PROCESS FOR MICROSATELLITE INSTABILITY DETECTION

Information

  • Patent Application
  • 20190169685
  • Publication Number
    20190169685
  • Date Filed
    November 29, 2018
    5 years ago
  • Date Published
    June 06, 2019
    5 years ago
Abstract
The invention provides methods for determining the MSI status of a patient by liquid biopsy with sample preparation using hybrid capture and non-unique barcodes. In certain aspects, the invention provides a method of detecting microsatellite instability (MSI). The method includes obtaining cell-free DNA (cfDNA) from a sample of blood or plasma from a patient and sequencing portions of the cfDNA to obtain sequences of a plurality of tracts of nucleotide repeats in the cfDNA. A report is provided describing an MSI status in the patient when a distribution of lengths of the plurality of tracts has peaks that deviate significantly from peaks in a reference distribution.
Description
TECHNICAL FIELD

The present invention relates generally to the detection, monitoring, and treatment of cancer and more specifically to determining the MSI status of a patient by liquid biopsy.


BACKGROUND

Cancer causes more than a half a million deaths each year in the United States alone. The success of current treatments depends on the type of cancer and the stage at which it is detected. Many treatments include costly and painful surgeries and chemotherapies, and are often unsuccessful. Early and accurate detection of mutations is essential for effective cancer therapy.


Many cancers involve the accumulation of mutations that results from failure of the DNA mismatch-repair (MMR). One important marker of MMR deficiency is microsatellite instability (MSI), a polymorphism of tandem nucleotide repeat lengths ubiquitously distributed throughout the genome. The presence of MMR-deficiency or MSI may serve as a marker for immunotherapy response with checkpoint inhibition. Knowledge of MSI status is thus important and valuable for the treatment of cancer. While it may be possible to determine MSI status by sequencing DNA from a tumor sample, such as a formalin-fixed paraffin-embedded (FFPE) tumor tissue specimen, there are patients for whom tumor material is not readily obtained.


Absent a fixed tissue specimen, a potential source for tumor information is through the analysis of circulating tumor DNA (ctDNA). ctDNA is released from tumor tissue into the blood and can be analyzed by liquid biopsy. Liquid biopsies potentially allow for the detection and characterization of cancer. However, liquid biopsies present their own inherent challenges associated with low circulating tumor DNA (ctDNA) levels as well as problems with faithfully amplifying and sequencing regions of DNA characterized by tracts of mononucleotide repeats.


SUMMARY OF THE INVENTION

The present invention is based on the seminal discovery that a circulating tumor DNA based approach is useful for the detection of high tumor mutation burden and microsatellite instability in cancer patients with advanced disease and can be used to predict responders to immune checkpoint blockade.


The invention provides methods for determining the MSI status of a patient by liquid biopsy. Methods include a sample preparation using hybrid capture and non-unique barcodes. The sample preparation both compensates for errors such as sequencing artifacts and polymerase slippage and provides for the successful capture of target DNA even when present only at a very low fraction of total DNA. Methods include sequencing tracts of mononucleotide repeats within captured sample and modelling the distribution of lengths of those tracts. A peak-finding operation evaluates peaks in the modelled distribution and reveals MSI in the patient when the peaks deviate from a reference distribution (e.g., such as by indicating that the tracts of mononucleotide repeats in the patient's DNA are markedly shorter than in healthy DNA).


Methods of the disclosure are amenable to implementation in conjunction with other genomic screenings such as screening panels of markers, genes, or whole genomes to report mutations or mutational burden. Methods may be implemented by including MSI markers within any suitable liquid-biopsy based sequencing assay and may evaluate MSI status by interrogating MSI markers such as BAT-25, BAT-26, MONO-27, NR-21, and NR-24, BAT-40, TGFβ RII, IGFIIR, hMSH3, BAX and dinucleotide D2S123, D9S283, D9S1851 and D18S58 loci, by way of example, or by modeling distributions of lengths of any other suitable set(s) of repeats in the genome.


In certain aspects, the invention provides a method of detecting microsatellite instability (MSI). The method includes obtaining cell-free DNA (cfDNA) from a sample of plasma from a patient and sequencing portions of the cfDNA to obtain sequences of a plurality of tracts of nucleotide repeats in the cfDNA. A report is provided describing an MSI status in the patient when a distribution of lengths of the plurality of tracts has peaks that deviate significantly from peaks in a reference distribution. Obtaining the cfDNA may include capturing target portions of DNA with probes, fragmenting the target portions to yield fragments, and attaching barcodes to the fragments. In preferred embodiments, the barcodes are non-unique barcodes that include duplicates such that different ones of the fragments are attached to identical barcodes.


The method may include amplifying the fragments to produce amplicons that include barcode information and copies of the fragments, wherein the sequencing step comprises sequencing the amplicons. In one aspect, the sequencing is next-generation, short-read sequencing. The obtained sequences may include a plurality of sequence reads and the method may include aligning the sequence reads to a reference, and identifying groups of sequence reads that originated from a unique segment of the cfDNA by means of the barcode information and position or content of the sequence reads.


The use of the non-unique barcodes to identify groups of sequence reads that originated from a unique segment of the cfDNA allows for the lengths of the plurality of tracts to be determined correctly by correcting for errors introduced by sequencing artifacts or polymerase slippage during the amplifying step.


Preferably, the target portions are markers for MSI such as one or more of BAT25, BAT26, MON027, NR21, NR24, Penta C, and Penta D. For example, the markers may include all of BAT25, BAT26, MON027, NR21, and NR24. In certain embodiments, each of the microsatellite markers is selected from the group consisting of BAT-25, BAT-26, MONO-27, NR-21, NR-24, Penta C, and Penta D, BAT-40, TGFβ RII, IGFIIR, hMSH3, BAX and dinucleotide D2S123, D9S283, D9S1851 and D18S58 loci, by way of example.


In some embodiments, the method includes recommending a treatment for the patient based on the MSI status. Where the MSI status indicates that the patient is microsatellite instable, the treatment may include an immune checkpoint inhibitor. In certain embodiments, the method includes administering the treatment (e.g., the immune checkpoint inhibitor) to the patient. The immune checkpoint inhibitor may be, for example, an antibody such as an anti-PD-1 antibody; an anti-IDO antibody; anti-CTLA-4 antibody; an anti-PD-L1 antibody; or an anti-LAG-3 antibody.


Related aspects provide a method of detecting microsatellite instability (MSI) that includes obtaining a sample comprising fragments of cell-free DNA from a patient; attaching barcodes to the fragments, wherein at least some of the barcodes are not unique; sequencing the barcodes to obtain sequences of a plurality of markers in the DNA; determining a distribution of lengths of the plurality of markers; and providing a report describing MSI in the patient when peaks in the distribution deviate significantly from expected peaks in a modeled healthy distribution.





BRIEF DESCRIPTION OF THE DRAWINGS


FIG. 1 diagrams a method for determining MSI status.



FIG. 2 shows a system for performing methods of the invention.



FIG. 3 shows a model of length distribution of mononucleotide repeats.



FIG. 4 shows a report provided by systems and methods of the invention.



FIGS. 5A-5D Plasma-Based Detection of Microsatellite Instability. (A) Prior to error correction and Digital Peak Finding (DPF) (light pink), the mononucleotide count distribution demonstrated high background noise due to sequencing related aberrations and polymerase slippage in library preparation PCR and sequencing. These are subsequently resolved after error correction and DPF (dark pink) to create distinct distributions for MSI and MSS alleles. (B) Across the BAT25, BAT26, MON027, NR21, and NR24 mononucleotide loci in 163 healthy donor plasma specimens, the error corrected mononucleotide count distribution was assessed with a DPF algorithm to identify mononucleotide alleles and determine MSI status. Prior to error correction and DPF (light pink), the majority of healthy donor samples exhibit alleles below the MSI cutoff (hashed line). Kaplan-Meier curves for progression free survival (C) and overall survival (D) among patients with progressive metastatic carcinoma were determined using MSI status from pre-treatment plasma specimens. In MSI patients (n=9*), median progression free survival was 16.17 months, while median overall survival was not reached. In MSS patients (n=7*), median progression free survival and median overall survival were 2.81 and 7.6 months, respectively. *Three patients with a tissue enrollment status of MSI-H were classified as MSS using pre-treatment baseline cfDNA obtained from plasma.



FIGS. 6A-6E Plasma-Based Detection of High Tumor Mutation Burden. (A) Using whole exome sequencing data derived from The Cancer Genome Atlas (TCGA), a significant positive correlation between the tumor mutation burden (TMB) evaluated in the 98 kb targeted regions compared to the whole exome analyses was observed (r=0.91, p<0.0001; Pearson correlation). (B) Comparison of the accuracy for determination of the TMB derived from the targeted panel in plasma compared to whole-exome analyses of matched archival tissue samples in 13 patients yielded a significant positive correlation (r=0.693, p=0.007; Pearson correlation). (C) The overall TMB status at baseline was assigned as TMB-High or TMB-Low using a cutoff of 50.8 mutations/Mbp sequenced. In total, six patients were categorized as TMB-High and ten patients as TMB-Low, with a median load of 132 mutations/Mbp sequenced and 15.2 mutations/Mbp sequenced, respectively. Additionally, 163 healthy donor cases were evaluated, all of which were determined to be TMB-Low, with a median load of 0 mutations/Mbp sequenced across the panel. Kaplan-Meier curves for progression free survival (D) and overall survival (E) among this same cohort of patients were determined using TMB status from pre-treatment plasma specimens with a cutoff of 50.8 mutations/Mbp sequenced. In TMB-High patients (n=6), median progression free survival and median overall survival were not reached. In TMB-Low patients (n=10), median progression free survival and median overall survival were 2.84 and 7.62 months, respectively.



FIGS. 7A-7F Serial Plasma-Based Overall Survival Analysis for Patients Treated with Immune Checkpoint Blockade. (A) Evaluation of overall survival with the protein biomarker level at last dose (CEA or CA19-9). A significant inverse correlation was observed between the overall survival in months when compared to the residual protein biomarker (r=−0.99, p=<0.001; Pearson correlation). (B) Kaplan-Meier curves for overall survival among patients with tissue enrollment status of MSI and detectable protein biomarker levels (n=8). For patients with >80% reduction in protein biomarker levels (n=4), median overall survival was not reached. For patients with ≤80% reduction in protein biomarker levels (n=4), median overall survival was 5.26 months. (C) Evaluation of overall survival compared to residual MSI allele levels at last dose. A significant inverse correlation was observed between the overall survival when compared to the residual MSI allele levels (r=−0.70, p=0.034; Pearson correlation). (D) Kaplan-Meier curves for overall survival among patients with tissue enrollment status of MSI and detectable MSI status at baseline (n=9). For patients with two consecutive timepoints displaying no residual MSI alleles (n=4) median overall survival was not reached. For patients with multiple timepoints containing residual MSI alleles (n=5) median overall survival was 7.64 months. (E) Evaluation of overall survival compared to residual TMB levels at last dose. A significant inverse correlation was observed between the overall survival in months when compared to the residual TMB levels (r=−0.95, p=<0.001; Pearson correlation). (F) Kaplan-Meier curves for overall survival among patients with tissue enrollment status of MSI and detectable TMB levels at baseline (n=11). For patients with >90% reduction in TMB levels (n=4), median overall survival was not reached. For patients with ≤90% reduction in TMB levels (n=7), median overall survival was 7.64 months. “/” indicates a censored datapoint; “*” indicates cases where baseline protein biomarker, MSI or TMB was not detected and were not included in the subsequent analyses; In cases where residual protein biomarker, MSI or TMB levels increased when compared to baseline, values of greater than 100% are indicated.



FIGS. 8A-8D Monitoring of Patients During Immune Checkpoint Blockade. For three patients with a complete response to immune checkpoint blockade (CS97 (A), CS98 (B), and CS00 (C) and one patient with progressive disease (CS05 (D)), circulating protein biomarkers (CEA, ng/mL and CA19-9, units/mL), residual alleles exhibiting MSI, and TMB levels were evaluated over time during treatment. In each case exhibiting a complete response, residual MSI and TMB alleles were reduced to 0% mutant allele fraction (MAF) between 0.6 and 4.8 months after first dose.



FIGS. 9A-9D Archival Tissue-Based Detection of Microsatellite Instability and High Tumor Mutation Burden. Kaplan-Meier curves for progression free survival (A) and overall survival (B) among patients with progressive metastatic carcinoma were determined using MSI status from archival tissue. In MSI patients (n=12), median progression free survival and median overall survival were 4.23 and 20.69 months, respectively. In MSS patients (n=4), median progression free survival and median overall survival were 2.81 and 6.31 months, respectively. Kaplan-Meier curves for progression free survival (C) and overall survival (D) among patients with progressive metastatic carcinoma were determined. In TMB-High patients (n=10), median progression free survival was 10.81 months, while median overall survival was not reached. In TMB-Low patients (n=3), median progression free survival and median overall survival were 2.81 and 5.02 months, respectively.



FIGS. 10A-10F Plasma-Based Progression Free Survival Analysis for Patients Treated with Immune Checkpoint Blockade. (A) Evaluation of progression free survival with the protein biomarker level at last dose (CEA or CA19-9). An inverse correlation was observed between the progression free survival in months when compared to the residual protein biomarker (r=−0.92, p=0.001; Pearson correlation). (B) Kaplan-Meier curves for progression free survival among patients with tissue enrollment status of MSI and detectable protein biomarker levels (n=8). For patients with >80% reduction in protein biomarker levels (n=4), median progression free survival was not reached. For patients with ≤80% reduction in protein biomarker levels (n=4), median progression free survival was 2.63 months. (C) Evaluation of progression free survival compared to residual MSI allele levels at last dose. A significant inverse correlation was observed between the progression free survival in months when compared to the residual MSI allele levels (r=−0.84, p=0.004; Pearson correlation). (D) Kaplan-Meier curves for progression free survival among patients with tissue enrollment status of MSI and detectable MSI status at baseline (n=9). For patients with two consecutive timepoints displaying no residual MSI alleles (n=4) median progression free survival was not reached. For patients with multiple timepoints containing residual MSI alleles (n=5) median progression free survival was 3.01 months. (E) Evaluation of progression free survival compared to residual TMB levels at last dose. A significant inverse correlation was observed between the progression free survival in months when compared to the residual TMB levels (r=−0.98, p=<0.001; Pearson correlation). (F) Kaplan-Meier curves for progression free survival among patients with tissue enrollment status of MSI and detectable TMB levels at baseline (n=11). For patients with >90% reduction in TMB levels (n=4), median progression free survival was not reached. For patients with ≤90% reduction in TMB levels (n=7), median progression free survival was 2.88 months. “/” indicates a censored datapoint; “*” indicates cases where baseline protein biomarker, MSI or TMB was not detected and were not included in the subsequent analyses; In cases where residual protein biomarker, MSI or TMB levels increased when compared to baseline, values of greater than 100% are indicated.



FIG. 11 Radiographic Imaging of Case CS98 Displaying a Complete Response to Immune Checkpoint Blockade. After 20 weeks of treatment with immune checkpoint blockade, radiographic imaging was performed and revealed potential lesions in the liver, but later disappeared, so likely instead represented inflammatory liver nodules.





DETAILED DESCRIPTION OF THE INVENTION

The present invention relates to the discovery that microsatellite instability (MSI) and high tumor mutation burden (TMB-High) are pan-tumor biomarkers used to select patients for treatment with immune checkpoint blockade. The present invention shows a plasma-based approach for detection of MSI and TMB-High in patients with advanced cancer. To detect sequence alterations across a 98 kilobase panel, including those in microsatellite regions, the inventors developed an error correction approach with specificities >99% (n=163) and sensitivities of 75% (n=12) and 60% (n=10), respectively, for MSI and TMB-High. For patients treated with PD-1 blockade, the data demonstrate that MSI and TMB-High in pre-treatment plasma predicted progression-free survival (hazard ratios 0.2 and 0.12, p=0.01 and 0.004, respectively). The data shows the results when plasma during therapy was analyzed in order to develop a prognostic signature for patients who achieved durable response to PD-1 blockade. These analyses demonstrate the feasibility of non-invasive pan-cancer screening and monitoring of patients who exhibit MSI or TMB-High and have a high likelihood of responding to immune checkpoint blockade.


The disclosure provides for the detection of MSI by liquid biopsy. While plasma is the illustrative example provided herein, it is understood that a liquid biopsy can be performed with a biological sample including blood, plasma, saliva, urine, feces, tears, mucosal secretions and other biological fluids.


In particular, methods of the disclosure provide and include the analytical validation of an integrated NGS-based liquid biopsy approach for the detection of microsatellite instability associated with cancers such as pancreatic, colon, gastric, endometrial, cholangiocarcinoma, breast, lung, head and neck, kidney, bladder, or prostate cancer, as well as hematopoietic cancers, among others. Failure of the DNA mismatch repair (MMR) pathway during DNA replication in cancer leads to the increased accumulation of somatic mutations. One important marker of MMR deficiency is microsatellite instability (MSI), which presents as polymorphism of tandem nucleotide repeat lengths ubiquitously distributed throughout the genome. Methods of the disclosure are offered to assay for and detect those markers via liquid biopsy. Additionally, since the presence of MMR-deficiency or MSI may serve as a marker for immunotherapy response with checkpoint inhibition, methods may be used to determine a course of treatment such as immunotherapy or the administration of a checkpoint inhibitor.


Microsatellite instability (MSI) and mismatch repair (MMR) deficiency have recently been demonstrated to predict immune checkpoint blockade response. The checkpoint inhibitor pembrolizumab is now indicated for the treatment of adult and pediatric patients with any unresectable or metastatic solid tumors identified as having either of these biomarkers. This indication covers patients with solid tumors that have progressed following prior treatment and have no satisfactory alternative treatment options.


Cancer is characterized by the accumulation of somatic mutations that have the potential to result in the expression of neoantigens, which may elicit T-cell-dependent immune responses against tumors. MMR is a mechanism by which post-replicative mismatches in daughter DNA strands are repaired and replaced with the correct DNA sequence. MMR deficiency results in both MSI and high tumor mutation burden (TMB-High), which increases the likelihood that acquired somatic mutations may be transcribed and translated into proteins that are recognized as immunogenic neoantigens. Historically, testing for MSI has been restricted to screening for Hereditary Non-Polyposis Colorectal Cancer (HNPCC), which is often characterized by early age onset colorectal cancer and endometrial cancer, as well as other extracolonic tumors. HNPCC, commonly referred to as Lynch Syndrome, is caused by mutations in the DNA mismatch repair genes (MLH1, MSH2, MSH6 and PMS2), as well as the more recently described, EPCAM(16). In addition to familial conditions, MSI can occur sporadically in cancer, and both hereditary and sporadic MSI patients respond to immune checkpoint blockade(1,2). A recent study, conducted across 39 tumor types and 11,139 patients to determine the landscape of MSI prevalence, concluded that 3.8% of these cancers across 27 tumor types displayed MSI, including 31.4% of uterine/endometrial carcinoma, 19.7% of colon adenocarcinoma, and 19.1% of stomach adenocarcinoma.


MSI can be detected through alterations in the length of microsatellite sequences typically due to deletions of repeating units of DNA to create novel allele lengths in tumor-derived DNA when compared to a matched-normal or a reference population. Current methods for MSI testing, using tissue biopsies and resection specimens, include PCR-based amplification followed by capillary electrophoresis, and more recently, next-generation sequencing (NGS) based approaches, which are used to quantify microsatellite allele lengths. The challenge associated with application of the former approach are polymerase induced errors (stutter bands), particularly in samples with low tumor purity, such as cell-free DNA (cfDNA), which can mask true biological alleles exhibiting MSI. In the case of NGS based approaches, sensitivity is typically limited by the accuracy for determination of homopolymer lengths. A novel method was recently described for determination of MSI using pre-PCR elimination of wild-type DNA homopolymers in liquid biopsies. However, given the low prevalence of MSI across cancer, it would be preferable to develop an NGS profiling approach which can include other clinically actionable alterations in cancer, including TMB, sequence mutations, copy number alterations, and translocations.


In addition to the technical challenges associated with MSI detection, it is often not possible to readily obtain biopsy or resection tissue for genetic testing due to insufficient material (biopsy size and tumor cellularity), exhaustion of the limited material available after prior therapeutic stratification, logistical considerations for tumor and normal sample acquisition after initial diagnosis, or safety concerns related to additional tissue biopsy interventions(26). In contrast, plasma-based approaches offer the unique opportunity to obtain a rapid and real-time view of the primary tumor and metastatic lesions along with associated response to therapy. Circulating tumor DNA can be used to monitor and assess residual disease in response to clinical intervention, such as surgery or chemotherapy(27-33), which can directly impact patient care. To determine the clinical impact of identifying tumors that harbor MSI or TMB-High using cfDNA, we developed and applied a 98 kb 58-gene targeted panel to cancer patients with advanced disease treated with PD-1 blockade. FIG. 1 diagrams a method 101 of detecting microsatellite instability (MSI). The method 101 includes obtaining 107 cell-free DNA (cfDNA) from a sample of plasma from a patient. Preferably, non-unique barcode are attached 111. Portions of the cfDNA are sequenced 115 to obtain sequences of a plurality of tracts of nucleotide repeats in the cfDNA. The method 101 includes modeling 121 a distribution of lengths of tracts of nucleotide repeats. A report is provided 125 describing an MSI status in the patient when a distribution of lengths of the plurality of tracts has peaks that deviate significantly from peaks in a reference distribution. Obtaining the cfDNA may include capturing target portions of DNA with probes, fragmenting the target portions to yield fragments, and attaching barcodes to the fragments.


Briefly, cell-free DNA may be extracted from cell line or blood or plasma specimens and prepared into a genomic library suitable for next-generation sequencing with oligonucleotide barcodes through end-repair, A-tailing and adapter ligation. An in-solution hybrid capture, utilizing for example, 120 base-pair (bp) RNA oligonucleotides may be performed.


In one embodiment, at least about 10-100 ng, such as 50 ng of DNA in 100 microliters of TE is fragmented in a sonicator to a size of about 150-450 bp. To remove fragments smaller than 150 bp, DNA may be purified using Agencourt AMPure XP beads (Beckman Coulter, IN) in a ratio of 1.0 to 0.9 of PCR product to beads twice and, e.g., washed using 70% ethanol per the manufacturer's instructions. Purified, fragmented DNA is mixed with H2O, End Repair Reaction Buffer, End Repair Enzyme Mix (cat# E6050, NEB, Ipswich, Mass.). The mixture is incubated then purified using Agencourt AMPure XP beads (Beckman Coulter, IN) in a ratio of 1.0 to 1.25 of PCR product to beads and washed using 70% ethanol per the manufacturer's instructions. To A-tail, end-repaired DNA is mixed with Tailing Reaction Buffer and Klenow (exo-) (cat# E6053, NEB, Ipswich, Mass.). The mixture is incubated at 37 degree C. for 30 min and purified using Agencourt AMPure XP beads (Beckman Coulter, IN) in a ratio of 1.0 to 1.0 of PCR product to beads and washed using 70% ethanol per the manufacturer's instructions. For adaptor ligation, A-tailed DNA is mixed with H2O, PE-adaptor (Illumina), Ligation buffer and Quick T4 DNA ligase (cat# E6056, NEB, Ipswich, Mass.). The ligation mixture was incubated, then amplified.


Exonic or targeted regions were captured in solution using the Agilent SureSelect v.4 kit according to the manufacturer's instructions (Agilent, Santa Clara, Calif.). The captured library was then purified with a Qiagen MinElute column purification kit. To purify PCR products, a NucleoSpin Extract II purification kit (Macherey-Nagel, PA) may be used before sequencing.


Targeted sequencing is performed. Two technical challenges to implementing these approaches in the form of a liquid biopsy include the limited amount of DNA obtained and the low mutant allele frequency associated with the MSI markers. It may be that as few as several thousand genomic equivalents are obtained per milliliter of plasma, and the mutant allele frequency can range from <0.01% to >50% total cfDNA. see Bettegowda, 2014, Detection of circulating tumor DNA in early- and late-stage human malignancies, Sci Trans Med 6(224):224ra24, incorporated by reference. The disclosed techniques overcome such problems and improve test sensitivity, optimized methods for conversion of cell-free DNA into a genomic library, and digital sequencing approaches to improve the specificity of next-generation sequencing approaches.


Methods may include extracting and isolating cell-free DNA from a blood or plasma sample and assigning an exogenous barcode to each fragment to generate a DNA library. The exogenous barcodes are from a limited pool of non-unique barcodes, for example 8 different barcodes. The barcoded fragments are differentiated based on the combination of their exogenous barcode and the information about the reads that results from sequencing such as the sequence of the reads (effectively, an endogenous barcode) or position information (e.g., stop and/or start position) of the read mapped to a reference. The DNA library is redundantly sequenced 115 and the sequences with matching barcodes are reconciled. The reconciled sequences may be aligned to a human genome reference.


The invention recognizes that completely unique barcode sequences are unnecessary. Instead, a combination of predefined set of non-unique sequences together with the endogenous barcodes can provide the same level of sensitivity and specificity that unique barcodes could for biologically relevant DNA amounts and can, in-fact, correct for sequencing artifacts or polymerase slippage. A limited pool of barcodes is more robust than a conventional unique set and easier to create and use. Methods include obtaining a sample comprising nucleic acid fragments, providing a plurality of sets of non-unique barcodes, and tagging 111 the nucleic acid fragments with the barcodes to generate a genomic library, wherein each nucleic acid fragment is tagged with the same barcode as another different nucleic acid fragment in the genomic library.


In embodiments, the plurality of sets is limited to twenty or fewer unique barcodes. In other embodiments, the plurality of sets is limited to ten or fewer unique barcodes.


According to the present invention, a small pool of non-unique exogenous barcodes can be used to provide a robust assay that achieves levels of sensitivity that are comparable to traditional, more complex barcoding schemes, while vastly reducing cost and complication.


After processing steps such as those described above, nucleic acids can be sequenced. Sequencing may be by any method known in the art. DNA sequencing techniques include classic dideoxy sequencing reactions (Sanger method) using labeled terminators or primers and gel separation in slab or capillary, and next generation sequencing methods such as sequencing by synthesis using reversibly terminated labeled nucleotides, pyrosequencing, 454 sequencing, Illumina/Solexa sequencing, allele specific hybridization to a library of labeled oligonucleotide probes, sequencing by synthesis using allele specific hybridization to a library of labeled clones that is followed by ligation, real time monitoring of the incorporation of labeled nucleotides during a polymerization step, polony sequencing, and SOLiD sequencing. Separated molecules may be sequenced by sequential or single extension reactions using polymerases or ligases as well as by single or sequential differential hybridizations with libraries of probes.


A sequencing technique that can be used includes, for example, use of sequencing-by-synthesis systems sold under the trademarks GS JUNIOR, GS FLX+ and 454 SEQUENCING by 454 Life Sciences, a Roche company (Branford, Conn.), and described by Margulies, M. et al., Genome sequencing in micro-fabricated high-density picotiter reactors, Nature, 437:376-380 (2005); U.S. Pat. No. 5,583,024; U.S. Pat. No. 5,674,713; and U.S. Pat. No. 5,700,673, the contents of which are incorporated by reference herein in their entirety.


Other examples of DNA sequencing techniques include SOLiD technology by Applied Biosystems from Life Technologies Corporation (Carlsbad, Calif.) and ion semiconductor sequencing using, for example, a system sold under the trademark ION TORRENT by Ion Torrent by Life Technologies (South San Francisco, Calif.). Ion semiconductor sequencing is described, for example, in Rothberg, et al., An integrated semiconductor device enabling non-optical genome sequencing, Nature 475:348-352 (2011); U.S. Pub. 2010/0304982; U.S. Pub. 2010/0301398; U.S. Pub. 2010/0300895; U.S. Pub. 2010/0300559; and U.S. Pub. 2009/0026082, the contents of each of which are incorporated by reference in their entirety.


Another example of a sequencing technology that can be used is Illumina sequencing. Illumina sequencing is based on the amplification of DNA on a solid surface using fold-back PCR and anchored primers. Adapters are added to the 5′ and 3′ ends of DNA that is either naturally or experimentally fragmented. DNA fragments that are attached to the surface of flow cell channels are extended and bridge amplified. The fragments become double stranded, and the double stranded molecules are denatured. Multiple cycles of the solid-phase amplification followed by denaturation can create several million clusters of approximately 1,000 copies of single-stranded DNA molecules of the same template in each channel of the flow cell. Primers, DNA polymerase and four fluorophore-labeled, reversibly terminating nucleotides are used to perform sequential sequencing. After nucleotide incorporation, a laser is used to excite the fluorophores, and an image is captured and the identity of the first base is recorded. The 3′ terminators and fluorophores from each incorporated base are removed and the incorporation, detection and identification steps are repeated. Sequencing according to this technology is described in U.S. Pat. No. 7,960,120; U.S. Pat. No. 7,835,871; U.S. Pat. No. 7,232,656; U.S. Pat. No. 7,598,035; U.S. Pat. No. 6,911,345; U.S. Pat. No. 6,833,246; U.S. Pat. No. 6,828,100; U.S. Pat. No. 6,306,597; U.S. Pat. No. 6,210,891; U.S. Pub. 2011/0009278; U.S. Pub. 2007/0114362; U.S. Pub. 2006/0292611; and U.S. Pub. 2006/0024681, each of which are incorporated by reference in their entirety.


Preferably sequencing is done redundantly for deep coverage, preferably at least 30× coverage or 100×. DNA libraries may be sequenced using paired-end 111umina HiSeq 2500 sequencing chemistry to an average target total coverage of either >20,000-fold or >5,000-fold coverage for each targeted base. Sequence data may be mapped to the reference human genome. Preferably, the sequencing is next-generation, short-read sequencing. The obtained sequences may include a plurality of sequence reads and the method may include aligning the sequence reads to a reference, and identifying groups of sequence reads that originated from a unique segment of the cfDNA by means of the barcode information and position or content of the sequence reads. Primary processing of sequence data may be performed using Illumina CASAVA software (v1.8), including masking of adapter sequences. Sequence reads may bealigned against the human reference genome (version hg18) using ELAND with additional realignment of select regions using the Needleman-Wunsch method.


In some embodiments, the barcodes are non-unique barcodes that include duplicates such that different ones of the fragments are attached to identical barcodes. The high clinical efficacy of MSI status now requires a fast, objective, highly sensitive screening method, particularly in late-stage patients where tumor material may not be readily obtained. However, to extend this approach to a liquid biopsy panel requires technological advances to both overcome the inherent challenges associated with low circulating tumor DNA (ctDNA) levels which is compounded by polymerase slippage in mononucleotide repeat regions during PCR amplification as well as other sequencing artifacts.


To overcome these limitations, we applied error correction approach using molecular barcoding together with high sequencing depth and a novel peak finding algorithm to more accurately identify the specific mononucleotide sequences in cell-free DNA (cfDNA) analyses of a 64 gene panel, by way of illustration. The MSI markers can be sequenced in conjunction with such 64 gene panel, or in isolation (e.g,. just sequence the markers) or in conjunction with any other gene panel (e.g., >300 genes) or with whole genome or whole exome sequencing.


The method may include amplifying the fragments to produce amplicons that include barcode information and copies of the fragments, wherein the sequencing step comprises sequencing the amplicons.


The use of the non-unique barcodes to identify groups of sequence reads that originated from a unique segment of the cfDNA allows for the lengths of the plurality of tracts to be determined correctly by correcting for errors introduced by sequencing artifacts or polymerase slippage during the amplifying step. By eliminating a significant majority of sequencing errors and polymerase slippage artifacts, we were able to reduce background error rates by >90%. Combined with implementation of a distribution modeling and a peak finding algorithm, we were able to accurately sequence the mononucleotide tracts to minimize false discovery rates for cfDNA analyses.



FIG. 2 shows a system 901 for performing methods of the disclosure. The system 901 includes a computer 933, and may optionally include a server computer 909. In certain embodiments, the system 901 includes a sequencing instrument 955 (such as an Illumina HiSeq device) which may itself include an instrument computer 951 (e.g., onboard in the sequencing instrument). Any of the computers may communicate via network 915. Each computer preferably includes at least one tangible, non-transitory memory device 975 and any input/output devices coupled to a processor. The memory may include instructions executable by the processor(s) to perform methods such as a method of detecting microsatellite instability (MSI) that includes obtaining a sample comprising fragments of cell-free DNA from a patient; attaching barcodes to the fragments, wherein at least some of the barcodes are not unique; sequencing the barcodes to obtain sequences of a plurality of markers in the DNA; determining a distribution of lengths of the plurality of markers; and providing a report describing MSI in the patient when peaks in the distribution deviate significantly from expected peaks in a modeled healthy distribution.



FIG. 3 illustrates distribution modeling for peak finding. In the illustrated distribution model 301, a model 307 of a distribution of lengths of tracts of nucleotide repeats is determined. It may be compared to a reference distribution 305 and an operation may be performed to find a peak 313 for the patient data 307 and/or the reference distribution 305 (which may be from patient healthy sample DNA or from a human genome reference or any other suitable source. In some embodiments, when the peak finding operation determines that the patient peak 313 is sufficiently deviant from a location of a reference peak, the method and system report the patient as MSI (microsatellite instable) for the relevant marker. Most preferably, the peak finding and distribution modeling is performed for each MSI marker. A benefit of the described method is that the distribution modeling and peak finding may be reliably implemented and automated in a high-throughput system.


MSI may be assayed by hybrid capture and NGS to address such markers as mononucleotide repeat markers such as BAT25, BAT26, MON027, NR21, and NR24. See U.S. Pub. 2017/0267760, incorporated by reference. Knowledge of MSI status is important and valuable in the treatment of many cancers, and there are patients for whom tumor material is not readily obtained. Tumors deficient in mismatch repair are particularly susceptible to a particular form of immunotherapy because this phenotype results in ongoing accumulation of mutations at a high frequency. Methods may include recommending or administering treatment for cancer patients that display the microsatellite instability phenotype or other high mutational burden. The treatment involves an inhibitory antibody for an immune checkpoint. Such checkpoints include PD-1, IDO, CTLA-4, PD-L1, and LAG-3 by way of example. Other immune checkpoints can be used as well. Antibodies can be administered by any means that is convenient, including but not limited to intravenous infusion, oral administration, subcutaneous administration, sublingual administration, ocular administration, nasal administration, and the like.


Preferably, the method 101 includes providing 125 a report with MSI status.



FIG. 4 shows a report 410 that includes a status of “instable” for certain MSI markers. Preferably, the target portions are markers for MSI such as one or more of BAT25, BAT26, MON027, NR21, NR24, Penta C, and Penta D. For example, the markers may include all of BAT25, BAT26, MON027, NR21, and NR24. In certain embodiments, each of the microsatellite markers is selected from the group consisting of BAT-25, BAT-26, MONO-27, NR-21, NR-24, Penta C, and Penta D.


In some embodiments, the method includes recommending a treatment for the patient based on the MSI status. Where the MSI status indicates that the patient is microsatellite instable, the treatment may include an immune checkpoint inhibitor. In certain embodiments, the method includes administering the treatment (e.g., the immune checkpoint inhibitor) to the patient. The immune checkpoint inhibitor may be, for example, an antibody such as an anti-PD-1 antibody; an anti-IDO antibody; anti-CTLA-4 antibody; an anti-PD-L1 antibody; or an anti-LAG-3 antibody. Types of antibodies which can be used include any that are developed for the immune checkpoint inhibitors. These can be monoclonal or polyclonal. They may be single chain fragments or other fragments of full antibodies, including those made by enzymatic cleavage or recombinant DNA techniques. They may be of any isotype, including but not limited to IgG, IgM, IgE. The antibodies may be of any species source, including human, goat, rabbit, mouse, cow, chimpanzee. The antibodies may be humanized or chimeric. The antibodies may be conjugated or engineered to be attached to another moiety, whether a therapeutic molecule or a tracer molecule. The therapeutic molecule may be a toxin, for example. The present invention is more particularly described in the following examples which are intended as illustrative only since numerous modifications and variations therein will be apparent to those skilled in the art. The following examples are intended to illustrate but not limit the invention.


EXAMPLES
Example 1
Methods

Patients and Sample Collection


Formalin fixed paraffin embedded (FFPE) tumor and matched normal buffy coat specimens (n=61) from individuals with cancer were obtained after surgical resection through commercial biorepositories from BioIVT (Hicksville, N.Y., USA), Indivumed (Hamburg, Germany), and iSpecimen (Lexington, Mass., USA). Plasma samples from healthy individuals (n=163) were procured through BioIVT (Hicksville, N.Y., USA) during routine screening with negative results and no prior history of cancer. Human cells from previously characterized MSI cell lines were obtained from ATCC (Manassas, Va., USA) (n=5; LS180, LS411N, SNU-C2B, RKO, and SNU-C2A). Finally, baseline and serial plasma samples from cancer patients with progressive metastatic carcinoma (n=16; 11 colorectal, 3 ampullary, and 2 small intestine) were obtained while patients were enrolled in a phase 2 clinical trial to evaluate immune checkpoint blockade with pembrolizumab(1,2). Radiographic and serum protein biomarker data for CEA and CA19-9 were collected as a part of routine clinical care. All samples were obtained under Institutional Review Board approved protocols with informed consent for research.


Orthogonal Testing of FFPE Tissue for MSI Status


The Promega MSI analysis system (Madison, Wis., USA) was used to assess MSI status in DNA derived from FFPE tumor tissue together with matched normal buffy coat by multiplex PCR and fluorescent capillary electrophoresis. Tumors were classified as MSI if two or more of the five mononucleotide markers (BAT25, BAT26, MONO27, NR21, and NR24) had significant length differences compared to the matched normal allele lengths. Additionally, 2-pentanucleotide repeat loci (PentaC and PentaD) were used to confirm case identity between normal and tumor samples.


Sample Preparation and Next-Generation Sequencing


FFPE Tumor and Normal Analyses


Sample processing from tissue or buffy coat, library preparation, hybrid capture, and sequencing were performed as previously described at Personal Genome Diagnostics (Baltimore, Md.)(34,36). Briefly, DNA was extracted from FFPE tissue and matched normal buffy coat cells using the Qiagen FFPE Tissue Kit and DNA Blood Mini Kit, respectively (Qiagen, Hilden, Germany). Genomic DNA was sheared using a Covaris sonicator (Woburn, Mass., USA) to a size range of 150-450 bp, and subsequently used to generate a genomic library using the New England Biolabs (Ipswich, Mass., USA) end-repair, A-tailing, and adapter ligation modules. Finally, genomic libraries were amplified and captured using the Agilent SureSelect XT in-solution hybrid capture system with a custom 120 bp RNA panel targeting the pre-defined regions of interest across 125 genes (Table 1). Captured libraries were sequenced on the Illumina HiSeq 2000 or 2500 (Illumina, San Diego, Calif., USA) with 100 bp paired end reads.


Plasma Analyses


Sample processing from plasma, library preparation, hybrid capture, and sequencing were performed as previously described at Personal Genome Diagnostics (Baltimore, Md.)(34). Briefly, blood was collected in EDTA tubes and centrifuged at 800 g for 10 minutes at 4° C. to separate plasma from white blood cells. Cell-free DNA was extracted from plasma using the QIAamp Circulating Nucleic Acid Kit (Qiagen, Hilden, Germany). Libraries were prepared with 5-250 ng of cfDNA using the NEBNext DNA Library Prep Kit (New England Biolabs, Ipswich, Mass., USA). After end repair and a-tailing, a pool of eight unique Illumina dual index adapters with 8 bp barcodes were ligated to cfDNA to allow for accurate error correction of duplicate reads, followed by 12 cycles of amplification. Targeted hybrid capture was performed using Agilent SureSelect XT in-solution hybrid capture system with a custom 120 bp RNA panel targeting the pre-defined regions of interest across 58 genes (Table 4) according to the manufacturer protocol (Agilent Technologies, Santa Clara, Calif., USA). Captured libraries were sequenced on the Illumina HiSeq 2000 or 2500 (Illumina, San Diego, Calif., USA) with 100 bp paired end reads.


Example 2
Microsatellite Instability Analyses by Next-Generation Sequencing

Sequence data were aligned to the human reference genome assembly (hg19) using BWA-MEM(37). Reads mapping to microsatellites were excised using Samtools(38) and analyzed for insertion and deletion events (indels). In most cases, alignment and variant calling did not generate accurate indel calls in repeated regions due to low quality bases surrounding the microsatellites. Therefore, a secondary local realignment and indel quantitation was performed. Reads were considered for an expanded indel analysis if (i) the mononucleotide repeat was contained to more than eight bases inside of the start and end of the read, (ii) the indel length was ≤12 bases from the reference length, (iii) there were no single base changes found within the repeat region, (iv) the read had a mapping score of 60, and (v) ≤20 bases of the read were soft clipped for alignment. After read specific mononucleotide length analysis, error correction was performed to allow for an aggregated and accurate quantitation among duplicated fragments using molecular barcoding. Reads were aggregated into barcode families by using the ordered and combined read 1 and read 2 alignment positions with the molecular barcode. Barcode families were considered for downstream analysis if they comprised of at least 2 reads and >50% of reads had consistent mononucleotide lengths. The error corrected mononucleotide length distribution was subjected to a peak finding algorithm where local maxima were required to be greater than the error corrected distinct fragment counts of the adjacent lengths ±2 bp. Identified peaks were further filtered to only include those which had >3 error corrected distinct fragments at ≥1% of the absolute coverage. The shortest identified mononucleotide allele length was compared to the hg19 reference length. If the allele length was ≥3 bp shorter than the reference length, the given mononucleotide loci was classified as exhibiting instability. This approach was applied across all mononucleotide loci. Samples were classified as MSI-H if ≥20% of loci were MSI. In the targeted 58 gene plasma panel, BAT25, BAT26, MONO27, NR21, and NR24 mononucleotide loci were for the determination of MSI status. In the targeted 125 gene targeted tissue panel, an additional 65 microsatellite regions were used for MSI classification.


Example 3
Tumor Mutational Burden Analyses by Next-Generation Sequencing

Next generation sequencing data were processed and variants were identified using the VariantDx custom software as previously described(34). A final set of candidate somatic mutations were selected for tumor mutational burden analyses based on: (i) variants enriched due to sequencing or alignment error were removed (≤5 observations or <0.30% mutant allele fraction), (ii) nonsynonymous and synonymous variants were included, but variants arising in non-coding regions were removed, (iii) hotspot variants annotated in COSMIC (version 72) were not included to reduce bias toward driver alterations, (iv) common germline SNPs found in dbSNP (version 138) were removed as well as variants deemed private germline variants based on the variant allele frequency, and (v) variants associated with clonal hematopoietic expansion were not included in the candidate variant set(39).


In Silico TCGA Analyses


In order to evaluate the accuracy of the 98 kb targeted panel for prediction of TMB, a comparison to whole-exome sequencing data derived from The Cancer Genome Atlas (TCGA)(35) was performed by considering synonymous and nonsynonymous alterations, excluding known hotspot mutations which may not be representative of TMB in the tumor. The cutoff for consideration as TMB-High was set to 5 candidate variants (50.8 mutations/Mbp sequenced) based on in silico analyses utilizing the TCGA data to achieve >95% accuracy (>36 mutations/Mbp).


Statistical Analyses


Due to small sample size, Firth's Penalized Likelihood was used to evaluate significant differences between Kaplan-Meier curves for progression free survival and overall survival with the classifiers baseline MSI status, baseline TMB status, two consecutive timepoints with >80% reduction in baseline protein biomarker levels, two consecutive timepoints with 0% residual MSI alleles on treatment, and two consecutive timepoints with >90% reduction in baseline TMB levels. Pearson correlations were used to evaluate significant association between TMB in the 58 gene targeted panel compared to whole-exome analyses, progression free and overall survival compared to residual protein biomarker levels, and progression free and overall survival compared to residual MSI and TMB allele levels. A student t-test was used to evaluate significant differences between the mean TMB level in TMB-High and TMB-Low patients. Response rate was calculated as the number of patients exhibiting a complete or partial response as a proportion of the total patients considered, and then evaluated using a Fisher's exact test.


Example 4
Development of an Assay to Identify MSI in Cell-Free DNA

To identify MSI in tumor-derived cfDNA, a method to detect length polymorphisms in mononucleotide tract alleles in circulating tumor DNA (ctDNA), which occur at low frequency in plasma, is needed. To overcome this issue, we developed a highly sensitive error-correction approach incorporating the commonly-used mononucleotide tracts BAT25, BAT26, MONO27, NR21, and NR24 for the determination of MSI status in tissue and plasma specimens using NGS. DNA was converted into an NGS compatible library using molecular barcoding, after which these targeted microsatellite loci were enriched using in-solution hybrid capture chemistry together with the regions associated with other clinically relevant genomic alterations.


To address the technical challenges associated with detection of low level allele length polymorphisms obtained from NGS, we combined an error correction approach for accurate determination of insertions and deletions (indels) present in the cfDNA fragments, together with a digital peak finding (DPF) method for quantification of MSI and MSS alleles. Redundant sequencing of each cfDNA fragment was performed, and reads were aligned to the five microsatellite loci contained in the human reference genome (hg19). cfDNA sequences were then analyzed for indels through a secondary local alignment at these five microsatellite loci to more accurately determine the indel length. To perform the error correction, duplicated reads associated with each cfDNA molecule were consolidated, only recognizing indels present throughout barcoded DNA fragment replicates obtained through redundant sequencing. Finally, the DPF approach was applied across the error corrected distribution of indels to identify high confidence alleles which exhibit microsatellite instability (FIGS. 5A and 5B).


To demonstrate the capability of this approach, we first evaluated the performance of the method for detection of MSI in formalin fixed, paraffin embedded (FFPE) tumor tissue specimens obtained from 31 MSI-High (MSI-H) and 30 microsatellite stable (MSS) tumors previously characterized with the PCR-based Promega MSI analysis system. In addition to these five mononucleotide markers, we sequenced 125 selected cancer genes which harbor clinically actionable genetic alterations consisting of sequence mutations (single base substitutions and indels), copy number alterations, and gene rearrangements in cancer (Table 1). Analyses of these 61 colorectal tumors yielded 193 Gb of total sequence data, corresponding to 832-fold distinct coverage on average across the 979 kb panel (Table 2). Analysis of these five mononucleotide loci, together with 65 additional microsatellite regions contained within the 125 gene panel resulted in 100% sensitivity (31/31) and 100% specificity (30/30) for determination of MSI status using the patient-matched tumor and normal samples (Table 3) Similarly, analysis of tumor NGS data using the DPF approach without the patient-matched normal sample yielded 100% concordance (61/61).


Next, we evaluated the signal-to-noise ratio in homopolymer regions from next-generation sequencing data obtained using cfDNA extracted from plasma. Together with the five mononucleotide loci, we developed a 98 kb, 58 gene panel for sequence mutation (single base substitutions and indels) analyses of clinically actionable genetic alterations in cancer (Table 4). To demonstrate the specificity of this approach for direct detection of MSI, we first obtained plasma from healthy donors (n=163), all of which would be expected to be tumor-free and MSS. These analyses yielded over 1.2 Tb of total sequence data, corresponding to 2,600-fold distinct coverage on average across the 98 kb targeted panel, and resulted in a per-patient specificity of 99.4% (162/163) for determination of MSI status (FIG. 5B, Tables 5 and 6).


Because ctDNA, even in patients with advanced cancer, may be present at mutant allele fractions (MAFs) less than 5%, we characterized the ability of DPF for sensitive and reproducible detection of MSI at low MAFs. Five previously characterized MSI cell line samples obtained from ATCC (LS180, LS411N, SNU-C2B, RKO, and SNU-C2A) were sheared to a fragment profile simulating cfDNA and diluted with normal DNA to yield a total of 25 ng evaluated at 1% MAF. Additionally, three of these cell lines (LS180, LS411N, and SNU-C2B) were evaluated at 1% MAF in triplicate within, and triplicate across library preparation and sequencing runs (Table 5). Based on the MAF observed in the parental cell line, the cases detected as MSI were computationally confirmed to contain MSI allele MAFs of 0.35%-1.87%, with a median MSI allele MAF of 0.92%. In total, MSI was detected in 90% (18/20) of samples and demonstrated 93.3% (14/15) repeatability and reproducibility within and across runs (Table 6). For one case which was not detected as MSI, one MSI allele was identified at 0.33% MAF and for the other case, no MSI alleles were detected.


Example 5
Assessment of MSI in cfDNA in Patients Treated with PD-1 Blockade

To evaluate the analytical and clinical performance of this approach for determination of MSI in cfDNA from patients with late-stage cancers, we obtained baseline and serial plasma from patients with metastatic cancers (including 11 colorectal, 3 ampullary, and 2 small intestine), with or without MMR deficiency, while enrolled in a clinical trial to evaluate immune checkpoint blockade with the PD-1 blocking antibody, pembrolizumab(1,2)(Table 7). In total, 12 MSI-H cases and 4 MSS cases, determined through archival tissue-based analyses, were evaluated across at least two timepoints, including baseline, and after approximately 2 weeks, 10 weeks, 20 weeks, and >100 weeks.


Patients with MSI tumors as determined by archival tissue analyses had improved progression-free survival (hazard ratio, 0.25; p=0.05, likelihood ratio test) and overall survival (hazard ratio, 0.24; p=0.041, likelihood ratio test) (FIGS. 9A and 9B and Table 8). In cfDNA, we could detect MSI in 75% (9/12) of the previously characterized MSI-H patients, and correctly identified 100% (4/4) of the MSS patients (Table 6). Of the three cases that were MSI in the tumor tissue and MSS in the cfDNA, one was a colorectal tumor (patient exhibited progressive disease) and two were small intestinal tumors (one patient exhibited a partial response and one exhibited progressive disease) with relatively low levels of ctDNA with MAF of 0.4%, 1.1%, and no detectable ctDNA in the third case(34) (Table 7).


We then evaluated pre-treatment MSI status in ctDNA to predict response and clinical outcome to treatment with PD-1 blockade. We assessed radiographic response, progression-free and overall survival to predict clinical outcome. When compared to progression free survival, direct detection of MSI in baseline cfDNA could be used to predict response to immune checkpoint blockade (hazard ratio, 0.2; p=0.01, likelihood ratio test) (FIGS. 5C and 5D).


Estimating Tumor Mutation Burden in ctDNA


In addition to MSI status, we also evaluated the ability of our cfDNA panel to predict TMB across a range of tumor types, using whole exome sequencing data derived from The Cancer Genome Atlas (TCGA)(35). We considered synonymous and nonsynonymous alterations identified by TCGA and excluded known hotspot mutations which may not be representative of TMB in the tumor. These analyses demonstrated a positive correlation between predicted TMB from our targeted 58 gene plasma panel compared to the TCGA whole exome analyses (r=0.91, p<0.0001; Pearson correlation) (FIG. 6A). We determined that a cutoff of five mutations (50.8 mutations/Mbp sequenced) in the targeted plasma panel could be used to identify tumors with exceptionally high TMB related to MMR deficiency (>36 mutations/Mbp) at >95% accuracy.


Patients with TMB-High tumors as determined by archival tissue analyses (≥10 mutations/Mbp) had improved progression-free survival (hazard ratio, 0.19; p=0.041, likelihood ratio test) and overall survival (hazard ratio, 0.18; p=0.047, likelihood ratio test) (FIGS. 9C and 9D). We also evaluated the accuracy of TMB derived from the targeted panel in 13 baseline plasma cases, compared to whole-exome analyses of tumor and matched normal tissue in the same patients(1,2), and a similar correlation was identified (r=0.69, p=0.007; Pearson correlation) (FIG. 6B). These patients were classified as either TMB-High or TMB-Low using a cutoff of 50.8 mutations/Mbp sequenced, which captured six of the ten tumors categorized as TMB-High by archival tissue and provided a statistically significant difference in the TMB classification (p=0.0072, t-test) (FIG. 6C). This algorithm was applied to the same 163 healthy donor plasma samples and 100% (163/163) were determined to be TMB-Low (FIG. 6C). When considering TMB classification as a predictor of clinical outcome from the same phase 2 study cohort, TMB-High status was associated with favorable progression free survival (hazard ratio, 0.12; p=0.004 likelihood ratio test) and overall survival (hazard ratio, 0.16; p=0.014, likelihood ratio test) (FIGS. 6D and 6E). Interestingly, all four MSI-H enrolled patients exhibiting a complete response were classified as TMB-High, and all five enrolled MSI-H patients with progressive disease were classified as TMB-Low (Table 7).


Example 6
Assessment of Molecular Remission and Biomarker Dynamics in Patients Treated with PD-1 Blockade

In addition to baseline plasma analyses, we also hypothesized that the molecular remission, as measured by ctDNA during treatment, would be predictive of long term durable response to immune checkpoint blockade. We first evaluated the utility of monitoring serum tumor protein biomarkers CEA or CA19-9 for determination of response and found that multiple consecutive timepoints with a >80% reduction in the baseline protein biomarker level resulted in improved overall and progression free survival (hazard ratio, 0.05; p=0.01 and hazard ratio, 0.05; p=0.01, likelihood ratio test, respectively) (FIGS. 7A and 7B and FIGS. 10A and 10B). When evaluating the on-treatment serial plasma samples for residual ctDNA levels, there was a significant inverse correlation between the overall and progression free survival when compared to the residual MSI allele levels at last dose (r=−0.70, p=0.034 and r=−0.84, p=0.004, respectively; Pearson correlation) (FIGS. 7C and FIG. 10C). We were able to correctly identify four of the six MSI patients who would achieve a long term durable clinical response requiring multiple consecutive on-treatment time points with 0% residual alleles displaying MSI, all four of which displayed a complete response (hazard ratio, 0.09; p=0.032, likelihood ratio test for overall survival) (FIG. 7D and FIG. 10D). A similar trend was observed when considering patients with a >90% decrease in overall TMB across two timepoints when compared to baseline (hazard ratio, 0.07; p=0.013, likelihood ratio test for overall survival) (FIGS. 7E and 7F and FIGS. 10E and 10F).


Additionally, for three patients (CS97, CS98, and CS00) with a complete response to immune checkpoint blockade, and one patient (CS05) without a response to immune checkpoint blockade, circulating protein biomarkers (CEA, ng/mL or CA19-9, units/mL) and residual alleles exhibiting MSI and TMB were evaluated over time during treatment (FIG. 8). In each of the patients exhibiting a complete response, there was a concurrent decrease in the circulating protein biomarker levels, the residual MSI alleles, and TMB levels, which correlated with reduced overall tumor volume as assessed by radiographic imaging. Protein biomarker levels decreased by more than 80% between 1.3 to 2.3 months after first dose. Residual MSI alleles and TMB levels were reduced by >90% between 0.6 and 4.8 months after first dose for these three cases. However, for patient CS05 with progressive disease, the protein biomarker levels remained relatively constant, but there was an increase in residual alleles exhibiting MSI and TMB of 78% and 50%, respectively, at 4.8 months. This correlated with a 13% increase in tumor volume as assessed by radiographic imaging at 5 months.


Patient CS97 demonstrated a partial radiographic response at 10.6 months, however, achieved a 100% reduction in residual MSI and TMB levels at 2.8 months. CS97 then went on to a complete radiographic response at 20.2 months (Table 7). A different patient, CS98, appeared to develop new liver lesions at 20 weeks suggestive of progressive disease (FIG. 11). However, following an initial spike, protein biomarkers and residual MSI and TMB levels demonstrated a biochemical tumor response at 1.3 and 4.8 months. A liver biopsy demonstrated only inflammatory changes in the location where new lesions were noted, suggesting checkpoint therapy induced inflammation. Radiographic imaging finally demonstrated resolution of any hepatic lesions and a 100% reduction in tumor volume at 16.8 months. A similar pattern was observed for patient CS00 where significant reduction in protein biomarker and residual MSI and TMB levels occurred at 1.5 and 0.6 months, respectively, however, radiographic imaging did not demonstrate a 100% reduction in tumor volume until 17 months. These data suggest that the residual MSI allele burden and TMB prognostic signature are indicative of overall tumor response to immune checkpoint blockade.


Discussion

The checkpoint inhibitor pembrolizumab is now indicated for the treatment of adult and pediatric patients with unresectable or metastatic solid tumors identified as having MSI or MMR deficiency(1,2). This represents the first pan-cancer biomarker indication, and now covers patients with solid tumors that have progressed following prior treatment and have no satisfactory alternative treatment options, as well as patients with colorectal cancer that have progressed following treatment with certain chemotherapy drugs. However, it is often not possible to readily obtain biopsy or resection tissue for genetic testing due to insufficient material, exhaustion of the limited material available after prior therapeutic stratification, logistical considerations for tumor and normal sample acquisition after initial diagnosis, or safety concerns related to additional tissue biopsy interventions(26).


We have described the development of a method for simultaneous detection of MSI and TMB-High directly from cfDNA and demonstrated proof of concept for the clinical utility afforded through these analyses for the prediction of response to immune checkpoint blockade. Additionally, given the concordance with circulating protein biomarker data while these patients were on treatment, these data suggest that the residual MSI allele burden and TMB prognostic signature could be applied to other tumor types where standardized protein biomarkers do not exist and may be an earlier predictor of response than radiographic imaging.


These methods described herein provide feasibility for a viable diagnostic approach for screening and monitoring of patients who exhibit MSI or TMB-High and may respond to immune checkpoint blockade.









TABLE 1







125 Gene List for FFPE Tissue Analyses










Gene
Sequence Mutations
Translocations
Amplifications


(n = 125)
(n = 117)
(n = 29)
(n = 41)





ABL1
Yes




AKT1
Yes

Yes


ALK
Yes
Yes
Yes


AR
Yes

Yes


ATM
Yes




ATRX
Yes




AXL
Yes
Yes
Yes


BCL2
Yes
Yes
Yes


BCR

Yes



BRAF
Yes
Yes
Yes


BRCA1
Yes
Yes



BRCA2
Yes
Yes



CBFB

Yes



CCND1
Yes

Yes


CCND2
Yes

Yes


CCND3
Yes

Yes


CDK4
Yes

Yes


CDK6
Yes

Yes


CDKN2A
Yes




CHEK2
Yes




CREBBP
Yes




CSF1R
Yes

Yes


CTNNB1
Yes




DDR2
Yes




DNMT3A
Yes




EGFR
Yes
Yes
Yes


EP300
Yes




EPHA2
Yes




ERBB2
Yes

Yes


ERBB3
Yes

Yes


ERBB4
Yes




ERCC3
Yes




ERG
Yes
Yes



ESR1
Yes




ETV1

Yes



ETV4

Yes



ETV5

Yes



ETV6

Yes



EWSR1

Yes



EZH2
Yes




FANCA
Yes




FANCD2
Yes




FANCG
Yes




FBXW7
Yes




FGFR1
Yes
Yes
Yes


FGFR2
Yes
Yes
Yes


FGFR3
Yes
Yes
Yes


FGFR4
Yes

Yes


FLT1
Yes

Yes


FLT3
Yes

Yes


FLT4
Yes

Yes


FOXL2
Yes




GNA11
Yes




GNAQ
Yes




GNAS
Yes




HDAC2
Yes




HNF1A
Yes




HRAS
Yes




IDH1
Yes




IDH2
Yes




JAK1
Yes




JAK2
Yes

Yes


JAK3
Yes




KDR
Yes

Yes


KEAP1
Yes




KIT
Yes

Yes


KMT2A
Yes




KRAS
Yes

Yes


MAP2K1
Yes




MAP2K2
Yes




MEN1
Yes




MET
Yes

Yes


MLH1
Yes




MLH3
Yes




MPL
Yes




MRE11A
Yes




MSH2
Yes




MSH6
Yes




MST1R
Yes

Yes


MTOR
Yes




MYC
Yes
Yes
Yes


MYCN
Yes

Yes


MYD88
Yes




NBN
Yes




NF1
Yes




NOTCH1
Yes




NPM1
Yes




NRAS
Yes




NTRK1
Yes
Yes
Yes


NTRK2
Yes
Yes
Yes


NTRK3
Yes
Yes
Yes


PALB2
Yes




PDGFRA
Yes
Yes
Yes


PDGFRB
Yes
Yes
Yes


PIK3CA
Yes

Yes


PIK3CB
Yes

Yes


PIK3R1
Yes




PMS2
Yes




POLD1
Yes




POLE
Yes




PTCH1
Yes




PTEN
Yes




PTPN11
Yes




RAD51
Yes




RAF1
Yes
Yes



RARA
Yes
Yes



RB1
Yes




RET
Yes
Yes
Yes


RNF43
Yes




ROS1
Yes
Yes
Yes


RUNX1
Yes

Yes


SDHB
Yes




SMAD4
Yes




SMARCB1
Yes




SMO
Yes




SRC
Yes




STK11
Yes




TERT
Yes




TET2
Yes




TMPRSS2

Yes



TP53
Yes




TSC1
Yes




TSC2
Yes




VEGFA
Yes

Yes


VHL
Yes


















TABLE 2







Summary of Next Generation Sequencing Statistics for FFPE Tumor and Matched Normal Samples



























Orthog-
DPF












onal
Matched












Promega
and
DPF








Bases Mapped
Average
Average
MSI
Tumor
Tumor







Bases
to Targeted
Total
Distinct
Analysis
Normal
Only


Case
Sample

Tumor
Bases
Mapped to
Regions of
Coverage
Coverage
System
Analysis
Analysis


ID
Type
Tumor Type
Purity
Sequenced
Genome
Interest
(Fold)
(Fold)
Result
Result
Result





















T1
Tumor
Colorectal Cancer
40%
3,166,928,200
2,938,394,500
1,273,077,559
1,158
927
MSI-H
MSI-H
MSI-H


T2
Tumor
Colorectal Cancer
40%
2,956,194,800
2,731,020,900
1,118,791,878
1,102
769
MSI-H
MSI-H
MSI-H


T2
Tumor
Colorectal Cancer
80%
4,620,105,200
4,266,153,700
1,719,208,838
1,705
1,151
MSI-H
MSI-H
MSI-H


T4
Tumor
Colorectal Cancer
60%
3,830,551,400
3,577,875,700
1,587,036,697
1,584
1,047
MSI-H
MSI-H
MSI-H


T5
Tumor
Colorectal Cancer
60%
3,694,440,800
3,417,070,000
1,421,110,311
1,423
1,021
MSI-H
MSI-H
MSI-H


T6
Tumor
Colorectal Cancer
70%
2,781,902,600
2,581,541,800
1,314,484,935
1,308
509
MSI-H
MSI-H
MSI-H


T7
Tumor
Colorectal Cancer
50%
2,946,039,800
2,766,061,900
1,287,341,543
1,287
870
MSI-H
MSI-H
MSI-H


T8
Tumor
Colorectal Cancer
40%
3,418,941,400
3,141,032,200
1,134,791,347
1,128
699
MSI-H
MSI-H
MSI-H


T9
Tumor
Colorectal Cancer
60%
2,554,068,000
2,397,514,900
1,059,806,697
1,065
789
MSI-H
MSI-H
MSI-H


T10
Tumor
Colorectal Cancer
70%
2,490,357,800
2,325,119,000
1,041,133,466
1,045
577
MSI-H
MSI-H
MSI-H


T11
Tumor
Colorectal Cancer
70%
2,802,989,100
2,574,326,700
1,021,889,116
1,028
611
MSI-H
MSI-H
MSI-H


T12
Tumor
Colorectal Cancer
60%
2,732,188,800
2,532,625,600
1,102,256,555
1,106
809
MSS
MSS
MSS


T13
Tumor
Colorectal Cancer
60%
3,374,700,400
3,160,846,000
1,444,383,958
1,452
856
MSS
MSS
MSS


T14
Tumor
Colorectal Cancer
30%
4,449,316,000
4,158,857,900
1,912,478,277
1,908
1,254
MSS
MSS
MSS


T15
Tumor
Colorectal Cancer
40%
3,221,878,600
2,990,670,400
1,289,368,393
1,297
984
MSS
MSS
MSS


T16
Tumor
Colorectal Cancer
30%
2,706,508,600
2,523,131,100
1,106,624,877
1,112
859
MSS
MSS
MSS


T17
Tumor
Colorectal Cancer
25%
3,251,114,200
2,856,483,800
961,918,119
966
736
MSS
MSS
MSS


T18
Tumor
Colorectal Cancer
30%
3,231,913,800
3,009,768,900
1,360,021,648
1,348
991
MSS
MSS
MSS


T19
Tumor
Colorectal Cancer
25%
3,363,038,600
3,113,118,000
1,384,620,931
1,376
997
MSS
MSS
MSS


T20
Tumor
Colorectal Cancer
15%
2,438,680,600
2,276,538,900
1,062,441,883
1,068
664
MSS
MSS
MSS


T21
Tumor
Colorectal Cancer
50%
3,835,047,200
3,616,937,100
1,599,268,481
1,585
1,070
MSS
MSS
MSS


T22
Tumor
Colorectal Cancer
50%
3,571,104,800
3,364,850,000
1,560,304,548
1,549
1,027
MSS
MSS
MSS


T23
Tumor
Colorectal Cancer
50%
3,358,858,000
3,148,152,000
1,543,361,455
1,531
381
MSS
MSS
MSS


T24
Tumor
Colorectal Cancer
30%
3,800,714,600
3,454,271,300
1,451,100,665
1,437
1,021
MSS
MSS
MSS


T25
Tumor
Colorectal Cancer
70%
2,786,623,600
2,616,808,300
1,300,533,976
1,308
839
MSS
MSS
MSS


T26
Tumor
Colorectal Cancer
70%
2,745,441,000
2,560,749,200
1,144,854,802
1,150
831
MSS
MSS
MSS


T27
Tumor
Colorectal Cancer
50%
2,718,178,000
2,492,681,200
1,007,856,362
1,009
772
MSS
MSS
MSS


T28
Tumor
Colorectal Cancer
50%
3,811,856,800
3,469,104,100
1,178,261,647
1,164
881
MSS
MSS
MSS


T29
Tumor
Colorectal Cancer
60%
2,836,284,200
2,619,980,200
1,070,525,027
1,076
803
MSS
MSS
MSS


T30
Tumor
Colorectal Cancer
60%
3,054,498,800
2,864,552,700
1,219,192,787
1,225
918
MSS
MSS
MSS


T31
Tumor
Colorectal Cancer
80%
2,580,688,000
2,400,044,300
974,909,901
980
778
MSS
MSS
MSS


T32
Tumor
Colorectal Cancer
15%
2,794,799,600
2,572,462,100
1,100,684,869
1,106
852
MSS
MSS
MSS


T33
Tumor
Colorectal Cancer
25%
4,145,168,800
3,782,601,800
1,600,830,943
1,581
1,023
MSS
MSS
MSS


T34
Tumor
Colorectal Cancer
30%
2,805,656,200
2,574,761,000
1,165,019,629
1,164
830
MSS
MSS
MSS


T35
Tumor
Colorectal Cancer
25%
3,314,533,600
3,084,210,800
1,337,527,973
1,324
978
MSS
MSS
MSS


T36
Tumor
Colorectal Cancer
40%
3,083,111,800
2,855,861,400
1,234,493,152
1,237
871
MSS
MSS
MSS


T37
Tumor
Colorectal Cancer
50%
2,944,656,600
2,738,021,600
1,185,096,762
1,184
871
MSI-H
MSI-H
MSI-H


T38
Tumor
Colorectal Cancer
50%
2,753,927,200
2,556,267,100
l,111,967,617
1,107
826
MSI-H
MSI-H
MSI-H


T39
Tumor
Colorectal Cancer
50%
2,909,479,000
2,736,312,200
1,240,864,480
1,245
880
MSI-H
MSI-H
MSI-H


T40
Tumor
Colorectal Cancer
50%
2,861,106,600
2,664,312,500
1,238,921,489
1,135
816
MSI-H
MSI-H
MSI-H


T41
Tumor
Colorectal Cancer
50%
3,067,986,400
2,803,426,000
1,187,719,134
1,191
807
MSI-H
MSI-H
MSI-H


T42
Tumor
Colorectal Cancer
50%
2,575,126,400
2,352,723,700
985,780,477
986
729
MSI-H
MSI-H
MSI-H


T43
Tumor
Colorectal Cancer
50%
3,553,245,200
3,291,761,000
1,519,022,764
1,520
569
MSI-H
MSI-H
MSI-H


T44
Tumor
Colorectal Cancer
50%
3,879,433,600
3,543,367,700
1,412,136,767
1,401
951
MSI-H
MSI-H
MSI-H


T45
Tumor
Colorectal Cancer
50%
2,906,836,200
2,640,548,900
1,084,265,479
1,083
708
MSI-H
MSI-H
MSI-H


T46
Tumor
Colorectal Cancer
50%
3,691,316,800
3,373,293,300
1,490,830,422
1,477
633
MSI-H
MSI-H
MSI-H


T47
Tumor
Colorectal Cancer
50%
3,682,074,800
3,431,016,500
1,578,799,330
1,565
1,099
MSI-H
MSI-H
MSI-H


T48
Tumor
Colorectal Cancer
50%
3,219,857,800
2,968,614,400
1,345,719,299
1,346
953
MSI-H
MSI-H
MSI-H


T49
Tumor
Colorectal Cancer
50%
3,682,200,200
3,314,391,200
1,446,490,248
1,448
827
MSI-H
MSI-H
MSI-H


T50
Tumor
Colorectal Cancer
50%
2,698,383,600
2,488,106,900
1,176,178,010
1,178
824
MSI-H
MSI-H
MSI-H


T51
Tumor
Colorectal Cancer
50%
3319,692,800
2,956,351,200
1,252,235,401
1,242
798
MSI-H
MSI-H
MSI-H


T52
Tumor
Colorectal Cancer
50%
3360,317,400
3,095,067,800
1,411,042,834
1,410
1,024
MSI-H
MSI-H
MSI-H


T53
Tumor
Colorectal Cancer
25%
2,961,110,600
2,731,451,400
1,142,603,080
1,149
597
MSI-H
MSI-H
MSI-H


T54
Tumor
Colorectal Cancer
25%
2,777,509,400
2,589,331,600
1,149,189,533
1,155
769
MSI-H
MSI-H
MSI-H


T53
Tumor
Colorectal Cancer
25%
2,639,198,800
2,442,326,100
911,827,087
916
617
MSS
MSS
MSS


T56
Tumor
Colorectal Cancer
20%
2,755,033,400
2,531,769,000
1,000,935,424
1,007
681
MSI-H
MSI-H
MSI-H


T57
Tumor
Colorectal Cancer
20%
2,447,814,600
2,287,722,900
1,015,055,726
1,020
696
MSS
MSS
MSS


T58
Tumor
Colorectal Cancer
20%
3,286,578,200
3,057,086,600
1,307,312,335
1,314
961
MSS
MSS
MSS


T59
Tumor
Colorectal Cancer
25%
2,903,957,600
2,684,504,100
1,230,635,449
1,238
569
MSS
MSS
MSS


T60
Tumor
Colorectal Cancer
30%
3,057,011,000
2,834,612,800
1,299,500,617
1,298
678
MSS
MSS
MSS


T61
Tumor
Colorectal Cancer
30%
3,524,443,800
3,332,286,300
1,616,082,336
1,617
890
MSI-H
MSI-H
MSI-H


N1
Normal
NA
NA
1,544,823,000
1,450,418,000
585,339,892
586
521
NA
NA
NA


N2
Normal
NA
NA
1,902,151,400
1,773,538,400
686,222,130
686
604
NA
NA
NA


N3
Normal
NA
NA
1,845,286,600
1,717,939,600
660,169,442
663
574
NA
NA
NA


N4
Normal
NA
NA
1,747,777,400
1,604,382,200
578,803,459
581
507
NA
NA
NA


N5
Normal
NA
NA
1,358,892,200
1,270,257,700
532,386,922
536
466
NA
NA
NA


N6
Normal
NA
NA
1,403,909,400
1,328,105,100
603,030,506
606
525
NA
NA
NA


N7
Normal
NA
NA
1,477,544,600
1,386,426,800
600,947,522
604
517
NA
NA
NA


N8
Normal
NA
NA
1,922,041,400
1,784,316,400
701,537,824
704
613
NA
NA
NA


N9
Normal
NA
NA
1,389,792,400
1,302,753,500
551,063,430
556
478
NA
NA
NA


N10
Normal
NA
NA
1,368,669,200
1,282,523,900
527,619,827
533
468
NA
NA
NA


N11
Normal
NA
NA
1,124,099,400
1,056,703,600
434,115,592
439
390
NA
NA
NA


N12
Normal
NA
NA
1,297,100,600
1,221,405,100
504,944,038
510
450
NA
NA
NA


N13
Normal
NA
NA
1,320,243,600
1,222,723,200
477,732,672
482
362
NA
NA
NA


N14
Normal
NA
NA
2,096,304,800
1,924,550,100
629,989,948
634
563
NA
NA
NA


N15
Normal
NA
NA
1,857,918,400
1,749,514,000
741,540,523
745
637
NA
NA
NA


N16
Normal
NA
NA
1,296,158,400
1,225,401,300
539,144,010
545
481
NA
NA
NA


N17
Normal
NA
NA
1,172,080,200
1,072,384,000
351,008,283
354
321
NA
NA
NA


N18
Normal
NA
NA
2,197,386,400
2,043,193,200
792,440,480
793
683
NA
NA
NA


N19
Normal
NA
NA
1,126,031,600
1,057,174,000
429,799,839
435
388
NA
NA
NA


N20
Normal
NA
NA
2,203,340,000
2,079,985,600
923,434,168
927
780
NA
NA
NA


N21
Normal
NA
NA
1,999,881,200
1,849,375,700
663,902,271
666
578
NA
NA
NA


N22
Normal
NA
NA
1,919,525,200
1,795,389,600
721,484,943
723
621
NA
NA
NA


N23
Normal
NA
NA
1,331,809,200
1,260,705,600
596,587,867
602
517
NA
NA
NA


N24
Normal
NA
NA
1,903,783,600
1,792,126,800
790,507,492
792
691
NA
NA
NA


N25
Normal
NA
NA
1,386,758,000
1,304,498,200
573,306,885
579
511
NA
NA
NA


N26
Normal
NA
NA
1,288,502,200
1,211,465,700
518,972,645
524
465
NA
NA
NA


N27
Normal
NA
NA
2,531,366,000
2,385,834,400
1,019,543,903
1,027
883
NA
NA
NA


N28
Normal
NA
NA
1,992,785,000
1,873,292,800
802,670,846
804
687
NA
NA
NA


N29
Normal
NA
NA
1,455,104,600
1,366,776,900
575,386,950
581
510
NA
NA
NA


N30
Normal
NA
NA
1,664,936,200
1,561,641,100
646,468,058
652
572
NA
NA
NA


N31
Normal
NA
NA
1,200,639,800
1,128,641,300
463,140,630
467
416
NA
NA
NA


N32
Normal
NA
NA
1,167,761,400
1,092,348,500
438,958,934
442
396
NA
NA
NA


N33
Normal
NA
NA
1,665,825,400
1,541,475,700
609,072,888
612
544
NA
NA
NA


N34
Normal
NA
NA
1,367,489,200
1,290,133,900
556,470,787
563
499
NA
NA
NA


N35
Normal
NA
NA
1,503,631,400
1,412,540,500
580,358,931
585
509
NA
NA
NA


N36
Normal
NA
NA
1,549,988,400
1,458,406,100
628,637,957
634
560
NA
NA
NA


N37
Normal
NA
NA
1,568,304,000
1,468,430,300
616,176,975
620
519
NA
NA
NA


N38
Normal
NA
NA
1,701,739,600
1,592,992,100
659,495,963
662
556
NA
NA
NA


N39
Normal
NA
NA
1,309,687,400
1,234,750,100
546,420,462
551
464
NA
NA
NA


N40
Normal
NA
NA
1,712,442,800
1,608,493,700
702,640,817
705
588
NA
NA
NA


N41
Normal
NA
NA
1,191,012,000
1,122,882,200
485,771,595
491
415
NA
NA
NA


N42
Normal
NA
NA
2,147,527,600
2,012,745,300
865,817,634
870
721
NA
NA
NA


N43
Normal
NA
NA
2,632,733,800
2,480,587,000
1,109,697,317
1,120
878
NA
NA
NA


N44
Normal
NA
NA
1,425,864,600
1,323,295,400
519,416,405
524
445
NA
NA
NA


N45
Normal
NA
NA
1,155,307,800
1,076,440,400
436,138,888
439
377
NA
NA
NA


N46
Normal
NA
NA
1,221,955,800
1,150,302,700
518,176,709
523
444
NA
NA
NA


N47
Normal
NA
NA
1,869,437,000
1,735,921,400
728,621,647
732
602
NA
NA
NA


N48
Normal
NA
NA
1,587,657,000
1,487,987,900
631,794,049
637
537
NA
NA
NA


N49
Normal
NA
NA
1,781,366,200
1,673,812,500
729,333,555
734
619
NA
NA
NA


N50
Normal
NA
NA
1,148,277,400
1,076,124,300
342,181,739
346
294
NA
NA
NA


N51
Normal
NA
NA
1,904,405,800
1,764,410,700
680,778,768
687
576
NA
NA
NA


N52
Normal
NA
NA
1,640,495,200
1,534,588,100
662,986,346
669
549
NA
NA
NA


N53
Normal
NA
NA
1,495,963,000
1,388,748,600
604,616,038
612
448
NA
NA
NA


N54
Normal
NA
NA
1,411,743,800
1,318,632,100
570,759,302
578
432
NA
NA
NA


N55
Normal
NA
NA
1,254,353,600
1,146,429,600
439,784,552
445
346
NA
NA
NA


N56
Normal
NA
NA
1,358,890,600
1,256,167,000
500,055,838
506
375
NA
NA
NA


N57
Normal
NA
NA
1,257,193,000
1,177,020,700
528,523,113
535
410
NA
NA
NA


NS8
Normal
NA
NA
1,315,380,800
1,233,333,300
528,722,180
535
432
NA
NA
NA


N59
Normal
NA
NA
1,275,383,800
1,172,703,600
504,548,697
511
383
NA
NA
NA


N60
Normal
NA
NA
2,296,267,800
2,109,785,900
916,969,966
925
645
NA
NA
NA


N61
Normal
NA
NA
1,433,371,200
1,350,514,800
632,414,511
639
475
NA
NA
NA
















TABLE 3







Comparison of Microsatellite Status Determined through FFPE Tissue


Analyses










Promega




MSI Analysis



System












FFPE Tissue Analysis

MSI-H
MSS
















125 Gene Targeted Panel
MSI
31
0




MSS
0
30

















TABLE 4







58 Gene List for Plasma Analyses










Gene (n = 58)
Sequence Region Covered







AKT1
Hot Exon Analysis



ALK
Full RefSeq/CCDS Coding Sequence



AR
Full RefSeq/CCDS Coding Sequence



ATM
Hot Exon Analysis



BRAF
Full RefSeq/CCDS Coding Sequence



BRCA1
Hot Exon Analysis



BRCA2
Hot Exon Analysis



CCND1
Hot Exon Analysis



CCND2
Hot Exon Analysis



CCND3
Hot Exon Analysis



CD274
Full RefSeq/CCDS Coding Sequence



CDK4
Full RefSeq/CCDS Coding Sequence



CDK6
Full RefSeq/CCDS Coding Sequence



CDKN2A
Hot Exon Analysis



CTNNB1
Hot Exon Analysis



DNMT3A
Hot Exon Analysis



EGFR
Full RefSeq/CCDS Coding Sequence



ERBB2
Full RefSeq/CCDS Coding Sequence



ESR1
Hot Exon Analysis



EZH2
Hot Exon Analysis



FGFR1
Hot Exon Analysis



FGFR2
Hot Exon Analysis



FGFR3
Hot Exon Analysis



FLT3
Hot Exon Analysis



GNAS
Hot Exon Analysis



HRAS
Hot Exon Analysis



IDH1
Hot Exon Analysis



IDH2
Hot Exon Analysis



JAK2
Hot Exon Analysis



KIT
Full RefSeq/CCDS Coding Sequence



KRAS
Full RefSeq/CCDS Coding Sequence



MAP2K1
Kinase Domain



MET
Hot Exon Analysis + Adjacent Exon 14 Introns



MTOR
Hot Exon Analysis



MYC
Hot Exon Analysis



MYCN
Hot Exon Analysis



NPM1
Hot Exon Analysis



NRAS
Hot Exon Analysis



NTRK1
Hot Exon Analysis



NTRK2
Hot Exon Analysis



NTRK3
Hot Exon Analysis



PALB2
Hot Exon Analysis



PIK3CA
Hot Exon Analysis



PIK3CB
Hot Exon Analysis



PIK3R1
Hot Exon Analysis



POLD1
Exonuclease Domain



POLE
Exonuclease Domain



PTCH1
Hot Exon Analysis



PTEN
Hot Exon Analysis



RB1
Hot Exon Analysis



RET
Full RefSeq/CCDS Coding Sequence



RNF43
Hot Exon Analysis



ROS1
Kinase and Catalytic Domain



TERT
Hot Exon Analysis + Promoter



TP53
Full RefSeq/CCDS Coding Sequence



TSC1
Hot Exon Analysis



TSC2
Hot Exon Analysis



VHL
Hot Exon Analysis

















TABLE 5







Summary of Next Generation Sequencing Statistics for Healthy Donor Samples, Contrived Samples, and Clinical Plasma Samples




















Bases


Clinical








Mapped to
Average
Average
Tissue
Plasma
Plasma






Targeted
Total
Distinct
Trial
MSI
TMB


Case
Sample
Bases
Bases Mapped
Regions of
Coverage
Coverage
Enrollment
Analysis
Analysis


ID
Type
Sequenced
to Genome
Interest
(Fold)
(Fold)
MSI Status
Result
Result



















HD1
Healthy
4,610,602,000
4,591,372,800
2,313,780,895
23,135
1,326
NA
MSS
TMB-Low



Donor










HD2
Healthy
8,891,644,000
8,866,148,100
4,437,521,303
44,386
2,567
NA
MSS
TMB-Low



Donor










HD3
Healthy
5,591,552,000
5,569,655,300
2,532,932,984
25,273
1,413
NA
MSS
TMB-Low



Donor










HD4
Healthy
5,573,545,400
5,543,102,100
2,255,758,545
22,481
1,654
NA
MSS
TMB-Low



Donor










HD5
Healthy
5,207,559,600
5,185,499,400
2,470,481,571
24,671
1,860
NA
MSS
TMB-Low



Donor










HD6
Healthy
6,388,732,200
6,377,549,900
3,432,543,524
34,319
3,762
NA
MSS
TMB-Low



Donor










HD7
Healthy
4,734,677,000
4,712,020,800
2,345,085,749
23,450
1,514
NA
MSS
TMB-Low



Donor










HD8
Healthy
5,302,549,600
5,278,776,000
2,691,437,847
26,923
1,141
NA
MSS
TMB-Low



Donor










HD9
Healthy
7,465,978,000
7,443,127,900
3,937,377,476
39,278
2,632
NA
MSS
TMB-Low



Donor










HD10
Healthy
6,074,039,400
6,052,256,300
3,176,126,200
31,723
1,707
NA
MSS
TMB-Low



Donor










HD11
Healthy
6,213,183,600
6,193,263,500
3,215,135,348
31,924
1,629
NA
MSS
TMB-Low



Donor










HD12
Healthy
7,312,985,200
7,287,955,400
3,361,626,922
33,392
2,219
NA
MSS
TMB-Low



Donor










HD13
Healthy
6,510,483,400
6,494,893,800
2,976,435,079
29,539
4,803
NA
MSS
TMB-Low



Donor










HD14
Healthy
8,627,645,800
8,610,309,100
4,370,055,158
43,159
4,240
NA
MSS
TMB-Low



Donor










HD15
Healthy
8,091,438,800
8,070,832,700
4,064,773,281
40,137
2,755
NA
MSS
TMB-Low



Donor










HD16
Healthy
8,479,048,000
8,460,878,600
4,274,823,876
42,218
2,387
NA
MSS
TMB-Low



Donor










HD17
Healthy
9,956,617,400
9,928,487,500
4,056,620,966
40,013
6,204
NA
MSS
TMB-Low



Donor










HD18
Healthy
8,764,661,800
8,741,658,300
4,365,489,881
43,257
1,659
NA
MSS
TMB-Low



Donor










HD19
Healthy
7,889,783,000
7,869,480,500
3,564,990,160
35,371
3,264
NA
MSS
TMB-Low



Donor










HD20
Healthy
7,633,405,000
7,615,920,000
3,881,573,734
38,491
1,890
NA
MSS
TMB-Low



Donor










HD21
Healthy
7,861,255,200
7,840,463,800
3,898,962,179
38,636
1,558
NA
MSS
TMB-Low



Donor










HD22
Healthy
4,781,596,200
4,700,767,800
2,047,246,059
20,023
  914
NA
MSS
TMB-Low



Donor










HD23
Healthy
6,681,047,200
6,637,094,200
3,496,324,530
34,651
1,777
NA
MSS
TMB-Low



Donor










HD24
Healthy
7,177,461,600
7,153,542,900
3,634,434,110
36,048
1,926
NA
MSS
TMB-Low



Donor










HD25
Healthy
7,434,671,400
7,407,050,700
3,898,804,784
38,653
2,302
NA
MSS
TMB-Low



Donor










HD26
Healthy
7,429,101,000
7,401,652,100
3,673,202,567
36,392
2,038
NA
MSS
TMB-Low



Donor










HD27
Healthy
8,503,220,200
8,481,220,900
4,481,836,913
44,189
4,007
NA
MSS
TMB-Low



Donor










HD28
Healthy
7,913,436,400
7,891,591,400
3,999,331,489
39,604
6,476
NA
MSS
TMB-Low



Donor










HD29
Healthy
4,614,537,000
4,554,941,500
2,105,579,210
20,597
  697
NA
MSS
TMB-Low



Donor










HD30
Healthy
7,492,256,600
7,465,117,200
3,476,188,532
34,328
2,857
NA
MSS
TMB-Low



Donor










HD31
Healthy
8,328,282,600
8,286,892,200
4,210,419,884
41,650
3,095
NA
MSS
TMB-Low



Donor










HD32
Healthy
7,016,633,400
6,995,998,500
3,531,038,365
34,933
1,236
NA
MSS
TMB-Low



Donor










HD33
Healthy
8,194,639,600
8,172,001,600
4,176,117,225
41,166
2,952
NA
MSS
TMB-Low



Donor










HD34
Healthy
6,007,170,600
5,988,709,000
2,841,711,258
28,277
3,526
NA
MSS
TMB-Low



Donor










HD35
Healthy
7,712,474,800
7,687,926,200
3,538,858,830
34,870
3,962
NA
MSS
TMB-Low



Donor










HD36
Healthy
6,447,382,600
6,427,425,700
3,393,662,901
33,415
2,859
NA
MSS
TMB-Low



Donor










HD37
Healthy
8,134,672,200
8,105,317,200
4,054,212,131
39,967
1,544
NA
MSS
TMB-Low



Donor










HD38
Healthy
5,535,483,200
5,524,427,300
2,816,615,357
28,054
1,983
NA
MSS
TMB-Low



Donor










HD39
Healthy
7,564,324,200
7,546,630,900
3,764,230,400
37,490
4,300
NA
MSS
TMB-Low



Donor










HD40
Healthy
8,036,286,000
8,014,954,300
4,048,484,998
40,197
3,096
NA
MSS
TMB-Low



Donor










HD41
Healthy
7,640,735,400
7,622,537,500
3,929,173,586
39,049
1,971
NA
MSS
TMB-Low



Donor










HD42
Healthy
6,677,376,600
6,656,214,200
2,797,826,119
27,836
1,938
NA
MSS
TMB-Low



Donor










HD43
Healthy
8,409,420,800
8,391,690,200
4,451,721,807
43,978
3,316
NA
MSS
TMB-Low



Donor










HD44
Healthy
8,467,700,000
8,440,226,300
3,675,196,602
36,497
5,083
NA
MSS
TMB-Low



Donor










HD45
Healthy
7,197,353,200
7,170,267,700
3,698,926,248
36,497
1,831
NA
MSS
TMB-Low



Donor










HD46
Healthy
8,318,236,800
8,281,776,900
4,148,545,120
40,815
1,773
NA
MSS
TMB-Low



Donor










HD47
Healthy
9,006,412,400
8,978,934,000
4,760,007,319
46,907
2,891
NA
MSS
TMB-Low



Donor










HD48
Healthy
7,344,659,400
7,321,998,700
3,843,864,822
37,828
1,883
NA
MSS
TMB-Low



Donor










HD49
Healthy
8,288,914,400
8,270,435,900
4,445,831,613
43,940
3,272
NA
MSS
TMB-Low



Donor










HD50
Healthy
8,639,110,000
8,615,224,600
4,502,933,702
44,423
2,244
NA
MSS
TMB-Low



Donor










HD51
Healthy
7,575,511,200
7,555,273,900
3,939,210,286
39,053
3,320
NA
MSS
TMB-Low



Donor










HD52
Healthy
8,427,667,800
8,400,593,500
4,420,970,865
43,296
3,497
NA
MSS
TMB-Low



Donor










HD53
Healthy
8,542,647,000
8,516,087,000
4,385,330,122
42,944
3,771
NA
MSS
TMB-Low



Donor










HD54
Healthy
8,453,014,000
8,428,500,700
4,387,819,781
43,296
2,325
NA
MSS
TMB-Low



Donor










HD55
Healthy
9,298,955,400
9,271,088,500
4,819,496,438
47,546
2,341
NA
MSS
TMB-Low



Donor










HD56
Healthy
8,478,268,200
8,444,312,700
4,094,638,378
40,360
2,050
NA
MSS
TMB-Low



Donor










HD57
Healthy
8,199,783,400
8,170,058,600
3,957,778,287
39,001
2,457
NA
MSS
TMB-Low



Donor










HD58
Healthy
9,346,566,000
9,314,731,900
4,924,333,229
48,509
1,727
NA
MSS
TMB-Low



Donor










HD59
Healthy
8,919,385,800
8,892,662,500
4,390,523,968
43,293
3,513
NA
MSS
TMB-Low



Donor










HD60
Healthy
8,389,446,000
8,370,187,100
4,369,738,805
43,148
1,477
NA
MSS
TMB-Low



Donor










HD61
Healthy
9,905,663,600
9,881,313,200
4,974,727,427
49,048
6,168
NA
MSS
TMB-Low



Donor










HD62
Healthy
9,174,224,000
9,148,992,500
4,535,168,624
44,655
2,138
NA
MSS
TMB-Low



Donor










HD63
Healthy
8,084,463,600
8,057,658,800
3,968,831,440
38,915
1,992
NA
MSS
TMB-Low



Donor










HD64
Healthy
8,983,082,400
8,950,678,700
3,826,555,016
37,464
3,223
NA
MSS
TMB-Low



Donor










HD65
Healthy
7,442,509,800
7,422,697,300
3,854,553,775
38,261
2,341
NA
MSS
TMB-Low



Donor










HD66
Healthy
8,337,674,200
8,316,432,900
4,007,211,501
39,529
2,369
NA
MSS
TMB-Low



Donor










HD67
Healthy
7,154,104,000
7,129,207,200
3,440,461,741
34,040
2,871
NA
MSS
TMB-Low



Donor










HD68
Healthy
8,659,740,200
8,618,184,400
4,184,609,095
41,321
3,957
NA
MSS
TMB-Low



Donor










HD69
Healthy
7,771,232,400
7,753,568,600
3,681,096,130
36,485
3,124
NA
MSS
TMB-Low



Donor










HD70
Healthy
4,405,077,200
4,384,751,000
1,552,688,622
15,295
2,094
NA
MSS
TMB-Low



Donor










HD71
Healthy
5,920,713,000
5,898,405,200
2,633,560,458
26,073
1,061
NA
MSS
TMB-Low



Donor










HD72
Healthy
7,579,429,200
7,554,654,800
2,748,860,562
27,222
2,043
NA
MSS
TMB-Low



Donor










HD73
Healthy
8,631,626,800
8,607,231,300
3,343,564,755
33,121
3,291
NA
MSS
TMB-Low



Donor










HD74
Healthy
6,949,033,000
6,931,273,900
3,127,174,082
30,958
3,031
NA
MSS
TMB-Low



Donor










HD75
Healthy
5,875,099,600
5,864,389,100
2,962,088,150
29,390
4,277
NA
MSS
TMB-Low



Donor










HD76
Healthy
6,626,185,400
6,609,772,800
3,084,072,011
30,517
2,136
NA
MSS
TMB-Low



Donor










HD77
Healthy
11,291,302,400
11,238,394,500
5,110,554,826
49,923
3,453
NA
MSS
TMB-Low



Donor










HD78
Healthy
5,515,433,800
5,483,434,100
1,965,775,908
19,120
  834
NA
MSS
TMB-Low



Donor










HD79
Healthy
6,954,396,400
6,931,242,800
3,311,490,206
32,327
3,145
NA
MSS
TMB-Low



Donor










HD80
Healthy
6,152,936,200
6,131,263,700
2,720,849,245
26,546
2,270
NA
MSS
TMB-Low



Donor










HD81
Healthy
8,733,434,600
8,702,900,400
4,271,410,537
41,795
3,890
NA
MSS
TMB-Low



Donor










HD82
Healthy
6,720,050,800
6,692,163,400
2,871,213,549
28,127
2,200
NA
MSS
TMB-Low



Donor










HD83
Healthy
7,729,687,400
7,705,631,600
3,769,457,577
37,031
3,098
NA
MSS
TMB-Low



Donor










HD84
Healthy
8,665,550,000
8,633,041,400
4,135,285,473
40,550
2,542
NA
MSS
TMB-Low



Donor










HD85
Healthy
7,972,481,400
7,950,462,100
3,776,002,282
37,290
3,000
NA
MSS
TMB-Low



Donor










HD86
Healthy
8,250,349,800
8,215,560,400
4,149,026,011
40,906
2,274
NA
MSS
TMB-Low



Donor










HD87
Healthy
7,218,789,600
7,194,779,100
3,266,906,096
32,493
3,137
NA
MSS
TMB-Low



Donor










HD88
Healthy
6,682,720,200
6,654,240,600
3,392,475,194
33,738
2,498
NA
MSS
TMB-Low



Donor










HD89
Healthy
6,871,691,000
6,856,541,800
3,521,282,340
34,894
2,744
NA
MSS
TMB-Low



Donor










HD90
Healthy
8,772,448,000
8,749,280,600
4,178,273,953
41,258
1,494
NA
MSS
TMB-Low



Donor










HD91
Healthy
7,480,832,800
7,457,217,500
3,595,805,873
35,471
2,266
NA
MSS
TMB-Low



Donor










HD92
Healthy
5,975,083,600
5,958,618,100
2,873,989,002
28,615
1,701
NA
MSS
TMB-Low



Donor










HD93
Healthy
5,375,821,400
5,360,555,500
2,567,619,902
25,583
2,090
NA
MSS
TMB-Low



Donor










HD94
Healthy
6,280,445,200
6,260,287,600
3,139,767,399
31,191
2,533
NA
MSS
TMB-Low



Donor










HD95
Healthy
8,135,958,600
8,115,624,700
4,130,225,448
40,731
2,317
NA
MSS
TMB-Low



Donor










HD96
Healthy
7,017,152,200
7,000,775,900
3,355,455,453
33,091
2,169
NA
MSS
TMB-Low



Donor










HD97
Healthy
7,423,045,000
7,401,835,700
3,612,561,174
35,619
1,489
NA
MSS
TMB-Low



Donor










HD98
Healthy
7,575,649,400
7,542,405,300
3,306,732,212
32,637
1,985
NA
MSS
TMB-Low



Donor










HD99
Healthy
8,101,683,000
8,073,383,300
3,916,838,207
38,584
2,250
NA
MSS
TMB-Low



Donor










HD100
Healthy
8,227,634,200
8,195,376,800
3,707,557,242
36,571
1,908
NA
MSS
TMB-Low



Donor










HD101
Healthy
7,409,985,800
7,378,039,500
2,943,037,267
29,002
1,815
NA
MSS
TMB-Low



Donor










HD102
Healthy
7,813,906,600
7,786,978,900
3,615,038,253
35,836
3,086
NA
MSS
TMB-Low



Donor










HD103
Healthy
7,127,926,200
7,092,785,200
2,681,989,940
26,428
1,720
NA
MSS
TMB-Low



Donor










HD104
Healthy
7,010,324,000
6,980,650,200
2,688,769,169
26,520
1,612
NA
MSS
TMB-Low



Donor










HD105
Healthy
7,822,779,200
7,785,449,900
3,212,888,783
31,319
1,715
NA
MSS
TMB-Low



Donor










HD106
Healthy
7,364,897,200
7,339,416,100
3,586,900,299
35,320
2,452
NA
MSS
TMB-Low



Donor










HD107
Healthy
8,493,402,800
8,458,319,500
3,260,053,476
32,076
3,556
NA
MSS
TMB-Low



Donor










HD108
Healthy
9,834,233,000
9,805,201,200
4,706,975,042
46,015
2,353
NA
MSS
TMB-Low



Donor










HD109
Healthy
6,747,679,000
6,733,999,600
3,110,916,970
30,972
1,938
NA
MSS
TMB-Low



Donor










HD110
Healthy
7,069,059,200
7,054,426,600
3,473,558,000
34,223
2,862
NA
MSS
TMB-Low



Donor










HD111
Healthy
10,374,032,800
10,334,762,500
5,226,977,407
51,127
3,737
NA
MSS
TMB-Low



Donor










HD112
Healthy
9,373,668,400
9,330,427,800
3,826,086,189
37,392
2,960
NA
MSS
TMB-Low



Donor










HD113
Healthy
6,510,073,600
6,495,529,700
3,083,434,125
30,646
2,714
NA
MSS
TMB-Low



Donor










HD114
Healthy
5,788,275,000
5,775,686,700
2,790,114,913
27,752
1,891
NA
MSS
TMB-Low



Donor










HD115
Healthy
5,628,781,800
5,608,788,400
2,324,130,331
23,040
1,336
NA
MSS
TMB-Low



Donor










HD116
Healthy
6,622,736,800
6,595,711,900
2,853,684,669
28,019
1,732
NA
MSS
TMB-Low



Donor










HD117
Healthy
8,235,416,200
8,206,562,800
4,066,198,888
40,037
2,147
NA
MSS
TMB-Low



Donor










HD118
Healthy
8,142,539,800
8,113,673,000
3,498,426,122
34,518
2,319
NA
MSS
TMB-Low



Donor










HD119
Healthy
6,567,610,600
6,552,480,100
3,520,404,897
35,102
1,423
NA
MSS
TMB-Low



Donor










HD120
Healthy
8,172,503,000
8,146,438,000
3,738,973,301
36,834
2,391
NA
MSS
TMB-Low



Donor










HD121
Healthy
7,086,855,800
7,066,717,300
3,531,225,022
35,183
2,010
NA
MSS
TMB-Low



Donor










HD122
Healthy
6,632,081,800
6,613,761,500
2,824,890,605
28,087
4,498
NA
MSS
TMB-Low



Donor










HD123
Healthy
8,716,718,200
8,692,336,500
4,249,954,641
42,108
2,897
NA
MSS
TMB-Low



Donor










HD124
Healthy
5,846,065,600
5,827,023,000
2,398,968,620
23,846
1,985
NA
MSS
TMB-Low



Donor










HD125
Healthy
5,987,677,000
5,975,740,400
3,037,726,557
30,200
1,896
NA
MSS
TMB-Low



Donor










HD126
Healthy
6,450,910,600
6,433,946,800
2,901,586,776
28,839
1,732
NA
MSS
TMB-Low



Donor










HD127
Healthy
6,521,277,600
6,505,071,200
3,237,555,270
32,254
1,946
NA
MSS
TMB-Low



Donor










HD128
Healthy
5,183,805,800
5,174,096,900
2,624,001,866
26,114
1,626
NA
MSS
TMB-Low



Donor










HD129
Healthy
6,060,923,800
6,032,344,900
3,294,503,839
33,028
2,041
NA
MSS
TMB-Low



Donor










HD130
Healthy
6,931,215,400
6,664,206,100
2,861,090,396
27,128
2,616
NA
MSS
TMB-Low



Donor










HD131
Healthy
6,881,530,800
6,868,642,100
3,207,081,669
31,800
5,415
NA
MSS
TMB-Low



Donor










HD132
Healthy
8,447,741,200
8,422,297,100
4,171,378,734
41,085
2,511
NA
MSS
TMB-Low



Donor










HD133
Healthy
6,647,519,000
6,618,288,100
3,015,315,572
29,699
  974
NA
MSI
TMB-Low



Donor










HD134
Healthy
9,017,435,200
8,992,906,300
4,677,984,882
46,146
3,595
NA
MSS
TMB-Low



Donor










HD135
Healthy
6,184,647,200
6,158,774,700
2,847,813,354
28,060
1,659
NA
MSS
TMB-Low



Donor










HD136
Healthy
7,819,615,400
7,794,596,100
3,895,863,689
38,457
2,214
NA
MSS
TMB-Low



Donor










HD137
Healthy
9,297,185,200
9,266,997,400
4,986,929,926
49,160
3,316
NA
MSS
TMB-Low



Donor










HD138
Healthy
6,088,725,600
6,071,004,400
2,871,318,994
28,468
1,376
NA
MSS
TMB-Low



Donor










HD139
Healthy
7,078,148,600
7,064,739,600
3,681,471,294
36,397
3,204
NA
MSS
TMB-Low



Donor










HD140
Healthy
7,991,284,600
7,973,783,500
3,998,658,283
39,667
3,312
NA
MSS
TMB-Low



Donor










HD141
Healthy
8,078,032,000
8,054,876,800
4,060,189,135
40,087
1,762
NA
MSS
TMB-Low



Donor










HD142
Healthy
7,768,653,400
7,744,059,500
3,415,300,515
33,884
1,990
NA
MSS
TMB-Low



Donor










HD143
Healthy
6,099,199,600
6,080,885,000
2,775,841,946
27,543
3,106
NA
MSS
TMB-Low



Donor










HD144
Healthy
7,710,555,200
7,694,753,100
3,714,118,122
36,768
3,794
NA
MSS
TMB-Low



Donor










HD145
Healthy
7,799,141,400
7,769,239,200
3,941,900,921
38,944
2,524
NA
MSS
TMB-Low



Donor










HD146
Healthy
6,726,282,600
6,712,047,100
3,413,345,451
33,896
2,071
NA
MSS
TMB-Low



Donor










HD147
Healthy
7,976,941,800
7,958,488,700
3,953,916,025
39,148
3,075
NA
MSS
TMB-Low



Donor










HD148
Healthy
6,773,777,600
6,756,376,400
3,398,770,425
33,685
1,878
NA
MSS
TMB-Low



Donor










HD149
Healthy
7,241,584,800
7,214,148,600
3,197,797,413
31,671
1,957
NA
MSS
TMB-Low



Donor










HD150
Healthy
8,772,019,000
8,744,087,400
4,198,896,205
41,505
2,404
NA
MSS
TMB-Low



Donor










HD151
Healthy
9,597,923,600
9,554,309,500
4,304,212,463
42,013
1,918
NA
MSS
TMB-Low



Donor










HD152
Healthy
9,766,675,200
9,730,121,800
4,232,551,267
41,381
2,131
NA
MSS
TMB-Low



Donor










HD153
Healthy
7,964,424,400
7,902,830,700
3,958,568,012
38,872
2,430
NA
MSS
TMB-Low



Donor










HD154
Healthy
8,703,468,000
8,679,744,500
4,526,278,703
44,722
5,251
NA
MSS
TMB-Low



Donor










HD155
Healthy
7,877,226,800
7,859,052,900
3,985,810,986
39,418
3,773
NA
MSS
TMB-Low



Donor










HD156
Healthy
7,747,095,200
7,729,059,800
3,978,272,416
39,396
3,948
NA
MSS
TMB-Low



Donor










HD157
Healthy
7,689,538,200
7,670,324,700
3,678,385,491
36,403
2,146
NA
MSS
TMB-Low



Donor










HD158
Healthy
6,036,060,400
6,021,050,700
2,871,615,508
28,452
1,793
NA
MSS
TMB-Low



Donor










HD159
Healthy
9,284,331,400
9,261,722,300
4,572,013,112
45,219
4,489
NA
MSS
TMB-Low



Donor










HD160
Healthy
8,039,083,200
8,017,829,900
3,668,403,298
36,433
2,958
NA
MSS
TMB-Low



Donor










HD161
Healthy
6,337,931,000
6,315,879,600
2,863,889,709
28,364
1,286
NA
MSS
TMB-Low



Donor










HD162
Healthy
10,292,765,800
10,260,537,400
5,291,273,703
51,768
3,546
NA
MSS
TMB-Low



Donor










HD163
Healthy
9,258,149,800
9,233,715,900
4,485,768,075
43,995
4,143
NA
MSS
TMB-Low



Donor










CL1
LS180
7,612,745,000
7,589,215,100
3,392,459,288
33,523
2,083
MSI
MSI
N/A


CL2
LS411N
7,678,713,000
7,654,819,800
3,291,800,936
32,532
2,149
MSI
MSI
N/A


CL3
SNU-
6,256,132,400
6,240,909,800
2,807,306,207
27,761
2,420
MSI
MSI
N/A



C2B










CL4
RKO
7,066,840,000
7,048,897,500
3,177,373,078
31,421
2,085
MSI
MSS
N/A


CL5
SNU-
7,669,517,600
7,650,812,200
3,439,833,485
34,079
3,069
MSI
MSI
N/A



C2A










CL6
LS180
8,691,502,000
8,658,803,000
3,445,624,572
33,838
2,426
MSI
MSI
N/A


CL7
LS180
8,535,101,200
8,503,984,000
3,893,865,285
38,211
2,595
MSI
MSI
N/A


CL8
LS180
8,083,764,400
8,056,986,800
3,780,724,828
37,152
2,455
MSI
MSI
N/A


CL9
LS180
7,904,478,600
7,881,702,700
3,696,511,241
36,324
2,407
MSI
MSI
N/A


CL10
LS180
7,764,828,000
7,737,044,900
3,531,063,394
34,455
2,138
MSI
MSI
N/A


CL11
LS411N
8,245,419,000
8,222,207,200
3,748,315,492
36,967
2,471
MSI
MSI
N/A


CL12
LS411N
6,575,842,800
6,554,550,700
3,030,898,415
29,795
2,430
MSI
MSS
N/A


CL13
LS411N
8,271,559,000
8,245,273,600
3,762,761,032
36,919
2,295
MSI
MSI
N/A


CL14
LS411N
7,934,153,000
7,905,178,000
3,458,080,463
33,948
2,451
MSI
MSI
N/A


CL15
LS411N
7,108,328,800
7,085,747,100
3,057,622,227
30,157
2,159
MSI
MSI
N/A


CL16
SNU-
8,456,505,800
8,424,591,600
3,925,699,391
38,462
2,482
MSI
MSI
N/A



C2B










CL17
SNU-
7,577,529,000
7,556,499,800
3,380,433,809
33,424
2,261
MSI
MSI
N/A



C2B










CL18
SNU-
6,993,859,200
6,976,543,600
3,225,795,617
31,918
2,171
MSI
MSI
N/A



C2B










CL19
SNU-
5,882,123,600
5,860,372,800
2,447,923,970
24,221
2,066
MSI
MSI
N/A



C2B










CL20
SNU-
7,878,616,400
7,858,594,100
3,506,238,369
34,685
2,058
MSI
MSI
N/A



C2B










CS94P1
Clinical
9,263,762,400
9,244,770,800
3,825,312,992
37,868
8,416
MSI
MSI
TMB-Low


CS94P2
Clinical
8,813,423,000
8,792,480,600
3,978,488,566
39,021
8,506
Timepoint
MSI
N/A









Sample




CS94P3
Clinical
8,964,792,200
8,937,833,100
3,676,739,247
36,159
9,963
Timepoint
MSS
N/A









Sample




CS95P1
Clinical
7,636,898,200
7,570,902,200
2,114,468,194
20,804
2,175
MSS
MSI
TMB-Low


CS95P2
Clinical
8,719,884,400
8,686,639,300
3,776,909,959
37,371
3,279
Timepoint
MSS
N/A









Sample




CS95P3
Clinical
7,946,606,600
7,923,725,300
3,681,799,356
36,417
3,069
Timepoint
MSI
N/A









Sample




CS96P1
Clinical
8,340,755,000
8,311,711,700
3,710,084,604
36,690
5,686
MSS
MSS
TMB-Low


CS96P2
Clinical
6,198,454,200
6,168,225,100
2,565,799,692
24,735
5,781
Timepoint
MSS
N/A









Sample




CS96P3
Clinical
5,912,813,200
5,893,031,300
2,980,746,401
28,844
7,161
Timepoint
MSS
N/A









Sample




CS97P1
Clinical
7,017,701,200
6,998,604,600
3,287,599,575
31,839
8,100
MSI
MSI
TMB-High


CS97P2
Clinical
7,308,707,000
7,285,576,000
3,660,108,635
35,033
8,563
Timepoint
MSI
N/A









Sample




CS97P3
Clinical
5,469,610,600
5,445,096,000
2,704,635,549
25,902
4,807
Timepoint
MSS
N/A









Sample




CS97P4
Clinical
6,624,844,800
6,602,615,600
3,295,259,498
31,752
5,692
Timepoint
MSS
N/A









Sample




CS97P5
Clinical
7,934,394,400
7,916,551,300
3,787,601,674
36,642
7,400
Timepoint
MSS
N/A









Sample




CS97P6
Clinical
5,527,711,600
5,504,889,500
2,466,586,812
23,568
3,104
Timepoint
MSS
N/A









Sample




CS98P1
Clinical
6,412,760,400
6,389,582,100
3,056,873,694
29,246
6,535
MSI
MSI
TMB-High


CS98P2
Clinical
6,672,529,200
6,656,140,900
3,244,885,418
31,287
6,232
Timepoint
MSI
N/A









Sample




CS98P3
Clinical
7,239,611,200
7,210,354,900
3,337,128,346
31,960
4,571
Timepoint
MSS
N/A









Sample




CS98P4
Clinical
4,884,469,600
4,870,410,300
2,398,774,907
23,146
3,886
Timepoint
MSS
N/A









Sample




CS98P5
Clinical
6,684,455,800
6,629,107,600
3,043,981,443
29,758
2,048
Timepoint
MSS
N/A









Sample




CS99P1
Clinical
7,515,207,800
7,492,829,500
3,558,158,353
35,107
3,567
MSS
MSS
TMB-Low


CS99P2
Clinical
7,295,781,200
7,266,983,100
3,137,279,510
30,900
2,698
Timepoint
MSS
N/A









Sample




CS99P3
Clinical
8,069,010,600
8,015,635,800
3,679,851,982
36,047
4,776
Timepoint
MSS
N/A









Sample




CS99P4
Clinical
7,293,700,400
7,259,226,900
3,264,415,728
32,140
2,399
Timepoint
MSS
N/A









Sample




CS00P1
Clinical
6,374,270,600
6,354,057,400
3,037,772,591
29,333
4,464
MSI
MSI
TMB-High


CS00P2
Clinical
7,800,574,000
7,772,352,900
3,639,270,013
35,789
7,769
Timepoint
MSS
N/A









Sample




CS00P3
Clinical
8,999,308,800
8,975,347,800
4,386,806,111
43,097
7,970
Timepoint
MSS
N/A









Sample




CS00P4
Clinical
8,380,704,400
8,356,079,400
4,080,252,921
40,115
6,470
Timepoint
MSS
N/A









Sample




CS00P5
Clinical
9,582,201,400
9,546,328,100
3,353,260,614
32,916
6,017
Timepoint
MSS
N/A









Sample




CS00P6
Clinical
10,156,844,200
10,115,758,100
4,837,193,865
47,487
3,644
Timepoint
MSS
N/A









Sample




CS01P1
Clinical
8,967,808,600
8,936,498,200
2,764,042,296
27,189
7,682
MSS
MSS
TMB-Low


CS01P2
Clinical
7,912,113,000
7,890,663,500
4,022,370,530
38,855
8,822
Timepoint
MSS
N/A









Sample




CS01P3
Clinical
6,484,354,600
6,455,565,800
3,188,722,876
30,729
7,517
Timepoint
MSS
N/A









Sample




CS02P1
Clinical
4,189,797,200
4,152,277,300
1,904,218,928
18,244
1,223
MSI
MSS
TMB-Low


CS02P2
Clinical
10,780,428,800
10,746,068,700
5,446,642,208
52,607
4,747
Timepoint
MSS
N/A









Sample




CS03P1
Clinical
7,050,276,200
7,025,996,100
3,272,196,914
31,585
4,411
MSI
MSI
TMB-High


CS03P2
Clinical
7,863,350,800
7,834,129,600
3,881,768,067
37,547
4,004
Timepoint
MSS
N/A









Sample




CS03P3
Clinical
5,886,551,400
5,855,839,700
2,592,954,330
25,054
1,821
Timepoint
MSS
N/A









Sample




CS03P4
Clinical
5,120,290,200
5,089,916,800
2,462,851,445
23,841
1,370
Timepoint
MSS
N/A









Sample




CS04P1
Clinical
7,761,417,200
7,737,522,100
3,680,626,639
35,734
5,451
MSS
MSS
TMB-Low


CS04P2
Clinical
7,248,720,000
7,230,958,400
3,711,116,296
36,019
5,988
Timepoint
MSS
N/A









Sample




CS04P3
Clinical
6,981,545,200
6,963,312,600
3,392,271,571
32,934
6,670
Timepoint
MSS
N/A









Sample




CS04P4
Clinical
8,074,351,200
8,052,943,600
3,852,185,218
37,331
8,083
Timepoint
MSS
N/A









Sample




CS04P5
Clinical
5,970,210,800
5,949,218,300
2,795,371,329
27,047
8,641
Timepoint
MSS
N/A









Sample




CS05P1
Clinical
5,968,039,800
5,946,488,700
2,729,734,351
26,410
5,574
MSI
MSI
TMB-Low


CS05P2
Clinical
6,623,933,000
6,600,539,800
3,226,910,099
31,277
6,687
Timepoint
MSI
N/A









Sample




CS05P3
Clinical
4,496,120,400
4,477,585,500
2,194,338,785
21,245
3,297
Timepoint
MSI
N/A









Sample




CS06P1
Clinical
8,211,159,400
8,186,988,800
4,156,128,019
40,389
8,002
MSI
MSI
TMB-High


CS06P2
Clinical
6,178,650,200
6,150,639,400
2,894,449,740
27,945
6,038
Timepoint
MSI
N/A









Sample




CS06P3
Clinical
6,478,543,800
6,455,283,900
3,163,299,171
30,602
4,990
Timepoint
MSS
N/A









Sample




CS06P4
Clinical
6,548,847,200
6,526,980,300
3,098,470,879
30,056
6,184
Timepoint
MSI
N/A









Sample




CS06P5
Clinical
5,595,054,000
5,528,052,700
2,505,771,405
23,684
3,530
Timepoint
MSI
N/A









Sample




CS07P1
Clinical
10,952,067,600
10,913,583,200
2,792,942,847
27,492
8,179
MSI
MSI
TMB-High


CS07P2
Clinical
10,529,570,200
10,492,696,000
4,154,390,112
40,862
7,298
Timepoint
MSI
N/A









Sample




CS07P3
Clinical
9,716,580,000
9,688,288,100
4,626,575,627
45,358
9,111
Timepoint
MSI
N/A









Sample




CS08P1
Clinical
6,015,494,400
5,979,103,500
2,862,781,196
27,006
7,909
MSI
MSI
TMB-Low


CS08P2
Clinical
6,132,402,200
6,089,687,400
3,043,241,991
28,664
9,537
Timepoint
MSI
N/A









Sample




CS08P3
Clinical
6,909,139,800
6,867,393,100
3,360,042,873
31,839
7,216
Timepoint
MSI
N/A









Sample




CS09P1
Clinical
5,711,066,800
5,673,140,500
2,598,635,892
24,508
4,781
MSI
MSS
TMB-Low


CS09P2
Clinical
6,038,788,400
6,017,962,600
2,867,990,049
27,725
6,346
Timepoint
MSS
N/A









Sample


















TABLE 6





Comparison of Microsatellite Status Determined through Healthy Donor,


Contrived, and Clinical Plasma Analyses




















Healthy Donors and

Expected Status













Contrived Sample Analysis

MSI-H
MSS
















58 Gene Targeted Panel
MSI
18
1




MSS
2
162














Tissue MSI Status













Clinical Plasma Analysis

MSI-H
MSS
















58 Gene Targeted Panel
MSI
9
0




MSS
3
4

















TABLE 7





Summary of Clinical Information for 16 Patients Evaluated for Response to Immune Checkpoint Blockade






























Lynch








Tissue


Metastases
Syndrome

Time to


Duration


Case
Enrollment
Tumor
Stage
Detected
(Medical
Best
Best Response
Time to ORR
Time to CR
of Response


ID
MSI Status
Type
(On Study)
At Baseline
History)
Reponse
(Months)
(Months)
(Months)
(Months)





CS94
MSI-H
Ampulla of Vater
IV
Y
Lynch syndrome
PD
N/A
N/A
N/A
N/A


CS95
MSI-H
Small Intestine
IV
Y
Lynch syndronte
PD
N/A
N/A
N/A
N/A


CS96
MSS
Colorectal
IV
Y
No
PD
N/A
N/A
N/A
N/A


CS97
MSI-H
Colorectal
IV
Y
Lynch syndrome
CR
20.2
6.5
20.2
34.7


CS98
MSI-H
Colorectal
IV
Y
Lynch syndrome
CR
16.9
12.4
16.9
36.2


CS99
MSS
Colorectal
IV
Y
No
PD
N/A
N/A
N/A
N/A


CS00
MSI-H
Ampulla of Vater
IV
Y
Lynch syndrome
CR
17.1
2.4
17.0
45.4


CS01
MSS
Colorectal
IV
Y
No
PD
N/A
N/A
N/A
N/A


CS02
MSI-H
Small Intestine
IV
Y
No
PR
2.6
2.6
N/A
4.8


CS03
MSI-H
Colorectal
IV
Y
Lynch syndrome
CR
15.2
2.9
15.2
39.1


CS04
MSS
Colorectal
IV
Y
No
PD
N/A
N/A
N/A
N/A


CS05
MSI-H
Colorectal
IV
Y
Lynch syndrome
PD
N/A
N/A
N/A
N/A


CS06
MSI-H
Colorectal
IV
Y
Lynch syndrome
PR
2.6
2.6
N/A
13.6


CS07
MSI-H
Ampulla of Vater
IV
Y
Lynch syndrome
NE
N/A
N/A
N/A
N/A


CS08
MSI-H
Colorectal
IV
Y
No
PD
N/A
N/A
N/A
N/A


CS09
MSI-H
Colorectal
IV
Y
Unknown
PD
N/A
N/A
N/A
N/A


















Progression





Two Consecutive Timepoints



Free
Overall
Last


Protein
with >80% Reduction


Case
Survival
Survival
Dose
Censored
Censored
Biomarkers
in Baseline Protein


ID
(Months)
(Months)
(Months)
(Progression)
(Overall)
Evaluated
Biomarker Levels





CS94
3.0
3.6
3.0
1
1
CEA
No


CS95
2.8
20.7
5.6
1
1
CEA
N/A-Baseline









Normal Reference









Range


CS96
2.8
5.0
2.4
1
1
CEA
No


CS97
41.2
48.8
10.6
0
0
CEA
Yes


CS98
48.6
48.8
23.8
0
0
CEA
Yes


CS99
2.8
8.8
2.3
1
1
CEA
No


CS00
47.8
47.8
23.9
0
0
CEA;
CEA: N/A-Baseline








CA19-9
Normal Reference









Range









CA19-9: Yes


CS01
1.7
4.9
1.8
1
1
CEA
No


CS02
5.5
43.9
23.6
1
0
CEA:
CEA: N/A-Baseline








CA19-9
Normal Reference









Range









CA19-9: N/A-Baseline









Normal Reference









Range


CS03
42.0
42.0
23.8
0
0
CEA
N/A - Baseline









Normal Reference









Range


CS04
2.9
7.6
3.8
1
1
CEA
No


CS05
2.9
15.9
4.8
1
1
CEA
No


CS06
16.2
40.0
23.7
1
0
CEA
No


CS07
2.4
2.4
1.4
1
1
CEA
No


CS08
3.0
7.6
3.4
1
1
CEA
N/A-Baseline









Normal Reference









Range


CS09
1.4
6.9
4.5
1
1
CEA
No




















Total
Plasma
Plasma
Plasma
Plasma
Plasma
Plasma
Exome
Plasma



Plasma
Time
Time
Time
Time
Time
Time
Mutation
Mutation


Case
Samples
Point 1
Point 2
Point 3
Point 4
Point 5
Point 6
Load
Load


ID
Evaluated
(Months)
(Months)
(Months)
(Months)
(Months)
(Months)
(mutations/Mbp)
(mutations/Mbp Sequenced)





C594
3
0.1
0.5
3.0
N/A
N/A
N/A
23.1
40.6


C595
3
0.0
0.5
6.5
N/A
N/A
N/A
64.0
10.2


C596
3
0.0
0.5
2.8
N/A
N/A
N/A
0.1
10.2


C597
6
0.0
0.5
2.8
10.6
12.5
22.8
70.2
111.7


C598
5
0.0
0.5
4.8
14.0
28.7
N/A
120.5
203.2


C599
4
0.0
0.5
0.9
2.8
N/A
N/A
N/A
10.2


CSOO
6
0.0
0.6
2.9
4.4
11.7
25.9
139.2
152.4


CS01
3
0.0
0.5
0.9
N/A
N/A
N/A
2.3
20.3


CSO2
2
0.0
0.6
N/A
N/A
N/A
N/A
40.8
0.0


CSO3
4
0.0
0.6
12.8
27.3
N/A
N/A
28.2
50.8


CS04
5
0.0
0.6
1.3
2.9
4.5
N/A
0.8
20.3


CS05
3
0.0
0.5
4.8
N/A
N/A
N/A
39.8
10.2


CS06
5
0.0
0.5
5.1
11.1
23.7
N/A
11.0
91.4


CS07
3
0.0
0.4
0.9
N/A
N/A
N/A
68.7
233.6


CS08
3
0.0
0.7
3.0
N/A
N/A
N/A
N/A
40.6


CS09
2
0.0
0.7
N/A
N/A
N/A
N/A
N/A
20.3

















Time








Difference

Baseline






Between

Plasma






Tissue
Average
Tumor
Two Consecutive Timepoints
Baseline




and Plasma
ctDNA
Mutation
with >90%
Plasma
Two Consecutive Timepoints


Case
Collection
Level at
Burden
Reduction inTMB
MSI
with 0% Residual MSI


ID
(Months)
Baseline
Status
Levels on Treatment
Status
Alleles on Treatment





CS94
10.6
 1.3%
TMB-Low
No
MSI-H
No


CS95
54.0
 0.4%
TMB-Low
No
MSS
N/A


CS96
25.3
 2.3%
TMB-Low
No
MSS
N/A


CS97
4.9
 7.1%
TMB-High
Yes
MSI-H
Yes


CS98
30.3
 5.5%
TMB-High
Yes
MSI-H
Yes


CS99
N/A
15.0%
TMB-Low
No
MSS
N/A


CS00
13.7
 2.3%
TMB-High
Yes
MSI-H
Yes


CS01
76.2
 0.8%
TMB-Low
No
MSS
N/A


CS02
0.0
 0.0%
TMB-Low
N/A
MSS
N/A


CS03
18.5
 0.5%
TMB-High
Yes
MSI-H
Yes


CS04
48.8
 2.3%
TMB-Low
No
MSS
N/A


CS05
6.2
 0.7%
TMB-Low
No
MSI-H
No


CS06
16.3
 4.6%
TMB-High
No
MSI-H
No


CS07
16.3
 7.9%
TMB-High
No
MSI-H
No


CS08
N/A
 7.2%
TMB-Low
No
MSI-H
No


CS09
N/A
 1.1%
TMB-Low
No
MSS
N/A
















TABLE 8





Comparison of Tumor Mutation Burden and Microsatellite Status for


Patients Evaluated for Response to Immune Checkpoint Blockade


















Tissue
Plasma











Response
MSI-H
MSS
MSI
MSS





Complete Response
4
0
4
0


Partial Response
2
0
1
1


Progressive Disease
5
4
3
6


Not Evaluable
1
0
1
0













Tissue
Plasma











Response
TMB-High
TMB-Low
TMB-High
TMB-Low





Complete Response
4
0
4
0


Partial Response
2
0
1
1


Progressive Disease
3
3
0
9


Not Evaluable
1
0
1
0





TMB-H is classified as ≥10 mutations/Mbp sequenced for tissue and ≥50.8 mutations/Mbp sequenced for plasma






REFERENCES



  • 1. Le D T, Durham J N, Smith K N, Wang H, Bartlett B R, Aulakh L K, et al. Mismatch repair deficiency predicts response of solid tumors to PD-1 blockade. Science 2017;357(6349):409-13 doi 10.1126/science.aan6733.

  • 2. Le DT, Uram J N, Wang H, Bartlett B R, Kemberling H, Eyring A D, et al. PD-1 Blockade in Tumors with Mismatch-Repair Deficiency. The New England journal of medicine 2015;372(26):2509-20 doi 10.1056/NEJMoa1500596.

  • 3. Vogelstein B, Papadopoulos N, Velculescu V E, Zhou S, Diaz L A, Jr., Kinzler K W. Cancer genome landscapes. Science 2013;339(6127):1546-58 doi 10.1126/science.1235122.

  • 4. Schumacher T N, Schreiber R D. Neoantigens in cancer immunotherapy. Science 2015;348(6230):69-74 doi 10.1126/science.aaa4971.

  • 5. Segal N H, Parsons D W, Peggs K S, Velculescu V, Kinzler K W, Vogelstein B, et al. Epitope landscape in breast and colorectal cancer. Cancer research 2008;68(3):889-92 doi 10.1158/0008-5472.CAN-07-3095.

  • 6. Warthin A. Heredity with reference to carcinoma: As shown by the study of the cases examined in the pathological laboratory of the university of michigan, 1895-1913. Archives of internal medicine 1913;XII(5):546-55 doi 10.1001/archinte.1913.00070050063006.

  • 7. Lynch H T, Shaw M W, Magnuson C W, Larsen A L, Krush A J. Hereditary factors in cancer. Study of two large midwestern kindreds. Archives of internal medicine 1966;117(2):206-12.

  • 8. Nicolaides N C, Papadopoulos N, Liu B, Wei Y F, Carter K C, Ruben S M, et al. Mutations of two PMS homologues in hereditary nonpolyposis colon cancer. Nature 1994;371(6492):75-80 doi 10.1038/371075a0.

  • 9. Hendriks Y M, Wagner A, Morreau H, Menko F, Stormorken A, Quehenberger F, et al. Cancer risk in hereditary nonpolyposis colorectal cancer due to MSH6 mutations: impact on counseling and surveillance. Gastroenterology 2004;127(1):17-25.

  • 10. Papadopoulos N, Nicolaides N C, Wei Y F, Ruben S M, Carter K C, Rosen C A, et al. Mutation of a mutL homolog in hereditary colon cancer. Science 1994;263(5153):1625-9.

  • 11. Bronner C E, Baker S M, Morrison P T, Warren G, Smith L G, Lescoe M K, et al. Mutation in the DNA mismatch repair gene homologue hMLH1 is associated with hereditary non-polyposis colon cancer. Nature 1994;368(6468):258-61 doi 10.1038/368258a0.

  • 12. Leach F S, Nicolaides N C, Papadopoulos N, Liu B, Jen J, Parsons R, et al. Mutations of a mutS homolog in hereditary nonpolyposis colorectal cancer. Cell 1993;75(6):1215-25.

  • 13. Fishel R, Lescoe M K, Rao M R, Copeland N G, Jenkins N A, Garber J, et al. The human mutator gene homolog MSH2 and its association with hereditary nonpolyposis colon cancer. Cell 1993;75(5):1027-38.

  • 14. Lindblom A, Tannergard P, Werelius B, Nordenskjold M. Genetic mapping of a second locus predisposing to hereditary non-polyposis colon cancer. Nature genetics 1993;5(3):279-82 doi 10.1038/ng1193-279.

  • 15. Peltomaki P, Aaltonen L A, Sistonen P, Pylkkanen L, Mecklin J P, Jarvinen H, et al. Genetic mapping of a locus predisposing to human colorectal cancer. Science 1993;260(5109):810-2.

  • 16. Kempers M J, Kuiper R P, Ockeloen C W, Chappuis P O, Hutter P, Rahner N, et al. Risk of colorectal and endometrial cancers in EPCAM deletion-positive Lynch syndrome: a cohort study. The Lancet Oncology 2011;12(1):49-55 doi 10.1016/S1470-2045(10)70265-5.

  • 17. Hause R J, Pritchard C C, Shendure J, Salipante S J. Classification and characterization of microsatellite instability across 18 cancer types. Nature medicine 2016;22(11):1342-50 doi 10.1038/nm.4191.

  • 18. Bonneville R, Krook M A, Kautto E A, Miya J, Wing M R, Chen H-Z, et al. Landscape of Microsatellite Instability Across 39 Cancer Types. JCO Precision Oncology 2017(1):1-15 doi 10.1200/po.17.00073.

  • 19. Murphy K M, Zhang S, Geiger T, Hafez M J, Bacher J, Berg K D, et al. Comparison of the microsatellite instability analysis system and the Bethesda panel for the determination of microsatellite instability in colorectal cancers. The Journal of molecular diagnostics: JMD 2006;8(3):305-11 doi 10.2353/jmoldx.2006.050092.

  • 20. Niu B, Ye K, Zhang Q, Lu C, Xie M, McLellan M D, et al. MSIsensor: microsatellite instability detection using paired tumor-normal sequence data. Bioinformatics 2014;30(7):1015-6 doi 10.1093/bioinformatics/btt755.

  • 21. Foltz S M, Liang W W, Xie M, Ding L. MIRMMR: binary classification of microsatellite instability using methylation and mutations. Bioinformatics 2017;33(23):3799-801 doi 10.1093/bioinformatics/btx507.

  • 22. Kautto E A, Bonneville R, Miya J, Yu L, Krook M A, Reeser J W, et al. Performance evaluation for rapid detection of pan-cancer microsatellite instability with MANTIS. Oncotarget 2017;8(5):7452-63 doi 10.18632/oncotarget.13918.

  • 23. Huang M N, McPherson J R, Cutcutache I, Teh B T, Tan P, Rozen S G. MSIseq: Software for Assessing Microsatellite Instability from Catalogs of Somatic Mutations. Scientific reports 2015;5:13321 doi 10.1038/srep13321.

  • 24. Salipante S J, Scroggins S M, Hampel H L, Turner E H, Pritchard C C. Microsatellite instability detection by next generation sequencing. Clinical chemistry 2014;60(9):1192-9 doi 10.1373/clinchem.2014.223677.

  • 25. Ladas I, Yu F, Leong K W, Fitarelli-Kiehl M, Song C, Ashtaputre R, et al. Enhanced detection of microsatellite instability using pre-PCR elimination of wild-type DNA homo-polymers in tissue and liquid biopsies. Nucleic acids research 2018 doi 10.1093/nar/gky251.

  • 26. Tsai E B, Pomykala K, Ruchalski K, Genshaft S, Abtin F, Gutierrez A, et al. Feasibility and Safety of Intrathoracic Biopsy and Repeat Biopsy for Evaluation of Programmed Cell Death Ligand-1 Expression for Immunotherapy in Non-Small Cell Lung Cancer. Radiology 2018;287(1):326-32 doi 10.1148/radio1.2017170347.

  • 27. Sausen M, Leary R J, Jones S, Wu J, Reynolds C P, Liu X, et al. Integrated genomic analyses identify ARID1A and ARID1B alterations in the childhood cancer neuroblastoma. Nature genetics 2013;45(1):12-7 doi 10.1038/ng.2493.

  • 28. Leary R J, Kinde I, Diehl F, Schmidt K, Clouser C, Duncan C, et al. Development of personalized tumor biomarkers using massively parallel sequencing. Science translational medicine 2010;2(20):20ra14 doi 10.1126/scitranslmed.3000702.

  • 29. Forshew T, Murtaza M, Parkinson C, Gale D, Tsui D W, Kaper F, et al. Noninvasive identification and monitoring of cancer mutations by targeted deep sequencing of plasma DNA. Science translational medicine 2012;4(136):136ra68 doi 10.1126/scitranslmed.3003726.

  • 30. McBride D J, Orpana A K, Sotiriou C, Joensuu H, Stephens P J, Mudie L J, et al. Use of cancer-specific genomic rearrangements to quantify disease burden in plasma from patients with solid tumors. Genes, chromosomes & cancer 2010;49(11):1062-9 doi 10.1002/gcc.20815.

  • 31. Diehl F, Schmidt K, Choti M A, Romans K, Goodman S, Li M, et al. Circulating mutant DNA to assess tumor dynamics. Nature medicine 2008;14(9):985-90 doi 10.1038/nm.1789.

  • 32. Dawson S J, Rosenfeld N, Caldas C. Circulating tumor DNA to monitor metastatic breast cancer. The New England journal of medicine 2013;369(1):93-4 doi 10.1056/NEJMc1306040.

  • 33. Sausen M, Phallen J, Adleff V, Jones S, Leary R J, Barrett M T, et al. Clinical implications of genomic alterations in the tumour and circulation of pancreatic cancer patients. Nature communications 2015;6:7686 doi 10.1038/ncomms8686.

  • 34. Phallen J, Sausen M, Adleff V, Leal A, Hruban C, White J, et al. Direct detection of early-stage cancers using circulating tumor DNA. Science translational medicine 2017;9(403) doi 10.1126/scitranslmed.aan2415.

  • 35. Ellrott K, Bailey M H, Saksena G, Covington K R, Kandoth C, Stewart C, et al. Scalable Open Science Approach for Mutation Calling of Tumor Exomes Using Multiple Genomic Pipelines. Cell systems 2018;6(3):271-81 e7 doi 10.1016/j.cels.2018.03.002.

  • 36. Jones S, Anagnostou V, Lytle K, Parpart-Li S, Nesselbush M, Riley D R, et al. Personalized genomic analyses for cancer mutation discovery and interpretation. Science translational medicine 2015;7(283):283ra53 doi 10.1126/scitranslmed.aaa7161.

  • 37. Li H, Durbin R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 2009;25(14):1754-60 doi 10.1093/bioinformatics/btp324.

  • 38. Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 2009;25(16):2078-9 doi 10.1093/bioinformatics/btp352.

  • 39. Xie M, Lu C, Wang J, McLellan M D, Johnson K J, Wendl M C, et al. Age-related mutations associated with clonal hematopoietic expansion and malignancies. Nature medicine 2014;20(12):1472-8 doi 10.1038/nm.3733.



Any and all references and citations to other documents, such as patents, patent applications, patent publications, journals, books, papers, web contents, that have been made throughout this disclosure are hereby incorporated herein by reference in their entirety for all purposes.


Although the present invention has been described with reference to specific details of certain embodiments thereof in the above examples, it will be understood that modifications and variations are encompassed within the spirit and scope of the invention. Accordingly, the invention is limited only by the following claims.

Claims
  • 1. A method of determining micro satellite instability (MSI) comprising: obtaining cell-free DNA (cfDNA) from a liquid sample from a patient;sequencing fragments of the cfDNA to obtain sequences of a plurality of tracts of nucleotide repeats in the cfDNA; andproviding a report describing an MSI status of the patient depicting a distribution of lengths of the plurality of tracts that have peaks that deviate from peaks in a reference distribution.
  • 2. The method of claim 1, wherein the reference distribution is from a matched normal DNA sample.
  • 3. The method of claim 2, further comprising attaching non-unique barcodes to the fragments.
  • 4. The method of claim 3, further comprising amplifying the fragments to produce amplicons that include barcode information and copies of the fragments, wherein the sequencing step comprises sequencing the amplicons.
  • 5. The method of claim 4, wherein the obtained sequences include a plurality of sequence reads and the method includes: aligning the sequence reads to a reference, and identifying groups of sequence reads that originated from a unique cfDNA fragment by means of the barcode information and position or content of the sequence reads.
  • 6. The method of claim 5, wherein the use of the non-unique barcodes to identify groups of sequence reads that originated from a unique cfDNA fragment which allows for the lengths of the plurality of tracts to be determined correctly by correcting for errors introduced by sequencing artifacts or polymerase slippage during the amplifying step.
  • 7. The method of claim 4, wherein the target portions are markers for MSI.
  • 8. The method of claim 7, wherein the markers include one or more of BAT25, BAT26, MON027, NR21, NR24, Penta C, and Penta D.
  • 9. The method of claim 7, wherein the markers include BAT25, BAT26, MON027, NR21, and NR24.
  • 10. The method of claim 7, wherein the one or more microsatellite markers are selected from the group consisting of BAT-25, BAT-26, MONO-27, NR-21, NR-24, Penta C, and Penta D.
  • 11. The method of claim 7, further comprising recommending a treatment for the patient based on the MSI status.
  • 12. The method of claim 11, wherein the MSI status indicates that the patient is microsatellite instable and the treatment comprises an immune checkpoint inhibitor.
  • 13. The method of claim 12, further comprising administering the immune checkpoint inhibitor to the patient.
  • 14. The method of claim 13, wherein the immune checkpoint inhibitor comprises an antibody.
  • 15. The method of claim 14, wherein the antibody is selected from the group consisting of: an anti-PD-1 antibody; an anti-IDO antibody; anti-CTLA-4 antibody; an anti-PD-L1 antibody; and an anti-LAG-3 antibody.
  • 16. The method of claim 1, wherein the sequencing is next-generation, short-read sequencing.
  • 17. The method of claim 1, wherein the sample is from blood, serum, plasma, urine, saliva or other biological fluid.
  • 18. A method of detecting microsatellite instability (MSI), the method comprising: obtaining a sample comprising fragments of cell-free DNA (cfDNA) from a patient;attaching barcodes to the fragments, wherein at least some of the barcodes are not unique;sequencing the barcodes to obtain sequences of a plurality of markers in the DNA;determining a distribution of lengths of the plurality of markers;providing a report describing MSI in the patient when peaks in the distribution deviate from expected peaks in a modeled healthy or reference distribution.
  • 19. A method of detecting micro satellite instability (MSI), the method comprising: obtaining fragments cell-free DNA (cfDNA) from a sample of plasma from a patient; sequencing portions of the cfDNA to obtain sequences of a plurality of tracts of nucleotide repeats in the cfDNA;assembling the sequences using information from endogenous barcodes at the starts and ends of the fragments; andproviding a report describing an MSI status in the patient when a distribution of lengths of the plurality of tracts has peaks that deviate significantly from peaks in a reference distribution.
  • 20. The method of claim 7, wherein the markers comprise any polynucleotide tract.
  • 21. The method of any of the preceding claims, wherein the method is used in screening for Lynch syndrome.
  • 22. A method for generating a personalized cancer treatment report, the method comprising: acquiring a liquid sample from a subject having or suspected of having cancer, determining the MSI status of the subject by the method of claim 1 and generating a personalized cancer treatment report to memorialize the presence or absence of microsatellite instability in the subject.
  • 23. The method of claim 22, wherein the cancer treatment report comprises one or more of the following: (i) information on prognosis, resistance, or potential therapeutic options; (ii) information on the likely effectiveness of a therapeutic option; (iii) the acceptability of a therapeutic option, or the advisability of applying the therapeutic option to the subject; or (iv) information on the administration of a drug.
  • 24. The method of claim 22, wherein the sample comprises cfDNA.
  • 25. The method of claim 1, wherein the subject has a cancer selected from pancreatic, colon, gastric, endometrial, cholangiocarcinoma, breast, lung, head and neck, kidney, bladder, prostate cancer, or hematopoietic cancers.
  • 26. The method of claim 18, wherein the subject has a cancer selected from pancreatic, colon, gastric, endometrial, cholangiocarcinoma, breast, lung, head and neck, kidney, bladder, prostate cancer, or hematopoietic cancers.
  • 27. The method of claim 19, wherein the subject has a cancer selected from pancreatic, colon, gastric, endometrial, cholangiocarcinoma, breast, lung, head and neck, kidney, bladder, prostate cancer, or hematopoietic cancers.
  • 28. The method of claim 22, wherein the subject has a cancer selected from pancreatic, colon, gastric, endometrial, cholangiocarcinoma, breast, lung, head and neck, kidney, bladder, prostate cancer, or hematopoietic cancers.
  • 29. A method for generating a personalized cancer treatment report, the method comprising: acquiring a liquid sample from a subject having or suspected of having cancer, determining the MSI status of the subject by the method of claim 18 and generating a personalized cancer treatment report to memorialize the presence or absence of microsatellite instability in the subject.
  • 30. A method for generating a personalized cancer treatment report, the method comprising: acquiring a liquid sample from a subject having or suspected of having cancer, determining the MSI status of the subject by the method of claim 19 and generating a personalized cancer treatment report to memorialize the presence or absence of microsatellite instability in the subject.
CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims benefit of priority under 35 U.S.C. § 119(e) of U.S. Ser. No. 62/593,664 filed Dec. 1, 2017, and of U.S. Ser. No. 62/741,448 filed Oct. 4, 2018, the entire contents of which is incorporated herein by reference in its entirety.

Provisional Applications (2)
Number Date Country
62593664 Dec 2017 US
62741448 Oct 2018 US