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Background: Inactivating mutations in homologous recombination repair (HRR) genes are associated with a response to PARP inhibitors and platinum-based therapies. However, secondary reversion mutations in HRR genes remain a major mechanism of resistance. We sought to characterize the prevalence and spectrum of these reversions in advanced cancer patients in BRCA1/2 and other HRR genes, such as ATM and CDK12. Methods: We analyzed cfDNA from >75,000 late-stage cancer patients previously tested as part of routine clinical care using Guardant360®, a 73- to 74-gene panel. This assay detects single-nucleotide variants (SNVs), indels, fusions, and copy-number alterations in cfDNA with a reportable range of ≥0.04%, 0.02%, 0.04%, and ≥2.12 copies, respectively. Secondary reversion mutations were identified by re-analysis using a bespoke HRR deficiency (HRD) Module, which annotates SNV/indels and calls multi-exon deletions and copy-number loss with a 95% limit of detection of ≥ 0.1% and 10 to 25% tumor fraction, respectively. Results: Among >20,000 breast, ovarian, prostate and pancreatic cancer patients, deleterious germline or somatic mutations were detected in BRCA1 (2.7%), BRCA2 (4.8%), ATM (2.9%) and CDK12 (1.7%). The majority of reversions occurred in BRCA1/2-mut (8.7%), compared to 0.07% of CDK12-mut and none in ATM. Reversions showed selective gene and allele representation. In breast cancer, reversions in BRCA2-mut patients were twice as frequent as those in BRCA1-mut (12.9% vs 5.5%, p-value=0.0046), while the near opposite was true for ovarian (7.6% vs 12.8%, p-value=0.73). In pancreatic cancer (>7,500 patients), reversions presented exclusively in BRCA2-mut (8.4%, n=131 vs 0 in BRCA1-mut, n=63). CDK12 reversions were relatively rare: 1 detected in >5,000 prostate cancer patients. Across all genes, the majority of reversions were small indels while 18% of reversions removed the exon entirely. Pathogenic hotspots BRCA2 codons A938 and S1982 - accounted for ≥10% of reversions and were more likely to revert compared to other deleterious sites (p-value Conclusions: cfDNA has been previously shown to detect BRCA1/2 germline, somatic and polyclonal reversion mutations acquired over PARP and platinum-based therapy. We demonstrate that the Guardant HRD Module can robustly identify these reversions in the cfDNA of metastatic patients. We emphasize that reversions were frequent among BRCA1/2-mut breast, ovarian, prostate and pancreatic cancers, with preferential enrichment based on gene and tumor type. Understanding resistance patterns may aid in the treatment selection for progressing patients. Further work is needed to define these ideal treatment options. Citation Format: Yu Fu, Jennifer Yen, Alexander Aartyomenko, Sante Gnerre, Caroline Weiport, Elena Helman, Arielle Yablonovitch, Stephen Pettitt, Christopher Llord, Andrew Tutt, Rebecca Nagy, Justin Odegaard, Darya Chudova, AmirAli Talasaz. Reversion patterns in homologous recombination repair genes in metastatic cancer patients profiled by cell-free DNA [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 25.

Background: NTRK rearrangements, though rare in common cancers, are clinically actionable targets with two FDA-approved drugs for pan-cancer indications, larotrectinib and entrectinib. Fusion detection from ctDNA provides an opportunity to facilitate screening for this biomarker, though technical challenges such as heterogeneity in fusion partners remain. To address this, we developed an assembly-based de novo fusion algorithm that does not rely on a fixed set of partner genes and applied it to >18,000 clinical samples to detect NTRK1 fusions. Methods: A cohort of 18,867 patients across multiple cancer types (lung adenocarcinoma, breast cancer, and colorectal adenocarcinoma), plus 276 healthy control samples were previously tested with Guardant360(R), a CLIA-validated 74-gene cfDNA NGS-based assay. The median unique molecule coverage was approximately 3,000 molecules sequenced to 15,000x read depth. Samples were reanalyzed in silico using the de novo fusion algorithm: in brief, reads aligned to candidate fusion breakpoints were assembled into de Bruijn graphs. Resulting contigs were aligned to the reference and analytical filters were applied to achieve high specificity. Results: NTRK1 fusions were detected in 0.13% of patients, a similar prevalence to what has been previously described in tissue (Rosen et al., 2020). No fusions were identified in 276 healthy control samples. When examining intergenic fusions, 53% of fusions included previously characterized partner genes (TPM3, TPR, LMNA, and TP53), while the remaining fusions contained novel partners.The majority of NTRK1 fusion partners (76%) were detected only once, consistent with previous studies. KRAS G12D and BRAF V600E were detected in 17% and 13% of NTRK1 fusion-positive patients, respectively, and occurred predominantly in the colorectal cohort; most variants (60%) were subclonal to the NTRK1 fusion, and have been previously shown to be associated with resistance to TRK inhibitors through downstream activation of the MAPK pathway. The on-target resistance variant NTRK1 G595R was detected in 8% of patients positive for NTRK1 fusions, all of which were subclonal to the fusion. EGFR L858R was detected in 13% of NTRK1 fusion-positive patients, all of which were clonal to the fusion and in the lung cohort, in accordance with previous studies showing NTRK1 fusions as a resistance mechanism to TKIs in EGFR-positive NSCLC (Xia et al., 2020). Conclusions: NTRK1 fusions were detected in cfDNA at a similar prevalence to tissue NGS, demonstrating high sensitivity of plasma-based assays to detect these fusions. NTRK1 fusion partners were diverse, with the majority of partner genes observed only once across the cohort. Both clonal and subclonal NTRK1 rearrangements were detected, affirming that this biomarker can emerge as an oncogenic driver or as a mechanism of resistance. Citation Format: Arielle Yablonovitch, Sante Gnerre, Jennifer Yen, Scott Shell, Elena Helman, Stephen Fairclough, Rebecca J. Nagy, Justin Odegaard, Darya Chudova, AmirAli Talasaz. NTRK1 fusion detection from clinical cfDNA NGS using a de novo fusion caller [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 537.

Background: Molecular response (MR) estimated as a change in circulating tumor (ctDNA) load between an early on treatment sample (usually 2-9 weeks post treatment start) and pre-treatment baseline has been shown to predict patient response and outcomes across solid tumors and therapy types in many retrospective studies. There is no consensus, however, regarding the best method for assessing molecular response. Therefore, we aimed to assess several molecular response calculations and determine the optimal method for predicting outcomes in individual advanced cancer patients. Method: Aggregate results of > 4,000 patient sample pairs (3-10 weeks apart), > 1000 patient sample technical replicates, > 100 contrived sample dilutions, and in silico simulations were analyzed with Guardant360TM or GuardantOMNITM. Baseline and on-treatment paired patient samples were collected from advanced cancer patients with over 12 tumor types, including lung, colon, and breast. MR calculations included variant allele fractions (VAFs) of somatic SNVs, indels and fusions. Methods including Ratio treatment/baseline (R) of Maximum VAF (maxVAF), Ratio of Mean VAF (mVAF), and Mean of VAF Ratios were compared, with consideration of VAF precision. Analytical accuracy, reproducibility and limit of detection (LoD) were assessed. Results: Comparison of methods for calculating net change in ctDNA load on > 1500 sample pairs showed high correlation (ρ ranged from 0.93 to 0.98) and categorical agreement split by the median (93%). Therefore selecting an optimal method based on outcome prediction would require prohibitively large patient cohorts. Analytical evaluation and in silico simulations can predict the behavior of each method. Simulations of changes in tumor fraction of real pre-treatment samples found that RmVAF or RmaxVAF are more accurate than mVAFR, which can be skewed by low VAF ratios. Almost 25% of sample pairs have a tumor driver or resistance mutation that is not the maxVAF, suggesting tumor dynamics are better captured by mVAF than maxVAF. Newly-detected on-treatment variants can be an important signal of rising ctDNA levels, impacting MR in approximately 2% of sample pairs. Importantly, MR accuracy for all methods decreases as maxVAF approaches or falls below the variant LoD, due to both stochastic detection and higher CV of variants at low VAF. Thus the assay variant LoD is a key determinant of the fraction of patients who can receive MR evaluation. Technical replicates identified the variant criteria at which a 50% change in tumor fraction differs significantly from technical variation, and could define analytical reporting limits. Conclusions: Comparison of MR methods in a large set of patient samples and simulations supports RmVAF with inclusion of newly-detected mutations. Clinical validation of these methods will support patient-specific MR prediction of outcomes. Citation Format: Allysia J. Mak, Katie J. Quinn, Carin Espenschied, Kyle Chang, Han-Yu Chuang, Elena Helman, Darya Chudova, Justin Odegaard, Becky Nagy, AmirAli Talasaz. Comparison of molecular response calculations for prediction of patient outcome [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 401.

With the advent of next-generation sequencing techniques, tumor mutations that guide therapeutic decisions can be identified on a large scale. The ability to accurately detect these mutations is often impaired by obstacles such as sequencing coverage gaps, small sample amount, low cellularity, and tumor heterogeneity. Two methods commonly used to interrogate the genomic profile of a tumor are: i) cancer gene panels, where a small number of genes (in the range of ~5-500, depending on the specific panel design) is assessed, and ii) cancer exome sequencing, where most human genes are evaluated. Additionally, tumor RNA can be studied to elucidate gene fusions and expression features that may impact treatment. Depending on the goals of the investigator, both methods have certain advantages and disadvantages. Differences between these assays must be clearly understood in order for cancer researchers to make the best decision to achieve their objective. We developed a set of unique sequencing assays to augment and improve detection of major cancer mutations by enhancing coverage over known sequencing gaps and GC-rich regions. We utilize these protocols to create an accuracy and content enhanced whole-exome (ACE Exome) as well as an extended, augmented cancer gene panel (ACE Extended Cancer Panel), targeting over 1,300 cancer genes and 200 miRNA genes at very high depth. We compared these ACE assays to standard exome and gene panels currently available. We then compared the ACE panel to the ACE exome using a set of well-characterized cancer cell lines, cell line mixtures with known genomic perturbations, and primary tumor and matched normal pairs involved in drug resistance. We also performed whole-transcriptome and targeted RNA sequencing as companion assays to the DNA exome and panel, respectively. All data were analyzed using a cancer bioinformatics pipeline optimized for detection of low-representation small variants and indels, as well as somatic copy-number aberrations, gene expression and fusions. The ACE assays were able to detect somatic variants with higher accuracy than standard assays across regions targeted by both. For example, a low-representation BRAF mutation detected by standard exome was confirmed as a false positive using the ACE exome due to 90 times more coverage over that region. Between the ACE assays, we found that 88% of somatic variants identified through ACE exome were also identified with our comprehensive panel within regions targeted by both. Conversely, more than half of the somatic variants discovered in the ACE panel were missed in the ACE exome due to insufficient read depth. For instance, the panel detected two EGFR variants known to be present at 1% that were missed in the ACE exome. About 20 times more somatic variants across some ~18,000 genes were detected in whole-exome that are not targeted on the augmented gene panel. These genes have not yet been associated with cancer in current literature, but may be of potential interest for discovery research. Copy number profiles, although more comprehensive in the exome, were recapitulated using the panel, despite large distances between panel targets. Finally, the integration of RNA analysis to the DNA panel was able to detect expression and fusion patterns. In particular, we detected a DNA mutation associated with resistance in a post-treatment specimen, but accompanying RNA data proved this mutant unexpressed and thereby unlikely to be the driver of resistance. For NGS-based cancer analysis to effectively guide clinical decisions and research, accuracy across all relevant cancer genes is of paramount importance. An enhanced exome may offer a solution for researchers interested in novel cancer associations who desire broad, even coverage. A comprehensive, high-depth panel targeting a large set of genes associated with cancer, accompanied by RNA analysis, may accomplish research goals across a wide range of translational cancer research. Citation Format: Elena Helman, Michael James Clark, Sean Boyle, Richard Chen, Shujun Luo, Christian Haudenschild, Jason Harris, Gabor Bartha, Deanna Church, John West. Augmented targeted NGS in cancer diagnostics: Comparing gene panels and whole exome sequencing for accurate detection of driver mutations. [abstract]. In: Proceedings of the AACR Special Conference on Translation of the Cancer Genome; Feb 7-9, 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 1):Abstract nr A2-39.

Background: In addition to BRCA1/2 germline and somatic inactivating mutations, loss of heterozygosity (LOH) and biallellic somatic copy number loss are associated with BRCA1/2 loss of function. Patients with cancers harboring these somatic features may benefit from treatment with PARP inhibitors. ctDNA analysis has proven a viable alternative to tumor tissue genotyping, especially in tissue- or time-limited clinical scenarios. However, ctDNA assessment of LOH and biallelic copy number loss is challenging given that ctDNA is diluted by cell-free leukocytic DNA making it difficult to confidently call these genomic events. We developed a method to identify somatic biallelic copy number loss in ctDNA using targeted sequencing of cell-free (cfDNA) across a wide range of cancer types. Methods: A novel statistical model was developed using coverage profiles and single nucleotide polymorphism (SNP) allelic frequencies estimated from plasma samples to determine the presence of LOH and biallellic copy number loss in BRCA1 or BRCA2. For each gene of interest, the model uses observed coverage and fragment size distribution, together with allele frequency of germline SNPs. The model was applied to plasma samples from 28,000 patients with advanced solid tumors sequenced using a 73-gene next generation sequencing ctDNA panel (Guardant360®, Guardant Health, Redwood City, CA). Results: The model was analytically validated using in-silico simulations in order to assess both the limit of blank and limit of detection. This method shows 95% sensitivity in detecting LOH and bi-allellic copy number loss for samples with a maximum somatic variant allele frequency as low as 9%. Sensitivity is mainly driven by the panel size and the depth of coverage of the gene of interest. The model was then applied to the 28,199 patient cohort. BRCA1 and BRCA2 LOH was observed in 2.4% (134/5568) and 7.4% (415/5568) of classic homologous recombination deficient (HRD) cancers including breast, ovarian, prostate, and pancreas. BRCA1 and BRCA2biallelic copy number loss was observed in 0.3% (19/5568) and 0.6% (31/5568) of this same group of HRD cancers. Discussion: In this cohort of 5,568 patients with classic HRD associated cancers, somatic LOH and biallelic copy number loss was detected in BRCA1 in 2.7% of samples and in BRCA2 in 8.0% of samples, which is aligned with previously reported tissue prevalence. BRCA1/2 somatic LOH and biallelic copy number loss can accurately be detected in ctDNA utilizing likelihood-based scores based on coverage and germline SNPs allele frequencies. The ability to identify this therapeutically targetable genomic alteration through a non-invasive ctDNA assessment has significant clinical implications. Citation Format: Catalin Barbacioru, Victoria M. Raymond, Marcin Sikora, Elena Helman, Sante Gnerre, Stephen Fairclough, Darya Chudova, Richard B. Lanman, AmirAli Talasaz. Cell-free circulating tumor DNA (ctDNA) detects somatic copy number loss in homologous recombination repair genes [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 435.

Background: Tumor mutational burden (TMB) has emerged as a predictive biomarker of response to immune checkpoint inhibitor (ICI) therapy. Current panel-based TMB algorithms aggregate signal from certain types of somatic variants (e.g. non-synonymous coding SNVs); however, delineating the contributions of these and other types of mutations may refine TMB calculation from gene panels. Moreover, early studies suggest other possible genomic correlates of patient outcome to ICI which may be complementary to TMB. Here, we explore the landscape of mutations comprising TMB and other genomic features correlating with TMB on a subset of several thousand late-stage plasma samples run on GuardantOMNITM (OMNI), a highly sensitive 500-gene cfDNA sequencing platform. Methods: We developed a cfDNA-based TMB algorithm which is robust to variable tumor shedding levels and presence of clonal hematopoiesis. We assessed cfDNA-based TMB in over 1,000 plasma samples across six tumor types, including lung and prostate. We examine the contribution of nonsynonymous, synonymous, intronic SNVs, and indels to TMB score. We investigate correlations between TMB and additional genomic features, including chromosomal instability, loss of HLA-bearing chromosome 6p, microsatellite instability (MSI), and common oncogenic and resistance mutations. Results: We found that the distribution of WES-calibrated TMB scores across this cohort of samples is consistent with TCGA, with median 10 mutations/Mb and upper-tertile of 14 mutations/Mb across tumor types. The number of non-synonymous coding SNVs per sample correlated highly with synonymous coding SNV and intronic SNV counts (Pearson’s r > 0.7 for each). Including this additional signal in TMB calculation improves clinical sensitivity by up to 5%. In MSS samples, indels were highly correlated with SNVs, indicating that both likely arise from a similar underlying mechanism. We found no clear correlation between high TMB and chromosomal instability, with high TMB samples exemplifying a range of tumor ploidies. TMB association with oncogenic drivers is consistent with existing literature, with lower median TMB in EGFR-driven lung tumors (p < 0.01), but little to no correlation between TMB and KRAS or PIK3CA driver status, or STK11 loss of function (p > 0.05), suggesting these latter events could be independent clinical biomarkers to TMB. Conclusions: Panel-based TMB scores can leverage synonymous and non-coding mutations to strengthen the signal of exome-wide mutation load. As more patient outcome data becomes available, TMB algorithms and orthogonal biomarkers of tumor genome immunogenicity will evolve further for improved guidance of patient response to immunotherapy. Sequencing panels with high sensitivity for TMB, via large panel space, and the ability to detect copy-number variations and MSI-status, will be important for biomarker development and clinical applications. Citation Format: Katie Quinn, Elena Helman, Tracy Nance, Jennifer Yen, John Latham, Kristin Gleitsman, Ravi Vijaya-Satya, Carlo Artieri, Alex Artyomenko, Marcin Sikora, Darya Chudova, Richard B. Lanman, AmirAli Talasaz. Landscape and genomic correlates of ctDNA-based tumor mutational burden across six solid tumor types [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 3404.

Background: Late stage therapy selection is advancing beyond the detection of classical driver alterations (SNV, Indel, CNV, Fusion) to the use of aggregate biomarkers comprising combinations of SNVs, Indels, and fusions, that reflect the underlying mutational process, indicate the cellular dysfunction, and suggest the neoantigen signature. GuardantOMNI (OMNI), is a highly sensitive 500-gene cfDNA sequencing test designed to detect primary somatic mutations as well as aggregate biomarkers including TMB and DDR pathway defects. The newest version of OMNI introduces MSI biomarker detection, which is an indication for the first FDA-approved pan-cancer therapy. Here we present the first analytical validation study of OMNI MSI-H detection. Methods: Probabilistic models for MSI detection were designed to interrogate instability across >1300 loci, differentiating PCR and sequencing induced artifacts from true biological signals using molecular barcoding as implemented in the Guardant digital sequencing platform. Cut-offs were selected based on the analysis of the training healthy donor cohort and locked prior to validation. Analytical performance was assessed using pre-characterized cell lines, cancer-free donor-samples, and clinical patient samples. Qualitative and quantitative orthogonal confirmation was provided by next-generation sequencing (NGS) and published information. Results: 350 samples were processed for this study, using both 5ng and 30ng cfDNA input levels, and all samples passed sequencing QC metrics established prior to testing. Cell line-based dilution studies performed in triplicate spanning 5ng and 30ng input range as well as in silico simulation demonstrated 95% limit of detection (LoD) at 0.1% Analysis of 121 healthy donor and 16 MSS advanced cancer patient samples identified no false positives (FP). In 240 patient samples with orthogonal confirmation of MSI status from tissue or panel NGS, GuardantOMNI demonstrated 100% analytic sensitivity (3/3) and 100% analytic specificity (237/237) with a PPV of 1 and and NPV 1. Retrospective analysis of 2000+ clinical samples detected MSI high in 16% of colorectal cancers, 4% of prostate cancers, and 0.6% of non-small cell lung cancers. Conclusions: Here we have demonstrated the performance of MSI-H detection with GuardantOMNI (OMNI), which has the potential to accelerate clinical trial enrollment, research and discovery with a single, non-invasive blood sample. Citation Format: Jing Zhao, Alexander Artyomenko, Carlo Artieri, John Latham, Stephen R. Fairclough, Catalin Barbacioru, Elena Helman, John Strickler, Darya Chudova, Richard Lanman, AmirAli Talasaz. Analytical validation of MSI High detection with GuardantOMNI [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 1675.

Major international projects are underway that are aimed at creating a comprehensive catalogue of all the genes responsible for the initiation and progression of cancer. These studies involve the sequencing of matched tumour-normal samples followed by mathematical analysis to identify those genes in which mutations occur more frequently than expected by random chance. Here we describe a fundamental problem with cancer genome studies: as the sample size increases, the list of putatively significant genes produced by current analytical methods burgeons into the hundreds. The list includes many implausible genes (such as those encoding olfactory receptors and the muscle protein titin), suggesting extensive false-positive findings that overshadow true driver events. We show that this problem stems largely from mutational heterogeneity and provide a novel analytical methodology, MutSigCV, for resolving the problem. We apply MutSigCV to exome sequences from 3,083 tumour-normal pairs and discover extraordinary variation in mutation frequency and spectrum within cancer types, which sheds light on mutational processes and disease aetiology, and in mutation frequency across the genome, which is strongly correlated with DNA replication timing and also with transcriptional activity. By incorporating mutational heterogeneity into the analyses, MutSigCV is able to eliminate most of the apparent artefactual findings and enable the identification of genes truly associated with cancer.

Aberrant DNA hypermethylation of tumor suppressor genes is thought to be an early event in tumorigenesis. Many studies have reported the methylation status of individual genes with known involvement in cancer, but an unbiased assessment of the biological function of the collective of hypermethylated genes has not been conducted so far. Based on the observation that a variety of human cancers recapitulate developmental gene expression patterns (that is activate genes normally expressed in early development and suppress late developmental genes), we hypothesized that the silencing of differentiation-associated genes in cancer could be attributed in part to DNA hypermethylation. To this end, we investigated the developmental expression patterns of genes with hypermethylated CpG islands in primary human lung carcinomas and lung cancer cell lines. We found that DNA hypermethylation primarily affects genes that are expressed in late stages of murine lung development. Gene ontology characterization of these genes shows that they are almost exclusively involved in morphogenetic differentiation processes. Our results indicate that DNA hypermethylation in cancer functions as a selective silencing mechanism of genes that are required for the maintenance of a differentiated state. The process of cellular de-differentiation that is evident on both the microscopic and transcriptional level in cancer might at least partly be mediated by these epigenetic events. Our observations provide a mechanistic explanation for induction of differentiation upon treatment with DNA methyltransferase inhibitors.

Background: Translational research and enrollment in clinical trials are limited by the rarity of individual mutations and lack of sufficient tissue for comprehensive testing. To address these limitations, we developed GuardantOMNI (OMNI), a highly sensitive 500-gene cfDNA sequencing test requiring as little as 2 mL of plasma and designed for broad genomic detection of somatic single-nucleotide variants (SNVs) and small indels in 497 genes, copy number amplifications (CNAs) in 106 genes, and fusions in 21 genes. Additionally, the OMNI panel enables assessment of tumor mutational burden (TMB), and DNA damage and mismatch repair, with coverage of over 30 genes associated with the DDR pathway. Here we present the first analytical validation study of OMNI. Methods: Analytical performance was assessed as per Nex-StoCT Working Group guidelines using precharacterized cell lines and healthy normal donor-derived samples. Qualitative and quantitative orthogonal confirmation was provided by exome sequencing, microarrays, and data from published compendia. Results: Seventy-three validation and 150 development plasma samples were processed for this study, using both 5ng and 30ng cfDNA input levels, and all samples passed sequencing QC metrics established prior to testing. Reportable ranges for SNVs were ≥0.04% variant allele fraction (VAF), ≥0.02% for indels, ≥2 supporting molecules for fusions, and ≥2.18 copies for CNAs. Cell line-based dilution studies demonstrated 95% limits of detection (LoD) of 0.24-0.6% VAF for SNVs (depending on known cancer association), 0.4-0.8% for non-homopolymeric indels (depending on clinical relevance), 0.1-0.2% for fusions, and 2.2-2.9 copies for 90% of CNA genes targeted. Comparison of diluted cell line and healthy donor samples to orthogonal sequencing and published genotype data demonstrated accuracies of 98.7% for SNVs, 97.2% for indels, and 100% for CNAs and fusions across the reportable range. The analytical false-positive rate per sample measured across 24 healthy donors was 0.25 for SNVs, 0.04 for indels, and 0 for CNAs and fusions, with positive predictive values (PPVs) of 97.5% for SNVs, 98% for indels, and 100% for CNAs and fusions. Quantitative correlation of allele fraction with confirmatory methods was high (r2 > 0.99). Conclusions: To our knowledge, OMNI is the largest comprehensive ctDNA cancer gene panel available. It detects alterations in genes under study in over 98% of current clinical trials with sensitivity, specificity, and accuracy similar to currently available targeted ctDNA sequencing tests. OMNI has the potential to accelerate clinical trial enrollment, research and discovery with a single, noninvasive blood sample. Citation Format: Elena Helman, Carlo Artieri, James V. Vowles, Jennifer Yen, Tracy Nance, Marcin Sikora, Joshua Gourneau, Mohit Goel, Stefanie Mortimer, Darya Chudova, Justin Odegaard, Richard B. Lanman, AmirAli Talasaz. Analytical validation of a comprehensive 500-gene ctDNA panel designed for immuno-oncology and DNA damage research [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 5603.

Background: DCC-2618 is a potent switch control inhibitor of KIT and PDGFRα kinases maintains potent inhibition of mutant forms across all exon regions in preclinical models. Gastrointestinal stromal tumor (GIST) is an important disease to achieve a proof-of-concept due to the heterogeneity of resistance mutations in KIT which emerge on treatment with approved KIT inhibitors. In later lines of therapy resistance mechanisms independent of the KIT gene have also been described. Methods: The ongoing phase 1, PK-guided dose escalation study of DCC-2618 given orally BID [28-day cycle] tested doses from 20 mg to 200 mg in patients (pts) with advanced solid tumors including GIST (NCT02571036). We report preliminary longitudinal results of plasma cell free (cf) DNA sequencing by Guardant 360 collected throughout the study and levels of circulating tumor cells (CTCs) based on a viral telomerase promoter-driven GFP expression assay. Results: To date, 24 out of 31 enrolled pts had metastatic KITm GIST refractory to standard therapy. A high, total mean exposure of DCC-2618 and its active metabolite was achieved at 100 and 150 mg BID, affording steady state Cmax >5 µM in Cycle 1. Starting with 50 mg BID dose level, concentrations of total drug exceeding IC90 of the most resistant mutations to DCC-2618 were achieved. Next-generation sequencing of plasma cfDNA revealed a total of 40 KIT mutations in 16 of 18 GIST pts at baseline. DCC-2618 led to rapid decrease and/or clearance of the heterogeneous array of KIT mutations from plasma cfDNA including exons 9, 11, 13, 14, 17, and 18. Independent of suppressed KIT mutation burden, longitudinal monitoring of cfDNA revealed changes in non-KIT oncogenic mutations which may contribute to heterogenous mechanisms of resistance. KIT mutation burden will be correlated with metabolic response assessment by PET scans and exposure to DCC-2618. CTCs have been detected in blood from all GIST patients at baseline using a non-biased assay capable of identifying sarcoma cells. Preliminary result show that CTCs with immunofluorescent detection of KIT or PDGFRα, corresponding to their respective mutational status, show 1 of 3 patterns when compared to radiologic response: most pts show relatively stable low levels at stable disease (SD), a minority of pts with prolonged SD a decline in CTCs and 2 pts with progressive disease had significant increase in KIT positive CTCs. Conclusions: DCC-2618 and its active metabolite achieved high plasma concentrations sufficient to inhibit the most resistant KIT mutations at well-tolerated exposures. Monitoring of cfDNA KIT mutation frequency demonstrates rapid clearance of a broad spectrum of KIT mutations in this heavily pretreated GIST patient population and suggests candidate resistance genes that are independent of KIT. Our data provide a first signal that CTC monitoring might represent a potential marker for tumor control in KIT mutant GIST. Citation Format: Filip Janku, Albi Razak, Michael Gordon, David Brooks, Daniel Flynn, Anu Gupta, Michael Kaufman, Cynthia Leary, Bryan Smith, Deb Westwood, Neeta Somaiah, Elena Helman, Eric Gerstenberger, Oliver Rosen, Suzanne George. Translational research in a phase I proof-of-concept study supports that DCC-2618 is a pan-KIT inhibitor [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr LB-039. doi:10.1158/1538-7445.AM2017-LB-039

Background: First and second-generation EGFR tyrosine kinase inhibitors (TKIs) have benefited patients with EGFR-mutated non-small cell lung cancer (NSCLC), but resistance invariably develops after a median of 9-16 months. In ~60% of patients, resistance is mediated by a second mutation in EGFR, namely T790M. Hence, third-generation EGFR TKIs such as osimertinib and rociletinib were developed to target both activating EGFR mutations as well as T790M. Unfortunately, patients also develop resistance to these therapies through mechanisms that have not yet been thoroughly explored. Since repeat tissue biopsies pose potential complications from invasive procedures, circulating tumor DNA (ctDNA) testing is increasingly used in the clinical setting to identify potentially targetable mechanisms of resistance. Methods: Matched pre-treatment and progression plasma from 57 patients with EGFR-mutated NSCLC treated with rociletinib were profiled using a 70-gene ctDNA targeted next-generation sequencing panel (Guardant360) that detects somatic single nucleotide variants, short insertions and deletions, fusions, and copy number variants. Pre-treatment EGFR ctDNA allele fractions were also determined by BEAMing, a technique based on droplet digital PCR followed by flow cytometry. Pre-treatment tumor EGFR status was assessed by the therascreen EGFR test. Results: In all 57 pre-treatment samples profiled, plasma-based ctDNA analysis detected the initial EGFR driver and T790M resistance mutations that were identified in the matched tumor. Interestingly, we found that 12% (7/57) of patients had evidence of compound EGFR driver mutations at baseline, including E709A-L858R, K860I-L858R, and L718V-L858R. EGFR T790M mutations in plasma were observed subclonally (present on average at 40% of the allele fraction of the driver mutation), suggesting tumor heterogeneity at baseline. The correlation coefficients (r) between Guardant360 and BEAMing for EGFR L858R, Exon19Del, and T790M were 0.90, 0.92, 0.95, respectively. Upon progression on rociletinib, 5% of patients (3/57) developed the EGFR C797S resistance mutation, 5% (3/57) developed focal MET amplification, and 2% (1/57) developed a NTRK1 fusion that were not present in the matched baseline plasma. Additionally, 4 deleterious BRCA1/2 alterations (2 germline and 2 somatic) were identified, with the somatic alterations emerging at progression. In 14% (8/57) of the patients, mutations in genes involved in the RAS/RAF signaling pathway, including KRAS Q61H, KRAS K117N and NF1 Q1822*, emerged or increased at progression. Conclusions: Plasma ctDNA revealed heterogeneity and multiple mechanisms of resistance in rociletinib treated patients. Thus comprehensive ctDNA sequencing allows for the identification of potentially actionable alterations and may help inform the choice of next therapy for patients progressing on a third-generation EGFR TKI. Citation Format: Elena Helman, Andrew D. Simmons, Chris A. Karlovich, Thomas C. Harding, Mitch Raponi, Darya I. Chudova, Daniel A. Simon, Richard B. Lanman, AmirAli Talasaz. Comprehensive ctDNA sequencing reveals mechanisms of resistance to rociletinib in EGFR T790M-mutated NSCLC [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 1009. doi:10.1158/1538-7445.AM2017-1009

Summary Recent genomic analyses of pathologically defined tumor types identify "within-a-tissue" disease subtypes. However, the extent to which genomic signatures are shared across tissues is still unclear. We performed an integrative analysis using five genome-wide platforms and one proteomic platform on 3,527 specimens from 12 cancer types, revealing a unified classification into 11 major subtypes. Five subtypes were nearly identical to their tissue-of-origin counterparts, but several distinct cancer types were found to converge into common subtypes. Lung squamous, head and neck, and a subset of bladder cancers coalesced into one subtype typified by TP53 alterations, TP63 amplifications, and high expression of immune and proliferation pathway genes. Of note, bladder cancers split into three pan-cancer subtypes. The multiplatform classification, while correlated with tissue-of-origin, provides independent information for predicting clinical outcomes. All data sets are available for data-mining from a unified resource to support further biological discoveries and insights into novel therapeutic strategies.

Targeted sequencing assays are increasingly used to identify tumor mutations that guide therapeutic decisions. Interpretation of a cancer variant's origin and therapeutic impact poses analytical challenges. Recent studies have indicated that jointly analyzing a tumor with its matched normal can accurately discriminate between tumor-specific (somatic) and inherited (germline) mutations. Moreover, a NHGRI/NCI Clinical Sequencing Exploratory Research Consortium Tumor Working Group just released a set of guidelines recommending that laboratories performing cancer sequencing tests should include germline variants. However, procurement of a matched sample is often logistically impractical. In the absence of a matched normal, large databases and analytical techniques are currently used to identify cancer variants in tumor sequencing data. Whether the benefits outweigh the additional burden of sequencing the matched normal for accurate detection of cancer-relevant mutations remains an open question. To compare tumor-only and tumor/normal analysis of cancer samples, we collected a set of >100 formalin-fixed (FFPE) and fresh frozen cancer samples of various tumor types, where matched normal blood or adjacent tissue was available. We performed augmented target enrichment sequencing (exome and large cancer gene panel) of both DNA and RNA. The data was analyzed using cancer bioinformatics pipelines that detect base substitutions, small insertions/deletions, copy number alterations, and gene fusions in both tumor-only and tumor/normal modes. Variants were annotated using described clinical actionability filtering strategies. Analysis of germline variants for secondary findings was performed. We find that 67% of mutations detected in tumor-only mode are reclassified as germline variants when analyzed together with the matched normal sample. These include mutations in hereditary cancer predisposition genes, such as BRCA1, VHL, and other genes with ACMG guidelines that warrant germline variant classification and appropriate management. Clinically actionable mutations may be miscalled as somatic when a matched normal is not available; however, we find the definition of ‘actionable’ can greatly impact the results of this analysis. Finally, the use of newly available large datasets, such as ExAC, substantially decreases the number of miscalled somatic variants in the absence of a matched normal. The effects of administering targeted therapies to patients with germline mutations in the relevant gene are largely unknown. Mutations of putative germline origin may be important for hereditary cancer knowledge and tumor treatment, and should be reported as such. For NGS-based cancer interpretation to guide clinical decisions in a practical and cost-effective manner, highly optimized tumor-only and tumor/normal analyses must be available with proper attention to germline consent, classification and education. Citation Format: Elena Helman, Michael J. Clark, Ravi Alla, Sean M. Boyle, Shujun Luo, Selene Virk, Deanna Church, Parin Sripakdeevong, Jason Harris, Mirian karbelashvili, Christian Haudenschild, John West, Richard Chen. The benefits and burdens of assaying matched normal tissue when sequencing cancer genomes. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 3169.

11561Background: Interpretation of a cancer variant’s origin and therapeutic impact poses analytical challenges. Recent studies have indicated that analyzing a tumor with its matched normal can dis...

The estrogen receptor (ER or ESR1) drives proliferation and growth of luminal type breast cancers. Endocrine therapies are highly effective in a majority of these cancers types; however, disease progression eventually occurs due to acquired resistance resulting in hormone-independent breast cancer. One mechanism of resistance is acquired mutations at codons 537 and 538 in the ligand binding domain of the receptor which are found in ∼12% of pretreated, ER+ patients. These mutations result in constitutive ER activation and hormone-independent progressive disease. Although ESR1 mutated breast cancers are insensitive to endocrine therapy, novel agents targeting these mutations may be effective; however few preclinical models of ESR1-mutant breast cancer are available for preclinical analysis. To this end we have established two patient derived xenograft (PDX) models harboring mutations at codon 537; ST941 with ESR1Y537S and ST1799 with ESR1Y537C. We have characterized these models using genomic analysis and compared with analysis from paired or serially collected clinical tissue and blood. Dependence on estradiol for model growth and sensitivity to endocrine therapies were also evaluated and compared with a hormone-dependent breast cancer models. The ESR1 mutations were not present in primary clinical tissue while several additional mutations were identified including in TP53 and PIK3CA genes. In vivo the ESR1 mutant models grew in the presence or absence of exogenous estradiol and demonstrated reduced sensitivity to endocrine therapies compared with hormone-dependent breast cancer models. ST941 treated with tamoxifen, letrozole or fulvestrant reported moderate tumor growth inhibition versus control while tumor stasis or regressions were reported in MCF-7 and hormone-dependent PDX breast models. Overall we have established and characterized two models of ESR1 mutant breast cancer which can be utilized for development of targeted therapies. Citation Format: Wick MJ, Helman E, Meade J, Clark MJ, Vaught T, Tolcher AW, Rasco D, Patnaik A, Lang A, Beeram M, Papadopoulos KP. Establishment and characterization of ESR1-mutant breast cancer PDX models. [abstract]. In: Proceedings of the Thirty-Eighth Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2015 Dec 8-12; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2016;76(4 Suppl):Abstract nr P3-03-04.

Background: Anaplastic large cell lymphoma (ALCL) is an uncommon, aggressive CD30-positive T-cell lymphoma exhibiting or lacking chromosomal translocation involving the Anaplastic Lymphoma Kinase (ALK) gene and expression of ALK protein. ALK positive ALCL (ALK+-ALCL) typically has an improved prognosis compared with ALK-negative; however, patients often respond favorably to standard chemotherapy and CD30-directed immunoconjugates only to progress and are left with few treatment options. To help identify additional treatment options, we collected tumor tissue and established, characterized and evaluated in vivo an ALK+-ALCL patient-derived xenograft (PDX) model from a thirty one year old Caucasian female with relapsed ALCL following initial therapy with a pediatric S-phase specific regimen. Methods: The ST2698 ALK+-ALCL model was established in CB17 SCID mice using tissue collected from a lymph node tumor biopsy. Clinical tissue, patient blood and the PDX model were subjected to WGS sequencing using an augmented and content-enhanced exome. The augmented exome is optimized to detect major cancer mutations by enhancing coverage over known sequencing gaps and GC-rich regions across >1300 cancer and 200 miRNA genes. We also performed whole-transcriptome sequencing on the PDX model. All data were analyzed using a cancer bioinformatics pipeline optimized for high accuracy detection of small variants and indels, somatic copy-number aberrations, gene expression and fusions. Drug sensitivity studies were performed evaluating sensitivity of the model to patient's current clinical treatment and relevant targeted therapies; study endpoints included tumor volume and time from treatment initiation with tumor growth inhibition, delay and regression reported at study completion. Results: Exome analysis identified several variants which were confirmed by transcriptome data. Efficacy studies confirmed model sensitivity to the patient's current clinical course (CHOP). In addition, ST2698 was found moderately sensitive to the IMiD lenalidomide (T/C=47%). However, treatment with the ALK-inhibitors crizotinib or ceritinib resulted in tumor regressions including durable complete responses in some mice. Conclusion: We have established and characterized a patient-derived ALK+-ALCL xenograft model using DNA and RNA-based analysis. In addition we evaluated the model in vivo and confirmed sensitivity to the patient's current clinical course and identified two ALK inhibitors as active, including reported complete tumor regressions. Disclosures Rasco: Celgene: Research Funding; Asana BioSciences, LLC: Research Funding. Tolcher:Asana BioSciences, LLC: Consultancy, Research Funding; AbbVie: Consultancy; ArQule: Consultancy; Bayer: Consultancy; BioMed Valley Discoveries: Research Funding; Janssen R&D: Consultancy.

Background: A subset of non-small cell lung cancer (NSCLC) is driven by mutations or deletions in the epithelial growth factor receptor (EGFR). Several targeted therapies including gefitinib and erlotinib are initially effective towards EGFR-mutant NSCLC; however, resistance to therapy often occurs during the course of treatment. To better understand the mechanisms for resistance to targeted therapies in EGFR-mutant NSCLC we collected tissue from a patient prior to and following response and progression to erlotinib and established xenografts for preclinical drug screening. Clinical and PDX tissue were characterized and compared using DNA and RNA analysis and the models compared in vivo for sensitivity to erlotinib and other therapies. Methods: The NSCLC adenocarcinoma PDX models were established in CB17 SCID mice using tissue collected from a lung biopsy (ST551) or ascites (ST551C). Clinical tissue, patient blood and the PDX model were subjected to WGS sequencing using an augmented and content-enhanced exome. The augmented exome is optimized to detect major cancer mutations by enhancing coverage over known sequencing gaps and GC-rich regions across >1300 cancer and 200 miRNA genes. We also performed whole-transcriptome sequencing on the PDX model. All data were analyzed using a cancer bioinformatics pipeline optimized for high accuracy detection of small variants and indels, somatic copy-number aberrations, gene expression and fusions. Drug sensitivity studies were performed evaluating sensitivity of the model to patient's current clinical treatment and relevant targeted therapies; study endpoints included tumor volume and time from treatment initiation with tumor growth inhibition, delay and regression reported at study completion. In erlotinib efficacy studies the therapy was administered daily via oral gavage at 35 mg/kg for sixty days. Results: Genomic analysis identified several variants which were confirmed by transcriptome data including the previously identified EGFR deletion as well as an MGMT deletion. Efficacy studies reported activity of erlotinib towards the ST551 model with a tumor growth inhibition of 112% including tumor regressions during treatment. However erlotinib was found less activity towards the ST551C model with a tumor growth inhibition of 55% and no tumor regressions. Conclusion: We have established a pair of PDX models from the same patient representing erlotinib sensitive and resistant NSCLC and have characterized the model using comparative genomic analysis and in in vivo efficacy testing. These models can be utilized to better understand mechanisms of resistance to EGFR-targeted therapies and represent unique tools in developing novel therapies to EGFR resistance. Citation Format: Michael J. J. Wick, Elena Helman, Teresa L. Vaught, Monica Farley, Lizette Gamez, Alyssa Moriarty, Scott Ulmer, Anthony W. Tolcher, Drew W. Rasco, Michael Clark, Amita Patnaik, Kyriakos P. Papadopoulos. Establishment and characterization of serial patient derived xenograft models representing erlotinib sensitive/resistant non-small cell lung cancer. [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2015 Nov 5-9; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2015;14(12 Suppl 2):Abstract nr A18.

Next-generation sequencing is now commonly applied to measure genomic modifications, providing the opportunity for a more accurate view of cancer progression. That being said, several challenges including tumor purity, heterogeneity and the broad set of cancer associated cellular functions, increase the difficulty of interpreting cancer genomes and make analyzing them distinct from analysis of germline diseases. The ability to detect low allelic mutations in the broad set of cancer-associated genes is critically important. To address these issues, we developed an extended cancer panel, a targeted enrichment sequencing platform that includes over 1,300 cancer genes and 200 miRNAs that improves coverage in traditionally difficult to sequence NGS regions. In this study, we apply an integrated RNA/DNA analysis approach as well as our extended cancer panel to explore mutations in metastatic renal cell carcinoma (RCC) tissue samples before and after treatment. Our goal with this study was to test the importance of integrating DNA and RNA analysis, which allows for detection of mutational events that cannot be observed by either method alone. With RNA analysis it is possible to detect gene fusion events, expression of small variants (SNVs/indels), and gene expression levels while DNA analysis allows for detection of loss of heterozygosity (LOH), copy number variants (CNVs), and small variants. By integrating both the RNA and DNA analysis, it is possible to identify unique variant classes, such as unexpressed variants, allele specific expression (ASE), effects of CNVs on gene expression levels, and how fusion gene integration sites affect CNVs. In our study, each variant class was interpreted pre- and post-treatment, allowing for optimal analysis of treatment effectiveness. By applying both our RNA/DNA integration approach and our extended cancer panel to investigate metastatic RCC samples, we were able to classify many genomic alterations that drive cancer progression. For example, we found that anywhere from 5% to 30% of small variant mutations in driver genes called in the DNA reside in genes that are unexpressed in the RNA. Likewise, we noted multiple cases of ASE, where high profile variants observed in DNA were entirely absent in the RNA despite high expression levels. Strong correlations between CNVs observed in DNA and gene expression changes found in RNA were also detected. We also observed that about half of the RCC samples we tested were biallelic for VHL mutations, suggesting inherited predisposition combined with second hit somatic mutations, facilitating cancer progression. This analysis demonstrates how combining an integrated RNA/DNA approach with a cancer focused augmented enrichment panel allows for detection of both low allelic representation variants and unique variant classes, both of which are critical for accurate interpretation of cancer samples. Citation Format: Sean M. Boyle, Michael J. Clark, Elena Helman, Alexander S. Parker, Thai Ho, Shujun Luo, Scott Kirk, Parin Sripakdeevong, Mirian Karbelashvili, Deanna M. Church, Michael Snyder, John West, Rich Chen. Integrating RNA/DNA analysis with a comprehensive cancer panel to improve interpretations of stage four metastatic renal cell carcinoma. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 3899. doi:10.1158/1538-7445.AM2015-3899

Choosing a genomic assay for cancer research is complicated by trade-offs. Cancer gene panels are a common choice, but they target mutational hotspots in a relatively small number of genes, often for cancers that are most commonly tested and that have common genetic etiologies. A common alternative is exome sequencing, which includes all the coding genes but, due to its larger genomic footprint, cannot reach the same depths as panels and therefore is less able to deal with low tumor purity and heterogeneity. Whole genome sequencing trades off very shallow depth and coverage over vast regions of uninterpretable genomic sequence in exchange for the identification of intergenic variants and structural variant breakpoints. All of these assays can be supplemented with RNA sequencing in order to capture gene fusions, allelic expression, splice isoforms, and gene expression. RNAseq comes with its own costs: the need to extract RNA from the same tissue, the need to perform a second assay, and the need to analyze a very different type of data from DNA sequencing. The trade-offs generally come down to three major issues: depth of sequencing, specific genes targeted, and cost. To solve these, we designed an extended, optimized cancer gene panel facilitating high depth sequencing at low cost. We started by identifying a comprehensive list of over 1,300 cancer genes. These genes were chosen through exhaustive cancer gene database and literature curation, and include genes from all major cancer pathways and from the Cancer Gene Census. We then took this gene list and applied an augmented targeting design strategy that we have previously used to create an augmented exome enrichment platform which fills in gaps that standard technical exomes miss. To validate the panel and analysis, we identified test samples including well-described cancer cell lines, cell line mixtures with engineered cancer variations, and formalin-fixed neoplastic tissues. We then performed a series of tests with these samples to measure the panel's small variant sensitivity and specificity, gauge its limits of detection, validate the detection of gene fusions, and demonstrate its ability to identify copy number alterations and loss of heterozygosity. In engineered cell lines, we detected 100% of small variants down to 5% allele frequency. We also mixed the cancer cell lines in various ratios and found similarly high sensitivity as well as very high specificity for small variant detection. We further compared our structural variation calls in the DNA and our fusion calls in the RNA with known data and found that we had very high concordance with known variations. These studies demonstrate that an extended, augmented cancer gene panel strategy solves many genomic assay trade-offs and leads to high accuracy and variant yield for cancer research applications. Citation Format: Michael J. Clark, Sean M. Boyle, Elena Helman, Shujun Luo, Gabor Bartha, Massimo Morra, Anil Patwardhan, Christian Haudenschild, Mirian Karbelashvili, Parin Sripakdeevong, Jason Harris, Deanna Church, Stephen Chervitz, John West, Richard Chen. Solving genomic assay trade-offs with an optimized, extended cancer gene panel for research and clinical applications. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 4744. doi:10.1158/1538-7445.AM2015-4744

HER2 amplification/overexpression occurs in 20-30% of breast cancers and is associated with poor prognosis and increased metastatic potential. Patients with HER2+ breast cancers who have progressed on trastuzumab and lapatinib are often prescribed the antibody-drug conjugate, do-trastuzumab emtansine (T-DM1). Results from the TH3RESA and EMILIA studies showed that T-DM1 increased progression-free and overall survival compared to standard therapy and suggested it be considered as standard of care. Despite these favorable efficacy results, most patients treated with T-DM1 eventually progress, but the mechanisms of resistance are not understood. Acquired resistance to T-DM1 has been shown in-vitro, but has not been examined in an in-vivo system. An FNA from a metastatic lung lesion was used to establish a human PDX model from a patient with metastatic HER2+ invasive ductal carcinoma. This model was found resistant to T-DM1 administered weekly at 3 m/k and every three weeks at 10 m/k. We performed whole-exome sequencing on both the metastatic tissue and the PDX model using a content-enhanced exome technology we developed. Our augmented exome is optimized to detect major cancer mutations by enhancing coverage over known sequencing gaps and GC-rich regions across >1300 cancer and 200 miRNA genes. We also performed whole-transcriptome sequencing on the PDX model. All data were analyzed using a cancer bioinformatics pipeline optimized for high-accuracy detection of small variants and indels, somatic copy-number aberrations, gene expression and fusions. We comprehensively profiled the exomes of a metastatic HER2+ ductal carcinoma resistant to T-DM1 treatment and a PDX model derived from it. We found that the PDX model was highly consistent with the neoplastic tissue, with respect to both somatic variants and copy-number alterations, establishing it as an accurate representation of a patient-derived T-DM1-resistant tumor. We verified only 0.27% contamination by mouse DNA in the PDX model. Specifically, we confirmed the continued amplification of HER2 as well as CCNE1 and MYC. Loss-of-heterozygosity of TP53 was coupled with a clonal damaging point mutation. Of note, we found a non-synonymous mutation in HER2, suggesting possible involvement in resistance mechanisms. Transcriptome data confirmed mutation expression in the RNA and gene expression changes of amplified/deleted genes. We present the first preclinical model of a human xenograft derived from a HER2+ metastatic breast cancer with acquired T-DM1 resistance. T-DM1 is effective in treating advanced HER2+ breast cancer in patients who have progressed on standard therapies, but this efficacy is short-lived. Here, we used whole-exome and transcriptome sequencing to characterize the genomic profile of tumors that have become resistant to T-DM1 and present a patient-derived model to reveal insights into acquired T-DM1 resistance mechanisms. Citation Format: Elena Helman, Michael J. Wick, Michael J. Clark, Lizette Gamez, Sean Boyle, Kyriakos P. Papadopoulos, Shujun Luo, Anthony W. Tolcher, Parin Sripakdeevong, Mirian Karbelashvili, Deanna Church, Richard Chen, John West. Genomic characterization of a PDX model of T-DM1-resistant HER2+ invasive ductal carcinoma using augmented exome sequencing. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 1457. doi:10.1158/1538-7445.AM2015-1457

e12547 Background: Genetic testing is an increasingly accepted form of molecular testing in cancer. Specific genetic lesions can determine prognosis and have diagnostic value, assisting in therapeutic decisions. Next-generation sequencing facilitates the detection of a wide array of somatic mutations, copy number alterations (CNAs), and gene fusions all in a single assay. With increased sequencing depth, mutations present in only a fraction of the neoplastic tissue can be identified. Detection of these mutations after sequencing DNA and RNA involves a complex set of variant detection algorithms tailored specifically for cancer analysis. Methods: We designed a comprehensive cancer panel using an augmented target enrichment strategy optimized for even coverage across the entire span of gene content. This approach includes multiple nucleic acid enrichments to deal with high-GC content and includes custom oligonucleotides designed to compensate for challenging regions. The panel includes over 1,200 cancer-rel...

Exome and genome sequencing are increasingly utilized to diagnose and direct treatment for cancer patients. However, there are many barriers to sequencing cancer exomes in an accurate and comprehensive way that assesses all crucial cancer mutations. Critical sequencing coverage gaps in cancer genes, challenges calling structural variants, difficulty isolating DNA from FFPE and small biopsy samples, tumor heterogeneity and cellularity, and accurate annotation of mutations remain significant hurdles to accurate clinical cancer interpretation. We have developed a comprehensive approach aimed at improving accuracy and completeness of sequencing, structural variant calling, and annotation of cancer samples. Whole exome sequencing in particular is susceptible to missing key parts of the cancer genome. We have designed a unique targeted assay and set of protocols to augment and improve the standard exome sequencing assay in order to assess all major cancer genome mutations, including important structural variations. We have optimized DNA isolation and library creation from FFPE tumor samples and small sample sizes. To improve sequencing coverage of 494 cancer related genes and over 7,000 other genes of medical importance, we include additional optimized targeted enrichments on top of the standard exome. We also utilize altered sequencing protocols to fill in regions containing genomic elements that are typically hard to sequence by standard protocols. We compared coverage over 494 cancer genes with our augmented exome approach to two different standard exome capture kits, all at 12G of sequencing. In this comparison, we defined “finishing” a gene to mean greater than 99% of bases within the gene at greater than 20x average coverage. Our augmented exome approach finished 459 of 494 (93%) cancer genes. The standard exome platforms only finished 276 (56%) and 318 (64%) cancer genes at this level. While finishing cancer genes is paramount to detecting small mutations, structural variants (SVs) are also a very important class of mutations when assessing clinical cancer samples. In addition to our augmented exome, we also perform whole genome sequencing to facilitate detection of structural variants. We utilize a combined algorithm for structural variant calling which leverages multiple lines of genomic evidence to identify and score SVs. By combining orthogonal SV detection algorithms with local reassembly, we achieve greater sensitivity (96.27% compared to 55.6% average for each algorithm independently), and a lower false discovery rate (1.37% compared to 27.55% average) on SV detection in genome sequence than any of the methods used independently. By comprehensively and accurately assessing cancer mutations and leveraging public and privately curated databases for annotation, we are able to sequence and interpret clinical grade cancer exomes to a much higher level than standard exome sequencing. Citation Format: Michael J. Clark, Deanna Church, Mark Pratt, Elena Helman, Gabor Bartha, Stephen Chervitz, Sarah Garcia, Shujun Luo, Jason Harris, Anil Patwardhan, Richard Chen, John West. Creating and accurately interpreting clinical grade cancer exomes: Challenges and solutions. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 3576. doi:10.1158/1538-7445.AM2014-3576

Retrotransposons comprise over 40% of the human genome and represent an important class of genetic variation. Though most elements are stationary ancient relics, it has recently been shown that some 100 elements remain highly active, copying and pasting themselves throughout the genome with each generation. Retrotransposon insertions can disrupt gene function, modulate gene expression, and lead to genomic rearrangement. These events have previously been implicated in cancer, including an account of insertional mutagenesis in APC as an early event in colon cancer progression. Due to the inherent difficulty in localizing repeat elements, however, an extensive investigation of retrotransposons’ role in cancer has not yet been performed. We developed RetroSeq, a computational framework to identify novel insertions of retrotransposons from paired-end sequencing data. In simulated data, RetroSeq identifies retrotransposon insertions with 99% sensitivity and 99% specificity. We ran RetroSeq on whole-genome sequencing data from a panel of tumors (including 9 colorectal, 20 non-small cell lung, 24 prostate and 22 breast tumors) and matched normal samples to determine the extent of somatic retrotransposon activity. We find a broad range of novel retrotransposon insertion events specific to each tumor, with some insertions falling in exonic as well as intronic and intergenic regions. These events were validated experimentally and integrated with expression and methylation data to provide a global view of retrotransposon activity in these samples. In sum, using RetroSeq we are able to localize novel retrotransposon insertions in paired-end sequencing data and provide evidence for the reactivation of retrotransposons in cancer. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 5060. doi:1538-7445.AM2012-5060