Biology subjects

Reinhold, W. C.

Publications and source records attributed to Reinhold, W. C..

6 recordsLinked to original sources

bioRxiv2025

Bimodal genomic approach predicting Semaphorin 7A (SEMA7A) as prognostic biomarker in adrenocortical carcinoma

Adrenocortical carcinoma (ACC) is a rare and aggressive endocrine malignancy with high mortality and poor prognosis. To elucidate the genetic underpinnings of ACCs, we have analyzed the transcriptome data of 112 ACC tumor samples from patients enrolled in the TCGA and NCI. Among 72 bimodally expressed genes stratifying patients into prognostic groups, we focused on SEMA7A, as it encodes a glycosylphosphatidylinositol-anchored membrane glycoprotein (Semaphorin 7a) regulating integrin-mediated signaling, cell migration and immune responses. We find that high SEMA7A gene expression is associated with poor prognosis (hazard ratio = 4.27; p-value < 0.001). In hormone-producing ACCs, SEMA7A expression is elevated and positively correlated with genes driving steroidogenesis, aldosterone and cortisol synthesis, including CYP17A1, CYP11A1, INHA, DLK1, NR5A1 and MC2R. Correlation analyses show that SEMA7A is co-expressed with the integrin-{beta}1, FAK (focal adhesion kinase) and MAPK/ERK (mitogen-activated protein kinase/extracellular signal regulated kinases) signaling pathways. Immunohistochemistry (IHC) staining demonstrates the feasibility of evaluating SEMA7A in ACC tissues and shows significant correlation between gene expression (RNA-Seq) and protein expression (IHC). These findings suggest SEMA7A as a candidate for further research in ACC biology, a candidate for cancer therapy, as well as a potential prognosis biomarker for ACC patients. Translational relevanceAdrenocortical cancer (ACC) remains a challenging disease primarily due to the scarcity of reliable biomarkers for predicting patient outcomes and informing innovative therapeutic strategies, as well as its rarity, which restricts the scope of clinical trials. In our study, we performed RNAseq and IHC analyses of ACC samples sourced from The Cancer Genome Atlas (TCGA), tissue microarray slide, and National Cancer Institute (NCI) cancer patient samples. Our findings indicate that a substantial proportion of ACC tumors exhibit expression of SEMA7A, a glycoprotein involved in Semaphorin cell surface signaling. Notably, elevated levels of SEMA7A were identified as a poor prognostic biomarker and were associated with activation of the integrin-ERK-MAPK kinase signaling pathways. These results suggest that ACC tumors with high SEMA7A expression should be considered at elevated risk, and SEMA7A may serve as a potential target for immunotherapeutic strategies, including antibody-drug conjugates, T-cell engagers, and/or small molecule inhibitors targeting the MAPK pathway.

cancer biology↗

bioRxiv2024

Ribosomal RNA transcription governs splicing through ribosomal protein RPL22

Ribosome biosynthesis is a cancer vulnerability executed by targeting RNA polymerase I (Pol I) transcription. We developed advanced, specific Pol I inhibitors to identify drivers of this sensitivity. By integrating multi-omics features and drug sensitivity data from a large cancer cell panel, we discovered that RPL22 frameshift mutation conferred Pol I inhibitor sensitivity in microsatellite instable cancers. Mechanistically, RPL22 directly interacts with 28S rRNA and mRNA splice junctions, functioning as a splicing regulator. RPL22 deficiency, intensified by 28S rRNA sequestration, promoted the splicing of its paralog RPL22L1 and p53 negative regulator MDM4. Chemical and genetic inhibition of rRNA synthesis broadly remodeled mRNA splicing controlling hundreds of targets. Strikingly, RPL22-dependent alternative splicing was reversed by Pol I inhibition revealing a ribotoxic stress-initiated tumor suppressive pathway. We identify a mechanism that robustly connects rRNA synthesis activity to splicing and reveals their coordination by ribosomal protein RPL22.

molecular biology↗

bioRxiv2022

Proteomic Dynamics of Breast Cancers Identifies Potential Therapeutic Protein Targets

Treatment and relevant targets for breast cancer (BC) remain limited, especially for triple-negative BC (TNBC). We quantified the proteomes of 76 human BC cell lines using data independent acquisition (DIA) based proteomics, identifying 6091 proteins. We then established a 24-protein panel distinguishing TNBC from other BC types. Integrating prior multi-omics datasets with the present proteomic results to predict the sensitivity of 90 drugs, we found that proteomics data improved drug sensitivity predictions. The sensitivity of the 90 drugs was mainly associated with cell cytoskeleton, signal transduction and mitochondrial function. We next profiled the proteome changes of nine cell lines (five TNBC cell lines, four non-TNBC cell lines) perturbated by EGFR/AKT/mTOR inhibitors. In the TNBC cell lines, metabolism pathways were dysregulated after EGFR/mTOR inhibitors treatment, while RNA modification and cell cycle pathways were dysregulated after AKT inhibitor treatment. Our study presents a systematic multi-omics and in-depth analysis of the proteome of BC cells. This work aims to aid in prioritization of potential therapeutic targets for TNBC as well as to provide insight into adaptive drug resistance in TNBC.

systems biology↗

bioRxiv2020

SCLC_CellMiner: Integrated Genomics and Therapeutics Predictors of Small Cell Lung Cancer Cell Lines based on their genomic signatures

Model systems are necessary to understand the biology of SCLC and develop new therapies against this recalcitrant disease. Here we provide the first online resource, CellMiner-SCLC (https://discover.nci.nih.gov/SclcCellMinerCDB) incorporating 118 individual SCLC cell lines and extensive omics and drug sensitivity datasets, including high resolution methylome performed for the purpose of the current study. We demonstrate the reproducibility of the cell lines and genomic data across the CCLE, GDSC, CTRP, NCI and UTSW datasets. We validate the SCLC classification based on four master transcription factors: NEUROD1, ASCL1, POU2F3 and YAP1 (NAPY classification) and show transcription networks connecting each them with their downstream and upstream regulators as well as with the NOTCH and HIPPO pathways and the MYC genes (MYC, MYCL1 and MYCN). We find that each of the 4 subsets express specific surface markers for antibody-targeted therapies. The SCLC-Y cell lines differ from the other subsets by expressing the NOTCH pathway and the antigen-presenting machinery (APM), and responding to mTOR and AKT inhibitors. Our analyses suggest the potential value of NOTCH activators, YAP1 inhibitors and immune checkpoint inhibitors in SCLC-Y tumors that can now be independently validated. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=151 SRC="FIGDIR/small/980623v2_ufig1.gif" ALT="Figure 1"> View larger version (65K): org.highwire.dtl.DTLVardef@10930e7org.highwire.dtl.DTLVardef@699d9corg.highwire.dtl.DTLVardef@1ea773corg.highwire.dtl.DTLVardef@3a1589_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LISCLC-CellMiner provides the most extensive SCLC resource in terms of number of cell lines (118 cell lines), extensive omics data (exome, microarray, RNA-seq, copy number, methylomes and microRNA) and drug sensitivity testing. C_LIO_LIWe find evidence of distinct epigenetic profile of SCLC cell lines (global hypomethylation and histone gene methylation), which is consistent with their plasticity. C_LIO_LITranscriptome analyses demonstrate the coherent transcriptional networks associated with the 4 main genomic subgroups (NEUROD1, ASCL1, POU2F3 & YAP1 = NAPY classification) and their connection with the NOTCH and HIPPO signaling pathways. C_LIO_LISCLC-CellMiner provides a conceptual framework for the selection of therapies for SCLC in a personalized fashion allowing putative biomarkers according molecular classifications and molecular characteristics. C_LIO_LISCLC-Y cell lines differ from the other cancer cell lines; their transcriptome resemble NSCLC cell lines. YAP1 cell lines while being the most resistant to standard of care treatments (etoposide, cisplatin and topotecan) respond to mTOR and AKT inhibitors and present native immune predisposition suggesting sensitivity to immune checkpoint inhibitors. C_LI

cancer biology↗

bioRxiv2019

Tyrosyl-DNA phosphodiesterase 1 and topoisomerase I activities as predictive indicators for Glioblastoma susceptibility to genotoxic agents

BackgroundGlioblastoma (GBM) patients have an estimated survival of [~]15 months with treatment, and the standard of care only modestly enhances patient survival. Identifying biomarkers representing vulnerabilities may allow for selection of efficacious chemotherapy options to address personalized variations in GBM tumors. Irinotecan, currently in clinical trials for GBM, targets topoisomerase I (TOP1) by forming a ternary DNA-TOP1 cleavage complex (TOP1cc) inducing apoptosis. Tyrosyl-DNA phosphodiesterase 1 (TDP1) is a crucial repair enzyme that may reduce the effectiveness of irinotecan.\n\nMethodsWe treated GBM cell lines with increasing concentrations of irinotecan and compared the IC50 values. TOP1 and TDP1 protein levels from each cell type as well as GBM patient tumors were determined by Western blot analysis, while activity levels were ascertained by specific enzymatic assays. Cellular TDP1 was elevated by ectopic expression of wild-type or mutant TDP1.\n\nResultsAfter comparing cellular susceptibility to TDP1 and TOP1 concentrations and activities, we found that the TDP1/TOP1 activity ratio had the strongest correlation (Pearson correlation coefficient R = 0.92) with IC50 values following irinotecan treatment. Increasing the TDP1/TOP1 activity ratio by ectopic expression of wild-type TDP1 increased in irinotecan IC50, while expression of the TDP1 catalytic-null mutant did not alter the susceptibility to irinotecan.\n\nConclusionsTDP1/TOP1 activity ratio may be a new predictive indicator for GBM vulnerability to irinotecan, allowing for selection of individual patients for irinotecan treatment based on risk-benefit. Moreover, TDP1 inhibitors may be a novel combination treatment with irinotecan to improve GBM patient responsiveness to genotoxic chemotherapies.\n\nKey PointsO_LITDP1/TOP1 activity ratio correlates with irinotecan sensitivity in GBM cell lines.\nC_LIO_LITDP1 and TOP1 protein levels are not reliable predictors for irinotecan activity.\nC_LIO_LITDP1 inhibition is a plausible approach to improve irinotecan effectiveness in GBM.\nC_LI\n\nImportance of the StudyThe current standard of care (surgery, radiation, and chemotherapy) for GBM patients modestly enhances survival beyond [~]15 months. Thus, there is a great need for effective therapies and biomarkers that address personalized variations in GBM tumors to improve treatment outcome. Topoisomerase I (TOP1) is the target of irinotecan. The repair enzyme tyrosyl-DNA phosphodiesterase 1 (TDP1) is known to excise irinotecan-induced TOP1-DNA cleavage complexes (TOP1ccs). Consequently, this study examines the relationship between TOP1 and TDP1 expression and activities in GBM cells and their correlation with irinotecan sensitivity. The results reveal that the TDP1/TOP1 activity ratio predicts irinotecan vulnerability in GBM cell lines. TDP1/TOP1 activity ratio was found to vary among GBM patient tumors. This potential predictive indicator may permit selection of patients responsive to irinotecan based on the capacity to repair TOP1cc. Moreover, inhibitors of TDP1 may represent a promising approach to enhance irinotecan efficacy in GBM patients.

cancer biology↗

bioRxiv2018

Integrative analysis of pharmacogenomics in major cancer cell line databases using CellMinerCDB

As precision medicine demands molecular determinants of drug response, CellMinerCDB provides (https://discover.nci.nih.gov/cellminercdb/) a web-based portal for multiple forms of pharmacological, molecular, and genomic analyses, unifying the richest cancer cell line datasets (NCI-60, NCI-SCLC, Sanger/MGH GDSC, and Broad CCLE/CTRP). CellMinerCDB enables genomic and pharmacological data queries for identifying pharmacogenomic determinants, drug signatures, and gene regulatory networks for researchers without requiring specialized bioinformatics support. It leverages overlaps of cell lines and tested drugs to allow assessment of data reproducibility. It builds on the complementarity and strength of each dataset. A panel of 41 drugs evaluated in parallel in the NCI-60 and GDSC is reported, supporting drug reproducibility across databases, repositioning of bisacodyl and acetalax for triple negative breast cancer, and identifying novel drug response determinants and genomic signatures for topoisomerase inhibitors and schweinfurthins in development. CellMinerCDB also allowed the identification of LIX1L as a novel mesenchymal gene regulating cellular migration and invasiveness.

bioinformatics↗