Bioinformatics for precision medicine in oncology: principles and application to the SHIVA clinical trial

Abstract

Precision medicine (PM) requires the delivery of individually adapted medical care based on the genetic characteristics of each patient and his/her tumor. The last decade witnessed the development of high-throughput technologies such as microarrays and next-generation sequencing which paved the way to PM in the field of oncology. While the cost of these technologies decreases, we are facing an exponential increase in the amount of data produced. Our ability to use this information in daily practice relies strongly on the availability of an efficient bioinformatics system that assists in the translation of knowledge from the bench towards molecular targeting and diagnosis. Clinical trials and routine diagnoses constitute different approaches, both requiring a strong bioinformatics environment capable of (i) warranting the integration and the traceability of data, (ii) ensuring the correct processing and analyses of genomic data, and (iii) applying well-defined and reproducible procedures for workflow management and decision-making. To address the issues, a seamless information system was developed at Institut Curie which facilitates the data integration and tracks in real-time the processing of individual samples. Moreover, computational pipelines were developed to identify reliably genomic alterations and mutations from the molecular profiles of each patient. After a rigorous quality control, a meaningful report is delivered to the clinicians and biologists for the therapeutic decision. The complete bioinformatics environment and the key points of its implementation are presented in the context of the SHIVA clinical trial, a multicentric randomized phase II trial comparing targeted therapy based on tumor molecular profiling versus conventional therapy in patients with refractory cancer. The numerous challenges faced in practice during the setting up and the conduct of this trial are discussed as an illustration of PM application.

Problems, challenges and promises: perspectives on precision medicine

“…The practice of medicine is currently undergoing a paradigm shift from the primarily reactive medicine of the past to a more proactive predictive medicine aimed at disease prevention [1][2][3][4][5][6][7][8][9]. Rather than treating a disease, there is a shift towards the treatment of individual patients, based on a personalised data driven approach.…”

“…Even when omics-scale or biomarker panels are used, treatment decisions are based on a univariate decision rule, which fails to account for interactions between the biomarkers [6], let alone to incorporate or consider any additional factors (such as omic or environmental data) that can regulate the functioning of the biomarker panel.…”

“…The utility of precision medicine to daily clinical practice relies strongly on the availability of efficient tools to translate an individual's data into diagnosis and targeted treatments [6].…”

“…Servant and his colleagues [6] distil the requirements of such tools as data integration and traceability, safeguards to ensure correct processing and analysis and finally the application of well-defined and reproducible procedures for workflow management and decision-making.…”

“…Oncology is the medical field most rapidly adopting genomic-era medicine, with most major cancer centres developing personalised medicine initiatives [6,12,[60][61][62][63][64][65]. As such, oncology also appears poised to be the pioneer of precision medicine approaches as it seeks to interpret ever growing datasets for individual patients.…”