New strategies for targeting kinase networks in cancer

Identification and validation a novel kinase-related gene signature for predicting prognosis and responsiveness to immunotherapy in hepatocellular carcinoma

Liver cancer research highlights the kinome's critical role in disease initiation and progression. However, comprehensive data analysis on the kinome's impact on hepatocellular carcinoma (HCC) prognosis is limited. We used the TCGA-LIHC mRNA expression profiles, analyzing them with various R packages. Key methods included univariate Cox regression for prognostic gene identification, consensus clustering for subtype classification, Gene Set Enrichment Analysis (GSEA), and immune landscape evaluation. A prognostic model was developed using LASSO Cox regression, and chemotherapy drug sensitivity was assessed using the pRRophetic package. We identified 45 kinases-related differentially expressed genes (DEGs), with 27 linked to HCC prognosis. Cluster analysis divided these genes into two subtypes, with distinct prognoses. We discovered 157 DEGs between kinase-related subtypes, 120 of which were prognostically relevant. A kinase-related gene signature (KRS) was developed for prognostic prediction. The high-KRS group showed poorer survival in TCGA-LIHC and validation cohorts, with notable differences in immune cell infiltration and checkpoint gene expression. This group also showed varying sensitivity to common drugs and anti-PD-L1 treatment. In contrast, the low-KRS group might respond better to anti-PD-1 immunotherapy. Our study introduces a kinase-related gene signature as a novel tool for predicting HCC prognosis. This signature aids in tailoring personalized treatment strategies, potentially improving clinical outcomes in HCC patients.

Discovery of 1-phenyl-1,2,3-triazole ureas as dual VEGFR-2/JNK-1 type II kinase inhibitors targeting pancreatic cancer

In oncology, pancreatic cancer (PC) continues to be a problem that requires creative approaches to therapy. This research aims to create dual kinase inhibitors that target VEGFR-2 and JNK-1, two important factors in the angiogenesis and progression of PC. We found compounds with promising anticancer action using phenyltriazolyl piperazine/(piperidine) carboxamides (PTPCs) and phenyltriazolyl phenylureas (PTPUs). Compound 12b was the most effective in inhibiting VEGFR-2 (IC50: 46 nM) and JNK-1 (IC50: 35 nM) and showed the highest activity against PANC-1 cancer cells (IC50: 1.05 μM). Furthermore, 12b altered the caspase-3, Bcl-2, and Bax apoptotic markers. The binding interactions of 12b with target kinases were discovered by in silico investigations. This study emphasizes how dual kinase inhibitors may be a viable way to improve the effectiveness of cancer treatments and deal with resistance mechanisms.

Vertical inhibition of p110α/AKT and N-cadherin enhances treatment efficacy in PIK3CA-aberrated ovarian cancer cells

Phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit alpha [PIK3CA, encoding PI3Kalpha (also known as p110α)] is one of the most commonly aberrated genes in human cancers. In serous ovarian cancer, PIK3CA amplification is highly frequent but PIK3CA point mutation is rare. However, whether PIK3CA amplification and PIK3CA driver mutations have the same functional impact in the disease is unclear. Here, we report that both PIK3CA amplification and E545K mutation are tumorigenic. While the protein kinase B (AKT) signaling axis was activated in both E545K knock-in cells and PIK3CA-overexpressing cells, the mitogen-activated protein kinase 3/1 (ERK1/2) pathway was induced selectively by E545K mutation but not PIK3CA amplification. Intriguingly, AKT signaling in these PIK3CA-aberrated cells increased transcriptional coactivator YAP1 (YAP) Ser127 phosphorylation and thereby cytoplasmic YAP levels, which in turn increased cell migration through Ras-related C3 botulinum toxin substrate 1 (RAC1) activation. In addition to the altered YAP signaling, AKT upregulated N-cadherin expression, which also contributed to cell migration. Pharmacological inhibition of N-cadherin reduced cell migratory potential. Importantly, co-targeting N-cadherin and p110α/AKT caused additive reduction in cell migration in vitro and metastases formation in vivo. Together, this study reveals the molecular pathways driven by the PIK3CA aberrations and the exploitable vulnerabilities in PIK3CA-aberrated serous ovarian cancer cells.

Development of natural product-based targeted protein degraders as anticancer agents

Targeted protein degradation (TPD) has emerged as a powerful approach for eliminating cancer-causing proteins through an "event-driven" pharmacological mode. Proteolysis-targeting chimeras (PROTACs), molecular glues (MGs), and hydrophobic tagging (HyTing) have evolved into three major classes of TPD technologies. Natural products (NPs) are a primary source of anticancer drugs and have played important roles in the development of TPD technology. NPs potentially expand the toolbox of TPD by providing a variety of E3 ligase ligands, protein of interest (POI) warheads, and hydrophobic tags (HyTs). As a promising direction in the TPD field, NP-based degraders have shown great potential for anticancer therapy. In this review, we summarize recent advances in the development of NP-based degraders (PROTACs, MGs and HyTing) with anticancer applications. Moreover, we put forward the challenges while presenting potential opportunities for the advancement of future targeted protein degraders derived from NPs.

Compressive stresses in cancer: characterization and implications for tumour progression and treatment

Beyond their many well-established biological aberrations, solid tumours create an abnormal physical microenvironment that fuels cancer progression and confers treatment resistance. Mechanical forces impact tumours across a range of biological sizes and timescales, from rapid events at the molecular level involved in their sensing and transmission, to slower and larger-scale events, including clonal selection, epigenetic changes, cell invasion, metastasis and immune response. Owing to challenges with studying these dynamic stimuli in biological systems, the mechanistic understanding of the effects and pathways triggered by abnormally elevated mechanical forces remains elusive, despite clear correlations with cancer pathophysiology, aggressiveness and therapeutic resistance. In this Review, we examine the emerging and diverse roles of physical forces in solid tumours and provide a comprehensive framework for understanding solid stress mechanobiology. We first review the physiological importance of mechanical forces, especially compressive stresses, and discuss their defining characteristics, biological context and relative magnitudes. We then explain how abnormal compressive stresses emerge in tumours and describe the experimental challenges in investigating these mechanically induced processes. Finally, we discuss the clinical translation of mechanotherapeutics that alleviate solid stresses and their potential to synergize with chemotherapy, radiotherapy and immunotherapies.

Emerging and Biological Concepts in Pediatric High-Grade Gliomas

Primary central nervous system tumors are the most frequent solid tumors in children, accounting for over 40% of all childhood brain tumor deaths, specifically high-grade gliomas. Compared with pediatric low-grade gliomas (pLGGs), pediatric high-grade gliomas (pHGGs) have an abysmal survival rate. The WHO CNS classification identifies four subtypes of pHGGs, including Grade 4 Diffuse midline glioma H3K27-altered, Grade 4 Diffuse hemispheric gliomas H3-G34-mutant, Grade 4 pediatric-type high-grade glioma H3-wildtype and IDH-wildtype, and infant-type hemispheric gliomas. In recent years, we have seen promising advancements in treatment strategies for pediatric high-grade gliomas, including immunotherapy, CAR-T cell therapy, and vaccine approaches, which are currently undergoing clinical trials. These therapies are underscored by the integration of molecular features that further stratify HGG subtypes. Herein, we will discuss the molecular features of pediatric high-grade gliomas and the evolving landscape for treating these challenging tumors.

Repurposing approved protein kinase inhibitors as potent anti-leishmanials targeting Leishmania MAP kinases

AIMS

Leishmaniasis, caused by the protozoan parasite poses a significant health burden globally. With a very few specific drugs, increased drug resistance it is important to look for drug repurposing along with the identification of pre-clinical candidates against visceral leishmaniasis. This study aims to identify potential drug candidates against visceral leishmaniasis by targeting leishmanial MAP kinases and screening FDA approved protein kinase inhibitors.

MATERIALS AND METHODS

MAP kinases were identified from the Leishmania genome. 12 FDA approved protein kinase inhibitors were screened against Leishmania MAP kinases. Binding affinity, ADME and toxicity of identified drug candidates were profiled. The anti-proliferative effects and mechanism of action were assessed in Leishmania, including changes in cell morphology, flagellar length, cell cycle progression, reactive oxygen species (ROS) generation, and intra-macrophage parasitic burden.

KEY FINDINGS

23 MAP kinases were identified from the Leishmania genome. Sorafenib and imatinib emerged as repurposable drug candidates and demonstrated excellent anti-proliferative effects in Leishmania. Treatment with these inhibitors resulted in significant changes in cell morphology, flagellar length, and cell cycle arrest. Furthermore, sorafenib and imatinib promoted ROS generation and reduced intra-macrophage parasitic burden, and elicited anti-leishmanial activity in in vivo experimental VL models.

SIGNIFICANCE

Collectively, these results imply involvement of MAP kinases in infectivity and survival of the parasite and can pave the avenue for repurposing sorafenib and imatinib as anti-leishmanial agents. These findings contribute to the exploration of new treatment options for visceral leishmaniasis, particularly in the context of emerging drug resistance.

A first-in-class selective inhibitor of EGFR and PI3K offers a single-molecule approach to targeting adaptive resistance

Despite tremendous progress in precision oncology, adaptive resistance mechanisms limit the long-term effectiveness of molecularly targeted agents. Here we evaluated the pharmacological profile of MTX-531 that was computationally designed to selectively target two key resistance drivers, epidermal growth factor receptor and phosphatidylinositol 3-OH kinase (PI3K). MTX-531 exhibits low-nanomolar potency against both targets with a high degree of specificity predicted by cocrystal structural analyses. MTX-531 monotherapy uniformly resulted in tumor regressions of squamous head and neck patient-derived xenograft (PDX) models. The combination of MTX-531 with mitogen-activated protein kinase kinase or KRAS-G12C inhibitors led to durable regressions of BRAF-mutant or KRAS-mutant colorectal cancer PDX models, resulting in striking increases in median survival. MTX-531 is exceptionally well tolerated in mice and uniquely does not lead to the hyperglycemia commonly seen with PI3K inhibitors. Here, we show that MTX-531 acts as a weak agonist of peroxisome proliferator-activated receptor-γ, an attribute that likely mitigates hyperglycemia induced by PI3K inhibition. This unique feature of MTX-531 confers a favorable therapeutic index not typically seen with PI3K inhibitors.

Oxygen tension-dependent variability in the cancer cell kinome impacts signaling pathways and response to targeted therapies

Most cells in solid tumors are exposed to oxygen levels between 0.5% and 5%. We developed an approach that allows collection, processing, and evaluation of cancer and non-cancer cells under physioxia, while preventing exposure to ambient air. This aided comparison of baseline and drug-induced changes in signaling pathways under physioxia and ambient oxygen. Using tumor cells from transgenic models of breast cancer and cells from breast tissues of clinically breast cancer-free women, we demonstrate oxygen-dependent differences in cell preference for epidermal growth factor receptor (EGFR) or platelet-derived growth factor receptor beta (PDGFRβ) signaling. Physioxia caused PDGFRβ-mediated activation of AKT and extracellular regulated kinase (ERK) that reduced sensitivity to EGFR and phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit alpha (PIK3CA) inhibition and maintained PDGFRβ+ epithelial-mesenchymal hybrid cells with potential cancer stem cell (CSC) properties. Cells in ambient air displayed differential EGFR activation and were more sensitive to targeted therapies. Our data emphasize the importance of oxygen considerations in preclinical cancer research to identify effective drug targets and develop combination therapy regimens.

Molecular pathways and therapeutic strategies in dermatofibrosarcoma protuberans (DFSP): unravelling the tumor's genetic landscape

Dermatofibrosarcoma Protuberans (DFSP) is a rare soft tissue sarcoma distinguished by its infiltrative growth pattern and recurrence potential. Understanding the molecular characteristics of DFSP is essential for enhancing its diagnosis, prognosis, and treatment strategies. The paper provides an overview of DFSP, highlighting the significance of its molecular understanding. The gene expression profiling has uncovered unique molecular signatures in DFSP, highlighting its heterogeneity and potential therapeutic targets. The Platelet-Derived Growth Factor Receptors (PDGFRs) and Fibroblast Growth Factor Receptors (FGFRs) signaling pathways play essential roles in the progression and development of DFSP. The abnormal activation of these pathways presents opportunities for therapeutic interventions. Several emerging therapies, i.e., immunotherapies, immunomodulatory strategies, and immune checkpoint inhibitors, offer promising alternatives to surgical resection. In DFSP management, combination strategies, including rational combination therapies, aim to exploit the synergistic effects and overcome resistance. The article consisting future perspectives and challenges includes the discovery of prognostic and predictive biomarkers to improve risk stratification and treatment selection. Preclinical models, such as Patient-derived xenografts (PDX) and genetically engineered mouse models, help study the biology of DFSP and evaluate therapeutic interventions. The manuscript also covers small-molecule inhibitors, clinical trials, immune checkpoint inhibitors for DFSP treatment, combination therapies, rational therapies, and resistance mechanisms, which are unique and not broadly covered in recent pieces of literature. See also the graphical abstract(Fig. 1).

Combined kinome inhibition states are predictive of cancer cell line sensitivity to kinase inhibitor combination therapies

Protein kinases are a primary focus in targeted therapy development for cancer, owing to their role as regulators in nearly all areas of cell life. Recent strategies targeting the kinome with combination therapies have shown promise, such as trametinib and dabrafenib in advanced melanoma, but empirical design for less characterized pathways remains a challenge. Computational combination screening is an attractive alternative, allowing in-silico filtering prior to experimental testing of drastically fewer leads, increasing efficiency and effectiveness of drug development pipelines. In this work, we generated combined kinome inhibition states of 40,000 kinase inhibitor combinations from kinobeads-based kinome profiling across 64 doses. We then integrated these with transcriptomics from CCLE to build machine learning models with elastic-net feature selection to predict cell line sensitivity across nine cancer types, with accuracy R2 ∼ 0.75-0.9. We then validated the model by using a PDX-derived TNBC cell line and saw good global accuracy (R2 ∼ 0.7) as well as high accuracy in predicting synergy using four popular metrics (R2 ∼ 0.9). Additionally, the model was able to predict a highly synergistic combination of trametinib and omipalisib for TNBC treatment, which incidentally was recently in phase I clinical trials. Our choice of tree-based models for greater interpretability allowed interrogation of highly predictive kinases in each cancer type, such as the MAPK, CDK, and STK kinases. Overall, these results suggest that kinome inhibition states of kinase inhibitor combinations are strongly predictive of cell line responses and have great potential for integration into computational drug screening pipelines. This approach may facilitate the identification of effective kinase inhibitor combinations and accelerate the development of novel cancer therapies, ultimately improving patient outcomes.

Integrated single-dose kinome profiling data is predictive of cancer cell line sensitivity to kinase inhibitors

Protein kinase activity forms the backbone of cellular information transfer, acting both individually and as part of a broader network, the kinome. Their central role in signaling leads to kinome dysfunction being a common driver of disease, and in particular cancer, where numerous kinases have been identified as having a causal or modulating role in tumor development and progression. As a result, the development of therapies targeting kinases has rapidly grown, with over 70 kinase inhibitors approved for use in the clinic and over double this number currently in clinical trials. Understanding the relationship between kinase inhibitor treatment and their effects on downstream cellular phenotype is thus of clear importance for understanding treatment mechanisms and streamlining compound screening in therapy development. In this work, we combine two large-scale kinome profiling data sets and use them to link inhibitor-kinome interactions with cell line treatment responses (AUC/IC50). We then built computational models on this data set that achieve a high degree of prediction accuracy (R2 of 0.7 and RMSE of 0.9) and were able to identify a set of well-characterized and understudied kinases that significantly affect cell responses. We further validated these models experimentally by testing predicted effects in breast cancer cell lines and extended the model scope by performing additional validation in patient-derived pancreatic cancer cell lines. Overall, these results demonstrate that broad quantification of kinome inhibition state is highly predictive of downstream cellular phenotypes.

Combined kinome inhibition states are predictive of cancer cell line sensitivity to kinase inhibitor combination therapies

Protein kinases are a primary focus in targeted therapy development for cancer, owing to their role as regulators in nearly all areas of cell life. Kinase inhibitors are one of the fastest growing drug classes in oncology, but resistance acquisition to kinase-targeting monotherapies is inevitable due to the dynamic and interconnected nature of the kinome in response to perturbation. Recent strategies targeting the kinome with combination therapies have shown promise, such as the approval of Trametinib and Dabrafenib in advanced melanoma, but similar empirical combination design for less characterized pathways remains a challenge. Computational combination screening is an attractive alternative, allowing in-silico screening prior to in-vitro or in-vivo testing of drastically fewer leads, increasing efficiency and effectiveness of drug development pipelines. In this work, we generate combined kinome inhibition states of 40,000 kinase inhibitor combinations from kinobeads-based kinome profiling across 64 doses. We then integrated these with baseline transcriptomics from CCLE to build robust machine learning models to predict cell line sensitivity from NCI-ALMANAC across nine cancer types, with model accuracy R2 ~ 0.75-0.9 after feature selection using elastic-net regression. We further validated the model's ability to extend to real-world examples by using the best-performing breast cancer model to generate predictions for kinase inhibitor combination sensitivity and synergy in a PDX-derived TNBC cell line and saw reasonable global accuracy in our experimental validation (R2 ~ 0.7) as well as high accuracy in predicting synergy using four popular metrics (R2 ~ 0.9). Additionally, the model was able to predict a highly synergistic combination of Trametinib (MEK inhibitor) and Omipalisib (PI3K inhibitor) for TNBC treatment, which incidentally was recently in phase I clinical trials for TNBC. Our choice of tree-based models over networks for greater interpretability also allowed us to further interrogate which specific kinases were highly predictive of cell sensitivity in each cancer type, and we saw confirmatory strong predictive power in the inhibition of MAPK, CDK, and STK kinases. Overall, these results suggest that kinome inhibition states of kinase inhibitor combinations are strongly predictive of cell line responses and have great potential for integration into computational drug screening pipelines. This approach may facilitate the identification of effective kinase inhibitor combinations and accelerate the development of novel cancer therapies, ultimately improving patient outcomes.

Phosphoproteomics guides effective low-dose drug combinations against pancreatic ductal adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is a devastating disease with a limited set of known driver mutations but considerable cancer cell heterogeneity. Phosphoproteomics provides a readout of aberrant signaling and has the potential to identify new targets and guide treatment decisions. Using two-step sequential phosphopeptide enrichment, we generate a comprehensive phosphoproteome and proteome of nine PDAC cell lines, encompassing more than 20,000 phosphosites on 5,763 phospho-proteins, including 316 protein kinases. By using integrative inferred kinase activity (INKA) scoring, we identify multiple (parallel) activated kinases that are subsequently matched to kinase inhibitors. Compared with high-dose single-drug treatments, INKA-tailored low-dose 3-drug combinations against multiple targets demonstrate superior efficacy against PDAC cell lines, organoid cultures, and patient-derived xenografts. Overall, this approach is particularly more effective against the aggressive mesenchymal PDAC model compared with the epithelial model in both preclinical settings and may contribute to improved treatment outcomes in PDAC patients.

Differential Regulation of GPVI-Induced Btk and Syk Activation by PKC, PKA and PP2A in Human Platelets

Bruton's tyrosine kinase (Btk) and spleen tyrosine kinase (Syk) are major signaling proteins in human platelets that are implicated in atherothrombosis and thrombo-inflammation, but the mechanisms controlling their activities are not well understood. Previously, we showed that Syk becomes phosphorylated at S297 in glycoprotein VI (GPVI)-stimulated human platelets, which limits Syk activation. Here, we tested the hypothesis that protein kinases C (PKC) and A (PKA) and protein phosphatase 2A (PP2A) jointly regulate GPVI-induced Btk activation in platelets. The GPVI agonist convulxin caused rapid, transient Btk phosphorylation at S180 (pS180↑), Y223 and Y551, while direct PKC activation strongly increased Btk pS180 and pY551. This increase in Btk pY551 was also Src family kinase (SFK)-dependent, but surprisingly Syk-independent, pointing to an alternative mechanism of Btk phosphorylation and activation. PKC inhibition abolished convulxin-stimulated Btk pS180 and Syk pS297, but markedly increased the tyrosine phosphorylation of Syk, Btk and effector phospholipase Cγ2 (PLCγ2). PKA activation increased convulxin-induced Btk activation at Y551 but strongly suppressed Btk pS180 and Syk pS297. PP2A inhibition by okadaic acid only increased Syk pS297. Both platelet aggregation and PLCγ2 phosphorylation with convulxin stimulation were Btk-dependent, as shown by the selective Btk inhibitor acalabrutinib. Together, these results revealed in GPVI-stimulated platelets a transient Syk, Btk and PLCγ2 phosphorylation at multiple sites, which are differentially regulated by PKC, PKA or PP2A. Our work thereby demonstrated the GPVI-Syk-Btk signalosome as a tightly controlled protein kinase network, in agreement with its role in atherothrombosis.

Receptor tyrosine kinase inhibitors in cancer

Targeted therapy is a new cancer treatment approach, involving drugs that particularly target specific proteins in cancer cells, such as receptor tyrosine kinases (RTKs) which are involved in promoting growth and proliferation, Therefore inhibiting these proteins could impede cancer progression. An understanding of RTKs and the relevant signaling cascades, has enabled the development of many targeted drug therapies employing RTK inhibitors (RTKIs) some of which have entered clinical application. Here we discuss RTK structures, activation mechanisms and functions. Moreover, we cover the potential effects of combination drug therapy (including chemotherapy or immunotherapy agents with one RTKI or multiple RTKIs) especially for drug resistant cancers.

Mapping the Protein Kinome: Current Strategy and Future Direction

The kinome includes over 500 different protein kinases, which form an integrated kinase network that regulates cellular phosphorylation signals. The kinome plays a central role in almost every cellular process and has strong linkages with many diseases. Thus, the evaluation of the cellular kinome in the physiological environment is essential to understand biological processes, disease development, and to target therapy. Currently, a number of strategies for kinome analysis have been developed, which are based on monitoring the phosphorylation of kinases or substrates. They have enabled researchers to tackle increasingly complex biological problems and pathological processes, and have promoted the development of kinase inhibitors. Additionally, with the increasing interest in how kinases participate in biological processes at spatial scales, it has become urgent to develop tools to estimate spatial kinome activity. With multidisciplinary efforts, a growing number of novel approaches have the potential to be applied to spatial kinome analysis. In this paper, we review the widely used methods used for kinome analysis and the challenges encountered in their applications. Meanwhile, potential approaches that may be of benefit to spatial kinome study are explored.

Deciphering the Role and Signaling Pathways of PKCα in Luminal A Breast Cancer Cells

Protein kinase C (PKC) comprises a family of highly related serine/threonine protein kinases involved in multiple signaling pathways, which control cell proliferation, survival, and differentiation. The role of PKCα in cancer has been studied for many years. However, it has been impossible to establish whether PKCα acts as an oncogene or a tumor suppressor. Here, we analyzed the importance of PKCα in cellular processes such as proliferation, migration, or apoptosis by inhibiting its gene expression in a luminal A breast cancer cell line (MCF-7). Differential expression analysis and phospho-kinase arrays of PKCα-KD vs. PKCα-WT MCF-7 cells identified an essential set of proteins and oncogenic kinases of the JAK/STAT and PI3K/AKT pathways that were down-regulated, whereas IGF1R, ERK1/2, and p53 were up-regulated. In addition, unexpected genes related to the interferon pathway appeared down-regulated, while PLC, ERBB4, or PDGFA displayed up-regulated. The integration of this information clearly showed us the usefulness of inhibiting a multifunctional kinase-like PKCα in the first step to control the tumor phenotype. Then allowing us to design a possible selection of specific inhibitors for the unexpected up-regulated pathways to further provide a second step of treatment to inhibit the proliferation and migration of MCF-7 cells. The results of this study suggest that PKCα plays an oncogenic role in this type of breast cancer model. In addition, it reveals the signaling mode of PKCα at both gene expression and kinase activation. In this way, a wide range of proteins can implement a new strategy to fine-tune the control of crucial functions in these cells and pave the way for designing targeted cancer therapies.

ERK2 MAP kinase regulates SUFU binding by multisite phosphorylation of GLI1

Crosstalk between the Hedgehog and MAPK signaling pathways occurs in several types of cancer and contributes to clinical resistance to Hedgehog pathway inhibitors. Here we show that MAP kinase-mediated phosphorylation weakens the binding of the GLI1 transcription factor to its negative regulator SUFU. ERK2 phosphorylates GLI1 on three evolutionarily conserved target sites (S102, S116, and S130) located near the high-affinity binding site for SUFU; these phosphorylations cooperate to weaken the affinity of GLI1-SUFU binding by over 25-fold. Phosphorylation of any one, or even any two, of the three sites does not result in the level of SUFU release seen when all three sites are phosphorylated. Tumor-derived mutations in R100 and S105, residues bordering S102, also diminish SUFU binding, collectively defining a novel evolutionarily conserved SUFU affinity-modulating region. In cultured mammalian cells, GLI1 variants containing phosphomimetic substitutions of S102, S116, and S130 displayed an increased ability to drive transcription. We conclude that multisite phosphorylation of GLI1 by ERK2 or other MAP kinases weakens GLI1-SUFU binding, thereby facilitating GLI1 activation and contributing to both physiological and pathological crosstalk.

Targeting mitochondrial metabolism for metastatic cancer therapy

Primary tumors evolve metabolic mechanisms favoring glycolysis for adenosine triphosphate (ATP) generation and antioxidant defenses. In contrast, metastatic cells frequently depend on mitochondrial respiration and oxidative phosphorylation (OxPhos). This reliance of metastatic cells on OxPhos can be exploited using drugs that target mitochondrial metabolism. Therefore, therapeutic agents that act via diverse mechanisms, including the activation of signaling pathways that promote the production of reactive oxygen species (ROS) and/or a reduction in antioxidant defenses may elevate oxidative stress and inhibit tumor cell survival. In this review, we will provide (1) a mechanistic analysis of function-selective extracellular signal-regulated kinase-1/2 (ERK1/2) inhibitors that inhibit cancer cells through enhanced ROS, (2) a review of the role of mitochondrial ATP synthase in redox regulation and drug resistance, (3) a rationale for inhibiting ERK signaling and mitochondrial OxPhos toward the therapeutic goal of reducing tumor metastasis and treatment resistance. Recent reports from our laboratories using metastatic melanoma and breast cancer models have shown the preclinical efficacy of novel and rationally designed therapeutic agents that target ERK1/2 signaling and mitochondrial ATP synthase, which modulate ROS events that may prevent or treat metastatic cancer. These findings and those of others suggest that targeting a tumor's metabolic requirements and vulnerabilities may inhibit metastatic pathways and tumor growth. Approaches that exploit the ability of therapeutic agents to alter oxidative balance in tumor cells may be selective for cancer cells and may ultimately have an impact on clinical efficacy and safety. Elucidating the translational potential of metabolic targeting could lead to the discovery of new approaches for treatment of metastatic cancer.

Integrated Drug Mining Reveals Actionable Strategies Inhibiting Plexiform Neurofibromas

Neurofibromatosis Type 1 (NF1) is one of the most common genetic tumor predisposition syndromes, affecting up to 1 in 2500 individuals. Up to half of patients with NF1 develop benign nerve sheath tumors called plexiform neurofibromas (PNs), characterized by biallelic NF1 loss. PNs can grow to immense sizes, cause extensive morbidity, and harbor a 15% lifetime risk of malignant transformation. Increasingly, molecular sequencing and drug screening data from various preclinical murine and human PN cell lines, murine models, and human PN tissues are available to help identify salient treatments for PNs. Despite this, Selumetinib, a MEK inhibitor, is the only currently FDA-approved pharmacotherapy for symptomatic and inoperable PNs in pediatric NF1 patients. The discovery of alternative and additional treatments has been hampered by the rarity of the disease, which makes prioritizing drugs to be tested in future clinical trials immensely important. Here, we propose a gene regulatory network-based integrated analysis to mine high-throughput cell line-based drug data combined with transcriptomes from resected human PN tumors. Conserved network modules were characterized and served as drug fingerprints reflecting the biological connections among drug effects and the inherent properties of PN cell lines and tissue. Drug candidates were ranked, and the therapeutic potential of drug combinations was evaluated via computational predication. Auspicious therapeutic agents and drug combinations were proposed for further investigation in preclinical and clinical trials.

A Stochastic Binary Model for the Regulation of Gene Expression to Investigate Responses to Gene Therapy

In this manuscript, we use an exactly solvable stochastic binary model for the regulation of gene expression to analyze the dynamics of response to a treatment aiming to modulate the number of transcripts of a master regulatory switching gene. The challenge is to combine multiple processes with different time scales to control the treatment response by a switching gene in an unavoidable noisy environment. To establish biologically relevant timescales for the parameters of the model, we select the RKIP gene and two non-specific drugs already known for changing RKIP levels in cancer cells. We demonstrate the usefulness of our method simulating three treatment scenarios aiming to reestablish RKIP gene expression dynamics toward a pre-cancerous state: (1) to increase the promoter's ON state duration; (2) to increase the mRNAs' synthesis rate; and (3) to increase both rates. We show that the pre-treatment kinetic rates of ON and OFF promoter switching speeds and mRNA synthesis and degradation will affect the heterogeneity and time for treatment response. Hence, we present a strategy for reaching increased average mRNA levels with diminished heterogeneity while reducing drug dosage by simultaneously targeting multiple kinetic rates that effectively represent the chemical processes underlying the regulation of gene expression. The decrease in heterogeneity of treatment response by a target gene helps to lower the chances of emergence of resistance. Our approach may be useful for inferring kinetic constants related to the expression of antimetastatic genes or oncogenes and for the design of multi-drug therapeutic strategies targeting the processes underpinning the expression of master regulatory genes.

Adaptive chromatin remodeling and transcriptional changes of the functional kinome in tumor cells in response to targeted kinase inhibition

Pharmacological inhibition of protein kinases induces adaptive reprogramming of tumor cell regulatory networks by altering expression of genes that regulate signaling, including protein kinases. Adaptive responses are dependent on transcriptional changes resulting from remodeling of enhancer and promoter landscapes. Enhancer and promoter remodeling in response to targeted kinase inhibition is controlled by changes in open chromatin state and by activity of specific transcription factors, such as c-MYC. This review focuses on the dynamic plasticity of protein kinase expression of the tumor cell kinome and the resulting adaptive resistance to targeted kinase inhibition. Plasticity of the functional kinome has been shown in patient window trials where triple-negative and human epidermal growth factor receptor 2–positive breast cancer patient tumors were characterized by RNAseq after biopsies before and after 1 week of therapy. The expressed kinome changed dramatically during drug treatment, and these changes in kinase expression were shown in cell lines and xenografts in mice to be correlated with adaptive tumor cell drug resistance. The dynamic transcriptional nature of the kinome also differs for inhibitors targeting different kinase signaling pathways (e.g., BRAF-MEK-ERK versus PI3K-AKT) that are commonly activated in cancers. Heterogeneity arising from differences in gene regulation and mutations represents a challenge to therapeutic durability and prevention of clinical drug resistance with drug-tolerant tumor cell populations developing and persisting through treatment. We conclude that understanding the heterogeneity of kinase expression at baseline and in response to therapy is imperative for development of combinations and timing intervals of therapies making interventions durable.

Is the Fixed Periodic Treatment Effective for the Tumor System without Complete Information?

Objective

The treatment plans designed with the guidance of the mathematical model and adaptive strategy can trap tumor subpopulations in a periodic and controllable loop. But this process requires detailed information about the tumor system, which is difficult to obtain. Therefore, we wondered whether the fixed periodic treatment plans designed with the typical values of population parameters could be applied to a similar tumor system without complete information.

Methods

A binary tumor system constructed by an EGFR-mutant and a KRAS-mutant cell line was used to explore the applicability of the fixed periodic treatment plans. The dynamics of this system were described by combining the Lotka-Volterra model with the framework of the nonlinear mixed-effects model. The typical values of population parameters were used to design the plans, and the robust plans were screened through parameter variation. These screened plans were examined their applicability in animal experiments and simulations.

Results

In animal experiments where system parameters vary from -30% to 30%, the "osimertinib administration, withdrawal, FK866 administration and withdrawal" plan can trap subpopulations of the system in periodic cycles. In simulation, when there was an unknown resistant subpopulation, the screened fixed periodic treatment plans can still delay the evolution of resistance. The median outcomes of screened plans were better than conventional sequential treatment in most cases. There was no significant difference between the outcomes of the screened plan with median stability and the optimal therapy. The evolutionary trajectories of these two plans were similar.

Conclusion

According to the results, these fixed periodic plans should be tried in treatment even the information of the tumor system was incomplete.