Markers Of Drug Response Research Articles

Abstract LB-35: Living biobank of cancer organoids for drug development and screening

Abstract Future cancer therapy will rely on treatment with drugs that are specific for individual tumours and patients. However, predicting clinical responses from genomic data remains highly challenging. Recently, systematic screens utilizing large panels of tumor cell lines have been used to correlate drug sensitivity with tumor genotype to identify predictive genomic markers of drug response. However, tumour cell lines have several drawbacks including being of limited genetic diversity; pre-clinical models that are more representative of the ‘natural’ mutational diversity of cancer are urgently required. The Clevers lab recently developed methods for long-term culture of human primary tissues and cancers (‘organoids’). By establishing a large collection of tumour-specific organoids, the genetic spectrum of tumours can be captured as a pre-clinical model. This will allow stratification of tumours based on their genomic footprint and drug sensitivity, and may reveal correlations between drug sensitivity and this footprint. We have now established the organoid technology and the first biobank of Colon, Pancreas, and Lung primary carcinomas and metastasis. Importantly, whole genome sequencing of the initial tumour material and long term cultures demonstrate that, similar to healthy organoids, also tumour organoids are stable. The first screens with sets of drugs that are clinically available shows that the organoids respond as would be predicted by their sequence analysis, such as KRAS mutant organoids not responding to the EGFR inhibitor Cetuximab. We do however find patient specific differences which points to a promising role for organoids in drug development, patient stratification and possibly diagnosis. Finally, genetic analysis has indicated that we are efficiently generating organoids of the full range of genetic alterations previously identified in patients for these tumour types, including specific genotypes that are poorly or not represented in any other in vitro model at the moment. Citation Format: Robert G.J. Vries, Hans Clevers. Living biobank of cancer organoids for drug development and screening. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr LB-35. doi:10.1158/1538-7445.AM2014-LB-35

Serum neurofilament light chain is a biomarker of human spinal cord injury severity and outcome

BackgroundNeurofilaments (Nf) are major structural proteins that occur exclusively in neurons. In spinal cord injury (SCI), the severity of disease is quantified by clinical measures that have limited sensitivity and...

MiR-942 decreases TRAIL-induced apoptosis through ISG12a downregulation and is regulated by AKT.

Tumor necrosis factor-related apoptosis-inducing ligand (TRAIL) is an attractive death ligand in targeted cancer therapy. Many cancer cells are refractory to TRAIL-induced cell death and the mechanisms underlying resistance are unclear. The molecular mechanisms of HCC and gastric cancer cells resistant to TRAIL-induced apoptosis were explored using molecular biological and immunological methods. In vivo experiments were conducted to study the effect of interferon stimulated gene 12a (ISG12a) on human liver cancer xenografts in mice. ISG12a decreases in TRAIL-resistant cancer cells. ISG12a regulates the sensitivity of cancer cells to TRAIL in vitro and in vivo. MicroRNA-942 (miR-942) is inversely correlated with ISG12a expression in cancer cells and tissues. Forced expression of miR-942 in TRAIL-sensitive cells significantly reduces endogenous ISG12a level and changes the TRAIL sensitive phenotype to a resistant one. Knockdown of miR-942 expression in TRAIL-resistant cells restores the expression of ISG12a and sensitizes the cells to TRAIL treatment. AKT control TRAIL resistance of cancer cells through downregulation of ISG12a by miR-942. Downregulation of ISG12a by miR-942 is needed to maintain the TRAIL-resistant phenotype of cancer cells and favors cancer cell survival. MiR-942 may offer a novel drug response marker with important implications in designing new therapeutics for TRAIL resistant tumors.

Pharmacogenomics: Current State-of-the-Art.

The completion of the human genome project 10 years ago was met with great optimism for improving drug therapy through personalized medicine approaches, with the anticipation that an era of genotype-guided patient prescribing was imminent. To some extent this has come to pass and a number of key pharmacogenomics markers of inter-individual drug response, for both safety and efficacy, have been identified and subsequently been adopted in clinical practice as pre-treatment genetic tests. However, the universal application of genetics in treatment guidance is still a long way off. This review will highlight important pharmacogenomic discoveries which have been facilitated by the human genome project and other milestone projects such as the International HapMap and 1000 genomes, and by the continued development of genotyping and sequencing technologies, including rapid point of care pre-treatment genetic testing. However, there are still many challenges to implementation for the many other reported biomarkers which continue to languish within the discovery phase. As technology advances over the next 10 years, and the costs fall, the field will see larger genetic data sets, including affordable whole genome sequences, which will, it is hoped, improve patient outcomes through better diagnostic, prognostic and predictive biomarkers.

PKP-016 Pharmacogenetics in allogeneic stem cell transplant patients: Mind the Mix

BackgroundCurrently, pharmacogenetic information is accumulating rapidly and is beginning to show consistent reproducible results for an increasing number of genetic markers for drug response. An increasing number of medical centres...

Is Swollen to Tender Joint Count Ratio a New and Useful Clinical Marker for Biologic Drug Response in Rheumatoid Arthritis? Results From a Swedish Cohort

To study the impact of swollen to tender joint count ratio (STR) and other baseline characteristics on treatment response to a first course of anti-tumor necrosis factor (anti-TNF) therapy in rheumatoid arthritis (RA) patients. Patients with RA initiating their first course of anti-TNF treatment were included in a structured clinical followup protocol. Based on pragmatic thresholds and plausibility, patients were categorized as having low (STR <0.5), moderate (0.5 ≤ STR ≤ 1.0), or high (STR >1.0) joint count ratios. The data were collected and followed during the period of March 1999 through December 2010. A total of 2,507 patients were included in the study (median age 56 years, 78% women). Of these patients, 344 (14%) had a low STR, 1,180 (47%) had a moderate STR, and 983 (39%) had a high STR. According to these STR thresholds, 23% of patients (95% confidence interval [95% CI] 18-29%) with low, 39% (95% CI 35-43%) with moderate, and 40% (95% CI 36-44%) with high STR achieved the American College of Rheumatology criteria for 50% improvement (ACR50) response at 6 months after initiation. Correlation tests showed that STR was associated with ACR50 response independent of both swollen and tender joint counts. Logistic regression analysis consistently showed that moderate STR, high STR, not using prednisolone, high baseline Disease Activity Score in 28 joints, and low baseline Health Assessment Questionnaire scores were significantly associated with favorable ACR50 response with odds ratios of 1.93 (P < 0.01), 2.82 (P < 0.01), 0.65 (P < 0.01), 1.49 (P < 0.01), and 0.47 (P < 0.01), respectively. STR is a new and feasible predictor of treatment response in RA. RA patients with a moderate to high STR have a 2- to 3-fold increased likelihood of responding according to ACR50 criteria.

Abstract B131: A prioritization pipeline for functionally annotating oncogenic somatic driver aberrations in cancer.

Abstract The Cancer Genome Atlas (TCGA) and similar projects are cataloging genomic aberrations across major cancer lineages with the goal of identifying the most promising therapeutic targets and diagnostic biomarkers. These efforts have revealed an extraordinary level of genome complexity made up of not only key “driver” events critical to pathogenesis, but also numerous biologically-neutral “passengers” that accompany unstable tumor genomes. The challenge now is to find ways to identify functional driver aberrations, as targeting such events or their activated pathways has great potential for improving patient outcomes. To complement the power of RNAi-based approaches, we are implementing a scalable gain-of-function screening infrastructure aimed at accelerating functional validation of oncogenic driver events. To do this, we devised a high-throughput mutagenesis and molecular barcoding (HiTMMoB) platform. The platform allows introduction of individual DNA mutations and small DNA insertions/deletions into a collection of over 32,000 sequence-verified human open reading frame (ORF) clones. In addition to permitting efficient modeling of gene aberrations, HiTMMoB also allows simultaneous "barcoding” of ORFs with unique 24-nuleotide DNA segments. Each barcode serves as a surrogate identifier for its associated ORF, thus permitting detection and quantitation of ORFs following pooled screens. We are employing HiTMMoB to functionally annotate gene aberrations across multiple cancer types, work that is funded in part by the NCI's Cancer Target Discovery and Development (CTD2) Network whose collective goal is to extract therapeutic targets and drug response markers from TCGA and similar efforts. ORFs processed through HiTMMoB are enlisted into a variety of in vitro screening platforms including those that annotate ORFs for their ability to drive colony formation in soft agar, cell survival and proliferation using the Ba/F3 myeloid cell system and drug resistance of sensitive cell models among other assays. While tractable, in vitro models do not recapitulate all hallmarks of tumorigenesis and metastasis. To address this challenge, we developed a Context-Specific Screen (CSS) platform that interrogates the tumorigenic and metastatic potency of candidate driver genes under the appropriate in vivo genetic and microenvironment contexts. These in vivo screens utilize genetically defined target cells and are performed at orthotopic sites in the mouse to ensure the correct microenvironment context. We identify putative driver ORFs from primary tumors and metastases by sequencing barcode pools from genomic DNA in parental (i.e., injected) cells, primary tumors and metastases. As expected, our data indicate that, relative to injected cells, primary tumors and metastases positively select driver genes and lose those with no role in driving tumorigenesis and metastasis in a given model (i.e., passengers). We have employed the CSS approach across tumors of multiple lineages that include pancreas, lung, breast and melanoma, work that has already revealed multiple novel drivers currently undergoing secondary validation and follow-up mechanistic studies. In summary, our aberration “prioritization pipeline” will provide the cancer research community information on the most promising somatic aberrations for downstream biomarker and drug development. Citation Information: Mol Cancer Ther 2013;12(11 Suppl):B131. Citation Format: Turgut Dogruluk, Yiu Huen Tsang, Nikitha Nair, Jianhua Zhang, Timmothy Heffernan, Lynda Chin, Gordon Mills, Kenneth L. Scott. A prioritization pipeline for functionally annotating oncogenic somatic driver aberrations in cancer. [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2013 Oct 19-23; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2013;12(11 Suppl):Abstract nr B131.

Pharmacogenomics, ancestry and clinical decision making for global populations

Pharmacogenomically relevant markers of drug response and adverse drug reactions are known to vary in frequency across populations. We examined minor allele frequencies (MAFs), genetic diversity (FST) and population structure of 1156 genetic variants (including 42 clinically actionable variants) in 212 genes involved in drug absorption, distribution, metabolism and excretion (ADME) in 19 populations (n=1478). There was wide population differentiation in these ADME variants, reflected in the range of mean MAF (ΔMAF) and FST. The largest mean ΔMAF was observed in African ancestry populations (0.10) and the smallest mean ΔMAF in East Asian ancestry populations (0.04). MAFs ranged widely, for example, from 0.93 for single-nucleotide polymorphism (SNP) rs9923231, which influences warfarin dosing to 0.01 for SNP rs3918290 associated with capecitabine metabolism. ADME genetic variants show marked variation between and within continental groupings of populations. Enlarging the scope of pharmacogenomics research to include multiple global populations can improve the evidence base for clinical translation to benefit all peoples.

A gene expression profile indicative of early stage HER2 tyrosine kinase inhibitor response.

e11536 Background: Lapatinib, afatinib and neratinib are tyrosine kinase inhibitors (TKIs) of HER2 and EGFR growth receptors. A panel of breast cancer cell lines was treated with these agents and gefintib (EGFR inhibitor) and the expression pattern of a specific panel of genes investigated as a potential marker of early drug response. Methods: RNA was extracted from breast cancer cell lines (BT474, SKBR3 and MDAMB453) with differing HER2 expression patterns and sensitivity to lapatinib before and 12hrs after treatment with 1 µM of lapatinib, 150nM of afatinib, 150nM of neratinib or 1µM of gefitinib. Gene expression changes were measured by Taqman RT-PCR and RQ values were determined using the comparative cycle threshold (Ct) method. Results: The expression of RB1CC1, ERBB3, FOXO3a, NR3C1 was directly correlated with the degree of sensitivity of the cell line to lapatinib and was observed to “switch” from up-regulated to down-regulated in the HER2 expressing lapatinib insensitive cell line (MDAMD453). The CCND1 gene (functionally linked to the other four genes) demonstrated an inversely proportional response to drug exposure; showing a trend of strong down-regulation in lapatinib-sensitive lines. A similar expression pattern was observed following the treatment with both neratinib and afatinib. In contrast, gefitinib treatment, resulted in a completely different expression pattern change. Conclusions: In these HER2-expressing cell models, lapatinib, neratinib and afatinib treatment generated a common, characteristic and specific gene expression response, proportionate to the sensitivity of the cell lines to the HER2 inhibitor. Characterisation of changes in these genes shortly after drug treatment may therefore give a valuable predictor of the likely extent and specificity of tumour HER2 inhibitor response in patients, potentially guiding more specific use of these agents.

Abstract 5245: C-Kit isoforms predict melanoma drug sensitivity.

Abstract The receptor tyrosine kinase c-Kit is an essential regulator of melanocyte and melanoma development. Alternative splicing of c-Kit produces one long variant, c-Kit(+), with a tetrapeptide GNNK located in the extracellular juxtamembrane domain, and one short variant, c-Kit(-), which lacks this sequence. In this study we characterize for the first time the function of the GNNK tetrapeptide sequence in melanoma. Our results show that step by step elimination of the GNNK tetrapeptide gradually increases receptor tyrosine phosphorylation, ubiquitination and downstream MAP kinase ERK activation. Successively increasing insert length, progressively leads to reduced melanoma cell survival during dacarbazine or rapamycin drug treatment. Our results indicate that the ratio between c-Kit isoforms might be useful as a prognostic marker for melanoma drug response. Citation Format: Bengt Phung, Eiríkur Steingrímsson, Lars Rönnstrand. C-Kit isoforms predict melanoma drug sensitivity. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 5245. doi:10.1158/1538-7445.AM2013-5245

Abstract 2206: Genomics of Drug Sensitivity in Cancer (GDSC): A resource for therapeutic biomarker discovery in cancer cells.

Abstract The Genomic of Drug Sensitivity in Cancer (GDSC; www.cancerRxgene.org) resource facilitates development of targeted cancer therapies through pre-clinical identification of therapeutic biomarkers. GDSC is the largest public resource for information on drug sensitivity in cancer cells and links these data to extensive genomic information to identify molecular features that influence anticancer drug response. There is compelling evidence that alterations in cancer genomes strongly influence clinical responses to anticancer therapies. There are several examples where genomic changes are used as molecular biomarkers to stratify patients most likely to benefit from a treatment (e.g. BRAF in melanoma). Despite these successes, the majority of cancer drugs have not been linked to specific molecular features that could be used to direct their clinical use to maximize patient benefit. We are using pharmacogenomic profiling in cancer cell lines as a biomarker discovery platform by systematically linking pharmacological data with genomic information in cancer cells. The GDSC database contains drug sensitivity data generated from high-throughput screening performed by the Cancer Genome Project at the Wellcome Trust Sanger Institute and the Center for Molecular Therapeutics at Massachusetts General Hospital using a collection of &amp;gt;1,200 cancer cell lines. GDSC release v3 (November 2012) contains drug sensitivity data for almost 80,000 experiments, describing response to 142 anticancer drugs across over 700 cancer cell lines. To identify molecular markers of drug response, cell line drug sensitivity data are integrated with large genomic datasets obtained from COSMIC (Catalogue of Somatic Mutations in Cancer), including information on somatic mutations in cancer genes, gene amplification and deletion, tissue type and transcriptional data. Analysis of GDSC data is through a web portal based on queries of specific anticancer drugs or cancer genes. Interactive graphical representations of the data are used throughout with links to related resources, and all datasets are freely available and downloadable. The GDSC database will undergo significant expansion in coming years as drug sensitivity and genomic datasets increase in size and complexity. GDSC provides a unique public resource incorporating large drug sensitivity and genomic datasets to facilitate discovery of new therapeutic biomarkers for cancer therapies. Citation Format: Wanjuan Yang, Jorge Soares, Patricia Greninger, Elena Edelman, Howard Lightfoot, Simon Forbes, Ramaswamy Sridhar, P. Andrew Futreal, Daniel Haber, Michael Stratton, Cyril Benes, Ultan McDermott, Mathew Garnett. Genomics of Drug Sensitivity in Cancer (GDSC): A resource for therapeutic biomarker discovery in cancer cells. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 2206. doi:10.1158/1538-7445.AM2013-2206

A gene expression profile indicative of early stage HER2 targeted therapy response

BackgroundEfficacious application of HER2-targetting agents requires the identification of novel predictive biomarkers. Lapatinib, afatinib and neratinib are tyrosine kinase inhibitors (TKIs) of HER2 and EGFR growth factor receptors. A panel of breast cancer cell lines was treated with these agents, trastuzumab, gefitinib and cytotoxic therapies and the expression pattern of a specific panel of genes using RT-PCR was investigated as a potential marker of early drug response to HER2-targeting therapies.ResultsTreatment of HER2 TKI-sensitive SKBR3 and BT474 cell lines with lapatinib, afatinib and neratinib induced an increase in the expression of RB1CC1, ERBB3, FOXO3a and NR3C1. The response directly correlated with the degree of sensitivity. This expression pattern switched from up-regulated to down-regulated in the HER2 expressing, HER2-TKI insensitive cell line MDAMB453. Expression of the CCND1 gene demonstrated an inversely proportional response to drug exposure. A similar expression pattern was observed following the treatment with both neratinib and afatinib. These patterns were retained following exposure to traztuzumab and lapatinib plus capecitabine. In contrast, gefitinib, dasatinib and epirubicin treatment resulted in a completely different expression pattern change.ConclusionsIn these HER2-expressing cell line models, lapatinib, neratinib, afatinib and trastuzumab treatment generated a characteristic and specific gene expression response, proportionate to the sensitivity of the cell lines to the HER2 inhibitor.Characterisation of the induced changes in expression levels of these genes may therefore give a valuable, very early predictor of the likely extent and specificity of tumour HER2 inhibitor response in patients, potentially guiding more specific use of these agents.

Adaptation of a chemosensitivity assay to accurately assess pemetrexed in ex vivo cultures of lung cancer

Purpose: Pemetrexed is the only FDA approved treatment for mesothelioma and is a second line agent for treatment of non-small cell lung carcinoma (NSCLC). Pemetrexed is inhibited by folate and its analogs, which are components of many culture media, making it challenging to study pemetrexed in vitro. In order to accurately evaluate pemetrexed’s effects in vitro, the protocol for a standard chemosensitivity assay, the ChemoFx drug response marker, had to be modified.Experimental Design: Novel rinse and media change steps were assessed and then added to the assay protocol in order to observe pemetrexed activity. The intraday and interday stability of pemetrexed were also established under the adapted protocol. Then, the modified protocol was used to examine pemetrexed in 65 ex vivo lung cancer specimens.Results: Substituting 5% RPMI + EGF for BEGM allowed pemetrexed to exert its anticancer activity in the ChemoFx DRM. ChemoFx classified 6.2% of the lung specimens as responsive, 9.2% as intermediate responsive and 84.6% as non-responsive to pemetrexed.Conclusions: Adapting the ChemoFx protocol allowed for the accurate evaluation of pemetrexed anticancer activity in ex vivo lung specimens. ChemoFx evaluation may provide an indication of a patient’s clinical response to the drug prior to pemetrexed treatment. Having this information when treatment options are being considered could avoid wasted time, unnecessary costs and needless side effects that are the result of an inappropriate chemotherapy regimen.

Genomics of Drug Sensitivity in Cancer (GDSC): a resource for therapeutic biomarker discovery in cancer cells

Alterations in cancer genomes strongly influence clinical responses to treatment and in many instances are potent biomarkers for response to drugs. The Genomics of Drug Sensitivity in Cancer (GDSC) database (www.cancerRxgene.org) is the largest public resource for information on drug sensitivity in cancer cells and molecular markers of drug response. Data are freely available without restriction. GDSC currently contains drug sensitivity data for almost 75 000 experiments, describing response to 138 anticancer drugs across almost 700 cancer cell lines. To identify molecular markers of drug response, cell line drug sensitivity data are integrated with large genomic datasets obtained from the Catalogue of Somatic Mutations in Cancer database, including information on somatic mutations in cancer genes, gene amplification and deletion, tissue type and transcriptional data. Analysis of GDSC data is through a web portal focused on identifying molecular biomarkers of drug sensitivity based on queries of specific anticancer drugs or cancer genes. Graphical representations of the data are used throughout with links to related resources and all datasets are fully downloadable. GDSC provides a unique resource incorporating large drug sensitivity and genomic datasets to facilitate the discovery of new therapeutic biomarkers for cancer therapies.

Pharmacoproteomic investigation into antidepressant response in two mouse inbred strains

In this study, we present a pharmacoproteomic investigation of response to antidepressants two inbred strains. Our aim was to uncover molecular mechanisms underlying antidepressant action and identify new biomarkers to determine therapeutic response to two antidepressants with proven efficacy in the treatment of depression but divergent mechanisms of action. Mice were treated with the pro-noradrenergic drug nortriptyline, the pro-serotonergic drug escitalopram or saline. Quantitative proteomic analyses were undertaken on hippocampal tissue from a study design that used two inbred mouse strains, two depressogenic protocols and a control condition, (maternal separation, chronic mild stress, control), two antidepressant drugs and two dosing protocols. The proteomic analysis was aimed at the identification of specific drug-response markers. Complementary approaches, 2DE and isobaric tandem mass tagging (TMT), were applied to the selected experimental groups. To investigate the relationship between proteomic profiles, depressogenic protocols and drug response, 2DE and TMT data sets were analysed using multivariate methods. The results highlighted significant strain- and stress-related differences across both 2DE and TMT data sets and identified the three gene products involved in serotonergic (PXBD5, YHWAB, SLC25A4) and one in noradrenergic antidepressant action (PXBD6).

Abstract 3611: Identifying gene expression markers of anticancer drug response using large scale genomic and drug response databases established from patient derived tumors

Abstract Chemotherapy has a well-established history of checkered success for treatment in a number of solid and hematological malignancies alike. Numerous reports have demonstrated heightened levels of response to some of these agents within subpopulations of cancer patients, leading to the notion that we have effective pharmaceuticals; however, we lack reliable means to identify these subpopulations prior to administering therapy. We report here an integrated series of preclinical and translational research studies aimed at identifying those populations by capturing predictive markers of response to specific anticancer agents. Our approach to identifying molecular markers of response relies on a biobank of more than 140,000 patient derived tumor samples, and an accompanying database of ex-vivo drug resistance and sensitivity profiles for a broad range of chemotherapeutic agents. Briefly, the drug response database was created by dissociating fresh tumor into single cell suspension, and treating each cell preparation in an anchorage independent growth matrix with antineoplastic agents relevant to tumor type. Drug resistance was scored as Extreme (E), Intermediate (I), or Low (L), with the Low score providing a correlative measure of sensitivity. We identified subsets of tumors featuring differential drug response profiles, and performed Affymetrix gene expression microarray profiling to create data sets that could be interrogated to identify gene expression markers predictive of response. In all, more than 300 tumors were profiled covering 7 indications: prostate, lung, colon, breast, melanoma, ovarian, and lymphoma. Statistical analysis and biological interpretation of genomic data was performed using Ingenuity iReport. iReport, powered by the Ingenuity® Knowledge Base, identified biological processes, pathways, and cellular phenotypes most perturbed in the tumor samples, providing insight into the molecular basis of drug response in these tumor cells. This focused the analysis down to a subset of genes anchored on pathways representative of the drug response and cellular properties of the tumor samples and prioritized a set of candidate markers for further study. We were able to identify gene expression markers predictive of drug response and evaluate those markers based on functional roles consistent with pathways and processes. Additional studies are on-going to validate these potentially novel markers in a series of primary tumor cell models established directly from patient tumor biopsies registered in the Molecular Response tumor bank. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 3611. doi:1538-7445.AM2012-3611

Abstract 5273: XenTech patient-derived xenograft (PDX) panels: a clinically relevant platform for drug efficacy, target validation and predictive biomarker discovery studies

Abstract Despite considerable efforts in understanding the biology and genetics of cancer, most currently available treatments fail to achieve tumor eradication in the majority of patients. Key to more effective therapies is adequate disease classification and subsequent patient stratification. In addition, it is important to understand the mechanisms of drug-response or resistance and identify novel targets amenable to therapeutic intervention. It is increasingly recognized that at the preclinical stage, testing therapeutic strategies and validating target relevance in more predictive models closely mimicking clinical disease such as patient derived xenografts (PDXs), may translate into improved clinical efficacy and lower rate of drug attrition. XenTech collection of over 120 runing PDX models is one of the largest in the world. PDX models were established by grafting post-surgery human tumor fragments in the interscapular region of immunodeficient mice. These deeply characterized PDX models can be used for in vivo preclinical assays. Such preclinical platform is a reliable surrogate of patient cohorts and can address several aims: 1. Evaluate tumor response to treatment. PDXs can be subjected to parallel evaluation of tumor response to various treatment protocols. Drug-response profile is linked to tumor histotype and molecular features in order to identify predictive markers of drug response to assist treatment choice. 2. Assess treatment-driven tumor eradication. The ability of a treatment to induce complete tumor response is assessed by monitoring tumor regression over a long period. Most tumors, despite complete macroscopic regression, are still present as latent microscopic nodular islands that may give rise to tumor recurrence. Molecular characterization of tumor foci responsible for tumor relapse may be performed to identify genes/pathways involved in residual tumor cell survival, which may provide new diagnostic and/or therapeutic targets for designing novel adjuvant treatment strategies. 3. Development of bioluminescent metastatic models to study the mechanisms of tumor invasion and to test anti-metastatic therapy. 4. Non-invasive molecular imaging technology to monitor tumor metabolism, vascularization and apoptosis. 5. Constitution of preclinical panels of rare malignancies to obtain phase II-like tumor cohorts. Development of new therapies for rare tumors is rendered difficult by the unavailability of patient cohorts wide enough to set up robust clinical trials. To assist the clinical need, these panels would allow the evaluation of new and more efficient therapies. We describe here in detail our PDX collection and illustrate how it represents a powerful tool to identify preferential therapeutic options for patients by exploring and improving anti-cancer therapeutic strategies. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 5273. doi:1538-7445.AM2012-5273

The transcriptomic analysis from pbmc is a powerful approach to identify specific signatures able to predict responses for each tnfalpha blocking agent

BackgroundThe number of RA biological treatments is increasing but 30 to 40% of patients do not respond to tumour necrosis factor α (TNFα) blocking agents. One way to optimise the...

Abstract B29: Alternative splicing studies for the identification of novel cancer targets and markers.

Abstract Abnormal alternative splicing occurs in cancer, resulting in the production of novel transcript variants. Understanding the diverse mechanisms by which splicing dysregulation contributes to human disease will generate new perspectives for drug development and biomarkers identification. The overall aim of this work is the generation of libraries of alternative splicing events that are deregulated in cancer and during anti-cancer treatments. The goal of these libraries is to be interrogated for the identification of novel biomarkers, allowing to monitor disease status, progression/relapse, and specificity/selectivity of drug response. The platform used is Exonhit's Genome Wide SpliceArray, a new generation of microarray that extends transcriptomic profiling to the monitoring of alternative splicing, thereby increasing the discriminatory power of the analyses. Here, we profiled different cancers to identify novel targets and markers that are either commonly regulated across multiple cancers or specific of a given cancer type. Alternatively spliced transcripts were isolated from breast, colon, and lung tumors and their corresponding adjacent normal tissues (20 each). Different splicing patterns were evidenced in tumoral versus normal tissues and from specificity analysis performed across a pool of 20 normal organs. Events of interest, focused on splicing events that generate potential novel amino acid sequences, were selected based on combination of statistical analysis of probe sets deregulations, protein knowledge and pathway analyses. The events were subsequently validated by QPCR analysis in these 3 cancers. These validated events will be used to identify novel cell surface epitopes for antibody development with therapeutic or diagnostic usefulness. Following this same approach, we also performed a comparative analysis of the transcriptomic response of endothelial cells to tubulin polymerization inhibitors, in order to test the feasibility of identifying differential drug response markers. To do so, we profiled various tubulin inhibitors in an in vitro angiogenesis inhibition assay. HUVEC cells were induced to form neovessels in vitro in the presence or absence of the tested tubulin inhibitors. Then, microarray profiling was performed, followed by a detailed analysis with Ingenuity, focusing on up-regulated genes with biomarker potential. Our results demonstrate that alternative RNA splicing offers a currently underexploited source of biological information for cancer research. Platforms dedicated to alternative splicing such as the SpliceArray can be integrated into discovery processes to allow identification of novel targets for drug discovery and biomarkers identification. Further, such platforms may also provide guidance in the selection and follow-up of patients in clinical trials. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2011 Nov 12-16; San Francisco, CA. Philadelphia (PA): AACR; Mol Cancer Ther 2011;10(11 Suppl):Abstract nr B29.

Prioritizing cancer-related genes with aberrant methylation based on a weighted protein-protein interaction network

BackgroundAs an important epigenetic modification, DNA methylation plays a crucial role in the development of mammals and in the occurrence of complex diseases. Genes that interact directly or indirectly may have the same or similar functions in the biological processes in which they are involved and together contribute to the related disease phenotypes. The complicated relations between genes can be clearly represented using network theory. A protein-protein interaction (PPI) network offers a platform from which to systematically identify disease-related genes from the relations between genes with similar functions.ResultsWe constructed a weighted human PPI network (WHPN) using DNA methylation correlations based on human protein-protein interactions. WHPN represents the relationships of DNA methylation levels in gene pairs for four cancer types. A cancer-associated subnetwork (CASN) was obtained from WHPN by selecting genes associated with seed genes which were known to be methylated in the four cancers. We found that CASN had a more densely connected network community than WHPN, indicating that the genes in CASN were much closer to seed genes. We prioritized 154 potential cancer-related genes with aberrant methylation in CASN by neighborhood-weighting decision rule. A function enrichment analysis for GO and KEGG indicated that the optimized genes were mainly involved in the biological processes of regulating cell apoptosis and programmed cell death. An analysis of expression profiling data revealed that many of the optimized genes were expressed differentially in the four cancers. By examining the PubMed co-citations, we found 43 optimized genes were related with cancers and aberrant methylation, and 10 genes were validated to be methylated aberrantly in cancers. Of 154 optimized genes, 27 were as diagnostic markers and 20 as prognostic markers previously identified in literature for cancers and other complex diseases by searching PubMed manually. We found that 31 of the optimized genes were targeted as drug response markers in DrugBank.ConclusionsHere we have shown that network theory combined with epigenetic characteristics provides a favorable platform from which to identify cancer-related genes. We prioritized 154 potential cancer-related genes with aberrant methylation that might contribute to the further understanding of cancers.