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Published Findings from Pangea Biomed Validate AI Method for Predicting Cancer Treatment Responses from Tumor Images

* The study, published in Nature Cancer, validates Pangea’s innovative ENLIGHT-DeepPT method

* ENLIGHT-DP successfully predicted treatment responses across five independent patient cohorts, covering four different treatments and six cancer types, increasing the baseline response rate by 39.5%


Pangea Biomed, the company behind the world’s most advanced cancer response predictor, ENLIGHT, today announced that new findings establishing the efficacy of its novel ENLIGHT-DP platform have been published in Nature Cancer. The study, conducted in collaboration with researchers from the Australian National University and from the US National Cancer Institute, reveals that combining ENLIGHT with the DeepPT approach for inferring gene expression from H&E slide scans significantly advanced the ability to predict therapeutic responses across multiple cancer types and drugs from histopathology images.

Existing approaches for predicting treatment response directly from histopathology slides are limited due to a need for large datasets of matched imaging and response data for each specific treatment, which are often unavailable. This scarcity limits applicability and raises concerns about generalizability to different patient populations and treatment regimens, underscoring the need for more versatile and broadly applicable predictive tools in precision oncology.

The ENLIGHT-DP method was developed to overcome these limitations with an indirect approach that bypasses the need for large, matched datasets. It utilizes a two-step process involving DeepPT, a novel deep-learning framework that predicts genome-wide tumor mRNA expression from hematoxylin and eosin (H&E)-stained slides, and ENLIGHT, which predicts treatment responses based on the inferred expression values.

“ENLIGHT-DP bypasses the data availability limitations that hinder existing approaches by eliminating the need for dedicated training on new cohorts for each drug treatment,” said Ranit Aharonov, Pangea’s CTO, who co-led the study. “This versatile solution can be applied across various cancer types and therapies, potentially transforming clinical practices and significantly improving patient outcomes.”

Prediction of cancer treatment response from histopathology images through imputed transcriptomics” evaluated the effectiveness of ENLIGHT-DP across five independent patient cohorts involving four different treatments and six cancer types, covering 234 patients. It demonstrated that the odds of patients to respond more than doubled if they received a treatment matched to their tumor by ENLIGHT-DP (an overall odds ratio of 2.28), and that the response rate among patients matched to their treatment by ENLIGHT-DP was 39.5% higher than the baseline response rate. Furthermore, ENLIGHT-DP showed comparable accuracy to supervised methods requiring treatment-specific training data. The authors also demonstrated that the underlying DeepPT framework is superior to other available methods in predicting tumor mRNA expression.

[p"These findings highlight the potential of using AI and digital pathology to enhance precision oncology, making advanced cancer treatment predictions more accessible and accurate,” said Tuvik Beker, CEO of Pangea Biomed. [/p]

The method has the potential to provide rapid and precise treatment recommendations, particularly for low- and middle-income countries where access to comprehensive cancer diagnostics is limited.

Further validation and prospective testing are planned to solidify ENLIGHT-DP’s clinical utility and regulatory approval.

The paper’s authors include Danh-Tai Hoang, Gal Dinstag, Eldad D. Shulman, Leandro C. Hermida, Doreen S. Ben-Zvi, Efrat Elis, Katherine Caley, Stephen-John Sammut, Sanju Sinha, Neelam Sinha, Christopher H. Dampier, Chani Stossel, Tejas Patil, Arun Rajan, Wiem Lassoued, Julius Strauss, Shania Bailey, Clint Allen, Jason Redman, Tuvik Beker, Peng Jiang, Talia Golan, Scott Wilkinson, Adam G. Sowalsky, Sharon R. Pine, Carlos Caldas, James L. Gulley, Kenneth Aldape, Ranit Aharonov, Eric A. Stone, and Eytan Ruppin.

To learn more about Pangea’s ENLIGHT-DP platform, please visit

About Pangea Biomed:

Founded in 2018, Pangea Biomed developed ENLIGHT – the world’s most advanced multi-cancer, multi-therapy response predictor. By combining machine learning and deep RNA analysis, the company is mapping tumor molecular signatures to dynamically and adaptively personalize cancer care for a healthier world. Pangea aims to bring effective precision oncology to cancer patients, improve oncology drug development and empower oncologists to treat patients with success. Pangea is backed by NFX, and its technology has been published in leading journals, including Cell, Med, Science Advances, Cancer Cell, Journal for ImmunoTherapy of Cancer and Nature Communications.


 Precision Oncology
 Artificial Intelligence
 Digital Pathology
 Cancer Research
 Pangea Biomed

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