Abstract P5-07-12: Local nuclear architecture features from H&amp;E images predict early versus distant recurrence in lymph node negative, ER+ breast cancers
Authors: S Ali, D Rimm, S Ganesan, A Madabhushi
Journal: Cancer Research (2016)

Abstract Introduction: Breast cancer (BCa) Patients with ER+ tumors that are lymph node negative (LN-) typically receive hormonal therapy. There is a need to identify ER+ LN- patients that will not benefit from adjuvant chemotherapy and will respond to hormonal therapy alone. Oncotype DX, a quantitative prognostic and predictive gene assay, provides a recurrence score that has been correlated with distant and early recurrence. In this work we present an approach that employ computer extracted features of nuclear architecture and morphology from routine H&amp;E slides alone that can distinguish early and distant recurrence in ER+ breast cancers. By constructing graph networks within epithelium and stroma regions, built using nuclei as vertices and edge connections between proximal nuclei, local nuclear architecture can be quantitatively characterized. Hosoya index (HI) (originally introduced for analysis of chemical bonds) is a measure of a bond (in this context nuclei connections in a graph). In this work, we leverage HI to measure structural similarities of graphs across the populations that are indicative of recurrence in LN- ER+ breast cancer tissue microarray (TMA) images. Design: In this study we considered two tissue microarrays (TMAs) comprising 453 early-stage lymph-node negative (LN-) estrogen receptor positive (ER+) breast cancer (BCa) patients (diagnosed with invasive ductal carcinoma), with a total of N=90 patients experiencing lifetime distant recurrence and N=343 patients who did not. All TMA cores were digitized at 20x magnification (0.33 um/pixel spatial resolution) using a digital whole-slide scanner. Each nucleus was identified via an automated computerized image analysis algorithm developed by our group. Then, using a cluster cell graph that encodes a link between a pair of nodes based on proximity, a series of graphs are constructed for a TMA. A HI value was then assigned to each local graph. A support vector machine classifier was trained in conjunction with the distribution of HI values for the early and distant recurrence cases on the training TMA (n=243, 50 early recurrences). Independent validation of the SVM classifier was performed on the second TMA (n=210, 40 early recurrences). Results: For the LN- ER+ breast cancer dataset, our method was able to distinguish tumors with early and distant recurrence with an accuracy of 75.4%, a positive predictive value of 78.6% and a negative predictive value of 76.4%. The separation between the Kaplan-Meier curves for early and distant recurrence of LN-, ER+ breast cancers on the validation set was statistically significant (p &amp;lt; 0.00102). Conclusion: Based only on tiny H&amp;E punches, a computer-aided morphometric classifier appears to identify lymph node negative, ER+ breast cancers with a low likelihood of recurrence. With further validation, this approach could be developed into an image based assay which could serve as a lower cost alternative to Oncotype DX. Citation Format: Ali S, Rimm D, Ganesan S, Madabhushi A. Local nuclear architecture features from H&amp;E images predict early versus distant recurrence in lymph node negative, ER+ breast cancers. [abstract]. In: Proceedings of the Thirty-Eighth Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2015 Dec 8-12; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2016;76(4 Suppl):Abstract nr P5-07-12.