Anticancer Drug Response Research Articles

Channel-assembling tumor microenvironment on-chip for evaluating anticancer drug efficacy.

Organ-on-a-chip is an advanced system for evaluating drug response in diseases. It simulates the in vivo tumor microenvironment, aiding in the understanding of drug mechanisms and tumor responses. It mimics the structure of the tumor microenvironment and the dynamic conditions within the body. As a result, it holds the potential for applications in precision and personalized medicine. However, there are still limitations in sequential development processes and complex structures, resulting in time-consuming molecular interference during system development. In this study, we developed a channel-assembling tumor microenvironment-on-chip (CATOC) system to overcome these limitations. CATOC was easily segmented into blood vessels and a tumor microenvironment-on-chip, which can be independently developed. The tumor microenvironment-on-chip consists of two independent channels for evaluating drug responses in different types of tumor microenvironments. Each fully developed system was physically interconnected to create a CATOC. Interconnected CATOC was used to validate chemical and targeted anticancer drug responses in different subtypes of the breast tumor microenvironment. We also emphasized the significance of on-chip experiments by observing the drug response of tumor spheroids on CATOC and scaffold-free platforms.

Drug Sensitivity Prediction Based on Multi-stage Multi-modal Drug Representation Learning.

Accurate prediction of anticancer drug responses is essential for developing personalized treatment plans in order to improve cancer patient survival rates and reduce healthcare costs. To this end, we propose a drug sensitivity prediction model based on multi-stage multi-modal drug representations (ModDRDSP) to reflect the properties of drugs more comprehensively, and to better model the complex interactions between cells and drugs. Specifically, we adopt the SMILES representation learning method based on the deep hierarchical bi-directional GRU network (DSBiGRU) and the molecular graph representation learning method based on the deep message-crossing network (DMCN) for the multi-modal information of drugs. Additionally, we integrate the multi-omics information of cell lines based on a convolutional neural network (CNN). Finally, we use an ensemble deep forest algorithm for the prediction of drug sensitivity. After validation, the ModDRDSP shows impressive performance which outperforms the four current industry-leading models. More importantly, ablation experiments demonstrate the validity of each module of the proposed model, and case studies show the good results of ModDRDSP for predicting drug sensitivity, further establishing the superiority of ModDRDSP in terms of performance.

Developing Anticancer Drug Response System Using Deep Learning System with Hybrid Genomic and Chemical Features

Developing Anticancer Drug Response System Using Deep Learning System with Hybrid Genomic and Chemical Features

Therapeutic Responses to Two New SN-38 Derivatives in Colorectal Cancer Patient-Derived Xenografts and Respective 3D In Vitro Cultures.

SN-38, an active metabolite of irinotecan, exhibits toxicity to all proliferating cells, causing dose-limiting and potentially life-threatening side effects. Newly established water-soluble derivatives of SN-38, 7-ethyl-9-(N-morpholinyl)methyl-10-hydroxycamptothecin (BN-MOA) and 7-ethyl-9-(N-methylamino)methyl-10-hydroxycamptothecin (BN-NMe), exhibit a unique mechanism of spontaneous alkylation of aromatic bases in DNA and show greater in vitro activity on cancer cell lines than SN-38. The aim of this study was to compare the therapeutic responses to irinotecan, BN-MOA and BN-NMe in vivo and in vitro in 3D cultures using colorectal cancer (CRC) patient derived xenografts (PDX). Seven established PDX tissues were subcutaneously grown on the flanks of NSG or NSG-SGM3 mice and tumor diameters were measured with a caliper. Compounds were administrated intraperitoneally at 40 mg/kg every five days. 3D PDX cultures were performed on 96-well LifeGel plates and cell viability was determined with the CellTiter Glo 3D reagent. Treatment with irinotecan significantly delayed or stopped the growth of 5 out of 7 PDXs, with a greater level of inhibition from BN-MOA compared to irinotecan and BN-NMe. In vitro studies exhibited the same trends in SN-38 and BN-NMe but not in BN-MOA. The new SN-38 derivatives, BN-MOA and BN-NMe, showed enhanced therapeutic effects compared to irinotecan in CRC models. BN-MOA demonstrated superior tumor inhibition in vivo, while BN-NMe had similar in vitro activity to SN-38. These findings highlight the potential of BN-MOA for greater antitumor efficacy in vivo, with BN-NMe showing comparable effectiveness to SN-38 in vitro. Future studies should optimize growth models to better predict anticancer drug responses.

BANDRP: a bilinear attention network for anti-cancer drug response prediction based on fingerprint and multi-omics.

Predicting anti-cancer drug response can help with personalized cancer treatment and is an important topic in modern oncology research. Although some methods have been used for anti-cancer drug response prediction, how to effectively integrate various features related to cancer cell lines, drugs, and their known responses is still affected by the redundant information of input features and the complex interactions between features. In this study, we propose a bilinear attention model, named BANDRP, based on multiple omics data of cancer cell lines and multiple molecular fingerprints of drugs to predict potential anti-cancer drug responses. Compared with existing models, BANDRP uses gene expression data to calculate pathway enrichment scores to enrich the features of cancer cell lines and can automatically learn the interactive information of cancer cell lines and drugs through bilinear attention networks. Benchmarking and independent tests demonstrate that BANDRP surpasses baseline models and exhibits robust generalization performance. Ablation experiments affirm the optimality of the current model architecture and feature selection scheme for our prediction task. Furthermore, analytical experiments and case studies on unknown anti-cancer drug response predictions underscore BANDRP's potential as a potent and reliable framework for predicting anti-cancer drug response.

Exploring the Dark Matter of Human Proteome: The Emerging Role of Non-Canonical Open Reading Frame (ncORF) in Cancer Diagnosis, Biology, and Therapy.

Cancer develops from abnormal cell growth in the body, causing significant mortalities every year. To date, potent therapeutic approaches have been developed to eradicate tumor cells, but intolerable toxicity and drug resistance can occur in treated patients, limiting the efficiency of existing treatment strategies. Therefore, searching for novel genes critical for cancer progression and therapeutic response is urgently needed for successful cancer therapy. Recent advances in bioinformatics and proteomic techniques have allowed the identification of a novel category of peptides encoded by non-canonical open reading frames (ncORFs) from historically non-coding genomic regions. Surprisingly, many ncORFs express functional microproteins that play a vital role in human cancers. In this review, we provide a comprehensive description of different ncORF types with coding capacity and technological methods in discovering ncORFs among human genomes. We also summarize the carcinogenic role of ncORFs such as pTINCR and HOXB-AS3 in regulating hallmarks of cancer, as well as the roles of ncORFs such as HOXB-AS3 and CIP2A-BP in cancer diagnosis and prognosis. We also discuss how ncORFs such as AKT-174aa and DDUP are involved in anti-cancer drug response and the underestimated potential of ncORFs as therapeutic targets.

Malignancy progression and treatment efficacy estimation of osteosarcoma patients based on in vitro cell culture model and analysis

BackgroundOsteosarcoma (OS) usually happens in patients under 20 years old and is notorious for its low survivorship and limb loss. Personalized medicine is a viable approach to increase the efficiency of chemotherapy which is the main prognostic factor for survivorship after surgical treatment. MethodsIn this five-year prospective observational study, we collected primary cells of osteosarcoma from 15 patients, and examined the correlation between clinical characters of patients and cell properties characterized using various in vitro assays. The properties including genes expression, pro-angiogenic capability and anti-cancer drug response are characterized respectively by using RT-PCR, tube formation assay, osteogenesis assay and drug testing on 3D tumor spheroid model. ResultThe results suggest that OS patients with higher MMP9 expression levels have higher probability to develop skip metastasis (p = 0.041). The 3D tumor spheroid test based on the median lethal dose from 2D culture provides some prognostic value. Patients do not response well to methotrexate (MTX) show higher percentage of high pathology grade (p = 0.009) and lung metastasis (p = 0.044). Also, patients respond well to ifosfamide (IFO) have higher probability to achieve high tumor necrosis rate (p = 0.007). ConclusionThe association between cell properties and clinical characters of patients provided by our data can act as potential prognostic factors to help physicians to develop effective personalized chemotherapy for osteosarcoma treatments.

Fully automated viability and toxicity screening—A reliable all‐in‐one attempt

AbstractBackgroundThe CRISPR/Cas9 technology is nowadays a common tool for genome editing to achieve new insights into, for example, diagnostics and therapeutics in cancer and genetic disorders. Cell proliferation and anticancer drug response studies are widely used to evaluate the impact of editing. However, these assays are often time‐consuming, expensive, and reproducibility is an issue. To overcome this, we developed a fast and cheap assay that combines a fully automated multispectral fluorescence microscopy platform with a nuclei staining and open‐source software analysis.MethodsHere, we generated different LEDGF/p75 model cell lines to validate the effect on proliferation and chemosensitivity. Therefore, a fast protocol for an optimized all‐in‐one attempt for cytotoxicity screenings and proliferation analysis of adherent cells in a 96‐well plate format was established using differential staining with two fluorescent dyes (Hoechst 33342 and propidium iodide) for live/dead cell discrimination. Subsequently, an automated cell nuclei count and analysis were performed using bioimage informatics.ResultsWith the new established assay technology, up to 50,000 cells/well can be detected and analyzed in a 96‐well plate, resulting in a fast and accurate verification of viability and proliferation with consistency of 98% compared to manual counting. Our screening revealed that LEDGF depletion using CRISPR/Cas9 showed a diminished proliferation and chemosensitivity independent of cell line origin. Moreover, LEDGF depletion caused a significant increase in 𝛾H2AX foci, indicating a substantial increase in DNA double strand breaks. LEDGF/p75 overexpression enhanced proliferation and chemoresistance underlining the role of LEDGF in DNA damage response.ConclusionIndependent of cancer cell type, LEDGF/p75 is a central player in DNA damage repair and is implicated in chemoresistance. Moreover, our automated fluorescence biosensor technology allowed fast and reliable data acquisition without any fixation or additional washing steps. Additionally, data analysis was implemented using the modular open‐source software that can be adapted as needed.

A hierarchical attention network integrating multi-scale relationship for drug response prediction

Anticancer drug response prediction with deep learning technology has become the foundation of precision medicine. It is essential for anticancer drug response prediction to incorporate multi-scale relationships within feature items and biomedical entities. Therefore, we propose MultiDRP that develops the hierarchical attention networks integrating multi-scale relationship for drug response prediction. MultiDRP can fuse both internal correlation of feature items and external relationship of biomedical entities by hierarchically integrating graph attention and self-attention networks to improve the anticancer drug response prediction. A variety of results showed that MultiDRP generated the great representation by integrating multi-scale relationships, and achieved higher performance compared to existing methods on various prediction scenarios. The results of network proximity, gene ontology biological process (GOBP) enrichment, and drug pathway association analysis show that MultiDRP can accurately screen the sensitive and resistant drugs for cancer cell lines. In vitro experiments, eight novel drugs predicted by MultiDRP exhibited high sensitivity to lung cancer cell line NCI-H23, seven of which showed IC50 values of less than 10 nM. These results further suggest that MultiDRP can serve as a powerful tool for anticancer drug response prediction. The source data and code are available at https://github.com/pengsl-lab/MultiDRP.git.

A gray box framework that optimizes a white box logical model using a black box optimizer for simulating cellular responses to perturbations

Predicting cellular responses to perturbations requires interpretable insights into molecular regulatory dynamics to perform reliable cell fate control, despite the confounding non-linearity of the underlying interactions. There is a growing interest in developing machine learning-based perturbation response prediction models to handle the non-linearity of perturbation data, but their interpretation in terms of molecular regulatory dynamics remains a challenge. Alternatively, for meaningful biological interpretation, logical network models such as Boolean networks are widely used in systems biology to represent intracellular molecular regulation. However, determining the appropriate regulatory logic of large-scale networks remains an obstacle due to the high-dimensional and discontinuous search space. To tackle these challenges, we present a scalable derivative-free optimizer trained by meta-reinforcement learning for Boolean network models. The logical network model optimized by the trained optimizer successfully predicts anti-cancer drug responses of cancer cell lines, while simultaneously providing insight into their underlying molecular regulatory mechanisms.

Developing MLP based prediction system for anticancer drug response using hybrid features of genomics and cheminformatics

Traditional cancer treatment methods have become less effective due to the increasing diversity of cancer types. To address this, precision medicine has gained support within the medical community. This approach tailors treatment to individual patients based on their specific disease characteristics. However, a major challenge lies in accurately predicting how a patient will respond to a specialized drug. Numerous machine learning-based predictive systems have been developed to address this challenge. These systems utilize genomic signatures and the chemical structure of drugs to predict drug activity. In this paper, we introduce a Multi-Layer Perceptron (MLP) based system for predicting the response of anticancer drugs. Our system utilizes hybrid features derived from both genetic expression and the chemical structure of drugs. It is developed using the well-known GDSC dataset (Genomics of Drug Sensitivity in Cancer). Our system achieved a lower Root Mean Square Error (RMSE) value of 0.889, in contrast to the RMSE value of 0.983 obtained by the current state-of-the-art (SOTA) system, SwNet. This indicates superior predictive accuracy. The findings suggest that our proposed research holds promise for the development of targeted drugs for anticancer treatments.

Abstract A101: A novel ovarian cancer organotypic tumor slice culture model

Abstract A101: A novel ovarian cancer organotypic tumor slice culture model

A 3D In Vitro Triculture Hybrid Model Recapitulating Tumor Stromal Interaction of Triple‐Negative Breast Cancer as a High Throughput Anticancer Drug Screening Platform

AbstractBreast cancer (BC) progression is substantially driven by cellular cross‐talk between tumor and stromal cells within the tumor microenvironment (TME). However, lack of precise recapitulation of the heterocellular complexity, interactions in oversimplified 2D models, and physiological differences in animal models lead to discrepancy in anticancer drug response. Three‐dimensional (3D) in vitro bioengineered models that physiologically resemble in vivo TME are apt to overcome the discrepancy in the preclinical drug testing outcome. Here, a compartmentalized 3D in vitro triculture triple‐negative breast cancer (TC‐TNBC) model is bioengineered using silk‐fibroin (SF) scaffold and GelMA hydrogel for recapitulating stromal and tumor extracellular niche, respectively. The model features the cellular heterogeneity of stromal niche by incorporating most representative cell types in breast tissue, i.e., adipocytes and endothelial cells, and MDA‐MB‐231 cells in the tumor niche. The TC‐TNBC model exhibits enhanced tumorigenic potential in the presence of stromal cells. Screening of model anticancer drugs (doxorubicin (Dox) and cisplatin (Cis)) exhibits an increase in IC50 concentration in TC‐TNBC as compared to monoculture, suggesting crucial role of stromal cells in drug response. Drug sensitivity and cytotoxicity behavior mimick the in vivo like drug response. This model provides a facile and adaptable platform to represent tumor‐stromal heterogeneity and high‐throughput anticancer drug screening.

Liprin-α1 contributes to oncogenic MAPK signaling by counteracting ERK activity.

PTPRF interacting protein alpha 1 (PPFIA1) encodes for liprin-α1, a member of the leukocyte common antigen-related protein tyrosine phosphatase (LAR-RPTPs)-interacting protein family. Liprin-α1 localizes to adhesive and invasive structures in the periphery of cancer cells, where it modulates migration and invasion in head and neck squamous cell carcinoma (HNSCC) and breast cancer. To study the possible role of liprin-α1 in anticancer drug responses, we screened a library of oncology compounds in cell lines with high endogenous PPFIA1 expression. The compounds with the highest differential responses between high PPFIA1-expressing and silenced cells across cell lines were inhibitors targeting mitogen-activated protein kinase kinase (MEK)/extracellular signal-regulated kinases (ERK) signaling. KRAS proto-oncogene, GTPase (KRAS)-mutated MDA-MB-231 cells were more resistant to trametinib upon PPFIA1 knockdown compared with control cells. In contrast, liprin-α1-depleted HNSCC cells with low RAS activity showed a context-dependent response to MEK/ERK inhibitors. Importantly, we showed that liprin-α1 depletion leads to increased p-ERK1/2 levels in all our studied cell lines independent of KRAS mutational status, suggesting a role of liprin-α1 in the regulation of MAPK oncogenic signaling. Furthermore, liprin-α1 depletion led to more pronounced redistribution of RAS proteins to the cell membrane. Our data suggest that liprin-α1 is an important contributor to oncogenic RAS/MAPK signaling, and the status of liprin-α1 may assist in predicting drug responses in cancer cells in a context-dependent manner.

ShinyDeepDR: A user-friendly R Shiny app for predicting anti-cancer drug response using deep learning

Advancing precision oncology requires accurate prediction of treatment response and accessible prediction models. To this end, we present shinyDeepDR, a user-friendly implementation of our innovative deep learning model, DeepDR, for predicting anti-cancer drug sensitivity. The web tool makes DeepDR more accessible to researchers without extensive programming experience. Using shinyDeepDR, users can upload mutation and/or gene expression data from a cancer sample (cell line or tumor) and perform two main functions: "Find Drug," which predicts the sample's response to 265 approved and investigational anti-cancer compounds, and "Find Sample," which searches for cell lines in the Cancer Cell Line Encyclopedia (CCLE) and tumors in The Cancer Genome Atlas (TCGA) with genomics profiles similar to those of the query sample to study potential effective treatments. shinyDeepDR provides an interactive interface to interpret prediction results and to investigate individual compounds. In conclusion, shinyDeepDR is an intuitive and free-to-use web tool for in silico anti-cancer drug screening.

Text-mining-based feature selection for anticancer drug response prediction.

Predicting anticancer treatment response from baseline genomic data is a critical obstacle in personalized medicine. Machine learning methods are commonly used for predicting drug response from gene expression data. In the process of constructing these machine learning models, one of the most significant challenges is identifying appropriate features among a massive number of genes. In this study, we utilize features (genes) extracted using the text-mining of scientific literatures. Using two independent cancer pharmacogenomic datasets, we demonstrate that text-mining-based features outperform traditional feature selection techniques in machine learning tasks. In addition, our analysis reveals that text-mining feature-based machine learning models trained on in vitro data also perform well when predicting the response of in vivo cancer models. Our results demonstrate that text-mining-based feature selection is an easy to implement approach that is suitable for building machine learning models for anticancer drug response prediction. https://github.com/merlab/text_features.

Predicting Clinical Anticancer Drug Response of Patients by using Domain Alignment and Prototypical Learning.

Anticancer drug response prediction is crucial in developing personalized treatment plans for cancer patients. However, High-quality patient anticancer drug response data are scarce and cell line data and patient data have different distributions, models trained solely on cell line data perform poorly. Some existing methods predict anticancer drug response by transferring knowledge from the cell line domain to the patient domain using transfer learning. However, the robustness of these classifiers is affected by anomalies in the cell line data, and they do not utilize the knowledge in the unlabeled target domain data. To this end, we proposed a model called DAPL to predict patient responses to anticancer drugs. The model extracts domain-invariant features from cell lines and patients by constructing multiple VAEs and extracts drug features using GNNs. These features are then combined for prototypical learning to train a classifier, resulting in better predictions of patient anticancer drug response. We used the cell line datasets CCLE and GDSC as source domains and the patient datasets TCGA and PDTC as target domains and conducted experiments. The results indicate that DAPL shows excellent performance in predicting patient anticancer drug response compared to other state-of-the-art methods.

Hierarchical graph representation learning with multi-granularity features for anti-cancer drug response prediction.

Patients with the same type of cancer often respond differently to identical drug treatments due to unique genomic traits. Accurately predicting a patient's response to drug is crucial in guiding treatment decisions, alleviating patient suffering, and improving cancer prognosis. Current computational methods utilize deep learning models trained on extensive drug screening data to predict anti-cancer drug responses based on features of cell lines and drugs. However, the interaction between cell lines and drugs is a complex biological process involving interactions across various levels, from internal cellular and drug structures to the external interactions among different molecules.To address this complexity, we propose a novel Hierarchical graph representation Learning with Multi-Granularity features (HLMG) algorithm for predicting anti-cancer drug responses. The HLMG algorithm combines features at two granularities: the overall gene expression and pathway substructures of cell lines, and the overall molecular fingerprints and substructures of drugs. Subsequently, it constructs a heterogeneous graph including cell lines, drugs, known cell line-drug responses, and the associations between similar cell lines and similar drugs. Through a graph convolutional network model, the HLMG learns the final cell line and drug representations by aggregating features of their multi-level neighbor in the heterogeneous graph. The multi-level neighbors consist of the node self, directly related drugs/cell lines, and indirectly related similar drugs/cell lines. Finally, a linear correlation coefficient decoder is employed to reconstruct the cell line-drug correlation matrix to predict anti-cancer drug responses. Our model was tested on the Genomics of Drug Sensitivity in Cancer (GDSC) and the Cancer Cell Line Encyclopedia (CCLE) databases. Results indicate that HLMG outperforms other state-of-the-art methods in accurately predicting anti-cancer drug responses.

Identification of hub genes to determine drug-disease correlation in breast carcinomas.

The exact molecular mechanism underlying the heterogeneous drug response against breast carcinoma remains to be fully understood. It is urgently required to identify key genes that are intricately associated with varied clinical response of standard anti-cancer drugs, clinically used to treat breast cancer patients. In the present study, the utility of transcriptomic data of breast cancer patients in discerning the clinical drug response using machine learning-based approaches were evaluated. Here, a computational framework has been developed which can be used to identify key genes that can be linked with clinical drug response and progression of cancer, offering an immense opportunity to predict potential prognostic biomarkers and therapeutic targets. The framework concerned utilizes DeSeq2, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Cytoscape, and machine learning techniques to find these crucial genes. Total RNA extraction and qRT-PCR were performed to quantify relative expression of few hub genes selected from the networks. In our study, we have experimentally checked the expression of few key hub genes like APOA2, DLX5, APOC3, CAMK2B, and PAK6 that were predicted to play an immense role in breast cancer tumorigenesis and progression in response to anti-cancer drug Paclitaxel. However, further experimental validations will be required to get mechanistic insights of these genes in regulating the drug response and cancer progression which will likely to play pivotal role in cancer treatment and precision oncology.

Improving anti-cancer drug response prediction using multi-task learning on graph convolutional networks

Predicting the therapeutic effect of anti-cancer drugs on tumors based on the characteristics of tumors and patients is one of the important contents of precision oncology. Existing computational methods regard the drug response prediction problem as a classification or regression task. However, few of them consider leveraging the relationship between the two tasks. In this work, we propose a Multi-task Interaction Graph Convolutional Network (MTIGCN) for anti-cancer drug response prediction. MTIGCN first utilizes an graph convolutional network-based model to produce embeddings for both cell lines and drugs. After that, the model employs multi-task learning to predict anti-cancer drug response, which involves training the model on three different tasks simultaneously: the main task of the drug sensitive or resistant classification task and the two auxiliary tasks of regression prediction and similarity network reconstruction. By sharing parameters and optimizing the losses of different tasks simultaneously, MTIGCN enhances the feature representation and reduces overfitting. The results of the experiments on two in vitro datasets demonstrated that MTIGCN outperformed seven state-of-the-art baseline methods. Moreover, the well-trained model on the in vitro dataset GDSC exhibited good performance when applied to predict drug responses in in vivo datasets PDX and TCGA. The case study confirmed the model's ability to discover unknown drug responses in cell lines.