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In the United States, great inequities exist within the science, technology, engineering, and math (STEM) fields in individuals who self-identify as belonging to underrepresented groups (e.g., from socioeconomically disadvantaged backgrounds, people with disabilities, and Black/African American, Hispanic/Latino, American Indian, Alaska Native, Native Hawaiian, or Pacific Islander). Underrepresentation in STEM careers has been documented to be due to a lack of qualified and diverse teaching personnel, limited access to resources in disadvantaged schools, and implicit bias within US institutions. In recognition of these inequities, an interprofessional team of biomedical faculty members and diversity, equity and inclusion (DEI) educators from the Louisiana State University Health Science Center (LSUHSC) in New Orleans developed and implemented a STEM educational training pipeline for elementary, middle, and high school (K-12), undergraduate, and medical students, with accompanying DEI training for LSUHSC mentors and staff to mitigate implicit bias and promote a welcoming environment for participants from different backgrounds. Our findings focus on one social determinant of health: access and quality of education. Other institutions can implement a program as described in the present study to address the educational and health inequities in their own communities.

A critical unmet medical need in prostate cancer (PCa) clinical management centers around distinguishing indolent from aggressive tumors. Traditionally, Gleason grading has been utilized for this purpose. However, tumor classification using Gleason Grade 7 is often ambiguous, as the clinical behavior of these tumors follows a variable clinical course. This study aimed to investigate the application of machine learning techniques (ML) to classify patients into indolent and aggressive PCas. We used gene expression data from The Cancer Genome Atlas and compared gene expression levels between indolent and aggressive tumors to identify features for developing and validating a range of ML and stacking algorithms. ML algorithms accurately distinguished indolent from aggressive PCas. With the accuracy of 96%, the stacking model was superior to individual ML algorithms when all samples with primary Gleason Grades 6 to 10 were used. Excluding samples with Gleason Grade 7 improved accuracy to 97%. This study shows that ML algorithms and stacking models are powerful approaches for the accurate classification of indolent versus aggressive PCas. Future implementation of this methodology may significantly impact clinical decision making and patient outcomes in the clinical management of prostate cancer.

Abstract Patients with triple-negative breast cancer (TNBC) have a poor prognosis and high relapse rate due to limited therapeutic options. This study was conducted to determine the mechanisms of action of panobinostat, a pan-inhibitor of histone deacetylase (HDAC) and FDA-approved medication for multiple myeloma, in TNBC and to provide a rationale for effective drug combinations against this aggressive disease. RNA sequencing analyses of the claudin-low (CL) TNBC (MDA-MB-231) cells untreated or treated with panobinostat were performed to identify the differentially expressed genes. Adaptive alterations in gene expression were analyzed and validated in additional CL TNBC cells. Tumor xenograft models were used to test the in vivo antitumor activity of panobinostat alone or its combinations with gefitinib, an EGFR-tyrosine kinase inhibitor (TKI). Panobinostat potently inhibited proliferation and induced apoptosis in all TNBC cells tested. However, in CL TNBC cells, this HDAC inhibitor markedly enhanced expression of HER3, which interacted with EGFR to activate both receptors and Akt signaling pathways. Combinations of panobinostat and gefitinib synergistically suppressed CL TNBC cell proliferation and promoted apoptosis in vitro and in vivo. Upregulation of HER3 compromises the efficacy of panobinostat in CL TNBC. Inactivation of HER3 combined with panobinostat represents a practical approach to combat CL TNBC.

IntroductionRapid advancements in Next Generation Sequencing (NGS) and bioinformatics tools have allowed physicians to obtain genetic testing results in a more rapid, cost-effective, and comprehensive manner than ever before. Around 50% of pediatric sensorineural hearing loss (SNHL) cases are due to a genetic etiology, thus physicians regularly utilize targeted sequencing panels that identify variants in genes related to SNHL. These panels allow for early detection of pathogenic variants which allows physicians to provide anticipatory guidance to families. Molecular testing does not always reveal a clear etiology due to the presence of multigenic variants with varying classifications, including the presence of Variants of Uncertain Significance (VUS). This study aims to perform a preliminary bioinformatics characterization of patients with variants associated with Type II Usher Syndrome in the presence of other multigenic variants. We also provide an interpretation algorithm for physicians reviewing molecular results with medical geneticists.MethodsReview of records for multigenic and/or VUS results identified several potential subjects of interest. For the purposes of this study, two ADGRV1 compound heterozygotes met inclusion criteria. Sequencing, data processing, and variant calling (the process by which variants are identified from sequence data) was performed at Invitae (San Francisco CA). The preliminary analysis followed the recommendations outlined by the American College of Medical Genetics and Association for Molecular Pathology (ACMG-AMP) in 2015 and 2019. The present study utilizes computational analysis, predictive data, and population data as well as clinical information from chart review and publicly available information in the ClinVar database.ResultsTwo subjects were identified as compound heterozygotes for variants in the gene ADGRV1. Subject 1's variants were predicted as deleterious, while Subject 2's variants were predicted as non-deleterious. These results were based on known information of the variants from ClinVar, multiple lines of computational data, population databases, as well as the clinical presentation.DiscussionEarly molecular diagnosis through NGS is ideal, as families are then able to access a wide range of resources that will ultimately support the child as their condition progresses. We recommend that physicians build strong relationships with medical geneticists and carefully review their interpretation before making recommendations to families, particularly when addressing the VUS. Reclassification efforts of VUS are supported by studies like ours that provide evidence of pathogenic or benign effects of variants.

IntroductionTriple-negative breast cancer (TNBC) comprises a heterogeneous group of clinically aggressive tumors with high risk of recurrence and metastasis. Current pharmacological treatment options remain largely limited to chemotherapy. Despite promising results, the efficacy of immunotherapy and chemo-immunotherapy in TNBC remains limited. There is strong evidence supporting the involvement of Notch signaling in TNBC progression. Expression of Notch1 and its ligand Jagged1 correlate with poor prognosis. Notch inhibitors, including g-secretase inhibitors (GSIs), are quite effective in preclinical models of TNBC. However, the success of GSIs in clinical trials has been limited by their intestinal toxicity and potential for adverse immunological effects, since Notch plays key roles in T-cell activation, including CD8 T-cells in tumors. Our overarching goal is to replace GSIs with agents that lack their systemic toxicity and ideally, do not affect tumor immunity. We identified sulindac sulfide (SS), the active metabolite of FDA-approved NSAID sulindac, as a potential candidate to replace GSIs.MethodsWe investigated the pharmacological and immunotherapeutic properties of SS in TNBC models in vitro, ex-vivo and in vivo.ResultsWe confirmed that SS, a known γ-secretase modulator (GSM), inhibits Notch1 cleavage in TNBC cells. SS significantly inhibited mammosphere growth in all human and murine TNBC models tested. In a transplantable mouse TNBC tumor model (C0321), SS had remarkable single-agent anti-tumor activity and eliminated Notch1 protein expression in tumors. Importantly, SS did not inhibit Notch cleavage in T- cells, and the anti-tumor effects of SS were significantly enhanced when combined with a-PD1 immunotherapy in our TNBC organoids and in vivo.DiscussionOur data support further investigation of SS for the treatment of TNBC, in conjunction with chemo- or -chemo-immunotherapy. Repurposing an FDA-approved, safe agent for the treatment of TNBC may be a cost-effective, rapidly deployable therapeutic option for a patient population in need of more effective therapies.

Background: A critical unmet medical need in clinical management of colorectal cancer (CRC) pivots around lack of noninvasive and or minimally invasive techniques for early diagnosis and prognostic prediction of clinical outcomes. Because DNA methylation can capture the regulatory landscape of tumors and can be measured in body fluids, it provides unparalleled opportunities for the discovery of early diagnostic and prognostics markers predictive of clinical outcomes. Here we investigated use of DNA methylation for the discovery of potential clinically actionable diagnostic and prognostic markers for predicting survival in CRC. Methods: We analyzed DNA methylation patterns between tumor and control samples to discover signatures of CpG sites and genes associated with CRC and predictive of survival. We conducted functional analysis to identify molecular networks and signaling pathways driving clinical outcomes. Results: We discovered a signature of aberrantly methylated genes associated with CRC and a signature of thirteen (13) CpG sites predictive of survival. We discovered molecular networks and signaling pathways enriched for CpG sites likely to drive clinical outcomes. Conclusions: The investigation revealed that CpG sites can predict survival in CRC and that DNA methylation can capture the regulatory state of tumors through aberrantly methylated molecular networks and signaling pathways.

COVID-19 ranges from asymptomatic in 35% of cases to severe in 20% of patients. Differences in the type and degree of inflammation appear to determine the severity of the disease. Recent reports show an increase in circulating monocytic-myeloid-derived suppressor cells (M-MDSC) in severe COVID 19 that deplete arginine but are not associated with respiratory complications. Our data shows that differences in the type, function and transcriptome of granulocytic-MDSC (G-MDSC) may in part explain the severity COVID-19, in particular the association with pulmonary complications. Large infiltrates by Arginase 1+ G-MDSC (Arg+G-MDSC), expressing NOX-1 and NOX-2 (important for production of reactive oxygen species) were found in the lungs of patients who died from COVID-19 complications. Increased circulating Arg+G-MDSC depleted arginine, which impaired T cell receptor and endothelial cell function. Transcriptomic signatures of G-MDSC from patients with different stages of COVID-19, revealed that asymptomatic patients had increased expression of pathways and genes associated with type I interferon (IFN), while patients with severe COVID-19 had increased expression of genes associated with arginase production, and granulocyte degranulation and function. These results suggest that asymptomatic patients develop a protective type I IFN response, while patients with severe COVID-19 have an increased inflammatory response that depletes arginine, impairs T cell and endothelial cell function, and causes extensive pulmonary damage. Therefore, inhibition of arginase-1 and/or replenishment of arginine may be important in preventing/treating severe COVID-19.

Delayed surgery is associated with worse lung cancer outcomes. Social determinants can influence health disparities. This study aimed to examine the potential racial disparity and the effects from social determinants on receipt of timely surgery among lung cancer patients in Louisiana, a southern state in the U.S. White and black stage I–IIIA non-small cell lung cancer patients diagnosed in Louisiana between 2004 and 2016, receiving surgical lobectomy or a more extensive surgery, were selected. Diagnosis-to-surgery interval >6 weeks were considered as delayed surgery. Social determinants included marital status, insurance, census tract level poverty, and census tract level urbanicity. Multivariable logistic regression and generalized multiple mediation analysis were conducted. A total of 3,616 white (78.9%) and black (21.1%) patients were identified. The median time interval from diagnosis to surgery was 27 days in whites and 42 days in blacks (P < 0.0001). About 28.7% of white and 48.4% of black patients received delayed surgery (P < 0.0001). Black patients had almost two-fold odds of receiving delayed surgery than white patients (adjusted odds ratio: 1.91; 95% confidence interval: 1.59–2.30). Social determinants explained about 26% of the racial disparity in receiving delayed surgery. Having social support, private insurance, and living in census tracts with lower poverty level were associated with improved access to timely surgery. The census tract level poverty level a stronger effect on delayed surgery in black patients than in white patients. Tailored interventions to improve the timely treatment in NSCLC patients, especially black patients, are needed in the future.

Abstract Background Prostate cancer (PCa) is the most common diagnosed malignancy and the second leading cause of cancer-related deaths among men in the United States. High-throughput genotyping has enabled discovery of germline genetic susceptibility variants (herein referred to as germline mutations) associated with an increased risk of developing PCa. However, germline mutation information has not been leveraged and integrated with information on acquired somatic mutations to link genetic susceptibility to tumorigenesis. The objective of this exploratory study was to address this knowledge gap. Methods Germline mutations and associated gene information were derived from genome-wide association studies (GWAS) reports. Somatic mutation and gene expression data were derived from 495 tumors and 52 normal control samples obtained from The Cancer Genome Atlas (TCGA). We integrated germline and somatic mutation information using gene expression data. We performed enrichment analysis to discover molecular networks and biological pathways enriched for germline and somatic mutations. Results We discovered a signature of 124 genes containing both germline and somatic mutations. Enrichment analysis revealed molecular networks and biological pathways enriched for germline and somatic mutations, including, the PDGF, P53, MYC, IGF-1, PTEN and Androgen receptor signaling pathways. Conclusion Integrative genomic analysis links genetic susceptibility to tumorigenesis in PCa and establishes putative functional bridges between the germline and somatic variation, and the biological pathways they control.

Abstract Background Large-scale “omics” datasets have not been leveraged and integrated with functional analyses to discover potential drivers of cardiomyopathy. This study addresses the knowledge gap. Methods We coupled RNA sequence (RNA-Seq) variant detection and transcriptome profiling with pathway analysis to model drug refractory dilated cardiomyopathy (drDCM) using the BaseSpace sequencing hub and Ingenuity Pathway Analysis. We used RNA-Seq case-control datasets (n = 6 cases, n = 4 controls), exome sequence familial DCM datasets (n = 3 Italians, n = 5 Italians, n = 5 Chinese), and controls from the HapMap project (n = 5 Caucasians, and n = 5 Asians) for disease modeling and putative mutation discovery. Variant replication datasets: n = 128 cases and n = 15 controls. Source of datasets: NCBI Sequence Read Archive. Statistics: Pairwise differential expression analyses to determine differentially expressed genes and t-tests to calculate p-values. We adjusted for false discovery rates and reported q-values. We used chi-square tests to assess independence among variables, the Fisher’s Exact Tests and overlap p-values for the pathways and p-scores to rank network. Results Data revealed that ECHS1(enoyl-CoA hydratase, short chain 1(log2(foldchange) = 1.63329) hosts a mirtron, MIR3944 expressed in drDCM (FPKM = 5.2857) and not in controls (FPKM = 0). Has-miR3944-3p is a putative target of BAG1 (BCL2 associated athanogene 1(log2(foldchange) = 1.31978) and has-miR3944-5p of ITGAV (integrin subunit alpha V(log2(foldchange) = 1.46107) and RHOD (ras homolog family member D(log2(foldchange) = 1.28851). There is an association between ECHS1:11 V/A(rs10466126) and drDCM (p = 0.02496). The interaction (p = 2.82E-07) between ECHS1:75 T/I(rs1049951) and ECHS1:rs10466126 is associated with drDCM (p

Extensive research has examined socioeconomic factors influencing prostate cancer (PCa) disparities. However, to what extent molecular and genetic mechanisms may also contribute to these inequalities still remains elusive. Although various in vitro, in vivo, and population studies have originated to address this issue, they are often very costly and time-consuming by nature. In this work, we attempt to explore this problem by a preliminary study, where a joint deep latent variable model (DLVM) is proposed to in silico quantify the personalized and race-specific effects that a genomic aberration may exert on the Gleason Score (GS) of each individual PCa patient. The core of the proposed model is a deep variational autoencoder (VAE) framework, which follows the causal structure of inference with proxies. Extensive experimental results on The Cancer Genome Atlas (TCGA) 270 European-American (EA) and 43 African-American (AA) PCa patients demonstrate that ERG fusions, somatic mutations in SPOP and ATM, and copy number alterations (CNAs) in ERG are the statistically significant genomic factors across all low-, intermediate-, and high-grade PCa that may explain the disparities between these two groups. Moreover, compared to a state-of-the-art deep inference method, our proposed method achieves much higher precision in causal effect inference in terms of the impact of a studied genomic aberration on GS. Further validation on an independent set and the assessment of the genomic-risk scores along with corresponding confidence intervals not only validate our results but also provide valuable insight to the observed racial disparity between these two groups regarding PCa metastasis. The pinpointed significant genomic factors may shed light on the molecular mechanism of cancer disparities in PCa and warrant further investigation.

Background. Breast cancer development and progression involve both germline and somatic mutations. High-throughput genotyping and next-generation sequencing technologies have enabled discovery of genetic risk variants and acquired somatic mutations driving the disease. However, the possible oncogenic interactions between germline genetic risk variants and somatic mutations in triple-negative breast cancer (TNBC) and non-triple-negative breast cancer (non-TNBC) have not been characterized. Here, we delineated the possible oncogenic interactions between genes containing germline and somatic mutations in TNBC and non-TNBC and investigated whether there are differences in gene expression and mutation burden between the two types of breast cancer. Methods. We addressed this problem by integrating germline mutation information from genome-wide association studies with somatic mutation information from next-generation sequencing using gene expression data as the intermediated phenotype. We performed network and pathway analyses to discover molecular networks and signalling pathways enriched for germline and somatic mutations. Results. The investigation revealed signatures of differentially expressed and differentially somatic mutated genes between TNBC and non-TNBC. Network and pathway analyses revealed functionally related genes interacting in gene regulatory networks and multiple signalling pathways enriched for germline and somatic mutations for each type of breast cancer. Among the signalling pathways discovered included the DNA repair and Androgen and ATM signalling pathways for TNBC and the DNA damage response, molecular mechanisms of cancer, and ATM and GP6 signalling pathways for non-TNBC. Conclusions. The results show that integrative genomics is a powerful approach for delineating oncogenic interactions between genes containing germline and genes containing somatic mutations in TNBC and non-TNBC and establishes putative functional bridges between genetic and somatic alterations and the pathways they control in the two types of breast cancer.

Prostate cancer (PCa) is the most common diagnosed malignancy and the second leading cause of cancer-related deaths among men in the USA. Advances in high-throughput genotyping and next generation sequencing technologies have enabled discovery of germline genetic susceptibility variants and somatic mutations acquired during tumor formation. Emerging evidence indicates that germline variations may interact with somatic events in carcinogenesis. However, the possible oncogenic interactions and cooperation between germline and somatic variation and their role in aggressive PCa remain largely unexplored. Here we investigated the possible oncogenic interactions and cooperation between genes containing germline variation from genome-wide association studies (GWAS) and genes containing somatic mutations from tumor genomes of 305 men with aggressive tumors and 52 control samples from The Cancer Genome Atlas (TCGA). Network and pathway analysis were performed to identify molecular networks and biological pathways enriched for germline and somatic mutations. The analysis revealed 90 functionally related genes containing both germline and somatic mutations. Transcriptome analysis revealed a 61-gene signature containing both germline and somatic mutations. Network analysis revealed molecular networks of functionally related genes and biological pathways including P53, STAT3, NKX3-1, KLK3, and Androgen receptor signaling pathways enriched for germline and somatic mutations. The results show that integrative analysis is a powerful approach to uncovering the possible oncogenic interactions and cooperation between germline and somatic mutations and understanding the broader biological context in which they operate in aggressive PCa.

Next generation sequencing (NGS) is becoming increasingly integrated into oncological practice and clinical research. NGS methods have also provided evidence for clonal evolution of cancers during disease progression and treatment. The number of variants associated with response to specific therapeutic agents keeps increasing. However, the identification of novel driver mutations as opposed to passenger (phenotypically silent or clinically irrelevant) mutations remains a major challenge. We conducted targeted exome sequencing of advanced solid tumors from 44 pre-treated patients with solid tumors including breast, colorectal and lung carcinomas, neuroendocrine tumors, sarcomas and others. We catalogued established driver mutations and putative new drivers as predicted by two distinct algorithms. The established drivers we detected were consistent with published observations. However, we also detected a significant number of mutations with driver potential never described before in each tumor type we studied. These putative drivers belong to key cell fate regulatory networks, including potentially druggable pathways. Should our observations be confirmed, they would support the hypothesis that new driver mutations are selected by treatment in clinically aggressive tumors, and indicate a need for longitudinal genomic testing of solid tumors to inform second line cancer treatment.

Advances in sequencing technologies and increased understanding of the contribution of genetics to congenital sensorineural hearing loss have led to vastly improved outcomes for patients and their families. Next-generation sequencing and diagnostic panels have become increasingly reliable and less expensive for clinical use. Despite these developments, the diagnosis of genetic sensorineural hearing loss still presents challenges for healthcare providers. Inherited sensorineural hearing loss has high levels of genetic heterogeneity and variable expressivity. Additionally, syndromic hearing loss (hearing loss and additional clinical abnormalities) should be distinguished from non-syndromic (hearing loss is the only clinical symptom). Although the diagnosis of genetic sensorineural hearing loss can be challenging, the patient’s family history and ethnicity may provide critical information, as certain genetic mutations are more common in specific ethnic populations. The early identification of the cause of deafness can benefit patients and their families by estimating recurrence risks for future family planning and offering the proper interventions to improve their quality of life. Collaboration between pediatricians, audiologists, otolaryngologists, geneticists, and other specialists are essential in the diagnosis and management of patients with hearing disorders. An early diagnosis is vital for proper management and care, as some clinical manifestations of syndromic sensorineural hearing loss are not apparent at birth and have a delayed age of onset. We present a case of Usher syndrome (congenital deafness and childhood-onset blindness) illustrating the challenges encountered in the diagnosis and management of children presenting with congenital genetic sensorineural hearing loss, along with helpful resources for clinicians and families.

MicroRNAs (miRNAs) regulate the expression of protein-coding genes and represent potential biomarkers for childhood acute lymphoblastic leukemia (ALL). However, information linking miRNAs with their messenger RNA (mRNA) target genes modulating white blood cell (WBC) count is lacking. Here, we analyzed miRNAs and gene expression data from pediatric patients with ALL to identify a signature of miRNAs involved in ALL and their mRNA target genes, molecular networks, and biological pathways modulating WBC. We discovered a signature of miRNAs differentially expressed in ALL and a signature of mRNA target genes distinguishing patients with high WBC from patients with low WBC. In addition, we identified molecular networks and biological pathways, among them PI3/AKT, JAK/STAT, IL-17, TGF-β, apoptosis, IL-15, STAT3, IGF-1, FGF, mTOR, VEGF, NF-kB, and P53 signaling pathways, enriched for or targeted by miRNAs. The discovered miRNAs and their target genes and pathways represent potential clinically actionable biomarkers and therapeutic targets.

High-throughput genotyping has enabled discovery of genetic variants associated with an increased risk of developing prostate cancer using genome-wide association studies (GWAS). The goal of this study was to associate GWAS information of patients with primary organ–confined and metastatic prostate cancer using gene expression data and to identify molecular networks and biological pathways enriched for genetic susceptibility variants involved in the 2 disease states. The analysis revealed gene signatures for the 2 disease states and a gene signature distinguishing the 2 patient groups. In addition, the analysis revealed molecular networks and biological pathways enriched for genetic susceptibility variants. The discovered pathways include the androgen, apoptosis, and insulinlike growth factor signaling pathways. This analysis established putative functional bridges between GWAS discoveries and the biological pathways involved in primary organ–confined and metastatic prostate cancer.