Dynamics of Adaptation and Resistance in Cancer: MApping and conTrolling Transcriptional and Epigenetic Recurrence

Descripción del proyecto

Comprender la farmacorresistencia tumoral

Los tumores evolucionan y pasan de ser enfermedades curables en estadios tempranos a neoplasias farmacorresistentes, lo que hace de la resistencia a los tratamientos un gran reto en oncología. Se cree que gran parte de esa resistencia se debe a cambios epigenéticos hereditarios en las células cancerosas, a menudo denominados la «materia oscura» de la evolución tumoral. Esas adaptaciones genéticas y epigenéticas se pueden producir a la vez dentro del mismo tumor. Sin embargo, la limitada comprensión de estos mecanismos dificulta el desarrollo de nuevos tratamientos. El proyecto DARC MATTER, financiado por el Consejo Europeo de Investigación, tiene como objetivo examinar la farmacorresistencia en el cáncer colorrectal. En el proyecto se emplearán modelos de organoides derivados de pacientes para investigar la evolución de los organoides durante el tratamiento antineoplásico. Se emplearán técnicas avanzadas para identificar nuevos mecanismos de resistencia y desarrollar modelos predictivos, con el fin último de mejorar las estrategias de politerapia y los resultados en los enfermos.

Objetivo

Tumours evolve, transforming from early-stage curable disease into treatment-refractory, deadly cancer. Therapy resistance is arguably the biggest problem in oncology today, and much of it remains unexplained.

The central hypothesis of this proposal is that a large proportion of unexplained drug resistance is due to heritable epigenetic alterations, and non-heritable transcriptional plasticity in cancer cells. I refer to these mechanisms as the dark matter of cancer evolution. Genetic, epigenetic and transcriptional adaptation, together with changes in the tumour microenvironment, may happen at the same time in the same tumour. Lack of knowledge of these mechanisms hinders the development of new treatments strategies. Tackling drug resistance requires a unique combination of clinical cohorts, experimental models, evolutionary biology and computational methods.

I will map and quantify the mechanisms and evolutionary dynamics of genetic and non-genetic drug resistance at unprecedented scale. I will focus on colorectal cancer, the third most common cancer and second leading cause of cancer-related death worldwide. I will use patient-derived organoid models, matched to clinical cohorts followed longitudinally. I will measure organoid evolution under the pressure of cancer drugs, with and without the tumour microenvironment. I will track cell lineages with lentiviral barcoding and perform longitudinal single cell multi-omics, measuring genomes, epigenomes and transcriptomes of the same cell. I will interpret the results within a unique computational framework that brings together evolutionary theory with machine learning to measure, predict and control resistance.

This project will identify new mechanisms and dynamics of cancer drug resistance, deliver new predictive models, and find novel collateral drug sensitivities. This will allow designing rational drug combinations and schedules that will prevent or delay resistance, drastically improving patient outcome.