Mechanisms Of Drug Resistance In Cancer Research Articles

Base editing screens define the genetic landscape of cancer drug resistance mechanisms

Drug resistance is a principal limitation to the long-term efficacy of cancer therapies. Cancer genome sequencing can retrospectively delineate the genetic basis of drug resistance, but this requires large numbers of post-treatment samples to nominate causal variants. Here we prospectively identify genetic mechanisms of resistance to ten oncology drugs from CRISPR base editing mutagenesis screens in four cancer cell lines using a guide RNA library predicted to install 32,476 variants in 11 cancer genes. We identify four functional classes of protein variants modulating drug sensitivity and use single-cell transcriptomics to reveal how these variants operate through distinct mechanisms, including eliciting a drug-addicted cell state. We identify variants that can be targeted with alternative inhibitors to overcome resistance and functionally validate an epidermal growth factor receptor (EGFR) variant that sensitizes lung cancer cells to EGFR inhibitors. Our variant-to-function map has implications for patient stratification, therapy combinations and drug scheduling in cancer treatment.

Exploring the potential of nanoparticles as a drug delivery system for cancer therapy: A review

In this review, we discuss the use of organic nanoparticles such as liposomes, polymeric nanoparticles, dendrimers, and inorganic nanoparticles such as gold nanoparticles and mesoporous silica nanoparticles in drug delivery in chemotherapy for cancer therapy. Another strategy is the use of nanoparticle vehicles for drug delivery through the natural openings on tumor vessels called fenestrations. Both of these can be functionalized to attach angiotensin to lymphocytes or any certain part of the cancer cell membrane, microenvironment, or cytoplasmic or nuclear receptor sites, and therefore a high concentration of drug delivery to targeted cancer cells is achieved, but with less or no toxicity to normal tissue. The potential advantages evident in this technology are useful approaches to established high-concentration drug treatment of cancer tissues without harming normal cells. Some features of a nanoparticle-based drug delivery system include the durability of the vehicle, biocompatibility, permeation, and ability to target specific types of cells. Organic and inorganic nanoparticles have developed this kind of drug-carrier system. Particulate systems are also still being explored for cancer drug resistance mechanisms, and their function in immunotherapy is expanding.

Network-based elucidation of colon cancer drug resistance mechanisms by phosphoproteomic time-series analysis

Aberrant signaling pathway activity is a hallmark of tumorigenesis and progression, which has guided targeted inhibitor design for over 30 years. Yet, adaptive resistance mechanisms, induced by rapid, context-specific signaling network rewiring, continue to challenge therapeutic efficacy. Leveraging progress in proteomic technologies and network-based methodologies, we introduce Virtual Enrichment-based Signaling Protein-activity Analysis (VESPA)—an algorithm designed to elucidate mechanisms of cell response and adaptation to drug perturbations—and use it to analyze 7-point phosphoproteomic time series from colorectal cancer cells treated with clinically-relevant inhibitors and control media. Interrogating tumor-specific enzyme/substrate interactions accurately infers kinase and phosphatase activity, based on their substrate phosphorylation state, effectively accounting for signal crosstalk and sparse phosphoproteome coverage. The analysis elucidates time-dependent signaling pathway response to each drug perturbation and, more importantly, cell adaptive response and rewiring, experimentally confirmed by CRISPR knock-out assays, suggesting broad applicability to cancer and other diseases.

Research Progress of Plant-Derived Natural Products against Drug-Resistant Cancer.

As one of the malignant diseases globally, cancer seriously endangers human physical and mental health because of its high morbidity and mortality. Conventional cancer treatment strategies, such as surgical resection and chemoradiotherapy, are effective at the early stage of cancer but have limited efficacy for advanced cancer. Along with cancer progress and treatment, resistance develops gradually within the population of tumor cells. As a consequence, drug resistance become the major cause that leads to disease progression and poor clinical prognosis in some patients. The mechanisms of cancer drug resistance are quite complex and involve various molecular and cellular mechanisms. Therefore, exploring the mechanisms and finding specific targets are becoming imperative to overcome drug resistance. In recent years, plant-derived natural products have been evaluated as potential therapeutic candidates against cancer with drug resistance due to low side effects and high anticancer efficacy. A growing number of studies have shown that natural products can achieve superior antitumor effects through multiple signaling pathways. The mechanisms include regulation of multiple drug resistance (MDR)-related genes, inhibition of the phosphatidylinositol 3-kinase (PI3K)/protein kinase B (AKT) signaling pathway, induction of autophagy, and blockade of the cell cycle. This paper reviews the molecular and cellular mechanisms of cancer drug resistance, as well as the therapeutic effects and mechanisms of plant-derived natural products against cancer drug resistance. It provides references for developing therapeutic medication for drug-resistant cancer treatment with high efficacy and low side effects.

Mathematical Modelling and Bioinformatics Analyses of Drug Resistance for Cancer Treatment

Abstract: Cancer is a leading cause of human death worldwide. Drug resistance, mainly caused by gene mutation, is a key obstacle to tumour treatment. Therefore, studying the mechanisms of drug resistance in cancer is extremely valuable for clinical applications. : This paper aims to review bioinformatics approaches and mathematical models for determining the evolutionary mechanisms of drug resistance and investigating their functions in designing therapy schemes for cancer diseases. We focus on the models with drug resistance based on genetic mutations for cancer therapy and bioinformatics approaches to study drug resistance involving gene co-expression networks and machine learning algorithms. : We first review mathematical models with single-drug resistance and multidrug resistance. The resistance probability of a drug is different from the order of drug administration in a multidrug resistance model. Then, we discuss bioinformatics methods and machine learning algorithms that are designed to develop gene co-expression networks and explore the functions of gene mutations in drug resistance using multi-omics datasets of cancer cells, which can be used to predict individual drug response and prognostic biomarkers. : It was found that the resistance probability and expected number of drug-resistant tumour cells increase with the increase in the net reproductive rate of resistant tumour cells. Constrained models, such as logistical growth resistance models, can be used to identify more clinically realistic treatment strategies for cancer therapy. In addition, bioinformatics methods and machine learning algorithms can also lead to the development of effective therapy schemes.

Mathematical modeling and bifurcation analysis for a biological mechanism of cancer drug resistance

Mathematical modeling and bifurcation analysis for a biological mechanism of cancer drug resistance

Understanding Cancer's Defense against Topoisomerase-Active Drugs: A Comprehensive Review.

In recent years, the emergence of cancer drug resistance has been one of the crucial tumor hallmarks that are supported by the level of genetic heterogeneity and complexities at cellular levels. Oxidative stress, immune evasion, metabolic reprogramming, overexpression of ABC transporters, and stemness are among the several key contributing molecular and cellular response mechanisms. Topo-active drugs, e.g., doxorubicin and topotecan, are clinically active and are utilized extensively against a wide variety of human tumors and often result in the development of resistance and failure to therapy. Thus, there is an urgent need for an incremental and comprehensive understanding of mechanisms of cancer drug resistance specifically in the context of topo-active drugs. This review delves into the intricate mechanistic aspects of these intracellular and extracellular topo-active drug resistance mechanisms and explores the use of potential combinatorial approaches by utilizing various topo-active drugs and inhibitors of pathways involved in drug resistance. We believe that this review will help guide basic scientists, pre-clinicians, clinicians, and policymakers toward holistic and interdisciplinary strategies that transcend resistance, renewing optimism in the ongoing battle against cancer.

Parsing mechanisms of cancer drug resistance using distributed supercomputing

Parsing mechanisms of cancer drug resistance using distributed supercomputing

Different Mechanisms of Cancer Drug Resistance: A Brief Review

Treatments in oncology have advanced over a long time. At the same pace, steady care for patients getting cancer treatment has extremely advanced, permitting patients to get the most current propels in treatment in both an inpatient and outpatient premise. The acknowledgment of the part of disease control and avoidance (infection prevention and control) within the results of patients living with cancer has been such that it is presently a requirement for healing centers and includes multidisciplinary groups. Today, an expansive volume of data is accessible on sedate resistance components of cancer cells. Opposite to the advance of treatment with chemotherapy drugs, the defensive instruments of cells against cytotoxic compounds are considered an enormous deterrent within the way to effective treatment of cancer. Growing data approximately sedate resistance components might be compelling in planning methodologies to overcome sedate resistance for creating modern drugs with less resistance. A few of the data gotten approximately sedate resistance uncovers modern components that are related to the dispersion of drugs within the body, and these data may be supportive in progressing the dispersion of drugs to completely different patients.

A Review and Meta-analysis on Trastuzumab Resistance in Patients with HER2+ Breast Cancer.

Trastuzumab is a monoclonal antibody that revolutionized the treatment of HER2+ breast cancer. However, about 30% of patients demonstrate resistance to this drug. The purpose of this study is to identify the mechanisms involved in resistance to treatment. with trastuzumab in women undergoing HER2+ breast cancer treatment. A wide review and meta-analysis were performed in the PubMed and Scielo databases up to January 2022. All articles that analyzed the efficacy of the drug in HER2+ human patients treated with trastuzumab were selected, except reviews, meta-analyses, and reports. Egger's test was applied to verify publication bias. Forest plot and PRISMA flowchart were employed. 60 articles were selected for the review and 15 included in the meta-analysis. A total of 102 resistance mechanisms were identified, 73 of which are different from each other. The mechanisms have been classified into 5 different categories. The main resistance mechanisms found are in the PI3K/Akt/mTOR pathway or related to low HER2, often resulting from failure to assess HER2 status. Both groups presented statistical significance. The two groups were not significantly different from each other. Drug resistance is the main challenge of trastuzumab-based treatment. To overcome this challenge, it is important to continue efforts to understand the mechanisms of cancer drug resistance, identify therapies that can treat refractory cancer to current therapies, and possibly create a panel of genes that predict resistance, avoiding symptomatic and economic costs. The main limitation of this study was the selection and population bias.

CRISPR technology: A versatile tool to model, screen, and reverse drug resistance in cancer

BackgroundDrug resistance is a serious challenge in cancer treatment that can render chemotherapy a failure. Understanding the mechanisms behind drug resistance and developing novel therapeutic approaches are cardinal steps in overcoming this issue. Clustered regularly interspaced short palindrome repeats (CRISPR) gene-editing technology has proven to be a useful tool to study cancer drug resistance mechanisms and target the responsible genes. In this review, we evaluated original research studies that used the CRISPR tool in three areas related to drug resistance, namely screening resistance-related genes, generating modified models of resistant cells and animals, and removing resistance by genetic manipulation. We reported the targeted genes, study models, and drug groups in these studies. In addition to discussing different applications of CRISPR technology in cancer drug resistance, we analyzed drug resistance mechanisms and provided examples of CRISPR’s role in studying them. Although CRISPR is a powerful tool for examining drug resistance and sensitizing resistant cells to chemotherapy, more studies are required to overcome its disadvantages, such as off-target effects, immunotoxicity, and inefficient delivery of CRISPR/cas9 into the cells.

Extrachromosomal circular DNA in cancer drug resistance and its potential clinical implications.

Chemotherapy is widely used to treat patients with cancer. However, resistance to chemotherapeutic drugs remains a major clinical concern. The mechanisms of cancer drug resistance are extremely complex and involve such factors such as genomic instability, DNA repair, and chromothripsis. A recently emerging area of interest is extrachromosomal circular DNA (eccDNA), which forms owing to genomic instability and chromothripsis. eccDNA exists widely in physiologically healthy individuals but also arises during tumorigenesis and/or treatment as a drug resistance mechanism. In this review, we summarize the recent progress in research regarding the role of eccDNA in the development of cancer drug resistance as well as the mechanisms thereof. Furthermore, we discuss the clinical applications of eccDNA and propose some novel strategies for characterizing drug-resistant biomarkers and developing potential targeted cancer therapies.

Lactic acid fermentation: A maladaptive mechanism and an evolutionary throwback boosting cancer drug resistance

After four decades of research primarily focused on tumour genetics, the importance of metabolism in tumour biology is receiving renewed attention. Cancer cells undergo energy, biosynthetic and metabolic rewiring, which involves several pathways with a prevalent change from oxidative phosphorylation (OXPHOS) to lactic acid fermentation, known as the Warburg effect. During carcinogenesis, microenvironmental changes can trigger the transition from OXPHOS to lactic acid fermentation, an ancient form of energy supply, mimicking the behaviour of certain anaerobic unicellular organisms according to "atavistic" models of cancer. However, the role of this transition as a mechanism of cancer drug resistance is unclear. Here, we hypothesise that the metabolic rewiring of cancer cells to fermentation can be triggered, enhanced, and sustained by exposure to chronic or high-dose chemotherapy, thereby conferring resistance to drug therapy. We try to expand on the idea that metabolic reprogramming from OXPHOS to lactate fermentation in drug-resistant tumour cells occurs as a general phenotypic mechanism in any type of cancer, regardless of tumour cell heterogeneity, biodiversity, and genetic characteristics. This metabolic response may therefore represent a common feature in cancer biology that could be exploited for therapeutic purposes to overcome chemotherapy resistance, which is currently a major challenge in cancer treatment.

The extracellular RNA and drug resistance in cancer: a narrative review

Background and Objective: Extracellular RNA (exRNA) is an evolving concept. In one hand, more endogenous RNA types are detected in various biofluids or found in local extracellular space. These endogenous exRNAs include microRNAs (miRNAs), long non-coding RNAs (lncRNAs), circular RNAs (circRNAs), piwi-interacting RNAs (piRNAs), transfer RNAs (tRNAs) and protein-coding messenger RNAs (mRNAs). ExRNA profilings are associated with many physiopathological conditions, including drug resistance in cancer. Endogenous exRNAs are also significantly involved in the molecular mechanisms of cancer drug resistance. In the other hand, exRNA types are expanded to include exogenous RNA therapeutics which started to headline in recent years with the approval of several RNA-based drugs. This review discusses current knowledge of exRNAs function in cancer drug resistance, including their value as biomarkers, the significant roles of modulating cancer drug resistance and the usage of exogenous RNA-based therapeutics to relieve cancer drug resistance.

Mechanistic insights into cancer drug resistance through optogenetic PI3K signaling hyperactivation

Hyperactivation of phosphatidylinositol 3-kinase (PI3K) signaling is a prominent feature in cancer cells. However, the mechanism underlying malignant behaviors in the state remains unknown. Here, we describe a mechanism of cancer drug resistance through the protein synthesis pathway, downstream of PI3K signaling. An optogenetic tool (named PPAP2) controlling PI3K signaling was developed. Melanoma cells stably expressing PPAP2 (A375-PPAP2) acquired resistance to a cancer drug in the hyperactivation state. Proteome analyses revealed that expression of the antiapoptotic factor tumor necrosis factor alpha-induced protein 8 (TNFAIP8) was upregulated. TNFAIP8 upregulation was mediated by protein translation from preexisting mRNA. These results suggest that cancer cells escape death via upregulation of TNFAIP8 expression from preexisting mRNA even though alkylating cancer drugs damage DNA.

Nanomedicines for Overcoming Cancer Drug Resistance.

Clinically, cancer drug resistance to chemotherapy, targeted therapy or immunotherapy remains the main impediment towards curative cancer therapy, which leads directly to treatment failure along with extended hospital stays, increased medical costs and high mortality. Therefore, increasing attention has been paid to nanotechnology-based delivery systems for overcoming drug resistance in cancer. In this respect, novel tumor-targeting nanomedicines offer fairly effective therapeutic strategies for surmounting the various limitations of chemotherapy, targeted therapy and immunotherapy, enabling more precise cancer treatment, more convenient monitoring of treatment agents, as well as surmounting cancer drug resistance, including multidrug resistance (MDR). Nanotechnology-based delivery systems, including liposomes, polymer micelles, nanoparticles (NPs), and DNA nanostructures, enable a large number of properly designed therapeutic nanomedicines. In this paper, we review the different mechanisms of cancer drug resistance to chemotherapy, targeted therapy and immunotherapy, and discuss the latest developments in nanomedicines for overcoming cancer drug resistance.

Predicting Anticancer Drug Resistance Mediated by Mutations.

Cancer drug resistance presents a challenge for precision medicine. Drug-resistant mutations are always emerging. In this study, we explored the relationship between drug-resistant mutations and drug resistance from the perspective of protein structure. By combining data from previously identified drug-resistant mutations and information of protein structure and function, we used machine learning-based methods to build models to predict cancer drug resistance mutations. The performance of our combined model achieved an accuracy of 86%, a Matthews correlation coefficient score of 0.57, and an F1 score of 0.66. We have constructed a fast, reliable method that predicts and investigates cancer drug resistance in a protein structure. Nonetheless, more information is needed concerning drug resistance and, in particular, clarification is needed about the relationships between the drug and the drug resistance mutations in proteins. Highly accurate predictions regarding drug resistance mutations can be helpful for developing new strategies with personalized cancer treatments. Our novel concept, which combines protein structure information, has the potential to elucidate physiological mechanisms of cancer drug resistance.

DNA damage and metabolic mechanisms of cancer drug resistance.

Cancer drug resistance is one of the main barriers to overcome to ensure durable treatment responses. While many pivotal advances have been made in first combination therapies, then targeted therapies, and now broadening out to immunomodulatory drugs or metabolic targeting compounds, drug resistance is still ultimately universally fatal. In this brief review, we will discuss different strategies that have been used to fight drug resistance from synthetic lethality to tumor microenvironment modulation, focusing on the DNA damage response and tumor metabolism both within tumor cells and their surrounding microenvironment. In this way, with a better understanding of both targetable mutations in combination with the metabolism, smarter drugs may be designed to combat cancer drug resistance.

Modeling adaptive drug resistance of colorectal cancer and therapeutic interventions with tumor spheroids.

Drug resistance is a major barrier against successful treatments of cancer patients. Various intrinsic mechanisms and adaptive responses of tumor cells to cancer drugs often lead to failure of treatments and tumor relapse. Understanding mechanisms of cancer drug resistance is critical to develop effective treatments with sustained anti-tumor effects. Three-dimensional cultures of cancer cells known as spheroids present a biologically relevant model of avascular tumors and have been increasingly incorporated in tumor biology and cancer drug discovery studies. In this review, we discuss several recent studies from our group that utilized colorectal tumor spheroids to investigate responses of cancer cells to cytotoxic and molecularly targeted drugs and uncover mechanisms of drug resistance. We highlight our findings from both short-term, one-time treatments and long-term, cyclic treatments of tumor spheroids and discuss mechanisms of adaptation of cancer cells to the treatments. Guided by mechanisms of resistance, we demonstrate the feasibility of designing specific drug combinations to effectively block growth and resistance of cancer cells in spheroid cultures. Finally, we conclude with our perspectives on the utility of three-dimensional tumor models and their shortcomings and advantages for phenotypic and mechanistic studies of cancer drug resistance.

Metronomic Chemotherapy Modulates Clonal Interactions to Prevent Drug Resistance in Non-Small Cell Lung Cancer.

Simple SummaryInside a tumor mass, drug resistant and sensitive cell populations co-exist, leading to a therapeutic challenge in oncology. In this study, we developed 2D and 3D co-culture systems and mathematical modeling to better understand how these different populations impact each other. We demonstrated that drug-sensitive cell populations inhibit the growth of drug-resistant populations. Mathematical modeling predicted that metronomic schedules, using chronic administration of drugs at low doses, could better control intratumor cell dynamics. We validated our in silico data in 3D and in vivo models. Finally, we demonstrated that metabolic cell activity of drug-sensitive cells could play a key role in controlling the proliferation of drug-resistant cells. Altogether, our study reports a new mechanism of action of metronomic therapy and paves the way for using it to better control drug resistance.Despite recent advances in deciphering cancer drug resistance mechanisms, relapse is a widely observed phenomenon in advanced cancers, mainly due to intratumor clonal heterogeneity. How tumor clones progress and impact each other remains elusive. In this study, we developed 2D and 3D non-small cell lung cancer co-culture systems and defined a phenomenological mathematical model to better understand clone dynamics. Our results demonstrated that the drug-sensitive clones inhibit the proliferation of the drug-resistant ones under untreated conditions. Model predictions and their experimental in vitro and in vivo validations indicated that a metronomic schedule leads to a better regulation of tumor cell heterogeneity over time than a maximum-tolerated dose schedule, while achieving control of tumor progression. We finally showed that drug-sensitive and -resistant clones exhibited different metabolic statuses that could be involved in controlling the intratumor heterogeneity dynamics. Our data suggested that the glycolytic activity of drug-sensitive clones could play a major role in inhibiting the drug-resistant clone proliferation. Altogether, these computational and experimental approaches provide foundations for using metronomic therapy to control drug-sensitive and -resistant clone balance and highlight the potential of targeting cell metabolism to manage intratumor heterogeneity.