Androgen-indifferent prostate cancer (AIPC) is increasingly common and particularly lethal. Data describing these tumors are sparse, and AIPC remains a poorly understood malignancy. Utilizing the Oncology Research Information Exchange Network (ORIEN) database, we enriched for tumors with features of AIPC using previously described characteristics. Our AIPC cohort included three subgroups: aggressive variant prostate cancer (AVPC), neuroendocrine PC (NEPC), and double-negative PC (DNPC). Of 1496 total PC patients available for analysis, we identified 323 (22%) as MCRPC. Of those, 39 (12%) met AIPC criteria (17 AVPC, 13 NEPC, 9 DNPC) and 284 (88%) were non-AIPC. Forty-three percent of AIPC patients had de novo metastatic disease vs. 15% for non-AIPC (p = 0.003). Homologous recombination deficiency (HRD) and tumor mutational burden (TMB) did not differ between cohorts, but microsatellite instability scores (MSI) were significantly higher in AIPC (p = 0.019). Using Gene Set Enrichment Analysis (GSEA), we found that genes defining response to androgens and genes involved in oxidative phosphorylation were the most downregulated, whereas genes involved in epithelial–mesenchymal transition (EMT) and immune signaling were significantly upregulated in AIPC vs. non-AIPC. Our study demonstrates the potential for predefined criteria that aim to enrich for AIPC and suggests opportunities for therapeutic investigation.

Thyroid diseases are common and highly heritable. Under the Global Biobank Meta-analysis Initiative, we performed a meta-analysis of genome-wide association studies from 19 biobanks for five thyroid diseases: thyroid cancer, benign nodular goiter, Graves’ disease, lymphocytic thyroiditis, and primary hypothyroidism. We analyzed genetic association data from ∼2.9 million genomes and identified 235 known and 501 novel independent variants significantly linked to thyroid diseases. We discovered genetic correlations between thyroid cancer, benign nodular goiter, and autoimmune thyroid diseases ( r ² =0.21-0.97). Telomere maintenance genes contribute to benign and malignant thyroid nodular disease risk, whereas cell cycle, DNA repair, and DNA damage response genes are predominantly associated with thyroid cancer. We proposed a paradigm explaining genetic predisposition to benign and malignant thyroid nodules. We evaluated thyroid cancer polygenic risk scores (PRS) for clinical applications in thyroid cancer diagnosis. We found PRS associations with thyroid cancer risk features: multifocality, lymph node metastases, and extranodal extension.

Background: Prostate cancer is a heterogeneous disease with variable clinical outcomes. If localized, the patient may be cured. However, prostate cancer is lethal if recurrence/progression to metastatic castrate resistant disease occurs. Thus, there is an unmet need to further understand the molecular underpinnings of this progression. Epidemiologic studies show that increased risk of developing and dying from prostate cancer has been associated with elevated serum IGF-1 levels, hyperinsulinemia and metabolic syndrome. Alterations in insulin pathway genes, such as PTEN, FOXO, and PIK3CA, are mutated in up to 32%, 15%, and 11% of localized prostate tumors, respectively. We aimed to further characterize expression of insulin pathway genes in localized prostate cancers in an effort to (1) provide insights into potential mechanisms of progression to metastatic disease and (2) try to further enrich for those prostate tumors that portend worse survival outcomes. Methods: Using the multi-institutional Oncology Research Information Exchange Network (ORIEN) database, gene expression data was analyzed from localized prostate cancer tumors. The raw counts were first normalized, and 176 genes related to the insulin receptor and its downstream pathways were then subset and used for clustering using the non-negative matrix factorization (NMF). The NMF cluster analysis was performed in an attempt to separate gene expression into two groups. Gene Set Enrichment Analysis (GSEA) was then performed between the two groups that had been separated by cluster analysis to determine homology between other GSEA sets. Kaplan–Meier curves were used to assess median overall survival. Cox analysis was performed to generate the adjusted KM curve. Mediation analysis was conducted to determine the relationship between cluster status, TN stage, and survival. Results: Cluster analysis revealed two distinct groups of insulin gene expression, cluster 1 (n = 96) and cluster 2 (n = 337). Compared with cluster 2, cluster 1 consisted of decreased expression of PTEN (p < 0.001) and PIK3R1 (p < 0.001), along with increases in the expression of AKT1 (p < 0.001), IRS1/2 (p < 0.001), FASN (p < 0.001), IGFBP2 (p < 0.001), and MTOR (p < 0.001). GSEA analysis revealed changes in lipid metabolism and WNT secretion pathways in cluster 1. Cluster 2 GSEA showed pathway changes related to DNA damage repair and testosterone. Patient characteristics between clusters differed significantly in the T and N stages of tumor but not in other ways. In unadjusted analysis, median overall survival was estimated at 117 months and 232 months for cluster 1 and cluster 2, respectively (p < 0.05). The proportion of patients who went on to develop metastases (p < 0.05) or need chemotherapy (p < 0.05) was increased in cluster 1 compared to cluster 2. Repeat survival analysis adjusted for confounders (T stage, N stage, age at diagnosis, pathologic grade) showed no difference in survival between clusters. Mediation analysis showed that the contribution of cluster status to survival was independent of T or N stage. Conclusions: A subset of localized prostate cancer patients demonstrated linked insulin pathway changes that are consistent with prior studies describing a pattern of insulin dysregulation. Though the group characterized by insulin dysregulation initially showed worse survival outcomes, this difference disappeared when controlling for confounders. Though baseline differences in tumor stage seemed to most readily explain the difference in survival between clusters, mediation analysis showed that the effect of cluster status on survival was independent of tumor stage. This suggests that other confounders, such as pathologic grade or baseline age, may explain the survival difference.