Tackling the development of hormone therapy resistance in breast cancer through mathematical modelling

Patients suffering from estrogen-driven breast cancer frequently develop hardly predictable resistance to hormone therapy, which significantly complicates treatment. Current approaches for tackling this problem include cell models and clinical studies, both supported by sequencing technologies like RNA-seq, and offering different strengths and limitations. This dissertation addresses the challenge of predicting resistance to hormone therapy in breast cancer by merging advances in bioinformatics and Bayesian statistics, and applying them to two types of data – RNA-seq data and clinical data. First, we explore the statistical analysis of clinical data through Bayesian inference combined with enhanced Markov Chain Monte Carlo techniques, and introduce a novel algorithm for adaptive integration in prospective Modified Hamiltonian Monte Carlo (MHMC) methods. We demonstrate its positive effect on performance of MHMC in biomedical applications using clinical data of breast cancer patients. Next, we propose and implement an RNA-seq pipeline within our interactive web-app for the analysis of resistant breast cancer cell lines sequenced at CIC bioGUNE. Finally, we propose an original approach based on a Bayesian logistics regression model coupled with a simulated annealing-like algorithm for a combined analysis of RNA-seq and clinical data, and apply it to ad hoc data to obtain and validate in-silico and in-vitro a novel 6-gene signature for stratifying patient response to hormone therapy

The effect of hydrogen sulphide on ammonium bisulphite when used as an oxygen scavenger in aqueous solutions

Peer reviewedPostprin

Numerical simulation of a binary communication channel: Comparison between a replica calculation and an exact solution

The mutual information of a single-layer perceptron with $N$ Gaussian inputs and $P$ deterministic binary outputs is studied by numerical simulations. The relevant parameters of the problem are the ratio between the number of output and input units, $\alpha = P/N$, and those describing the two-point correlations between inputs. The main motivation of this work refers to the comparison between the replica computation of the mutual information and an analytical solution valid up to $\alpha \sim O(1)$. The most relevant results are: (1) the simulation supports the validity of the analytical prediction, and (2) it also verifies a previously proposed conjecture that the replica solution interpolates well between large and small values of $\alpha$.Comment: 6 pages, 8 figures, LaTeX fil

Auto and crosscorrelograms for the spike response of LIF neurons with slow synapses

An analytical description of the response properties of simple but realistic neuron models in the presence of noise is still lacking. We determine completely up to the second order the firing statistics of a single and a pair of leaky integrate-and-fire neurons (LIFs) receiving some common slowly filtered white noise. In particular, the auto- and cross-correlation functions of the output spike trains of pairs of cells are obtained from an improvement of the adiabatic approximation introduced in \cite{Mor+04}. These two functions define the firing variability and firing synchronization between neurons, and are of much importance for understanding neuron communication.Comment: 5 pages, 3 figure

Classification of tolerable/intolerable mucosal toxicity of head-and-neck radiotherapy schedules with a biomathematical model of cell dynamics

Purpose: The purpose of this study is to present a biomathematical model based on the dynamics of cell populations to predict the tolerability/intolerability of mucosal toxicity in head-and-neck radiotherapy. Methods and Materials: Our model is based on the dynamics of proliferative and functional cell populations in irradiated mucosa, and incorporates the three As: Accelerated proliferation, loss of Asymmetric proliferation, and Abortive divisions. The model consists of a set of delay differential equations, and tolerability is based on the depletion of functional cells during treatment. We calculate the sensitivity (sen) and specificity (spe) of the model in a dataset of 108 radiotherapy schedules, and compare the results with those obtained with three phenomenological classification models, two based on a biologically effective dose (BED) function describing the tolerability boundary (Fowler and Fenwick) and one based on an equivalent dose in 2 Gy fractions (EQD2) boundary (Strigari). We also perform a machine learning-like cross-validation of all the models, splitting the database in two, one for training and one for validation. Results: When fitting our model to the whole dataset, we obtain predictive values (sen + spe) up to 1.824. The predictive value of our model is very similar to that of the phenomenological models of Fowler (1.785), Fenwick (1.806), and Strigari (1.774). When performing a k = 2 cross-validation, the specificity and sensitivity in the validation dataset decrease for all models, from ˜1.82 to ˜1.55–1.63. For Fowler, the worsening is higher, down to 1.49. Conclusions: Our model has proved useful to predict the tolerability/intolerability of a dataset of 108 schedules. As the model is more mechanistic than other available models, it could prove helpful when designing unconventional dose fractionations, schedules not covered by datasets to which phenomenological models of toxicity have been fitted

Fuentes de conocimiento externo en el sector arqueológico español: Mapeo de la fase emergente en una actividad empresarial

[EN] Recent studies of innovation highlight the importance of external knowledge sourcing. Existing empirical works are based on national surveys and specific industries. The present study contributes to the analysis of strategies for sourcing external knowledge, based on a specific case study and moment in time: the Spanish archaeological sector and its emergence as a new business activity. Our results show that external knowledge sourcing involves diverse mechanisms, agents and two main strategies: cooperation and knowledge acquisition. In an expanding knowledge-based sector emerging in an uncertain context and whose sources of knowledge are scattered, innovation strategy should focus on the search for external knowledge ¿cooperation and acquisition strategies-, rather than on internal sources.[ES] Estudios recientes señalan la importancia de las fuentes externas de conocimiento como estrategia para innovar. La evidencia empírica se fundamenta en encuestas nacionales y en industrias específicas. El presente estudio contribuye al análisis de las estrategias de incorporación de conocimiento externo mediante un caso de estudio y en un momento concreto: el sector arqueológico español y su emergencia como nueva actividad económica. Los resultados muestran que las fuentes de conocimiento externo implican diversos mecanismos, agentes y dos estrategias principalmente: cooperar y adquirir conocimiento. En un sector en expansión, basado en el conocimiento, que surge en un contexto incierto y cuyas fuentes de conocimiento están dispersas, el foco de la innovación puede encontrarse en las estrategias de búsqueda de conocimiento externo -cooperación y adquisición-, más que en fuentes internas.This work was supported by the ACE (Archaeology in Contemporary Europe) Project, funded by the European Commission Cultural Programme between 2008 and 2012. The authors are grateful to the archaeologists and archaeological firms that participated in the study. Special thanks go to the Institute of Heritage Sciences (Incipit-CSIC) and INGENIO (CSIC-UPV) for the support and help. We also thank the anonymous reviewers for their helpful comments. Finally, we appreciate the English text editing by Cynthia Little, a specialist in our area, which always helps us in this activity.Parga-Dans, E.; Castro-Martínez, E.; Sánchez-Barrioluengo, M. (2017). External knowledge sourcing in the Spanish archaeological sector: Mapping the emergent stage of a business activity. Revista española de Documentación Científica. 40(1):1-14. doi:10.3989/redc.2017.1.1380S114401Langvall, O. (2011). Impact of climate change, seedling type and provenance on the risk of damage to Norway spruce (Picea abies (L.) Karst.) seedlings in Sweden due to early summer frosts. Scandinavian Journal of Forest Research, 26(S11), 56-63. doi:10.1080/02827581.2011.564399Nielsen, U. B., Madsen, P., Hansen, J. K., Nord-Larsen, T., & Nielsen, A. T. (2014). Production potential of 36 poplar clones grown at medium length rotation in Denmark. Biomass and Bioenergy, 64, 99-109. doi:10.1016/j.biombioe.2014.03.03

Nueva técnica de pegado con resina acrílica para preparar láminas delgadas para microscopía óptica

After a short comparative analysis of the most usual bonding techniques for rock thin section preparation, a new fast and reliable bonding technique with acrylic resin is described. A new design for a U.V. translucent press necessary for both a minimun glue thickness and as a requirement for the acrylic bonding to be completed, is also presented. Tests about thin section quality, mechanical resistance of the bond, refractive index and ageing performed on different hard rocks and impregnated sediments and soils, have given excellent results

A Novel Mathematical Approach for Analysis of Integrated Cell–Patient Data Uncovers a 6-Gene Signature Linked to Endocrine Therapy Resistance

A significant number of breast cancers develop resistance to hormone therapy. This progression, while posing a major clinical challenge, is difficult to predict. Despite important contributions made by cell models and clinical studies to tackle this problem, both present limitations when taken individually. Experiments with cell models are highly reproducible but do not reflect the indubitable heterogeneous landscape of breast cancer. On the other hand, clinical studies account for this complexity but introduce uncontrolled noise due to external factors. Here, we propose a new approach for biomarker discovery that is based on a combined analysis of sequencing data from controlled MCF7 cell experiments and heterogeneous clinical samples that include clinical and sequencing information from The Cancer Genome Atlas. Using data from differential gene expression analysis and a Bayesian logistic regression model coupled with an original simulated annealing-type algorithm, we discovered a novel 6-gene signature for stratifying patient response to hormone therapy. The experimental observations and computational analysis built on independent cohorts indicated the superior predictive performance of this gene set over previously known signatures of similar scope. Together, these findings revealed a new gene signature to identify patients with breast cancer with an increased risk of developing resistance to endocrine therapy