Diagnostic yield and clinical utility of a comprehensive gene panel for hereditary tumor syndromes

Incidental findings in a series of 2500 gene panel tests for a genetic predisposition to cancer: Results and impact on patients

With next generation sequencing, physicians are faced with more complex and uncertain data, particularly incidental findings (IF). Guidelines for the return of IF have been published by learned societies. However, little is known about how patients are affected by these results in a context of oncogenetic testing. Over 4 years, 2500 patients with an indication for genetic testing underwent a gene cancer panel. If an IF was detected, patients were contacted by a physician/genetic counsellor and invited to take part in a semi-structured interview to assess their understanding of the result, the change in medical care, the psychological impact, and the transmission of results to the family. Fourteen patients (0.56%) were delivered an IF in a cancer predisposition gene (RAD51C, PMS2, SDHC, RET, BRCA2, CHEK2, CDKN2A, CDH1, SUFU). Two patients did not collect the results and another two died before the return of results. Within the 10 patients recontacted, most of them reported surprise at the delivery of IF, but not anxiety. The majority felt they had chosen to obtain the result and enough information to understand it. They all initiated the recommended follow-up and did not regret the procedure. Information regarding the IF was transmitted to their offspring but siblings or second-degree relatives were not consistently informed. No major adverse psychological events were found in our experience. IF will be inherent to the development of sequencing, even for restricted gene panels, so it is important to increase our knowledge on the impact of such results in different contexts.

The role of genomics in global cancer prevention

Despite improvements in the understanding of cancer causation, much remains unknown regarding the mechanisms by which genomic and non-genomic factors initiate carcinogenesis, drive cell invasion and metastasis, and enable cancer to develop. Technological advances have enabled the analysis of whole genomes, comprising thousands of tumours across populations worldwide, with the aim of identifying mutation signatures associated with particular tumour types. Large collaborative efforts have resulted in the identification and improved understanding of causal factors, and have shed light on new opportunities to prevent cancer. In this new era in cancer genomics, discoveries from studies conducted on an international scale can inform evidence-based strategies in cancer control along the cancer care continuum, from prevention to treatment. In this Review, we present the relevant history and emerging frontiers of cancer genetics and genomics from the perspective of global cancer prevention. We highlight the importance of local context in the adoption of new technologies and emergent evidence, with illustrative examples from worldwide. We emphasize the challenges in implementing important genomic findings in clinical settings with disparate resource availability and present a conceptual framework for the translation of such findings into clinical practice, and evidence-based policies in order to maximize the utility for a population.

Machine Learning Approaches for the Prioritization of Genomic Variants Impacting Pre-mRNA Splicing

Defects in pre-mRNA splicing are frequently a cause of Mendelian disease. Despite the advent of next-generation sequencing, allowing a deeper insight into a patient's variant landscape, the ability to characterize variants causing splicing defects has not progressed with the same speed. To address this, recent years have seen a sharp spike in the number of splice prediction tools leveraging machine learning approaches, leaving clinical geneticists with a plethora of choices for in silico analysis. In this review, some basic principles of machine learning are introduced in the context of genomics and splicing analysis. A critical comparative approach is then used to describe seven recent machine learning-based splice prediction tools, revealing highly diverse approaches and common caveats. We find that, although great progress has been made in producing specific and sensitive tools, there is still much scope for personalized approaches to prediction of variant impact on splicing. Such approaches may increase diagnostic yields and underpin improvements to patient care.

How I approach hereditary cancer predisposition in a child with cancer

Approximately 10% of all children with cancer are affected by a monogenic cancer predisposition syndrome. This has important implications for both the child and her/his family. The assessment of hereditary cancer predisposition is a challenging task for clinicians and genetic counselors in daily routine. It includes consideration of tumor genetics, specific features of the patient, and the medical/family history. To keep up with the pace of this rapidly evolving and increasingly complex field of genetic susceptibility, we suggest a systematic approach for the evaluation of the child with cancer and her/his family by an interdisciplinary team specialized in hereditary cancer predisposition.