Enrique J. deAndrés-Galiana

Supervised Classification by Filter Methods and Recursive Feature Elimination Predicts Risk of Radiotherapy-Related Fatigue in Patients with Prostate Cancer

Background Fatigue is a common side effect of cancer (CA) treatment. We used a novel analytical method to identify and validate a specific gene cluster that is predictive of fatigue risk in prostate cancer patients (PCP) treated with radiotherapy (RT). Methods A total of 44 PCP were categorized into high-fatigue (HF) and low-fatigue (LF) cohorts based on fatigue score change from baseline to RT completion. Fold-change differential and Fishers linear discriminant analyses (LDA) from 27 subjects with gene expression data at baseline and RT completion generated a reduced base of most discriminatory genes (learning phase). A nearest-neighbor risk (k-NN) prediction model was developed based on small-scale prognostic signatures. The predictive model validity was tested in another 17 subjects using baseline gene expression data (validation phase). Result The model generated in the learning phase predicted HF classification at RT completion in the validation phase with 76.5% accuracy. Conclusion The results suggest that a novel analytical algorithm that incorporates fold-change differential analysis, LDA, and a k-NN may have applicability in predicting regimen-related toxicity in cancer patients with high reliability, if we take into account these results and the limited amount of data that we had at disposal. It is expected that the accuracy will be improved by increasing data sampling in the learning phase.

Genomic Data Integration in Chronic Lymphocytic Leukemia

B‐cell chronic lymphocytic leukemia (CLL) is a heterogeneous disease and the most common adult leukemia in western countries. IgVH mutational status distinguishes two major types of CLL, each associated with a different prognosis and survival. Sequencing identified NOTCH1 and SF3B1 as the two main recurrent mutations. We described a novel method to clarify how these mutations affect gene expression by finding small‐scale signatures that predict the IgVH, NOTCH1 and SF3B1 mutations. We subsequently defined the biological pathways and correlation networks involved in disease development, with the potential goal of identifying new drugable targets.

Sensitivity analysis of gene ranking methods in phenotype prediction.

INTRODUCTION It has become clear that noise generated during the assay and analytical processes has the ability to disrupt accurate interpretation of genomic studies. Not only does such noise impact the scientific validity and costs of studies, but when assessed in the context of clinically translatable indications such as phenotype prediction, it can lead to inaccurate conclusions that could ultimately impact patients. We applied a sequence of ranking methods to damp noise associated with microarray outputs, and then tested the utility of the approach in three disease indications using publically available datasets. MATERIALS AND METHODS This study was performed in three phases. We first theoretically analyzed the effect of noise in phenotype prediction problems showing that it can be expressed as a modeling error that partially falsifies the pathways. Secondly, via synthetic modeling, we performed the sensitivity analysis for the main gene ranking methods to different types of noise. Finally, we studied the predictive accuracy of the gene lists provided by these ranking methods in synthetic data and in three different datasets related to cancer, rare and neurodegenerative diseases to better understand the translational aspects of our findings. RESULTS AND DISCUSSION In the case of synthetic modeling, we showed that Fishers Ratio (FR) was the most robust gene ranking method in terms of precision for all the types of noise at different levels. Significance Analysis of Microarrays (SAM) provided slightly lower performance and the rest of the methods (fold change, entropy and maximum percentile distance) were much less precise and accurate. The predictive accuracy of the smallest set of high discriminatory probes was similar for all the methods in the case of Gaussian and Log-Gaussian noise. In the case of class assignment noise, the predictive accuracy of SAM and FR is higher. Finally, for real datasets (Chronic Lymphocytic Leukemia, Inclusion Body Myositis and Amyotrophic Lateral Sclerosis) we found that FR and SAM provided the highest predictive accuracies with the smallest number of genes. Biological pathways were found with an expanded list of genes whose discriminatory power has been established via FR. CONCLUSIONS We have shown that noise in expression data and class assignment partially falsifies the sets of discriminatory probes in phenotype prediction problems. FR and SAM better exploit the principle of parsimony and are able to find subsets with less number of high discriminatory genes. The predictive accuracy and the precision are two different metrics to select the important genes, since in the presence of noise the most predictive genes do not completely coincide with those that are related to the phenotype. Based on the synthetic results, FR and SAM are recommended to unravel the biological pathways that are involved in the disease development.

Relationship of Mitochondrial Enzymes to Fatigue Intensity in Men With Prostate Cancer Receiving External Beam Radiation Therapy

Purpose: Mitochondrial dysfunction is a plausible biological mechanism for cancer-related fatigue. Specific aims of this study were to (1) describe the levels of mitochondrial oxidative phosphorylation complex (MOPC) enzymes, fatigue, and health-related quality of life (HRQOL) before and at completion of external beam radiation therapy (EBRT) in men with nonmetastatic prostate cancer (PC); (2) examine relationships over time among levels of MOPC enzymes, fatigue, and HRQOL; and (3) compare levels of MOPC enzymes in men with clinically significant and nonsignificant fatigue intensification during EBRT. Methods: Fatigue was measured by the revised Piper Fatigue Scale and the Functional Assessment of Cancer Therapy–Fatigue subscale (FACT-F). MOPC enzymes (Complexes I–V) and mitochondrial antioxidant superoxide dismutase 2 were measured in peripheral blood using enzyme-linked immunosorbent assay at baseline and completion of EBRT. Participants were categorized into high or low fatigue (HF vs. LF) intensification groups based on amount of change in FACT-F scores during EBRT. Results: Fatigue reported by the 22 participants with PC significantly worsened and HRQOL significantly declined from baseline to EBRT completion. The HF group comprised 12 men with clinically significant change in fatigue (HF) during EBRT. Although no significant changes were observed in MOPC enzymes from baseline to EBRT completion, there were important differences in the patterns in the levels of MOPC enzymes between HF and LF groups. Conclusion: Distinct patterns of changes in the absorbance of MOPC enzymes delineated fatigue intensification among participants. Further investigation using a larger sample is warranted.

Sampling Defective Pathways in Phenotype Prediction Problems via the Holdout Sampler.

In this paper, we introduce the holdout sampler to find the defective pathways in high underdetermined phenotype prediction problems. This sampling algorithm is inspired by the bootstrapping procedure used in regression analysis to established confidence bounds. We show that working with partial information (data bags) serves to sample the linear uncertainty region in a simple regression problem, mainly along the axis of greatest uncertainty that corresponds to the smallest singular value of the system matrix. This procedure applied to a phenotype prediction problem, considered as a generalized prediction problem between the set of genetic signatures and the set of classes in which the phenotype is divided, serves to unravel the ensemble of altered pathways in the transcriptome that are involved in the disease development. The algorithm looks for the minimum-scale genetic signature in each random holdout and the likelihood (predictive accuracy) is established using the validation dataset via a nearest-neighbor classifier. The posterior analysis serves to identify the header genes that most-frequently appear in the different hold-outs and are therefore robust to a partial lack of samples. These genes are used to establish the genetic pathways and the biological processes involved in the disease progression. This algorithm is much faster, robust and simpler than Bayesian Networks. We show its application to a microarray dataset concerning a type of breast cancers with poor prognoses (TNBC).

NK-cell Editing Mediates Epithelial-to-Mesenchymal Transition via Phenotypic and Proteomic Changes in Melanoma Cell Lines.

Tumor cell plasticity is a major obstacle for the cure of malignancies as it makes tumor cells highly adaptable to microenvironmental changes, enables their phenotype switching among different forms, and favors the generation of prometastatic tumor cell subsets. Phenotype switching toward more aggressive forms involves different functional, phenotypic, and morphologic changes, which are often related to the process known as epithelial-mesenchymal transition (EMT). In this study, we report natural killer (NK) cells may increase the malignancy of melanoma cells by inducing changes relevant to EMT and, more broadly, to phenotype switching from proliferative to invasive forms. In coculture, NK cells induced effects on tumor cells similar to those induced by EMT-promoting cytokines, including upregulation of stemness and EMT markers, morphologic transition, inhibition of proliferation, and increased capacity for Matrigel invasion. Most changes were dependent on the engagement of NKp30 or NKG2D and the release of cytokines including IFNγ and TNFα. Moreover, EMT induction also favored escape from NK-cell attack. Melanoma cells undergoing EMT either increased NK-protective HLA-I expression on their surface or downregulated several tumor-recognizing activating receptors on NK cells. Mass spectrometry-based proteomic analysis revealed in two different melanoma cell lines a partial overlap between proteomic profiles induced by NK cells or by EMT cytokines, indicating that various processes or pathways related to tumor progression are induced by exposure to NK cells.Significance: NK cells can induce prometastatic properties on melanoma cells that escape from killing, providing important clues to improve the efficacy of NK cells in innovative antitumor therapies. Cancer Res; 78(14); 3913-25. ©2018 AACR.

Design of Biomedical Robots for the Analysis of Cancer, Neurodegenerative and Rare Diseases

Studies of genomics make use of high throughput technology to discover and characterize genes associated with cancer and other illnesses. Genomics may be of particular value in discovering mechanisms and interventions for neurodegenerative and rare diseases in the quest for orphan drugs. To expedite risk prediction, mechanism of action and drug discovery, effectively, analytical methods, especially those that translate to clinical relevant outcomes, are highly important. In this paper, we define the term biomedical robot as a novel tool for genomic analysis in order to improve phenotype prediction, identifying disease pathogenesis and significantly defining therapeutic targets. The implementation of a biomedical robot in genomic analysis is based on the use of feature selection methods and ensemble learning techniques. Mathematically, a biomedical robot exploits the structure of the uncertainty space of any classification problem conceived as in an ill-posed optimization problem, that is, given a classifier several equivalent low scale signatures exist providing similar prediction accuracies. As an example, we applied this method to the analysis of three different expression microarrays publically available concerning Chronic Lymphocytic Leukemia, Inclusion Body Myositis/Polimyositis (IBM-PM) and Amyotrophic Lateral Sclerosis (ALS). Using these examples we showed the value of the biomedical robot concept to improve knowledge discovery and provide decision systems in order to optimize diagnosis, treatment and prognosis. The goal of the FINISTERRAE project is to leverage publically available genetic databases of rare and neurodegenerative diseases and construct a relational database with the genes and genetic pathways involved, which can be used to accelerate translational research in this domain.

Sampling Defective Pathways in Phenotype Prediction Problems via the Fisher’s Ratio Sampler

In this paper, we introduce the Fisher’s ratio sampler that serves to unravel the defective pathways in highly underdetermined phenotype prediction problems. This sampling algorithm first selects the most discriminatory genes, that are at the same time differentially expressed, and samples the high discriminatory genetic networks with a prior probability that it is proportional to their individual Fisher’s ratio. The number of genes of the different networks is randomly established taking into account the length of the minimum-scale signature of the phenotype prediction problem which is the one that contains the most discriminatory genes with the maximum predictive power. The likelihood of the different networks is established via leave-one-out-cross-validation. Finally, the posterior analysis of the most frequently sampled genes serves to establish the defective biological pathways. This novel sampling algorithm is much faster and simpler than Bayesian Networks. We show its application to a microarray dataset concerning a type of breast cancers with very bad prognosis (TNBC). In these kind of cancers, the breast cancer cells have tested negative for hormone epidermal growth factor receptor 2 (HER-2), estrogen receptors (ER), and progesterone receptors (PR). This lack causes that common treatments like hormone therapy and drugs that target estrogen, progesterone, and HER-2 are ineffective. We believe that the genetic pathways that are identified via the Fisher’s ratio sampler, which are mainly related to signaling pathways, provide new insights about the molecular mechanisms that are involved in this complex disease. The Fisher’s ratio sampler can be also applied to the genetic analysis of other complex diseases.

Comparison of Different Sampling Algorithms for Phenotype Prediction

In this paper, we compare different sampling algorithms used for identifying the defective pathways in highly underdetermined phenotype prediction problems. The first algorithm (Fisher’s ratio sampler) selects the most discriminatory genes and samples the high discriminatory genetic networks according to a prior probability that it is proportional to their individual Fisher’s ratio. The second one (holdout sampler) is inspired by the bootstrapping procedure used in regression analysis and uses the minimum-scale signatures found in different random hold outs to establish the most frequently sampled genes. The third one is a pure random sampler which randomly builds networks of differentially expressed genes. In all these algorithms, the likelihood of the different networks is established via leave one out cross-validation (LOOCV), and the posterior analysis of the most frequently sampled genes serves to establish the altered biological pathways. These algorithms are compared to the results obtained via Bayesian Networks (BNs). We show the application of these algorithms to a microarray dataset concerning Triple Negative Breast Cancers. This comparison shows that the Random, Fisher’s ratio and Holdout samplers are most effective than BNs, and all provide similar insights about the genetic mechanisms that are involved in this disease. Therefore, it can be concluded that all these samplers are good alternatives to Bayesian Networks which much lower computational demands. Besides this analysis confirms the insight that the altered pathways should be independent of the sampling methodology and the classifier that is used to infer them.

mGluR5 mediates post-radiotherapy fatigue development in cancer patients

Cancer-related fatigue (CRF) is a common burden in cancer patients and little is known about its underlying mechanism. The primary aim of this study was to identify gene signatures predictive of post-radiotherapy fatigue in prostate cancer patients. We employed Fisher Linear Discriminant Analysis (LDA) to identify predictive genes using whole genome microarray data from 36 men with prostate cancer. Ingenuity Pathway Analysis was used to determine functional networks of the predictive genes. Functional validation was performed using a T lymphocyte cell line, Jurkat E6.1. Cells were pretreated with metabotropic glutamate receptor 5 (mGluR5) agonist (DHPG), antagonist (MPEP), or control (PBS) for 20 min before irradiation at 8 Gy in a Mark-1 γ-irradiator. NF-κB activation was assessed using a NF-κB/Jurkat/GFP Transcriptional Reporter Cell Line. LDA achieved 83.3% accuracy in predicting post-radiotherapy fatigue. “Glutamate receptor signaling” was the most significant (p = 0.0002) pathway among the predictive genes. Functional validation using Jurkat cells revealed clustering of mGluR5 receptors as well as increased regulated on activation, normal T cell expressed and secreted (RANTES) production post irradiation in cells pretreated with DHPG, whereas inhibition of mGluR5 activity with MPEP decreased RANTES concentration after irradiation. DHPG pretreatment amplified irradiation-induced NF-κB activation suggesting a role of mGluR5 in modulating T cell activation after irradiation. These results suggest that mGluR5 signaling in T cells may play a key role in the development of chronic inflammation resulting in fatigue and contribute to individual differences in immune responses to radiation. Moreover, modulating mGluR5 provides a novel therapeutic option to treat CRF.