Integration of pan-cancer cell line and single-cell transcriptomic profiles enables inference of therapeutic vulnerabilities in heterogeneous tumors.

Single-cell RNA-sequencing (scRNA-seq) greatly advanced the understanding of intratumoral heterogeneity by identifying distinct cancer cell subpopulations. However, translating biological differences into treatment strategies is challenging due to a lack of tools to facilitate efficient drug discovery that tackles heterogeneous tumors. Developing such approaches requires accurate prediction of drug response at the single-cell level to offer therapeutic options to specific cell subpopulations. Here, we developed a transparent computational framework (nicknamed scIDUC) to predict therapeutic efficacies on an individual-cell basis by integrating single-cell transcriptomic profiles with large, data-rich pan-cancer cell line screening datasets. This method achieved high accuracy in separating cells into their correct cellular drug response statuses. In three distinct prospective tests covering different diseases (rhabdomyosarcoma, pancreatic ductal adenocarcinoma, and castration-resistant prostate cancer), the predicted results using scIDUC were accurate and mirrored biological expectations. In the first two tests, the framework identified drugs for cell subpopulations that were resistant to standard-of-care (SOC) therapies due to intrinsic resistance or tumor microenvironmental effects, and the results showed high consistency with experimental findings from the original studies. In the third test using newly generated SOC therapy resistant cell lines, scIDUC identified efficacious drugs for the resistant line, and the predictions were validated with in vitro experiments. Together, this study demonstrates the potential of scIDUC to quickly translate scRNA-seq data into drug responses for individual cells, displaying the potential as a tool to improve treatment of heterogenous tumors.

A Web Application for Predicting Drug Combination Efficacy Using Monotherapy Data and IDACombo.

We recently reported a computational method (IDACombo) designed to predict the efficacy of cancer drug combinations using monotherapy response data and the assumptions of independent drug action. Given the strong agreement between IDACombo predictions and measured drug combination efficacy in vitro and in clinical trials, we believe IDACombo can be of immediate use to researchers who are working to develop novel drug combinations. While we previously released our method as an R package, we have now created an R Shiny application to allow researchers without programming experience to easily utilize this method. The app provides a graphical interface which enables users to easily generate efficacy predictions with IDACombo using provided data from several high-throughput cell line screens or using custom, user-provided data.

Sex-related differences in delayed doxorubicin-induced cardiac dysfunction in C57BL/6 mice.

Cancer survivors may experience long-term cardiovascular complications due to chemotherapeutic drugs such as doxorubicin (DOX). The exact mechanism of delayed DOX-induced cardiotoxicity has not been fully elucidated. Sex is an important risk factor for DOX-induced cardiotoxicity. In the current study, we identified sex differences in delayed DOX-induced cardiotoxicity and determined the underlying molecular determinants of the observed sexual dimorphism. Five-week-old male and female mice were administered intraperitoneal injections of DOX (4 mg/kg/week) or saline for 6 weeks. Echocardiography was performed 5 weeks after the last dose of DOX to evaluate cardiac function. Thereafter, mice were sacrificed and gene expression of markers of apoptosis, senescence, and inflammation was measured by PCR in hearts and livers. Proteomic profiling of the heart from both sexes was conducted to determine differentially expressed proteins (DEPs). Only DOX-treated male, but not female, mice demonstrated cardiac dysfunction, cardiac atrophy, and upregulated cardiac expression of Nppb and Myh7. No sex-related differences were observed in DOX-induced expression of most apoptotic, senescence, and pro-inflammatory markers. However, the gene expression of Trp53 was significantly reduced in hearts of DOX-treated female mice only. The anti-inflammatory marker Il-10 was significantly reduced in hearts of DOX-treated male mice only, while the pro-inflammatory marker Il-1α was significantly reduced in livers of DOX-treated female mice only. Gene expression of Tnf-α was reduced in hearts of both DOX-treated male and female mice. Proteomic analysis identified several DEPs after DOX treatment in a sex-specific manner, including anti-inflammatory acute phase proteins. This is the first study to assess sex-specific proteomic changes in a mouse model of delayed DOX-induced cardiotoxicity. Our proteomic analysis identified several sexually dimorphic DEPs, many of which are associated with the anti-inflammatory marker Il-10.

A review of computational methods for predicting cancer drug response at the single-cell level through integration with bulk RNAseq data.

Cancer treatment failure is often attributed to tumor heterogeneity, where diverse malignant cell clones exist within a patient. Despite a growing understanding of heterogeneous tumor cells depicted by single-cell RNA sequencing (scRNA-seq), there is still a gap in the translation of such knowledge into treatment strategies tackling the pervasive issue of therapy resistance. In this review, we survey methods leveraging large-scale drug screens to generate cellular sensitivities to various therapeutics. These methods enable efficient drug screens in scRNA-seq data and serve as the bedrock of drug discovery for specific cancer cell groups. We envision that they will become an indispensable tool for tailoring patient care in the era of heterogeneity-aware precision medicine.

Inferring therapeutic vulnerability within tumors through integration of pan-cancer cell line and single-cell transcriptomic profiles.

Single-cell RNA sequencing greatly advanced our understanding of intratumoral heterogeneity through identifying tumor subpopulations with distinct biologies. However, translating biological differences into treatment strategies is challenging, as we still lack tools to facilitate efficient drug discovery that tackles heterogeneous tumors. One key component of such approaches tackles accurate prediction of drug response at the single-cell level to offer therapeutic options to specific cell subpopulations. Here, we present a transparent computational framework (nicknamed scIDUC) to predict therapeutic efficacies on an individual-cell basis by integrating single-cell transcriptomic profiles with large, data-rich pan-cancer cell line screening datasets. Our method achieves high accuracy, with predicted sensitivities easily able to separate cells into their true cellular drug resistance status as measured by effect size (Cohen's d > 1.0). More importantly, we examine our method's utility with three distinct prospective tests covering different diseases (rhabdomyosarcoma, pancreatic ductal adenocarcinoma, and castration-resistant prostate cancer), and in each our predicted results are accurate and mirrored biological expectations. In the first two, we identified drugs for cell subpopulations that are resistant to standard-of-care (SOC) therapies due to intrinsic resistance or effects of tumor microenvironments. Our results showed high consistency with experimental findings from the original studies. In the third test, we generated SOC therapy resistant cell lines, used scIDUC to identify efficacious drugs for the resistant line, and validated the predictions with in-vitro experiments. Together, scIDUC quickly translates scRNA-seq data into drug response for individual cells, displaying the potential as a first-line tool for nuanced and heterogeneity-aware drug discovery.

Revealing Pan-Histology Immunomodulatory Targets in Pediatric Central Nervous System Tumors.

BACKGROUND: The application of immunotherapy for pediatric CNS malignancies has been limited by the poorly understood immune landscape in this context. The aim of this study was to uncover the mechanisms of immune suppression common among pediatric brain tumors. METHODS: We apply an immunologic clustering algorithm validated by The Cancer Genome Atlas Project to an independent pediatric CNS transcriptomic dataset. Within the clusters, the mechanisms of immunosuppression are explored via tumor microenvironment deconvolution and survival analyses to identify relevant immunosuppressive genes with translational relevance. RESULTS: High-grade diseases fall predominantly within an immunosuppressive subtype (C4) that independently lowers overall survival time and where common immune checkpoints (e.g., PDL1, CTLA4) are less relevant. Instead, we identify several alternative immunomodulatory targets with relevance across histologic diseases. Specifically, we show how the mechanism of EZH2 inhibition to enhance tumor immunogenicity in vitro via the upregulation of MHC class 1 is applicable to a pediatric CNS oncologic context. Meanwhile, we identify that the C3 (inflammatory) immune subtype is more common in low-grade diseases and find that immune checkpoint inhibition may be an effective way to curb progression for this subset. CONCLUSIONS: Three predominant immunologic clusters are identified across pediatric brain tumors. Among high-risk diseases, the predominant immune cluster is associated with recurrent immunomodulatory genes that influence immune infiltrate, including a subset that impacts survival across histologies.

Simplicity: web-based visualization and analysis of high-throughput cancer cell line screens.

High-throughput drug screens are a powerful tool for cancer drug development. However, the results of such screens are often made available only as raw data, which is intractable for researchers without informatic skills, or as highly processed summary statistics, which can lack essential information for translating screening results into clinically meaningful discoveries. To improve the usability of these datasets, we developed Simplicity, a robust and user-friendly web interface for visualizing, exploring, and summarizing raw and processed data from high-throughput drug screens. Importantly, Simplicity allows for easy recalculation of summary statistics at user-defined drug concentrations. This allows Simplicity's outputs to be used with methods that rely on statistics being calculated at clinically relevant doses. Simplicity can be freely accessed at https://oncotherapyinformatics.org/simplicity/.

A systematic review on sex differences in adverse drug reactions related to psychotropic, cardiovascular, and analgesic medications.

Background and objective: Adverse drug reactions (ADRs) are the main safety concerns of clinically used medications. Accumulating evidence has shown that ADRs can affect men and women differently, which suggests sex as a biological predictor in the risk of ADRs. This review aims to summarize the current state of knowledge on sex differences in ADRs with the focus on the commonly used psychotropic, cardiovascular, and analgesic medications, and to aid clinical decision making and future mechanistic investigations on this topic. Methods: PubMed search was performed with combinations of the following terms: over 1,800 drugs of interests, sex difference (and its related terms), and side effects (and its related terms), which yielded over 400 unique articles. Articles related to psychotropic, cardiovascular, and analgesic medications were included in the subsequent full-text review. Characteristics and the main findings (male-biased, female-biased, or not sex biased ADRs) of each included article were collected, and the results were summarized by drug class and/or individual drug. Results: Twenty-six articles studying sex differences in ADRs of six psychotropic medications, ten cardiovascular medications, and one analgesic medication were included in this review. The main findings of these articles suggested that more than half of the ADRs being evaluated showed sex difference pattern in occurrence rate. For instance, lithium was found to cause more thyroid dysfunction in women, and amisulpride induced prolactin increase was more pronounced in women than in men. Some serious ADRs were also found to exert sex difference pattern, such as clozapine induced neutropenia was more prevalent in women whereas simvastatin/atorvastatin-related abnormal liver functions were more pronounced in men.

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.

Cooperativity between H3.3K27M and PDGFRA poses multiple therapeutic vulnerabilities in human iPSC-derived diffuse midline glioma avatars.

Diffuse midline glioma (DMG) is a leading cause of brain tumor death in children. In addition to hallmark H3.3K27M mutations, significant subsets also harbor alterations of other genes, such as TP53 and PDGFRA. Despite the prevalence of H3.3K27M, the results of clinical trials in DMG have been mixed, possibly due to the lack of models recapitulating its genetic heterogeneity. To address this gap, we developed human iPSC-derived tumor models harboring TP53R248Q with or without heterozygous H3.3K27M and/or PDGFRAD842V overexpression. The combination of H3.3K27M and PDGFRAD842V resulted in more proliferative tumors when gene-edited neural progenitor (NP) cells were implanted into mouse brains compared to NP with either mutation alone. Transcriptomic comparison of tumors and their NP cells of origin identified conserved JAK/STAT pathway activation across genotypes as characteristic of malignant transformation. Conversely, integrated genome-wide epigenomic and transcriptomic analyses, as well as rational pharmacologic inhibition, revealed targetable vulnerabilities unique to the TP53R248Q; H3.3K27M; PDGFRAD842V tumors and related to their aggressive growth phenotype. These include AREG-mediated cell cycle control, altered metabolism, and vulnerability to combination ONC201/trametinib treatment. Taken together, these data suggest that cooperation between H3.3K27M and PDGFRA influences tumor biology, underscoring the need for better molecular stratification in DMG clinical trials.

Deciphering genetic causes for sex differences in human health through drug metabolism and transporter genes.

Sex differences have been widely observed in human health. However, little is known about the underlying mechanism behind these observed sex differences. We hypothesize that sex-differentiated genetic effects are contributors of these phenotypic differences. Focusing on a collection of drug metabolism enzymes and transporters (DMET) genes, we discover sex-differentiated genetic regulatory mechanisms between these genes and human complex traits. Here, we show that sex-differentiated genetic effects were present at genome-level and at DMET gene regions for many human complex traits. These sex-differentiated regulatory mechanisms are reflected in the levels of gene expression and endogenous serum biomarkers. Through Mendelian Randomization analysis, we identify putative sex-differentiated causal effects in each sex separately. Furthermore, we identify and validate sex differential gene expression of a subset of DMET genes in human liver samples. We observe higher protein abundance and enzyme activity of CYP1A2 in male-derived liver microsomes, which leads to higher level of an active metabolite formation of clozapine, a commonly prescribed antipsychotic drug. Taken together, our results demonstrate the presence of sex-differentiated genetic effects on DMET gene regulation, which manifest in various phenotypic traits including disease risks and drug responses.

Sex dimorphism in response to targeted therapy and immunotherapy in non-small cell lung cancer patients: a narrative review.

Background and Objective: Multiple agents have been developed for treating non-small cell lung cancer (NSCLC). However, patients' response to these therapies vary drastically, which indicates a need to tailor therapy. Sex is a readily usable clinical characteristic that has been shown to impact patients' response to drugs. The main objective of this narrative review is to summarize the current state of knowledge, compiled from meta-analyses, on sex differences in treatment efficacy for targeted therapy and immunotherapy in NSCLC. We discuss the interplay of patient characteristics, both molecular and demographic, with sex on how they impact therapeutic response. Methods: PubMed search was performed with the term "sex/gender differences" with currently FDA approved targeting therapy and immunotherapy agents in treating NSCLC. Key Content and Findings: For targeted therapy, women tend to benefit more in terms of progression-free survival upon receiving first-generation anti-epidermal growth factor receptor (EGFR) treatment than men. On the other hand, there is an ongoing debate on sex differences in response to immunotherapy. Although preliminary, whether sex differences were observed depends on treatment settings, patient characteristics, and molecular features. Importantly, incorporating sex as a biological component in the biomarker discovery seems to reveal novel insights in immunotherapy response. Conclusions: Taken together, sex differences in responding to standard care have been observed in clinical settings for NSCLC patients. A better understanding of sex-associated treatment response and the underlying biology will improve cancer prognosis and eliminate these sex differences.

A 10-miRNA risk score-based prediction model for pathological complete response to neoadjuvant chemotherapy in hormone receptor-positive breast cancer.

Patients with hormone receptor (HR)-positive tumors breast cancer usually experience a relatively low pathological complete response (pCR) to neoadjuvant chemotherapy (NAC). Here, we derived a 10-microRNA risk score (10-miRNA RS)-based model with better performance in the prediction of pCR and validated its relation with the disease-free survival (DFS) in 755 HR-positive breast cancer patients (273, 265, and 217 in the training, internal, and external validation sets, respectively). This model, presented as a nomogram, included four parameters: the 10-miRNA RS found in our previous study, progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2) status, and volume transfer constant (Ktrans). Favorable calibration and discrimination of 10-miRNA RS-based model with areas under the curve (AUC) of 0.865, 0.811, and 0.804 were shown in the training, internal, and external validation sets, respectively. Patients who have higher nomogram score (>92.2) with NAC treatment would have longer DFS (hazard ratio=0.57; 95%CI: 0.39-0.83; P=0.004). In summary, our data showed the 10-miRNA RS-based model could precisely identify more patients who can attain pCR to NAC, which may help clinicians formulate the personalized initial treatment strategy and consequently achieves better clinical prognosis for patients with HR-positive breast cancer.

Opposing transcriptional programs of KLF5 and AR emerge during therapy for advanced prostate cancer.

Endocrine therapies for prostate cancer inhibit the androgen receptor (AR) transcription factor. In most cases, AR activity resumes during therapy and drives progression to castration-resistant prostate cancer (CRPC). However, therapy can also promote lineage plasticity and select for AR-independent phenotypes that are uniformly lethal. Here, we demonstrate the stem cell transcription factor Krüppel-like factor 5 (KLF5) is low or absent in prostate cancers prior to endocrine therapy, but induced in a subset of CRPC, including CRPC displaying lineage plasticity. KLF5 and AR physically interact on chromatin and drive opposing transcriptional programs, with KLF5 promoting cellular migration, anchorage-independent growth, and basal epithelial cell phenotypes. We identify ERBB2 as a point of transcriptional convergence displaying activation by KLF5 and repression by AR. ERBB2 inhibitors preferentially block KLF5-driven oncogenic phenotypes. These findings implicate KLF5 as an oncogene that can be upregulated in CRPC to oppose AR activities and promote lineage plasticity.

Long Non-Coding RNA ANRIL as a Potential Biomarker of Chemosensitivity and Clinical Outcomes in Osteosarcoma.

Osteosarcoma has a poor prognosis due to chemo-resistance and/or metastases. Increasing evidence shows that long non-coding RNAs (lncRNAs) can play an important role in drug sensitivity and cancer metastasis. Using osteosarcoma cell lines, we identified a positive correlation between the expression of a lncRNA and ANRIL, and resistance to two of the three standard-of-care agents for treating osteosarcoma-cisplatin and doxorubicin. To confirm the potential role of ANRIL in chemosensitivity, we independently inhibited and over-expressed ANRIL in osteosarcoma cell lines followed by treatment with either cisplatin or doxorubicin. Knocking-down ANRIL in SAOS2 resulted in a significant increase in cellular sensitivity to both cisplatin and doxorubicin, while the over-expression of ANRIL in both HOS and U2OS cells led to an increased resistance to both agents. To investigate the clinical significance of ANRIL in osteosarcoma, we assessed ANRIL expression in relation to clinical phenotypes using the osteosarcoma data from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) dataset. Higher ANRIL expression was significantly associated with increased rates of metastases at diagnosis and death and was a significant predictor of reduced overall survival rate. Collectively, our results suggest that the lncRNA ANRIL can be a chemosensitivity and prognosis biomarker in osteosarcoma. Furthermore, reducing ANRIL expression may be a therapeutic strategy to overcome current standard-of-care treatment resistance.

A Long Noncoding RNA, GAS5 Can Be a Biomarker for Docetaxel Response in Castration Resistant Prostate Cancer.

While functional studies of long noncoding RNAs (lncRNAs) have mostly focused on how they influence disease diagnosis and prognosis, the pharmacogenomic relevance of lncRNAs remains largely unknown. Here, we test the hypothesis that the expression of a lncRNA, grow arrest-specific 5 (GAS5) can be a biomarker for docetaxel response in castration resistant prostate cancer (CRPC) using both prostate cancer (PCa) cell lines and CRPC patient datasets. Our results suggest that lower GAS5 expression is associated with docetaxel resistance in both PCa cell lines and CRPC patients. Further experiments also suggest that GAS5 is downregulated in docetaxel resistant CRPC cell lines, which reinforces its potential as a biomarker for docetaxel response. To examine the underlying biological mechanisms, we transiently knockdown GAS5 expression in PCa cell lines and then subject the cells to docetaxel treatment overtime. We did not observe a decrease in docetaxel induced growth inhibition or apoptosis in the siRNA treated cells. The findings suggest that there is no direct causal relationship between change in GAS5 expression and docetaxel response. Subsequently, we explored the indirect regulation among GAS5, ATP binding cassette subfamily B member 1 (ABCB1), and docetaxel sensitivity. We showed that transient knockdown GAS5 did not lead to significant changes in ABCB1 expression. Therefore, we rule out the hypothesis that GAS5 directly down regulate ABCB1 that lead to docetaxel sensitivity. In conclusion, our work suggests that GAS5 can serve as a predictive biomarker for docetaxel response in CRPC; however, the exact mechanism behind the observed correlation remain to be elucidated.

Pharmacogenomics education, research and clinical implementation in the state of Minnesota.

Several healthcare organizations across Minnesota have developed formal pharmacogenomic (PGx) clinical programs to increase drug safety and effectiveness. Healthcare professional and student education is strong and there are multiple opportunities in the state for learners to gain workforce skills and develop advanced competency in PGx. Implementation planning is occurring at several organizations and others have incorporated structured utilization of PGx into routine workflows. Laboratory-based and translational PGx research in Minnesota has driven important discoveries in several therapeutic areas. This article reviews the state of PGx activities in Minnesota including educational programs, research, national consortia involvement, technology, clinical implementation and utilization and reimbursement, and outlines the challenges and opportunities in equitable implementation of these advances.

Facilitating Drug Discovery in Breast Cancer by Virtually Screening Patients Using In Vitro Drug Response Modeling.

(1) Background: Drug imputation methods often aim to translate in vitro drug response to in vivo drug efficacy predictions. While commonly used in retrospective analyses, our aim is to investigate the use of drug prediction methods for the generation of novel drug discovery hypotheses. Triple-negative breast cancer (TNBC) is a severe clinical challenge in need of new therapies. (2) Methods: We used an established machine learning approach to build models of drug response based on cell line transcriptome data, which we then applied to patient tumor data to obtain predicted sensitivity scores for hundreds of drugs in over 1000 breast cancer patients. We then examined the relationships between predicted drug response and patient clinical features. (3) Results: Our analysis recapitulated several suspected vulnerabilities in TNBC and identified a number of compounds-of-interest. AZD-1775, a Wee1 inhibitor, was predicted to have preferential activity in TNBC (p < 2.2 × 10-16) and its efficacy was highly associated with TP53 mutations (p = 1.2 × 10-46). We validated these findings using independent cell line screening data and pathway analysis. Additionally, co-administration of AZD-1775 with standard-of-care paclitaxel was able to inhibit tumor growth (p < 0.05) and increase survival (p < 0.01) in a xenograft mouse model of TNBC. (4) Conclusions: Overall, this study provides a framework to turn any cancer transcriptomic dataset into a dataset for drug discovery. Using this framework, one can quickly generate meaningful drug discovery hypotheses for a cancer population of interest.

Oncogene or tumor suppressor? Long noncoding RNAs role in patient's prognosis varies depending on disease type.

Functional studies of long noncoding RNAs (lncRNAs) are often performed in the context of only a single cancer type. However, the tissue-specific expression patterns of lncRNAs raise the question of whether lncRNA associations identified in one cancer type are relevant to other cancer types. Here, we examine the relationships between the expression levels of 50 cancer-related lncRNAs and survival data from 24 types of cancer in The Cancer Genome Atlas (TCGA) with the goal of identifying prognosis related lncRNAs. Our results suggest that high expression levels of certain lncRNAs are consistently associated with worse/better survival in a number of cancers, while other lncRNAs have different prognostic roles in different types of cancer. Our analysis also identifies 20 novel unadjusted associations that have not been reported before. In addition, in low-grade glioma (LGG), prognostic-related lncRNAs are identified after conditioning on known clinical biomarker and common therapy, revealing that 2 lncRNAs, FOXP4-AS1, and NEAT1, are associated with temozolomide response-a standard-of-care in LGG. Pathway analysis suggests NF-kB/STAT3 signaling pathway enrichment in LGG patients with high NEAT1 expression and DNA repair/myc gene set enrichment in LGG patients with high expression of FOXP4-AS1. Our work demonstrates the context dependency of lncRNAs across cancer types and highlights a number of lncRNAs as potential novel cancer prognosis markers.