This document discusses building tools for personalized treatment of diseases through a multi-step analysis of patient data. It proposes an end-to-end analysis tool that can perform image analysis, biomarker analysis, gene expression analysis, and blood analysis for each patient to predict their response to various therapies. A key point is that treatment should not use a "one-size-fits-all" approach, but rather identify patients who will respond to specific medicines based on their biomarkers.

1. Madan Dabbeeru
November 9, 2019
Rakuten Institute of Technology
Rakuten India Enterprise PVT. LTD

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Personalization in Curing
Disease
http://woked.co/goldman-sachs-curing-cancer-business/

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Building the Next Generation AI tools for Personalized Treatment

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Personalized treatment can be achieved through building the end-to-end
analysis tool, which can perform the image analysis to Biomarker Analysis
to Gene Analysis to Blood Analysis of each patient to predict their response
to any therapy
Image Analysis
Bio-Marker
Analysis
Gene Expression
Analysis
Blood Analysis
A treatment should not be “one-fits-all” approach
Identify the patients who respond to the medicine based on their biomarkers

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http://www.applyscience.it/life-science-division/
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classifier identifies patients with clinical heart failure using whole-slide images of H&E
tissue. PLoS ONE. 13. 10.1371/journal.pone.0192726.

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*PDL-1 and PANCK are biomarkers expressed by cells

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Biomarker
Analysis
• Which Biomarkers are more effective for a patient to respond to the
therapy?
• Spatial Configuration of Biomarkers in a responding and non-responding
patients

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• *Units: Measurement scale to be obtained from CRO/medical team
• More generalized comparison can be done at responding non-responding patients level once sufficient data is provided

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