Publications

Survival analysis is a technique for identifying prognostic biomarkers and genetic vulnerabilities in cancer studies. Large-scale consortium-based projects have profiled >11 000 adult and >4000 pediatric tumor cases with clinical outcomes and multiomics approaches. This provides a resource for investigating molecular-level cancer etiologies using clinical correlations. Although cancers often arise from multiple genetic vulnerabilities and have deregulated gene sets (GSs), existing survival analysis protocols can report only on individual genes. Additionally, there is no systematic method to connect clinical outcomes with experimental (cell line) data. To address these gaps, we developed cSurvival (https://tau.cmmt.ubc.ca/cSurvival). cSurvival provides a user-adjustable analytical pipeline with a curated, integrated database and offers three main advances: (i) joint analysis with two genomic predictors to identify interacting biomarkers, including new algorithms to identify optimal cutoffs for two continuous predictors; (ii) survival analysis not only at the gene, but also the GS level; and (iii) integration of clinical and experimental cell line studies to generate synergistic biological insights. To demonstrate these advances, we report three case studies. We confirmed findings of autophagy-dependent survival in colorectal cancers and of synergistic negative effects between high expression of SLC7A11 and SLC2A1 on outcomes in several cancers. We further used cSurvival to identify high expression of the Nrf2-antioxidant response element pathway as a main indicator for lung cancer prognosis and for cellular resistance to oxidative stress-inducing drugs. Altogether, these analyses demonstrate cSurvival's ability to support biomarker prognosis and interaction analysis via gene- and GS-level approaches and to integrate clinical and experimental biomedical studies.

Glioblastomas (GBMs) are aggressive brain tumors characterized by extensive inter- and intratumor heterogeneity. Patient-derived models, such as organoids and explants, have recently emerged as useful models to study such heterogeneity, although the extent to which they can recapitulate GBM genomic features remains unclear. Here, we analyze bulk exome and single-cell genome and transcriptome profiles of 12 IDH wild-type GBMs, including two recurrent tumors, and of patient-derived explants (PDEs) and gliomasphere (GS) lines derived from these tumors. We find that PDEs are genetically similar to, and variably retain gene expression characteristics of, their parent tumors. Notably, PDEs appear to exhibit similar levels of transcriptional heterogeneity compared with their parent tumors, whereas GS lines tend to be enriched for cells in a more uniform transcriptional state. The approaches and datasets introduced here will provide a valuable resource to help guide experiments using GBM-derived models, especially in the context of studying cellular heterogeneity.

In this study, we evaluate the impact of whole genome and transcriptome analysis (WGTA) on predictive molecular profiling and histologic diagnosis in a cohort of advanced malignancies. WGTA was used to generate reports including molecular alterations and site/tissue of origin prediction. Two reviewers analyzed genomic reports, clinical history, and tumor pathology. We used National Comprehensive Cancer Network (NCCN) consensus guidelines, Food and Drug Administration (FDA) approvals, and provincially reimbursed treatments to define genomic biomarkers associated with approved targeted therapeutic options (TTOs). Tumor tissue/site of origin was reassessed for most cases using genomic analysis, including a machine learning algorithm (Supervised Cancer Origin Prediction Using Expression [SCOPE]) trained on The Cancer Genome Atlas data. WGTA was performed on 652 cases, including a range of primary tumor types/tumor sites and 15 malignant tumors of uncertain histogenesis (MTUH). At the time WGTA was performed, alterations associated with an approved TTO were identified in 39 (6%) cases; 3 of these were not identified through routine pathology workup. In seven (1%) cases, the pathology workup either failed, was not performed, or gave a different result from the WGTA. Approved TTOs identified by WGTA increased to 103 (16%) when applying 2021 guidelines. The histopathologic diagnosis was reviewed in 389 cases and agreed with the diagnostic consensus after WGTA in 94% of non-MTUH cases (n = 374). The remainder included situations where the morphologic diagnosis was changed based on WGTA and clinical data (0.5%), or where the WGTA was non-contributory (5%). The 15 MTUH were all diagnosed as specific tumor types by WGTA. Tumor board reviews including WGTA agreed with almost all initial predictive molecular profile and histopathologic diagnoses. WGTA was a powerful tool to assign site/tissue of origin in MTUH. Current efforts focus on improving therapeutic predictive power and decreasing cost to enhance use of WGTA data as a routine clinical test.

Autism spectrum disorder (ASD) describes a complex and heterogenous group of neurodevelopmental disorders. Whole genome sequencing continues to shed light on the multifactorial etiology of ASD. Dysregulated transcriptional pathways have been implicated in neurodevelopmental disorders. Emerging evidence suggests that de novo POLR2A variants cause a newly described phenotype called 'Neurodevelopmental Disorder with Hypotonia and Variable Intellectual and Behavioral Abnormalities' (NEDHIB). The variable phenotype manifests with a spectrum of features; primarily early onset hypotonia and delay in developmental milestones. In this study, we investigate a patient with complex ASD involving epilepsy and strabismus. Whole genome sequencing of the proband-parent trio uncovered a novel de novo POLR2A variant (c.1367T>C, p. Val456Ala) in the proband. The variant appears deleterious according to in silico tools. We describe the phenotype in our patient, who is now 31 years old, draw connections between the previously reported phenotypes and further delineate this emerging neurodevelopmental phenotype. This study sheds new insights into this neurodevelopmental disorder, and more broadly, the genetic etiology of ASD.

Introduction: Severe internal carotid stenosis, if left untreated, can pose serious risks for ischemic stroke and cognitive impairments. The effects of revascularization on any aspects of cognition, however, are not well understood, as conflicting results are reported, which have mainly been centered on paper-based cognitive analyses. Here, we summarized and evaluated the publications to date of functional MRI (fMRI) studies that examined the mechanisms of functional brain activation and connectivity as a way to reflect cognitive effects of revascularization on patients with carotid stenosis.

Methods: A PubMed and Google Scholar (covering the relevant literature until November 1, 2021) search yielded eight original studies of the research line, including seven resting-state and one task-based fMRI reports.

Results: Findings demonstrated treatment-related alterations in fMRI signal intensity and symmetry level, regional fMRI activation pattern, and functional brain network connectivity. The functional brain changes were associated largely with improvement in cognitive function assessed using standard cognitive test scores.

Conclusions: These findings support the contribution of fMRI to the understanding of brain functional activation and connectivity changes revealing cognitive effects of revascularization in the management of severe carotid stenosis. The review also highlighted the importance of reproducibility through enhancing experimental designs and cognitive task applications with future research for potential clinical translation.

Background: A previous International Lymphoma Epidemiology (InterLymph) Consortium evaluation of joint associations between five immune gene variants and autoimmune conditions reported interactions between B-cell response-mediated autoimmune conditions and the rs1800629 genotype on risk of B-cell NHL subtypes. Here, we extend that evaluation using NHL subtype-specific polygenic risk scores (PRS) constructed from loci identified in genome-wide association studies of three common B-cell NHL subtypes.

Methods: In a pooled analysis of NHL cases and controls of Caucasian descent from 14 participating InterLymph studies, we evaluated joint associations between B-cell mediated autoimmune conditions and tertile (T) of PRS for risk of diffuse large B-cell lymphoma (DLBCL, n=1914), follicular lymphoma (FL, n=1733) and marginal zone lymphoma (MZL, n=407), using unconditional logistic regression.

Results: We demonstrated a positive association of DLBCL PRS with DLBCL risk (T2 vs T1: odds ratio, OR=1.24, 95% confidence interval, CI=1.08-1.43; T3 vs T1: OR=1.81, 95% CI=1.59-2.07; P-trend<0.0001). DLBCL risk also increased with increasing PRS tertile among those with an autoimmune condition, being highest for those with a B-cell mediated autoimmune condition and a T3 PRS (OR=6.46 vs no autoimmune condition and a T1 PRS, P-trend<0.0001, p-interaction=0.49). FL and MZL risk demonstrated no evidence of joint associations or significant p-interaction.

Conclusions: Our results suggest that PRS constructed from currently known subtype-specific loci may not necessarily capture biological pathways shared with autoimmune conditions.

Impact: Targeted genetic (PRS) screening among population subsets with autoimmune conditions may offer opportunities for identifying those at highest risk for (and early detection from) DLBCL.

Despite advances in genomic classification of breast cancer, current clinical tests and treatment decisions are commonly based on protein level information. Formalin-fixed paraffin-embedded (FFPE) tissue specimens with extended clinical outcomes are widely available. Here, we perform comprehensive proteomic profiling of 300 FFPE breast cancer surgical specimens, 75 of each PAM50 subtype, from patients diagnosed in 2008-2013 (n = 178) and 1986-1992 (n = 122) with linked clinical outcomes. These two cohorts are analyzed separately, and we quantify 4214 proteins across all 300 samples. Within the aggressive PAM50-classified basal-like cases, proteomic profiling reveals two groups with one having characteristic immune hot expression features and highly favorable survival. Her2-Enriched cases separate into heterogeneous groups differing by extracellular matrix, lipid metabolism, and immune-response features. Within 88 triple-negative breast cancers, four proteomic clusters display features of basal-immune hot, basal-immune cold, mesenchymal, and luminal with disparate survival outcomes. Our proteomic analysis characterizes the heterogeneity of breast cancer in a clinically-applicable manner, identifies potential biomarkers and therapeutic targets, and provides a resource for clinical breast cancer classification.