Peer-reviewed publications

Background: This study is the first survey of the genomic literacy of medical oncologists as co-investigators on a trial using medical genomic “big data”. The Personalized Onco-Genomics Program (POG) conducts whole genome DNA and RNA sequencing and in-depth bioinformatic analyses on patients with metastatic cancers to identify somatic variants and gene expression changes that may be targetable cancer “drivers”. Aberrant pathways are matched to drug databases and this data is reported to the clinician for each individual patient.

Methods: We conducted a survey of medical oncologists based at the six tertiary care cancer hospitals of the BC Cancer Agency (n = 31, 52.5% response rate) who enroll patients into POG. We measured oncologists’ level of genomic knowledge and their experience and attitudes about genomic science and technologies.

Results: We found a low to moderate level of genomic literacy amongst the oncologists as 48% reported having little knowledge about newer genetic/genomic technologies. Clinicians outside of the Vancouver area (the major urban centre) reported having less knowledge about new genetics technologies compared to those located in the Vancouver area (26.7% vs 73.3%, P < 0.07, Fisher exact test). 42% of all clinicians think medical education programs do not offer enough genomics training. The majority of the respondents envision that in the next 5-years genomic technologies will have a major impact on drug discovery (67.7%) and on assisting in treatment selection (58%). The three top concerning issues pertaining to the application of genomics science and technologies into clinical practices were: cost (61.3%), patients’ genomic literacy (48.3%), and clinical utility of genomic data (42%).

Conclusions: The data suggests a high need to increase genomic literacy amongst oncologists beginning in medical school and with ongoing educational tools. Although these oncologists had variable experiences with POG directly informing treatment decisions; there was overall agreement that genomics and big data will play an increasingly important role in cancer care decision-making

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Inhibitors of the programmed cell death 1 (PD-1) signaling axis have recently demonstrated efficacy and are rapidly being incorporated into the treatment of non-small cell lung cancers (NSCLCs). Despite clear benefits to certain patients, the association of these responses with a predictive biomarker remains uncertain. Several different biomarkers have been proposed, with differing results and conclusions. This study compares multiple methods of biomarker testing for treatment of NSCLCs with PD1-axis inhibitors. Tissue microarrays of matched primary and metastatic NSCLCs were used to compare four different PD-1 ligand (PD-L1) IHC techniques, as well as RNA ISH. Additional cases with whole genome and transcriptome data were assessed for molecular correlates of PD-L1 overexpression. Eighty cases were included in the IHC study. Multiple IHC methodologies showed a high rate of agreement (Kappa = 0.67). When calibrated to RNA expression, agreement improved significantly (Kappa = 0.90, p=0.0049). PD-L1 status of primary and metastatic tumors was discordant in 17 (22%) cases. This study suggests that different IHC methodologies for PD-L1 assessment provide slightly different results. There is significant discordance between the PD-L1 status of primary tumors and lymph node metastases. RNA ISH may be a useful adjunct to complement PD-L1 IHC testing.

Inflammatory myofibroblastic tumor (IMT) is a genetically heterogenous tumor of the viscera and soft tissues, with multiple molecular features having been demonstrated in this tumor type. About 50% of cases harbor an anaplastic lymphoma kinase (ALK) gene rearrangement, and recent studies have described novel fusions involving the ROS1 and PDGFRβ genes in a subset of ALK-negative cases. However, the molecular features of the remaining subset of cases are not yet defined. We report a case of a large, highly aggressive IMT of the lung in a 17-year-old girl. This case was molecularly characterized through whole-genome and transcriptome sequencing. Subsequently, we investigated a cohort of 15 ALK-negative IMTs of various anatomic sites. All cases were screened using fluorescence in situ hybridization (FISH) for rearrangement of the ETV6 locus and with reverse transcription polymerase chain reaction (RT-PCR) for the ETV6-NTRK3 fusion transcript. Whole-genome and transcriptome sequencing revealed an ETV6-NTRK3 fusion transcript in our index case. This was confirmed by FISH studies for ETV6 gene rearrangement, as well as by RT-PCR. In addition, 2 additional cases in our cohort demonstrated ETV6 rearrangement by FISH. The presence of ETV6-NTRK3 fusion transcript was demonstrated by RT-PCR in one of these additional cases. In summary, we demonstrate the expression of the ETV6-NTRK3 fusion oncogene in a small subset of IMTs, lending further support to the role of oncogenic tyrosine kinases in the pathophysiology of this tumor type. Our data also further expand the growing spectrum of tumor types expressing the ETV6-NTRK3 fusion.

In an attempt to assess potential treatment options, whole-genome and transcriptome sequencing were performed on a patient with an unclassifiable small lymphoproliferative disorder. Variants from genome sequencing were prioritized using a combination of comparative variant distributions in a spectrum of lymphomas, and meta-analyses of gene expression profiling. In this patient, the molecular variants that we believe to be most relevant to the disease presentation most strongly resemble a diffuse large B-cell lymphoma (DLBCL), whereas the gene expression data are most consistent with a low-grade chronic lymphocytic leukemia (CLL). The variant of greatest interest was a predicted NOTCH2-truncating mutation, which has been recently reported in various lymphomas.

Given the success of targeted agents in specific populations it is expected that some degree of molecular biomarker testing will become standard of care for many, if not all, cancers. To facilitate this, cancer centers worldwide are experimenting with targeted “panel” sequencing of selected mutations. Recent advances in genomic technology enable the generation of genome-scale data sets for individual patients. Recognizing the risk, inherent in panel sequencing, of failing to detect meaningful somatic alterations, we sought to establish processes to integrate data from whole-genome analysis (WGA) into routine cancer care. Between June 2012 and August 2014, 100 adult patients with incurable cancers consented to participate in the Personalized OncoGenomics (POG) study. Fresh tumor and blood samples were obtained and used for whole-genome and RNA sequencing. Computational approaches were used to identify candidate driver mutations, genes, and pathways. Diagnostic and drug information were then sought based on these candidate “drivers.” Reports were generated and discussed weekly in a multidisciplinary team setting. Other multidisciplinary working groups were assembled to establish guidelines on the interpretation, communication, and integration of individual genomic findings into patient care. Of 78 patients for whom WGA was possible, results were considered actionable in 55 cases. In 23 of these 55 cases, the patients received treatments motivated by WGA. Our experience indicates that a multidisciplinary team of clinicians and scientists can implement a paradigm in which WGA is integrated into the care of late stage cancer patients to inform systemic therapy decisions.

Peritoneal mesothelioma is a rare and sometimes lethal malignancy that presents a clinical challenge for both diagnosis and management. Recent studies have led to a better understanding of the molecular biology of peritoneal mesothelioma. Translation of the emerging data into better treatments and outcome is needed. From two patients with peritoneal mesothelioma, we derived whole genome sequences, RNA expression profiles, and targeted deep sequencing data. Molecular data were made available for translation into a clinical treatment plan. Treatment responses and outcomes were later examined in the context of molecular findings. Molecular studies presented here provide the first reported whole genome sequences of peritoneal mesothelioma. Mutations in known mesothelioma-related genes NF2, CDKN2A, LATS2, amongst others, were identified. Activation of MET-related signaling pathways was demonstrated in both cases. A hypermutated phenotype was observed in one case (434 vs. 18 single nucleotide variants) and was associated with a favourable outcome despite sarcomatoid histology and multifocal disease. This study represents the first report of whole genome analyses of peritoneal mesothelioma, a key step in the understanding and treatment of this disease.

Extraordinary advancements in sequencing technology have made what was once a decade‐long multi‐institutional endeavor into a methodology with the potential for practical use in a clinical setting. We therefore set out to examine the clinical value of next‐generation sequencing by enrolling patients with incurable or ambiguous tumors into the Personalized OncoGenomics initiative at the British Columbia Cancer Agency whereby whole genome and transcriptome analyses of tumor/normal tissue pairs are completed with the ultimate goal of directing therapeutics. First, we established that the sequencing, analysis, and communication with oncologists could be completed in less than 5 weeks. Second, we found that cancer diagnostics is an area that can greatly benefit from the comprehensiveness of a whole genome analysis. Here, we present a scenario in which a metastasized sphenoid mass, which was initially thought of as an undifferentiated squamous cell carcinoma, was rediagnosed as an SMARCB1 ‐negative rhabdoid tumor based on the newly acquired finding of homozygous SMARCB1 deletion. The new diagnosis led to a change in chemotherapy and a complete nodal response in the patient. This study also provides additional insight into the mutational landscape of an adult SMARCB1 ‐negative tumor that has not been explored at a whole genome and transcriptome level.