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This work extends Receiver Operating Characteristic (ROC) curve to the situation where some cases, falling in an intermediate “indeterminacy zone” of the predictor, are not classified. It addresses two challenges: definition of sensitivity and specificity bounds for this case; and summarization of the large number of possibilities arising from different choices of indeterminacy zones.

Background Growing evidence shows that clinical and molecular subtypes of prostate cancer (PCa) have specific risk factors. Observational studies suggest that physical activity may lower the risk of aggressive PCa. To our knowledge, the association between physical activity and PCa defined by TMPRSS2:ERG has not been evaluated. Objective To prospectively examine the association between physical activity and risk of PCa defined by clinical features and TMPRSS2:ERG. Design, setting, and participants We studied 49 160 men aged 40–75 yr in the Health Professionals Follow-up Study from 1986 to 2012. Data was collected at baseline and every 2 yr with >90% follow-up. Total and vigorous physical activity were measured in metabolic equivalent of task (MET)-h/wk. Outcome measures and statistical analysis Advanced PCa was defined as stage T3b, T4, N1, or M1 at diagnosis and lethal PCa as distant metastases or death due to disease over follow-up. Presence of TMPRSS2:ERG was estimated by immunohistochemistry of ERG protein expression. Cox proportional hazards models were used to obtain multivariable hazard ratios (HRs) and 95% confidence intervals (CIs) for incidence of subtype-specific PCa. Results and limitations During 26 yr of follow-up, 6411 developed PCa overall and 888 developed lethal disease. There were no significant associations between total physical activity and risk of PCa in the overall cohort. In multivariable-adjusted models, men in the highest quintile of vigorous activity had a significant 30% lower risk of advanced PCa (HR: 0.70, 95% CI: 0.53–0.92) and 25% lower risk of lethal PCa (HR: 0.75, 95% CI: 0.59–0.94) than men in the lowest quintile of vigorous activity. The association was independent of screening history. Vigorous activity was not associated with total PCa in the overall cohort but was inversely associated among highly screened men (top vs bottom quintile, HR: 0.83, 95% CI: 0.70–0.97). Of all cases, 945 were assayed for ERG (48% ERG-positive). Men with higher vigorous activity had a lower risk of ERG-positive PCa (top vs bottom quintile, HR: 0.71, 95% CI: 0.52–0.97). There was no significant association with the risk of ERG-negative disease (p heterogeneity = 0.09). Conclusions Our study confirms that vigorous physical activity is associated with lower risk of advanced and lethal PCa and provides novel evidence for a lower risk of TMPRSS2:ERG-positive disease. Patient summary The identification of modifiable lifestyle factors for prevention of clinically important prostate cancer (PCa) is needed. In this report, we compared risk of PCa in men with different levels of physical activity. Men with higher vigorous activity had a lower risk of developing advanced and lethal PCa and PCa with the common TMPRSS2:ERG gene fusion.

Integrative analyses of multiple gene expression studies are frequently performed. In the setting of two studies, integrative correlation (IGC) can be used to assess the consistency of co-expression of a given gene. For three or more studies, an extension of IGC gives a global score per gene. We propose to extend IGC and use factor analysis to assess the study-specific consistency of co-expression of genes when there are three or more studies, possibly on different platforms. Our method is able to identify studies whose expression patterns are different from others. Filtering genes based on our score is shown to improve the concordance of association with phenotype across studies.

Background On average 10% of SMM patients progress to symptomatic MM per year with in first 5 years of diagnosis. However, a subset of SMM patients re-classified as high risk patients on the basis of risk markers which identify risk of progression within 2 years. Although recent studies have evaluated the high-risk SMM, genomic background of SMM patients who do not progress to MM after long-term follow-up (>=5 years) has not been described. Methods Here, we evaluated transcriptomic and genomic changes enriched in non-progressor (NP) (no progression after 5 years of follow-up) precursor conditions (N=31) with those progressed within short period time (N=71) and compared them with changes observed in newly diagnosed MM (N=192). Additionally, using transcriptome, epigenome and whole genome profiling we also studied additional unique samples from 18 patients at their precursor stage as well as when progressed to MM. Results Overall, we have observed significantly lower mutational load for NP SMM (median SNV 5460 vs. 7018, p 90% accuracy and >0.80 area under the curve on ROC using ten-fold cross validation. This indicated that not only the load but also the patterns of mutations (type, location, frequency) are different between two conditions. We also found that NP samples have significantly lower heterogeneity (p 80%) with AUC score 0.80. Our transcriptomic analysis measured the distance between progressor and NP SMM as well as MM and found that NP SMM are less similar to MM which is closer to progressor SMM. Epigenomic analysis yield that 75 SEs regions were differentially utilized between precursor and symptomatic MM stage. The targeted genes included BMP6, PRDM1, STAT1 and RAB21 and possibly regulating genes related to oncogenic KRAS activities. Conclusions In conclusion, our results now provide the basis to develop genomic definition of SMM as well as risk driving features.

Archival formalin-fixed, paraffin-embedded (FFPE) tissue specimens represent a readily available but largely untapped resource for gene expression profiling-based biomarker discovery. Several technologies have been proposed to cope with the bias from RNA cross-linking and degradation associated with archival specimens to generate data comparable with RNA from fresh-frozen materials. Direct comparison studies of these RNA expression platforms remain rare. We compared two commercially available platforms for RNA expression profiling of archival FFPE specimens from clinical studies of prostate and ovarian cancer: the Affymetrix Human Gene 1.0ST Array following whole-transcriptome amplification using the NuGen WT-Ovation FFPE System V2, and the NanoString nCounter without amplification. For each assay, we profiled 7 prostate and 11 ovarian cancer specimens, with a block age of 4 to 21 years. Both platforms produced gene expression profiles with high sensitivity and reproducibility through technical repeats from FFPE materials. Sensitivity and reproducibility remained high across block age within each cohort. A strong concordance was shown for the transcript expression values for genes detected by both platforms. We showed the biological validity of specific gene signatures generated by both platforms for both cohorts. Our study supports the feasibility of gene expression profiling and large-scale signature validation on archival prostate and ovarian tumor specimens using commercial platforms. These approaches have the potential to aid precision medicine with biomarker discovery and validation.

Transcriptomic deconvolution in cancer and other heterogeneous tissues remains challenging. Available methods lack the ability to estimate both component-specific proportions and expression profiles for individual samples. We present DeMixT, a new tool to deconvolve high dimensional data from mixtures of more than two components. DeMixT implements an iterated conditional mode algorithm and a novel gene-set-based component merging approach to improve accuracy. In a series of experimental validation studies and application to TCGA data, DeMixT showed high accuracy. Improved deconvolution is an important step towards linking tumor transcriptomic data with clinical outcomes.

The availability of the human genome sequence and progress in sequencing and bioinformatic technologies have enabled genome-wide investigation of somatic mutations in human cancers. This article briefly reviews challenges arising in the statistical analysis of mutational data of this kind. A first challenge is that of designing studies that efficiently allocate sequencing resources. We show that this can be addressed by two-stage designs and demonstrate via simulations that even relatively small studies can produce lists of candidate cancer genes that are highly informative for future research efforts. A second challenge is to distinguish mutated genes that are selected for by cancer (drivers) from mutated genes that have no role in the development of cancer and simply happened to mutate (passengers). We suggest that this question is best approached as a classification problem and discuss some of the difficulties of more traditional testing-based approaches. A third challenge is to identify biologic processes affected by the driver genes. This can be pursued by gene set analyses. These can reliably identify functional groups and pathways that are enriched for mutated genes even when the individual genes involved in those pathways or sets are not mutated at sufficient frequencies to provide conclusive evidence as drivers.

Retrotransposons have shaped eukaryotic genomes for millions of years. To analyze the consequences of human L1 retrotransposition, we developed a genetic system to recover many new L1 insertions in somatic cells. Forty-two de novo integrants were recovered that faithfully mimic many aspects of L1s that accumulated since the primate radiation. Their structures experimentally demonstrate an association between L1 retrotransposition and various forms of genetic instability. Numerous L1 element inversions, extra nucleotide insertions, exon deletions, a chromosomal inversion, and flanking sequence comobilization (called 5′ transduction) were identified. In a striking number of integrants, short identical sequences were shared between the donor and the target site's 3′ end, suggesting a mechanistic model that helps explain the structure of L1 insertions.

A recent national panel on cost-effectiveness in health and medicine has recommended that cost-effectiveness analysis (CEA) of randomized controlled trials (RCTs) should reflect the effect of treatments on long-term outcomes. Because the follow-up period of RCTs tends to be relatively short, long-term implications of treatments must be assessed using other sources. We used a comprehensive simulation model of the natural history of stroke to estimate long-term outcomes after a hypothetical RCT of an acute stroke treatment. The RCT generates estimates of short-term quality-adjusted survival and cost and also the pattern of disability at the conclusion of follow-up. The simulation model incorporates the effect of disability on long-term outcomes, thus supporting a comprehensive CEA. Treatments that produce relatively modest improvements in the pattern of outcomes after ischemic stroke are likely to be cost-effective. This conclusion was robust to modifying the assumptions underlying the analysis. More effective treatments in the acute phase immediately following stroke would generate significant public health benefits, even if these treatments have a high price and result in relatively small reductions in disability. Simulation-based modeling can provide the critical link between a treatment's short-term effects and its long-term implications and can thus support comprehensive CEA.

SummaryThe problem of interpreting missense mutations of disease-causing genes is an increasingly important one. Because these point mutations result in alteration of only a single amino acid of the protein product, it is often unclear whether this change alone is sufficient to cause disease. We propose a Bayesian approach that utilizes genetic information on affected relatives in families ascertained through known missense-mutation carriers. This method is useful in evaluating known disease genes for common disease phenotypes, such as breast cancer or colorectal cancer. The posterior probability that a missense mutation is disease causing is conditioned on the relationship of the relatives to the proband, the population frequency of the mutation, and the phenocopy rate of the disease. The approach is demonstrated in two cancer data sets: BRCA1 R841W and APC I1307K. In both examples, this method helps establish that these mutations are likely to be disease causing, with Bayes factors in favor of causality of 5.09 and 66.97, respectively, and posterior probabilities of .836 and .985. We also develop a simple approximation for rare alleles and consider the case of unknown penetrance and allele frequency.

SummaryBreast cancer–susceptibility genes BRCA1 and BRCA2 have recently been identified on the human genome. Women who carry a mutation of one of these genes have a greatly increased chance of developing breast and ovarian cancer, and they usually develop the disease at a much younger age, compared with normal individuals. Women can be tested to see whether they are carriers. A woman who undergoes genetic counseling before testing can be told the probabilities that she is a carrier, given her family history. In this paper we develop a model for evaluating the probabilities that a woman is a carrier of a mutation of BRCA1 and BRCA2, on the basis of her family history of breast and ovarian cancer in first- and second-degree relatives. Of special importance are the relationships of the family members with cancer, the ages at onset of the diseases, and the ages of family members who do not have the diseases. This information can be elicited during genetic counseling and prior to genetic testing. The carrier probabilities are obtained from Bayes's rule, by use of family history as the evidence and by use of the mutation prevalences as the prior distribution. In addressing an individual's carrier probabilities, we incorporate uncertainty about some of the key inputs of the model, such as the age-specific incidence of diseases and the overall prevalence of mutations. There is some evidence that other, undiscovered genes may be important in explaining familial breast cancer. Users of the current version of the model should be aware of this limitation. The methodology that we describe can be extended to more than two genes, should data become available about other genes.

Despite evidence that differences in gene expression levels contribute significantly to phenotypic variation across individuals, there has been only limited effort to study gene expression variation in human tissue. To characterize expression variation in the normal human retina, the authors utilized a custom retinal microarray to analyze 33 normal retinas from 19 donors, aged 29 to 90 years. Statistical models were designed to separate and quantify biological and technical sources of variation, including age, gender, eye laterality, gene function, and age-by-gender interaction. Although the majority of the 9406 genes analyzed showed relatively stable expression across different donors (for an average gene the expression level value of 95 out of a 100 individual fell within a 1.23-fold range), 2.6% of genes showed significant donor-to-donor variation, with a false discovery rate of 10%. The mean expression ratio standard deviation was 0.15 ± 0.8, log 2 , with a range of 0.09 to 0.99. Genes selectively expressed in photoreceptors showed higher expression variation than other gene classes. Gender, age, and other donor-specific factors contributed significantly to the expression variation of multiple genes, and groups of genes with age- or gender-associated expression pattern were identified. These findings show that a significant fraction of gene expression variation in the normal human retina is attributable to identifiable biologic factors. The greater expression variability of many genes central to retinal function (including photoreceptor-specific gene) may be partially explained by the dynamics of the vision process, and raises the possibility that photoreceptor gene expression levels may contribute to phenotypic diversity across normal adult retinas. Additionally, as such diversity may result in different levels of disease susceptibility, exploring its sources may provide insights into the pathogenesis of retinal disease.—Hans E. Grossniklaus