Biology subjects

Kalari, K. R.

Publications and source records attributed to Kalari, K. R..

17 recordsLinked to original sources

bioRxiv2025

Molecular Mechanisms Underlying the Regulation of VCAM-1 Expression by the Short-Chain Fatty Acid Butyrate

Over the past decade, cerebrovascular inflammation has been increasingly recognized as a contributor to the progression of neurodegenerative diseases, particularly Alzheimers disease (AD). One of the molecular hallmarks of cerebrovascular inflammation is the increased expression of vascular cell adhesion molecule (VCAM)-1 on blood-brain barrier (BBB) endothelial cells. Exposure to amyloid beta (A{beta}) peptides, one of the primary hallmarks of AD, and pro-inflammatory cytokines such as tumor necrosis factor-alpha (TNF-) induces VCAM-1 expression on the BBB endothelium, which facilitates extravasation of leukocytes into the brain thereby promoting an inflammatory response. Therefore, it is crucial to explore therapeutic agents that can inhibit VCAM-1 expression induced by A{beta} and TNF-. Short-chain fatty acids, such as butyrate, produced by the gut microbiota as byproducts of dietary fiber metabolism, are recognized for their anti-inflammatory properties. In this study, we successfully tested the hypothesis that butyrate mitigates A{beta} and TNF--induced VCAM-1 expression in polarized human cerebral microvascular endothelial cell monolayers, a widely used BBB in vitro model. Our findings indicated that pre-treatment with butyrate significantly reduced A{beta}42 and TNF- mediated upregulation of VCAM-1. Furthermore, we have shown STAT3/GATA6 axis as a key mediator of anti-inflammatory effects of butyrate. These findings provide mechanistic insight into butyrates protective role and highlight its potential to mitigate A{beta} and TNF--induced cerebrovascular inflammation in AD.

molecular biology↗

bioRxiv2025

NVUAtlas: A Comprehensive Single-Nucleus RNA-Seq Resource for the Human Neurovascular Unit in Alzheimer's Disease

BackgroundNeurovascular unit (NVU) dysfunction is being recognized as one of the earliest contributors to Alzheimers disease (AD) pathogenesis. However, systematic investigation of NVU dysfunction is currently limited by lack of access to molecular level information due to underrepresentation of vascular and mural cells in standard single-nucleus RNA sequencing (snRNA-seq) datasets. Consequently, existing transcriptomic atlases lack the resolution necessary to capture the coordinated intercellular signaling and dysfunction across vascular components of NVU, including endothelial cells and pericytes. MethodsWe constructed the Human NVU Atlas by integrating 11 publicly available snRNA-seq datasets, including vascular-enriched samples, from the human prefrontal cortex. This comprehensive dataset aggregates over 4.2 million nuclei from 748 donors, including AD patients and age-matched controls. We utilized a unified probabilistic pipeline based on deep generative models (SCVI) to perform batch-aware integration and employed an ensemble of supervised and deep-learning classifiers to rigorously re-annotate cell types. Differential expression and ligand-receptor interaction analyses were subsequently performed to identify cell-type-specific disruptions in males versus females. ResultsThe atlas successfully curated vascular populations from 11 studies to assemble the largest publically available NVU cohort of endothelial cells (2.8%) and pericytes (1.9%) alongside astrocytes and neurons. Differential expression analysis revealed that while neurons predominantly exhibited gene downregulation in AD, vascular cells displayed a pattern of transcriptional hyperactivity with significant gene upregulation. We also identified pronounced sex-specific vulnerabilities; females exhibited distinct inflammatory signatures and downregulation of basement membrane collagen genes (e.g., COL4A1, COL4A2) in pericytes, whereas these changes were not observed in males. Moreover, cell-cell interaction analysis revealed a widespread loss of collagen-integrin signaling between pericytes and neurons, suggesting the involvement of extracellular matrix disruptions in NVU dysfunction observed in AD. ConclusionThe Human NVU Atlas provides a high-resolution, integrated transcriptomic framework for dissecting the cellular heterogeneity of the neurovascular unit. By uncovering sex-specific vascular mechanisms and disrupted intercellular communication, this resource highlights the critical role of vascular cells in AD progression and serves as a foundational reference for investigating cerebrovascular contribution to AD.

neuroscience↗

bioRxiv2025

ACUTE AMYLOID-B 40 EXPOSURE DISRUPTS METABOLIC INSULIN SIGNALING IN BLOOD-BRAIN BARRIER ENDOTHELIAL CELLS

BackgroundBrain insulin resistance and cerebrovascular dysfunction emerge early in late-onset Alzheimers disease, but how amyloid-{beta} (A{beta}) disrupts insulin signaling at the cerebrovascular blood-brain barrier--the principal site of insulin receptor signaling and transport into the brain--remains unclear. MethodsWe exposed two distinct human blood-brain-barrier endothelial cell models to soluble A{beta}40 or A{beta}42 for 1 h, followed by 100 nM insulin for 10 min. Protein and phosphoprotein responses were quantified by reverse-phase protein array, and differential expression was evaluated using empirical-Bayes-stabilized linear models with FDR correction. ResultsA{beta}40 reduced insulin-stimulated Akt phosphorylation and converted insulins normal inhibition of AMPK into modest stimulation, increasing AMPK Thr172 and ACC Ser79 phosphorylation. A{beta}42 did not alter insulin-stimulated Akt signaling, indicating isoform-specific disruption of endothelial insulin responses. Without insulin, both A{beta}40 and A{beta}42 showed modest stimulation of ribosomal protein s6. ConclusionsThese findings show that A{beta}40 acutely impairs insulin signal transduction in BBB endothelial cells, supporting a model in which vascular amyloid exposure contributes directly to the early development of brain insulin resistance in AD.

molecular biology↗

bioRxiv2025

Characterization of transcriptomic changes in the neurovascular unit of Alzheimiers transgenic mouse models using digital spatial profiling

Alzheimers disease (AD) affects 40 million individuals globally and is characterized by the accumulation of amyloid-beta (A{beta}) proteins, which aggregate and form plaques. BBB dysfunction drives AD cerebrovascular pathology and BBB integrity is maintained by neurovascular unit (NVU). Specifically, within the NVU, the cerebral endothelial cells maintain vascular homeostasis. In this study, we isolated endothelial-enriched regions of interest (ROIs) using the Nanostring GeoMx digital spatial profiler and employed a deconvolution model to evaluate transcriptomic changes. We observed dysregulation of cellular signaling potentially disrupting the APP+ BBB integrity. Analysis of ligand-receptor pairings that are the foundation of the NVU intercellular signaling indicated that the endothelial vasculature completes a feedback loop with the NVU to the regulating astroctyes. Further, we identified potentially antagonistic signaling roles for opioid receptor species that should be further investigated for potential therapeutic targets. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=159 HEIGHT=200 SRC="FIGDIR/small/640886v2_ufig1.gif" ALT="Figure 1"> View larger version (52K): org.highwire.dtl.DTLVardef@d89b56org.highwire.dtl.DTLVardef@be2d96org.highwire.dtl.DTLVardef@1a2e7bforg.highwire.dtl.DTLVardef@1cdc9f_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2025

Influence of Gut Microbiota on Oral Drug Absorption and Metabolism

Gut bacteria influence host intestinal phenotype in ways that impact drug absorption and metabolism. By comparing colonic tissue from germ-free mice to that of mice colonized with human microbiota, this study evaluates microbiota-driven differences in gene expression, mucosal permeability, and P-gp efflux capacity. Transcriptomic analysis revealed upregulation of genes coding ATP-binding cassette drug transporters P-gp (Abcb1a, Abcb1b), BCRP (Abcg2), and MRP3 (Abcc3), along with increased expression of solute carriers MCT1 (Slc16a1) and OCTN2 (Slc22a5). Immunohistochemistry indicated greater P-gp expression and apical localization in humanized mice. No relevant gene expression changes were observed in human homologs of drug-metabolizing cytochrome P450 enzymes, though cytochrome P450 oxidoreductase (Por) upregulation suggests increased cytochrome activity. Other phase I drug-metabolizing enzymes, including multiple homologs of human carboxylesterase 2 (CES2) and several reductases, were upregulated with high significance. Regarding phase II metabolism, genes encoding most UDP-glucuronosyltransferases and glutathione S-transferases were upregulated, along with the enzymes responsible for synthesizing their corresponding co-substrates, UDP-glucuronic acid and glutathione. Small intestinal mucosal explants demonstrated higher permeability to 14C-labeled polyethylene glycol 4000 (14C-PEG4000) in germ-free mice than in humanized mice. The 14C-PEG4000 permeability differences are in agreement with changes in paracellular tight junctional proteins such as claudins, zonula occludens, and myosin II-related genes. These results demonstrate the broader impact of gut microbiome on oral drug absorption and metabolism and implicate the contributions of gut microbiome to individual variations in drug efficacy and toxicity.

pharmacology and toxicology↗

bioRxiv2025

Elucidating Molecular Mechanisms Governing TNF-Alpha-Mediated Regulation of Amyloid Beta 42 Uptake in Blood-Brain Barrier Endothelial Cells

Cerebrovascular inflammation is prevalent in a majority of Alzheimers patients. Inflammatory cytokines, such as tumor necrosis factor-alpha (TNF-alpha), circulating in the plasma have been shown to cause the inflammation of blood-brain barrier (BBB) endothelium lining the cerebral microvasculature. The BBB inflammation has been implicated in the increase of toxic A{beta} accumulation within Alzheimers disease (AD) brain. TNF-alpha in the peripheral circulation can aggravate the accumulation of amyloid-beta (A{beta}) peptides in Alzheimers disease brain. In the current study, we have shown that the exposure to TNF-alpha leads to an increase in A{beta}42 accumulation in mice and BBB endothelial cells in vitro. Moreover, dynamic SPECT/CT imaging in wild-type (WT) mice infused with TNF-alpha increased the permeability and influx of A{beta}42 into the mice brain. In addition, our results show that TNF-alpha modifies the expression of cofilin, actin, and dynamin, which are critical components for A{beta} endocytosis by BBB endothelial cells. These results offer a mechanistic understanding of how TNF-alpha may promote A{beta} accumulation at the BBB and the underlying interactions between inflammation and A{beta} exposure that drives BBB dysfunction. Hence, a therapeutic intervention aimed at addressing cerebrovascular inflammation in Alzheimers disease may potentially reduce A{beta} induced cerebrovascular toxicity in Alzheimers disease brain. Significance statementIncreased levels of TNF-alpha circulating in the plasma are considered significant factors in the consequences of A{beta} pathology in Alzheimers disease, where it can promote cerebrovascular inflammation and BBB dysfunction. However, the role of TNF-alpha, in exacerbating A{beta} pathology by increasing A{beta} accumulation at the BBB endothelial cells remains only partially understood. In this study, we demonstrated that TNF-alpha enhances A{beta}42 accumulation in the BBB endothelium by altering the expression of the BBB endocytosis machinery, specifically cofilin, actin, and dynamin. These findings are anticipated to contribute to the development of therapeutic approaches aimed at addressing elevated cytokine levels in Alzheimers disease. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=162 SRC="FIGDIR/small/635286v2_ufig1.gif" ALT="Figure 1"> View larger version (45K): org.highwire.dtl.DTLVardef@6b27a4org.highwire.dtl.DTLVardef@f7b37dorg.highwire.dtl.DTLVardef@28efbcorg.highwire.dtl.DTLVardef@193408c_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGraphical AbstractC_FLOATNO C_FIG

molecular biology↗

bioRxiv2024

Nomination of a novel plasma protein biomarker panel capable of classifying Alzheimer's disease dementia with high accuracy in an African American cohort

IntroductionAfrican Americans (AA) are widely underrepresented in plasma biomarker studies for Alzheimers disease (AD) and current diagnostic biomarker candidates do not reflect the heterogeneity of AD. MethodsUntargeted proteome measurements were obtained using the SomaScan 7k platform to identify novel plasma biomarkers for AD in a cohort of AA clinically diagnosed as AD dementia (n=183) or cognitively unimpaired (CU, n=145). Machine learning approaches were implemented to identify the set of plasma proteins that yields the best classification accuracy. ResultsA plasma protein panel achieved an area under the curve (AUC) of 0.91 to classify AD dementia vs CU. The reproducibility of this finding was observed in the ANMerge plasma and AMP-AD Diversity brain datasets (AUC=0.83; AUC=0.94). DiscussionThis study demonstrates the potential of biomarker discovery through untargeted plasma proteomics and machine learning approaches. Our findings also highlight the potential importance of the matrisome and cerebrovascular dysfunction in AD pathophysiology.

neuroscience↗

bioRxiv2024

OmicsFootPrint: a framework to integrate and interpret multi-omics data using circular images and deep neural networks

The OmicsFootPrint framework addresses the need for advanced multi-omics data analysis methodologies by transforming data into intuitive two-dimensional circular images and facilitating the interpretation of complex diseases. Utilizing Deep Neural Networks and incorporating the SHapley Additive exPlanations (SHAP) algorithm, the framework enhances model interpretability. Tested with The Cancer Genome Atlas (TCGA) data, OmicsFootPrint effectively classified lung and breast cancer subtypes, achieving high Area Under Curve (AUC) scores-- 0.98{+/-}0.02 for lung cancer subtype differentiation, 0.83{+/-}0.07 for breast cancer PAM50 subtypes, and successfully distinguished between invasive lobular and ductal carcinomas in breast cancer, showcasing its robustness. It also demonstrated notable performance in predicting drug responses in cancer cell lines, with a median AUC of 0.74, surpassing nine existing methods. Furthermore, its effectiveness persists even with reduced training sample sizes. OmicsFootPrint marks an enhancement in multi-omics research, offering a novel, efficient, and interpretable approach that contributes to a deeper understanding of disease mechanisms.

bioinformatics↗

bioRxiv2023

Microbiome Prevents Sudden Death Through Occult Cardiac Sub-micrometastasis in Mice

Sudden cardiac deaths (SCDs) pose a formidable clinical challenge, and their underlying risk mechanisms are poorly understood. Using a gnotobiotic, germ-free mouse model, we fortuitously discovered SCD incidences resulting from occult cardiac metastases. Female germ-free C57BL/6 mice (n=22) were raised in isolation and injected with mammary Py230 cells. Significantly higher SCD probabilities (36.3%) were observed in germ-free mice compared to gut-colonized groups (0%). Extensive examinations revealed no physical anomalies but demonstrated occult cardiac sub-micrometastasis in three out of four sudden death cases. Further analysis supported the role of occult cardiac sub-micrometastasis as the leading cause of SCDs. The remaining germ-free mice exhibited minimal primary tumors but high levels of cardiac metastases and morbidity. The gnotobiotic SCD model represents a crucial milestone in our understanding of the complex interplay between the gut microbiota and the development of occult oncological processes that ultimately culminate in SCDs and warrants further investigation into their mechanisms.

cancer biology↗

bioRxiv2022

Integration of multi-omics data shows downregulation of mismatch repair, purin, and tublin pathways in AR-negative triple-negative chemotherapy-resistant breast tumors

Triple negative breast cancer (TNBC) patients who fail to achieve a pathological complete response to neoadjuvant chemotherapy (NAC) will likely experience recurrence of the disease within 3-4 years. Prognostic assessment of early recurrence could facilitate clinical decisions and impact disease survival. This study investigated pre- and post-NAC multi-omics data from both an in-house and a public clinical study to identify biomarkers of NAC response. We observed significant transcriptional differences associated with response in the residual disease (post-NAC biopsies), which did not exist in the pre-NAC biopsies. We further refined the post-NAC transcriptional changes to a 17-gene signature and machine learning models were applied to evaluate the signatures diagnostic potential (area under the curve [≥] 0.8). Interestingly, the signature was enriched with down-regulated immune genes and several of these genes demonstrated prognostic potential in basal TNBC tumors. Our 17-gene signature provides additional insight into TNBC recurrence, which warrants further investigation.

cancer biology↗

bioRxiv2022

Identification of transcriptional network disruptions in drug-resistant prostate cancer with TraRe

Metastatic castration-resistant prostate cancer (mCRPC) presents very low survival rates due to lack of response or acquired resistance to the available therapies. To date no molecular mechanisms of resistance have been identified, pointing out their complex dynamics. To identify key genes and processes associated with phenotypically-driven regulatory differences, we developed TraRe, a computational method that provides a three-tier analysis: i) at the network level, inferring differentially regulated modules; ii) at the regulon level, identifying regulatory relationships linked to phenotypic differences; and iii) at the single gene level, identifying TFs consistently linked to rewired modules. We applied TraRe (available in Bioconductor with full documentation) to transcriptomic data from 46 mCRPC patients with Abiraterone-response clinical data and uncovered abrogated immune response regulatory modules that showed strong differential regulation in Abi-resistant patients. These modules were replicated in an independent mCRPC study. Further, we experimentally validated key rewiring predictions and their associated transcription factors. Among them, ELK3, MXD1, and MYB were found to have a differential role in cell survival for Abi-response-specific settings. Moreover, we identified the role of ELK3 in cell migration capacity, which could have direct impact on mCRPC. Collectively, these findings shed light on the underlying regulatory mechanisms driving abiraterone response.

bioinformatics↗

bioRxiv2021

Barcoded Competitive Clone-Initiating Cell (BC-CIC) Analysis Reveals Differences in Ovarian Cancer Cell Genotype and Niche Specific Clonal Fitness During Growth and Metastasis In Vivo

During oncogenesis, pathogenic clones develop which contain cells capable of spreading throughout the body, ultimately compromising vital organ functions and physiology. Understanding how metastatic clones develop and spread is critical for improving cancer treatments. However, our understanding of these processes has been hampered by a paucity of quantitative methodologies to comprehensively map, track and characterize such clones. To address this shortcoming, we have developed a DNA barcoding and next-generation sequencing based system-wide clonal tracking technology integrated with a computational data analysis pipeline called Clone-Initiating Cell (CIC) Calculator. The CIC Calculator interfaces with the CIC Morbus Mandala (CIC-MM) plot, a novel tool to visually comprehend and detect four distinct categories that explains their complex relationships with various tissues/organ sites. Further, we describe machine learning approaches to study CIC number, frequency, and estimate clone size and distribution demonstrating distinct growth patterns, and their inter-relationships and their routes of metastatic spread at clonal resolution. We demonstrate these methodologies, using our novel multifunctional lentiviral barcode libraries, and specifically barcoded tubal-ovarian metastatic OVCAR5 cell lines (engineered to express varying levels of metastasis promoting LRRC15 gene) and co-injected cells in a competitive CIC assay into tubal or ovarian sites in highly immunodeficient NSG mice. DNA was isolated from primary tumors, omental/bowel metastasis and system-wide anatomical site/organs. Amplicon sequencing libraries were constructed with spike-in-control barcodes (serving as internal calibration controls) to estimate absolute clone sizes. The computational pipeline CIC Calculator was then used to deconvolute and filter the data, set stringent thresholds, and generate high-quality information on CIC numbers and frequencies, clone sizes, linkages across sites and classify clones based on their extent of metastatic activity. Using of CIC-MM plot, statistical models and machine learning approaches, we generated high-resolution clonal maps of metastasis for each animal. The information generated included clone types and system-wide metastasis, similar and dissimilar clonal patterns of dominance at heterotopic sites and their routes of metastases. The data revealed previously unknown influences of cellular genotype and their implanted sites on selecting certain clones with specific system-wide clonal patterns, and identified rare LRRC15 expressor clones (classified as CIC.Toti) predisposed to exploit all sites, albeit at varying degrees of dominance. The genomic technology and computational methodology described here are tissue-agnostic. They enable rapid adoption for an investigation into various stages of system-wide metastasis and growth of transplantable malignant cells at the highest clonal resolution.

cancer biology↗

bioRxiv2021

Clustering-based time series analysis on insulin response in the blood-brain barrier

BackgroundCritical functions of the blood-brain barrier (BBB), including cerebral blood flow and vascular response, are regulated by insulin signaling pathways. Therefore, endothelial insulin resistance could lead to vascular dysfunction, which is associated with neurodegenerative diseases such as Alzheimers disease (AD). ObjectiveThe objective of the current study is to map the dynamics of insulin-responsive pathways in polarized human cerebral microvascular endothelial cells (hCMEC/D3) cell monolayers, a widely used BBB cell culture model, to identify molecular mechanisms underlying BBB dysfunction in AD. MethodsRNA-Sequencing (RNA-Seq) was performed on hCMEC/D3 cell monolayers with and without insulin treatment at various time points. The Short Time-series Expression Miner (STEM) method was used to identify clusters of genes with distinct and representative patterns. Functional annotation and pathway analysis of the genes from top clusters were conducted using the Webgestalt and Ingenuity Pathway Analysis (IPA) software, respectively. ResultsQuantitative expression differences of 19,971 genes between the insulin-treated and control monolayers at five-time points were determined. STEM software identified 11 clusters with 3061 genes across that displayed various temporal patterns. Gene ontology enrichment analysis performed using the top 5 clusters demonstrated that these genes were enriched in various biological processes associated with AD pathophysiology. The IPA analyses revealed that signaling pathways exacerbating AD pathology such as inflammation were downregulated after insulin treatment (clusters 1 to 3). In contrast, pathways attenuating AD pathology were upregulated, including synaptogenesis and BBB repairment (clusters 4 and 5). ConclusionsThese findings unravel the dynamics of insulin action on the BBB endothelium and inform about downstream signaling cascades that potentially regulate neurovascular unit (NVU) functions that are disrupted in AD.

bioinformatics↗

bioRxiv2019

Frequency of MicroRNA Response Elements Identifies Pathologically Relevant Signaling Pathways in Cancers

Complex interactions between mRNAs and microRNAs influence cellular functions. The interactions between mRNA and microRNAs also determine the post-transcriptional availability of free mRNAs and unbound microRNAs. The microRNAs bind to one or more microRNA Response Elements (MREs) predominantly located on the 3untranslated regions (3UTR) of mRNAs. In this study, we leveraged MRE sites and their frequencies in transcriptomes of cancer and matched normal tissues to obtain insights into disease-specific interactions between mRNAs and microRNAs. Toward this, we developed a novel bioinformatics method called ReMIx that utilizes RNA-Seq data to quantify MRE frequencies at 3UTR of genes across the transcriptome. We applied ReMIx to The Cancer Genome Atlas (TCGA) Triple Negative (TN) breast cancer tumor-normal adjacent pairs (N=13) and identified distinctly and differentially expressed MREs specific to the TN tumors. Novel data generated by ReMIx identified candidate mRNAs and microRNAs in the MAPK signaling cascade of the TN tumors. We further analyzed the MAPK endogenous RNA network to establish regulatory microRNA partners, along with interacting protein-coding mRNAs that influence and modulate MAPK signaling in TN breast cancers.

bioinformatics↗

bioRxiv2019

Knowledge-guided analysis of ‘omics’ data using the KnowEnG cloud platform

We present KnowEnG, a free-to-use computational system for analysis of genomics data sets, designed to accelerate biomedical discovery. It includes tools for popular bioinformatics tasks such as gene prioritization, sample clustering, gene set analysis and expression signature analysis. The system offers knowledge-guided data-mining and machine learning algorithms, where user-provided data are analyzed in light of prior information about genes, aggregated from numerous knowledge-bases and encoded in a massive Knowledge Network. KnowEnG adheres to FAIR principles: its tools are easily portable to diverse computing environments, run on the cloud for scalable and cost-effective execution of compute-intensive and data-intensive algorithms, and are interoperable with other computing platforms. They are made available through multiple access modes including a web-portal, and include specialized visualization modules. We present use cases and re-analysis of published cancer data sets using KnowEnG tools and demonstrate its potential value in democratization of advanced tools for the modern genomics era.

cancer biology↗

bioRxiv2017

PANOPLY: A computational method for identification of promising drugs for a patient based on multidimensional data

PurposeThe majority of cancer patients receive treatments that are minimally informed by omics data. We propose a precision medicine computational framework (PANOPLY: Precision cancer genomic report: single sample inventory) to identify and prioritize drug targets and cancer therapy regimens.\n\nMethodsThe PANOPLY approach integrates clinical data with germline and somatic features obtained from multi-omics platforms, and apply machine learning, and network analysis approaches in the context of the individual patient and matched controls. The PANOPLY workflow employs four steps (i) selection of matched controls to the case of interest (ii) identification of case-specific genomic events (iii) identification of suitable drugs using the driver-gene network and random forest analyses and (iv) provide an integrated multi-omics case report of the patient with prioritization of anti-cancer drugs.\n\nResultsThe PANOPLY workflow can be executed on a stand-alone virtual machine and is also available for download as an R package. We applied the method to an institutional breast cancer neoadjuvant chemotherapy study which collected clinical and genomic data as well as patient-derived xenografts (PDXs) to investigate the prioritization offered by PANOPLY. In a chemotherapy-resistant PDX model, we found that that the prioritized drug, olaparib, was more effective than placebo in treating the tumor (P < 0.05). We also applied PANOPLY to in-house and publicly accessible multi-omics tumor datasets with therapeutic response or survival data available.\n\nConclusionPANOPLY shows promise as a means to prioritize drugs based on clinical and multi-omics data for an individual cancer patient. Additional studies are needed to confirm this approach.

bioinformatics↗

bioRxiv2016

Knowledge-Guided Prioritization of Genes Determinant of Drug Response using ProGENI

BackgroundIdentification of genes whose basal mRNA expression predicts the sensitivity of tumor cells to cytotoxic treatments can play an important role in individualized cancer medicine. It enables detailed characterization of the mechanism of action of drugs. Furthermore, screening the expression of these genes in the tumor tissue may suggest the best course of chemotherapy or a combination of drugs to overcome drug resistance.\n\nResultsWe developed a computational method called ProGENI to identify genes most associated with the variation of drug response across different individuals, based on gene expression data. In contrast to existing methods, ProGENI also utilizes prior knowledge of protein-protein and genetic interactions, using random walk techniques. Analysis of two relatively new and large datasets including gene expression data on hundreds of cell lines and their cytotoxic responses to a large compendium of drugs reveals a significant improvement in prediction of drug sensitivity using genes identified by ProGENI compared to other methods. Our siRNA knockdown experiments on ProGENI-identified genes confirmed the role of many new genes in sensitivity to three chemotherapy drugs: cisplatin, docetaxel and doxorubicin. Based on such experiments and extensive literature survey, we demonstrate that about 73% our top predicted genes modulate drug response in selected cancer cell lines. In addition, global analysis of genes associated with groups of drugs uncovered pathways of cytotoxic response shared by each group.\n\nConclusionsOur results suggest that knowledge-guided prioritization of genes using ProGENI gives new insight into mechanisms of drug resistance and identifies genes that may be targeted to overcome this phenomenon.

bioinformatics↗