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WO2025181348A1 - Procédé permettant de déterminer l'origine de l'adn circulant - Google Patents

Procédé permettant de déterminer l'origine de l'adn circulant

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Publication number
WO2025181348A1
WO2025181348A1 PCT/EP2025/055553 EP2025055553W WO2025181348A1 WO 2025181348 A1 WO2025181348 A1 WO 2025181348A1 EP 2025055553 W EP2025055553 W EP 2025055553W WO 2025181348 A1 WO2025181348 A1 WO 2025181348A1
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Prior art keywords
cfdna
cancer
origin
dna
cell
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Benjamin BERMAN
Heather Wilson-Robles
Mark Edward Eccleston
Theresa K. Kelly
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Belgian Volition SPRL
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Belgian Volition SPRL
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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
    • C12Q1/6881—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for tissue or cell typing, e.g. human leukocyte antigen [HLA] probes
    • 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
    • C12Q1/6844—Nucleic acid amplification reactions
    • C12Q1/6858—Allele-specific amplification
    • 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
    • C12Q1/6869—Methods for sequencing
    • 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

Definitions

  • the present invention relates to methods of determining the origin of cell free (circulating) DNA using a nanopore sequencer.
  • Fragmentation patterns can also be used to understand the origin of circulating DNA, although this is complicated by the fact that DNAse activity is not restricted to these cells but also occurs during circulation. Unlike bisulfite sequencing, which results in additional fragmentation of input DNA and has been used for many of the circulating cancer DNA studies, nanopore sequencing preserves native fragmentation properties.
  • One of the important global fragmentation changes associated with cancer is the shortening of the typical mono-nucleosomes and di-nucleosome fragments. While this shortening process disproportionally affects DNA derived from cancer relative to other non-cancer cell types, it appears to affect non-cancer DNA as well.
  • FIGURE 1 Nanopore sequencing of a selected subset of 37 cancer samples and 21 healthy samples.
  • A ichorCNA tumor fractions for all samples sequenced using Oxford Nanopore Technologies (ONT).
  • C H3.1 nucleosome levels vs. Illumina tumor fractions or ONT tumor fractions, for the same set of samples.
  • D Comparison of ONT ichorCNA cancer detection and DNA methylation-based cancer detection.
  • FIGURE 2 Identification of cancer-associated fragment length features.
  • A-B Principal component analysis of all cancer and healthy samples, using either unnormalized (A) or z-score normalized (B) counts for each fragment length bin. Lines indicate values above 50 for PC1 and values above -2 for PC2.
  • C Unnormalized counts of all cancer and healthy samples, ordered by PC1 and then PC2. Red line selects PC2 values greater than - 2. Loadings for each of the PCs are shown above ONT samples. Three samples with significant fragments over 10kb are separated out as “Healthy-Ultralong” samples.
  • D Same as C, but using bin counts z-score normalized using mean and standard deviation of healthy samples.
  • FIGURE S (A) Typical “spun” plasma processing workflow and alternative “unspun” workflow. (B) All ONT samples, showing proportion of DNA in fragments greater than 7.5kb in length. (C) Bioanalyzer trace showing one of the Healthy-Ultralong samples, together with a pre-centrifugation (“Unspun”) aliquot of the same plasma. (D) H3.1 concentration and (E) cfDNA concentration for 3 outlier healthy samples and 4 other healthy samples. (F-G) Repeated PCA analysis run including the three Unspun counterparts of the three Healthy-Ultralong samples. The right side of (E) shows the fraction of sequenced DNA in each of the fragment length ranges defined by each of the 3 principal components (right).
  • H-J Proportion of fragments greater than 500bp (H), 1 ,000 bp (I) and 7,500 bp (J).
  • K Proportion of fragment ends starting with CC in ONT sequencing, as a function of fragment length, for 4 typical healthy volunteers (black) and the three outlier healthy volunteers processed with the spun workflow (red) and the unspun workflow (blue).
  • L The proportion of fragment ends starting with CCCA in ONT sequencing, in typical length fragments (100- 400bp, x axis) vs. fragments greater than 7.5kb (y axis).
  • FIGURE 4 Unsupervised clustering by fragment length.
  • A All samples ordered by first three principal components. The first 3 sample groups are cancer samples with the cancer type indicated by color, and the second 3 groups are healthy samples. Bar plots on the left show total cfDNA concentration (ng/mL), total plasma H3.1 nucleosome concentration (ng/mL), tumor DNA fraction from ichorCNA from ONT sequencing (“CNA ONT”) and Illumina sequencing (“CAN ILLUMINA”), and the fraction of ONT fragments ending with the motif CCCA. In all bar plots, higher bars are also indicated with darker colors. The heatmap to the right of these bar plots shows the proportion of DNA sequenced in different fragment length bins, on a log scale from 50 bp to 50,000 bp.
  • the left heatmap shows the unnormalized proportion of sequenced DNA in each bin
  • the right heatmap shows z-scores which are column normalized according to the mean and standard deviation across all spun healthy samples.
  • Above the heatmaps are the PC loadings for each of the first 3 PCs.
  • the heatmap to the right shows the same data, but each fragment length bin/column is z-score normalized according to the mean and standard deviation across all healthy samples (not including “pre-spin” samples).
  • the bar plots on the right show the eigenvalues for the 3 principal components (“PC scores”), and the percentage of sequenced DNA in the ranges defined the by those PCs (“Frac DNA”).
  • PC scores principal components
  • Frac DNA percentage of sequenced DNA in the ranges defined the by those PCs
  • One sample with a sub-threshold but positive PC1 value is marked with an asterisk.
  • C CC end motif frequencies as a function of fragment length, for the 6 cancer samples with the highest PC3 scores, and the 6 cancer samples with the lowest PC3-scores.
  • FIGURE S Origins of short mono and dinucleosome fragments (75-145bp, 245- 295 bp)
  • A All samples, showing Cell of Origin (COO) scores for relevant cell types. 10 samples that had positive PC3 scores and >1.5M reads are shown with asterisk or “X”. Those with asterisk have a top COO cell type matching the cancer type, whereas the correct cell type was not detected in the four marked with “X”.
  • B ichorCNA tumor fraction for the 10 samples described above, for only the typical mononucleosome fragments (x axis) and the shorter mono-nucleosome and di-nucleosome fragments associated with PC3 (y axis).
  • C Methylated percentage averaged across CpGs from 2,274 CpG islands commonly hypermethylated in cancer. Elevated methylation at these regions was observed in the same six samples with elevated tumor fraction (Prostate cancer CA-14, Colorectal cancers CA-35, and CA-75, Non Hodgkins Lymphomas CB-64, CA-19, and CA-07)
  • D Cell type fraction for the 4 cell types correctly identified among the 10 samples, as estimated using COO deconvolution.
  • FIGURE S Results for DNASE1L3-associated CC motifs in long fragments. CC end motif frequency is plotted as a function of fragment length.
  • FIGURE 7 Links between hyperfragmentation and elevated circulating chromatin levels in cancer.
  • A 30 cancer samples from the PC3-high and PC1-3 low groups, ordered by PC3 value. Heatmap rows show sample features clustered by hierarchical clustering based on 1-p (Spearman’s rho). Heatmap rows are normalized to row min and max values.
  • B Spearman correlation values for all pairwise comparisons.
  • FIGURE 8 Cancer-Specific DNA Methylation Patterns in Client-Owned Dogs.
  • WGM Global methylation changes
  • (B) H3.1 plasma nucleosome concentrations in dogs newly diagnosed with large cell lymphoma (n 15) before and 24 hours after the first dose of chemotherapy (week 1, cycle 1). H3.1 concentration in dogs with (C) increases and (D) decreases, in nucleosomes 24 hours after chemotherapy.
  • FIGURE 9 Hyperfragmentation is a Strong Predictor of Healthy vs. Cancer Status in Dogs.
  • A Hyperfragmentation component (pel) in the 21 “ultra-shallow” healthy vs. cancer dog cohort.
  • B-D Bona-fide cancer DNA markers in the same cohort: global methylation (WGM) (B), tumor fraction from ichorCNA Copy Number Alterations (C), and average methylation at cancer-associated CpG Islands (CGI) (D).
  • E Receiver Operating Characteristics (ROC) using Multivariable Logistic Regression (MLR) to predict healthy vs cancer status using a combination of global methylation, CpG Island methylation, and tumor fraction as inputs.
  • MLR Multivariable Logistic Regression
  • AUC Area Under the Curve
  • LOOCV Leave-One-Out Cross Validation
  • FIGURE 10 Hyperfragmentation is a Strong Predictor of Inflammation vs. Cancer Status in Dogs.
  • A Hyperfragmentation component (pel) in “shallow” inflammation vs. cancer cohort (“INF” is inflammatory inflammation, “LSA” is lymphoma”, and “HSS” is hemangiosarcoma).
  • B-D Bona-fide cancer DNA markers in the same cohort: global methylation (WGM) (B), tumor fraction from ichorCNA Copy Number Alterations (C), and average methylation at cancer-associated CpG Islands (CGI) (D).
  • WGM global methylation
  • C cancer-associated CpG Islands
  • FIGURE 11 Using Hyperfragmentation and Cancer DNA Markers to Monitor Dogs during and After Treatment.
  • FIGURE 12 Hyperfragmentation changes 24 hours post chemotherapy in dogs.
  • A For 13 lymphoma cases from the “shallow” ONT sequencing cohort, pre-treatment (“Diagnosis”) values vs. 24 hours post chemotherapy values are shown for H3.1 nucleosome levels (left) and values of the hyperfragmentation component (pel), with both generally showing increases at the post chemotherapy timepoint.
  • B Bona-fide cancer DNA markers for the same samples.
  • C Pre-therapy (“Diagnosis”), the fragment length distribution for patient “HR” including all cancer-associated CpG islands shows longer fragments for both the cancer and non-cancer fragments, while post-chemotherapy fragments are shorter (hyperfragmentation) in both the cancer and non-cancer fragment groups.
  • FIGURE 13 Enrichment of Long Fragments in Clinical Sepsis.
  • B shows the fraction of fragments in each sample that have lengths between 900bp to 4,300bp.
  • C shows the fraction of granulocyte (neutrophil) DNA estimated using methylation-based cell of origin deconvolution from the Non-Negative Least Squares (NNLS) method of the CelFiE-ISH package.
  • NLS Non-Negative Least Squares
  • Circulating tumor DNA (ctDNA) is routinely identified in cancer patients and increasingly used for monitoring and early detection.
  • concentration of immune cell DNA is elevated in cancer, suggesting major changes in immune cell turnover or cell free DNA (cfDNA) homeostasis.
  • the present inventors used nanopore sequencing to identify a general shortening cfDNA in most cancers, and methylation-based cell of origin analysis revealed this phenomenon affects both cancer and non-cancer DNA. Therefore, incorporation of long-read sequencing provides an important parameter to consider when determining the tissue of origin of cfDNA and in methods of detecting ctDNA for liquid biopsy.
  • a tissue of origin a cell type of origin, origination from a cancerous or immune cell, or a combination thereof of cell-free DNA (cfDNA) in an animal subject, the method comprising:
  • Nanopore sequencing is more appropriate for longer range alterations such as aneuploidy and chromosomal arm amplifications or deletions particularly where rapid results are required.
  • One advantage we have found for nanopore sequencing is to provide direct methylated and/or hydroxymethylated DNA sequence results for cfDNA in real time (with no chemical pretreatment).
  • Nanopore sequencer instruments can also be small, low cost and suitable for use near to the patient. Moreover, when sufficient data has been obtained in real time, sequencing can be terminated avoiding the unnecessary use of further reagents leading to economy of use. Furthermore, nanopore sequencers are small and so can be used broadly and closer to the patient.
  • Nanopore DNA sequencing (for example the sequencing methods employed by Oxford Nanopore Technology DNA instruments) is becoming more commonly employed by workers in the field.
  • nanopore sequencing involves the passage of DNA strands through electrically charged nanopores.
  • passing is translocating.
  • an electrical disturbance characteristic of the nucleotide is induced and this is detected by a sensor connected to the nanopore.
  • Nanopore sequencing typically produces lower coverage results and less accurate DNA sequence results than sequencing by synthesis (“SBS”, for example the sequencing methods employed by Illumina NGS instruments are sequencing by synthesis) but has a number of advantages including the ability to sequence long DNA chains without fragmentation, the ability to directly sequence non-standard nucleotides in addition to adenine, thymine, cytosine and guanine (for example 5-methylcytosine and 5-hydroxymethylcytosine) and the generation of sequence data in real time and without library amplification. This facilitates the obtaining of sequence data in a shorter time of a few hours or less. Nanopore sequencing is expensive if used for high coverage sequencing of whole genomes but more economic for shallow sequencing (e.g. 1X coverage) or ultra-shallow (e.g. 0.05x coverage) of cfDNA fragments.
  • the nanopore sequencer is an Oxford Nanopore sequencer.
  • Methods of the invention may use a computer-based machine learning system to analyse the fragment length data.
  • Algorithms e.g. machine learning algorithms
  • executed by a computer can be used to automate analytical model building, e.g., for clustering, classification or pattern recognition.
  • Machine learning algorithms may be supervised or unsupervised. Learning algorithms include, for example, artificial neural networks, discriminant analyses (e.g. Bayesian classifier or Fischer analysis), support vector machines, decision trees (e.g. recursive partitioning processes such as CART - classification and regression trees, or random forests), linear classifiers (e.g. multiple linear regression (MLR), partial least squares (PLS) regression, and principal components regression), hierarchical clustering, and cluster analysis.
  • discriminant analyses e.g. Bayesian classifier or Fischer analysis
  • support vector machines e.g. recursive partitioning processes such as CART - classification and regression trees, or random forests
  • linear classifiers e.g. multiple linear regression (MLR), partial least square
  • analysis of the fragment length data is performed on the cfDNA after passing the cfDNA through the nanopore sequencer by clustering the data based on the length of said cfDNA.
  • the clustering is unsupervised clustering.
  • a classifier is used to cluster the data based on the length of said cfDNA.
  • the term “classifier” generally refers to algorithm computer code that receives, as input, test data and produces, as output, a classification of the input data as belonging to one or another class (e.g. length of fragment).
  • analysis of the fragment length data comprises transforming a fraction of sequencing reads from the fragment length data to an estimated fraction of total cfDNA present in the sample.
  • said transforming is performed prior to clustering.
  • clustering the data comprises separating the data into short, mid and long cfDNA lengths.
  • the short cfDNA length comprises 50-500 base pairs (bp) in length. In a further embodiment, the short cfDNA length comprises about 60-300 bp in length, such as about 75-145 and about 245-294 bp in length.
  • the mid cfDNA length comprises 501-5,000 bp in length. In a further embodiment, the mid cfDNA length comprises about 900-4,300 bp in length.
  • the long cfDNA length comprises 5,001 bp or more in length, such as 5,001-60,000 bp in length. In a further embodiment, the long cfDNA length comprises about 7,500-53,000 bp in length.
  • the method additionally comprises extracting the cfDNA from circulating chromatin fragments in the sample and sequencing the extracted cfDNA using the nanopore sequencer.
  • the cfDNA is unamplified after it is extracted from a sample from a subject. Unlike most cfDNA sequencing approaches, amplification is not required (i.e. the method may be “amplification free”), which may provide an even more accurate representation of fragmentation features.
  • the cfDNA is modified with a sequencing adapter. Therefore, the method may comprise ligating an adapter sequence below 75 nucleotides in length to the cfDNA to produce adapter ligated cfDNA.
  • Said adapter sequence may comprise a nucleic acid barcode that uniquely identifies a source sample (i.e. the sample from which the cfDNA is obtained) of the cfDNA.
  • Using adapter sequences can produce an adapter ligated cfDNA library for analysis.
  • the adapter ligated cfDNA library may then be passed through a nanopore sequencer to produce a sequence of the cfDNA.
  • the method further comprises performing an additional analysis on the cfDNA.
  • the additional analysis is performed on the cfDNA after passing the cfDNA through the nanopore sequencer.
  • fragmentation location analysis is performed on the cfDNA after passing the cfDNA through the nanopore sequencer and said fragmentation location analysis is used in combination with the fragment length data to determine a tissue of origin, cell type of origin, origination from a cancerous or immune cell or a combination thereof of cfDNA.
  • the fragmentation location analysis comprises fragment end motif analysis.
  • the fragment end motif analysis is performed with the sequence determined from sequencing a plurality of cfDNAs.
  • the method additionally comprises:
  • RNA sequencing RNA-seq
  • DNase deoxyribonuclease
  • RT-PCR Reverse Transcription Polymerase Chain Reaction
  • DNase enzymes catalyse the hydrolytic cleavage of phosphodiester linkages in the DNA backbone, thus degrading DNA.
  • DNase I deoxyribonuclease I
  • DNase II deoxyribonuclease II
  • the DNase enzyme is a DNase I enzyme.
  • the DNase I enzyme is DNASE1 L3.
  • the end sequence is an end 4 nucleotides.
  • the end sequences are the sequences provided in Chan et al., “Plasma DNA Profile Associated with DNASE1L3 Gene Mutations: Clinical Observations, Relationships to Nuclease Substrate Preference, and In Vivo Correction”, American Journal of Human Genetics, Vol. 107, No. 5 (2020), herein incorporated by reference in its entirety.
  • the end sequences are the sequences provided in Serpas et al., “Dnase1l3 deletion causes aberrations in length and end-motif frequencies in plasma DNA”. Proc. Natl Acad. Sci. Vol.
  • the end sequence is selected from CCCA, CCAG, CCTG, CCAA, CCCT, CCTT, CCAT, CAAA, CCTC, CCAC, TGAA, TAAA, CCTA, CCCC, TGAG, TGTT, CAAG, CTTT, AAAA, TGTG, CATT, CACA, CAGA, TATT, and CAGG.
  • the end sequence is CCCA.
  • the presence of a specific end fragment sequence indicates the DNA is from a cancer cell.
  • the presence of a specific end fragment sequence indicates the DNA is from an immune cell.
  • an enrichment of a specific end fragment sequence indicates the sample is from a subject that has cancer.
  • the presence or absence of a specific end fragment sequence is indicative of DNASE1L3 activity.
  • a reduction or absence of the end sequence e.g. CCCA, CCTG, CCAG, CCAA, CCAT and CCTC, in particular CCCA indicates loss of function of DNASE1L3.
  • DNA modification data refers to the information of the modification of a portion of bases in the DNA molecule.
  • the modification is an epigenetic modification, such as an epigenetically modified base.
  • the DNA modification data is selected from: methylation data, hydroxymethylation data and both, and said DNA modification data is used in combination with the fragment length data to determine a tissue of origin, cell type of origin, origination from a cancerous or immune cell or a combination thereof of cfDNA.
  • the methylation data is 5-methylcytosine (5mC) methylation.
  • the hydroxymethylation data is 5-hydroxymethylcytosine (5hmC) hydroxymethylation.
  • methylation data refers to the information of the methylation status of a portion of the bases in a DNA molecule.
  • hydroxymethylation data refers to the information of the hydroxymethylation status of a portion of the bases in a DNA molecule.
  • a portion is all of the bases.
  • the bases are cytosines.
  • copy number analysis is performed on the cfDNA after passing the cfDNA through the nanopore sequencer and said copy number analysis is used in combination with the fragment length data to determine a tissue of origin, cell type of origin, origination from a cancerous or immune cell or a combination thereof of cfDNA.
  • the fragment length data is combined with copy number analysis and one or more signatures derived from DNA modification data (such as methylation data) to determine a tissue of origin, cell type of origin, origination from a cancerous or immune cell or a combination thereof of cfDNA.
  • DNA modification data such as methylation data
  • the copy number analysis results in the detection of an oncogene amplification and further comprising administering an agent that targets said oncogene.
  • the identifying is based on the fragment length and the fragmentation location analysis. In some embodiments, the identifying is based on the fragment length and DNA modification data. In some embodiments, the identifying is based on the fragment length and copy number analysis. In some embodiments, the identifying is based on the fragment length and the level of circulating nucleosomes and/or total level of cfDNA. In some embodiments, the identifying is based on the fragment length, the fragmentation location analysis, the DNA modification data and the level of circulating nucleosomes. In some embodiments, the identifying is based on the fragment length, the fragmentation location analysis, the DNA modification data and the total level of cfDNA.
  • the identifying is based on the fragment length, the fragmentation location analysis, the DNA modification data, the copy number analysis and the level of circulating nucleosomes. In some embodiments, the identifying is based on the fragment length, the fragmentation location analysis, the DNA modification data, the copy number analysis and the total level of cfDNA.
  • the present inventors have found a distinct hyperfragmentation pattern occurs across a large fraction of cancers and are associated with markers of altered DNase activity.
  • the results show that fragments derived from cancer can be distinguished by a shorter length distribution (e.g. 75-145 bp and 245-295 bp) and end motif patterns consistent with loss of DNASE1L3 fragmentation activity. Therefore, the DNase activity, in particular DNASE1 L3 activity, can be used in the determination of a tissue of origin, cell type of origin, origination from a cancerous or immune cell or a combination thereof of cfDNA.
  • the method additionally comprises detecting DNASE1L3 activity.
  • said detecting comprises: (a) fragment end motif analysis, such as analysing the presence or absence of a specific end fragment sequence which is indicative of DNASE1L3 activity;
  • RNA sequencing (b) performing RNA sequencing (RNA-seq) on the body fluid sample and detecting a level of DNASE1L3 mRNA expression;
  • the method is a method of determining origination from a cancerous cell and further comprises identifying a cancer-specific DNA modification change in said cancerous cell.
  • the method is a method of determining origination from an immune cell, which optionally further comprises identifying an immune- specific sequence in said immune cell.
  • the method additionally comprises centrifuging the sample prior to passing the cfDNA through a nanopore sequencer.
  • the sample may be any biological fluid (or body fluid) sample taken from a subject including, without limitation, cerebrospinal fluid (CSF), whole blood, blood serum, plasma, menstrual blood, endometrial fluid, urine, saliva, or other bodily fluid (stool, tear fluid, synovial fluid, sputum), breath, e.g. as condensed breath, or an extract or purification therefrom, or dilution thereof.
  • CSF cerebrospinal fluid
  • whole blood blood serum, plasma, menstrual blood, endometrial fluid, urine, saliva, or other bodily fluid (stool, tear fluid, synovial fluid, sputum), breath, e.g. as condensed breath, or an extract or purification therefrom, or dilution thereof.
  • blood, serum or plasma samples are used.
  • plasma samples are used.
  • Plasma samples may be collected in collection tubes containing one or more anticoagulants such as ethylenediamine tetraacetic acid (EDTA), heparin, or sodium cit
  • biomarker means a distinctive biological or biologically derived indicator of a process, event, or condition. Used herein, the biomarker may refer to fragment length data obtained by the nanopore sequencer. Biomarkers can be used in methods of diagnosis, e.g. clinical screening, and prognosis assessment and in monitoring the results of therapy, identifying patients most likely to respond to a particular therapeutic treatment, drug screening and development. Biomarkers and uses thereof are valuable for identification of new drug treatments and for discovery of new targets for drug treatment.
  • a size profile obtained by nanopore sequencing of all circulating chromatin fragments present in a body fluid sample to identify a patient suitable for cancer treatment, such as immunotherapy.
  • Cancerous cells of interest in the present invention are derived from a cancer.
  • the cancer is a haematological cancer including without limitation leukaemias, lymphomas (including canine lymphoma), myelomas and angiosarcomas (including canine hemangiosarcoma).
  • the cancer disease is a solid cancer including without limitation, lung cancer, liver cancer, prostate cancer, breast cancer, gastric cancer, colorectal cancer, thyroid cancer, skin cancer (e.g. melanoma), bladder cancer, cervical cancer, pancreatic cancer, brain cancer, ovarian cancer, endometrial cancer or renal cancer.
  • Haematological cancers are cancers of the blood, therefore may also be referred to as “liquid or blood cancers”. There are 3 principal types of haematological cancers: leukaemias, which are caused by the rapid production of abnormal white blood cells; lymphomas which are caused by abnormal lymphoma cells; and myelomas, which is a cancer of the plasma cells.
  • a blood cancer may be considered to be any cancer in direct contact with the circulation.
  • the cancerous cells are derived from hemangiosarcoma.
  • Leukaemia is cancer of the blood cells which usually starts in the bone marrow and travels through the bloodstream.
  • the bone marrow produces mutated cells and spreads them into the blood, where they grow and crowd out healthy blood cells.
  • Lymphoma diseases affect the cells in the lymphatic system.
  • lymphomas immune cells called lymphocytes grow out of control and collect in lymph nodes, the spleen, in other lymph tissues or in neighbouring organs.
  • Myeloma also known as multiple myeloma, develops in the bone marrow and affects plasma cells, which produce antibodies that attack infections and diseases.
  • Examples of blood cancers include Acute Lymphoblastic Leukaemia (ALL), Acute Myeloid Leukaemia (AML), Hodgkin Lymphoma (HL) and Non-Hodgkin Lymphoma (NHL).
  • references to “acute leukaemia” means the cancer progresses quickly and aggressively, usually requiring immediate treatment.
  • ALL involves the development of large numbers of immature lymphocytes which are unable to fight infection. This causes the patient to have less room for healthy white blood cells, red blood cells, and platelets in the circulation. As a result, the patient usually suffers from a weakened immune system and the symptoms of anaemia, such as tiredness, breathlessness and an increased risk of excessive bleeding.
  • the risk for developing ALL is highest in children younger than 5 years of age and it is the most common type of leukaemia that affects children. The risk then declines slowly until the mid-20s, and begins to rise again slowly after age 50. Overall, about 4 of every 10 cases of ALL are in adults.
  • AML affects myeloblasts which results in the accumulation of abnormal monocytes and granulocytes in the bone marrow. AML may also affect myeloid stem cells resulting in abnormal red blood cells or platelets. As with ALL, this causes the patient to have lower levels of healthy white blood cells, red blood cells, and platelets in the circulation. AML is one of the most common types of leukaemia in adults and the average age at diagnosis is 68.
  • HL and NHL are the two main types of lymphoma.
  • HL has a particular appearance under the microscope and contains cells called Reed-Sternberg cells (a type of B lymphocyte that has become cancerous), whereas NHL looks different under the microscope and does not contain Reed-Sternberg cells.
  • Most lymphomas are NHL and only about 1 in 5 are HL.
  • NHL is a cancer affecting lymphocytes and usually starts in lymph nodes or lymph tissue. It is one of the more common cancers among children, teens and young adults.
  • CBC complete blood count
  • WBC white blood cell count
  • X-ray, CT or PET scan can be used to detect swollen lymph nodes, however this is also non-specific.
  • a bone marrow or lymph node biopsy is required.
  • Immune cells of interest in the present invention include, but are not limited to, CD34+ cells, B-Cells, CD45+ (lymphocyte common antigen) cells, Alpha-Beta T-cells, Cytotoxic T-cells, Helper T-cells, Plasma Cells, Neutrophils, Monocytes, Macrophages, Red Blood Cells, Platelets, Dendritic Cells, Phagocytes, Granulocytes, Innate lymphoid cells, Natural Killer (NK) cells and Gamma Delta T-cells.
  • immune cells are classified with the aid of combinatorial cell surface molecule analysis (e.g. via flow cytometry) to identify or group or cluster to differentiate immune cells into sub-populations. These can be then still further sub-divided with additional analysis.
  • Uses and methods may additionally comprise measuring or detecting the level of chromatin fragments in the body fluid sample and said level is used in combination with the fragment length data to determine a tissue of origin, cell type of origin, origination from a cancerous or immune cell or a combination thereof of cfDNA.
  • chromatin fragment refers to a complex of proteins and nucleic acid whose origin lies in the chromosome or mitochondria of a cell. The term encompasses chromatin fragments found outside of cells, which may also be referred to as “cell free chromatin fragments”.
  • a fragment of chromatin may contain a nucleosome and/or associated DNA and/or any of a huge variety of non-histone chromatin associated proteins in a multi-protein-nucleic acid complex.
  • Some examples of non-histone chromatin associated proteins include transcription factors, cofactors, co-activators, co-repressors, RNA polymerase moieties, elongation factors, chromatin remodelling factors, mediators, STAT moieties, upstream binding factor (UBF) and others.
  • Chromatin fragments, cfDNA or cf-nucleosomes may be measured by many methods including, for example without limitation, binding methods such as immunochemical or immunoassay methods or binding by DNA intercalating dyes, sequencing (for example to determine read numbers), rtPCR methods and spectroscopic methods.
  • the method additionally comprises measuring or detecting the level of circulating (cell free) nucleosomes in the body fluid sample and said level is used in combination with the fragment length data to determine a tissue of origin, cell type of origin, origination from a cancerous or immune cell or a combination thereof of cfDNA.
  • the method additionally comprises measuring or detecting the level of circulating nucleosomes in the body fluid sample and said level is used in combination with the fragment length data to determine a tissue of origin, cell type of origin, origination from a cancerous or immune cell or a combination thereof of cfDNA. Sequencing can only provide relative cell type proportions therefore in order to address the question of globally elevated levels of chromatin, the inventors profiled not only DNA concentration in these individuals, but also absolute concentrations of circulating nucleosomes.
  • the nucleosome is the basic unit of chromatin structure and consists of a protein complex of eight highly conserved core histones (comprising of a pair of each of the histones H2A, H2B, H3, and H4). Around this complex is wrapped approximately 146 base pairs of DNA. Another histone, H1 or H5, acts as a linker and is involved in chromatin compaction.
  • the DNA is wound around consecutive nucleosomes in a structure often said to resemble “beads on a string” and this forms the basic structure of open or euchromatin. In compacted or heterochromatin this string is coiled and super coiled into a closed and complex structure (Herranz and Esteller (2007) Methods Mol. Biol. 361: 25-62).
  • NETs Neutrophil extracellular traps
  • ETs extracellular traps
  • nucleosome may refer to “cell free nucleosome” when detected in body fluid samples. It will be appreciated that the term cell free nucleosome throughout this document is intended to include any circulating chromatin fragment that includes one or more nucleosomes. “Epigenetic features”, “epigenetic signal features” or “epigenetic signal structures” of a cell free nucleosome as referred herein may comprise, without limitation, one or more histone post-translational modifications, histone isoforms, modified nucleotides and/or proteins bound to a nucleosome in a nucleosome-protein adduct.
  • the cell free nucleosome may be detected by binding to a component thereof.
  • component thereof refers to a part of the nucleosome, i.e. the whole nucleosome does not need to be detected.
  • the component of the cell free nucleosomes may be selected from the group consisting of: a histone protein ⁇ i.e. histone H1, H2A, H2B, H3 or H4), a histone post-translational modification, a histone variant or isoform, a protein bound to the nucleosome i.e.
  • nucleosome-protein adduct a nucleosome-protein adduct
  • DNA fragment associated with the nucleosome and/or a modified nucleotide associated with the nucleosome.
  • the component thereof may be histone (isoform) H3.1 or histone H1 or DNA.
  • the component of the nucleosome is a histone protein.
  • histone refers to histones and modifications thereof, as described herein (e.g. post-translational modifications, mutations, isoforms, variants and fragments of histones, such as clipped histones).
  • nucleosomes per se refers to the total nucleosome level or concentration present in the sample, regardless of any epigenetic features the nucleosomes may or may not include. Detection of the total nucleosome level typically involves detecting a histone protein common to all nucleosomes, such as histone H4. Therefore, nucleosomes per se may be measured by detecting a core histone protein, such as histone H4. As described herein, histone proteins form structural units known as nucleosomes which are used to package DNA in eukaryotic cells and also form the repeating units present in ETs and NETs.
  • Mononucleosomes and oligonucleosomes can be detected by Enzyme-Linked ImmunoSorbant Assay (ELISA) and several methods have been reported (e.g. Salgame et al. (1997); Holdenrieder et al. (2001); van Nieuwenhuijze et al. (2003)). These assays typically employ an anti-histone antibody (for example anti-H2B, anti-H3 or anti-H1, H2A, H2B, H3 and H4) as capture antibody and an anti-DNA or anti-H2A-H2B-DNA complex antibody as detection antibody.
  • an anti-histone antibody for example anti-H2B, anti-H3 or anti-H1, H2A, H2B, H3 and H4
  • Circulating nucleosomes are not a homogeneous group of protein-nucleic acid complexes. Rather, they are a heterogeneous group of chromatin fragments originating from the digestion of chromatin on cell death and include an immense variety of epigenetic structures including particular histone isoforms (or variants), post-translational histone modifications, nucleotides or modified nucleotides, and protein adducts.
  • Uses and methods of the invention may include data for additional biomarkers, such as the level of cell free nucleosomes per se and/or an epigenetic feature of a cell free nucleosome.
  • additional biomarkers such as the level of cell free nucleosomes per se and/or an epigenetic feature of a cell free nucleosome.
  • the terms “epigenetic signal structure” and “epigenetic feature” are used interchangeably herein. They refer to particular features of the nucleosome that may be detected.
  • the epigenetic feature of the nucleosome is selected from the group consisting of: a post-translational histone modification, a histone variant, a particular nucleotide and a protein adduct.
  • the epigenetic feature of the nucleosome is the histone isoform H3.1.
  • the structure of a nucleosome may vary by the inclusion of alternative histone isoforms or variants which are different gene or splice products and have different amino acid sequences.
  • the epigenetic feature of the nucleosome comprises a histone variant or isoform.
  • histone variant and “histone isoform” may be used interchangeably herein.
  • Histone isoforms are known in the art. Histone isoforms can be classed into a number of families which are subdivided into individual types. The sequences of a large number of histone isoforms are known and publicly available for example in the National Human Genome Research Institute NHGRI Histone Database (Marino-Ramirez et al.
  • the Histone Database an integrated resource for histones and histone fold-containing proteins.
  • GenBank NIH genetic sequence
  • EMBL Nucleotide Sequence Database the DNA Data Bank of Japan (DDBJ).
  • isoforms of histone H2 include H2A1, H2A2, mH2A1, mH2A2, H2AX and H2AZ.
  • histone isoforms of H3 include H3.1, H3.2 and H3t.
  • the histone isoform is H3.1.
  • the epigenetic feature is a mutated histone.
  • the mutation is in histone 3 (H3).
  • the mutation in H3 is when lysine 27 is replaced by a methionine (H3K27M).
  • the structure of nucleosomes can vary by post translational modification (PTM) of histone proteins.
  • PTM of histone proteins typically occurs on the tails of the core histones and common modifications include acetylation, methylation or ubiquitination of lysine residues as well as citrullination or methylation of arginine residues and phosphorylation of serine residues and many others.
  • a histone PTM may occur on different isoforms (variants) of the histone.
  • the lysine residues that occur on the tail of histone H3 isoforms H3.1, H3.2 and H3.3 may be modified by acetylation or methylation.
  • the epigenetic feature of the cell free nucleosome may be a histone post translational modification (PTM).
  • the histone PTM may be present on a core nucleosome histone (e.g. H2A, H2B, H3 or H4), or a linker histone (e.g. H1 or H5). Examples of PTMs are described in WO 2005/019826 and WO 2017/068359.
  • the histone PTMs are selected from acetylation, methylation (which may be mono-, di- or tri-methylation), phosphorylation, ribosylation, citrullination, ubiquitination, hydroxylation, glycosylation, nitrosylation, glutamination and isomerisation.
  • the histone PTM is methylation of a lysine residue.
  • the methylation is of a histone 3 lysine residue.
  • the histone PTM is selected from H3K4Me, H3K4Me2, H3K9Me, H3K9Me3, H3K27Me3 or H3K36Me3.
  • the histone PTM is acetylation of a lysine residue. In a further embodiment, the acetylation is of a histone 3 lysine residue. In a yet further embodiment, the histone PTM is selected from H3K9Ac, H3K14AC, H3K18AC or H3K27AC. In another embodiment, the histone PTM is H4PanAc. In one embodiment, the histone PTM is phosphorylation of a serine residue. In a further embodiment, the phosphorylation is of an isoform X of histone 2A (H2AX) serine residue or phosphorylation of a histone 3 serine residue.
  • H2AX histone 2A
  • the histone PTM is selected from pH2AX or H3S10Ph. In one embodiment, the histone PTM is selected from citrullination or ribosylation. In a further embodiment, the histone PTM is citrullinated H3 (H3cit) or citrullinated H4 (H4cit). In a further embodiment, the histone PTM is citrullination of a histone 3 arginine residue. In a yet further embodiment, the histone PTM is H3R8Cit.
  • the histone PTM is selected from the group consisting of: H3K4Me, H3K4Me2, H3K9Me, H3K9Me3, H3K27Me3, H3K36Me3, H3K9Ac, H3K14AC, H3K18AC, H3K27AC, H4PanAc, pH2AX, H3S10Ph and H3R8Cit.
  • a group or class of related histone post translational modifications may also be detected.
  • a typical example, without limitation, would involve a 2-site immunoassay employing one antibody or other selective binder directed to bind to nucleosomes and one antibody or other selective binder directed to bind the group of histone modifications in question.
  • Examples of such antibodies directed to bind to a group of histone modifications would include, for illustrative purposes and without limitation, anti-pan- acetylation antibodies (e.g. a Pan-acetyl H4 antibody [H4panAc]), anti-citrullination antibodies or anti-ubiquitin antibodies.
  • the epigenetic feature is a DNA modification.
  • nucleosomes also differ in their nucleotide and modified nucleotide composition. Some nucleosomes may comprise more 5-methylcytosine residues, or 5-hydroxymethylcytosine residues or other nucleotides or modified nucleotides, than other nucleosomes.
  • the epigenetic feature is a DNA modification selected from 5-methylcytosine or 5-hydroxymethylcytosine.
  • the defined calibrated DNA modification is 5-methylcytosine or 5-hydroxymethylcytosine.
  • nucleosome protein adducts A further type of circulating nucleosome subset is nucleosome protein adducts. It has been known for many years that chromatin comprises a large number of non-histone proteins bound to its constituent DNA and/or histones. These chromatin associated proteins are of a wide variety of types and have a variety of functions including transcription factors, transcription enhancement factors, transcription repression factors, histone modifying enzymes, DNA damage repair proteins and many more. These chromatin fragments including nucleosomes and other non-histone chromatin proteins or DNA and other nonhistone chromatin proteins are described in the art. Therefore, in one embodiment, the epigenetic feature comprises one or more protein-nucleosome adducts or complexes.
  • epigenetic feature of cell free nucleosomes may be detected in methods and uses of the invention.
  • the epigenetic features may be the same type (e.g. PTMs, histone isoforms, nucleotides or protein adducts) or different types (e.g. a PTM in combination with a histone isoform).
  • a post-translational histone modification and a histone variant may be detected (/.e. more than one type of epigenetic feature is detected).
  • more than one type of post-translational histone modification is detected, or more than one type of histone isoform is detected.
  • the method may additionally comprise measuring or detecting the level of circulating cell free nucleosomes.
  • Said measurement or detection comprises methods described hereinbefore, such as an immunoassay, immunochemical, mass spectroscopy, chromatographic, chromatin immunoprecipitation or biosensor method.
  • the measurement or detection employs a single binding agent.
  • the measurement or detection comprises a 2-site immunometric assay employing two binding agents.
  • the terms “antibody”, “binder” or “ligand” as used herein are not limiting but are intended to include any binder capable of specifically binding to particular molecules or entities and that any suitable binder can be used in the method of the invention.
  • the binding agent is an antibody.
  • the binding agent is a chromatin binding protein.
  • the most commonly used epitope binders in the art are antibodies or derivatives of an antibody that contain a specific binding domain.
  • the antibody may be a polyclonal antibody or a monoclonal antibody or a fragment thereof capable of specific binding to the epitope.
  • any binder capable of binding to a particular epitope may be used for the purposes of the invention.
  • the reagents may comprise one or more ligands or binders, for example, naturally occurring or chemically synthesised compounds, capable of specific binding to the desired target.
  • a ligand or binder may comprise a peptide, an antibody or a fragment thereof, or a synthetic ligand such as a plastic antibody, or an aptamer or oligonucleotide, capable of specific binding to the desired target.
  • the antibody can be a monoclonal antibody or a fragment thereof. It will be understood that if an antibody fragment is used then it retains the ability to bind the biomarker so that the biomarker may be detected (in accordance with the present invention).
  • a ligand/binder may be labelled with a detectable marker, such as a luminescent, fluorescent, enzyme or radioactive marker; alternatively or additionally a ligand according to the invention may be labelled with an affinity tag, e.g. a biotin, avidin, streptavidin or His (e.g. hexa-His) tag.
  • a detectable marker such as a luminescent, fluorescent, enzyme or radioactive marker
  • an affinity tag e.g. a biotin, avidin, streptavidin or His (e.g. hexa-His) tag.
  • affinity tag e.g. a biotin, avidin, streptavidin or His (e.g. hexa-His) tag.
  • ligand binding may be determined using a label-free technology for example that of ForteBio Inc.
  • the terms antibody or binder as used herein are interchangeable and refer to any moiety capable of specific binding to an epitope.
  • the binding agent is directed to a histone, nucleosome core protein, DNA epitope or a protein adducted to a nucleosome.
  • the binding agent is directed to a histone isoform, such as a histone isoform of a core histone, in particular a histone H3 isoform.
  • the binding agent specifically binds to histone isoform H3.1.
  • a binding agent is considered to “specifically bind” if there is a greater than 10 fold difference, and preferably a 25, 50 or 100 fold difference between the binding of the agent to a particular target epitope compared to an non-target epitope.
  • the binding agent may comprise an MHC molecule or part thereof which comprises the peptide binding groove.
  • the agent may comprise an anti-peptide antibody.
  • antibody includes a whole immunoglobulin molecule or a part thereof or a bioisostere or a mimetic thereof or a derivative thereof or a combination thereof. Examples of a part thereof include: Fab, F(ab)'2; and Fv. Examples of a bioisostere include single chain Fv (scFv) fragments, chimeric antibodies, bifunctional antibodies.
  • the term “mimetic” relates to any chemical which may be a peptide, polypeptide, antibody or other organic chemical which has the same binding specificity as the antibody.
  • derivative as used herein in relation to antibodies includes chemical modification of an antibody. Illustrative of such modifications would be replacement of hydrogen by an alkyl, acyl, or amino group.
  • the binding agent may be an aptamer or a non- immunoglobulin scaffold such as an affibody, an affilin molecule, an AdNectin, a lipocalin mutein, a DARPin, a Knottin, a Kunitz-type domain, an Avimer, a Tetranectin or a transbody.
  • a non- immunoglobulin scaffold such as an affibody, an affilin molecule, an AdNectin, a lipocalin mutein, a DARPin, a Knottin, a Kunitz-type domain, an Avimer, a Tetranectin or a transbody.
  • the method of measuring the level of nucleosomes comprises contacting the sample with a solid phase comprising a binding agent that detects nucleosomes or a component thereof, and detecting binding to said binding agent.
  • the method of measuring the level of nucleosomes may comprise: (a) contacting the sample with a first binding agent which binds to an epigenetic feature of a cell free nucleosome; (b) contacting the sample bound by the first binding agent in step (a) with a second binding agent which binds to cell free nucleosomes; and (c) detecting or quantifying the binding of the second binding agent in the sample.
  • the measuring the level of nucleosomes may comprise: (a) contacting the sample with a first binding agent which binds to cell free nucleosomes; (b) contacting the sample bound by the first binding agent in step (a) with a second binding agent which binds to an epigenetic feature of the cell free nucleosome; and (c) detecting or quantifying the binding of the second binding agent in the sample.
  • the binding agent is linked to a solid phase. Therefore, the circulating chromatin fragment (e.g. nucleosome) may be bound and isolated from the sample before analysis.
  • Methods of the invention may be for use in cancer detection or diagnosis, early cancer screening, residual disease detection, relapse detection, metastasis detection or a combination thereof.
  • detecting or “diagnosing” as used herein encompasses identification, confirmation, and/or characterisation of a disease state.
  • Methods of detecting, monitoring and of diagnosis according to the invention are useful to confirm the existence of a disease, to monitor development of the disease by assessing onset and progression, or to assess amelioration or regression of the disease.
  • Methods of detecting, monitoring and of diagnosis are also useful in methods for assessment of clinical screening, prognosis, choice of therapy, evaluation of therapeutic benefit, i.e. for drug screening and drug development.
  • the method described herein is repeated on multiple occasions. This embodiment provides the advantage of allowing the detection results to be monitored over a time period. Such an arrangement will provide the benefit of monitoring or assessing the efficacy of treatment of a disease state. Such monitoring methods of the invention can be used to monitor onset, progression, stabilisation, amelioration, relapse and/or remission.
  • test samples may be taken on two or more occasions.
  • the method may further comprise comparing the level of the biomarker(s) present in the test sample with one or more control(s) and/or with one or more previous test sample(s) taken earlier from the same test subject, e.g. prior to commencement of therapy, and/or from the same test subject at an earlier stage of therapy.
  • the method may comprise detecting a change in the nature or amount of the biomarker(s) in test samples taken on different occasions.
  • a change in the level of the biomarker in the test sample relative to the level in a previous test sample taken earlier from the same test subject may be indicative of a beneficial effect, e.g. stabilisation or improvement, of said therapy on the disorder or suspected disorder.
  • the method of the invention may be periodically repeated in order to monitor for the recurrence of a disease.
  • Methods of the invention may be used to identify a patient suitable for cancer treatment, such as immunotherapy. Therefore, methods of the invention may be for use in a method for monitoring the efficacy of a therapy in a subject having, suspected of having, or being predisposed to cancer.
  • Methods for monitoring efficacy of a therapy can be used to monitor the therapeutic effectiveness of existing therapies and new therapies in human subjects and in non-human animals (e.g. in animal models). These monitoring methods can be incorporated into screens for new drug substances and combinations of substances. [109] In a further embodiment the monitoring of more rapid changes due to fast acting therapies may be conducted at shorter intervals of hours or days.
  • Biomarkers for detecting the presence of a disease are essential targets for discovery of novel targets and drug molecules that retard or halt progression of the disorder. As the level of the biomarker is indicative of disorder and of drug response, the biomarker is useful for identification of novel therapeutic compounds in in vitro and/or in vivo assays. Biomarkers described herein can be employed in methods for screening for compounds that modulate the activity of the biomarker.
  • biomarkers for a disease state permits integration of diagnostic procedures and therapeutic regimes.
  • the biomarkers provide the means to indicate therapeutic response, failure to respond, unfavourable side-effect profile, degree of medication compliance and achievement of adequate serum drug levels.
  • the biomarkers may be used to provide warning of adverse drug response. Biomarkers are useful in development of personalized therapies, as assessment of response can be used to fine-tune dosage, minimise the number of prescribed medications, reduce the delay in attaining effective therapy and avoid adverse drug reactions.
  • biomarker of the invention can be used to titrate the optimal dose, predict a positive therapeutic response and identify those subjects at high risk of severe side effects.
  • Biomarker-based tests provide a first line assessment of ‘new’ subjects, and provide objective measures for accurate and rapid diagnosis, not achievable using the current measures.
  • Biomarker monitoring methods are also vital as subject monitoring tools, to enable the physician to determine whether relapse is due to worsening of the disorder. If pharmacological treatment is assessed to be inadequate, then therapy can be reinstated or increased; a change in therapy can be given if appropriate. As the biomarkers are sensitive to the state of the disorder, they provide an indication of the impact of drug therapy.
  • the subject is suspected of relapse to a cancer.
  • Minimal residual disease is the name given to small numbers of cancer cells that remain in the person during treatment, or after treatment when the patient is in remission (/.e. patients with no symptoms or signs of disease).
  • MRD is the major cause of relapse in cancer. Methods of the invention are therefore useful in monitoring patients who are suspected of relapse, particularly patients who are in remission from cancer.
  • the subject tested using the methods described herein may present with symptoms indicative of cancer, for example the symptoms of a haematological cancer may include anaemia, leucocytosis and/or swollen lymph nodes.
  • the subject has a high level of leucocytosis. This may also be referred to a “high white blood cell count”.
  • Haematological cancers typically cause increased proliferation of abnormal white or red blood cells which results in a high white blood cell count.
  • leucocytosis is not sufficient to diagnose a patient with a haematological cancer (in particular leukaemia) because it is frequently a sign of an inflammatory response, most commonly the result of infection. Therefore, methods of the invention are able to provide a more specific differential method to identify patients who are likely to be suffering from cancer or an inflammatory condition.
  • Cut-off values can be predetermined by analysing results from multiple patients and controls, and determining a suitable value for classifying a subject as with or without the disease. For example, for diseases where the level of biomarker is higher in patients suffering from the disease, then if the level detected is higher than the cut-off, the patient is indicated to suffer from the disease. Alternatively, for diseases where the level of biomarker is lower in patients suffering from the disease, then if the level detected is lower than the cut-off, the patient is indicated to suffer from the disease.
  • the advantages of using simple cut-off values include the ease with which clinicians are able to understand the test and the elimination of any need for software or other aids in the interpretation of the test results. Cut-off levels can be determined using methods in the art.
  • control subjects may be selected on a variety of basis which may include, for example, subjects known to be free of the disease or may be subjects with a different disease (for example, for the investigation of differential diagnosis).
  • the “control” may comprise a healthy subject, a non-diseased subject and/or a subject without a haematological cancer. Comparison with a control is well known in the field of diagnostics.
  • Both positive and negative controls may be used.
  • the presence of a cancer disease in a subject may be confirmed by comparison of results with known cancer controls (positive control) as well as with known disease free or non-cancer controls (negative control).
  • the method additionally comprises determining at least one clinical parameter for the patient.
  • This parameter can be used in the interpretation of results.
  • Clinical parameters may include any relevant clinical information for example, without limitation, gender, weight, Body Mass Index (BMI), smoking status, temperature and dietary habits. Therefore, in one embodiment, the clinical parameter is selected from the group consisting of: age, sex and body mass index (BMI).
  • the method of the invention is performed to identify a subject at high risk of having a cancer and therefore in need of further testing (/.e. further cancer investigations).
  • the further testing may involve one or more of: biopsy (such as bone marrow biopsy or lymph node biopsy), cytogenetic testing, immunophenotyping, CT scanning, X-ray (in particular chest X-ray to identify swollen lymph nodes) and/or lumbar puncture.
  • Methods and biomarkers described herein may be used to identify if a patient is in need of a biopsy, in particular a bone marrow or lymph node biopsy (e.g. for patients with suspected haematological cancer). Therefore, according to a further aspect of the invention there is provided a method of identifying a patient in need of a biopsy comprising performing the method of the invention and using the results obtained to identify whether the patient is in need of a biopsy.
  • the subject may be a human or an animal subject.
  • the subject is a human subject.
  • the subject is a (non-human) animal subject.
  • the animal is a companion animal (also referred to as a pet or domestic animal).
  • Companion animals include, for example dogs, cats, rabbits, ferrets, horses, cows, or the like.
  • the companion animal is a dog or cat, particularly a dog. The methods described herein may be performed in vitro, or ex vivo.
  • Plasma samples were procured through the following Commercial Biobanks: Discovery Life Sciences (DLS), Alternative Research, and Bay Biosciences. Selection of 52 self-reported healthy individuals for Illumina sequencing were selected based on donor age criteria: an even distribution of ages from 40 to 90 years old. Selection of 207 cancer cases was based on Stage lll/IV status and diversity of cancer types. Selection of healthy samples for Nanopore sequencing was based on volume of plasma available and the age of the donor. Samples were evenly distributed across ages with 10 samples in each of the following age groups: 40-49, 50-59, and 60-69 years old. 15 samples were procured in the age group 70-79, and 5 samples in the age group 80-89. Healthy samples were only considered on a lack of cancer diagnosis. Cancer samples were selected based on late stage diagnosis (III or IV), type of cancer, treatment status (untreated) and large sample volume.
  • Plasma samples were received and subsequently stored at -80°C. Aliquots were thawed at room temperature (RT) for up to 2 hours, depending on the aliquot volume. Plasma was then spun at 14000 x g for 2 minutes at room temperature and transferred to a new tube avoiding any pelleted fraction.
  • Nucleosome levels in plasma were quantified using the Nu.Q® H3.1 assay developed by Volition for use on the IDS i 10 instrument, using 250 uL of plasma according to the manufacturer’s recommendations. Samples were completed either in duplicate or triplicate. Raw RLU (Relative Light Units) were converted to concentrations of nucleosomes (ng/mL) using the provided kit standards, and replicate measurements were averaged.
  • DNA from the basic characterization step was used for Illumina Sequencing. If more was needed, additional DNA from 1-5 mL of plasma was extracted using the QIAamp® Circulating Nucleic Acid Kit (Qiagen, catalogue # 55114).
  • Illumina sequencing libraries were constructed using SRSLY® PicoPlus DNA NGS Library Preparation Kit (ClaretBio - Cat: CBS-K250B). Libraries were sequenced by Discovery Life Sciences using the NovaSeq 6000 S4 200 cycle flow cell. Genome mapping to hg38 was performed using the Illumina BaseSpace DRAGEN pipeline.
  • methylation BED files were created using modkit v. 0.1.5 with the command “modkit pileup --cpg --combine-strands --ignore h --filter-threshold 0.9 --bedgraph”.
  • Modkit pileup --cpg --combine-strands --ignore h --filter-threshold 0.9 --bedgraph For methylation deconvolution, Minimap BAM files were used directly (details below).
  • Fragment lengths were extracted from ONT Minimap BAMs using the script “fragmentationReports.py”, which uses Pysam and defines the fragment length based on the primary alignment, calculating the difference between the start and end coordinates on the reference genome.
  • Fragment length distributions for PCA analysis were created by defining bins as 10 A x where x contains a range 50 to 50,000. Raw fragment lengths are log transformed and assigned to the closest bin by rounding to the nearest increment of 10 A 0.01. The proportion of fragments is defined as the number of fragments in a given bin divided by the total number of fragments in all bins. For the proportion of total DNA, each fragment in a bin is multiplied by the number of base pairs in the fragment and summed to get the bin base pair total. Each bin total is then divided by the sum of all base pair sums in all bins.
  • Minimap BAMs were filtered using the script “stratifyBamByFraglen.py”, which uses the same Pysam code as above to calculate the fragment length of each read, and write that read to the output BAM file only if it is within the correct length range.
  • the code to perform this analysis is available as the script “fraglens_to_histogram.py”.
  • ONT Minimap BAMs were processed using the script “fragmentationReports.py”, which uses Pysam to perform the following analysis. We collect all reads that contain a perfect match to the final 5 base pairs of the ONT adapter sequence (“CACCT”) as the final sequence in the soft-clipped portion of the read. We then collect the first 5 base pairs of the aligned portion of the read, which is by definition adjacent to the adapter sequence. This is the “end motif”. If any of the adjacent 5 base pairs have a basecalling quality less than 20, the end is not counted. We count each of the two ends of each fragment as independent observations.
  • CACCT ONT adapter sequence
  • DNA proportions were calculated for fragment length bins as described above. PCA was performed using the sklearn. decomposition Python package. PCA was performed on column normalized versions of the fragment length distributions. These were defined by taking the proportion of DNA in each bin for of a given sample, and calculating a z-score based on the mean and standard deviation of the bin across all 21 healthy samples (not including the 3 “unspun” healthy samples).
  • Oxford Nanopore Technologies may be used to identify DNA methylation states which can be used to determine cell-of-origin (COO) from circulating plasma DNA.
  • ONT Oxford Nanopore Technologies
  • each cancer and healthy sample was represented as the fraction of DNA sequenced, in length bins from 50-60,000 bp. Fragment lengths are often quantified as the fraction of sequencing reads in each bin. However, we reasoned that for ONT sequencing it would be preferable to analyze the amount of DNA present in each bin, rather than the sequence counts (thus, a 1kb fragment will be weighted 10x more than a 100bp fragment). One reason is that ONT sequencing is asynchronous and thus longer DNA fragments take proportionally longer to sequence than short fragments (unlike short-read sequencing). We also reasoned that quantifying the proportion of DNA rather than fragment count would better reflect the overall cell of origin of the DNA, since 1 cell yields a constant number of base pairs, not a constant number of sequence fragments.
  • PCA Principal Component Analysis
  • Figure 2A Principal Component Analysis
  • the first component (PC1) bifurcated a majority of the cancers (in yellow) from a set of 7 other cancers (in red) - the majority of these PC1-positive cancers were AML.
  • the second component (PC2) separated most cancers from the healthy samples. Because our aim is to identify aberrations from normal variation in healthy individuals, we z-score normalized all bins by their mean and standard deviation in normal samples, and re-performed PCA, which allowed better resolution of cancer vs. healthy as well as the set of 7 outlier cancers (Figure 2B). The top two components of this bin-normalized PCA captured 87% of variance.
  • ONT sequencing can analyze long cfDNA fragments, but some of these may represent contamination from genomic DNA released as part of sample processing, especially from cell lysis during freezing and thawing of samples.
  • Our standard processing protocol includes a post-thaw high-speed spin to reduce the number of such fragments (Figure 3A).
  • Figure 3A the “healthy ultralong”, Figure 2C-D and Figure 3B). While this pattern was rare and thus not captured by the first 4 components of our PCA analysis, we found it interesting because the fragments were longer than the PC1 “laddered fragments” (or “hypofragmented” fragments) observed in AML and other cancers.
  • PCA Principal Component Analysis
  • PC1-high The hypofragmented cancer group (PC1-high) was strongly distinguished from the 3 healthy samples with likely genomic contamination, which are high in PC2 ( Figure 4A bottom).
  • PC2 is defined by a size range of 7,500-53,000 bp. Consistent with our earlier analysis, the post-spin samples had much lower PC2 scores than the unspun samples (Figure 4A, PC scores), and much less total cfDNA in the 7,500-53,000 bp bin ( Figure 4A “Frac DNA”, 4-9% for post-spin samples and 38-48% for pre-spin samples). Aside from these three sample groups, all other healthy volunteers and cancers had low values for PC1 and PC2 ( Figure 4A, “PC scores”).
  • PC3 The third principal component (PC3) defined a hyperfragmented set of 17 cancers (Figure 4C, “PC3-high cancers”). These had elevated cfDNA in the range 75-145 bp and 245-295 bp (corresponding to short mono-nucleosomes and short di-nucleosomes, respectively). Longer fragments, including those greater than 1kb, were under-represented. Interestingly, PC3-high cancers tended to have lower frequencies of the DNASE1L3- associated CC end motifs ( Figure 4C).
  • PCA analysis in this Example included the 3 unspun samples. To ensure that these were not influencing the clustering, we performed PCA with these samples excluded. This produced nearly identical versions of PC1 and PC3 (as the first two components), and identical grouping of all cancer samples.
  • EXAMPLE 5 Origins of short mono and dinucleosome fragments (75-145bp, 245-295 bp)
  • PC3 mono and di-nucleosomes
  • DNA methylation allows for “deconvolution” of plasma DNA into its constituent cell types using reference methylation atlases.
  • For the 5 samples with detectable CNAs we were able to identify strong signals from the correct cell of origin, compared to only 1 of the 5 samples without CNAs (Figure 5A). While our ability to accurately deconvolute cell types at this sequencing depth is limited, this does provide additional evidence that the samples without CNAs had low cancer content.
  • the absolute fraction of the cancer cell of origin (COO) was never above 50%, consistent with CNA analysis indicating that a significant contribution to elevated cfDNA levels is not attributed to cancer DNA.
  • DNASE1L3-associated CC motifs were also investigated in long fragments.
  • the results shown in Figure 6 focus on the differences between the PC1 group and the PC2 group. All samples from the PC1 class, totalling seven samples, and six samples from the PC2 class were investigated. Three of the six samples from the PC2 class represent the three healthy individuals that have been spun, showing a high level of PC2 fragments, and then the same three samples unspun, displaying PC2 fragments. Each one of these is aligned in the heat map of Figure 6, where the plot illustrates the CC end motif frequency as a function of fragment length for these samples.
  • laddered fragments in this size range can be released by necrotic cells (lingerer et al. 2021). It is also possible that active DNASE1L3 during blood handling could fragment longer DNA from either necrosing or lysed cells.
  • Nucleosome concentrations were quantified using the Nu.Q® H3.1 ELISA assay.
  • DNA libraries were prepared and sequenced using Oxford Nanopore Technologies Native Barcoding Kit v. 14, PromethlON R10.4.1 flow cells, and the P2 Solo sequencer. Base and modification calling were performed using the ONT Dorado basecaller v. 0.4.1 with the dna_r10.4.1_e8.2_400bps_hac@v4.2.0_5mCG_5hmCG modification calling model. DNA modifications were extracted using Oxford Nanopore modkit 0.1.13. CpG islands for canFam5 were taken from the UCSC genome browser.
  • the commonly hypermethylated human CpG island promoters described above were mapped to orthologous gene promoters in the canFam5 genome using orthologous gene mappings from the Zoonomia TOGA dataset (Kirilenko, 2023). This set of promoters was then filtered for those where the promoter (transcription start site) overlapped a CpG island in the canFam5 UCSC CpG island track. Finally, we removed CpG island promoters that were longer than 20kb in the canFam5 genome, since most indicated a canFam5 genome assembly errors within repetitive telomeric regions upon manual inspection. The canine CpG island methylation signature was the mean of the resulting 1,272 resulting CpG islands.
  • PCA Principal Component Analysis
  • CGI CpG island
  • EXAMPLE 9 Hyperfragmentation is a strong predictor of healthy vs. cancer status and inflammation vs. cancer status in client-owned dogs
  • Inflammation is often associated with increased levels of circulating nucleosomes and DNA in humans and dogs, including in the canine inflammatory condition Pyometra (a uterine infection; while most commonly associated with dogs, the infection has also been identified in other animals, such as cattle, swine, cats and many rodent animals).
  • Pyometra a uterine infection; while most commonly associated with dogs, the infection has also been identified in other animals, such as cattle, swine, cats and many rodent animals.
  • INF high circulating nucleosomes
  • LSA treatment naive lymphoma samples
  • HSA treatment naive hemangiosarcoma samples
  • the first principal component (pel) had values that were positively correlated to the fraction of fragments less than 150 bp in length, and thus we termed this the “hyperfragmentation” component.
  • Figure 10B global methylation
  • Figure 10C tumor fraction from ichorCNA analysis
  • CGI CpG island methylation
  • MLR multivariable logistic regression
  • EXAMPLE 10 Using Hyperfragmentation and cancer DNA markers to monitor dogs during and after treatment
  • DNA libraries were prepared and sequenced using Oxford Nanopore Technologies Native Barcoding Kit v. 14, PromethlON R10.4.1 flow cells, and the P2 Solo sequencer, to a “shallow” depth of 1-2x genomic coverage.
  • Base and modification calling were performed using the ONT Dorado basecaller v. 0.4.1 with the dna_r10.4.1_e8.2_400bps_hac@v4.2.0_5mCG_5hmCG modification calling model.
  • DNA modifications were extracted using Oxford Nanopore modkit 0.1.13. CpG islands for canFam5 were taken from the UCSC genome browser.
  • the “--modbam_qual 0.9” setting was used to filter out any modification base with a modification probability score less than 0.9.
  • NETs Neutrophil Extracellular Traps
  • Kirilenko et al. 2023 Integrating gene annotation with orthology inference at scale. Science 380: eabn3107.
  • Dnase1l3 deletion causes aberrations in length and end-motif frequencies in plasma DNA. Proc Natl Acad Sci 116: 641-649.
  • CelFiE-ISH a probabilistic model for multi-cell type deconvolution from single-molecule DNA methylation haplotypes. Genome Biol. 25: 151.
  • Van Der Pol et al. 2023 Real-time analysis of the cancer genome and fragmentome from plasma and urine cell-free DNA using nanopore sequencing. EMBO Mol Med 15: e 17282.

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Abstract

L'invention concerne des procédés pour déterminer un tissu d'origine, un type cellulaire d'origine, l'origine d'une cellule cancéreuse ou immunitaire, ou une combinaison de ceux-ci d'ADN acellulaire par l'utilisation de données liées à la longueur des fragments.
PCT/EP2025/055553 2024-03-01 2025-02-28 Procédé permettant de déterminer l'origine de l'adn circulant Pending WO2025181348A1 (fr)

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WO2013030579A1 (fr) 2011-09-01 2013-03-07 Singapore Volition Pte Limited Procédé de détection de nucléosomes contenant des variants d'histones
WO2013030578A2 (fr) 2011-09-01 2013-03-07 Singapore Volition Pte Limited Procédé de détection de nucléosomes
WO2013084002A2 (fr) 2011-12-07 2013-06-13 Singapore Volition Pte Limited Procédé de détection de produits d'addition à base de nucléosomes
WO2017068359A1 (fr) 2015-10-21 2017-04-27 Belgian Volition Sprl Procédé de détection de nucléosomes contenant des modifications et variants d'histone
WO2023067597A1 (fr) * 2021-10-18 2023-04-27 Yissum Research Development Company Of The Hebrew University Of Jerusalem Ltd. Utilisation du séquençage par nanopores pour déterminer l'origine de l'adn circulant
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WO2005019826A1 (fr) 2003-08-18 2005-03-03 Chroma Therapeutics Limited Detection d'une modification des histones dans des nucleosomes acellulaires
WO2013030579A1 (fr) 2011-09-01 2013-03-07 Singapore Volition Pte Limited Procédé de détection de nucléosomes contenant des variants d'histones
WO2013030578A2 (fr) 2011-09-01 2013-03-07 Singapore Volition Pte Limited Procédé de détection de nucléosomes
WO2013084002A2 (fr) 2011-12-07 2013-06-13 Singapore Volition Pte Limited Procédé de détection de produits d'addition à base de nucléosomes
WO2017068359A1 (fr) 2015-10-21 2017-04-27 Belgian Volition Sprl Procédé de détection de nucléosomes contenant des modifications et variants d'histone
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WO2023067597A1 (fr) * 2021-10-18 2023-04-27 Yissum Research Development Company Of The Hebrew University Of Jerusalem Ltd. Utilisation du séquençage par nanopores pour déterminer l'origine de l'adn circulant
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