Cancer genomics: from discovery science to personalized medicine

TS mRNA levels can predict pemetrexed and raltitrexed sensitivity in colorectal cancer

PURPOSE

The purpose of the study is to analyze the relationship between tumor thymidylate synthase (TS) mRNA expression levels and raltitrexed/pemetrexed/5-FU sensitivity.

PATIENTS AND METHODS

We collected freshly removed colorectal tumor specimens from 50 patients. Chemosensitivities to anticancer drugs were evaluated by histoculture drug response assay. We adopted quantitative reverse transcription polymerase chain reaction for TS mRNA detection and immunohistochemical staining for assessing TS expression in tumor tissues.

RESULTS

There is a significant relationship between TS mRNA expression levels and in vitro chemosensitivity of freshly removed colorectal tumor specimens to pemetrexed (P < 0.001)/raltitrexed (P = 0.004)/5-FU (P = 0.007).

CONCLUSIONS

TS mRNA expression levels can predict pemetrexed and raltitrexed sensitivity in colorectal cancer.

Enhancing systems medicine beyond genotype data by dynamic patient signatures: having information and using it too

In order to establish systems medicine, based on the results and insights from basic biological research applicable for a medical and a clinical patient care, it is essential to measure patient-based data that represent the molecular and cellular state of the patient's pathology. In this paper, we discuss potential limitations of the sole usage of static genotype data, e.g., from next-generation sequencing, for translational research. The hypothesis advocated in this paper is that dynOmics data, i.e., high-throughput data that are capable of capturing dynamic aspects of the activity of samples from patients, are important for enabling personalized medicine by complementing genotype data.

Activating ESR1 mutations in hormone-resistant metastatic breast cancer

Breast cancer is the most prevalent cancer in women, and over two-thirds of cases express estrogen receptor-α (ER-α, encoded by ESR1). Through a prospective clinical sequencing program for advanced cancers, we enrolled 11 patients with ER-positive metastatic breast cancer. Whole-exome and transcriptome analysis showed that six cases harbored mutations of ESR1 affecting its ligand-binding domain (LBD), all of whom had been treated with anti-estrogens and estrogen deprivation therapies. A survey of The Cancer Genome Atlas (TCGA) identified four endometrial cancers with similar mutations of ESR1. The five new LBD-localized ESR1 mutations identified here (encoding p.Leu536Gln, p.Tyr537Ser, p.Tyr537Cys, p.Tyr537Asn and p.Asp538Gly) were shown to result in constitutive activity and continued responsiveness to anti-estrogen therapies in vitro. Taken together, these studies suggest that activating mutations in ESR1 are a key mechanism in acquired endocrine resistance in breast cancer therapy.

Network-based stratification of tumor mutations

Many forms of cancer have multiple subtypes with different causes and clinical outcomes. Somatic tumor genome sequences provide a rich new source of data for uncovering these subtypes but have proven difficult to compare, as two tumors rarely share the same mutations. Here we introduce network-based stratification (NBS), a method to integrate somatic tumor genomes with gene networks. This approach allows for stratification of cancer into informative subtypes by clustering together patients with mutations in similar network regions. We demonstrate NBS in ovarian, uterine and lung cancer cohorts from The Cancer Genome Atlas. For each tissue, NBS identifies subtypes that are predictive of clinical outcomes such as patient survival, response to therapy or tumor histology. We identify network regions characteristic of each subtype and show how mutation-derived subtypes can be used to train an mRNA expression signature, which provides similar information in the absence of DNA sequence.

Impact of exome sequencing in inflammatory bowel disease

Approaches to understanding the genetic contribution to inflammatory bowel disease (IBD) have continuously evolved from family- and population-based epidemiology, to linkage analysis, and most recently, to genome-wide association studies (GWAS). The next stage in this evolution seems to be the sequencing of the exome, that is, the regions of the human genome which encode proteins. The GWAS approach has been very fruitful in identifying at least 163 loci as being associated with IBD, and now, exome sequencing promises to take our genetic understanding to the next level. In this review we will discuss the possible contributions that can be made by an exome sequencing approach both at the individual patient level to aid with disease diagnosis and future therapies, as well as in advancing knowledge of the pathogenesis of IBD.

Optical spectroscopic methods for intraoperative diagnosis

Molecular analytical methods are increasingly needed for a quick and reliable analysis of tissue in an operating room to provide more information during operations. In this Trends article, we highlight the current state and the developments of optical spectroscopic methods as intra operative tools. The clinical problem and challenges are illustrated on the example of brain tumor surgery. While fluorescence microscopy is already used, vibrational spectroscopy techniques will complement the standard method for brain tissue diagnostics. New portable instruments are currently available and can be stationed in the operating room for quick evaluation of tissue. The promise and limitations of fluorescence and vibrational spectroscopy as intraoperative tools are surveyed in this report.

Cancer genomics identifies disrupted epigenetic genes

Latest advances in genome technologies have greatly advanced the discovery of epigenetic genes altered in cancer. The initial single candidate gene approaches have been coupled with newly developed epigenomic platforms to hasten the convergence of scientific discoveries and translational applications. Here, we present an overview of the evolution of cancer epigenomics and an updated catalog of disruptions in epigenetic pathways, whose misregulation can culminate in cancer. The creation of these basic mutational catalogs in cell lines and primary tumors will provide us with enough knowledge to move diagnostics and therapy from the laboratory bench to the bedside.

Single-cell mutational profiling and clonal phylogeny in cancer

The development of cancer is a dynamic evolutionary process in which intraclonal, genetic diversity provides a substrate for clonal selection and a source of therapeutic escape. The complexity and topography of intraclonal genetic architectures have major implications for biopsy-based prognosis and for targeted therapy. High-depth, next-generation sequencing (NGS) efficiently captures the mutational load of individual tumors or biopsies. But, being a snapshot portrait of total DNA, it disguises the fundamental features of subclonal variegation of genetic lesions and of clonal phylogeny. Single-cell genetic profiling provides a potential resolution to this problem, but methods developed to date all have limitations. We present a novel solution to this challenge using leukemic cells with known mutational spectra as a tractable model. DNA from flow-sorted single cells is screened using multiplex targeted Q-PCR within a microfluidic platform allowing unbiased single-cell selection, high-throughput, and comprehensive analysis for all main varieties of genetic abnormalities: chimeric gene fusions, copy number alterations, and single-nucleotide variants. We show, in this proof-of-principle study, that the method has a low error rate and can provide detailed subclonal genetic architectures and phylogenies.

CancerDR: cancer drug resistance database

Cancer therapies are limited by the development of drug resistance, and mutations in drug targets is one of the main reasons for developing acquired resistance. The adequate knowledge of these mutations in drug targets would help to design effective personalized therapies. Keeping this in mind, we have developed a database “CancerDR”, which provides information of 148 anti-cancer drugs, and their pharmacological profiling across 952 cancer cell lines. CancerDR provides comprehensive information about each drug target that includes; (i) sequence of natural variants, (ii) mutations, (iii) tertiary structure, and (iv) alignment profile of mutants/variants. A number of web-based tools have been integrated in CancerDR. This database will be very useful for identification of genetic alterations in genes encoding drug targets, and in turn the residues responsible for drug resistance. CancerDR allows user to identify promiscuous drug molecules that can kill wide range of cancer cells. CancerDR is freely accessible at http://crdd.osdd.net/raghava/cancerdr/

Patient-derived xenografts, the cancer stem cell paradigm, and cancer pathobiology in the 21st century

Cancer is a heterogeneous disease manifest in many forms. Tumor histopathology can differ significantly among patients and cellular heterogeneity within tumors is common. A primary goal of cancer biologists is to better understand tumorigenesis and cancer progression; however, the complex nature of tumors has posed a substantial challenge to unlocking cancer's secrets. The cancer stem cell (CSC) paradigm for the pathobiology of solid tumors appropriately acknowledges phenotypic and functional tumor cell heterogeneity observed in solid tumors and accounts for the disconnect between drug approval based on response and the general inability of approved therapies to meaningfully impact survival due to their failure to eradicate these most important of cellular targets. First proposed to exist decades ago, CSC have only recently begun to be precisely identified due to technical advancements that facilitate identification, isolation, and interrogation of distinct tumor cell subpopulations with differing ability to form and perpetuate tumors. Precise identification of CSC populations and the complete hierarchy of cells within solid tumors will facilitate more accurate characterization of patient subtypes and ultimately contribute to more personalized and effective therapies. Rapid advancement in the understanding of tumor biology as it exists in patients requires cooperation among institutions, surgeons, pathologists, cancer biologists and patients alike, primarily because this translational research is best done with patient-derived tissue grown in the xenograft setting as patient-derived xenografts. This review calls for a broader change in the approaches taken to study cancer pathobiology, highlights what implications the CSC paradigm has for pathologists and cancer biologists alike, and calls for greater collaboration between institutions, physicians and scientists in order to more rapidly advance our collective understanding of cancer.

A novel algorithm for simplification of complex gene classifiers in cancer

The clinical application of complex molecular classifiers as diagnostic or prognostic tools has been limited by the time and cost needed to apply them to patients. Using an existing 50-gene expression signature known to separate two molecular subtypes of the pediatric cancer rhabdomyosarcoma, we show that an exhaustive iterative search algorithm can distill this complex classifier down to two or three features with equal discrimination. We validated the two-gene signatures using three separate and distinct datasets, including one that uses degraded RNA extracted from formalin-fixed, paraffin-embedded material. Finally, to show the generalizability of our algorithm, we applied it to a lung cancer dataset to find minimal gene signatures that can distinguish survival. Our approach can easily be generalized and coupled to existing technical platforms to facilitate the discovery of simplified signatures that are ready for routine clinical use.

Structural analysis of mitochondrial mutations reveals a role for bigenomic protein interactions in human disease

Mitochondria are the energy producing organelles of the cell, and mutations within their genome can cause numerous and often severe human diseases. At the heart of every mitochondrion is a set of five large multi-protein machines collectively known as the mitochondrial respiratory chain (MRC). This cellular machinery is central to several processes important for maintaining homeostasis within cells, including the production of ATP. The MRC is unique due to the bigenomic origin of its interacting proteins, which are encoded in the nucleus and mitochondria. It is this, in combination with the sheer number of protein-protein interactions that occur both within and between the MRC complexes, which makes the prediction of function and pathological outcome from primary sequence mutation data extremely challenging. Here we demonstrate how 3D structural analysis can be employed to predict the functional importance of mutations in mtDNA protein-coding genes. We mined the MITOMAP database and, utilizing the latest structural data, classified mutation sites based on their location within the MRC complexes III and IV. Using this approach, four structural classes of mutation were identified, including one underexplored class that interferes with nuclear-mitochondrial protein interactions. We demonstrate that this class currently eludes existing predictive approaches that do not take into account the quaternary structural organization inherent within and between the MRC complexes. The systematic and detailed structural analysis of disease-associated mutations in the mitochondrial Complex III and IV genes significantly enhances the predictive power of existing approaches and our understanding of how such mutations contribute to various pathologies. Given the general lack of any successful therapeutic approaches for disorders of the MRC, these findings may inform the development of new diagnostic and prognostic biomarkers, as well as new drugs and targets for gene therapy.

The autoimmune side of heart and lung diseases

The elevated cardiovascular morbidity in rheumatoid arthritis, systemic lupus erythematosus, and the antiphospholipid syndrome is well known, as well as the pulmonary involvement observed in these conditions and to a major extent in systemic sclerosis. These manifestations constitute a major challenge for clinicians involved in patient management. Moreover, several issues regarding the link between autoimmune rheumatic diseases and cardio pulmonary morbidity remain largely enigmatic. The mechanistic role of certain autoantibodies frequently observed in association with heart and lung diseases or the pathogenetic link between chronic inflammation and the pathways leading to atherosclerosis or pulmonary vascular changes are yet to be elucidated. As such, these questions as well as treatment strategies are of common interest to rheumatologists, immunologist, pulmonologists, and cardiologists and thus call for an interdisciplinary approach. This paradigm has been well established for rare conditions such as the Churg-Strauss syndrome. Nowadays, it seems that this approach should be expanded to encompass more common conditions such as coronary heart disease, pulmonary arterial hypertension or dilated cardiomyopathy. The present issue of Clinical Reviews in Allergy and Immunology addresses the new knowledge and concepts of autoimmune-related cardiopulmonary diseases. The issue derives from the 2010 International Autoimmunity Meeting held in Ljubljana, Slovenia and is thus timely and dedicated to the latest developments in this new multidisciplinary field.

Integrating multi-platform genomic data using hierarchical Bayesian relevance vector machines

Background

Recent advances in genome technologies and the subsequent collection of genomic information at various molecular resolutions hold promise to accelerate the discovery of new therapeutic targets. A critical step in achieving these goals is to develop efficient clinical prediction models that integrate these diverse sources of high-throughput data. This step is challenging due to the presence of high-dimensionality and complex interactions in the data. For predicting relevant clinical outcomes, we propose a flexible statistical machine learning approach that acknowledges and models the interaction between platform-specific measurements through nonlinear kernel machines and borrows information within and between platforms through a hierarchical Bayesian framework. Our model has parameters with direct interpretations in terms of the effects of platforms and data interactions within and across platforms. The parameter estimation algorithm in our model uses a computationally efficient variational Bayes approach that scales well to large high-throughput datasets.

Results

We apply our methods of integrating gene/mRNA expression and microRNA profiles for predicting patient survival times to The Cancer Genome Atlas (TCGA) based glioblastoma multiforme (GBM) dataset. In terms of prediction accuracy, we show that our non-linear and interaction-based integrative methods perform better than linear alternatives and non-integrative methods that do not account for interactions between the platforms. We also find several prognostic mRNAs and microRNAs that are related to tumor invasion and are known to drive tumor metastasis and severe inflammatory response in GBM. In addition, our analysis reveals several interesting mRNA and microRNA interactions that have known implications in the etiology of GBM.

Conclusions

Our approach gains its flexibility and power by modeling the non-linear interaction structures between and within the platforms. Our framework is a useful tool for biomedical researchers, since clinical prediction using multi-platform genomic information is an important step towards personalized treatment of many cancers. We have a freely available software at: http://odin.mdacc.tmc.edu/~vbaladan.

Whole-genome sequencing identifies recurrent mutations in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is one of the most deadly cancers worldwide and has no effective treatment, yet the molecular basis of hepatocarcinogenesis remains largely unknown. Here we report findings from a whole-genome sequencing (WGS) study of 88 matched HCC tumor/normal pairs, 81 of which are Hepatitis B virus (HBV) positive, seeking to identify genetically altered genes and pathways implicated in HBV-associated HCC. We find beta-catenin to be the most frequently mutated oncogene (15.9%) and TP53 the most frequently mutated tumor suppressor (35.2%). The Wnt/beta-catenin and JAK/STAT pathways, altered in 62.5% and 45.5% of cases, respectively, are likely to act as two major oncogenic drivers in HCC. This study also identifies several prevalent and potentially actionable mutations, including activating mutations of Janus kinase 1 (JAK1), in 9.1% of patients and provides a path toward therapeutic intervention of the disease.

A poor prognosis subtype of HNSCC is consistently observed across methylome, transcriptome, and miRNome analysis

PURPOSE

Distant metastasis after treatment is observed in about 20% of squamous cell carcinoma of the head and neck (HNSCC). In the absence of any validated robust biomarker, patients at higher risk for metastasis cannot be provided with tailored therapy. To identify prognostic HNSCC molecular subgroups and potential biomarkers, we have conducted genome-wide integrated analysis of four omic sets of data.

EXPERIMENTAL DESIGN

Using state-of-the-art technologies, a core set of 45 metastasizing and 55 nonmetastasizing human papillomavirus (HPV)-unrelated HNSCC patient samples were analyzed at four different levels: gene expression (transcriptome), DNA methylation (methylome), DNA copy number (genome), and microRNA (miRNA) expression (miRNome). Molecular subgroups were identified by a model-based clustering analysis. Their clinical relevance was evaluated by survival analysis, and functional significance by pathway enrichment analysis.

RESULTS

Patient subgroups selected by transcriptome, methylome, or miRNome integrated analysis are associated with shorter metastasis-free survival (MFS). A common subgroup, R1, selected by all three omic approaches, is statistically more significantly associated with MFS than any of the single omic-selected subgroups. R1 and non-R1 samples display similar DNA copy number landscapes, but more frequent chromosomal aberrations are observed in the R1 cluster (especially loss at 13q14.2-3). R1 tumors are characterized by alterations of pathways involved in cell-cell adhesion, extracellular matrix (ECM), epithelial-to-mesenchymal transition (EMT), immune response, and apoptosis.

CONCLUSIONS

Integration of data across several omic profiles leads to better selection of patients at higher risk, identification of relevant molecular pathways of metastasis, and potential to discover biomarkers and drug targets.

Revealing selection in cancer using the predicted functional impact of cancer mutations. Application to nomination of cancer drivers

Every malignant tumor has a unique spectrum of genomic alterations including numerous protein mutations. There are also hundreds of personal germline variants to be taken into account. The combinatorial diversity of potential cancer-driving events limits the applicability of statistical methods to determine tumor-specific "driver" alterations among an overwhelming majority of "passengers". An alternative approach to determining driver mutations is to assess the functional impact of mutations in a given tumor and predict drivers based on a numerical value of the mutation impact in a particular context of genomic alterations.Recently, we introduced a functional impact score, which assesses the mutation impact by the value of entropic disordering of the evolutionary conservation patterns in proteins. The functional impact score separates disease-associated variants from benign polymorphisms with an accuracy of ~80%. Can the score be used to identify functionally important non-recurrent cancer-driver mutations? Assuming that cancer-drivers are positively selected in tumor evolution, we investigated how the functional impact score correlates with key features of natural selection in cancer, such as the non-uniformity of distribution of mutations, the frequency of affected tumor suppressors and oncogenes, the frequency of concurrent alterations in regions of heterozygous deletions and copy gain; as a control, we used presumably non-selected silent mutations. Using mutations of six cancers studied in TCGA projects, we found that predicted high-scoring functional mutations as well as truncating mutations tend to be evolutionarily selected as compared to low-scoring and silent mutations. This result justifies prediction of mutations-drivers using a shorter list of predicted high-scoring functional mutations, rather than the "long tail" of all mutations.

Inside out: targeting NHE1 as an intracellular and extracellular regulator of cancer progression

The sodium hydrogen exchanger isoform one is a critical regulator of intracellular pH, serves as an anchor for the formation of cytoplasmic signaling complexes, and modulates cytoskeletal organization. There is a growing interest in the potential for sodium hydrogen exchanger isoform one as a therapeutic target against cancer. Sodium hydrogen exchanger isoform one transport drives formation of membrane protrusions essential for cell migration and contributes to the establishment of a tumor microenvironment that leads to the rearrangement of the extracellular matrix further supporting tumor progression. Here, we focus on the potential impact that an inexpensive, $100 genome would have in identifying prospective therapeutic targets to treat tumors based upon changes in gene expression and variation of sodium hydrogen exchanger isoform one regulators. In particular, we will focus on the ezrin, radixin, moesin family proteins, calcineurin B homologous proteins, Ras/Raf/MEK/ERK signaling, and phosphoinositide signaling as they relate to the regulation of sodium hydrogen exchanger isoform one in cancer progression.

Molecular classification of non-small-cell lung cancer: diagnosis, individualized treatment, and prognosis

Non-small-cell lung cancer (NSCLC) is the most common cause of premature death among the malignant diseases worldwide. The current staging criteria do not fully capture the complexity of this disease. Molecular biology techniques, particularly gene expression microarrays, proteomics, and next-generation sequencing, have recently been developed to facilitate effectively its molecular classification. The underlying etiology, pathogenesis, therapeutics, and prognosis of NSCLC based on an improved molecular classification scheme may promote individualized treatment and improve clinical outcomes. This review focuses on the molecular classification of NSCLC based on gene expression microarray technology reported during the past decade, as well as their applications for improving the diagnosis, staging and treatment of NSCLC, including the discovery of prognostic markers or potential therapeutic targets. We highlight some of the recent studies that may refine the identification of NSCLC subtypes using novel techniques such as epigenetics, proteomics, or deep sequencing.

From finger pricks to point-and-click

In this issue of Immunity, Obermoser et al. (2013) systematically analyze and compare the blood-transcriptomic response of the pneumococcal and influenza vaccines in humans over multiple time points spanning hours to tens of days. They then present web-based interactive figures that facilitate exploration of this large, complex data set.

Identification of targetable FGFR gene fusions in diverse cancers

Through a prospective clinical sequencing program for advanced cancers, four index cases were identified which harbor gene rearrangements of FGFR2, including patients with cholangiocarcinoma, breast cancer, and prostate cancer. After extending our assessment of FGFR rearrangements across multiple tumor cohorts, we identified additional FGFR fusions with intact kinase domains in lung squamous cell cancer, bladder cancer, thyroid cancer, oral cancer, glioblastoma, and head and neck squamous cell cancer. All FGFR fusion partners tested exhibit oligomerization capability, suggesting a shared mode of kinase activation. Overexpression of FGFR fusion proteins induced cell proliferation. Two bladder cancer cell lines that harbor FGFR3 fusion proteins exhibited enhanced susceptibility to pharmacologic inhibition in vitro and in vivo. Because of the combinatorial possibilities of FGFR family fusion to a variety of oligomerization partners, clinical sequencing efforts, which incorporate transcriptome analysis for gene fusions, are poised to identify rare, targetable FGFR fusions across diverse cancer types.

The Warburg effect version 2.0: metabolic reprogramming of cancer stem cells

When fighting cancer, knowledge on metabolism has always been important. Today, it matters more than ever. The restricted cataloging of cancer genomes is quite unlikely to achieve the task of curing cancer, unless it is integrated into metabolic networks that respond to and influence the constantly evolving cancer stem cell (CSC) cellular states. Once the genomic era of carcinogenesis had pushed the 1920s Otto Warburg's metabolic cancer hypothesis into obscurity for decades, the most recent studies begin to support a new developing paradigm, in which the molecular logic behind the conversion of non-CSCs into CSCs can be better understood in terms of the "metabolic facilitators" and "metabolic impediments" that operate as proximate openings and roadblocks, respectively, for the transcriptional events and signal transduction programs that ultimately orchestrate the intrinsic and/or microenvironmental paths to CSC cellular states. Here we propose that a profound understanding of how human carcinomas install a proper "Warburg effect version 2.0" allowing them to "run" the CSCs' "software" programs should guide a new era of metabolo-genomic-personalized cancer medicine. By viewing metabolic reprogramming of CSCs as an essential characteristic that allows dynamic, multidimensional and evolving cancer populations to compete successfully for their expansion on the organism, we now argue that CSCs bioenergetics might be another cancer hallmark. A definitive understanding of metabolic reprogramming in CSCs may complement or to some extent replace, the 30-y-old paradigm of targeting oncogenes to treat human carcinomas, because it can be possible to metabolically create non-permissive or "hostile" metabotypes to prevent the occurrence of CSC cellular states with tumor- and metastasis-initiating capacity.

Integrative molecular analysis of intrahepatic cholangiocarcinoma reveals 2 classes that have different outcomes

BACKGROUND & AIMS

Cholangiocarcinoma, the second most common liver cancer, can be classified as intrahepatic cholangiocarcinoma (ICC) or extrahepatic cholangiocarcinoma. We performed an integrative genomic analysis of ICC samples from a large series of patients.

METHODS

We performed a gene expression profile, high-density single-nucleotide polymorphism array, and mutation analyses using formalin-fixed ICC samples from 149 patients. Associations with clinicopathologic traits and patient outcomes were examined for 119 cases. Class discovery was based on a non-negative matrix factorization algorithm and significant copy number variations were identified by Genomic Identification of Significant Targets in Cancer (GISTIC) analysis. Gene set enrichment analysis was used to identify signaling pathways activated in specific molecular classes of tumors, and to analyze their genomic overlap with hepatocellular carcinoma (HCC).

RESULTS

We identified 2 main biological classes of ICC. The inflammation class (38% of ICCs) is characterized by activation of inflammatory signaling pathways, overexpression of cytokines, and STAT3 activation. The proliferation class (62%) is characterized by activation of oncogenic signaling pathways (including RAS, mitogen-activated protein kinase, and MET), DNA amplifications at 11q13.2, deletions at 14q22.1, mutations in KRAS and BRAF, and gene expression signatures previously associated with poor outcomes for patients with HCC. Copy number variation-based clustering was able to refine these molecular groups further. We identified high-level amplifications in 5 regions, including 1p13 (9%) and 11q13.2 (4%), and several focal deletions, such as 9p21.3 (18%) and 14q22.1 (12% in coding regions for the SAV1 tumor suppressor). In a complementary approach, we identified a gene expression signature that was associated with reduced survival times of patients with ICC; this signature was enriched in the proliferation class (P < .001).

CONCLUSIONS

We used an integrative genomic analysis to identify 2 classes of ICC. The proliferation class has specific copy number alterations, activation of oncogenic pathways, and is associated with worse outcome. Different classes of ICC, based on molecular features, therefore might require different treatment approaches.

Development of therapeutic combinations targeting major cancer signaling pathways

Signaling networks play key homeostatic processes in living organisms but are commonly hijacked in oncogenesis. Prominent examples include genetically altered receptor tyrosine kinases and dysregulated intracellular signaling molecules. The discovery and development of targeted therapies against such oncogenic proteins has imparted clinical benefit. Nevertheless, concerns remain about the limited single-agent efficacy and narrow therapeutic indices of many of these antitumor agents. Moreover, it is apparent that oncogenic proteins comprise complex signaling networks that interact through crosstalk and feedback loops, which modify therapeutic vulnerability. These complexities mandate the study of drug combinations, which will also become necessary to reverse tumor drug resistance. Here, we outline the challenges associated with rational drug codevelopment strategies, with a focus on the importance of analytically validated biomarkers for patient selection and pharmacokinetic-pharmacodynamic (PK-PD) studies. Overall, the most informative clinical studies of novel combinations will have the following characteristics: robust scientific hypotheses leading to their selection; supportive preclinical data from contextually appropriate preclinical model systems; sufficient preclinical PK data to inform on the risk of drug-drug interactions; and detailed PD studies to determine the biologically active dose range for each agent. Toward this end, several novel clinical trial designs may be envisioned to accelerate successful drug combination development while minimizing the risk of late drug combination attrition. Although considerable challenges remain, these efforts may enable important steps to be taken toward more durable therapeutic control of many cancers.

Genotyping and genomic profiling of non-small-cell lung cancer: implications for current and future therapies

Substantial advances have been made in understanding critical molecular and cellular mechanisms driving tumor initiation, maintenance, and progression in non-small-cell lung cancer (NSCLC). Over the last decade, these findings have led to the discovery of a variety of novel drug targets and the development of new treatment strategies. Already, the standard of care for patients with advanced-stage NSCLC is shifting from selecting therapy empirically based on a patient's clinicopathologic features to using biomarker-driven treatment algorithms based on the molecular profile of a patient's tumor. This approach is currently best exemplified by treating patients with NSCLC with first-line tyrosine kinase inhibitors when their cancers harbor gain-of-function hotspot mutations in the epidermal growth factor receptor (EGFR) gene or anaplastic lymphoma kinase (ALK) gene rearrangements. These genotype-based targeted therapies represent the first step toward personalizing NSCLC therapy. Recent technology advances in multiplex genotyping and high-throughput genomic profiling by next-generation sequencing technologies now offer the possibility of rapidly and comprehensively interrogating the cancer genome of individual patients from small tumor biopsies. This advance provides the basis for categorizing molecular-defined subsets of patients with NSCLC in whom a growing list of novel molecularly targeted therapeutics are clinically evaluable and additional novel drug targets can be discovered. Increasingly, practicing oncologists are facing the challenge of determining how to select, interpret, and apply these new genetic and genomic assays. This review summarizes the evolution, early success, current status, challenges, and opportunities for clinical application of genotyping and genomic tests in therapeutic decision making for NSCLC.

Next-generation sequencing discoveries in lymphoma

Since the mapping of the human genome and the advent of next-generation sequencing technology thorough examination of the cancer genome has become a reality. Over the last few years several studies have used next-generation sequencing technology to investigate the genetic landscape of Hodgkin and non-Hodgkin lymphomas, identifying novel genetic mutations and gene rearrangements that have shed new light on the underlying tumor biology in these diseases as well as identifying possible targets for directed therapy. This review covers the major discoveries in lymphoma using next-generation sequencing technology.

Chemical kinetic mechanistic models to investigate cancer biology and impact cancer medicine

Traditional experimental biology has provided a mechanistic understanding of cancer in which the malignancy develops through the acquisition of mutations that disrupt cellular processes. Several drugs developed to target such mutations have now demonstrated clinical value. These advances are unequivocal testaments to the value of traditional cellular and molecular biology. However, several features of cancer may limit the pace of progress that can be made with established experimental approaches alone. The mutated genes (and resultant mutant proteins) function within large biochemical networks. Biochemical networks typically have a large number of component molecules and are characterized by a large number of quantitative properties. Responses to a stimulus or perturbation are typically nonlinear and can display qualitative changes that depend upon the specific values of variable system properties. Features such as these can complicate the interpretation of experimental data and the formulation of logical hypotheses that drive further research. Mathematical models based upon the molecular reactions that define these networks combined with computational studies have the potential to deal with these obstacles and to enable currently available information to be more completely utilized. Many of the pressing problems in cancer biology and cancer medicine may benefit from a mathematical treatment. As work in this area advances, one can envision a future where such models may meaningfully contribute to the clinical management of cancer patients.

Activating HER2 mutations in HER2 gene amplification negative breast cancer

Data from 8 breast cancer genome-sequencing projects identified 25 patients with HER2 somatic mutations in cancers lacking HER2 gene amplification. To determine the phenotype of these mutations, we functionally characterized 13 HER2 mutations using in vitro kinase assays, protein structure analysis, cell culture, and xenograft experiments. Seven of these mutations are activating mutations, including G309A, D769H, D769Y, V777L, P780ins, V842I, and R896C. HER2 in-frame deletion 755-759, which is homologous to EGF receptor (EGFR) exon 19 in-frame deletions, had a neomorphic phenotype with increased phosphorylation of EGFR or HER3. L755S produced lapatinib resistance, but was not an activating mutation in our experimental systems. All of these mutations were sensitive to the irreversible kinase inhibitor, neratinib. These findings show that HER2 somatic mutation is an alternative mechanism to activate HER2 in breast cancer and they validate HER2 somatic mutations as drug targets for breast cancer treatment.

SIGNIFICANCE

We show that the majority of HER2 somatic mutations in breast cancer patients are activating mutations that likely drive tumorigenesis. Several patients had mutations that are resistant to the reversible HER2 inhibitor lapatinib, but are sensitive to the irreversible HER2 inhibitor, neratinib. Our results suggest that patients with HER2 mutation–positive breast cancers could benefit from existing HER2-targeted drugs.

Collections of simultaneously altered genes as biomarkers of cancer cell drug response

Computational analysis of cancer pharmacogenomics data has resulted in biomarkers predictive of drug response, but the majority of response is not captured by current methods. Methods typically select single biomarkers or groups of related biomarkers but do not account for response that is strictly dependent on many simultaneous genetic alterations. This shortcoming reflects the combinatorics and multiple-testing problem associated with many-body biologic interactions. We developed a novel approach, Multivariate Organization of Combinatorial Alterations (MOCA), to partially address these challenges. Extending on previous work that accounts for pairwise interactions, the approach rapidly combines many genomic alterations into biomarkers of drug response, using Boolean set operations coupled with optimization; in this framework, the union, intersection, and difference Boolean set operations are proxies of molecular redundancy, synergy, and resistance, respectively. The algorithm is fast, broadly applicable to cancer genomics data, is of immediate use for prioritizing cancer pharmacogenomics experiments, and recovers known clinical findings without bias. Furthermore, the results presented here connect many important, previously isolated observations.

Personalizing lung cancer prevention through a reverse migration strategy

Lung cancer is the deadliest cancer in the United States and worldwide. Tobacco use is the one of the primary causes of lung cancer and smoking cessation is an important step towards prevention, but patients who have quit smoking remain at risk for lung cancer. Finding pharmacologic agents to prevent lung cancer could potentially save many lives. Unfortunately, despite extensive research, there are no known effective chemoprevention agents for lung cancer. Clinical trials in the past, using agents without a clear target in an unselected population, have shown pharmacologic interventions to be ineffective or even harmful. We propose a new approach to drug development in the chemoprevention setting: reverse migration, that is, drawing on our experience in the treatment of advanced cancer to bring agents, biomarkers, and study designs into the prevention setting. By identifying molecular drivers of lung neoplasia and using matched targeted agents, we hope to personalize therapy to each individual to develop more effective, tolerable chemoprevention. Also, advances in risk modeling, using not only clinical characteristics but also biomarkers, may help us to select patients better for chemoprevention efforts, thus sparing patients at low risk for cancer the potential toxicities of treatment. Our institution has experience with biomarker-driven clinical trials, as in the recently reported Biomarker-integrated Approaches of Targeted Therapy for Lung Cancer Elimination (BATTLE) trial, and we now propose to bring this trial design into the prevention setting.

Drug discovery in rare indications: opportunities and challenges

Over the past decade, the number of new therapies developed for the treatment of rare diseases continues to increase. The most rapid growth has been in the development of new drugs for oncology indications. One focus in drug discovery for oncology indications is the development of targeted therapies for select patient subgroups characterized by genetic alterations. The identification of these patient subgroups has increased in the past decade and has resulted in a corresponding increase in the development of new drugs for genetically defined patient subgroups. As an example of the development of new therapeutics for rare indications, I describe here the drug discovery efforts leading to the development of DOT1L inhibitors for the treatment of MLL-rearranged leukemia.

Strategies to modulate heritable epigenetic defects in cellular machinery: lessons from nature

Natural epigenetic processes precisely orchestrate the intricate gene network by expressing and suppressing genes at the right place and time, thereby playing an essential role in maintaining the cellular homeostasis. Environment-mediated alteration of this natural epigenomic pattern causes abnormal cell behavior and shifts the cell from the normal to a diseased state, leading to certain cancers and neurodegenerative disorders. Unlike heritable diseases that are caused by the irreversible mutations in DNA, epigenetic errors can be reversed. Inheritance of epigenetic memory is also a major concern in the clinical translation of the Nobel Prize-winning discovery of induced pluripotent stem cell technology. Consequently, there is an increasing interest in the development of novel epigenetic switch-based therapeutic strategies that could potentially restore the heritable changes in epigenetically inherited disorders. Here we give a comprehensive overview of epigenetic inheritance and suggest the prospects of therapeutic gene modulation using epigenetic-based drugs, in particular histone deacetylase inhibitors. This review suggests that there is a need to develop therapeutic strategies that effectively mimic the natural environment and include the ways to modulate the gene expression at both the genetic and epigenetic levels. The development of tailor-made small molecules that could epigenetically alter DNA in a sequence-specific manner is a promising approach for restoring defects in an altered epigenome and may offer a sustainable solution to some unresolved clinical issues.

It's about time: lessons for solid tumors from chronic myelogenous leukemia therapy

The use of imatinib in chronic myelogenous leukemia (CML) transformed the disease, rapidly changing the median survival from 4 years to at least 20 years. In this review, we outline the causes of this revolution, including the identification of a critical driving molecular aberration, BCR-ABL, and the development of a potent and specific inhibitor, imatinib. Equally important was the timing of the targeted therapy, specifically its administration to patients with newly diagnosed disease. In solid tumors, targeted therapies are often both developed and used in metastatic malignancies after conventional approaches have failed. We postulate that this strategy is similar to using imatinib in blast-crisis CML, in which response rates are less than 15%, all patients relapse, and median survival remains only about 1 year. We hypothesize that the imatinib-led revolution in CML, including the critically important factor of timing, may be applicable to other cancers as well. Therefore, it will be important to use promising targeted therapies in the earliest phases of biomarker-defined solid tumors, before metastatic progression, to determine if outcomes can be significantly improved and, thus, establish if the success of imatinib in CML is an anomaly or a paradigm.

Comprehensive genome sequencing of the liver cancer genome

Hepatocellular carcinoma (HCC) is the third leading cause of cancer-related death worldwide. Recently, comprehensive whole genome and exome sequencing analyses for HCC revealed new cancer-associated genes and a variety of genomic alterations. In particular, frequent genetic alterations of the chromatin remodeling genes were observed, suggesting a new potential therapeutic target for HCC. Sequencing analysis has further identified the molecular complexities of multicentric lesions and intratumoral heterogeneity. Detailed analyses of the somatic substitution pattern of the cancer genome and the HBV virus genome integration sites by using whole-genome sequencing will elucidate the molecular basis and diverse etiological factors involved in liver cancer development.

Constitutional genetic variants as predictors of antiangiogenic therapy outcome in renal cell carcinoma

The development of specific angiogenesis inhibitors has drastically improved renal cancer treatment in recent years. Currently, four VEGF receptor inhibitors (sorafenib, sunitinib, pazopanib and axitinib), one anti-VEGF monoclonal antibody (bevacizumab) and two inhibitors of the mTOR pathway (temsirolimus and everolimus) have been approved to treat renal cell carcinoma (RCC), and several other molecules are under investigation. However, lack of response to antiangiogenic drugs and adverse drug reactions leading to treatment suspension are critical clinical problems that need to be solved. Because antiangiogenic drugs act on nonmalignant endothelial cells, the genetic background of the patient may play a crucial role determining the efficacy of these drugs. This article focuses on the identification of polymorphisms associated with antiangiogenic drugs outcome in RCC patients. It reviews and summarizes our current knowledge on this area and discusses future strategies to identify new biomarkers that could be used to personalize RCC management.

Cellular heterogeneity and molecular evolution in cancer

Intratumor heterogeneity represents a major obstacle to effective cancer treatment and personalized medicine. However, investigators are now elucidating intratumor heterogeneity at the single-cell level due to improvements in technologies. Better understanding of the composition of tumors, and monitoring changes in cell populations during disease progression and treatment, will improve cancer diagnosis and therapeutic design. Measurements of intratumor heterogeneity may also be used as biomarkers to predict the risk of progression and therapeutic resistance. We summarize important considerations related to intratumor heterogeneity during tumor evolution. We also discuss experimental approaches that are commonly used to infer intratumor heterogeneity and describe how these methodologies can be translated into clinical practice.