Mechanisms Of Drug Response Research Articles

Geometrical Features of Epidermal Growth Factor Receptor-Related Dimers Reveal the Mechanisms of Drug Resistance in Lung Cancer Patients

Epidermal growth factor receptor (EGFR) plays an important role in lung cell proliferation. Dimerization of EGFR family members and other receptor tyrosine kinases (RTKs) act as a vital controller for lung cell life cycle signals. Mutations in the kinase domain of EGFR may disorder the signaling networks and lead to cancer. Drug resistance occurs in several generations of EGFR drugs due to genetic mutations, and there is a very less understanding about the mechanism of EGFR-mutated drug resistance. In this work, we investigate the mechanism of wild type EGFR protein and its drug-sensitive and drug-resistant mutations. We performed molecular dynamics (MD) simulation for the 10-ns EGFR-drug mutant complex and investigated the structures’ geometrical properties. With features extracted by alpha shape modeling, different geometrical properties, such as matching rates of atom solid angles at interaction sites and centroid distances between interfacial atoms, were calculated to characterize the binding intensity. Wilcoxon rank sum test was applied to reveal the differences between mutations based on extracted properties. We have developed a framework that explains the drug resistance mechanism based on geometrical properties and binding free energy. Results revealed that drug-sensitive mutants have tighter interactions with corresponding RTK in the complex for all protein-drug systems, while drug-resistant mutants are bound looser. The extracted geometrical properties of the drug mutant complex help understand the drug response mechanism at atomic level.

MNBDR: A Module Network Based Method for Drug Repositioning.

Drug repurposing/repositioning, which aims to find novel indications for existing drugs, contributes to reducing the time and cost for drug development. For the recent decade, gene expression profiles of drug stimulating samples have been successfully used in drug repurposing. However, most of the existing methods neglect the gene modules and the interactions among the modules, although the cross-talks among pathways are common in drug response. It is essential to develop a method that utilizes the cross-talks information to predict the reliable candidate associations. In this study, we developed MNBDR (Module Network Based Drug Repositioning), a novel method that based on module network to screen drugs. It integrated protein–protein interactions and gene expression profile of human, to predict drug candidates for diseases. Specifically, the MNBDR mined dense modules through protein–protein interaction (PPI) network and constructed a module network to reveal cross-talks among modules. Then, together with the module network, based on existing gene expression data set of drug stimulation samples and disease samples, we used random walk algorithms to capture essential modules in disease development and proposed a new indicator to screen potential drugs for a given disease. Results showed MNBDR could provide better performance than popular methods. Moreover, functional analysis of the essential modules in the network indicated our method could reveal biological mechanism in drug response.

Abstract 3681: Selective vulnerability of GBM PDX to a panel of EGFR tyrosine kinase inhibitors

Abstract Glioblastoma (GBM), a particularly aggressive form of primary brain tumor, has poor survival due to a lack of effective treatments and high recurrence. Epidermal growth factor receptor (EGFR) (mapped to 7p 11.2), is frequently amplified and mutated (>50%) in GBM, and is suggested as a potential therapeutic target. To correlate chemo-vulnerability with genomic aberrations and further determine biomarkers for targeted therapies, a panel of seven EGFR tyrosine kinase inhibitors were tested in a selected nine human GBM patient-derived xenograft (PDX) panel; these preclinical models harbor different mutation combinations of CDKN2A deletion (C), PTEN deletion (P), wild-type EGFR (E) and/or EGFRvIII (Ev3) overexpression. A single agent drug dose response assay (DDR) was performed on GBM PDX neurospheres using drug concentrations ranging from 100uM to 0.5nM with a 12-point serial 3-fold dilution scheme. The cytotoxic efficacy of EGFR inhibition was dependent on both the PDX line and the EFGR inhibitor applied. GBM59 and GBM76 – both triple CEv3P mutants – were the most responsive to EGFR inhibition relative to the other GBM models tested. For GBM59, the IC50 values of afatinib, canertinib and neratinib, were sub-micromolar, and low micromolar (<3uM) for erlotinib, gefitinib and lapatinib. GBM76 had IC50 values of 1-8uM with all seven EGFR inhibitors including AZD3759. However, GBM155 and GBM126 with CEv3 but wildtype PTEN only responded to afatinib, canertinib and neratinib at low micromolar IC50 values (<3uM) but not to erlotinib, gefitinib and lapatinib. A deletion in PTEN may sensitize GBM cells with CEv3 mutations to erlotinib, gefitinib and lapatinib in vitro. The other group of GBM PDX lines (C only) represented by GBM122, GBM150 and GBM182 responded well to afatinib, canertinib and neratinib with IC50 values of 2- 9uM. GBM156 with CE had a similar profile to the group of C only or CEv3. GBM56 with CP showed the most resistance with mild responses to afatinib, canertinib and neratinib, as observed in its high micromolar IC50 values (6-9uM). Overall, neratinib is the most effective compound among the tested EGFR inhibitors based on IC50 values. Canertinib, the triple tyrosine kinase inhibitor against EGFR/HER2/ErbB-4, is the second most effective agent. Afatinib has similar drug efficacy to canertinib. All three compounds moderately inhibit all GBM PDX lines. The drug response mechanism and its correlation with the mutation status of CDKN2A, EGFR, and PTEN will be further investigated cross a broad array of GBM PDX models. Supported by NIH NCI R01 CA204136 Citation Format: Nanyun Tang, George Reid, C Ryan Miller, Michael E. Berens. Selective vulnerability of GBM PDX to a panel of EGFR tyrosine kinase inhibitors [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 3681.

Estimation and inference for the indirect effect in high-dimensional linear mediation models

Mediation analysis is difficult when the number of potential mediators is larger than the sample size. In this paper we propose new inference procedures for the indirect effect in the presence of high-dimensional mediators for linear mediation models. We develop methods for both incomplete mediation, where a direct effect may exist, and complete mediation, where the direct effect is known to be absent. We prove consistency and asymptotic normality of our indirect effect estimators. Under complete mediation, where the indirect effect is equivalent to the total effect, we further prove that our approach gives a more powerful test compared to directly testing for the total effect. We confirm our theoretical results in simulations, as well as in an integrative analysis of gene expression and genotype data from a pharmacogenomic study of drug response. We present a novel analysis of gene sets to understand the molecular mechanisms of drug response, and also identify a genome-wide significant noncoding genetic variant that cannot be detected using standard analysis methods.

Distinct Colorectal Cancer-Associated APC Mutations Dictate Response to Tankyrase Inhibition.

The majority of colorectal cancers show hyperactivated WNT signaling due to inactivating mutations in the adenomatous polyposis coli (APC) tumor suppressor. Genetically restoring APC suppresses WNT and induces rapid and sustained tumor regression, implying that reengaging this endogenous tumor-suppressive mechanism may be an effective therapeutic strategy. Here, using new animal models, human cell lines, and ex vivo organoid cultures, we show that tankyrase (TNKS) inhibition can control WNT hyperactivation and provide long-term tumor control in vivo, but that effective responses are critically dependent on how APC is disrupted. Mutant APC proteins truncated within the mutation cluster region physically engage the destruction complex and suppress the WNT transcriptional program, while APC variants with early truncations (e.g., Apc Min) show limited interaction with AXIN1 and β-catenin, and do not respond to TNKS blockade. Together, this work shows that TNKS inhibition, like APC restoration, can reestablish endogenous control of WNT/β-catenin signaling, but that APC genotype is a crucial determinant of this response. SIGNIFICANCE: This study reveals how subtle changes to the mutations in a critical colorectal tumor suppressor, APC, influence the cellular response to a targeted therapy. It underscores how investigating the specific genetic alterations that occur in human cancer can identify important biological mechanisms of drug response and resistance.This article is highlighted in the In This Issue feature, p. 1325.

Abstract 2153: Ex vivo drug sensitivity testing of primary cells for precision cancer medicine

Abstract Introduction/Purpose: Cancer therapy is increasingly moving towards individualized care and therapy, but there are still gaps between what is known and described on the molecular level about cancers and what is applied in the clinic. In an attempt to bridge the knowledge gap, we at the Institute for Molecular Medicine Finland (FIMM) have set up an Individualized Systems Medicine program that integrates clinical information, molecular profiling and functional information about individual patients’ cancers (Pemovska et al, Cancer Discov, 2013). Central to this program is the Drug Sensitivity and Resistance Testing (DSRT) where we functionally profile the responses of primary cancer cells to a comprehensive clinical oncology and signal transduction inhibitor drug collection of 528 compounds. Methods: Acoustic dispensing platforms are integral to the success of this profiling activity. We have to date produced approximately 3000 drug sets as dose response assay ready plates. The acoustic dispensing allows for making pre-drugged single drug plate sets and/or drug combination plates within hours after sampling of the cells. The plates are also readily sent to researchers anywhere in the world for running comparable assays at other sites. The drugging reproducibility is excellent generating results with correlations of 0.98 or higher in replicate assays. We have developed in-house software solutions to aid these processes: a script for quick creation of transfer list for combination plates and automated analysis pipelines with web-based software interfaces to enable the screening biologists to analyze the screening results effectively. Results: The results of these assays are used to explore and understand cancer biology in terms of druggability, functional heterogeneity and mechanism of drug response and resistance. The profiling data can be used to stratify and position the relevance of specific drugs in different diseases and has been used to identify novel clinically relevant activities of existing and investigational drugs (see e.g. Pemovska et al, Nature, 2015). This information is further utilized to establish hypotheses on drug combinations selectively targeting individual cancers and their predictive biomarkers, which can be explored in the clinic by our clinical collaborators to guide the treatment of the individual patient. Conclusions: In summary, we describe our platform for a functional drug sensitivity testing within our individualized cancer systems medicine program, which generates consistent biological and clinically relevant data. Citation Format: Sergey G. Kuznetsov, Alexander Ianevski, Evgeny Kulessky, Karoliina Laamanen, Elina Lehtinen, Maria Nurmi, Swapnil Potdar, Jani Saarela, Katja Suomi, Laura Turunen, Krister Wennerberg, Päivi Tammela. Ex vivo drug sensitivity testing of primary cells for precision cancer medicine [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 2153.

Abstract 3578: Comprehensive pharmacogenomic analysis establishes lncRNAs as protein-coding independent biomarker of drug response in human cancers

Abstract Somatic alterations in the cancer genome influence a patient’s response to anti-cancer therapeutics. Thus, identifying context-specific alterations associated with differential drug sensitivity can improve patient response prediction and therapeutic selection. The discovery of pervasive dysregulation of long non-coding RNAs (lncRNA) in human cancers and their perceived role in gene regulation has led to the speculation that lncRNAs may also play a role in determining cancer drug response. However, it is unclear whether lncRNAs can augment the existing, predominantly protein coding, repertoire of pharmacogenomic biomarkers of anti-cancer agents. We performed comprehensive analysis of whole genome and transcriptome data from over 1000 cancer cell lines and drug screening data for over 500 anti-cancer agents to systematically analyze over 5 million lncRNA-drug associations. We developed statistical and machine-learning frameworks to study these associations while controlling for the effects of confounding PCGs. Sparse regression and network-based methods demonstrated the predictive ability and biological relevance of literature-supported and novel lncRNAs biomarkers. Using regression analysis of lncRNA expression controlling for neighboring PCGs against drug response, we established that a large proportion of the lncRNAs candidates are PCG-independent biomarkers and potent predictors of cancer drug response. We further identified response-associated somatic alterations specifically in lncRNA genome that do not overlap PCGs. In addition, we demonstrated that collinear lncRNAs might be biologically relevant determinants of drug response. As an example, we identified EGFR-AS1 and MIR205HG as novel predictors of anti-EGFR therapy, which explain a significantly larger proportion of variability in erlotinib and gefitinib response as compared to EGFR mutations and copy-number alterations in drug screens and in patient data. We validated our findings in 16 non-small cell lung cancer and erlotinib-resistant cell lines. Our knockdown experiments revealed mechanisms of erlotinib sensitivity mediated by the two lncRNAs without influencing EGFR expression. In conclusion, our comprehensive analysis generated unprecedented insights into the role of lncRNAs in cancer drug response that reaches beyond PCGs. This study will serve as a foundation for future cancer pharmacogenomic studies and will be an invaluable resource for investigators seeking insights into mechanisms of drug response. Citation Format: Aritro Nath, Eunice Y. Lau, Adam M. Lee, Paul Geeleher, William C. Cho, R. Stephanie Huang. Comprehensive pharmacogenomic analysis establishes lncRNAs as protein-coding independent biomarker of drug response in human cancers [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 3578.

Genome sequencing analysis of blood cells identifies germline haplotypes strongly associated with drug resistance in osteosarcoma patients

BackgroundOsteosarcoma is the most common malignant bone tumor in children. Survival remains poor among histologically poor responders, and there is a need to identify them at diagnosis to avoid delivering ineffective therapy. Genetic variation contributes to a wide range of response and toxicity related to chemotherapy. The aim of this study is to use sequencing of blood cells to identify germline haplotypes strongly associated with drug resistance in osteosarcoma patients.MethodsWe used sequencing data from two patient datasets, from Inova Hospital and the NCI TARGET. We explored the effect of mutation hotspots, in the form of haplotypes, associated with relapse outcome. We then mapped the single nucleotide polymorphisms (SNPs) in these haplotypes to genes and pathways. We also performed a targeted analysis of mutations in Drug Metabolizing Enzymes and Transporter (DMET) genes associated with tumor necrosis and survival.ResultsWe found intronic and intergenic hotspot regions from 26 genes common to both the TARGET and INOVA datasets significantly associated with relapse outcome. Among significant results were mutations in genes belonging to AKR enzyme family, cell-cell adhesion biological process and the PI3K pathways; as well as variants in SLC22 family associated with both tumor necrosis and overall survival. The SNPs from our results were confirmed using Sanger sequencing. Our results included known as well as novel SNPs and haplotypes in genes associated with drug resistance.ConclusionWe show that combining next generation sequencing data from multiple datasets and defined clinical data can better identify relevant pathway associations and clinically actionable variants, as well as provide insights into drug response mechanisms.

A potential novel therapy for FGFR1-amplified pancreatic cancer with bone metastasis, screened by next-generation sequencing and a patient-derived xenograft model.

Effective therapies are limited for pancreatic cancer, particularly for those with distant tumour metastases. Therefore, more individualised drug screening is urgently required. Next-generation sequencing (NGS) is a powerful tool to investigate the genomic landscape of patients and the mechanism of drug response, which may provide a broader vision for potential clinical drug screening. Patient-derived xenograft (PDX) models may have a significant advantage in predicting clinical treatment response. In our previous study, a PDX of pancreatic cancer bone metastasis was established, and NGS was conducted to investigate the molecular information. In the present study, these data were further analysed and fibroblast growth factor receptor 1 (FGFR1) amplification was identified in a panel of 416 cancer-associated genes. Thus, AZD4547, an inhibitor against FGFR, was selected as a potential therapy, and was evaluated using the PDX model. AZD4547 was shown to exhibit antitumor activity by reducing the expression of FGFR1 and its targets. The present study also demonstrated the high potential of the novel NGS/PDX-based drug screening platform to improve individualised cancer treatment.

PO-456 Cell line panel profiling of all clinically approved kinase inhibitors for cancer treatment

IntroductionProfiling of drugs on cancer cell line panels can uncover new insights into the mechanisms of drug response. We have established a panel of 102 genetically well-characterised cell lines from distinct tumour origins, called Oncolines. Earlier work on kinase inhibitor profiling showed that our workflow generates highly reproducible data.1 Here we present the profiling of seventeen kinase inhibitors which have been approved since November 2014 in the Oncolines panel.2Material and methodsThe profiled agents include the ALK inhibitors ceritinib, brigatinib, and alectinib, the CDK4/6 inhibitors palbociclib, abemaciclib, and ribociclib, the BTK inhibitors ibrutinib, and acalabrutinib, and nine other novel marketed inhibitors that were not profiled earlier.2 The cell lines were screened in parallel in a high-throughput proliferation assay based on ATP-lite read-out, at 9 duplicate concentrations of each inhibitor. Drug response was expressed as IC50 and GR50 values.3Drug response metrics were associated with genomic alterations in the cell lines. These were retrieved from the Catalogue of Somatic Mutations in Cancer database and filtered for relevance in primary patient tumours.In addition, we calculated correlations between the log IC50s and basal gene expression levels of 18 900 genes, retrieved from the Cancer Cell Line Encyclopaedia. Correlations were filtered by the Oncolines database of 150 previously profiled anti-cancer agents.1Results and discussionsOf the ALK inhibitors, alectinib was more selective than ceritinib and brigatinib. Cell lines harbouring the NPM-ALK translocation were potently inhibited by all three inhibitors. There was a strong correlation between drug sensitivity and high ALK expression levels. Furthermore, elevated expression of JAK3 and other JAK-STAT signalling genes correlated with drug sensitivity, indicating a potential role of JAK-STAT signalling in response to ALK inhibitors. Elevated expression of the transcription factor MYCN was indicative of the sensitivity of NPM-ALK negative cell lines for ceritinib and brigatinib.ConclusionBy profiling all clinically approved kinase inhibitors, we were able to compare their selectivity and identify novel response biomarkers. These data will be important in guiding novel applications of these inhibitors as well as future drug development in the kinase field.ReferencesUitdehaag, et al. Mol. Cancer Therap 2016;15:3091–3109.Uitdehaag, et al. PLoS One 2014;9:e92146.Hafner, et al. Nat. Methods 2016;13:521–527.

PO-491 Single-cell phenotypic profiling of breast cancerpatient-derived tumour xenografts using mass cytometry

IntroductionDespite the advances in patient stratification and the understanding of basic molecular mechanisms underlining breast cancer (BC), 30% of patients with early stages of the disease later relapse. To offer more effective treatments to these patients, we need to adapt pre-clinical drug development to account for the main feature of human cancer: heterogeneity. Our group has established a biobank of 160 BC patient-derived tumour xenografts (PDTXs) and has robustly shown that PDTXs and their matching patient-derived tumour cells (PDTCs) preserve most of the originating sample’s molecular features, including intra-tumour clonal diversity (Bruna et al. Cell 2016). This integrated platform permits high-throughput (HT) assessment of drug responses ex vivo in PDTCs and their validation in vivo in PDTXs. Thus, this PDTX/PDTC biobank represents a powerful resource for pre-clinical drug development in BC.Material and methodsHere, we aim to add a crucial layer of molecular characterisation at single-cell resolution by studying the phenotypic tumour heterogeneity in BC PDTXs and its role in drug response mechanisms. To achieve this goal, we employed the state-of-the-art HT single-cell method called mass cytometry (CYTOF), which combines the principles of flow cytometry and mass spectrometry allowing the simultaneous analysis of about 40 molecular events using metal-conjugated antibodies.Results and discussionsWe designed and validated a BC-specific panel of 35 antibodies to investigate: a)the epithelial tumour compartment; b)the stroma tumour compartment; c)oncogenic signalling; d)cell cycle and apoptosis. We used this panel in CyTOF experiments to perform an in-depth phenotypic characterisation of a representative collection of BC cell lines and molecularly distinct PDTXs. Using data-driven unsupervised methods to analyse the CyTOF data focusing on the epithelial compartment, we show that we capture different degrees of both inter- and intra-tumour functional heterogeneity. Importantly, we show that CyTOF can be used to characterise the heterogeneity in signalling dynamics within the different tumour compartments under the perturbation of therapy.ConclusionCyTOF is a novel approach to characterise heterogeneity and drug responses in BC PDTXs. The integration of high-level features, such as single-cell phenotypic profiles, and functional features, such as drug response data, obtained with CyTOF will facilitate the pre-clinical development of novel therapeutic strategies and will help to uncover mechanisms of drug resistance.

Exploration of drug-response mechanism by integrating genetics and epigenetics across cancers.

To discover CpG island methylator phenotype (CIMP) as a predictor for cancer drug-response mechanism. CIMP classification of 966 cancer cell lines was determined according to identified copy number alteration and differential methylation by DNA methylation profiles. CIMP-related drugs were analyzed by analysis of variance. Tissue-cell-drug networks were developed to predict drug response of individual samples. One hundred and thirty-six copy number gain and 142 copy number loss cell lines were classified into CIMP-high and CIMP-low groups, meanwhile 9 and 24 CIMP-associated drugs were identified, respectively. Specially, breast invasive carcinoma samples primarily composed by HCC1419 were predicted to be sensitive to GSK690693. The study provides guidance for drug response in cancer therapy through genome-wide DNA methylation.

Predicting Cancer Drug Response using a Recommender System.

As we move toward an era of precision medicine, the ability to predict patient-specific drug responses in cancer based on molecular information such as gene expression data represents both an opportunity and a challenge. In particular, methods are needed that can accommodate the high-dimensionality of data to learn interpretable models capturing drug response mechanisms, as well as providing robust predictions across datasets. We propose a method based on ideas from 'recommender systems' (CaDRReS) that predicts cancer drug responses for unseen cell-lines/patients based on learning projections for drugs and cell-lines into a latent 'pharmacogenomic' space. Comparisons with other proposed approaches for this problem based on large public datasets (CCLE and GDSC) show that CaDRReS provides consistently good models and robust predictions even across unseen patient-derived cell-line datasets. Analysis of the pharmacogenomic spaces inferred by CaDRReS also suggests that they can be used to understand drug mechanisms, identify cellular subtypes and further characterize drug-pathway associations. Source code and datasets are available at https://github.com/CSB5/CaDRReS. Supplementary data are available at Bioinformatics online.

Abstract B155: Combining cell panel screening with analysis of gene expression levels reveals features of drug response and resistance

Abstract Screening of new drugs on large cell panels is an important tool to study the biologic mechanism of drug response. From a combination of parallel in vitro tests and bio-informatics analysis, important conclusions can be drawn, for instance about drug selectivity in a cellular context, about resistance mechanisms, and about the patient population in which a drug is effective. This makes cell panel screening indispensable in modern drug discovery. We have set up a platform called Oncolines™ that comprises 102 cell lines from diverse tumor tissues. All cell lines are screened in parallel in high-throughput proliferation assays based on ATP-lite™. Compounds are tested with 9 point dose-response curves in duplicate. Curves are visually inspected. In the past, we have shown that this workflow leads to highly reproducible IC50s, which are necessary for genomic biomarker discovery [1]. For instance, the IC50s have been coupled to curated databases of somatic mutations and copy numbers (1). However, these changes only reflect a small percentage of oncogenic transformations. A more comprehensive view of oncogenic signaling inside a cell can be obtained from mRNA expression levels (2). Here, we describe a workflow to investigate drug response in the 102 cell line Oncolines™ panel based on basal gene expression levels. Correlations between gene expression levels and the log IC50s were calculated for more than 18,000 genes. To reduce the number of false-positive correlations, we considered only genes with a known biologic role in cancer or that were clinically actionable. Secondly, we filtered out genes that correlated indiscriminately with drug responses, by correcting for the average correlations seen in our profiling database of more than 150 inhibitors. This filtered gene list was structured graphically by combining it with information on protein-protein interactions (using the StringDB) or information on which genes are part of the same pathways (a method called Gene Set Analysis). Our method reveals that gene overexpression correlates with drug responses for a number of targets. For instance, EGFR, IGFR, or ALK kinase inhibitors have more potent effects in cell lines that overexpress EGFR, IGFR, or ALK, respectively, and the MDM2 antagonist nutlin is more active in MDM2-overexpressing cell lines. This is irrespective of the genomic alterations driving overexpression of these genes. The distinct response profiles of the spectrum-selective BTK/EGFR/ERBB2 inhibitor ibrutinib and the more selective BTK inhibitor alcalabrutinib could be distinguished as well. Finally, the analysis shows mechanisms of drug resistance. For instance, the response of vincristine strongly correlates to cellular ABCB1 expression, the P-glycoprotein/MDR1 drug transporter, but also to ABCC3, another ABC transporter. Combining basal gene expression levels with cell panel data therefore allows discovery of novel mechanisms of drug response and resistance.

PDX-MI: Minimal Information for Patient-Derived Tumor Xenograft Models.

Patient-derived tumor xenograft (PDX) mouse models have emerged as an important oncology research platform to study tumor evolution, mechanisms of drug response and resistance, and tailoring chemotherapeutic approaches for individual patients. The lack of robust standards for reporting on PDX models has hampered the ability of researchers to find relevant PDX models and associated data. Here we present the PDX models minimal information standard (PDX-MI) for reporting on the generation, quality assurance, and use of PDX models. PDX-MI defines the minimal information for describing the clinical attributes of a patient's tumor, the processes of implantation and passaging of tumors in a host mouse strain, quality assurance methods, and the use of PDX models in cancer research. Adherence to PDX-MI standards will facilitate accurate search results for oncology models and their associated data across distributed repository databases and promote reproducibility in research studies using these models. Cancer Res; 77(21); e62-66. ©2017 AACR.

Mechanistic and Preclinical Insights from Mouse Models of Hematologic Cancer Characterized by Hyperactive Ras.

RAS genes are mutated in 5%-40% of a spectrum of myeloid and lymphoid cancers with NRAS affected 2-3 times more often than KRAS Genomic analysis indicates that RAS mutations generally occur as secondary events in leukemogenesis, but are integral to the disease phenotype. The tractable nature of the hematopoietic system has facilitated generating accurate mouse models of hematologic malignancies characterized by hyperactive Ras signaling. These strains provide robust platforms for addressing how oncogenic Ras expression perturbs proliferation, differentiation, and self-renewal programs in stem and progenitor cell populations, for testing potential therapies, and for investigating mechanisms of drug response and resistance. This review summarizes recent insights from key studies in mouse models of hematologic cancer that are broadly relevant for understanding Ras biology and for ongoing efforts to implement rational therapeutic strategies for cancers with oncogenic RAS mutations.

Progress and impact of clinical phosphoproteomics on precision oncology

: Advances in phosphoproteomic technologies have enabled a step-change in the ability to make precise measurements of global protein phosphorylation states within tumours, providing critical information on the activation status of key oncogenic pathways driving disease. As an ever increasing number of molecularly targeted drugs enact their effect through modulation of such pathways, translational phosphoproteomic studies represent an avenue for biomarker discovery that provides insight into the direct functional impact of drug treatment whilst uncovering previously unrecognised mechanisms of drug response and resistance. Array- and mass spectrometry (MS)-based techniques allow for the characterisation of hundreds to thousands of phosphorylation sites that in principle can be readily integrated with genomic and transcriptomic datasets to produce highly detailed molecular portraits of human cancers. However, in practice, the routine application of phosphoproteomics to clinically-derived material has been limited by factors relating to sample integrity, assay resolution and computational challenges. In this perspective, we highlight a number of recent studies that have applied phosphoproteomics to the analysis of tumour specimens and discuss their impact on our knowledge of dynamic pathway biology and response to targeted therapies. We provide examples of pioneering studies that have integrated phosphoproteomic data with genomic methods to demonstrate the utility of these approaches in identifying clinically important biology for applications in personalised medicine. Collectively, these studies suggest that the increasing use of phosphoproteomics in translational precision oncology holds the promise of contributing to practice-changing discoveries over the coming decade.

Resistance to dasatinib is associated with the activation of Akt in oral squamous cell carcinoma

Objectives: Overexpression and aberrant activation of Src promote the development of oral squamous cell carcinoma (OSCC), thus therapies targeting Src-related kinases may afford an improvement in patient survival. However, limited clinical activity of the Src-targeted drug, dasatinib, in cancer patients warrants further investigation to better understand the underlying basis of resistance to dasatinib in OSCC. Methods: Response to dasatinib was evaluated in a panel of oral cancer cell lines by 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide, DNA synthesis, cell cycle and apoptosis analysis. The underlying mechanism of drug response was investigated using immunoblotting. Xenograft models were used to test efficacy. Results: All cell lines were sensitive to dasatinib (IC50 < 250 nM), but this was not associated with CDKN2a/p14ARF mutations. Dasatinib-induced cell cycle arrest and apoptosis, while inhibiting Src, Akt and FAK activity in all lines tested. However, dasatinib failed to inhibit tumour growth in xenograft models and treated tissues showed Akt activity despite the loss of Src activity. Conclusions: Our data revealed that reactivation of Akt (Ser473) could be a compensatory mechanism that bypasses Src inhibition by dasatinib, providing important clues that could improve treatment strategies to overcome dasatinib resistance.

Genomic analysis of circulating tumor DNA to predict endocrine resistance and clonal evolution in patients with prostate cancer: Clinical perspectives and research opportunities

Metastatic castration-resistant prostate cancer (mCRPC) is a multi-faceted disease and clinicians treating and managing the disease face several challenges. Progressive disease on androgen deprivation therapy (ADT) commonly occurs. With the approval of several survival-prolonging endocrine agents, the question of sequential treatment administration becomes increasingly important and new biomarkers beyond PSA should be identified to predict resistance and prognosis of mCRPC. A step forward has been made with the identification of splicing variants of androgen receptor (AR) associated with a resistance to ADT. The direct identification of AR mutants from patients’ serum, and the functional characterization of these mutants may provide personalized recommendations regarding the best future therapy. Genotyping circulating tumor DNA in blood samples can be used to identify the molecular profile of prostate cancer and to closely follow its evolution during therapy. This approach can also be used to detect minimal residual disease after surgery and to identify actionable therapeutic targets, and uncovering mechanisms of endocrine resistance ex-vivo . Importantly, by identifying mechanism of drug response by functional characterization of AR, novel compounds may be introduced in the treatment of mCRPC.

Abstract 2378: Responses of AML patients to tailored drug regimens: monitoring cancer subclones by ultra-deep resequencing

Abstract As part of our individualized systems medicine (ISM) program, personalized treatment options are provided to clinicians based on in-depth genomic and molecular profiling as well as ex vivo drug sensitivity and resistance testing (DSRT) of leukemia patients (Pemovska et al. Cancer Discovery, 2013). In chemorefractory AML patients (n = 17), the ISM strategy has resulted in up to 35% response rate when individually selected targeted drugs have been applied in patient treatment. The responses achieved have, however, been transient and patients have typically relapsed quickly. Here, we aimed to understand the molecular basis of such treatment failures by quantitating the kinetics of individual cancer subclones before, during and after targeted treatments, as well as at the time of relapse and disease progression. Longitudinal serial samples from 13 AML patients were studied at multiple steps during leukemia progression and drug response. Clonal evolution of leukemic subclones was studied by both exome sequencing to get genome-wide overviews of disease progression, as well as by ultra-deep (>10,000x) amplicon resequencing with unique molecular identifiers to identify rare clones carrying specific cancer-relevant mutations. Nine of the 13 patients (69%) had multiple clones by exome sequencing and displayed branching evolution. In five patients who received treatment with targeted inhibitors we observed a significant differential therapeutic response of the individual AML subclones during therapy. In some cases, this could be directly attributed to the molecular mechanisms of drug response and resistance, such as the loss of NF1 in a subclone leading to cytarabine resistance or the loss of the FLT3-positive subclone in a patient responding to sunitinib treatment. Despite a prominent drug response at the level of the subclone carrying the driver mutations, in all these patients a new subclone emerged that led to progression of the disease. In three of the patients, the dominant clone appearing at relapse was already detected as a minor subclone in the diagnostic sample by amplicon resequencing. Amplicon sequencing enabled us to detect these minor subclones (down to 0,5% frequency) that were missed by exome sequencing. The results suggest that relapses in AML may arise because the drug-resistant subclone exists already before the onset of therapy. Overall, it is necessary to quantify tumor evolution and drug responses at the level of cancer subclones. Ultra-deep resequencing can be used to monitor drug responses at the subclone level, even at very low frequencies. This could facilitate early detection of small subclones with important prognostic implications, as well as the design of intelligent combinations of targeted drugs that could block such subclones. Citation Format: Poojitha N Ojamies, Mika Kontro, Henrik Edgren, Pekka Ellonen, Sonja Lagstrom, Henrikki Almusa, Timo Miettinen, Samuli Eldfors, David Tamborero, Krister Wennerberg, Caroline Heckman, Kimmo Porkka, Maija Wolf, Olli Kallioniemi. Responses of AML patients to tailored drug regimens: monitoring cancer subclones by ultra-deep resequencing. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 2378.