Multiple studies have identified transcriptome subtypes of high-grade serous ovarian carcinoma (HGSOC), but their interpretation and translation are complicated by tumor evolution and polyclonality accompanied by extensive accumulation of somatic aberrations, varying cell type admixtures, and different tissues of origin. In this study, we examined the chronology of HGSOC subtype evolution in the context of these factors using a novel integrative analysis of absolute copy-number analysis and gene expression in The Cancer Genome Atlas complemented by single-cell analysis of six independent tumors. Tumor purity, ploidy, and subclonality were reliably inferred from different genomic platforms, and these characteristics displayed marked differences between subtypes. Genomic lesions associated with HGSOC subtypes tended to be subclonal, implying subtype divergence at later stages of tumor evolution. Subclonality of recurrent HGSOC alterations was evident for proliferative tumors, characterized by extreme genomic instability, absence of immune infiltration, and greater patient age. In contrast, differentiated tumors were characterized by largely intact genome integrity, high immune infiltration, and younger patient age. Single-cell sequencing of 42,000 tumor cells revealed widespread heterogeneity in tumor cell type composition that drove bulk subtypes but demonstrated a lack of intrinsic subtypes among tumor epithelial cells. Our findings prompt the dismissal of discrete transcriptome subtypes for HGSOC and replacement by a more realistic model of continuous tumor development that includes mixtures of subclones, accumulation of somatic aberrations, infiltration of immune and stromal cells in proportions correlated with tumor stage and tissue of origin, and evolution between properties previously associated with discrete subtypes.Significance: This study infers whether transcriptome-based groupings of tumors differentiate early in carcinogenesis and are, therefore, appropriate targets for therapy and demonstrates that this is not the case for HGSOC.

The functional complexity of higher organisms is not easily accounted for by the size of their genomes. Rather, complexity appears to be generated by transcriptional, translational, and post-translational mechanisms and tissue organization that produces a context-dependent response of cells to specific stimuli. One property of gene products that likely increases the ability of cells to respond to stimuli with complexity is the multifunctionality of expressed proteins. Receptor for hyaluronan-mediated motility (RHAMM) is an example of a multifunctional protein that controls differential responses of cells in response-to-injury contexts. Here, we trace its evolution into a sensor-transducer of tissue injury signals in higher organisms through the detection of hyaluronan (HA) that accumulates in injured microenvironments. Our goal is to highlight the domain and isoform structures that generate RHAMM’s function complexity and model approaches for targeting its key functions to control cancer progression.

Comprehensive identification of insertions/deletions (indels) across the full size spectrum from second generation sequencing is challenging due to the relatively short read length inherent in the technology. Different indel calling methods exist but are limited in detection to specific sizes with varying accuracy and resolution. We present ScanIndel, an integrated framework for detecting indels with multiple heuristics including gapped alignment, split reads and de novo assembly. Using simulation data, we demonstrate ScanIndel’s superior sensitivity and specificity relative to several state-of-the-art indel callers across various coverage levels and indel sizes. ScanIndel yields higher predictive accuracy with lower computational cost compared with existing tools for both targeted resequencing data from tumor specimens and high coverage whole-genome sequencing data from the human NIST standard NA12878. Thus, we anticipate ScanIndel will improve indel analysis in both clinical and research settings. ScanIndel is implemented in Python, and is freely available for academic use at https://github.com/cauyrd/ScanIndel .

Background: Indeterminate thyroid fine-needle aspiration (FNA) cytology, including atypia of undetermined significance (AUS/FLUS) and suspicious for follicular neoplasm (SFN), continues to generate uncertainty about the presence of malignancy, resulting in repeated follow-up, repeat FNA, or diagnostic surgery. Mutational panel testing may improve the malignancy risk prediction in indeterminate nodules, but the general application of such testing has not been investigated extensively. Methods: A retrospective review was performed of all patients undergoing thyroidectomy at a tertiary care facility over a two-year period. Mutational panel test results, when present, were analyzed relative to FNA cytologic result and surgical histopathologic diagnosis. Malignancy rates, sensitivity, specificity, positive predictive values (PPV), negative predictive values (NPV) and positive and negative likelihood ratios (LR) were calculated. Results: A total of 261 operated thyroid nodules had the following initial FNA cytology results: 2% non-diagnostic, 23% benign, 28% AUS/FLUS, 11% SFN, 9% suspicious for malignancy (SUSP), and 27% malignant. The histopathologic malignancy rate was 48%, subcategorized by cytology into benign 7%, AUS/FLUS 30%, SFN 38%, and SUSP 83%. Mutations were more frequent in indeterminate nodules that were histologically malignant versus benign ( p < 0.0001) or versus adenoma ( p = 0.001). Mutational analysis in 44 AUS/FLUS nodules resulted in a malignancy detection sensitivity of 85%, a specificity of 65%, a PPV of 50%, a NPV of 91%, and a positive LR of 2.4. In 12 SFN nodules analyzed with ThyroSeq ^® testing, sensitivity was 100%, specificity 57%, PPV 63%, NPV 100%, and LR 2.3. Performance of the seven-gene mutational panel was not significantly different from the ThyroSeq ^® panel in the AUS/FLUS group. The malignancy yield, comparing the mutation positive AUS/FLUS group with the untested AUS/FLUS surgical cohort, did not reach statistical significance ( p = 0.17). Conclusions: In a surgical cohort, a similar NPV but a lower PPV was found with the use of mutational panel testing compared to the published literature. Following the identification of a mutation, the prevalence of malignancy in the AUS/FLUS or SFN category was increased by nearly 15% to 45% and 53%, respectively. Further study is needed to confirm these results and to analyze clinical outcome subcategories relative to the utility of mutational testing.

Cancer has been conceptualized as a chronic wound with a predominance of tumor promoting inflammation. Given the accumulating evidence that the microenvironment supports tumor growth, we investigated hyaluronan (HA)-CD44 interactions within breast cancer cells, to determine whether this axis directly impacts the formation of an inflammatory microenvironment. Our results demonstrate that breast cancer cells synthesize and fragment HA and express CD44 on the cell surface. Using RNA sequencing approaches, we found that loss of CD44 in breast cancer cells altered the expression of cytokine-related genes. Specifically, we found that production of the chemokine CCL2 by breast cancer cells was significantly decreased after depletion of either CD44 or HA. In vivo, we found that CD44 deletion in breast cancer cells resulted in a delay in tumor formation and localized progression. This finding was accompanied by a decrease in infiltrating CD206+ macrophages, which are typically associated with tumor promoting functions. Importantly, our laboratory results were supported by human breast cancer patient data, where increased HAS2 expression was significantly associated with a tumor promoting inflammatory gene signature. Because high levels of HA deposition within many tumor types yields a poorer prognosis, our results emphasize that HA-CD44 interactions potentially have broad implications across multiple cancers.