With more than 40 peer-reviewed scientific publications, findings from the POG program are influencing precision oncology approaches around the world.
Uveal melanoma is the most common intraocular malignancy and has a poor prognosis compared to other melanoma subtypes with a median overall survival of 6-10 months. With immune checkpoint inhibitor therapy, either PD-1 inhibitor alone or combination ipilimumab/nivolumab (anti-CTLA-4/anti-PD-1), responses are rare and often not durable. We present a case report of a now 66-year-old woman with diffuse metastatic uveal melanoma previously treated with a combination of ipilimumab/nivolumab, followed by maintenance nivolumab. Almost complete resolution of all sites of metastatic disease was observed except for one liver metastasis which regressed partially on immunotherapy. Notably, the patient had a significantly elevated BMI and developed widespread vitiligo on treatment. Whole-genome and transcriptome analysis was performed on the residual liver biopsy and molecular markers that may have contributed to the exceptional response were investigated. Several alterations were observed in genes involved in T-cell responses. Estimates of tumour infiltrating immune cells indicated a high level of plasma cells compared to other uveal melanoma cases, a finding previously associated with indolent disease. The patient also carried several germline SNPs that may have contributed to her treatment response as well as widespread vitiligo. Whole-genome and transcriptome sequencing have provided insight into potential molecular underpinnings of an exceptional treatment response in a tumour type typically associated with poor prognosis. Immunological findings suggest a role for plasma cells in the tumour microenvironment. Elevated BMI and the development of vitiligo may be clinically relevant factors for predicting response to immune checkpoint inhibitor therapy, warranting further studies in patients with uveal melanoma.
Background: Recent advances are enabling delivery of precision genomic medicine to cancer clinics. While the majority of approaches profile panels of selected genes or hotspot regions, comprehensive data provided by whole genome and transcriptome sequencing and analysis (WGTA) presents an opportunity to align a much larger proportion of patients to therapies.
Patients and methods: Samples from 570 patients with advanced or metastatic cancer of diverse types enrolled in the Personalized OncoGenomics (POG) program underwent WGTA. DNA-based data, including mutations, copy number, and mutation signatures, were combined with RNA-based data, including gene expression and fusions, to generate comprehensive WGTA profiles. A multidisciplinary molecular tumour board used WGTA profiles to identify and prioritize clinically actionable alterations and inform therapy. Patient responses to WGTA-informed therapies were collected.
Results: Clinically actionable targets were identified for 83% of patients, 37% of whom received WGTA-informed treatments. RNA expression data were particularly informative, contributing to 67% of WGTA-informed treatments; 25% of treatments were informed by RNA expression alone. Of a total 248 WGTA-informed treatments, 46% resulted in clinical benefit. RNA expression data were comparable to DNA-based mutation and copy number data in aligning to clinically beneficial treatments. Genome signatures also guided therapeutics including platinum, PARP inhibitors, and immunotherapies. Patients accessed WGTA-informed treatments through clinical trials (19%), off-label use (35%), and as standard therapies (46%) including those which would not otherwise have been the next choice of therapy, demonstrating the utility of genomic information to direct use of chemotherapies as well as targeted therapies.
Conclusions: Integrating RNA expression and genome data illuminated treatment options that resulted in 46% of treated patients experiencing positive clinical benefit, supporting the use of comprehensive WGTA profiling in clinical cancer care.
Adrenocortical cancer (ACC) is a rare cancer of the adrenal gland. Several driver mutations have been identified in both primary and metastatic ACCs, but the therapeutic options are still limited. We performed whole-genome and transcriptome sequencing on seven patients with metastatic ACC. Integrative analysis of mutations, RNA expression changes, mutation signature, and homologous recombination deficiency (HRD) analysis was performed. Mutations affecting CTNNB1 and TP53 and frequent loss of heterozygosity (LOH) events were observed in our cohort. Alterations affecting genes involved in cell cycle (RB1, CDKN2A, CDKN2B), DNA repair pathways (MUTYH, BRCA2, ATM, RAD52, MLH1, MSH6), and telomere maintenance (TERF2 and TERT) consisting of somatic and germline mutations, structural variants, and expression outliers were also observed. HRDetect, which aggregates six HRD-associated mutation signatures, identified a subset of cases as HRD. Genomic alterations affecting genes involved in epigenetic regulation were also identified, including structural variants (SWI/SNF genes and histone methyltransferases), and copy gains and concurrent high expression of KDM5A, which may contribute to epigenomic deregulation. Findings from this study highlight HRD and epigenomic pathways as potential therapeutic targets and suggest a subgroup of patients may benefit from a diverse array of molecularly targeted therapies in ACC, a rare disease in urgent need of therapeutic strategies.
Tumor mutation burden (TMB) is a measure to predict patient responsiveness to immune checkpoint immunotherapy since with increased mutation frequency, the likelihood of a greater neoantigen burden is increased. Although neoantigen prediction tools exist, tumor neoantigen burden (TNB) has not been adopted as measures to predict immunotherapy response. With both measures, current guidelines are limited to the coding regions, but ectopic expression of sequences in the noncoding space may potentially be a source of neoantigens. Here we analyzed a pan-cancer cohort of 574 advanced disease stage patients with whole genome and transcriptome sequencing to report mutation burden and neoantigen counts within the coding and noncoding regions. We evaluated the efficacy of TNB, reported as tumor neoantigen counts (TNC), including neoantigens derived from the expression of noncoding regions, compared to TMB as a predictor of response to immunotherapy for 80 patients who had received treatment. We found that TMB was the best predictor of response to immunotherapy, whereas expression derived TNC from the noncoding regions did not improve prediction of response. Therefore, there is minimal benefit in extending the calculation of TNC to the noncoding space for the purposes of predicting response. However, it is likely that there is a wealth of neoantigens derived from the noncoding space that may impact patient outcomes and treatments.
Genomic research is driving discovery for future population beneft. Limited evidence exists on immediate patient and health system impacts of research participation. This study uses real-world data and quasi-experimental matching to examine early-stage cost and health impacts of research-based genomic sequencing. British Columbia’s Personalized OncoGenomics (POG) single-arm program applies whole genome and transcriptome analysis (WGTA) to characterize genomic landscapes in advanced cancers. Our cohort includes POG patients enrolled between 2014 and 2015 and 1:1 genetic algorithm–matched usual care controls. We undertake a cost consequence analysis and estimate 1-year efects of WGTA on patient management, patient survival, and health system costs reported in 2015 Canadian dollars. WGTA costs are imputed and forecast using system of equations modeling. We use Kaplan-Meier survival analysis to explore survival diferences and inverse probability of censoring weighted linear regression to estimate mean 1-year survival times and costs. Non-parametric bootstrapping simulates sampling distributions and enables scenario analysis, revealing drivers of incremental costs, survival, and net monetary beneft for assumed willingness to pay thresholds. We identifed 230 POG patients and 230 matched controls for cohort inclusion. The mean period cost of research-funded WGTA was $26,211 (SD: $14,191). Sequencing costs declined rapidly, with WGTA forecasts hitting $13,741 in 2021. The incremental healthcare system efect (non-research expenditures) was $5203 (95% CI: 75, 10,424) compared to usual care. No overall survival diferences were observed, but outcome heterogeneity was present. POG patients receiving WGTA-informed treatment experienced incremental survival gains of 2.49 months (95% CI: 1.32, 3.64). Future cost consequences became favorable as WGTA cost drivers declined and WGTAinformed treatment rates improved to 60%. Our study demonstrates the ability of real-world data to support evaluations of only-in-research health technologies. We identify situations where precision oncology research initiatives may produce survival beneft at a cost that is within healthcare systems’ willingness to pay. This economic evidence informs the early-stage healthcare impacts of precision oncology research.
Single-arm trials are common in precision oncology. Owing to the lack of randomized counterfactual, resultant data are not amenable to comparative outcomes analyses. Difference-in-difference (DID) methods present an opportunity to generate causal estimates of time-varying treatment outcomes. Using DID, our study estimates within-cohort effects of genomics-informed treatment versus standard care on clinical and cost outcomes.
This is the first report of a NACC2-NTRK2 fusion in a histological glioblastoma. Oncogenomic analysis revealed this actionable fusion oncogene in a pediatric cerebellar glioblastoma, which would not have been identified through routine diagnostics, demonstrating the value of clinical genome profiling in cancer care.
Randomized controlled trials (RCTs) are uncommon in precision oncology. We provide an introduction and illustrative example of matching methods for evaluating precision oncology in the absence of RCTs. We focus on British Columbia's Personalized OncoGenomics (POG) program, which applies whole-genome and transcriptome analysis (WGTA) to inform advanced cancer care.
Purpose: Structural variants (SVs) may be an underestimated cause of hereditary cancer syndromes given the current limitations of short-read next-generation sequencing. Here we investigated the utility of long-read sequencing in resolving germline SVs in cancer susceptibility genes detected through short-read genome sequencing.
Methods: Known or suspected deleterious germline SVs were identified using Illumina genome sequencing across a cohort of 669 advanced cancer patients with paired tumor genome and transcriptome sequencing. Candidate SVs were subsequently assessed by Oxford Nanopore long-read sequencing.
Results: Nanopore sequencing confirmed eight simple pathogenic or likely pathogenic SVs, resolving three additional variants whose impact could not be fully elucidated through short-read sequencing. A recurrent sequencing artifact on chromosome 16p13 and one complex rearrangement on chromosome 5q35 were subsequently classified as likely benign, obviating the need for further clinical assessment. Variant configuration was further resolved in one case with a complex pathogenic rearrangement affecting TSC2.
Conclusion: Our findings demonstrate that long-read sequencing can improve the validation, resolution, and classification of germline SVs. This has important implications for return of results, cascade carrier testing, cancer screening, and prophylactic interventions.
Keywords: genome sequencing; hereditary cancer; long-read sequencing; structural variants; variant interpretation.
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Purpose: Gene fusions are important oncogenic drivers and many are actionable. Whole-genome and transcriptome (WGS and RNA-seq, respectively) sequencing can discover novel clinically relevant fusions.
Experimental design: Using WGS and RNA-seq, we reviewed the prevalence of fusions in a cohort of 570 patients with cancer, and compared prevalence to that predicted with commercially available panels. Fusions were annotated using a consensus variant calling pipeline (MAVIS) and required that a contig of the breakpoint could be constructed and supported from ≥2 structural variant detection approaches.
Results: In 570 patients with advanced cancer, MAVIS identified 81 recurrent fusions by WGS and 111 by RNA-seq, of which 18 fusions by WGS and 19 by RNA-seq were noted in at least 3 separate patients. The most common fusions were EML4-ALK in thoracic malignancies (9/69, 13%), and CMTM8-CMTM7 in colorectal cancer (4/73, 5.5%). Combined genomic and transcriptomic analysis identified novel fusion partners for clinically relevant genes, such as NTRK2 (novel partners: SHC3, DAPK1), and NTRK3 (novel partners: POLG, PIBF1).
Conclusions: Utilizing WGS/RNA-seq facilitates identification of novel fusions in clinically relevant genes, and detected a greater proportion than commercially available panels are expected to find. A significant benefit of WGS and RNA-seq is the innate ability to retrospectively identify variants that becomes clinically relevant over time, without the need for additional testing, which is not possible with panel-based approaches.
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Manual interpretation of variants remains rate limiting in precision oncology. The increasing scale and complexity of molecular data generated from comprehensive sequencing of cancer samples requires advanced interpretative platforms as precision oncology expands beyond individual patients to entire populations. To address this unmet need, we introduce a Platform for Oncogenomic Reporting and Interpretation (PORI), comprising an analytic framework that facilitates the interpretation and reporting of somatic variants in cancer. PORI integrates reporting and graph knowledge base tools combined with support for manual curation at the reporting stage. PORI represents an open-source platform alternative to commercial reporting solutions suitable for comprehensive genomic data sets in precision oncology. We demonstrate the utility of PORI by matching 9,961 pan-cancer genome atlas tumours to the graph knowledge base, calculating therapeutically informative alterations, and making available reports describing select individual samples.
The global impact of somatic structural variants (SVs) on gene regulation in advanced tumors with complex treatment histories has been mostly uncharacterized. Here, using whole-genome and RNA sequencing from 570 recurrent or metastatic tumors, we report the altered expression of hundreds of genes in association with nearby SV breakpoints, including oncogenes and G-protein-coupled receptor-related genes such as PLEKHG2. A significant fraction of genes with SV-expression associations correlate with worse patient survival in primary and advanced cancers, including SRD5A1. In many instances, SV-expression associations involve retrotransposons being translocated near genes. High overall SV burden is associated with treatment with DNA alkylating agents or taxanes and altered expression of metabolism-associated genes. SV-expression associations within tumors from topoisomerase I inhibitor-treated patients include chromatin-related genes. Within anthracycline-treated tumors, SV breakpoints near chromosome 1p genes include PDE4B. Patient treatment and history can help understand the widespread SV-mediated cis-regulatory alterations found in cancer.
RNA sequencing (RNAseq) has been widely used to generate bulk gene expression measurements collected from pools of cells. Only relatively recently have single-cell RNAseq (scRNAseq) methods provided opportunities for gene expression analyses at the single-cell level, allowing researchers to study heterogeneous mixtures of cells at unprecedented resolution. Tumors tend to be composed of heterogeneous cellular mixtures and are frequently the subjects of such analyses. Extensive method developments have led to several protocols for scRNAseq but, owing to the small amounts of RNA in single cells, technical constraints have required compromises. For example, the majority of scRNAseq methods are limited to sequencing only the 3' or 5' termini of transcripts. Other protocols that facilitate full-length transcript profiling tend to capture only polyadenylated mRNAs and are generally limited to processing only 96 cells at a time. Here, we address these limitations and present a novel protocol that allows for the high-throughput sequencing of full-length, total RNA at single-cell resolution. We demonstrate that our method produced strand-specific sequencing data for both polyadenylated and non-polyadenylated transcripts, enabled the profiling of transcript regions beyond only transcript termini, and yielded data rich enough to allow identification of cell types from heterogeneous biological samples.
Background: RNA-sequencing-based classifiers can stratify pancreatic ductal adenocarcinoma (PDAC) into prognostically significant subgroups but are not practical for use in clinical workflows. Here, we assess whether histomorphological features may be used as surrogate markers for predicting molecular subgroup and overall survival in PDAC.
Methods: Ninety-six tissue samples from 50 patients with non-resectable PDAC were scored for gland formation, stromal maturity, mucin, necrosis, and neutrophil infiltration. Prognostic PDAC gene expression classifiers were run on all tumors using whole transcriptome sequencing data from the POG trial (NCT02155621). Findings were validated using digital TCGA slides (n = 50). Survival analysis used multivariate Cox proportional-hazards tests and log-rank tests.
Results: The combination of low gland formation and low neutrophil infiltration was significantly associated with the poor prognosis PDAC molecular subgroup (basal-like or squamous) and was an independent predictor of shorter overall survival, in both frozen section (n = 47) and formalin-fixed paraffin-embedded (n = 49) tissue samples from POG patients, and in the TCGA samples. This finding held true in the subgroup analysis of primary (n = 17) and metastatic samples (n = 79). The combination of high gland formation and high neutrophils had low sensitivity but high specificity for favorable prognosis subgroups.
Conclusions: The assessment of gland formation and neutrophil infiltration on routine histological sections can aid in prognostication and allow inferences to be made about molecular subtype, which may help guide patient management decisions and contribute to our understanding of heterogeneity in treatment response.
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The practical application of genome-scale technologies to precision oncology research requires flexible tissue processing strategies that can be used to differentially select both tumour and normal cell populations from formalin-fixed paraffin-embedded tissues. As tumour sequencing scales towards clinical implementation, practical difficulties in scheduling and obtaining fresh tissue biopsies at scale, including blood samples as surrogates for matched "normal" DNA, have focused attention on the use of formalin-preserved clinical samples collected routinely for diagnostic purposes. In practice, such samples often contain both tumour and normal cells which, if correctly partitioned, could be used to profile both tumour and normal genomes, thus identifying somatic alterations. Here we report a semi-automated method for laser microdissecting entire slide-mounted tissue sections to enrich for cells of interest with sufficient yield for whole genome and transcriptome sequencing. Using this method, we demonstrated enrichment of tumour material from mixed tumour-normal samples by up to 67%. Leveraging new methods that allow for the extraction of high-quality nucleic acids from small amounts of formalin-fixed tissues, we further showed that the method was successful in yielding sequence data of sufficient quality for use in BC Cancer's Personalized OncoGenomics (POG) program.
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Background RNA-sequencing-based subtyping of pancreatic ductal adenocarcinoma (PDAC) has been reported by multiple research groups, each using different methodologies and patient cohorts. 'Classical' and 'basal-like' PDAC subtypes are associated with survival differences, with basal-like tumors associated with worse prognosis. We amalgamated various PDAC subtyping tools to evaluate the potential of such tools to be reliable in clinical practice. Methods Sequencing data for 574 PDAC tumors was obtained from prospective trials and retrospective public databases. Six published PDAC subtyping strategies (Moffitt regression tools, clustering-based Moffitt, Collisson, Bailey, and Karasinska subtypes) were employed on each sample, and results were tested for subtype call consistency and association with survival. Results Basal-like and classical subtype calls were concordant in 88% of patient samples, and survival outcomes were significantly different (p<0.05) between prognostic subtypes. 12% of tumors had subtype-discordant calls across the different methods, showing intermediate survival in univariate and multivariate survival analyses. Transcriptional profiles compatible with that of a hybrid subtype signature were observed for subtype-discordant tumors, in which classical and basal-like genes were concomitantly expressed. Subtype-discordant tumors showed intermediate molecular characteristics, including subtyping gene expression (p<0.0001) and mutant KRAS allelic imbalance (p<0.001). Conclusions Nearly one in six patients with PDAC have tumors that fail to reliably fall into the classical or basal-like PDAC subtype categories, based on two regression tools aimed towards clinical practice. Rather, these patient tumors show intermediate prognostic and molecular traits. We propose close consideration of the non-binary nature of PDAC subtypes for future incorporation of subtyping into clinical practice.
Purpose: With the rising incidence of early-onset pancreatic cancer (EOPC), molecular characteristics that distinguish early-onset pancreatic ductal adenocarcinoma (PDAC) tumors from those arising at a later age are not well understood.
Experimental design: We performed bioinformatic analysis of genomic and transcriptomic data generated from 269 advanced (metastatic or locally advanced) and 277 resectable PDAC tumor samples. Patient samples were stratified into EOPC (age of onset ≤55 years; n = 117), intermediate (age of onset 55-70 years; n = 264), and average (age of onset ≥70 years; n = 165) groups. Frequency of somatic mutations affecting genes commonly implicated in PDAC, as well as gene expression patterns, were compared between EOPC and all other groups.
Results: EOPC tumors showed significantly lower frequency of somatic single-nucleotide variant (SNV)/insertions/deletions (indel) in CDKN2A (P = 0.0017), and were more likely to achieve biallelic mutation of CDKN2A through homozygous copy loss as opposed to heterozygous copy loss coupled with a loss-of-function SNV/indel mutation, the latter of which was more common for tumors with later ages of onset (P = 1.5e-4). Transcription factor forkhead box protein C2 (FOXC2) was significantly upregulated in EOPC tumors (P = 0.032). Genes significantly correlated with FOXC2 in PDAC samples were enriched for gene sets related to epithelial-to-mesenchymal transition (EMT) and included VIM (P = 1.8e-8), CDH11 (P = 6.5e-5), and CDH2 (P = 2.4e-2).
Conclusions: Our comprehensive analysis of sequencing data generated from a large cohort of PDAC patient samples highlights a distinctive pattern of biallelic CDKN2A mutation in EOPC tumors. Increased expression of FOXC2 in EOPC, with the correlation between FOXC2 and EMT pathways, represents novel molecular characteristics of EOPC.
Next-generation sequencing of solid tumors has revealed variable signatures of immunogenicity across tumors, but underlying molecular characteristics driving such variation are not fully understood. While expression of endogenous retrovirus (ERV)-containing transcripts can provide a source of tumor-specific neoantigen in some cancer models, associations between ERV levels and immunogenicity across different types of metastatic cancer are not well established. We performed bioinformatics analysis of genomic, transcriptomic and clinical data across an integrated cohort of 199 metastatic breast, colorectal and pancreatic ductal adenocarcinoma (PDAC) patient tumors. Within each cancer type, we identified a subgroup of viral mimicry tumors in which increased ERV levels were coupled with transcriptional signatures of autonomous antiviral response and immunogenicity. In addition, viral mimicry colorectal and pancreatic tumors showed increased expression of DNA demethylation gene TET2. Taken together, these data demonstrate the existence of an ERV-associated viral mimicry phenotype across three distinct metastatic cancer types, while indicating links between ERV abundance, epigenetic dysregulation and immunogenicity.
Importance: Pediatric cancers are epigenetic diseases; therefore, considering tumor gene expression information is necessary for a complete understanding of the tumorigenic processes.
Objective: To evaluate the feasibility and utility of incorporating comparative gene expression information into the precision medicine framework for difficult-to-treat pediatric and young adult patients with cancer.
Design, setting, and participants: This cohort study was conducted as a consortium between the University of California, Santa Cruz (UCSC) Treehouse Childhood Cancer Initiative and clinical genomic trials. RNA sequencing (RNA-Seq) data were obtained from the following 4 clinical sites and analyzed at UCSC: British Columbia Children's Hospital (n = 31), Lucile Packard Children's Hospital at Stanford University (n = 80), CHOC Children's Hospital and Hyundai Cancer Institute (n = 46), and the Pacific Pediatric Neuro-Oncology Consortium (n = 24). The study dates were January 1, 2016, to March 22, 2017.
Exposures: Participants underwent tumor RNA-Seq profiling as part of 4 separate clinical trials at partner hospitals. The UCSC either downloaded RNA-Seq data from a partner institution for analysis in the cloud or provided a Docker pipeline that performed the same analysis at a partner institution. The UCSC then compared each participant's tumor RNA-Seq profile with more than 11 000 uniformly analyzed tumor profiles from pediatric and young adult patients with cancer, downloaded from public data repositories. These comparisons were used to identify genes and pathways that are significantly overexpressed in each patient's tumor. Results of the UCSC analysis were presented to clinical partners.
Main outcomes and measures: Feasibility of a third-party institution (UCSC Treehouse Childhood Cancer Initiative) to obtain tumor RNA-Seq data from patients, conduct comparative analysis, and present analysis results to clinicians; and proportion of patients for whom comparative tumor gene expression analysis provided useful clinical and biological information.
Results: Among 144 samples from children and young adults (median age at diagnosis, 9 years; range, 0-26 years; 72 of 118 [61.0%] male [26 patients sex unknown]) with a relapsed, refractory, or rare cancer treated on precision medicine protocols, RNA-Seq-derived gene expression was potentially useful for 99 of 144 samples (68.8%) compared with DNA mutation information that was potentially useful for only 34 of 74 samples (45.9%).
Conclusions and relevance: This study's findings suggest that tumor RNA-Seq comparisons may be feasible and highlight the potential clinical utility of incorporating such comparisons into the clinical genomic interpretation framework for difficult-to-treat pediatric and young adult patients with cancer. The study also highlights for the first time to date the potential clinical utility of harmonized publicly available genomic data sets.
The analysis of cell-free circulating tumor DNA (ctDNA) is potentially a less invasive, more dynamic assessment of cancer progression and treatment response than characterizing solid tumor biopsies. Standard isolation methods require separation of plasma by centrifugation, a time-consuming step that complicates automation. To address these limitations, we present an automatable magnetic bead-based ctDNA isolation method that eliminates centrifugation to purify ctDNA directly from peripheral blood (PB). To develop and test our method, ctDNA from cancer patients was purified from PB and plasma. We found that allelic fractions of somatic single-nucleotide variants from target gene capture libraries were comparable, indicating that the PB ctDNA purification method may be a suitable replacement for the plasma-based protocols currently in use.