Revistas
Revista:
NPJ SYSTEMS BIOLOGY AND APPLICATIONS
ISSN:
2056-7189
Año:
2023
Vol.:
9
N°:
1
Págs.:
32
Synthetic lethality (SL) is a promising concept in cancer research. A wide array of computational tools has been developed to predict and exploit synthetic lethality for the identification of tumour-specific vulnerabilities. Previously, we introduced the concept of genetic Minimal Cut Sets (gMCSs), a theoretical approach to SL developed for genome-scale metabolic networks. The major challenge in our gMCS framework is to go beyond metabolic networks and extend existing algorithms to more complex protein-protein interactions. In this article, we take a step further and incorporate linear regulatory pathways into our gMCS approach. Extensive algorithmic modifications to compute gMCSs in integrated metabolic and regulatory models are presented in detail. Our extended approach is applied to calculate gMCSs in integrated models of human cells. In particular, we integrate the most recent genome-scale metabolic network, Human1, with 3 different regulatory network databases: Omnipath, Dorothea and TRRUST. Based on the computed gMCSs and transcriptomic data, we discovered new essential genes and their associated synthetic lethal for different cancer cell lines. The performance of the different integrated models is assessed with available large-scale in-vitro gene silencing data. Finally, we discuss the most relevant gene essentiality predictions based on published literature in cancer research.
Revista:
PLOS COMPUTATIONAL BIOLOGY
ISSN:
1553-7358
Año:
2022
Vol.:
18
N°:
3
Págs.:
e1009395
Synthetic Lethality (SL) is currently defined as a type of genetic interaction in which the loss of function of either of two genes individually has limited effect in cell viability but inactivation of both genes simultaneously leads to cell death. Given the profound genomic aberrations acquired by tumor cells, which can be systematically identified with -omics data, SL is a promising concept in cancer research. In particular, SL has received much attention in the area of cancer metabolism, due to the fact that relevant functional alterations concentrate on key metabolic pathways that promote cellular proliferation. With the extensive prior knowledge about human metabolic networks, a number of computational methods have been developed to predict SL in cancer metabolism, including the genetic Minimal Cut Sets (gMCSs) approach. A major challenge in the application of SL approaches to cancer metabolism is to systematically integrate tumor microenvironment, given that genetic interactions and nutritional availability are interconnected to support proliferation. Here, we propose a more general definition of SL for cancer metabolism that combines genetic and environmental interactions, namely loss of gene functions and absence of nutrients in the environment. We extend our gMCSs approach to determine this new family of metabolic synthetic lethal interactions. A computational and experimental proof-of-concept is presented for predicting the lethality of dihydrofolate reductase (DHFR) inhibition in different environments. Finally, our approach is applied to identify extracellular nutrient dependences of tumor cells, elucidating cholesterol and myo-inositol depletion as potential vulnerabilities in different malignancies.
Revista:
PLOS COMPUTATIONAL BIOLOGY
ISSN:
1553-7358
Año:
2022
Vol.:
18
N°:
5
Págs.:
e1010180
With the frenetic growth of high-dimensional datasets in different biomedical domains, there is an urgent need to develop predictive methods able to deal with this complexity. Feature selection is a relevant strategy in machine learning to address this challenge. We introduce a novel feature selection algorithm for linear regression called BOSO (Bilevel Optimization Selector Operator). We conducted a benchmark of BOSO with key algorithms in the literature, finding a superior accuracy for feature selection in high-dimensional datasets. Proof-of-concept of BOSO for predicting drug sensitivity in cancer is presented. A detailed analysis is carried out for methotrexate, a well-studied drug targeting cancer metabolism.
Revista:
AMERICAN JOURNAL OF HEMATOLOGY
ISSN:
0361-8609
Año:
2022
Vol.:
97
N°:
3
Págs.:
E113 - E117
Nacionales y Regionales
Título:
Nueva aproximación computacional para predecir letalidad sintética en cáncer
Código de expediente:
PID2019-110344RB-I00
Investigador principal:
Ángel Rubio Díaz-Cordovés, Francisco Javier Planes Pedreño
Financiador:
MINISTERIO DE CIENCIA E INNOVACIÓN
Convocatoria:
2019 AEI PROYECTOS I+D+i (incluye Generación del conocimiento y Retos investigación)
Fecha de inicio:
01/06/2020
Fecha fin:
31/05/2023
Importe concedido:
90.750,00€
Otros fondos:
-
Título:
nG23 - Enfoque inteligente para el diseño de dispositivos de detección micro-nano-fluídica y nuevas funcionalidades para el análisis fluídico
Código de expediente:
KK-2023/00001
Investigador principal:
Idoia Ochoa Álvarez
Financiador:
EUSKO JAURLARITZA - GOBIERNO VASCO
Convocatoria:
ELKARTEK 2023. Programa de Ayudas a la Investigación Colaborativa en áreas estratégicas
Fecha de inicio:
01/03/2023
Fecha fin:
31/12/2024
Importe concedido:
198.730,16€
Otros fondos:
-
Otros (PIUNA, fundaciones, contratos…)
Título:
Precision medicine approach to target deregulated metabolism in multiple mieloma
Investigador principal:
Francisco Javier Planes Pedreño
Financiador:
FUNDACION RAMON ARECES
Convocatoria:
2019 FD Ramón Areces - Ampliación de Estudios en el extranjero en Ciencias de la Vida y de la Materia
Fecha de inicio:
03/04/2019
Fecha fin:
02/04/2022
Importe concedido:
121.500,00€
Título:
Identification of the epigenomic and transcriptomic alterations in the transformation of benign monoclonal gammapathies to symptomatic multiple myeloma.
Investigador principal:
Felipe Luis Prósper Cardoso
Financiador:
FUNDACION BBVA
Convocatoria:
2021 Fundación BBVA Proyectos de investigación
Fecha de inicio:
01/07/2022
Fecha fin:
30/06/2024
Importe concedido:
149.984,50€