Patent Application
20230014674
The present invention relates in part to methods for detecting the presence of target DNA, such as circulating tumour DNA (ctDNA) from, e.g., a cell-free DNA (cfDNA) source, such as blood plasma or other biological fluid. In particular, the methods of the invention find use in the diagnosis, treatment and especially monitoring of cancer.
Blood plasma of cancer patients contains circulating tumor DNA (ctDNA), but this valuable source of information is diluted by much larger quantities of DNA of non-cancerous origins: ctDNA therefore represents only a small fraction of the total cell-free DNA (cfDNA) (1, 2). High-depth targeted sequencing of selected genomic regions can be used to detect low levels of ctDNA, but broader analysis with methods such as whole exome sequencing (WES) and shallow whole genome sequencing (sWGS) are only generally informative when ctDNA levels are ˜10% or greater (3-5). The concentration of ctDNA can exceed 10% of the total cfDNA in patients with advanced-stage cancers (6-8), but is much lower in patients with low tumor burden (9-12) and in patients with some cancer types such as gliomas and renal cancers (6). Current strategies to improve ctDNA detection rely on increasing depth of sequencing coupled with various error-correction methods (2, 13, 14). However, approaches that focus only on mutation analysis do not take advantage of the potential differences in chromatin organization or fragment size in ctDNA (15-17). Results of ever-deeper sequencing are also confounded by the likelihood of false positive results from detection of mutations from non-cancerous cells or clonal expansions in normal epithelia, or clonal hematopoiesis of indeterminate potential (CHIP) (13, 18, 19).
The cell of origin and the mechanism of cfDNA release into blood can mark cfDNA with specific fragmentation signatures, potentially providing precise information about cell type, gene expression, oncogenic potential or action of treatment (15, 16, 20). cfDNA fragments commonly show a prominent mode at 167 bp, suggesting release from apoptotic caspase-dependent cleavage (21-24). Circulating fetal DNA has been shown to be shorter than maternal DNA in plasma, and these size differences have been used to improve sensitivity of non-invasive prenatal diagnosis (22, 25-27). The size distribution of tumor-derived cfDNA has only been investigated in a few studies, encompassing a small number of cancer types and patients, and shows conflicting results (28-33). A limitation of previous studies is that determining the specific sizes of tumor-derived DNA fragments requires detailed characterization of matched tumor-derived alterations (30, 33), and the broader understanding and implications of potential biological differences have not previously been explored. Mouliere, Pikorz, Chandrananda, Moore et al., 2017, BioRxiv Preprint, doi: http://dx.doi.org/10.1101/134437 reports that selecting short fragments in plasma improves detection of circulating tumour DNA (ctDNA) in patients having recurrent high-grade serous ovarian cancer.
While detection of ctDNA shows promise in the field of cancer care, there remains an unmet need for methods and systems that maximise signal-to-noise ratio in the context of ctDNA detection. A related problem is the need to distinguish somatic cancer mutations from mutations present in non-cancerous cells, clonal expansions of normal epithelia or CHIP. The present invention seeks to provide solutions to these needs and provides further related advantages.
The present inventors hypothesised that differences in fragment lengths of circulating DNA could be exploited to enhance sensitivity for detecting the presence of ctDNA and for non-invasive genomic analysis of cancer. As described in detail herein, analysis of size-selected cfDNA identified clinically actionable mutations and copy number alterations that were otherwise not detected. Identification of patients with advanced cancer was improved by predictive models integrating fragment length and copy number analysis of cfDNA with AUC>0.99 compared to AUC<0.80 without fragmentation features. Increased detection of ctDNA from patients with glioma, renal and pancreatic cancer patients was achieved with AUC>0.91, compared to AUC<0.5 without fragmentation features. Detection of ctDNA from glioma, which does not metastasize beyond the central nervous system (CNS) has previously been reported to be very challenging (6). Fragment-size analysis and selective sequencing of specific fragment sizes can boost ctDNA detection, and could be an alternative to deeper mutation sequencing for clinical applications, earlier diagnosis and to study tumor biology.
Accordingly, in a first aspect the present invention provides a computer-implemented method for detecting variant nucleic acid (e.g. DNA or RNA) from a cell-free nucleic acid (e.g. DNA or RNA)-containing sample, comprising:
In some embodiments the cell-free nucleic acid-containing sample is a cell-free DNA (cfDNA)-containing sample, and wherein the variant nucleic acid is variant DNA. In particular, the variant DNA may be selected from the group consisting of: circulating tumour DNA (ctDNA), circulating bacterial DNA, circulating pathogen DNA, circulating mitochondrial DNA, circulating foetal DNA, circulating DNA derived from a donor organ or donor tissue, circulating DNA release by a cell or tissue with an altered physiology, circulating extra chromosomal DNA, and a double minute of circular DNA. In a particularly preferred embodiment the variant DNA is ctDNA.
In some embodiments the data representing fragment sizes of the nucleic acid fragments (e.g. DNA or RNA fragments) comprise fragment sizes inferred from sequence reads, fragment sizes determined by fluorimetry, or fragment sizes determined by densitometry.
In some embodiments the present invention provides a computer-implemented method for detecting variant DNA from a cell-free DNA (cfDNA)-containing sample, comprising:
In some embodiments the classification algorithm operates to classify sample data into one of said at least two, three, four, or at least five classes based on at least a plurality of cfDNA fragment size features selected from the group consisting of:
In some embodiments the plurality of cfDNA fragment size features comprise: P(160-180), P(180-220), P(250-320) and the amplitude oscillations in fragment size density with 10 bp periodicity. As described in the Examples herein, both a linear and a non-linear machine learning algorithm independently identified the same four fragment size features P(160-180), P(180-220), P(250-320) and the amplitude oscillations in fragment size density with 10 bp periodicity, along with the SCNA feature (i.e. trimmed Median Absolute Deviation from copy number neutrality (t-MAD) score), albeit with some differences in the rank order of the features. Classification with high accuracy was obtained using only the four fragmentation features (see
In some embodiments the classification algorithm operates to classify sample data into one of said at least two classes based on at least a deviation from copy number neutrality feature which is a trimmed Median Absolute Deviation from copy number neutrality (t-MAD) score or an ichorCNA feature.
ichorCNA is a tool for estimating the fraction of tumor in cell-free DNA from ultra-low-pass whole genome sequencing (ULP-WGS, 0.1× coverage). The code for ichorCNA is available at the following URL: https://github.com/broadinstitute/ichorCNA.ichorCNA uses a probabilistic model, implemented as a hidden Markov model (HMM), to simultaneously segment the genome, predict large-scale copy number alterations, and estimate the tumor fraction of a ultra-low-pass whole genome sequencing sample (ULP-WGS). The methodology and probabilistic model are described in: Adalsteinsson, Ha, Freeman, et al. Scalable whole-exome sequencing of cell-free DNA reveals high concordance with metastatic tumors. (2017) Nature Communications November 6; 8(1):1324. doi: 10.1038/s41467-017-00965-y (the contents of which are incorporated herein by reference). The analysis workflow consists of 2 tasks:
GC-content bias correction (using HMMcopy)
a. Computing read coverage from ULP-WGS
b. Data correction and normalization
CNA prediction and estimation of tumor fraction of cfDNA.
In particular, when the deviation from copy number neutrality feature comprise a t-MAD score, the score may be determined by trimming regions of genome that exhibit high copy number variability in whole genome datasets derived from healthy subjects and then calculating the median absolute deviation from log2R=0 of the non-trimmed regions of the genome.
In some embodiments in accordance with the present invention the classification algorithm performs random forests (RF) analysis, logistic regression (LR) analysis, or support vector machine (SVM) analysis. The classification algorithm may provide an output that is a probability of correct classification, e.g., a probability that the sample in question has been classified correctly to the healthy class or cancerous class per the training set on which the classification algorithm has been trained.
In some embodiments the performance of the classification algorithm when trained on the training set is assessed by the area under the curve (AUC) value from a receiver operating characteristic (ROC) analysis. Generally the classification algorithm model showing the highest AUC value is selected as having the best performance.
In some embodiments the classification algorithm has been trained on a training set comprising at least 10, 20, 30, 40 or at least 50 samples from healthy subjects and at least 10, 20, 30, 40 or at least 50 samples from subjects known to have a cancer. In particular, the samples employed in the training set may be those shown in Table 2.
In some embodiments the sequence data provided in step a) represent whole-genome sequence (WGS) reads, Tailored Panel Sequencing (TAPAS) sequence reads, Integration of Variant Reads (INVAR) TAPAS (see co-pending patent application GB1803596.4 filed 6 Mar. 2018, incorporated herein by reference), hybrid-capture sequence reads, Tagged-Amplicon Deep Sequencing (TAm-Seq) reads, focused-exome sequence reads or whole-exome sequence reads. In particular, the sequence data provided in step a) may represent shallow whole-genome sequence (sWGS) reads, optionally 0.4× depth WGS reads.
In some embodiments the data provided in step a) represent fragment sizes of multiple nucleic acid fragments (e.g. DNA fragments) from a substantially cell-free liquid sample from a subject having or suspected as having a cancer.
In some embodiments the sequence data provided in step a) represent sequence reads of multiple DNA fragments from a substantially cell-free liquid sample from a subject having or suspected as having a cancer.
In some embodiments, the cancer may be selected from melanoma, lung cancer, cholangiocarcinoma, bladder cancer, oesophageal cancer, colorectal cancer, ovarian cancer, glioma, pancreatic cancer, renal cancer and breast cancer.
In some embodiments the sample is a plasma sample, a urine sample, a saliva sample, a cerebrospinal fluid sample, a serum sample or other nucleic acid containing (e.g. DNA-containing) biological liquid sample.
In some embodiments, wherein the variant DNA is ctDNA, the method is for detecting the presence of, growth of, prognosis of, regression of, treatment response of, or recurrence of a cancer in a subject from which the sample has been obtained.
In some embodiments the presence of ctDNA in the sample is distinguished from cfDNA containing somatic mutations of non-cancerous origin. It is specifically contemplated herein that including fragment size information on each read may enhance mutation calling algorithms from high depth sequencing so as to distinguish tumour-derived mutations from other sources of somatic variants (including clonal expansions of non-cancerous cells) or background sequencing noise. In certain embodiments the method may distinguish variant sequence reads representing clonal expansions of normal epithelia or clonal haematopoiesis of indeterminate potential (CHIP) from variant sequence reads representing ctDNA.
In certain embodiments the fragment size data provided in step a) represent sequence reads of multiple DNA fragments from a substantially cell-free liquid sample from a subject and wherein the method is for determining whether the sample contains ctDNA or contains cfDNA from CHIP. In particular, the classification algorithm may have been trained on a training set further comprising a plurality of samples of cfDNA obtained from subjects having CHIP, and wherein said at least two classes further comprise a third class containing CHIP-derived cfDNA based on a plurality of cfDNA fragment size features and/or a deviation from copy number neutrality feature.
In a second aspect the present invention provides a method for detecting variant nucleic acid from a cell-free nucleic acid-containing sample, comprising:
In some embodiments said analysing comprises:
In some embodiments the present invention provides a method for detecting variant DNA from a cell-free DNA (cfDNA)-containing sample, comprising:
In some embodiments the sequencing comprises generating a sequencing library from the sample and performing whole-genome sequencing, Tailored Panel Sequencing (TAPAS) sequencing, hybrid-capture sequencing, TAm-Seq sequencing, focussed-exome sequencing or whole-exome sequencing, optionally generating an indexed sequencing library and performing shallow whole genome sequencing (e.g. to a depth of 0.4×).
In some embodiments processing the sequence reads comprises one or more of the following steps:
In some embodiments the variant DNA is selected from the group consisting of: circulating tumour DNA (ctDNA), circulating bacterial DNA, circulating pathogen DNA, circulating mitochondrial DNA, circulating foetal DNA, and circulating DNA derived from a donor organ or donor tissue, circulating DNA release by a cell or tissue with an altered physiology, circulating extra chromosomal DNA, and a double minute of circular DNA.
In some embodiments processing the sequence reads to determine sequence data representing fragment sizes of cfDNA fragments obtained from said sample and/or representing a measure of deviation from copy number neutrality of the cfDNA fragments obtained from said sample comprises determining one or more (e.g. 2, 3, 4, 5 or more) features selected from the group consisting of:
In some embodiments the plurality of cfDNA fragment size features comprise: P(160-180), P(180-220), P(250-320) and the amplitude oscillations in fragment size density with 10 bp periodicity.
In some embodiments the fragment sizes of cfDNA fragments are inferred from sequence reads using the mapping locations of the read ends in the genome following alignment of the sequence reads with the reference genome of the species from which the sample was obtained.
In some embodiments processing the sequence reads to determine sequence data representing a measure of deviation from copy number neutrality of the cfDNA fragments obtained from said sample comprises determining a trimmed Median Absolute Deviation from copy number neutrality (t-MAD) score or an ichorCNA score. In particular, the t-MAD score may be determined by trimming regions of genome that exhibit high copy number variability in whole genome datasets derived from healthy subjects and then calculating the median absolute deviation from log2R=0 of the non-trimmed regions of the genome.
In some embodiments the sample contains multiple DNA fragments from a substantially cell-free liquid from a subject having or suspected as having a cancer. In particular cases, the cancer may be selected from melanoma, lung cancer, cholangiocarcinoma, bladder cancer, oesophageal cancer, colorectal cancer, ovarian cancer, glioma, pancreatic cancer, renal cancer and breast cancer.
In some embodiments the sample is a plasma sample, a urine sample, a saliva sample, a cerebrospinal fluid sample, a serum sample or other DNA-containing biological liquid sample.
In accordance with any aspect of the present invention the sample may be or may have been subjected to one or more processing steps to remove whole cells, for example by centrifugation.
In certain embodiments, wherein the variant DNA is ctDNA, the method may be for detecting the presence of, growth of, prognosis of, regression of, treatment response of, or recurrence of a cancer in a subject from which the sample has been obtained.
In some embodiments the presence of ctDNA is distinguished from the presence of cfDNA containing somatic mutations of non-cancerous origin, optionally from CHIP origin.
In some embodiments a somatic mutation containing cfDNA fragment is classified as being of tumour origin or being of CHIP origin based on a plurality of fragment size features determined from the sequence reads.
In some embodiments the variant DNA is ctDNA and the classification of the sample as containing ctDNA or not, or the determined probability that the sample contains ctDNA is used to predict whether said sample or a further sample from the same subject will be susceptible to further ctDNA analysis.
In some cases the further ctDNA analysis comprises sequencing to a greater sequencing depth and/or targeted sequencing of ctDNA in said sample.
In some embodiments, when the probability that the sample contains ctDNA as determined by the classification algorithm is at least 0.5 (e.g. at least 0.6 or at least 0.75), the sample is subjected to said further ctDNA analysis.
In some embodiments:
In a third aspect the present invention provides a method for improving the detection of circulating tumour DNA (ctDNA) in a cell-free DNA (cfDNA) containing sample, comprising performing an in vitro and/or in silico size selection to enrich for DNA fragments of less than 167 bp in length and/or to enrich for DNA fragments in the size range 250 to 320 bp. In some embodiments the size selection is to enrich for DNA fragments in the range 90 to 150 bp in length. In some cases the size selection may comprise excluding high molecular weight DNA such as that derived from white blood cells when the sample comprises a serum sample.
In some embodiments the sample may have been obtained from a subject having or suspected as having a cancer selected from the group consisting of melanoma, cholangiocarcinoma, colorectal cancer, glioma, pancreatic cancer, renal cancer and breast cancer.
In some embodiments the size selection comprises an in vitro size selection that is performed on DNA extracted from a cfDNA containing sample and/or is performed on a library created from DNA extracted from a cfDNA containing sample. In particular, the in vitro size selection may comprise agarose gel electrophoresis.
In some embodiments the size selection comprises an in silico size selection that is performed on sequence reads.
In particular cases the sequence reads may comprise paired-end reads generated by sequencing DNA from both ends of the fragments present in a library generated from the cfDNA containing sample. The original length of the DNA fragments in the cfDNA containing sample may be inferred using the mapping locations of the read ends in the genome following alignment of the sequence reads with the reference genome of the species from which the sample was obtained (e.g. the human reference genome GRCh37 for a human subject).
In some embodiments DNA fragments outside the range 90 to 150 bp in length are substantially excluded (see, e.g.,
In some embodiments the size selection is performed on a genome wide basis or an exome wide basis. As described herein, the present inventors identified size differences between mutant an non-mutant cfDNA on a genome-wide and pan-cancer scale in contrast to previous studies that were limited to specific genomic loci, cancer types or cases (30, 32, 33).
In certain embodiments the in vitro size selection is performed prior to shallow whole genome sequencing (sWGS) or the in silico size selection is performed on sWGS sequencing reads.
In certain embodiments the method further comprises performing somatic copy number aberration analysis and/or mutation calling on the sequence reads subsequent to the size selection. In particular cases somatic copy number aberration analysis may comprise processing the sequence reads to determine a trimmed Median Absolute Deviation from copy number neutrality (t-MAD) score or an ichorCNA score. For example, the t-MAD score may be determined by trimming regions of genome that exhibit high copy number variability in whole genome datasets derived from healthy subjects and then calculating the median absolute deviation from log2R=0 of the non-trimmed regions of the genome.
In certain embodiments somatic copy number aberration analysis may comprise detecting amplifications in one or more genes selected from NF1, TERT, and MYC. As described in the Examples herein, analysis of plasma cfDNA after size selection revealed a large number of SCNAs that were not observed in the same samples without size selection.
In certain embodiments mutation calling comprises detecting mutations in one or more genes selected from BRAF, ARID1A, and NFL As described in the Examples herein, size selection enriched the mutant allele fraction (MAF) for nearly all mutations.
In some embodiments the cancer is a high ctDNA cancer selected from the group consisting of: colorectal, cholangiocarcinoma, breast and melanoma.
In some embodiments the cancer is a low ctDNA cancer selected from the group consisting of: pancreatic cancer, renal cancer and glioma.
In certain embodiments the sample may be a plasma sample, a urine sample, a saliva sample, a cerebrospinal fluid sample, a serum sample or other DNA-containing biological liquid sample.
In some embodiments the method further comprises detecting the presence of, growth of, prognosis of, regression of, treatment response of, or recurrence of a cancer in a subject from which the sample has been obtained. Improving the detection of ctDNA, mutation calling and/or SCNA detection in accordance with the methods of this aspect of the invention may assist with the early detection of cancer and with ongoing cancer monitoring, and may inform treatment strategies.
In some embodiments the method may carried out on a sample obtained prior to a cancer treatment of the subject and on a sample obtained following the cancer treatment of the subject. As described herein, size selected samples indicated tumour progression 69 and 87 days before detection by imaging or non-size selected t-MAD analysis (see
In accordance with any aspect of the present invention, the subject may be a human, a companion animal (e.g. a dog or cat), a laboratory animal (e.g. a mouse, rat, rabbit, pig or non-human primate), a domestic or farm animal (e.g. a pig, cow, horse or sheep). Preferably, the subject is a human patient. In some cases, the subject is a human patient who has been diagnosed with, is suspected of having or has been classified as at risk of developing, a cancer.
Embodiments of the present invention will now be described by way of example and not limitation with reference to the accompanying figures. However various further aspects and embodiments of the present invention will be apparent to those skilled in the art in view of the present disclosure.
The present invention includes the combination of the aspects and preferred features described except where such a combination is clearly impermissible or is stated to be expressly avoided. These and further aspects and embodiments of the invention are described in further detail below and with reference to the accompanying examples and figures.
Aspects and embodiments of the present invention will now be discussed with reference to the accompanying figures. Further aspects and embodiments will be apparent to those skilled in the art. All documents mentioned in this text are incorporated herein by reference.
In describing the present invention, the following terms will be employed, and are intended to be defined as indicated below.
“Computer-implemented method” where used herein is to be taken as meaning a method whose implementation involves the use of a computer, computer network or other programmable apparatus, wherein one or more features of the method are realised wholly or partly by means of a computer program.
A “sample” as used herein may be a biological sample, such as a cell-free DNA sample, a cell (including a circulating tumour cell) or tissue sample (e.g. a biopsy), a biological fluid, an extract (e.g. a protein or DNA extract obtained from the subject). In particular, the sample may be a tumour sample, a biological fluid sample containing DNA, a blood sample (including plasma or serum sample), a urine sample, a cervical smear, a cerebrospinal fluid sample, or a non-tumour tissue sample. It has been found that urine and cervical smears contains cells, and so may provide a suitable sample for use in accordance with the present invention. Other sample types suitable for use in accordance with the present invention include fine needle aspirates, lymph nodes, surgical margins, bone marrow or other tissue from a tumour microenvironment, where traces of tumour DNA may be found or expected to be found. The sample may be one which has been freshly obtained from the subject (e.g. a blood draw) or may be one which has been processed and/or stored prior to making a determination (e.g. frozen, fixed or subjected to one or more purification, enrichment or extractions steps, including centrifugation). The sample may be derived from one or more of the above biological samples via a process of enrichment or amplification. For example, the sample may comprise a DNA library generated from the biological sample and may optionally be a barcoded or otherwise tagged DNA library. A plurality of samples may be taken from a single patient, e.g. serially during a course of treatment. Moreover, a plurality of samples may be taken from a plurality of patients. Sample preparation may be as described in the Materials and Methods section herein. “and/or” where used herein is to be taken as specific disclosure of each of the two specified features or components with or without the other. For example “A and/or B” is to be taken as specific disclosure of each of (i) A, (ii) B and (iii) A and B, just as if each is set out individually herein.
The sequence reads data may be provided or obtained directly, e.g., by sequencing the cfDNA sample or library or by obtaining or being provided with sequencing data that has already been generated, for example by retrieving sequence read data from a non-volatile or volatile computer memory, data store or network location. Where the sequence reads are obtained by sequencing a sample, the median mass of input DNA may in some cases be in the range 1-100 ng, e.g., 2-50 ng or 3-10 ng. The DNA may be amplified to obtain a library having, e.g. 100-1000 ng of DNA. The sequence reads may be in a suitable data format, such as FASTQ.
The sequence read data, e.g., FASTQ files, may be subjected to one or more processing or clean-up steps prior to or as part of the step of reads collapsing into read families. For example, the sequence data files may be processed using one or more tools selected from as FastQC v0.11.5, a tool to remove adaptor sequences (e.g. cutadapt v1.9.1). The sequence reads (e.g. trimmed sequence reads) may be aligned to an appropriate reference genome, for example, the human reference genome GRCh37 for a human subject.
As used herein “read” or “sequencing read” may be taken to mean the sequence that has been read from one molecule and read once. Each molecule can be read any number of times, depending on the sequencing performed.
“Classifier” or “classification algorithm” may be a model or algorithm that maps input data, such as a cfDNA fragment size features, to a category, such as cancerous or non-cancerous origin. In some embodiments, the present invention provides methods for detecting, classifying, prognosticating, or monitoring cancer in subjects. In particular, data obtained from sequence analysis, such as fragment length and/or copy number (e.g. trimmed median absolute deviation from copy-number neutrality “t-MAD”) of may be evaluated using one or more pattern recognition algorithms. Such analysis methods may be used to form a predictive model, which can be used to classify test data. For example, one convenient and particularly effective method of classification employs multivariate statistical analysis modelling, first to form a model (a “predictive mathematical model”) using data (“modelling data”) from samples of known category (e.g., from subjects known to have a particular cancer), and second to classify an unknown sample (e.g., “test sample”) according to category.
Pattern recognition is the use of multivariate statistics, both parametric and non-parametric, to analyse data, and hence to classify samples and to predict the value of some dependent variable based on a range of observed measurements. There are two main approaches. One set of methods is termed “unsupervised” and these simply reduce data complexity in a rational way and also produce display plots which can be interpreted by the human eye. However, this type of approach may not be suitable for developing a clinical assay that can be used to classify samples derived from subjects independent of the initial sample population used to train the prediction algorithm.
The other approach is termed “supervised” whereby a training set of samples with known class or outcome is used to produce a mathematical model which is then evaluated with independent validation data sets. Here, a “training set” of sequence information, e.g. fragmentation features and/or copy number features, is used to construct a statistical model that predicts correctly the class of each sample. This training set is then tested with independent data (referred to as a test or validation set) to determine the robustness of the computer-based model. These models are sometimes termed “expert systems,” but may be based on a range of different mathematical procedures such as support vector machine (SVM), decision trees, k-nearest neighbour and naïve Bayes, each of which are contemplated herein for use in accordance with the present invention. As detailed in the Examples herein, logistic regression (LR) and Random Forests (RF) were used for variable selection and the classification of samples as “healthy” or “cancer”. Supervised methods can use a data set with reduced dimensionality (for example, the first few principal components), but typically use unreduced data, with all dimensionality. The robustness of the predictive models can also be checked using cross-validation, by leaving out selected samples from the analysis.
As used herein tailored panel sequencing refers to sequencing of targeted regions and/or genes. This may employ selected or custom capture panels that target genes of interest, such as genes commonly mutated in cancer and/or genes found to carry mutations in a tumour of the subject of interest (e.g. identified by sequencing matched tumor tissue DNA and plasma DNA samples). In some cases the capture panels may range in size from 0.5-5 Mb, e.g. 1-3 Mb.
The following is presented by way of example and is not to be construed as a limitation to the scope of the claims.
344 plasma samples from 200 patients with multiple cancer types, and 65 plasma samples from 65 healthy controls, were collected. Among the patients, 172 individuals were recruited through prospective clinical studies at Addenbrooke's Hospital, Cambridge, UK, approved by the local research ethics committee (REC reference numbers: 07/Q0106/63; and NRES Committee East of England—Cambridge Central 03/018). Written informed consent was obtained from all patients and blood samples were collected before and after initiation of treatment with surgery or chemotherapeutic agents. DNA was extracted from 2 mL of plasma using the QIAamp circulating nucleic acid kit (Qiagen) or QIAsymphony (Qiagen) according to the manufacturer's instructions. In addition, 28 patients were recruited as part of the Copenhagen Prospective Personalized Oncology (CoPPO) program (Ref: PMID: 25046202) at Rigshospitalet, Copenhagen, Denmark, approved by the local research ethics committee. Baseline tumor tissue biopsies were available from all 28 patients, together with re-biopsies collected at relapse from two patients, including matched plasma samples. Brain tumor patients were recruited at the Addenbrooke's Hospital, Cambridge, UK, as part of the BLING study (REC—15/EE/0094). Bladder cancer patients were recruited at the Netherlands Cancer Institute, Amsterdam, The Netherlands, and approval was in accordance with national guidelines(N13KCM/CFMPB250) (47). 65 plasma samples were obtained from healthy control individuals using a similar protocol (Seralab). Plasma samples were freeze-thawed no more than 2 times to reduce artifactual fragmentation of cfDNA.
Between 8-20 ng of DNA were loaded into a 3% agarose cassette (HTC3010, Sage Bioscience) and size selection was performed on a PippinHT (Sage Bioscience) according to the manufacturer's protocol. Quality controls of in vitro size selection were performed on 20 healthy controls samples. Duplicate reads observed with in vitro selection were removed for any downstream size selection analysis. A QC metric called the median absolute pairwise difference (MAPD) algorithm was used to determine the sequencing noise. MAPD measured the absolute difference between the log2 CN ratios of every pair of neighboring bins and determined the median across all bins. Higher MAPD scores reflected greater noise, typically associated with poor-quality samples. All samples exhibited a MAPD score of 0.01 (+−0.01), irrespective of the size selection condition.
Tagged-Amplicon Deep Sequencing libraries were prepared as previously described (34), using primers designed to assess single nucleotide variants (SNV) and small indels across selected hotspots and the entire coding regions of TP53. Libraries were sequenced using MiSeq or HiSeq 4000 (Illumina).
Shallow Whole Genome Sequencing (sWGS)
Indexed sequencing libraries were prepared using commercially available kits (ThruPLEX-Plasma Seq and/or Tag-Seq, Rubicon Genomics). Libraries were pooled in equimolar amounts and sequenced to <0.4× depth of coverage on a HiSeq 4000 (Illumina) generating 150-bp paired-end reads. Sequence data were analyzed using an in-house pipeline. Paired end sequence reads were aligned to the human reference genome (GRCh37) using BWA-mem following the removal of contaminating adapter sequences (48). PCR and optical duplicates were marked using MarkDuplicates (Picard Tools) feature and these were excluded from downstream analysis along with reads of low mapping quality and supplementary alignments. When necessary, reads were down-sampled to 10 million in all samples for comparison purposes.
The analysis was performed in R using a software suite for shallow Whole Genome Sequencing copy number analysis named CNAclinic (https://github.com/sdchandra/CNAclinic) as well as the QDNAseq pipeline (49). Sequencing reads were randomly sampled to 10 million reads per dataset and allocated into equally sized (30 Kbp) non-overlapping bins throughout the length of the genome. Read counts in each bin were corrected to account for sequence GC content and mappability. Bins overlapping ‘blacklisted’ regions (derived from the ENCODE Project and the 1000 Genomes Project database) prone to artefacts were excluded from downstream analysis. Read counts in test samples were normalized by the counts from an identically processed healthy individual and log2 transformed to obtained copy number ratio values per genomic bin. Read counts in healthy controls were normalized by their median genome-wide count. Bins were then segmented using both Circular Binary Segmentation and Hidden Markov Model algorithms. An averaged log2R value per bin was calculated.
An in-house empirical blacklist of aberrant read count regions was constructed. Firstly, 65 sWGS datasets from healthy plasma were used to calculate median read counts per 30 Kbp genomic bin as a function of GC content and mappability. A 2D LOESS surface was then applied and the difference between the actual count and the LOESS fitted values were calculated. The median of these residual values across the 65 controls were calculated per genomic bin and regions with median residuals greater than 4 standard deviations were blacklisted. The averaged segmental log2R values in each test sample that overlap this cfDNA blacklist were trimmed and the median absolute value was calculated. This score was defined as the trimmed median absolute deviation (t-MAD) from log2R=0. The R code to reproduce this analysis is provided in https://github.com/sdchandra/tMAD (incorporated herein by reference in its entirety).
Indexed sequencing libraries were prepared as described above (see Methods, sWGS). Plasma DNA libraries from each sample were made and pooled together for exome capture (TruSeq Exome Enrichment Kit, Illumina). Pools were concentrated using a SpeedVac vacuum concentrator (Eppendorf). Exome enrichment was performed following the manufacturer's protocol. Enriched libraries were quantified using quantitative PCR (KAPA library quantification, KAPA Biosystems), and DNA fragments sizes observed by Bioanalyzer (2100 Bioanalyzer, Agilent Genomics) and pooled in equimolar ratios for paired-end next generation sequencing on a HiSeq4000 (Illumina). Sequencing reads were de-multiplexed allowing zero mismatches in barcodes. Paired-end alignment to the GRCh37 reference genome was performed using BWA-mem for all exome sequencing data (germline/plasma/tumor tissue DNA). PCR duplicates were marked using Picard. Base quality score recalibration and local realignment were performed using Genome Analysis Tool Kit (GATK).
Mutation allele fractions (MAFs) for each single-base locus were calculated with MuTect2 for all bases with PHRED quality 30. Filtering parameters were then applied so that a mutation was called if no mutant reads for an allele were observed in germline DNA at a locus that was covered at least 10×, and if at least 4 reads supporting the mutant were found in the plasma data with at least 1 read on each strand (forward and reverse). At loci with <10× coverage in normal DNA and no mutant reads, mutations were called in plasma if a prior plasma sample showed no evidence of a mutation and was covered adequately (10× or more). A method called Integrated Signal Amplification for Non-invasive Interrogation of Tumors was used to aggregate mutations called before and after size selection. This method combined different subsets of mutations called from the same plasma DNA sample using different processing approaches. The mutation aggregation as used in this study was formalized as follows: aggregated mutations=mutations detected without size selection U (mutations detected with in vitro size selection U mutations detected with in silico size selection).
Paired-end reads are generated by sequencing DNA from both ends of the fragments present in the library. The original length of the DNA can be inferred using the mapping locations of the read ends in the genome. Once alignment is complete, Samtools software is used to select paired reads that correspond to fragment lengths in a specific range. Mutect2 is used to call mutations from this in silico size selected data as described in the previous section.
Matched tumor tissue DNA and plasma DNA samples of 19 patients collected from the RigsHospitalet (Copenhagen, Denmark) with advanced cancer were sequenced by WES. Variants were called from these samples by mutation calling (see above). Hybrid-based capture for longitudinal plasma samples analysis were designed to cover these variants for each patient using SureDesign (Agilent). A median of 160 variants were included per patient, and in addition, 41 common genes of interest for pan-cancer analysis were included in the tumor-guided sequencing panel. Indexed sequencing libraries were prepared as per sWGS (see above). Plasma DNA libraries from each sample were made and pooled together for tumor-guided capture sequencing (SureSelect, Agilent). Pools were concentrated using a SpeedVac vacuum concentrator (Eppendorf). Capture enrichment was performed following the manufacturer's protocol. Enriched libraries were quantified using quantitative PCR (KAPA library quantification, KAPA Biosystems), and DNA fragments sizes controlled by Bioanalyzer (2100 Bioanalyzer, Agilent Genomics) and pooled in equimolar ratio for paired-end next generation sequencing on a HiSeq4000 (Illumina). Sequencing reads were de-multiplexed allowing zero mismatches in barcodes. Paired-end alignment to the GRCh37 reference genome was performed using BWA-mem for all exome sequencing data including germline, plasma and tumor tissue DNA where generated. PCR duplicates were marked using Picard. Base quality score recalibration and local realignment were performed using Genome Analysis Tool Kit (GATK).
The preliminary analysis was carried out on 304 samples (182 high ctDNA cancer samples, 57 low ctDNA cancer samples and 65 healthy controls). For each sample the following features were calculated from sWGS data: t-MAD, amplitude_10 bp, P(20-150), P(160-180), P(20-150)/P(160-180), P(100-150), P(100-150)/P(163-169), P(180-220), P(250-320), P(20-150)/P(180-220) (see Table 2). The data was arranged in a matrix where the rows represent each sample and the columns held the aforementioned features with an extra “class” column with the binary labels of “cancer”/“healthy”. The following analysis was carried out in R utilising RandomForest, caret, and pROC packages. The caret package is available and is described at the following URL: http://topepo.github.io/caret/index.html. Exemplary source code for the classification algorithms described in the Examples herein is shown below in the section headed “Code”. The pairwise correlations between the features were calculated to assess multi-collinearity in the dataset. A single variable was selected for removal from pairs with Pearson correlation >0.75. Highly correlated fragmentation features that were composite of individual variables already in the dataset such as P(20-150)/P(180-220), were prioritized for removal. The features were also assessed for zero variance and linear dependencies but none were flagged. After this pre-processing the following 5 variables were selected for further analysis: t-MAD, amplitude_10 bp, P(160-180), P(180-220) and P(250-320) (see Table 2). All 57 low ctDNA samples were set aside for validation of the models. The data matrix for the remaining high ctDNA cancer samples and healthy controls (n=247) were randomly partitioned in a 60:40 split into 1 training and 1 validation dataset with the different cancer types and healthy samples represented in similar proportions. Hence, the training data contained 153 samples (cancer=114, healthy=39) while the first validation set of high ctDNA cancers contained 94 samples (cancer=68, healthy=26). This validation dataset was only utilized for final assessment of the classifiers.
Classification of samples as healthy or cancer was performed using one linear and one non-linear machine learning algorithm, namely logistic regression (LR), and random forest (RF). Each algorithm was paired with recursive feature selection in order to identify the best predictor variables. This analysis was carried out with caret within the framework of 5 repeats of 10-fold cross-validation on the training set. The algorithm was configured to explore all possible subsets of the features. The optimal model for each classifier was selected using ROC metric. Separately, a logistic regression model was trained only using t-MAD as a predictor in order to assess the difference in performance without the addition of fragmentation features. Finally, the 68 high ctDNA cancer samples, 57 low ctDNA cancer samples and 26 healthy controls set aside for validation were used to test the classifiers, utilizing area under the curve in a ROC analysis to quantify their performance.
A secondary analysis was carried out on the same training and validation cohorts with the only difference being the features used in the model. Here, we tested predictive ability of fragmentation features without the addition of information from SCNAs (i.e. t-MAD). Hence the features utilized were: amplitude_10 bp, P(160-180), P (180-220) and P(250-320).
The amplitude of the 10 bp periodic oscillation observed in the size distribution of cfDNA samples was determined from the sWGS data as follows. Local maxima and minima in the range 75 bp to 150 bp were identified. The average of their positions across the samples was calculated (for minima: 84, 96, 106, 116, 126, 137, 148, and maxima: 81, 92, 102, 112, 122, 134, 144). To compute the amplitude of the oscillations with 10 bp periodicity observed below 150 bp, the sum of the minima were subtracted from the sum of the heights of the maxima. The larger this difference, the more distinct the peaks. The height of the x bp peak is defined as the number of fragments with length x divided by the total number of fragments. To define local maxima, y positions were selected such that y was the largest value in the interval [y−2, y+2]. The same rationale was used to pick minima.
A catalogue of cfDNA fragmentation features was generated using 344 plasma samples from 200 patients with 18 different cancer types, and an additional 65 plasma samples from healthy controls (
The size profile of mutant ctDNA in plasma was determined using two high specificity approaches. First, the specific size profile of ctDNA and non-tumor cfDNA was inferred with sWGS from the plasma of mice bearing human ovarian cancer xenografts (
These data indicated that ctDNA is shorter than non-tumor cfDNA and suggested that biological differences in fragment lengths could be harnessed to improve ctDNA detection. The feasibility of selective sequencing of shorter fragments was determined using in vitro size selection with a bench-top microfluidic device followed by sWGS, in 48 plasma samples from 35 patients with high-grade serous ovarian cancer (HGSOC) (
To quantitatively assess the enrichment after size selection on a genome-wide scale, a metric from sWGS data (<0.4× coverage) called t-MAD (trimmed Median Absolute Deviation from copy-number neutrality, see
Using t-MAD ctDNA was detected from 69% (130/189) of the samples from cancer types where ctDNA levels have been shown to be high (
To explore whether size selected sequencing could improve the detection of response or disease progression, sWGS of longitudinal plasma samples from six cancer patients (
The ability of size selection to increase the sensitivity for detecting new mutations in cfDNA was examined. To test effects on copy number aberrations, 35 patients with HGSOC were studied as this is the archetypal copy-number driven cancer (35). t-MAD was used to quantify the enrichment of ctDNA with in vitro size selection in 48 plasma samples, including samples collected before and after initiation of chemotherapy treatment. In vitro size selection resulted in an increase in the calculated t-MAD score from the sWGS data for 47/48 of the plasma samples (98%, t-test, p=0.06) with a mean 2.5 and median 2.1-fold increase (
This was then investigated to determine if improved sensitivity resulted in the detection of SCNAs with potential clinical value. Across the genome, t-MAD scores evaluating SCNAs were higher after size selection in 33/35 (94%) HGSOC patients, and the absolute level of the copy number (log2ratio) values significantly increased after in vitro size selection (t-test for the means, p=0.003) (
To exclude the possibilty that size selection might only increase the sensitivity for sWGS analysis, it was examined if enrichment was seen for substitutions. Whole exome sequencing of plasma cfDNA from 23 patients with 7 cancer types was performed (
Size selection with both in vitro and in silico methods increased the number of mutations detected by WES by an average of 53% compared to no size selection (
It is important to note that although in vitro and in silico size selection increase the sensitivity of detection, they also result in a loss of cfDNA for analysis. Regions of the cancer genome which are not altered by mutation also excluded and cannot contribute to the analysis (
The sWGS data defined other cfDNA fragmentation features including (1) the proportion of fragments in multiple size ranges, (2) the ratios of proportions of fragments in different sizes and (3) the amplitude of oscillations in fragment-size density with 10 bp periodicity (
Furthermore, the potential of fragmentation features to enhance the detection of tumor DNA in plasma samples was explored. A predictive analysis was performed using the t-MAD score and 9 fragmentation features across 304 samples (239 from cancers patients and 65 from healthy controls) (
Variable selection and the classification of samples as “healthy” or “cancer” were performed using logistic regression (LR) and random forests (RF) trained on 153 samples, and validated on two datasets of 94 and 83 independent samples (
A random forest (RF) model in accordance with the present invention and as described in Example 7 was based on the density or proportion of plasma cell-free DNA fragments with length 20-150, 100-150, 160-180, 163-169, 180-220 and 250-320 bp, as well as the amplitude of the oscillations with 10 bp periodicity and can predict the probability that a given plasma sample has been collected from an individual with cancer.
In addition, our data indicates that the output of this same RF classification model might allow for the triage of cancer patient fluid samples into those with sufficiently high levels of ctDNA for detection by other methods (including those with greater sensitivity and/or that allow targeted analysis of specific somatic mutations), and those without.
After applying the RF model to plasma samples from patients with renal cell carcinoma (RCC), of those with >50% probability of cancer by the RF model:
In summary, this analysis has the potential to highlight those cancer patients in which ctDNA analysis (by more sensitive or targeted methods such as INVAR-TAPAS) is more likely to yield informative output. In-turn these samples are more likely to prove clinically useful, potentially allowing, for example, prediction of response to therapy through identification of resistance mutations, disease prognostication, and assessment of clonal evolution through application of targeted methods. This may prove particularly relevant in those cancer types in which ctDNA detection is unreliable (such as renal cancer and glioblastoma), even at later stages of disease at which ctDNA detection would be expected to be reliable (based on equivalent data from other cancer types). Moreover, preliminary results (not shown) suggest that the above findings for RCC are corroborated in a glioblastoma cohort.
Our results indicate that exploiting fundamental properties of cfDNA with fragment specific analyses can provide more sensitive analysis of ctDNA. We based the selection criteria on a biological observation that ctDNA fragment size distribution is shifted from normal cfDNA. Our work builds on a comprehensive survey of plasma cfDNA fragmentation patterns across 200 patients with multiple cancer types and 65 healthy individuals. We identified features that could determine the presence and amount of ctDNA in plasma samples, without a priori knowledge of somatic aberrations. Although this catalogue is the first of its kind, we note that it employed double-stranded DNA from plasma samples, and is subject to potential biases incurred by the DNA extraction and sequencing methods we used. Additional biological effects could contribute to further selective analysis of cfDNA. Other bodily fluids (urine, cerebrospinal fluid, saliva), different nucleic acids and structures, altered mechanisms of release into circulation, or sample processing methods could exhibit varying fragment size signatures and could offer additional exploitable biological patterns for selective sequencing.
Previous work has reported the size distributions of mutant ctDNA, but only considered limited genomic loci, cancer types, or cases (30, 32, 33). We identified the size differences between mutant and non-mutant DNA on a genome-wide and pan-cancer scale. We developed a method to size mutant ctDNA without using high-depth WGS. By sequencing >150 mutations per patient at high depth we obtained large numbers of reads that could be unequivocally identified as tumor-derived, and thus determined the size distribution of mutant ctDNA and non-mutant cfDNA in cancer patients. A potential limitation of our approach is that capture-based sequencing is biased by probe capture efficiency and therefore our data may not accurately reflect ctDNA fragments <100 bp or >300 bp.
Our work provides strong evidence that the modal size of ctDNA for many cancer types is less than 167 bp, which is the length of DNA wrapped around the chromatosome. In addition, our work also shows that there is a high level of enrichment of mutant DNA fragments at sizes greater than 167 bp, notably in the range 250-320 bp. These longer fragments may explain previous observations that longer ctDNA can be detected in the plasma of cancer patients (29, 32). The origin of these long fragments is still unknown, and their observation could be linked to technical factors. However, it is likely that mechanisms of compaction and release of cfDNA into circulation, which may differ depending on its origin, will be reflected by different fragment sizes (38). Improving the characterization of these fragments will be important, especially for future work combining ctDNA analysis with other entities in blood such as microvesicles and tumor-educated platelets (39, 40). Fragment specific analyses not only increase the sensitivity for detection of rare mutations, but could be used to track modifications in the size distribution of ctDNA. Future work should address whether this approach could be used to elucidate mechanistic effects of treatment on tumor cells, for example by distinguishing between necrosis and apoptosis based on fragment size (41).
Genome-wide and exome sequencing of plasma DNA at multiple time-points during cancer treatment have been proposed as non-invasive means to study cancer evolution and for the identification of possible resistance mechanisms to treatment (3). However, WGS and WES approaches are costly and have thus far been applicable only in samples for which the tumor DNA fraction was >5-10% (3-5, 42). We demonstrated that we could exploit the differences in fragment lengths using in vitro and in silico size selection to enrich for tumor content in plasma samples which improved mutation and SCNA detection in sWGS and WES data. We demonstrated that size selection improved the detection of mutations that are present in plasma at low allelic fractions, while maintaining low sequencing depth by sWGS and WES. Size selection can be achieved with simple means and at low cost, and is compatible with a wide range of downstream genome-wide and targeted genomic analyses, greatly increasing the potential value and utility of liquid biopsies.
Size selection can be applied in silico, which incurs no added costs, or in vitro, which adds a simple and low-cost intermediate step that can be applied to either the extracted DNA or the libraries created from it. This approach, applied prospectively to new studies, could boost the clinical utility of ctDNA detection and analysis, and creates an opportunity for re-analysis of large volumes of existing data (4, 34, 43). The limitation of this technique is a potential loss of material and information, since some of the informative fragments may be found in size ranges that are filtered out or de-prioritized in the analysis. This may be particularly problematic if only a few copies of the fragments of interest are present in plasma. Despite potential loss of material, we demonstrated that classification algorithms can learn from cfDNA fragmentation features and SCNAs analysis and improve the detection of ctDNA with a cheap sequencing approach (
Analysis of fragment sizes could provide improvements in other applications. Introducing fragment size information on each read could enhance mutation-calling algorithms from high depth sequencing, to identify tumor-derived mutations from other sources such as somatic variants or background sequencing noise. In addition, cfDNA analysis in patients with CHIP is likely to be structurally different from ctDNA released during tumor cell proliferation (18, 19). Thus, fragmentation analysis or selective sequencing strategies could be applied to distinguish clinically relevant tumor mutations from those present in clonal expansions of normal cells. This will be critical for the development of cfDNA-based methods for identification of patients with early stage cancer.
Size selection could also have an impact on the detection of other types of DNA in body fluids or to enrich signals for circulating bacterial or pathogen DNA and mitochondrial DNA. These DNA fragments are not associated with nucleosomes and are often highly fragmented below 100 bp. Filtering such fragments may prove to be important in light of the recently established link between the microbiome and treatment efficiency (17, 44). Moreover, recent work highlights a stronger correlation between ctDNA detection and cellular proliferation, rather than cell-death (45). We hypothesize that the mode of the distribution of ctDNA fragment sizes at 145 bp could reflect cfDNA released during cell proliferation, and the fragments at 167 bp may reflect cfDNA released by apoptosis or maturation/turnover of blood cells. The effect of other cancer hallmarks (46) on ctDNA biology, structure, concentration and release is yet unknown.
In summary, ctDNA fragment size analysis, via size selection and machine learning approaches, boosts non-invasive genomic analysis of tumor DNA. Size selection of shorter plasma DNA fragments enriches ctDNA, and leads to the identification of a greater number of genomic alterations with both targeted and untargeted sequencing at a minimal additional cost. Combining cfDNA fragment size analysis and the detection of SCNAs with a non-linear classification algorithm improved the discrimination between samples from cancer patients and healthy individuals. As the analysis of fragment sizes is based on the structural property of ctDNA, size selection could be used with any downstream sequencing applications. Our work could help overcome current limitations of sensitivity for liquid biopsy, supporting expanded clinical and research applications. Our results indicate that exploiting the endogenous biological properties of cfDNA provides an alternative paradigm to deeper sequencing of ctDNA.
The following exemplary analysis code for the classification algorithms described in the Examples above is in the R programming environment (see https://www.r-project.org/about.html). The features may be taken from Table 2, wherein the samples are separated into group A cancers (“high ctDNA cancers”) and group B “low ctDNA cancer”), and wherein healthy controls are used in each (i.e. a copy in each of the files).
All references cited herein are incorporated herein by reference in their entirety and for all purposes to the same extent as if each individual publication or patent or patent application was specifically and individually indicated to be incorporated by reference in its entirety.
The specific embodiments described herein are offered by way of example, not by way of limitation. Any sub-titles herein are included for convenience only, and are not to be construed as limiting the disclosure in any way.
Caldas, N. Rosenfeld, Analysis of Circulating Tumor DNA to Monitor Metastatic Breast Cancer, N. Engl. J. Med. 368, 1199-1209 (2013).
| Number | Date | Country | Kind |
|---|---|---|---|
| 1818159.4 | Nov 2018 | GB | national |
| Filing Document | Filing Date | Country | Kind |
|---|---|---|---|
| PCT/EP2019/080506 | 11/7/2019 | WO |
