Finding the Goldilocks zones of cell signaling pathways in cancer therapy

• Grant number: 19/21619-5
• Support Opportunities: Regular Research Grants
• Start date: May 01, 2020
• End date: April 30, 2022
• Field of knowledge: Physical Sciences and Mathematics - Computer Science
• Principal Investigator: Marcelo da Silva Reis
• Grantee: Marcelo da Silva Reis
• Host Institution: Instituto Butantan. Secretaria da Saúde (São Paulo - Estado). São Paulo , SP, Brazil
• Associated researchers: Edmilson Ozorio dos Santos ; Hugo Aguirre Armelin ; Juliane Liepe ; Junior Barrera ; Ronaldo Fumio Hashimoto ; Rosangela Aparecida Wailemann Mansano
• Associated scholarship(s): 20/10329-3 - Bayesian model selection of cell signaling pathways and designing of cell growth classifiers, BP.TT; 20/08555-5 - Designing of a biochemical reaction database for model selection of cell signaling pathways, BP.TT

Abstract

The increase of cancer cell fitness during oncogenesis is accompanied by an increase of several cellular stresses, a phenomenon that may be exacerbated through mitogenic overstimulation. Recently, our group reported an unusual approach to cancer therapy, which relies on mitogenic overstimulation followed by inhibition of stress mitigation pathways. Although mitogenic overstimulation of cancer cells can often be achieved using natural mitogens, which generally are harmless for healthy cells, some types of cancer do not respond well to them. In this case, it might be necessary interventions downstream of mitogenic signaling pathways, a procedure that might also be cytotoxic to healthy cells. Therefore, we hypothesize the existence of a Goldilocks zone, that is, a just right set of such interventions that maximizes treatment efficacy at the same time sparing healthy cells from deleterious effects. To test this hypothesis, we propose to develop a methodology that encompasses: i) a Bayesian model selection procedure that draws candidate models from a reactions database and takes into account the lack of isolation problem that arises during the estimation of signaling pathways models; ii) the designing of classifiers of cell proliferation as a function of signaling pathways dynamics; iii) the incorporation of (i) and (ii) into a combinatorial optimization search for best sets of interventions. To test the proposed methodology, we plan to apply it into synthetic data and on results from experimental assays using cancer and non-tumorigenic cell lines. We also intend to experimentally validate promising predictions obtained with those wet lab assays. Testing this Goldilocks zones hypothesis is a timely endeavor, which could assist the discovery of new mitogenic signaling pathway activators for that novel cancer therapy approach. (AU)

Scientific publications

(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)

BARRERA, JUNIOR; HASHIMOTO, RONALDO F.; HIRATA, NINA S. T.; HIRATA, R., JR.; REIS, MARCELO S.. From Mathematical Morphology to machine learning of image operators. SAO PAULO JOURNAL OF MATHEMATICAL SCIENCES, v. 16, n. 1, p. 42-pg., 2022-04-21. (15/22308-2, 13/07467-1, 19/21619-5, 17/25835-9)

MONTONI, FABIO; DE SOUSA, RONALDO N.; DE LIMA JUNIOR, MARCELO B.; CAMPOS, CRISTIANO G. S.; WANG, WILLIAN; CONSTANTINO, VIVIAN M.; SANCTOS, CASSIA S.; ARMELIN, HUGO A.; REIS, MARCELO S.; IEEE. Anguix: Cell Signaling Modeling Improvement through Sabio-RK association to Reactome. 2022 IEEE 18TH INTERNATIONAL CONFERENCE ON E-SCIENCE (ESCIENCE 2022), v. N/A, p. 2-pg., 2022-01-01. (20/08555-5, 13/07467-1, 21/04355-4, 20/10329-3, 19/24580-2, 19/21619-5)