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More MH, Varankar SS, Naik RR, Dhake RD, Ray P, Bankar RM, Mali AM, Subbalakshmi AR, Chakraborty P, Jolly MK, Bapat SA. A Multistep Tumor Growth Model of High-Grade Serous Ovarian Carcinoma Identifies Hypoxia-Associated Signatures. Cells Tissues Organs 2022; 213:79-95. [PMID: 35970135 DOI: 10.1159/000526432] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Accepted: 08/04/2022] [Indexed: 11/19/2022] Open
Abstract
High-grade serous ovarian carcinoma (HGSC) is associated with late-stage disease presentation and poor prognosis, with a limited understanding of early transformation events. Our study analyzes HGSC tumor progression and organ-specific metastatic dissemination to identify hypoxia-associated molecular, cellular, and histological alterations. Clinical characteristics of the HGSC were replicated in orthotopic xenografts, which involve metastatic dissemination and the prevalence of group B tumors (volume: >0.0625 ≤ 0.5 cm3). Enhanced hyaluronic acid (HA) deposition, expanded tumor vasculature, and increased necrosis contributed to the remodeling of tumor tissue architecture. The proliferative potential of tumor cells and the ability to form glands were also altered during tumor growth. Flow cytometry and label chase-based molecular profiling across the tumor regenerative hierarchy identified the hypoxia-vasculogenic niche and the hybrid epithelial-mesenchymal tumor-cell state as determinants of self-renewal capabilities of progenitors and cancer stem cells. A regulatory network and mathematical model based on tumor histology and molecular signatures predicted hypoxia-inducible factor 1-alpha (HIF1A) as a central node connecting HA synthesis, epithelial-mesenchymal transition, metabolic, vasculogenic, inflammatory, and necrotic pathways in HGSC tumors. Thus, our findings provide a temporal resolution of hypoxia-associated events that sculpt HGSC tumor growth; an in-depth understanding of it may aid in the early detection and treatment of HGSC.
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Affiliation(s)
- Madhuri H More
- National Centre for Cell Science, Savitribai Phule Pune University, Pune, India
| | - Sagar S Varankar
- National Centre for Cell Science, Savitribai Phule Pune University, Pune, India
| | - Rutika R Naik
- National Centre for Cell Science, Savitribai Phule Pune University, Pune, India
| | - Rahul D Dhake
- Department of Histopathology, Inlaks and Budhrani Hospital, Morbai Naraindas Cancer Institute, Pune, India
| | - Pritha Ray
- Advance Centre for Treatment, Research and Education in Cancer, Tata Memorial Centre, Navi Mumbai, India
| | - Rahul M Bankar
- National Centre for Cell Science, Savitribai Phule Pune University, Pune, India
| | - Avinash M Mali
- National Centre for Cell Science, Savitribai Phule Pune University, Pune, India
| | | | - Priyanka Chakraborty
- Centre for BioSystems Science and Engineering, Indian Institute of Science, Bengaluru, India
| | - Mohit Kumar Jolly
- Centre for BioSystems Science and Engineering, Indian Institute of Science, Bengaluru, India
| | - Sharmila A Bapat
- National Centre for Cell Science, Savitribai Phule Pune University, Pune, India
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Vanhaelen Q. Web-based Tools for Drug Repurposing: Successful Examples of Collaborative Research. Curr Med Chem 2021; 28:181-195. [PMID: 32003659 DOI: 10.2174/0929867327666200128111925] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2019] [Revised: 11/23/2019] [Accepted: 11/30/2019] [Indexed: 11/22/2022]
Abstract
Computational approaches have been proven to be complementary tools of interest in identifying potential candidates for drug repurposing. However, although the methods developed so far offer interesting opportunities and could contribute to solving issues faced by the pharmaceutical sector, they also come with their constraints. Indeed, specific challenges ranging from data access, standardization and integration to the implementation of reliable and coherent validation methods must be addressed to allow systematic use at a larger scale. In this mini-review, we cover computational tools recently developed for addressing some of these challenges. This includes specific databases providing accessibility to a large set of curated data with standardized annotations, web-based tools integrating flexible user interfaces to perform fast computational repurposing experiments and standardized datasets specifically annotated and balanced for validating new computational drug repurposing methods. Interestingly, these new databases combined with the increasing number of information about the outcomes of drug repurposing studies can be used to perform a meta-analysis to identify key properties associated with successful drug repurposing cases. This information could further be used to design estimation methods to compute a priori assessment of the repurposing possibilities.
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Affiliation(s)
- Quentin Vanhaelen
- Insilico Medicine, 307A, Core Building 1, 1 Science Park East Avenue, Hong Kong Science Park, Pak Shek Kok, Hong Kong
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Vanhaelen Q, Mamoshina P, Aliper AM, Artemov A, Lezhnina K, Ozerov I, Labat I, Zhavoronkov A. Design of efficient computational workflows for in silico drug repurposing. Drug Discov Today 2016; 22:210-222. [PMID: 27693712 DOI: 10.1016/j.drudis.2016.09.019] [Citation(s) in RCA: 99] [Impact Index Per Article: 12.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2016] [Revised: 08/26/2016] [Accepted: 09/21/2016] [Indexed: 12/22/2022]
Abstract
Here, we provide a comprehensive overview of the current status of in silico repurposing methods by establishing links between current technological trends, data availability and characteristics of the algorithms used in these methods. Using the case of the computational repurposing of fasudil as an alternative autophagy enhancer, we suggest a generic modular organization of a repurposing workflow. We also review 3D structure-based, similarity-based, inference-based and machine learning (ML)-based methods. We summarize the advantages and disadvantages of these methods to emphasize three current technical challenges. We finish by discussing current directions of research, including possibilities offered by new methods, such as deep learning.
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Affiliation(s)
- Quentin Vanhaelen
- Insilico Medicine Inc., Johns Hopkins University, ETC, B301, MD 21218, USA.
| | - Polina Mamoshina
- Insilico Medicine Inc., Johns Hopkins University, ETC, B301, MD 21218, USA
| | - Alexander M Aliper
- Insilico Medicine Inc., Johns Hopkins University, ETC, B301, MD 21218, USA
| | - Artem Artemov
- Insilico Medicine Inc., Johns Hopkins University, ETC, B301, MD 21218, USA
| | - Ksenia Lezhnina
- Insilico Medicine Inc., Johns Hopkins University, ETC, B301, MD 21218, USA
| | - Ivan Ozerov
- Insilico Medicine Inc., Johns Hopkins University, ETC, B301, MD 21218, USA
| | - Ivan Labat
- BioTime Inc., 1010 Atlantic Avenue, 102, Alameda, CA 94501, USA
| | - Alex Zhavoronkov
- Insilico Medicine Inc., Johns Hopkins University, ETC, B301, MD 21218, USA
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