Jose Miguel Sanchez

Algorithm to find gene expression profiles of deregulation and identify families of disease-altered genes

MOTIVATION Alteration of gene expression often results in up- or down-regulated genes and the most common analysis strategies look for such differentially expressed genes. However, molecular disease mechanisms typically constitute abnormalities in the regulation of genes producing strong alterations in the expression levels. The search for such deregulation states in the genomic expression profiles will help to identify disease-altered genes better. RESULTS We have developed an algorithm that searches for the genes which present a significant alteration in the variability of their expression profiles, by comparing an altered state with a control state. The algorithm provides groups of genes and assigns a statistical measure of significance to each group of genes selected. The method also includes a prefilter tool to select genes with a threshold of differential expression that can be set by the user ad casum. The method is evaluated using an experimental set of microarrays of human control and cancer samples from patients with acute promyelocytic leukemia.

Strategies to improve usability and preserve accuracy in biological sequence databases

Biology is increasingly dependent on large‐scale analysis, such as proteomics, creating a requirement for efficient bioinformatics. Bioinformatic predictions of biological functions rely upon correctly annotated database sequences, and the presence of inaccurately annotated or otherwise poorly described sequences introduces noise and bias to biological analyses. Accurate annotations are, for example, pivotal for correct identification of polypeptide fragments. However, standards for how sequence databases are organized and presented are currently insufficient. Here, we propose five strategies to address fundamental issues in the annotation of sequence databases: (i) to clearly separate experimentally verified and unverified sequence entries; (ii) to enable a system for tracing the origins of annotations; (iii) to separate entries with high‐quality, informative annotation from less useful ones; (iv) to integrate automated quality‐control software whenever such tools exist; and (v) to facilitate postsubmission editing of annotations and metadata associated with sequences. We believe that implementation of these strategies, for example as requirements for publication of database papers, would enable biology to better take advantage of large‐scale data.

Efficient exploration of pan-cancer networks by generalized covariance selection and interactive web content

Statistical network modeling techniques are increasingly important tools to analyze cancer genomics data. However, current tools and resources are not designed to work across multiple diagnoses and technical platforms, thus limiting their applicability to comprehensive pan-cancer datasets such as The Cancer Genome Atlas (TCGA). To address this, we describe a new data driven modeling method, based on generalized Sparse Inverse Covariance Selection (SICS). The method integrates genetic, epigenetic and transcriptional data from multiple cancers, to define links that are present in multiple cancers, a subset of cancers, or a single cancer. It is shown to be statistically robust and effective at detecting direct pathway links in data from TCGA. To facilitate interpretation of the results, we introduce a publicly accessible tool (cancerlandscapes.org), in which the derived networks are explored as interactive web content, linked to several pathway and pharmacological databases. To evaluate the performance of the method, we constructed a model for eight TCGA cancers, using data from 3900 patients. The model rediscovered known mechanisms and contained interesting predictions. Possible applications include prediction of regulatory relationships, comparison of network modules across multiple forms of cancer and identification of drug targets.

System-Scale Network Modeling of Cancer Using EPoC

One of the central problems of cancer systems biology is to understand the complex molecular changes of cancerous cells and tissues, and use this understanding to support the development of new targeted therapies. EPoC (Endogenous Perturbation analysis of Cancer) is a network modeling technique for tumor molecular profiles. EPoC models are constructed from combined copy number aberration (CNA) and mRNA data and aim to (1) identify genes whose copy number aberrations significantly affect target mRNA expression and (2) generate markers for long- and short-term survival of cancer patients. Models are constructed by a combination of regression and bootstrapping methods. Prognostic scores are obtained from a singular value decomposition of the networks. We have previously analyzed the performance of EPoC using glioblastoma data from The Cancer Genome Atlas (TCGA) consortium, and have shown that resulting network models contain both known and candidate disease-relevant genes as network hubs, as well as uncover predictors of patient survival. Here, we give a practical guide how to perform EPoC modeling in practice using R, and present a set of alternative modeling frameworks.

Abstract 265: Cotargeting EGFR, STAT3 and Src-Notch pathways: a promising approach to improve the efficacy of EGFR-TKIs in the treatment of NSCLC patients

Intrinsic or acquired resistance limits the clinical effectiveness of EGFR tyrosine kinase inhibitors (TKIs) for non-small cell lung cancer (NSCLC) patients (p) with EGFR mutations. One of the signaling mediators downstream of activated EGFR is signal transducer and activator of transcription 3 (STAT3). Not only does gefitinib not inhibit STAT3, but it also augments STAT3 tyrosine phosphorylation. EGFR blockade enriches lung cancer stem cells (CSCs) through NOTCH3-dependent signaling. A co-receptor of IL-6 (gp130) associates with Src and triggers activation of YAP and NOTCH. Our study is designed with three parallel objectives: firstly, to demonstrate that single EGFR TKI treatment cannot abrogate STAT3 and Src in EGFR mutant NSCLC cell lines; secondly, to examine whether the combination of gefitinib with compounds that target STAT3, (TPCA-1) and Src (saracatinib), suppresses the mechanisms of resistance; thirdly, to identify biomarkers in clinical tumor samples that may help us predict the outcome of EGFR TKIs and design effective combination therapies. Cell viability assay (MTT), western blotting, quantitative-real time PCR (qRT-PCR) and aldefluor assay-flow cytometry were used. We found that gefitinib increases pSTAT3 Y705 in PC-9 cells (that harbor the exon 19 deletion) in a time- and dose-dependent manner. Nine days after gefitinib treatment STAT3 mRNA level was significantly elevated. PC-9 cells showed dramatic increase in the fraction of ALDH+ cells upon treatment with gefitinib. TPCA-1 increased sensitivity to gefitinib in the PC-9 cells. Combination of gefitinib with TPCA-1 abrogated pSTAT3 Y705 but neither inhibited pPaxillin Y118 (Src induced) and pYAP S127 nor prevented the increment in the ALDH+ CSCs subpopulation. The triple combination of gefitinib, TPCA-1 and saracatinib was highly synergistic and abrogated pSTAT3 Y705, pPaxillin Y118 and pYAP S127. We performed qRT-PCR at baseline tumor samples of 64 EGFR mutant NSCLC p treated with first line EGFR TKIs and found that high expression of STAT3 and YAP were significantly correlated with shorter median progression-free survival (mPFS). mPFS was 9.6 months (m) (95% CI, 5.9 to 14.1) for p with low STAT3 and 18.4m (95% CI, 8.8 to 30.2) for p with high STAT3 mRNA expression (P Citation Format: Niki Karachaliou, Imane Chaib, Sara Pilotto, Jordi Codony, Xueting Cai, Xuefei Li, Ana Drozdowskyj, Carles Codony, Andres Felipe Cardona, Guillermo Lopez-Vivanco, Alain Vergnenegre, Jose Miguel Sanchez, Mariano Provencio, Filippo de Marinis, Enric Carcereny, Noemi Reguart, Rosario Garcia-Campelo, Silvia Marin, Cristina Teixido, Isabella Sperduti, Sonia Rodriguez, Roger Estrada, Raimon Puig de la Bellacasa, Jose Luis Ramirez, Miguel Angel Molina-Vila, Caicun Zhou, Peng Cao, Patrick Ma, Trever Bivona, Rafael Rosell. Cotargeting EGFR, STAT3 and Src-Notch pathways: a promising approach to improve the efficacy of EGFR-TKIs in the treatment of NSCLC patients. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 265.

Abstract B2-35: Efficient exploration of multi-cancer networks by generalized covariance selection and interactive web content

Statistical network modeling techniques are increasingly important tools to analyze cancer genomics data. However, current tools for network construction and interpretation are not designed to work across multiple diagnoses and technical platforms, thus limiting their applicability to comprehensive pan-cancer datasets such as the Cancer Genome Atlas (TCGA). Here, we describe a novel strategy to construct and analyze integrative network models heterogeneous data from multiple cancers. First, we introduce a generalization of sparse inverse covariance selection (SICS) designed to integrate genetic, epigenetic and transcriptional data from multiple cancers into a comparative network. The algorithm is shown to be statistically robust, effective at detecting direct pathway links in data from The Cancer Genome Atlas (TCGA), and uses a new strategy involving non-informative priors to balance different cancers and data types. Second, we propose to rationalize the interpretation of the derived networks by a new and publicly accessible tool (cancerlandscapes.org), in which derived models are explored as interactive web content, linked to several pathway and pharmacological databases. To evaluate the performance of the method, we constructed a model of genetic, epigenetic and transcriptional data for eight TCGA cancers, using data from 3900 patients. The derived model rediscovered known mechanisms and contained interesting predictions. Possible applications include the prediction of regulatory relationships between genes in particular cancers, comparison of network modules in across multiple forms of cancer, and identification of drug targets in relation to network structure. Citation Format: Teresia Kling, Patrik Johansson, Jose Sanchez, Voichita D. Marinescu, Rebecka Jornsten, Sven Nelander. Efficient exploration of multi-cancer networks by generalized covariance selection and interactive web content. [abstract]. In: Proceedings of the AACR Special Conference on Computational and Systems Biology of Cancer; Feb 8-11 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 2):Abstract nr B2-35.

SCAT trial: Phase III Spanish customized adjuvant treatment according BRCA1 mRNA levels in stage II-IIIA non-small cell lung cancer.

TPS208 Background: Postop platinum-based CT improves outcomes in completely resected NSCLC with nodal involvement, (St II-IIIA). Standard adjuvant CT is still insufficient to provide optimal survival . Improvement could be obtained through better risk selection or customized therapy. mRNA BRCA1 levels are prognostic in early NSCLC (Rosell et al 2007) and could be a predictive marker for CT activity. BRCA1 expression level could acts as a differential modulator of CT. In advanced disease p with low BRCA1 benefit from cisplatin-doublets meanwhile p with high levels BRCA1 attained longer survival when were treated with taxanes (Rosell et al 2008). Pharmacogenomic customization is feasible in adjuvant setting due to tissue availability and timelines after surgery. Previous pilot trial p with high-expression levels treated with postoperative CT singel agent taxane do not showed inferior outcomes compared to the groups treated with cisplatin-doublets (Massutí et al 2009 WLCC). METHODS Phase III prospective randomized trial of 4 cycles selected vs non-selected adjuvant CT. Eligibility criteria: NSCLC, complete R0 resection, pN1 or pN2, KI > 70, recovered from surgery, ANC > 1,500/µL, Hb > 10 g/dL, platelets > 100,000/µL, bilirrubin < 1.0 mg/dL, GOT & GPT < 1.5 x UNL, creatinine clearance > 60 mL/min, no prior RT or CT, age > 18 y, signed informed consent. Stratification factors: N1 vs N2; Histology (squamous vs non-squamous), extent of resection (lobectomy vs pneumonectomy). Central lab for measurement of mRNA BRCA1 levels and quartile distribution. Primary end-point: Disease free survival. Secondary end-points: Survival, Toxicity profiles, Recurrence pattern. Tissue and serum samples collected for Translational research. Statistical hypothesis: Randomization: 1:3 (control/experimental arms); Accrual: 432 patients; 5-year survival rate 45% control group; Absolute improvement of 20% in experimental group; 80% powered; 2-sided type I error of 5%; Anticipated loss of 10% cases. Trial design is shown in the table. [Table: see text].

DAB2 interactive protein (DAB2IP) methylation in serum DNA of non-small cell lung cancer (NSCLC) patients (p) with epidermal growth factor receptor (EGFR) mutations.

7593 Background: DAB2IP loss promotes primary tumor growth by activating Ras and drives metastasis through NFkB, serving as a signaling scaffold to coordinately regulate these pathways. DAB2IP is frequently methylated in lung cancer, and methylation in the m2a region is a key regulatory factor for DAB2IP expression in prostate cancer. We examined DAB2IP methylation in cell lines and in serum from erlotinib-treated NSCLC p with EGFR mutations. Methods: In human lung, breast and colorectal cancer cell lines, we analyzed DAB2IP promoter methylation in regions m2a and m2b by methylation-specific PCR (MSP) and bisulfite genomic sequencing. In circulating serum DNA from 152 erlotinib-treated NSCLC p with EGFR mutations, we analyzed methylation in the m2a and m2b promoter regions of DAB2IP by MSP. Methylation status was correlated with clinical outcome. Results: Methylation was detected in the m2a region of 42 (27.63%) p, and in the m2b region in 51 (33.55%) p. There were no major differences in clinical charact...