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Background: The impact of tumour mutational burden (TMB) on outcome with molecularly matched therapy is unknown. Higher TMB could predict resistance to molecularly matched therapy through co-occurring driver mutations., Methods: One hundred and four patients with advanced cancers underwent molecular profiling in the DKTK-MASTER program. Fifty-five patients received systemic therapy excluding immunotherapy. Patients with molecularly matched (n = 35) or non-molecularly informed therapy (n = 20) were analysed for TMB and survival. Results were validated in an independent cohort of patients receiving molecularly matched (n = 68) or non-molecularly informed therapy (n = 40). Co-occurring driver mutations and TMB were analysed in the exploratory cohort and The Cancer Genome Atlas (TCGA) datasets., Results: Patients were stratified by the median TMB of 1.67 mutations per Megabase (mut/Mb) of 35 patients receiving molecularly matched therapy into TMB-high or TMB-low groups. Median overall survival (4 months [95% CI, 3.3-7.6] versus 12.8 months [95% CI, 10-not reached], p < 0.001) and progression-free survival (1.8 months [95% CI, 1.1-3.7] versus 7.9 months [95% CI, 2.8-17.0], p = 0.003) were significantly shorter in the TMB-high group compared to the TMB-low group. In the validation cohort, shorter OS and PFS were identified in the TMB-high group (TMB cut-off of 4 mut/Mb) treated with molecularly matched therapy. No differences were observed in patients receiving non-molecularly informed systemic therapy. A significant correlation between co-occurring driver mutations and TMB (n = 104, r = 0.78 [95% CI, 0.68-0.85], p < 0.001) was found in the exploratory cohort as well as the majority (24/33) of TCGA studies., Conclusion: A high TMB was associated with unfavourable outcome in patients receiving molecularly matched therapy, indicating untargeted resistance pathways. Therefore, TMB should be further investigated as a predictive biomarker in precision oncology programs., Competing Interests: Declaration of Competing Interest Till de Bortoli, Manuela Benary, Mario Lamping, Reinhold Schäfer and Frederick Klauschen report no potential conflicts of interest. Peter Horak reported consulting or advisory board membership for Platomics and honoraria from Platomics, Roche. Sebastian Stintzing has received honoraria/consulting or advisory role from Amgen, Bayer, Lilly, Merck KGaA, MSD, Pierre Fabre, Roche, Sanofi, Servier, Taiho Pharmaceuticals, Takeda, Boehringer Ingelheim, research funding from Merck Serono, Pierre Fabre, Roche Molecular Diagnostics, travel, accommodation and expenses support from Amgen, Bayer, Lilly, Merck KgaA, Pierre Fabre, Roche, Sanofi, Sirtex Medical and Takeda. Inge Tinhofer reports honoraria/consulting or advisory role for Merck KgaA and MerckSerono. Serge Leyvraz reports consulting or advisory role and travel expenses support from Bayer. Ulrich Keller reports a consulting role for Roche, Janssen-Cilag, Takeda, BMS, Gilead, Hexal, Pfizer, Astra-Zeneca, Pentixapharm and honoraria from Gilead, Amgen, Novartis, BMS, Roche, Takeda, MSD, as well as research funding from Celgene, Takeda, BMS, Roche, Astra-Zeneka, Novartis, MSD, Janssen-Cilag, Pfizer. Other support was declared from Roche, BMS, Gilead, Takeda, Janssen-Cilag and Celgene. Albrecht Stenzinger reported Advisory Board/Speaker’s Bureau from Aignostics, Astra Zeneca, AGCT, Bayer, BMS, Eli Lilly, Illumina, Incyte, Janssen, MSD, Novartis, Pfizer, Roche, Seattle Genetics, Takeda, Thermo Fisher and research grants from Bayer, BMS, Chugai, Incyte. Stefan Fröhling reported consulting or advisory board membership from Bayer, Illumina, Roche; honoraria: Amgen, Eli Lilly, PharmaMar, Roche and research funding from AstraZeneca, Pfizer, PharmaMar, Roche, as well as travel or accommodation expenses support from Amgen, Eli Lilly, Illumina, PharmaMar, Roche. Razelle Kurzrock has received research funding from Biological Dynamics, Boehringer Ingelheim, Debiopharm, Foundation Medicine, Genentech, Grifols, Guardant, Incyte, Konica Minolta, Medimmune, Merck Serono, Omniseq, Pfizer, Sequenom, Takeda, and TopAlliance; as well as consultant and/or speaker fees and/or advisory board for Actuate Therapeutics, AstraZeneca, Bicara Therapeutics, Biological Dynamics, EISAI, EOM Pharmaceuticals, Iylon, Merck, NeoGenomics, Neomed, Pfizer, Prosperdtx, Roche, TD2/Volastra, Turning Point Therapeutics, X-Biotech and has an equity interest in CureMatch Inc., CureMetrix, and IDbyDNA; She also serves on the Board of CureMatch and CureMetrix,and is a co-founder of CureMatch. Ulrich Keilholz has received advisory board/speaker bureau, trial support, research collaboration or research support from Amgen, AstraZeneca, BMS, Boehringer Ingelheim, Glycotope, Innate, Lilly, Medimmune, MerckSerono, MSD/Merck, Novartis, Pfizer, Roche/Genentech and Sirtex. Damian Rieke has received consultant and/or advisory board and/or speaker fees from Bayer, Bristol-Myers Squibb and Lilly. Ivan Jelas has received consultant and/or advisory board and/or speaker fees from Bristol-Myers Squibb, Merck and Roche., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

CIViC (Clinical Interpretation of Variants in Cancer; civicdb.org) is a crowd-sourced, public domain knowledgebase composed of literature-derived evidence characterizing the clinical utility of cancer variants. As clinical sequencing becomes more prevalent in cancer management, the need for cancer variant interpretation has grown beyond the capability of any single institution. CIViC contains peer-reviewed, published literature curated and expertly-moderated into structured data units (Evidence Items) that can be accessed globally and in real time, reducing barriers to clinical variant knowledge sharing. We have extended CIViC's functionality to support emergent variant interpretation guidelines, increase interoperability with other variant resources, and promote widespread dissemination of structured curated data. To support the full breadth of variant interpretation from basic to translational, including integration of somatic and germline variant knowledge and inference of drug response, we have enabled curation of three new Evidence Types (Predisposing, Oncogenic and Functional). The growing CIViC knowledgebase has over 300 contributors and distributes clinically-relevant cancer variant data currently representing >3200 variants in >470 genes from >3100 publications., (© The Author(s) 2022. Published by Oxford University Press on behalf of Nucleic Acids Research.)

Background: Structured and harmonized implementation of molecular tumor boards (MTB) for the clinical interpretation of molecular data presents a current challenge for precision oncology. Heterogeneity in the interpretation of molecular data was shown for patients even with a limited number of molecular alterations. Integration of high-dimensional molecular data, including RNA- (RNA-Seq) and whole-exome sequencing (WES), is expected to further complicate clinical application. To analyze challenges for MTB harmonization based on complex molecular datasets, we retrospectively compared clinical interpretation of WES and RNA-Seq data by two independent molecular tumor boards., Methods: High-dimensional molecular cancer profiling including WES and RNA-Seq was performed for patients with advanced solid tumors, no available standard therapy, ECOG performance status of 0-1, and available fresh-frozen tissue within the DKTK-MASTER Program from 2016 to 2018. Identical molecular profiling data of 40 patients were independently discussed by two molecular tumor boards (MTB) after prior annotation by specialized physicians, following independent, but similar workflows. Identified biomarkers and resulting treatment options were compared between the MTBs and patients were followed up clinically., Results: A median of 309 molecular aberrations from WES and RNA-Seq (n = 38) and 82 molecular aberrations from WES only (n = 3) were considered for clinical interpretation for 40 patients (one patient sequenced twice). A median of 3 and 2 targeted treatment options were identified per patient, respectively. Most treatment options were identified for receptor tyrosine kinase, PARP, and mTOR inhibitors, as well as immunotherapy. The mean overlap coefficient between both MTB was 66%. Highest agreement rates were observed with the interpretation of single nucleotide variants, clinical evidence levels 1 and 2, and monotherapy whereas the interpretation of gene expression changes, preclinical evidence levels 3 and 4, and combination therapy yielded lower agreement rates. Patients receiving treatment following concordant MTB recommendations had significantly longer overall survival than patients receiving treatment following discrepant recommendations or physician's choice., Conclusions: Reproducible clinical interpretation of high-dimensional molecular data is feasible and agreement rates are encouraging, when compared to previous reports. The interpretation of molecular aberrations beyond single nucleotide variants and preclinically validated biomarkers as well as combination therapies were identified as additional difficulties for ongoing harmonization efforts., (© 2022. The Author(s).)

Precision oncology relies on accurate discovery and interpretation of genomic variants, enabling individualized diagnosis, prognosis and therapy selection. We found that six prominent somatic cancer variant knowledgebases were highly disparate in content, structure and supporting primary literature, impeding consensus when evaluating variants and their relevance in a clinical setting. We developed a framework for harmonizing variant interpretations to produce a meta-knowledgebase of 12,856 aggregate interpretations. We demonstrated large gains in overlap between resources across variants, diseases and drugs as a result of this harmonization. We subsequently demonstrated improved matching between a patient cohort and harmonized interpretations of potential clinical significance, observing an increase from an average of 33% per individual knowledgebase to 57% in aggregate. Our analyses illuminate the need for open, interoperable sharing of variant interpretation data. We also provide a freely available web interface (search.cancervariants.org) for exploring the harmonized interpretations from these six knowledgebases.

Background: Reliable and reproducible interpretation of molecular aberrations constitutes a bottleneck of precision medicine. Evidence-based decision management systems may improve rational therapy recommendations. To cope with an increasing amount of complex molecular data in the clinical care of patients with cancer, we established a workflow for the interpretation of molecular analyses., Methods: A specialized physician screened results from molecular analyses for potential biomarkers, irrespective of the diagnostic modality. Best available evidence was retrieved and categorized through establishment of an in-house database and interrogation of publicly available databases. Annotated biomarkers were ranked using predefined evidence levels and subsequently discussed at a molecular tumour board (MTB), which generated treatment recommendations. Subsequent translation into patient treatment and clinical outcomes were followed up., Results: One hundred patients were discussed in the MTB between January 2016 and May 2017. Molecular data were obtained for 70 of 100 patients (50 whole exome/RNA sequencing, 18 panel sequencing, 2 immunohistochemistry (IHC)/microsatellite instability analysis). The MTB generated a median of two treatment recommendations each for 63 patients. Thirty-nine patients were treated: 6 partial responses and 12 stable diseases were achieved as best responses. Genetic counselling for germline events was recommended for seven patients., Conclusion: The development of an evidence-based workflow allowed for the clinical interpretation of complex molecular data and facilitated the translation of personalized treatment strategies into routine clinical care. The high number of treatment recommendations in patients with comprehensive genomic data and promising responses in patients treated with combination therapy warrant larger clinical studies., Competing Interests: Conflict of interest statement M.L., M.B., C.M., E.B., K.J., S.B., K.K., S.O., R.S., and F.K. reported no conflicts of interest. S.L. received support and consulting compensation from Bayer. M.L.Y., T.K. and M.S. are employees of Alacris Theranostics. D.L. is currently an employee of AstraZeneca and reported employment, stock ownership and patents/royalties/intellectual property for her spouse at Bayer. C.S. received honoraria from Merck Serono, I.T. took part in an advisory board by Merck Serono. D.B. is a shareholder of MicroDiscovery GmbH. U.K. reported research funding from Merck Sharp & Dohme, Novartis, Bristol-Myers Squibb, Roche, GlaxoSmithKline, AstraZeneca and Merck Serono and served as a consultant or as a member of the advisory board with Merck Sharp & Dohme, Bristol-Myers Squibb, AstraZeneca, Merck Serono and GlaxoSmithKline. D.T.R. received payments as a consultant for Alacris Theranostics and honoraria from Bristol-Myers Squibb., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

Every tumour is different. They arise in patients with different genomes, from cells with different epigenetic modifications, and by random processes affecting the genome and/or epigenome of a somatic cell, allowing it to escape the usual controls on its growth. Tumours and patients therefore often respond very differently to the drugs they receive. Cancer precision medicine aims to characterise the tumour (and often also the patient) to be able to predict, with high accuracy, its response to different treatments, with options ranging from the selective characterisation of a few genomic variants considered particularly important to predict the response of the tumour to specific drugs, to deep genome analysis of both tumour and patient, combined with deep transcriptome analysis of the tumour. Here, we compare the expected results of carrying out such analyses at different levels, from different size panels to a comprehensive analysis incorporating both patient and tumour at the DNA and RNA levels. In doing so, we illustrate the additional power gained by this unusually deep analysis strategy, a potential basis for a future precision medicine first strategy in cancer drug therapy. However, this is only a step along the way of increasingly detailed molecular characterisation, which in our view will, in the future, introduce additional molecular characterisation techniques, including systematic analysis of proteins and protein modification states and different types of metabolites in the tumour, systematic analysis of circulating tumour cells and nucleic acids, the use of spatially resolved analysis techniques to address the problem of tumour heterogeneity as well as the deep analyses of the immune system of the patient to, e.g., predict the response of the patient to different types of immunotherapy. Such analyses will generate data sets of even greater complexity, requiring mechanistic modelling approaches to capture enough of the complex situation in the real patient to be able to accurately predict his/her responses to all available therapies., (© 2017 S. Karger AG, Basel.)