The cancer multiple: Producing and translating genomic big data into oncology care

Abstract

This article provides an ethnographic account of how Big Data biology is produced, interpreted, debated, and translated in a Big Data-driven cancer clinical trial, entitled “Personalized OncoGenomics,” in Vancouver, Canada. We delve into epistemological differences between clinical judgment, pathological assessment, and bioinformatic analysis of cancer. To unpack these epistemological differences, we analyze a set of gazes required to produce Big Data biology in cancer care: clinical gaze, molecular gaze, and informational gaze. We are concerned with the interactions of these bodily gazes and their interdependence on each other to produce Big Data biology and translate it into clinical knowledge. To that end, our central research questions ask: How do medical practitioners and data scientists interact, contest, and collaborate to produce and translate Big Data into clinical knowledge? What counts as actionable and reliable data in cancer decision-making? How does the explicability or translatability of genomic Big Data come to redefine or contradict medical practice? The article contributes to current debates on whether Big Data engenders new questions and approaches to biology, or Big Data biology is merely an extension of early modern natural history and biology. This ethnographic account will highlight how genomic Big Data, which underpins the mechanism of personalized medicine, allows oncologists to understand and diagnose cancer in a different light, but it does not revolutionize or disrupt medical oncology on an institutional level. Rather, personalized medicine is interdependent on different styles of (medical) thought, gaze, and practice to be produced and made intelligible.

Groundwork for AI: Enforcing a benchmark for neoantigen prediction in personalized cancer immunotherapy

“…BCL and FASTQ), distributed computing, reference databases, visualization software (e.g. IVG) and countless norms, best practices and gold standards to make, sometimes, genomic big data actionable ( Nelson et al, 2013 ) for diagnosis, prognosis or therapeutics ( Hà & Chow-White, 2021 ). The acronym NGS refers to something far larger than a set of technologies: It refers to a sociotechnical process that comprises concrete locations and instruments, but also people and ways of doing and thinking that mutually constitute each other and, by this fact, participate in the constitution of sequencing-derived biomedical knowledge ( Cambrosio, Keating, et al, 2018 ).…”