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Increased MAPK signaling is a hallmark of various cancers and is a central regulator of cell survival. Direct ERK1/2 inhibition is considered a promising approach to avoid ERK1/2 reactivation caused by upstream kinases BRAF, MEK1/2, and KRAS, as well as by receptor tyrosine kinase inhibitors, but the dynamics and selectivity of ERK1/2 inhibitors are much less studied compared with BRAF or MEK inhibitors. Using ERK1/2 and downstream kinase ELK1 reporter cell lines of lung cancer (H1299; NRAS Q61K ), colon cancer (HCT-116; KRAS G13D ), neuroblastoma (SH-SY5Y), and leukemia (U937), we examined the relationship between ERK inhibition and drug-induced toxicity for five ERK inhibitors: SCH772984, ravoxertinib, LY3214996, ulixertinib, and VX-11e, as well as one MEK inhibitor, PD0325901. Comparing cell viability and ERK inhibition revealed different ERK dependencies for these cell lines. We identify several drugs, such as SCH772984 and VX-11e, which induce excessive toxicity not directly related to ERK1/2 inhibition in specific cell lines. We also show that PD0325901, LY3214996, and ulixertinib are prone to ERK1/2 reactivation over time. We distinguished two types of ERK1/2 reactivation: the first could be reversed by adding a fresh dose of inhibitors, while the second persists even after additional treatments. We also showed that cells that became resistant to the MEK1/2 inhibitor PD0325901 due to ERK1/2 reactivation remained sensitive to ERK1/2 inhibitor ulixertinib. Our data indicate that correlation of ERK inhibition with drug-induced toxicity in multiple cell lines may help to find more selective and effective ERK1/2 inhibitors., Competing Interests: Conflict of interest The authors declare that they have no conflicts of interest with the contents of this article., (Copyright © 2022 The Authors. Published by Elsevier Inc. All rights reserved.)

LINE1 retrotransposons, which are thought to be the remnants of ancient integrations of retrovirus-like elements, are aberrantly (re)activated in many cancer cells. Due to LINE1-induced alterations in target gene expression and/or chromosomal rearrangements, they may be important drivers of tumorigenesis. Moreover, LINE1 encoded proteins, Open Reading Frame (ORF)1 and ORF2, may have pro-oncogenic potential through inductors of oncogenic transcription factors or inhibitors of cell cycle suppressors. The current study therefore aimed to investigate in vitro and in vivo anti-tumorigenic effects of two well-known antiretroviral drugs, zidovudine, a nucleoside analogue inhibitor of RT (NRTI), and efavirenz, a non-nucleoside RT inhibitor (NNRTI). Our data demonstrate that both drugs in clinically relevant doses significantly reduced the proliferation of murine and human cancer cell lines, as well as growth of tumors in a murine subcutaneous model. Intriguingly, we found that the combination of both zidovudine and efavirenz almost entirely blocked tumorigenesis in vivo. Because both drugs are FDA-approved agents and the combination was very well tolerated in mice, the combination therapy as presented in our paper might be an opportunity to treat colorectal tumors and metastasis to the liver in an inexpensive way.

We describe here the Oncobox method for scoring efficiencies of anticancer target drugs (ATDs) using high throughput gene expression data. The method rationale, design, and validation are given along with the examples of its practical applications in biomedicine. The method is based on the analysis of intracellular molecular pathways activation and measuring expressions of molecular target genes for every ATD under consideration. Using Oncobox method requires collection of normal (control) expression profiles and annotated databases of molecular pathways and drug target genes. Both microarray and RNA sequencing profiles are acceptable, although the latter type of data prevails in the most recent applications of this technique.

DNA mutations govern cancer development. Cancer mutation profiles vary dramatically among the individuals. In some cases, they may serve as the predictors of disease progression and response to therapies. However, the biomarker potential of cancer mutations can be dramatically (several orders of magnitude) enhanced by applying molecular pathway-based approach. We developed Oncobox system for calculation of pathway instability (PI) values for the molecular pathways that are aggregated mutation frequencies of the pathway members normalized on gene lengths and on number of genes in the pathway. PI scores can be effective biomarkers in different types of comparisons, for example, as the cancer type biomarkers and as the predictors of tumor response to target therapies. The latter option is implemented using mutation drug score (MDS) values, which algorithmically rank the drugs capacity of interfering with the mutated molecular pathways. Here, describe the mathematical basis and algorithms for PI and MDS values calculation, validation and implementation. The example analysis is provided encompassing 5956 human tumor mutation profiles of 15 cancer types from The Cancer Genome Atlas (TCGA) project, that totally make 2,316,670 mutations in 19,872 genes and 1748 molecular pathways, thus enabling ranking of 128 clinically approved target drugs. Our results evidence that the Oncobox PI and MDS approaches are highly useful for basic and applied aspects of molecular oncology and pharmacology research.

Anticancer target drugs (ATDs) specifically bind and inhibit molecular targets that play important roles in cancer development and progression, being deeply implicated in intracellular signaling pathways. To date, hundreds of different ATDs were approved for clinical use in the different countries. Compared to previous chemotherapy treatments, ATDs often demonstrate reduced side effects and increased efficiency, but also have higher costs. However, the efficiency of ATDs for the advanced stage tumors is still insufficient. Different ATDs have different mechanisms of action and are effective in different cohorts of patients. Personalized approaches are therefore needed to select the best ATD candidates for the individual patients. In this review, we focus on a new generation of biomarkers - molecular pathway activation - and on their applications for predicting individual tumor response to ATDs. The success in high throughput gene expression profiling and emergence of novel bioinformatic tools reinforced quick development of pathway related field of molecular biomedicine. The ability to quantitatively measure degree of a pathway activation using gene expression data has revolutionized this field and made the corresponding analysis quick, robust and inexpensive. This success was further enhanced by using machine learning algorithms for selection of the best biomarkers. We review here the current progress in translating these studies to clinical oncology and patient-oriented adjustment of cancer therapy., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

Biological activity of the new antimicrobial peptide polyphemusin III from the horseshoe crab Limulus polyphemus was examined against bacterial strains and human cancer, transformed, and normal cell cultures. Polyphemusin III has the amino acid sequence RRGCFRVCYRGFCFQRCR and is homologous to other β-hairpin peptides from the horseshoe crab. Antimicrobial activity of the peptide was evaluated and MIC (minimal inhibitory concentration) values were determined. IC 50 (half-maximal inhibitory concentration) values measured toward human cells revealed that polyphemusin III showed a potent cytotoxic activity at concentrations of <10 μM. Polyphemusin III caused fast permeabilization of the cytoplasmic membrane of human leukemia cells HL-60, which was measured with trypan blue exclusion assay and lactate dehydrogenase-release assay. Flow cytometry experiments for annexin V-FITC/ propidium iodide double staining revealed that the caspase inhibitor, Z-VAD-FMK, did not abrogate disruption of the plasma membrane by polyphemusin III. Our data suggest that polyphemusin III disrupts the plasma membrane integrity and induces cell death that is apparently not related to apoptosis. In comparison to known polyphemusins and tachyplesins, polyphemusin III demonstrates a similar or lower antimicrobial effect, but significantly higher cytotoxicity against human cancer and transformed cells in vitro.

Personalized medicine implies that distinct treatment methods are prescribed to individual patients according several features that may be obtained from, e.g., gene expression profile. The majority of machine learning methods suffer from the deficiency of preceding cases, i.e. the gene expression data on patients combined with the confirmed outcome of known treatment methods. At the same time, there exist thousands of various cell lines that were treated with hundreds of anti-cancer drugs in order to check the ability of these drugs to stop the cell proliferation, and all these cell line cultures were profiled in terms of their gene expression. Here we present a new approach in machine learning, which can predict clinical efficiency of anti-cancer drugs for individual patients by transferring features obtained from the expression-based data from cell lines. The method was validated on three datasets for cancer-like diseases (chronic myeloid leukemia, as well as lung adenocarcinoma and renal carcinoma) treated with targeted drugs - kinase inhibitors, such as imatinib or sorafenib.

The development of resistance remains a major obstacle to long-term disease control in cancer patients treated with targeted therapies. In BRAF-mutant mouse models, we demonstrate that although targeted inhibition of either BRAF or VEGF initially suppresses the growth of BRAF-mutant tumors, combined inhibition of both pathways results in apoptosis, long-lasting tumor responses, reduction in lung colonization, and delayed onset of acquired resistance to the BRAF inhibitor PLX4720. As well as inducing tumor vascular normalization and ameliorating hypoxia, this approach induces remodeling of the extracellular matrix, infiltration of macrophages with an M1-like phenotype, and reduction in cancer-associated fibroblasts. At the molecular level, this therapeutic regimen results in a de novo transcriptional signature, which sustains and explains the observed efficacy with regard to cancer progression. Collectively, our findings offer new biological rationales for the management of clinical resistance to BRAF inhibitors based on the combination between BRAF V 600E inhibitors with anti-angiogenic regimens., (© 2016 The Authors. Published under the terms of the CC BY 4.0 license.)

A new generation of anticancer therapeutics called target drugs has quickly developed in the 21st century. These drugs are tailored to inhibit cancer cell growth, proliferation, and viability by specific interactions with one or a few target proteins. However, despite formally known molecular targets for every "target" drug, patient response to treatment remains largely individual and unpredictable. Choosing the most effective personalized treatment remains a major challenge in oncology and is still largely trial and error. Here we present a novel approach for predicting target drug efficacy based on the gene expression signature of the individual tumor sample(s). The enclosed bioinformatic algorithm detects activation of intracellular regulatory pathways in the tumor in comparison to the corresponding normal tissues. According to the nature of the molecular targets of a drug, it predicts whether the drug can prevent cancer growth and survival in each individual case by blocking the abnormally activated tumor-promoting pathways or by reinforcing internal tumor suppressor cascades. To validate the method, we compared the distribution of predicted drug efficacy scores for five drugs (Sorafenib, Bevacizumab, Cetuximab, Sorafenib, Imatinib, Sunitinib) and seven cancer types (Clear Cell Renal Cell Carcinoma, Colon cancer, Lung adenocarcinoma, non-Hodgkin Lymphoma, Thyroid cancer and Sarcoma) with the available clinical trials data for the respective cancer types and drugs. The percent of responders to a drug treatment correlated significantly (Pearson's correlation 0.77 p = 0.023) with the percent of tumors showing high drug scores calculated with the current algorithm.

Effective choice of anticancer drugs is important problem of modern medicine. We developed a method termed OncoFinder for the analysis of new type of biomarkers reflecting activation of intracellular signaling and metabolic molecular pathways. These biomarkers may be linked with the sensitivity to anticancer drugs. In this study, we compared the experimental data obtained in our laboratory and in the Genomics of Drug Sensitivity in Cancer (GDS) project for testing response to anticancer drugs and transcriptomes of various human cell lines. The microarray-based profiling of transcriptomes was performed for the cell lines before the addition of drugs to the medium, and experimental growth inhibition curves were built for each drug, featuring characteristic IC50 values. We assayed here four target drugs - Pazopanib, Sorafenib, Sunitinib and Temsirolimus, and 238 different cell lines, of which 11 were profiled in our laboratory and 227 - in GDS project. Using the OncoFinder-processed transcriptomic data on ~600 molecular pathways, we identified pathways showing significant correlation between pathway activation strength (PAS) and IC50 values for these drugs. Correlations reflect relationships between response to drug and pathway activation features. We intersected the results and found molecular pathways significantly correlated in both our assay and GDS project. For most of these pathways, we generated molecular models of their interaction with known molecular target(s) of the respective drugs. For the first time, our study uncovered mechanisms underlying cancer cell response to drugs at the high-throughput molecular interactomic level.

Fifty-two years have passed since President Nixon launched the "War on Cancer". Despite unparalleled efforts and funds allocated worldwide, the outlined goals were not achieved because cancer treatment approaches such as chemotherapy, radiation therapy, hormonal and targeted therapies have not fully met the expectations. Based on the recent literature, a new direction in cancer therapy can be proposed which targets connections between cancer cells and their microenvironment by chemical means. Cancer–stromal synapses such as immunological synapses between cancer and immune cells provide an attractive target for this approach. Such synapses form ligand–receptor clusters on the interface of the interacting cells. They share a common property of involving intercellular clusters of spatially proximate and cooperatively acting proteins. Synapses provide the space for the focused intercellular signaling molecules exchange. Thus, the disassembly of cancer–stromal synapses may potentially cause the collapse of various tumors. Additionally, the clustered arrangement of synapse components offers opportunities to enhance treatment safety and precision by using targeted crosslinking chemical agents which may inactivate cancer synapses even in reduced concentrations. Furthermore, attaching a cleavable cell-permeable toxic agent(s) to a crosslinker may further enhance the anti-cancer effect of such therapeutics. The highlighted approach promises to be universal, relatively simple and cost-efficient. We also hope that, unlike chemotherapeutic and immune drugs that interact with a single target, by using supramolecular large clusters that include many different components as a target, the emergence of a resistance characteristic of chemo- and immunotherapy is extremely unlikely. [ABSTRACT FROM AUTHOR]

Azvudine (FNC) is a novel cytidine analogue that has both antiviral and anticancer activities. This minireview focuses on its underlying molecular mechanisms of suppressing viral life cycle and cancer cell growth and discusses applications of this nucleoside drug for advanced therapy of tumors and malignant blood diseases. FNC inhibits positive-stand RNA viruses, like HCV, EV, SARS-COV-2, HBV, and retroviruses, including HIV, by suppressing their RNA-dependent polymerase enzymes. It may also inhibit such enzyme (reverse transcriptase) in the human retrotransposons, including human endogenous retroviruses (HERVs). As the activation of retrotransposons can be the major factor of ongoing cancer genome instability and consequently higher aggressiveness of tumors, FNC has a potential to increase the efficacy of multiple anticancer therapies. Furthermore, FNC also showed other aspects of anticancer activity by inhibiting adhesion, migration, invasion, and proliferation of malignant cells. It was also reported to be involved in cell cycle arrest and apoptosis, thereby inhibiting the progression of cancer through different pathways. To the date, the grounds of FNC effects on cancer cells are not fully understood and hence additional studies are needed for better understanding molecular mechanisms of its anticancer activities to support its medical use in oncology. [ABSTRACT FROM AUTHOR]

Molecular diagnostics is becoming one of the major drivers of personalized oncology. With hundreds of different approved anticancer drugs and regimens of their administration, selecting the proper treatment for a patient is at least nontrivial task. This is especially sound for the cases of recurrent and metastatic cancers where the standard lines of therapy failed. Recent trials demonstrated that mutation assays have a strong limitation in personalized selection of therapeutics, consequently, most of the drugs cannot be ranked and only a small percentage of patients can benefit from the screening. Other approaches are, therefore, needed to address a problem of finding proper targeted therapies. The analysis of RNA expression (transcriptomic) profiles presents a reasonable solution because transcriptomics stands a few steps closer to tumor phenotype than the genome analysis. Several recent studies pioneered using transcriptomics for practical oncology and showed truly encouraging clinical results. The possibility of directly measuring of expression levels of molecular drugs' targets and profiling activation of the relevant molecular pathways enables personalized prioritizing for all types of molecular-targeted therapies. RNA sequencing is the most robust tool for the high throughput quantitative transcriptomics. Its use, potentials, and limitations for the clinical oncology will be reviewed here along with the technical aspects such as optimal types of biosamples, RNA sequencing profile normalization, quality controls and several levels of data analysis. [ABSTRACT FROM AUTHOR]

Despite the significant achievements in chemotherapy, cancer remains one of the leading causes of death. Target therapy revolutionized this field, but efficiencies of target drugs show dramatic variation among individual patients. Personalization of target therapies remains, therefore, a challenge in oncology. Here, we proposed molecular pathway-based algorithm for scoring of target drugs using high throughput mutation data to personalize their clinical efficacies. This algorithm was validated on 3,800 exome mutation profiles from The Cancer Genome Atlas (TCGA) project for 128 target drugs. The output values termed Mutational Drug Scores (MDS) showed positive correlation with the published drug efficiencies in clinical trials. We also used MDS approach to simulate all known protein coding genes as the putative drug targets. The model used was built on the basis of 18,273 mutation profiles from COSMIC database for eight cancer types. We found that the MDS algorithm-predicted hits frequently coincide with those already used as targets of the existing cancer drugs, but several novel candidates can be considered promising for further developments. Our results evidence that the MDS is applicable to ranking of anticancer drugs and can be applied for the identification of novel molecular targets. [ABSTRACT FROM AUTHOR]

Background: Inevitable recurrence after radiochemotherapy is the major problem in the treatment of glioblastoma, the most prevalent type of adult brain malignancy. Glioblastomas are notorious for a high degree of intratumor heterogeneity manifest through a diversity of cell types and molecular patterns. The current paradigm of understanding glioblastoma recurrence is that cytotoxic therapy fails to target effectively glioma stem cells. Recent advances indicate that therapy-driven molecular evolution is a fundamental trait associated with glioblastoma recurrence. There is a growing body of evidence indicating that intratumor heterogeneity, longitudinal changes in molecular biomarkers and specific impacts of glioma stem cells need to be taken into consideration in order to increase the accuracy of molecular diagnostics still relying on readouts obtained from a single tumor specimen. Methods: This study integrates a multisampling strategy, longitudinal approach and complementary transcriptomic investigations in order to identify transcriptomic traits of recurrent glioblastoma in whole-tissue specimens of glioblastoma or glioblastoma stem cells. In this study, 128 tissue samples of 44 tumors including 23 first diagnosed, 19 recurrent and 2 secondary recurrent glioblastomas were analyzed along with 27 primary cultures of glioblastoma stem cells by RNA sequencing. A novel algorithm was used to quantify longitudinal changes in pathway activities and model efficacy of anti-cancer drugs based on gene expression data. Results: Our study reveals that intratumor heterogeneity of gene expression patterns is a fundamental characteristic of not only newly diagnosed but also recurrent glioblastomas. Evidence is provided that glioblastoma stem cells recapitulate intratumor heterogeneity, longitudinal transcriptomic changes and drug sensitivity patterns associated with the state of recurrence. Conclusions: Our results provide a transcriptional rationale for the lack of significant therapeutic benefit from temozolomide in patients with recurrent glioblastoma. Our findings imply that the spectrum of potentially effective drugs is likely to differ between newly diagnosed and recurrent glioblastomas and underscore the merits of glioblastoma stem cells as prognostic models for identifying alternative drugs and predicting drug response in recurrent glioblastoma. With the majority of recurrent glioblastomas being inoperable, glioblastoma stem cell models provide the means of compensating for the limited availability of recurrent glioblastoma specimens. [ABSTRACT FROM AUTHOR]

Sequential courses of anticancer target therapy lead to selection of drug-resistant cells, which results in continuous decrease of clinical response. Here we present a new approach for predicting effective combinations of target drugs, which act in a synergistic manner. Synergistic combinations of drugs may prevent or postpone acquired resistance, thus increasing treatment efficiency. We cultured human ovarian carcinoma SKOV-3 and neuroblastoma NGP-127 cancer cell lines in the presence of Tyrosine Kinase Inhibitors (Pazopanib, Sorafenib, and Sunitinib) and Rapalogues (Temsirolimus and Everolimus) for four months and obtained cell lines demonstrating increased drug resistance. We investigated gene expression profiles of intact and resistant cells by microarrays and analyzed alterations in 378 cancer-related signaling pathways using the bioinformatical platform Oncobox. This revealed numerous pathways linked with development of drug resistant phenotypes. Our approach is based on targeting proteins involved in as many as possible signaling pathways upregulated in resistant cells. We tested 13 combinations of drugs and/or selective inhibitors predicted by Oncobox and 10 random combinations. Synergy scores for Oncobox predictions were significantly higher than for randomly selected drug combinations. Thus, the proposed approach significantly outperforms random selection of drugs and can be adopted to enhance discovery of new synergistic combinations of anticancer target drugs. [ABSTRACT FROM AUTHOR]