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Empirical determination of the limit of detection. Defined oncogenic driver and background SNVs (a), oncogene and tumor suppressor indels (b), and oncogenic fusions (c) were titrated to specified VAFs, and multiple replicates were measured to determine the 95% LOD. (d) Cell lines comprising defined gene amplifications were titrated to bracket the expected LOD, and the 95% LOD was determined from statistical detection models as per the CLSI Classical Approach.

Genes on the Guardant360 panel.

Supplementary Figure Legends

Supplemental Methods, Supplemental References, Supplemental Figure Legends, Figure S1-13, Table S1-5 and S14

Supplemental Figure 1. Glesatinib is a potent and selective MET inhibitor. Supplemental Figure 2. Waterfall plot of glesatinib activity across pre-clinical tumor models. Supplemental Figure 3. SNU-638 Criz-res line is homogeneous and harbors the F1200L and Y1230H resistance mutations on the same allele. Supplemental Figure 4. Long term treatment of a METex14 del mutant and amplified PDX model (PULM-039) leads to capmatinib and crizotinib-resistant tumors but not glesatinib-resistant tumors. Supplemental Figure 5. Molecular basis for resistance that arises from the G1163R mutation. Supplemental Figure 6. Glesatinib is active against type I MET inhibitor-resistance mutations in a spheroid growth assay.

Digital Sequencing analytical performance metrics summary table.

cfDNA variants associated with resistance to on-label targeted therapies across six cancer types

Purpose: To analytically and clinically validate a circulating cell-free tumor DNA sequencing test for comprehensive tumor genotyping and demonstrate its clinical feasibility.Experimental Design: Analytic validation was conducted according to established principles and guidelines. Blood-to-blood clinical validation comprised blinded external comparison with clinical droplet digital PCR across 222 consecutive biomarker-positive clinical samples. Blood-to-tissue clinical validation comprised comparison of digital sequencing calls to those documented in the medical record of 543 consecutive lung cancer patients. Clinical experience was reported from 10,593 consecutive clinical samples.Results: Digital sequencing technology enabled variant detection down to 0.02% to 0.04% allelic fraction/2.12 copies with ≤0.3%/2.24–2.76 copies 95% limits of detection while maintaining high specificity [prevalence-adjusted positive predictive values (PPV) >98%]. Clinical validation using orthogonal plasma- and tissue-based clinical genotyping across >750 patients demonstrated high accuracy and specificity [positive percent agreement (PPAs) and negative percent agreement (NPAs) >99% and PPVs 92%–100%]. Clinical use in 10,593 advanced adult solid tumor patients demonstrated high feasibility (>99.6% technical success rate) and clinical sensitivity (85.9%), with high potential actionability (16.7% with FDA-approved on-label treatment options; 72.0% with treatment or trial recommendations), particularly in non–small cell lung cancer, where 34.5% of patient samples comprised a directly targetable standard-of-care biomarker.Conclusions: High concordance with orthogonal clinical plasma- and tissue-based genotyping methods supports the clinical accuracy of digital sequencing across all four types of targetable genomic alterations. Digital sequencing's clinical applicability is further supported by high rates of technical success and biomarker target discovery. Clin Cancer Res; 24(15); 3539–49. ©2018 AACR.

Purpose: MET exon 14 deletion (METex14 del) mutations represent a novel class of non–small cell lung cancer (NSCLC) driver mutations. We evaluated glesatinib, a spectrum-selective MET inhibitor exhibiting a type II binding mode, in METex14 del–positive nonclinical models and NSCLC patients and assessed its ability to overcome resistance to type I MET inhibitors.Experimental Design: As most MET inhibitors in clinical development bind the active site with a type I binding mode, we investigated mechanisms of acquired resistance to each MET inhibitor class utilizing in vitro and in vivo models and in glesatinib clinical trials.Results: Glesatinib inhibited MET signaling, demonstrated marked regression of METex14 del-driven patient-derived xenografts, and demonstrated a durable RECIST partial response in a METex14 del mutation-positive patient enrolled on a glesatinib clinical trial. Prolonged treatment of nonclinical models with selected MET inhibitors resulted in differences in resistance kinetics and mutations within the MET activation loop (i.e., D1228N, Y1230C/H) that conferred resistance to type I MET inhibitors, but remained sensitive to glesatinib. In vivo models exhibiting METex14 del/A-loop double mutations and resistance to type I inhibitors exhibited a marked response to glesatinib. Finally, a METex14 del mutation-positive NSCLC patient who responded to crizotinib but later relapsed, demonstrated a mixed response to glesatinib including reduction in size of a MET Y1230H mutation-positive liver metastasis and concurrent loss of detection of this mutation in plasma DNA.Conclusions: Together, these data demonstrate that glesatinib exhibits a distinct mechanism of target inhibition and can overcome resistance to type I MET inhibitors. Clin Cancer Res; 23(21); 6661–72. ©2017 AACR.

Driver alteration prevalence in the cfDNA cohort versus in TCGA

Quantitative precision of Digital Sequencing results. (a) SNV VAFs in 375 consecutive SNV control runs. (b) Indel and fusion VAFs (left y-axis) and CNA copy number (right y-axis) in consecutive CFI control runs. (c) Variation distribution of DS results in 375 consecutive analyses of a single SNV control lot (blue) and multiple consecutive lots of indel (red), fusion (grey), and CNA (orange) controls.

Detailed results of Digital Sequencing and clinical ddPCR comparison. Results shown in individual 2x2 tables for each biomarker tested. *Note, all discordances were below DFCI ddPCR's reportable range. Detailed clinical information for each is presented in Supplemental Table 3.

Discordant results between Digital Sequencing and clinical ddPCR. Details of the cases discordant between DS and Dana-Farber Clinical Laboratory's ddPCR results with orthogonal follow-up data. *Note, while approved specifically for T790M+ NSCLC, osimertinib has been reported to have clinical activity in T790-wild type patients as well; however, osimertinib efficacy in T790M-wild type patients resistant to 1st generation EGFR TKIs is unclear.

Purpose: Cell-free DNA (cfDNA) sequencing provides a noninvasive method for obtaining actionable genomic information to guide personalized cancer treatment, but the presence of multiple alterations in circulation related to treatment and tumor heterogeneity complicate the interpretation of the observed variants.Experimental Design: We describe the somatic mutation landscape of 70 cancer genes from cfDNA deep-sequencing analysis of 21,807 patients with treated, late-stage cancers across >50 cancer types. To facilitate interpretation of the genomic complexity of circulating tumor DNA in advanced, treated cancer patients, we developed methods to identify cfDNA copy-number driver alterations and cfDNA clonality.Results: Patterns and prevalence of cfDNA alterations in major driver genes for non–small cell lung, breast, and colorectal cancer largely recapitulated those from tumor tissue sequencing compendia (The Cancer Genome Atlas and COSMIC; r = 0.90–0.99), with the principal differences in alteration prevalence being due to patient treatment. This highly sensitive cfDNA sequencing assay revealed numerous subclonal tumor-derived alterations, expected as a result of clonal evolution, but leading to an apparent departure from mutual exclusivity in treatment-naïve tumors. Upon applying novel cfDNA clonality and copy-number driver identification methods, robust mutual exclusivity was observed among predicted truncal driver cfDNA alterations (FDR = 5 × 10−7 for EGFR and ERBB2), in effect distinguishing tumor-initiating alterations from secondary alterations. Treatment-associated resistance, including both novel alterations and parallel evolution, was common in the cfDNA cohort and was enriched in patients with targetable driver alterations (>18.6% patients).Conclusions: Together, these retrospective analyses of a large cfDNA sequencing data set reveal subclonal structures and emerging resistance in advanced solid tumors. Clin Cancer Res; 24(15); 3528–38. ©2018 AACR.

Mutual exclusivity statistics for cfDNA alterations in lung adenocarcinoma, breast cancer, and colorectal cancer

Per-sample mutation burden distribution by tumor type. Per-sample adjusted mutation count relative to other samples within clinical indication. Green bars indicate the 95th percentile.

Quantitative accuracy of Digital Sequencing. Quantitative correlation between expected and observed VAFs for SNVs (a) and CNAs (b) in the analytical validation studies. (c) DS copy number calls compared to those reported in the Cancer Cell Line Encyclopedia (CCLE).

Background In high-risk pregnant women, noninvasive prenatal testing with the use of massively parallel sequencing of maternal plasma cell-free DNA (cfDNA testing) accurately detects fetal autosomal aneuploidy. Its performance in low-risk women is unclear. Methods At 21 centers in the United States, we collected blood samples from women with singleton pregnancies who were undergoing standard aneuploidy screening (serum biochemical assays with or without nuchal translucency measurement). We performed massively parallel sequencing in a blinded fashion to determine the chromosome dosage for each sample. The primary end point was a comparison of the false positive rates of detection of fetal trisomies 21 and 18 with the use of standard screening and cf DNA testing. Birth outcomes or karyotypes were the reference standard. Results The primary series included 1914 women (mean age, 29.6 years) with an eligible sample, a singleton fetus without aneuploidy, results from cfDNA testing, and a risk classification based on standard screening. For trisomies 21 and 18, the false positive rates with cfDNA testing were significantly lower than those with standard screening (0.3% vs. 3.6% for trisomy 21, P