Abstract
BackgroundBreast cancer intrinsic molecular subtype (IMS) as classified by the expression-based PAM50 assay is considered a strong prognostic feature, even when controlled for by standard clinicopathological features such as age, grade, and nodal status, yet the molecular testing required to elucidate these subtypes is not routinely performed. Furthermore, when such bulk assays as RNA sequencing are performed, intratumoral heterogeneity that may affect prognosis and therapeutic decision-making can be missed.MethodsAs a more facile and readily available method for determining IMS in breast cancer, we developed a deep learning approach for approximating PAM50 intrinsic subtyping using only whole-slide images of H&E-stained breast biopsy tissue sections. This algorithm was trained on images from 443 tumors that had previously undergone PAM50 subtyping to classify small patches of the images into four major molecular subtypes—Basal-like, HER2-enriched, Luminal A, and Luminal B—as well as Basal vs. non-Basal. The algorithm was subsequently used for subtype classification of a held-out set of 222 tumors.ResultsThis deep learning image-based classifier correctly subtyped the majority of samples in the held-out set of tumors. However, in many cases, significant heterogeneity was observed in assigned subtypes across patches from within a single whole-slide image. We performed further analysis of heterogeneity, focusing on contrasting Luminal A and Basal-like subtypes because classifications from our deep learning algorithm—similar to PAM50—are associated with significant differences in survival between these two subtypes. Patients with tumors classified as heterogeneous were found to have survival intermediate between Luminal A and Basal patients, as well as more varied levels of hormone receptor expression patterns.ConclusionsHere, we present a method for minimizing manual work required to identify cancer-rich patches among all multiscale patches in H&E-stained WSIs that can be generalized to any indication. These results suggest that advanced deep machine learning methods that use only routinely collected whole-slide images can approximate RNA-seq-based molecular tests such as PAM50 and, importantly, may increase detection of heterogeneous tumors that may require more detailed subtype analysis.
Highlights
Breast cancer intrinsic molecular subtype (IMS) as classified by the expression-based Predictor Analysis of Microarray 50 (PAM50) assay is considered a strong prognostic feature, even when controlled for by standard clinicopathological features such as age, grade, and nodal status, yet the molecular testing required to elucidate these subtypes is not routinely performed
RNA-based signature assays such as MammaPrint, Oncotype DX, and Predictor Analysis of Microarray 50 (PAM50) [2] are being employed as supplementary prognostic indicators due to studies demonstrating more significant differential survival between identified subtypes when compared to standard clinicopathological factors [3,4,5]
The PAM50-defined intrinsic molecular subtype (IMS) classifications include Luminal A (LumA), Luminal B (LumB), HER2-enriched (HER2), Basal-like (Basal), and Normal-like (Normal); while there is some correlation between receptor status and IMS, the latter is determined by consideration of gene expression beyond receptors
Summary
Breast cancer intrinsic molecular subtype (IMS) as classified by the expression-based PAM50 assay is considered a strong prognostic feature, even when controlled for by standard clinicopathological features such as age, grade, and nodal status, yet the molecular testing required to elucidate these subtypes is not routinely performed. When such bulk assays as RNA sequencing are performed, intratumoral heterogeneity that may affect prognosis and therapeutic decision-making can be missed. The molecular signature-based tests are not, as ubiquitously employed as IHC-based receptor subtyping in part due to their high cost, extended processing times, and requirement for appropriate tissue samples. A method for tumor classification beyond receptor subtyping that approximates PAM50 subtyping that is practical, cost-effective, and utilizes readily available samples could be of great utility
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