Navigating Multi-Scale Cancer Systems Biology Towards Model-Driven Clinical Oncology and Its Applications in Personalized Therapeutics

Gondal, Mahnoor Naseer; Chaudhary, Safee Ullah

doi:10.3389/fonc.2021.712505

Cited by 7 publications

(5 citation statements)

References 329 publications

(447 reference statements)

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“…Therefore, the identification of synergistic combinations to evaluate for cancer treatment is of great importance. To this end, computational methods based on transcriptomics and protein interaction networks [ 187 ], as well as drug repurposing opportunities arising from artificial intelligence-based analyses [ 188 ], represent valuable tools to limit the number of drug combinations for screening.…”

Section: Discussionmentioning

confidence: 99%

Targeting the Ubiquitin–Proteasome System and Recent Advances in Cancer Therapy

Spano,

Catara

2023

Cells

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Ubiquitination is a reversible post-translational modification based on the chemical addition of ubiquitin to proteins with regulatory effects on various signaling pathways. Ubiquitination can alter the molecular functions of tagged substrates with respect to protein turnover, biological activity, subcellular localization or protein–protein interaction. As a result, a wide variety of cellular processes are under ubiquitination-mediated control, contributing to the maintenance of cellular homeostasis. It follows that the dysregulation of ubiquitination reactions plays a relevant role in the pathogenic states of human diseases such as neurodegenerative diseases, immune-related pathologies and cancer. In recent decades, the enzymes of the ubiquitin–proteasome system (UPS), including E3 ubiquitin ligases and deubiquitinases (DUBs), have attracted attention as novel druggable targets for the development of new anticancer therapeutic approaches. This perspective article summarizes the peculiarities shared by the enzymes involved in the ubiquitination reaction which, when deregulated, can lead to tumorigenesis. Accordingly, an overview of the main pharmacological interventions based on targeting the UPS that are in clinical use or still in clinical trials is provided, also highlighting the limitations of the therapeutic efficacy of these approaches. Therefore, various attempts to circumvent drug resistance and side effects as well as UPS-related emerging technologies in anticancer therapeutics are discussed.

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Section: Discussionmentioning

confidence: 99%

Targeting the Ubiquitin–Proteasome System and Recent Advances in Cancer Therapy

Spano,

Catara

2023

Cells

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“…In terms of limitations and future extensions, CanSeer can cue the development of a multi-scale modeling pipeline around the framework [134] by integrating regulations from different spatiotemporal scales. Bootstrapping and applying CanSeer to a large cohort of patient samples can help elucidate the bigger landscape of association between patient-specific omics data and therapeutic combinations.…”

Section: Discussionmentioning

confidence: 99%

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

Butt

Bibi

Sultan

et al. 2022

Preprint

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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 'CanSee' 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. CanSeer's 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 CanSeer's 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.

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“…Cancer is a complex disease characterized by its highly heterogeneous and multifactorial nature ( Gondal and Chaudhary, 2021 ). Traditional approaches to studying cancer, such as bulk RNA sequencing constitute a mixture of the cellular composition in tumors and often fail to accurately capture cancer cell-specific gene expression ( Ding et al, 2020 ; Huang et al, 2023 ).…”

Section: Landscape Of Single-cell Transcriptomics Databasesmentioning

confidence: 99%

A systematic overview of single-cell transcriptomics databases, their use cases, and limitations

Gondal,

Shah,

Chinnaiyan

et al. 2024

Front. Bioinform.

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Rapid advancements in high-throughput single-cell RNA-seq (scRNA-seq) technologies and experimental protocols have led to the generation of vast amounts of transcriptomic data that populates several online databases and repositories. Here, we systematically examined large-scale scRNA-seq databases, categorizing them based on their scope and purpose such as general, tissue-specific databases, disease-specific databases, cancer-focused databases, and cell type-focused databases. Next, we discuss the technical and methodological challenges associated with curating large-scale scRNA-seq databases, along with current computational solutions. We argue that understanding scRNA-seq databases, including their limitations and assumptions, is crucial for effectively utilizing this data to make robust discoveries and identify novel biological insights. Such platforms can help bridge the gap between computational and wet lab scientists through user-friendly web-based interfaces needed for democratizing access to single-cell data. These platforms would facilitate interdisciplinary research, enabling researchers from various disciplines to collaborate effectively. This review underscores the importance of leveraging computational approaches to unravel the complexities of single-cell data and offers a promising direction for future research in the field.

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Navigating Multi-Scale Cancer Systems Biology Towards Model-Driven Clinical Oncology and Its Applications in Personalized Therapeutics

Cited by 7 publications

References 329 publications

Targeting the Ubiquitin–Proteasome System and Recent Advances in Cancer Therapy

Targeting the Ubiquitin–Proteasome System and Recent Advances in Cancer Therapy

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

A systematic overview of single-cell transcriptomics databases, their use cases, and limitations

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