Understanding Of Drug Resistance Research Articles

Identification of hub genes involved in cisplatin resistance in head and neck cancer

BackgroundCisplatin resistance is one of the major contributors to the poor survival rate among head and neck cancer (HNC) patients. Focusing on the protein–protein interaction rather than a single protein could provide a better understanding of drug resistance. Thus, this study aimed to identify hub genes in a complex network of cisplatin resistance associated genes in HNC chemotherapy via a series of bioinformatic tools. MethodsThe genes involved in cisplatin resistance were retrieved from the NCBI gene database using “head and neck cancer” and “cisplatin resistance” as key words. The human genes retrieved were analyzed for their interactions and enriched using the STRING database. The interaction between KEGG pathways and genes was visualized in Cytoscape 3.7.2. Further, the hub gene was identified using the Cytohubba plugin of Cytoscape and validated using UALCAN and Human Protein Atlas database. Validated genes were investigated for the drug–gene interaction using the DGIbd database. ResultsOut of 137 genes obtained using key words, 133 were associated with cisplatin resistance in the human species. A total of 150 KEGG pathways, 82 cellular components, 123 molecular functions, and 1752 biological processes were modulated on enrichment analysis. Out of 37 hub genes, CCND1, AXL, CDKN2A, TERT, and EXH2 genes were found to have significant (p < 0.05) mRNA expression and effect on overall survival whereas protein expression was found to be positive for all the significant genes except TERT. Thus, they can be targeted with palbociclib, methotrexate, bortezomib and fluorouracil, sorafenib, dasatinib, carboplatin, paclitaxel, gemcitabine, imatinib, doxorubicin, and vorinostat. ConclusionAs the pathogenesis of head and neck cancer is complex, targeting hub genes and associated pathways involved in cisplatin resistance could bring a milestone change in the drug discovery and management of drug resistance which might uplift overall survival among HNC patients.

Mirvetuximab Soravtansine in Platinum-Resistant Ovarian Cancer.

Ovarian cancer is the second leading cause of death from gynecologic malignancies worldwide. Management of platinum-resistant disease is challenging and clinical outcomes with standard chemotherapy are poor. Over the past decades, significant efforts have been made to understand drug resistance and develop strategies to overcome treatment failure. Antibody drug conjugates (ADCs) are a rapidly growing class of oncologic therapeutics, which combine the ability to target tumor-specific antigens with the cytotoxic effects of chemotherapy. Mirvetuximab soravtansine is an ADC comprising an IgG1 monoclonal antibody against the folate receptor alpha (FRα) conjugated to the cytotoxic maytansinoid effector molecule DM4 that has shown promising clinical activity in patients with FR-α-positive ovarian cancer. This review summarizes current evidence of mirvetuximab soravtansine in platinum-resistant ovarian cancer, focusing on clinical activity, toxicity, and future directions.

Keeping RelApse in Chk: molecular mechanisms of Chk1 inhibitor resistance in lymphoma.

Chk1 is a member of the DNA damage response pathway, whose loss leads to replication stress and genome instability. Because of its protective role against lethal levels of DNA replication stress, Chk1 has been studied as a valuable and intriguing target for cancer therapy. However, one of the most prominent challenges with this strategy is development of resistance to Chk1 inhibitors, rendering the treatment ineffective. In their recent papers, Hunter and colleagues demonstrate multiple mechanisms by which Chk1 inhibitor resistance can arise in lymphomas. Specifically, this series of papers identify the relationship between dysfunction in NF-κB and the development of Chk1 inhibitor resistance through a loss of Chk1 activity in mouse models of lymphoma. They identify that cells lacking Chk1 activity can compensate for this loss through up-regulation of alternative pathways, such as PI3K/AKT. Finally, this work also identifies a novel role for Claspin, an important Chk1 activator, in female fertility and cancer development, furthering our understanding of how dysfunction in the Claspin/Chk1 signaling pathway affects disease states. These findings improve our understanding of drug resistance in cancer therapy, which has important implications for clinical use of Chk1 inhibitors.

Umbrella Trial in Myeloid Malignancies: The Myelomatch National Clinical Trials Network Precision Medicine Initiative

Introduction: To accelerate myeloid cancer therapeutics, the National Clinical Trials Network (NCTN) is launching the National Cancer Institute (NCI) Myeloid Malignancies Molecular Analysis for Therapy Choice (myeloMATCH) precision medicine clinical trial. Sponsored by NCI and conducted across the entire NCTN system with initial trials to be led by SWOG, Alliance, ECOG-ACRIN, and CCTG, the initiative leverages public-private partnerships with pharmaceutical industry and biotech companies to create an efficient regulatory model incorporating cross-company novel-novel combinations in trials for acute myeloid leukemia (AML) and myelodysplastic syndromes (MDS). The goal is to create a portfolio of rationally designed treatment substudies onto which patients sequentially enroll over their treatment journey. As patients transition to higher tiers with increasingly lower remaining tumor burden, the focus will be to target residual disease more precisely. Methods: Newly diagnosed patients are enrolled onto the Master Screening and Reassessment Protocol (MSRP) for baseline clinical and laboratory evaluation. Specimens are sent to the Molecular Diagnostics Network (MDNet) with a 72-hour turn-around for patient assignment to an initial treatment substudy via an integrated informatics system developed by the NCI Precision Medicine Analysis and Coordination Center (PMACC). Assignments will be based on algorithms adjusted for prevalence of co-mutations to enhance accrual of rare molecular subsets to specific targeted treatment trials. As shown in figure 1, there are 4 tiers and 5 clinical baskets. Tier-1 is for initial therapy grouped by MDS, younger AML, and older AML. These are typically randomized phase 2 studies testing sensitivity to novel drug combinations with measurable residual disease (MRD) assessment conducted centrally by MDNet. Subsequent therapy occurs in higher tiers. These assignments are made by MDNet/PMACC based on prior treatment substudy outcome. Flow cytometry and duplex sequencing will be employed in Tier-4 clinical trials that will target residual disease. Statistical designs will evaluate the clinical utility of the assays and biomarkers to determine if targeting residual disease confers clinical benefit. Planned activation is quarter 4 of 2022 with the MSPRP, 2-young adult tier-1 studies and 1 tier-2 study. These are testing combinations of azacitidine, venetoclax, CPX351, 7+3 for ELN defined high risk AML, standard risk AML, and in tier-2 the ability to "erase” residual disease after tier-1 treatment. Additional studies in development include agents for mutant TP53, KIT, FLT-3, NPM1, IDH 1/2, higher and lower-risk MDS and a study for reduced intensity transplant and maintenance to include efforts for diverse populations. Launch for these studies is planned for mid 2023. Tier-4 studies to target KIT, IDH, FLT3 and others are in discussion. Discussion: MyeloMATCH is the largest focused investment of infrastructure and researchers ever coordinated by the US Network Groups and NCI that follows patients from diagnosis thorough all treatment in a single disease area. The charge is to rapidly advance therapeutics in myeloid malignancies. MyeloMATCH is designed to efficiently screen and assign patients to precision treatment trials of promising therapeutic combinations. By using early endpoints to identify large activity signals, myeloMATCH will generate data with promising findings for definitive study. The clinical and laboratory data can be interrogated across the initiative to generate hypotheses for additional focused testing. Participants receiving their treatment journey through myeloMATCH will contribute to a unique clinically annotated database with specimens for "omics” serially collected from pre-treatment and through follow up. This will provide a national resource for understanding drug sensitivity and resistance, as well as clonal evolution. In this manner, myeloMATCH aims to reduce the time and investment in failed phase 3 studies, and instead aims to provide high-quality randomized trial data that will enhance selection of phase 3 priorities. We believe this new paradigm for the collaborative conduct of clinical trials may provide meaningful advances for patients with AML and MDS, mentor investigators, and accelerate drug development. Updates and specific treatment-trial designs will be discussed at the meeting. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

Novel 17-Plex Flow Cytometry Assay for Comprehensive Immunophenotyping and Characterization of Plasma Cells (PC) in Relapsed/Refractory (RR) Multiple Myeloma (MM) Patients

Background: Multiple Myeloma (MM) is a hematological malignancy also known as plasma cell myeloma or myeloma, a type of bone marrow cancer characterized by the proliferation of clonal malignant plasma cells (PCs). Characterization of multiple myeloma plasma cells (MMPCs) remains challenging due to their plasticity, especially in response to emerging targeted therapies such as anti-BCMA and anti-CD38. The need for novel options for comprehensive phenotyping remains high due to the evolving target expression profiles and dynamics across MM disease evolution and treatment lines. Recent progress in flow cytometry offers the opportunity to analyze more than 30 surface markers in one panel. This technology advance will help to address the current challenges, answer clinically relevant questions and generate data to enable translational research and identification of new drug targets or combination partners. Methods: We present a novel 17-plex flow cytometry assay for immunophenotyping of PCs in RRMM patients enrolled in a global Phase I multi-center clinical study (NCT04557150). The panel relies on 5 markers for the identification of malignant plasma cells: CD38, CD138, CD45, CD319 and CD229. The inclusion of less commonly used markers (CD229, CD319) allows to overcome some of the challenges in MMPCs identification, such as CD138 instability and CD38 downregulation due to prior therapies. In addition, the assay includes markers commonly used to differentiate normal from malignant PCs (CD19, CD56, CD27, CD81, CD117, CD28). The panel also includes 2 immunotherapy target ligands (PD-L1 and 4-1BBL) and offers the possibility to measure another 3 clinically relevant targets on MMPCs, including BCMA. With this configuration, the assay allows relevant target antigen density quantification via the use of molecules of equivalent soluble fluorochrome (MESF) beads in 3 channels. Results: We present examples of how to apply this strategy for a reliable identification of MMPCs in RRMM patient samples after up to 14 previous lines of therapies. The assay design permits a deep phenotypical characterization of MMPCs and pharmacodynamic changes in markers targeted by immunotherapy drugs, like anti-CD38 or anti-BCMA. As an example, we characterize BCMA and CD38 expression on the surface of MMPCs at the time of enrolment into a Phase I study. Finally, with the goal of discovering new biomarkers that could be overlooked in the traditional manual gating hierarchy applied in flow cytometry, we have visualized the results using dimensionality reduction analysis (Uniform Manifold Approximation and Projection, UMAP). Using this approach, we have observed high inter-patient heterogeneity of MMPC phenotypes. Furthermore, we could compare intra-patient differences in MMPC phenotype in two compartments, whole blood and bone marrow aspirate. Updated data will be presented. Conclusion: The multi parameter flow cytometry assay described here represents a novel tool to reliably identify MMPCs in RRMM patients independently of commonly used unstable surface markers. Furthermore, it represents an important tool in the era of surface antigen targeted therapies (e.g., anti-BCMA) helping to understand drug resistance due to target loss. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

The Potential Role of 3D In Vitro Acute Myeloid Leukemia Culture Models in Understanding Drug Resistance in Leukemia Stem Cells.

The complexity of the bone marrow (BM) microenvironment makes studying hematological malignancies in vitro a challenging task. Three-dimensional cell cultures are being actively studied, particularly due to their ability to serve as a bridge of the gap between 2D cultures and animal models. The role of 3D in vitro models in studying the mechanisms of chemotherapeutic resistance and leukemia stem cells (LSCs) in acute myeloid leukemia (AML) is not well-reviewed. We present an overview of 3D cell models used for studying AML, emphasizing the recent advancements in microenvironment modeling, chemotherapy testing, and resistance.

Hyaluronic Acid-Based Nanomaterials Applied to Cancer: Where Are We Now?

Cancer cells normally develop the ability to rewire or reprogram themselves to become resistant to treatments that were previously effective. Despite progress in understanding drug resistance, knowledge gaps remain regarding the underlying biological causes of drug resistance and the design of cancer treatments to overcome it. So, resistance acquisition remains a major problem in cancer treatment. Targeted therapeutics are considered the next generation of cancer therapy because they overcome many limitations of traditional treatments. Numerous tumor cells overexpress several receptors that have a high binding affinity for hyaluronic acid (HA), while they are poorly expressed in normal body cells. HA and its derivatives have the advantage of being biocompatible and biodegradable and may be conjugated with a variety of drugs and drug carriers for developing various formulations as anticancer therapies such as micelles, nanogels, and inorganic nanoparticles. Due to their stability in blood circulation and predictable delivery patterns, enhanced tumor-selective drug accumulation, and decreased toxicity to normal tissues, tumor-targeting nanomaterial-based drug delivery systems have been shown to represent an efficacious approach for the treatment of cancer. In this review, we aim to provide an overview of some in vitro and in vivo studies related to the potential of HA as a ligand to develop targeted nanovehicles for future biomedical applications in cancer treatment.

Abstract PR006: Multi-modal single-cell profiling of sarcomas from archival tissue reveals mechanisms of resistance to immune checkpoint inhibitors

Abstract Single-cell genomics is an enabling technology that may inform the molecular underpinnings of drug response and resistance in patient biopsies. These methods are difficult to implement in the study of rare diseases such as sarcomas due to specimen requirements and technical limitations. Here, we evolved novel methods that we recently reported in melanoma (Wang, Fan, et al., bioRxiv, 2022) which enable single-nucleus RNA, T cell receptor (snRNA/TCR-seq), and pool-matched whole-genome sequencing (WGS) from archival frozen sarcoma tissue. This enabled profiling of 75,716 cells and 788 matched TCR clonotypes from six patients with intimal sarcoma (INS) and undifferentiated pleomorphic sarcoma (UPS), including two matched pair samples from pre/post-immune checkpoint inhibitor (ICI). Our analysis revealed substantial transcriptional cancer cell heterogeneity driven by varying copy number alterations (CNAs). In one patient with INS with a complete response to ICI followed by an isolated recurrence, we identified a rare cancer cell clone defined by CNA (confirmed with WGS) and resulting transcriptional outputs that pre-existed and emerged during resistance. Furthermore, in a UPS patient with intrinsic resistance to ICI, we find adequate T cell clonal expansion, activation, and differentiation, suggesting appropriate T cell response to ICI dampened by intrinsic mechanisms of ICI resistance within the cancer cells. Non-negative matrix factorization (NMF) analysis identified cell states associated with either intrinsic or adaptive resistance to ICI that was distinct from resistance to doxorubicin. These observations are consistent with those previously reported from sequential biopsies obtained from KEYNOTE-001 in metastatic melanoma (Wang, Fan, et al., bioRxiv, 2022), which also revealed emergence of pre-existing populations of resistant clones defined by their underlying aneuploidy patterns. Together, these results demonstrate feasibility of implementing single-cell genomics from archival tissue to study sarcoma and propel our understanding of drug resistance. Conceptually, this work suggests that large-scale CNAs may allow us to identify cell sub-populations associated with ICI resistance in sarcoma and in other diseases. Citation Format: Sminu Bose, Blake Caldwell, Amit Dipak Amin, Johannes Melms, Somnath Tagore, Matthew Ingham, Benjamin Izar, Gary K. Schwartz. Multi-modal single-cell profiling of sarcomas from archival tissue reveals mechanisms of resistance to immune checkpoint inhibitors [abstract]. In: Proceedings of the AACR Special Conference: Sarcomas; 2022 May 9-12; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2022;28(18_Suppl):Abstract nr PR006.

Genomic analysis of single nucleotide polymorphisms in malaria parasite drug targets

Malaria is a life-threatening parasitic disease caused by members of the genus Plasmodium. The development and spread of drug-resistant strains of Plasmodium parasites represent a major challenge to malaria control and elimination programmes. Evaluating genetic polymorphism in a drug target improves our understanding of drug resistance and facilitates drug design. Approximately 450 and 19 whole-genome assemblies of Plasmodium falciparum and Plasmodium vivax, respectively, are currently available, and numerous sequence variations have been found due to the presence of single nucleotide polymorphism (SNP). In the study reported here, we analysed global SNPs in the malaria parasite aminoacyl-tRNA synthetases (aaRSs). Our analysis revealed 3182 unique SNPs in the 20 cytoplasmic P. falciparum aaRSs. Structural mapping of SNPs onto the three-dimensional inhibitor-bound complexes of the three advanced drug targets within aaRSs revealed a remarkably low mutation frequency in the crucial aminoacylation domains, low overall occurrence of mutations across samples and high conservation in drug/substrate binding regions. In contrast to aaRSs, dihydropteroate synthase (DHPS), also a malaria drug target, showed high occurrences of drug resistance-causing mutations. Our results show that it is pivotal to screen potent malaria drug targets against global SNP profiles to assess genetic variances to ensure success in designing drugs against validated targets and tackle drug resistance early on.Graphical

Multi-modal single-cell profiling of sarcomas from archival tissue reveals mechanisms of resistance to immune checkpoint inhibitors.

e23518 Background: Single-cell RNA-seq is an enabling technology that may inform the molecular underpinnings of drug response and resistance in patient biopsies. This method is difficult to implement in the study of rare diseases such as sarcomas due to specimen requirements and technical limitations. Methods: Here, we evolved novel methods that we recently reported in melanoma ( Wang, Fan, et al., bioRxiv, 2022), which enable single-nucleus RNA, T cell receptor (snRNA/TCR)-seq, and pool-matched whole-genome sequencing (WGS) from archival, frozen sarcoma tissue. Results: This enabled profiling of 75,716 cells and 788 matched TCR clonotypes from six patients with intimal sarcoma (INS) and undifferentiated pleomorphic sarcomas (UPS), including two matched pair samples from pre/post-immune checkpoint inhibitor (ICI). Our analysis revealed substantial transcriptional cancer cell heterogeneity driven by varying copy number alterations (CNAs). In one patient with INS with a complete response to ICI followed by an isolated recurrence, we identified a rare cancer cell clone defined by CNA (confirmed with WGS) and resulting transcriptional outputs that pre-existed and emerged during resistance. Furthermore, in a patient with UPS with intrinsic resistance to ICI, we find adequate T cell clonal expansion and activation, suggesting appropriate T cell response to ICI dampened by intrinsic mechanisms of ICI resistance within the cancer cells. Non-negative matrix factorization (NMF) analysis identified cell states associated with either intrinsic or adaptive resistance to ICI that was distinct from resistance to doxorubicin. These observations are consistent with those previously reported from sequential biopsies obtained from KEYNOTE-001 in metastatic melanoma ( Wang, Fan, et al., bioRxiv, 2022), which also revealed emergence of pre-existing populations of resistant clones defined by their underlying aneuploidy patterns. Conclusions: Together, these results demonstrate feasibility of implementing single-cell genomics from archival tissue to study sarcoma and propel our understanding of drug resistance. Conceptually, this work suggests that large-scale CNAs may drive cell sub-populations associated with ICI resistance in sarcoma and in other diseases.

Lysosomal Exocytosis of Olivacine on the Way to Explain Drug Resistance in Cancer Cells.

Ellipticine is an indole alkaloid with proven antitumor activity against various tumors in vitro and a diverse mechanism of action, which includes topoisomerase II inhibition, intercalation, and cell cycle impact. Olivacine—ellipticine’s isomer—shows similar properties. The objectives of this work were as follows: (a) to find a new path of olivacine synthesis, (b) to study the cytotoxic properties of olivacine and ellipticine in comparison to doxorubicin as well as their impact on the cell cycle, and (c) to investigate the cellular pharmacokinetics of the tested compounds to understand drug resistance in cancer cells better. SRB and MTT assays were used to study the anticancer activity of olivacine and ellipticine in vitro. Both compounds showed a cytotoxic effect on various cell lines, most notably on the doxorubicin-resistant LoVo/DX model, with olivacine’s cytotoxicity approximately three times higher than doxorubicin. Olivacine proved to be less effective against cancer cells and less cytotoxic to normal cells than ellipticine. Olivacine proved to have fluorescent properties. Microscopic observation of cells treated with olivacine showed the difference in sensitivity depending on the cell line, with A549 cells visibly affected by a much lower concentration of olivacine than normal NHDF cells. An increased percentage of cells in G0/G1 was observed after treatment with olivacine and ellipticine, suggesting an impact on cell cycle progression, potentially via higher p53 protein expression, which blocks the transition from G0/G1 to the S phase. Ellipticine induced apoptosis at a concentration as low as 1 μM. It has been proved that the tested compounds (ellipticine and olivacine) undergo lysosomal exocytosis. Reducing exocytosis is possible through the use of compounds that inhibit the activity of the proton pump. Olivacine and ellipticine exhibited diverse cytotoxicity against a panel of cancer cells. Analysis of the lysosomal exocytosis of olivacine and ellipticine shows the need to look for derivatives with comparable anticancer activity but reduced weak base character.

DualGCN: a dual graph convolutional network model to predict cancer drug response

BackgroundDrug resistance is a critical obstacle in cancer therapy. Discovering cancer drug response is important to improve anti-cancer drug treatment and guide anti-cancer drug design. Abundant genomic and drug response resources of cancer cell lines provide unprecedented opportunities for such study. However, cancer cell lines cannot fully reflect heterogeneous tumor microenvironments. Transferring knowledge studied from in vitro cell lines to single-cell and clinical data will be a promising direction to better understand drug resistance. Most current studies include single nucleotide variants (SNV) as features and focus on improving predictive ability of cancer drug response on cell lines. However, obtaining accurate SNVs from clinical tumor samples and single-cell data is not reliable. This makes it difficult to generalize such SNV-based models to clinical tumor data or single-cell level studies in the future.ResultsWe present a new method, DualGCN, a unified Dual Graph Convolutional Network model to predict cancer drug response. DualGCN encodes both chemical structures of drugs and omics data of biological samples using graph convolutional networks. Then the two embeddings are fed into a multilayer perceptron to predict drug response. DualGCN incorporates prior knowledge on cancer-related genes and protein–protein interactions, and outperforms most state-of-the-art methods while avoiding using large-scale SNV data.ConclusionsThe proposed method outperforms most state-of-the-art methods in predicting cancer drug response without the use of large-scale SNV data. These favorable results indicate its potential to be extended to clinical and single-cell tumor samples and advancements in precision medicine.

Understanding drug resistance patterns across different classes of antiretrovirals used in HIV-1-infected treatment-Naïve and experienced patients in Mumbai, India.

The aim of this study is to find out the proportion of treatment-naïve (Tn) and treatment-experienced (Te) patients experiencing HIV drug resistance (DR) to different classes of antiretrovirals (ARVs) being used for HIV treatment and their in class DR correlation. A cross-sectional study was done on 109 HIV patients enrolled at a private hospital in Thane, India, from 2014 to 2019. All patients were tested for CD4 count, viral load, and resistance to ARVs. Sixty-six patients were Tn and 43 patients were Te. Among Tn and Te patients, the percentage of high-level resistance (HLR) for nonnucleoside reverse transcriptase inhibitors (NNRTI) was 4.55% and 37.8%, respectively, for nucleoside reverse transcriptase inhibitors (NRTI) was 0.43% and 36.4%, respectively. No HLR was observed for protease inhibitors (PIs) among Tn patients, while Te patients showed 2.62% HLR. Tn and Te patients showed high susceptibility for Darunavir (98.48% and 95.34%, respectively) followed by Atazanavir and Lopinavir (96.96%, each and 90.69%, each). Tn patients showed HLR for Lamivudine and Emtricitabine (1.52%, each). Integrase Strand Transfer Inhibitors were susceptible (100%) in both Tn and Te patients. A positive correlation was observed for within class across ARVs. An increased incidence of HLR was observed for NNRTI as compared to NRTI while PIs and integrase strand transfer inhibitors (INSTIs) demonstrated no HLR in either group of patients. When selecting a regimen for Tn patients consisting of NRTIs + NNRTIs genotypic DR test is essential. While with PIs or INSTIs its optional. Among Te patients, DR testing is recommended for all classes of drugs.

Computational Methods for the Analysis and Prediction of EGFR-Mutated Lung Cancer Drug Resistance: Recent Advances in Drug Design, Challenges and Future Prospects.

Lung cancer is a major cause of cancer deaths worldwide, and has a very low survival rate. Non-small cell lung cancer (NSCLC) is the largest subset of lung cancers, which accounts for about 85% of all cases. It has been well established that a mutation in the epidermal growth factor receptor (EGFR) can lead to lung cancer. EGFR Tyrosine Kinase Inhibitors (TKIs) are developed to target the kinase domain of EGFR. These TKIs produce promising results at the initial stage of therapy, but the efficacy becomes limited due to the development of drug resistance. In this paper, we provide a comprehensive overview of computational methods, for understanding drug resistance mechanisms. The important EGFR mutants and the different generations of EGFR-TKIs, with the survival and response rates are discussed. Next, we evaluate the role of important EGFR parameters in drug resistance mechanism, including structural dynamics, hydrogen bonds, stability, dimerization, binding free energies, and signaling pathways. Personalized drug resistance prediction models, drug response curve, drug synergy, and other data-driven methods are also discussed. Recent advancements in deep learning; such as AlphaFold2, deep generative models, big data analytics, and the applications of statistics and permutation are also highlighted. We explore limitations in the current methodologies, and discuss strategies to overcome them. We believe this review will serve as a reference for researchers; to apply computational techniques for precision medicine, analyzing structures of protein-drug complexes, drug discovery, and understanding the drug response and resistance mechanisms in lung cancer patients.

Intratumor heterogeneity and clonal evolution revealed in castration-resistant prostate cancer by longitudinal genomic analysis

Intratumor heterogeneity is a key driver for local relapse and treatment failure. Thus, using multifocal prostate cancer as a model to investigate tumor inter-clonal relationships and tumor evolution could aid in our understanding of drug resistance. Previous studies discovered genomic alterations by comparing hormone-sensitive prostate cancer (HSPC) with castration-resistant prostate cancer (CRPC) in large cohorts. However, most studies did not sequentially sample tumors from the same patient. In our study, we performed whole-exome sequencing (WES) on 14 specimens from five locally relapsed patients before and after androgen-deprivation therapy. We described the landscape of genomic alterations before and after treatment and identified critical driver events that could have contributed to the evolution of CRPC. In addition to confirming known cancer genes such as TP53 and CDK12, we also identified new candidate genes that may play a role in the progression of prostate cancer, including MYO15A, CHD6 and LZTR1. At copy number alteration (CNA) level, gain of 8q24.13-8q24.3 was observed in 60% of patients and was the most commonly altered locus in both HSPC and CRPC tumors. Finally, utilizing phylogenetic reconstruction, we explored the clonal progression pattern from HSPC to CRPC in each patient. Our findings highlight the complex and heterogeneous mechanisms underlying the development of drug resistance, and underscore the potential value of monitoring tumor clonal architectures during disease progression in a clinical setting.

Phosphoproteomic Analysis of Primary Myeloma Patient Samples Identifies Distinct Phosphorylation Signatures Correlating with Chemo-Sensitivity Profiles in an Ex Vivo Drug Sensitivity Testing Platform

Introduction: Multiple myeloma (MM) is characterized by the clonal expansion of plasma cells in the bone marrow resulting in end-organ damage. Despite an extensive increase in the five-year survival rate in recent years, MM is still considered an incurable disease as patients will repeatedly relapse and develop resistance to current chemotherapies. A key focus for the personalization of myeloma therapy is understanding the biological mechanisms of drug resistance and identifying clinically useful biomarkers of therapeutic response. Highly efficient techniques for the enrichment of phosphorylated peptides followed by high resolution mass spectrometry facilitates the quantitation of thousands of site-specific phosphorylation events. Here, we have performed a phosphoproteomic analysis on MM cell lysates stratified based on their ex vivo drug response profiles to advance our understanding of drug resistance mechanisms.Materials and Methods: CD138 + plasma cells were isolated from 20 adult MM patient bone marrow aspirates at diagnosis (n=7) or relapse (n=13). Samples were grouped based on ex vivo drug sensitivity and resistance testing (DSRT) as follows: highly sensitive (Group I), sensitive (Group II), resistant (Group III), highly resistant (Group IV) [1]. For the phosphoproteomic analysis, peptides were generated and purified using the filter aided sample preparation (FASP) protocol. Peptide tandem mass tag (TMT) labelling, Fe 3+ immobilized metal ion affinity chromatography (IMAC), synchronous precursor selection (SPS), and triple stage tandem mass spectrometry (MS3) was performed. Nonenriched peptides were used for proteomic analysis. Resulting data was analysed using MaxQuant, followed by normalization of phosphosite intensities using the internal reference scale (IRS) method, and statistical analysis using Perseus. Functional enrichment and kinase enrichment analyses were performed on significant phosphoproteins using g:profiler and KEA2, respectively.Results: Our quantitative MS-based phosphoproteomic analysis identified 2,945 phosphorylation sites on 2,232 phosphopeptides from 690 phosphoproteins. Of these phosphorylation sites, 176 were significantly changed between all four DSRT groups and 267 were significantly changed between Group I and Group IV (False Discovery Rate (FDR) < 0.05). Hierarchical clustering was performed to highlight the distinct phosphoproteomic profiles associated with each DSRT group, of which the very sensitive (Group I) and very resistant (Group IV) subgroups demonstrated a well-defined separation (Fig. 1A, 1B). KEGG pathway analysis and gene ontology (GO) analysis of significantly increased phosphorylated proteins in Group IV compared to Group I MM patients demonstrated an increased phosphorylation of proteins associated with tight junctions, the Rap1 signalling pathway and the phosphatidylinositol signalling system; indicating an upregulation of cell adhesion associated processes in drug resistant MM. Phosphoproteins increased in abundance in Group I compared to Group IV MM patients revealed an increased phosphorylation of proteins involved in translation and RNA processing including the spliceosome, RNA transport and RNA binding pathways (Fig. 1C). We identified filamin A serine 2152, RAS guanyl-releasing protein 2 serine 576 and proto-oncogene tyrosine-protein kinase Src serine 17 as increased in Group IV MM, and nuclease-sensitive element-binding protein 1 (YBX1) serine 165, CD44 serine 697 and Bcl2-associated agonist of cell death (BAD) serine 99 as increased in Group I MM. KEA of the upregulated phosphoproteome in Group IV revealed an enrichment of cyclin dependent kinase 1 (CDK1) and ribosomal s6 kinases (RPS6K) whereas casein kinase 2 (CK2) and the glycolysis-associated kinases were enriched in Group I (Fig. 1D).Conclusion: Our study has generated a phosphoproteomic dataset demonstrating distinct phosphorylation signatures associated with drug sensitivity in clinical MM plasma cells. The identification of phosphorylation events associated with drug resistance provides a basis for further exploration of these events and associated signalling pathways to further understand drug resistance mechanisms in MM and identify potential biomarkers of therapeutic response and targets for drug re-sensitization in MM.

Network-driven analysis of human\u2013Plasmodium falciparum interactome: processes for malaria drug discovery and extracting in silico targets

BackgroundThe emergence and spread of malaria drug resistance have resulted in the need to understand disease mechanisms and importantly identify essential targets and potential drug candidates. Malaria infection involves the complex interaction between the host and pathogen, thus, functional interactions between human and Plasmodium falciparum is essential to obtain a holistic view of the genetic architecture of malaria. Several functional interaction studies have extended the understanding of malaria disease and integrating such datasets would provide further insights towards understanding drug resistance and/or genetic resistance/susceptibility, disease pathogenesis, and drug discovery.MethodsThis study curated and analysed data including pathogen and host selective genes, host and pathogen protein sequence data, protein–protein interaction datasets, and drug data from literature and databases to perform human-host and P. falciparum network-based analysis. An integrative computational framework is presented that was developed and found to be reasonably accurate based on various evaluations, applications, and experimental evidence of outputs produced, from data-driven analysis.ResultsThis approach revealed 8 hub protein targets essential for parasite and human host-directed malaria drug therapy. In a semantic similarity approach, 26 potential repurposable drugs involved in regulating host immune response to inflammatory-driven disorders and/or inhibiting residual malaria infection that can be appropriated for malaria treatment. Further analysis of host–pathogen network shortest paths enabled the prediction of immune-related biological processes and pathways subverted by P. falciparum to increase its within-host survival.ConclusionsHost–pathogen network analysis reveals potential drug targets and biological processes and pathways subverted by P. falciparum to enhance its within malaria host survival. The results presented have implications for drug discovery and will inform experimental studies.

RNA-Sequencing-Based Transcriptomic Score with Prognostic and Theranostic Values in Multiple Myeloma.

Multiple myeloma (MM) is the second most frequent hematological cancer and is characterized by the clonal proliferation of malignant plasma cells. Genome-wide expression profiling (GEP) analysis with DNA microarrays has emerged as a powerful tool for biomedical research, generating a huge amount of data. Microarray analyses have improved our understanding of MM disease and have led to important clinical applications. In MM, GEP has been used to stratify patients, define risk, identify therapeutic targets, predict treatment response, and understand drug resistance. In this study, we built a gene risk score for 267 genes using RNA-seq data that demonstrated a prognostic value in two independent cohorts (n = 674 and n = 76) of newly diagnosed MM patients treated with high-dose Melphalan and autologous stem cell transplantation. High-risk patients were associated with the expression of genes involved in several major pathways implicated in MM pathophysiology, including interferon response, cell proliferation, hypoxia, IL-6 signaling pathway, stem cell genes, MYC, and epigenetic deregulation. The RNA-seq-based risk score was correlated with specific MM somatic mutation profiles and responses to targeted treatment including EZH2, MELK, TOPK/PBK, and Aurora kinase inhibitors, outlining potential utility for precision medicine strategies in MM.

The Non-Coding RNAs Inducing Drug Resistance in Ovarian Cancer: A New Perspective for Understanding Drug Resistance.

Ovarian cancer, a common malignant tumor, is one of the primary causes of cancer-related deaths in women. Systemic chemotherapy with platinum-based compounds or taxanes is the first-line treatment for ovarian cancer. However, resistance to these chemotherapeutic drugs worsens the prognosis. The underlying mechanism of chemotherapeutic resistance in ovarian cancer remains unclear. Non-coding RNAs, including long non-coding RNAs, microRNAs, and circular RNAs, have been implicated in the development of drug resistance. Abnormally expressed non-coding RNAs can promote ovarian cancer resistance by inducing apoptosis inhibition, protective autophagy, abnormal tumor cell proliferation, epithelial-mesenchymal transition, abnormal glycolysis, drug efflux, and cancer cell stemness. This review summarizes the role of non-coding RNAs in the development of chemotherapeutic resistance in ovarian cancer, including their mechanisms, targets, and potential signaling pathways. This will facilitate the development of novel chemotherapeutic agents that can target these non-coding RNAs and improve ovarian cancer treatment.

The high-performance technology CRISPR/Cas9 improves knowledge and management of acute myeloid leukemia.

Knowledge on acute myeloid leukemia pathogenesis and treatment has progressed recently, but not enough to provide ideal management. Improving the prognosis of acute myeloid leukemia patients depends on advances in molecular biology for the detection of new therapeutic targets and the production of effective drugs. The CRISPR/Cas9 technology allows gene insertions and deletions and it is the first step in investigating the function of their encoded proteins. Thus, new experimental models have been developed and progress has been made in understanding protein metabolism, antitumor activity, leukemic cell maintenance, differentiation, growth, apoptosis, and self-renewal, the combined pathogenetic mechanisms involved in leukemogenesis. The CRISPR/Cas9 system is used to understand drug resistance and find solutions to overcome it. The therapeutic progress achieved using the CRISPR/Cas9 system is remarkable. FST gene removal inhibited acute myeloid leukemia cell growth. Lysine acetyltransferase gene deletion contributed to decreased proliferation rate, increased apoptosis, and favored differentiation of acute myelid leukemia cells carrying MLL-X gene fusions. The removal of CD38 gene from NK cells decreased NK fratricidal cells contributing to increased efficacy of new CD38 CAR-NK cells to target leukemic blasts. BCL2 knockout has synergistic effects with FLT3 inhibitors. Exportin 1 knockout is synergistic with midostaurin treatment in acute myeloid leukemia with FLT3-ITD mutation. Using the results of CRISPR/Cas9 libraries and technology application will allow us to get closer to achieving the goal of curing acute myeloid leukemia in the coming decades.