A thermodynamic-based approach for the resolution and prediction of protein network structures

© 2018 Elsevier B.V. The rapid accumulation of omics data from biological specimens has revolutionized the field of cancer research. The generation of computational techniques attempting to study these masses of data and extract the significant signals is at the forefront. We suggest studying cancer from a thermodynamic-based point of view. We hypothesize that by modelling biological systems based on physico-chemical laws, highly complex systems can be reduced to a few parameters, and their behavior under varying conditions, including response to therapy, can be predicted. Here we validate the predictive power of our thermodynamic-based approach, by uncovering the protein network structure that emerges in MCF10a human mammary cells upon exposure to epidermal growth factor (EGF), and anticipating the consequences of treating the cells with the Src family kinase inhibitor, dasatinib

Personalized disease signatures through information-theoretic compaction of big cancer data

Accurate cancer diagnostics is a prerequisite for optimal personalized cancer medicine. We propose an information-theoretic cancer diagnosis that identifies signatures comprising patient-specific oncogenic processes rather than cancer type-specific biomarkers. Such comprehensive transcriptional signatures should allow for more accurate classification of cancer patients and better patient-specific diagnostics. The approach that we describe herein allows decoding of large-scale molecular-level information and elucidating patient-specific transcriptional altered network structures. Thereby, we move from cancer type-associated biomarkers to unbiased patient-specific unbalanced oncogenic processes.Every individual cancer develops and grows in its own specific way, giving rise to a recognized need for the development of personalized cancer diagnostics. This suggested that the identification of patient-specific oncogene markers would be an effective diagnostics approach. However, tumors that are classified as similar according to the expression levels of certain oncogenes can eventually demonstrate divergent responses to treatment. This implies that the information gained from the identification of tumor-specific biomarkers is still not sufficient. We present a method to quantitatively transform heterogeneous big cancer data to patient-specific transcription networks. These networks characterize the unbalanced molecular processes that deviate the tissue from the normal state. We study a number of datasets spanning five different cancer types, aiming to capture the extensive interpatient heterogeneity that exists within a specific cancer type as well as between cancers of different origins. We show that a relatively small number of altered molecular processes suffices to accurately characterize over 500 tumors, showing extreme compaction of the data. Every patient is characterized by a small specific subset of unbalanced processes. We validate the result by verifying that the processes identified characterize other cancer patients as well. We show that different patients may display similar oncogene expression levels, albeit carrying biologically distinct tumors that harbor different sets of unbalanced molecular processes. Thus, tumors may be inaccurately classified and addressed as similar. These findings highlight the need to expand the notion of tumor-specific oncogenic biomarkers to patient-specific, comprehensive transcriptional networks for improved patient-tailored diagnostics

Exploring Alzheimer’s Disease Molecular Variability via Calculation of Personalized Transcriptional Signatures

Despite huge investments and major efforts to develop remedies for Alzheimer’s disease (AD) in the past decades, AD remains incurable. While evidence for molecular and phenotypic variability in AD have been accumulating, AD research still heavily relies on the search for AD-specific genetic/protein biomarkers that are expected to exhibit repetitive patterns throughout all patients. Thus, the classification of AD patients to different categories is expected to set the basis for the development of therapies that will be beneficial for subpopulations of patients. Here we explore the molecular heterogeneity among a large cohort of AD and non-demented brain samples, aiming to address the question whether AD-specific molecular biomarkers can progress our understanding of the disease and advance the development of anti-AD therapeutics. We studied 951 brain samples, obtained from up to 17 brain regions of 85 AD patients and 22 non-demented subjects. Utilizing an information-theoretic approach, we deciphered the brain sample-specific structures of altered transcriptional networks. Our in-depth analysis revealed that 7 subnetworks were repetitive in the 737 diseased and 214 non-demented brain samples. Each sample was characterized by a subset consisting of ~1–3 subnetworks out of 7, generating 52 distinct altered transcriptional signatures that characterized the 951 samples. We show that 30 different altered transcriptional signatures characterized solely AD samples and were not found in any of the non-demented samples. In contrast, the rest of the signatures characterized different subsets of sample types, demonstrating the high molecular variability and complexity of gene expression in AD. Importantly, different AD patients exhibiting similar expression levels of AD biomarkers harbored distinct altered transcriptional networks. Our results emphasize the need to expand the biomarker-based stratification to patient-specific transcriptional signature identification for improved AD diagnosis and for the development of subclass-specific future treatment