Targeting colorectal cancer via its microenvironment by inhibiting IGF-1 receptor-insulin receptor substrate and STAT3 signaling.

The tumor microenvironment (TME) exerts critical pro-tumorigenic effects through cytokines and growth factors that support cancer cell proliferation, survival, motility and invasion. Insulin-like growth factor-1 (IGF-1) and signal transducer and activator of transcription 3 (STAT3) stimulate colorectal cancer development and progression via cell autonomous and microenvironmental effects. Using a unique inhibitor, NT157, which targets both IGF-1 receptor (IGF-1R) and STAT3, we show that these pathways regulate many TME functions associated with sporadic colonic tumorigenesis in CPC-APC mice, in which cancer development is driven by loss of the Apc tumor suppressor gene. NT157 causes a substantial reduction in tumor burden by affecting cancer cells, cancer-associated fibroblasts (CAF) and myeloid cells. Decreased cancer cell proliferation and increased apoptosis were accompanied by inhibition of CAF activation and decreased inflammation. Furthermore, NT157 inhibited expression of pro-tumorigenic cytokines, chemokines and growth factors, including IL-6, IL-11 and IL-23 as well as CCL2, CCL5, CXCL7, CXCL5, ICAM1 and TGFβ; decreased cancer cell migratory activity and reduced their proliferation in the liver. NT157 represents a new class of anti-cancer drugs that affect both the malignant cell and its supportive microenvironment

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

Understanding cancer phenomena using a thermodynamic-based approach

We seek to address fundamental questions in cancer biology by an experimental-theoretical approach based on physicochemical laws. We have recently pioneered the application of the thermodynamic-based surprisal analysis, which has been previously applied to systems in chemistry and physics, to biological processes. We have shown that through the accurate resolution of the protein networks that deviate the cancer system from its balanced state, various biological phenotypes can be predicted. For example, we have demonstrated that using a thermodynamic-based proteomic analysis in varying cell-cell distances, the direction of movement of brain cancer cells can be predicted and experimentally manipulated. Here we present single cell and bulk proteomic methods integrated with thermodynamic-derived information theory. We demonstrate how complex biological phenomena, such as cellular tumor architectures or inter-tumor variability can be modeled using a limited number of key physical parameters. Furthermore we show how these parameters are used to predict cellular architectures or to design high-precision, patient-specific drug cocktails. Generally speaking, this approach provides a framework that models biological systems in order to learn how to predict and manipulate their behavior

Understanding cancer phenomena using a thermodynamic-based approach

We seek to address fundamental questions in cancer biology by an experimental-theoretical approach based on physicochemical laws. We have recently pioneered the application of the thermodynamic-based surprisal analysis, which has been previously applied to systems in chemistry and physics, to biological processes. We have shown that through the accurate resolution of the protein networks that deviate the cancer system from its balanced state, various biological phenotypes can be predicted. For example, we have demonstrated that using a thermodynamic-based proteomic analysis in varying cell-cell distances, the direction of movement of brain cancer cells can be predicted and experimentally manipulated. Here we present single cell and bulk proteomic methods integrated with thermodynamic-derived information theory. We demonstrate how complex biological phenomena, such as cellular tumor architectures or inter-tumor variability can be modeled using a limited number of key physical parameters. Furthermore we show how these parameters are used to predict cellular architectures or to design high-precision, patient-specific drug cocktails. Generally speaking, this approach provides a framework that models biological systems in order to learn how to predict and manipulate their behavior

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

Targeting colorectal cancer via its microenvironment by inhibiting IGF-1 receptor-insulin receptor substrate and STAT3 signaling.

The tumor microenvironment (TME) exerts critical pro-tumorigenic effects through cytokines and growth factors that support cancer cell proliferation, survival, motility and invasion. Insulin-like growth factor-1 (IGF-1) and signal transducer and activator of transcription 3 (STAT3) stimulate colorectal cancer development and progression via cell autonomous and microenvironmental effects. Using a unique inhibitor, NT157, which targets both IGF-1 receptor (IGF-1R) and STAT3, we show that these pathways regulate many TME functions associated with sporadic colonic tumorigenesis in CPC-APC mice, in which cancer development is driven by loss of the Apc tumor suppressor gene. NT157 causes a substantial reduction in tumor burden by affecting cancer cells, cancer-associated fibroblasts (CAF) and myeloid cells. Decreased cancer cell proliferation and increased apoptosis were accompanied by inhibition of CAF activation and decreased inflammation. Furthermore, NT157 inhibited expression of pro-tumorigenic cytokines, chemokines and growth factors, including IL-6, IL-11 and IL-23 as well as CCL2, CCL5, CXCL7, CXCL5, ICAM1 and TGFβ; decreased cancer cell migratory activity and reduced their proliferation in the liver. NT157 represents a new class of anti-cancer drugs that affect both the malignant cell and its supportive microenvironment