Skip to main content

Fco. Xavier De la Cruz Montserrat

Our main research aims at understanding the molecular basis of hereditary disease, integrating two complementary aspects: the molecular impact of causative variants and how genetic background regulates the propagation of this impact. At a technical level, to reach our objective, we integrate the results of the most advanced genomic experiments (single-cell, Hi-C, etc.) using state-of-the-art machine learning tools. To enhance the biomedical reach of our research, we work in collaboration with clinical groups from different hospitals. As a result of these efforts, we have recently made significant advances in understanding the functional effect of BRCA1/2 protein variants underlying hereditary breast and ovarian cancers. Finally, mention that we are also devoting an important part of our efforts to the fundamental study of epigenetic processes, to reach a full picture of which phenomena contribute to the generation of phenotype and, more precisely, of clinical phenotype.

Institutions of which they are part

Head of group
Clinical and Translational Bioinformatics
Vall Hebron Institut de Recerca
My interests are devoted to studying the impact of pathogenic mutations and transform the
results into clinically useful Artificial Intelligence (AI) models.

We ranked second in the groups' classification at the international challenge for in silico methods, CAGI 5 (inherited breast/ovarian cancers section). We won the Best Poster Prize of the Editor of 'Science' in HGM 2018 (Japan). In 2015, we participated in the identification of the glioblastoma signature in cerebro-spinal fluid (De Mattos-Arruda et al., Nature Communications, 6:8839), a project that won the Prize "La Vanguardia de la Ciencia".

.- Ph.D. Thesis
Thesis (1990-1993, UPC, Barcelona). I worked in a relationship between atomic areas
and free energies (de la Cruz et al., J.Mol.Graphics, 10:96, 1992). During a stay in the lab of
van Gunsteren (ETH-Z, Zürich), I shifted toward the study of the structure/function relationship (de la Cruz et al., J. Mol. Biol., 236:1186, 1994).
Fellowships: FPI Generalitat (1990-93, UPC); EMBO Short Term (1992, ETH-Z).

.- Post-doctoral stays (1993-2000)
Two post-doctoral stays: NIH, USA (4 years) and UCL-EBI, UK (3 years). In these stays, I increased my background on structure/function relationship (analog structure comparisons: de la Cruz & Lee, Protein Sci., 5:857, 1996; beta-turn prediction: de la Cruz et al., PNAS, 99:11157, 2002).
Fellowships: Fogarty Fellowship (1993-97, USA); MEC fellowship for doctors (1997); Human
Frontier Science Program (HFSP) (1997-99, UK).

PRESENT (2000-**) ICREA Research Professor
Our research aims at understanding/predicting the impact of pathogenic of variants using AI. Our initial approach (Ferrer-Costa et al., J. Mol. Biol. 2002; Proteins, 2004; Bioinformatics, 2005) has been broadly followed (>920 citations). After joining VHIR (2012) we have designed a novel approach to pathogenicity prediction (Riera et al., Hum. Mut., 2016). This crystallized in the model for BRCA1/2 variants (Padilla et al., Hum. Mut., 2019) that we presented at the international CAGI contest (see above). We have further tested this result to extend it to the clinical genome (Özkan et al., IJMS, 2020). Our expertise is reflected in our collaborations (see below for our collaboration with E. Bhoj at the Children’s Hospital of Philadelphia).

We have worked also in the epigenetic regulation of gene expression, where I would like to
single out two of our publications (co-corresponding author in both), one in PNAS (Pappa et
al., 2019) about DNA damage prevention and the second in Development (Estarás et al., 2012)
on the regulation of neural development by JMJD3.
Contracts: Reincorporación MEC (2000-2001), ICREA Research Professor (2001-nowadays).

-Skills. We have a deep understanding of the molecular basis of hereditary diseases.
Technically, we are proficient in the development of AI tools for pathogenicity prediction. We
are also proficient in the structural/functional characterization of protein mutations using
computational/bioinformatics approaches.

-International collaborations. We have an ongoing collaboration with E. Bhoj (Children’s
Hospital of Philadelphia) for the clinical characterization of histone H3.3 variants. This work
has lead to a high-impact publication (Bryant et al., Science Advances, 2020) in which we
leadered the in silico analyses of the whole consortium (137 authors) in a study resulting in the discovery of a new neurodegenerative disorder.

-Training researchers. I have supervised 11 Ph.D. theses. The majority of the students work
in bioinformatics/computation.

Research lines

Prediction of pathological mutations

123 Since a few years ago, we have been working in the development of computational tools for the prediction of pathological mutations in proteins (PMID: 11812146, 15390262, 15879453, 16208716, 17059831). In this work we have followed a two-step approach. First, we have characterized pathological mutations in terms of sequence, structure and evolutionary properties (PMID: 11812146). Second, we have used the results of this descriptive work to identify those parameters with the best predictive power. Also we have used these results to train a neural network and obtain an empirical model that allows the identification of disease-causing mutations with moderately high accuracies (around 70%; PMID: 15390262, 15879453, 16208716). At present, we are applying this approach to the case of specific diseases. We believe that this is a natural step towards the advancement of personalized medicine. Within this context, we are characterizing the impact on the structure and function of alpha-galactosidase of Fabry disease-causing mutations. Again our approach is the same as in the general case, but here we are benefitting from the close collaboration with the group of Dr. Joan Montaner.

IP: Fco. Xavier De la Cruz Montserrat


Una aproximación traslacional a la interpretación de variantes de secuencia en proteínas: integrando impacto molecular, regulación por el entorno genético y coste clínico

IP: Fco. Xavier De la Cruz Montserrat
Collaborators: Natalia Padilla Sirera
Funding agency: Ministerio de Ciencia e Innovación-MICINN
Funding: 133100
Reference: PID2019-111217RB-I00
Duration: 01/06/2020 - 31/05/2023

Ministerio de Ciencia

Nuevas aproximaciones para identificar variantes patogénicas en experimentos de secuenciación masiva basadas en la integración de impacto molecular e información biomédica

IP: Fco. Xavier De la Cruz Montserrat
Collaborators: -
Funding agency: Ministerio Economía, Industria y Competitividad
Funding: 96800
Reference: SAF2016-80255-R
Duration: 30/12/2016 - 29/12/2019

PIREPRED: Sol·licitud de projecte coordinat per part del departament de Bioinformàtica Translacional juntament amb 5 altres centres.

IP: Fco. Xavier De la Cruz Montserrat
Collaborators: -
Funding: 100888.78
Duration: 01/09/2016 - 30/06/2021

Desarrollo de herramientas bioinformáticas para la predicción y compresión de dos rasgos fundamentales de las patologías hereditarias: severidad y fenotipo tisular

IP: Fco. Xavier De la Cruz Montserrat
Collaborators: Miriam Izquierdo Sans
Funding agency: Fundació Institut de Recerca HUVH
Funding: 45135
Reference: PRED-VHIR-2013-06
Duration: 26/09/2014 - 25/09/2017

Related professionals

Natalia Padilla Sirera

Natalia Padilla Sirera

Research technician
Clinical and Translational Bioinformatics
Read more
Selen Ozkan

Selen Ozkan

Predoctoral researcher
Clinical and Translational Bioinformatics
Read more
Fco. Xavier De la Cruz Montserrat

Fco. Xavier De la Cruz Montserrat

Head of group
Clinical and Translational Bioinformatics
Read more

Subscriu-te als nostres butlletíns i forma part de la vida del Campus

El Vall d’Hebron Barcelona Hospital Campus és un parc sanitari de referència mundial on assistència, recerca, docència i innovació es donen la mà.