Understanding the Genetic Mechanisms of Cancer Drug Resistance Using Genomic Approaches

Research progress in MCM family: Focus on the tumor treatment resistance

Malignant tumors constitute a significant category of diseases posing a severe threat to human survival and health, thereby representing one of the most challenging and pressing issues in the field of biomedical research. Due to their malignant nature, which is characterized by a high potential for metastasis, rapid dissemination, and frequent recurrence, the prevailing approach in clinical oncology involves a comprehensive treatment strategy that combines surgery with radiotherapy, chemotherapy, targeted drug therapies, and other interventions. Treatment resistance remains a major obstacle in the comprehensive management of tumors, serving as a primary cause for the failure of integrated tumor therapies and a critical factor contributing to patient relapse and mortality. The Minichromosome Maintenance (MCM) protein family comprises functional proteins closely associated with the development of resistance in tumor therapy.The influence of MCMs manifests through various pathways, encompassing modulation of DNA replication, cell cycle regulation, and DNA damage repair mechanisms. Consequently, this leads to an enhanced tolerance of tumor cells to chemotherapy, targeted drugs, and radiation. Consequently, this review explores the specific roles of the MCM family in various cancer treatment strategies. Its objective is to enhance our comprehension of resistance mechanisms in tumor therapy, thereby presenting novel targets for clinical research aimed at overcoming resistance in cancer treatment. This bears substantial clinical relevance.

Sublethal engagement of apoptotic pathways in residual cancer

Cytotoxic chemo-, radio-, and targeted therapies frequently elicit apoptotic cancer cell death. Mitochondrial outer membrane permeabilization (MOMP) is a critical, regulated step in this apoptotic pathway. The residual cancer cells that survive treatment serve as the seeds of eventual relapse and are often functionally characterized by their transient tolerance of multiple therapeutic treatments. New studies suggest that, in these cells, a sublethal degree of MOMP, reflective of incomplete apoptotic commitment, is widely observed. Here, we review recent evidence that this sublethal MOMP drives the aggressive features of residual cancer cells while templating a host of unique vulnerabilities, highlighting how failed apoptosis may counterintuitively enable new therapeutic strategies to target residual disease (RD).

Aneuploidy in human cancer: new tools and perspectives

Chromosome copy number imbalances, otherwise known as aneuploidies, are a common but poorly understood feature of cancer. Here, we describe recent advances in both detecting and manipulating aneuploidies that have greatly advanced our ability to study their role in tumorigenesis. In particular, new clustered regularly interspaced short palindromic repeats (CRISPR)-based techniques have been developed that allow the creation of isogenic cell lines with specific chromosomal changes, thereby facilitating experiments in genetically controlled backgrounds to uncover the consequences of aneuploidy. These approaches provide increasing evidence that aneuploidy is a key driver of cancer development and enable the identification of multiple dosage-sensitive genes encoded on aneuploid chromosomes. Consequently, measuring aneuploidy may inform clinical prognosis, while treatment strategies that target aneuploidy could represent a novel method to counter malignant growth.

HGCLMDA: Predicting mRNA-Drug Sensitivity Associations via Hypergraph Contrastive Learning

The identification of drug sensitivity to mRNA interactions is crucial for drug development and disease treatment, but traditional experimental methods for verifying mRNA-drug sensitivity associations are labor-intensive and time-consuming. In this study, we present a hypergraph contrastive learning approach, HGCLMDA, to predict potential mRNA-drug sensitivity associations. HGCLMDA integrates a graph convolutional network-based method with a hypergraph convolutional network to mine high-order relationships between mRNA-drug association pairs. The proposed cross-view contrastive learning architecture improves the model's learning ability, and the inner product is used to obtain the mRNA-drug sensitivity association score. Our experiments on three mRNA-drug sensitivity association data sets show that HGCLMDA outperforms traditional graph convolutional network-based methods, graph augmentation-based contrastive learning methods, and state-of-the-art association prediction methods. The visualization experiment demonstrates the strong discrimination ability of the mRNA and drug embeddings learned by HGCLMDA, and experiments on sparse data sets showcase the performance and robustness of the method. In-depth analysis of hypergraph structures reveals a crucial role that hypergraphs play in enhancing the performance of models. The case study highlights the potential of HGCLMDA as a valuable tool for predicting mRNA-drug sensitivity associations. The interpretive analysis reveals that HGCLMDA effectively models the similarity between mRNA-mRNA and drug-drug interactions.

Identification of VPS34-PI(3)P-FEN1-mediated DNA repair pathway as a potential drug target to overcome chemoresistance

Intrinsic or acquired chemoresistance represents a major obstacle in cancer treatment. Multiple mechanisms can contribute to cancer cells' resistance to chemotherapy. Among them, an aberrantly strengthened DNA repair mechanism is responsible for a large proportion of drug resistance to alkylating agents and radiation therapy. In cancer cells, damping overactivated DNA repair system can overcome survival advantages conferred by chromosomal translocations or mutations and lead to cytostatic effects or cytotoxic. Therefore, selectively targeting DNA repair system in cancer cells holds promise for overcoming chemoresistance. In this study, we revealed that the endonuclease Flap Endonuclease 1 (FEN1), essential for DNA replication and repair, directly interacts with phosphatidylinositol 3-phosphate [PI(3)P], and FEN1-R378 is the primary PI(3)P-binding site. PI(3)P-binding deficient FEN1 mutant (FEN1-R378A) cells exhibited abnormal chromosomal structures and were hypersensitized to DNA damage. The PI(3)P-mediated FEN1 functionality was essential for repairing DNA damages caused by multiple mechanisms. Furthermore, VPS34, the major PI(3)P synthesizing enzyme, was negatively associated with patients' survival in various cancer types, and VPS34 inhibitors significantly sensitized chemoresistant cancer cells to genotoxic agents. These findings open up an avenue for counteracting chemoresistance by targeting VPS34-PI(3)P-mediated DNA repair pathway, and call for assessing the efficacy of this strategy in patients suffering from chemoresistance-mediated cancer recurrence in clinical trials.

IMOPAC: A web server for interactive multiomics and pharmacological analyses of patient-derived cancer cell lines

Large-scale multidimensional cancer genomic and pharmacological profiles have been created by several large consortium projects, including NCI-60, GDSC and DepMap, providing novel opportunities for data mining and further understanding of intrinsic therapeutic response mechanisms. However, it is increasingly challenging for experimental biologists, especially those without a bioinformatic background, to integrate, explore, and analyse these tremendous pharmacogenomics. To address this gap, IMOPAC, an interactive and easy-to-use web-based tool, was introduced to provide rapid visualizations and customizable functionalities on the basis of these three publicly available databases, which may reduce pharmacogenomic profiles from cell lines into readily understandable genetic, epigenetic, transcriptionomic, proteomic, metabolomic, and pharmacological events. The user-friendly query interface together with customized data storage enables users to interactively investigate and visualize multiomics alterations across genes and pathways and to link these alterations with drug responses across cell lines from diverse cancer types. The analyses in our portal include pancancer expression, drug-omics/pathway correlation, cancer subtypes, omics-omics (cis-/trans-regulation) correlation, fusion query analysis, and drug response prediction analysis. The comprehensive multiomics and pharmacogenomic analyses with simple clicking through IMOPAC will significantly benefit cancer precision medicine, contribute to the discoveries of potential biological mechanisms and facilitate pharmacogenomics mining in the identification of clinically actionable biomarkers for both basic researchers and clinical practitioners. IMOPAC is freely available at http://www.hbpding.com/IMOPAC.

Understanding and targeting resistance mechanisms in cancer

Resistance to cancer therapies has been a commonly observed phenomenon in clinical practice, which is one of the major causes of treatment failure and poor patient survival. The reduced responsiveness of cancer cells is a multifaceted phenomenon that can arise from genetic, epigenetic, and microenvironmental factors. Various mechanisms have been discovered and extensively studied, including drug inactivation, reduced intracellular drug accumulation by reduced uptake or increased efflux, drug target alteration, activation of compensatory pathways for cell survival, regulation of DNA repair and cell death, tumor plasticity, and the regulation from tumor microenvironments (TMEs). To overcome cancer resistance, a variety of strategies have been proposed, which are designed to enhance the effectiveness of cancer treatment or reduce drug resistance. These include identifying biomarkers that can predict drug response and resistance, identifying new targets, developing new targeted drugs, combination therapies targeting multiple signaling pathways, and modulating the TME. The present article focuses on the different mechanisms of drug resistance in cancer and the corresponding tackling approaches with recent updates. Perspectives on polytherapy targeting multiple resistance mechanisms, novel nanoparticle delivery systems, and advanced drug design tools for overcoming resistance are also reviewed.

CircRNA-CREIT inhibits stress granule assembly and overcomes doxorubicin resistance in TNBC by destabilizing PKR

Background

Circular RNAs (circRNAs) represent a novel type of regulatory RNA characterized by high evolutionary conservation and stability. CircRNAs are expected to be potential diagnostic biomarkers and therapeutic targets for a variety of malignancies. However, the regulatory functions and underlying mechanisms of circRNAs in triple-negative breast cancer (TNBC) are largely unknown.

Methods

By using RNA high-throughput sequencing technology, qRT-PCR and in situ hybridization assays, we screened dysregulated circRNAs in breast cancer and TNBC tissues. Then in vitro assays, animal models and patient-derived organoids (PDOs) were utilized to explore the roles of the candidate circRNA in TNBC. To investigate the underlying mechanisms, RNA pull-down, RNA immunoprecipitation (RIP), co immunoprecipitation (co-IP) and Western blotting assays were carried out.

Results

In this study, we demonstrated that circRNA-CREIT was aberrantly downregulated in doxorubicin resistant triple-negative breast cancer (TNBC) cells and associated with a poor prognosis. The RNA binding protein DHX9 was responsible for the reduction in circRNA-CREIT by interacting with the flanking inverted repeat Alu (IRAlu) sequences and inhibiting back-splicing. By utilizing in vitro assays, animal models and patient-derived organoids, we revealed that circRNA-CREIT overexpression significantly enhanced the doxorubicin sensitivity of TNBC cells. Mechanistically, circRNA-CREIT acted as a scaffold to facilitate the interaction between PKR and the E3 ligase HACE1 and promoted proteasomal degradation of PKR protein via K48-linked polyubiquitylation. A reduced PKR/eIF2α signaling axis was identified as a critical downstream effector of circRNA-CREIT, which attenuated the assembly of stress granules (SGs) to activate the RACK1/MTK1 apoptosis signaling pathway. Further investigations revealed that a combination of the SG inhibitor ISRIB and doxorubicin synergistically inhibited TNBC tumor growth. Besides, circRNA-CREIT could be packaged into exosomes and disseminate doxorubicin sensitivity among TNBC cells.

Conclusions

Our study demonstrated that targeting circRNA-CREIT and SGs could serve as promising therapeutic strategies against TNBC chemoresistance.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13045-022-01345-w.

Patient-derived cancer models: Valuable platforms for anticancer drug testing

Molecularly targeted treatments and immunotherapy are cornerstones in oncology, with demonstrated efficacy across different tumor types. Nevertheless, the overwhelming majority metastatic disease is incurable due to the onset of drug resistance. Preclinical models including genetically engineered mouse models, patient-derived xenografts and two- and three-dimensional cell cultures have emerged as a useful resource to study mechanisms of cancer progression and predict efficacy of anticancer drugs. However, variables including tumor heterogeneity and the complexities of the microenvironment can impair the faithfulness of these platforms. Here, we will discuss advantages and limitations of these preclinical models, their applicability for drug testing and in co-clinical trials and potential strategies to increase their reliability in predicting responsiveness to anticancer medications.

Multidrug Resistance in Cancer: Understanding Molecular Mechanisms, Immunoprevention and Therapeutic Approaches

Cancer is one of the leading causes of death worldwide. Several treatments are available for cancer treatment, but many treatment methods are ineffective against multidrug-resistant cancer. Multidrug resistance (MDR) represents a major obstacle to effective therapeutic interventions against cancer. This review describes the known MDR mechanisms in cancer cells and discusses ongoing laboratory approaches and novel therapeutic strategies that aim to inhibit, circumvent, or reverse MDR development in various cancer types. In this review, we discuss both intrinsic and acquired drug resistance, in addition to highlighting hypoxia- and autophagy-mediated drug resistance mechanisms. Several factors, including individual genetic differences, such as mutations, altered epigenetics, enhanced drug efflux, cell death inhibition, and various other molecular and cellular mechanisms, are responsible for the development of resistance against anticancer agents. Drug resistance can also depend on cellular autophagic and hypoxic status. The expression of drug-resistant genes and the regulatory mechanisms that determine drug resistance are also discussed. Methods to circumvent MDR, including immunoprevention, the use of microparticles and nanomedicine might result in better strategies for fighting cancer.

Therapy resistance: opportunities created by adaptive responses to targeted therapies in cancer

Normal cells explore multiple states to survive stresses encountered during development and self-renewal as well as environmental stresses such as starvation, DNA damage, toxins or infection. Cancer cells co-opt normal stress mitigation pathways to survive stresses that accompany tumour initiation, progression, metastasis and immune evasion. Cancer therapies accentuate cancer cell stresses and invoke rapid non-genomic stress mitigation processes that maintain cell viability and thus represent key targetable resistance mechanisms. In this Review, we describe mechanisms by which tumour ecosystems, including cancer cells, immune cells and stroma, adapt to therapeutic stresses and describe three different approaches to exploit stress mitigation processes: (1) interdict stress mitigation to induce cell death; (2) increase stress to induce cellular catastrophe; and (3) exploit emergent vulnerabilities in cancer cells and cells of the tumour microenvironment. We review challenges associated with tumour heterogeneity, prioritizing actionable adaptive responses for optimal therapeutic outcomes, and development of an integrative framework to identify and target vulnerabilities that arise from adaptive responses and engagement of stress mitigation pathways. Finally, we discuss the need to monitor adaptive responses across multiple scales and translation of combination therapies designed to take advantage of adaptive responses and stress mitigation pathways to the clinic.

Informed Attentive Predictors: A Generalisable Architecture for Prior Knowledge-Based Assisted Diagnosis of Cancers

Due to the high mortality of many cancers and their related diseases, the prediction and prognosis techniques of cancers are being extensively studied to assist doctors in making diagnoses. Many machine-learning-based cancer predictors have been put forward, but many of them have failed to become widely utilised due to some crucial problems. For example, most methods require too much training data, which is not always applicable to institutes, and the complicated genetic mutual effects of cancers are generally ignored in many proposed methods. Moreover, a majority of these assist models are actually not safe to use, as they are generally built on black-box machine learners that lack references from related field knowledge. We observe that few machine-learning-based cancer predictors are capable of employing prior knowledge (PrK) to mitigate these issues. Therefore, in this paper, we propose a generalisable informed machine learning architecture named the Informed Attentive Predictor (IAP) to make PrK available to the predictor’s decision-making phases and apply it to the field of cancer prediction. Specifically, we make several implementations of the IAP and evaluate its performance on six TCGA datasets to demonstrate the effectiveness of our architecture as an assist system framework for actual clinical usage. The experimental results show a noticeable improvement in IAP models on accuracies, f1-scores and recall rates compared to their non-IAP counterparts (i.e., basic predictors).

The Role of Extracellular Vesicles in Mediating Resistance to Anticancer Therapies

Although advances in targeted therapies have driven great progress in cancer treatment and outcomes, drug resistance remains a major obstacle to improving patient survival. Several mechanisms are involved in developing resistance to both conventional chemotherapy and molecularly targeted therapies, including drug efflux, secondary mutations, compensatory genetic alterations occurring upstream or downstream of a drug target, oncogenic bypass, drug activation and inactivation, and DNA damage repair. Extracellular vesicles (EVs) are membrane-bound lipid bilayer vesicles that are involved in cell-cell communication and regulating biological processes. EVs derived from cancer cells play critical roles in tumor progression, metastasis, and drug resistance by delivering protein and genetic material to cells of the tumor microenvironment. Understanding the biochemical and genetic mechanisms underlying drug resistance will aid in the development of new therapeutic strategies. Herein, we review the role of EVs as mediators of drug resistance in the context of cancer.

Building patient-specific models for receptor tyrosine kinase signaling networks

Cancer progresses due to changes in the dynamic interactions of multidimensional factors associated with gene mutations. Cancer research has actively adopted computational methods, including data-driven and mathematical model-driven approaches, to identify causative factors and regulatory rules that can explain the complexity and diversity of cancers. A data-driven, statistics-based approach revealed correlations between gene alterations and clinical outcomes in many types of cancers. A model-driven mathematical approach has elucidated the dynamic features of cancer networks and identified the mechanisms of drug efficacy and resistance. More recently, machine learning methods have emerged that can be used for mining omics data and classifying patient. However, as the strengths and weaknesses of each method becoming apparent, new analytical tools are emerging to combine and improve the methodologies and maximize their predictive power for classifying cancer subtypes and prognosis. Here, we introduce recent advances in cancer systems biology aimed at personalized medicine, with focus on the receptor tyrosine kinase signaling network.

Strongly preserved modules between cancer tissue and cell line contribute to drug resistance analysis across multiple cancer types

The existence and emergence of drug resistance in tumor cells is the main burden of cancer treatment. Most cancer drug resistance analyses are based entirely on cell line data and ignore the discordance between human tumors and cell lines, leading to biased preclinical model transformation. Based on cancer tissue data in TCGA and cancer cell line data in CCLE, this study identified and excluded non-preserved module (NP module) between cancer tissue and cell lines. We used strongly preserved module (SP module) for clinically relevant drug resistance analysis and identified 2068 "cancer-drug-module" pairs of 7 cancer types and 212 drugs based on data in GDSC. Furthermore, we identified potentially ineffective combination therapy (PICT) from multiple perspectives. Finally, we found 1608 sets of predictors that can predict drug response. These results provide insights and clues for the clinical selection of effective chemotherapy drugs to overcome cancer resistance in a new perspective.

Molecular modeling approaches to address drug-metabolizing enzymes (DMEs) mediated chemoresistance: a review

Resistance against clinically approved anticancer drugs is the main roadblock in cancer treatment. Drug metabolizing enzymes (DMEs) that are capable of metabolizing a variety of xenobiotic get overexpressed in malignant cells, therefore, catalyzing drug inactivation. As evident from the literature reports, the levels of DMEs increase in cancer cells that ultimately lead to drug inactivation followed by drug resistance. To puzzle out this issue, several strategies inclusive of analog designing, prodrug designing, and inhibitor designing have been forged. On that front, the implementation of computational tools can be considered a fascinating approach to address the problem of chemoresistance. Various research groups have adopted different molecular modeling tools for the investigation of DMEs mediated toxicity problems. However, the utilization of these in-silico tools in maneuvering the DME mediated chemoresistance is least considered and yet to be explored. These tools can be employed in the designing of such chemotherapeutic agents that are devoid of the resistance problem. The current review canvasses various molecular modeling approaches that can be implemented to address this issue. Special focus was laid on the development of specific inhibitors of DMEs. Additionally, the strategies to bypass the DMEs mediated drug metabolism were also contemplated in this report that includes analogs and pro-drugs designing. Different strategies discussed in the review will be beneficial in designing novel chemotherapeutic agents that depreciate the resistance problem.

Targeting CTGF in Cancer: An Emerging Therapeutic Opportunity

Despite the dramatic advances in cancer research over the decades, effective therapeutic strategies are still urgently needed. Increasing evidence indicates that connective tissue growth factor (CTGF), a multifunctional signaling modulator, promotes cancer initiation, progression, and metastasis by regulating cell proliferation, migration, invasion, drug resistance, and epithelial-mesenchymal transition (EMT). CTGF is also involved in the tumor microenvironment in most of the nodes, including angiogenesis, inflammation, and cancer-associated fibroblast (CAF) activation. In this review, we comprehensively discuss the expression of CTGF and its regulation, oncogenic role, clinical relevance, targeting strategies, and therapeutic agents. Herein, we propose that CTGF is a promising cancer therapeutic target that could potentially improve the clinical outcomes of cancer patients.

An elementary mathematical modeling of drug resistance in cancer

Targeted therapy is one of the promising strategies for the treatment of cancer. However, resistance to anticancer drug strongly limits the long-term effectiveness of treatment, which is a major obstacle for successfully treating cancer. In this paper, we analyze a linear system of ordinary differential equations for cancer multi-drug resistance induced mainly by random genetic point mutation. We investigate that the resistance generated before the beginning of the treatment is greater than that developed during-treatment. This result depends on the concentration of the drug, which holds only when the concentration of the drug reaches a lower limit. Moreover, no matter how many drugs are used in the treatment, the amount of resistance (generated at the beginning of the treatment and within a certain period of time after the treatment) always depends on the turnover rate. Using numerical simulations, we also evaluate the response of the mutant cancer cell population as a function of time under different treatment strategies. At appropriate dosages, combination therapy produces significant effects for the treatment with low-turnover rate cancer. For cancer with very high-turnover rate (close to 1), combination therapy can not significantly reduce the amount of resistant mutants compared to monotherapy, so in this case, combination therapy would not have advantage over monotherapy.

Establishing patient-tailored variability-based paradigms for anti-cancer therapy: Using the inherent trajectories which underlie cancer for overcoming drug resistance

Drug resistance is a major obstacle for successful therapy of many malignancies and is affecting the loss of response to chemotherapy and immunotherapy. Tumor-related compensatory adaptation mechanisms contribute to the development of drug resistance. Variability is inherent to biological systems and altered patterns of variability are associated with disease conditions. The marked intra and inter patient tumor heterogeneity, and the diverse mechanism contributing to drug resistance in different subjects, which may change over time even in the same patient, necessitate the development of personalized dynamic approaches for overcoming drug resistance. Altered dosing regimens, the potential role of chronotherapy, and drug holidays are effective in cancer therapy and immunotherapy. In the present review we describe the difficulty of overcoming drug resistance in a dynamic system and present the use of the inherent trajectories which underlie cancer development for building therapeutic regimens which can overcome resistance. The establishment of a platform wherein patient-tailored variability signatures are used for overcoming resistance for ensuing long term sustainable improved responses is presented.

ATM Inhibitor Suppresses Gemcitabine-Resistant BTC Growth in a Polymerase θ Deficiency-Dependent Manner

Patients with advanced biliary tract cancer (BTC) inevitably experience progression after first-line, gemcitabine-based chemotherapy, due to chemo-resistance. The genetic alterations of DNA damage repair (DDR) genes are usually determined in BTC tumors. In this study, we found that the POLQ mRNA levels are downregulated and the ataxia-telangiectasia mutated (ATM) inhibitor AZD0156 was more sensitive in gemcitabine-resistant BTC sublines than in the parental cell lines. The knockdown of DNA polymerase θ does not affect cell proliferation, but its combination with the ATM inhibitor facilitated cell death in gemcitabine-resistant and gemcitabine-intensive BTC cells. Moreover, in the DNA damage caused by photon, hydrogen peroxide, or chemotherapy drugs, synthetic lethal interactions were found in combination with ATM inhibition by AZD0156 and DNA polymerase θ depletion, resulting in increased DNA damage accumulation and micronucleus formation, as well as reduced cell survival and colony formation. Collectively, our results reveal that ATM acts as a potential target in gemcitabine-resistant and DNA polymerase θ-deficient BTC.

Patient-Derived, Drug-Resistant Colon Cancer Cells Evade Chemotherapeutic Drug Effects via the Induction of Epithelial-Mesenchymal Transition-Mediated Angiogenesis

Cancer cells can exhibit resistance to different anticancer drugs by acquiring enhanced anti-apoptotic potential, improved DNA injury resistance, diminished enzymatic inactivation, and enhanced permeability, allowing for cell survival. However, the genetic mechanisms for these effects are unknown. Therefore, in this study, we obtained drug-sensitive HT-29 cells (commercially) and drug-resistant cancer cells (derived from biochemically and histologically confirmed colon cancer patients) and performed microarray analysis to identify genetic differences. Cellular proliferation and other properties were determined after treatment with oxaliplatin, lenvatinib, or their combination. In vivo, tumor volume and other properties were examined using a mouse xenograft model. The oxaliplatin and lenvatinib cotreatment group showed more significant cell cycle arrest than the control group and groups treated with either agent alone. Oxaliplatin and lenvatinib cotreatment induced the most significant tumor shrinkage in the xenograft model. Drug-resistant and metastatic colon cancer cells evaded the anticancer drug effects via angiogenesis. These findings present a breakthrough strategy for treating drug-resistant cancer.

Whole-exome sequencing reveals potential mechanisms of drug resistance to FGFR3-TACC3 targeted therapy and subsequent drug selection: towards a personalized medicine

Background

Drug resistance is a major obstacle to effective cancer therapy. In order to detect the change in tumor genomic states under drug selection pressure, we use next-generation sequencing technology to investigate the underlying potential mechanisms of drug resistance.

Methods

In our study, we presented a bladder cancer patient who had been a bona fide responder to first-line gemcitabine plus cisplatin regimen and second-line pazopanib (tyrosine kinase inhibitor (TKI) for FGFR3-TACC3 fusion) but finally had disease progression as an ideal case for showing genomic alteration during drug resistance. We applied whole-exome sequencing and ultra-deep target sequencing to the patient pre- and post- pazopanib resistance. Protein-protein interaction (PPI) network and Gene Ontology (GO) analyses were used to analysis protein interactions and genomic alterations. Patient-derived xenograft (PDX) model was built to test drug sensitivity.

Results

Twelve mutations scattered in 12 genes were identified by WES pre- pazopanib resistance, while 63 mutations in 50 genes arose post- pazopanib resistance. PPI network showed proteins from multiple epigenetic regulator families were involved post- pazopanib resistance, including subunits of chromatin remodeler SWI/SNF complex ARID1A/1B and SMARCA4, histone acetylation writers CREBBP, histone methylation writer NSD1 and erasers KDM6A/5A. GO enrichment analysis showed pazopanib resistance genes were prominently tagged for chromatin modification, transcription, as well as gland development, leaving genes with the best adaptive FGFR TKI-coping mechanisms. In addition, significantly elevated tumor mutational burden suggested possible utility of immunotherapy. Intriguingly, PDX model suggested that, sensitivity to original chemotherapy regimen (cisplatin) was restored in patient tumor post-pazopanib.

Conclusions

Epigenetic regulation may play a role in acquired TKI resistance. Our study traced the complete tumor genomic variation course from chemo-resistant but TKI-sensitive to TKI-resistant but chemo-(re) sensitive, revealing the potential complex dynamic drug-driven mechanisms of resistance.

Progress of long noncoding RNAs in anti-tumor resistance

The resistance of cancer cells to anti-cancer drugs is an important reason for the failure of treatment. Overcoming drug resistance can achieve long-lasting and efficient cancer treatment. Long non-coding RNA (lncRNA) is a class of RNA molecules that does not encode protein and has more than 200 nucleotides. LncRNA not only has a regulatory role in the occurrence and development of malignant tumors, but also has been found to have a potential impact on anti-tumor resistance. Abnormal expression of lncRNA can cause tumor cells to develop resistance to anti-tumor drugs. This article reviews the recent research progress of lncRNA in various tumor resistances and the mechanism of lncRNA acting on tumor drug resistance.

Lenvatinib for hepatocellular carcinoma: From preclinical mechanisms to anti-cancer therapy

Lenvatinib, a multi-target tyrosine kinase inhibitor (TKI), is an emerging first-line therapy for hepatocellular carcinoma (HCC). Its application has changed the status of sorafenib as the only first-line TKI treatment for HCC for more than a decade. Evidence has shown that lenvatinib possesses antitumor proliferation and immunomodulatory activity in preclinical studies. In comparison, lenvatinib was non-inferior to sorafenib in overall survival (OS), and even shows superiority with regard to all the secondary efficacy endpoints. Immune-checkpoint inhibitors(ICIs)are now being incorporated into HCC treatment. Positive outcomes have been achieved in the combination of lenvatinib plus ICIs, bringing broader prospects for HCC. This review presents an overview on the therapeutic mechanisms and clinical efficacy of lenvatinib in HCC, and we discuss the future perspectives of lenvatinib in HCC management with focus on biomarker-guided precision medicine.

RNAactDrug: a comprehensive database of RNAs associated with drug sensitivity from multi-omics data

Drug sensitivity has always been at the core of individualized cancer chemotherapy. However, we have been overwhelmed by large-scale pharmacogenomic data in the era of next-generation sequencing technology, which makes it increasingly challenging for researchers, especially those without bioinformatic experience, to perform data integration, exploration and analysis. To bridge this gap, we developed RNAactDrug, a comprehensive database of RNAs associated with drug sensitivity from multi-omics data, which allows users to explore drug sensitivity and RNA molecule associations directly. It provides association data between drug sensitivity and RNA molecules including mRNAs, long non-coding RNAs (lncRNAs) and microRNAs (miRNAs) at four molecular levels (expression, copy number variation, mutation and methylation) from integrated analysis of three large-scale pharmacogenomic databases (GDSC, CellMiner and CCLE). RNAactDrug currently stores more than 4 924 200 associations of RNA molecules and drug sensitivity at four molecular levels covering more than 19 770 mRNAs, 11 119 lncRNAs, 438 miRNAs and 4155 drugs. A user-friendly interface enriched with various browsing sections augmented with advance search facility for querying the database is offered for users retrieving. RNAactDrug provides a comprehensive resource for RNA molecules acting in drug sensitivity, and it could be used to prioritize drug sensitivity-related RNA molecules, further promoting the identification of clinically actionable biomarkers in drug sensitivity and drug development more cost-efficiently by making this knowledge accessible to both basic researchers and clinical practitioners. Database URL: http://bio-bigdata.hrbmu.edu.cn/RNAactDrug.

TRAIL-induced variation of cell signaling states provides nonheritable resistance to apoptosis

TNFα-related apoptosis-inducing ligand (TRAIL), specifically initiates programmed cell death, but often fails to eradicate all cells, making it an ineffective therapy for cancer. This fractional killing is linked to cellular variation that bulk assays cannot capture. Here, we quantify the diversity in cellular signaling responses to TRAIL, linking it to apoptotic frequency across numerous cell systems with single-cell mass cytometry (CyTOF). Although all cells respond to TRAIL, a variable fraction persists without apoptotic progression. This cell-specific behavior is nonheritable where both the TRAIL-induced signaling responses and frequency of apoptotic resistance remain unaffected by prior exposure. The diversity of signaling states upon exposure is correlated to TRAIL resistance. Concomitantly, constricting the variation in signaling response with kinase inhibitors proportionally decreases TRAIL resistance. Simultaneously, TRAIL-induced de novo translation in resistant cells, when blocked by cycloheximide, abrogated all TRAIL resistance. This work highlights how cell signaling diversity, and subsequent translation response, relates to nonheritable fractional escape from TRAIL-induced apoptosis. This refined view of TRAIL resistance provides new avenues to study death ligands in general.

Mathematical modeling and computational prediction of cancer drug resistance

Diverse forms of resistance to anticancer drugs can lead to the failure of chemotherapy. Drug resistance is one of the most intractable issues for successfully treating cancer in current clinical practice. Effective clinical approaches that could counter drug resistance by restoring the sensitivity of tumors to the targeted agents are urgently needed. As numerous experimental results on resistance mechanisms have been obtained and a mass of high-throughput data has been accumulated, mathematical modeling and computational predictions using systematic and quantitative approaches have become increasingly important, as they can potentially provide deeper insights into resistance mechanisms, generate novel hypotheses or suggest promising treatment strategies for future testing. In this review, we first briefly summarize the current progress of experimentally revealed resistance mechanisms of targeted therapy, including genetic mechanisms, epigenetic mechanisms, posttranslational mechanisms, cellular mechanisms, microenvironmental mechanisms and pharmacokinetic mechanisms. Subsequently, we list several currently available databases and Web-based tools related to drug sensitivity and resistance. Then, we focus primarily on introducing some state-of-the-art computational methods used in drug resistance studies, including mechanism-based mathematical modeling approaches (e.g. molecular dynamics simulation, kinetic model of molecular networks, ordinary differential equation model of cellular dynamics, stochastic model, partial differential equation model, agent-based model, pharmacokinetic–pharmacodynamic model, etc.) and data-driven prediction methods (e.g. omics data-based conventional screening approach for node biomarkers, static network approach for edge biomarkers and module biomarkers, dynamic network approach for dynamic network biomarkers and dynamic module network biomarkers, etc.). Finally, we discuss several further questions and future directions for the use of computational methods for studying drug resistance, including inferring drug-induced signaling networks, multiscale modeling, drug combinations and precision medicine.

mTCTScan: a comprehensive platform for annotation and prioritization of mutations affecting drug sensitivity in cancers

Cancer therapies have experienced rapid progress in recent years, with a number of novel small-molecule kinase inhibitors and monoclonal antibodies now being widely used to treat various types of human cancers. During cancer treatments, mutations can have important effects on drug sensitivity. However, the relationship between tumor genomic profiles and the effectiveness of cancer drugs remains elusive. We introduce Mutation To Cancer Therapy Scan (mTCTScan) web server (http://jjwanglab.org/mTCTScan) that can systematically analyze mutations affecting cancer drug sensitivity based on individual genomic profiles. The platform was developed by leveraging the latest knowledge on mutation-cancer drug sensitivity associations and the results from large-scale chemical screening using human cancer cell lines. Using an evidence-based scoring scheme based on current integrative evidences, mTCTScan is able to prioritize mutations according to their associations with cancer drugs and preclinical compounds. It can also show related drugs/compounds with sensitivity classification by considering the context of the entire genomic profile. In addition, mTCTScan incorporates comprehensive filtering functions and cancer-related annotations to better interpret mutation effects and their association with cancer drugs. This platform will greatly benefit both researchers and clinicians for interrogating mechanisms of mutation-dependent drug response, which will have a significant impact on cancer precision medicine.

Personalized medicine: From diagnostic to adaptive

Personalized therapy has made great strides but suffers from the lack of companion diagnostics. With the dawn of extracellular vesicle (EV) based liquid biopsies fast approaching, this article proposes a novel approach to cancer treatment – adaptive therapy. Already being implemented in the field of radiation oncology, adaptive radiation therapy utilizes cutting-edge imaging techniques as a viable means to monitor a patient's tumor throughout the entire treatment cycle by adapting the dosage and alignment to match the dynamic tumor. Through an EV liquid biopsy, medical oncologists will also soon have the means to continuously monitor a patient's tumor as it changes over time. With this information, physicians will be able to “adapt” pre-planned therapies concurrently with the fluctuating tumor environment, thus creating a more precise personalized medicine. In this article, a theory for adaptive medicine and the current state of the field with an outlook on future challenges are discussed.

Emerging role of nuclear factor erythroid 2-related factor 2 in the mechanism of action and resistance to anticancer therapies

Nuclear factor E2-related factor 2 (NRF2), a transcription factor, is a master regulator of an array of genes related to oxidative and electrophilic stress that promote and maintain redox homeostasis. NRF2 function is well studied in in vitro, animal and general physiology models. However, emerging data has uncovered novel functionality of this transcription factor in human diseases such as cancer, autism, anxiety disorders and diabetes. A key finding in these emerging roles has been its constitutive upregulation in multiple cancers promoting pro-survival phenotypes. The survivability pathways in these studies were mostly explained by classical NRF2 activation involving KEAP-1 relief and transcriptional induction of reactive oxygen species (ROS) neutralizing and cytoprotective drug-metabolizing enzymes (phase I, II, III and 0). Further, NRF2 status and activation is associated with lowered cancer therapeutic efficacy and the eventual emergence of therapeutic resistance. Interestingly, we and others have provided further evidence of direct NRF2 regulation of anticancer drug targets like receptor tyrosine kinases and DNA damage and repair proteins and kinases with implications for therapy outcome. This novel finding demonstrates a renewed role of NRF2 as a key modulatory factor informing anticancer therapeutic outcomes, which extends beyond its described classical role as a ROS regulator. This review will provide a knowledge base for these emerging roles of NRF2 in anticancer therapies involving feedback and feed forward models and will consolidate and present such findings in a systematic manner. This places NRF2 as a key determinant of action, effectiveness and resistance to anticancer therapy.

Reactive oxygen species induced by icaritin promote DNA strand breaks and apoptosis in human cervical cancer cells

Increased production of reactive oxygen species (ROS) is a distinct feature of various types of cancer. ROS drive tumor progression and render cancer cells vulnerable to additional oxidative insult. The various natural herb compounds have been shown to induce additional production of ROS in cancer cells, although the physiological implications of ROS under these conditions are not fully determined. In the present study, icaritin, a natural compound derived from the medicinal plants Epimedium, was demonstrated to potently suppresses the proliferation of human HeLa and SiHa cervical cancer cells, without similar affects on non-cancerous CCD-1095Sk fibroblasts and 293 cells, as measured by MTT and colony formation assays. Icaritin treatment caused a rapid increase in ROS in HeLa and SiHa cells, which was followed by a prominent increase in the number of DNA strand breaks. Consequently, the levels of the pro-apoptotic protein Bax and activated caspase 3 and 9 enzymes were increased, while the levels of the anti-apoptotic proteins Bcl-2 and XIAP were downregulated. These protein expression changes were accompanied by marked induction of apoptosis in icaritin-treated cancer cells. The results suggested that the icaritin-induced ROS overload promoted cancer cell death via induction of extensive oxidative DNA damage, which subsequently resulted in large numbers of DNA strand breaks and the activation of the intrinsic apoptotic pathway.

Companion animals in comparative oncology: One Medicine in action

Comparative oncology is poised to have a far-reaching impact on both animals and human beings with cancer. The field is gaining momentum and has repeatedly proven its utility in various aspects of oncology, including study of the genetics, development, progression, immunology and therapy of cancer. Companion animals provide many advantages over both traditional rodent models and human beings for studying cancer biology and accelerating the development of novel anti-cancer therapies. In this review, several examples of the ability of companion animals with spontaneous cancers to fill a unique niche in the field of oncology are discussed. In addition, potential caveats of the use of companion animals in research are reviewed, as well as ethical considerations and efforts to standardize veterinary clinical trials.

FusionPathway: Prediction of pathways and therapeutic targets associated with gene fusions in cancer

Numerous gene fusions have been uncovered across multiple cancer types. Although the ability to target several of these fusions has led to the development of some successful anti-cancer drugs, most of them are not druggable. Understanding the molecular pathways of a fusion is important in determining its function in oncogenesis and in developing therapeutic strategies for patients harboring the fusion. However, the molecular pathways have been elucidated for only a few fusions, in part because of the labor-intensive nature of the required functional assays. Therefore, we developed a domain-based network approach to infer the pathways of a fusion. Molecular interactions of a fusion are first predicted by using its protein domain composition, and its associated pathways are then inferred from these molecular interactions. We demonstrated the capabilities of this approach by primarily applying it to the well-studied BCR-ABL1 fusion. The approach was also applied to two undruggable fusions in sarcoma, EWS-FL1 and FUS-DDIT3. We successfully identified known genes and pathways associated with these fusions and satisfactorily validated these predictions using several benchmark sets. The predictions of EWS-FL1 and FUS-DDIT3 also correlate with results of high-throughput drug screening. To our best knowledge, this is the first approach for inferring pathways of fusions.

We present a computational framework, FusionPathway, to infer the oncogenesis pathways of a fusion and help develop therapeutic strategies in these pathways for patients harboring the fusion. In this work, we successfully validated the capabilities of this approach through its application to the well-studied BCR-ABL1 fusion and two undruggable fusions in sarcoma, EWS-FL1 and FUS-DDIT3. Especially, the predictions of EWS-FL1 and FUS-DDIT3 correlate well with results of high-throughput drug screening in sarcoma cells. Therefore, FusionPathway can be an effective method to infer pathways and potential therapeutic targets that are associated with those undruggable fusions. Our results of BCR-ABL1 also suggest that FusionPathway may be able to help elucidate pathway-dependent mechanisms of resistances to those kinase fusion-targeting therapies and develop strategies to overcome the resistances. In addition, the developed R package of FusionPathways (https://github.com/perwu/FusionPathway/) can help people easily apply our approach to study other important fusions in cancer.

Current Molecular-Targeted Therapies in NSCLC and Their Mechanism of Resistance

Lung cancer is treated with many conventional therapies, such as surgery, radiation, and chemotherapy. However, these therapies have multiple undesirable side effects. To bypass the side effects elicited by these conventional treatments, molecularly-targeted therapies are currently in use or under development. Current molecularly-targeted therapies effectively target specific biomarkers, which are commonly overexpressed in lung cancers and can cause increased tumorigenicity. Unfortunately, several molecularly-targeted therapies are associated with initial dramatic responses followed by acquired resistance due to spontaneous mutations or activation of signaling pathways. Acquired resistance to molecularly targeted therapies presents a major clinical challenge in the treatment of lung cancer. Therefore, to address this clinical challenge and to improve lung cancer patient prognosis, we need to understand the mechanism of acquired resistance to current therapies and develop additional novel therapies. This review concentrates on various lung cancer biomarkers, including EGFR, ALK, and BRAF, as well as their potential mechanisms of drug resistance.

Application of the CRISPR/Cas9 System to Drug Resistance in Breast Cancer

Clinical evidence indicates that drug resistance is a great obstacle in breast cancer therapy. It renders the disease uncontrollable and causes high mortality. Multiple mechanisms contribute to the development of drug resistance, but the underlying cause is usually a shift in the genetic composition of tumor cells. It is increasingly feasible to engineer the genome with the clustered regularly interspaced short palindromic repeats (CRISPR)/associated (Cas)9 technology recently developed, which might be advantageous in overcoming drug resistance. This article discusses how the CRISPR/Cas9 system might revert resistance gene mutations and identify potential resistance targets in drug-resistant breast cancer. In addition, the challenges that impede the clinical applicability of this technology and highlight the CRISPR/Cas9 systems are presented. The CRISPR/Cas9 system is poised to play an important role in preventing drug resistance in breast cancer therapy and will become an essential tool for personalized medicine.

Big Data Approaches for Modeling Response and Resistance to Cancer Drugs

Despite significant progress in cancer research, current standard-of-care drugs fail to cure many types of cancers. Hence, there is an urgent need to identify better predictive biomarkers and treatment regimes. Conventionally, insights from hypothesis-driven studies are the primary force for cancer biology and therapeutic discoveries. Recently, the rapid growth of big data resources, catalyzed by breakthroughs in high-throughput technologies, has resulted in a paradigm shift in cancer therapeutic research. The combination of computational methods and genomics data has led to several successful clinical applications. In this review, we focus on recent advances in data-driven methods to model anticancer drug efficacy, and we present the challenges and opportunities for data science in cancer therapeutic research.

The Current Landscape of 3D In Vitro Tumor Models: What Cancer Hallmarks Are Accessible for Drug Discovery?

Cancer prognosis remains a lottery dependent on cancer type, disease stage at diagnosis, and personal genetics. While investment in research is at an all-time high, new drugs are more likely to fail in clinical trials today than in the 1970s. In this review, a summary of current survival statistics in North America is provided, followed by an overview of the modern drug discovery process, classes of models used throughout different stages, and challenges associated with drug development efficiency are highlighted. Then, an overview of the cancer hallmarks that drive clinical progression is provided, and the range of available clinical therapies within the context of these hallmarks is categorized. Specifically, it is found that historically, the development of therapies is limited to a subset of possible targets. This provides evidence for the opportunities offered by novel disease-relevant in vitro models that enable identification of novel targets that facilitate interactions between the tumor cells and their surrounding microenvironment. Next, an overview of the models currently reported in literature is provided, and the cancer biology they have been used to explore is highlighted. Finally, four priority areas are suggested for the field to accelerate adoption of in vitro tumour models for cancer drug discovery.

Genome-Scale Signatures of Gene Interaction from Compound Screens Predict Clinical Efficacy of Targeted Cancer Therapies

Identifying reliable drug response biomarkers is a significant challenge in cancer research. We present computational analysis of resistance (CARE), a computational method focused on targeted therapies, to infer genome-wide transcriptomic signatures of drug efficacy from cell line compound screens. CARE outputs genome-scale scores to measure how the drug target gene interacts with other genes to affect the inhibitor efficacy in the compound screens. Such statistical interactions between drug targets and other genes were not considered in previous studies but are critical in identifying predictive biomarkers. When evaluated using transcriptome data from clinical studies, CARE can predict the therapy outcome better than signatures from other computational methods and genomics experiments. Moreover, the CARE signatures for the PLX4720 BRAF inhibitor are associated with an anti-programmed death 1 clinical response, suggesting a common efficacy signature between a targeted therapy and immunotherapy. When searching for genes related to lapatinib resistance, CARE identified PRKD3 as the top candidate. PRKD3 inhibition, by both small interfering RNA and compounds, significantly sensitized breast cancer cells to lapatinib. Thus, CARE should enable large-scale inference of response biomarkers and drug combinations for targeted therapies using compound screen data.

Molecular mechanisms of drug resistance in ovarian cancer

Ovarian cancer is the most lethal malignancy among the gynecological cancers, with a 5-year survival rate, mainly due to being diagnosed at advanced stages, recurrence and resistance to the current chemotherapeutic agents. Drug resistance is a complex phenomenon and the number of known involved genes and cross-talks between signaling pathways in this process is growing rapidly. Thus, discovering and understanding the underlying molecular mechanisms involved in chemo-resistance are crucial for management of treatment and identifying novel and effective drug targets as well as drug discovery to improve therapeutic outcomes. In this review, the major and recently identified molecular mechanisms of drug resistance in ovarian cancer from relevant literature have been investigated. In the final section of the paper, new approaches for studying detailed mechanisms of chemo-resistance have been briefly discussed.

A Functionally Robust Phenotypic Screen that Identifies Drug Resistance-associated Genes Using 3D Cell Culture

Drug resistance is a major obstacle in cancer treatment: A case in point is the failure of cancer patients to respond to tyrosine kinase inhibitors (TKI) of EGFR, a receptor that is highly expressed in many cancers. Identification of the targets and delineation of mechanisms of drug resistance remain major challenges. Traditional pharmacological assays of drug resistance measure the response of tumor cells using cell proliferation or cell death as readouts. These assays performed using traditional plastic tissue culture plates (2D) do not translate to in vivo realities. Here, we describe a genetic screen based on phenotypic changes that can be completed over a period of 1-1½ months using functional endpoints in physiologically relevant 3D culture models. This phenotype-based assay could lead to the discovery of previously unknown therapeutic targets and could explain the source of the resistance and relapse. As a proof of principle, we performed our 3D culture assay with a small cDNA library in that yielded five unknown intermediates in EGFR and PI3K signaling pathways. Here, we describe the screening method and the characterization of one of the five molecules, but this approach could be easily expanded for a high-throughput screening to identify or evaluate many more unknown intermediates in oncogenic signaling pathways.

Antifibrotic effects of pentoxifylline improve the efficacy of gemcitabine in human pancreatic tumor xenografts

We investigated the combinatorial effects of pentoxifylline (PTX) on the efficacy of gemcitabine (GEM) in a human pancreatic tumor xenograft model. PTX significantly improved the efficacy of GEM, as shown by a 50% reduction in tumor growth rate at 4 weeks of treatment compared with that in animals given GEM alone. The fluorescent drug doxorubicin (DOX) was used to test whether drug delivery was improved by PTX, contributing to the improved efficacy of GEM. PTX given for 2 weeks prior to giving DOX improved drug distribution by 1.8‐ to 2.2‐fold with no changes in vessel density, suggesting that improvement in drug delivery was not related to the vascular mechanism. Instead, collagen I content in tumor stroma was significantly reduced, as was the expression of alpha‐smooth muscle actin of cancer‐associated fibroblasts and connective tissue growth factor (CTGF) by PTX pretreatment. Overall, our data demonstrated that increased efficacy of GEM by PTX was associated with improved drug delivery to tumor tissue, which may be attributed to decreased expression of CTGF and subsequent reduction in the stromal collagen matrix in the pancreatic ductal adenocarcinoma tumor. These results support the usefulness of PTX in combination with chemotherapy for targeting drug delivery barriers associated with the stromal matrix, which should be further evaluated for clinical development.

Targeting Polo-like kinase 1 in SMARCB1 deleted atypical teratoid rhabdoid tumor

Atypical teratoid rhabdoid tumor (ATRT) is an aggressive and malignant pediatric brain tumor. Polo-like kinase 1 (PLK1) is highly expressed in many cancers and essential for mitosis. Overexpression of PLK1 promotes chromosome instability and aneuploidy by overriding the G2-M DNA damage and spindle checkpoints. Recent studies suggest that targeting PLK1 by small molecule inhibitors is a promising approach to tumor therapy. We investigated the effect of PLK1 inhibition in ATRT. Gene expression analysis showed that PLK1 was overexpressed in ATRT patient samples and tumor cell lines. Genetic inhibition of PLK1 with shRNA potently suppressed ATRT cell growth in vitro. Treatment with the PLK1 inhibitor BI 6727 (Volasertib) significantly decreased cell growth, inhibited clonogenic potential, and induced apoptosis. BI6727 treatment led to G2-M phase arrest, consistent with PLK1's role as a critical regulator of mitosis. Moreover, inhibition of PLK1 by BI6727 suppressed the tumor-sphere formation of ATRT cells. Treatment also significantly decreased levels of the DNA damage proteins Ku80 and RAD51 and increased γ-H2AX expression, indicating that BI 6727 can induce DNA damage. Importantly, BI6727 significantly enhanced radiation sensitivity of ATRT cells. In vivo, BI6727 slowed growth of ATRT tumors and prolonged survival in a xenograft model. PLK1 inhibition is a compelling new therapeutic approach for treating ATRT, and the use of BI6727 should be evaluated in clinical studies.

Characterization of In Vivo Resistance to Osimertinib and JNJ-61186372, an EGFR/Met Bispecific Antibody, Reveals Unique and Consensus Mechanisms of Resistance

Approximately 10% of non-small cell lung cancer (NSCLC) patients in the United States and 40% of NSCLC patients in Asia have activating epidermal growth factor receptor (EGFR) mutations and are eligible to receive targeted anti-EGFR therapy. Despite an extension of life expectancy associated with this treatment, resistance to EGFR tyrosine kinase inhibitors and anti-EGFR antibodies is almost inevitable. To identify additional signaling routes that can be cotargeted to overcome resistance, we quantified tumor-specific molecular changes that govern resistant cancer cell growth and survival. Mass spectrometry-based quantitative proteomics was used to profile in vivo signaling changes in 41 therapy-resistant tumors from four xenograft NSCLC models. We identified unique and tumor-specific tyrosine phosphorylation rewiring in tumors resistant to treatment with the irreversible third-generation EGFR-inhibitor, osimertinib, or the novel dual-targeting EGFR/Met antibody, JNJ-61186372. Tumor-specific increases in tyrosine-phosphorylated peptides from EGFR family members, Shc1 and Gab1 or Src family kinase (SFK) substrates were observed, underscoring a differential ability of tumors to uniquely escape EGFR inhibition. Although most resistant tumors within each treatment group displayed a marked inhibition of EGFR as well as SFK signaling, the combination of EGFR inhibition (osimertinib) and SFK inhibition (saracatinib or dasatinib) led to further decrease in cell growth in vitro This result suggests that residual SFK signaling mediates therapeutic resistance and that elimination of this signal through combination therapy may delay onset of resistance. Overall, analysis of individual resistant tumors captured unique in vivo signaling rewiring that would have been masked by analysis of in vitro cell population averages. Mol Cancer Ther; 16(11); 2572-85. ©2017 AACR.

Improved therapeutic efficacy of mammalian expressed-recombinant interferon gamma against ovarian cancer cells

Human interferon gamma (hIFNγ) affects tumour cells and modulates immune responses, showing promise as an anti-cancer biotherapeutic. This study investigated the effect of glycosylation and expression system of recombinant hIFNγ in ovarian carcinoma cell lines, PEO1 and SKOV3. The efficacy of E. coli- and mammalian-expressed hIFNγ (hIFNγ-CHO and HEK293, glycosylated/de-glycosylated) on cytostasis, cell death (MTT, and Guava-ViaCount^® flow-cytometry) and apoptotic signalling (Western blot of Cdk2, histone H3, procaspase-3, FADD, cleaved PARP, and caspase-3) was examined. Hydrophilic Interaction Liquid Chromatography determined the structure of N-linked glycans present in HEK293-expressed hIFNγ (hIFNγ-HEK). PEO1 was more sensitive to hIFNγ than SKOV3, but responses were dose-dependent and expression platform/glycosylation status-independent, whereas SKOV3 responded to mammalian-expressed hIFNγ in a dose-independent manner, only. Complex-type oligosaccharides dominated the N-glycosylation pattern of hIFNγ-HEK with some terminal sialylation and core fucosylation. Cleaved PARP and cleaved caspase-3 were not detected in either cell line, but FADD was expressed in SKOV3 with levels increased following treatment. In conclusion, hIFNγ did not induce apoptosis in either cell line. Mammalian- expressed hIFNγ increased cell death in the drug-resistant SKOV3. The presence of FADD in SKOV3, which may inhibit apoptosis through activation of NF-κB, could serve as a novel therapeutic target.

Genome-wide chemical mutagenesis screens allow unbiased saturation of the cancer genome and identification of drug resistance mutations

Drug resistance is an almost inevitable consequence of cancer therapy and ultimately proves fatal for the majority of patients. In many cases, this is the consequence of specific gene mutations that have the potential to be targeted to resensitize the tumor. The ability to uniformly saturate the genome with point mutations without chromosome or nucleotide sequence context bias would open the door to identify all putative drug resistance mutations in cancer models. Here, we describe such a method for elucidating drug resistance mechanisms using genome-wide chemical mutagenesis allied to next-generation sequencing. We show that chemically mutagenizing the genome of cancer cells dramatically increases the number of drug-resistant clones and allows the detection of both known and novel drug resistance mutations. We used an efficient computational process that allows for the rapid identification of involved pathways and druggable targets. Such a priori knowledge would greatly empower serial monitoring strategies for drug resistance in the clinic as well as the development of trials for drug-resistant patients.

Co-Targeting IGF-1R and Autophagy Enhances the Effects of Cell Growth Suppression and Apoptosis Induced by the IGF-1R Inhibitor NVP-AEW541 in Triple-Negative Breast Cancer Cells

BACKGROUND

Triple-negative breast cancer (TNBC) is the most intractable type of breast cancer, and there is a lack of effective targeted therapy. Insulin-like growth factor-1 receptor (IGF-1R) is reportedly a potential target for TNBC treatment. However, satisfying treatment outcomes in breast cancer patients have yet to be achieved with IGF-1R-targeted agents.

METHODS

To confirm whether inhibiting IGF-1R could induce autophagy, we detected autophagy-related proteins by western blotting and immunofluorescence staining of LC3-II. The IGF-1R inhibitor NVP-AEW541, autophagy inhibitor 3-methyladenine (3-MA) and Atg7 small interfering RNA (siRNA) were used to further investigate the effects of autophagy induced by IGF-1R inhibition in TNBC cells. The CCK8 assay, EdU assay, apoptosis and cell cycle analyses were applied to test cell function after treatment.

RESULTS

NVP-AEW541 markedly induced autophagy in TNBC cells by increasing the levels of the autophagy-related protein Beclin-1 and the LC3-II/LC-I ratio and reducing the selective autophagy substrate p62. Joint application of 3-MA or Atg7 siRNA enhanced the cell growth inhibition and apoptosis effects of NVP-AEW541 by arresting cells at G1/G0 phase and increasing Bax expression and decreasing that of Bcl-2.

CONCLUSION

Targeting IGF-1R in TNBC induces cell-protective autophagy, thereby weakening the therapeutic effect of agents directed toward IGF-1R. Our findings reveal that combined use autophagy-disrupting agents can enhance the therapeutic efficacy of IGF-1R inhibitors in TNBC cells and may provide a valuable treatment strategy for IGF-1R inhibitor-based therapies for TNBC and other IGF-1 signaling-associated tumors.