The NCI's Therapeutically Applicable Research to Generate Effective Treatments (TARGET) is a comprehensive genomic characterization initiative to determine the molecular changes that drive the development and progression of certain childhood cancers. TARGET seeks to improve treatment of these cancers through identification of valid molecular targets, in and across each disease, and ultimately lead to the rapid development of novel, more effective therapies. The TARGET projects include primarily high-risk for relapse cases. Most TARGET disease groups also have relapse samples available for study, thereby allowing the determination of changes that occur during the disease progression. To accelerate translation into the clinic, TARGET data is readily available to the research community for further investigation. There are currently around 2,000 TARGET BAM files from next generation platforms currently available to investigators, and additional sequencing files are regularly being submitted to expand the TARGET datasets. All TARGET cases have either whole genome or exome sequencing of matched tumor and normal pairs (relapse sample when available), and transcriptome sequences exist for most TARGET cases as well. In addition, all TARGET cases have clinical annotation, array-based gene expression and copy number data available to researchers, most also have methylation profiles, to allow for a more complete understanding of the molecular basis of these childhood cancers. Some projects have further included miRNA characterization data as well.

Molecular chaperones help nascent polypeptides fold correctly and multimeric protein complexes assemble productively, while minimizing the danger of aggregation in the protein-rich intracellular environment. Heat shock protein 90 (Hsp90) is an evolutionarily conserved molecular chaperone that participates in stabilizing and activating more than 200 proteins - referred to as Hsp90 ‘clients' - many of which are essential for constitutive cell signalling and adaptive responses to stress. To accomplish this task, Hsp90 and additional proteins termed co-chaperones form a dynamic protein complex known as the Hsp90 chaperone machine. The complex nature of Hsp90 regulation and the many ways in which it participates in cell physiology have been clarified in recent years, in part with the help of small molecule Hsp90 inhibitors. Considerable progress has been made in understanding the dynamic conformational flexibility of Hsp90 and in recognizing the importance of post-translational modifications in regulating Hsp90 function. Hsp90 function has been co-opted by cancer cells to preserve their survival in the face of numerous environmental insults, and growing clinical evidence suggests a benefit for targeting Hsp90, alone or in combination with other agents, in cancer.

Although genome-wide association studies (GWAS) have identified many single nucleotide polymorphisms (SNPs) associated with cancer, the contribution to risk of these variants is small, rendering them of little use clinically. Whereas GWAS focus on main effects, complex diseases are also influenced by many environmental factors that may affect the association between SNPs and disease. We hypothesized that a variant identified by GWAS to have a small effect in the general population may actually exert a large effect in a subset of individuals sharing a common environmental context. To test this, we performed GWAS of two therapy-induced cancers: radiation therapy-induced second cancers after Hodgkin lymphoma, and therapy-related acute myeloid leukemia. We found that common variants can have large effects in the context of specific etiological exposures. Thus, genomic studies incorporating exposures may reveal patient subsets for whom specific SNPs contribute meaningfully to disease. In the future, a major challenge will be to design genetic studies taking advantage of exposures shared by cases and controls when the contribution to disease of the exposure is less obvious than for therapy-induced cancers. Learning objectives: 1. To understand the implications of cancer as a complex disease for studies investigating genetic susceptibilities to cancer. 2. To understand the strengths and limitations of genome-wide association studies (GWAS). 3. To understand the role of PRDM1 in defending against radiation-induced second cancers.

Copy number variation, a major cause of structural variation in the genome, plays an important role in human disease. Determination of copy number variation requires both precision and accuracy. Real-time PCR is commonly used to validate copy number variation in populations. However, it may require many replicates to get the precision needed. Digital PCR allows you to go beyond the limits of real-time PCR. With the QuantStudio 3D Digital PCR System, a simple and affordable digital PCR platform, obtaining precise and accurate absolute quantification for your nucleic acid targets is attainable.

Pancreatic cancer is the 4th most common cause of cancer deaths in the United States. Due to the aggressive nature of this cancer and the lack of biomarkers for early detection, the incidence and mortality rates for pancreatic cancer are nearly equivalent. The presence of oncogenically mutated K-Ras in 90% of human pancreatic ductal adenocarcinomas (PDAC) strongly suggests a critical role for this genetic mutation in the development of this disease. We and others demonstrate that mutated K-Ras can activate one or more downstream signaling pathways, including the RalGTPase pathway, and lead to transformed growth in PDAC. However, despite many years of research, there are no anti-Ras therapies that have successfully reached the clinic. One of the genes that have been found by microarrays to be overexpressed after oncogenic K-ras is Pim-1 kinase. Most recently, another member of the Pim family, Pim-3 kinase, was shown to be aberrantly expressed and to block apoptosis in PDAC cell lines and patient tumors. With these and other observations implicating Pim kinases as oncogenes and inhibitors of apoptosis, the overall goal of our research is to determine the functional significance of the Pim kinase family in PDAC growth. We hypothesize that inhibition of one or more Pims will be an effective approach for antagonizing the aberrant growth of pancreatic carcinoma. Our results will help to define the role of K-Ras activation in Pim upregulation and determine the consequences of Pim inhibition on PDAC growth. Furthermore, these findings will allow us to critically validate these kinases as novel therapeutic targets for PDAC treatment. Learning objectives: 1. Discuss the importance of oncogenic K-Ras and downstream signaling pathways in the development and progression of pancreatic cancer. 2. Discuss the roles of oncogenic Pim kinases in pancreatic cancer growth and as a potential drug target.

Invading cancer cells leave the tumor to form distant metastases and are ultimately responsible for 90% of deaths in cancer. Reducing the ability of cancer cells to invade and metastasize could extend the life of cancer patients. However, our current understanding of the conditions that trigger and guide the invasion of cancer cells is insufficient and our abilities to interfere with these processes are limited. By using novel microfluidic tools, we uncovered an unexpected ability of cancer cells to navigate and exit microscopic mazes along the shortest path. To explain this behavior, we propose a novel mechanism that guides cancer cell migration. This mechanism depends on the generation of spatial chemical gradients by the cancer cell themselves, through the competition between epidermal growth factor (EGF) uptake by the cells and the restricted diffusion of EGF from surrounding microenvironment to the cells. Employing this strategy when placed in uniform but confined environments, cancer cells can self-generate spatial gradients of EGF, effectively mapping the environment, and guiding their own escape from the confinement. Better understanding of the cancer cell guidance strategy by self-generated gradients could lead to approaches for restricting the migration of malignant cells to delay local invasion and distant metastases. Learning objectives: - Identify the environment settings that trigger self-guided cell migration; - Recognize the conditions that favor cancer cell invasion; - Predict the patterns of cancer cell migration in various conditions of confinement; - Suggest interventions that may perturb the self-guidance of cancer cells.

The discovery of proteinaceous disease biomarkers and their clinical validation is critically important for the enablement of molecular diagnostics and ultimately, precision medicine. In spite of the importance of biomarkers, research done in the last two decades has yielded limited success in enhancing the repertoire of protein biomarkers of disease. This apparent dichotomy is in large part due to the fact that the process from discovery to validation is complex and involves a multidisciplinary collaboration across clinical, biochemical and biophysical disciplines and that that many initiatives sought to identify unique markers for a given disease with perfect correlation. Recent advances in better biomarker discovery strategies, clinical sampling and sample storage, better sample preparation methods that consider the inherent instabilities associated with clinical specimens, better discovery methods and analytical instrumentation suited for assay validation and clinical assays together promise to generate better panel tests for disease biomarkers of clinical significance.

Invading cancer cells leave the tumor to form distant metastases and are ultimately responsible for 90% of deaths in cancer. Reducing the ability of cancer cells to invade and metastasize could extend the life of cancer patients. However, our current understanding of the conditions that trigger and guide the invasion of cancer cells is insufficient and our abilities to interfere with these processes are limited. By using novel microfluidic tools, we uncovered an unexpected ability of cancer cells to navigate and exit microscopic mazes along the shortest path. To explain this behavior, we propose a novel mechanism that guides cancer cell migration. This mechanism depends on the generation of spatial chemical gradients by the cancer cell themselves, through the competition between epidermal growth factor (EGF) uptake by the cells and the restricted diffusion of EGF from surrounding microenvironment to the cells. Employing this strategy when placed in uniform but confined environments, cancer cells can self-generate spatial gradients of EGF, effectively mapping the environment, and guiding their own escape from the confinement. Better understanding of the cancer cell guidance strategy by self-generated gradients could lead to approaches for restricting the migration of malignant cells to delay local invasion and distant metastases. Learning objectives: - Identify the environment settings that trigger self-guided cell migration; - Recognize the conditions that favor cancer cell invasion; - Predict the patterns of cancer cell migration in various conditions of confinement; - Suggest interventions that may perturb the self-guidance of cancer cells.

Personalized medicine is transforming biomedical research and healthcare service delivery. Disease definition, diagnosis, treatment, and prevention are being fundamentally altered by the capacity to routinely perform comprehensive molecular characterization. Nowhere is this change happening faster than in the field of cancer. Increasingly sophisticated technology provides the capacity to describe, in multiple molecular dimensions, the tumor and the individual in which it has developed. These technologies identify the millions of variants present in normal individuals and thousands of alterations that occur during the course of the disease process. This systems-wide molecular analysis of constitutional and somatic tissues has identified a complex cacophony of inherited and acquired variation. Coherence emerges from these data when evaluated using biologic networks as analytic frameworks. These networks account for the individual heterogeneity in underlying etiology as well as the diversity of events necessary to generate a complex phenotype such as cancer. Emerging collections of analytic approaches permit analysis using genome-wide data sets and established biologic networks as models. The generation of this unprecedented amount of data presents us with the challenge contextualizing that data and converting into actionable information. The integration and interpretation of this complex multidimensional information into the evidence necessary to support clinical care exceeds the raw human cognitive capacity. Information systems have the capacity to provide the needed tool to tackle this challenge to generate the necessary evidence to support the delivery of personalized medicine. Arizona State Universitys (ASU) Complex Adaptive Systems team is building such an Evidence Engine in its Next Generation Cyber Capability (NGCC). The ASU NGCC composed of networks, hardware, software, and people transforms Big Data to information and creates the evidence necessary to enable personalized medicine. Learning Objectives: *Understand that cancer displays the properties of a complex adaptive system. *Learn that the chaotic molecular patterns observed in cancer obtain coherence when examined in the context of biologic networks.

Curative therapy for metastatic disease in solid malignancies remains frustratingly elusive due to the long recognized problem of tumor cell heterogeneity and emergence of treatment resistant lesions. Cancer is a complex adaptive system. Tumor progression reflects the co-evolutionary dynamics of host-tumor interactions and the progressive selection of tumor clones of increasing fitness able to survive in varied host tissue microenvironments and to circumvent host defenses and therapeutic intervention. Improved success in confronting this intrinsic biological complexity requires major changes in current approaches to the discovery and clinical validation of new therapeutics. The following needs must be accorded higher priority. *new tools to map the dynamic topology of perturbed molecular signaling networks in malignancies arising in different cell lineages to better predict the compensatory pathways most likely to confer resistance to guide new drug development and as companion diagnostics for monitoring emergence of resistance *new approaches for therapeutic action at multiple targets in perturbed signaling networks via new combinations of targeted therapies or, more challenging, the design of promiscuous polypharmacology in a single agent *new minimally invasive blood-based diagnostics such as CTCs, ctDNA or exosomes for faster detection of resistance and agile changes in treatment regimens *new adaptive clinical trial protocols such as I-SPY for simultaneous evaluation of multiple agents and agile switching of agent combinations guided by biomarker profiling of response/resistance patterns. This will also require new regulatory paradigms to accommodate the increased statistical and logistical complexities of adaptive clinical trials and efficacy assessment of combinations of investigational agents and companion biomarkers *new value propositions for payors (and patients) linking reimbursement and pricing policies to more substantial improvements in clinical outcomes *new approaches for rapid updating of standard-of-care (SOC) guidelines and improved physician education to reflect advances in molecular oncology and greater adoption of routine molecular profiling of patients for improved therapy selection *new patient advocacy approaches to enhance patient education about the value of molecular profiling in treatment choice and increase the enrollment of eligible patients into investigational trials

Prostate cancer epithelial cells depend on androgens for their survival. Because of this dependence, androgen deprivation therapy is the major treatment of advanced prostate cancer. Such therapy is effective in delaying progression of the disease. However, inevitably with time, the tumors continue to grow. This stage of the disease is referred to as Castration Resistant Prostate Cancer (CRPC). A key question has been how CRPC can survive and then grow in the face of castrate levels of androgens. Dr. Tindalls laboratory has discovered a potential mechanism by which prostate cancer cells survive following androgen deprivation therapy. His laboratory team has shown for the first time that naturally occurring androgen receptor variants can support the expression of many endogenous androgen dependent target genes, as well as the growth of CRPC cells in the absence of androgens. These variants lack the androgen-binding-domain of the receptor protein and are constitutively active. Splice variants increase following androgen depletion and may play an important role in driving proliferation of cells in CRPC. This discovery provides important information to the pharmaceutical industry and may lead to new therapeutics for prostate cancer patients.

Cancer metastases develop when tumor cells known as circulating tumor cells (CTCs) are shed from a tumor, circulate through the blood stream and colonize a distant tissue. The number of CTCs detected in a metastatic breast cancer patient is currently used as an independent predictor of outcome and for serial monitoring of treatment response. There are many methods currently being developed to detect, capture, and analyze CTCs. Most methods including the only FDA-cleared method (CellSearch) are based on the detection of epithelial markers on the surface of CTCs. However, 40% of patients with metastatic breast cancer have no detectable CTCs using these criteria. The objective of this project was to develop a method to identify and define CTC subtypes (phenotypes) based on surface markers. The novel identification and subtyping we propose will improve the currently used clinical diagnostic which only enumerates CTCs with an epithelial phenotype. Our proposed method will detect a more comprehensive set of CTCs and determine a patients unique CTC subtype profile associated with a specific cancer outcome. Our strategy is to use a combination of antibodies to devise a subtyping schema capable of differentiating potential subtypes. We have tested this approach by using a library of antibodies to detect a panel of breast cancer cell lines including 4 different molecular categories of breast cancer cell lines: MCF-7 (luminal); MDA-MB-231 (mesenchymal-like); HCC1954 (basal A); and, SK-Br-3 (HER2). Our data indicates that we are able to subtype each of these 4 cell lines and provides preliminary evidence that this approach holds promise for identifying and subtyping heterogeneous circulating breast cancer cells. The ability to subtype CTCs will provide a non-invasive method to predict specific cancer outcomes such as metastatic potential, the location of organ-specific metastasis, dormancy versus non-dormancy, as well as to monitor treatment. The ability to predict future organ-specific metastases will greatly improve personalized treatment regimens and prevent the development of metastatic cancer.

DNASTAR offers an integrated suite of software for assembling and analyzing data from all major next-generation sequencing platforms. The software supports a variety of reference guided and de novo assembly workflows, including whole genomes, transcriptomes, targeted resequencing, ChIP-Seq and miRNA. In this presentation, we will show two different assembly projects that can be completed on a desktop computer using DNASTAR software: 1) bacterial genome assembly with gap closure using a reference genome from a related organism and 2) alignment of multiple human exomes against a reference genome that includes GenBank, dbSNP, Cosmic, and GERP annotations. After each assembly, the software can be used to view depth of coverage, read alignment, and SNPs and indels. SNPs can be filtered based on a variety of factors, including impact on gene function, phenotypic and ontology data for associated genes, and presence/absence in dbSNP and other databases. In the case of multiple samples, SNPs can also be compared across samples or groups. The integration of all these tools into one software package facilitates fast, comprehensive analysis, helping scientists move quickly from raw next-gen sequencing data to meaningful results. By using innovative algorithms within the software, scientists can have all of the assembly and analysis capabilities available to them on their desktop computer. Learner Objectives: Have a better understanding of sequence assembly and analysis software More familiarity with large-scale targeted re-sequencing (exome) assembly and analysis More familiarity with RNA-Seq expression analysis

Developments in DNA sequencing technology have provided a unique opportunity for diagnosis and discovery of genetic alterations for rare diseases. Partnering with 20 academic centers in the US and globally, we have coordinated and facilitated NGS projects for rare disease. We will summarize what we learned from all these sites for the entire process from consent, sample collection, library prep, sequencing, bioinformatics, interpretation, counseling, and clinical reimbursement.

Many drugs currently used for anti-cancer therapy demonstrate significant inter-individual variability that cannot be normalized using body weight or body surface area. There is an increasing amount of evidence in the scientific literature describing the use of therapeutic drug monitoring (TDM) to inform dosing of these drugs can lead to more effective treatment. This presentation will discuss the scientific literature addressing inter-individual variability of these drugs, the concept of maximum tolerated dose (MTD), and why an approach using maximum tolerated exposure (MTE) might be a better way to manage some chemotherapeutic drugs. Specific examples of TDM in oncology will be discussed including 5-fluorouracil, taxanes, and imatinib.

RNA-Seq allows the simultaneous observation of gene expression levels, mutations in the coding sequences, splice variants and gene fusions, which are especially important in cancer studies. Archived FFPE collections, which are stored for decades, must be unlocked to discover clinically relevant biomarkers. With the advent of new molecular techniques, specifically targeting FFPE and advances in automation, it is foreseeable that faster, more cost-efficient and reliable tools will facilitate the discovery of clinically relevant biomarkers. This study describes automated RNA isolation from archival FFPE tissue specimens using Agencourt FormaPure Kit and RNA library construction using the Beckman Coulter Biomek FXP. The library preparation method is based on the ScriptSeq RNA sample preparation protocol (Complete Gold low input). The ScriptSeq method is a streamlined, time-saving method that proved amenable to automation with limited optimization required. This automated method is suitable for up to ninety-six library constructions in parallel. As part of this automated method development we assessed; the overall RNA quality of the FFPE sample cohort, the total RNA input amount, SPRI bead-based clean-up strategies and optimization of key preparation steps. Libraries prepped on the Biomek automated platform provide end-users with a more reliable way to generate libraries from precious clinical samples in a faster turnaround time.

Recent data have confirmed the ability of cardiac troponin (cTn) measurements to identify those who are developing cardiotoxicity in response to potentially cardiotoxic chemotherapeutic agents. Indeed, in some studies, the use of antioxidant treatments have been shown to reduce the increases in cTn and with that the eventual development of reduced ventricular performance. The next iteration of cardiac troponin assays are substantially more sensitive than conventional assays and thus able to identify problems at a far earlier stage. Indeed, the pharmaceutical industry has been using such testing to determine during the developmental phases of discovery whether or not specific agents might be cardiotoxic. Thus, there is substantial promise that these new assays will facilitate these sorts of evaluations in patients receiving chemotherapy as well. However, these very sensitive assays detect a variety of cardiovascular comorbidities. These include not only coronary artery disease but also structural heart disease. Thus, baseline values will be essential the proper interpretation of subsequent values. In addition, because these assays are sensitive, careful sample acquisition and preparation will be essential and it is likely that sex specific cut off values will be necessary.  Of course, the early detection of cardiotoxicity is only half of the problem. We then will need to figure out how to optimally inhibit this toxicity while making sure we do not negatively impact on the therapy of the cancer itself.

Whenever there is cell death, apoptotic cell free DNA fragments appear in the circulation of the host. These fragments, typically 145-160 base pairs in size, represent a minute fraction of total DNA in the host circulation. If care is taken to avoid host cell breakage, the apoptotic DNA fragments can be analyzed to provide information about the genome of the cells that produced them. In pregnancy, analysis of apoptotic DNA fragments has allowed the development of very effective noninvasive aneuploidy testing. Indeed it is possible to reconstruct the entire fetal genomic DNA sequence from these fragments. In cancer it is possible to view copy number, sequence, and epigenetic differences between the tumor and the host by analyzing circulating free DNA. While this work is still at early stages it is reasonable to predict that an array of useful noninvasive cancer diagnostic tests should soon result from such studies. It is furthermore likely that a careful search for informative apoptotic DNA fragments that differ in methylation from DNA in the circulation of a normal host might lead to methods for the early detection of other clinical situations that are characterized by inappropriate cell death. Learning Objectives: Understand why stochastic noise limits the kinds of molecular testing possible from dilute sources like plasma Understand why non invasive prenatal testing can be extremely accurate but not perfect because of biological complexities

The widespread adoption of precision medicine in oncology requires: a compendium of therapies targeting the genetic vulnerabilities of cancer; the diagnostic tools capable of generating a precise molecular diagnosis; and importantly, the information systems to connect a patients molecular diagnosis to optimal treatment hypotheses. Here, we will present Life Technologies work to accelerate (1) defining the landscape of actionable driver genetic events in cancer, (2) developing a next-generation sequencing screen on the Ion Torrent semiconductor-sequencing platform, (3) assembling treatment-related information relevant for clinical research, and (4) devising a turnkey bioinformatics solution to democratize clinical sequencing.

While Melanoma is very curable and treatable when detected early, late stage melanoma has had a very poor outcome. In 2013, it is estimated that over 80,000 patients in the US will develop melanoma and 8,000 patients will die of this disease. Until 2011, the choices for the treatment of advanced melanoma included only chemotherapy of the immune cytokine, IL-2. Neither of these options was proven to prolong survival and neither worked very frequently (Response Rates ~15%). Recently, however, there have been 2 major advances in melanoma. One has been the discovery of highly specific BRAF inhibitors that can produce responses in 50-80% of patients with BRAF mutant melanoma. These patients constitute a sizable fraction of melanoma patients (50%) so can benefit a lot of patients. Another important advance has been the finding that immune checkpoint inhibitors such as ipilimumab (anti CTLA4 antibody) or anti-PD-1 antibodies can result in long lasting responses in a larger fraction of patients. Together, these and other drugs in the pipeline promise to transform the landscape of melanoma care in the next few years.

Deregulation of ACK1, a non-receptor tyrosine kinase originally identified by its ability to bind to GTP-bound Cdc42 (hence the name: activated Cdc42-associated kinase)(Manser et al., 1993), correlates with the ability of primary tumors to metastasize (Mahajan et al., 2007; Mahajan et al., 2005; van der Horst et al., 2005). To understand how specific mutations in ACK1 promote oncogenic processes, we have used Drosophila to investigate the roles of Ack family kinases during animal development. The general domain organization of Ack family genes includes a N-terminal SAM (sterile ? motif), tyrosine kinase, SH3 (Src homology 3), CRIB (Cdc42/Rac interacting binding domain), proline-rich region, and C-terminal UBA (ubiquitin-associating domain). Like mammals, Drosophila has two Ack homologs, dAck and dPR2, differing by the presence of the CRIB domain. Although dAck is more similar to ACK1 in amino acid sequence, it lacks the CRIB domain, raising the question whether dAck is a true homolog of ACK1. Using molecular genetics, we show that dAck is the functional homolog of ACK1 and its tyrosine kinase activity is essential for spermatogenesis in a cell autonomous manner. dAck forms a complex with Dock (dreadlocks, the Drosophila homolog of Nck), and its kinase activity is critical for Dock subcellular localization in differentiating male germ cells. Based on these results, we propose that dAck, via SH2-phosphotyrosine interaction, recruits Dock, which assembles multi-protein complexes with its SH3 domains to specific sites to promote sperm morphogenesis.

Non Small Cell Lung Cancer (NSCLC) is one of the most significant causes of cancer-related mortality worldwide. In about 6-7% of NSCLC, rearrangements involving the Anaplastic Lymphoma Kinase (ALK) play a strong driver role in tumor progression. Indeed, blocking ALKs kinase activity with the use of selective kinase inhibitors, such as crizotinib, induces an exceptionally high rate of clinical responses in ALK-rearranged NSCLC. Unfortunately, almost all patients relapse due to resistance to crizotinib developed in tumors by various molecular mechanisms. Therefore, developing additional therapeutic options for ALK-rearranged NSCLC is a pressing clinical need. The ALK protein has many, quite unique, features of an ideal tumor onco-antigen: it is specifically expressed by tumor cells with very low and limited expression in normal tissues, it is naturally immunogenic in humans, as indicated by studies on patients with ALK-rearranged tumors, and it is required for tumor survival and growth, which greatly decreases the chances of ALK negative clones to escape the immune system. In preclinical experiments in mouse models of ALK-driven cancers, we demonstrated that DNA-based ALK vaccine elicited a specific and powerful immune response against the ALK protein that was sufficient to strongly reduce the growth of ALK-rearranged lymphoma and lung carcinoma. Based on this data, we predict that a highly potent and safe ALK-targeted vaccine will be beneficial for the treatment of lung cancer and lymphoma patients. In principle, this vaccine could be extended to any ALK-expressing cancer.

While the Prostate Specific Antigen (PSA) blood test has been available since 1986 and FDA-approved for the early detection of prostate cancer since the early 1990s, 2012 marked a critical inflection point when the US Preventative Services Task Force (USPSTF) gave PSA a D-rating and generally discouraged its use. This D-rating was based on one randomized controlled trial (RCT) that showed no survival benefit to screening and another RCT that showed a survival benefit but at the cost of over-detection of non-life-threatening tumors. Despite the USPSTF, the PSA test remains widely used and many experts feel that the PSA test is still valuable when used in a more intelligent way. In using PSA more effectively, the American Urological Association (AUA) and the National Comprehensive Cancer Network (NCCN) suggest that all men have a baseline PSA test at age 40 to assess their future risk of prostate cancer. Using this baseline along with changes over time in the form of PSA Velocity (PSAV) or PSA Doubling Time (PSA-DT) can help doctors pick of prostate cancer in young men where finding cancers is more relevant. Further ways to use PSA smarter include age-adjusting PSA levels to increase detection of important cancers in young men while avoiding detection of non-life threatening cancers in older men. Taking into account other high risk features such as family history and race/ethnicity can fine tune early detection efforts. In addition to using total PSA, other PSA tools include the percent free PSA, complexed PSA, and the urine-based molecular marker, PCA-3 can further improve the accuracy of prostate cancer early detection efforts. Finally, if a prostate cancer is detected, better risk stratification can help doctors decide if a patient requires active treatment with surgery or radiation or active surveillance.

The remarkable diversity we see between different cell types in the human body is governed by the specificity attained through transcriptional and epigenetic regulatory programs. Cancer is a disease that targets specific tissues, and in the case of cancer-causing germline mutations, it is perplexing that primary tumors arise in a restricted subset of tissues only. Understanding why a mutation can be suppressed in one tissue but not others stands to unlock insights into tissue-specific transcriptional regulation and how these programs promote fragility or resistance of cancer-causing mutations. We have been studying cancer-causing germline mutations in the context of cell type-specific gene regulatory networks. Using a comprehensive tissue expression atlas from the FANTOM5 consortium, we have access to CAGE sequencing data that captures promoter usage and gene expression in over 1000 human samples, including primary cells, tissues and cell lines. Levering information from COSMIC, the Cancer Gene Census, and FANTOM5, two classes of genes that have tissues-specific, cancer-causing mutations have been identified (1) genes that are expressed in the cell type where the cancer occurs, (2) genes that are expressed ubiquitously across many different cell types. For this second class, we have begun comparing regulatory networks associated with these genes in susceptible versus resistant cell types to identify changes in network topology that may change a cell types oncogenic potential. Learning Objectives *To understand how normal human tissues use gene networks differently. *To appreciate that some genes with cancer-causing mutations are expressed in a wide range of tissues but tumors develop in only a restricted subset of those tissues.

Cancer cells have historically been classified by microscopic analysis of blood smears and tissue sections. Current technologies use molecular techniques to categorize and classify tumor cells by the identification of chromosomal aberrations, and the increased or decreased expression of genes and gene products. Both genomics (the study of genes) and proteomics (the study of proteins) are therefore essential components of molecular diagnostics, which determines how genes and proteins interact in a cell. The molecular signature or profile of a cancer cell can be determined by the presence or absence of gene activity in cancer and pre-cancerous cells by real time PCR, fluorescence in situ hybridization, microarrays, etc. This profile allows for enhanced detection, earlier and more accurate diagnosis and prognosis, and can aid the physician in determining if cancer cells persist after treatment, all of which, are factors that contribute to the highest quality in patient care. Objectives: 1. Contrast oncogenes with tumor-suppressor genes and list three of each 2. Explain loss of heterozygosity and a common mechanism of its detection 3. Describe FISH and its application in one type of cancer

While androgen deprivation therapy (ADT) remains the primary treatment modality for patients with metastatic prostate cancer (PCa), treatment is uniformly marked by progression to castration-resistant prostate cancer (CRPC). Accumulating data emphasize that androgen independent or hormone refractory tumors remain sensitive to hormonal activation and suggest that despite suppression of circulating testosterone (T), persistent activation of androgen receptor (AR) signaling plays a dominant role in driving progression. The introduction of novel agents targeting the AR signaling axis, including steroid synthesis inhibitors such as abiraterone and potent AR antagonists such as enzalutamide, have demonstrated striking responses in men with CRPC confirming the ongoing importance of AR signaling. However, not all patients respond, a majority develop clinical and PSA evidence of disease progression, and mechanisms of resistance to these agents are only beginning to be elucidated. In this presentation we will review AR-dependent mechanisms underlying disease progression, including tumoral appropriation of alternative androgen sources, alterations in AR expression, AR mutations, truncated AR variants, alterations and cross-talk in recruitment of co-factors to AR binding sites in the genome, and AR-driven oncogenic gene fusions. We will discuss the mechanism of action of new hormone-based therapies targeting the AR axis currently in clinical and pre-clinical development, and review emerging data that have begun to identify mechanisms of resistance to abiraterone and enzalutamide. Finally, we discuss current issues in the development of sequencing and combination treatment strategies, and in determining optimal patient selection for these agents. At the conclusion of this presentation the listener will 1) recognize the continued role of AR signaling in CRPC; 2) be able to identify tumor androgens and AR splice variants as key mechanisms mediating resistance to novel AR directed therapies, and 3) understand the opportunities for optimizing patient selection and the rationale for combination treatment strategies in the use of novel targeted agents in CRPC.

The effective implementation of personalised cancer therapeutic regimens depends on the successful identification and translation of informative biomarkers to aid clinical decision making. The utility and importance of cancer biomarkers has been recognised by substantial public and private funding, and biomarker discovery efforts are now commonplace in both academic and industrial settings. Advances in omic technologies, especially transcriptomics and proteomics, have led to a proliferation of potential cancer biomarkers. However, the pace of biomarker validation has not kept up with the extensive strides made in discovery. Key to successful translation of biomarkers into clinical settings is the establishment of robust, reproducible and informative assays. Antibody-based proteomics occupies a pivotal space in the cancer biomarker discovery and validation pipeline, facilitating the high-throughput evaluation of candidate markers. Although the clinical utility of such approaches remains to be established, the traditional use of antibodies as affinity reagents in clinical diagnostic and predictive assays suggests that rapid translation is an achievable goal. Furthermore, in combination with, or as alternatives to, genomic and transcriptomic methods for patient stratification, antibody-based proteomics approaches offer the promise of additional insight into cancer disease states. In this presentation, the current status of antibody-based proteomics will be covered, as will its contribution to the development of new assays that are crucial for the realisation of individualised cancer therapy. Moreover, the utility of digital pathology, particularly pertaining to automated assessment of tissue-derived biomarker expression, will also be described. The presentation will also provide a more general perspective of the advent of molecular diagnostics within the oncology arena, as well as touch upon several large-scale collaborative cancer research projects which are taking place at the international level that are addressing key issues in this space.

The eukaryotic translation initiation factor eIF4E is a potent oncogene estimated to be elevated in about 30% of human cancers including cancers of the breast, prostate, lung, colon as well as others. eIF4E is also elevated in hematological malignancies including M4 and M5 subtypes of acute myeloid leukemia (AML). eIF4E overexpression leads to oncogenic transformaiton in cell lines and to tumours in animal models. eIF4E plays critical roles in mRNA metabolism via its interactions with the methyl-7 guanosine (m7G) cap on the 5 end of mRNAs. eIF4E is best known for its role in mRNA translation but also plays roles in the export of mRNAs from the nucleus to the cytoplasm. Both of these functions are relevant to its oncogenic potential. New findings will be discussed with regard to newly defined roles of eIF4E in mRNA export. In particular, the ability of eIF4E to modulate the nuclear pore complex (NPC) in order to enhance the export function of eIF4E. In fact, these studies directly link its transformation capacity to its ability to modulate export of certain mRNAs. Further, these studies demonstrate that certain component of the NPC can suppress eIF4E mediated transformation. Finally, the clinical findings targeting eIF4E activity will be discussed. Our studies, including new NMR studies show that ribavirin directly binds the m7G cap-binding pocket of eIF4E. Ribavirin represses eIF4E dependent mRNA translation and mRNA export. In a Phase II clinical trial, ribavirin monothreapy treatment led to targeting of eIF4E activity in M4 /M5 AML patients and this correlated to clinical responses in patients. In an ongoing Phase I trial with ribavirin combined with cytarabine, eIF4E targeting and clinical responses have also been observed. Thus targeting eIF4E activity appears beneficial to patients. Learner Objectives: *Discuss the importance and possibility of targeting eIF4E in cancer *Discuss new mechanisms by which eIF4E can, in part, transform cells

Cancer and diabetes are complex diseases that have proven difficult to treat in the clinics. Until recently, most efforts have focused on hitting individual therapeutic targets cleanly. Unfortunately, success rates in clinical trials have been low: in the case of cancer, success rates for individual drugs have commonly been in single digits. This suggests that our models are poorly predictive of useful therapeutics. One issue is disease complexity. Another is the importance of modeling disease in the context of the whole animal. I will discuss our efforts using Drosophila to build complex cancer and diabetes models in the context of the whole animal. We utilize genomic sequencing data to create multi-hit fly models, either of disease populations or of individual patients. We then use automated systems to screen libraries for useful drugs. In addition, I will describe how we combine Drosophila genetics plus medicinal chemistry to create new generation candidate therapeutics.

Survival rates for early stage non-small cell lung cancer (NSCLC) remain unacceptably low compared to other common solid tumors. This mortality reflects a weakness in conventional staging, as 30-45% even of stage I patients harbor undetected metastasis. The need for better discrimination of high-risk patients in both stages I and II NSCLC is underscored by the documentation that adjuvant chemotherapy offers the possibility of cure for occult metastasis of NSCLC. Despite this possibility to improve survival through early intervention, however, many stage II and nearly all stage I patients with occult metastasis still forgo adjuvant chemotherapy because there has been no well-validated means of identifying those patients at highest risk. Gene expression profiles have become the gold standard for delineating risk and improving clinical decisions in early stage breast cancer. Previous efforts to develop a similarly practical and effective means of improving decision-making in the management of early stage NSCLC, however, have lacked both a clinically relevant approach and adequate large-scale validation. A PCR-based, 14-gene expression assay that uses paraffin-embedded tissue was recently reported to discriminate high- and low-risk patients with non-squamous NSCLC, and has been validated in ~1,500 patients in two large scale, international, independent, blinded studies (Lancet 2012;379:823, JAMA 2012;308:1629). The molecular profile outperforms all conventional risk criteria and staging methods at identifying patients at the highest risk of mortality after an attempt at curative surgery, even within individual staging groups. Published guidelines already recommend adjuvant chemotherapy for high-risk stage I and stage II patients; this molecular assay therefore represents a more effective means of implementing the current standard of care and of improving clinical decision-making in patients with early stage non-squamous, NSCLC.

"SuperSelective" primers, by virtue of their unique design, enable only a few molecules of a mutant sequence to generate amplicons in conventional, real-time PCR assays without interference from extremely abundant wild-type molecules, even if the only difference between the mutant sequence and the wild-type sequence is a single-nucleotide polymorphism. The basis of this extraordinary selectivity is two-fold: (1) short, perfectly complementary mutant hybrids formed by the primers are thermodynamically more likely to be present at any given moment than the even shorter, mismatched wild-type hybrids; and (2) because the mean persistence time of mismatched wild-type hybrids (measured in milliseconds) is significantly shorter than the mean persistence time of perfectly complementary mutant hybrids, DNA polymerase molecules are kinetically more likely to encounter mutant hybrids before those hybrids dissociate, compared to the probability of their encountering the shorter-lived wild-type hybrids before they dissociate. Consequently, as few as 10 mutant molecules can routinely be distinguished and quantitated in samples containing 1,000,000 wild-type molecules. Learner Objectives: After completing this activity, the learner will be able to design polymerase chain reaction assays for the detection of rare mutant cells for the sensitive detection of circulating tumor cell DNA, for the quantitation of minimal residual disease, and for the early detection of mutations associated with resistance to therapeutic drugs.

The current paradigm of detecting established cancer (often too late) and treating routinely with cytotoxic drugs is beginning to change. Advanced technologies such as whole genome sequencing (WGS/NGS), nanotechnology, imaging, bioinformatics and the convergence of the biological and physical sciences portends a future where personalized cancer medicine will be possible maybe even routine. However, this paradigm shift will require that a number of technical and scientific challenges be met and overcome. For example, WGS/NGS is producing multidimensional genomic and phenotypic multi-dimensional data at an unprecedented rate. Large scale genomic efforts such as The Cancer Genome Atlas are already producing multi-dimensional data sets that are unmanageable in the hands of single investigators. Cancer is quickly becoming digital information which means that we already require trained computational scientists and sophisticated analytical tools both of which are in short supply. Moreover, cancer occurs in context, so it is increasingly critical that mechanistic understanding of genomic alterations consider the cellular architecture/microenvironment in which these changes are decoded. Obviously true understanding of pathway dysfunction must also be interpreted across scales. This genomics-driven data tsunami represents major challenges on multiple fronts data quality, robust experimental design(s), standards and analysis to name a few. The massive amount of data needed to appropriately evaluate even a single patient will require teams of scientists and clinicians and large broadly available high quality data sets. Capitalizing on these opportunities for progress will require biospecimen and technology standards and appropriately qualified biomarkers developed through and end-to-end approaches that range from discovery to regulatory review. Finally, fully leveraging the outputs from the molecular sciences to both understand and control metastatic cancer will require de-convoluting the complexity of the disease. The thinking and new ideas to confront this level of complexity will likely to come from physicists, mathematicians and engineers working closely with cancer researchers. So while molecularly based medicine may well be our best strategy to date to finally defeat cancer, it is far from certain that this goal will be easily achieved or comprehensive in scope for the very complex set of diseases we collectively label cancer.

It is now appreciated that breast cancer is not a single disease, but instead is a spectrum of tumor subtypes with distinct cellular origins, somatic changes and somewhat predictable clinical behaviors. Gene expression data coming from DNA microarrays, and now sequencing-based technologies, has provided additional insights into the biology of breast cancer that has resulted in the development of a number of clinically useful assays. Our work on breast tumors has led to a new molecular taxonomy that identifies at least five subtypes of breast cancers (Luminal A, Luminal B, HER2-enriched, Basal-like and Claudin-low) and a normal breast-like group. Known as the intrinsic subtypes, these groups have revealed critical differences in incidence, survival, metastatic site specificity, and response to treatment. In addition to the intrinsic subtypes, many other important prognostic and predictive gene expression-based profiles have been identified. These tests have lead to the identification of a subset of ER-positive patients that have an extremely good outcome, and thus, for whom adjuvant endocrine therapy alone appears sufficient. Alternatively, the remaining patients/subtypes including Luminal B, HER2-enriched and Basal-like, show significantly worse prognoses, although targeted therapies for HER2+ patients have greatly improved their outcomes. To address the need for the targeting of Luminal B and Basal-like subtypes, genome sequencing approaches are being employed to provide a means of rationally selecting new targeted agents. Other genomics findings with therapeutic potential include the discovery that a subtype of breast cancer (i.e. the Basal-like subtype) shows significant genetic and mRNA expression similarities with Serous Ovarian Cancers; these similarities include TP53 mutation (>85% with almost no other commonly mutated genes), wide spread genomic instability, RB1-deficiency or Cyclin E1 amplification, BRCA1 inactivation, cMYC amplification, and high expression of AKT3. These common genetic/genomic features suggest that drug regimens that are used for Ovarian Cancer (i.e. a taxane and cis/carboplatin regimen) may also be effective on Basal-like breast cancers, which is being tested at a number of sites worldwide. Data from whole exome sequencing of Basal-like cancers will be presented and used as a model of how genome sequencing can contribute to the identification of potentially targetable driver mutations.

Cancer cells are characterized by major alterations in both cellular metabolism and epigenetic profiles. Current understanding of links between metabolism and chromatin in the context of cancer is currently very limited. We have previously demonstrated that acetylation of histones is sensitive to glucose availability through the enzyme ATP-citrate lyase (ACL), which produces acetyl-CoA from citrate. While this is likely to impact gene expression and other chromatin-dependent processes, the molecular mechanisms and functional significance of metabolic regulation of lysine acetylation are poorly understood. In this presentation, I will focus on current efforts to elucidate how metabolic signals are conveyed to chromatin. I will also discuss recent findings demonstrating a novel link between ACL and another chromatin-modifying enzyme, DNA methyltransferase 1, during adipocyte differentiation. Learning Objectives: Understand how changes in cellular metabolism impact chromatin modifications such as histone acetylation. Learn about the role of ATP-citrate lyase in regulating gene expression and epigenetics during adipocyte differentiation.

The adjuvant therapy of choice for superficial bladder cancer is the intravesical instillation of live Mycobacterium bovis Bacillus Calmette-Guerin (BCG). In spite of the fact that this therapy is the most effective treatment for superficial bladder cancer, intravesical administration of BCG is associated with high local morbidity and the potential for systemic infection. Therefore, there is a need for the development of safer, less toxic approaches to fight this disease. Since fibronectin attachment protein (FAP) is a key element in BCG retention and targeting to cells, we hypothesize that this protein can be used as targeting agent to deliver cytotoxic cargo for the treatment of bladder tumors. Here we evaluated the ability of bladder tumor cells to bind and endocytose FAP via fibronectin-integrin complexes. We found that micro-aggregation induced by an anti-FAP polyclonal antibody accelerated FAP uptake by bladder tumor cells. FAP-FBN-Integrin complexes were determined to be internalized via a clathrin-independent, caveolae-dependent mechanism. Further, once in the endosomal compartment, FAP was targeted to the lysosomal compartment with negligible recycling to the plasma membrane. Importantly, we demonstrated that FAP micro-aggregation and internalization could also be triggered by multivalent Ni2+NTA-bearing liposomes. Overall, our studies validate the use of FAP as a targeting vector and provide the foundations for the design of more effective, less toxic bladder cancer therapeutics.

With the rapid rise in the number of therapeutic options for men with castration-resistant prostate cancer (CRPC) comes increasingly complicated treatment decision-making, emphasizing the need for biomarkers that can identify appropriate patients for specific treatments and accurately assess benefit. While prognostic factors describe natural history, predictive biomarkers are factors related to the disease or the host that are associated with improvements in outcomes, such as survival, due to specific therapies. Surrogate biomarkers provide an early estimate of treatment effect as an intermediate endpoint for outcomes such as survival. Such biomarkers have become of increasing importance in oncology to maximize the benefits of novel systemic agents while minimizing the harms to individual patients and the costs to society. Given the number of newly approved and expensive systemic therapies, including novel hormonal therapies, chemotherapies, immunotherapies, and bone microenvironment-targeting therapies, predictive biomarkers are needed to give physicians a more rational sense of matching the right patient to the right therapy sequence at a given time. Here I discuss prognostic and potential predictive biomarkers in men with CRPC and discuss their potential role in the management of men with metastatic disease. Learning objectives: 1. Define prognostic and predictive biomarkers and apply this knowledge to metastatic prostate cancer patients based on established biomarkers in the castration-resistant setting. 2. Understand the role of surrogate biomarkers in drug development, particularly with respect to metastatic prostate cancer.

The knowledge of molecular alterations involved in colon carcinoma (CRC) and non-small-cell lung carcinoma (NSCLC) has significantly increased in the past few years. Molecular subgroups of tumors carrying mutations in druggable genes have been identified in CRC and NSCLC. In this respect, the availability of approaches that are compatible with small amounts of input DNA from FFPE tissue and that allow a rapid screening of a large number of somatic mutations is definitely needed. Ion AmpliSeq technology introduces a groundbreaking workflow enabling the rapid sequencing of hundreds of mutations with low allele frequency using as little as 10ng of DNA per reaction. The OncoNetwork Consortium is a European collaborative effort of 8 Cancer Translational Research Institutions to evaluate a custom panel targeting hotspots mutations in 22 genes implicated in CRC and NSCLC. In particular, 7 labs will test the gene panel in 3 phases using 180 retrospective FFPE samples previously tested with orthogonal technologies, and 1 lab will perform confirmatory testing on novel SNPs.