Biology subjects

Giri, A. K.

Publications and source records attributed to Giri, A. K..

7 recordsLinked to original sources

bioRxiv2023

Single-cell transcriptomes identify patient-tailored therapies for selective co-inhibition of cancer clones

Intratumoral cellular heterogeneity necessitates multi-targeting therapies for improved clinical benefits in patients with advanced malignancies. However, systematic identification of patient-specific treatments that selectively co-inhibit cancerous cell populations poses a combinatorial challenge, since the number of possible drug-dose combinations vastly exceeds what could be tested in scarce patient cells. Here, we developed scTherapy, a machine learning model that leverages single-cell transcriptomic profiles to prioritize multi-targeting treatment options for individual patients with hematological cancers or solid tumors.

bioinformatics↗

bioRxiv2020

ETV5 expression positively correlates with promoter methylation and predicts response for 5-FU-based adjuvant therapy response in proximal colon cancer

Discovery of markers predictive for 5-Fluorouracil (5-FU)-based adjuvant chemotherapy (adjCTX) response in patients with locally advanced stage II and III colorectal cancer (CRC) is necessary for early identification of potential responders as only 20-65% of CRC patients benefit from the treatment. PEA3 subfamily of ETS transcription factors (ETV1, ETV4, and ETV5) are upregulated in multiple cancers including colon cancers. However, the underlying epigenetic mechanism regulating their overexpression and their role in predicting therapy response in colon cancer is largely unexplored. In this study, using gene expression and methylation data from The Cancer Genome Atlas (TCGA) project, we showed that promoter DNA methylation negatively correlates with ETV4 expression ({rho}= -0.17, p=5.6x10-3) and positively correlates with ETV5 expression ({rho}= 0.22, p=1.43x10-4) in colon cancer tissue. Further, our analysis in 662 colon cancer patients treated with 5-FU-based-adjCTX revealed that higher ETV5 expression associated with shorter relapse-free survival (RFS) of treated patients with proximal tumors (Hazard ratio = 3.30 - 6.22, p=0.005-0.02). We also observed higher expression of signaling molecules involved in cellular proliferation (e.g. GNB5, DUSP4, FYN) in patients with high ETV5 level, suggesting that the increased cellular proliferation due to overexpression of these genes could drive the therapy resistance. The present study suggests ETV5 expression as a strong predictive biomarker for 5-FU-based adjCTX response in stage II/III CRC patients with proximal tumors.

cancer biology↗

bioRxiv2020

Patient-tailored design of AML cell subpopulation-selective drug combinations

The extensive primary and secondary drug resistance in acute myeloid leukemia (AML) requires rational approaches to design personalized combinatorial treatments that exploit patient-specific therapeutic vulnerabilities to optimally target disease-driving AML cell subpopulations. However, the large number of AML-relevant drug combinations makes the testing impossible in scarce primary patient cells. This combinatorial problem is further exacerbated by the translational challenge of how to design such personalized and selective drug combinations that do not only show synergistic effect in overall AML cell killing but also result in minimal toxic side effects on non-malignant cells. To solve these challenges, we implemented a systematic computational-experimental approach for identifying potential drug combinations that have a desired synergy-efficacy-toxicity balance. Our mechanism-agnostic approach combines single-cell RNA-sequencing (scRNA-seq) with ex vivo single-agent viability testing in primary patient cells. The data integration and predictive modelling are carried out at a single-cell resolution by means of a machine learning model that makes use of compound-target interaction networks to narrow down the massive search space of potentially effective drug combinations. When applied to two diagnostic and two refractory AML patient cases, each having a different genetic background, our integrated approach predicted a number of patient-specific combinations that were shown to result not only in synergistic cancer cell inhibition but were also capable of targeting specific AML cell subpopulations that emerge in differing stages of disease pathogenesis or treatment regimens. Overall, 53% of the 59 predicted combinations were experimentally confirmed to show synergy, and 83% were non-antagonistic, as validated with viability assays, which is a significant improvement over the success rate of randomly guessing a synergistic drug combination (5%). Importantly, 67% of the predicted combinations showed low toxicity to non-malignant cells, as validated with flow-based population assays, suggesting their selective killing of AML cell populations. Our data-driven approach provides an unbiased means for systematic prioritization of patient-specific drug combinations that selectively inhibit AML cells and avoid co-inhibition of non-malignant cells, thereby increasing their likelihood for clinical translation. The approach uses only a limited number of patient primary cells, and it is widely applicable to hematological cancers that are accessible for scRNA-seq profiling and ex vivo compound testing.

systems biology↗

bioRxiv2019

Fully-automated cell-type identification with specific markers extracted from single-cell transcriptomic data

Single-cell transcriptomics enables systematic charting of cellular composition of complex tissues. Identification of cell populations often relies on unsupervised clustering of cells based on the similarity of their scRNA-seq profiles, followed by manual annotation of cell clusters using established marker genes. However, manual selection of marker genes is a time-consuming process that may lead to sub-optimal annotation results as the selected markers must be informative of both the individual cell clusters and various cell types present in the complex samples. Here, we developed a computational platform, termed ScType, which enables data-driven, fully-automated and ultra-fast cell-type identification based solely on given scRNA-seq data, combined with our comprehensive cell marker database as background information. Using a compendium of six scRNA-seq datasets from various human and mouse tissues, we show how ScType provides an unbiased and accurate cell-type annotation by guaranteeing the specificity of positive and negative marker genes both across cell clusters and cell types. We also demonstrate how ScType enables distinguishing between healthy and malignant cell populations, based on single-cell calling of single-nucleotide variants, making it a versatile tool for exploration and use of single-cell transcriptomic data for anticancer applications. The widely-applicable method is deployed both as an interactive web-tool (https://sctype.app), and as an open-source R-package, connected with a comprehensive ScType database of specific markers.

systems biology↗

bioRxiv2019

SynToxProfiler: an approach for top drug combination selection based on integrated profiling of synergy, toxicity and efficacy

Drug combinations are becoming a standard treatment of many complex diseases due to their capability to overcome resistance to monotherapy. Currently, in the preclinical drug combination screening, the top hits for further study are often selected based on synergy alone, without considering the combination efficacy and toxicity effects, even though these are critical determinants for the clinical success of a therapy. To promote the prioritization of drug combinations based on integrated analysis of synergy, efficacy and toxicity profiles, we implemented a web-based open-source tool, SynToxProfiler (Synergy-Toxicity-Profiler). When applied to 20 anti-cancer drug combinations tested both in healthy control and T-cell prolymphocytic leukemia (T-PLL) patient cells, as well as to 77 anti-viral drug pairs tested on Huh7 liver cell line with and without Ebola virus infection, SynToxProfiler was shown to prioritize synergistic drug pairs with higher selective efficacy (difference between efficacy and toxicity level) as top hits, which offers improved likelihood for clinical success.

bioinformatics↗

bioRxiv2019

Multifaceted genome-wide study identifies novel regulatory loci for body mass index in Indians

Obesity, a risk factor for various human diseases originates through complex interactions between genes and prevailing environment that varies across populations. Indians exhibit a unique obesity phenotype likely attributed by specific gene pool and environmental factors. Here, we present genome-wide association study (GWAS) of 7,259 Indians to understand the genetic architecture of body mass index (BMI) in the population. Our study revealed novel association of variants in BAI3 (rs6913677) and SLC22A11 (rs2078267) at GWAS significance, and of ZNF45 (rs8100011) with near GWAS significance. As genetic loci may dictate the phenotype through modulation of epigenetic processes, we overlapped discovered genetic signatures with DNA methylation patterns of 236 Indian individuals, and analyzed expression of the candidate genes using publicly available data. The variants in BAI3 and SLC22A11 were found to dictate methylation patterns at unique CpGs harboring critical cis- regulatory elements. Further, BAI3, SLC22A11 and ZNF45 variants were found to overlie repressive chromatin, active enhancer, and active chromatin regions, in that order, in human subcutaneous adipose tissue in ENCODE database. Besides, the identified genomic regions represented potential binding sites for key transcription factors implicated in obesity and/or metabolic disorders. Interestingly, rs8100011 (ZNF45) acted as a robust cis-expression quantitative trait locus (cis-eQTL) in subcutaneous adipose tissue in GTEx portal, and ZNF45 gene expression showed an inverse correlation with BMI in skeletal muscle of Indian subjects. Further, gene-based GWAS analysis revealed CPS1 and UPP2 as additional leads regulating BMI in Indians. Our study decodes potential genomic mechanisms underlying obesity phenotype in Indians.

genomics↗

bioRxiv2018

DNMT inhibitors increase methylation of CpGs related to p53 pathway genes in colon, lymphoma, bladder, ovarian, and breast cancer cells

BackgroundDNA methyltransferase inhibitors (DNMTi) decitabine and azacytidine are approved therapies for acute myeloid leukemia and myelodysplastic syndrome. Identification of CpGs violating demethylaion due to DNMTi treatment may help to understand their resistance mechanisms.\n\nMaterials and MethodsTo identify such CpGs, we analysed publicly available 450K methylation data of multiple cancer type cell lines.\n\nResultsWe identified 637 CpGs corresponding to genes enriched for p53 and olfactory receptor pathways with a transient increase in methylation (median {Delta}{beta} = 0.12) after decitabine treatment in HCT116 cells. Azacytidine treatment also increased methylation of identified CpGs in 9 colon, 9 ovarian, 3 breast, and 1 lymphoma cancer cell lines.\n\nConclusionDNMTi treatment increases methylation of subset of CpGs in cancer genome.

pharmacology and toxicology↗