Exploring the feasibility and utility of exome-scale tumour sequencing in a clinical setting

Factors for a broad technology assessment of comprehensive genomic profiling in advanced cancer, a systematic review

Comprehensive Genomic Profiling (CGP) allows for the identification of many targets. Reimbursement decision-making is, however, challenging because besides the health benefits of on-label treatments and costs, other factors related to diagnostic and treatment pathways may also play a role. The aim of this study was to identify which other factors are relevant for the technology assessment of CGP and to summarize the available evidence for these factors. After a scoping search and two expert sessions, five factors were identified: feasibility, test journey, wider implications of diagnostic results, organisation of laboratories, and "scientific spillover". Subsequently, a systematic search identified 83 studies collecting mainly evidence for the factors "test journey" and "wider implications of diagnostic results". Its nature was, however, of limited value for decision-making. We recommend the use of comparative strategies, uniformity in outcome definitions, and the inclusion of a comprehensive set of factors in future evidence generation.

Multicenter Comparison of Molecular Tumor Boards in The Netherlands: Definition, Composition, Methods, and Targeted Therapy Recommendations

BACKGROUND

Molecular tumor boards (MTBs) provide rational, genomics-driven, patient-tailored treatment recommendations. Worldwide, MTBs differ in terms of scope, composition, methods, and recommendations. This study aimed to assess differences in methods and agreement in treatment recommendations among MTBs from tertiary cancer referral centers in The Netherlands.

MATERIALS AND METHODS

MTBs from all tertiary cancer referral centers in The Netherlands were invited to participate. A survey assessing scope, value, logistics, composition, decision-making method, reporting, and registration of the MTBs was completed through on-site interviews with members from each MTB. Targeted therapy recommendations were compared using 10 anonymized cases. Participating MTBs were asked to provide a treatment recommendation in accordance with their own methods. Agreement was based on which molecular alteration(s) was considered actionable with the next line of targeted therapy.

RESULTS

Interviews with 24 members of eight MTBs revealed that all participating MTBs focused on rare or complex mutational cancer profiles, operated independently of cancer type-specific multidisciplinary teams, and consisted of at least (thoracic and/or medical) oncologists, pathologists, and clinical scientists in molecular pathology. Differences were the types of cancer discussed and the methods used to achieve a recommendation. Nevertheless, agreement among MTB recommendations, based on identified actionable molecular alteration(s), was high for the 10 evaluated cases (86%).

CONCLUSION

MTBs associated with tertiary cancer referral centers in The Netherlands are similar in setup and reach a high agreement in recommendations for rare or complex mutational cancer profiles. We propose a "Dutch MTB model" for an optimal, collaborative, and nationally aligned MTB workflow.

IMPLICATIONS FOR PRACTICE

Interpretation of genomic analyses for optimal choice of target therapy for patients with cancer is becoming increasingly complex. A molecular tumor board (MTB) supports oncologists in rationalizing therapy options. However, there is no consensus on the most optimal setup for an MTB, which can affect the quality of recommendations. This study reveals that the eight MTBs associated with tertiary cancer referral centers in The Netherlands are similar in setup and reach a high agreement in recommendations for rare or complex mutational profiles. The Dutch MTB model is based on a collaborative and nationally aligned workflow with interinstitutional collaboration and data sharing.

Analysis and Interpretation of the Impact of Missense Variants in Cancer

Large scale genome sequencing allowed the identification of a massive number of genetic variations, whose impact on human health is still unknown. In this review we analyze, by an in silico-based strategy, the impact of missense variants on cancer-related genes, whose effect on protein stability and function was experimentally determined. We collected a set of 164 variants from 11 proteins to analyze the impact of missense mutations at structural and functional levels, and to assess the performance of state-of-the-art methods (FoldX and Meta-SNP) for predicting protein stability change and pathogenicity. The result of our analysis shows that a combination of experimental data on protein stability and in silico pathogenicity predictions allowed the identification of a subset of variants with a high probability of having a deleterious phenotypic effect, as confirmed by the significant enrichment of the subset in variants annotated in the COSMIC database as putative cancer-driving variants. Our analysis suggests that the integration of experimental and computational approaches may contribute to evaluate the risk for complex disorders and develop more effective treatment strategies.

The pan-cancer landscape of prognostic germline variants in 10,582 patients

Background

While clinical factors such as age, grade, stage, and histological subtype provide physicians with information about patient prognosis, genomic data can further improve these predictions. Previous studies have shown that germline variants in known cancer driver genes are predictive of patient outcome, but no study has systematically analyzed multiple cancers in an unbiased way to identify genetic loci that can improve patient outcome predictions made using clinical factors.

Methods

We analyzed sequencing data from the over 10,000 cancer patients available through The Cancer Genome Atlas to identify germline variants associated with patient outcome using multivariate Cox regression models.

Results

We identified 79 prognostic germline variants in individual cancers and 112 prognostic germline variants in groups of cancers. The germline variants identified in individual cancers provide additional predictive power about patient outcomes beyond clinical information currently in use and may therefore augment clinical decisions based on expected tumor aggressiveness. Molecularly, at least 12 of the germline variants are likely associated with patient outcome through perturbation of protein structure and at least five through association with gene expression differences. Almost half of these germline variants are in previously reported tumor suppressors, oncogenes or cancer driver genes with the other half pointing to genomic loci that should be further investigated for their roles in cancers.

Conclusions

Germline variants are predictive of outcome in cancer patients and specific germline variants can improve patient outcome predictions beyond predictions made using clinical factors alone. The germline variants also implicate new means by which known oncogenes, tumor suppressor genes, and driver genes are perturbed in cancer and suggest roles in cancer for other genes that have not been extensively studied in oncology. Further studies in other cancer cohorts are necessary to confirm that germline variation is associated with outcome in cancer patients as this is a proof-of-principle study.

Electronic supplementary material

The online version of this article (10.1186/s13073-020-0718-7) contains supplementary material, which is available to authorized users.