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WO2019053244A1 - Biomarqueurs pour la thérapie par inhibiteur de point de contrôle de sensibilité - Google Patents

Biomarqueurs pour la thérapie par inhibiteur de point de contrôle de sensibilité Download PDF

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WO2019053244A1
WO2019053244A1 PCT/EP2018/075017 EP2018075017W WO2019053244A1 WO 2019053244 A1 WO2019053244 A1 WO 2019053244A1 EP 2018075017 W EP2018075017 W EP 2018075017W WO 2019053244 A1 WO2019053244 A1 WO 2019053244A1
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relative frequency
responders
cells
checkpoint
therapy
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WO2019053244A9 (fr
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Carsten KRIEG
Mitchell LEVESQUE
Burkhard Becher
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Zurich Universitaet Institut fuer Medizinische Virologie
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Zurich Universitaet Institut fuer Medizinische Virologie
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/53—Immunoassay; Biospecific binding assay; Materials therefor
    • G01N33/569—Immunoassay; Biospecific binding assay; Materials therefor for microorganisms, e.g. protozoa, bacteria, viruses
    • G01N33/56966—Animal cells
    • G01N33/56972—White blood cells
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/53—Immunoassay; Biospecific binding assay; Materials therefor
    • G01N33/574—Immunoassay; Biospecific binding assay; Materials therefor for cancer
    • G01N33/57407—Specifically defined cancers
    • G01N33/57434—Specifically defined cancers of prostate
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2800/00—Detection or diagnosis of diseases
    • G01N2800/52—Predicting or monitoring the response to treatment, e.g. for selection of therapy based on assay results in personalised medicine; Prognosis

Definitions

  • the present invention relates to the use of biomarkers in order to stratify patients before checkpoint inhibitor therapy.
  • CIT Checkpoint Inhibitor Therapy
  • immune checkpoints The physiological function of immune checkpoints is to prevent autoimmunity by down- regulating the immune system and promoting self tolerance. Tumor cells however use this system to evade detection and attack by the immune system.
  • PD-1 Programmed cell death protein 1
  • Immunotherapy with anti-PD-1 aims to block the interaction of tumor-reactive T cells with PD-1 ligands (PD-L1 and PD-L2) expressed on various cells types including leukocytes and the tumor cells themselves.
  • PD-1 ligands PD-L1 and PD-L2
  • Clinical trials on PD-1 and PD-L1 blockade for patients with advanced melanoma have demonstrated consistent therapeutic responses, thus prompting their application to several other cancers.
  • Nivolumab an anti-PD-1 monoclonal antibody, has been approved by the US FDA for the treatment of patients with metastatic melanoma, non-small cell lung carcinoma (NSCLC), metastatic renal cell carcinoma, metastatic squamous NSCLC and refractory Hodgkin's lymphoma.
  • NSCLC non-small cell lung carcinoma
  • Pembrolizumab has shown similar efficacy and is now FDA approved as a second line treatment drug for melanoma and for the treatment of patients with NSCLC, advanced gastric cancer, advanced bladder cancer, head and neck cancer, classical Hodgkin's lymphoma, and triple negative breast cancer.
  • Further anti-PD-L1 mAbs have been developed for the treatment of advanced human cancers including metastatic urothelial bladder cancer.
  • Clinical outcomes of anti-PD-1 immunotherapy are however highly variable, with only a fraction of patients showing durable responses, some with early progression and others with late response, while the majority of treated patients show no clinical benefit.
  • the discovery of reliable biomarkers to stratify patients prior to treatment is urgently needed in order to improve identification of responders to anti-PD-1 immunotherapy.
  • the objective of the present invention is to provide means and methods to identify and discriminate responder and non-responder to anti- PD-1 immunotherapy and to establish new methods for patient stratification. This objective is attained by the claims of the present specification.
  • checkpoint inhibitory therapy relates to a therapy that overrides an immune checkpoint mechanism and enables the immune system to attack tumor cells.
  • the agents used in checkpoint inhibitory therapy may be checkpoint inhibitory agents or checkpoint agonist agents.
  • checkpoint inhibitory agent or checkpoint inhibitory antibody is meant to encompass an agent, particularly an antibody (or antibody-like molecule) capable of disrupting the signal cascade leading to T cell inhibition after T cell activation as part of what is known in the art the immune checkpoint mechanism.
  • a checkpoint inhibitory agent or checkpoint inhibitory antibody include antibodies to CTLA-4 (Uniprot P16410), PD-1 (Uniprot Q151 16), PD-L1 (Uniprot Q9NZQ7), B7H3 (CD276; Uniprot Q5ZPR3), Tim-3, Gal9, VISTA, Lag3.
  • checkpoint agonist agent or checkpoint agonist antibody is meant to encompass an agent, particularly but not limited to an antibody (or antibody-like molecule) capable of engaging the signal cascade leading to T cell activation as part of what is known in the art the immune checkpoint mechanism.
  • Non-limiting examples of receptors known to stimulate T cell activation include CD122 and CD137 (4-1 BB; Uniprot Q0701 1 ).
  • the term checkpoint agonist agent or checkpoint agonist antibody encompasses agonist antibodies to CD137 (4-1 BB), CD134 (OX40), CD357 (GITR) CD278 (ICOS), CD27, CD28.
  • antibody in its meaning known in the art of cell biology and immunology; it refers to whole antibodies including but not limited to immunoglobulin type G (IgG), type A (IgA), type D (IgD), type E (IgE) or type M (IgM), any antigen binding fragment or single chains thereof and related or derived constructs.
  • a whole antibody is a glycoprotein comprising at least two heavy (H) chains and two light (L) chains inter-connected by disulfide bonds.
  • Each heavy chain is comprised of a heavy chain variable region (VH) and a heavy chain constant region (CH).
  • the heavy chain constant region is comprised of three domains, CH1 , CH2 and CH3.
  • Each light chain is comprised of a light chain variable region (abbreviated herein as VL) and a light chain constant region (CL).
  • the light chain constant region is comprised of one domain, CL.
  • the variable regions of the heavy and light chains contain a binding domain that interacts with an antigen.
  • the constant regions of the antibodies may mediate the binding of the immunoglobulin to host tissues or factors, including various cells of the immune system (e.g., effector cells) and the first component of the classical complement system.
  • antibody-like molecule in the context of the present specification refers to a molecule capable of specific binding to another molecule or target with high affinity / a Kd ⁇ 10E-8 mol/l.
  • An antibody-like molecule binds to its target similarly to the specific binding of an antibody.
  • antibody-like molecule encompasses a repeat protein, such as a designed ankyrin repeat protein (Molecular Partners, Zurich), a polypeptide derived from armadillo repeat proteins, a polypeptide derived from leucine-rich repeat proteins and a polypeptide derived from tetratricopeptide repeat proteins.
  • antibody-like molecule further encompasses a polypeptide derived from protein A domains, a polypeptide derived from fibronectin domain FN3, a polypeptide derived from consensus fibronectin domains, a polypeptide derived from lipocalins, a polypeptide derived from Zinc fingers, a polypeptide derived from Src homology domain 2 (SH2), a polypeptide derived from Src homology domain 3 (SH3), a polypeptide derived from PDZ domains, a polypeptide derived from gamma-crystallin, a polypeptide derived from ubiquitin, a polypeptide derived from a cysteine knot polypeptide and a polypeptide derived from a knottin.
  • SH2 Src homology domain 2
  • SH3 polypeptide derived from Src homology domain 3
  • PDZ domains a polypeptide derived from gamma-crystallin
  • CD14 refers to a protein identified by UniProt ID P08571 .
  • CD16 refers to CD16a, identified by UniProt ID P08637 and/or CD16b, identified by UniProt ID 075015.
  • CD33 refers to a protein identified by UniProt ID P20138.
  • HLA-DR refers to "Human Leukocyte Antigen - antigen D Related", encoded by the human leukocyte antigen complex on chromosome 6 region 6p21 .31 .
  • the designation "positive” or “+” with regard to the expression of a certain marker molecule is used in its meaning known in the field of immunology.
  • a cell population is designated “positive” or “+” with regard to the expression of a certain marker molecule, this shall mean that the cell population can be stained by a common fluorescent-dye-labelled antibody against the marker molecule and will give a fluorescence signal of at least 30% higher intensity, particularly of at least double the intensity, more particularly of at least one, two or three log higher intensity compared to unlabelled cells or compared to cells labelled with the same antibody but commonly known as not expressing said marker molecule or compared to cells labelled with an isotype control antibody.
  • the designation "negative” or “-” with regard to the expression of a certain marker molecule is used in its meaning known in the field of immunology.
  • a cell population is designated “negative” or “-” with regard to the expression of a certain marker molecule, this shall mean that a cell population cannot be stained by a fluorescent-dye labelled antibody as described above against the marker molecule.
  • the designation "hi” with regard to the expression of a certain marker molecule is used in its meaning known in the field of immunology.
  • a cell population is designated “hi” with regard to the expression of a certain marker molecule, this shall mean that the cell population stained by a common fluorescent-dye-labelled antibody against the marker molecule will give a fluorescence signal significantly stronger, particularly at least 2 x stronger or 10 x stronger than that given by a cell population designated "+” with regard to the expression of said marker molecule.
  • classical monocytes is used in its meaning known in the field of immunology.
  • the term refers to monocytes that are characterized by expression of the CD14 cell surface receptor and absence of expression of the CD16 cell surface receptor (CD14 + CD16 " ).
  • a first aspect of the invention relates to a method of prognosis, specifically of assigning to a patient suffering from cancer a likelihood of being responsive to checkpoint inhibitor therapy.
  • the method comprises the steps of
  • a second aspect of the invention relates to a method of therapeutic scheduling, specifically of assigning a patient suffering from cancer to checkpoint inhibitor therapy.
  • the method comprises the steps of
  • the classical monocytes are characterized by expression of CD14 + CD16 " CD33 hi HLA-DR hi .
  • the threshold is 1 .6 times (x) the average relative frequency of classical monocytes in a blood sample obtained from a healthy subject. In certain embodiments, the threshold is 1 .7 x the average relative frequency of classical monocytes in a blood sample obtained from a healthy subject. In certain embodiments, the threshold is 1 .8 x the average relative frequency of classical monocytes in a blood sample obtained from a healthy subject. In certain embodiments, the threshold is 1 .9 x the average relative frequency of classical monocytes in a blood sample obtained from a healthy subject.
  • the threshold is 2.0 x the average relative frequency of classical monocytes in a blood sample obtained from a healthy subject. In certain embodiments, the threshold is 2.1 x the average relative frequency of classical monocytes in a blood sample obtained from a healthy subject. In certain embodiments, the threshold is 12.2 x the average relative frequency of classical monocytes in a blood sample obtained from a healthy subject.
  • the relative frequency of classical monocytes is determined by flow cytometry.
  • the cancer is melanoma. In certain embodiments, the cancer is metastatic melanoma.
  • the checkpoint inhibitor therapy comprises treatment with a checkpoint inhibitory agent.
  • the checkpoint inhibitor therapy is selected from the group comprising anti-PD-1 immunotherapy, anti-PD-L1 immunotherapy, anti-CTLA-4 immunotherapy, anti-TIM-3 immunotherapy, anti- Lag-3 immunotherapy, or a combination of said therapies.
  • the checkpoint inhibitor therapy is anti-PD-1 immunotherapy.
  • the anti-PD-1 immunotherapy comprises administration of an anti-PD-1 immunotherapy agent selected from the group comprising Nivolumab and Pembrolizumab.
  • the level of a marker selected from expression of albumin, expression of c-reactive protein and relative frequency of immature granulocytes is determined in the blood sample and wherein an increase in the additional marker is predictive of the patient being responsive to checkpoint inhibitor therapy.
  • a checkpoint inhibitory agent or a checkpoint agonist agent for use in the therapy of cancer wherein a patient receiving the checkpoint inhibitory agent or a checkpoint agonist agent is characterized by a relative frequency of CD14 + CD16 " CD33 hl HLA-DR hl monocytes determined as a percentage of classical monocytes in relation to the number of peripheral blood mononuclear cells determined in a blood sample obtained from the patient, and the relative frequency is above 1 .6 x, 1 .7 x, 1 .8 x, 1 .9 x, 2.0 x, 2.1 x, 2.2 x the average relative frequency of classical monocytes in a blood sample obtained from a healthy subject.
  • the cancer is melanoma, in particular metastatic melanoma.
  • the checkpoint inhibitory agent is selected from the group comprising an anti-PD-1 antibody, an anti-PD-L1 antibody, an anti- CTLA-4 antibody, an anti-TIM-3 antibody, an anti-Lag-3 antibody, or a combination of said antibodies.
  • a method of monitoring the success of checkpoint inhibitor therapy comprises the steps of
  • a diagnostic kit comprises ligands, in particular antibodies, binding to CD14, CD16, CD33 and HLA-DR.
  • the kit further comprises ligands, in particular antibodies, binding to CD3, CD4, CD1 1 b, CD19, CD45RO and CD56.
  • the kit further comprises at least one antibody binding to CD45, CD61 , CD66b, ICAM-1 , CD86, CD1 1 c, CD7 or PD-L1 .
  • the kit further comprises at least two ligands, in particular antibodies, each binding to one of CD45, CD61 , CD66b, ICAM-1 , CD86, CD1 1 c, CD7 and PD-L1 .
  • the kit further comprises at least 5 ligands, in particular antibodies, each binding to one of CD45, CD61 , CD66b, ICAM-1 , CD86, CD1 1 c, CD7 and PD-L1 .
  • the ligands comprise means for detection by flow cytometry. In certain embodiments, the ligands are fluorescently labeled.
  • Fig. 1 shows the stratification of responders and non-responders and identification of differences in immune cell populations using mass cytometry.
  • A Experimental setup for melanoma patient sample processing using metal-labeled antibodies and acquisition by mass cytometry.
  • B Dendrogram tree built on hierarchical clustering using Ward linkage of the normalized median marker expression from single cells of patient PBMCs. Bars on top of the heatmap represent individual samples from responders (green) versus non-responders (red).
  • C Heatmap of significantly differentially expressed markers between responders and non- responders before and 12 weeks after therapy initiation, in pre-processed live single cells.
  • D Cells from healthy donors and patients were used as an input for the FlowSOM algorithm. Thirteen algorithm- chosen markers were used to form 7 machine-assisted clusters. Visualization of 15 ⁇ 00 events in non-responders (NR), responders (R), and healthy donors (HD) using the tSNE algorithm.
  • the heatmap represents the expression of respective marker within a cellular cluster and was used to annotate clusters, which were overlaid in color code (panel on the right).
  • Fig. 2 shows the differences in T cell activation status and in the frequency of the T cell subpopulations before and after 12 weeks of therapy in responders and non-responders.
  • C and D FlowSOM was used to generate indicated T cell subpopulations and resultant cluster frequencies from batch 1 (circles) and batch 2 (triangles) are plotted as in Fig. 1 E.
  • Fig. 3 shows the increased activation in CD4 + or CD8 + CD69 + T mem/eff cells after immunotherapy start in responders.
  • CD4 + and CD8 + memory/effector T cells after therapy were extracted and activated polyclonally.
  • Frequencies of PD-1 , CTLA-4, and cytokines in (A) CD4 + CD69 + T mem/eff cells and (B) CD8 + CD69 + T mem/eff cells in responders (green) and non-responders (pink) were compared. Healthy subjects (grey) served as controls.
  • C A matrix using the afore-mentioned markers was created and cells were sorted into this matrix using FlowSOM.
  • Fig. 4 shows the patient stratification based on myeloid cell markers and expansion and enhanced activation of classical monocytes in responders.
  • A Dendrogram tree built using hierarchical clustering and Ward linkage as in Fig 1 B.
  • C Visualization of FlowSOM-generated myeloid clusters (CD3 negative CD19 negative) in non-responders (NR), responders (R) and health donors (HD) using tSNE. Per plot, 1 CT000 cells are displayed. CD7 and CD56 positive cells were excluded from analysis.
  • the heatmap represents the expression of respective markers within a cellular cluster.
  • E Heatmap comparing significantly differently expressed myeloid markers in CD14 + and CD33 + monocytes between responders and non-responders before and 12 weeks after therapy. Heat scale shows normalized median marker expression ranging from under expressed (blue) to over-expressed (orange).
  • Fig. 5 shows the simultaneous detection of T cell differentiation and activation markers in blood.
  • PBMCs from 5 healthy donors and 10 melanoma patients were stained with a panel of 31 antibodies and analyzed by mass cytometry.
  • Biaxial mass cytometry plots show the staining quality by gating on a respective positive and negative cell population of the shown differentiation and activation marker. Each plot shows a representation of four independent experiments.
  • Fig. 6 shows the simultaneous detection of TH and CTL profiles in human blood.
  • PBMC from melanoma patients were stimulated for 4 hours with PMA lonomycin in the presence of brefeldin A and monensin.
  • Two-dimensional mass cytometry plots show a representative experiment of four independent experiments.
  • Fig. 7 shows the characterization of the circulating myeloid compartment in the blood of melanoma patients. Shown are dot plots from mass cytometry staining panels on cells from human blood. Data is representative for one out of four independent experiments.
  • Fig. 8 shows the comparison of 30 clinical parameters including measure monocytes to progression free survival using a multivariate Cox-model on the patient samples used for discovery mass cytometry approach.
  • Fig. 9 shows the citrus analysis of the T cell panel.
  • A Depiction of Model Error Rate;
  • B significant different clusters extracted from the minimal cluster model;
  • C spanning tree of most relevant clusters in the minimal cluster model;
  • D heatmap of markers expressed in significant different clusters shown in B (R) responders, (NR) non-responders.
  • Fig. 10 shows the citrus analysis of the myeloid cell-enriched (CD3negCD19neg) panel.
  • A Depiction of Model Error Rate;
  • B significant different clusters extracted from the minimal cluster model;
  • C spanning tree of most relevant clusters in the minimal cluster model;
  • D heatmap of markers expressed in significant different clusters shown in B (R) resonders, (NR) non-responders.
  • Fig. 1 1 shows the FACS validation panel.
  • PBMC from an independent, randomized, blinded patient cohort were stained for CD3, CD4, CD1 1 b, CD14, CD19, CD16, CD33, CD45RO, CD56, and HLA-DR, acquired and analyzed using the above gating strategy.
  • Fig. 12 shows the comparison of 30 clinical parameters plus monocyte frequencies from the validation experiment to progression free survival using a multivariate Cox-model on the FACS validation cohort.
  • PD-1 programmed cell death 1
  • T effector cells T effector cells
  • PD- 1 ligands PD-L1 and PD-L2
  • Clinical trials on PD-1 and PD-L1 blockade for patients with advanced melanoma have demonstrated consistent therapeutic responses, thus prompting their application to several other cancers.
  • nivolumab an anti-PD-1 monoclonal antibody
  • NSCLC non-small cell lung carcinoma
  • Nivolumab has additionally been approved to treat patients with metastatic squamous NSCLC and refractory Hodgkirf s lymphoma.
  • Pembrolizumab has shown similar efficacy and is now FDA approved as a first line treatment drug for melanoma. Pembrolizumab is also effective in patients with NSCLC, advanced gastric cancer, advanced bladder cancer, head and neck cancer, classical Hodgkin ' s lymphoma and triple negative breast cancer.
  • mAbs Additional anti-PD-L1 monoclonal antibodies (mAbs) have been developed for the treatment of advanced human cancers including metastatic urothelial bladder cancer.There does not appear to be a significant difference among PD-1/PD-L1 mAbs, however there are no current side-by-side comparison studies.
  • PBMC peripheral blood mononuclear cells
  • the aim of this study was to identify biomarkers that could be used to predict responsiveness to anti-PD-1 immunotherapy.
  • Table 1 Characteristics of melanoma patients and healthy donors used for the biomarker discovery study. Numbers in parenthesis display the age range of subjects.
  • PBMCs were thawed and stained with three mass cytometry panels (Fig. 1A and Table 3). For all three panels, one phenotypic and one functional T cell panel, as well as one myeloid panel, the same number of PBMCs was used and labeled with barcodes that combine 2 or 3 (out of 5) metal tags.
  • the first staining panel contained 31 T cell markers to identify all major immune cell populations and cover all stages of T cell differentiation and activation (Table 3). After acquisition, each sample was debarcoded using Boolean gating. Staining quality was evaluated by defining a biological positive and negative control (Fig. 5).
  • the inventors next tested the hypothesis that the changes in normalized median marker expression were driven by changes in the relative abundance of the various cell populations between responders and non-responders. Therefore, the inventors analyzed the differential median expression of the 29 markers, comparing responders and non-responders, before and after therapy initiation (Fig. 1 C). Significant increases in the expression of HLA-DR, CTLA-4, CD56, CD45RO, CD1 1 a, CD25, and CCR5 and down-regulation of CD3, CD27, CD28, CD127, and CD4 was observed in responders versus non-responders.
  • the inventors manually annotated the seven major cell populations (CD4 T cells, CD8 T cells, NK cells, NKT cells, B cells, ⁇ cells, and myeloid cells) and then separated them into the three groups.
  • T cells are the major targets of anti-PD-1 immunotherapy and given the altered T cell composition in responders before immunotherapy, the inventors next compared the normalized median marker expressions on T cells between non-responders and responders before and after therapy.
  • CD4 + T cells in responders showed an up-regulation of CTLA-4, HLA- DR, CD69, BTLA, and CD1 1 a (Fig. 2A) already at baseline before therapy.
  • CD8 + T cells in responders showed an increase in CD45RO, CTLA-4, CD62L, CD69, CD1 1 a, CCR4, BTLA, PD-1 , CCR6, HLA-DR and granzyme-B expression (Fig. 2B).
  • CTLA-4 is also a marker of activated T cells.
  • the inventors found that T cell depletion in the peripheral blood of melanoma patients is more pronounced in responders compared to non-responders (Fig. 2C and D). This phenomenon may be due to their enhanced ability to migrate to the tumor site. Indeed, in the CD4 + T cell compartment of responder patients, the inventors also found an up-regulation of CD1 1 a, which has been shown to be essential for migration to lymph nodes and distal sites.
  • CD4 + T cells and CD8 + T cells from the FlowSOM- generated clusters in Fig. 1 C and subdivided them into CD45RO " CD62L + naive, CD45RO " CD62L- effector cells (TE), CD45RO + CD62L “ effector memory (EM) cells, CD45RO + CD62L + central memory (CM) cells or CD127 " CD25 + regulatory T cells (T reg s) using FlowSOM (Fig. 2C and D).
  • the inventors then compared the frequencies of resultant T cell sub-clusters between responders and non-responders before and 12 weeks after therapy.
  • the patients who eventually responded to the therapy showed a significant reduction in the CD4 + EM T cells, as well as reduction in CD8 + naive T cells population at baseline and after treatment (p-values: 8.21 e-03, 6.95e-03).
  • the CD8 + T cell subpopulation of responders showed an increase in CM T cells before and after treatment (Fig. 2C and D).
  • T cell development progresses linearly from naive via memory to effector cells along with the loss of some properties, such as the ability to self-renew, expand and persist but in turn gain effector function and tissue specificity in vivo.
  • some properties such as the ability to self-renew, expand and persist but in turn gain effector function and tissue specificity in vivo.
  • differentiated memory T cells can differentiate into potent effectors in vivo following interaction with their cognate antigen.
  • Anti-PD-1 immunotherapy alters the properties within the T cell compartment
  • PBMCs were processed as described above. Briefly, single cell suspensions were cultured for 4h in the presence of PMA/lonomycin, barcoded, stained, fixed and analyzed by mass cytometry.
  • activated CD69 + memory and effector T cells T mem/eff cells
  • cytokine IL-2, IL-4, IL-10, IL-13, IL-17A, GM-CSF, TNF-a, IFN- ⁇ , Grz-B
  • PD-1 and CTLA-4 positive T cell subpopulations were identified.
  • the inventors found no difference in cytokine production between responders and non-responders prior to therapy. However, after therapy the inventors found a significant up-regulation of PD-1 , IL-4, and granzyme-B in CD4 + CD69 +mem/eff T cells in responders, while IL-17A-positive cells were less abundant (Fig.
  • Fig. 3A For CD8 + CD69 +mem/eff T cells, an up-regulation of CTLA4 and granzyme-B was detected in responders (Fig. 3B). In order to link these signatures to a specific cell population, the inventors then created a matrix containing all possible marker combinations in CD4 + CD69 +mem/eff T cells (Fig3C) or CD8 + CD69 +mem/eff T cells (not shown). Using this approach, no differences in the CD8 + T cell subpopulations were found. Fig. 3D shows the different cell populations from this matrix when comparing CD4 + T cell subsets in responders to non-responders.
  • the decrease of IL-2 in CD4 + CD69 +mem/eff T cells from cluster 5 and the expansion of cluster 48 in responders reflect the higher activation status in the CD4 + T cell compartment that the inventors observed from panel 1.
  • Myeloid cells predict responsiveness to anti-PD-1 immunotherapy
  • the inventors next searched for changes in normalized median marker expression between non-responders and responders, before and 12 weeks after therapy, and the inventors found that 16 markers (i.e., CD86, HLA-DR, CD141 , ICAM-1 , CD1 1 c, PD-L1 , CD38, CD16, CD33, CD1 1 b, CD303, CD62L, CD1 c, CD64, CD14, and CD34) were significantly up-regulated in the myeloid compartment of responders (Fig. 4B).
  • 16 markers i.e., CD86, HLA-DR, CD141 , ICAM-1 , CD1 1 c, PD-L1 , CD38, CD16, CD33, CD1 1 b, CD303, CD62L, CD1 c, CD64, CD14, and CD34
  • FlowSOM was used to subdivide the myeloid compartment into 4 major clusters, which were annotated as CD14 + CD16 " HLA-DR hi classical monocytes, CD14 " CD33
  • CD14 + classical monocytes were significantly increased in responders before therapy, whereas the frequency of CD14 " CD33
  • 0W CD1 1 b + HLA-DR'° myeloid cells were decreased (both p-value 2.23e-02). Importantly, the expansion of CD14 + classical monocytes was maintained after anti-PD-1 therapy.
  • the inventors more specifically examined the marker expression of the myeloid cell clusters before and after therapy, as shown in Fig. 4C, and found an up-regulation of ICAM- 1 , CD1 1 b, CD1 1 c, HLA-DR, and PD-L1 in CD14 + classical monocytes of responders already before therapy (Fig. 4E).
  • CD14 " CD33 + myeloid cells also showed an activated phenotype by over-expressing HLA-DR, CD141 , CD33, CD1 1 c, CD1 1 b and CD86.
  • Citrus is a clustering-based supervised algorithm that identifies stratifying signatures, to compare the identified cell types and marker expression differences that could distinguish between non-responders and responders before therapy (Fig. 9 and 10). Citrus independently confirmed the reduction observed in the T cell compartment and the increase in the myeloid compartment before therapy, as shown in panels 1 and 3.
  • the inventors designed a flow cytometry-based validation panel using a reduced number of markers.
  • the inventors selected a combination of markers that were significantly differentially expressed in Fig. 1 C and 4B and markers known from the literature to define the cellular composition in the blood (Fig. 1 1 ).
  • a blinded validation was performed on PBMCs from a second independent cohort of 31 melanoma patients containing 15 responders and 16 non-responders before anti- PD-1 therapy (table 2).
  • the inventors assessed the correlation between commonly documented clinical factors and patients PFS under treatment, including the monocyte frequencies form the validation panel (Fig. 12).
  • the inventors In the inventors' cohort of melanoma patients, the inventors not only discovered a previously unappreciated higher frequency of classical monocytes (CD14 + CD16 " ) amongst responding patients before therapy, but the inventors also found higher levels of HLA-DR and ICAM-1 on these cells. Together with the finding that patients responding to the therapy have higher frequencies of central memory T cells in circulation and a more activated (CTLA-4 + , TNF-a + , PD-1 + , granzyme-B + and IL-2 + ) T cell compartment after therapy, the inventors' results suggest that the presence of highly activated classical monocytes may be a prerequisite for a successful response to anti-PD-1 immunotherapy.
  • the inventors propose to investigate the signature of PD-1 responders further in larger cohorts, as well as across the different applications of checkpoint inhibitor therapy. Altogether, besides representing a potentially powerful clinical determinant of response, the inventors' findings may help to elucidate the mechanisms underlying anti-PD-1 activity.
  • PBMC peripheral blood mononuclear cells
  • Progression was defined as either a significant increase in tumor size, new metastatic sites, or the need to treat the patient with a secondary treatment such as radiotherapy.
  • age- and sex-matched PBMCs were acquired from the Red Cross Blood Bank, Zurich, Switzerland. All human biological samples were collected after written informed consent of the patients and with approval of the Local Ethics Committee (Kantonale Ethikkommission Zurich, KEK-ZH authorization Nr. 2014-0425) in accordance to GCP guidelines and the Declaration of Helsinki. Patient data and analysis
  • Standard clinical parameters (30) were measured in responders and non-responders that were collected before and after anti-PD-1 treatment.
  • Age was considered as a continuous variable, whereas all other variable were dichotomized using clinical limit of normal as cut-offs.
  • Candidate prognostic factors with a significant p value ( ⁇ 0.05) were then included in the multivariate analysis.
  • LMM linear mixed models
  • the inventors used the generalized linear hypothesis (glht) function from the multcomp R package (Hothorn, T. et al., Biometrical Journal 50, 346-363, 2008) to test for the four following contrasts: (1 ) the difference in marker expression between responders and non-responders before therapy, (2) differences after therapy, (3) overall differences in both combined and (4) an interaction that is comparing differences before and after therapy. Except for functional components (Fig. 3), the inventors noted that in almost all cases the therapy did not have an impact on the observed significant differences. Based on this observation and in order to gain power, the inventors report results of the overall differences between responders and non-responders. To account for multiple cluster comparison, the inventors adjusted the resulting p-values using the Benjamini-Hochberg procedure of multiple-testing correction.
  • Differential marker expression is visualized using heatmaps as the change between responders and non-responders for significant markers (adjusted p-value ⁇ 0.1 ). Colours represent normalized median marker expressions to mean of 0 and standard deviation of 1 .
  • PCA principal component analysis
  • Top scoring Levine PCA score averaged across samples
  • the inventors used the SOM function from the FlowSOM R package 39 and ConsensusClusterPlus function from ConsensusClusterPlus R package (Wilkerson, M. D. & Hayes, D. N., Bioinformatics 26, 1572-1573, 2010), a combination of methods that is one of the best performing clustering approaches (Weber & Robinson, 2016).
  • SOM self-organizing map
  • the inventors used Flow SOM to assign cells to a 10 times 10 grid according to their similarity using the self-organizing map (SOM) algorithm.
  • the resulting 100 codes, vectors of marker expression representing the 100 grid nodes were clustered using ConsensusClusterPlus hierarchical clustering with average linkage.
  • ConsensusClusterPlus to cluster the codes into a range of clusters from 2 to 20 and to calculate a score (delta area), which the inventors used to define the appropriate number of clusters present in the data based on the so called elbow criterion.
  • tSNE dimension reduction to represent the annotated cell populations in a 2D map (Maaten & Hinton, 2008).
  • the inventors performed analysis analogous to differential marker expression analysis described above.
  • the response variable (y) was the number of cells in a given cluster in each sample, and instead of a LMM, a generalized linear mixed model (GLMM) with the binomial family was applied.
  • GLMM generalized linear mixed model
  • Bimatrix is a binary matrix with rows representing cells and columns corresponding to the cytokines of interest where each entry encodes whether a cell is positive (1 ) or negative (0) for a given cytokine. Thresholds for defining the positive status of a cell were defined for each batch of data individually by investigating expression profiles in FlowJo. Subsequently, the inventors performed two types of comparisons.
  • the differential frequency analysis based on GLMM which compare the abundance of positive cells in responders and non- responders for each individual cytokine (Fig. 3A and 3B).
  • the inventors considered an entire cytokine set profile of each cell.
  • Cells described by the bimatrix were clustered by using the SOM method into 49 clusters (7 times 7 grid) to generate profiles of the cytokine production (Fig. 3C) and the relative abundance of these profiles was compared between responders and non-responders using the GLMM approach described above.
  • the inventors generated a flow cytometry panel as described above.
  • the panel was based on a combination of markers with significantly different expression taken from Fig. 1 C and 4B and markers known from literature to define the cellular composition in the blood.
  • Validation samples were taken in a blinded fashion on PBMCs from a second independent cohort of 31 melanoma patients containing 15 responders and 16 non- responders before anti-PD-1 therapy.
  • CD56-Pe-Cy7 NCAM1
  • CD1 1 c- AlexaFluor700 B-Ly6
  • CD16-APC 3G8
  • CD45RO-ECD 2H4LDH1 1 LD89(2H4)
  • BD Fortessa flow cytometer
  • TriStar FlowJo software
  • the inventors applied a generalized linear model (GLM) with beta family, using the glmmADMB R package (link: httpglmmadmb.r-forge.r-project.org, accessed: 12 December 2016), where the response y is an relative abundance (proportion) of a cell population in the sample.
  • the contrast for the comparison between responders and non-responders was tested using the glht function and a Benjamini-Hochberg procedure was applied to correct the resulting p-values for multiple- testing.

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La présente invention concerne un procédé d'attribution à un patient souffrant d'un cancer d'une probabilité d'être sensible à une thérapie par inhibiteur de point de contrôle. Le procédé comprend les étapes de : 1) détermination dans un échantillon de sang obtenu auprès d'un patient de la fréquence relative des monocytes classiques, la fréquence relative étant le pourcentage de monocytes classiques sur le nombre de cellules mononucléaires du sang périphérique ; 2) comparaison de la fréquence relative à un seuil et 3) attribution d'une probabilité élevée d'être sensible à la thérapie par inhibiteur de point de contrôle au patient si la fréquence relative est supérieure au seuil.
PCT/EP2018/075017 2017-09-15 2018-09-17 Biomarqueurs pour la thérapie par inhibiteur de point de contrôle de sensibilité Ceased WO2019053244A1 (fr)

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JP2020187127A (ja) * 2019-05-15 2020-11-19 上準微流體股▲ふん▼有限公司ＭｉＣａｒｅｏ Ｔａｉｗａｎ Ｃｏ．， Ｌｔｄ． ガンを有する対象の免疫療法応答を予測するための方法
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EP3809136A1 (fr) * 2019-10-16 2021-04-21 Medizinische Hochschule Hannover Identification moléculaire de cellules t liées à la mutation et spécifiques de tumeur indépendante du tcr
WO2021074291A1 (fr) * 2019-10-16 2021-04-22 Medizinische Hochschule Hannover Identification moléculaire indépendante du tcr de lymphocytes t liés à une mutation et spécifiques d'une tumeur
WO2021091377A1 (fr) * 2019-11-05 2021-05-14 Stichting Euroflow Moyens et méthodes de sous-définition de leucocytes à base de cytométrie multiparamètre
NL2024163B1 (en) * 2019-11-05 2021-07-20 Univ Of Salamanca Means and methods for multiparameter cytometry-based leukocyte subsetting.
WO2022054796A1 (fr) * 2020-09-08 2022-03-17 学校法人 埼玉医科大学 Biomarqueur permettant de prédire la réponse à un traitement contre le cancer
WO2024108114A3 (fr) * 2022-11-18 2024-07-18 The Regents Of The University Of California Procédés de sélection de patients atteints d'un cancer réceptifs à un traitement avec un inhibiteur de point de contrôle immunitaire

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