Feedback analysis identifies a combination target for overcoming adaptive resistance to targeted cancer therapy

Targeting RAF dimers in RAS mutant tumors: From biology to clinic

RAS mutations occur in approximately 30% of tumors worldwide and have a poor prognosis due to limited therapies. Covalent targeting of KRAS G12C has achieved significant success in recent years, but there is still a lack of efficient therapeutic approaches for tumors with non-G12C KRAS mutations. A highly promising approach is to target the MAPK pathway downstream of RAS, with a particular focus on RAF kinases. First-generation RAF inhibitors have been authorized to treat BRAF mutant tumors for over a decade. However, their use in RAS-mutated tumors is not recommended due to the paradoxical ERK activation mainly caused by RAF dimerization. To address the issue of RAF dimerization, type II RAF inhibitors have emerged as leading candidates. Recent clinical studies have shown the initial effectiveness of these agents against RAS mutant tumors. Promisingly, type II RAF inhibitors in combination with MEK or ERK inhibitors have demonstrated impressive efficacy in RAS mutant tumors. This review aims to clarify the importance of RAF dimerization in cellular signaling and resistance to treatment in tumors with RAS mutations, as well as recent progress in therapeutic approaches to address the problem of RAF dimerization in RAS mutant tumors.

Global stabilizing control of large-scale biomolecular regulatory networks

MOTIVATION

Cellular behavior is determined by complex non-linear interactions between numerous intracellular molecules that are often represented by Boolean network models. To achieve a desired cellular behavior with minimal intervention, we need to identify optimal control targets that can drive heterogeneous cellular states to the desired phenotypic cellular state with minimal node intervention. Previous attempts to realize such global stabilization were based solely on either network structure information or simple linear dynamics. Other attempts based on non-linear dynamics are not scalable.

RESULTS

Here, we investigate the underlying relationship between structurally identified control targets and optimal global stabilizing control targets based on non-linear dynamics. We discovered that optimal global stabilizing control targets can be identified by analyzing the dynamics between structurally identified control targets. Utilizing these findings, we developed a scalable global stabilizing control framework using both structural and dynamic information. Our framework narrows down the search space based on strongly connected components and feedback vertex sets then identifies global stabilizing control targets based on the canalization of Boolean network dynamics. We find that the proposed global stabilizing control is superior with respect to the number of control target nodes, scalability, and computational complexity.

AVAILABILITY AND IMPLEMENTATION

We provide a GitHub repository that contains the DCGS framework written in Python as well as biological random Boolean network datasets (https://github.com/sugyun/DCGS).

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Evaluating a therapeutic window for precision medicine by integrating genomic profiles and p53 network dynamics

The response variation to anti-cancer drugs originates from complex intracellular network dynamics of cancer. Such dynamic networks present challenges to determining optimal drug targets and stratifying cancer patients for precision medicine, although several cancer genome studies provided insights into the molecular characteristics of cancer. Here, we introduce a network dynamics-based approach based on attractor landscape analysis to evaluate the therapeutic window of a drug from cancer signaling networks combined with genomic profiles. This approach allows for effective screening of drug targets to explore potential target combinations for enhancing the therapeutic window of drug responses. We also effectively stratify patients into desired/undesired response groups using critical genomic determinants, which are network-specific origins of variability to drug response, and their dominance relationship. Our methods provide a viable and quantitative framework to connect genotype information to the phenotypes of drug response with regard to network dynamics determining the therapeutic window.

NETISCE: a network-based tool for cell fate reprogramming

The search for effective therapeutic targets in fields like regenerative medicine and cancer research has generated interest in cell fate reprogramming. This cellular reprogramming paradigm can drive cells to a desired target state from any initial state. However, methods for identifying reprogramming targets remain limited for biological systems that lack large sets of experimental data or a dynamical characterization. We present NETISCE, a novel computational tool for identifying cell fate reprogramming targets in static networks. In combination with machine learning algorithms, NETISCE estimates the attractor landscape and predicts reprogramming targets using signal flow analysis and feedback vertex set control, respectively. Through validations in studies of cell fate reprogramming from developmental, stem cell, and cancer biology, we show that NETISCE can predict previously identified cell fate reprogramming targets and identify potentially novel combinations of targets. NETISCE extends cell fate reprogramming studies to larger-scale biological networks without the need for full model parameterization and can be implemented by experimental and computational biologists to identify parts of a biological system relevant to the desired reprogramming task.

Systematic Analysis of the Molecular Mechanisms of Cold and Hot Properties of Herbal Medicines

Effective treatments for patients experiencing temperature-related symptoms are limited. The hot and cold effects of traditional herbal medicines have been utilized to treat and manage these symptoms, but their molecular mechanisms are not fully understood. Previous studies with arbitrarily selected herbs and ingredients may have produced biased results. Here, we aim to systematically elucidate the molecular mechanisms of the hot and cold properties of herbal medicines through an unbiased large-scale investigation of herbal ingredients, their target genes, and the transcriptome signatures induced by them. Using data regarding 243 herbs retrieved from two herbal medicine databases, we statistically identify (R)-Linalool, (-)-alpha-pinene, peruviol, (L)-alpha-terpineol, and cymol as five new hot-specific ingredients that share a common target, a norepinephrine transporter. However, no significant ingredients are cold-specific. We also statistically identify 14 hot- and 8 cold-specific new target genes. Pathway enrichment analysis of hot-specific target genes reveals the associated pathways including neurotransmitter reuptake, cold-induced thermogenesis, blood pressure regulation, adrenergic receptor signaling, and cation symporter activity. Cold-specific target genes are associated with the steroid pathway. Transcriptome analysis also shows that hot herbs are more strongly associated with coagulation and synaptic transmission than cold herbs. Our results, obtained from novel connections between herbal ingredients, target genes, and pathways, may contribute to the development of pharmacological treatment strategies for temperature-related pain using medicinal plants.

Ras Pathway Activation and MEKi Resistance Scores Predict the Efficiency of MEKi and SRCi Combination to Induce Apoptosis in Colorectal Cancer

Colorectal cancer (CRC) is the second leading cause of cancer death in the United States. The RAS pathway is activated in more than 55% of CRC and has been targeted for therapeutic intervention with MEK inhibitors. Unfortunately, many patients have de novo resistance, or can develop resistance to this new class of drugs. We have hypothesized that much of this resistance may pass through SRC as a common signal transduction node, and that inhibition of SRC may suppress MEK inhibition resistance mechanisms. CRC tumors of the Consensus Molecular Subtype (CMS) 4, enriched in stem cells, are difficult to successfully treat and have been suggested to evade traditional chemotherapy agents through resistance mechanisms. Here, we evaluate targeting two pathways simultaneously to produce an effective treatment by overcoming resistance. We show that combining Trametinib (MEKi) with Dasatinib (SRCi) provides enhanced cell death in 8 of the 16 tested CRC cell lines compared to treatment with either agent alone. To be able to select sensitive cells, we simultaneously evaluated a validated 18-gene RAS pathway activation signature score along with a 13-gene MEKi resistance signature score, which we hypothesize predict tumor sensitivity to this dual targeted therapy. We found the cell lines that were sensitive to the dual treatment were predominantly CMS4 and had both a high 18-gene and a high 13-gene score, suggesting these cell lines had potential for de novo MEKi sensitivity but were subject to the rapid development of MEKi resistance. The 13-gene score is highly correlated to a score for SRC activation, suggesting resistance is dependent on SRC. Our data show that gene expression signature scores for RAS pathway activation and for MEKi resistance may be useful in determining which CRC tumors will respond to the novel drug combination of MEKi and SRCi.

Deepening into Intracellular Signaling Landscape through Integrative Spatial Proteomics and Transcriptomics in a Lymphoma Model

Human Proteome Project (HPP) presents a systematic characterization of the protein landscape under different conditions using several complementary-omic techniques (LC-MS/MS proteomics, affinity proteomics, transcriptomics, etc.). In the present study, using a B-cell lymphoma cell line as a model, comprehensive integration of RNA-Seq transcriptomics, MS/MS, and antibody-based affinity proteomics (combined with size-exclusion chromatography) (SEC-MAP) were performed to uncover correlations that could provide insights into protein dynamics at the intracellular level. Here, 5672 unique proteins were systematically identified by MS/MS analysis and subcellular protein extraction strategies (neXtProt release 2020-21, MS/MS data are available via ProteomeXchange with identifier PXD003939). Moreover, RNA deep sequencing analysis of this lymphoma B-cell line identified 19,518 expressed genes and 5707 protein coding genes (mapped to neXtProt). Among these data sets, 162 relevant proteins (targeted by 206 antibodies) were systematically analyzed by the SEC-MAP approach, providing information about PTMs, isoforms, protein complexes, and subcellular localization. Finally, a bioinformatic pipeline has been designed and developed for orthogonal integration of these high-content proteomics and transcriptomics datasets, which might be useful for comprehensive and global characterization of intracellular protein profiles.

A Computational Approach for Pathway-Based Systemic Drug Influence

Drug repositioning is a well-known method used to reduce the time, cost, and development risks involved in bringing a new drug to the market. The rapid expansion of high-throughput datasets has enabled computational research that can suggest new potential uses for existing drugs. Some computational methods allow the prediction of potential drug targets of a given disease from a systematic network. Despite numerous efforts, the path of many drugs’ efficacy in the human body remains unclear. Therefore, the present study attempted to understand drug efficacy by systematically focusing on functional gene sets. The purpose of this study was to carry out modeling to identify systemic gene networks (called drug paths) in drug-specific pathways. In our results, we found five different paths for five different drugs.

A New Computational Approach to Evaluating Systemic Gene–Gene Interactions in a Pathway Affected by Drug LY294002

In this study, we investigate how drugs systemically affect genes via pathways by integrating information from interactions between chemical compounds and molecular expression datasets, and from pathway information such as gene sets using mathematical models. First, we adopt drug-induced gene expression datasets; then, employ gene set enrichment analysis tools for selecting candidate enrichment pathways; and lastly, implement the inverse algorithm package for identifying gene–gene regulatory networks in a pathway. We tested LY294002-induced datasets of the MCF7 breast cancer cell lines, and found a CELL CYCLE pathway with 101 genes, ERBB signaling pathway consisting of 82 genes, and MTOR pathway consisting of 45 genes. We consider two interactions: quantity strength depending on number of interactions, and quality strength depending on weight of interaction as positive (+) and negative (−) interactions. Our methods revealed ANAPC1-CDK6 (−0.412) and ORC2L- CHEK1(0.951) for the CELL CYCLE pathway; INS-RPS6 (−3.125) and PRKAA2-PRKAA2 (+1.319) for the MTOR pathway; and CBLB-RPS6KB1 (−0.141), RPS6KB1-CBLC (+0.238) for the ERBB signaling pathway to be top quality interactions. Top quantity interactions discovered include 12; the CDC (−,+) gene family for the CELL CYCLE pathway, 20; PIK3 (−), 23; PIK3CG (+) for the MTOR pathway, 11; PAK (−), 10; PIK3 (+) for the ERBB signaling pathway.

Src Family Tyrosine Kinases in Intestinal Homeostasis, Regeneration and Tumorigenesis

Src, originally identified as an oncogene, is a membrane-anchored tyrosine kinase and the Src family kinase (SFK) prototype. SFKs regulate the signalling induced by a wide range of cell surface receptors leading to epithelial cell growth and adhesion. In the intestine, the SFK members Src, Fyn and Yes regulate epithelial cell proliferation and migration during tissue regeneration and transformation, thus implicating conserved and specific functions. In patients with colon cancer, SFK activity is a marker of poor clinical prognosis and a potent driver of metastasis formation. These tumorigenic activities are linked to SFK capacity to promote the dissemination and tumour-initiating capacities of epithelial tumour cells. However, it is unclear how SFKs promote colon tumour formation and metastatic progression because SFK-encoding genes are unfrequently mutated in human cancer. Here, we review recent findings on SFK signalling during intestinal homeostasis, regeneration and tumorigenesis. We also describe the key nongenetic mechanisms underlying SFK tumour activities in colorectal cancer, and discuss how these mechanisms could be exploited in therapeutic strategies to target SFK signalling in metastatic colon cancer.