Rhabdomyosarcoma arising in a giant congenital melanocytic nevus.

A number of lesions have been documented to arise within congenital melanocytic nevi (CMNs). Although the most frequent malignancy arising within a CMN is melanoma, the association between rhabdomyosarcoma and CMN has rarely been documented. We present a case arising in a 4-month-old girl with a giant CMN. She presented for evaluation of a pedunculated lesion at the superior gluteal crease that had been present since birth and exhibited rapid growth. Biopsy of the lesion revealed two distinct components: an expansile proliferation of pleomorphic cells with varying degrees of cellularity and a proliferation of banal-appearing melanocytic nevic cells. The cells of the expansile proliferation displayed a wide range of morphologic features, including nests of round cells, spindle-shaped cells, and more differentiated rhabdomyoblasts within a myxoid, highly vascularized stroma. Cross-striations, a marker of skeletal muscle differentiation, were present. These tumor cells were strongly immunoreactive with desmin, myo-D1, and myogenin. Fluorescence in situ hybridization analysis with PAX3/7-FKHR probes was negative. A diagnosis of embryonal rhabdomyosarcoma in association with CMN was made. Initial excision revealed tumor at the margins, and the patient underwent reexcision with subsequent chemotherapy with vincristine, actinomycin D, and cyclophosphamide. She was disease-free at the 6-year follow-up. It has been postulated that the combination of melanocytic and rhabdomyoblastic cells within the same lesion may imply derivation from a common pluripotent stem cell or neural crest cell. Clinicians following patients with giant CMN should consider rhabdomyosarcoma in the differential diagnosis of lesions arising within the nevus.

Human-interpretable image features derived from densely mapped cancer pathology slides predict diverse molecular phenotypes.

Computational methods have made substantial progress in improving the accuracy and throughput of pathology workflows for diagnostic, prognostic, and genomic prediction. Still, lack of interpretability remains a significant barrier to clinical integration. We present an approach for predicting clinically-relevant molecular phenotypes from whole-slide histopathology images using human-interpretable image features (HIFs). Our method leverages >1.6 million annotations from board-certified pathologists across >5700 samples to train deep learning models for cell and tissue classification that can exhaustively map whole-slide images at two and four micron-resolution. Cell- and tissue-type model outputs are combined into 607 HIFs that quantify specific and biologically-relevant characteristics across five cancer types. We demonstrate that these HIFs correlate with well-known markers of the tumor microenvironment and can predict diverse molecular signatures (AUROC 0.601-0.864), including expression of four immune checkpoint proteins and homologous recombination deficiency, with performance comparable to 'black-box' methods. Our HIF-based approach provides a comprehensive, quantitative, and interpretable window into the composition and spatial architecture of the tumor microenvironment.

Report on computational assessment of Tumor Infiltrating Lymphocytes from the International Immuno-Oncology Biomarker Working Group.

Assessment of tumor-infiltrating lymphocytes (TILs) is increasingly recognized as an integral part of the prognostic workflow in triple-negative (TNBC) and HER2-positive breast cancer, as well as many other solid tumors. This recognition has come about thanks to standardized visual reporting guidelines, which helped to reduce inter-reader variability. Now, there are ripe opportunities to employ computational methods that extract spatio-morphologic predictive features, enabling computer-aided diagnostics. We detail the benefits of computational TILs assessment, the readiness of TILs scoring for computational assessment, and outline considerations for overcoming key barriers to clinical translation in this arena. Specifically, we discuss: 1. ensuring computational workflows closely capture visual guidelines and standards; 2. challenges and thoughts standards for assessment of algorithms including training, preanalytical, analytical, and clinical validation; 3. perspectives on how to realize the potential of machine learning models and to overcome the perceptual and practical limits of visual scoring.