Alexander Zien

Src activation by adrenoreceptors is a key switch for tumour metastasis

Norepinephrine (NE) can modulate multiple cellular functions important for cancer progression; however, how this single extracellular signal regulates such a broad array of cellular processes is unknown. Here, we identify Src as a key regulator of phosphoproteomic signaling networks activated in response to beta-adrenergic signaling in cancer cells. These results also identify a new mechanism of Src phosphorylation that mediates beta-adrenergic/PKA regulation of downstream networks, thereby enhancing tumor cell migration, invasion and growth. In human ovarian cancer samples, high tumoral NE levels were correlated with high pSrcY419 levels. Moreover, among cancer patients, the use of beta blockers was significantly associated with reduced cancer-related mortality. Collectively, these data provide a pivotal molecular target for disrupting neural signaling in the tumor microenvironment.

Plasma microRNA profiles for bladder cancer detection

BACKGROUND Bladder cancer (BC) is a burdensome disease with significant morbidity, mortality, and cost. The development of novel plasma-based biomarkers for BC diagnosis and surveillance could significantly improve clinical outcomes and decrease health expenditures. Plasma miRNAs are promising biomarkers that have yet to be rigorously investigated in BC. OBJECTIVE To determine the feasibility and efficacy of detecting BC with plasma miRNA signatures. MATERIALS AND METHODS Plasma miRNA was isolated from 20 patients with bladder cancer and 18 noncancerous controls. Samples were analyzed with a miRNA array containing duplicate probes for each miRNA in the Sanger database. Logistic regression modeling was used to optimize diagnostic miRNA signatures to distinguish between muscle invasive BC (MIBC), non-muscle-invasive BC (NMIBC) and noncancerous controls. RESULTS Seventy-nine differentially expressed plasma miRNAs (local false discovery rate [FDR] <0.5) in patients with or without BC were identified. Some diagnostically relevant miRNAs, such as miR-200b, were up-regulated in MIBC patients, whereas others, such as miR-92 and miR-33, were inversely correlated with advanced clinical stage, supporting the notion that miRNAs released in the circulation have a variety of cellular origins. Logistic regression modeling was able to predict diagnosis with 89% accuracy for detecting the presence or absence of BC, 92% accuracy for distinguishing invasive BC from other cases, 100% accuracy for distinguishing MIBC from controls, and 79% accuracy for three-way classification between MIBC, NIMBC, and controls. CONCLUSIONS This study provides preliminary data supporting the use of plasma miRNAs as a noninvasive means of BC detection. Future studies will be required to further specify the optimal plasma miRNA signature, and to apply these signatures to clinical scenarios, such as initial BC detection and BC surveillance.

Abstract A34: Conquering resistance to targeted therapies through system‐based analysis of clinico‐molecular information

While the advent of targeted therapies has promised to revolutionize the success of cancer treatment, a critical review of clinical response rates provides a sobering perspective on the challenge at hand. New levels of discovery innovation are urgently required to redress this enormous medical need. Focusing on primary imatinib resistance in chronic myeloid leukemia, we demonstrate that the systems‐based modeling and analysis of clinico‐molecular information from responder and non‐responder patients can provide unique molecular insights into the sources of therapy resistance. By then utilizing these models to prioritize therapeutic drug space, the strategy elaborates rational multi‐component therapies to potentially redress the issue. Moreover, by hypothesizing that co‐medications may perturb the activity of resistance model components, we utilize prescription information to “probe” the functional network in search of key resistance associated targets. Not only does this method provide a novel approach to the analysis of patient information, it also permits the repositioning of drugs from independent therapeutic areas within predicted combinatorial regimens. Taken together, our results provide an exciting new approach to the computational analysis of clinico‐molecular data, and suggest a future where rationally designed theranostic‐linked combination therapies could significantly address the non‐responder problem. Citation Information: Mol Cancer Ther 2009;8(12 Suppl):A34.

Abstract A41: Automated retrieval and assessment of biomarker-related evidence for cancer treatment decision support

Introduction: Molecular characterization is rapidly becoming a standard of care in clinical oncology practice. Simultaneously, growing volumes of peer-reviewed literature reporting the relationships between genomic aberrations, tumor responsiveness to treatments, and associated patient outcomes has significantly increased the challenge facing clinicians to comprehensively identify and prioritize personalized treatments. This challenge is further compounded by a) the disease-specific nature of most predictive biomarkers, b) discordant published observations, c) the varying levels of clinical validity associated with most biomarker:drug response relationships, and d) the time constraints that all treating oncologists face. To overcome such challenges, we developed an automated computational system to assess the predicted effects of published genomic aberrations on drug responsiveness in a patient/disease-specific manner. Here we describe how computational selection and assessment of peer-reviewed published evidence relevant to a given cancer patient compares to that of a human expert with access to the same body of data. Methods: We developed a computational treatment decision support system (TDSS) for the analysis of cancer patients with a set of known mutations. The system holds a drug-response database (DRDB) containing expert curated information on three levels of clinical validity: 1) clinically endorsed (>100 FDA-approved facts), 2) clinically observed (>5,000 patients), and 3) translationally observed (∼4,000 model systems). Given a patient mutation profile, the TDSS retrieves related DRDB entries and weighs them according to validity level, relationship to patient disease (using the Medical Subject Headings ontology, MeSH), and RECIST response criteria. The TDSS then summarizes whether a given genotype is likely to confer response or resistance to available cancer drugs. To compare the performance of this system to that of a human molecular diagnostics expert, we created 48 virtual patients, each from one of five highly frequent cancer indications (lung, CRC, GIST, melanoma, breast) and with one or two randomly drawn predictive mutations. They were both processed by the TDSS and surveyed by an expert with electronically access to all DRDB data. The task was to recommend up to three treatments with highest likelihood of response, and identify up to three with highest risk of resistance for each case. Results: The TDSS required a few seconds of computation, compared to more than five hours of work by the expert. Both obtained a similar number of actionable predictions, for essentially the same 75% of the cases. For each case, the three treatments selected for response / resistance were compared and critically re-assessed. We found the error rates to be higher for the molecular diagnostics expert (9.6% of treatments for response, 2.1% for resistance) than for the TDSS (3.0% response, 1.4% resistance). Frequent causes of human error include a) overlooking evidence and b) failure to consider the numbers of patient cases observed. Conclusions: Computational aggregation and rules-based analysis of the clinical effects of tumor mutations on cancer drug responsiveness can greatly aid the evidence-based prioritization of treatment options for cancer patients with superior accuracy and turn-around-times. While a large-scale study is planned to compare TDSS performance against multiple experts in a real-life clinical setting, our preliminary insights suggest that the necessity for such computational systems will parallel both the emergence of clinico-molecular knowledge in this domain and the adoption rates for clinical use of genomic technologies. Citation Format: Alexander Zien, Francesca Diella, Anja Doerks, Theodoros Soldatos, Markus Hartenfeller, David B. Jackson. Automated retrieval and assessment of biomarker-related evidence for cancer treatment decision support. [abstract]. In: Proceedings of the AACR Precision Medicine Series: Drug Sensitivity and Resistance: Improving Cancer Therapy; Jun 18-21, 2014; Orlando, FL. Philadelphia (PA): AACR; Clin Cancer Res 2015;21(4 Suppl): Abstract nr A41.

Abstract A1-60: Gene mutation screening upstream of biomarkers has the potential to identify and fix compromized conclusions

The purpose of this study is to assess potential problems of single-nucleotide variant (SNV) biomarker testing for cancer treatment decision support. Specifically, we investigate whether mutations upstream of predictive biomarkers have the potential to invalidate the predictive conclusions drawn on the basis of just testing for the biomarker mutation itself and neglecting the context. We investigate 25 cases of endometrial cancer (EC) with whole exome sequencing data (from TCGA), and 23 cases of breast cancer (BC) sequenced for a panel of >600 cancer-related genes. Each case is investigated for the presence of any of 1825 SNVs that have been curated from the medical literature for potentially being predictive of treatment effect in at least one cancer type. For each of these SNVs, we look for other mutations that may compromize the biomarker by destroying the corresponding protein, specifically upstream frame-shift (fs) insertions or deletions (indels), splice-site disruptions (SSDs), and premature stop codons (PSCs). We count two types of dubious constellations. First, biomarker mutations found present along with a dramatic upstream mutation in the same gene are potentially false positive (FP), because altering a destroyed protein may be without impact. Second, where a biomarker mutation is absent, an equivalent effect may be caused by a deleterious upstream mutation, such that the situation amounts to a potential false negative (FN). Looking for misleading presence of biomarkers (FPs), we find potentially disruptive mutations upstream of known biomarkers in 5 of our 48 cases. Closer investigation suggests, however, that none of them are real FPs. The most important reason is that, for biomarkers that inactive the molecular function of the protein, any further damage to the protein would rather strengthen the effect. In contrast, potentially misleading absence of any inactivating biomarker in a gene that is disrupted by a different mutation is frequent in our cohort. Starting from a list of 716 human tumor suppressor genes taken from TSGene (Vanderbilt), we find that 23 have at least one associated biomarker SNV that is absent and at the same time have a deleterious upstream mutation in at least one patient in our cohort. For a more detailed investigation we focus on P53. In the EC cohort, we have 1 case with a known P53 biomarker, and among the 24 others 1 case with upstream disruptions (both fs-indel and SSD). In the BC set, there are 2 regular P53 biomarker calls, and 4 additional cases without biomarker mutation but with upstream disruptions (all fs-indels, 1 also with PSC). Reassuringly, the conclusions based on biomarker mutations found to be present seem to be reliable, at least in the investigated cohorts. However we do find disruptions upstream of called biomarker mutations in ~10% of the investigated patients, which demonstrates that there is a real danger of false positive findings. Just looking at P53, we find that considering disruptions in addition to the described biomarkers (3 cases), such that they formally differ from the biomarkers but may have similar consequences (5 cases), almost triples the number of hits. In contrast to hot-spot sequencing and SNP arrays, NGS-based whole gene sequencing allows for holistic assessment of biomarker genes. It consequently may allow more precise cancer diagnostics and may benefit treatment decision. This study may be expanded in several ways, including taking into account variant allele frequencies and investigating larger cohorts. In particular we plan to include metastatic cancer and cases of acquired resistance, which may have increased incidence of mutations that interfere with biomarkers. Citation Format: Sonia Vivas, Francesca Diella, Alexander Zien. Gene mutation screening upstream of biomarkers has the potential to identify and fix compromized conclusions. [abstract]. In: Proceedings of the AACR Special Conference on Translation of the Cancer Genome; Feb 7-9, 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 1):Abstract nr A1-60.

Abstract A1-33: Knowledge prioritization of cancer genomes using oncoscores and functional impact scores to support biomarker discovery and clinical decision making

Introduction: To accelerate the translation of genome information in important biological and clinical insights requires systematic integration and analysis of functional genomic and proteomic information. In this way molecular landscapes of cancer patients can be characterized efficiently to impact their care. To fully rationalize the potential treatment options for patients based on mechanistic considerations, we must understand two key parameters: (a) how important is each protein/gene to the known mechanisms underlying specific cancer types and (b) what are the functional implications of variants at the level of protein and system function. Of the many modules in our pipeline, here we present two of the components we have developed to address these questions. Methods: Based on expert-curated data, combined with bioinformatics and text-data mining methods we have modeled (a) the relative biological importance of each gene/protein to specific cancer types, with a measure we call ‘oncoscore’ (b) the functional effects of a variant with a process we call ‘functional impact score’. First, the oncoscore method relies on multidimensional data types that summarize real-time evidence regarding clinical and molecular importance with respect to specific cancer indications. Such features include, among others, gene/protein pathway inclusion facts, drug-targetness, disease association, interaction neighborhood, as well as indication-specific protein and druggability attributes. Applying these parameters across individual cancer indications provides us with a prioritization of the functionally most important genes associated with each cancer type. Secondly, to understand the impact of aberrations we contextualized structural, functional, drug response and safety information to provide a novel approach for the prediction of functionally important aberrations. Combining the two scores permits prioritization of the functionally most important genes and aberrations in any patient tumor. Interestingly, the strategy can also be applied in absence of DNA sequence information, where the oncoscore method alone can be used to prioritize potential drug targets, again based on levels of real-time evidence. Results: We present a subset of our results in the context of 25 different cancer conditions and demonstrate how the two scores can help prioritize the most important clinico-molecular players of a disease, decipher the most important aberrations found in patients9 molecular profiles, and respectively combine this information to prioritize treatments for each indication/patient. Conclusion: While our database contains curated information about the relationship between a gene/protein mutation and drug response within specific cancer types, we have devised two additional mechanisms to expand the clinical actionability of this information. The oncoscore and functional-impact scores provide an additional modus to decipher clinically actionable information from a patient tumor, especially when no known biomarkers are detected in the patient9s profile. These methods are also particularly applicable to the identification of novel treatment biomarkers. Another advantage is that they can be used to prioritize patient treatments in the absence of sequence information, a feature that can be helpful when it comes to non-resectable disease in rare cancers. In summary, our cancer-specific integration of biological and clinical knowledge allows us to predict potentially actionable mutations in patient tumors. This is an important extension to the identification of previously known predictive biomarkers and lends itself to translational level clinical applications including biomarker discovery, drug repositioning and clinical trial prioritization for ex-guideline patients. Citation Format: Theodoros G. Soldatos, Sasha Badbanchi, Sonia Vivas, Alexander Zien, Francesca Diella, Markus Hartenfeller, Alexander Picker, Martin A. Stein, David B. Jackson. Knowledge prioritization of cancer genomes using oncoscores and functional impact scores to support biomarker discovery and clinical decision making. [abstract]. In: Proceedings of the AACR Special Conference on Translation of the Cancer Genome; Feb 7-9, 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 1):Abstract nr A1-33.

Abstract P3-06-22: Identification of ERBB2 gene variants in HER2 positive disease associated with trastuzumab response in an adjuvant trastuzumab chemotherapy trial

Background Among 493 patients with early stage, lower risk (70% node negative), HER2 positive breast cancer, the 2-year DFS was 97% with adjuvant docetaxel and cyclophosphamide plus 1 year of trastuzumab (TCH) in a phase 2 study (Jones et al, Lancet Oncology 14: 1121, 2013). The objective of this work was to investigate the presence of ERBB2 specific DNA biomarkers in HER2 amplified tumors associated with relapse compared to those which did not recur during 3 years of followup. Methods The 26 coding exons of the ERBB2 gene were analyzed by next generation sequencing (NGS) on the HiSeq-2500 (Illumina) platform using DNA samples from the primary tumors of 13 of the 493 patients who progressed. Treatment refractory cases were analyzed in parallel with a clinically and pathologically matched group of 11 patients from the same trial with 2-year relapse free survival (RFS). Results ERBB2 gene variants were detected in all 11 relapse-free patients and 10/13 patients with relapse. Heterogeneity for sub-clonal ERBB2 variants at 30 ERBB2 SNVs. These sub-clonal ERBB2 SNVs were not detected in a comparison set of non-HER2 positive solid tumors. We did find variants that seem to be associated with later relapse, of which one is statistically significant (I655V; p=1.2%, Fisher exact test, no compensation for multiple testing), and further 5 have p Conclusions We find no evidence for known activating HER2 mutations to confer increased (nor decreased) risk of relapse after TCH therapy (p=30.0%). Remarkably, I655V is found significantly less often in the relapse group. This variant is known to increase BC risk by activating HER2 dimerization; an effect that may be muted by trastuzumab, in contrast to some other causes of BC. Moreover, I655V mutations with high TVF (>75%, hence suggesting homozygous germline presence) are exclusively found in the no-relapse group (p=3.1%). These biomarkers in combination with other molecular studies including immune function gene status may help define the subset of patients at risk for relapse during TCH therapy. The hyper-mutability genotype does not seem to correlate with later relapse, so it may be hypothized that the low TVF mutations are passengers rather than drivers. Further studies are needed to verify and precisely define the role of these ERBB2 biomarkers in HER2 positive breast cancer. Citation Format: Shelly Gunn, Alexander Zien, Markus Hartenfeller, Francesca Diella, Stephan Brock, Martin Stein, Sasha Badbanchi, Anja Doerks, David Jackson, Lina Asmar, Yunfei Wang, Steve Jones. Identification of ERBB2 gene variants in HER2 positive disease associated with trastuzumab response in an adjuvant trastuzumab chemotherapy trial [abstract]. In: Proceedings of the Thirty-Seventh Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2014 Dec 9-13; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2015;75(9 Suppl):Abstract nr P3-06-22.

Abstract 2983: Bisphosphonates: New strategies for targeting angiogenesis in ovarian cancer

Objectives: Bisphosphonates, known to have beneficial effects on bone and soft tissue metastases in breast cancer, are potent inhibitors of macrophages, but effects on the microenvironment are not fully understood. We investigated the biological effect of clodronate on macrophage and endothelial cell-driven angiogenesis in ovarian cancer. Methods: Using the FDA Adverse Event Reporting System (FAERS), we examined the effects of bisphosphonate use on overall cancer mortality. We examined the in vitro (endothelial cell migration, capillary formation and cytokine secretion) and in vivo (orthotopic mouse models) effects of clodronate on angiogenesis, macrophage infiltration, and tumor growth. Results: Using (FAERS) data, out of ∼17,000 patients with a cancer diagnosis co-medicated with a bisphosphonate, overall reported death rate was 36% lower (17.6% vs 27.7%, p Conclusions: Bisphosphonates modulate tumor angiogenesis through effects on macrophages and endothelial cells and are associated with overall decreased mortality in cancer patients, independent of chemotherapeutic agents. Bisphosphonates represent an unexplored, but attractive clinical strategy in ovarian cancer, with potential for synergistic combination with other anti-angiogenic agents. Citation Format: Heather J. Dalton, Nicole M. Reusser, Alexander Zien, David Jackson, Rebecca Previs, Rajesha Rupaimoole, Behrouz Zand, Gabriel Lopez-Berestein, Robert L. Coleman, Anil K. Sood. Bisphosphonates: New strategies for targeting angiogenesis in ovarian cancer. [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 2983. doi:10.1158/1538-7445.AM2014-2983

Abstract 56: MASE: A system for the molecular analysis of side effect information and its application to marketed cancer therapeutics

Proceedings: AACR 102nd Annual Meeting 2011‐‐ Apr 2‐6, 2011; Orlando, FL Adverse events (AEs) are a common and unavoidable consequence of therapeutic intervention. Nevertheless, available tomes of such data now provide us with an invaluable opportunity to study the relationship between human phenotype and drug-induced protein perturbations within a patient system. Deciphering the molecular basis of such adverse responses is not only paramount to the development of safer drugs, but it also presents a unique opportunity to dissect disease systems in search of novel response biomarkers, drug targets and efficacious combination therapies. Inspired by the potential application of this approach in clinical oncology, we have developed an in silico platform dedicated to the Molecular Analysis of Side Effect information (MASE). Combining data from the FDAs Adverse Event Reporting System (AERS) with highly curated knowledge about drug, target and pathway associations, MASE provides a biosystem level view on adverse event reports. In terms of data integration process, the free-text drug names provided by the AERS system are first mapped to standard drug synonyms. By then associating these small-molecule/biological drugs with known and predicted protein partners, we transform adverse events information from a purely drug-centric resource, to one that emphasizes the functional mediators of drug activity within the patient system. Employing this strategy, AE cases associated with a total of 97 marketed cancer drugs were curated and contextualized. A total of 208,364 adverse event cases (i.e. 14% of all) were reported as either involving a cancer drug or cancer indication, with 1243 other drugs (79%) reported as co-medicated throughout these cases. Using this cancer-focused subset, case-specific molecular views were generated highlighting all elements of the proteome perturbed through multi-component therapies – totaling 1663 direct targets, 49 metabolizing enzymes and 407 pathways. The system may be queried to analyze side effects and outcomes associated with specific combinations of drugs, targets, metabolizing enzymes, pathways or cancer indications. Importantly, we have developed a set of analytical approaches that mine MASE for evidence of 1) efficacious target combinations 2) target combinations with increased side effects, 3) target combinations that appear to attenuate certain drug side effects. In addition, we also report a strategy to predict novel targets of established drugs, based on side effect dissimilarities between otherwise structurally comparable agents. In summary, by permitting direct assessment relationships between the human proteome and drug-induced phenotypes, MASE provides a novel approach to the analysis and molecular dissection of AE information in oncology. Current developments are focused on the integration of patient specific clinico-molecular data and the combined application to treatment decision support. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 56. doi:10.1158/1538-7445.AM2011-56

Abstract 937: Molecular dynamics of SrcS17 activation in chronic stress-induced tumor growth

Clinical studies have demonstrated that chronic stress can influence cancer progression. However, the underlying mechanisms are not fully understood. To determine the molecular drivers of downstream signaling networks activated in response to chronic stress, we performed a phosphoproteomic analysis and determined that the non-receptor tyrosine kinase, Src, was the key regulator of these networks. Since Src plays an important role in cancer biology, we examined the biological and clinical significance of Src in stress-mediated tumor growth. Norepinephrine (NE) rapidly activated Src Y419 in β-adrenergic receptor (ADRB) positive ovarian cancer cell lines, but not in ADRB-null cells. Confocal microscopy showed that Src was rapidly recruited to the cellular membrane after NE exposure in ADRB positive ovarian cancer cells. Furthermore, treatment with different ADRB agonists and blockers determined that ADRB2 is required for Src Y419 phosphorylation. Treatment with a cAMP agonist or PKA agonist/antagonists demonstrated that cAMP/PKA signaling is required for NE-induced Src activation. The unexpected Src activation via cAMP/PKA was found to be mediated by direct phosphorylation of Src S17 following NE treatment. In Src-/- cells transiently expressing WT Src, NE caused Src Y419 phosphorylation, which was not observed when cells were transfected with a Src S17A construct. In order to investigate how S17 phosphorylation leads to Src activation, we performed molecular dynamic simulations and observed that upon Src S17 phosphorylation, Src undergoes significant structural changes that expose its Y419 residue. To understand the functional consequences of stress-induced Src activation, we performed migration and invasion assays. Exposure to NE resulted in an increase in ovarian cancer cell migration and invasion that was completely abrogated by Src-targeted siRNA (P Y419 was associated with worse patient survival (P Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 937. doi:10.1158/1538-7445.AM2011-937