Erich Huang

Predicting the clinical status of human breast cancer by using gene expression profiles

Prognostic and predictive factors are indispensable tools in the treatment of patients with neoplastic disease. For the most part, such factors rely on a few specific cell surface, histological, or gross pathologic features. Gene expression assays have the potential to supplement what were previously a few distinct features with many thousands of features. We have developed Bayesian regression models that provide predictive capability based on gene expression data derived from DNA microarray analysis of a series of primary breast cancer samples. These patterns have the capacity to discriminate breast tumors on the basis of estrogen receptor status and also on the categorized lymph node status. Importantly, we assess the utility and validity of such models in predicting the status of tumors in crossvalidation determinations. The practical value of such approaches relies on the ability not only to assess relative probabilities of clinical outcomes for future samples but also to provide an honest assessment of the uncertainties associated with such predictive classifications on the basis of the selection of gene subsets for each validation analysis. This latter point is of critical importance in the ability to apply these methodologies to clinical assessment of tumor phenotype.

Gene expression predictors of breast cancer outcomes

BACKGROUND Correlation of risk factors with genomic data promises to provide specific treatment for individual patients, and needs interpretation of complex, multivariate patterns in gene expression data, as well as assessment of their ability to improve clinical predictions. We aimed to predict nodal metastatic states and relapse for breast cancer patients. METHODS We analysed DNA microarray data from samples of primary breast tumours, using non-linear statistical analyses to assess multiple patterns of interactions of groups of genes that have predictive value for the individual patient, with respect to lymph node metastasis and cancer recurrence. FINDINGS We identified aggregate patterns of gene expression (metagenes) that associate with lymph node status and recurrence, and that are capable of predicting outcomes in individual patients with about 90% accuracy. The metagenes defined distinct groups of genes, suggesting different biological processes underlying these two characteristics of breast cancer. Initial external validation came from similarly accurate predictions of nodal status of a small sample in a distinct population. INTERPRETATION Multiple aggregate measures of profiles of gene expression define valuable predictive associations with lymph node metastasis and disease recurrence for individual patients. Gene expression data have the potential to aid accurate, individualised, prognosis. Importantly, these data are assessed in terms of precise numerical predictions, with ranges of probabilities of outcome. Precise and statistically valid assessments of risks specific for patients, will ultimately be of most value to clinicians faced with treatment decisions.

Role for E2F in Control of Both DNA Replication and Mitotic Functions as Revealed from DNA Microarray Analysis

ABSTRACT We have used high-density DNA microarrays to provide an analysis of gene regulation during the mammalian cell cycle and the role of E2F in this process. Cell cycle analysis was facilitated by a combined examination of gene control in serum-stimulated fibroblasts and cells synchronized at G1/S by hydroxyurea block that were then released to proceed through the cell cycle. The latter approach (G1/S synchronization) is critical for rigorously maintaining cell synchrony for unambiguous analysis of gene regulation in later stages of the cell cycle. Analysis of these samples identified seven distinct clusters of genes that exhibit unique patterns of expression. Genes tend to cluster within these groups based on common function and the time during the cell cycle that the activity is required. Placed in this context, the analysis of genes induced by E2F proteins identified genes or expressed sequence tags not previously described as regulated by E2F proteins; surprisingly, many of these encode proteins known to function during mitosis. A comparison of the E2F-induced genes with the patterns of cell growth-regulated gene expression revealed that virtually all of the E2F-induced genes are found in only two of the cell cycle clusters; one group was regulated at G1/S, and the second group, which included the mitotic activities, was regulated at G2. The activation of the G2 genes suggests a broader role for E2F in the control of both DNA replication and mitotic activities.

Myc requires distinct E2F activities to induce S phase and apoptosis.

Previous work has shown that the Myc transcription factor induces transcription of the E2F1, E2F2, and E2F3 genes. Using primary mouse embryo fibroblasts deleted for individual E2F genes, we now show that Myc-induced S phase and apoptosis requires distinct E2F activities. The ability of Myc to induce S phase is impaired in the absence of either E2F2 or E2F3 but not E2F1 or E2F4. In contrast, the ability of Myc to induce apoptosis is markedly reduced in cells deleted for E2F1 but not E2F2 or E2F3. From this data, we propose that the induction of specific E2F activities is an essential component in the Myc pathways that control cell proliferation and cell fate decisions.

Genomic Prediction of Locoregional Recurrence After Mastectomy in Breast Cancer

PURPOSE This study aims to explore gene expression profiles that are associated with locoregional (LR) recurrence in breast cancer after mastectomy. PATIENTS AND METHODS A total of 94 breast cancer patients who underwent mastectomy between 1990 and 2001 and had DNA microarray study on the primary tumor tissues were chosen for this study. Eligible patient should have no evidence of LR recurrence without postmastectomy radiotherapy (PMRT) after a minimum of 3-year follow-up (n = 67) and any LR recurrence (n = 27). They were randomly split into training and validation sets. Statistical classification tree analysis and proportional hazards models were developed to identify and validate gene expression profiles that relate to LR recurrence. RESULTS Our study demonstrates two sets of gene expression profiles (one with 258 genes and the other 34 genes) to be of predictive value with respect to LR recurrence. The overall accuracy of the prediction tree model in validation sets is estimated 75% to 78%. Of patients in validation data set, the 3-year LR control rate with predictive index more than 0.8 derived from 34-gene prediction models is 91%, and predictive index 0.8 or less is 40% (P = .008). Multivariate analysis of all patients reveals that estrogen receptor and genomic predictive index are independent prognostic factors that affect LR control. CONCLUSION Using gene expression profiles to develop prediction tree models effectively identifies breast cancer patients who are at higher risk for LR recurrence. This gene expression-based predictive index can be used to select patients for PMRT.

Systematic Analysis of Challenge-Driven Improvements in Molecular Prognostic Models for Breast Cancer

An open challenge to model breast cancer prognosis revealed that collaboration and transparency enhanced the power of prognostic models. DREAMing of Biomedicine’s Future Although they no longer live in the lab, scientific editors still enjoy doing experiments. The simultaneous publication of two unusual papers offered Science Translational Medicine’s editors the chance to conduct an investigation into peer-review processes for competition-based crowdsourcing studies designed to address problems in biomedicine. In a Report by Margolin et al. (which was peer-reviewed in the traditional way), organizers of the Sage Bionetworks/DREAM Breast Cancer Prognosis Challenge (BCC) describe the contest’s conception, execution, and insights derived from its outcome. In the companion Research Article, Cheng et al. outline the development of the prognostic computational model that won the Challenge. In this experiment in scientific publishing, the rigor of the Challenge design and scoring process formed the basis for a new style of publication peer review. DREAM—Dialogue for Reverse Engineering Assessments and Methods—conducts a variety of computational Challenges with the goal of catalyzing the “interaction between theory and experiment, specifically in the area of cellular network inference and quantitative model building in systems biology.” Previous Challenges involved, for example, modeling of protein-protein interactions for binding domains and peptides and the specificity of transcription factor binding. In the BCC—which was a step in the translational direction—participants competed to create an algorithm that could predict, more accurately than current benchmarks, the prognosis of breast cancer patients from clinical information (age, tumor size, histological grade), genome-scale tumor mRNA expression data, and DNA copy number data. Participants were given Web access to such data for 1981 women diagnosed with breast cancer and used it to train computational models that were then submitted to a common, open-access computational platform as re-runnable source code. The predictive value of each model was assessed in real-time by calculating a concordance index (CI) of predicted death risks compared to overall survival in a held-out data set, and CIs were posted on a public leaderboard. The winner of the Challenge was ultimately determined when a select group of top models were validated in a new breast cancer data set. The winning model, described by Cheng et al., was based on sets of genes (signatures)—called attractor metagenes—that the same research group had previously shown to be associated, in various ways, with multiple cancer types. Starting with these gene sets and some other clinical and molecular features, the team modeled various feature combinations, selecting ones that improved performance of their prognostic model until they ultimately fashioned the winning algorithm. Before the BCC was initiated, Challenge organizers approached Science Translational Medicine about the possibility of publishing a Research Article that described the winning model. The Challenge prize would be a scholarly publication—a form of “academic currency.” The editors pondered whether winning the Challenge, with its built-in transparency and check on model reproducibility, would be sufficient evidence in support of the model’s validity to substitute for traditional peer review. Because the specific conditions of a Challenge are critical in determining the meaningfulness of the outcome, the editors felt it was not. Thus, they chose to arrange for peer-reviewers, chosen by the editors, to be embedded within the challenge process, as members of the organizing team—a so-called Challenge-assisted review. The editors also helped to develop criteria for determining the winning model, and if the criteria were not met, there would have been no winner—and no publication. Last, the manuscript was subjected to advisory peer review after it was submitted to the journal. So what new knowledge was gained about reviewing an article in which the result is an active piece of software? Reviewing such a model required that referees have access to the data and platform used for the Challenge and have the ability to re-run each participant’s code; in the context of the BCC, this requirement was easily achievable, because Challenge-partner Sage Bionetworks had created a platform (Synapse) with this goal in mind. In fact, both the training and validation datasets for the BCC are available to readers via links into Synapse (for a six month period of time). In general, this requirement should not be an obstacle, as there are code-hosting sites such as GitHub and TopCoder.com that can accommodate data sharing. Mechanisms for confidentiality would need to be built into any computational platform to be used for peer review. Finally, because different conventions are used in divergent scientific fields, communicating the science to an interdisciplinary audience is not a trivial endeavor. The architecture of the Challenge itself is critical in determining the real-world importance of the result. The question to be investigated must be framed so as to capture a significant outcome. In the BCC, participants’ models had to score better than a set of 60 different prognostic models developed by a team of expert programmers during a Challenge precompetition as well as a previously described first-generation 70-gene risk predictor. Thus, the result may or may not be superior to existing gene expression profiling tests used in clinical practice. This remains to be tested. It also remains to be seen whether prize-based crowdsourcing contests can make varied and practical contributions in the clinic. Indeed, DREAM and Sage Bionetworks have immediate plans to collaborate on new clinically relevant Challenges. But there is no doubt that the approach has value in solving big-data problems. For example, in a recent contest, non-immunologists generated a method for annotating the complex genome sequence of the antibody repertoire when the contest organizers translated the problem into generic language. In the BCC, the Challenge winners used a mathematical approach to identify biological modules that might, with continued investigation, teach us something about cancer biology. These examples support the notion that harnessing the expertise of contestants outside of traditional biological disciplines may be a powerful way to accelerate the translation of biomedical science to the clinic. Although molecular prognostics in breast cancer are among the most successful examples of translating genomic analysis to clinical applications, optimal approaches to breast cancer clinical risk prediction remain controversial. The Sage Bionetworks–DREAM Breast Cancer Prognosis Challenge (BCC) is a crowdsourced research study for breast cancer prognostic modeling using genome-scale data. The BCC provided a community of data analysts with a common platform for data access and blinded evaluation of model accuracy in predicting breast cancer survival on the basis of gene expression data, copy number data, and clinical covariates. This approach offered the opportunity to assess whether a crowdsourced community Challenge would generate models of breast cancer prognosis commensurate with or exceeding current best-in-class approaches. The BCC comprised multiple rounds of blinded evaluations on held-out portions of data on 1981 patients, resulting in more than 1400 models submitted as open source code. Participants then retrained their models on the full data set of 1981 samples and submitted up to five models for validation in a newly generated data set of 184 breast cancer patients. Analysis of the BCC results suggests that the best-performing modeling strategy outperformed previously reported methods in blinded evaluations; model performance was consistent across several independent evaluations; and aggregating community-developed models achieved performance on par with the best-performing individual models.

Quantitative MRI radiomics in the prediction of molecular classifications of breast cancer subtypes in the TCGA/TCIA data set.

Using quantitative radiomics, we demonstrate that computer-extracted magnetic resonance (MR) image-based tumor phenotypes can be predictive of the molecular classification of invasive breast cancers. Radiomics analysis was performed on 91 MRIs of biopsy-proven invasive breast cancers from National Cancer Institute’s multi-institutional TCGA/TCIA. Immunohistochemistry molecular classification was performed including estrogen receptor, progesterone receptor, human epidermal growth factor receptor 2, and for 84 cases, the molecular subtype (normal-like, luminal A, luminal B, HER2-enriched, and basal-like). Computerized quantitative image analysis included: three-dimensional lesion segmentation, phenotype extraction, and leave-one-case-out cross validation involving stepwise feature selection and linear discriminant analysis. The performance of the classifier model for molecular subtyping was evaluated using receiver operating characteristic analysis. The computer-extracted tumor phenotypes were able to distinguish between molecular prognostic indicators; area under the ROC curve values of 0.89, 0.69, 0.65, and 0.67 in the tasks of distinguishing between ER+ versus ER−, PR+ versus PR−, HER2+ versus HER2−, and triple-negative versus others, respectively. Statistically significant associations between tumor phenotypes and receptor status were observed. More aggressive cancers are likely to be larger in size with more heterogeneity in their contrast enhancement. Even after controlling for tumor size, a statistically significant trend was observed within each size group (P=0.04 for lesions ⩽2 cm; P=0.02 for lesions >2 to ⩽5 cm) as with the entire data set (P-value=0.006) for the relationship between enhancement texture (entropy) and molecular subtypes (normal-like, luminal A, luminal B, HER2-enriched, basal-like). In conclusion, computer-extracted image phenotypes show promise for high-throughput discrimination of breast cancer subtypes and may yield a quantitative predictive signature for advancing precision medicine.

Multicenter imaging outcomes study of The Cancer Genome Atlas glioblastoma patient cohort: imaging predictors of overall and progression-free survival

BACKGROUND Despite an aggressive therapeutic approach, the prognosis for most patients with glioblastoma (GBM) remains poor. The aim of this study was to determine the significance of preoperative MRI variables, both quantitative and qualitative, with regard to overall and progression-free survival in GBM. METHODS We retrospectively identified 94 untreated GBM patients from the Cancer Imaging Archive who had pretreatment MRI and corresponding patient outcomes and clinical information in The Cancer Genome Atlas. Qualitative imaging assessments were based on the Visually Accessible Rembrandt Images feature-set criteria. Volumetric parameters were obtained of the specific tumor components: contrast enhancement, necrosis, and edema/invasion. Cox regression was used to assess prognostic and survival significance of each image. RESULTS Univariable Cox regression analysis demonstrated 10 imaging features and 2 clinical variables to be significantly associated with overall survival. Multivariable Cox regression analysis showed that tumor-enhancing volume (P = .03) and eloquent brain involvement (P < .001) were independent prognostic indicators of overall survival. In the multivariable Cox analysis of the volumetric features, the edema/invasion volume of more than 85 000 mm(3) and the proportion of enhancing tumor were significantly correlated with higher mortality (Ps = .004 and .003, respectively). CONCLUSIONS Preoperative MRI parameters have a significant prognostic role in predicting survival in patients with GBM, thus making them useful for patient stratification and endpoint biomarkers in clinical trials.

Design of Phase I Combination Trials: Recommendations of the Clinical Trial Design Task Force of the NCI Investigational Drug Steering Committee

Anticancer drugs are combined in an effort to treat a heterogeneous tumor or to maximize the pharmacodynamic effect. The development of combination regimens, while desirable, poses unique challenges. These include the selection of agents for combination therapy that may lead to improved efficacy while maintaining acceptable toxicity, the design of clinical trials that provide informative results for individual agents and combinations, and logistic and regulatory challenges. The phase I trial is often the initial step in the clinical evaluation of a combination regimen. In view of the importance of combination regimens and the challenges associated with developing them, the Clinical Trial Design (CTD) Task Force of the National Cancer Institute Investigational Drug Steering Committee developed a set of recommendations for the phase I development of a combination regimen. The first two recommendations focus on the scientific rationale and development plans for the combination regimen; subsequent recommendations encompass clinical design aspects. The CTD Task Force recommends that selection of the proposed regimens be based on a biologic or pharmacologic rationale supported by clinical and/or robust and validated preclinical evidence, and accompanied by a plan for subsequent development of the combination. The design of the phase I clinical trial should take into consideration the potential pharmacokinetic and pharmacodynamic interactions as well as overlapping toxicity. Depending on the specific hypothesized interaction, the primary endpoint may be dose optimization, pharmacokinetics, and/or pharmacodynamics (i.e., biomarker). Clin Cancer Res; 20(16); 4210–7. ©2014 AACR.

Integrating Factor Analysis and a Transgenic Mouse Model to Reveal a Peripheral Blood Predictor of Breast Tumors

BackgroundTransgenic mouse tumor models have the advantage of facilitating controlled in vivo oncogenic perturbations in a common genetic background. This provides an idealized context for generating transcriptome-based diagnostic models while minimizing the inherent noisiness of high-throughput technologies. However, the question remains whether models developed in such a setting are suitable prototypes for useful human diagnostics. We show that latent factor modeling of the peripheral blood transcriptome in a mouse model of breast cancer provides the basis for using computational methods to link a mouse model to a prototype human diagnostic based on a common underlying biological response to the presence of a tumor.MethodsWe used gene expression data from mouse peripheral blood cell (PBC) samples to identify significantly differentially expressed genes using supervised classification and sparse ANOVA. We employed these transcriptome data as the starting point for developing a breast tumor predictor from human peripheral blood mononuclear cells (PBMCs) by using a factor modeling approach.ResultsThe predictor distinguished breast cancer patients from healthy individuals in a cohort of patients independent from that used to build the factors and train the model with 89% sensitivity, 100% specificity and an area under the curve (AUC) of 0.97 using Youdens J-statistic to objectively select the models classification threshold. Both permutation testing of the model and evaluating the model strategy by swapping the training and validation sets highlight its stability.ConclusionsWe describe a human breast tumor predictor based on the gene expression of mouse PBCs. This strategy overcomes many of the limitations of earlier studies by using the model system to reduce noise and identify transcripts associated with the presence of a breast tumor over other potentially confounding factors. Our results serve as a proof-of-concept for using an animal model to develop a blood-based diagnostic, and it establishes an experimental framework for identifying predictors of solid tumors, not only in the context of breast cancer, but also in other types of cancer.