Quantitative Framework for Bench-to-Bedside Cancer Research

SIMPLE SUMMARY: Technological advancements and emerging high throughput molecular data have transformed biology into a more quantitative and multidisciplinary discipline. This has accelerated the translation of laboratory based findings into applied and clinically relevant applications and therapeutics. A shared practice for quantifying and statistical rank-ordering the effects of such translational applications and for understanding their underlying mode-of-action is now critical. In this manuscript, we discuss some of the major types of quantitative translational research and the best practices. We propose that adherence to these guidelines will improve assay design and reduce missteps in translational biomarker and therapeutics clinical application and adoption. ABSTRACT: Bioscience is an interdisciplinary venture. Driven by a quantum shift in the volume of high throughput data and in ready availability of data-intensive technologies, mathematical and quantitative approaches have become increasingly common in bioscience. For instance, a recent shift towards a quantitative description of cells and phenotypes, which is supplanting conventional qualitative descriptions, has generated immense promise and opportunities in the field of bench-to-bedside cancer OMICS, chemical biology and pharmacology. Nevertheless, like any burgeoning field, there remains a lack of shared and standardized framework for quantitative cancer research. Here, in the context of cancer, we present a basic framework and guidelines for bench-to-bedside quantitative research and therapy. We outline some of the basic concepts and their parallel use cases for chemical–protein interactions. Along with several recommendations for assay setup and conditions, we also catalog applications of these quantitative techniques in some of the most widespread discovery pipeline and analytical methods in the field. We believe adherence to these guidelines will improve experimental design, reduce variabilities and standardize quantitative datasets.