Faculty Bibliography

Papillary renal neoplasm with reverse polarity (PRNRP) has been proposed as a distinct subtype of renal cell neoplasm with recurrent KRAS mutations and indolent behavior. However, its epigenetic landscape is poorly understood. In this study, 12 PRNRPs and a PRNRP initially diagnosed as "papillary adenoma" were analyzed. All 13 cases underwent targeted next-generation sequencing for driver mutations. Eleven PRNRPs were profiled using the Illumina MethylationEPIC array and compared with a reference cohort of 71 common renal cell tumors. KRAS mutations were identified in 12 of 13 (92%) cases of PRNRP. Copy-number analysis from methylation profiling showed that 9 of 11 (82%) PRNRPs lacked copy-number changes. Two cases showed a focal loss of chromosome 8 and a gain of chromosome 16, respectively. Unsupervised clustering based on methylation data showed that PRNRPs form a distinct epigenetic group, separate from papillary renal cell carcinomas (pRCCs) and other major renal tumors, but with the closest affinity to clear cell papillary renal cell tumors. In addition, DNA methylation analysis suggested PRNRP may arise from the distal nephron, in contrast to pRCC, which appears to recapitulate proximal tubules. These findings support PRNRP as a subtype of renal cell neoplasm with a distinct epigenetic signature.

Renal neoplasms are morphologically and molecularly heterogeneous, with their diagnosis often hindered by interobserver variability and overlapping microscopic features. A subset of cases is unclassifiable despite immunohistochemical, mutation, and cytogenetic-based diagnostic workup. Through examination of the genome-wide DNA methylation signatures of over 2000 renal neoplasms, we identified 23 coherent groups that correlate with known neoplasm types and identified novel clinically relevant subtypes of existing neoplasm types. We used machine learning models to develop and validate a classifier trained on DNA methylation profiles of 1284 samples. The classifier was tested on an external data set of 287 renal neoplasms with >90% concordance between expected neoplasm type and high-score DNA methylation-based classification. Discordance between the original histologic label and methylation class led to potential reclassification of some cases. This work demonstrates proof of principle for the feasibility of a DNA methylation classifier as a clinically useful tool to assist in the diagnosis of renal neoplasms.

The aggressiveness of prostate cancer is primarily assessed from histopathological data using the Gleason scoring system. Conventional artificial intelligence (AI) approaches can predict Gleason scores, but often lack explainability, which may limit clinical acceptance. Here, we present an alternative, inherently explainable AI that circumvents the need for post-hoc explainability methods. The model was trained on 1,015 tissue microarray core images, annotated with detailed pattern descriptions by 54 international pathologists following standardized guidelines. It uses pathologist-defined terminology and was trained using soft labels to capture data uncertainty. This approach enables robust Gleason pattern segmentation despite high interobserver variability. The model achieved comparable or superior performance to direct Gleason pattern segmentation (Dice score:

Cancer of unknown primary (CUP) constitutes between 2% and 5% of human malignancies and is among the most common causes of cancer death in the United States. Brain metastases are often the first clinical presentation of CUP; despite extensive pathological and imaging studies, 20%-45% of CUP are never assigned a primary site. DNA methylation array profiling is a reliable method for tumor classification but tumor-type-specific classifier development requires many reference samples. This is difficult to accomplish for CUP as many cases are never assigned a specific diagnosis. Recent studies identified subsets of methylation quantitative trait loci (mQTLs) unique to specific organs, which could help increase classifier accuracy while requiring fewer samples. We performed a retrospective genome-wide methylation analysis of 759 carcinoma samples from formalin-fixed paraffin-embedded tissue samples using Illumina EPIC array. Utilizing mQTL specific for breast, lung, ovarian/gynecologic, colon, kidney, or testis (BLOCKT) (185k total probes), we developed a deep learning-based methylation classifier that achieved 93.12% average accuracy and 93.04% average F1-score across a 10-fold validation for BLOCKT organs. Our findings indicate that our organ-based DNA methylation classifier can assist pathologists in identifying the site of origin, providing oncologists insight on a diagnosis to administer appropriate therapy, improving patient outcomes.

Programmed death-ligand 1 (PD-L1) expression in terms of the tumor proportion score (TPS) is the main predictive biomarker approved for immunotherapy against lung nonsmall cell carcinoma. Although some studies have explored the associations between histology and PD-L1 expression in pulmonary adenocarcinoma, they have been limited in sample size and/or extent of examined histologic variables, which may have resulted in conflicting information. In this observational retrospective study, we identified primary and metastatic lung adenocarcinoma cases in the span of 5 years and tabulated the detailed histopathologic features, including pathological stage, tumor growth pattern, tumor grade, lymphovascular and pleural invasion, molecular alterations, and the associated PD-L1 expression for each case. Statistical analyses were performed to detect associations between PD-L1 and these features. Among 1658 cases, 643 were primary tumor resections, 751 were primary tumor biopsies, and 264 were metastatic site biopsies or resections. Higher TPS significantly correlated with high-grade growth patterns, grade 3 tumors, higher T and N stage, presence of lymphovascular invasion, and presence of MET and TP53 alterations, whereas lower TPS correlated with lower-grade tumors and presence of EGFR alterations. There was no difference in PD-L1 expression in matched primary and metastases, although higher TPS was observed in metastatic tumors due to the presence of high-grade patterns in these specimens. TPS showed a strong association with a histologic pattern. Higher-grade tumors had higher TPS, which is also associated with more aggressive histologic features. Tumor grade should be kept in mind when selecting cases and blocks for PD-L1 testing.