Abstract 3678: Beyond detection: AI-based classification of breast cancer invasiveness using cell-free orphan non-coding RNAs

Abstract Background: Approximately 1 in 8 women will be impacted by breast cancer in their lifetimes. Earlier detection of breast cancer through screening has improved survival. Liquid biopsies have the potential to complement existing screening methods by enabling earlier detection and differentiating invasive cancer (IBC) from ductal carcinoma in-situ (DCIS). We previously demonstrated high sensitivity and specificity for early detection of IBC by using a blood-based liquid biopsy platform to analyze a novel category of cancer-associated small RNAs, termed orphan RNAs (oncRNAs). Here, we developed a test that could not only detect the presence of cancer, but also classify the invasiveness of breast cancer. Methods: We utilized The Cancer Genome Atlas (TCGA) small RNA profiles to discover a library of 20,538 oncRNAs that were significantly enriched among 1,103 breast tumors compared to 349 controls from normal tissues spanning multiple tissue sites, limited to female samples. The diagnostic performance of these oncRNAs was assessed in an independent cohort of serum samples from 708 women, including 380 breast cancer patients (221 IBC and 159 DCIS; mean age: 58.0 ± 13.4 years) and 328 age-matched controls (mean age: 58.4 ± 13.7 years). We sequenced the small RNA content from 1 ml of serum from these patients at an average depth of 21.6 million 50-bp single-end reads. We detected 19,736 (96%) of the breast cancer-specific oncRNA library within at least one sample in this cohort. We then trained a multi-class generative AI model using 5-fold cross-validation to predict IBC, DCIS, and absence of breast cancer (IBC or DCIS). Results: Our oncRNA-based generative AI model achieved an overall AUC of 0.95 (95% CI: 0.94-0.97) for prediction of breast cancer versus cancer-free controls. At 90% specificity, overall model sensitivity is 90.0% (86.5%-92.8%). For DCIS and stage I IBC, the model has a sensitivity of 88.1% (82.0%-92.7%) and 90.4% (81.9%-95.8%) respectively, both at 90% specificity. In the second step, restricting to samples flagged as cancer, we observed an overall AUC of 0.9 (0.87-0.93) and sensitivity of 62.4% (55.3%-69.1%) at 90% specificity for discriminating against invasive breast cancer. Conclusions: We have demonstrated the potential utility of oncRNAs as the foundation for a liquid biopsy platform for sensitive and accurate early detection of breast cancer. Our liquid biopsy assay has the potential to complement standard of care by not only detecting breast cancer but also differentiating IBC from DCIS. Citation Format: Mehran Karimzadeh, Taylor B. Cavazos, Nae-Chyun Chen, Noura K. Tbeileh, David Siegel, Amir Momen-Roknabadi, Jennifer Yen, Jeremy Ku, Selina Chen, Diana Corti, Alice Huang, Dang Nguyen, Rose Hanna, Ti Lam, Seda Kilinc, Philip Murzynowski, Jieyang Wang, Xuan Zhao, Andy Pohl, Babak Behsaz, Helen Li, Lisa Fish, Kim H. Chau, Marra S. Francis, Laura J. Van't Veer, Laura J. Esserman, Patrick A. Arensdorf, Hani Goodarzi, Fereydoun Hormozdiari, Babak Alipanahi. Beyond detection: AI-based classification of breast cancer invasiveness using cell-free orphan non-coding RNAs [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3678.