Original ResearchSupport of a molecular tumour board by an evidence-based decision management system for precision oncology
Introduction
Basket trials and targeted therapies based on well-defined biomarkers have proven the concept of precision oncology (PO) [1].
The feasibility of PO programs for unselected patient populations has been shown by several centres [[2], [3], [4], [5]].
Yet, the molecularly targeted therapy based on tumour molecular profiling versus conventional therapy for advanced cancer (SHIVA) trial, a prospective randomized study in an unselected patient population, did not show a benefit with fixed biomarker-treatment associations [6]. More flexible treatment allocation strategies are now increasingly studied, albeit also with limited patient benefit [[7], [8], [9]].
Virtually all published data rely on the analysis of a limited set of alterations, obtained by panel sequencing (Panel-Seq). Yet, uncertainty existed among tumour boards even with such limited datasets [10].
Published guidelines mainly address reporting and stratification of identified biomarkers, whereas the translation of findings into clinical application is not well defined [11,12]. The available literature suggests the use of evidence levels (EvLs) to prioritize treatment recommendations in accordance with the best available evidence [5,[13], [14], [15]].
This remains a challenging task because the amount of information is rapidly increasing with biomedical literature. More complex results are generated by comprehensive sequencing technologies and novel types of biomarkers need to be integrated.
To integrate these requirements for results from an increasing amount of comprehensive genomic analyses in clinical routine at the Charité Comprehensive Cancer Center, we established and tested a decision support system and here report features and results in the first 100 patients.
Section snippets
Molecular tumour board
A weekly interdisciplinary molecular tumour board (MTB) was established, comprising clinicians, pathologists, molecular and tumour biologists, medical oncologists and bioinformaticians, as well as additional experts, as needed. The MTB was supported by dedicated physicians who developed and applied the following workflow.
Development and application of a decision support system.
An annotation workflow was developed (Fig. 1). Results were pre-annotated to identify a list of potential biomarkers including diagnostic, predictive, prognostic, predisposing
Patient characteristics
One hundred patients were discussed in the MTB between 20th of January 2016 and 23rd of May 2017. Fifty-four were male and 46 were female with a median age of 51 years (range r: 21–86). Patients had received a median of three (r: 0–12) prior systemic therapy lines (Table 1). Prior therapy included chemotherapy with a median of two chemotherapy protocols per patient (r: 0–9), immunotherapy in 36 patients and targeted therapy in 77 patients. Tumour types were heterogeneous with a predominance of
Discussion
We here report the design and clinical evaluation of a decision support system for PO, allowing for the integration of different data types and complex biomarkers (e.g. WES, RNA-Seq, TMB, protein expression, combinations of biomarkers) into routine clinical care. The design of underlying workflows has not systematically been reported (for published PO studies and associated data, Suppl. Table S4 [[2], [3], [4], [5], [6], [7], [8],[19], [20], [21], [22], [23], [24], [25], [26], [27], [28], [29],
Funding
This work was supported in part by the German Federal Ministry of Education and Research (BMBF) [Grant numbers 031L0030E, 031L0023B and 031A512]. The DKTK MASTER program is funded by the Deutsches Konsortium für translationale Krebsforschung (DKTK). D.T.R. is a participant in the Berlin Institute of Health – Charité Clinical Scientist Program funded by the Charité – Universitätsmedizin Berlin and the Berlin Institute of Health.
The funding sources provided financial support for the conception,
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
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2021, PathologyCitation Excerpt :Once incorporated as an integral part of screening, diagnosis and follow-up of human cancer in the near future, democratisation of availability and accessibility of personalised medicine and personalised pathology to the entire population would be possible. Molecular tumour boards (MTB) are replacing traditional oncology multidisciplinary team (MDT) meetings.127 In this model, the traditional group of clinicians has embraced bioinformaticians and scientists to collectively provide patients with advanced and/or treatment resistant cancer, with opportunity to access personalised and targeted therapeutic plans that take into account the genomic mutations in an N-of-One management plan.127,128
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2020, Cancer Treatment ReviewsCitation Excerpt :An enormous discrepancy between targetability of 40%, matching of therapy in 12% and objective response (OR) in 0.8–3.0%, assessed in more than 13,000 patients, was reported by Tannock & Hickman [44]. This discrepancy could be confirmed by an own analysis of more than 40,000 patients enrolled into the trials that are enumerated in Table A.1 [16–34]. Actionability of median 56% was observed for the eligible patients.
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These authors contributed equally.