Proteomic Screening and Lasso Regression Reveal Differential Signaling in Insulin and Insulin-like Growth Factor I (IGF1) Pathways

MOBILE pipeline enables identification of context-specific networks and regulatory mechanisms

Robust identification of context-specific network features that control cellular phenotypes remains a challenge. We here introduce MOBILE (Multi-Omics Binary Integration via Lasso Ensembles) to nominate molecular features associated with cellular phenotypes and pathways. First, we use MOBILE to nominate mechanisms of interferon-γ (IFNγ) regulated PD-L1 expression. Our analyses suggest that IFNγ-controlled PD-L1 expression involves BST2, CLIC2, FAM83D, ACSL5, and HIST2H2AA3 genes, which were supported by prior literature. We also compare networks activated by related family members transforming growth factor-beta 1 (TGFβ1) and bone morphogenetic protein 2 (BMP2) and find that differences in ligand-induced changes in cell size and clustering properties are related to differences in laminin/collagen pathway activity. Finally, we demonstrate the broad applicability and adaptability of MOBILE by analyzing publicly available molecular datasets to investigate breast cancer subtype specific networks. Given the ever-growing availability of multi-omics datasets, we envision that MOBILE will be broadly useful for identification of context-specific molecular features and pathways.

Use of Computation Ecosystems to Analyze the Kidney-Heart Crosstalk

The identification of mediators for physiologic processes, correlation of molecular processes, or even pathophysiological processes within a single organ such as the kidney or heart has been extensively studied to answer specific research questions using organ-centered approaches in the past 50 years. However, it has become evident that these approaches do not adequately complement each other and display a distorted single-disease progression, lacking holistic multilevel/multidimensional correlations. Holistic approaches have become increasingly significant in understanding and uncovering high dimensional interactions and molecular overlaps between different organ systems in the pathophysiology of multimorbid and systemic diseases like cardiorenal syndrome because of pathological heart-kidney crosstalk. Holistic approaches to unraveling multimorbid diseases are based on the integration, merging, and correlation of extensive, heterogeneous, and multidimensional data from different data sources, both -omics and nonomics databases. These approaches aimed at generating viable and translatable disease models using mathematical, statistical, and computational tools, thereby creating first computational ecosystems. As part of these computational ecosystems, systems medicine solutions focus on the analysis of -omics data in single-organ diseases. However, the data-scientific requirements to address the complexity of multimodality and multimorbidity reach far beyond what is currently available and require multiphased and cross-sectional approaches. These approaches break down complexity into small and comprehensible challenges. Such holistic computational ecosystems encompass data, methods, processes, and interdisciplinary knowledge to manage the complexity of multiorgan crosstalk. Therefore, this review summarizes the current knowledge of kidney-heart crosstalk, along with methods and opportunities that arise from the novel application of computational ecosystems providing a holistic analysis on the example of kidney-heart crosstalk.

MEMMAL: A tool for expanding large-scale mechanistic models with machine learned associations and big datasets

Computational models that can explain and predict complex sub-cellular, cellular, and tissue-level drug response mechanisms could speed drug discovery and prioritize patient-specific treatments (i.e., precision medicine). Some models are mechanistic with detailed equations describing known (or supposed) physicochemical processes, while some are statistical or machine learning-based approaches, that explain datasets but have no mechanistic or causal guarantees. These two types of modeling are rarely combined, missing the opportunity to explore possibly causal but data-driven new knowledge while explaining what is already known. Here, we explore combining machine learned associations with mechanistic models to develop computational models that could more fully represent cellular behavior. In this proposed MEMMAL (MEchanistic Modeling with MAchine Learning) framework, machine learning/statistical models built using omics datasets provide predictions for new interactions between genes and proteins where there is physicochemical uncertainty. These interactions are used as a basis for new reactions in mechanistic models. As a test case, we focused on incorporating novel IFNγ/PD-L1 related associations into a large-scale mechanistic model for cell proliferation and death to better recapitulate the recently released NIH LINCS Consortium MCF10A dataset and enable description of the cellular response to checkpoint inhibitor immunotherapies. This work is a template for combining big-data-inferred interactions with mechanistic models, which could be more broadly applicable for building multi-scale precision medicine and whole cell models.

Breast tumor IGF1R regulates cell adhesion and metastasis: alignment of mouse single cell and human breast cancer transcriptomics

Introduction

The acquisition of a metastatic phenotype is the critical event that determines patient survival from breast cancer. Several receptor tyrosine kinases have functions both in promoting and inhibiting metastasis in breast tumors. Although the insulin-like growth factor 1 receptor (IGF1R) has been considered a target for inhibition in breast cancer, low levels of IGF1R expression are associated with worse overall patient survival.

Methods

To determine how reduced IGF1R impacts tumor phenotype in human breast cancers, we used weighted gene co-expression network analysis (WGCNA) of Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) patient data to identify gene modules associated with low IGF1R expression. We then compared these modules to single cell gene expression analyses and phenotypes of mouse mammary tumors with reduced IGF1R signaling or expression in a tumor model of triple negative breast cancer.

Results

WGCNA from METABRIC data revealed gene modules specific to cell cycle, adhesion, and immune cell signaling that were inversely correlated with IGF1R expression in human breast cancers. Integration of human patient data with single cell sequencing data from mouse tumors revealed similar pathways necessary for promoting metastasis in basal-like mammary tumors with reduced signaling or expression of IGF1R. Functional analyses revealed the basis for the enhanced metastatic phenotype including alterations in E- and P-cadherins.

Discussion

Human breast and mouse mammary tumors with reduced IGF1R are associated with upregulation of several pathways necessary for promoting metastasis supporting the conclusion that IGF1R normally helps maintain a metastasis suppressive tumor microenvironment. We further found that reduced IGF1R signaling in tumor epithelial cells dysregulates cadherin expression resulting in reduced cell adhesion.

Loss of E-cadherin Induces IGF1R Activation and Reveals a Targetable Pathway in Invasive Lobular Breast Carcinoma

No special-type breast cancer [NST; commonly known as invasive ductal carcinoma (IDC)] and invasive lobular carcinoma (ILC) are the two major histological subtypes of breast cancer with significant differences in clinicopathological and molecular characteristics. The defining pathognomonic feature of ILC is loss of cellular adhesion protein, E-cadherin (CDH1). We have previously shown that E-cadherin functions as a negative regulator of the IGF1R and propose that E-cadherin loss in ILC sensitizes cells to growth factor signaling that thus alters their sensitivity to growth factor-signaling inhibitors and their downstream activators. To investigate this potential therapeutic vulnerability, we generated CRISPR-mediated CDH1 knockout (CDH1 KO) IDC cell lines (MCF7, T47D, and ZR75.1) to uncover the mechanism by which loss of E-cadherin results in IGF pathway activation. CDH1 KO cells demonstrated enhanced invasion and migration that was further elevated in response to IGF1, serum and collagen I. CDH1 KO cells exhibited increased sensitivity to IGF resulting in elevated downstream signaling. Despite minimal differences in membranous IGF1R levels between wild-type (WT) and CDH1 KO cells, significantly higher ligand-receptor interaction was observed in the CDH1 KO cells, potentially conferring enhanced downstream signaling activation. Critically, increased sensitivity to IGF1R, PI3K, Akt, and MEK inhibitors was observed in CDH1 KO cells and ILC patient-derived organoids.

IMPLICATIONS

Overall, this suggests that these targets require further exploration in ILC treatment and that CDH1 loss may be exploited as a biomarker of response for patient stratification.

Heat treatment reduced the expression of miR-7-5p to facilitate insulin-stimulated lactate secretion by targeting IRS2 in boar Sertoli cells

Insulin dysfunction of diabetes mellitus (DM) disorders the glucose metabolism in Sertoli cells (SCs), resulting in the impairment of spermatogenesis.Insulin signaling system in Sertoli cells (SCs) plays an important role in regulating lactate secretion. Heat treatment could increase the lactate secretion of boar SCs, but whether heat treatment participates in lactate secretion by improving the sensitivity of insulin is unknown. In the current study, the primary SCs from 21-day-old boar were employed to treat with 100 nM insulin for 24 h or heat treatment (43 °C, 30 min). Heat treatment strengthened the effect of insulin on the effect of lactate secretion. In addition, heat treatment increased the expression of insulin-induced insulin receptor substrate 2 (IRS2), but reduced the expression of miR-7-5p. Using dual luciferase reporter assay and Western blot, the study found that IRS2 is a potential target gene of miR-7-5p. Heat treatment also enhanced the Phosphorylation of insulin-stimulated PI3K/Akt, and increased lactate secretion by promoting the expression of Glucose Transporter 3 (GLUT3), Lactate Dehydrogenase-A (LDHA) and monocarboxylate transporter 1 (MCT1). Furthermore, miR-7-5p inhibitor could partly mimic the effects of heat treatment on lactate production of SCs, indicating that heat treatment improves insulin sensitivity by regulating the expression of miR-7-5p/IRS2/PI3K/Akt. These results reveal a novel miRNA-mediated mechanism of heat treatment on the regulation of lactate metabolism production, and suggest that targeting miR-7-5p is a probably therapeutic method to insulin dysfunction-induced metabolic diseases.

Inhibition of RPS6K reveals context-dependent Akt activity in luminal breast cancer cells

Aberrant signaling through insulin (Ins) and insulin-like growth factor I (IGF1) receptors contribute to the risk and advancement of many cancer types by activating cell survival cascades. Similarities between these pathways have thus far prevented the development of pharmacological interventions that specifically target either Ins or IGF1 signaling. To identify differences in early Ins and IGF1 signaling mechanisms, we developed a dual receptor (IGF1R & InsR) computational response model. The model suggested that ribosomal protein S6 kinase (RPS6K) plays a critical role in regulating MAPK and Akt activation levels in response to Ins and IGF1 stimulation. As predicted, perturbing RPS6K kinase activity led to an increased Akt activation with Ins stimulation compared to IGF1 stimulation. Being able to discern differential downstream signaling, we can explore improved anti-IGF1R cancer therapies by eliminating the emergence of compensation mechanisms without disrupting InsR signaling.

Biomarkers of Insulin and the Insulin-Like Growth Factor Axis in Relation to Breast Cancer Risk in Chinese Women

BACKGROUND

The interplay between biomarkers of insulin and the insulin-like growth factor (IGF) axis in the context of breast cancer risk is unclear.

METHODS

We measured the concentrations of insulin, C-peptide, IGF1, and IGF binding protein 3 (IGFBP3) and calculated the homeostasis model assessment of insulin resistance (HOMA-IR) index and the IGF1/IGFBP3 ratio among 2536 patients with breast cancer and 2528 patients with benign breast disease recruited from Ruijin Hospital, Shanghai, China, between 2012 and 2017.

RESULTS

Multivariable-adjusted odds ratios (ORs) for breast cancer associated with the highest quartiles versus the lowest quartiles of insulin and IGF factors were 1.45 (95% CI, 1.20-1.75) for insulin, 1.32 (1.08-1.60) for C-peptide, 1.53 (1.26-1.85) for HOMA-IR, and 1.27 (1.05-1.53) for IGF1; these associations did not differ substantially across stratifications of age, body mass index, age at menarche, or menopausal status (all P for interaction >0.05). In the joint analysis, the highest quartile of IGF1 was associated with the greatest risk of breast cancer in the highest quartiles of insulin (OR, 1.77; 95% CI, 1.29-2.44), C-peptide (1.60; 1.17-2.20), and HOMA-IR (1.90; 1.38-2.62), compared with the risks associated with the combination of the lowest quartiles of IGF1 and each insulin factor. In stratification analysis, the positive association between IGF1 and breast cancer was stronger in the highest quartiles of insulin (P[interaction] = 0.29), C-peptide (P[interaction] = 0.020), and HOMA-IR (P[interaction] = 0.075).

CONCLUSION

Our findings indicate effect modifications of insulin, C-peptide, and insulin resistance on the relationship between IGF1 and breast cancer risk in Chinese women.

Harnessing Human Microphysiology Systems as Key Experimental Models for Quantitative Systems Pharmacology

Two technologies that have emerged in the last decade offer a new paradigm for modern pharmacology, as well as drug discovery and development. Quantitative systems pharmacology (QSP) is a complementary approach to traditional, target-centric pharmacology and drug discovery and is based on an iterative application of computational and systems biology methods with multiscale experimental methods, both of which include models of ADME-Tox and disease. QSP has emerged as a new approach due to the low efficiency of success in developing therapeutics based on the existing target-centric paradigm. Likewise, human microphysiology systems (MPS) are experimental models complementary to existing animal models and are based on the use of human primary cells, adult stem cells, and/or induced pluripotent stem cells (iPSCs) to mimic human tissues and organ functions/structures involved in disease and ADME-Tox. Human MPS experimental models have been developed to address the relatively low concordance of human disease and ADME-Tox with engineered, experimental animal models of disease. The integration of the QSP paradigm with the use of human MPS has the potential to enhance the process of drug discovery and development.

Loss of E-cadherin Enhances IGF1-IGF1R Pathway Activation and Sensitizes Breast Cancers to Anti-IGF1R/InsR Inhibitors

Purpose: Insulin-like growth factor 1 (IGF1) signaling regulates breast cancer initiation and progression and associated cancer phenotypes. We previously identified E-cadherin (CDH1) as a repressor of IGF1 signaling and in this study examined how loss of E-cadherin affects IGF1R signaling and response to anti-IGF1R/insulin receptor (InsR) therapies in breast cancer.Experimental Design: Breast cancer cell lines were used to assess how altered E-cadherin levels regulate IGF1R signaling and response to two anti-IGF1R/InsR therapies. In situ proximity ligation assay (PLA) was used to define interaction between IGF1R and E-cadherin. TCGA RNA-seq and RPPA data were used to compare IGF1R/InsR activation in estrogen receptor-positive (ER+) invasive lobular carcinoma (ILC) and invasive ductal carcinoma (IDC) tumors. ER+ ILC cell lines and xenograft tumor explant cultures were used to evaluate efficacy to IGF1R pathway inhibition in combination with endocrine therapy.Results: Diminished functional E-cadherin increased both activation of IGF1R signaling and efficacy to anti-IGF1R/InsR therapies. PLA demonstrated a direct endogenous interaction between IGF1R and E-cadherin at points of cell-cell contact. Increased expression of IGF1 ligand and levels of IGF1R/InsR phosphorylation were observed in E-cadherin-deficient ER+ ILC compared with IDC tumors. IGF1R pathway inhibitors were effective in inhibiting growth in ER+ ILC cell lines and synergized with endocrine therapy and similarly IGF1R/InsR inhibition reduced proliferation in ILC tumor explant culture.Conclusions: We provide evidence that loss of E-cadherin hyperactivates the IGF1R pathway and increases sensitivity to IGF1R/InsR targeted therapy, thus identifying the IGF1R pathway as a potential novel target in E-cadherin-deficient breast cancers. Clin Cancer Res; 24(20); 5165-77. ©2018 AACR.

Protein network construction using reverse phase protein array data

In this paper, we introduce a novel computational method for constructing protein networks based on reverse phase protein array (RPPA) data to identify complex patterns in protein signaling. The method is applied to phosphoproteomic profiles of basal expression and activation/phosphorylation of 76 key signaling proteins in three breast cancer cell lines (MCF7, LCC1, and LCC9). Temporal RPPA data are acquired at 48h, 96h, and 144h after knocking down four genes in separate experiments. These genes are selected from a previous study as important determinants for breast cancer survival. Interaction networks are constructed by analyzing the expression levels of protein pairs using a multivariate analysis of variance model. A new scoring criterion is introduced to determine relevant protein pairs. Through a network topology based analysis, we search for wiring patterns to identify key proteins that are associated with significant changes in expression levels across various experimental conditions.