PathAI Present Machine Learning Models that Predict the Homologous Recombination Deficiency Status of Breast Cancer Biopsies at the 2020 SABCS – PR…

BOSTON (PRWEB) December 09, 2020

PathAI, a global provider of AI-powered technology applied to pathology research, today announced the result of a proof-of-concept investigation into ML model prediction of HRD directly from H&E-stained biopsy slides. DNA damage repair pathways, such as homologous recombination, have essential roles in healthy cells, and mutations in these pathways are closely associated with an increased risk for cancer, as well as cancer progression. HRD results from mutations in BRCA1/2, as well as other genes that encode the homologous recombination components that are responsible for error-free repair of double-strand breaks in DNA. HRD tumors are sensitive to poly-ADP ribose polymerase (PARP) inhibitors and platinum-based chemotherapy, making determination of a patients tumor HRD status clinically important. Genomic sequencing is currently the gold standard to classify a tumor as HRD or homologous recombination proficient, but this method has a high error rate, leaving a great unmet need to develop robust and reliable HRD scoring tools.

Identifying the underlying molecular drivers of cancer has tremendous significance not only for our fundamental understanding of the disease biology, but because these image-based assays may also play an important role in making patient treatment decisions in the future, like choosing the most effective therapeutic, said PathAI co-founder and Chief Executive Officer Andy Beck MD, PhD. Our ability to find these signatures in widely available H&E images suggests that our models could have a great impact, and we look forward to investigating this further and validating these results in future studies.

PathAI used two different approaches to predict the HRD status of a tumor from the H&E-stained tissue biopsy. Models were trained using breast cancer tumor biopsy images from TCGA and HRD scores of these same biopsies generated by Knijnenburg and colleagues (published in Cell Reports. 2018. 23:239-254). The Human Interpretable Features (HIF)-based model was trained using thousands of expert pathologist annotations of cell- and tissue-level features of the TCGA images to predict HRD status from HIF-based correlations, whereas the end-to-end model learned to predict HRD status directly from the biopsy image.

Both models predicted HRD with high accuracy, with the HIF-based model having an AUROC of 0.87, and the end-to-end model a AUROC of 0.80. The HIF-based model also revealed that HRD tumors have greater degree of necrosis and also more lymphocytes within the tumor itself than homologous recombination proficient tumors. These results show the enormous potential for digital pathology to identify clinically-significant genomic phenotypes that could not be detected using traditional pathology methods. PathAI will continue to develop and validate these important models for future clinical application.

About PathAIPathAI is a leading provider of AI-powered research tools and services for pathology. PathAIs platform promises substantial improvements to the accuracy of diagnosis and the efficacy of treatment of diseases like cancer, leveraging modern approaches in machine and deep learning. Based in Boston, PathAI works with leading life sciences companies and researchers to advance precision medicine. To learn more, visit https://www.pathai.com.

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PathAI Present Machine Learning Models that Predict the Homologous Recombination Deficiency Status of Breast Cancer Biopsies at the 2020 SABCS - PR...