Integrative Pharmacogenomics Analysis of Patient-Derived Xenografts

Patient-Derived Tumor Xenograft Study with CDK4/6 Inhibitor Plus AKT Inhibitor for the Management of Metastatic Castration-Resistant Prostate Cancer

Metastatic castration-resistant prostate cancer (mCRPC) is an aggressive malignancy with poor outcomes. To investigate novel therapeutic strategies, we characterized three new metastatic prostate cancer patient derived-tumor xenograft (PDTX) models and developed 3D spheroids from each to investigate molecular targeted therapy combinations including CDK4/6 inhibitors (CDK4/6i) with AKT inhibitors (ATKi). Metastatic prostate cancer tissue was collected and three PDTX models were established and characterized using whole-exome sequencing. PDTX 3D spheroids were developed from these three PDTXs to show resistance patterns and test novel molecular-targeted therapies. CDK4/6i's were combined with AKTi's to assess synergistic antitumor response to prove our hypothesis that blockade of AKT overcomes drug resistance to CDK4/6i. This combination was evaluated in PDTX three-dimensional (3D) spheroids and in vivo experiments with responses measured by tumor volumes, PSA, and Ga-68 PSMA-11 PET-CT imaging. We demonstrated CDK4/6i's with AKTi's possess synergistic antitumor activity in three mCRPC PDTX models. These models have multiple unique pathogenic and deleterious genomic alterations with resistance to single-agent CDK4/6i's. Despite this, combination therapy with AKTi's was able to overcome resistance mechanisms. The IHC and Western blot analysis confirmed on target effects, whereas tumor volume, serum PSA ELISA, and radionuclide imaging demonstrated response to therapy with statistically significant SUV differences seen with Ga-68 PSMA-11 PET-CT. These preclinical data demonstrating antitumor synergy by overcoming single-agent CDK 4/6i as well as AKTi drug resistance provide the rational for a clinical trial combining a CDK4/6i with an AKTi in patients with mCRPC whose tumor expresses wild-type retinoblastoma 1.

Representation of genomic intratumor heterogeneity in multi-region non-small cell lung cancer patient-derived xenograft models

Patient-derived xenograft (PDX) models are widely used in cancer research. To investigate the genomic fidelity of non-small cell lung cancer PDX models, we established 48 PDX models from 22 patients enrolled in the TRACERx study. Multi-region tumor sampling increased successful PDX engraftment and most models were histologically similar to their parent tumor. Whole-exome sequencing enabled comparison of tumors and PDX models and we provide an adapted mouse reference genome for improved removal of NOD scid gamma (NSG) mouse-derived reads from sequencing data. PDX model establishment caused a genomic bottleneck, with models often representing a single tumor subclone. While distinct tumor subclones were represented in independent models from the same tumor, individual PDX models did not fully recapitulate intratumor heterogeneity. On-going genomic evolution in mice contributed modestly to the genomic distance between tumors and PDX models. Our study highlights the importance of considering primary tumor heterogeneity when using PDX models and emphasizes the benefit of comprehensive tumor sampling.

Impact of STAT-signaling pathway on cancer-associated fibroblasts in colorectal cancer and its role in immunosuppression

Colorectal cancer (CRC) remains one of the most commonly diagnosed and deadliest types of cancer worldwide. CRC displays a desmoplastic reaction (DR) that has been inversely associated with poor prognosis; less DR is associated with a better prognosis. This reaction generates excessive connective tissue, in which cancer-associated fibroblasts (CAFs) are critical cells that form a part of the tumor microenvironment. CAFs are directly involved in tumorigenesis through different mechanisms. However, their role in immunosuppression in CRC is not well understood, and the precise role of signal transducers and activators of transcription (STATs) in mediating CAF activity in CRC remains unclear. Among the myriad chemical and biological factors that affect CAFs, different cytokines mediate their function by activating STAT signaling pathways. Thus, the harmful effects of CAFs in favoring tumor growth and invasion may be modulated using STAT inhibitors. Here, we analyze the impact of different STATs on CAF activity and their immunoregulatory role.

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

Motivation

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.

Results

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.

Availability and implementation

https://github.com/merlab/text_features.

Spatiotemporal modeling of chemoresistance evolution in breast tumors uncovers dependencies on SLC38A7 and SLC46A1

In solid tumors, drug concentrations decrease with distance from blood vessels. However, cellular adaptations accompanying the gradated exposure of cancer cells to drugs are largely unknown. Here, we modeled the spatiotemporal changes promoting chemotherapy resistance in breast cancer. Using pairwise cell competition assays at each step during the acquisition of chemoresistance, we reveal an important priming phase that renders cancer cells previously exposed to sublethal drug concentrations refractory to dose escalation. Therapy-resistant cells throughout the concentration gradient display higher expression of the solute carriers SLC38A7 and SLC46A1 and elevated intracellular concentrations of their associated metabolites. Reduced levels of SLC38A7 and SLC46A1 diminish the proliferative potential of cancer cells, and elevated expression of these SLCs in breast tumors from patients correlates with reduced survival. Our work provides mechanistic evidence to support dose-intensive treatment modalities for patients with solid tumors and reveals two members of the SLC family as potential actionable targets.

Differential DNA damage repair and PARP inhibitor vulnerability of the mammary epithelial lineages

It is widely assumed that all normal somatic cells can equally perform homologous recombination (HR) and non-homologous end joining in the DNA damage response (DDR). Here, we show that the DDR in normal mammary gland inherently depends on the epithelial cell lineage identity. Bioinformatics, post-irradiation DNA damage repair kinetics, and clonogenic assays demonstrated luminal lineage exhibiting a more pronounced DDR and HR repair compared to the basal lineage. Consequently, basal progenitors were far more sensitive to poly(ADP-ribose) polymerase inhibitors (PARPis) in both mouse and human mammary epithelium. Furthermore, PARPi sensitivity of murine and human breast cancer cell lines as well as patient-derived xenografts correlated with their molecular resemblance to the mammary progenitor lineages. Thus, mammary epithelial cells are intrinsically divergent in their DNA damage repair capacity and PARPi vulnerability, potentially influencing the clinical utility of this targeted therapy.

A Multipronged Unbiased Strategy Guides the Development of an Anti-EGFR/EPHA2-Bispecific Antibody for Combination Cancer Therapy

PURPOSE

Accumulating analyses of pro-oncogenic molecular mechanisms triggered a rapid development of targeted cancer therapies. Although many of these treatments produce impressive initial responses, eventual resistance onset is practically unavoidable. One of the main approaches for preventing this refractory condition relies on the implementation of combination therapies. This includes dual-specificity reagents that affect both of their targets with a high level of selectivity. Unfortunately, selection of target combinations for these treatments is often confounded by limitations in our understanding of tumor biology. Here, we describe and validate a multipronged unbiased strategy for predicting optimal co-targets for bispecific therapeutics.

EXPERIMENTAL DESIGN

Our strategy integrates ex vivo genome-wide loss-of-function screening, BioID interactome profiling, and gene expression analysis of patient data to identify the best fit co-targets. Final validation of selected target combinations is done in tumorsphere cultures and xenograft models.

RESULTS

Integration of our experimental approaches unambiguously pointed toward EGFR and EPHA2 tyrosine kinase receptors as molecules of choice for co-targeting in multiple tumor types. Following this lead, we generated a human bispecific anti-EGFR/EPHA2 antibody that, as predicted, very effectively suppresses tumor growth compared with its prototype anti-EGFR therapeutic antibody, cetuximab.

CONCLUSIONS

Our work not only presents a new bispecific antibody with a high potential for being developed into clinically relevant biologics, but more importantly, successfully validates a novel unbiased strategy for selecting biologically optimal target combinations. This is of a significant translational relevance, as such multifaceted unbiased approaches are likely to augment the development of effective combination therapies for cancer treatment. See related commentary by Kumar, p. 2570.

The future of patient-derived xenografts in prostate cancer research

Patient-derived xenografts (PDXs) are generated by engrafting human tumours into mice. Serially transplantable PDXs are used to study tumour biology and test therapeutics, linking the laboratory to the clinic. Although few prostate cancer PDXs are available in large repositories, over 330 prostate cancer PDXs have been established, spanning broad clinical stages, genotypes and phenotypes. Nevertheless, more PDXs are needed to reflect patient diversity, and to study new treatments and emerging mechanisms of resistance. We can maximize the use of PDXs by exchanging models and datasets, and by depositing PDXs into biorepositories, but we must address the impediments to accessing PDXs, such as institutional, ethical and legal agreements. Through collaboration, researchers will gain greater access to PDXs representing diverse features of prostate cancer.

Patient-derived xenograft models in cancer therapy: technologies and applications

Patient-derived xenograft (PDX) models, in which tumor tissues from patients are implanted into immunocompromised or humanized mice, have shown superiority in recapitulating the characteristics of cancer, such as the spatial structure of cancer and the intratumor heterogeneity of cancer. Moreover, PDX models retain the genomic features of patients across different stages, subtypes, and diversified treatment backgrounds. Optimized PDX engraftment procedures and modern technologies such as multi-omics and deep learning have enabled a more comprehensive depiction of the PDX molecular landscape and boosted the utilization of PDX models. These irreplaceable advantages make PDX models an ideal choice in cancer treatment studies, such as preclinical trials of novel drugs, validating novel drug combinations, screening drug-sensitive patients, and exploring drug resistance mechanisms. In this review, we gave an overview of the history of PDX models and the process of PDX model establishment. Subsequently, the review presents the strengths and weaknesses of PDX models and highlights the integration of novel technologies in PDX model research. Finally, we delineated the broad application of PDX models in chemotherapy, targeted therapy, immunotherapy, and other novel therapies.

Data augmentation and multimodal learning for predicting drug response in patient-derived xenografts from gene expressions and histology images

Patient-derived xenografts (PDXs) are an appealing platform for preclinical drug studies. A primary challenge in modeling drug response prediction (DRP) with PDXs and neural networks (NNs) is the limited number of drug response samples. We investigate multimodal neural network (MM-Net) and data augmentation for DRP in PDXs. The MM-Net learns to predict response using drug descriptors, gene expressions (GE), and histology whole-slide images (WSIs). We explore whether combining WSIs with GE improves predictions as compared with models that use GE alone. We propose two data augmentation methods which allow us training multimodal and unimodal NNs without changing architectures with a single larger dataset: 1) combine single-drug and drug-pair treatments by homogenizing drug representations, and 2) augment drug-pairs which doubles the sample size of all drug-pair samples. Unimodal NNs which use GE are compared to assess the contribution of data augmentation. The NN that uses the original and the augmented drug-pair treatments as well as single-drug treatments outperforms NNs that ignore either the augmented drug-pairs or the single-drug treatments. In assessing the multimodal learning based on the MCC metric, MM-Net outperforms all the baselines. Our results show that data augmentation and integration of histology images with GE can improve prediction performance of drug response in PDXs.

NRF2 and FXR dual signaling pathways cooperatively regulate the effects of oleanolic acid on cholestatic liver injury

BACKGROUND

Previous studies have shown that the anti-cholestatic effect of oleanolic acid (OA) is associated with FXR and NRF2. However, how the two signaling pathways cooperate to regulate the anti-cholestatic effect of OA remains unclear.

PURPOSE

This study aimed to further demonstrate the effect of OA on alpha-naphthyl isothiocyanate (ANIT)-induced cholestatic liver injury and the interaction mechanism between NRF2 and FXR signaling pathways in maintaining bile acid homeostasis.

METHODS

Gene knockout animals and cell models, metabolomics analysis, and co-immunoprecipitation were used to investigate the mechanism of OA against cholestatic liver injury.

RESULTS

The effect of OA against ANIT-induced liver injury in rats was dramatically reduced after Nrf2 gene knockdown. With the silencing of Fxr, the hepatoprotective effect of OA was weakened, but it still effectively alleviated cholestatic liver injury in rats. In L02 cells, OA can up-regulate the levels of NRF2, FXR, BSEP and UGT1A1, and reduce the expression of CYP7A1. Silencing of NRF2 or FXR significantly attenuated the protective effect of OA on ANIT-induced L02 cell injury and its regulation on downstream target genes, and the influence of NRF2 gene silencing on OA appeared to be greater. The NRF2 activator sulforaphane, and the FXR activator GW4064 both remarkably promoted NRF2 binding to P300 and FXR to RXRα, but reduced β-catenin binding to P300 and β-catenin binding to FXR.

CONCLUSION

The effect of OA on cholestatic liver injury is closely related to the simultaneous activation of NRF2 and FXR dual signaling pathways, in which NRF2 signaling pathway plays a more important role. The dual signaling pathways of NRF2 and FXR cooperatively regulate bile acid metabolic homeostasis through the interaction mechanism with β-catenin/P300.

A Genomically and Clinically Annotated Patient-Derived Xenograft Resource for Preclinical Research in Non-Small Cell Lung Cancer

Patient-derived xenograft (PDX) models are an effective preclinical in vivo platform for testing the efficacy of novel drugs and drug combinations for cancer therapeutics. Here we describe a repository of 79 genomically and clinically annotated lung cancer PDXs available from The Jackson Laboratory that have been extensively characterized for histopathologic features, mutational profiles, gene expression, and copy-number aberrations. Most of the PDXs are models of non-small cell lung cancer (NSCLC), including 37 lung adenocarcinoma (LUAD) and 33 lung squamous cell carcinoma (LUSC) models. Other lung cancer models in the repository include four small cell carcinomas, two large cell neuroendocrine carcinomas, two adenosquamous carcinomas, and one pleomorphic carcinoma. Models with both de novo and acquired resistance to targeted therapies with tyrosine kinase inhibitors are available in the collection. The genomic profiles of the LUAD and LUSC PDX models are consistent with those observed in patient tumors from The Cancer Genome Atlas and previously characterized gene expression-based molecular subtypes. Clinically relevant mutations identified in the original patient tumors were confirmed in engrafted PDX tumors. Treatment studies performed in a subset of the models recapitulated the responses expected on the basis of the observed genomic profiles. These models therefore serve as a valuable preclinical platform for translational cancer research.

SIGNIFICANCE

Patient-derived xenografts of lung cancer retain key features observed in the originating patient tumors and show expected responses to treatment with standard-of-care agents, providing experimentally tractable and reproducible models for preclinical investigations.

CombPDX: a unified statistical framework for evaluating drug synergism in patient-derived xenografts

Anticancer combination therapy has been developed to increase efficacy by enhancing synergy. Patient-derived xenografts (PDXs) have emerged as reliable preclinical models to develop effective treatments in translational cancer research. However, most PDX combination study designs focus on single dose levels, and dose-response surface models are not appropriate for testing synergism. We propose a comprehensive statistical framework to assess joint action of drug combinations from PDX tumor growth curve data. We provide various metrics and robust statistical inference procedures that locally (at a fixed time) and globally (across time) access combination effects under classical drug interaction models. Integrating genomic and pharmacological profiles in non-small-cell lung cancer (NSCLC), we have shown the utilities of combPDX in discovering effective therapeutic combinations and relevant biological mechanisms. We provide an interactive web server, combPDX ( https://licaih.shinyapps.io/CombPDX/ ), to analyze PDX tumor growth curve data and perform power analyses.

Discovery of BRAF/HDAC Dual Inhibitors Suppressing Proliferation of Human Colorectal Cancer Cells

The combination of histone deacetylase inhibitor and BRAF inhibitor (BRAFi) has been shown to enhance the antineoplastic effect and reduce the progress of BRAFi resistance. In this study, a series of (thiazol-5-yl)pyrimidin-2-yl)amino)-N-hydroxyalkanamide derivatives were designed and synthesized as novel dual inhibitors of BRAF and HDACs using a pharmacophore hybrid strategy. In particular, compound 14b possessed potent activities against BRAF, HDAC1, and HDAC6 enzymes. It potently suppressed the proliferation of HT-29 cells harboring BRAF^V600E mutation as well as HCT116 cells with wild-type BRAF. The dual inhibition against BRAF and HDAC downstream proteins was validated in both cells. Collectively, the results support 14b as a promising lead molecule for further development and a useful tool for studying the effects of BRAF/HDAC dual inhibitors.

Bimodal gene expression in cancer patients provides interpretable biomarkers for drug sensitivity

Identifying biomarkers predictive of cancer cell response to drug treatment constitutes one of the main challenges in precision oncology. Recent large-scale cancer pharmacogenomic studies have opened new avenues of research to develop predictive biomarkers by profiling thousands of human cancer cell lines at the molecular level and screening them with hundreds of approved drugs and experimental chemical compounds. Many studies have leveraged these data to build predictive models of response using various statistical and machine learning methods. However, a common pitfall to these methods is the lack of interpretability as to how they make predictions, hindering the clinical translation of these models. To alleviate this issue, we used the recent logic modeling approach to develop a new machine learning pipeline that explores the space of bimodally expressed genes in multiple large in vitro pharmacogenomic studies and builds multivariate, nonlinear, yet interpretable logic-based models predictive of drug response. The performance of this approach was showcased in a compendium of the three largest in vitro pharmacogenomic data sets to build robust and interpretable models for 101 drugs that span 17 drug classes with high validation rates in independent datasets. These results along with in vivo and clinical validation, support a better translation of gene expression biomarkers between model systems using bimodal gene expression.

Novel Targeted Therapeutic Strategies for Ewing Sarcoma

Simple Summary

Ewing sarcoma is an uncommon cancer that arises in mesenchymal tissues and represents the second most widespread malignant bone neoplasm after osteosarcoma in children. Therapy has increased the 5-year survival rate in the last 40 years, although the recurrence rate has remained high. There is an immediate and unmet need for the development of novel Ewing sarcoma therapies. We offer new prospective targets for the therapy of Ewing sarcoma. The EWSR1/FLI1 fusion protein, which is identified in 85–90% of Ewing sarcoma tumors, and its direct targets are given special focus in this study. Experimantal therapy that targets multiple signaling pathways activated during ES progression, alone or in combination with existing regimens, may become the new standard of care for Ewing sarcoma patients, improving patient survival.

Abstract

Ewing sarcoma (ES) is an uncommon cancer that arises in mesenchymal tissues and represents the second most widespread malignant bone neoplasm after osteosarcoma in children. Amplifications in genomic, proteomic, and metabolism are characteristics of sarcoma, and targeting altered cancer cell molecular processes has been proposed as the latest promising strategy to fight cancer. Recent technological advancements have elucidated some of the underlying oncogenic characteristics of Ewing sarcoma. Offering new insights into the physiological basis for this phenomenon, our current review examines the dynamics of ES signaling as it related to both ES and the microenvironment by integrating genomic and proteomic analyses. An extensive survey of the literature was performed to compile the findings. We have also highlighted recent and ongoing studies integrating metabolomics and genomics aimed at better understanding the complex interactions as to how ES adapts to changing biochemical changes within the tumor microenvironment.

Patient-derived tumor models are attractive tools to repurpose drugs for ovarian cancer treatment: Pre-clinical updates

Despite advances in understanding of ovarian cancer biology, the progress in translation of research findings into new therapies is still slow. It is associated in part with limitations of commonly used cancer models such as cell lines and genetically engineered mouse models that lack proper representation of diversity and complexity of actual human tumors. In addition, the development of de novo anticancer drugs is a lengthy and expensive process. A promising alternative to new drug development is repurposing existing FDA-approved drugs without primary oncological purpose. These approved agents have known pharmacokinetics, pharmacodynamics, and toxicology and could be approved as anticancer drugs quicker and at lower cost. To successfully translate repurposed drugs to clinical application, an intermediate step of pre-clinical animal studies is required. To address challenges associated with reliability of tumor models for pre-clinical studies, there has been an increase in development of patient-derived xenografts (PDXs), which retain key characteristics of the original patient’s tumor, including histologic, biologic, and genetic features. The expansion and utilization of clinically and molecularly annotated PDX models derived from different ovarian cancer subtypes could substantially aid development of new therapies or rapid approval of repurposed drugs to improve treatment options for ovarian cancer patients.

Patient-derived xenograft models of neuroendocrine prostate cancer

In recent years, patient-derived xenografts (PDXs) have attracted much attention as clinically relevant models for basic and translational cancer research. PDXs retain the principal histopathological and molecular heterogeneity of their donor tumors and remain stable across passages. These characteristics allow PDXs to offer a reliable platform for better understanding cancer biology, discovering biomarkers and therapeutic targets, and developing novel therapies. A growing interest in generating neuroendocrine prostate cancer (NEPC) PDX models has been demonstrated, and such models have proven useful in several areas. This review provides a comprehensive summary of currently available NEPC PDX collections, encompassing 1) primary or secondary sites where patient samples were collected, 2) donor patients' treatment histories, 3) morphological features (i.e., small cell and large cell), and 4) genomic alterations. We also highlight suitable models for various research purposes, including identifying therapeutic targets and evaluating drug responses in models with specific genomic backgrounds. Finally, we provide perspectives on the current knowledge gaps and shed light on future applications and improvements of NEPC PDXs.

Assessing therapy response in patient-derived xenografts

[Figure: see text].

Orchestrating and sharing large multimodal data for transparent and reproducible research

Reproducibility is essential to open science, as there is limited relevance for findings that can not be reproduced by independent research groups, regardless of its validity. It is therefore crucial for scientists to describe their experiments in sufficient detail so they can be reproduced, scrutinized, challenged, and built upon. However, the intrinsic complexity and continuous growth of biomedical data makes it increasingly difficult to process, analyze, and share with the community in a FAIR (findable, accessible, interoperable, and reusable) manner. To overcome these issues, we created a cloud-based platform called ORCESTRA ( orcestra.ca ), which provides a flexible framework for the reproducible processing of multimodal biomedical data. It enables processing of clinical, genomic and perturbation profiles of cancer samples through automated processing pipelines that are user-customizable. ORCESTRA creates integrated and fully documented data objects with persistent identifiers (DOI) and manages multiple dataset versions, which can be shared for future studies.

CTR-DB, an omnibus for patient-derived gene expression signatures correlated with cancer drug response

To date, only some cancer patients can benefit from chemotherapy and targeted therapy. Drug resistance continues to be a major and challenging problem facing current cancer research. Rapidly accumulated patient-derived clinical transcriptomic data with cancer drug response bring opportunities for exploring molecular determinants of drug response, but meanwhile pose challenges for data management, integration, and reuse. Here we present the Cancer Treatment Response gene signature DataBase (CTR-DB, http://ctrdb.ncpsb.org.cn/), a unique database for basic and clinical researchers to access, integrate, and reuse clinical transcriptomes with cancer drug response. CTR-DB has collected and uniformly reprocessed 83 patient-derived pre-treatment transcriptomic source datasets with manually curated cancer drug response information, involving 28 histological cancer types, 123 drugs, and 5139 patient samples. These data are browsable, searchable, and downloadable. Moreover, CTR-DB supports single-dataset exploration (including differential gene expression, receiver operating characteristic curve, functional enrichment, sensitizing drug search, and tumor microenvironment analyses), and multiple-dataset combination and comparison, as well as biomarker validation function, which provide insights into the drug resistance mechanism, predictive biomarker discovery and validation, drug combination, and resistance mechanism heterogeneity.

Drug sensitivity prediction from cell line-based pharmacogenomics data: guidelines for developing machine learning models

The goal of precision oncology is to tailor treatment for patients individually using the genomic profile of their tumors. Pharmacogenomics datasets such as cancer cell lines are among the most valuable resources for drug sensitivity prediction, a crucial task of precision oncology. Machine learning methods have been employed to predict drug sensitivity based on the multiple omics data available for large panels of cancer cell lines. However, there are no comprehensive guidelines on how to properly train and validate such machine learning models for drug sensitivity prediction. In this paper, we introduce a set of guidelines for different aspects of training gene expression-based predictors using cell line datasets. These guidelines provide extensive analysis of the generalization of drug sensitivity predictors and challenge many current practices in the community including the choice of training dataset and measure of drug sensitivity. The application of these guidelines in future studies will enable the development of more robust preclinical biomarkers.

Suppressed mitochondrial respiration via NOX5-mediated redox imbalance contributes to the antitumor activity of anlotinib in oral squamous cell carcinoma

Anlotinib, a novel multitarget tyrosine kinase inhibitor, has shown promising results in the management of various carcinomas. This study aimed to investigate the antitumor activity of anlotinib in oral squamous cell carcinoma (OSCC) and the underlying molecular mechanism. A retrospective clinical study revealed that anlotinib improved the median progression-free survival (mPFS) and median overall survival (mOS) of patients with recurrent and metastatic (R/M) OSCC, respectively. Functional studies revealed that anlotinib markedly inhibited in vitro proliferation of OSCC cells and impeded in vivo tumor growth of OSCC patient-derived xenograft models. Mechanistically, RNA-sequencing identified that oxidative stress, oxidative phosphorylation and AKT/mTOR signaling were involved in anlotinib-treated OSCC cells. Anlotinib upregulated NADPH oxidase 5 (NOX5) expression, elevated reactive oxygen species (ROS) production, impaired mitochondrial respiration, and promoted apoptosis. Moreover, anlotinb also inhibited phospho-Akt (p-AKT) expression and elevated p-eIF2α expression in OSCC cells. NOX5 knockdown attenuated these inhibitory effects and cytotoxicity in anlotinib-treated OSCC cells. Collectively, we demonstrated that anlotinib monotherapy demonstrated favorable anticancer activity and manageable toxicities in patients with R/M OSCC. The antitumor activity of anlotinib in OSCC may be mainly involved in the suppression of mitochondrial respiration via NOX5-mediated redox imbalance and the AKT/eIF2α pathway.

Machine learning in the prediction of cancer therapy

Resistance to therapy remains a major cause of cancer treatment failures, resulting in many cancer-related deaths. Resistance can occur at any time during the treatment, even at the beginning. The current treatment plan is dependent mainly on cancer subtypes and the presence of genetic mutations. Evidently, the presence of a genetic mutation does not always predict the therapeutic response and can vary for different cancer subtypes. Therefore, there is an unmet need for predictive models to match a cancer patient with a specific drug or drug combination. Recent advancements in predictive models using artificial intelligence have shown great promise in preclinical settings. However, despite massive improvements in computational power, building clinically useable models remains challenging due to a lack of clinically meaningful pharmacogenomic data. In this review, we provide an overview of recent advancements in therapeutic response prediction using machine learning, which is the most widely used branch of artificial intelligence. We describe the basics of machine learning algorithms, illustrate their use, and highlight the current challenges in therapy response prediction for clinical practice.

Artificial intelligence, machine learning, and drug repurposing in cancer

Introduction: Drug repurposing provides a cost-effective strategy to re-use approved drugs for new medical indications. Several machine learning (ML) and artificial intelligence (AI) approaches have been developed for systematic identification of drug repurposing leads based on big data resources, hence further accelerating and de-risking the drug development process by computational means.Areas covered: The authors focus on supervised ML and AI methods that make use of publicly available databases and information resources. While most of the example applications are in the field of anticancer drug therapies, the methods and resources reviewed are widely applicable also to other indications including COVID-19 treatment. A particular emphasis is placed on the use of comprehensive target activity profiles that enable a systematic repurposing process by extending the target profile of drugs to include potent off-targets with therapeutic potential for a new indication.Expert opinion: The scarcity of clinical patient data and the current focus on genetic aberrations as primary drug targets may limit the performance of anticancer drug repurposing approaches that rely solely on genomics-based information. Functional testing of cancer patient cells exposed to a large number of targeted therapies and their combinations provides an additional source of repurposing information for tissue-aware AI approaches.

Conservation of copy number profiles during engraftment and passaging of patient-derived cancer xenografts

Patient-derived xenografts (PDXs) are resected human tumors engrafted into mice for preclinical studies and therapeutic testing. It has been proposed that the mouse host affects tumor evolution during PDX engraftment and propagation, affecting the accuracy of PDX modeling of human cancer. Here, we exhaustively analyze copy number alterations (CNAs) in 1,451 PDX and matched patient tumor (PT) samples from 509 PDX models. CNA inferences based on DNA sequencing and microarray data displayed substantially higher resolution and dynamic range than gene expression-based inferences, and they also showed strong CNA conservation from PTs through late-passage PDXs. CNA recurrence analysis of 130 colorectal and breast PT/PDX-early/PDX-late trios confirmed high-resolution CNA retention. We observed no significant enrichment of cancer-related genes in PDX-specific CNAs across models. Moreover, CNA differences between patient and PDX tumors were comparable to variations in multiregion samples within patients. Our study demonstrates the lack of systematic copy number evolution driven by the PDX mouse host.

Personalized cancer therapy prioritization based on driver alteration co-occurrence patterns

Identification of actionable genomic vulnerabilities is key to precision oncology. Utilizing a large-scale drug screening in patient-derived xenografts, we uncover driver gene alteration connections, derive driver co-occurrence (DCO) networks, and relate these to drug sensitivity. Our collection of 53 drug-response predictors attains an average balanced accuracy of 58% in a cross-validation setting, rising to 66% for a subset of high-confidence predictions. We experimentally validated 12 out of 14 predictions in mice and adapted our strategy to obtain drug-response models from patients’ progression-free survival data. Our strategy reveals links between oncogenic alterations, increasing the clinical impact of genomic profiling.

Systematic Establishment of Robustness and Standards in Patient-Derived Xenograft Experiments and Analysis

Patient-derived xenografts (PDX) are tumor-in-mouse models for cancer. PDX collections, such as the NCI PDXNet, are powerful resources for preclinical therapeutic testing. However, variations in experimental and analysis procedures have limited interpretability. To determine the robustness of PDX studies, the PDXNet tested temozolomide drug response for three prevalidated PDX models (sensitive, resistant, and intermediate) across four blinded PDX Development and Trial Centers using independently selected standard operating procedures. Each PDTC was able to correctly identify the sensitive, resistant, and intermediate models, and statistical evaluations were concordant across all groups. We also developed and benchmarked optimized PDX informatics pipelines, and these yielded robust assessments across xenograft biological replicates. These studies show that PDX drug responses and sequence results are reproducible across diverse experimental protocols. In addition, we share the range of experimental procedures that maintained robustness, as well as standardized cloud-based workflows for PDX exome-sequencing and RNA-sequencing analyses and for evaluating growth. SIGNIFICANCE: The PDXNet Consortium shows that PDX drug responses and sequencing results are reproducible across diverse experimental protocols, establishing the potential for multisite preclinical studies to translate into clinical trials.

Computational Methods for the Integrative Analysis of Genomics and Pharmacological Data

Since the pioneering NCI-60 panel of the late'80's, several major screenings of genetic profiling and drug testing in cancer cell lines have been conducted to investigate how genetic backgrounds and transcriptional patterns shape cancer's response to therapy and to identify disease-specific genes associated with drug response. Historically, pharmacogenomics screenings have been largely heterogeneous in terms of investigated cell lines, assay technologies, number of compounds, type and quality of genomic data, and methods for their computational analysis. The analysis of this enormous and heterogeneous amount of data required the development of computational methods for the integration of genomic profiles with drug responses across multiple screenings. Here, we will review the computational tools that have been developed to integrate cancer cell lines' genomic profiles and sensitivity to small molecule perturbations obtained from different screenings.

Know Thy PDX Model

Patient-derived tumor xenograft (PDX) models are frequently used to study cancer mechanisms and potential therapeutics, however, differences in tumor evolution between models and patients have called into question their clinical relevance. In this issue, Mer and colleagues describe the Xenograft Visualization and Analysis (Xeva) software tool that empowers pharmacogenomic analysis through integration of PDX model tumor-drug response with genetic data. By performing the largest PDX model meta-analysis of its kind, the authors demonstrate PDX models are robust platforms for cancer treatment studies. With a clear need for more integrative studies, Xeva is well placed to make more important contributions to pharmacogenomic discovery.See related article by Mer et al., p. 4539.