Pharmacoproteomic characterisation of human colon and rectal cancer

Most molecular cancer therapies act on protein targets but data on the proteome status of patients and cellular models for proteome-guided pre-clinical drug sensitivity studies are only beginning to emerge. Here, we profiled the proteomes of 65 colorectal cancer (CRC) cell lines to a depth of > 10,000 proteins using mass spectrometry. Integration with proteomes of 90 CRC patients and matched transcriptomics data defined integrated CRC subtypes, highlighting cell lines representative of each tumour subtype. Modelling the responses of 52 CRC cell lines to 577 drugs as a function of proteome profiles enabled predicting drug sensitivity for cell lines and patients. Among many novel associations, MERTK was identified as a predictive marker for resistance towards MEK1/2 inhibitors and immunohistochemistry of 1,074 CRC tumours confirmed MERTK as a prognostic survival marker. We provide the proteomic and pharmacological data as a resource to the community to, for example, facilitate the design of innovative prospective clinical trials. © 2017 The Authors. Published under the terms of the CC BY 4.0 licens

Gene expression and immunohistochemical analyses identify SOX2 as major risk factor for overall survival and relapse in Ewing sarcoma patients

BACKGROUND: Up to 30-40% of Ewing sarcoma (EwS) patients with non-metastatic disease develop local or metastatic relapse within a time span of 2-10 years. This is in part caused by the absence of prognostic biomarkers that can identify high-risk patients and thus assign them to risk-adapted monitoring and treatment regimens. Since cancer stemness has been associated with tumour relapse and poor patient outcomes, we investigated in the current study the prognostic potential SOX2 (sex determining region Y box 2) - a major transcription factor involved in development and stemness - which was previously described to contribute to the undifferentiated phenotype of EwS. METHODS: Two independent patient cohorts, one consisting of 189 retrospectively collected EwS tumours with corresponding mRNA expression data (test-cohort) and the other consisting of 141 prospectively collected formalin-fixed and paraffin-embedded resected tumours (validation and cohort), were employed to analyse SOX2 expression levels through DNA microarrays or immunohistochemistry, respectively, and to compare them with clinical parameters and patient outcomes. Two methods were employed to test the validity of the results at both the mRNA and protein levels. FINDINGS: Both cohorts showed that only a subset of EwS patients (16-20%) expressed high SOX2 mRNA or protein levels, which significantly correlated with poor overall survival. Multivariate analyses of our validation-cohort revealed that high SOX2 expression represents a major risk-factor for poor survival (HR = 3·19; 95%CI 1·74-5·84; p < 0·01) that is independent from metastasis and other known clinical risk-factors at the time of diagnosis. Univariate analyses demonstrated that SOX2-high expression was correlated with tumour relapse (p = 0·002). The median first relapse was at 14·7 months (range: 3·5-180·7). INTERPRETATION: High SOX2 expression constitutes an independent prognostic biomarker for EwS patients with poor outcomes. This may help to identify patients with localised disease who are at high risk for tumour relapse within the first two years after diagnosis. FUNDING: The laboratory of T. G. P. Grünewald is supported by grants from the 'Verein zur Förderung von Wissenschaft und Forschung an der Medizinischen Fakultät der LMU München (WiFoMed)', by LMU Munich's Institutional Strategy LMUexcellent within the framework of the German Excellence Initiative, the 'Mehr LEBEN für krebskranke Kinder - Bettina-Bräu-Stiftung', the Walter Schulz Foundation, the Wilhelm Sander-Foundation (2016.167.1), the Friedrich-Baur foundation, the Matthias-Lackas foundation, the Barbara & Hubertus Trettner foundation, the Dr. Leopold & Carmen Ellinger foundation, the Gert & Susanna Mayer foundation, the Deutsche Forschungsgemeinschaft (DFG 391665916), and by the German Cancer Aid (DKH-111886 and DKH-70112257). J. Li was supported by a scholarship of the China Scholarship Council (CSC), J. Musa was supported by a scholarship of the Kind-Philipp foundation, and T. L. B. Hölting by a scholarship of the German Cancer Aid. M. F. Orth and M. M. L. Knott were supported by scholarships of the German National Academic Foundation. G. Sannino was supported by a scholarship from the Fritz-Thyssen Foundation (FTF-40.15.0.030MN). The work of U. Dirksen is supported by grants from the German Cancer Aid (DKH-108128, DKH-70112018, and DKH-70113419), the ERA-Net-TRANSCAN consortium (project number 01KT1310), and Euro Ewing Consortium (EEC, project number EU-FP7 602,856), both funded under the European Commission Seventh Framework Program FP7-HEALTH (http://cordis.europa.eu/), the Barbara & Hubertus Trettner foundation, and the Gert & Susanna Mayer foundation. G. Hardiman was supported by grants from the National Science Foundation (SC EPSCoR) and National Institutes of Health (U01-DA045300). The laboratory of J. Alonso was supported by Instituto de Salud Carlos III (PI12/00816; PI16CIII/00026); Asociación Pablo Ugarte (TPY-M 1149/13; TRPV 205/18), ASION (TVP 141/17), Fundación Sonrisa de Alex & Todos somos Iván (TVP 1324/15).The laboratory of T. G. P. Grünewald is supported by grants from the ‘Verein zur Förderung von Wissenschaft und Forschung an der Medizinischen Fakultät der LMU München (WiFoMed)’, by LMU Munich's Institutional Strategy LMUexcellent within the framework of the German Excellence Initiative, the ‘Mehr LEBEN für krebskranke Kinder – Bettina-Bräu-Stiftung’, the Walter Schulz Foundation, the Wilhelm Sander-Foundation (2016.167.1), the Friedrich-Baur foundation, the Matthias-Lackas foundation, the Barbara & Hubertus Trettner foundation, the Dr. Leopold und Carmen Ellinger foundation, the Gert & Susanna Mayer foundation, the Rolf M. Schwiete foundation, the Deutsche Forschungsgemeinschaft (DFG 391665916), and by the German Cancer Aid (DKH-111886 and DKH-70112257). J. Li was supported by a scholarship of the China Scholarship Council (CSC), J. Musa was supported by a scholarship of the Kind-Philipp foundation, and T. L. B. Hölting by a scholarship of the German Cancer Aid. M. F. Orth and M. M. L. Knott were supported by scholarships of the German National Academic Foundation. G. Sannino was supported from a scholarship from the Fritz-Thyssen Foundation (FTF-40.15.0.030MN). The work of U. Dirksen is supported by grants from the German Cancerr Aid (DKH-108128, DKH-70112018, and DKH-70113419), the ERA-Net-TRANSCAN consortium (project number 01KT1310), and Euro Ewing Consortium (EEC, project number EU-FP7 602856), both funded under the European Commission Seventh Framework Program FP7-HEALTH (http://cordis.europa.eu/), the Barbara & Hubertus Trettner foundation, and the Gert & Susanna Mayer foundation. G. Hardiman was supported by grants from the National Science Foundation (SC EPSCoR) and National Institutes of Health (U01-DA045300). The laboratory of J. Alonso was supported by Instituto de Salud Carlos III (PI12/00816; PI16CIII/00026); Asociación Pablo Ugarte (TPY-M 1149/13; TRPV 205/18), ASION (TVP 141/17), Fundación Sonrisa de Alex & Todos somos Iván (TVP 1324/15).S

DBnorm as an R package for the comparison and selection of appropriate statistical methods for batch effect correction in metabolomic studies.

As a powerful phenotyping technology, metabolomics provides new opportunities in biomarker discovery through metabolome-wide association studies (MWAS) and the identification of metabolites having a regulatory effect in various biological processes. While mass spectrometry-based (MS) metabolomics assays are endowed with high throughput and sensitivity, MWAS are doomed to long-term data acquisition generating an overtime-analytical signal drift that can hinder the uncovering of real biologically relevant changes. We developed "dbnorm", a package in the R environment, which allows for an easy comparison of the model performance of advanced statistical tools commonly used in metabolomics to remove batch effects from large metabolomics datasets. "dbnorm" integrates advanced statistical tools to inspect the dataset structure not only at the macroscopic (sample batches) scale, but also at the microscopic (metabolic features) level. To compare the model performance on data correction, "dbnorm" assigns a score that help users identify the best fitting model for each dataset. In this study, we applied "dbnorm" to two large-scale metabolomics datasets as a proof of concept. We demonstrate that "dbnorm" allows for the accurate selection of the most appropriate statistical tool to efficiently remove the overtime signal drift and to focus on the relevant biological components of complex datasets

Genes and Gene Networks Related to Age-associated Learning Impairments

The incidence of cognitive impairments, including age-associated spatial learning impairment (ASLI), has risen dramatically in past decades due to increasing human longevity. To better understand the genes and gene networks involved in ASLI, data from a number of past gene expression microarray studies in rats are integrated and used to perform a meta- and network analysis. Results from the data selection and preprocessing steps show that for effective downstream analysis to take place both batch effects and outlier samples must be properly removed. The meta-analysis undertaken in this research has identified significant differentially expressed genes across both age and ASLI in rats. Knowledge based gene network analysis shows that these genes affect many key functions and pathways in aged compared to young rats. The resulting changes might manifest as various neurodegenerative diseases/disorders or syndromic memory impairments at old age. Other changes might result in altered synaptic plasticity, thereby leading to normal, non-syndromic learning impairments such as ASLI. Next, I employ the weighted gene co-expression network analysis (WGCNA) on the datasets. I identify several reproducible network modules each highly significant with genes functioning in specific biological functional categories. It identifies a “learning and memory” specific module containing many potential key ASLI hub genes. Functions of these ASLI hub genes link a different set of mechanisms to learning and memory formation, which meta-analysis was unable to detect. This study generates some new hypotheses related to the new candidate genes and networks in ASLI, which could be investigated through future research

Clinical biomarkers of response to neoadjuvant endocrine therapy in breast cancer: exploring the potential of gene expression data integration

Introduction Aromatase inhibitors (AIs) have an established role in the treatment of estrogen receptor alpha positive (ER+) post-menopausal breast cancer. However, response rates are only 50-70% in the neoadjuvant setting and lower in advanced disease. There is a need to identify pre- or early on-treatment biomarkers to predict sensitivity which outperform those currently used, in a move towards stratified treatments and improved patient care. Given the heterogeneity known to exist in the breast cancer population, and the limited availability of matched pre- and on-treatment clinical material, this study also sought to develop novel data integration approaches allowing for the inclusion of similar previously published datasets, thus maximising the power of this study. Experimental Design Pre- and on-treatment (at 14 days and 3-months) biopsies were obtained from 34 postmenopausal women with ER+ breast cancer receiving 3 months of neoadjuvant letrozole. Illumina Beadarray gene expression data from these samples were combined with Affymetrix GeneChip data from a similar published study (n=55) and crossplatform integration approaches were evaluated. Dynamic clinical response was assessed for each patient from periodic 3D ultrasound measurements during treatment. Results Despite intrinsic differences between different microarray technologies, suitably similar studies can be directly integrated for robust and meaningful meta-analysis with improved statistical power. After mapping probe sequences to Ensembl genes it was demonstrated that, ComBat and cross platform normalisation (XPN), significantly outperform mean-centering and distance-weighted discrimination (DWD) in terms of minimising inter-platform variance. In particular it was observed that DWD, a popular method used in a number of previous studies, removed systematic bias at the expense of genuine biological variability, potentially reducing legitimate biological differences from integrated datasets. A pipeline for the successful integration of microarray datasets from different platforms was developed. Using this approach a classifier of clinical response to endocrine therapy in the neoadjuvant setting based on the expression of 4 genes was developed which predicted response with 96% and 91% accuracy in training (n=73) and independent validation (n=44) datasets respectively. An early on-treatment biopsy was found to improve predictive power in addition to pre-treatment alone. Conclusions Using a novel data integration approach developed as part of this study, a model comprising 4 novel biomarkers for accurate and robust prediction of clinical response to AIs by two weeks of treatment has been generated and validated. On-going work will investigate the applicability to other anti-estrogens, and the adjuvant setting and will assess the potential for a new therapy response test

Multiplexed affinity peptidomic assays: multiplexing and applications for testing protein biomarkers

Biomarkers are increasingly used in a wide range of areas such as sports and clinical diagnostics, biometric applications, forensic analysis and population screening. Testing for such biomarkers requires substantial resources and has traditionally involved centralised laboratory testing. From cancer diagnosis to COVID testing, there is an increasing demand for protein based assays that are portable, easy to use and ideally multiplexed, so that more than one biomarker can be tested at the same time, thus increasing the throughput and reducing time of the analysis and potentially the costs. Events in recent years, not least the ongoing investigations into claims of widespread state-sponsored doping schemes in sport and the COVID-19 pandemic of 2020 highlight the ever-growing requirement and importance of such tests across multiple frontiers. The project evaluated the feasibility of new antipeptide affinity reagents and suitable technologies for application to multiplexed affinity assays geared towards quantitatively analysing a range of analytes. In the first part of this project, key protein biomarkers available from blood serum and covering a range of conditions including cancer, inflammation, and various behavioural traits were chosen from the literature. Peptide antigens for the development of antipeptide polyclonal antibodies for each protein were selected following in silico proteolysis and ranking of the peptides using an algorithm devised as part of this research. A microarray format was used to achieve spatial multiplexing and increase throughput of the assays. The arrays were evaluated experimentally and were tested for their usability for studying up/down regulation of the target biomarkers in human sera samples. Another protein assay format tested for compatibility with affinity peptidomics approach was a gold nanoparticle based lateral flow test. An affinity-based lateral flow test device was built and used for the detection of the benzodiazepine Valium. Here spectral multiplexing of detection was considered. The principle was tested using quantum dot nanoparticles instead of traditionally used gold nanoparticles. The spectral deconvolution was achieved for mixtures containing up to six differently sized quantum dots. In the final part of this project, a search for novel peptide affinity reagents against insulin growth-like factor 1 (IGF-1) was conducted using phage display. Four peptides were identified after screening a phage display library, and the binding of these peptides to IGF-1 was compared to that of traditional antibody

Molecular and computational approach to the link between nutrition and cancer

Tesis Doctoral inédita leída en la Universidad Autónoma de Madrid, Facultad de Ciencias, Departamento de Química Física Aplicada. Fecha de lectura: 22-11-201

Data harmonisation for information fusion in digital healthcare: A state-of-the-art systematic review, meta-analysis and future research directions.

Removing the bias and variance of multicentre data has always been a challenge in large scale digital healthcare studies, which requires the ability to integrate clinical features extracted from data acquired by different scanners and protocols to improve stability and robustness. Previous studies have described various computational approaches to fuse single modality multicentre datasets. However, these surveys rarely focused on evaluation metrics and lacked a checklist for computational data harmonisation studies. In this systematic review, we summarise the computational data harmonisation approaches for multi-modality data in the digital healthcare field, including harmonisation strategies and evaluation metrics based on different theories. In addition, a comprehensive checklist that summarises common practices for data harmonisation studies is proposed to guide researchers to report their research findings more effectively. Last but not least, flowcharts presenting possible ways for methodology and metric selection are proposed and the limitations of different methods have been surveyed for future research