sections were counterstained with hematoxylin. The following primary antibodies were used: anti-B220 (BD 550286) and anti-Ki67 (Cell Signaling Technology 12202).
  • CD80-BV421 clone 16-10A1, BioLegend 104725, dilution 1:300
  • CD86-BV605 clone GL-1, BioLegend 105037, dilution 1:200
  • NP-PE N-5070-1, diluted to 5pg/mL
  • the Dual-Luciferase Reporter 1000 Assay System (Promega #E1980) was used to determine Luciferase activity in the RIVA and Raji cell lines. Specifically, 125,000 cells from each sample were gathered and spun down at 300xg for 5 minutes. Each of these cell pellets was then reconstituted in 200ul of media that contained either DMSO (Sigma Aldrich D8418-500ML) or PMA (Santa cruz Biotechnology SC-3576A) and ionomycin (Santa cruz Biotechnology I0634-1MG), serving to stimulate the cells. The resuspended samples were then transferred to a 96- well U-bottom plate and incubated at 37 °C for an hour.
  • DMSO Sigma Aldrich D8418-500ML
  • PMA Santa cruz Biotechnology SC-3576A
  • ionomycin Santa cruz Biotechnology I0634-1MG
  • the plate was centrifuged again at 300xg for 5 minutes. The supernatant was carefully discarded, and the cell pellets were rinsed with 200ul of IX DPBS. This centrifugation process was then repeated, after which the cells were lysed using 50ul of PLB lysis Buffer (Promega E194A). The plate was then subjected to shaking for 15 minutes at RT. Once the incubation was complete, a 20ul aliquot from each sample was transferred to a white flat-bottom 96-well plate. This plate was then loaded into the Biotek plate reader to determine the background luminescence without the addition of any substrate.
  • 5.5x106 cells were resuspended in ImL of buffer 1 (0.3M Sucrose, 15mM Tris pH 7.5, 60mM KC1, 15mM NaCl, 5mM MgC12, 2mM EDTA, 0.5mM DTT, lx PIC, 0.2mM spermine, ImM spermidine) with 0.02% NP-40 and incubated on ice for 5 minutes. Samples were then spun down at 300xg for 5 min at 4C.
  • buffer 1 0.3M Sucrose, 15mM Tris pH 7.5, 60mM KC1, 15mM NaCl, 5mM MgC12, 2mM EDTA, 0.5mM DTT, lx PIC, 0.2mM spermine, ImM spermidine
  • the pellets were resuspended in ImL of buffer 2 (0.3M Sucrose, 15mM Tris pH 7.5, 60mM KC1, 15mM NaCl, 5mM MgC12, 0.5mM DTT, lx PIC, 0.2mM spermine, ImM spermidine) and spun down at 300xg for 5 min at 4C. Nuclei integrity was checked before continuing with protocol and only samples that had nuclei recovery >85% were processed.
  • buffer 2 0.3M Sucrose, 15mM Tris pH 7.5, 60mM KC1, 15mM NaCl, 5mM MgC12, 0.5mM DTT, lx PIC, 0.2mM spermine, ImM spermidine
  • Pellets were resuspended in 400ul MNase buffer (0.3M Sucrose, 50mM Tris ph 7.5, 4mM MgC12, ImM CaC12, lx PIC) and 3-5U of MNase S7 micrococcal nuclease (Roche) was added. Samples were incubated at 37C for 30 minutes. EDTA (5mM) was added to stop the reaction. SDS (1% final concentration) and Proteinase K (200ug/mL) were added and samples were incubated for 1 hour at 55C with shaking. DNA was purified using the Qiagen PCR Purification Kit and digestion efficiency was assessed using an Agilent Bioanalyzer. The mononucleasomal fraction was purified using Ampure XP beads (1.1X) and libraries were prepared using NEBNext Ultra II Library Preparation Kit, using 8 PCR cycles and lOOng of starting DNA.
  • MNase buffer 0.3M Sucrose, 50mM Tris ph 7.5, 4mM
  • RNA sequencing Single-cell RNA sequencing (scRNA-seq) and single-cell ATAC sequencing (scATAC-seq) libraries were prepared employing the 10X Genomics Single Cell Multiome assay kit (10X Genomics; 1000230, 1000283, 1000494, 1000215, 1000212) strictly following the manufacturer's guidelines. Briefly, GC cells from splenocytes were sorted using antibody cocktails described above (Flow Cytometry), rinsed with 0.04% BSA PBS, and 100,000 cells underwent the manufacturer's nuclei preparation procedure. Then, about 20,000 nuclei were placed into a lOx Chromium X instrument. The remainder of the library preparation steps were conducted in accordance with the manufacturer's protocol. Libraries were evaluated using an Agilent Bioanalyzer, quantified utilizing a KAPA Library Quantification Kit, and sequenced on an Illumina Novaseq platform.
  • 10X Genomics Single Cell Multiome assay kit 10X Genomics; 1000230, 1000283, 1000494, 1000215, 1000212
  • TSCA TruSeq Custom Amplicon assay
  • Raw reads were checked by FastQC (Andrews, 2010). Sequence adapters were trimmed by Trim Galore! and the resulting reads were aligned to the mm 10 or hg38 genome reference with BWA (Krueger, 2016; H. Li & Durbin, 2009). Duplicate reads were marked by Picard (Broad Institute, 2016). After the alignment, mitochondrial reads, duplicate reads, reads in blacklisted regions, secondary alignments and multimapping reads, reads with insert size greater than 2kb or wrong orientation of the pairs, or orphan reads were filtered. Peaks were called using MACS2 narrow peak calling with shift of -75 bp and extension of 150 bp (Gaspar, 2018).
  • ATAC-seq quality was performed by checking the fragment length distribution, FRIP scores, TSS enrichment and high quality autosomal reads using Picard, ataqv and MultiQC (Ewels et al., 2016; Orchard et al., 2020). Consensus peaks were called by taking the overlap of regions covered in at least 2 of the samples. Reads within peaks were quantified using featureCounts (Liao et al., 2014). Variance stabilizing transform (vst) was used for normalizing the data for visualizations and unsupervised clustering (Love et al., 2014).
  • the differential peak accessibility was called by negative binomial model using DESeq2 using multivariate models and filtering for an absolute log2-fold-change value of log2(1.5) (0.58) and FDR adjusted p-value of 0.01 (Love et al., 2014).
  • the peaks were annotated and collapsed to genes by taking the maximally changing peak with respect to p- value within 5kb of each transcription start site and assigning that peak to each gene by their promoter, or alternatively putative proximal enhancers in their regulatory regions defined by GREAT (McLean et al., 2010).
  • the genes were ranked by the log2-fold-change value or the Wald statistic and fed into GSEA using MSigDB database and the curated gene signatures from the RNA-seq experiments as well as literature. An adjusted p-value of 0.05 was used to filter the resulting gene sets (Liberzon et al., 2011; Subramanian et al., 2005).
  • Regulatory potential of transcription factors was calculated by comparing all the mammalian motifs in JASPAR within the sequences of accessible peaks (Castro-Mondragon et al., 2022; Doane et al., 2021).
  • the log2 fold-changes of peak accessibility were modeled against the presence of such motifs in a multivariate manner while correcting for GC content, i.e. log2FC ⁇ pO + pi* xl + P2*%2+ ... + piV*GC , where xi denotes a Boolean variable for the presence of a TF motif in a peak as a binary variable and GC is the GC-content of the peak.
  • RNA-seq The correlation between RNA-seq and ATAC-seq was performed by taking all the ATAC-seq peaks within lOkb of the transcription start site of every gene and binning the genes into differentially opening or closing and non-differential categories, and plotting the log2-fold-change of expression differences.
  • Sequencing QC and adapter trimming were performed by FastQC and Trimmomatic (Andrews, 2010; Bolger et al., 2014). Trimmed sequences were aligned to the hg38 genome for RIVA samples by Bowtie2, and filtered for mapping reads with proper pairs and duplicate marked using samtools (Langmead & Salzberg, 2013; H. Li et al., 2009). All read fragments were utilized during the analysis of histone marks and BAF complex proteins. For all the other transcription factors the sequences were filtered to fragments with insert sizes less than 120 base pairs. Resulting alignments were converted into track files by RPKM normalization using deepTools (Ramirez et al., 2016).
  • Peaks were called using seacr with stringent method and 1% threshold (Meers et al., 2019). Consensus peaks between different replicates and conditions were merged by collapsing all peaks by any amount of overlap, then filtering to merged peaks that have at least 3 replicates as called peaks. Reads within the peaks were quantified using featureCounts and normalized using variance-stabilizing transform in DESeq2 for visualization purposes, such as heatmaps, unsupervised clustering, t-SNE and PCA(Liao et al., 2014; Love et al., 2014; Van Der Maaten & Hinton, 2008). Differential peak binding was calculated with a negative binomial model and TMM normalization using DESeq2.
  • Raw read QC and adapter trimming were performed by FastQC and TrimGalore!, and reads were aligned to the hg38 genome reference using BWA (Andrews, 2010; Krueger, 2016; H. Li & Durbin, 2009). Duplicate reads were marked by Picard (Broad Institute, 2016). After the alignment, duplicate reads, reads in blacklisted regions, secondary alignments and multimapping reads, reads with insert size greater than 2kb or wrong orientation of the pairs, or orphan reads were filtered. Peaks were called for each IP against the control samples using MACS2 (Gaspar, 2018; Liu, 2014).
  • MNase-seq data was processed and filtered in a similar manner to ChlPseq, utilizing FastQC, Trim Galore!, BWA alignment, Picard and samtools filtering (Andrews, 2010; Broad Institute, 2016; Krueger, 2016; H. Li et al., 2009; H. Li & Durbin, 2009).
  • DANPOS3 was used for nucleosome occupancy prediction, nucleosome fuzziness, and differential nucleosome binding (Chen et al., 2013, 2015). Smoothed predicted nucleosome occupancy tracks were quantile normalized for each condition.
  • the workflow for segmenting chromatin into regions with distinct epigenomic marks comprised four main steps: 1- Defining regions via peak clustering, 2- Quantifying these regions, 3- Clustering these regions, and 4- Matching the regions with read-out values from other epigenomic marks.
  • peak calls from every mark relevant for genome segmentation (e.g., ATACseq, H3K27ac, H3K27me3) were extended to at least 500bp.
  • a graph which represented the combined data from all replicates of all marks, was created.
  • Each peak was treated as a node, and nodes were connected with a vertex only if the corresponding peaks overlapped by 50% in both directions.
  • two peaks situated roughly in the same location and having similar sizes shared an edge.
  • a broad peak encompassing a much smaller peak did not have an edge with the smaller one if the latter was less than 50% the size of the broad peak.
  • the counts were then normalized to log(FPKM + 1). For each segment, the median log FPKM value was calculated for all replicates of a specific mark and genotype. For each mark, the log2 fold-change for Het vs WT and KD vs WT was calculated based on the difference between the log FPKM values. This yielded baseline WT logFPKM values and Het Vs WT log2 fold-change values for ATACseq, H3K27ac, and H3K27me3 marks, and an additional KD vs WT value for ATACseq. These seven metrics were used to cluster all regions into 16 groups using k- means clustering. Marks that were not considered during peak definition or clustering were used as read-outs.
  • Multiome fastq files were aligned and processed with CellRanger Arc version 2.0.0 (lOx Genomics). CellRanger outputs were then further processed individually with Signac version 1.5.0 (Stuart et al., 2021). Peaks were called with MACS2 using default settings (Zhang et al., 2008). Samples were combined together, and a combined peak set was generated from all samples. Peaks larger than lOkb and less than 20 bases were filtered from this combined peak set. Harmony version 0.1.0 was used on PCA based on RNA and LSI based on ATAC to correct batch effect between experiments (Korsunsky et al., 2019).
  • Signac FindMultiModalNeighbors was used on these Harmony corrected values to generate multimodal nearest neighbors upon which the UMAP is based.
  • Cell type labels were assigned based on RNA expression using the Seurat TransferLabel function and reference datasets (Rivas et al., 2021).
  • Gene signature module scores for expression were calculated using the AddModuleScore function, with a control value of 5.
  • Slingshot version 2.8.0 was used to generate Pseudotime based on the first and second Harmony corrected PCA of expression, with the starting anchor being Centroblasts. Density and difference in density of cells for WT and ARID 1 A HET along this pseudotime was caluclated, with significance being tested using wilcoxon rank sum of this pseudotime.
  • ChromVAR scores for the JASPAR2020 core, vertebrates motif list were calculated with Signac’s AddMotif and RunChromVAR functions on the filtered, combined peak set.
  • the indicated gene signature or ChromVAR accessibility of the indicated motif were plotted against pseudotime, which was broken into 10 deciles. Expression of these gene signatures or ChromVAR accessibility for each decile was then tested for significance between WT and ARID 1 A HET using wilcoxon rank sum. The difference in splines were plotted and colored according to decile significance.
  • Pre-plasma cells were defined as cycling BCL6- ⁇ ow non-pre-memory cells. Code for multiome processing can be found at https://github.com/crchin/multiome_process.
  • GEO super series code GSE237990.
  • Example 2 Aridla deletion disrupts germinal center dynamics and progression
  • GCs are composed of several major subpopulations of cells. The most abundant of these are called centroblasts (CBs) which experience proliferative bursting and somatic hypermutation. These are complemented by post-replicative centrocytes (CCs), which compete interact with T follicular helper cells for positive selection based on their B-cell receptor affinity for antigen (Mesin et al., 2016).
  • CBs centroblasts
  • CCs post-replicative centrocytes
  • Example 3 - ARID 1 A loss leads to widespread chromatin repression and decrease in active nucleosome turnover
  • GC cells are characterized by high chromatin plasticity and an open chromatin structure, which allow for their high proliferation rate and rapid cell-fate transitions (Doane et al., 2021). We therefore aimed to determine if this plasticity is regulated by the BAF complex.
  • FIG. 2A The upper section of Figure 2A shows gain of signal flanking ATAC peak midpoints, with midpoint accessibility being mostly unchanged. We posited that these signals reflect gain of positioned nucleosomes. Indeed, this signal was no longer observed when plotting only those ATAC fragments shorter than 120bp, thus excluding nucleosomal length reads, but still conserving many shorter reads that correspond to TF binding sites at peak midpoints (Figure 12A).
  • heterozygous (cKO/WT) profiles yielded a partial reduction of accessibility at the same regions affected (more severely) by homozygous loss of Aridla (cKO/cKO), indicating loss of function in cells heterozygous for Aridla.
  • ARID 1 A protein expression was rescued upon CRISPR-editing in WT/WT cells compared to parental Q474*/WT cells as shown via immunoblot ( Figure 12B).
  • an inducible short hairpin (sh) knockdown construct within the miR-30 backbone, carrying the GFP reporter was integrated into the Q474*/WT cells (sh-KD) to further suppress ARID 1 A levels beyond Q474*/WT levels (Figure 2D).
  • Efficiency of the knockdown was robust, exhibiting high inducibility (>90%) and negligible cassette expression pre-induction ( ⁇ 0.5%: Figure 12E and 12F).
  • nucleosome occupancy was altered at ATAC peaks.
  • Chromatin remodelers are recognized for their role in regulating well-positioned nucleosome placement adjacent to DNA binding motifs. To ascertain whether the increase in nucleosome occupancy was accompanied by a decrease in well-defined nucleosome positions, we assessed nucleosome fuzziness on a genome-wide scale (Chen et al., 2013). Nucleosome fuzziness refers to the variation in nucleosome positions (within each nucleosome peak) from the most preferred nucleosome position. As a measure of this variation, we utilized the standard deviation of the positions of reads in each peak.
  • Example 4 - ARID1A enables chromatin opening for PU.l and NF-kB factors during germinal center B-cell transitions
  • cluster 4 was most consistently linked to canonical CC/GC exit programs, including genes induced by the CD40, NF-kB signaling, STAT3, IL2, IL4, IL6 and Notch pathways, as well as IRF4 and other plasma cell differentiation signatures (as compared to the other clusters; Figure 3G).
  • Example 5 - PU.l and NF-kB factors require ARID1A for binding and transcriptional activation of cytokine-induced genes in lymphoma cells
  • Example 6 Co-dependency of PU.l and NF-kB marks the regulation of accessibility at germinal center cell-fate genes
  • the reporter also contained PU.1 DNA motifs in proximity to the NF-kB response element in our reporter.
  • CRISPR CRISPR to disrupt the strongest PU.l motif, which was separated from the NF-kB response element by half of nucleosomal length (75bp).
  • Figure 15B 48h after CRISPR modification, we observed a significant >3-fold reduction in luciferase expression ( Figure 5D), showing requirement of PU.1 DNA binding motif for NF-kB -mediated gene activation.
  • Example 8 - ARID1A deficiency drives transcriptional programming and cell-fate decision toward a pre-memory B-cell state
  • RNA and AT AC Multiome
  • UMAP Uniform Manifold Approximation and Projection
  • Example 9 - ARID1A enables sequential and cooperative function of PU.l and NF-kB in germinal center B cells
  • Example 10 promotes memory B-cell transcriptional programming in lymphoma cells
  • Example 11 - Aridla deletion enhances germinal center production of immature memory B cells
  • MB cells can arise both from GCs as well as extrafollicular populations (Elsner & Shlomchik, 2020).
  • NP-KLH extrafollicular populations
  • MB cells with a CD8CFPDL2- (double negative) immunophenotype are generally IgM + and are prone to re-enter GC reactions (Zuccarino- Catania et al., 2014).
  • CD80 + PDL2 + (double positive) MB cells are generally IgG+, cannot re-enter GCs, and tend to differentiate into plasma cells upon antigenic recall.
  • cKO/WT antigen experienced GC-derived MB cells B220+CD138-IgD-FAS+CD38-NP+
  • Example 12 - ARID1A is a haplo-insufficient tumor-suppressor in mouse germinal center B cells
  • Bcl2;Aridla cKO/WT mice also manifested greater spleen size vs. Bcl2;Aridla WT/WT ( Figure 8E), indicative of greater disease burden. Histologic analysis revealed a more advanced lymphoma phenotype in Bcl2;Aridla cKO/WT mice.
  • Bcl2;Aridla cKO/WT lymphomas featured large and dysplastic immunoblasts, with large, irregular nuclei and presence of mitotic figures ( Figure 8F and 18A), which are similar traits as human DLBCLs. Notably, these aberrant cells were frequently found outside the follicular regions leading to marked disruption of the splenic architecture.
  • Example 13 - ARID1A mutations are associated with the FL memory B cell subgroup prone to transformation to DLBCL
  • Example 14 - ARID1A -mutant lymphoma cells show enhanced vulnerability to BAF complex inhibition
  • lymphoid neoplasms manifested the greatest dependency on ARID 1 A (i.e. canonical BAF complex) among all tumor types ( Figure 9D).
  • non-Hodgkin lymphomas were the most dependent subset ( Figure 9E); consistent with this notion, we were unable to generate DLBCL cell lines with homozygous CRISPR knockout of ARID1 A (RIVA/RL1, OCI-LY1, OCLLY7; 438 clones screened).
  • ARID 1 A mutant lymphoma cells exhibit significant vulnerability to dual SMARCA4 and SMARCA2 ATPase inhibitor, linked at least in part to the disruption of transcription factor PU.1 function, suggesting a promising role for BAF inhibitors in treating cBAF-deficient B cell lymphomas.
  • ARID 1 A deficiency favors the production of IgM+ CD80-PDL2- MB cells, recognized for their augmented potential to re-enter new GC reactions. This paves the way for additional rounds of somatic hypermutation and lymphomagenesis, positioning these cells as putative clonal precursor cells.
  • TFs are known to cooperate on chromatin, as many cannot independently occupy their target sites, highlighting their interdependence in gene regulation (Iwafuchi-Doi & Zaret, 2014; Zaret & Carroll, 2011).
  • pioneering TFs possess the unique ability to bind both nucleosomal DNA and partially exposed DNA motifs, but still require nucleosome remodeling for chromatin opening (Michael et al., 2020; Soufi et al., 2015). It has been shown that certain pioneering factors (OCT4, SOX2, API) require the BAF complex to mediate nearby nucleosome remodeling (Barisic et al., 2019; King & Klose, 2017; Wolf et al., 2023).
  • the phosphate-regulated yeast PHO5 promoter contains an array of highly positioned nucleosomes when the PHO5 gene is transcriptionally inactive (Svaren & Hdrz, 1997). In low phosphate conditions, nucleosomes are evicted from the promoter to expose binding sites for transcription factors (Bryant et al., 2008) by a complex network of five chromatin remodeling complexes (Musladin et al., 2014).
  • the transcriptional activator Pho4 and subsequently the TATA-box binding protein (TBP) bind the promoter and activate PHO5 expression illustrating how specific nucleosome position and occupancy inhibits transcription factor binding and downstream gene activation in yeast.
  • TATA-box binding protein TATA-box binding protein
  • PU.l binds extensively to genes architecturally remodeled and activated in the GC reaction (Bunting et al., 2016). While an early study did not report any defects in GC upon PU.l knockout (Polli et al., 2005), a more recent investigation using a Cre allele activated in mature B cells revealed a decrease in GC B cells, a reduction in IgGl+ cells, and a trend towards a reduction in long-lived post-GC plasma cells (Willis et al., 2017).
  • PU.l deficient B cells were also shown to be less responsive to CD40 and IL4 signaling and showed a downregulation of CD40 along with several other genes enriched in the ARID 1 A signature (Willis et al., 2017) (also refer to Figure 3J). These observations notably mirror our findings, thereby supporting the idea that a compromised PU.l functionality is a key factor in our observed GC Aridla haploinsufficient phenotype. Moreover, we extend these findings by detailing the implications of these alterations on lymphoma progression.
  • GC B cells stand out due to their exceptional phenotypic plasticity, which allows them to transform into a diverse range of memory B cells. Moreover, they are even capable of changing lineages to adopt the dramatically different transcriptional, chromatin, and 3D architectural characteristics of plasma cells.
  • Our findings suggest a crucial role for ARID 1 A in steering GC B cells through these decisions by promoting PU.l -dependent, NFkB-driven chromatin and transcriptional programming towards the plasma cell phenotype.
  • ARID 1 A haploinsufficiency induces a gain of memory B cell programming state in GC B cells, which could further influence their ultimate trajectories.
  • FDCs follicular dendritic cells
  • TNF follicular helper T
  • the MB gene expression profiles might result from an undiscovered influence of BAF haploinsufficiency on other transcription factors.
  • BAF complexes might govern distinct GC B cell fates, as the pan-BAF loss of function triggered by SMARCA4 haploinsufficiency has shown a notably distinct phenotype with GC hyperplasia and heightened light zone to dark zone re-cycling (see accompanying article).
  • IgM+ MB cells have been proposed as likely clonal precursor cells for FL and other lymphomas, have been extensively studied (Milpied et al., 2021). IgM+ MB cells in particular were described as potential normal equivalents to various IgM+ lymphoma subtypes (Klein et al., 1997), and quiescent clonal interfollicular memory-like cells were suggested to represent tumor reservoirs linked to FL therapy resistance (A. Dogan et al., 1998).
  • BCL2 translocated t( 14; 18) positive B cells have been identified in normal donors (Limpens et al., 1995), and were shown to derive from GCs and often have an IgM + IgD + CD27 + MB-like phenotype (Roulland et al., 2006).
  • IgM+ and IgG+ memory B cells follow unique paths, with the former exhibiting a lower somatic hypermutation load and the ability to enter new GCs, whereas the latter were more mutated, did not re-enter the GC reaction, and mainly differentiated into plasma cells upon immune challenge (I. Dogan et al., 2009; Pape et al., 2011).
  • CD80 + PDL2 + MBs primarily formed plasma cells (Zuccarino- Catania et al., 2014).
  • ARIDlA-deficient MB cells display a competitive advantage over their WT counterparts when placed in the same microenvironment, and are generated at a higher rate.
  • the re-entry of ARIDlA-deficient IgM+CD80-PDL2- MB cells into new GCs would provide an opportunity for clonal precursor cells to accumulate additional oncogenic mutations and initiate malignant transformation.
  • ARIDlA-induced precursor cells differ from the proposed autoimmune B cell clonal precursor cells that contribute to many activated B-cell-like (ABC)-DLBCLs (Venturutti et al., 2023), which could potentially shed light on the underlying biology behind transcriptional and phenotypic differences between GC-like tumors and post-GC tumors.
  • the differences between clonal precursor cells specific to various immunoglobulin isotypes may have clinical significance.
  • memory B-cell-like IgM+ follicular lymphomas have been demonstrated to carry a higher risk of transforming into aggressive follicular lymphomas, as opposed to the typically IgG+ germinal center-like follicular lymphomas (X. Wang et al., 2022).
  • Discovering the mechanisms and biomarkers associated with this transformation represents a crucial unmet medical need, as patients identified as high-risk could potentially benefit from more intensive treatments or participation in clinical trials aiming to delay or prevent this often lethal progression.
  • ARID 1 A as a biomarker could be used by determining the mutation status of ARID 1 A gene in FL patients using precision medicine genotyping methods for diagnosis. Furthermore, a Flow cytometry method could be used to determine the intracellular levels of ARID 1 A protein as a diagnostic marker.
  • ARID 1 A mutated cells.
  • the inhibitors are:
  • Drug treatment of a different lymphoma cell line (Burkitt lymphoma), showing that the same vulnerability of ARID 1 A mutated tumor cells to treatment is observed in other tumor types (FIG. 22).
  • Trimmomatic A flexible trimmer for Illumina sequence data. Bioinformatics . https://doi.org/10.1093/bioinformatics/btul70
  • JASPAR 2022 The 9th release of the open-access database of transcription factor binding profiles. Nucleic Acids Research. http s ://doi . org/ 10.1093/nar/gkab 1113
  • OCT2 pre-positioning facilitates cell fate transition and chromatin architecture changes in humoral immunity. Nature Immunology, 22(10), 1327-1340. https://doi.org/10.1038/s41590-021-01025-w
  • VavP-Bcl2 transgenic mice develop follicular lymphoma preceded by germinal center hyperplasia. Blood, 103(6), 2276-2283. https://doi.org/10.1182/blood-2003-07-2469
  • Eph-related tyrosine kinase ligand Ephrin-Bl marks germinal center and memory precursor B cells. Journal of Experimental Medicine, 214(3), 639-649. https://doi.org/10.1084/jem.20161461
  • chromVAR inferring transcription-factor-associated accessibility from single-cell epigenomic data. Nature Methods, 14(10), 975-978. https://doi.org/10.1038/nmeth.4401
  • ISWI a member of the SW12/SNF2 ATPase family, encodes the 140 kDa subunit of the nucleosome remodeling factor. Cell, 83(6), 1021-1026. https://doi.org/10.1016/0092-8674(95)90217-l

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Abstract

La présente invention concerne des compositions et des procédés pour diagnostiquer des sujets atteints d'un lymphome folliculaire (LF) présentant un risque de transformation en lymphome diffus à grandes cellules B (LDGCB), des procédés de prédiction de réponses de sujets atteints de LF ou LDGCB à l'inhibition pharmacologique de SMARCA4/2, ainsi que des procédés de prévention et/ou de traitement de LF et/ou LDGCB sur la base du génotype, du niveau et/ou de l'activité d'ARID1A.
PCT/US2024/054163 2023-11-01 2024-11-01 Arid1a, marqueur pronostique pour la transformation agressive du lymphome et à vulnérabilité à l'inhibition pharmacologique de smarca4/2 Pending WO2025096976A1 (fr)

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