m6A epitranscriptome analysis reveals differentially methylated transcripts that drive early chemoresistance in bladder cancer.

N 6-Methyladenosine (m6A) RNA modifications dynamically regulate messenger RNA processing, differentiation and cell fate. Given these functions, we hypothesized that m6A modifications play a role in the transition to chemoresistance. To test this, we took an agnostic discovery approach anchored directly to chemoresistance rather than to any particular m6A effector protein. Specifically, we used methyl-RNA immunoprecipitation followed by sequencing (MeRIP-seq) in parallel with RNA sequencing to identify gene transcripts that were both differentially methylated and differentially expressed between cisplatin-sensitive and cisplatin-resistant bladder cancer (BC) cells. We filtered and prioritized these genes using clinical and functional database tools, and then validated several of the top candidates via targeted quantitative polymerase chain reaction (qPCR) and MeRIP-PCR. In cisplatin-resistant cells, SLC7A11 transcripts had decreased methylation associated with decreased m6A reader YTHDF3 binding, prolonged RNA stability, and increased RNA and protein levels, leading to reduced ferroptosis and increased survival. Consistent with this, cisplatin-sensitive BC cell lines and patient-derived organoids exposed to cisplatin for as little as 48 h exhibited similar mechanisms of SLC7A11 upregulation and chemoresistance, trends that were also reflected in public cancer survival databases. Collectively, these findings highlight epitranscriptomic plasticity as a mechanism of rapid chemoresistance and a potential therapeutic target.

Florida-California Cancer Research, Education and Engagement (CaRE2) Health Equity Center: Structure, Innovations, and Initial Outcomes.

INTRODUCTION: The Florida-California Cancer Research, Education, and Engagement (CaRE2) Health Equity Center is a triad partnership committed to increasing institutional capacity for cancer disparity research, the diversity of the cancer workforce, and community empowerment. This article provides an overview of the structure, process innovations, and initial outcomes from the first 4 years of the CaRE2 triad partnership. METHODS: CaRE2 serves diverse populations in Florida and California using a "molecule to the community and back" model. We prioritize research on the complex intersection of biological, environmental, and social determinants health, working together with scientific and health disparities communities, sharing expertise across institutions, bidirectional training, and community outreach. Partnership progress and outcomes were assessed using mixed methods and four Program Steering Committee meetings. RESULTS: Research capacity was increased through development of a Living Repository of 81 cancer model systems from minority patients for novel cancer drug development. CaRE2 funded 15 scientific projects resulting in 38 publications. Workforce diversity entailed supporting 94 cancer trainees (92 URM) and 34 ESIs (32 URM) who coauthored 313 CaRE2-related publications and received 48 grants. Community empowerment was promoted via outreaching to more than 3000 individuals, training 145 community cancer advocates (including 28 Community Scientist Advocates), and publishing 10 community reports. CaRE2 members and trainees together have published 639 articles, received 61 grants, and 57 awards. CONCLUSION: The CaRE2 partnership has achieved its initial aims. Infrastructure for translational cancer research was expanded at one partner institution, and cancer disparities research was expanded at the two cancer centers.

Phosphorylation and stabilization of EZH2 by DCAF1/VprBP trigger aberrant gene silencing in colon cancer.

Our recent work has shown that DCAF1 (also known as VprBP) is overexpressed in colon cancer and phosphorylates histone H2AT120 to drive epigenetic gene inactivation and oncogenic transformation. We have extended these observations by investigating whether DCAF1 also phosphorylates non-histone proteins as an additional mechanism linking its kinase activity to colon cancer development. We now demonstrate that DCAF1 phosphorylates EZH2 at T367 to augment its nuclear stabilization and enzymatic activity in colon cancer cells. Consistent with this mechanistic role, DCAF1-mediated EZH2 phosphorylation leads to elevated levels of H3K27me3 and altered expression of growth regulatory genes in cancer cells. Furthermore, our preclinical studies using organoid and xenograft models revealed that EZH2 requires phosphorylation for its oncogenic function, which may have therapeutic implications for gene reactivation in colon cancer cells. Together, our data define a mechanism underlying DCAF1-driven colonic tumorigenesis by linking DCAF1-mediated EZH2 phosphorylation to EZH2 stability that is crucial for establishing H3K27me3 and gene silencing program.

Validation of Myc-Associated Protein X (MAX) regulation in growth hormone secreting and nonfunctional pituitary adenoma.

INTRODUCTION: Many patients with growth hormone-secreting pituitary adenoma (GHPA) fail to achieve biochemical remission, warranting investigation into epigenetic and molecular signatures associated with tumorigenesis and hormonal secretion. Prior work exploring the DNA methylome showed Myc-Associated Protein X (MAX), a transcription factor involved in cell cycle regulation, was differentially methylated between GHPA and nonfunctional pituitary adenoma (NFPA). We aimed to validate the differential DNA methylation and related MAX protein expression profiles between NFPA and GHPA. METHODS: DNA methylation levels were measured in 52 surgically resected tumors (37 NFPA, 15 GHPA) at ~100,000 known MAX binding sites derived using ChIP-seq analysis from ENCODE. Findings were correlated with MAX protein expression using a constructed tissue microarray (TMA). Gene ontology analysis was performed to explore downstream genetic and signaling pathways regulated by MAX. RESULTS: GHPA had more hypomethylation events across all known MAX binding sites. Of binding sites defined using ChIP-seq analysis, 1,551 sites had significantly different methylation patterns between the two cohorts; 432 occurred near promoter regions potentially regulated by MAX, including promoters of TNF and MMP9. Gene ontology analysis suggested enrichment in genes involved in oxygen response, immune system regulation, and cell proliferation. Thirteen MAX binding sites were within coding regions of genes. GHPA demonstrated significantly increased expression of MAX protein compared to NFPA. CONCLUSION: GHPA have significantly different DNA methylation and downstream protein expression levels of MAX compared to NFPA. These differences may influence mechanisms involved with cellular proliferation, tumor invasion and hormonal secretion.

Cell-free DNA Methylation as a Predictive Biomarker of Response to Neoadjuvant Chemotherapy for Patients with Muscle-invasive Bladder Cancer in SWOG S1314.

BACKGROUND: Neoadjuvant chemotherapy (NAC) is the standard of care in muscle-invasive bladder cancer (MIBC). However, treatment is intense, and the overall benefit is small, necessitating effective biomarkers to identify patients who will benefit most. OBJECTIVE: To characterize cell-free DNA (cfDNA) methylation in patients receiving NAC in SWOG S1314, a prospective cooperative group trial, and to correlate the methylation signatures with pathologic response at radical cystectomy. DESIGN, SETTING, AND PARTICIPANTS: SWOG S1314 is a prospective cooperative group trial for patients with MIBC (cT2-T4aN0M0, ≥5 mm of viable tumor), with a primary objective of evaluating the coexpression extrapolation (COXEN) gene expression signature as a predictor of NAC response, defined as achieving pT0N0 or ≤pT1N0 at radical cystectomy. For the current exploratory analysis, blood samples were collected prospectively from 72 patients in S1314 before and during NAC, and plasma cfDNA methylation was measured using the Infinium MethylationEPIC BeadChip array. INTERVENTION: No additional interventions besides plasma collection. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Differential methylation between pathologic responders (≤pT1N0) and nonresponders was analyzed, and a classifier predictive of treatment response was generated using the Random Forest machine learning algorithm. RESULTS AND LIMITATIONS: Using prechemotherapy plasma cfDNA, we developed a methylation-based response score (mR-score) predictive of pathologic response. Plasma samples collected after the first cycle of NAC yielded mR-scores with similar predictive ability. Furthermore, we used cfDNA methylation data to calculate the circulating bladder DNA fraction, which had a modest but independent predictive ability for treatment response. In a model combining mR-score and circulating bladder DNA fraction, we correctly predicted pathologic response in 79% of patients based on their plasma collected at baseline and after one cycle of chemotherapy. Limitations of this study included a limited sample size and relatively low circulating bladder DNA levels. CONCLUSIONS: Our study provides the proof of concept that cfDNA methylation can be used to generate classifiers of NAC response in bladder cancer patients. PATIENT SUMMARY: In this exploratory analysis of S1314, we demonstrated that cell-free DNA methylation can be profiled to generate biomarker signatures associated with neoadjuvant chemotherapy response. With validation in additional cohorts, this minimally invasive approach may be used to predict chemotherapy response in locally advanced bladder cancer and perhaps also in metastatic disease.

Radiogenomic Associations Clear Cell Renal Cell Carcinoma: An Exploratory Study.

INTRODUCTION: This study investigates how quantitative texture analysis can be used to non-invasively identify novel radiogenomic correlations with clear cell renal cell carcinoma (ccRCC) biomarkers. METHODS: The Cancer Genome Atlas-Kidney Renal Clear Cell Carcinoma open-source database was used to identify 190 sets of patient genomic data that had corresponding multiphase contrast-enhanced CT images in The Cancer Imaging Archive. 2,824 radiomic features spanning fifteen texture families were extracted from CT images using a custom-built MATLAB software package. Robust radiomic features with strong inter-scanner reproducibility were selected. Random forest, AdaBoost, and elastic net machine learning (ML) algorithms evaluated the ability of the selected radiomic features to predict the presence of 12 clinically relevant molecular biomarkers identified from the literature. ML analysis was repeated with cases stratified by stage (I/II vs. III/IV) and grade (1/2 vs. 3/4). 10-fold cross validation was used to evaluate model performance. RESULTS: Before stratification by tumor grade and stage, radiomics predicted the presence of several biomarkers with weak discrimination (AUC 0.60-0.68). Once stratified, radiomics predicted KDM5C, SETD2, PBRM1, and mTOR mutation status with acceptable to excellent predictive discrimination (AUC ranges from 0.70 to 0.86). CONCLUSIONS: Radiomic texture analysis can potentially identify a variety of clinically relevant biomarkers in patients with ccRCC and may have a prognostic implication.

Evidence of Novel Susceptibility Variants for Prostate Cancer and a Multiancestry Polygenic Risk Score Associated with Aggressive Disease in Men of African Ancestry.

BACKGROUND: Genetic factors play an important role in prostate cancer (PCa) susceptibility. OBJECTIVE: To discover common genetic variants contributing to the risk of PCa in men of African ancestry. DESIGN, SETTING, AND PARTICIPANTS: We conducted a meta-analysis of ten genome-wide association studies consisting of 19378 cases and 61620 controls of African ancestry. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Common genotyped and imputed variants were tested for their association with PCa risk. Novel susceptibility loci were identified and incorporated into a multiancestry polygenic risk score (PRS). The PRS was evaluated for associations with PCa risk and disease aggressiveness. RESULTS AND LIMITATIONS: Nine novel susceptibility loci for PCa were identified, of which seven were only found or substantially more common in men of African ancestry, including an African-specific stop-gain variant in the prostate-specific gene anoctamin 7 (ANO7). A multiancestry PRS of 278 risk variants conferred strong associations with PCa risk in African ancestry studies (odds ratios [ORs] >3 and >5 for men in the top PRS decile and percentile, respectively). More importantly, compared with men in the 40-60% PRS category, men in the top PRS decile had a significantly higher risk of aggressive PCa (OR = 1.23, 95% confidence interval = 1.10-1.38, p = 4.4 × 10-4). CONCLUSIONS: This study demonstrates the importance of large-scale genetic studies in men of African ancestry for a better understanding of PCa susceptibility in this high-risk population and suggests a potential clinical utility of PRS in differentiating between the risks of developing aggressive and nonaggressive disease in men of African ancestry. PATIENT SUMMARY: In this large genetic study in men of African ancestry, we discovered nine novel prostate cancer (PCa) risk variants. We also showed that a multiancestry polygenic risk score was effective in stratifying PCa risk, and was able to differentiate risk of aggressive and nonaggressive disease.

Genomic, epigenomic, and transcriptomic signatures of prostate cancer between African American and European American patients.

Prostate cancer is the second most common cancer in men in the United States, and racial disparities are greatly observed in the disease. Specifically, African American (AA) patients have 60% higher incidence and mortality rates, in addition to higher grade and stage prostate tumors, than European American (EA) patients. In order to narrow the gap between clinical outcomes for these two populations, genetic and molecular signatures contributing to this disparity have been characterized. Over the past decade, profiles of prostate tumor samples from different ethnic groups have been developed using molecular and functional assays coupled with next generation sequencing or microarrays. Comparative genome-wide analyses of genomic, epigenomic, and transcriptomic profiles from prostate tumor samples have uncovered potential race-specific mutations, copy number alterations, DNA methylation, and gene expression patterns. In this study, we reviewed over 20 published studies that examined the aforementioned molecular contributions to racial disparities in AA and EA prostate cancer patients. The reviewed genomic studies revealed mutations, deletions, amplifications, duplications, or fusion genes differentially enriched in AA patients relative to EA patients. Commonly reported genomic alterations included mutations or copy number alterations of FOXA1, KMT2D, SPOP, MYC, PTEN, TP53, ZFHX3, and the TMPRSS2-ERG fusion. The reviewed epigenomic studies identified that CpG sites near the promoters of PMEPA1, RARB, SNRPN, and TIMP3 genes were differentially methylated between AA and EA patients. Lastly, the reviewed transcriptomic studies identified genes (e.g. CCL4, CHRM3, CRYBB2, CXCR4, GALR1, GSTM3, SPINK1) and signaling pathways dysregulated between AA and EA patients. The most frequently found dysregulated pathways were involved in immune and inflammatory responses and neuroactive ligand signaling. Overall, we observed that the genomic, epigenomic, and transcriptomic alterations evaluated between AA and EA prostate cancer patients varied between studies, highlighting the impact of using different methods and sample sizes. The reported genomic, epigenomic, and transcriptomic alterations do not only uncover molecular mechanisms of tumorigenesis but also provide researchers and clinicians valuable resources to identify novel biomarkers and treatment modalities to improve the disparity of clinical outcomes between AA and EA patients.

Characterizing chromatin interactions of regulatory elements and nucleosome positions, using Hi-C, Micro-C, and promoter capture Micro-C.

BACKGROUND: Regulatory elements such as promoters, enhancers, and insulators interact each other to mediate molecular processes. To capture chromatin interactions of regulatory elements, 3C-derived methods such as Hi-C and Micro-C are developed. Here, we generated and analyzed Hi-C, Micro-C, and promoter capture Micro-C datasets with different sequencing depths to study chromatin interactions of regulatory elements and nucleosome positions in human prostate cancer cells. RESULTS: Compared to Hi-C, Micro-C identifies more high-resolution loops, including ones around structural variants. By evaluating the effect of sequencing depth, we revealed that more than 2 billion reads of Micro-C are needed to detect chromatin interactions at 1 kb resolution. Moreover, we found that deep-sequencing identifies additional long-range loops that are longer than 1 Mb in distance. Furthermore, we found that more than 50% of the loops are involved in insulators while less than 10% of the loops are promoter-enhancer loops. To comprehensively capture chromatin interactions that promoters are involved in, we performed promoter capture Micro-C. Promoter capture Micro-C identifies loops near promoters with a lower amount of sequencing reads. Sequencing of 160 million reads of promoter capture Micro-C resulted in reaching a plateau of identifying loops. However, there was still a subset of promoters that are not involved in loops even after deep-sequencing. By integrating Micro-C with NOMe-seq and ChIP-seq, we found that active promoters involved in loops have a more accessible region with lower levels of DNA methylation and more highly phased nucleosomes, compared to active promoters that are not involved in loops. CONCLUSION: We determined the required sequencing depth for Micro-C and promoter capture Micro-C to generate high-resolution chromatin interaction maps and loops. We also investigated the effect of sequencing coverage of Hi-C, Micro-C, and promoter capture Micro-C on detecting chromatin loops. Our analyses suggest the presence of distinct regulatory element groups, which are differently involved in nucleosome positions and chromatin interactions. This study does not only provide valuable insights on understanding chromatin interactions of regulatory elements, but also present guidelines for designing research projects on chromatin interactions among regulatory elements.

Characterizing and Targeting Genes Regulated by Transcription Factor MYBL2 in Lung Adenocarcinoma Cells.

Overexpression of MYBL2 is associated with poor survival of lung adenocarcinoma patients, but the molecular mechanism by which it regulates transcription and carcinogenesis has not yet been elucidated. In this study, we performed ChIP-seq using an MYBL2-targeted antibody and discovered that MYBL2 primarily binds to the promoters of highly expressed genes in lung adenocarcinoma cells. Using a knockdown experiment of MYBL2 and global transcriptome profiling, we identified that over a thousand genes are dysregulated by MYBL2, and MYBL2 acts as a transcriptional activator in lung adenocarcinoma cells. Moreover, we revealed that the binding sites of FOXM1 are largely shared with MYBL2 binding sites, and genes involved in cell cycle phase transitions are regulated by these transcription factors. We furthermore investigated the effect of a previously reported FOXM1 inhibitor, FDI-6, in lung adenocarcinoma cells. We demonstrated that FDI-6 decreases the proliferation of lung adenocarcinoma cells and inhibits the activities of FOXM1 as well as MYBL2. Moreover, we found that genes involved in cell death and cell cycle are inhibited by FDI-6. Overall, our findings suggest that MYBL2 and FOXM1 activate cell cycle genes together, acting as oncogenic transcription factors in lung adenocarcinoma cells, and they are potential treatment targets for the disease.

Feasibility of using CT radiomic signatures for predicting CD8-T cell infiltration and PD-L1 expression in renal cell carcinoma.

Objectives: To identify computed tomography (CT)-based radiomic signatures of cluster of differentiation 8 (CD8)-T cell infiltration and programmed cell death ligand 1 (PD-L1) expression levels in patients with clear-cell renal cell carcinoma (ccRCC). Methods: Seventy-eight patients with pathologically confirmed localized ccRCC, preoperative multiphase CT and tumor resection specimens were enrolled in this retrospective study. Regions of interest (ROI) of the ccRCC volume were manually segmented from the CT images and processed using a radiomics panel comprising of 1708 metrics. The extracted metrics were used as inputs to three machine learning classifiers: Random Forest, AdaBoost, and ElasticNet to create radiomic signatures for CD8-T cell infiltration and PD-L1 expression, respectively. Results: Using a cut-off of 80 lymphocytes per high power field, 59 % were classified to CD8 highly infiltrated tumors and 41 % were CD8 non highly infiltrated tumors, respectively. An ElasticNet classifier discriminated between these two groups of CD8-T cells with an AUC of 0.68 (95 % CI, 0.55-0.80). In addition, based on tumor proportion score with a cut-off of > 1 % tumor cells expressing PD-L1, 76 % were PD-L1 positive and 24 % were PD-L1 negative. An Adaboost classifier discriminated between PD-L1 positive and PD-L1 negative tumors with an AUC of 0.8 95 % CI: (0.66, 0.95). 3D radiomics metrics of graylevel co-occurrence matrix (GLCM) and graylevel run-length matrix (GLRLM) metrics drove the performance for CD8-Tcell and PD-L1 classification, respectively. Conclusions: CT-radiomic signatures can differentiate tumors with high CD8-T cell infiltration with moderate accuracy and positive PD-L1 expression with good accuracy in ccRCC.

Epigenetic dysregulation in meningiomas.

Background: Meningiomas are the most common primary brain tumor. Though typically benign with a low mutational burden, tumors with benign histology may behave aggressively and there are no proven chemotherapies. Although DNA methylation patterns distinguish subgroups of meningiomas and have higher predictive value for tumor behavior than histologic classification, little is known about differences in DNA methylation between meningiomas and surrounding normal dura tissue. Methods: Whole-exome sequencing and methylation array profiling were performed on 12 dura/meningioma pairs (11 WHO grade I and 1 WHO grade II). Single-nucleotide polymorphism (SNP) genotyping and methylation array profiling were performed on an additional 19 meningiomas (9 WHO grade I, 5 WHO grade II, 4 WHO grade III). Results: Using multimodal studies of meningioma/dura pairs, we identified 4 distinct DNA methylation patterns. Diffuse DNA hypomethylation of malignant meningiomas readily facilitated their identification from lower-grade tumors by unsupervised clustering. All clusters and 12/12 meningioma-dura pairs exhibited hypomethylation of the gene promoters of a module associated with the craniofacial patterning transcription factor FOXC1 and its upstream lncRNA FOXCUT. Furthermore, we identified an epigenetic continuum of increasing hypermethylation of polycomb repressive complex target promoters with increasing histopathologic grade. Conclusion: These findings support future investigations of the role of epigenetic dysregulation of FOXC1 and cranial patterning genes in meningioma formation as well as studies of the utility of polycomb inhibitors for the treatment of malignant meningiomas.

Methylation Subtypes of Primary Prostate Cancer Predict Poor Prognosis.

BACKGROUND: Patients with prostate cancer experience heterogeneous outcomes after radical prostatectomy. Genomic studies including The Cancer Genome Atlas (TCGA) have reported molecular signatures of prostate cancer, but few studies have assessed the prognostic effects of DNA methylation profiles. METHODS: We conducted the largest methylome subtyping analysis for primary prostate tumors to date, using methylome data from three patient populations: TCGA, a prostate cancer cohort study conducted at the Fred Hutchinson Cancer Research Center (FH; Seattle, WA), and the Canadian International Cancer Genome Consortium (ICGC) cohort. Four subtypes were detected in the TCGA dataset, then independently assigned to FH and ICGC cohort data. The identified methylation subtypes were assessed for association with cancer prognosis in the above three patient populations. RESULTS: Using a set of hypermethylated CpG sites, four methylation subtypes were identified in TCGA. Compared with subtype 1, subtype 4 had an HR of 2.09 (P = 0.029) for biochemical recurrence (BCR) in TCGA patients. HRs of 2.76 (P = 0.002) for recurrence and 9.73 (P = 0.002) for metastatic-lethal (metastasis or prostate cancer-specific death) outcomes were observed in the FH cohort. A similar pattern of association was noted in the Canadian ICGC cohort, though HRs were not statistically significant. CONCLUSIONS: A hypermethylated subtype was associated with an increased hazard of recurrence and mortality in three studies with prostate tumor methylome data. Further molecular work is needed to understand the effect of methylation subtypes on cancer prognosis. IMPACT: This study identified a DNA methylation subtype that was associated with worse prostate cancer prognosis after radical prostatectomy.

FOXC1 Binds Enhancers and Promotes Cisplatin Resistance in Bladder Cancer.

Chemotherapy resistance is traditionally attributed to DNA mutations that confer a survival advantage under drug selection pressure. However, in bladder cancer and other malignancies, we and others have previously reported that cancer cells can convert spontaneously to an aggressive drug-resistant phenotype without prior drug selection or mutational events. In the current work, we explored possible epigenetic mechanisms behind this phenotypic plasticity. Using Hoechst dye exclusion and flow cytometry, we isolated the aggressive drug-resistant cells and analyzed their chromatin accessibility at regulatory elements. Compared to the rest of the cancer cell population, the aggressive drug-resistant cells exhibited enhancer accessibility changes. In particular, we found that differentially accessible enhancers were enriched for the FOXC1 transcription factor motif, and that FOXC1 was the most significantly overexpressed gene in aggressive drug-resistant cells. ChIP-seq analysis revealed that differentially accessible enhancers in aggressive drug-resistant cells had a higher FOXC1 binding, which regulated the expression of adjacent cancer-relevant genes like ABCB1 and ID3. Accordingly, cisplatin treatment of bladder cancer cells led to an increased FOXC1 expression, which mediated cell survival and conversion to a drug-resistant phenotype. Collectively, these findings suggest that FOXC1 contributes to phenotypic plasticity by binding enhancers and promoting a mutation-independent shift towards cisplatin resistance in bladder cancer.

MMP-9 drives the melanomagenic transcription program through histone H3 tail proteolysis.

Melanoma is a type of skin cancer that develops in pigment-producing melanocytes and often spreads to other parts of the body. Aberrant gene expression has been considered as a crucial step for increasing the risk of melanomagenesis, but how chromatin reorganization contributes to this pathogenic process is still not well understood. Here we report that matrix metalloproteinase 9 (MMP-9) localizes to the nucleus of melanoma cells and potentiates gene expression by proteolytically clipping the histone H3 N-terminal tail (H3NT). From genome-wide studies, we discovered that growth-regulatory genes are selectively targeted and activated by MMP-9-dependent H3NT proteolysis in melanoma cells. MMP-9 cooperates functionally with p300/CBP because MMP-9 cleaves H3NT in a manner that is dependent on p300/CBP-mediated acetylation of H3K18. The functional significance of MMP-9-dependent H3NT proteolysis is further underscored by the fact that RNAi knockdown and small-molecule inhibition of MMP-9 and p300/CBP impede melanomagenic gene expression and melanoma tumor growth. Together, our data establish new functions and mechanisms for nuclear MMP-9 in promoting melanomagenesis and demonstrate how MMP-9-dependent H3NT proteolysis can be exploited to prevent and treat melanoma skin cancer.

CT-based radiomics stratification of tumor grade and TNM stage of clear cell renal cell carcinoma.

OBJECTIVES: To evaluate the utility of CT-based radiomics signatures in discriminating low-grade (grades 1-2) clear cell renal cell carcinomas (ccRCC) from high-grade (grades 3-4) and low TNM stage (stages I-II) ccRCC from high TNM stage (stages III-IV). METHODS: A total of 587 subjects (mean age 60.2 years ± 12.2; range 22-88.7 years) with ccRCC were included. A total of 255 tumors were high grade and 153 were high stage. For each subject, one dominant tumor was delineated as the region of interest (ROI). Our institutional radiomics pipeline was then used to extract 2824 radiomics features across 12 texture families from the manually segmented volumes of interest. Separate iterations of the machine learning models using all extracted features (full model) as well as only a subset of previously identified robust metrics (robust model) were developed. Variable of importance (VOI) analysis was performed using the out-of-bag Gini index to identify the top 10 radiomics metrics driving each classifier. Model performance was reported using area under the receiver operating curve (AUC). RESULTS: The highest AUC to distinguish between low- and high-grade ccRCC was 0.70 (95% CI 0.62-0.78) and the highest AUC to distinguish between low- and high-stage ccRCC was 0.80 (95% CI 0.74-0.86). Comparable AUCs of 0.73 (95% CI 0.65-0.8) and 0.77 (95% CI 0.7-0.84) were reported using the robust model for grade and stage classification, respectively. VOI analysis revealed the importance of neighborhood operation-based methods, including GLCM, GLDM, and GLRLM, in driving the performance of the robust models for both grade and stage classification. CONCLUSION: Post-validation, CT-based radiomics signatures may prove to be useful tools to assess ccRCC grade and stage and could potentially add to current prognostic models. Multiphase CT-based radiomics signatures have potential to serve as a non-invasive stratification schema for distinguishing between low- and high-grade as well as low- and high-stage ccRCC. KEY POINTS: • Radiomics signatures derived from clinical multiphase CT images were able to stratify low- from high-grade ccRCC, with an AUC of 0.70 (95% CI 0.62-0.78). • Radiomics signatures derived from multiphase CT images yielded discriminative power to stratify low from high TNM stage in ccRCC, with an AUC of 0.80 (95% CI 0.74-0.86). • Models created using only robust radiomics features achieved comparable AUCs of 0.73 (95% CI 0.65-0.80) and 0.77 (95% CI 0.70-0.84) to the model with all radiomics features in classifying ccRCC grade and stage, respectively.

Genetic discovery and risk characterization in type 2 diabetes across diverse populations.

Genomic discovery and characterization of risk loci for type 2 diabetes (T2D) have been conducted primarily in individuals of European ancestry. We conducted a multiethnic genome-wide association study of T2D among 53,102 cases and 193,679 control subjects from African, Hispanic, Asian, Native Hawaiian, and European population groups in the Population Architecture Genomics and Epidemiology (PAGE) and Diabetes Genetics Replication and Meta-analysis (DIAGRAM) Consortia. In individuals of African ancestry, we discovered a risk variant in the TGFB1 gene (rs11466334, risk allele frequency (RAF) = 6.8%, odds ratio [OR] = 1.27, p = 2.06 × 10-8), which replicated in independent studies of African ancestry (p = 6.26 × 10-23). We identified a multiethnic risk variant in the BACE2 gene (rs13052926, RAF = 14.1%, OR = 1.08, p = 5.75 × 10-9), which also replicated in independent studies (p = 3.45 × 10-4). We also observed a significant difference in the performance of a multiethnic genetic risk score (GRS) across population groups (pheterogeneity = 3.85 × 10-20). Comparing individuals in the top GRS risk category (40%-60%), the OR was highest in Asians (OR = 3.08) and European (OR = 2.94) ancestry populations, followed by Hispanic (OR = 2.39), Native Hawaiian (OR = 2.02), and African ancestry (OR = 1.57) populations. These findings underscore the importance of genetic discovery and risk characterization in diverse populations and the urgent need to further increase representation of non-European ancestry individuals in genetics research to improve genetic-based risk prediction across populations.

VprBP directs epigenetic gene silencing through histone H2A phosphorylation in colon cancer.

Histone modification is aberrantly regulated in cancer and generates an unbalanced state of gene transcription. VprBP, a recently identified kinase, phosphorylates histone H2A on threonine 120 (T120) and is involved in oncogenic transcriptional dysregulation; however, its specific role in colon cancer is undefined. Here, we show that VprBP is overexpressed in colon cancer and directly contributes to epigenetic gene silencing and cancer pathogenesis. Mechanistically, the observed function of VprBP is mediated through H2AT120 phosphorylation (H2AT120p)-driven transcriptional repression of growth regulatory genes, resulting in a significantly higher proliferative capacity of colon cancer cells. Our preclinical studies using organoid and xenograft models demonstrate that treatment with the VprBP inhibitor B32B3 impairs colonic tumor growth by blocking H2AT120p and reactivating a transcriptional program resembling that of normal cells. Collectively, our work describes VprBP as a master kinase contributing to the development and progression of colon cancer, making it a new molecular target for novel therapeutic strategies.

Molecular and computational approaches to map regulatory elements in 3D chromatin structure.

Epigenetic marks do not change the sequence of DNA but affect gene expression in a cell-type specific manner by altering the activities of regulatory elements. Development of new molecular biology assays, sequencing technologies, and computational approaches enables us to profile the human epigenome in three-dimensional structure genome-wide. Here we describe various molecular biology techniques and bioinformatic tools that have been developed to measure the activities of regulatory elements and their chromatin interactions. Moreover, we list currently available three-dimensional epigenomic data sets that are generated in various human cell types and tissues to assist in the design and analysis of research projects.