The digital redesign of mental health: leveraging connected digital technologies for agency-driven patient-focused care

Digital psychiatry could empower individuals to navigate their context-specific experiences outside healthcare visits. This editorial discusses how leveraging digital health technologies could dramatically transform how we conceptualise mental health and the mental health professional's day-day practice, and how patients could be enabled to navigate their mental health with greater agency.

Effect of self-managed lifestyle treatment on glycemic control in patients with type 2 diabetes

The lack of effective, scalable solutions for lifestyle treatment is a global clinical problem, causing severe morbidity and mortality. We developed a method for lifestyle treatment that promotes self-reflection and iterative behavioral change, provided as a digital tool, and evaluated its effect in 370 patients with type 2 diabetes (ClinicalTrials.gov identifier: NCT04691973). Users of the tool had reduced blood glucose, both compared with randomized and matched controls (involving 158 and 204 users, respectively), as well as improved systolic blood pressure, body weight and insulin resistance. The improvement was sustained during the entire follow-up (average 730 days). A pathophysiological subgroup of obese insulin-resistant individuals had a pronounced glycemic response, enabling identification of those who would benefit in particular from lifestyle treatment. Natural language processing showed that the metabolic improvement was coupled with the self-reflective element of the tool. The treatment is cost-saving because of improved risk factor control for cardiovascular complications. The findings open an avenue for self-managed lifestyle treatment with long-term metabolic efficacy that is cost-saving and can reach large numbers of people.

Stress-related consequences of the coronavirus disease 2019 pandemic on symptoms of Crohn's disease

OBJECTIVES

A link between stress and Crohn's disease activity suggests an association, but results have been conflicting. The purpose of this study was to assess whether the stress related to the coronavirus disease 2019 (COVID-19) pandemic affected disease activity in patients with Crohn's disease.

BASIC METHODS

An anonymous survey was distributed to patients through gastroenterology clinics and networks. Patients were asked to report their Crohn's disease symptoms in the months prior to the COVID-19 pandemic and again during the early stages of the COVID-19 pandemic using the Manitoba inflammatory bowel disease index in addition to questions about stress, perception of reasons for symptom change and personal impact.

MAIN RESULTS

Out of 243 individuals with a confirmed diagnosis of Crohn's disease, there was a 24% relative increase in active symptoms between the pre-COVID-19 period to the during-COVID-19 period (P < 0.0001) reflecting an absolute change from 45 to 56%, respectively. The most frequent reported reason for a change in symptoms was 'Increased stress/and or feeling overwhelmed' (118/236), and personal impact of the pandemic was, 'I'm worrying a lot about the future' (113/236), both reported by approximately half of respondents.

PRINCIPAL CONCLUSIONS

This study serves as a 'proof of concept' demonstrating the impact of a significant and uniquely uniform stressor as a natural experiment on Crohn's disease activity. The severity of symptoms of Crohn's disease increased during the COVID-19 pandemic. The primary reported reason for symptom change was an increase in stress, not a change in diet, exercise or other lifestyle behaviours, corroborating the hypothesis that stress affects Crohn's disease activity.

Detecting the impact of subject characteristics on machine learning-based diagnostic applications

Collection of high-dimensional, longitudinal digital health data has the potential to support a wide-variety of research and clinical applications including diagnostics and longitudinal health tracking. Algorithms that process these data and inform digital diagnostics are typically developed using training and test sets generated from multiple repeated measures collected across a set of individuals. However, the inclusion of repeated measurements is not always appropriately taken into account in the analytical evaluations of predictive performance. The assignment of repeated measurements from each individual to both the training and the test sets ("record-wise" data split) is a common practice and can lead to massive underestimation of the prediction error due to the presence of "identity confounding." In essence, these models learn to identify subjects, in addition to diagnostic signal. Here, we present a method that can be used to effectively calculate the amount of identity confounding learned by classifiers developed using a record-wise data split. By applying this method to several real datasets, we demonstrate that identity confounding is a serious issue in digital health studies and that record-wise data splits for machine learning- based applications need to be avoided.

Combining accurate tumor genome simulation with crowdsourcing to benchmark somatic structural variant detection

BACKGROUND

The phenotypes of cancer cells are driven in part by somatic structural variants. Structural variants can initiate tumors, enhance their aggressiveness, and provide unique therapeutic opportunities. Whole-genome sequencing of tumors can allow exhaustive identification of the specific structural variants present in an individual cancer, facilitating both clinical diagnostics and the discovery of novel mutagenic mechanisms. A plethora of somatic structural variant detection algorithms have been created to enable these discoveries; however, there are no systematic benchmarks of them. Rigorous performance evaluation of somatic structural variant detection methods has been challenged by the lack of gold standards, extensive resource requirements, and difficulties arising from the need to share personal genomic information.

RESULTS

To facilitate structural variant detection algorithm evaluations, we create a robust simulation framework for somatic structural variants by extending the BAMSurgeon algorithm. We then organize and enable a crowdsourced benchmarking within the ICGC-TCGA DREAM Somatic Mutation Calling Challenge (SMC-DNA). We report here the results of structural variant benchmarking on three different tumors, comprising 204 submissions from 15 teams. In addition to ranking methods, we identify characteristic error profiles of individual algorithms and general trends across them. Surprisingly, we find that ensembles of analysis pipelines do not always outperform the best individual method, indicating a need for new ways to aggregate somatic structural variant detection approaches.

CONCLUSIONS

The synthetic tumors and somatic structural variant detection leaderboards remain available as a community benchmarking resource, and BAMSurgeon is available at https://github.com/adamewing/bamsurgeon .

The Use of Smartphones for Health Research

Because of their growing popularity and functionality, smartphones are increasingly valuable potential tools for health and medical research. Using ResearchKit, Apple's open-source platform to build applications ("apps") for smartphone research, collaborators have developed apps for researching asthma, breast cancer, cardiovascular disease, type 2 diabetes, and Parkinson disease. These research apps enhance widespread participation by removing geographical barriers to participation, provide novel ways to motivate healthy behaviors, facilitate high-frequency assessments, and enable more objective data collection. Although the studies have great potential, they also have notable limitations. These include selection bias, identity uncertainty, design limitations, retention, and privacy. As smartphone technology becomes increasingly available, researchers must recognize these factors to ensure that medical research is conducted appropriately. Despite these limitations, the future of smartphones in health research is bright. Their convenience grants unprecedented geographic freedom to researchers and participants alike and transforms the way clinical research can be conducted.

Crowdsourced estimation of cognitive decline and resilience in Alzheimer's disease

Identifying accurate biomarkers of cognitive decline is essential for advancing early diagnosis and prevention therapies in Alzheimer's disease. The Alzheimer's disease DREAM Challenge was designed as a computational crowdsourced project to benchmark the current state-of-the-art in predicting cognitive outcomes in Alzheimer's disease based on high dimensional, publicly available genetic and structural imaging data. This meta-analysis failed to identify a meaningful predictor developed from either data modality, suggesting that alternate approaches should be considered for prediction of cognitive performance.

Analysis of 589,306 genomes identifies individuals resilient to severe Mendelian childhood diseases

Genetic studies of human disease have traditionally focused on the detection of disease-causing mutations in afflicted individuals. Here we describe a complementary approach that seeks to identify healthy individuals resilient to highly penetrant forms of genetic childhood disorders. A comprehensive screen of 874 genes in 589,306 genomes led to the identification of 13 adults harboring mutations for 8 severe Mendelian conditions, with no reported clinical manifestation of the indicated disease. Our findings demonstrate the promise of broadening genetic studies to systematically search for well individuals who are buffering the effects of rare, highly penetrant, deleterious mutations. They also indicate that incomplete penetrance for Mendelian diseases is likely more common than previously believed. The identification of resilient individuals may provide a first step toward uncovering protective genetic variants that could help elucidate the mechanisms of Mendelian diseases and new therapeutic strategies.

The mPower study, Parkinson disease mobile data collected using ResearchKit

Current measures of health and disease are often insensitive, episodic, and subjective. Further, these measures generally are not designed to provide meaningful feedback to individuals. The impact of high-resolution activity data collected from mobile phones is only beginning to be explored. Here we present data from mPower, a clinical observational study about Parkinson disease conducted purely through an iPhone app interface. The study interrogated aspects of this movement disorder through surveys and frequent sensor-based recordings from participants with and without Parkinson disease. Benefitting from large enrollment and repeated measurements on many individuals, these data may help establish baseline variability of real-world activity measurement collected via mobile phones, and ultimately may lead to quantification of the ebbs-and-flows of Parkinson symptoms. App source code for these data collection modules are available through an open source license for use in studies of other conditions. We hope that releasing data contributed by engaged research participants will seed a new community of analysts working collaboratively on understanding mobile health data to advance human health.

PERSONALIZED HYPOTHESIS TESTS FOR DETECTING MEDICATION RESPONSE IN PARKINSON DISEASE PATIENTS USING iPHONE SENSOR DATA

We propose hypothesis tests for detecting dopaminergic medication response in Parkinson disease patients, using longitudinal sensor data collected by smartphones. The processed data is composed of multiple features extracted from active tapping tasks performed by the participant on a daily basis, before and after medication, over several months. Each extracted feature corresponds to a time series of measurements annotated according to whether the measurement was taken before or after the patient has taken his/her medication. Even though the data is longitudinal in nature, we show that simple hypothesis tests for detecting medication response, which ignore the serial correlation structure of the data, are still statistically valid, showing type I error rates at the nominal level. We propose two distinct personalized testing approaches. In the first, we combine multiple feature-specific tests into a single union-intersection test. In the second, we construct personalized classifiers of the before/after medication labels using all the extracted features of a given participant, and test the null hypothesis that the area under the receiver operating characteristic curve of the classifier is equal to 1/2. We compare the statistical power of the personalized classifier tests and personalized union-intersection tests in a simulation study, and illustrate the performance of the proposed tests using data from mPower Parkinsons disease study, recently launched as part of Apples ResearchKit mobile platform. Our results suggest that the personalized tests, which ignore the longitudinal aspect of the data, can perform well in real data analyses, suggesting they might be used as a sound baseline approach, to which more sophisticated methods can be compared to.

The consensus molecular subtypes of colorectal cancer

Colorectal cancer (CRC) is a frequently lethal disease with heterogeneous outcomes and drug responses. To resolve inconsistencies among the reported gene expression-based CRC classifications and facilitate clinical translation, we formed an international consortium dedicated to large-scale data sharing and analytics across expert groups. We show marked interconnectivity between six independent classification systems coalescing into four consensus molecular subtypes (CMSs) with distinguishing features: CMS1 (microsatellite instability immune, 14%), hypermutated, microsatellite unstable and strong immune activation; CMS2 (canonical, 37%), epithelial, marked WNT and MYC signaling activation; CMS3 (metabolic, 13%), epithelial and evident metabolic dysregulation; and CMS4 (mesenchymal, 23%), prominent transforming growth factor-β activation, stromal invasion and angiogenesis. Samples with mixed features (13%) possibly represent a transition phenotype or intratumoral heterogeneity. We consider the CMS groups the most robust classification system currently available for CRC-with clear biological interpretability-and the basis for future clinical stratification and subtype-based targeted interventions.

Impact of centralization on aCGH-based genomic profiles for precision medicine in oncology

BACKGROUND

Comparative genomic hybridization (CGH) arrays are increasingly used in personalized medicine programs to identify gene copy number aberrations (CNAs) that may be used to guide clinical decisions made during molecular tumor boards. However, analytical processes such as the centralization step may profoundly affect CGH array results and therefore may adversely affect outcomes in the precision medicine context.

PATIENTS AND METHODS

The effect of three different centralization methods: median, maximum peak, alternative peak, were evaluated on three datasets: (i) the NCI60 cell lines panel, (ii) the Cancer Cell Line Encyclopedia (CCLE) panel, and (iii) the patients enrolled in prospective molecular screening trials (SAFIR-01 n = 283, MOSCATO-01 n = 309), and compared with karyotyping, drug sensitivity, and patient-drug matching, respectively.

RESULTS

Using the NCI60 cell lines panel, the profiles generated by the alternative peak method were significantly closer to the cell karyotypes than those generated by the other centralization strategies (P < 0.05). Using the CCLE dataset, selected genes (ERBB2, EGFR) were better or equally correlated to the IC50 of their companion drug (lapatinib, erlotinib), when applying the alternative centralization. Finally, focusing on 24 actionable genes, we observed as many as 7.1% (SAFIR-01) and 6.8% (MOSCATO-01) of patients originally not oriented to a specific treatment, but who could have been proposed a treatment based on the alternative peak centralization method.

CONCLUSION

The centralization method substantially affects the call detection of CGH profiles and may thus impact precision medicine approaches. Among the three methods described, the alternative peak method addresses limitations associated with existing approaches.

Abstract 359: An age-related 99-gene signature from glioblastoma implicates differences in survival related to RAS activation

Background: Glioblastoma (GBM) is the most common primary brain tumor with universally poor prognosis despite treatment with surgery, radiation and chemotherapy. One of the most significant prognostic factors is the patient's age at onset of disease, with younger patients demonstrating longer survival despite similar therapies. The aim of this work is to examine genomic differences that are tied to the age of onset that could explain the differences in outcomes.

Methods: We used the gene expression profiles from 559 GBM patients (pts) included in the Cancer Genome Atlas (TCGA) to build a gene expression model of the expression of 12185 genes to predict patient age using elasticnet with 500 bootstraps. This model was used to predict the age of patients in a testing set of 419 pts with gliomas included in Repository for Brain Neoplasia Data (REMBRANDT) (99 grade II, 71 grade III and 125 grade IV and 124 with no grade). Receiver-operating characteristic (ROC) and survival analysis were performed to measure the performance of the model in predicting age, gene-set enrichment analysis (GSEA) was used to reveal network topology perturbations, Cox proportional hazards were used to predict survival of the different predicted classes.

Results: Using a FDR cutoff of p=0.005, the bootstrapped elasticnet discovered 99 genes that were highly correlated with age in GBM patients that predicts age in the testing set of patients with gliomas with an area under the ROC curve of 0.97. GSEA motifs within these genes related to RAS activation in multiple cell lines, as well as genes related to immune modulation and increased CpG methylation. Survival analysis of the REMBRANDT pts showed that the cohort with an “older” gene signature had worse survival (median 27 months versus 15 months, log-rank p&lt;0.00005). When applied to pts in REMBRANDT for whom age was not recorded (n=36), the model predicted classes also had significantly different survival (median 6 versus 10 months, log-rank p=0.001).

Conclusions: We have developed a gene expression model related to age of onset of GBM, to investigate the differences inherent in the biology that may lead to better prognosis in younger patients. Of the 99 genes that are strongly predictive of age in an external validation set, many were related to RAS activation in cell lines, immune modulation and CpG methylation. RAS perturbations have been previously shown to induce gliomas in mice, and this study shows a relationship between RAS gene network activation and age that may play a role in the different survival outcomes between different ages. Future work will include the use of RAS targeted agents to elucidate their role in the management of gliomas for select pts.

Citation Format: Andrew D. Trister, Robert Rostomily, Stephen H. Friend. An age-related 99-gene signature from glioblastoma implicates differences in survival related to RAS activation. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 359. doi:10.1158/1538-7445.AM2014-359

The Stream algorithm: computationally efficient ridge-regression via Bayesian model averaging, and applications to pharmacogenomic prediction of cancer cell line sensitivity

Computational efficiency is important for learning algorithms operating in the "large p, small n" setting. In computational biology, the analysis of data sets containing tens of thousands of features ("large p"), but only a few hundred samples ("small n"), is nowadays routine, and regularized regression approaches such as ridge-regression, lasso, and elastic-net are popular choices. In this paper we propose a novel and highly efficient Bayesian inference method for fitting ridge-regression. Our method is fully analytical, and bypasses the need for expensive tuning parameter optimization, via cross-validation, by employing Bayesian model averaging over the grid of tuning parameters. Additional computational efficiency is achieved by adopting the singular value decomposition reparametrization of the ridge-regression model, replacing computationally expensive inversions of large p × p matrices by efficient inversions of small and diagonal n × n matrices. We show in simulation studies and in the analysis of two large cancer cell line data panels that our algorithm achieves slightly better predictive performance than cross-validated ridge-regression while requiring only a fraction of the computation time. Furthermore, in comparisons based on the cell line data sets, our algorithm systematically out-performs the lasso in both predictive performance and computation time, and shows equivalent predictive performance, but considerably smaller computation time, than the elastic-net.

Systematic assessment of analytical methods for drug sensitivity prediction from cancer cell line data

Large-scale pharmacogenomic screens of cancer cell lines have emerged as an attractive pre-clinical system for identifying tumor genetic subtypes with selective sensitivity to targeted therapeutic strategies. Application of modern machine learning approaches to pharmacogenomic datasets have demonstrated the ability to infer genomic predictors of compound sensitivity. Such modeling approaches entail many analytical design choices; however, a systematic study evaluating the relative performance attributable to each design choice is not yet available. In this work, we evaluated over 110,000 different models, based on a multifactorial experimental design testing systematic combinations of modeling factors within several categories of modeling choices, including: type of algorithm, type of molecular feature data, compound being predicted, method of summarizing compound sensitivity values, and whether predictions are based on discretized or continuous response values. Our results suggest that model input data (type of molecular features and choice of compound) are the primary factors explaining model performance, followed by choice of algorithm. Our results also provide a statistically principled set of recommended modeling guidelines, including: using elastic net or ridge regression with input features from all genomic profiling platforms, most importantly, gene expression features, to predict continuous-valued sensitivity scores summarized using the area under the dose response curve, with pathway targeted compounds most likely to yield the most accurate predictors. In addition, our study provides a publicly available resource of all modeling results, an open source code base, and experimental design for researchers throughout the community to build on our results and assess novel methodologies or applications in related predictive modeling problems.

Enabling transparent and collaborative computational analysis of 12 tumor types within The Cancer Genome Atlas

The Cancer Genome Atlas Pan-Cancer Analysis Working Group collaborated on the Synapse software platform to share and evolve data, results and methodologies while performing integrative analysis of molecular profiling data from 12 tumor types. The group's work serves as a pilot case study that provides (i) a template for future large collaborative studies; (ii) a system to support collaborative projects; and (iii) a public resource of highly curated data, results and automated systems for the evaluation of community-developed models.

Impact of bioinformatic procedures in the development and translation of high-throughput molecular classifiers in oncology

The progressive introduction of high-throughput molecular techniques in the clinic allows for the extensive and systematic exploration of multiple biologic layers of tumors. Molecular profiles and classifiers generated from these assays represent the foundation of what the National Academy describes as the future of "precision medicine". However, the analysis of such complex data requires the implementation of sophisticated bioinformatic and statistical procedures. It is critical that oncology practitioners be aware of the advantages and limitations of the methods used to generate classifiers to usher them into the clinic. This article uses publicly available expression data from patients with non-small cell lung cancer to first illustrate the challenges of experimental design and preprocessing of data before clinical application and highlights the challenges of high-dimensional statistical analysis. It provides a roadmap for the translation of such classifiers to clinical practice and makes key recommendations for good practice.

Improving breast cancer survival analysis through competition-based multidimensional modeling

Breast cancer is the most common malignancy in women and is responsible for hundreds of thousands of deaths annually. As with most cancers, it is a heterogeneous disease and different breast cancer subtypes are treated differently. Understanding the difference in prognosis for breast cancer based on its molecular and phenotypic features is one avenue for improving treatment by matching the proper treatment with molecular subtypes of the disease. In this work, we employed a competition-based approach to modeling breast cancer prognosis using large datasets containing genomic and clinical information and an online real-time leaderboard program used to speed feedback to the modeling team and to encourage each modeler to work towards achieving a higher ranked submission. We find that machine learning methods combined with molecular features selected based on expert prior knowledge can improve survival predictions compared to current best-in-class methodologies and that ensemble models trained across multiple user submissions systematically outperform individual models within the ensemble. We also find that model scores are highly consistent across multiple independent evaluations. This study serves as the pilot phase of a much larger competition open to the whole research community, with the goal of understanding general strategies for model optimization using clinical and molecular profiling data and providing an objective, transparent system for assessing prognostic models.

Systematic analysis of challenge-driven improvements in molecular prognostic models for breast cancer

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.

Abstract 1210: Periostin expression in glioma correlates with genes related to mesenchymal transition and survival

Background: Gliomas are the most common primary brain tumors and their prognosis is related to WHO histopathological grade. Glioblastoma (grade IV astrocytoma or GBM) is characterized by microscopic invasion into surrounding brain and universally poor prognosis despite treatment with surgery, radiation and chemotherapy . Recently, the matricellular protein periostin (POSTN) has been shown to be associated with increased parenchymal invasion (measured by edema on MRI) and poor prognosis in GBM. The specific aim of the present analysis was to determine the mechanistic impact of POSTN on glioma outcome.

Methods: We used the gene expression of POSTN from 559 GBM patients (pts) included in the Cancer Genome Atlas (TCGA) to build a gene expression model of the expression of 12184 genes to predict POSTN expression using elastic net. This model was used to predict the POSTN expression in a testing set of 419 pts with gliomas included in Repository for Brain Neoplasia Data (REMBRANDT) (99 grade II, 71 grade III and 125 grade IV and 124 with no grade). Receiver-operating characteristic (ROC) and survival analysis were performed to measure the performance of the model, and gene-set enrichment analysis (GSEA) was used to reveal network topology perturbations.

Results: The gene expression model discovered 721 genes highly correlated to POSTN expression that predict POSTN in the testing set with an area under the curve (AUC) of 0.96 on ROC curve. GSEA reveals genes involved in “mesenchymal transition,” transition of invasive ductal carcinoma (IDC) from ductal carcinoma in situ (DCIS) in breast cancer and stem cell signatures. Survival analysis of the TCGA GBM pts showed that the cohort with “high POSTN” had worse survival (median 12 months versus 15 months, log-rank p=0.0002). When applied to all pts in REMBRANDT, the model predicted classes also had significantly different survival (median 13.4 versus 38 months, log-rank p&lt;0.0001). Interestingly, this classification of survival persists in the subset of pts with grade III (n=71, median survival 17.7 versus 42.4 months, p=0.0125) and grade II (n=99, median survival 17.9 versus 50.8, p=0.02) gliomas.

Conclusions: We have developed a gene expression model related to POSTN, a gene linked to poor prognosis in GBM, to investigate the role correlated genes may play in the aggressive phenotype. Some of the genes found to be highly correlated to POSTN are related to mesenchymal transition, invasive behavior in breast cancer and stemness. We verify that high POSTN expression is a strong prognostic indicator for poor outcome in GBM, and reveal for the first time that pts with grade II and III glioma with a “high” POSTN signature have significantly worse survival. Given that low grade glioma pts often have less aggressive treatment at time of diagnosis, we propose studying the role of early chemoradiation in the subset of pts with poor POSTN signature to potentially improve their outcome.

Citation Format: Andrew D. Trister, Andrei M. Mikheev, Jason K. Rockhill, Stephen H. Friend, Robert C. Rostomily. Periostin expression in glioma correlates with genes related to mesenchymal transition and survival. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 1210. doi:10.1158/1538-7445.AM2013-1210

Predictive, personalized, preventive, participatory (P4) cancer medicine

Medicine will move from a reactive to a proactive discipline over the next decade--a discipline that is predictive, personalized, preventive and participatory (P4). P4 medicine will be fueled by systems approaches to disease, emerging technologies and analytical tools. There will be two major challenges to achieving P4 medicine--technical and societal barriers--and the societal barriers will prove the most challenging. How do we bring patients, physicians and members of the health-care community into alignment with the enormous opportunities of P4 medicine? In part, this will be done by the creation of new types of strategic partnerships--between patients, large clinical centers, consortia of clinical centers and patient-advocate groups. For some clinical trials it will necessary to recruit very large numbers of patients--and one powerful approach to this challenge is the crowd-sourced recruitment of patients by bringing large clinical centers together with patient-advocate groups.

Accelerating drug discovery: Open source cancer cell biology?

The possibility that experimental data from diverse cell biology experiments might shed light on other experiments has been generally outside the realm of cancer biologists. Recent experiments suggest that core RNA expression profiles distilled from experiments using a set of known members with related attributes may be used as query tools to probe expression profiles from other unrelated experiments. The potential benefit arises from the possibility to share findings without fully reconstructing the exact initial conditions. The limitations will be framed by the robustness of the hypotheses so generated.

Small interfering RNA screens reveal enhanced cisplatin cytotoxicity in tumor cells having both BRCA network and TP53 disruptions

RNA interference technology allows the systematic genetic analysis of the molecular alterations in cancer cells and how these alterations affect response to therapies. Here we used small interfering RNA (siRNA) screens to identify genes that enhance the cytotoxicity (enhancers) of established anticancer chemotherapeutics. Hits identified in drug enhancer screens of cisplatin, gemcitabine, and paclitaxel were largely unique to the drug being tested and could be linked to the drug's mechanism of action. Hits identified by screening of a genome-scale siRNA library for cisplatin enhancers in TP53-deficient HeLa cells were significantly enriched for genes with annotated functions in DNA damage repair as well as poorly characterized genes likely having novel functions in this process. We followed up on a subset of the hits from the cisplatin enhancer screen and validated a number of enhancers whose products interact with BRCA1 and/or BRCA2. TP53(+/-) matched-pair cell lines were used to determine if knockdown of BRCA1, BRCA2, or validated hits that associate with BRCA1 and BRCA2 selectively enhances cisplatin cytotoxicity in TP53-deficient cells. Silencing of BRCA1, BRCA2, or BRCA1/2-associated genes enhanced cisplatin cytotoxicity approximately 4- to 7-fold more in TP53-deficient cells than in matched TP53 wild-type cells. Thus, tumor cells having disruptions in BRCA1/2 network genes and TP53 together are more sensitive to cisplatin than cells with either disruption alone.

Emerging approaches in molecular profiling affecting oncology drug discovery

The purpose of this paper is to provide some perspectives on whether we are at a tipping point in understanding oncology and oncology drug discovery. It describes how model organisms have prepared us for more efficient drug discovery, lessons that are in use today. It provides examples of the emerging integration of biomarkers in patient care. It also details how over the next several years the processes of carrying out target identification and identifying responders to drugs will become more and more similar. In conclusion, a discussion is provided about who can do what to link the various components of this information-rich drug discovery process together.

Cancer biomarkers--an invitation to the table

The allure of the emerging genomic technologies in cancer is their ability to generate new biomarkers that predict how individual cancer patients will respond to various treatments. However, productive implementation of cancer biomarkers into patient care will require fundamental changes in how we consider approvals for cancer indications and how we track patient responses. Here we briefly describe ongoing efforts to identify and to validate cancer biomarkers, discuss the technological hurdles that lie ahead, and then focus on the more pressing political and cultural issues that, if left unheeded, could derail many of the anticipated benefits of biomarker research.

How molecular profiling could revolutionize drug discovery

Information from genomic, proteomic and metabolomic measurements has already benefited target discovery and validation, assessment of efficacy and toxicity of compounds, identification of disease subgroups and the prediction of responses of individual patients. Greater benefits can be expected from the application of these technologies on a significantly larger scale; by simultaneously collecting diverse measurements from the same subjects or cell cultures; by exploiting the steadily improving quantitative accuracy of the technologies; and by interpreting the emerging data in the context of underlying biological models of increasing sophistication. The benefits of applying molecular profiling to drug discovery and development will include much lower failure rates at all stages of the drug development pipeline, faster progression from discovery through to clinical trials and more successful therapies for patient subgroups. Upheavals in existing organizational structures in the current 'conveyor belt' models of drug discovery might be required to take full advantage of these methods.

Signatures of Environmental Exposures Using Peripheral Leukocyte Gene Expression: Tobacco Smoke

Functional biological markers of environmental exposures are important in epidemiological studies of disease risk. Such markers not only provide a measure of the exposure, they also reflect the degree of physiological and biochemical response to the exposure. In an observational study, using DNA microarrays, we show that it is possible to distinguish between 85 individuals exposed and unexposed to tobacco smoke on the basis of mRNA expression in peripheral leukocytes. Furthermore, we show that active exposure to tobacco smoke is associated with a biologically relevant mRNA expression signature. These findings suggest that expression patterns can be used to identify a complex environmental exposure in humans.

Signatures of environmental exposures using peripheral leukocyte gene expression: tobacco smoke

Functional biological markers of environmental exposures are important in epidemiological studies of disease risk. Such markers not only provide a measure of the exposure, they also reflect the degree of physiological and biochemical response to the exposure. In an observational study, using DNA microarrays, we show that it is possible to distinguish between 85 individuals exposed and unexposed to tobacco smoke on the basis of mRNA expression in peripheral leukocytes. Furthermore, we show that active exposure to tobacco smoke is associated with a biologically relevant mRNA expression signature. These findings suggest that expression patterns can be used to identify a complex environmental exposure in humans.

A new paradigm for drug discovery: integrating clinical, genetic, genomic and molecular phenotype data to identify drug targets

Application of statistical genetics approaches to variations in mRNA transcript abundances in segregating populations can be used to identify genes and pathways associated with common human diseases. The combination of this genetic information with gene expression and clinical trait data can also be used to identify subtypes of a disease and the genetic loci specific to each subtype. Here we highlight results from some of our recent work in this area and further explore the many possibilities that exist in employing a more comprehensive genetics and functional genomics approach to the functional annotation of genomes, and in applying such methods to the validation of targets for complex traits in the drug discovery process.

Genetics of gene expression surveyed in maize, mouse and man

Treating messenger RNA transcript abundances as quantitative traits and mapping gene expression quantitative trait loci for these traits has been pursued in gene-specific ways. Transcript abundances often serve as a surrogate for classical quantitative traits in that the levels of expression are significantly correlated with the classical traits across members of a segregating population. The correlation structure between transcript abundances and classical traits has been used to identify susceptibility loci for complex diseases such as diabetes and allergic asthma. One study recently completed the first comprehensive dissection of transcriptional regulation in budding yeast, giving a detailed glimpse of a genome-wide survey of the genetics of gene expression. Unlike classical quantitative traits, which often represent gross clinical measurements that may be far removed from the biological processes giving rise to them, the genetic linkages associated with transcript abundance affords a closer look at cellular biochemical processes. Here we describe comprehensive genetic screens of mouse, plant and human transcriptomes by considering gene expression values as quantitative traits. We identify a gene expression pattern strongly associated with obesity in a murine cross, and observe two distinct obesity subtypes. Furthermore, we find that these obesity subtypes are under the control of different loci.

A gene-expression signature as a predictor of survival in breast cancer

BACKGROUND

A more accurate means of prognostication in breast cancer will improve the selection of patients for adjuvant systemic therapy.

METHODS

Using microarray analysis to evaluate our previously established 70-gene prognosis profile, we classified a series of 295 consecutive patients with primary breast carcinomas as having a gene-expression signature associated with either a poor prognosis or a good prognosis. All patients had stage I or II breast cancer and were younger than 53 years old; 151 had lymph-node-negative disease, and 144 had lymph-node-positive disease. We evaluated the predictive power of the prognosis profile using univariable and multivariable statistical analyses.

RESULTS

Among the 295 patients, 180 had a poor-prognosis signature and 115 had a good-prognosis signature, and the mean (+/-SE) overall 10-year survival rates were 54.6+/-4.4 percent and 94.5+/-2.6 percent, respectively. At 10 years, the probability of remaining free of distant metastases was 50.6+/-4.5 percent in the group with a poor-prognosis signature and 85.2+/-4.3 percent in the group with a good-prognosis signature. The estimated hazard ratio for distant metastases in the group with a poor-prognosis signature, as compared with the group with the good-prognosis signature, was 5.1 (95 percent confidence interval, 2.9 to 9.0; P<0.001). This ratio remained significant when the groups were analyzed according to lymph-node status. Multivariable Cox regression analysis showed that the prognosis profile was a strong independent factor in predicting disease outcome.

CONCLUSIONS

The gene-expression profile we studied is a more powerful predictor of the outcome of disease in young patients with breast cancer than standard systems based on clinical and histologic criteria.

Gene expression profiling predicts clinical outcome of breast cancer

Breast cancer patients with the same stage of disease can have markedly different treatment responses and overall outcome. The strongest predictors for metastases (for example, lymph node status and histological grade) fail to classify accurately breast tumours according to their clinical behaviour. Chemotherapy or hormonal therapy reduces the risk of distant metastases by approximately one-third; however, 70-80% of patients receiving this treatment would have survived without it. None of the signatures of breast cancer gene expression reported to date allow for patient-tailored therapy strategies. Here we used DNA microarray analysis on primary breast tumours of 117 young patients, and applied supervised classification to identify a gene expression signature strongly predictive of a short interval to distant metastases ('poor prognosis' signature) in patients without tumour cells in local lymph nodes at diagnosis (lymph node negative). In addition, we established a signature that identifies tumours of BRCA1 carriers. The poor prognosis signature consists of genes regulating cell cycle, invasion, metastasis and angiogenesis. This gene expression profile will outperform all currently used clinical parameters in predicting disease outcome. Our findings provide a strategy to select patients who would benefit from adjuvant therapy.