Purpose: The objective of this study is to characterize the role of miRNAs in the classification of head and neck squamous cell carcinoma (HNSCC).Experimental Design: Here, we analyzed 562 HNSCC samples, 88 from a novel cohort and 474 from The Cancer Genome Atlas, using miRNA microarray and miRNA sequencing, respectively. Using an integrative correlations method followed by miRNA expression-based hierarchical clustering, we validated miRNA clusters across cohorts. Evaluation of clusters by logistic regression and gene ontology approaches revealed subtype-based clinical and biological characteristics.Results: We identified two independently validated and statistically significant (P < 0.01) tumor subtypes and named them "epithelial" and "stromal" based on associations with functional target gene ontology relating to differing stages of epithelial cell differentiation. miRNA-based subtypes were correlated with indi-vidual gene expression targets based on miRNA seed sequences, as well as with miRNA families and clusters including the miR-17 and miR-200 families. These correlated genes defined pathways relevant to normal squamous cell function and pathophysiology. miRNA clusters statistically associated with differential mutation patterns including higher proportions of TP53 mutations in the stromal class and higher NSD1 and HRAS mutation frequencies in the epithelial class. miRNA classes correlated with previously reported gene expression subtypes, clinical characteristics, and clinical outcomes in a multivariate Cox proportional hazards model with stromal patients demonstrating worse prognoses (HR, 1.5646; P ¼ 0.006).Conclusions: We report a reproducible classification of HNSCC based on miRNA that associates with known pathologically altered pathways and mutations of squamous tumors and is clinically relevant.

Bariatric surgery is becoming more prevalent as a sustainable weight loss approach, with vertical sleeve gastrectomy (VSG) being the first line of surgical intervention. We and others have shown that obesity exacerbates tumor growth while diet-induced weight loss impairs obesity-driven progression. It remains unknown how bariatric surgery-induced weight loss impacts cancer progression or alters responses to therapy. Using a pre-clinical model of diet induced obesity followed by VSG or diet-induced weight loss, breast cancer progression and immune checkpoint blockade therapy was investigated. Weight loss by bariatric surgery or weight matched dietary intervention before tumor engraftment protected against obesity-exacerbated tumor progression. However, VSG was not as effective as dietary intervention in reducing tumor burden despite achieving a similar extent of weight and adiposity loss. Circulating leptin did not associate with changes in tumor burden, however circulating IL-6 was elevated in mice after VSG. Uniquely, tumors in mice that received VSG displayed elevated inflammation and immune checkpoint ligand PD-L1+ myeloid and non-immune cells. Further, mice that received VSG had reduced tumor T lymphocytes and markers of cytolysis suggesting an ineffective anti-tumor microenvironment. VSG-associated elevation of PD-L1 prompted us to next investigate the efficacy of immune checkpoint blockade in lean, obese, and formerly obese mice that lost weight by VSG or weight matched controls. While obese mice were resistant to immune checkpoint blockade, anti-PD-L1 potently impaired tumor progression after VSG through improved anti-tumor immunity. Thus, in formerly obese mice, surgical weight loss followed by immunotherapy reduced breast cancer burden. Last, we compared transcriptomic changes in adipose tissue after bariatric surgery from both patients and mouse models that revealed a conserved bariatric surgery associated weight loss signature (BSAS). Importantly, BSAS significantly associated with decreased tumor volume. Our findings demonstrate conserved impacts of obesity and bariatric surgery-induced weight loss pathways associated with breast cancer progression.

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High-throughput sequencing protocols such as RNA-seq have made it possible to interrogate the sequence, structure and abundance of RNA transcripts at higher resolution than previous microarray and other molecular techniques. While many computational tools have been proposed for identifying mRNA variation through differential splicing/alternative exon usage, challenges in its analysis remain. Here, we propose a framework for unbiased and robust discovery of aberrant RNA transcript structures using short read sequencing data based on shape changes in an RNA-seq coverage profile. Shape changes in selecting sample outliers in RNA-seq, SCISSOR, is a series of procedures for transforming and normalizing base-level RNA sequencing coverage data in a transcript independent manner, followed by a statistical framework for its analysis (https://github.com/hyochoi/SCISSOR). The resulting high dimensional object is amenable to unsupervised screening of structural alterations across RNA-seq cohorts with nearly no assumption on the mutational mechanisms underlying abnormalities. This enables SCISSOR to independently recapture known variants such as splice site mutations in tumor suppressor genes as well as novel variants that are previously unrecognized or difficult to identify by any existing methods including recurrent alternate transcription start sites and recurrent complex deletions in 3′ UTRs.