Models Of Drug Response Research Articles

Modeling Drug Responses and Evolutionary Dynamics Using Patient-Derived Xenografts Reveals Precision Medicine Strategies for Triple-Negative Breast Cancer.

The intertumor and intratumor heterogeneity of triple-negative breast cancers, which is reflected in diverse drug responses, interplays with tumor evolution. In this study, we developed a preclinical experimental and analytical framework using patient-derived tumor xenografts (PDTX) from patients with treatment-naïve triple-negative breast cancers to test their predictive value in personalized cancer treatment approaches. Patients and their matched PDTXs exhibited concordant drug responses to neoadjuvant therapy using two trial designs and dosing schedules. This platform enabled analysis of nongenetic mechanisms involved in relapse dynamics. Treatment resulted in permanent phenotypic changes, with functional and therapeutic consequences. High-throughput drug screening methods in ex vivo PDTX cells revealed patient-specific drug response changes dependent on first-line therapy. This was validated in vivo, as exemplified by a change in olaparib sensitivity in tumors previously treated with clinically relevant cycles of standard-of-care chemotherapy. In summary, PDTXs provide a robust tool to test patient drug responses and therapeutic regimens and to model evolutionary trajectories. However, high intermodel variability and permanent nongenomic transcriptional changes constrain their use for personalized cancer therapy. This work highlights important considerations associated with preclinical drug response modeling and potential uses of the platform to identify efficacious and preferential sequential therapeutic regimens. Significance: Patient-derived tumor xenografts from treatment-naïve breast cancer samples can predict patient drug responses and model treatment-induced phenotypic and functional evolution, making them valuable preclinical tools.

Spatial proteomic profiling elucidates immune determinants of neoadjuvant chemo-immunotherapy in esophageal squamous cell carcinoma.

Esophageal squamous cell carcinoma (ESCC) presents significant clinical and therapeutic challenges due to its aggressive nature and generally poor prognosis. We initiated a Phase II clinical trial (ChiCTR1900027160) to assess the efficacy of a pioneering neoadjuvant chemo-immunotherapy regimen comprising programmed death-1 (PD-1) blockade (Toripalimab), nanoparticle albumin-bound paclitaxel (nab-paclitaxel), and the oral fluoropyrimidine derivative S-1, in patients with locally advanced ESCC. This study uniquely integrates clinical outcomes with advanced spatial proteomic profiling using Imaging Mass Cytometry (IMC) to elucidate the dynamics within the tumor microenvironment (TME), focusing on the mechanistic interplay of resistance and response. Sixty patients participated, receiving the combination therapy prior to surgical resection. Our findings demonstrated a major pathological response (MPR) in 62% of patients and a pathological complete response (pCR) in 29%. The IMC analysis provided a detailed regional assessment, revealing that the spatial arrangement of immune cells, particularly CD8+ T cells and B cells within tertiary lymphoid structures (TLS), and S100A9+ inflammatory macrophages in fibrotic regions are predictive of therapeutic outcomes. Employing machine learning approaches, such as support vector machine (SVM) and random forest (RF) analysis, we identified critical spatial features linked to drug resistance and developed predictive models for drug response, achieving an area under the curve (AUC) of 97%. These insights underscore the vital role of integrating spatial proteomics into clinical trials to dissect TME dynamics thoroughly, paving the way for personalized and precise cancer treatment strategies in ESCC. This holistic approach not only enhances our understanding of the mechanistic basis behind drug resistance but also sets a robust foundation for optimizing therapeutic interventions in ESCC.

Computational Modeling of Drug Response Identifies Mutant-Specific Constraints for Dosing panRAF and MEK Inhibitors in Melanoma.

This study explores the potential of pre-clinical in vitro cell line response data and computational modeling in identifying the optimal dosage requirements of pan-RAF (Belvarafenib) and MEK (Cobimetinib) inhibitors in melanoma treatment. Our research is motivated by the critical role of drug combinations in enhancing anti-cancer responses and the need to close the knowledge gap around selecting effective dosing strategies to maximize their potential. In a drug combination screen of 43 melanoma cell lines, we identified specific dosage landscapes of panRAF and MEK inhibitors for NRAS vs. BRAF mutant melanomas. Both experienced benefits, but with a notably more synergistic and narrow dosage range for NRAS mutant melanoma (mean Bliss score of 0.27 in NRAS vs. 0.1 in BRAF mutants). Computational modeling and follow-up molecular experiments attributed the difference to a mechanism of adaptive resistance by negative feedback. We validated the in vivo translatability of in vitro dose-response maps by predicting tumor growth in xenografts with high accuracy in capturing cytostatic and cytotoxic responses. We analyzed the pharmacokinetic and tumor growth data from Phase 1 clinical trials of Belvarafenib with Cobimetinib to show that the synergy requirement imposes stricter precision dose constraints in NRAS mutant melanoma patients. Leveraging pre-clinical data and computational modeling, our approach proposes dosage strategies that can optimize synergy in drug combinations, while also bringing forth the real-world challenges of staying within a precise dose range. Overall, this work presents a framework to aid dose selection in drug combinations.

Computational modeling of drug response identifies mutant-specific constraints for dosing panRAF and MEK inhibitors in melanoma.

This study explores the potential of preclinical in vitro cell line response data and computational modeling in identifying optimal dosage requirements of pan-RAF (Belvarafenib) and MEK (Cobimetinib) inhibitors in melanoma treatment. Our research is motivated by the critical role of drug combinations in enhancing anti-cancer responses and the need to close the knowledge gap around selecting effective dosing strategies to maximize their potential. In a drug combination screen of 43 melanoma cell lines, we identified unique dosage landscapes of panRAF and MEK inhibitors for NRAS vs BRAF mutant melanomas. Both experienced benefits, but with a notably more synergistic and narrow dosage range for NRAS mutant melanoma. Computational modeling and molecular experiments attributed the difference to a mechanism of adaptive resistance by negative feedback. We validated in vivo translatability of in vitro dose-response maps by accurately predicting tumor growth in xenografts. Then, we analyzed pharmacokinetic and tumor growth data from Phase 1 clinical trials of Belvarafenib with Cobimetinib to show that the synergy requirement imposes stricter precision dose constraints in NRAS mutant melanoma patients. Leveraging pre-clinical data and computational modeling, our approach proposes dosage strategies that can optimize synergy in drug combinations, while also bringing forth the real-world challenges of staying within a precise dose range.

Representing mutations for predicting cancer drug response.

Predicting cancer drug response requires a comprehensive assessment of many mutations present across a tumor genome. While current drug response models generally use a binary mutated/unmutated indicator for each gene, not all mutations in a gene are equivalent. Here, we construct and evaluate a series of predictive models based on leading methods for quantitative mutation scoring. Such methods include VEST4 and CADD, which score the impact of a mutation on gene function, and CHASMplus, which scores the likelihood a mutation drives cancer. The resulting predictive models capture cellular responses to dabrafenib, which targets BRAF-V600 mutations, whereas models based on binary mutation status do not. Performance improvements generalize to other drugs, extending genetic indications for PIK3CA, ERBB2, EGFR, PARP1, and ABL1 inhibitors. Introducing quantitative mutation features in drug response models increases performance and mechanistic understanding. Code and example datasets are available at https://github.com/pgwall/qms.

Rare histologic transformation of a CTNNB1 (β-catenin) mutated prostate cancer with aggressive clinical course

BackgroundCatenin (Cadherin-Associated Protein), Beta 1 (CTNNB1) genomic alterations are rare in prostate cancer (PCa). Gain-of-function mutations lead to overexpression of β-catenin, with consequent hyperactivation of the Wnt/β-catenin signaling pathway, implicated in PCa progression and treatment resistance. To date, successful targeted treatment options for Wnt/β-catenin - driven PCa are lacking.MethodsWe report a rare histologic transformation of a CTNNB1 (β-catenin) mutated metastatic castration resistant prostate cancer (mCRPC), clinically characterized by highly aggressive disease course. We histologically and molecularly characterized the liver metastatic tumor samples, as well as successfully generated patient-derived organoids (PDOs) and patient-derived xenograft (PDX) from a liver metastasis. We used the generated cell models for further molecular characterization and drug response assays.ResultsImmunohistochemistry of liver metastatic biopsies and PDX tumor showed lack of expression of typical PCa (e.g., AR, PSA, PSAP, ERG) or neuroendocrine markers (synaptophysin), compatible with double-negative CRPC, but was positive for nuclear β-catenin expression, keratin 7 and 34βE12. ERG rearrangement was confirmed by fluorescent in situ hybridization (FISH). Drug response assays confirmed, in line with the clinical disease course, lack of sensitivity to common drugs used in mCRPC (e.g., enzalutamide, docetaxel). The casein kinase 1 (CK1) inhibitor IC261 and the tankyrase 1/2 inhibitor G700-LK showed modest activity. Moreover, despite harbouring a CTNNB1 mutation, PDOs were largely insensitive to SMARCA2/4- targeting PROTAC degraders and inhibitor.ConclusionsThe reported CTNNB1-mutated mCRPC case highlights the potential challenges of double-negative CRPC diagnosis and underlines the relevance of further translational research to enable successful targeted treatment of rare molecular subtypes of mCRPC.

UNNT: A novel Utility for comparing Neural Net and Tree-based models.

The use of deep learning (DL) is steadily gaining traction in scientific challenges such as cancer research. Advances in enhanced data generation, machine learning algorithms, and compute infrastructure have led to an acceleration in the use of deep learning in various domains of cancer research such as drug response problems. In our study, we explored tree-based models to improve the accuracy of a single drug response model and demonstrate that tree-based models such as XGBoost (eXtreme Gradient Boosting) have advantages over deep learning models, such as a convolutional neural network (CNN), for single drug response problems. However, comparing models is not a trivial task. To make training and comparing CNNs and XGBoost more accessible to users, we developed an open-source library called UNNT (A novel Utility for comparing Neural Net and Tree-based models). The case studies, in this manuscript, focus on cancer drug response datasets however the application can be used on datasets from other domains, such as chemistry.

Integration of Computational Pipeline to Streamline Efficacious Drug Nomination and Biomarker Discovery in Glioblastoma.

Glioblastoma multiforme (GBM) is the deadliest, most heterogeneous, and most common brain cancer in adults. Not only is there an urgent need to identify efficacious therapeutics, but there is also a great need to pair these therapeutics with biomarkers that can help tailor treatment to the right patient populations. We built patient drug response models by integrating patient tumor transcriptome data with high-throughput cell line drug screening data as well as Bayesian networks to infer relationships between patient gene expression and drug response. Through these discovery pipelines, we identified agents of interest for GBM to be effective across five independent patient cohorts and in a mouse avatar model: among them are a number of MEK inhibitors (MEKis). We also predicted phosphoglycerate dehydrogenase enzyme (PHGDH) gene expression levels to be causally associated with MEKi efficacy, where knockdown of this gene increased tumor sensitivity to MEKi and overexpression led to MEKi resistance. Overall, our work demonstrated the power of integrating computational approaches. In doing so, we quickly nominated several drugs with varying known mechanisms of action that can efficaciously target GBM. By simultaneously identifying biomarkers with these drugs, we also provide tools to select the right patient populations for subsequent evaluation.

An integrated framework for prognosis prediction and drug response modeling in colorectal liver metastasis drug discovery

BackgroundColorectal cancer (CRC) is the third most prevalent cancer globally, and liver metastasis (CRLM) is the primary cause of death. Hence, it is essential to discover novel prognostic biomarkers and therapeutic drugs for CRLM.MethodsThis study developed two liver metastasis-associated prognostic signatures based on differentially expressed genes (DEGs) in CRLM. Additionally, we employed an interpretable deep learning model utilizing drug sensitivity databases to identify potential therapeutic drugs for high-risk CRLM patients. Subsequently, in vitro and in vivo experiments were performed to verify the efficacy of these compounds.ResultsThese two prognostic models exhibited superior performance compared to previously reported ones. Obatoclax, a BCL-2 inhibitor, showed significant differential responses between high and low risk groups classified by prognostic models, and demonstrated remarkable effectiveness in both Transwell assay and CT26 colorectal liver metastasis mouse model.ConclusionsThis study highlights the significance of developing specialized prognostication approaches and investigating effective therapeutic drugs for patients with CRLM. The application of a deep learning drug response model provides a new drug discovery strategy for translational medicine in precision oncology.

Abstract 234: Microfluidic chip for drug response studies in non-small cell lung carcinoma patient-derived organoids

Abstract Background. Organoids have been widely accepted as 3D tumor models for patient-specific drug response. However, research involving non-small cell lung cancer (NSCLC) organoids is hampered by the lack of appropriate platforms. Although dome cultures show promising results, the relatively large size variability of patient-derived organoids (PDOs) hinders their application in drug response studies. Here, we report a microfluidic chip for maintaining high uniformity and requiring miniscule sample volume for PDO drug screening. Methods. The microfluidic chip includes a bottom microwell layer and a top microchannel layer. Each chip has 5 channels for parallel treatment conditions, with 30 U-shaped microwells (400μm dia x 250μm deep) per channel. PDOs were loaded from side channels and trapped by filters adjacent to microwells. NSCLC PDOs F231/F671 (NCI PDMR) were cultured and treated by KRASG12C inhibitor adagrasib (0-2000nM, 72h). PDO drug response was assessed via viability using fluorescent dyes. Results. On-chip PDO size after one-week incubation exhibited the coefficient of variation (CV) of 28% for F231 and 36% for F671, which was notably lower than measurements obtained using Matrigel dome (126%, n=3) and low attachment plate (80%, n=4). The F231/F671 showed viability >81% after 1wk (n=20). On-chip cytotoxicity results showed that both KRASG12C PDOs exhibited sensitivity to adagrasib, matching the clinical response, with IC50 values of 830 nM (F231) and 1324nM (F671). When cultured with fibroblast (WI-38) supernatant, the cytotoxic effect was mitigated due to the fibroblast-induced resistance, with IC50 increasing ≥2 ×. No statistical difference was observed for the fibroblast effect in the open-format microwells and our microfluidic chip (69.6±16.9% to 80.2±9.0% vs. 69.0±12.2% to 82.2±4.1%, respectively, following 500nM adagrasib for 72h), further underscoring the reliability of our chip for PDOs’ drug response studies. Application of continuous perfusion at 70nL/min yielded no statistical difference in viability from static conditions. Conclusions. Our microfluidic chip offers uniform PDO growth with perfusion allowing drug screening in almost real time. The on-chip response to adagrasib was clinically relevant. Resistance due to tumor microenvironment (TME) can be readily assessed using fibroblast supernatant, and could be expanded to other TME components in future studies to investigate their impact on treatment resistance. Citation Format: Qiyue Luan, Ines Pulido, Angelique Isagirre, Jian Zhou, Takeshi Shimamura, Ian Papautsky. Microfluidic chip for drug response studies in non-small cell lung carcinoma patient-derived organoids [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 234.

Abstract 7393: Tumor model to tumor treatment: Applying deep learning approaches to map multimodal data from cancer model systems to patients

Abstract Despite large-scale efforts to measure the effect of drug screens in cancer cell lines, mapping the effects of drugs to patient samples has been a challenge. Biological differences between cell lines and patients, such as lack of immune system or microbiome, in-vitro survival adaptations, and biases in measurement technologies create differences across sample modalities that can confound analysis including prediction with machine learning. In this work, we propose a multiway batch correction strategy to enable algorithmic prediction of tumor drug response across model systems and patient data.Recent advances in batch correction algorithms have been motivated by the need to correct for batch effects in single-cell omics and include diverse approaches such as variational autoencoders (VAEs) and generative adversarial networks (GANs). Given the successes of these generative deep learning methods in single cell sequencing analysis, we worked to employ similar approaches to correct large omics measurements across various cancer datasets. Here, we describe mapping of datasets from diverse data sources and model systems to the same space, so that a predictive model of drug response built in a system such as cell lines can be used in biologically relevant models such as organoids, patient derived xenografts, and tumor data. Specifically, we introduce a modified loss function in a VAE using cosine similarity distance to minimize the effect of different cancer model systems in predicting cancer types. We evaluate the method on standard data types for drug response prediction - gene expression, copy number variation, and protein abundance. For this method, the cosine similarity is added as an additional term to the VAE reconstruction and Kullback-Leibler divergence loss terms. This injects a quantification of the dissimilarity between the tumor and tumor model distributions into the backpropagation and gradient descent for updating the model parameters resulting in an encoded representation of the data where the effect of data source has been attenuated while preserving the phenotypic signal. We evaluate our approach for biological signal preservation while reducing model system-specific noise with logistic regression and Euclidean distance. Our results show that the proposed VAE can effectively correct for platform effects and improve the accuracy of downstream integrative analyses. This study has the potential to improve the accuracy and translatability of proteogenomic drug response studies. The proposed modified VAE could be used to correct for platform effects in a variety of datasets, including those from different studies, different platforms, and different cancer types. This could lead to new insights into cancer biology, calibration of cancer patient digital twins, and the development of new diagnostic and therapeutic strategies. Citation Format: Brian Karlberg, Raphael Kirchgaessner, Jeremy R. Jacobson, Kyle Ellrott, Sara J. Gosline. Tumor model to tumor treatment: Applying deep learning approaches to map multimodal data from cancer model systems to patients [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7393.

Abstract A015: Predicting efficacious drugs and drug-biomarker relationships for targeted glioblastoma treatment

Abstract Glioblastoma multiforme (GBM) is the deadliest, most heterogeneous, and common brain cancer in adults. Despite major advancements in neurosurgery as well as chemotherapy and radiotherapy techniques, overall prognosis has changed little over the decades. Not only is there an urgent need to identify efficacious therapeutics but there is also a great need to pair these therapeutics with biomarkers that can help tailor treatment to the right patient populations given the heterogeneous nature of the disease. Here, we implement several machine learning and statistical based pipelines for drug and biomarker discovery, leveraging previous work building patient drug response models using high-throughput cell line drug screening data as well as Bayesian network structure learning. Through these discovery pipelines, we identified multiple agents of interest for GBM, including MEK inhibitors (MEKis). MEKis were consistently predicted to be effective against GBM in multiple cross platform (microarray and RNA-sequencing) patient and patient derived xenograft (PDX) datasets. Additionally, they were experimentally shown to be more efficacious in tumor samples than standard of care agents (Temozolomide and Carmustine). We also predicted PHGDH gene expression levels to be causally associated with MEKi efficacy, which increased tumor sensitivity to a MEKi (Trametinib) during experimental testing in GBM cell lines with knockdown. These findings support our drug and biomarker discovery pipelines as an effective strategy to unite multiple cross platform and cross species datasets to help achieve GBM precision medicine. They also demonstrate that patient PHGDH levels can help inform Trametinib response in the fight against GBM. Citation Format: Danielle Maeser, Robert Gruener, Robert Galvin, Stephanie Huang, Tomo Koga, Florina Grigore. Predicting efficacious drugs and drug-biomarker relationships for targeted glioblastoma treatment [abstract]. In: Proceedings of the AACR Special Conference on Brain Cancer; 2023 Oct 19-22; Minneapolis, Minnesota. Philadelphia (PA): AACR; Cancer Res 2024;84(5 Suppl_1):Abstract nr A015.

PANCDR: precise medicine prediction using an adversarial network for cancer drug response.

Pharmacogenomics aims to provide personalized therapy to patients based on their genetic variability. However, accurate prediction of cancer drug response (CDR) is challenging due to genetic heterogeneity. Since clinical data are limited, most studies predicting drug response use preclinical data to train models. However, such models might not be generalizable to external clinical data due to differences between the preclinical and clinical datasets. In this study, a Precision Medicine Prediction using an Adversarial Network for Cancer Drug Response (PANCDR) model is proposed. PANCDR consists of two sub-models, an adversarial model and a CDR prediction model. The adversarial model reduces the gap between the preclinical and clinical datasets, while the CDR prediction model extracts features and predicts responses. PANCDR was trained using both preclinical data and unlabeled clinical data. Subsequently, it was tested on external clinical data, including The Cancer Genome Atlas and brain tumor patients. PANCDR outperformed other machine learning models in predicting external test data. Our results demonstrate the robustness of PANCDR and its potential in precision medicine by recommending patient-specific drug candidates. The PANCDR codes and data are available at https://github.com/DMCB-GIST/PANCDR.

Interpretable Dynamic Directed Graph Convolutional Network for Multi-Relational Prediction of Missense Mutation and Drug Response.

Tumor heterogeneity presents a significant challenge in predicting drug responses, especially as missense mutations within the same gene can lead to varied outcomes such as drug resistance, enhanced sensitivity, or therapeutic ineffectiveness. These complex relationships highlight the need for advanced analytical approaches in oncology. Due to their powerful ability to handle heterogeneous data, graph convolutional networks (GCNs) represent a promising approach for predicting drug responses. However, simple bipartite graphs cannot accurately capture the complex relationships involved in missense mutation and drug response. Furthermore, Deep learning models for drug response are often considered "black boxes", and their interpretability remains a widely discussed issue. To address these challenges, we propose an Interpretable Dynamic Directed Graph Convolutional Network (IDDGCN) framework, which incorporates four key features: (1) the use of directed graphs to differentiate between sensitivity and resistance relationships, (2) the dynamic updating of node weights based on node-specific interactions, (3) the exploration of associations between different mutations within the same gene and drug response, and (4) the enhancement of interpretability models through the integration of a weighted mechanism that accounts for the biological significance, alongside a ground truth construction method to evaluate prediction transparency. The experimental results demonstrate that IDDGCN outperforms existing state-of-the-art models, exhibiting excellent predictive power. Both qualitative and quantitative evaluations of its interpretability further highlight its ability to explain predictions, offering a fresh perspective for precision oncology and targeted drug development.

Dynamic altruistic cooperation within breast tumors

BackgroundSocial behaviors such as altruism, where one self-sacrifices for collective benefits, critically influence an organism’s survival and responses to the environment. Such behaviors are widely exemplified in nature but have been underexplored in cancer cells which are conventionally seen as selfish competitive players. This multidisciplinary study explores altruism and its mechanism in breast cancer cells and its contribution to chemoresistance.MethodsMicroRNA profiling was performed on circulating tumor cells collected from the blood of treated breast cancer patients. Cancer cell lines ectopically expressing candidate miRNA were used in co-culture experiments and treated with docetaxel. Ecological parameters like relative survival and relative fitness were assessed using flow cytometry. Functional studies and characterization performed in vitro and in vivo include proliferation, iTRAQ-mass spectrometry, RNA sequencing, inhibition by small molecules and antibodies, siRNA knockdown, CRISPR/dCas9 inhibition and fluorescence imaging of promoter reporter-expressing cells. Mathematical modeling based on evolutionary game theory was performed to simulate spatial organization of cancer cells.ResultsOpposing cancer processes underlie altruism: an oncogenic process involving secretion of IGFBP2 and CCL28 by the altruists to induce survival benefits in neighboring cells under taxane exposure, and a self-sacrificial tumor suppressive process impeding proliferation of altruists via cell cycle arrest. Both processes are regulated concurrently in the altruists by miR-125b, via differential NF-κB signaling specifically through IKKβ. Altruistic cells persist in the tumor despite their self-sacrifice, as they can regenerate epigenetically from non-altruists via a KLF2/PCAF-mediated mechanism. The altruists maintain a sparse spatial organization by inhibiting surrounding cells from adopting the altruistic fate via a lateral inhibition mechanism involving a GAB1-PI3K-AKT-miR-125b signaling circuit.ConclusionsOur data reveal molecular mechanisms underlying manifestation, persistence and spatial spread of cancer cell altruism. A minor population behave altruistically at a cost to itself producing a collective benefit for the tumor, suggesting tumors to be dynamic social systems governed by the same rules of cooperation in social organisms. Understanding cancer cell altruism may lead to more holistic models of tumor evolution and drug response, as well as therapeutic paradigms that account for social interactions. Cancer cells constitute tractable experimental models for fields beyond oncology, like evolutionary ecology and game theory.

A 3D adrenocortical carcinoma tumor platform for preclinical modeling of drug response and matrix metalloproteinase activity

Adrenocortical carcinoma (ACC) has a poor prognosis, and no new drugs have been identified in decades. The absence of drug development can partly be attributed to a lack of preclinical models. Both animal models and 2D cell cultures of ACC fail to accurately mimic the disease, as animal physiology is inherently different than humans, and 2D cultures fail to represent the crucial 3D architecture. Organoids and other small 3D in vitro models of tissues or tumors can model certain complexities of human in vivo biology; however, this technology has largely yet to be applied to ACC. In this study, we describe the generation of 3D tumor constructs from an established ACC cell line, NCI-H295R. NCI-H295R cells were encapsulated to generate 3D ACC constructs. Tumor constructs were assessed for biomarker expression, viability, proliferation, and cortisol production. In addition, matrix metalloproteinase (MMP) functionality was assessed directly using fluorogenic MMP-sensitive biosensors and through infusion of NCI-H295R cells into a metastasis-on-a-chip microfluidic device platform. ACC tumor constructs showed expression of biomarkers associated with ACC, including SF-1, Melan A, and inhibin α. Treatment of ACC tumor constructs with chemotherapeutics demonstrated decreased drug sensitivity compared to 2D cell culture. Since most tumor cells migrate through tissue using MMPs to break down extracellular matrix, we validated the utility of ACC tumor constructs by integrating fluorogenic MMP-sensitive peptide biosensors within the tumor constructs. Lastly, in our metastasis-on-a-chip device, NCI-H295R cells successfully engrafted in a downstream lung cell line-based construct, but invasion distance into the lung construct was decreased by MMP inhibition. These studies, which would not be possible using 2D cell cultures, demonstrated that NCI-H295R cells secreted active MMPs that are used for invasion in 3D. This work represents the first evidence of a 3D tumor constructs platform for ACC that can be deployed for future mechanistic studies as well as development of new targets for intervention and therapies.

Analysis and modeling of cancer drug responses using cell cycle phase-specific rate effects

Identifying effective therapeutic treatment strategies is a major challenge to improving outcomes for patients with breast cancer. To gain a comprehensive understanding of how clinically relevant anti-cancer agents modulate cell cycle progression, here we use genetically engineered breast cancer cell lines to track drug-induced changes in cell number and cell cycle phase to reveal drug-specific cell cycle effects that vary across time. We use a linear chain trick (LCT) computational model, which faithfully captures drug-induced dynamic responses, correctly infers drug effects, and reproduces influences on specific cell cycle phases. We use the LCT model to predict the effects of unseen drug combinations and confirm these in independent validation experiments. Our integrated experimental and modeling approach opens avenues to assess drug responses, predict effective drug combinations, and identify optimal drug sequencing strategies.

Computational drug discovery for castration-resistant prostate cancers through in vitro drug response modeling

Prostate cancer (PC) is the most frequently diagnosed malignancy and a leading cause of cancer deaths in US men. Many PC cases metastasize and develop resistance to systemic hormonal therapy, a stage known as castration-resistant prostate cancer (CRPC). Therefore, there is an urgent need to develop effective therapeutic strategies for CRPC. Traditional drug discovery pipelines require significant time and capital input, which highlights a need for novel methods to evaluate the repositioning potential of existing drugs. Here, we present a computational framework to predict drug sensitivities of clinical CRPC tumors to various existing compounds and identify treatment options with high potential for clinical impact. We applied this method to a CRPC patient cohort and nominated drugs to combat resistance to hormonal therapies including abiraterone and enzalutamide. The utility of this method was demonstrated by nomination of multiple drugs that are currently undergoing clinical trials for CRPC. Additionally, this method identified the tetracycline derivative COL-3, for which we validated higher efficacy in an isogenic cell line model of enzalutamide-resistant vs. enzalutamide-sensitive CRPC. In enzalutamide-resistant CRPC cells, COL-3 displayed higher activity for inhibiting cell growth and migration, and for inducing G1-phase cell cycle arrest and apoptosis. Collectively, these findings demonstrate the utility of a computational framework for independent validation of drugs being tested in CRPC clinical trials, and for nominating drugs with enhanced biological activity in models of enzalutamide-resistant CRPC. The efficiency of this method relative to traditional drug development approaches indicates a high potential for accelerating drug development for CRPC.

Decellularized brain extracellular matrix slice glioblastoma culture model recapitulates the interaction between cells and the extracellular matrix without a nutrient-oxygen gradient interference.

Decellularized extracellular matrix (dECM) is a valuable tool for generating three-dimensional in vitro tumor models that effectively recapitulate tumor-extracellular matrix (ECM) interactions. However, in current culture models, the components and structures of dECM are enzymatically disrupted to form hydrogels, making it difficult to recapitulate the native ECM. Additionally, when studying ECM-cell interactions, large-volume tumor culture models are incompatible with traditional experimental techniques and the nutrient-oxygen concentration gradient, which is a significant confounding factor. To address these issues, we developed a decellularized brain extracellular matrix slice (dBECMS) glioblastoma (GBM) culture model. This model possesses good light transmittance and substance diffusivity, making it compatible with traditional experimental techniques without forming nutrient-oxygen concentration gradients. Through transcriptomic analysis, we found that native brain ECM has a broad impact on glioma cells; the impact involves the ECM-ECM receptor interactions and the ECM and metabolic reprogramming. Further experiments demonstrated that dBECMS promoted glucose consumption and lactate production in GBM cells. Silver staining experiments revealed abundant proteins in the media of dBECMS, suggesting the degradation of the brain ECM by GBM cells. Transcriptome analysis also showed that the dBECMS-GBM culture model more accurately recapitulated the transcriptional profile of GBM than the two-dimensional culture. We experimentally demonstrated that the dBECMS-GBM model enhanced the resistance of GBM cells to temozolomide and increased the stemness of GBM cells. Additionally, we demonstrated the feasibility of the dBECMS-GBM model as a platform for drug response modeling. STATEMENT OF SIGNIFICANCE: The decellularized brain extracellular matrix (ECM) slice glioblastoma culture model mimics the interaction between native brain ECM and glioblastoma when glioblastoma infiltrates the brain and reveals the effects of native brain ECM on glioblastoma metabolism, ECM reprogramming, drug responsiveness, and stemness.

DROEG: a method for cancer drug response prediction based on omics and essential genes integration.

Predicting therapeutic responses in cancer patients is a major challenge in the field of precision medicine due to high inter- and intra-tumor heterogeneity. Most drug response models need to be improved in terms of accuracy, and there is limited research to assess therapeutic responses of particular tumor types. Here, we developed a novel method DROEG (Drug Response based on Omics and Essential Genes) for prediction of drug response in tumor cell lines by integrating genomic, transcriptomic and methylomic data along with CRISPR essential genes, and revealed that the incorporation of tumor proliferation essential genes can improve drug sensitivity prediction. Concisely, DROEG integrates literature-based and statistics-based methods to select features and uses Support Vector Regression for model construction. We demonstrate that DROEG outperforms most state-of-the-art algorithms by both qualitative (prediction accuracy for drug-sensitive/resistant) and quantitative (Pearson correlation coefficient between the predicted and actual IC50) evaluation in Genomics of Drug Sensitivity in Cancer and Cancer Cell Line Encyclopedia datasets. In addition, DROEG is further applied to the pan-gastrointestinal tumor with high prevalence and mortality as a case study at both cell line and clinical levels to evaluate the model efficacy and discover potential prognostic biomarkers in Cisplatin and Epirubicin treatment. Interestingly, the CRISPR essential gene information is found to be the most important contributor to enhance the accuracy of the DROEG model. To our knowledge, this is the first study to integrate essential genes with multi-omics data to improve cancer drug response prediction and provide insights into personalized precision treatment.