Biology subjects

Nagaraj, S. H.

Publications and source records attributed to Nagaraj, S. H..

6 recordsLinked to original sources

bioRxiv2023

Pharmacological Induction of mesenchymal-epithelial transition chemosensitizes breast cancer cells and prevents metastatic progression

The epithelial-mesenchymal transition (EMT) is a developmental program co-opted by tumor cells that aids the initiation of the metastatic cascade. Tumor cells that undergo EMT are relatively chemoresistant, and there are currently no therapeutic avenues specifically targeting cells that have acquired mesenchymal traits. We show that treatment of mesenchymal-like triple-negative breast cancer (TNBC) cells with the microtubule-destabilizing chemotherapeutic eribulin, which is FDA-approved for the treatment of advanced breast cancer, leads to a mesenchymal-epithelial transition (MET). This MET is accompanied by loss of metastatic propensity and sensitization to subsequent treatment with other FDA-approved chemotherapeutics. We uncover a novel epigenetic mechanism of action that supports eribulin pretreatment as a path to MET induction that curtails metastatic progression and the evolution of therapy resistance. SummaryWhile the advent of targeted therapy has led to vast improvements in outcomes for certain types of breast cancer, a mainstay for triple-negative breast cancer (TNBC) remains cytotoxic chemotherapy. A major clinical hurdle in successfully managing this disease is the eventual development of therapeutic resistance and disease relapse in more aggressive forms. Our data reveal that epigenetic modulation of EMT state using the FDA-approved therapeutic eribulin curtails metastatic propensity of breast tumors and, when administered in the treatment-naive setting, sensitizes to subsequent treatment with other chemotherapeutics.

cancer biology↗

bioRxiv2023

Predicting Distant Recurrences in Invasive Breast Carcinoma Patients Using Clinicopathological Data: A cross-institutional and AI-based study

Breast cancer ranks second in the most common cancer in women worldwide with 30% of cases resulting into recurrence of the disease at distant organs post the treatment. While clinicians have utilized several clinicopathological measurements for prediction of distant recurrences in invasive breast carcinoma (IBC), none of those studies have showcased the potential of combining clinicopathological evaluations of IBC tumors pre and post therapies using machine learning (ML) or artificially intelligent (AI) models to predict the distant recurrence of the disease in respective patients. The goal of our study was to determine whether classification-based ML/AI techniques can predict distant recurrences in IBC patients using key clinicopathological measurements that includes pathological staging of tumor and surrounding lymph nodes deemed both pre- and post-neoadjuvant therapy, imaging-based therapy responses, and the status of adjuvant therapy administered to patients. We trained and tested clinicopathological ML/AI model using dataset from Duke University and validated it using external dataset from Dartmouth Hitchcock Medical Center (DHMC). Random Forest (RF) model performed best compared to C-Support Vector Classifier (SVC) and Multi-Layer Perceptron (MLP) yielding AUC ranging 0.75-1.0 (p<0.002) across both the institutions, thereby demonstrating the cross-institutional portability and validity of ML/AI models in the field of clinical research in cancer.

bioinformatics↗

bioRxiv2021

ImaGene: A web-based software platform for tumor radiogenomic evaluation and reporting

The field of radiomics has undergone several advancements in approaches to uncovering hidden quantitative features from tumor imaging data for use in guiding clinical decision-making for cancer patients. Radiographic imaging techniques provide insight into the imaging features of tumor regions of interest (ROIs), while immunohistochemistry and sequencing techniques performed on biopsy samples yield omics data. Potential associations between tumor genotype and phenotype can be identified from imaging and omics data via traditional correlation analysis, as well as through artificial intelligence (AI) models. However, at present the radiogenomics community lacks a unified software platform for which to conduct such analyses in a reproducible manner. To address this gap, we propose ImaGene, a web-based platform that takes tumor omics and imaging data sets as input, performs correlation analysis between them, and constructs AI models (optionally using only those features found to exhibit statistically significant correlation with some element of the opposing dataset). ImaGene has several modifiable configuration parameters, providing users complete control over their analysis. For each run, ImaGene produces a comprehensive report displaying a number of intuitive model diagnostics. To demonstrate the utility of ImaGene, exploratory studies surrounding Invasive Breast Carcinoma (IBC) and Head and Neck Squamous Cell Carcinoma (HNSCC) on datasets acquired from public databases are conducted. Potential associations are identified between several imaging features and 6 genes: CRABP1, SMTNL2, FABP1, HAS1, FAM163A and DSG1 for IBC, and 4 genes: CEACAM6, NANOG, ACSM2B, and UPK2 for HNSCC. In summary, the software provides researchers with a transparent tool for which to begin radiogenomic analysis and explore possible further directions in their research. We anticipate that ImaGene will become the standard platform for tumor analyses in the field of radiogenomics due to its ease of use, flexibility, and reproducibility, and that it can serve as an enabling centrepoint for an emerging radiogenomic knowledge base.

bioinformatics↗

bioRxiv2021

BGvar - a comprehensive resource for blood group immunogenetics

BackgroundBlood groups form the basis of effective and safe blood transfusion. There are about 41 well recognized human blood group systems presently known. Blood groups are molecularly determined by the presence of specific antigens on the red blood cells and are genetically determined and inherited following Mendelian principles. The lack of a comprehensive, relevant, manually compiled and genome-ready dataset of red cell antigens limited the widespread application of genomic technologies to characterise and interpret the blood group complement of an individual from genomic datasets. Materials and MethodsA range of public datasets were used to systematically annotate the variation compendium for its functionality and allele frequencies across global populations. Details on phenotype or relevant clinical importance were collated from reported literature evidence. ResultsWe have compiled the Blood Group Associated Genomic Variant Resource (BGvar), a manually curated online resource comprising all known human blood group related allelic variants including a total of 1672 ISBT approved alleles and 1552 alleles predicted and curated from literature reports. This repository includes 1606 Single Nucleotide Variations (SNVs), 270 Insertions, Deletions (InDels) and Duplications and about 1310 combination mutations corresponding to 41 human blood group systems and 2 transcription factors. This compendium also encompasses gene fusion and rearrangement events occurring in human blood group genes. ConclusionTo the best of our knowledge, BGvar is a comprehensive and a user friendly resource with most relevant collation of blood group alleles in humans. BGvar is accessible online at URL: http://clingen.igib.res.in/bgvar/

genomics↗

bioRxiv2021

RBCeq: An Integrated Bioinformatics Algorithm Designed to Improve Blood Type Compatibility Testing

While blood transfusion is an essential cornerstone of hematological care, patients that require repetitive transfusion remain at persistent risk of alloimmunization due to the diversity of human blood group polymorphisms. Next-generation sequencing (NGS) is an effective means of identifying genotypic and phenotypic variations among the blood groups, while the accurate interpretation of such NGS data is currently hampered by a lack of accessibility to bioinformatics support. To address this unmet need, we have developed the RBCeq (https://www.rbceq.org/) platform, which consists of a novel bioinformatics algorithm coupled with a user-friendly web server capable of comprehensively delineating different blood group variants from genomics data with advanced visualization of results. The software profiles genomic data for 36 blood group systems, including two transcription factors and can identify small genetic alterations, including small indels and copy number variants. The RBCeq algorithm was validated on 403 samples which include 58 complex serology cases from Australian Red Cross LifeBlood, 100 samples from The MedSeq Project (phs000958) and a further 245 from Indigenous Australian participants. The final blood typing data from RBCeq was 99.83% concordant for 403 samples (85 different antigens in 21 blood group systems) with that listed from the International Society for Blood Transfusion database.

bioinformatics↗

bioRxiv2020

Limiting Self-Renewal of the Basal Compartment Induces Differentiation and Alters Evolution of Mammary Tumors

Differentiation therapy is an approach that utilizes our understanding of the hierarchy of cellular systems to pharmacologically induce a shift towards terminal commitment. While this approach has been a paradigm in treating certain hematological malignancies, efforts to translate this success to solid tumors have proven challenging. In this study we show that activation of PKA drives aberrant mammary differentiation by diminishing the self-renewing potential of the basal compartment. PKA activation results in tumors that are more benign, exhibiting reduced metastatic propensity, loss of tumor-initiating potential and increased sensitivity to chemotherapy. Analysis of tumor histopathology revealed features of overt differentiation with papillary characteristics. Longitudinal single cell profiling at the hyperplasia and tumor stages uncovered an altered path of tumor evolution whereby PKA curtails the emergence of aggressive subpopulations. PKA activation represents a promising approach as an adjuvant to chemotherapy for certain breast cancers, reviving the paradigm of differentiation therapy for solid tumors.

cancer biology↗