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Deep Learning Artificial Intelligence Predicts Homologous Recombination Deficiency and Platinum Response From Histologic Slides

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Below is the abstract of the article. or on the link below.

Purpose

Cancers with homologous recombination deficiency (HRD) can benefit from platinum salts and poly(ADP-ribose) polymerase inhibitors. Standard diagnostic tests for detecting HRD require molecular profiling, which is not universally available.

Methods

We trained DeepHRD, a deep learning platform for predicting HRD from hematoxylin and eosin (H&E)–stained histopathological slides, using primary breast (n = 1,008) and ovarian (n = 459) cancers from The Cancer Genome Atlas (TCGA). DeepHRD was compared with four standard HRD molecular tests using breast (n = 349) and ovarian (n = 141) cancers from multiple independent data sets, including platinum-treated clinical cohorts with RECIST progression-free survival (PFS), complete response (CR), and overall survival (OS) endpoints.

Results

DeepHRD predicted HRD from held-out H&E-stained breast cancer slides in TCGA with an AUC of 0.81 (95% CI 0.77-0.85). This performance was confirmed in two independent primary breast cancer cohorts (AUC 0.76, 95% CI 0.71-0.82). In an external platinum-treated metastatic breast cancer cohort, samples predicted as HRD had higher complete CR (AUC 0.76, 95% CI 0.54-0.93) with 3.7-fold increase in median PFS (14.4 vs 3.9 months, P=0.0019) and hazard ratio (HR) of 0.45 (P=0.0047). There were no significant differences in nonplatinum treatment outcome by predicted HRD status in three breast cancer cohorts, including CR (AUC 0.39) and PFS (HR 0.98, P=0.95) in taxane-treated metastatic breast cancer. Through transfer learning to high-grade serous ovarian cancer, DeepHRD-predicted HRD samples had better OS after first-line (HR 0.46, P=0.030) and neoadjuvant (HR 0.49, P=0.015) platinum therapy in two cohorts.

Conclusion

DeepHRD can predict HRD in breast and ovarian cancers directly from routine H&E slides across multiple external cohorts, slide scanners, and tissue fixation variables. When compared with molecular testing, DeepHRD classified 1.8- to 3.1-fold more patients with HRD, which exhibited better OS in high-grade serous ovarian cancer and platinum-specific PFS in metastatic breast cancer.

Read an interview about the study here.

Read the full article

Deep Learning Artificial Intelligence Predicts Homologous Recombination Deficiency and Platinum Response From Histologic Slides

Primary Source

Journal of Clinical Oncology

Source Reference:

ASCO Publications Corner

ASCO Publications Corner