Developmental and therapeutic implications of IL4ra expression for rhabdomyosarcoma.

Rhabdomyosarcoma (RMS) is a solid tumor whose metastatic progression can be accelerated through interleukin-4 receptor alpha (Il4ra) mediated interaction with normal muscle stem cells (satellite cells). To understand the function of Il4ra in this tumor initiation phase of RMS, we conditionally deleted Il4ra in genetically-engineered RMS mouse models. Nullizygosity of Il4ra altered the latency, site and/or stage distribution of RMS tumors compared to IL4RA intact models. Primary tumor cell cultures taken from the genetically-engineered models then used in orthotopic allografts further defined the interaction of satellite cells and RMS tumor cells in the context of tumor initiation: in alveolar rhabdomyosarcoma (ARMS), satellite cell co-injection was necessary for Il4ra null tumor cells engraftment, whereas in embryonal rhabdomyosarcoma (ERMS), satellite cell co-injection decreased latency of engraftment of Il4ra wildtype tumor cells but not Il4ra null tumor cells. When refocusing on Il4ra wildtype tumors by single cell sequencing and cytokine studies, we have uncovered a putative signaling interplay of Il4 from T-lymphocytes being received by Il4ra + rhabdomyosarcoma tumor cells, which in turn express Ccl2, the ligand for Ccr2 and Ccr5. Taken together, these results suggest that mutations imposed during tumor initiation have different effects than genetic or therapeutic intervention imposed once tumors are already formed. We also propose that CCL2 and its cognate receptors CCR2 and/or CCR5 are potential therapeutic targets in Il4ra mediated RMS progression.

Machine learning for rhabdomyosarcoma histopathology.

Correctly diagnosing a rare childhood cancer such as sarcoma can be critical to assigning the correct treatment regimen. With a finite number of pathologists worldwide specializing in pediatric/young adult sarcoma histopathology, access to expert differential diagnosis early in case assessment is limited for many global regions. The lack of highly-trained sarcoma pathologists is especially pronounced in low to middle-income countries, where pathology expertise may be limited despite a similar rate of sarcoma incidence. To address this issue in part, we developed a deep learning convolutional neural network (CNN)-based differential diagnosis system to act as a pre-pathologist screening tool that quantifies diagnosis likelihood amongst trained soft-tissue sarcoma subtypes based on whole histopathology tissue slides. The CNN model is trained on a cohort of 424 centrally-reviewed histopathology tissue slides of alveolar rhabdomyosarcoma, embryonal rhabdomyosarcoma and clear-cell sarcoma tumors, all initially diagnosed at the originating institution and subsequently validated by central review. This CNN model was able to accurately classify the withheld testing cohort with resulting receiver operating characteristic (ROC) area under curve (AUC) values above 0.889 for all tested sarcoma subtypes. We subsequently used the CNN model to classify an externally-sourced cohort of human alveolar and embryonal rhabdomyosarcoma samples and a cohort of 318 histopathology tissue sections from genetically engineered mouse models of rhabdomyosarcoma. Finally, we investigated the overall robustness of the trained CNN model with respect to histopathological variations such as anaplasia, and classification outcomes on histopathology slides from untrained disease models. Overall positive results from our validation studies coupled with the limited worldwide availability of sarcoma pathology expertise suggests the potential of machine learning to assist local pathologists in quickly narrowing the differential diagnosis of sarcoma subtype in children, adolescents, and young adults.