The RAS isoforms are frequently mutated in many types of human cancers, including PAX3/PAX7 fusion-negative rhabdomyosarcoma. Pediatric RMS arises from skeletal muscle progenitor cells that have failed to differentiate normally. The role of mutant RAS in this differentiation blockade is incompletely understood. We demonstrate that oncogenic RAS, acting through the RAF–MEK [mitogen-activated protein kinase (MAPK) kinase]–ERK (extracellular signal–regulated kinase) MAPK effector pathway, inhibits myogenic differentiation in rhabdomyosarcoma by repressing the expression of the prodifferentiation myogenic transcription factor, MYOG. This repression is mediated by ERK2-dependent promoter-proximal stalling of RNA polymerase II at the MYOG locus. Small-molecule screening with a library of mechanistically defined inhibitors showed that RAS-driven RMS is vulnerable to MEK inhibition. MEK inhibition with trametinib leads to the loss of ERK2 at the MYOG promoter and releases the transcriptional stalling of MYOG expression. MYOG subsequently opens chromatin and establishes super-enhancers at genes required for late myogenic differentiation. Furthermore, trametinib, in combination with an inhibitor of IGF1R, potently decreases rhabdomyosarcoma cell viability and slows tumor growth in xenograft models. Therefore, this combination represents a potential therapeutic for RAS-mutated rhabdomyosarcoma.

Rational design of chimeric antigen receptor T (CAR-T) cells based on the recognition of antigenic epitopes capable of evoking the most potent CAR activation is an important objective in optimizing immune therapy. In solid tumors, the B7-H3 transmembrane protein is an emerging target that harbours two distinct epitope motifs, IgC and IgV, in its ectodomain. Here, we generate dromedary camel nanobodies targeting B7-H3 and demonstrate that CAR-T cells, based on the nanobodies recognizing the IgC but not IgV domain, had potent antitumour activity against large tumors in female mice. These CAR-T cells are characterized by highly activated T cell signaling and significant tumor infiltration. Single-cell transcriptome RNA sequencing coupled with functional T-cell proteomics analysis uncovers the top-upregulated genes that might be critical for the persistence of polyfunctional CAR-T cells in mice. Our results highlight the importance of the specific target antigen epitope in governing optimal CAR-T activity and provide a nanobody-based B7-H3 CAR-T product for use in solid tumor therapy.

Objective: The goal is to evaluate avelumab, an anti-PD-L1 monoclonal immunoglobulin G antibody labeled with zirconium-89 in human PD-L1-expressing cancer cells and mouse xenografts for clinical translation. Methods: [⁸⁹Zr]Zr-DFO-PD-L1 monoclonal antibody (mAb) was synthesized using avelumab conjugated to desferrioxamine. In vitro binding studies and biodistribution studies were performed with PD-L1+MDA-MB231 cells and MDA-MB231 xenograft mouse models, respectively. Biodistributions were determined at 1, 2, 3, 5, and 7 days post coinjection of [⁸⁹Zr]Zr-DFO-PD-L1 mAb without or with unlabeled avelumab (10, 20, 40, and 400 µg). Results: [⁸⁹Zr]Zr-DFO-PD-L1 mAb exhibited high affinity (K_d ∼ 0.3 nM) and detected moderate PD-L1 expression levels in MDA-MB231 cells. The spleen and lymph nodes exhibited the highest [⁸⁹Zr]Zr-DFO-PD-L1 mAb uptakes in all time points, while MDA-MB231 tumor uptakes were lower but highly retained. In the unlabeled avelumab dose escalation studies, spleen tissue–muscle ratios decreased in a dose-dependent manner indicating specific [⁸⁹Zr]Zr-DFO-PD-L1 mAb binding to PD-L1. In contrast, lymph node and tumor tissue–muscle ratios increased 4- to 5-fold at 20 and 40 µg avelumab doses. Conclusions: [⁸⁹Zr]Zr-DFO-PD-L1 mAb exhibited specific and high affinity for PD-L1 in vitro and had target tissue uptakes correlating with PD-L1 expression levels in vivo. [⁸⁹Zr]Zr-DFO-PD-L1 mAb uptake in PD-L1+tumors increased with escalating doses of avelumab.

The prognostic significance of histologic and clinical features was evaluated in a retrospective study of 70 dogs treated with nephrectomy for renal cell carcinoma. Dogs presenting with hematuria and cachexia had significantly decreased overall and tumor-specific survival. Mitotic index (MI), nuclear size, nuclear pleomorphism, tumor differentiation, invasiveness, Fuhrman nuclear grade, and clear cell morphology were significantly associated with survival times (overall and tumor specific) in univariate analyses. A multivariate Cox proportional hazards model was constructed using stepwise selection to evaluate potential histologic predictor variables. This multivariate analysis revealed MI, defined as the number of mitotic figures in ten 400× fields, as the sole independent prognostic variable. Median survival for dogs with an MI >30 was 187 days compared with 1184 days for dogs with an MI of <10. Dogs with an intermediate MI of 10 to 30 had a median survival of 452 days. Canine renal carcinomas were categorized into the following subtypes based on histologic features and histochemical and immunohistochemical staining: (1) clear cell, (2) chromophobe, (3) papillary, and (4) multilocular cystic renal cell carcinomas. Clear cell carcinoma was diagnosed in 6 of 70 (9%) canine tumors and was associated with a significantly decreased median survival time. Papillary carcinomas were identified in 15 of 70 tumors (21%), chromophobe in 6 of 70 (9%), and the multilocular cystic variant of canine renal cell carcinoma in 3 of 70 tumors (4%). These findings facilitate uniform categorization of canine renal cell carcinoma and provide veterinary pathologists with criteria to determine prognostic information.

Toxicologic pathology is transitioning from analog to digital methods. This transition seems inevitable due to a host of ongoing social and medical technological forces. Of these, artificial intelligence (AI) and in particular machine learning (ML) are globally disruptive, rapidly growing sectors of technology whose impact on the long-established field of histopathology is quickly being realized. The development of increasing numbers of algorithms, peering ever deeper into the histopathological space, has demonstrated to the scientific community that AI pathology platforms are now poised to truly impact the future of precision and personalized medicine. However, as with all great technological advances, there are implementation and adoption challenges. This review aims to define common and relevant AI and ML terminology, describe data generation and interpretation, outline current and potential future business cases, discuss validation and regulatory hurdles, and most importantly, propose how overcoming the challenges of this burgeoning technology may shape toxicologic pathology for years to come, enabling pathologists to contribute even more effectively to answering scientific questions and solving global health issues. [Box: see text]