Faculty Bibliography

Colorectal cancer (CRC) is the third most common cause of cancer related death in the United States (Jasperson et al. in Gastroenterology 138:2044-2058, https://doi.org/10.1053/j.gastro.2010.01.054 , 2010). Many studies have explored prognostic factors in CRC. Today, much focus has been placed on the tumor microenvironment, including different immune cells and the extracellular matrix (ECM). The present study aims to evaluate the role of V-domain immunoglobulin suppressor of T cell activation (VISTA). We utilized QuPath for whole slides image analysis, performing superpixel image segmentation (SIS) on a 226 patient-cohort. High VISTA expression correlated with better disease-free survival (DFS), high tumor infiltrative lymphocyte, microsatellite instability, BRAF mutational status as well as lower tumor stage. High VISTA expression was also associated with mature stromal differentiation (SD). When cohorts were separated based on SD and MMR, only patients with immature SD and microsatellite stability were found to correlate VISTA expression with DFS. Considering raised VISTA expression is associated with improved survival, TILs, mature SD, and MMR in CRC; careful, well-designed clinical trials should be pursued which incorporate the underlying tumoral microenvironment.

Spindle cell lesions of the breast comprise a diverse set of tumors; harboring significant histological and immunohistochemical (IHC) overlap. Accurate diagnosis and classification of spindle cell lesions in the breast remains challenging, especially in core biopsies. In the current study, we evaluated a spectrum of spindle cell lesion of the breast with a panel of IHC antibodies in an effort to differentiate metaplastic spindle cell carcinoma from its benign and malignant mimickers. Our study included 92 patients who underwent breast core biopsies or breast resections at Northwell Health who were diagnosed with benign and malignant tumor/tumor-like spindle cell lesions. Tumors subtypes in this the study included: angiosarcoma, nodular fasciitis, fibromatosis, myofibroblastoma, phyllodes tumors (benign, borderline and malignant), primary sarcomas and metaplastic spindle cell carcinoma. Our biomarker panel included high molecular weight keratin (HMWK), CAM5.2, AE1/AE3, p63, CD34 and GATA3. GATA3 expression was significantly higher in metaplastic carcinomas (88.9 % vs 4.1 %, p < 0.001), when compared to other spindle cell lesions. The sensitivity and specificity for detecting metaplastic carcinomas reached 84.2 % and 97.3 %, respectively. Regarding cytokeratin panels, none of the three individual markers were as sensitive or specific for metaplastic breast carcinoma. GATA3 is the most specific and sensitive marker forfor the identification of metaplastic spindle cell carcinoma of the breast.

Cancer comprises epithelial tumor cells and associated stroma, often times referred to as the "tumoral microenvironment". Cancer-associated fibroblasts (CAFs) are the most notable components of the tumor mesenchyme. CAFs promote the initiation of cancer through angiogenesis, invasion and metastasis. Histologically, the differentiation of stroma has been reported to correlate with prognostic outcomes in patients with colorectal cancer. This review summarizes our current understanding of the extracellular matrix (ECM) in colorectal carcinoma (CRC), showcasing the functions of CAFs and its role in stromal differentiation (SD). We also review current state-of-the-art biology, histopathology, computation, and genomics in the setting of the stroma. SD is distinctive morphologically, and is easily recognized by a surgical pathologist; we offer a lexicon and guide for discovering the essence of stroma, as well as an incipient vision of the future for computation and molecular genomics. We propose that the mesenchymal phenotype, which encompasses a cancer migratory/metastatic capacity, could occur through the process of SD. Looking forward, pathologists will need to invest time and energy into SD, embracing the concept and propagating its use. For patients with colorectal cancer, stroma is a brave new frontier, one not only rich in biologic diversity, but also potentially critical for therapeutic decision making.

Artificial intelligence (AI) is a new frontier and often enigmatic for medical professionals. Cloud computing could open up the field of computer vision to a wider medical audience and deep learning on the cloud allows one to design, develop, train and deploy applications with ease. In the field of histopathology, the implementation of various applications in AI has been successful for whole slide images rich in biological diversity. However, the analysis of other tissue medias, including electron microscopy, is yet to be explored. The present study aims to evaluate deep learning for the classification of medical kidney disease on electron microscopy images: amyloidosis, diabetic glomerulosclerosis, membranous nephropathy, membranoproliferative glomerulonephritis (MPGN), and thin basement membrane disease (TBMD). We found good overall classification with the MedKidneyEM-v1 Classifier and when looking at normal and diseased kidneys, the average area under the curve for precision and recall was 0.841. The average area under the curve for precision and recall on the disease only cohort was 0.909. Digital pathology will shape a new era for medical kidney disease and the present study demonstrates the feasibility of deep learning for electron microscopy. Future approaches could be used by renal pathologists to improve diagnostic concordance, determine therapeutic strategies, and optimize patient outcomes in a true clinical environment.