Anticancer Drug Response Research Articles

Integrative analysis reveals a four-gene signature for predicting survival and immunotherapy response in colon cancer patients using bulk and single-cell RNA-seq data.

Colon cancer (CC) ranks as one of the leading causes of cancer-related mortality globally. Single-cell transcriptome sequencing (scRNA-seq) offers precise gene expression data for distinct cell types. This study aimed to utilize scRNA-seq and bulk transcriptome sequencing (bulk RNA-seq) data from CC samples to develop a novel prognostic model. scRNA-seq data was downloaded from the GSE161277 database. R packages including "Seurat", "Harmony", and "singleR" were employed to categorize eight major cell types within normal and tumor tissues. By comparing tumor and normal samples, differentially expressed genes (DEGs) across these major cell types were identified. Gene Ontology (GO) enrichment analyses of DEGs for each cell type were conducted using "Metascape". DEGs-based signature construction involved Cox regression and least absolute shrinkage operator (LASSO) analyses, performed on The Cancer Genome Atlas (TCGA) training cohort. Validation occurred in the GSE39582 and GSE33382 datasets. The expression pattern of prognostic genes was verified using spatial transcriptome sequencing (ST-seq) data. Ultimately, an established prognostic nomogram based on the gene signature and age was established and calibrated. Sensitivity to chemotherapeutic drugs was predicted with the "oncoPredict" R package. Using scRNA-Seq data, we examined 33,213 cells, categorizing them into eight cell types within normal and tumor samples. GO enrichment analysis revealed various cancer-related pathways across DEGs in these cell types. Among the 55 DEGs identified via univariate Cox regression, four independent prognostic genes emerged: PTPN6, CXCL13, SPINK4, and NPDC1. Expression validation through ST-seq confirmed PTPN6 and CXCL13 predominance in immune cells, while SPINK4 and NPDC1 were relatively epithelial cell-specific. Creating a four-gene prognostic signature, Kaplan-Meier survival analyses emphasized higher risk scores correlating with unfavorable prognoses, confirmed across training and validation cohorts. The risk score emerged as an independent prognostic factor, supported by a reliable nomogram. Intriguingly, drug sensitivity analysis unveiled contrasting anti-cancer drug responses in the two risk groups, suggesting significant clinical implications. We developed a novel prognostic four-gene risk model, and these genes may act as potential therapeutic targets for CC.

Role of NF-κB pathway in kidney renal clear cell carcinoma and its potential therapeutic implications.

Kidney renal clear cell carcinoma (KIRC), a common malignant tumor of the urinary system, is the most aggressive renal tumor subtype. Since the discovery of nuclear factor kappa B (NF-κB) in 1986, many studies have demonstrated abnormal NF-κB signaling is associated with the development of various cancers, including kidney renal clear cell carcinoma. In this study, the relationship between NF-κB and kidney renal clear cell carcinoma was confirmed using bioinformatics analysis. First, we explored the differential expression of copy number variation (CNV), single nucleotide variant (SNV), and messenger RNA (mRNA) in NF-κB-related genes in different types of cancer, as well as the impact on cancer prognosis and sensitivity to common chemotherapy drugs. Then, we divided the mRNA expression levels of NF-κB-related genes in KIRC patients into three groups through GSVA cluster analysis and explored the correlation between the NF-κB pathway and clinical data of KIRC patients, classical cancer-related genes, common anticancer drug responsiveness, and immune cell infiltration. Finally, 11 tumor-related genes were screened using least absolute shrinkage and selection operator (LASSO) regression to construct a prognostic model. In addition, we used the UALCAN and HPA databases to verify the protein levels of three key NF-κB-related genes (CHUK, IKGGB, and IKBKG) in KIRC. In conclusion, our study established a prognostic survival model based on NF-κB-related genes, which can be used to predict the prognosis of patients with KIRC.

3D tumor spheroids: morphological alterations a yardstick to anti-cancer drug response

3D tumor spheroids: morphological alterations a yardstick to anti-cancer drug response

The Genetic, Pharmacogenomic, and Immune Landscapes Associated with Protein Expression across Human Cancers.

Comprehensive characterization of the relationship between protein expression and the genetic, pharmacogenomic, and immune landscape of tumors across cancer types provides a foundation for investigating the role of protein expression in cancer development and treatment.

NeuPD—A Neural Network-Based Approach to Predict Antineoplastic Drug Response

With the beginning of the high-throughput screening, in silico-based drug response analysis has opened lots of research avenues in the field of personalized medicine. For a decade, many different predicting techniques have been recommended for the antineoplastic (anti-cancer) drug response, but still, there is a need for improvements in drug sensitivity prediction. The intent of this research study is to propose a framework, namely NeuPD, to validate the potential anti-cancer drugs against a panel of cancer cell lines in publicly available datasets. The datasets used in this work are Genomics of Drug Sensitivity in Cancer (GDSC) and Cancer Cell Line Encyclopedia (CCLE). As not all drugs are effective on cancer cell lines, we have worked on 10 essential drugs from the GDSC dataset that have achieved the best modeling results in previous studies. We also extracted 1610 essential oncogene expressions from 983 cell lines from the same dataset. Whereas, from the CCLE dataset, 16,383 gene expressions from 1037 cell lines and 24 drugs have been used in our experiments. For dimensionality reduction, Pearson correlation is applied to best fit the model. We integrate the genomic features of cell lines and drugs’ fingerprints to fit the neural network model. For evaluation of the proposed NeuPD framework, we have used repeated K-fold cross-validation with 5 times repeats where K = 10 to demonstrate the performance in terms of root mean square error (RMSE) and coefficient determination (R2). The results obtained on the GDSC dataset that were measured using these cost functions show that our proposed NeuPD framework has outperformed existing approaches with an RMSE of 0.490 and R2 of 0.929.

CTDN (Convolutional Temporal Based Deep‐ Neural Network): An Improvised Stacked Hybrid Computational Approach for Anticancer Drug Response Prediction

The characterization of drug - metabolizing enzymes is a significant problem for customized therapy. It is important to choose the right drugs for cancer victims, and the ability to forecast how those drugs will react is usually based on the available information, genetic sequence, and structural properties. To the finest of our knowledge, this is the first study to evaluate optimization algorithms for selection of features and pharmacogenetics categorization using classification methods based on a successful evolutionary algorithm using datasets from the Cancer Cell Line Encyclopaedia (CCLE) and Genomics of Drug Sensitivity in Cancer (GDSC). The study proposes the uses of Firefly and Grey Wolf Optimization techniques for feature extraction, while comparing the traditional Machine Learning (ML), ensemble ML and Stacking Algorithm with the proposed Convolutional Temporal Deep Neural Network or CTDN. With the potential to increase efficiency from the suggested intelligible classifier model for a suggestive chemotherapeutic drugs response prediction, our study is important in particular for selecting an acceptable feature selection method. The comparison analysis demonstrates that the proposed model not only surpasses the prior state-of-the-art methods, but also uses Grey Wolf and Fire Fly Optimization to lessen multicollinearity and overfitting.

Abstract 2773: Homologous recombination deficiency in the CCLE cell lines correlates with in vitro resistance to platinum agents and PARP inhibitors

Abstract Background: Since the advent of high-throughput sequencing technologies, multi-omics data along with large-scale drug sensitivity screening data on cancer cell lines from the NCI-60 panel and the CCLE (Cancer Cell Line Encyclopedia) project have been established and widely used for cancer research. Although we previously reported the utility of homologous recombination deficiency (HRD), commonly found in ovarian and breast cancer, as a clinical biomarker across cancer types (PMID:35613413), whether HRD status in cancer cell lines can be an indicator of drug sensitivity to platinum agents or PARP inhibitors has not been fully investigated. Methods: From CCLE publicly available datasets, BRCA1/2 gene mutations and their loss of heterozygosity (LOH) status, HRD score, mutational signature 3 (SBS3), and BRCA1 methylation silencing were analyzed. In vitro drug screening data were obtained from the GDSC (Genomics of Drug Sensitivity in Cancer), CTRP (Cancer Therapeutic Response Portal), and PRISM (Profiling Relative Inhibition Simultaneously in Mixtures) databases. We additionally analyzed anticancer drug response in cell line-derived xenograft models using the dataset of ROADMAPS (Responses to Oncology Agents and Dosing in Models to Aid Preclinical Studies). Results: A total of 1260 cell lines from the CCLE were analyzed. HRD score and SBS3 were positively correlated (ρ=0.36). These scores were higher in samples with BRCA1 methylation and BRCA1/2 mutations involving locus-specific LOH (referred to collectively as BRCA alteration), but not in samples with BRCA1/2 mutations lacking locus-specific LOH. Per-drug correlation analysis between cell line HRD scores and the areas under the drug response curve (AUCs) showed statistically significant (p<0.05) positive correlations in 60% (6/10) of platinum compounds and in 46% (6/13) of PARP inhibitors, but no negative correlations in any of these drugs. A similar trend was observed in the analysis using SBS3 and in the two-group comparison with and without BRCA alteration, indicating that cell lines with HRD were more resistant to platinum agents or PARP inhibitors. In contrast, drug response analysis in xenograft models showed that HRD score and SBS3 were non-significantly higher in cisplatin-high-sensitivity cell lines than those with low sensitivity (median HRD score 45 vs 36, median SBS3 11.7 vs 3.5). Conclusion: When comparing in vitro drug sensitivity among cancer cell lines, HRD correlates with resistance rather than with response to platinum or PARP inhibitors. Thus, the association between HRD status and drug sensitivity of cancer cells is substantially different between tumors in dish culture and in clinical settings. This study provides critical insights that should be considered when using cancer cell lines and underscores the need for experimental models that better reflect efficacy in clinical tumors. Citation Format: Shiro Takamatsu, Noriomi Matsumura. Homologous recombination deficiency in the CCLE cell lines correlates with in vitro resistance to platinum agents and PARP inhibitors [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 2773.

Improving drug response prediction based on two-space graph convolution

Patients with the same cancer types may present different genomic features and therefore have different drug sensitivities. Accordingly, correctly predicting patients’ responses to the drugs can guide treatment decisions and improve the outcome of cancer patients. Existing computational methods leverage the graph convolution network model to aggregate features of different types of nodes in the heterogeneous network. They most fail to consider the similarity between homogeneous nodes. To this end, we propose an algorithm based on two-space graph convolutional neural networks, TSGCNN, to predict the response of anticancer drugs. TSGCNN first constructs the cell line feature space and the drug feature space and separately performs the graph convolution operation on the feature spaces to diffuse similarity information among homogeneous nodes. After that, we generate a heterogeneous network based on the known cell line and drug relationship and perform graph convolution operations on the heterogeneous network to collect the features of different types of nodes. Subsequently, the algorithm produces the final feature representations for cell lines and drugs by adding their self features, the feature space representations, and the heterogeneous space representations. Finally, we leverage the linear correlation coefficient decoder to reconstruct the cell line-drug correlation matrix for drug response prediction based on the final representations. We tested our model on the Cancer Drug Sensitivity Data (GDSC) and Cancer Cell Line Encyclopedia (CCLE) databases. The results indicate that TSGCNN shows excellent performance drug response prediction compared with other eight state-of-the-art methods.

DeepInsight-3D architecture for anti-cancer drug response prediction with deep-learning on multi-omics

Modern oncology offers a wide range of treatments and therefore choosing the best option for particular patient is very important for optimal outcome. Multi-omics profiling in combination with AI-based predictive models have great potential for streamlining these treatment decisions. However, these encouraging developments continue to be hampered by very high dimensionality of the datasets in combination with insufficiently large numbers of annotated samples. Here we proposed a novel deep learning-based method to predict patient-specific anticancer drug response from three types of multi-omics data. The proposed DeepInsight-3D approach relies on structured data-to-image conversion that then allows use of convolutional neural networks, which are particularly robust to high dimensionality of the inputs while retaining capabilities to model highly complex relationships between variables. Of particular note, we demonstrate that in this formalism additional channels of an image can be effectively used to accommodate data from different omics layers while implicitly encoding the connection between them. DeepInsight-3D was able to outperform other state-of-the-art methods applied to this task. The proposed improvements can facilitate the development of better personalized treatment strategies for different cancers in the future.

Preclinical-to-clinical Anti-cancer Drug Response Prediction and Biomarker Identification Using TINDL

Prediction of the response of cancer patients to different treatments and identification of biomarkers of drug response are two major goals of individualized medicine. Here, we developed a deep learning framework called TINDL, completely trained on preclinical cancer cell lines (CCLs), to predict the response of cancer patients to different treatments. TINDL utilizes a tissue-informed normalization to account for the tissue type and cancer type of the tumors and to reduce the statistical discrepancies between CCLs and patient tumors. Moreover, by making the deep learning black box interpretable, this model identifies a small set of genes whose expression levels are predictive of drug response in the trained model, enabling identification of biomarkers of drug response. Using data from two large databases of CCLs and cancer tumors, we showed that this model can distinguish between sensitive and resistant tumors for 10 (out of 14) drugs, outperforming various other machine learning models. In addition, our small interfering RNA (siRNA) knockdown experiments on 10 genes identified by this model for one of the drugs (tamoxifen) confirmed that tamoxifen sensitivity is substantially influenced by all of these genes in MCF7 cells, and seven of these genes in T47D cells. Furthermore, genes implicated for multiple drugs pointed to shared mechanism of action among drugs and suggested several important signaling pathways. In summary, this study provides a powerful deep learning framework for prediction of drug response and identification of biomarkers of drug response in cancer. The code can be accessed at https://github.com/ddhostallero/tindl.

Gamma distribution based predicting model for breast cancer drug response based on multi-layer feature selection.

In the pursuit of precision medicine for cancer, a promising step is to predict drug response based on data mining, which can provide clinical decision support for cancer patients. Although some machine learning methods for predicting drug response from genomic data already exist, most of them focus on point prediction, which cannot reveal the distribution of predicted results. In this paper, we propose a three-layer feature selection combined with a gamma distribution based GLM and a two-layer feature selection combined with an ANN. The two regression methods are applied to the Encyclopedia of Cancer Cell Lines (CCLE) and the Cancer Drug Sensitivity Genomics (GDSC) datasets. Using ten-fold cross-validation, our methods achieve higher accuracy on anticancer drug response prediction compared to existing methods, with an R 2 and RMSE of 0.87 and 0.53, respectively. Through data validation, the significance of assessing the reliability of predictions by predicting confidence intervals and its role in personalized medicine are illustrated. The correlation analysis of the genes selected from the three layers of features also shows the effectiveness of our proposed methods.

Rapid anticancer drug screening using patient-derived lung cancer organoids: a case report

A variety of anticancer drugs and targeted agents for lung cancer have been developed, but some patients do not respond to these medications as intended. Therefore, there is an urgent need to develop a tool for predicting the anticancer drug response of each patient. Patient-derived tumor organoids (PDOs) have emerged as reliable in vitro tools for developing precision medicine. Herein, we describe a case of a 50-year-old nonsmoking man who was diagnosed with non-small cell lung cancer. Initially, no clinical symptoms were found in this patient. Postoperative pathology confirmed a stage Ib tumor, and an epidermal growth factor receptor (EGFR) mutation (exon21p.L858R) was detected in the patient’s lung tumor specimen. The patient later showed intracranial relapse 17 months after complete resection. An organoid culture was established from the resected brain metastatic tissue and a drug sensitivity test showed, within 72 hours, that the organoids were resistant to gefitinib and osimertinib. Our results recapitulated the patient’s response to anticancer drugs, demonstrating the potential of PDOs for precision medicine.

Deep learning and multi-omics approach to predict drug responses in cancer

BackgroundCancers are genetically heterogeneous, so anticancer drugs show varying degrees of effectiveness on patients due to their differing genetic profiles. Knowing patient’s responses to numerous cancer drugs are needed for personalized treatment for cancer. By using molecular profiles of cancer cell lines available from Cancer Cell Line Encyclopedia (CCLE) and anticancer drug responses available in the Genomics of Drug Sensitivity in Cancer (GDSC), we will build computational models to predict anticancer drug responses from molecular features.ResultsWe propose a novel deep neural network model that integrates multi-omics data available as gene expressions, copy number variations, gene mutations, reverse phase protein array expressions, and metabolomics expressions, in order to predict cellular responses to known anti-cancer drugs. We employ a novel graph embedding layer that incorporates interactome data as prior information for prediction. Moreover, we propose a novel attention layer that effectively combines different omics features, taking their interactions into account. The network outperformed feedforward neural networks and reported 0.90 for R^2 values for prediction of drug responses from cancer cell lines data available in CCLE and GDSC.ConclusionThe outstanding results of our experiments demonstrate that the proposed method is capable of capturing the interactions of genes and proteins, and integrating multi-omics features effectively. Furthermore, both the results of ablation studies and the investigations of the attention layer imply that gene mutation has a greater influence on the prediction of drug responses than other omics data types. Therefore, we conclude that our approach can not only predict the anti-cancer drug response precisely but also provides insights into reaction mechanisms of cancer cell lines and drugs as well.

DRPreter: Interpretable Anticancer Drug Response Prediction Using Knowledge-Guided Graph Neural Networks and Transformer.

Some of the recent studies on drug sensitivity prediction have applied graph neural networks to leverage prior knowledge on the drug structure or gene network, and other studies have focused on the interpretability of the model to delineate the mechanism governing the drug response. However, it is crucial to make a prediction model that is both knowledge-guided and interpretable, so that the prediction accuracy is improved and practical use of the model can be enhanced. We propose an interpretable model called DRPreter (drug response predictor and interpreter) that predicts the anticancer drug response. DRPreter learns cell line and drug information with graph neural networks; the cell-line graph is further divided into multiple subgraphs with domain knowledge on biological pathways. A type-aware transformer in DRPreter helps detect relationships between pathways and a drug, highlighting important pathways that are involved in the drug response. Extensive experiments on the GDSC (Genomics of Drug Sensitivity and Cancer) dataset demonstrate that the proposed method outperforms state-of-the-art graph-based models for drug response prediction. In addition, DRPreter detected putative key genes and pathways for specific drug-cell-line pairs with supporting evidence in the literature, implying that our model can help interpret the mechanism of action of the drug.

NeuMF: Predicting Anti-cancer Drug Response Through a Neural Matrix Factorization Model

Background: Anti-cancer drug response is urgently required for individualized therapy. Measurements with wet experiments are costly and time-consuming. Artificial intelligence-based models are currently available for predicting drug response but still have challenges in prediction accuracy Objective: Construct a model to predict drug response values for unknown cell lines and analyze drug potential association properties in sparse data. Methods: Propose a Neural Matrix Factorization (NeuMF) framework to help predict the unknown responses of cell lines to drugs. The model uses a deep neural network to figure out drug and cell lines' latent variables. In NeuMF, the inputs and the parameters of the multi-layer neural network are simultaneously optimized by gradient descent to minimize the reconstruction errors between the predicted and natural values of the observed entries. Then the unknown entries can be readily recovered by propagating the latent variables to the output layer. Results: Experiments on the Cancer Cell Line Encyclopedia (CCLE) dataset and Genomics of Drug Sensitivity in Cancer (GDSC) dataset compare NeuMF with the other three state-of-the-art methods. NeuMF reduces constructing drug or cell line similarity and mines the response matrix itself for correlations in the network, avoiding the inclusion of redundant noise. NeuMF obtained drug averaged PCC_sr of 0.83 and 0.84 on both datasets. It demonstrates that NeuMF substantially improves the prediction. Some essential parameters in NeuMF, such as the strategy of global effect removal and the scales of the input layer, are also discussed. Finally, case studies have shown that NeuMF can better learn the latent characteristics of drugs, e.g., Irinotecan and Topotecan are found to act on the same pathway TOP1. The conclusions are in line with some existing biological findings. Results: Experiments on the Cancer Cell Line Encyclopedia (CCLE) dataset and Genomics of Drug Sensitivity in Cancer (GDSC) dataset compare NeuMF with the other three state-of-the-art methods. NeuMF reduces constructing drug or cell line similarity and mines the response matrix itself for correlations in the network, avoiding the inclusion of redundant noise. NeuMF obtained drug averaged PCC_sr of 0.83 and 0.84 on both datasets. It demonstrates that NeuMF substantially improves the prediction. Some essential parameters in NeuMF, such as the global effect removal strategy and the input layer scales, are also discussed. Finally, case studies have shown that NeuMF can better learn the latent characteristics of drugs, e.g., Irinotecan and Topotecan are found to act on the same pathway TOP1. The conclusions are in line with some existing biological findings. Conclusion: NeuMF achieves better prediction accuracy than existing models, and its output is biologically interpretable. NeuMF also helps analyze the correlations between drugs.

Neutrophil-related genes predict prognosis and response to immune checkpoint inhibitors in bladder cancer.

Neutrophils play a key role in the occurrence and development of cancer. However, the relationship between neutrophils and cancer prognosis remains unclear due to their great plasticity and diversity. To explore the effects of neutrophils on the clinical outcome of bladder cancer, we acquired and analyzed gene expression data and clinical information of bladder cancer patients from IMvigor210 cohort and The Cancer Genome Atlas dataset (TCGA) database. We established a neutrophil-based prognostic model incorporating five neutrophil-related genes (EMR3, VNN1, FCGRT, HIST1H2BC, and MX1) and the predictive value of the model was validated in both an internal and an external validation cohort. Multivariate Cox regression analysis further proved that the model remained an independent prognostic factor for overall survival and a nomogram was constructed for clinical practice. Additionally, FCGRT was identified as the key neutrophil-related gene linked to an adverse prognosis of bladder cancer. Up-regulation of FCGRT indicated activated cancer metabolism, immunosuppressive tumor environment, and dysregulated functional status of immune cells. FCGRT overexpression was also correlated with decreased expression of PD-L1 and low levels of tumor mutation burden (TMB). FCGRT predicted a poor response to immunotherapy and had a close correlation with chemotherapy sensitivity. Taken together, a novel prognostic model was developed based on the expression level of neutrophil-related genes. FCGRT served as a promising candidate biomarker for anti-cancer drug response, which may contribute to individualized prognostic prediction and may contribute to clinical decision-making.

Clinical review of alkalization therapy in cancer treatment.

One of the most unique characteristics of cancer metabolism is activated aerobic glycolysis, which is called the “Warburg effect”, and is a hallmark of cancer. An acidic tumor microenvironment (TME) resulting from activated anaerobic glycolysis is associated with cancer progression, multi-drug resistance, and immune escape. Several in vitro and in vivo studies reported that neutralization of the acidic TME by alkalizing agents, such as bicarbonate, resulted in the suppression of cancer progression and a potential benefit for anti-cancer drug responses. In clinical settings, alkalizing effects were achieved not only by alkalizing agents, but also by a following a particular diet. An epidemiological study demonstrated that more fruits and vegetables and less meat and dairy products are associated with an increase in urine pH, which may reflect the alkalizing effect on the body. However, it remains unclear whether alkaline dietary intervention improves the effects of cancer treatment. Moreover, there are few clinical reports to date regarding cancer treatments being performed on patients together with alkalization therapy. In this review, we investigated whether alkalization therapy, which includes an alkaline diet and/or alkalizing agents, improves cancer treatment.

Combinatorial Anticancer Drug Screen Identifies Off-Target Effects of Epigenetic Chemical Probes.

Anticancer drug response is determined by genetic and epigenetic mechanisms. To identify the epigenetic regulators of anticancer drug response, we conducted a chemical epigenetic screen using chemical probes that target different epigenetic modulators. In this screen, we tested 31 epigenetic probes in combination with 14 mechanistically diverse anticancer agents and identified 8 epigenetic probes that significantly potentiate the cytotoxicity of TAK-243, a first-in-class ubiquitin-activating enzyme (UBA1) inhibitor evaluated in several solid and hematologic malignancies. These probes are TP-472, GSK864, A-196, UNC1999, SGC-CBP30, and PFI-4 (and its related analogues GSK6853 and GSK5959), and they target BRD9/7, mutant IDH1, SUV420H1/2, EZH2/1, p300/CBP, and BRPF1B, respectively. In contrast to epigenetic probes, negative control compounds did not have a significant impact on TAK-243 cytotoxicity. Potentiation of TAK-243 cytotoxicity was associated with reduced ubiquitylation and induction of apoptosis. Mechanistically, these epigenetic probes exerted their potentiation by inhibiting the efflux transporter ATP-binding cassette subfamily G member 2 (ABCG2) without inducing significant changes in the ubiquitylation pathways or ABCG2 expression levels. As assessed by docking analysis, the identified probes could potentially interact with ABCG2. Based on these data, we have developed a cell-based assay that can quantitatively evaluate ABCG2 inhibition by drug candidates. In conclusion, our study identifies epigenetic probes that profoundly potentiate TAK-243 cytotoxicity through off-target ABCG2 inhibition. We also provide experimental evidence that several negative control compounds cannot exclude a subset of off-target effects of chemical probes. Finally, potentiation of TAK-243 cytotoxicity can serve as a quantitative measure of ABCG2-inhibitory activity.

Multiomic characterization and drug testing establish circulating tumor cells as an ex vivo tool for personalized medicine

SummaryMatching the treatment to an individual patient’s tumor state can increase therapeutic efficacy and reduce tumor recurrence. Circulating tumor cells (CTCs) derived from solid tumors are promising subjects for theragnostic analysis. To analyze how CTCs represent tumor states, we established cell lines from CTCs, primary and metastatic tumors from a mouse model and provided phenotypic and multiomic analyses of these cells. CTCs and metastatic cells, but not primary tumor cells, shared stochastic mutations and similar hypomethylation levels at transcription start sites. CTCs and metastatic tumor cells shared a hybrid epithelial/mesenchymal transcriptome state with reduced adhesive and enhanced mobilization characteristics. We tested anti-cancer drugs on tumor cells from a metastatic breast cancer patient. CTC responses mirrored the impact of drugs on metastatic rather than primary tumors. Our multiomic and clinical anti-cancer drug response results reveal that CTCs resemble metastatic tumors and establish CTCs as an ex vivo tool for personalized medicine.

Interleukin-6-derived cancer-associated fibroblasts activate STAT3 pathway contributing to gemcitabine resistance in cholangiocarcinoma.

Cancer-associated fibroblasts (CAFs) are the dominant component of the tumor microenvironment (TME) that can be beneficial to the generation and progression of cancer cells leading to chemotherapeutic failure via several mechanisms. Nevertheless, the roles of CAFs on anti-cancer drug response need more empirical evidence in cholangiocarcinoma (CCA). Herein, we examined the oncogenic roles of CAFs on gemcitabine resistance in CCA cells mediated via IL-6/STAT3 activation. Our findings showed that CCA-derived CAFs promote cell viability and enhance gemcitabine resistance in CCA cells through the activation of IL-6/STAT3 signaling. High expression of IL-6R was correlated with a poor overall survival rate and gemcitabine resistance in CCA, indicating that IL-6R can be a prognostic or predictive biomarker for the chemotherapeutic response of CCA patients. Blockade of IL-6R on CCA cells by tocilizumab, an IL-6R humanized antihuman monoclonal antibody, contributed to inhibition of the CAF-CCA interaction leading to enhancement of gemcitabine sensitivity in CCA cells. The results of this study should be helpful for modifying therapeutic regimens aimed at targeting CAF interacting with cancer cells resulting in the suppression of the tumor progression but enhancement of drug sensitivity.