More Like this

1. Comparing the Secretomes of Chemorefractory and Chemoresistant Ovarian Cancer Cell Populations

   https://doi.org/10.3390/cancers14061418  

   Lee, Amy H.; Mejia Peña, Carolina; Dawson, Michelle R. (March 2022, Cancers)

   High-grade serous ovarian cancer (HGSOC) constitutes the majority of all ovarian cancer cases and has staggering rates of both refractory and recurrent disease. While most patients respond to the initial treatment with paclitaxel and platinum-based drugs, up to 25% do not, and of the remaining that do, 75% experience disease recurrence within the subsequent two years. Intrinsic resistance in refractory cases is driven by environmental stressors like tumor hypoxia which alter the tumor microenvironment to promote cancer progression and resistance to anticancer drugs. Recurrent disease describes the acquisition of chemoresistance whereby cancer cells survive the initial exposure to chemotherapy and develop adaptations to enhance their chances of surviving subsequent treatments. Of the environmental stressors cancer cells endure, exposure to hypoxia has been identified as a potent trigger and priming agent for the development of chemoresistance. Both in the presence of the stress of hypoxia or the therapeutic stress of chemotherapy, cancer cells manage to cope and develop adaptations which prime populations to survive in future stress. One adaptation is the modification in the secretome. Chemoresistance is associated with translational reprogramming for increased protein synthesis, ribosome biogenesis, and vesicle trafficking. This leads to increased production of soluble proteins and extracellular vesicles (EVs) involved in autocrine and paracrine signaling processes. Numerous studies have demonstrated that these factors are largely altered between the secretomes of chemosensitive and chemoresistant patients. Such factors include cytokines, growth factors, EVs, and EV-encapsulated microRNAs (miRNAs), which serve to induce invasive molecular, biophysical, and chemoresistant phenotypes in neighboring normal and cancer cells. This review examines the modifications in the secretome of distinct chemoresistant ovarian cancer cell populations and specific secreted factors, which may serve as candidate biomarkers for aggressive and chemoresistant cancers. 

   more » « less
2. Integrating spatial transcriptomics and bulk RNA-seq: predicting gene expression with enhanced resolution through graph attention networks

   https://doi.org/10.1093/bib/bbae316  

   Baul, Sudipto; Tanvir_Ahmed, Khandakar; Jiang, Qibing; Wang, Guangyu; Li, Qian; Yong, Jeongsik; Zhang, Wei (July 2024, Briefings in Bioinformatics)

   Abstract Spatial transcriptomics data play a crucial role in cancer research, providing a nuanced understanding of the spatial organization of gene expression within tumor tissues. Unraveling the spatial dynamics of gene expression can unveil key insights into tumor heterogeneity and aid in identifying potential therapeutic targets. However, in many large-scale cancer studies, spatial transcriptomics data are limited, with bulk RNA-seq and corresponding Whole Slide Image (WSI) data being more common (e.g. TCGA project). To address this gap, there is a critical need to develop methodologies that can estimate gene expression at near-cell (spot) level resolution from existing WSI and bulk RNA-seq data. This approach is essential for reanalyzing expansive cohort studies and uncovering novel biomarkers that have been overlooked in the initial assessments. In this study, we present STGAT (Spatial Transcriptomics Graph Attention Network), a novel approach leveraging Graph Attention Networks (GAT) to discern spatial dependencies among spots. Trained on spatial transcriptomics data, STGAT is designed to estimate gene expression profiles at spot-level resolution and predict whether each spot represents tumor or non-tumor tissue, especially in patient samples where only WSI and bulk RNA-seq data are available. Comprehensive tests on two breast cancer spatial transcriptomics datasets demonstrated that STGAT outperformed existing methods in accurately predicting gene expression. Further analyses using the TCGA breast cancer dataset revealed that gene expression estimated from tumor-only spots (predicted by STGAT) provides more accurate molecular signatures for breast cancer sub-type and tumor stage prediction, and also leading to improved patient survival and disease-free analysis. Availability: Code is available at https://github.com/compbiolabucf/STGAT. 

   more » « less
3. Network-based multi-task learning models for biomarker selection and cancer outcome prediction

   https://doi.org/10.1093/bioinformatics/btz809  

   Wang, Zhibo; He, Zhezhi; Shah, Milan; Zhang, Teng; Fan, Deliang; Zhang, Wei; Cowen, Lenore (November 2019, Bioinformatics)

   Abstract Motivation Detecting cancer gene expression and transcriptome changes with mRNA-sequencing (RNA-Seq) or array-based data are important for understanding the molecular mechanisms underlying carcinogenesis and cellular events during cancer progression. In previous studies, the differentially expressed genes were detected across patients in one cancer type. These studies ignored the role of mRNA expression changes in driving tumorigenic mechanisms that are either universal or specific in different tumor types. To address the problem, we introduce two network-based multi-task learning frameworks, NetML and NetSML, to discover common differentially expressed genes shared across different cancer types as well as differentially expressed genes specific to each cancer type. The proposed frameworks consider the common latent gene co-expression modules and gene-sample biclusters underlying the multiple cancer datasets to learn the knowledge crossing different tumor types. Results Large-scale experiments on simulations and real cancer high-throughput datasets validate that the proposed network-based multi-task learning frameworks perform better sample classification compared with the models without the knowledge sharing across different cancer types. The common and cancer specific molecular signatures detected by multi-task learning frameworks on TCGA ovarian cancer, breast cancer, and prostate cancer datasets are correlated with the known marker genes and enriched in cancer relevant KEGG pathways and Gene Ontology terms. Availability and Implementation Source code is available at: https://github.com/compbiolabucf/NetML Supplementary information Supplementary data are available at Bioinformatics 

   more » « less
4. Abstract 7077: Immunofluorescence microtissue array (IMA) for detection of prognostic cancer stem cell biomarkers

   https://doi.org/10.1158/1538-7445.AM2025-7077  

   Das, Madhumita; Henry, Kymkecia; Armstrong, Djarie; Truman, Charle; Kendrick, Charlie; Saunders, Maya S; Anderson, Juan E; Lovett, Malcolm J; Stiffin, Rose; Huisso, Ayivi; et al (April 2025, Cancer Research)

   AACR (Ed.)

   Abstract Cancer is an intricate disease accountable for the deaths of over 10 million people per year in the United States of America. Several scientific studies showed that the cancer stem cell (CSC) markers have prognostic significance in various cancers and are crucial for designing anticancer drugs to lower cancer death. However, there was a lack of rapid, accurate identification, and analysis, of the prognostic cancer stem cell (CSC) biomarkers in numerous cancer patients. In our laboratory, we identified and analyzed prognostic lung cancer stem cell markers (LCSCs) by using the Immunofluorescence microtissue array (IMA) technique in different lung cancer patient’s tissue biopsy samples and observed that the increased expression of LCSCs principally, CD44 and CD80 in stage IIIA lung cancer tissues compared to normal lung biopsy tissues. We also investigated pancreatic cancer stem cell biomarkers (PAN CSCs) namely CD44 and CD80 with the IMA technique in pancreatic biopsy tissues. The CD44 fluorescence proved an increased expression in adenocarcinoma pancreatic cell tissues when compared to CD80. We also studied and analyzed the stage progression with ovarian cancer stem cell biomarkers (OCSCs) chiefly CD54 and CD44 using the IMA technique in ovarian cancer patients and normal biopsy tissues. The increased expression of CD44 and CD54 were observed in Stage III ovarian cancer tissues compared to normal ovarian tissue indicating the potential role of these OCSC’s biomarkers for the prognosis of ovarian cancer pathogenesis. Our results of prognostic cancer stem cell biomarkers of lung, pancreatic, and ovarian cancers have been analyzed by one-way ANOVA and bioinformatics software (Reactome, Cytoscape PSICQUIC services, STRING) to find underlying molecular mechanism of target gene regulation of increased expression of prognostic CSCs which may give a clue for the prevention and treatment of these cancers. Further research is warranted for these lung, pancreatic, and ovarian CSCs which could be valuable for clinical trials and drug discovery against these CSC biomarkers at early-stage development. Citation Format:Madhumita Das, Kymkecia Henry, Djarie Armstrong, Charle Truman, Charlie Kendrick, Maya S. Saunders, Juan E. Anderson, Malcolm J. Lovett, Rose Stiffin, Ayivi Huisso, Donrie Purcell, Marco Ruiz, Paulo Chaves, Jayanta Kumar Das. Immunofluorescence microtissue array (IMA) for detection of prognostic cancer stem cell biomarkers [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 7077. 

   more » « less
5. Network based analysis identifies TP53m-BRCA1/2wt-homologous recombination proficient (HRP) population with enhanced susceptibility to Vigil immunotherapy

   https://doi.org/10.1038/s41417-021-00400-x  

   Sliheet, Elyssa; Robinson, Molly; Morand, Susan; Choucair, Khalil; Willoughby, David; Stanbery, Laura; Aaron, Phylicia; Bognar, Ernest; Nemunaitis, John (November 2021, Cancer Gene Therapy)

   null (Ed.)

   Abstract Thus far immunotherapy has had limited impact on ovarian cancer. Vigil (a novel DNA-based multifunctional immune-therapeutic) has shown clinical benefit to prolong relapse-free survival (RFS) and overall survival (OS) in the BRCA wild type and HRP populations. We further analyzed molecular signals related to sensitivity of Vigil treatment. Tissue from patients enrolled in the randomized double-blind trial of Vigil vs. placebo as maintenance in frontline management of advanced resectable ovarian cancer underwent DNA polymorphism analysis. Data was generated from a 981 gene panel to determine the tumor mutation burden and classify variants using Ingenuity Variant Analysis software (Qiagen) or NIH ClinVar. Only variants classified as pathogenic or likely pathogenic were included. STRING application (version 1.5.1) was used to create a protein-protein interaction network. Topological distance and probability of co-mutation were used to calculated the C-score and cumulative C-score (cumC-score). Kaplan–Meier analysis was used to determine the relationship between gene pairs with a high cumC-score and clinical parameters. Improved relapse free survival in Vigil treated patients was found for the TP53 m- BRCA wt-HRP group compared to placebo (21.1 months versus 5.6 months p  = 0.0013). Analysis of tumor mutation burden did not reveal statistical benefit in patients receiving Vigil versus placebo. Results suggest a subset of ovarian cancer patients with enhanced susceptibility to Vigil immunotherapy. The hypothesis-generating data presented invites a validation study of Vigil in target identified populations, and supports clinical consideration of STRING-generated network application to biomarker characterization with other cancer patients targeted with Vigil. 

   more » « less