Procrustes is a machine-learning approach that removes cross-platform batch effects from clinical RNA sequencing data.

With the increased use of gene expression profiling for personalized oncology, optimized RNA sequencing (RNA-seq) protocols and algorithms are necessary to provide comparable expression measurements between exome capture (EC)-based and poly-A RNA-seq. Here, we developed and optimized an EC-based protocol for processing formalin-fixed, paraffin-embedded samples and a machine-learning algorithm, Procrustes, to overcome batch effects across RNA-seq data obtained using different sample preparation protocols like EC-based or poly-A RNA-seq protocols. Applying Procrustes to samples processed using EC and poly-A RNA-seq protocols showed the expression of 61% of genes (N = 20,062) to correlate across both protocols (concordance correlation coefficient > 0.8, versus 26% before transformation by Procrustes), including 84% of cancer-specific and cancer microenvironment-related genes (versus 36% before applying Procrustes; N = 1,438). Benchmarking analyses also showed Procrustes to outperform other batch correction methods. Finally, we showed that Procrustes can project RNA-seq data for a single sample to a larger cohort of RNA-seq data. Future application of Procrustes will enable direct gene expression analysis for single tumor samples to support gene expression-based treatment decisions.

Evolutionary dynamics of tipifarnib in HRAS mutated head and neck squamous cell carcinoma.

Head and neck squamous cell carcinoma (HNSCC) is a highly prevalent malignancy worldwide, with a significant proportion of patients developing recurrent and/or metastatic (R/M) disease. Despite recent advances in therapy, the prognosis for patients with advanced HNSCC remains poor. Here, we present the case of a patient with recurrent metastatic HNSCC harboring an HRAS G12S mutation who achieved a durable response to treatment with tipifarnib, a selective inhibitor of farnesyltransferase. The patient was a 48-year-old woman who had previously received multiple lines of therapy with no significant clinical response. However, treatment with tipifarnib resulted in a durable partial response that lasted 8 months. Serial genomic and transcriptomic analyses demonstrated upregulation of YAP1 and AXL in metastatic lesions compared with the primary tumor, the evolution of the tumor microenvironment from an immune-enriched to a fibrotic subtype with increased angiogenesis, and activation of the PI3K/AKT/mTOR pathway in tipifarnib treatment. Lastly, in HRAS-mutated PDXs and in the syngeneic HRAS model, we demonstrated that tipifarnib efficacy is limited by activation of the AKT pathway, and dual treatment with tipifarnib and the PI3K inhibitor, BYL719, resulted in enhanced anti-tumor efficacy. Our case study highlights the potential of targeting HRAS mutations with tipifarnib in R/M HNSCC and identifies potential mechanisms of acquired resistance to tipifarnib, along with immuno-, chemo-, and radiation therapy. Preclinical results provide a firm foundation for further investigation of drug combinations of HRAS-and PI3K -targeting therapeutics in R/M HRAS-driven HNSCC.