Faculty Bibliography

Colorectal cancer (CRC) is the most common malignancy of the gastrointestinal tract. The stroma and the tumoral microenvironment (TME) represent ecosystem-like biological networks and are new frontiers in CRC. The present study demonstrates the use of a novel machine learning-based superpixel approach for whole slide images to unravel this biology. Findings of significance include the association of low proportionated stromal area, high immature stromal percentage, and high myxoid stromal ratio (MSR) with worse prognostic outcomes in CRC. Overall, stromal computational markers outperformed all others at predicting clinical outcomes. MSR may be able to prognosticate patients independent of pathological stage, representing an optimal way to effectively prognosticate CRC patients which circumvents the need for more extensive molecular and/or computational profiling. The superpixel approaches to the TME demonstrated here can be performed by a trained pathologist and recorded during synoptic cancer reporting with appropriate quality assurance. Future clinical trials will have the ultimate say in determining whether we can better tailor the need for adjuvant therapy in patients with CRC.

The role of stromal differentiation (SD), program death-ligand 1 (PD-L1), and v-domain Ig suppressor of T cell activation (VISTA) in gastrointestinal stromal tumor (GIST) is largely unknown. Looking forward, the assessment of SD and immune check point inhibition will become more ubiquitous in surgical pathology. Immature, myxoid stroma has been found to be a poor prognostic signature in many cancer subtypes (colon, breast, cervix, esophagus, stomach); although little is known regarding its significance in GIST. For immune check-point inhibition, studies have demonstrated expression to be associated with patient outcomes in numerous cancer subtypes. The present body of work aims to evaluate SD, PD-L1 and VISTA; both in terms of its nature and significance in a clinical setting. Here we found PD-L1 expression in immune cells (IC) and immature SD to be associated with worse cancer free survival, while positive VISTA expression was found to be associated with improved outcomes. High-grade, immature SD had the highest propensity for death/recurrence and was the only variable found to have prognostic significance on multivariate analysis. Our findings support the evaluation of SD, PD-L1 and VISTA in GIST, with clinical practice implications for pathologists. Ultimately, we hope our findings lead to improved prognostication, further optimization of therapeutics, and improved outcomes in a true clinical environment. For GIST, PD-L1 and VISTA could be both clinically relevant and targetable, while SD may be the answer to clinical heterogeneity.

Coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has rapidly spread worldwide. Most of the infected patients present with respiratory symptoms and acute lung damage. Here, we present three cases of patients with COVID-19 disease whose main clinical manifestations are gastrointestinal symptoms. In our first case, we present a COVID-19 patient with histologic findings associated with ischemic necrosis of the small bowel. In the second and third cases, we demonstrate acute cholecystitis and histology showing microvascular thrombosis. These three cases highlight the ischemic and thrombotic changes seen in the setting of COVID-19 infection without classic respiratory symptoms, with resulting severe gastrointestinal and hepatobiliary disease requiring surgical management. Although the bile or stool viral load was not tested in these patients, the small intestine and gallbladder were infected with SARS-CoV-2, most likely via the epithelial angiotensin-converting enzyme 2 (ACE2) receptor.

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.

Colorectal cancer is a heterogenous disease with striking biological diversity. Colorectal carcinoma (CRC) is one of the most common malignancies, accounting for over 9% of all cancers worldwide. To put it in perspective, 5% of people will develop CRC in their lifetime. Biomarkers specific to a particular cancer type can assist in the evaluation of survival probability and help clinicians assess treatment modalities, an example being programmed death ligand-1 (PD-L1). With regards to PD-L1, this is the first study to evaluate the SP-142 antibody clone in CRC. The Ventana PD-L1 (SP-142) assay for PD-L1 expression identifies patients who may benefit from treatment with atezolizumab. SP-142 was chosen as large stage 3 clinical trials are being undertaken with atezolizumab in CRC. Indoleamine 2,3-dioxygenase (IDO-1) was also chosen as there are several ongoing trials for Epacadostat, the best-in-class oral IDO-1 enzyme inhibitor, in many solid tumors. For solid tumors, IDO-1-based immune escape has the potential to inhibit monotherapeutic efficacy of PD-L1-based therapeutics. In this study, a total of 223 cases of CRC were retrospectively reviewed and clinicopathologic data were analyzed in relation to PD-L1 and IDO-1 protein expression. Moreover, tumor-infiltrating lymphocytes, mismatch repair deficiency, high mitotic index, and worse survival outcomes were found in cohorts with significant PD-L1 and IDO-1 expression. Both PD-L1 and IDO-1 are actionable biomarkers, with potential therapeutic implications in CRC. Our findings support the theoretical foundation for targeting PD-L1 and IDO-1 in CRC, which now needs verification in well-designed robust clinical trials.