François Fauteux

Computational selection of antibody-drug conjugate targets for breast cancer

The selection of therapeutic targets is a critical aspect of antibody-drug conjugate research and development. In this study, we applied computational methods to select candidate targets overexpressed in three major breast cancer subtypes as compared with a range of vital organs and tissues. Microarray data corresponding to over 8,000 tissue samples were collected from the public domain. Breast cancer samples were classified into molecular subtypes using an iterative ensemble approach combining six classification algorithms and three feature selection techniques, including a novel kernel density-based method. This feature selection method was used in conjunction with differential expression and subcellular localization information to assemble a primary list of targets. A total of 50 cell membrane targets were identified, including one target for which an antibody-drug conjugate is in clinical use, and six targets for which antibody-drug conjugates are in clinical trials for the treatment of breast cancer and other solid tumors. In addition, 50 extracellular proteins were identified as potential targets for non-internalizing strategies and alternative modalities. Candidate targets linked with the epithelial-to-mesenchymal transition were identified by analyzing differential gene expression in epithelial and mesenchymal tumor-derived cell lines. Overall, these results show that mining human gene expression data has the power to select and prioritize breast cancer antibody-drug conjugate targets, and the potential to lead to new and more effective cancer therapeutics.

Glycoproteomic Comparison of Clinical Triple-Negative and Luminal Breast Tumors

Triple-negative (TN) breast cancer accounts for ∼ 15% of breast cancers and is characterized by a high likelihood of relapse and a lack of targeted therapies. In contrast, luminal-type tumors that express the estrogen and progesterone receptors (ER+/PR+) and lack expression of human epidermal growth factor receptor 2 (Her2-) are treated with targeted hormonal therapy and carry a better prognosis. To identify potential targets for the development of future therapeutics aimed specifically at TN breast cancers, we have used a hydrazide-based glycoproteomic workflow to compare protein expression in clinical tumors from nine TN (Her2-/ER-/PR-) and nine luminal (Her2-/ER+/PR+) patients. Using a label-free LC-MS based approach, we identified and quantified 2264 proteins. Of these, 90 proteins were more highly expressed and 86 proteins were underexpressed in the TN tumors relative to the luminal tumors. The expression level of four of these potential targets was verified in the original set of tumors by Western blot and correlated well with our mass-spectrometry-based quantification. Furthermore, 30% of the proteins differentially expressed between luminal and TN tumors were validated in a larger cohort of 406 TN and 469 luminal tumors through corresponding differences in their mRNA expression in publically available microarray data. A group of 29 of these differentially expressed proteins was shown to correctly classify 88% of TN and luminal tumors using microarray data of their associated mRNA levels. Interestingly, even within a group of TN breast cancer patients, the expression levels of these same mRNAs were able to significantly predict patient survival, suggesting that these proteins play a role in the aggressiveness seen in TN tumors. This study provides a comprehensive list of potential targets for the development of diagnostic and therapeutic agents specifically aimed at treating TN breast cancer and demonstrates the utility of using publicly available microarray data to further prioritize potential targets.

Bioinformatics identification of new targets for improving low temperature stress tolerance in spring and winter wheat

BackgroundPhenotypic studies in Triticeae have shown that low temperature-induced protective mechanisms are developmentally regulated and involve dynamic acclimation processes. Understanding these mechanisms is important for breeding cold-resistant wheat cultivars. In this study, we combined three computational techniques for the analysis of gene expression data from spring and winter wheat cultivars subjected to low temperature treatments. Our main objective was to construct a comprehensive network of cold response transcriptional events in wheat, and to identify novel cold tolerance candidate genes in wheat.ResultsWe assigned novel cold stress-related roles to 35 wheat genes, uncovered novel transcription (TF)-gene interactions, and identified 127 genes representing known and novel candidate targets associated with cold tolerance in wheat. Our results also show that delays in terms of activation or repression of the same genes across wheat cultivars play key roles in phenotypic differences among winter and spring wheat cultivars, and adaptation to low temperature stress, cold shock and cold acclimation.ConclusionsUsing three computational approaches, we identified novel putative cold-response genes and TF-gene interactions. These results provide new insights into the complex mechanisms regulating the expression of cold-responsive genes in wheat.

Data integration and knowledge discovery in life sciences

Recent advances in various forms of omics technologies have generated huge amount of data. To fully exploit these data sets that in many cases are publicly available, robust computational methodologies need to be developed to deal with the storage, integration, analysis, visualization, and dissemination of these data. In this paper, we describe some of our research activities in data integration leading to novel knowledge discovery in life sciences. Our multistrategy approach with integration of prior knowledge facilitates a novel means to identify informative genes that could have been missed by the commonly used methods. Our transcriptomics-proteomics integrative framework serves as a means to enhance the confidence of and also to complement transcriptomics discovery. Our new research direction in integrative data analysis of omics data is targeted to identify molecular associations to disease and therapeutic response signatures. The ultimate goal of this research is to facilitate the development of clinical test-kits for early detection, accurate diagnosis/prognosis of disease, and better personalized therapeutic management.

Digitization of trait representation in microarray data analysis of wheat infected by fusarium graminearum

Fusarium head blight (FHB) limits wheat yield and compromises grain quality. We investigated differentially expressed genes after FHB challenge. FHB-susceptible and -resistant common wheat (Triticum aestivum) cultivars were challenged with the toxigenic fungus Fusarium graminearum and gene expression was analyzed using 61K Affymetrix wheat microarrays. We digitized trait specificity in the susceptible and resistant lines with and without the infection in order to facilitate subsequent data mining. We discovered various patterns of differential gene expression between susceptible and resistant lines in response to the infection. We performed association network analysis among genes in clusters significantly correlated with one or more quantitative trait loci known to contribute to Fusarium resistance. We found 11 interconnected hub genes responsive to FHB infection and significantly correlated with wheat resistance to FHB, among which two are predicted to encode a polygalacturonase-inhibiting protein (PGIP1).

Personalized prediction of genes with tumor-causing somatic mutations based on multi-modal deep Boltzmann machine

Abstract When diagnosed at an advanced stage, most cancer patients suffer from treatment failure, recurrences and low survival. Taking advantage of high-throughput sequencing and deep learning techniques, we developed an early cancer monitoring method based on multi-modal deep Boltzmann machine to (1) learn association between matched germline and somatic mutations captured by whole exome sequencing from available samples of cancer patients, and (2) predict patient-specific high-risk genes whose somatic mutations are required to drive normal tissues to a tumor state. Our experiments on a set of breast cancer samples show that our method significantly outperforms the currently used frequency-based method in the personalized prediction of genes carrying critical mutations.

Chemical Derivatization Strategy for Extending the Identification of MHC Class I Immunopeptides

Neoantigen-based therapeutic vaccines have a high potential impact on tumor eradication and patient survival. Mass spectrometry (MS)-based immunopeptidomics has the capacity to identify tumor-associated epitopes and pinpoint mutation-bearing major histocompatibility complex (MHC)-binding peptides. This approach presents several challenges, including the identification of low-abundance peptides. In addition, MHC peptides have much lower MS/MS identification rates than tryptic peptides due to their shorter sequence and lack of basic amino acid at C-termini. In this study, we report the development and application of a novel chemical derivatization strategy that combines the analysis of native, dimethylated, and alkylamidated peptides by liquid chromatography-tandem mass spectrometry (LC-MS/MS) to expand the coverage of the MHC peptidome. The results revealed that dimethylation increases hydrophobicity and ionization efficiency of MHC class I peptides, while alkylamidation significantly improves the fragmentation by producing more y-ions during MS/MS fragmentation. Thus, the combination of dimethylation and alkylamidation enabled the identification of peptides that could not be identified from the analysis of their native form. Using this strategy, we identified 3148 unique MHC I peptides from HCT 116 cell lines, compared to only 1388 peptides identified in their native form. Among these, 10 mutation-bearing peptides were identified with high confidence, indicating that this chemical derivatization strategy is a promising approach for neoantigen discovery in clinical applications.

Abstract 3669: Screening platform for development of antibody-drug conjugates against novel targets at the National Research Council of Canada

One of the most promising of the next generation of biologic-based cancer therapeutics builds on the molecular targeting abilities of antibodies by combining them with drugs to generate highly specific antibody-drug conjugates (ADCs). However, the development of ADCs requires time-consuming selection of the antibody for every target and cancer type. High-throughput screening technologies based on the use of conjugated secondary antibodies provide a fast and efficient surrogate assay from which to identify which antibodies are best internalized and suitable for immunoconjugate development into ADCs. As part of its integrated antibody development initiative, NRC has isolated and characterized anti- mouse Fc and anti-human Fc monoclonal antibodies to serve as very selective detective reagents for various IgG isotypes. We have shown that these secondary antibodies are species specific, selective and of high affinity. When conjugated to pH sensitive fluorophores, we have used them to specifically identify internalizing antibodies against tumor targets, which were later validated as ADCs. Furthermore, these secondary conjugates exhibit high specific potency and low background toxicity once conjugated to linkered drugs. This approach allow us to optimize the selection of an antibody for a particular target, tumor type, linker and drug for ADC development. NRC will present results of a screen of 285 mouse antibodies against 20 different targets in 7 different cancer cell lines, using either MCC-DM1 or vc-MMAE-conjugated secondary antibodies. The NRC ADC discovery platform is combining this methodology with our proprietary mRNA and DNA expression database for the selection of appropriate ADC targets. NRC Biologics and Biomanufacturing program is in the process of screening thousands of NRC antibodies generated against a variety of cancer-associated cell surface targets to deliver a steady pipeline of ADCS as part of its drug discovery efforts. This functional screening platform further promotes the integration and advancement of NRC’s capabilities and strengths in the area of biologic-based therapeutics lead candidate selection, including quality attributes and characterization and biomanufacturing. The combined expertise in cell biology, high throughput screening, antibody generation and selection, bioinformatics and expression analysis forms the foundation by which NRC can establish strategic collaborations with other Canadian or international partners to develop antibodies into novel ADC biologics. Citation Format: Maria Luz Jaramillo, Luc Meury, Patrice Bouchard, Allan Matte, Anne Marcil, Mauro Acchione, Jennifer Hill, Francois Fauteux. Screening platform for development of antibody-drug conjugates against novel targets at the National Research Council of Canada [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 3669. doi:10.1158/1538-7445.AM2017-3669

Integration of 3D gene expression patterns and gene regulatory networks for clinical applications in epithelial ovarian cancer

In the past decades, many high-throughput studies have been performed to investigate molecular mechanisms underlying epithelial ovarian cancer (EOC), to improve treatments and to develop early detection and staging biomarkers. EOC is still a deadly disease due in part to a lack of screening tools and to the absence of subtype and stage-specific targeted treatments. Here, we applied an integrative three-dimensional clustering algorithm to analyze gene expression data from normal ovaries and four subtypes of EOC. Our analysis revealed major differences between subtypes and highlighted biological patterns linked with stages of the disease. These results may contribute to the understanding of molecular mechanisms underlying EOC and find applications in EOC detection and treatment.