Identifying new therapeutic targets in Pancreatic Ductal Adenocarcinoma by combining relevant models and approaches

• Grant number: 22/06305-7
• Support Opportunities: Generation Project Research Grant
• Start date: August 01, 2023
• End date: July 31, 2028
• Field of knowledge: Biological Sciences - Biology
• Principal Investigator: Pedro Luiz Porfirio Xavier
• Grantee: Pedro Luiz Porfirio Xavier
• Host Institution: Faculdade de Zootecnia e Engenharia de Alimentos (FZEA). Universidade de São Paulo (USP). Pirassununga , SP, Brazil
• Associated researchers: Claudia Madalena Cabrera Mori ; Fabiana Fernandes Bressan ; Heidge Fukumasu ; José Bento Sterman Ferraz ; Juliano Coelho da Silveira ; Paulo Lee Ho ; Ricardo de Francisco Strefezzi ; Rodrigo Alexandre Panepucci ; Susanne Müller-Knapp ; Talal Jamil Qazi ; Tathiane Maistro Malta Pereira ; Vanessa Cristina Oliveira Nogueira de Pontes
• Associated research grant(s): 23/14701-2 - A Brazil-Germany Collaboration Boosting Innovative Platforms and Models to Identify Therapeutic Targets in Pancreatic Ductal Adenocarcinoma, AP.R SPRINT
• Associated scholarship(s): 24/15154-8 - Evaluation of inhibition of therapeutic targets in Pancreatic Ductal Adenocarcinoma (PDAC) using three-dimensional tissue-specific models., BP.MS; 24/06143-2 - Establishment of a high-content screening platform in 3D in vitro models to screen and identify potential therapeutic targets for PDAC, BP.MS; 24/00428-5 - PDACombat: a platform for the dissemination of science and the fight against cancer, BP.JC; + associated scholarships 24/00444-0 - PDACombat: a platform for the dissemination of science and the fight against cancer, BP.JC; 24/00635-0 - Establishment and characterization of a 3D tissue-specific platform for Pancreatic Ductal Adenocarcinoma cells, BP.IC; 23/07358-0 - Establishment and Characterization of PDAC 3D spheroids in 384-well Ultra-Low Attachment plates, BP.IC; 23/05099-7 - Identifying new therapeutic targets in Pancreatic Ductal Adenocarcinoma by combining relevant models and approaches, BP.GR - associated scholarships

Abstract

Pancreatic Ductal Adenocarcinoma (PDAC) is one of the most challenging types of cancer exhibiting a lack of targetable pathways. Thus, PDAC has been considered a "graveyard" for drug development. Here we propose an ambitious project which aims to identify new therapeutic targets and develop the first steps of an innovative therapy for PDAC, using relevant models and approaches. For this, we will address three different scientific and technical challenges: 1) In the first challenge, we will screen a bespoke library of about 1,500 small-molecule inhibitors obtained in collaboration with the Structural Genome Consortium (SGC) and EubOPEN in PDAC tumor spheroids, observing the modulation of tumor phenotypes using a High Content Screening platform and identifying potential therapeutic targets for PDAC. 2) Then, we will establish 3D tissue-specific environments for PDAC spheroids, using a hydrogel system containing tissue-specific extracellular matrix proteins mimicking a microenvironment for PDAC in order to further evaluate the most promising small-molecule inhibitors selected in the scientific and technical challenge 1. 3) Finally, we will develop an innovative and promising therapeutic platform targeting PDAC through the generation of a recombinant NDV virus expressing a CRISPR/Cas9 system specifically inhibiting the therapeutic target previously selected. Thus, we can test the hypothesis that an NDV virus expressing a CRISPR/Cas9 system can exhibit potential antitumor effects through both virus-mediated oncolysis and CRISPR/Cas9-mediated gene editing in PDAC. At the end of these projects, new therapeutic targets and an innovative therapeutic approach will be identified and generated for PDAC. (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)

XAVIER, PEDRO L. P.; MARCAO, MAYCON; SIMOES, RENAN L. S.; JOB, MARIA EDUARDA G.; STREFEZZI, RICARDO DE FRANCISCO; FUKUMASU, HEIDGE; MALTA, TATHIANE M.. Machine learning determines stemness associated with simple and basal-like canine mammary carcinomas. HELIYON, v. 10, n. 5, p. 12-pg., 2024-02-28. (23/07358-0, 18/00583-0, 23/05099-7, 22/06305-7, 19/14928-1, 22/09378-5, 21/00283-9)