||1U01CA269181-01 Interpret this number
||An Ai-Enabled Digital Pathology Platform for Multi-Cancer Diagnosis, Prognosis and Prediction of Therapeutic Benefit
SUMMARY: Recognizing that over-diagnosis of many cancers is leading to over-treatment with adjuvant
chemotherapy or with radiation therapy boost, there is a growing appreciation for the need for prognostic and
predictive assays to identify those cancer patients who can benefit from therapy de-intensification. While multi-gene-expression based tests such as Oncotype DX and Decipher exist for identifying early-stage breast and
prostate cancer patients who could avoid adjuvant therapies and hence mitigate side-effects and complications,
the price of these tests ($3K-4K/patient) puts them beyond the reach of most patients in low- and middle-income
countries (LMICs). Ironically, the need for these prognostic and predictive tests is even more acute in LMICs like
India, where access to treatment resources like radiation and chemotherapy are limited and hence need to be
administered judiciously to those patients who stand to receive the most benefit from them.
Sophisticated digital pathomic analysis with computer vision and pattern recognition tools has been
shown to “unlock” sub-visual attributes about tumor behavior and patient outcomes from hematoxylin & eosin
(H&E)-stained slides alone. The Madabhushi team at Case Western Reserve University (CWRU) has extensively
shown the potential for these approaches for predicting outcome and therapeutic response for breast, head and
neck, lung and prostate cancer. The Madabhushi team working with collaborators Dr. Parmar and Dr. Desai at
the Tata Memorial Center (TMC), the largest cancer center in India, has shown that advanced pathomic analysis
is able to identify unique prognostic morphologic signatures of breast cancer that are different between South
Asian (SA) and Caucasian American (CA) women 1. In addition, the CWRU group has shown that digital pathomic
based image classifiers can significantly improve and even outperform the prognostic and predictive
performance of expensive gene-expression assays for breast (Oncotype Dx) and prostate cancer (Decipher) 2.
Building on the strong extant collaboration between CWRU and TMC 3, and a strong track record in digital
image based prognostic and predictive based assays, we propose to optimize and validate an AI-enabled Digital
Pathology Platform (ADAPT) for Multi-Cancer Diagnosis, Prognosis and Prediction of Therapeutic Benefit.
ADAPT will involve optimizing the previously developed image assays by the CWRU group in the context of SA
cancer patients. Furthermore, by integrating the AI-pathomic tools with PathPresenter, a widely used digital
pathology image analysis platform, ADAPT will have a global footprint for the prognostic and predictive tools.
Specifically, ADAPT will be validated for predicting outcome and benefit of adjuvant chemo- and radiation therapy
in the context of estrogen receptor positive (ER+) breast cancer (BC) and triple negative breast cancer (TNBC),
oral cavity squamous cell carcinoma (OC-SCC) and prostate cancer at TMC via a number of clinical trial datasets
in the US (SWOG S8814, RTOG 0920, 0521) and at TMC (AREST, POP-RT). Successful project completion
will establish ADAPT as an Affordable Precision Medicine (APM) solution for Indian cancer patients.
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