Using genomic sequencing technology to provide insight into cancer biology and their mechanisms

Genomic sequencing technology provides insight into cancer pathogenesis and tumoural mechanisms. Tumour RNA sequencing can be used to assess the functionality of genes by allowing for gene expression quantification and transcriptome analysis. Mutational signatures are somatic patterns of mutations arising from specific mutagenic processes such as exogenous and endogenous exposures, defective DNA repair mechanisms or DNA enzymatic editing. Such signatures are “genomic scars” informing on the underlying biological processes that led to cancer. Whole genome sequencing (WGS) of tumour DNA and matched blood DNA as well as whole transcriptome sequencing (WTS) of tumour RNA was performed in advanced cancers of diverse types as part of the Personalized OncoGenomics project. Germline single nucleotide variants (SNVs), copy number variants (CNVs) and structural variants (SVs) in 98 hereditary cancer genes were analyzed from germline WGS data. Somatic SNVs, CNVs and SVs were analyzed from tumour WGS and WTS data. Somatic SNVs profiles were used for mutational signature modelling. Gene expression was obtained from WTS. Transcriptome targeted assembly was performed for transcript splicing analysis. We present specific examples demonstrating the usefulness of combined genomic and bioinformatic approaches for understanding clinically unusual cases of cancer and their molecular mechanisms. We used somatic mutational signature profiling to determine the functional impact of germline and somatic variants in MUTYH, a base excision repair gene, on the overall mutational landscape. In Chapter 2, we present a case series of patients with germline MUTYH variants and diverse cancers. We identified two MUTYH variants for which the previous classification in public databases are inconsistent and we show that these variants cause aberrant splicing and base excision repair deficiency signatures enriched for C:G>A:T transversion mutations. Our results support the pathogenicity of these variants. In Chapter 3, we present the example of comprehensive genomic profiling of a rare and uncharacterized tumour, the eccrine porocarcinoma, in which CDKN2A was identified as a potential novel driver. In both chapters, we used transcriptome targeted assembly to detect and characterize aberrant splicing due to selected germline and somatic variants of interest.Science, Faculty ofGraduat

Performance of the McGill Interactive Pediatric OncoGenetic Guidelines for Identifying Cancer Predisposition Syndromes.

Importance Prompt recognition of a child with a cancer predisposition syndrome (CPS) has implications for cancer management, surveillance, genetic counseling, and cascade testing of relatives. Diagnosis of CPS requires practitioner expertise, access to genetic testing, and test result interpretation. This diagnostic process is not accessible in all institutions worldwide, leading to missed CPS diagnoses. Advances in electronic health technology can facilitate CPS risk assessment. Objective To evaluate the diagnostic accuracy of a CPS prediction tool (McGill Interactive Pediatric OncoGenetic Guidelines [MIPOGG]) in identifying children with cancer who have a low or high likelihood of having a CPS. Design, Setting, and Participants In this international, multicenter diagnostic accuracy study, 1071 pediatric (<19 years of age) oncology patients who had a confirmed CPS (12 oncology referral centers) or who underwent germline DNA sequencing through precision medicine programs (6 centers) from January 1, 2000, to July 31, 2020, were studied. Exposures Exposures were MIPOGG application in patients with cancer and a confirmed CPS (diagnosed through routine clinical care; n = 413) in phase 1 and MIPOGG application in patients with cancer who underwent germline DNA sequencing (n = 658) in phase 2. Study phases did not overlap. Data analysts were blinded to genetic test results. Main Outcomes and Measures The performance of MIPOGG in CPS recognition was compared with that of routine clinical care, including identifying a CPS earlier than practitioners. The tool's test characteristics were calculated using next-generation germline DNA sequencing as the comparator. Results In phase 1, a total of 413 patients with cancer (median age, 3.0 years; range, 0-18 years) and a confirmed CPS were identified. MIPOGG correctly recognized 410 of 412 patients (99.5%) as requiring referral for CPS evaluation at the time of primary cancer diagnosis. Nine patients diagnosed with a CPS by a practitioner after their second malignant tumor were detected by MIPOGG using information available at the time of the first cancer. In phase 2, of 658 children with cancer (median age, 6.6 years; range, 0-18.8 years) who underwent comprehensive germline DNA sequencing, 636 had sufficient information for MIPOGG application. When compared with germline DNA sequencing for CPS detection, the MIPOGG test characteristics for pediatric-onset CPSs were as follows: sensitivity, 90.7%; specificity, 60.5%; positive predictive value, 17.6%; and negative predictive value, 98.6%. Tumor DNA sequencing data confirmed the MIPOGG recommendation for CPS evaluation in 20 of 22 patients with established cancer-CPS associations. Conclusions and Relevance In this diagnostic study, MIPOGG exhibited a favorable accuracy profile for CPS screening and reduced time to CPS recognition. These findings suggest that MIPOGG implementation could standardize and rationalize recommendations for CPS evaluation in children with cancer