Molecular target class is predictive of in vitro response profile

Gastric cancer patient-derived organoids model for the therapeutic drug screening

BACKGROUND

Gastric cancer (GC) is a highly heterogeneous disease featuring many various histological and molecular subtypes. Therefore, it is imperative to have well-characterized in vitro models for personalized treatment development. Gastric cancer patient-derived organoids (PDOs), re-capitulating in vivo conditions, exhibit high clinical efficacy in predicting drug sensitivity to facilitate the development of cancer precision medicine.

METHODS

PDOs were established from surgically resected GC tumor tissues. Histological and molecular characterization of PDOs and primary tissues were performed via IHC and sequencing analysis. We also conducted drug sensitivity tests using PDO cultures with five chemotherapeutic drugs and twenty-two targeted drugs.

RESULTS

We have successfully constructed a PDOs biobank that included EBV+, intestinal/CIN, diffuse/GS, mixed and Her2+ GC subtypes, and these PDOs captured the pathological and genetic characteristics of corresponding tumors and exhibited different sensitivities to the tested agents. In a clinical case study, we performed an additional drug sensitivity test for a patient who reached an advanced progressive stage after surgery. We discovered that the combination of napabucasin and COTI-2 exhibited a stronger synergistic effect than either drug alone.

CONCLUSION

PDOs maintained the histological and genetic characteristics of original cancer tissues. PDOs biobank opens up new perspectives for studying cancer cell biology and personalized medicine as a preclinical study platform.

Genomic screening methodology not requiring barcoding: Single nucleotide polymorphism-based, mixed-cell screening (SMICS)

Although high-throughput, cancer cell-line screening is a time-honored, important tool for anti-cancer drug development, this process involves the testing of each, individual drug in each, individual cell-line. Despite the availability of robotic liquid handling systems, this process remains a time-consuming and costly investment. The Broad Institute developed a new method called Profiling Relative Inhibition Simultaneously in Mixtures (PRISM) to screen a mixture of barcoded, tumor cell-lines. Although this methodology significantly improved the efficiency of screening large numbers of cell-lines, the barcoding process itself was tedious that requires gene transfection and subsequent selection of stable cell-lines. In this study, we developed a new, genomic approach for screening multiple cancer cell-lines using endogenous "tags" that did not require prior barcoding: single nucleotide polymorphism-based, mixed-cell screening (SMICS). The code for SMICS is available at https://github.com/MarkeyBBSRF/SMICS.

A Scalable 3D High-Content Imaging Protocol for Measuring a Drug Induced DNA Damage Response Using Immunofluorescent Subnuclear γH2AX Spots in Patient Derived Ovarian Cancer Organoids

The high morbidity rate of ovarian cancer has remained unchanged during the past four decades, partly due to a lack of understanding of disease mechanisms and difficulties in developing new targeted therapies. Defective DNA damage detection and repair is one of the hallmarks of cancer cells and is a defining characteristic of ovarian cancer. Most in vitro studies to date involve viability measurements at scale using relevant cancer cell lines; however, the translation to the clinic is often lacking. The use of patient derived organoids is closing that translational gap, yet the 3D nature of organoid cultures presents challenges for assay measurements beyond viability measurements. In particular, high-content imaging has the potential for screening at scale, providing a better understanding of the mechanism of action of drugs or genetic perturbagens. In this study we report a semiautomated and scalable immunofluorescence imaging assay utilizing the development of a 384-well plate based subnuclear staining and clearing protocol and optimization of 3D confocal image analysis for studying DNA damage dose response in human ovarian cancer organoids. The assay was validated in four organoid models and demonstrated a predictable response to etoposide drug treatment with the lowest efficacy observed in the clinically most resistant model. This imaging and analysis method can be applied to other 3D organoid and spheroid models for use in high content screening.

Organoids in lung cancer: A teenager with infinite growth potential

Despite the rapid advancement in lung cancer research, morbidity and mortality remain high in recent years. Therefore, deeper learning of the underlying molecular mechanisms of pathogenesis and discovery of novel effective therapeutic strategies of treatment in lung cancer research are around the corner. Among these, applying an efficient and reliable preclinical model would be a critical step that exists throughout the whole process. Traditional 2D models used in lung cancer research, including lung cancer cell lines and cell-derived xenograft models, cannot recapitulate the situations of patients due to the lack of a tumor microenvironment or tumor heterogeneity. Organoids, newly developed 3D in vitro structures, more comprehensively imitate the architecture, interaction and genetics of human organs. Cancer organoids, especially those derived from individual patients, can better resemble primary tumor tissues and thus have a greater potential for making breakthroughs in future cancer studies. Here, we mainly review recent advances in the methodologies and applications of lung cancer organoids, which are just developing but have huge potential.

Computational estimation of quality and clinical relevance of cancer cell lines

Immortal cancer cell lines (CCLs) are the most widely used system for investigating cancer biology and for the preclinical development of oncology therapies. Pharmacogenomic and genome-wide editing screenings have facilitated the discovery of clinically relevant gene-drug interactions and novel therapeutic targets via large panels of extensively characterised CCLs. However, tailoring pharmacological strategies in a precision medicine context requires bridging the existing gaps between tumours and in vitro models. Indeed, intrinsic limitations of CCLs such as misidentification, the absence of tumour microenvironment and genetic drift have highlighted the need to identify the most faithful CCLs for each primary tumour while addressing their heterogeneity, with the development of new models where necessary. Here, we discuss the most significant limitations of CCLs in representing patient features, and we review computational methods aiming at systematically evaluating the suitability of CCLs as tumour proxies and identifying the best patient representative in vitro models. Additionally, we provide an overview of the applications of these methods to more complex models and discuss future machine-learning-based directions that could resolve some of the arising discrepancies.

Large-scale pharmacogenomic studies and drug response prediction for personalized cancer medicine

The response rate of most anti-cancer drugs is limited because of the high heterogeneity of cancer and the complex mechanism of drug action. Personalized treatment that stratifies patients into subgroups using molecular biomarkers is promising to improve clinical benefit. With the accumulation of preclinical models and advances in computational approaches of drug response prediction, pharmacogenomics has made great success over the last 20 years and is increasingly used in the clinical practice of personalized cancer medicine. In this article, we first summarize FDA-approved pharmacogenomic biomarkers and large-scale pharmacogenomic studies of preclinical cancer models such as patient-derived cell lines, organoids, and xenografts. Furthermore, we comprehensively review the recent developments of computational methods in drug response prediction, covering network, machine learning, and deep learning technologies and strategies to evaluate immunotherapy response. In the end, we discuss challenges and propose possible solutions for further improvement.

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.

N-acylsphingosine amidohydrolase 1 promotes melanoma growth and metastasis by suppressing peroxisome biogenesis-induced ROS production

OBJECTIVE

Metabolic deregulation is a key hallmark of cancer cells and has been shown to drive cancer growth and metastasis. However, not all metabolic drivers of melanoma are known. Based on our finding that N-acylsphingosine amidohydrolase 1 (ASAH1) is overexpressed in melanoma, the objective of these studies was to establish its role in melanoma tumor growth and metastasis, understand its mechanism of action, and evaluate ASAH1 targeting for melanoma therapy.

METHODS

We used publicly available melanoma datasets and patient-derived samples of melanoma and normal skin tissue and analyzed them for ASAH1 mRNA expression and ASAH1 protein expression using immunohistochemistry. ASAH1 was knocked down using short-hairpin RNAs in multiple melanoma cell lines that were tested in a series of cell culture-based assays and mouse-based melanoma xenograft assays to monitor the effect of ASAH1 knockdown on melanoma tumor growth and metastasis. An unbiased metabolomics analysis was performed to identify the mechanism of ASAH1 action. Based on the metabolomics findings, the role of peroxisome-mediated reactive oxygen species (ROS) production was explored in regard to mediating the effect of ASAH1. The ASAH1 inhibitor was used alone or in combination with a BRAFV600E inhibitor to evaluate the therapeutic value of ASAH1 targeting for melanoma therapy.

RESULTS

We determined that ASAH1 was overexpressed in a large percentage of melanoma cells and regulated by transcription factor E2F1 in a mitogen-activated protein (MAP) kinase pathway-dependent manner. ASAH1 expression was necessary to maintain melanoma tumor growth and metastatic attributes in cell cultures and mouse models of melanoma tumor growth and metastasis. To identify the mechanism by which ASAH1 facilitates melanoma tumor growth and metastasis, we performed a large-scale and unbiased metabolomics analysis of melanoma cells expressing ASAH1 short-hairpin RNAs (shRNAs). We found that ASAH1 inhibition increased peroxisome biogenesis through ceramide-mediated PPARγ activation. ASAH1 loss increased ceramide and peroxisome-derived ROS, which in turn inhibited melanoma growth. Pharmacological inhibition of ASAH1 also attenuated melanoma growth and enhanced the effectiveness of BRAF kinase inhibitor in the cell cultures and mice.

CONCLUSIONS

Collectively, these results demonstrate that ASAH1 is a druggable driver of melanoma tumor growth and metastasis that functions by suppressing peroxisome biogenesis, thereby inhibiting peroxisome-derived ROS production. These studies also highlight the therapeutic utility of ASAH1 inhibitors for melanoma therapy.

Towards the routine use of in silico screenings for drug discovery using metabolic modelling

Currently, the development of new effective drugs for cancer therapy is not only hindered by development costs, drug efficacy, and drug safety but also by the rapid occurrence of drug resistance in cancer. Hence, new tools are needed to study the underlying mechanisms in cancer. Here, we discuss the current use of metabolic modelling approaches to identify cancer-specific metabolism and find possible new drug targets and drugs for repurposing. Furthermore, we list valuable resources that are needed for the reconstruction of cancer-specific models by integrating various available datasets with genome-scale metabolic reconstructions using model-building algorithms. We also discuss how new drug targets can be determined by using gene essentiality analysis, an in silico method to predict essential genes in a given condition such as cancer and how synthetic lethality studies could greatly benefit cancer patients by suggesting drug combinations with reduced side effects.

SP3 is associated with migration, invasion, and Akt/PKB signalling in MDA-MB-231 breast cancer cells

Specificity proteins (SPs) have pro-oncogenic functions in cancer cells, ranging from cancer cell proliferation, migration, invasion, and angiogenesis. There is strong evidence that several antineoplastic drugs target depletion of SP proteins via different pathways. However, the mode of action of SP3 and the underlying consequences of its depletion are not well understood. Here, we demonstrate that SP3 is overexpressed in invasive breast cancer cells vs normal counterparts. The gene expression analysis from The Cancer Genome Atlas datasets indicated that SP3 is strongly correlated with Akt signalling-related proteins, G protein subunit alpha 13, and RAB33B (RAB33B, member RAS oncogene family). RNA interference of SP3 decreased active phosphorylation of Akt at serine and threonine sites. These findings indicate that SP3 exhibits a pro-oncogenic function, which clearly fits the description of an nononcogene addiction gene. Future analyses are prompted to uncover the SP3 gene regulation function and to reveal downstream targets of SP3 in breast cancer.

Human-Derived Model Systems in Gynecological Cancer Research

The human female reproductive tract (FRT) is a complex system that combines series of organs, including ovaries, fallopian tubes, uterus, cervix, vagina, and vulva; each of which possesses unique cellular characteristics and functions. This versatility, in turn, allows for the development of a wide range of epithelial gynecological cancers with distinct features. Thus, reliable model systems are required to better understand the diverse mechanisms involved in the regional pathogenesis of the reproductive tract and improve treatment strategies. Here, we review the current human-derived model systems available to study the multitude of gynecological cancers, including ovarian, endometrial, cervical, vaginal, and vulvar cancer, and the recent advances in the push towards personalized therapy.

Evaluating the consistency of large-scale pharmacogenomic studies

Recent years have seen an increase in the availability of pharmacogenomic databases such as Genomics of Drug Sensitivity in Cancer (GDSC) and Cancer Cell Line Encyclopedia (CCLE) that provide genomic and functional characterization information for multiple cell lines. Studies have alluded to the fact that specific characterizations may be inconsistent between different databases. Analysis of the potential discrepancies in the different databases is highly significant, as these sources are frequently used to analyze and validate methodologies for personalized cancer therapies. In this article, we review the recent developments in investigating the correspondence between different pharmacogenomics databases and discuss the potential factors that require attention when incorporating these sources in any modeling analysis. Furthermore, we explored the consistency among these databases using copulas that can capture nonlinear dependencies between two sets of data.

An Improved Anticancer Drug-Response Prediction Based on an Ensemble Method Integrating Matrix Completion and Ridge Regression

In this study, we proposed an ensemble learning method, simultaneously integrating a low-rank matrix completion model and a ridge regression model to predict anticancer drug response on cancer cell lines. The model was applied to two benchmark datasets, including the Cancer Cell Line Encyclopedia (CCLE) and the Genomics of Drug Sensitivity in Cancer (GDSC). As previous studies suggest, the dual-layer integrated cell line-drug network model was one of the best models by far and outperformed most state-of-the-art models. Thus, we performed a head-to-head comparison between the dual-layer integrated cell line-drug network model and our model by a 10-fold crossvalidation study. For the CCLE dataset, our model has a higher Pearson correlation coefficient between predicted and observed drug responses than that of the dual-layer integrated cell line-drug network model in 18 out of 23 drugs. For the GDSC dataset, our model is better in 26 out of 28 drugs in the phosphatidylinositol 3-kinase (PI3K) pathway and 26 out of 30 drugs in the extracellular signal-regulated kinase (ERK) signaling pathway, respectively. Based on the prediction results, we carried out two types of case studies, which further verified the effectiveness of the proposed model on the drug-response prediction. In addition, our model is more biologically interpretable than the compared method, since it explicitly outputs the genes involved in the prediction, which are enriched in functions, like transcription, Src homology 2/3 (SH2/3) domain, cell cycle, ATP binding, and zinc finger.

Deep learning for drug response prediction in cancer

Predicting the sensitivity of tumors to specific anti-cancer treatments is a challenge of paramount importance for precision medicine. Machine learning(ML) algorithms can be trained on high-throughput screening data to develop models that are able to predict the response of cancer cell lines and patients to novel drugs or drug combinations. Deep learning (DL) refers to a distinct class of ML algorithms that have achieved top-level performance in a variety of fields, including drug discovery. These types of models have unique characteristics that may make them more suitable for the complex task of modeling drug response based on both biological and chemical data, but the application of DL to drug response prediction has been unexplored until very recently. The few studies that have been published have shown promising results, and the use of DL for drug response prediction is beginning to attract greater interest from researchers in the field. In this article, we critically review recently published studies that have employed DL methods to predict drug response in cancer cell lines. We also provide a brief description of DL and the main types of architectures that have been used in these studies. Additionally, we present a selection of publicly available drug screening data resources that can be used to develop drug response prediction models. Finally, we also address the limitations of these approaches and provide a discussion on possible paths for further improvement. Contact:  mrocha@di.uminho.pt.

BCL9 provides multi-cellular communication properties in colorectal cancer by interacting with paraspeckle proteins

Colorectal cancer (CRC) is the third most commonly diagnosed cancer, which despite recent advances in treatment, remains incurable due to molecular heterogeneity of tumor cells. The B-cell lymphoma 9 (BCL9) oncogene functions as a transcriptional co-activator of the Wnt/β-catenin pathway, which plays critical roles in CRC pathogenesis. Here we have identified a β-catenin-independent function of BCL9 in a poor-prognosis subtype of CRC tumors characterized by expression of stromal and neural associated genes. In response to spontaneous calcium transients or cellular stress, BCL9 is recruited adjacent to the interchromosomal regions, where it stabilizes the mRNA of calcium signaling and neural associated genes by interacting with paraspeckle proteins. BCL9 subsequently promotes tumor progression and remodeling of the tumor microenvironment (TME) by sustaining the calcium transients and neurotransmitter-dependent communication among CRC cells. These data provide additional insights into the role of BCL9 in tumor pathogenesis and point towards additional avenues for therapeutic intervention.

Migration rather than proliferation transcriptomic signatures are strongly associated with breast cancer patient survival

The efficacy of prospective cancer treatments is routinely estimated by in vitro cell-line proliferation screens. However, it is unclear whether tumor aggressiveness and patient survival are influenced more by the proliferative or the migratory properties of cancer cells. To address this question, we experimentally measured proliferation and migration phenotypes across more than 40 breast cancer cell-lines. Based on the latter, we built and validated individual predictors of breast cancer proliferation and migration levels from the cells' transcriptomics. We then apply these predictors to estimate the proliferation and migration levels of more than 1000 TCGA breast cancer tumors. Reassuringly, both estimates increase with tumor's aggressiveness, as qualified by its stage, grade, and subtype. However, predicted tumor migration levels are significantly more strongly associated with patient survival than the proliferation levels. We confirmed these findings by conducting siRNA knock-down experiments on the highly migratory MDA-MB-231 cell lines and deriving gene knock-down based proliferation and migration signatures. We show that cytoskeletal drugs might be more beneficial in patients with high predicted migration levels. Taken together, these results testify to the importance of migration levels in determining patient survival.

Xenograft and organoid model systems in cancer research

Patient‐derived tumour xenografts and tumour organoids have become important preclinical model systems for cancer research. Both models maintain key features from their parental tumours, such as genetic and phenotypic heterogeneity, which allows them to be used for a wide spectrum of applications. In contrast to patient‐derived xenografts, organoids can be established and expanded with high efficiency from primary patient material. On the other hand, xenografts retain tumour–stroma interactions, which are known to contribute to tumorigenesis. In this review, we discuss recent advances in patient‐derived tumour xenograft and tumour organoid model systems and compare their promises and challenges as preclinical models in cancer research.

Current Status of Patient-Derived Ovarian Cancer Models

Ovarian cancer (OC) is one of the leading causes of female cancer death. Recent studies have documented its extensive variations as a disease entity, in terms of cell or tissue of origin, pre-cancerous lesions, common mutations, and therapeutic responses, leading to the notion that OC is a generic term referring to a whole range of different cancer subtypes. Despite such heterogeneity, OC treatment is stereotypic; aggressive surgery followed by conventional chemotherapy could result in chemo-resistant diseases. Whereas molecular-targeted therapies will become shortly available for a subset of OC, there still remain many patients without effective drugs, requiring development of groundbreaking therapeutic agents. In preclinical studies for drug discovery, cancer cell lines used to be the gold standard, but now this has declined due to frequent failure in predicting therapeutic responses in patients. In this regard, patient-derived cells and tumors are gaining more attention in precise and physiological modeling of in situ tumors, which could also pave the way to implementation of precision medicine. In this article, we comprehensively overviewed the current status of various platforms for patient-derived OC models. We highly appreciate the potentials of organoid culture in achieving high success rate and retaining tumor heterogeneity.

An Interactive Resource to Probe Genetic Diversity and Estimated Ancestry in Cancer Cell Lines

Recent work points to a lack of diversity in genomics studies from genome-wide association studies to somatic (tumor) genome analyses. Yet, population-specific genetic variation has been shown to contribute to health disparities in cancer risk and outcomes. Immortalized cancer cell lines are widely used in cancer research, from mechanistic studies to drug screening. Larger collections of cancer cell lines better represent the genomic heterogeneity found in primary tumors. Yet, the genetic ancestral origin of cancer cell lines is rarely acknowledged and often unknown. Using genome-wide genotyping data from 1,393 cancer cell lines from the Catalogue of Somatic Mutations in Cancer (COSMIC) and Cancer Cell Line Encyclopedia (CCLE), we estimated the genetic ancestral origin for each cell line. Our data indicate that cancer cell line collections are not representative of the diverse ancestry and admixture characterizing human populations. We discuss the implications of genetic ancestry and diversity of cellular models for cancer research and present an interactive tool, Estimated Cell Line Ancestry (ECLA), where ancestry can be visualized with reference populations of the 1000 Genomes Project. Cancer researchers can use this resource to identify cell line models for their studies by taking ancestral origins into consideration.

Predict drug sensitivity of cancer cells with pathway activity inference

Background

Predicting cellular responses to drugs has been a major challenge for personalized drug therapy regimen. Recent pharmacogenomic studies measured the sensitivities of heterogeneous cell lines to numerous drugs, and provided valuable data resources to develop and validate computational approaches for the prediction of drug responses. Most of current approaches predict drug sensitivity by building prediction models with individual genes, which suffer from low reproducibility due to biologic variability and difficulty to interpret biological relevance of novel gene-drug associations. As an alternative, pathway activity scores derived from gene expression could predict drug response of cancer cells.

Method

In this study, pathway-based prediction models were built with four approaches inferring pathway activity in unsupervised manner, including competitive scoring approaches (DiffRank and GSVA) and self-contained scoring approaches (PLAGE and Z-score). These unsupervised pathway activity inference approaches were applied to predict drug responses of cancer cells using data from Cancer Cell Line Encyclopedia (CCLE).

Results

Our analysis on all the 24 drugs from CCLE demonstrated that pathway-based models achieved better predictions for 14 out of the 24 drugs, while taking fewer features as inputs. Further investigation on indicated that pathway-based models indeed captured pathways involving drug-related genes (targets, transporters and metabolic enzymes) for majority of drugs, whereas gene-models failed to identify these drug-related genes, in most cases. Among the four approaches, competitive scoring (DiffRank and GSVA) provided more accurate predictions and captured more pathways involving drug-related genes than self-contained scoring (PLAGE and Z-Score). Detailed interpretation of top pathways from the top method (DiffRank) highlights the merit of pathway-based approaches to predict drug response by identifying pathways relevant to drug mechanisms.

Conclusion

Taken together, pathway-based modeling with inferred pathway activity is a promising alternative to predict drug response, with the ability to easily interpret results and provide biological insights into the mechanisms of drug actions.

Electronic supplementary material

The online version of this article (10.1186/s12920-018-0449-4) contains supplementary material, which is available to authorized users.

The genomic landscape of UM-SCC oral cavity squamous cell carcinoma cell lines

OBJECTIVES

We sought to describe the genetic complexity of 14 UM-SCC oral cavity cancer cell lines that have remained uncharacterized despite being used as model systems for decades.

MATERIALS AND METHODS

We performed exome sequencing on 14 oral cavity UM-SCC cell lines and denote the mutational profile of each line. We used a SNP array to profile the multiple copy number variations of each cell line and use immunoblotting to compare alterations to protein expression of commonly amplified genes (EGFR, PIK3CA, etc.). RNA sequencing was performed to characterize the expression of genes with copy number alterations.

RESULTS

The cell lines displayed a highly complex network of genetic aberrations that was consistent with alterations identified in the HNSCC TCGA project including PIK3CA amplification, CDKN2A deletion, as well as TP53 and CASP8 mutations, enabling genetic stratification of each cell line in the panel. Copy number FISH and spectral karyotyping analysis demonstrate that cell lines retain chromosomal heterogeneity.

CONCLUSIONS

Collectively, we developed an important resource for future oral cavity HNSCC cell line studies and highlight the complexity of genomic aberrations in cell lines.

More than fishing for a cure: The promises and pitfalls of high throughput cancer cell line screens

High-throughput screens in cancer cell lines (CCLs) have been used for decades to help researchers identify compounds with the potential to improve the treatment of cancer and, more recently, to identify genomic susceptibilities in cancer via genome-wide shRNA and CRISPR/Cas9 screens. Additionally, rich genomic and transcriptomic data of these CCLs has allowed researchers to pair this screening data with biological features, enabling efforts to identify biomarkers of treatment response and gene dependencies. In this paper, we review the major CCL screening efforts and the large datasets these screens have made available. We also assess the CCL screens collectively and include a resource with harmonized CCL and compound identifiers to facilitate comparisons across screens. The CCLs in these screens were found to represent a wide range of cancer types, with a strong correlation between the representation of a cancer type and its associated mortality. Patient ages and gender distributions of CCLs were generally as expected, with some notable exceptions of female underrepresentation in certain disease types. Also, ethnicity information, while largely incomplete, suggests that African American and Hispanic patients may be severely underrepresented in these screens. Nearly all genes were targeted in the genetic perturbations screens, but the compounds used for the drug screens target less than half of known cancer drivers, likely reflecting known limitations in our drug design capabilities. Finally, we discuss recent developments in the field and the promise they hold for enabling future screens to overcome previous limitations and lead to new breakthroughs in cancer treatment.

microRNA 125a Regulates MHC-I Expression on Esophageal Adenocarcinoma Cells, Associated With Suppression of Antitumor Immune Response and Poor Outcomes of Patients

BACKGROUND & AIMS

Immune checkpoint inhibition may affect growth or progression of highly aggressive cancers, such as esophageal adenocarcinoma (EAC). We investigated the regulation of expression of major histocompatibility complex, class 1 (MHC-I) proteins (encoded by HLA-A, HLA-B, and HLA-C) and the immune response to EACs in patient samples.

METHODS

We performed quantitative polymerase chain reaction array analyses of OE33 cells and OE19 cells, which express different levels of the ATP binding cassette subfamily B member 1 (TAP1) and TAP2, required for antigen presentation by MHC-I, to identify microRNAs (miRNAs) that regulate their expression. We performed luciferase assays to validate interactions between miRNAs and potential targets. We overexpressed candidate miRNAs in OE33, FLO-1, and OACP4 C cell lines and performed quantitative polymerase chain reaction, immunoblot, and flow cytometry analyses to identify changes in messenger RNA (mRNA) and protein expression; we studied the effects of cytotoxic T cells. We performed miRNA in situ hybridization, RNA-sequencing, and immunohistochemical analyses of tumor tissues from 51 untreated patients with EAC in the Netherlands. Clinical and survival data were collected for patients, and EAC subtypes were determined.

RESULTS

We found OE19 cells to have increased levels of 7 miRNAs. Of these, we found binding sites for miRNA 125a (MIR125a)-5p in the 3' untranslated region of the TAP2 mRNA and binding sites for MIR148a-3p in 3' untranslated regions of HLA-A, HLA-B, and HLA-C mRNAs. Overexpression of these miRNAs reduced expression of TAP2 in OE33, FLO-1, and OACP4 C cells, and reduced cell-surface levels of MHC-I. OE33 cells that expressed the viral peptide BZLF1 were killed by cytotoxic T cells, whereas OE33 that overexpressed MIR125a-5p or MIR 148a along with BZLF1 were not. In EAC and nontumor tissues, levels of MIR125a-5p correlated inversely with levels of TAP2 protein. High expression of TAP1 by EAC correlated with significantly shorter overall survival times of patients. EACs that expressed high levels of TAP1 and genes involved in antigen presentation also expressed high levels of genes that regulate the adaptive immune response, PD-L1, PD-L2, and IDO1; these EACs had a poor response to neoadjuvant chemoradiotherapy and associated with shorter overall survival times of patients.

CONCLUSIONS

In studies of EAC cell lines and tumor tissues, we found increased levels of MIR125a-5p and MIR148a-3p to reduce levels of TAP2 and MHC-I, required for antigen presentation. High expression of MHC-I molecules by EAC correlated with markers of an adaptive immune response and significantly shorter overall survival times of patients.

β-catenin/TCF activity regulates IGF-1R tyrosine kinase inhibitor sensitivity in colon cancer

The availability of large-scale drug screening data on cell line panels provides a unique opportunity to identify predictive biomarkers for targeted drug efficacy. Analysis of diverse drug data on ~990 cancer cell lines revealed enhanced sensitivity of insulin-like growth factor 1 receptor/ Insulin Receptor (IGF-1R/IR) tyrosine kinase inhibitors (TKIs) in colon cancer cells. Interestingly, β-catenin/TCF(T cell factor)-responsive promoter activity exhibited a significant positive association with IGF-1R/IR TKI response, while the mutational status of direct upstream genes, such as CTNNB1 and APC, was not significantly associated with the response. The β-catenin/TCF activity high cell lines express components of IGF-1R/IR signaling more than the low cell lines explaining their enhanced sensitivity against IGF-1R/IR TKI. Reinforcing β-catenin/TCF responsive promoter activity by introducing CTNNB1 gain-of-function mutations into IGF-1R/IR TKI-resistant cells increased the expression and activity of IGF-1R/IR signaling components and also sensitized the cells to IGF-1R/IR TKIs in vitro and in vivo. Analysis of TCGA data revealed that the stronger β-catenin/TCF responsive promoter activity was associated with higher IGF-1R and IGF2 transcription in human colon cancer specimens as well. Collectively, compared to the mutational status of upstream genes, β-catenin/TCF responsive promoter activity has potential to be a stronger predictive positive biomarker for IGF-1R/IR TKI responses in colon cancer cells. The present study highlights the potential of transcriptional activity as therapeutic biomarkers for targeted therapies, overcoming the limited ability of upstream genetic mutations to predict responses.

Implementation of a Multiplex and Quantitative Proteomics Platform for Assessing Protein Lysates Using DNA-Barcoded Antibodies

Molecular analysis of tumors forms the basis for personalized cancer medicine and increasingly guides patient selection for targeted therapy. Future opportunities for personalized medicine are highlighted by the measurement of protein expression levels via immunohistochemistry, protein arrays, and other approaches; however, sample type, sample quantity, batch effects, and "time to result" are limiting factors for clinical application. Here, we present a development pipeline for a novel multiplexed DNA-labeled antibody platform which digitally quantifies protein expression from lysate samples. We implemented a rigorous validation process for each antibody and show that the platform is amenable to multiple protocols covering nitrocellulose and plate-based methods. Results are highly reproducible across technical and biological replicates, and there are no observed "batch effects" which are common for most multiplex molecular assays. Tests from basal and perturbed cancer cell lines indicate that this platform is comparable to orthogonal proteomic assays such as Reverse-Phase Protein Array, and applicable to measuring the pharmacodynamic effects of clinically-relevant cancer therapeutics. Furthermore, we demonstrate the potential clinical utility of the platform with protein profiling from breast cancer patient samples to identify molecular subtypes. Together, these findings highlight the potential of this platform for enhancing our understanding of cancer biology in a clinical translation setting.

A new approach to assess drug sensitivity in cells for novel drug discovery

INTRODUCTION

There is a pressing need to improve strategies to select candidate drugs early on in the drug development pipeline, especially in oncology, as the efficiency of new drug approval has steadily declined these past years. Traditional methods of drug screening have relied on low-cost assays on cancer cell lines growing on plastic dishes. Recent massive-scale screens have generated big data amenable for sophisticated computational modeling and integration with clinical data. However, 2D culturing has several intrinsic limitations and novel methodologies have been devised for culturing in three dimensions, to include cells from the tumor immune microenvironment. These major improvements are bringing in vitro systems even closer to a physiological, more clinically relevant state. Areas covered: In this article, the authors review the literature on methodologies for early-phase drug screening, focusing on in vitro systems and analyzing both novel experimental and statistical approaches. The article does not cover the expanding literature on in vivo systems. Expert opinion: The popularity of three-dimensional systems is exploding, driven by the development of 'organoid' derivation technology in 2009. These assays are growing in sophistication to accommodate the increasing need by modern oncology to develop drugs that target the microenvironment.

ITGB1-dependent upregulation of Caveolin-1 switches TGFβ signalling from tumour-suppressive to oncogenic in prostate cancer

Caveolin-1 (CAV1) is over-expressed in prostate cancer (PCa) and is associated with adverse prognosis, but the molecular mechanisms linking CAV1 expression to disease progression are poorly understood. Extensive gene expression correlation analysis, quantitative multiplex imaging of clinical samples, and analysis of the CAV1-dependent transcriptome, supported that CAV1 re-programmes TGFβ signalling from tumour suppressive to oncogenic (i.e. induction of SLUG, PAI-1 and suppression of CDH1, DSP, CDKN1A). Supporting such a role, CAV1 knockdown led to growth arrest and inhibition of cell invasion in prostate cancer cell lines. Rationalized RNAi screening and high-content microscopy in search for CAV1 upstream regulators revealed integrin beta1 (ITGB1) and integrin associated proteins as CAV1 regulators. Our work suggests TGFβ signalling and beta1 integrins as potential therapeutic targets in PCa over-expressing CAV1, and contributes to better understand the paradoxical dual role of TGFβ in tumour biology.

Aiming for the Insulin-like Growth Factor-1 system in breast cancer therapeutics

Despite the major discoveries occurred in oncology the recent years, breast malignancies remain one of the most common causes of cancer-related deaths for women in developed countries. Development of HER2-targeting drugs has been considered a breakthrough in anti-cancer approaches and alluded to the potential of targeting growth factors in breast cancer (BrCa) therapeutics. More than twenty-five years have passed since the Insulin-like Growth Factor-1 (IGF-1) system was initially recognized as a potential target candidate in BrCa therapy. To date, a growing body of studies have implicated the IGF-1 signaling with the BrCa biology. Despite the promising experimental evidence, the impression from clinical trials is rather disappointing. Several reasons may account for this and the last word regarding the efficacy of this system as a target candidate in BrCa therapeutics is probably not written yet. Herein, we provide the theoretical basis, as well as, a comprehensive overview of the current literature, regarding the different strategies targeting the various components of the IGF-1/IGF-1R axis in several pathophysiological aspects of BrCa, including the tumor micro-environment and cancer stemness. In addition, we review the rationale for targeting the IGF-1 system in the different BrCa molecular subtypes and in treatment resistant breast tumors with a focus on both the molecular mechanisms and on the clinical perspectives of such approaches in specific population subgroups. We also discuss the future challenges, as well as, the development of novel molecules and strategies targeting the system and suggest potential improvements in the field.

Discordancy Partitioning for Validating Potentially Inconsistent Pharmacogenomic Studies

The Genomics of Drug Sensitivity in Cancer (GDSC) and Cancer Cell Line Encyclopedia (CCLE) are two major studies that can be used to mine for therapeutic biomarkers for cancers of a large variety. Model validation using the two datasets however has proved challenging. Both predictions and signatures do not consistently validate well for models built on one dataset and tested on the other. While the genomic profiling seems consistent, the drug response data is not. Some efforts at harmonizing experimental designs has helped but not entirely removed model validation difficulties. In this paper, we present a partitioning strategy based on a data sharing concept which directly acknowledges a potential lack of concordance between datasets and in doing so, also allows for extraction of reproducible novel gene-drug interaction signatures as well as accurate test set predictions. We demonstrate these properties in a re-analysis of the GDSC and CCLE datasets.

New insight for pharmacogenomics studies from the transcriptional analysis of two large-scale cancer cell line panels

One of the most challenging problems in the development of new anticancer drugs is the very high attrition rate. The so-called “drug repositioning process” propose to find new therapeutic indications to already approved drugs. For this, new analytic methods are required to optimize the information present in large-scale pharmacogenomics datasets. We analyzed data from the Genomics of Drug Sensitivity in Cancer and Cancer Cell Line Encyclopedia studies. We focused on common cell lines (n = 471), considering the molecular information, and the drug sensitivity for common drugs screened (n = 15). We propose a novel classification based on transcriptomic profiles of cell lines, according to a biological network-driven gene selection process. Our robust molecular classification displays greater homogeneity of drug sensitivity than cancer cell line grouped based on tissue of origin. We then identified significant associations between cell line cluster and drug response robustly found between both datasets. We further demonstrate the relevance of our method using two additional external datasets and distinct sensitivity metrics. Some associations were still found robust, despite cell lines and drug responses’ variations. This study defines a robust molecular classification of cancer cell lines that could be used to find new therapeutic indications to known compounds.

Drug sensitivity and resistance testing identifies PLK1 inhibitors and gemcitabine as potent drugs for malignant peripheral nerve sheath tumors

Patients with malignant peripheral nerve sheath tumor (MPNST), a rare soft tissue cancer associated with loss of the tumor suppressor neurofibromin (NF1), have poor prognosis and typically respond poorly to adjuvant therapy. We evaluated the effect of 299 clinical and investigational compounds on seven MPNST cell lines, two primary cultures of human Schwann cells, and five normal bone marrow aspirates, to identify potent drugs for MPNST treatment with few side effects. Top hits included Polo-like kinase 1 (PLK1) inhibitors (volasertib and BI2536) and the fluoronucleoside gemcitabine, which were validated in orthogonal assays measuring viability, cytotoxicity, and apoptosis. DNA copy number, gene expression, and protein expression were determined for the cell lines to assess pharmacogenomic relationships. MPNST cells were more sensitive to BI2536 and gemcitabine compared to a reference set of 94 cancer cell lines. PLK1, RRM1, and RRM2 mRNA levels were increased in MPNST compared to benign neurofibroma tissue, and the protein level of PLK1 was increased in the MPNST cell lines compared to normal Schwann cells, indicating an increased dependence on these drug targets in malignant cells. Furthermore, we observed an association between increased mRNA expression of PLK1, RRM1, and RRM2 in patient samples and worse disease outcome, suggesting a selective benefit from inhibition of these genes in the most aggressive tumors.

The Pursuit of Therapeutic Biomarkers with High-Throughput Cancer Cell Drug Screens

In the last decade we have witnessed tremendous advances in our understanding of the landscape of the molecular alterations that underpin many of the most prevalent cancers, in the use of automated high-throughput platforms for high-throughput drug screens in cancer cells, in the creation of more clinically relevant cancer cell models, and lastly in the development of more useful computational approaches in the pursuit of biomarkers of drug response. Separately, each of these improvements will undoubtedly lead to improvements in the treatment of cancer patients but to fulfill the promise of truly personalized cancer medicine, we must bring these disciplines together in a truly multidisciplinary fashion.

Predicting the Reliability of Drug-target Interaction Predictions with Maximum Coverage of Target Space

Many computational methods to predict the macromolecular targets of small organic molecules have been presented to date. Despite progress, target prediction methods still have important limitations. For example, the most accurate methods implicitly restrict their predictions to a relatively small number of targets, are not systematically validated on drugs (whose targets are harder to predict than those of non-drug molecules) and often lack a reliability score associated with each predicted target. Here we present a systematic validation of ligand-centric target prediction methods on a set of clinical drugs. These methods exploit a knowledge-base covering 887,435 known ligand-target associations between 504,755 molecules and 4,167 targets. Based on this dataset, we provide a new estimate of the polypharmacology of drugs, which on average have 11.5 targets below IC50 10 µM. The average performance achieved across clinical drugs is remarkable (0.348 precision and 0.423 recall, with large drug-dependent variability), especially given the unusually large coverage of the target space. Furthermore, we show how a sparse ligand-target bioactivity matrix to retrospectively validate target prediction methods could underestimate prospective performance. Lastly, we present and validate a first-in-kind score capable of accurately predicting the reliability of target predictions.

Ex vivo tumor culture systems for functional drug testing and therapy response prediction

Optimal patient stratification is of utmost importance in the era of personalized medicine. Prediction of individual treatment responses by functional ex vivo assays requires model systems derived from viable tumor samples, which should closely resemble in vivo tumor characteristics and microenvironment. This review discusses a broad spectrum of model systems, ranging from classic 2D monolayer culture techniques to more experimental ‘cancer-on-chip’ procedures. We mainly focus on organotypic tumor slices that take tumor heterogeneity and tumor–stromal interactions into account. These 3D model systems can be exploited for patient selection as well as for fundamental research. Selection of the right model system for each specific research endeavor is crucial and requires careful balancing of the pros and cons of each technology.

Selection of the right therapy for individual cancer patients is very important with the expanding number of possible treatments. How tumors respond to a therapy can be tested by treating a sample from the tumor outside the body. Various culture methods can be used to maintain this tumor sample. Each of these model systems has its own benefits and disadvantages. In this review, we discuss the advantages and drawbacks of the available model systems and how they can be used to guide personalized medicine.

Drug screening in 3D in vitro tumor models: overcoming current pitfalls of efficacy read-outs

There is cumulating evidence that in vitro 3D tumor models with increased physiological relevance can improve the predictive value of pre-clinical research and ultimately contribute to achieve decisions earlier during the development of cancer-targeted therapies. Due to the role of tumor microenvironment in the response of tumor cells to therapeutics, the incorporation of different elements of the tumor niche on cell model design is expected to contribute to the establishment of more predictive in vitro tumor models. This review is focused on the several challenges and adjustments that the field of oncology research is facing to translate these advanced tumor cells models to drug discovery, taking advantage of the progress on culture technologies, imaging platforms, high throughput and automated systems. The choice of 3D cell model, the experimental design, choice of read-outs and interpretation of data obtained from 3D cell models are critical aspects when considering their implementation in drug discovery. In this review, we foresee some of these aspects and depict the potential directions of pre-clinical oncology drug discovery towards improved prediction of drug efficacy.

Cell Panel Profiling Reveals Conserved Therapeutic Clusters and Differentiates the Mechanism of Action of Different PI3K/mTOR, Aurora Kinase and EZH2 Inhibitors

Cancer cell line panels are important tools to characterize the in vitro activity of new investigational drugs. Here, we present the inhibition profiles of 122 anticancer agents in proliferation assays with 44 or 66 genetically characterized cancer cell lines from diverse tumor tissues (Oncolines). The library includes 29 cytotoxics, 68 kinase inhibitors, and 11 epigenetic modulators. For 38 compounds this is the first comparative profiling in a cell line panel. By strictly maintaining optimized assay protocols, biological variation was kept to a minimum. Replicate profiles of 16 agents over three years show a high average Pearson correlation of 0.8 using IC₅₀ values and 0.9 using GI₅₀ values. Good correlations were observed with other panels. Curve fitting appears a large source of variation. Hierarchical clustering revealed 44 basic clusters, of which 26 contain compounds with common mechanisms of action, of which 9 were not reported before, including TTK, BET and two clusters of EZH2 inhibitors. To investigate unexpected clusterings, sets of BTK, Aurora and PI3K inhibitors were profiled in biochemical enzyme activity assays and surface plasmon resonance binding assays. The BTK inhibitor ibrutinib clusters with EGFR inhibitors, because it cross-reacts with EGFR. Aurora kinase inhibitors separate into two clusters, related to Aurora A or pan-Aurora selectivity. Similarly, 12 inhibitors in the PI3K/AKT/mTOR pathway separated into different clusters, reflecting biochemical selectivity (pan-PI3K, PI3Kβγδ-isoform selective or mTOR-selective). Of these, only allosteric mTOR inhibitors preferentially targeted PTEN-mutated cell lines. This shows that cell line profiling is an excellent tool for the unbiased classification of antiproliferative compounds. Mol Cancer Ther; 15(12); 3097-109. ©2016 AACR.

Large-Scale Drug Screens Support Precision Medicine

The major challenge underlying the emerging precision medicine initiative is to make links between cancer subsets and drugs that can be used to guide treatment of individual patients, leading to improved outcomes and decreased toxicity. Seashore-Ludlow and colleagues support this effort by reporting measurements of responses of 664 adherent cancer cell lines to 70 FDA-approved drugs, 100 experimental compounds, and 311 small-molecule probes. They use a novel Annotated Cluster Multidimensional Enrichment algorithm to identify drug mechanisms of action, molecular markers of response, responsive cancer subtypes, and compounds that produce synergistic cell inhibition.

Reproducible pharmacogenomic profiling of cancer cell line panels

The use of large-scale genomic and drug response screening of cancer cell lines depends crucially on the reproducibility of results. Here we consider two previously published screens, plus a later critique of these studies. Using independent data, we show that consistency is achievable, and provide a systematic description of the best laboratory and analysis practices for future studies.

Identifying the determinants of response to MDM2 inhibition

Previous reports have provided evidence that p53 mutation is a strong negative predictor of response to MDM2 inhibitors. However, this correlation is not absolute, as many p53^Mutant cell lines have been reported to respond to MDM2 inhibition, while many p53^WT cell lines have been shown not to respond. To better understand the nature of these exceptions, we screened a panel of 260 cell lines and noted similar discrepancies. However, upon extensive curation of this panel, these apparent exceptions could be eliminated, revealing a perfect correlation between p53 mutational status and MDM2 inhibitor responsiveness. It has been suggested that the MDM2-amplified subset of p53^WT tumors might be particularly sensitive to MDM2 inhibition. To facilitate clinical testing of this hypothesis, we identified a rationally derived copy number cutoff for assignment of functionally relevant MDM2 amplification. Applying this cutoff resulted in a pan-cancer MDM2 amplification rate far lower than previously published.

Focal Adhesion Kinase Regulates the DNA Damage Response and Its Inhibition Radiosensitizes Mutant KRAS Lung Cancer

PURPOSE

Non-small cell lung cancer (NSCLC) is the leading cause of cancer-related deaths worldwide due to the limited availability of effective therapeutic options. For instance, there are no effective strategies for NSCLCs that harbor mutant KRAS, the most commonly mutated oncogene in NSCLC. Thus, our purpose was to make progress toward the generation of a novel therapeutic strategy for NSCLC.

EXPERIMENTAL DESIGN

We characterized the effects of suppressing focal adhesion kinase (FAK) by RNA interference (RNAi), CRISPR/CAS9 gene editing or pharmacologic approaches in NSCLC cells and in tumor xenografts. In addition, we tested the effects of suppressing FAK in association with ionizing radiation (IR), a standard-of-care treatment modality.

RESULTS

FAK is a critical requirement of mutant KRAS NSCLC cells. With functional experiments, we also found that, in mutant KRAS NSCLC cells, FAK inhibition resulted in persistent DNA damage and susceptibility to exposure to IR. Accordingly, administration of IR to FAK-null tumor xenografts causes a profound antitumor effect in vivo CONCLUSIONS: FAK is a novel regulator of DNA damage repair in mutant KRAS NSCLC and its pharmacologic inhibition leads to radiosensitizing effects that could be beneficial in cancer therapy. Our results provide a framework for the rationale clinical testing of FAK inhibitors in NSCLC patients. Clin Cancer Res; 22(23); 5851-63. ©2016 AACR.

The genetics of drug efficacy: opportunities and challenges

Lack of sufficient efficacy is the most common cause of attrition in late-phase drug development. It has long been envisioned that genetics could drive stratified drug development by identifying those patient subgroups that are most likely to respond. However, this vision has not been realized as only a small proportion of drugs have been found to have germline genetic predictors of efficacy with clinically meaningful effects, and so far all but one were found after drug approval. With the exception of oncology, systematic application of efficacy pharmacogenetics has not been integrated into drug discovery and development across the industry. Here, we argue for routine, early and cumulative screening for genetic predictors of efficacy, as an integrated component of clinical trial analysis. Such a strategy would identify clinically relevant predictors that may exist at the earliest possible opportunity, allow these predictors to be integrated into subsequent clinical development and provide mechanistic insights into drug disposition and patient-specific factors that influence response, therefore paving the way towards more personalized medicine.

Computational selection of antibody-drug conjugate targets for breast cancer

The selection of therapeutic targets is a critical aspect of antibody-drug conjugate research and development. In this study, we applied computational methods to select candidate targets overexpressed in three major breast cancer subtypes as compared with a range of vital organs and tissues. Microarray data corresponding to over 8,000 tissue samples were collected from the public domain. Breast cancer samples were classified into molecular subtypes using an iterative ensemble approach combining six classification algorithms and three feature selection techniques, including a novel kernel density-based method. This feature selection method was used in conjunction with differential expression and subcellular localization information to assemble a primary list of targets. A total of 50 cell membrane targets were identified, including one target for which an antibody-drug conjugate is in clinical use, and six targets for which antibody-drug conjugates are in clinical trials for the treatment of breast cancer and other solid tumors. In addition, 50 extracellular proteins were identified as potential targets for non-internalizing strategies and alternative modalities. Candidate targets linked with the epithelial-to-mesenchymal transition were identified by analyzing differential gene expression in epithelial and mesenchymal tumor-derived cell lines. Overall, these results show that mining human gene expression data has the power to select and prioritize breast cancer antibody-drug conjugate targets, and the potential to lead to new and more effective cancer therapeutics.

Understanding the Genetic Mechanisms of Cancer Drug Resistance Using Genomic Approaches

A major obstacle in precision cancer medicine is the inevitable resistance to targeted therapies. Tremendous effort and progress has been made over the past few years to understand the biochemical and genetic mechanisms underlying drug resistance, with the goal to eventually overcome such daunting challenges. Diverse mechanisms, such as secondary mutations, oncogene bypass, and epigenetic alterations, can all lead to drug resistance, and the number of known involved genes is growing rapidly, thus providing many possibilities to overcome resistance. The finding of these mechanisms and genes invariably requires the application of genomic and functional genomic approaches to tumors or cancer models. In this review, we briefly highlight the major drug-resistance mechanisms known today, and then focus primarily on the technological approaches leading to the advancement of this field.

In vitro human cell line models to predict clinical response to anticancer drugs

In vitro human cell line models have been widely used for cancer pharmacogenomic studies to predict clinical response, to help generate pharmacogenomic hypothesis for further testing, and to help identify novel mechanisms associated with variation in drug response. Among cell line model systems, immortalized cell lines such as Epstein-Barr virus (EBV)-transformed lymphoblastoid cell lines (LCLs) have been used most often to test the effect of germline genetic variation on drug efficacy and toxicity. Another model, especially in cancer research, uses cancer cell lines such as the NCI-60 panel. These models have been used mainly to determine the effect of somatic alterations on response to anticancer therapy. Even though these cell line model systems are very useful for initial screening, results from integrated analyses of multiple omics data and drug response phenotypes using cell line model systems still need to be confirmed by functional validation and mechanistic studies, as well as validation studies using clinical samples. Future models might include the use of patient-specific inducible pluripotent stem cells and the incorporation of 3D culture which could further optimize in vitro cell line models to improve their predictive validity.

Differential regulation and synthetic lethality of exclusive RB1 and CDKN2A mutations in lung cancer

Genetic alterations in lung cancer are distinctly represented in non-small cell lung carcinoma (NSCLC) and small cell lung carcinoma (SCLC). Mutation of the RB1 and CDKN2A genes, which are tightly associated with cell cycle regulation, is exclusive to SCLC and NSCLC cells, respectively. Through the systematic analysis of transcriptome and proteome datasets for 318 cancer cell lines, we characterized differential gene expression and protein regulation in RB1-mutant SCLC and CDKN2A-mutant NSCLC. Many of the genes and proteins associated with RB1-mutant SCLC cell lines belong to functional categories of gene expression and transcription, whereas those associated with CDKN2A-mutant NSCLC cell lines were enriched in gene sets of the extracellular matrix and focal adhesion. These results indicate that the loss of RB1 and CDKN2A function induces distinctively different signaling cascades in SCLC and NSCLC cells. In addition, knockdown of the RB1 gene in CKDN2A-mutant cell lines (and vice versa) synergistically inhibits cancer cell proliferation. The present study on the exclusive role of RB1 and CDKN2A mutations in lung cancer subtypes demonstrates a synthetic lethal strategy for cancer regulation.

Publisher’s Note:Abstraction for data integration:Fusing mammalian molecular, cellular and phenotype big datasets for better knowledge extraction

With advances in genomics, transcriptomics, metabolomics and proteomics, and more expansive electronic clinical record monitoring, as well as advances in computation, we have entered the Big Data era in biomedical research. Data gathering is growing rapidly while only a small fraction of this data is converted to useful knowledge or reused in future studies. To improve this, an important concept that is often overlooked is data abstraction. To fuse and reuse biomedical datasets from diverse resources, data abstraction is frequently required. Here we summarize some of the major Big Data biomedical research resources for genomics, proteomics and phenotype data, collected from mammalian cells, tissues and organisms. We then suggest simple data abstraction methods for fusing this diverse but related data. Finally, we demonstrate examples of the potential utility of such data integration efforts, while warning about the inherit biases that exist within such data.