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Data-Driven Precision Medicine Part Four | Going beyond cancer.

Updated: Feb 24, 2021

Authored by Gaëtan Fraikin, CEO, Addictive Health

This is part four of a timely 4-part series covering key aspects of precision medicine for anyone interested in understanding (1) what it truly is, (2) how genomics powers it, (3) what genomics will expand to and what other data modalities will eventually be added to the mix, and (4) what disease areas will benefit from it.

Part 1 - Defining precision medicine and looking at its future

Part 2 - Leveraging genomics as foundation

Part 3 - Going beyond DNA and genomics

Part 4 - Going beyond cancer

Liquid biopsies - The next frontier

Tissue is no longer the only type of sample that can be used to detect cancer. Liquid Biopsy Analyses can detect both Circulating Tumor Cells (CTCs - intact tumor cells that are dislodged into the systemic circulation) and Cell-Free DNA (cfDNA -fragments of tumor DNA, around 160 to 180 base pairs in length with a higher prevalence of mutations in the smaller fragments). Beyond cancer, they can also detect the presence of pathogens for infectious disease applications.

Cancer - And beyond

In oncology, by taking into account the genetic make up of the patient and of his/her tumor, specific treatments or clinical trials can be selected to ensure a better chance of positive outcomes. Companies like Foundation Medicine and Tempus are at the forefront of data-driven precision medicine in that space. Most biopharma organizations work with companion diagnostics (CDx) providers to develop CDxs in tandem with drugs to test patients ahead of therapy selection.

As of June 9, 2020, the FDA cleared (approved) list of CDx amounts to fourty, developed by Myriad Genetics, Qiagen, Roche Molecular Diagnostics, Abbott Molecular, Foundation Medicine (Roche), Ventana Medical Systems (Roche), Illumina, Thermo Fisher, Invivoscribe, Leica Biosystems, Dako Denmark and Biomerieux with a vast majority focused on cancer.

In pharmacogenomics, for select drugs, researchers have identified gene variants that affect how people respond. In these cases, doctors can select the best treatment, dose, provide guidance on possible side effects, or differences in effectiveness for people with certain gene variants. Drug companies are also using pharmacogenomics to develop and market medicines for people with specific genetic profiles. By studying a drug only in people likely to benefit from it, drug companies might be able to speed up drug development, reduce cost of development, speed-up FDA or CE approval and maximize its therapeutic benefit.

A new space where precision medicine is now propelled forward, accelerated by the latest global pandemic triggered by SARS-CoV-2, is infectious diseases. In that space, the two main areas of focus are (1) the genome analysis of pathogens to help with identification and track transmission and (2) the genetic make-up of patients and their unique immune system system. Other ideas areas will follow with significant lag including ophthalmology, neurology, rheumatology and pulmonology.

My take

Data-driven precision medicine, leveraging Multi-Level genomic data, although highly challenging, is off to a great start. Leveraging other data types such as imaging and other modalities on top of genomics is even more experimental - but its potential ahead is one of the most exciting revolution in transforming healthcare and health. To help accelerate it, we will need (1) further democratization of genomic analysis (sequencing), (2) the expansion of reimbursement in genomic testing, (3) the ability of the various players across the genomic analysis ecosystem to come together and collaborate to build better integrated solutions, (4) the development of sample-to-answer diagnostic solutions at point-of-care, (5) the acceleration of regulatory approvals of candidate companion diagnostics and other medical devices, (6) advanced data management and processing solutions and (7) the integration of - or access to - multi-modality databases centered around patients to ensure models learn on real-world data and generate the most relevant, actionable and clinically successful and reporting and recommendations. In addition, funding needs to be increase downstream in other data types beyond DNA and and multi-dimensional models must be developed to make sense of that data and better understand health and disease. Stay tuned for regular updates. Full steam ahead.

By Gaëtan Fraikin / CEO / Addictive Health / / + 1 760 580 3908 /


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