Pathway‐extended gene expression signatures integrate novel biomarkers that improve predictions of patient responses to kinase inhibitors

Cancer chemotherapy responses have been related to multiple pharmacogenetic biomarkers, often for the same drug. This study utilizes machine learning to derive multi‐gene expression signatures that predict individual patient responses to specific tyrosine kinase inhibitors, including erlotinib, gefitinib, sorafenib, sunitinib, lapatinib and imatinib. Support vector machine (SVM) learning was used to train mathematical models that distinguished sensitivity from resistance to these drugs using a novel systems biology‐based approach. This began with expression of genes previously implicated in specific drug responses, then expanded to evaluate genes whose products were related through biochemical pathways and interactions. Optimal pathway‐extended SVMs predicted responses in patients at accuracies of 70% (imatinib), 71% (lapatinib), 83% (sunitinib), 83% (erlotinib), 88% (sorafenib) and 91% (gefitinib). These best performing pathway‐extended models demonstrated improved balance predicting both sensitive and resistant patient categories, with many of these genes having a known role in cancer aetiology. Ensemble machine learning‐based averaging of multiple pathway‐extended models derived for an individual drug increased accuracy to \u3e70% for erlotinib, gefitinib, lapatinib and sorafenib. Through incorporation of novel cancer biomarkers, machine learning‐based pathway‐extended signatures display strong efficacy predicting both sensitive and resistant patient responses to chemotherapy

Bioinformatics and Machine Learning for Cancer Biology

Cancer is a leading cause of death worldwide, claiming millions of lives each year. Cancer biology is an essential research field to understand how cancer develops, evolves, and responds to therapy. By taking advantage of a series of “omics” technologies (e.g., genomics, transcriptomics, and epigenomics), computational methods in bioinformatics and machine learning can help scientists and researchers to decipher the complexity of cancer heterogeneity, tumorigenesis, and anticancer drug discovery. Particularly, bioinformatics enables the systematic interrogation and analysis of cancer from various perspectives, including genetics, epigenetics, signaling networks, cellular behavior, clinical manifestation, and epidemiology. Moreover, thanks to the influx of next-generation sequencing (NGS) data in the postgenomic era and multiple landmark cancer-focused projects, such as The Cancer Genome Atlas (TCGA) and Clinical Proteomic Tumor Analysis Consortium (CPTAC), machine learning has a uniquely advantageous role in boosting data-driven cancer research and unraveling novel methods for the prognosis, prediction, and treatment of cancer

Biologically Interpretable, Integrative Deep Learning for Cancer Survival Analysis

Identifying complex biological processes associated to patients\u27 survival time at the cellular and molecular level is critical not only for developing new treatments for patients but also for accurate survival prediction. However, highly nonlinear and high-dimension, low-sample size (HDLSS) data cause computational challenges in survival analysis. We developed a novel family of pathway-based, sparse deep neural networks (PASNet) for cancer survival analysis. PASNet family is a biologically interpretable neural network model where nodes in the network correspond to specific genes and pathways, while capturing nonlinear and hierarchical effects of biological pathways associated with certain clinical outcomes. Furthermore, integration of heterogeneous types of biological data from biospecimen holds promise of improving survival prediction and personalized therapies in cancer. Specifically, the integration of genomic data and histopathological images enhances survival predictions and personalized treatments in cancer study, while providing an in-depth understanding of genetic mechanisms and phenotypic patterns of cancer. Two proposed models will be introduced for integrating multi-omics data and pathological images, respectively. Each model in PASNet family was evaluated by comparing the performance of current cutting-edge models with The Cancer Genome Atlas (TCGA) cancer data. In the extensive experiments, PASNet family outperformed the benchmarking methods, and the outstanding performance was statistically assessed. More importantly, PASNet family showed the capability to interpret a multi-layered biological system. A number of biological literature in GBM supported the biological interpretation of the proposed models. The open-source software of PASNet family in PyTorch is publicly available at https://github.com/DataX-JieHao

Tuneable 3D biocompatible scaffolds for biological and biophysical solid-tumour microenvironment studies; applications in Ovarian Cancer

Recently, three-dimensional (3D) tumour models mimicking the tumour microenvironment and reducing the use of experimental animals have been developed generating great interest to appraise tumour response to treatment strategies in cancer therapy. As tumours have distinct mechanics compared to normal tissues, biomaterials have also been utilized in 3D culture to model the mechanical properties of the tumour microenvironment, and to study the effects of extracellular matrix (ECM) mechanics on tumour development and progression. Mechanical cues regulate various cell behaviours through mechanotransduction, including proliferation, migration, and differentiation. In the context of cancer, both stromal cells (cancer associated fibroblasts) and tumour cells remodel the ECM and change its mechanical properties, and the altered mechanical niche in turn is likely to influence tumour progression. In this study, bovine derived collagen type I and Jellyfish derived marine collagen sources, were tested as biomaterial candidates for cancer studies, moulded to porous scaffolds with tuneable mechanical properties. The resulting interconnected network of collagen fibre constructs, fabricated using lyophilisation provide good control of scaffolding architecture, pore sizes range, high porosity levels, high level of cell viability and low production cost. Importantly these sponge scaffolds were, in the form of 3D models, compatible with a host of cellular and molecular biology assays used to investigate mechanical and biological effects of collagen crosslinking and (hyaluronic acid) HA inclusion on both fibroblasts and ovarian cancer cells. Stromal cells and cancer cells respond differently to the altered stiffness of their local microenvironment. Fibroblasts, once activated with TGF1, converge toward a ‘senescent-like phenotype’, blocking migration and matrix remodelling and promote tumour progression, probably through the secretion of tumour-promoting signals, in stiffer mechanical environments. Cancer cells, of both epithelial and mesenchymal phenotype, respond to increased local matrix stiffness by increasing proliferation while, at the same time, becoming more susceptible to treatment. Mechanically informative scaffolds resemble the physical characteristics of both normal and pathological ovarian tissue mechanics, where ovarian cancer originates. Physical changes observed in the later stage of ovarian cancer disease progression may therefore be fundamental for the increased cancer proliferation that drives metastatic progression, however opening an interesting window for cancer treatment. Bio-physical inclusive models not only lead the path to unveil complex interactions of biophysical and biological signals in the tumour microenvironment, but they represent a highly informative and effective platform to test novel target therapies with effective costs and high throughput. They can accommodate coculture systems and potentially patients-derived cell cultures, providing a platform to test current and new drugs and to evaluate drug efficacy following a precision medicine approach

Artificial neural network techniques to investigate potential interactions between biomarkers

High-throughput technologies in biomedical sciences, including gene microarrays, supposed to revolutionise the post-genomic era, have barely met the great expectations they inspired to the biomedical community at first. Current efforts are still focused toward improving the technology, its reproducibility and accuracy. In the meantime, computational techniques for the analysis of the data from these technologies have achieved great progresses and show encouraging results. New approaches have been developed to extract relevant information out from these results. However, important work needs to be further conducted in order to extract even more meaningful and relevant information. These techniques offer great possibilities to explore the overall dynamic held within a living organism. The potential information contained in their output can reveal important leads at deciphering the interconnection, interaction or regulation influences that can exist between several molecules. In front of an increasing interest of the scientific community toward the exploration of these dynamics, some groups have started to develop solutions based on different technologies to extract these information related to interactions. Here we present an Artificial Neural Network-based methodology for the study of interactions in gene transcriptomic data. This will be applied and validated in a breast cancer context

Non-parametric algorithms for evaluating gene expression in cancer using DNA microarray technology

Microarray technology has transformed the field of cancer biology by enabling the simultaneous evaluation of tens of thousands mRNA expression levels in a single experiment. This technology has been applied to medical science in order to find gene expression markers that cluster diseased and normal tissues, genes affected by treatments, and gene network interactions. All methods of microarray data analysis can be summarized as a study of differential gene expression. This study addresses three questions, 1) the roles of selectively expressed genes for the classification of cancer, 2) issues of accounting for both experimental and biological noise, and 3) issues of comparing data derived from different research groups using the Affymetrix GeneChipTM platform. A key finding of this study is that selectively expressed genes are very powerful when used for disease classification. A model was designed to reduce noise and eliminate false positives from true results. With this approach, data from different research groups can be integrated to increase information and enable a better understanding of cancer