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PurposeLynch syndrome is defined by germline pathogenic mutations involving DNA mismatch repair (MMR) genes and linked with the development of MMR-deficient colon and endometrial cancers. Whether breast cancers developing in the context of Lynch syndrome are causally related to MMR deficiency (MMRd), remains controversial. Thus, we explored the morphologic and genomic characteristics of breast cancers occurring in Lynch syndrome individuals.Experimental designA retrospective analysis of 20,110 patients with cancer who underwent multigene panel genetic testing was performed to identify individuals with a likely pathogenic/pathogenic germline variant in MLH1, MSH2, MSH6, or PMS2 who developed breast cancers. The histologic characteristics and IHC assessment of breast cancers for MMR proteins and programmed death-ligand 1 (PD-L1) expression were assessed on cases with available materials. DNA samples from paired tumors and blood were sequenced with Memorial Sloan Kettering-Integrated Mutation Profiling of Actionable Cancer Targets (≥468 key cancer genes). Microsatellite instability (MSI) status was assessed utilizing MSISensor. Mutational signatures were defined using SigMA.ResultsA total of 272 individuals with Lynch syndrome were identified, 13 (5%) of whom had primary breast cancers. The majority of breast cancers (92%) were hormone receptor-positive tumors. Five (42%) of 12 breast cancers displayed loss of MMR proteins by IHC. Four (36%) of 11 breast cancers subjected to tumor-normal sequencing showed dominant MSI mutational signatures, high tumor mutational burden, and indeterminate (27%) or high MSISensor scores (9%). One patient with metastatic MMRd breast cancer received anti-PD1 therapy and achieved a robust and durable response.ConclusionsA subset of breast cancers developing in individuals with Lynch syndrome are etiologically linked to MMRd and may benefit from anti-PD1/PD-L1 immunotherapy.

Pathologic analysis of surgical excision specimens for breast carcinoma is important to evaluate the completeness of surgical excision and has implications for future treatment. This analysis is performed manually by pathologists reviewing histologic slides prepared from formalin-fixed tissue. In this paper, we present Deep Multi-Magnification Network trained by partial annotation for automated multi-class tissue segmentation by a set of patches from multiple magnifications in digitized whole slide images. Our proposed architecture with multi-encoder, multi-decoder, and multi-concatenation outperforms other single and multi-magnification-based architectures by achieving the highest mean intersection-over-union, and can be used to facilitate pathologists' assessments of breast cancer., Comment: Accepted at Computerized Medical Imaging and Graphics