The impact of tumor profiling approaches and genomic data strategies for cancer precision medicine

Background: The diversity of clinical tumor profiling approaches (small panels to whole exomes with matched or unmatched germline analysis) may engender uncertainty about their benefits and liabilities, particularly in light of reported germline false positives in tumor-only profiling and use of global mutational and/or neoantigen data. The goal of this study was to determine the impact of genomic analysis strategies on error rates and data interpretation across contexts and ancestries. Methods: We modeled common tumor profiling modalities—large (n = 300 genes), medium (n = 48 genes), and small (n = 15 genes) panels—using clinical whole exomes (WES) from 157 patients with lung or colon adenocarcinoma. We created a tumor-only analysis algorithm to assess germline false positive rates, the impact of patient ancestry on tumor-only results, and neoantigen detection. Results: After optimizing a germline filtering strategy, the germline false positive rate with tumor-only large panel sequencing was 14 % (144/1012 variants). For patients whose tumor-only results underwent molecular pathologist review (n = 91), 50/54 (93 %) false positives were correctly interpreted as uncertain variants. Increased germline false positives were observed in tumor-only sequencing of non-European compared with European ancestry patients (p < 0.001; Fisher’s exact) when basic germline filtering approaches were used; however, the ExAC database (60,706 germline exomes) mitigated this disparity (p = 0.53). Matched and unmatched large panel mutational load correlated with WES mutational load (r2 = 0.99 and 0.93, respectively; p < 0.001). Neoantigen load also correlated (r2 = 0.80; p < 0.001), though WES identified a broader spectrum of neoantigens. Small panels did not predict mutational or neoantigen load. Conclusions: Large tumor-only targeted panels are sufficient for most somatic variant identification and mutational load prediction if paired with expanded germline analysis strategies and molecular pathologist review. Paired germline sequencing reduced overall false positive mutation calls and WES provided the most neoantigens. Without patient-matched germline data, large germline databases are needed to minimize false positive mutation calling and mitigate ethnic disparities. Electronic supplementary material The online version of this article (doi:10.1186/s13073-016-0333-9) contains supplementary material, which is available to authorized users

Mutational patterns in chemotherapy resistant muscle-invasive bladder cancer

Despite continued widespread use, the genomic effects of cisplatin-based chemotherapy and implications for subsequent treatment are incompletely characterized. Here, we analyze whole exome sequencing of matched pre- and post-neoadjuvant cisplatin-based chemotherapy primary bladder tumor samples from 30 muscle-invasive bladder cancer patients. We observe no overall increase in tumor mutational burden post-chemotherapy, though a significant proportion of subclonal mutations are unique to the matched pre- or post-treatment tumor, suggesting chemotherapy-induced and/or spatial heterogeneity. We subsequently identify and validate a novel mutational signature in post-treatment tumors consistent with known characteristics of cisplatin damage and repair. We find that post-treatment tumor heterogeneity predicts worse overall survival, and further observe alterations in cell-cycle and immune checkpoint regulation genes in post-treatment tumors. These results provide insight into the clinical and genomic dynamics of tumor evolution with cisplatin-based chemotherapy, suggest mechanisms of clinical resistance, and inform development of clinically relevant biomarkers and trials of combination therapies

Comprehensive Characterization of Cancer Driver Genes and Mutations

Identifying molecular cancer drivers is critical for precision oncology. Multiple advanced algorithms to identify drivers now exist, but systematic attempts to combine and optimize them on large datasets are few. We report a PanCancer and PanSoftware analysis spanning 9,423 tumor exomes (comprising all 33 of The Cancer Genome Atlas projects) and using 26 computational tools to catalog driver genes and mutations. We identify 299 driver genes with implications regarding their anatomical sites and cancer/cell types. Sequence- and structure-based analyses identified &gt;3,400 putative missense driver mutations supported by multiple lines of evidence. Experimental validation confirmed 60%-85% of predicted mutations as likely drivers. We found that &gt;300 MSI tumors are associated with high PD-1/PD-L1, and 57% of tumors analyzed harbor putative clinically actionable events. Our study represents the most comprehensive discovery of cancer genes and mutations to date and will serve as a blueprint for future biological and clinical endeavors

Integrated genomic characterization of pancreatic ductal adenocarcinoma

We performed integrated genomic, transcriptomic, and proteomic profiling of 150 pancreatic ductal adenocarcinoma (PDAC) specimens, including samples with characteristic low neoplastic cellularity. Deep whole-exome sequencing revealed recurrent somatic mutations in KRAS, TP53, CDKN2A, SMAD4, RNF43, ARID1A, TGFβR2, GNAS, RREB1, and PBRM1. KRAS wild-type tumors harbored alterations in other oncogenic drivers, including GNAS, BRAF, CTNNB1, and additional RAS pathway genes. A subset of tumors harbored multiple KRAS mutations, with some showing evidence of biallelic mutations. Protein profiling identified a favorable prognosis subset with low epithelial-mesenchymal transition and high MTOR pathway scores. Associations of non-coding RNAs with tumor-specific mRNA subtypes were also identified. Our integrated multi-platform analysis reveals a complex molecular landscape of PDAC and provides a roadmap for precision medicine

Molecular profile to cancer cell line matchmaking

Abstract Profile-to-cell line matchmaking is a computational protocol to identify cancer cell lines that are genomically similar to a patient’s case profile. In doing so, high-throughput drug screens applied to the same cancer cell lines may be used for therapeutic hypothesis generation in research settings and potentially in clinical settings. To evaluate the metrics of the matchmaking, a hold-one-out approach of the considered cancer cell lines is applied, and molecular similarity models are assessed based on their ability to identify cancer cell lines that share therapeutic sensitivity.</jats:p

From Orientation Needs to Developmental Realities: The Honors First-Year Seminar in a National Context

The transition into college remains one of the most formative and complex phases in an individual’s life. Institutions of higher learning have responded to the challenges facing first-year students in myriad ways, most often by offering summer orientation programs, dynamic living-learning environments, tailored academic and psychological support services, and dedicated first-year seminars (FYSs) that seek to engage students in a range of curricular and co-curricular experiences. FYSs—courses intended to enhance the academic skills and/or social development of first-year college students—have become the curricular anchors grounding this broad array of programming. While addressing the developmental needs of first-year students is the key driver of such seminars, they can also enhance student connection to the institution and have positive effects on retention, especially persistence to the sophomore year. A deep body of research exists on campus-wide FYS programs, and evidence suggests that the FYS is a recurring interest in honors communities as well. However, the honors community lacks a comprehensive analytical framework that might provide an informed approach to the honors FYS. Important topics related to honors FYSs include how prevalent they are on campuses across the U.S.; what distinguishes them from other FYS offerings on campus; what kinds of resources they share with broader-campus programs; what curricular structures and learning outcomes characterize them; and what types of considerations motivate the creation of distinct seminars for first-year honors students. The overview of the honors FYS that follows, based on a national survey of honors programs and colleges conducted in 2014, addresses these topics

Abstract 558: Computational analysis of clinically actionable genomic features: precision heuristics for interpreting the alteration landscape (PHIAL)

Abstract Background: PHIAL (Precision Heuristics for Interpreting the Alteration Landscape) was developed as a heuristic clinical interpretation algorithm for cancer genomic data to inform treatment decisions at the point of care and provide researchers with rapid assessment of tumor actionability. This approach used somatic whole exome sequencing data and a database of tumor alterations relevant for genomics driven therapy (TARGET). However, PHIAL was limited to first order genomic relationships, could not distinguish relative actionability given multiple actionable variants, did not maximize the richness of somatic-germline interactions, and could not leverage both exome and transcriptome data to move towards feature-based actionability. Towards that end, we developed a new interpretation methodology to address these areas and improve clinical actionability algorithms. Methods: We revised PHIAL to predict actionable alterations based on the presence of SNVs (in the context of allele specific expression from RNA-seq), indels, SCNAs, fusions, and global features (e.g., context-specific mutational burden) that imply actionability. Additionally, we refined and expanded the TARGET database to enable PHIAL to produce scores on multiple dimensions and reflect newly discovered relationships between genomics and clinical actions. Predictive implication values were assigned to reflect the validities of TARGET’s drug sensitivity, drug resistance, and prognostic claims. Results: We applied both the original (PHIAL1) and an updated version of PHIAL (PHIAL2) to a 255 patient cohort with whole exome/transcriptome sequencing data (146 castration-resistant prostate cancer and 109 metastatic melanoma samples). PHIAL1 identified 1,342 clinically actionable/biologically relevant events across the cohort with a median of 3 events per patient and 95% of patients having at least one event. PHIAL2 identified 2,508 events, with a median of 6 events per patient and 98.5% of patients harboring at least one event. Of these events, 8.12% were associated with an FDA-approved therapy and 2.09% with a clinical trial. PHIAL2 identified events in 9 patient samples that PHIAL1 associated with no events. Conclusion: PHIAL2 was able to identify and rank more putatively actionable alterations than PHIAL1, and effectively transitioned from a variant-based to a feature-based approach. This strategy may inform the utility of point-of-care whole-exome/transcriptome sequencing in larger contexts as these data emerge in clinical settings, and may bridge towards machine learning based approaches as patient outcomes are linked to genomic and transcriptomic features. Finally, PHIAL2 may ultimately provide a deeper understanding of, and suggest clinical actions for, cases in which there is no clear single genomic alteration associated with oncogenesis. Citation Format: Brendan Reardon, Nathanael Moore, Eliezer VanAllen. Computational analysis of clinically actionable genomic features: precision heuristics for interpreting the alteration landscape (PHIAL) [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 558. doi:10.1158/1538-7445.AM2017-558</jats:p

Expanding clinical actionability in individual patient profiles with the Molecular Oncology Almanac.

3015 Background: The clinical care of oncology patients is routinely informed by tumor and inherited genetic profiles. This is accomplished by molecular pathologists synthesizing the growing body of clinical guidelines and scientific evidence that associates cancer genome alterations and therapeutic response, and applying that knowledge during case reviews. Many academic medical centers formalize this process in the form of molecular tumor boards. As the number of cases for review and literature continue to increase, there is opportunity to leverage clinical interpretation algorithms to computationally prioritize molecular features and both enhance and automate the sample contextualization process. Here, we present the Molecular Oncology Almanac (MOAlmanac) to enable the rapid assessment of tumor actionability. Methods: Molecular Oncology Almanac is an open source clinical interpretation algorithm and paired knowledge base for precision cancer medicine. It is used to rapidly characterize and identify genomic features related to therapeutic sensitivity and resistance and of prognostic relevance. This is performed by assessing not only individual genomic features (e.g. somatic variants, copy number alterations, germline variants, and fusions) but also interactions between these events as well as secondary features such as mutational burden, mutational signatures, MSI status, and aneuploidy. MOAlmanac summarizes all clinically relevant findings into a web-based actionability report. The underlying knowledge base can be accessed through our API endpoints and web browser, and entries may be recommended through either Github or our browser extension. In addition, we developed a cloud-based web portal on top of the Terra framework to increase accessibility. Results: A total of 32,108 samples from 30,607 patients across 66 cancer types received targeted sequencing to characterize somatic variants, copy number alterations, and fusions from PROFILE’s Oncopanel and were evaluated with MOAlmanac. Based on Oncopanel’s tier 1 and tier 2 criteria for clinical actionability, we observed that 8,285 samples (26%, 0 - 69% by cancer type) of patients harbored at least one alteration suggesting therapeutic sensitivity based on FDA approvals or clinical guidelines. Actionability increases to 18,117 samples (56%, 0 - 85% by cancer type) when considering an expanded set of evidence to include relationships captured from clinical trials, clinical, preclinical, and inferential evidence; consequently providing at least one therapeutic hypothesis to otherwise variant-negative patients. Conclusions: Clinical actionability of molecular tumor data was increased in individual patients by expanding the set of evidence considered. Source code and a web portal for this project are available at moalmanac.org . </jats:p