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There is an increasing need for more advanced, composite biomarkers that can model the complex systems biology driving response and resistance to cancer therapy. However, many cancer diagnostic platforms to date, with their focus on mutational changes in a relatively small panel of genes, provide limited data to support integrative, multidimensional biomarkers that can better predict immunotherapy response. To enable the identification of composite biomarkers that combine tumor- and immune-related information from both DNA and RNA, we have developed ImmunoID NeXT, an enhanced exome/transcriptome-based diagnostic platform that can simultaneously profile the tumor and immune system from a single FFPE sample, across all of the approximately 20,000 genes. By co-optimizing assay and analytics design, we enable sensitive evaluation of clinically-relevant cancer biomarkers from >=25ng of co-extracted DNA/RNA, while also providing a broader evaluation of neoantigens, HLA typing and LOH, antigen processing machinery (APM), TCR/BCR repertoire, immune expression signatures, tumor-infiltrating lymphocytes (TILs), oncoviruses, and germline variants. Leveraging this expansive feature set, we developed methods that combine individual analytes to construct composite biomarker scores that correlate with immunotherapy response. Validation of ImmunoID NeXT demonstrated high sensitivity and specificity to somatic and structural variants across ~20,000 genes at allelic fractions as low as 5%, with clinical diagnostic reporting on actionable mutations (SNVs, indels, CNAs, fusions) in 248 cancer-driver genes that have been boosted further for higher sensitivity, as well as reporting on TMB and MSI status. For neoantigen prediction, immuno-peptidomic data from monoallelic HLA-transfected cell lines were used to train neural networks to predict pMHC binding with higher precision than public tools. For TCRα/β analysis in FFPE tumor samples, strong correlation with targeted TCR kit results was shown (R^2>0.9 and >0.94). For TILs, we developed signatures for eight immune cell types, demonstrating concordance with orthogonal immunofluorescence methods. We achieved genotyping accuracy of 99.1% for HLA Class I, and 95% for HLA Class II, and have developed and verified the performance of a tool for HLA LOH detection. In a cohort of 55 late-stage melanoma patients, the integration of neoantigen burden, HLA LOH, and APM mutational data formed a composite neoantigen score that more accurately predicted response to checkpoint blockade than other markers such as TMB. With ImmunoID NeXT, we have developed a broad diagnostic platform that can be leveraged for the development of advanced composite biomarkers (and novel resistance mechanisms) that combine both tumor and immune features from DNA and RNA; enabling more accurate stratification of patient response to immunotherapy. The platform has been validated and optimized for use with limited FFPE tissue samples, making it ideal for both research and clinical applications. Citation Format: Robert Peter Power, Gabor Bartha, Jason Harris, Sean M. Boyle, Eric Levy, Pamela Milani, Prateek Tandon, Paul McNitt, Mandy Lee, Massimo Morra, Sejal Desai, Sebastian Salvidar, Michael J. Clark, Christian Haudenschild, Sekwon Jang, John West, Richard Chen. A diagnostic platform for precision cancer therapy enabling composite biomarkers by combining tumor and immune features from an enhanced exome and transcriptome [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 1334.

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.

Immunogenomic profiling of the tumor and the tumor microenvironment (TME) is critical for identifying new biomarkers of immunotherapy response, understanding resistance, and enabling the development of personalized immunotherapies. However, running a comprehensive array of biomarker assays for each patient sample is often impractical given limited sample quantity, processing complexity, and prohibitive cost. To address these challenges, we developed a novel, augmented exome and transcriptome-based platform that simultaneously characterizes the tumor and TME from a single FFPE sample. We co-optimized the design of our sequencing assays and analytics to increase performance for the detection of somatic SNVs, indels, CNAs, and fusions across ~20,000 genes, as well as the evaluation of neoantigens, expression signatures, HLA typing and LOH, TCR/BCR repertoires, oncoviruses, tumor-infiltrating lymphocytes (TILs), clinically-actionable mutations, tumor mutational burden (TMB), and MSI status. We developed novel methods to sequence difficult regions of the exome and to extend coverage to key immunogenomic biomarkers. Analytic pipelines were designed to utilize assay optimizations to achieve higher accuracy than with other platforms. We then validated the platform for diagnostic and therapeutic use. With as little as 50ng of DNA per FFPE sample and co-extracted RNA, this platform completely covers between 17% to 40% more genes compared to a non-augmented exome, thus increasing sensitivity to somatic mutations and putative neoantigens. For neoantigen performance, we generated immune-peptidomic data from mono-allelic HLA transfected cell-lines and trained neural networks to predict neoepitope binding to MHC, demonstrating a higher precision (0.88) across alleles than publicly available tools (0.9 and >0.94, respectively). For TILs, we developed signatures for CD4, CD8 T-cells, and other immune cells, demonstrating concordance with synthetic and CyTOF-derived validation sets. For HLA typing, we achieve an accuracy of 99.1% for HLA Class I, and 95% for HLA Class II typing calls, and have developed a novel tool for HLA LOH detection. We demonstrate sensitive detection of HPV, EBV, HCV, HTLV, and KSHV in known samples, and accurate MSI and TMB assessment. Finally, for diagnostic reporting, we achieve high sensitivity and specificity for clinically-reportable mutations comparable with diagnostic cancer panels. With this platform, we have developed a novel immunogenomics platform that can characterize both the tumor and TME from a single sample. By co-optimizing our assay and analytics for immuno-oncology, we enhance biomarker sensitivity compared to non-optimized genomics assays. Validation of the platform extends its use to diagnostics and personalized immunotherapy development. Citation Format: Robert Power, Gabor Bartha, Jason Harris, Sean Michael Boyle, Eric Levy, Pamela Milani, Prateek Tandon, Robin Li, Manjula Chinnappa, Paul McNitt, Rena McClory, Massimo Morra, Sebastian Saldivar, Michael Clark, Christian Haudenschild, Erin Newburn, Christelle Johnson, John West. A comprehensive, highly accurate genomics platform for precision immunotherapy: Simultaneously characterize tumors and the TME from a single FFPE sample [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference on Molecular Targets and Cancer Therapeutics; 2019 Oct 26-30; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2019;18(12 Suppl):Abstract nr A051. doi:10.1158/1535-7163.TARG-19-A051

Tumor biopsies are often Formalin-Fixed and Paraffin-Embedded (FFPE) for histological staining, genetic testing and archival purposes. Formalin treatment preserves tissue by crosslinking proteins, but can lead to mutation of the nucleic acid bases that can be detected by next-generation sequencing (NGS) methods despite being unrelated to the cancer. Studies have shown that certain fixation protocols are compatible with high quality nucleic acid isolation and variant calling by NGS. These studies used tissue fixed with 10% neutral buffered formalin for 24 hours, a standard protocol in the pathology field. In practice, however, FFPE sample handling varies from site-to-site. As we processed FFPE samples from various sites, we found that the quality of the isolated nucleic acids and subsequent sequencing results varied substantially. We hypothesized that this is due to deviations from the standard protocol such as improperly buffered reagents, variation in the fixation time and temperature, microwaving, and varied storage conditions of the samples. To understand the role of formalin fixation on the quality of the isolated nucleic acids and subsequent NGS analyses, we subjected the widely studied cell line NA12878 to various formalin fixation conditions. We then performed an augmented target enrichment and sequencing assay on both the DNA and RNA isolated from fresh frozen (FF) and formalin fixed (FFPE) NA12878 samples. We assessed raw nucleic acid quality, library quality, alignment rate, duplication rate, on-target efficiency and variant concordance between FF and FFPE. We noted substantial negative effects on sequencing library quality associated with sample incubation in unbuffered formalin, at high temperatures, and long periods of time (e.g. 3 days rather than 1 day). These harshly treated samples also showed poor alignment qualities, higher duplication rates, and lower mapping qualities. At the small variant level, they showed an increase in global C>T deamination (CG->TA, 50% FFPE vs 35% in FF) and oxidation (CG->AT, 15% FFPE vs 10% FF) rates. Variants caused by formalin fixation were most commonly detected at low ( Using the results of this study, we intend to provide guidance on optimal fixation conditions for NGS applications. It is crucial to fix tissues in buffered formalin for less than 24hr at room temperature to preserve nucleic acid integrity and amenability for downstream NGS assays. We also demonstrate how a deeper understanding of the effects of formalin can improve variant calling from FFPE tissues, especially at lower AFs where formalin-related errors have the greatest impact. Citation Format: Ravi Alla, Shujun Luo, Elena Helman, Sean M. Boyle, Michael J. Clark, Kirk Scott, Parin Sripakdeevong, Mirian Karbelashvili, Deanna M. Church, Michael Snyder, John West, Richard Chen. Fix the fixation: effect of formalin fixation on targeted sequencing, variant calling and gene expression. [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 3616.

Accurate detection of somatic variants is a staple of both research and clinical cancer analysis, with applications ranging from detecting new common driver mutations in large patient cohorts to selecting therapeutic small molecule treatment courses for an individual patient. Recent research into neoantigens and immunotherapy has shown great promise as a precision therapeutic, and somatic variant detection by next-generation sequencing represents an ideal method of identifying candidate neoantigens. Somatic variant detection typically involves assaying the DNA for changes in gene sequences without assessing whether those variants are actually expressed in RNA. However, the expression of small variants is key because only expressed peptides will be displayed as neoantigens on the cell surface. From a technical standpoint, detection of somatic variants in the RNA represents additional challenges above and beyond those of somatic detection in DNA. The widely varying expression levels of cancer genes, alternative splicing, and RNA editing are all features that make somatic variant calling in RNA uniquely challenging. However, accurately detecting variants directly from expressed transcripts is beneficial to neoantigen prediction, and therefore we sought to create and validate a method for somatic variant calling in RNA. We have designed a highly accurate expression-based somatic variant detection pipeline utilizing extensive discovery and filtering methods to overcome the challenges inherent in RNA somatic variant calling. We validated our pipeline using a combination of well-characterized cell lines, commercially available reference standards, and real world FFPE patient samples. To our knowledge, this is the most extensive validation of its kind to date, representing over 29,158 small variants across 39 samples. In testing, we measured our detection method at >99% sensitivity and >99% PPV using a combination of gold set small variants and orthogonal validation. This method, in combination with our validated DNA somatic variant calling pipeline (>99% sensitivity and >99% PPV), enables precise detection of variant expression levels in a given sample, even at low allele frequency (5%). After validating our RNA somatic variant calling method, we applied it to detect candidate neoantigens in patient tumor samples. We performed HLA typing for each sample using HLAssign software and predicted MHC presentation of the expressed somatic variants. In ongoing studies, we are validating our most promising putative neoantigens using orthogonal technologies and demonstrating our ability to detect the most promising clinically effective peptides for therapy. Citation Format: Sean M. Boyle, Michael J. Clark, Ravi Alla, Shujun Luo, Deanna M. Church, Elena Helman, Parin Sripakdeevong, John West, Rich Chen. Accurately identifying expressed somatic variants for neoantigen detection and immuno-oncology. [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 533.

Cancer genomes are a hodgepodge of mutations. Single nucleotide somatic variants, complex structural rearrangements, whole and partial gene deletions and amplifications, splice variants and expression changes—these are the potential mutation types in cancer. Tumor heterogeneity is common, tumor samples can be solid or liquid biopsies, and they are often contaminated with adjacent normal tissue. Cancer samples are treated with formalin for archival purposes causing additional genomic variations. All of these factors combine to make genomic analysis in cancer far more complicated than that of normal germline tissue. Recent efforts to define gold standard human genomes lack many features characteristic of cancer genomes. A single cancer sample may contain a few distinct variant types, but typically not enough to confidently assess the accuracy of cancer variant detection algorithms. The Horizon Quantitative Multiplex, an engineered FFPE cell line mix with variants at allele frequencies (AFs) from 1.0 to 41.5%, and the Acrometrix Oncology Hotspot Control with >500 SNVs and small indels in one sample are available standards. In the Horizon cell line, we detected 18/18 SNVs and 4/4 indels. Meanwhile, we found that the artificial nature of the Acrometrix sample made it incompatible with next generation sequencing. While samples of this type have limited utility, there is a clear need for samples containing the full spectrum of mutation types and frequences. Since the commercially available standards are of limited utility, we aimed to create a cancer standard set that consists of many cancer cell lines in combination with some real primary cancer samples. We started with a collection of 28 cancer cell lines including 817 known SNVs, 62 small indels, 21 deletions, 23 amplifications, and 14 gene fusions. We simulated tumor heterogeneity by mixing the cell lines at various ratios, generating variant allele frequencies down to 1% and emulated reduced purity by mixing cell lines with paired normal samples at ratios down to 10%. This gave us our platform accuracy metrics for tumor-only somatic analysis. These experiments required sequencing over 200 samples and mixes. Using augmented exome and cancer gene panel (targeting >1,500 cancer genes), we were able to interrogate substantial quantities of variants. We detected 16136/16146 SNVs at 5% AF, 639/646 indels at 10% AF, 29/30 CNAs at 20% purity, and 14/14 gene fusions using the panel, for example. These high variant counts gave us tighter confidence intervals and accuracy metrics for detection of all major cancer variant types. After validating our variant calling approaches via this set, we applied our algorithms to primary tumor samples both formalin treated and fresh frozen, and also tested them both with and without a paired normal tissue sample. These gave us the ability to understand the fundamental effects of formalin fixation, and the benefits of including the paired normal sample, which will be covered in this presentation. Citation Format: Michael J. Clark. Answering the challenge of cancer genomic testing. [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 1830.

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.

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: 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

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