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Background Breast cancer patients with estrogen receptor (ER)+/HER2- (and usually node-negative) tumors can avail themselves of Oncotype DX Breast Recurrence Score (ODXRS) testing to predict their risk of distant recurrence within 9 years and, consequently, putative chemotherapy benefit. However, ODXRS testing requires sufficient tumour availability and specimen shipping, which imposes time and financial burdens to testing which have to be met by healthcare systems. The advent of digital pathology offers a potential avenue for exploring computer-aided diagnostic solutions which may overcome these hurdles by extracting the requisite information from hematoxylin and eosin (H&E)-stained tissue whole slide images (WSIs) alone. In turn, this technology could significantly reduce diagnostic turnaround times and cost, and improve accessibility and test reproducibility, thereby enabling healthcare systems to run more efficiently and offer patients more timely results. Ideally, such a platform should incorporate a measure of the underlying tumor biology to provide a fully explainable, white box solution, and may offer further insights into the identification of early recurrence events. Aims The aim of this study was to establish whether our computer-aided solution’s (Q-Plasia OncoReader Breast, QPORB) digital biomarker representing G1/S cell cycle deformations extracted from H&E WSIs was prognostic for disease-free survival (DFS) and could predict disease recurrence, particularly in the setting of low risk ODXRS breast cancers. Methods Primary breast cancer resection/excision specimens (n=70 cases) sent for ODXRS testing from St James’s University Hospital, UK (2016-2019) were collected. Anonymised diagnostic glass slides (n=198 slides) of H&E-stained tumors were scanned at x20 magnification on an Aperio AT2 scanner. In parallel, relevant clinical and histological data were collected from pathology reports and electronic patient records, including both ODXRS and recurrence events during follow-up. The QPORB recurrence scale (QPORB-RS), which combines statistical physics and tumor biology to identify image-based malignant cell cycle deformation, extracts prognostic information from WSIs. The contribution of potential confounders (age, stage, grade, lesion size, Nottingham prognostic index and Charlson score) were accounted for. Results The QPORB-RS was prognostic for DFS for patients with predominantly node-negative (including node micro-metastases) HR+/HER2- tumors over a median follow-up period of 5 years (P=0.02; dichotomized Kaplan Meyer with median cut-off). The QPORB-RS concurred with ODXRS’s high vs. low recurrence risk in 73% (19/26) and 61% (27/44) of cases, respectively, with an overall agreement of 66% (46/70). Moreover, the QPORB-RS identified all 5 patients who had recurrences (with ODXRS of 6, 9, 10, 21 and 26, and ages of 55, 66, 42, 35 and 50 years, respectively) as being high risk in the subset of those given a low (including historically intermediate) ODXRS and who did not receive chemotherapy. Conclusion The QPORB-RS is a good prognostic test of risk of disease recurrence in breast cancer patients with predominantly node-negative (including node micro-metastases) HR+/HER2- tumors within a median 5-year follow-up period. Our efforts are now focussed on extending this cohort and establishing the prognostic value of the QPORB-RS across all breast carcinomas, regardless of molecular subtype, stage/node positivity and menopausal status. Citation Format: Satabhisa Mukhopadhyay, Tathagata Dasgupta, Elizabeth Walsh, Rebecca Millican-Slater, Andrew hanby, Joanne Stephenson, Craig A. Bunnell, Nicolas M. Orsi. Prediction of disease recurrence in low risk Oncotype Dx breast cancers from digital H&E-stained whole slide images of pre-treatment resections alone [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P3-05-48. |
