Biology subjects

Gondal, M. N.

Publications and source records attributed to Gondal, M. N..

7 recordsLinked to original sources

bioRxiv2025

Amoeboid-Mesenchymal Transition and the Proteolytic Control of Cancer Invasion Plasticity

Invasion plasticity allows malignant cells to toggle between collective, mesenchymal and amoeboid phenotypes while traversing extracellular matrix (ECM) barriers. Current dogma holds that collective and mesenchymal invasion programs trigger the mobilization of proteinases that digest structural barriers dominated by type I collagen, while amoeboid activity allows cancer cells to marshal mechanical forces to traverse tissues independently of ECM proteolysis. Here, we use cancer spheroid-3-dimensional matrix models, single-cell RNA sequencing, and human tissue explants to identify the mechanisms controlling mesenchymal versus amoeboid invasion. Unexpectedly, collective/mesenchymal- and amoeboid-type invasion programs - though distinct - are each characterized by active tunneling through ECM barriers, with expression of matrix-degradative metalloproteinases. CRISPR/Cas9-mediated targeting of a single membrane-anchored collagenase, MMP14/MT1-MMP, ablates tissue-invasive activity while co-regulating cancer cell transcriptional programs. Though changes in matrix architecture, nuclear rigidity, and metabolic stress as well as the presence of cancer-associated fibroblasts are proposed to support amoeboid activity, none of these changes restore invasive activity of MMP14-targeted cancer cells. While a requirement for MMP14 is bypassed in low-density collagen hydrogels, invasion by the proteinase-deleted cells is associated with nuclear envelope and DNA damage, highlighting a proteolytic requirement for maintaining nuclear integrity. Nevertheless, when cancer cells confront explants of live human breast tissue, MMP14 is again required to support invasive activity. Corroborating these results, spatial transcriptomic and immunohistological analyses of invasive human breast cancers identified clear expression of MMP14 in invasive cells that were further associated with degraded collagen, underlining the pathophysiologic importance of this proteinase in directing invasive activity in vivo.

cancer biology↗

bioRxiv2024

Assessing DEI Bias in Gene Expression Omnibus (GEO) Datasets Based on Gender and Ethnicity

The Gene Expression Omnibus (GEO) is a widely used repository for gene expression data, but its datasets may be subject to biases in terms of diversity, equity, and inclusion. This study aims to assess DEI-related biases in GEO datasets specifically focusing on gender and ethnicity representation. We curated a subset of GEO datasets by applying filters for organism type, sample size, and DEI-related metadata (gender and ethnicity). Following rigorous data extraction and cleaning, we analyzed 211 datasets, evaluating gender balance and ethnicity/race distribution using quantitative metrics. For gender representation, we calculated a gender ratio for each dataset, with a ratio closer to 1 indicating balanced representation. Ethnicity/race representation was assessed using Chi-square goodness-of-fit tests to identify disparities in ethnic distribution. We ranked datasets based on these DEI criteria to identify those with the highest and lowest bias. Our findings revealed that while many datasets displayed balanced gender ratios, significant bias in ethnicity representation was observed, with a predominance of White and African American participants. Additionally, we observed some variation in DEI metrics for datasets published before and after 2015, suggesting a shift in gender recruitment practices. These results highlight the need for more inclusive data collection and reporting in genomic research. We emphasize the importance of incorporating DEI criteria into data curation and encourage future efforts to standardize DEI data reporting across repositories to mitigate bias and improve the generalizability of genomic research.

bioinformatics↗

bioRxiv2024

Integrated cancer cell-specific single-cell RNA-seq datasets of immune checkpoint blockade-treated patients

Immune checkpoint blockade (ICB) therapies have emerged as a promising avenue for the treatment of various cancers. Despite their success, the efficacy of these treatments is variable across patients and cancer types. Numerous single-cell RNA-sequencing (scRNA-seq) studies have been conducted to unravel cell-specific responses to ICB treatment. However, these studies are limited in their sample sizes and require advanced coding skills for exploration. Here, we have compiled eight scRNA-seq datasets from nine cancer types, encompassing 174 patients, and 90,270 cancer cells. This compilation forms a unique resource tailored for investigating how cancer cells respond to ICB treatment across cancer types. We meticulously curated, quality-checked, pre-processed, and analyzed the data, ensuring easy access for researchers. Moreover, we designed a user-friendly interface for seamless exploration. By sharing the code and data for creating these interfaces, we aim to assist fellow researchers. These resources offer valuable support to those interested in leveraging and exploring single-cell datasets across diverse cancer types, facilitating a comprehensive understanding of ICB responses.

cancer biology↗

bioRxiv2022

CanSeer: A Method for Development and Clinical Translation of Personalized Cancer Therapeutics

Computational modeling and analysis of biomolecular network models annotated with cancer patient-specific multi-omics data can enable the development of personalized therapies. Current endeavors aimed at employing in silico models towards personalized cancer therapeutics remain to be fully translated. In this work, we present "CanSeer" a novel multi-stage methodology for developing in silico models towards clinical translation of personalized cancer therapeutics. The proposed methodology integrates state-of-the-art dynamical analysis of biomolecular network models with patient-specific genomic and transcriptomic data to assess the individualized therapeutic responses to targeted drugs and their combinations. CanSeers translational approach employs transcriptomic data (RNA-seq based gene expressions) with genomic profile (CNVs, SMs, and SVs). Specifically, patient-specific cancer driver genes are identified, followed by the selection of druggable and/or clinically actionable targets for therapeutic interventions. To exemplify CanSeer, we have designed three case studies including (i) lung squamous cell carcinoma, (ii) breast invasive carcinoma, and (iii) ovarian serous cystadenocarcinoma. The case study on lung squamous cell carcinoma concluded that restoration of Tp53 activity together with an inhibition of EGFR as an efficacious combinatorial treatment for patients with Tp53 and EGFR cancer driver genes. The findings from the cancer case study helped identify personalized treatments including APR-246, APR-246+palbociclib, APR-246+osimertinib, APR-246+afatinib, APR-246+osimertinib+dinaciclib, and APR-246+afatinib+dinaciclib. The second case study on breast invasive carcinoma revealed CanSeers potential to elucidate drug resistance against targeted drugs and their combinations including KU-55933, afuresertib, ipatasertib, and KU-55933+afuresertib. Lastly, the ovarian cancer case study revealed the combinatorial efficacy of APR-246+carmustine, and APR-246+dinaciclib for treating ovarian serous cystadenocarcinoma. Taken together, CanSeer outlines a novel method for systematic identification of optimal tailored treatments with mechanistic insights into patient-to-patient variability of therapeutic response, drug resistance mechanism, and cytotoxicity profiling towards personalized medicine.

systems biology↗

bioRxiv2021

Navigating Multi-scale Cancer Systems Biology towards Model-driven Personalized Therapeutics

Rapid advancements in high-throughput omics technologies and experimental protocols have led to the generation of vast amounts of biomolecular data on cancer that now populates several online databases and resources. Cancer systems biology models built on top of this data have the potential to provide specific insights into complex multifactorial aberrations underpinning tumor initiation, development, and metastasis. Furthermore, the annotation of these single- or multi-scale models with patient data can additionally assist in designing personalized therapeutic interventions as well as aid in clinical decision-making. Here, we have systematically reviewed the emergence and evolution of (i) repositories with scale-specific and multiscale biomolecular cancer data, (ii) systems biology models developed using this data, (iii) associated simulation software for development of personalized cancer therapeutics, and (iv) translational attempts to pipeline multi-scale panomics data for data-driven in silico clinical oncology. The review concludes by highlighting that the absence of a generic, zero-code, panomics-based multi-scale modeling pipeline and associated software framework, impedes the development and seamless deployment of personalized in silico multi-scale models in clinical settings.

systems biology↗

bioRxiv2021

TISON: a next-generation multi-scale modeling theatre for in silico systems oncology

Multi-scale models integrating biomolecular data from genetic, transcriptional, and translational levels, coupled with extracellular microenvironments can assist in decoding the complex mechanisms underlying system-level diseases such as cancer. To investigate the emergent properties and clinical translation of such cancer models, we present Theatre for in silico Systems Oncology (TISON, https://tison.lums.edu.pk), a next-generation web-based multi-scale modeling and simulation platform for in silico systems oncology. TISON provides a "zero-code" environment for multi-scale model development by seamlessly coupling scale-specific information from biomolecular networks, microenvironments, cell decision circuits, in silico cell lines, and organoid geometries. To compute the temporal evolution of multi-scale models, a simulation engine and data analysis features are also provided. Furthermore, TISON integrates patient-specific gene expression data to evaluate patient-centric models towards personalized therapeutics. Several literature-based case studies have been developed to exemplify and validate TISONs modeling and analysis capabilities. TISON provides a cutting-edge multi-scale modeling pipeline for scale-specific as well as integrative systems oncology that can assist in drug target discovery, repositioning, and development of personalized therapeutics.

systems biology↗

bioRxiv2020

In silico Drosophila Patient Model Reveals Optimal Combinatorial Therapies for Colorectal Cancer

In silico models of biomolecular regulation in cancer, annotated with patient-specific gene expression data can aid in the development of novel personalized cancer therapeutics strategies. Drosophila melanogaster is a well-established animal model that is increasingly being employed to evaluate preclinical personalized cancer therapies. Here, we report five Boolean network models of biomolecular regulation in cells lining the Drosophila midgut epithelium and annotate them with patient-specific mutation data to develop an in silico Drosophila Patient Model (DPM). The network models were validated against cell-type-specific RNA-seq gene expression data from the FlyGut-seq database and through three literature-based case studies on colorectal cancer. The results obtained from the study help elucidate cell fate evolution in colorectal tumorigenesis, validate cytotoxicity of nine FDA-approved cancer drugs, and devise optimal personalized drug treatment combinations. The proposed personalized therapeutics approach also helped identify synergistic combinations of chemotherapy (paclitaxel) with targeted therapies (pazopanib, or ruxolitinib) for treating colorectal cancer. In conclusion, this work provides a novel roadmap for decoding colorectal tumorigenesis and in the development of personalized cancer therapeutics through a DPM.

systems biology↗