With more than 40 peer-reviewed scientific publications, findings from the POG program are influencing precision oncology approaches around the world.
Purpose: Identification of clinically actionable molecular subtypes of pancreatic ductal adenocarcinoma (PDAC) is key to improving patient outcome. Intertumoral metabolic heterogeneity contributes to cancer survival and the balance between distinct metabolic pathways may influence PDAC outcome. We hypothesized that PDAC can be stratified into prognostic metabolic subgroups based on alterations in the expression of genes involved in glycolysis and cholesterol synthesis.
Experimental design: We performed bioinformatics analysis of genomic, transcriptomic, and clinical data in an integrated cohort of 325 resectable and nonresectable PDAC. The resectable datasets included retrospective The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC) cohorts. The nonresectable PDAC cohort studies included prospective COMPASS, PanGen, and BC Cancer Personalized OncoGenomics program (POG).
Results: On the basis of the median normalized expression of glycolytic and cholesterogenic genes, four subgroups were identified: quiescent, glycolytic, cholesterogenic, and mixed. Glycolytic tumors were associated with the shortest median survival in resectable (log-rank test P = 0.018) and metastatic settings (log-rank test P = 0.027). Patients with cholesterogenic tumors had the longest median survival. KRAS and MYC-amplified tumors had higher expression of glycolytic genes than tumors with normal or lost copies of the oncogenes (Wilcoxon rank sum test P = 0.015). Glycolytic tumors had the lowest expression of mitochondrial pyruvate carriers MPC1 and MPC2. Glycolytic and cholesterogenic gene expression correlated with the expression of prognostic PDAC subtype classifier genes.
Conclusions: Metabolic classification specific to glycolytic and cholesterogenic pathways provides novel biological insight into previously established PDAC subtypes and may help develop personalized therapies targeting unique tumor metabolic profiles.
Purpose: Gene fusions involving neuregulin 1 (NRG1) have been noted in multiple cancer types and have potential therapeutic implications. Although varying results have been reported in other cancer types, the efficacy of the HER-family kinase inhibitor afatinib in the treatment of NRG1 fusion-positive pancreatic ductal adenocarcinoma is not fully understood.
Experimental design: Forty-seven patients with pancreatic ductal adenocarcinoma received comprehensive whole-genome and transcriptome sequencing and analysis. Two patients with gene fusions involving NRG1 received afatinib treatment, with response measured by pretreatment and posttreatment PET/CT imaging.
Results: Three of 47 (6%) patients with advanced pancreatic ductal adenocarcinoma were identified as KRAS wild type by whole-genome sequencing. All KRAS wild-type tumors were positive for gene fusions involving the ERBB3 ligand NRG1. Two of 3 patients with NRG1 fusion-positive tumors were treated with afatinib and demonstrated a significant and rapid response while on therapy.
Conclusions: This work adds to a growing body of evidence that NRG1 gene fusions are recurrent, therapeutically actionable genomic events in pancreatic cancers. Based on the clinical outcomes described here, patients with KRAS wild-type tumors harboring NRG1 gene fusions may benefit from treatment with afatinib.
Effective management of brain and spine tumors relies on a multidisciplinary approach encompassing surgery, radiation, and systemic therapy. In the era of personalized oncology, the latter is complemented by various molecularly targeting agents. Precise identification of cellular targets for these drugs requires comprehensive profiling of the cancer genome coupled with an efficient analytic pipeline, leading to an informed decision on drug selection, prognosis, and confirmation of the original pathological diagnosis. Acquisition of optimal tumor tissue for such analysis is paramount and often presents logistical challenges in neurosurgery. Here, we describe the experience and results of the Personalized OncoGenomics (POG) program with a focus on tumors of the central nervous system (CNS). Patients with recurrent CNS tumors were consented and enrolled into the POG program prior to accrual of tumor and matched blood followed by whole-genome and transcriptome sequencing and processing through the POG bioinformatic pipeline. Sixteen patients were enrolled into POG. In each case, POG analyses identified genomic drivers including novel oncogenic fusions, aberrant pathways, and putative therapeutic targets. POG has highlighted that personalized oncology is truly a multidisciplinary field, one in which neurosurgeons must play a vital role if these programs are to succeed and benefit our patients.
This study investigated therapeutic potential of integrated genome and transcriptome profiling of metastatic sarcoma, a rare but extremely heterogeneous group of aggressive mesenchymal malignancies with few systemic therapeutic options.
Forty-three adult patients with advanced or metastatic non-GI stromal tumor sarcomas of various histology subtypes who were enrolled in the Personalized OncoGenomics program at BC Cancer were included in this study. Fresh tumor tissues along with blood samples underwent whole-genome and transcriptome sequencing.
The most frequent genomic alterations in this cohort are large-scale structural variation and somatic copy number variation. Outlier RNA expression as well as somatic copy number variations, structural variations, and small mutations together suggest the presence of one or more potential therapeutic targets in the majority of patients in our cohort. Point mutations or deletions in known targetable cancer genes are rare; for example, tuberous sclerosis complex 2 provides a rationale for targeting the mammalian target of rapamycin pathway, resulting in a few patients with exceptional clinical benefit from everolimus. In addition, we observed recurrent 17p11-12 amplifications, which seem to be a sarcoma-specific event. This may suggest that this region harbors an oncogene(s) that is significant for sarcoma tumorigenesis. Furthermore, some sarcoma tumors carrying a distinct mutational signature suggestive of homologous recombination deficiency seem to demonstrate sensitivity to double-strand DNA–damaging agents.
Integrated large-scale genomic analysis may provide insights into potential therapeutic targets as well as novel biologic features of metastatic sarcomas that could fuel future experimental and clinical research and help design biomarker-driven basket clinical trials for novel therapeutic strategies.
Pancreatic neuroendocrine neoplasms (PanNENs) represent a minority of pancreatic neoplasms that exhibit variability in prognosis. Ongoing mutational analyses of PanNENs have found recurrent abnormalities in chromatin remodeling genes (e.g., DAXX and ATRX), and mTOR pathway genes (e.g., TSC2, PTEN PIK3CA, and MEN1), some of which have relevance to patients with related familial syndromes. Most recently, grade 3 PanNENs have been divided into two groups based on differentiation, creating a new group of well-differentiated grade 3 neuroendocrine tumors (PanNETs) that have had a limited whole-genome level characterization to date. In a patient with a metastatic well-differentiated grade 3 PanNET, our study utilized whole-genome sequencing of liver metastases for the comparative analysis and detection of single-nucleotide variants, insertions and deletions, structural variants, and copy-number variants, with their biologic relevance confirmed by RNA sequencing. We found that this tumor most notably exhibited a TSC1-disrupting fusion, showed a novel CHD7-BEND2 fusion, and lacked any somatic variants in ATRX, DAXX, and MEN1.
Importance: A molecular diagnostic method that incorporates information about the transcriptional status of all genes across multiple tissue types can strengthen confidence in cancer diagnosis.
Objective: To determine the practical use of a whole transcriptome-based pan-cancer method in diagnosing primary and metastatic cancers and resolving complex diagnoses.
Design, setting, and participants: This cross-sectional diagnostic study assessed Supervised Cancer Origin Prediction Using Expression (SCOPE), a machine learning method using whole-transcriptome RNA sequencing data. Training was performed on publicly available primary cancer data sets, including The Cancer Genome Atlas. Testing was performed retrospectively on untreated primary cancers and treated metastases from volunteer adult patients at BC Cancer in Vancouver, British Columbia, from January 1, 2013, to March 31, 2016, and testing spanned 10 822 samples and 66 output classes representing untreated primary cancers (n = 40) and adjacent normal tissues (n = 26). SCOPE's performance was demonstrated on 211 untreated primary mesothelioma cancers and 201 treatment-resistant metastatic cancers. Finally, SCOPE was used to identify the putative site of origin in 15 cases with initial presentation as cancers with unknown primary of origin.
Results: A total of 10 688 adult patient samples representing 40 untreated primary tumor types and 26 adjacent-normal tissues were used for training. Demographic data were not available for all data sets. Among the training data set, 5157 of 10 244 (50.3%) were male and the mean (SD) age was 58.9 (14.5) years. Testing was performed on 211 patients with untreated primary mesothelioma (173 [82.0%] male; mean [SD] age, 64.5 [11.3] years); 201 patients with treatment-resistant cancers (141 [70.1%] female; mean [SD] age, 55.6 [12.9] years); and 15 patients with cancers of unknown primary of origin; among the treatment-resistant cancers, 168 were metastatic, and 33 were the primary presentation. An accuracy rate of 99% was obtained for primary epithelioid mesotheliomas tested (125 of 126). The remaining 85 mesotheliomas had a mixed etiology (sarcomatoid mesotheliomas) and were correctly identified as a mixture of their primary components, with potential implications in resolving subtypes and incidences of mixed histology. SCOPE achieved an overall mean (SD) accuracy rate of 86% (11%) and F1 score of 0.79 (0.12) on the 201 treatment-resistant cancers and matched 12 of 15 of the putative diagnoses for cancers with indeterminate diagnosis from conventional pathology.
Conclusions and relevance: These results suggest that machine learning approaches incorporating multiple tumor profiles can more accurately identify the cancerous state and discriminate it from normal cells. SCOPE uses the whole transcriptomes from normal and tumor tissues, and results of this study suggest that it performs well for rare cancer types, primary cancers, treatment-resistant metastatic cancers, and cancers of unknown primary of origin. Genes most relevant in SCOPE's decision making were examined, and several are known biological markers of respective cancers. SCOPE may be applied as an orthogonal diagnostic method in cases where the site of origin of a cancer is unknown, or when standard pathology assessment is inconclusive.
We report a case of early-onset pancreatic ductal adenocarcinoma in a patient harboring biallelic MUTYH germline mutations, whose tumor featured somatic mutational signatures consistent with defective MUTYH-mediated base excision repair and the associated driver KRAS transversion mutation p.Gly12Cys. Analysis of an additional 730 advanced cancer cases (N = 731) was undertaken to determine whether the mutational signatures were also present in tumors from germline MUTYH heterozygote carriers or if instead the signatures were only seen in those with biallelic loss of function. We identified two patients with breast cancer each carrying a pathogenic germline MUTYH variant with a somatic MUTYH copy loss leading to the germline variant being homozygous in the tumor and demonstrating the same somatic signatures. Our results suggest that monoallelic inactivation of MUTYH is not sufficient for C:G>A:T transversion signatures previously linked to MUTYH deficiency to arise (N = 9), but that biallelic complete loss of MUTYH function can cause such signatures to arise even in tumors not classically seen in MUTYH-associated polyposis (N = 3). Although defective MUTYH is not the only determinant of these signatures, MUTYH germline variants may be present in a subset of patients with tumors demonstrating elevated somatic signatures possibly suggestive of MUTYH deficiency (e.g., COSMIC Signature 18, SigProfiler SBS18/SBS36, SignatureAnalyzer SBS18/SBS36).
The Personalized Onco-Genomics (POG) program at BC Cancer integrates whole-genome (DNA) and RNA sequencing into practice for metastatic malignancies. We examined the subgroup of patients with metastatic non-small-cell lung cancer (NSCLC) and report the prevalence of actionable targets, treatments, and outcomes. We identified patients who were enrolled in the POG program between 2012 and 2016 who had a tumor biopsy and blood samples with comprehensive DNA (80×, 40× normal) and RNA sequencing followed by in-depth bioinformatics to identify potential cancer drivers and actionable targets. In NSCLC cases, we compared the progression-free survival (PFS) of "POG-informed therapies" with the PFS of the last regimen prior to POG (PFS ratio). In 29 NSCLC cases, 11 were male (38%), the median age was 60.2 yr (range: 39.4-72.6), and histologies included were adenocarcinoma (93%) and squamous cell carcinoma (7%). Potential molecular targets (i.e., cancer drivers including TP53 mutations) were identified in 26 (90%), and 21 (72%) had actionable targets. Therapies based on standard-of-care mutation analysis, such as EGFR mutations, were not considered POG-informed therapies. Thirteen received POG-informed therapies, of which three had no therapy before POG; therefore a comparator PFS could not be obtained. Of 10 patients with POG-informed therapy, median PFS ratio was 0.94 (IQR 0.2-3.4). Three (30%) had a PFS ratio ≥1.3, and three (30%) had a PFS ratio ≥0.8 and <1.3. In this small cohort of NSCLC, 30% demonstrated longer PFS with POG-informed therapies. Larger studies will help clarify the role of whole-genome analysis in clinical practice.
Thyroid-like follicular renal cell carcinoma (TLFRCC) is a rare cancer with few reports of metastatic disease. Little is known regarding genomic characteristics and therapeutic targets. We present the clinical, pathologic, genomic, and transcriptomic analyses of a case of a 27-yr-old male with TLFRCC who presented initially with bone metastases of unknown primary. Genomic DNA from peripheral blood and metastatic tumor samples were sequenced. A transcriptome of 280 million sequence reads was generated from the same tumor sample. Tumor somatic expression profiles were analyzed to detect aberrant expression. Genomic and transcriptomic data sets were integrated to reveal dysregulation in pathways and identify potential therapeutic targets. Integrative genomic analysis with The Cancer Genome Atlas (TCGA) data set revealed the following outliers in gene expression profiles: CDK6 (81st percentile), MYC (99th percentile), AR (100th percentile), PDGFRA and PDGFRB (99th and 100th percentiles, respectively), and MAP2K2 (86th percentile). The patient received first-line sunitinib to target PDGFRA and PDGFRB and had stable disease for >6 mo, followed by nivolumab upon progression. To the authors’ knowledge, this is the first reported case of comprehensive somatic genomic analyses in a patient with metastatic TLFRCC. Somatic analyses provided molecular confirmation of the primary site of cancer and potential therapeutic strategies in a rare disease with little evidence of efficacy on systemic therapy.
Homologous recombination (HR) facilitates error-free repair of double-strand DNA breaks and interstrand crosslinks.1 Mutations in BRCA1, BRCA2, and other genes responsible for HR are prevalent among human cancers and cause HR deficiency (HRD) and genomic instability.2 Recent evidence has shown that BRCA1 and BRCA2 mutations are associated with improved outcomes on platinum-based chemotherapy in pancreatic cancer,3-5 which mirrors more-established findings from breast cancer.6
Whole-genome sequencing (WGS) efforts have identified mutational and structural rearrangement signatures linked to BRCA1 and BRCA2 mutations in breast and other cancers,7 which may predict response to platinum-based chemotherapy8 and poly (ADP-ribose) polymerase inhibitors.9 However, the role signature timing plays in treatment response has not been elucidated but could help to distinguish currently active, actionable mutational processes from historically active ones.
We present the first clinical application of HRD dynamics across spatially and temporally distinct biopsy specimens of a pancreatic ductal adenocarcinoma (PDAC). This approach helped to reconcile the following paradoxical findings: genomic stability and low HRD mutation signature despite a germline BRCA1 mutation and exceptional response to fluorouracil, oxaliplatin, leucovorin, and irinotecan (FOLFIRINOX). The findings highlight the potential value of considering timing in the clinical interpretation of mutation signatures.