Biology subjects

Gallinger, S.

Publications and source records attributed to Gallinger, S..

4 recordsLinked to original sources

bioRxiv2018

Meta-analysis of 1,200 transcriptomic profiles identifies a prognostic model for pancreatic ductal adenocarcinoma

BackgroundWith a dismal 8% median 5-year overall survival (OS), pancreatic ductal adenocarcinoma (PDAC) is highly lethal. Only 10-20% of patients are eligible for surgery, and over 50% of these will die within a year of surgery. Identify molecular predictors of early death would enable the selection of PDAC patients at high risk.\n\nMethodsWe developed the Pancreatic Cancer Overall Survival Predictor (PCOSP), a prognostic model built from a unique set of 89 PDAC tumors where gene expression was profiled using both microarray and sequencing platforms. We used a meta-analysis framework based on the binary gene pair method to create gene expression barcodes robust to biases arising from heterogeneous profiling platforms and batch effects. Leveraging the largest compendium of PDAC transcriptomic datasets to date, we show that PCOSP is a robust single-sample predictor of early death ([≤]1 yr) after surgery in a subset of 823 samples with available transcriptomics and survival data.\n\nResultsThe PCOSP model was strongly and significantly prognostic with a meta-estimate of the area under the receiver operating curve (AUROC) of 0.70 (P=1.9e-18) and hazard ratio (HR) of 1.95(1.6-2.3) (P=2.6e-16) for binary and survival predictions, respectively. The prognostic value of PCOSP was independent of clinicopathological parameters and molecular subtypes. Over-representation analysis of the PCOSP 2619 gene-pairs (1070 unique genes) unveiled pathways associated with Hedgehog signalling, epithelial mesenchymal transition (EMT) and extracellular matrix (ECM) signalling.\n\nConclusionPCOSP could improve treatment decision by identifying patients who will not benefit from standard surgery/chemotherapy and may benefit from alternate approaches.\n\nAbbreviations

genomics↗

bioRxiv2018

Ability of known susceptibility SNPs to predict colorectal cancer risk for persons with and without a family history

BackgroundA number of single nucleotide polymorphisms (SNPs), which are common inherited genetic variants, have been identified that are associated with risk of colorectal cancer. The aim of this study was to determine the ability of these SNPs to estimate colorectal cancer (CRC) risk for persons with and without a family history of CRC, and the screening implications.\n\nMethodsWe estimated the association with CRC of a 45 SNP-based risk using 1,181 cases and 999 controls, and its correlation (r) with CRC risk predicted from detailed family history. We estimated the predicted change in the distribution across predefined risk categories, and implications for recommended age to commence screening, from adding SNP-based risk to family history.\n\nResultsThe inter-quintile risk ratio for colorectal cancer risk of the SNP-based risk was 2.46 (95% CI 1.91 - 3.11). SNP-based and family history-based risks were not correlated (r = 0.02). For persons with no first-degree relatives with CRC, recommended screening would commence 2 years earlier for women (4 years for men) in the highest quintile of SNP-based risk, and 12 years later for women (7 years for men) in the lowest quintile. For persons with two first-degree relatives with CRC, recommended screening would commence 15 years earlier for men and women in the highest quintile, and 8 years earlier for men and women in the lowest quintile.\n\nConclusionsRisk reclassification by 45 SNPs could inform targeted screening for CRC prevention, particularly in clinical genetics settings when mutations in high-risk genes cannot be identified.

epidemiology↗

bioRxiv2017

DriverPower: Combined burden and functional impact tests for cancer driver discovery

We describe DriverPower, a software package that uses mutational burden and functional impact evidence to identify cancer driver mutations in coding and non-coding sites within cancer whole genomes. Using a total of 1,373 genomic features derived from public sources, DriverPowers background mutation model explains up to 93% of the regional variance in the mutation rate across a variety of tumour types. By incorporating functional impact scores, we are able to further increase the accuracy of driver discovery. Testing across a collection of 2,583 cancer genomes from the Pan-Cancer Analysis of Whole Genomes (PCAWG) project, DriverPower identifies 217 coding and 95 non-coding driver candidates. Comparing to six published methods used by the PCAWG Drivers and Functional Interpretation Group, DriverPower has the highest F1-score for both coding and non-coding driver discovery. This demonstrates that DriverPower is an effective framework for computational driver discovery.

bioinformatics↗

bioRxiv2017

Whole Genomes Define Concordance of Matched Primary, Xenograft, and Organoid Models of Pancreas Cancer

Pancreatic ductal adenocarcinoma (PDAC) has the worst prognosis among solid malignancies and improved therapeutic strategies are needed to improve outcomes. Patient-derived xenografts (PDX) and patient-derived organoids (PDO) serve as promising tools to identify new drugs with therapeutic potential in PDAC. For these preclinical disease models to be effective, they should both recapitulate the molecular heterogeneity of PDAC and validate patient-specific therapeutic sensitivities. To date however, deep characterization of PDAC PDX and PDO models and comparison with matched human tumour remains largely unaddressed at the whole genome level. We conducted a comprehensive assessment of the genetic landscape of 16 whole-genome pairs of tumours and matched PDX, from primary PDAC and liver metastasis, including a unique cohort of 5 trios of matched primary tumour, PDX, and PDO. We developed a new pipeline to score concordance between PDAC models and their paired human tumours for genomic events, including mutations, structural variations, and copy number variations. Comparison of genomic events in the tumours and matched disease models displayed single-gene concordance across major PDAC driver genes, and genome-wide similarities of copy number changes. Genome-wide and chromosome-centric analysis of structural variation (SV) events revealed high variability across tumours and disease models, but also highlighted previously unrecognized concordance across chromosomes that demonstrate clustered SV events. Our approach and results demonstrate that PDX and PDO recapitulate PDAC tumourigenesis with respect to simple somatic mutations and copy number changes, and capture major SV events that are found in both resected and metastatic tumours.

bioinformatics↗