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It is increasingly common clinically for cancer specimens to be examined using techniques that identify somatic mutations. In principle, these mutational profiles can be used to diagnose the tissue of origin, a critical task for the 3% to 5% of tumors that have an unknown primary site. Diagnosis of primary site is also critical for screening tests that employ circulating DNA. However, most mutations observed in any new tumor are very rarely occurring mutations, and indeed the preponderance of these may never have been observed in any previous recorded tumor. To create a viable diagnostic tool we need to harness the information content in this "hidden genome" of variants for which no direct information is available. To accomplish this we propose a multilevel meta‐feature regression to extract the critical information from rare variants in the training data in a way that permits us to also extract diagnostic information from any previously unobserved variants in the new tumor sample. A scalable implementation of the model is obtained by combining a high‐dimensional feature screening approach with a group‐lasso penalized maximum likelihood approach based on an equivalent mixed‐effect representation of the multilevel model. We apply the method to the Cancer Genome Atlas whole‐exome sequencing data set including 3702 tumor samples across seven common cancer sites. Results show that our multilevel approach can harness substantial diagnostic information from the hidden genome. [ABSTRACT FROM AUTHOR]

Inferring the cancer-type specificities of ultra-rare, genome-wide somatic mutations is an open problem. Traditional statistical methods cannot handle such data due to their ultra-high dimensionality and extreme data sparsity. To harness information in rare mutations, we have recently proposed a formal multilevel multilogistic "hidden genome" model. Through its hierarchical layers, the model condenses information in ultra-rare mutations through meta-features embodying mutation contexts to characterize cancer types. Consistent, scalable point estimation of the model can incorporate 10s of millions of variants across thousands of tumors and permit impressive prediction and attribution. However, principled statistical inference is infeasible due to the volume, correlation, and noninterpretability of mutation contexts. In this paper, we propose a novel framework that leverages topic models from computational linguistics to effectuate dimension reduction of mutation contexts producing interpretable, decorrelated meta-feature topics. We propose an efficient MCMC algorithm for implementation that permits rigorous full Bayesian inference at a scale that is orders of magnitude beyond the capability of existing out-of-the-box inferential high-dimensional multi-class regression methods and software. Applying our model to the Pan Cancer Analysis of Whole Genomes dataset reveals interesting biological insights including somatic mutational topics associated with UV exposure in skin cancer, aging in colorectal cancer, and strong influence of epigenome organization in liver cancer. Under cross-validation, our model demonstrates highly competitive predictive performance against blackbox methods of random forest and deep learning. [ABSTRACT FROM AUTHOR]

Cancer epidemiologic research has traditionally been guided by the premise that certain diseases share an underlying etiology, or cause. However, with the rise of molecular and genomic profiling, attention has increasingly focused on identifying subtypes of disease. As subtypes are identified, it is natural to ask the question of whether they share a common etiology or in fact arise from distinct sets of risk factors. In this context, epidemiologic questions of interest include (1) whether a risk factor of interest has the same effect across all subtypes of disease and (2) whether risk factor effects differ across levels of each individual tumor marker of which the subtypes are comprised. A number of statistical models have been proposed to address these questions. In an effort to determine the similarities and differences among the proposed methods, and to identify any advantages or disadvantages, we use a simplified data example to elucidate the interpretation of model parameters and available hypothesis tests, and we perform a simulation study to assess bias in effect size, type I error, and power. The results show that when the number of tumor markers is small enough that the cross-classification of markers can be evaluated in the traditional polytomous logistic regression framework, then the statistical properties are at least as good as the more complex modeling approaches that have been proposed. The potential advantage of more complex methods is in the ability to accommodate multiple tumor markers in a model of reduced parametric dimension. [ABSTRACT FROM AUTHOR]

Background: The somatic molecular profiles of basal-like breast cancers and high-grade serous ovarian cancers share many similarities, leading to the hypothesis that they have similar aetiologies, in which case they should occur together in the same patient more often than expected.Methods: We identified 545 women with double independent primary cancers of the breast and ovary reported to the California Cancer Registry from 1999 to 2013 and examined the coincidence of subtype combinations.Results: For most subtype combinations the observed frequencies were similar to their expected frequencies, but in 103 observed cases vs 43.8 expected (O/E=2.35; 95% CI 1.90-2.81) a triple-negative breast tumour (typically basal-like) was matched with a serous ovarian tumour (typically high-grade).Conclusions: The results provide compelling evidence that basal-like breast cancer and high-grade serous ovarian cancer share a much more similar aetiology than breast and ovarian cancers more broadly. Further research is needed to clarify the influence of germ-line BRCA1 mutations and other risk factors on these results. [ABSTRACT FROM AUTHOR]

With the advent of increasingly detailed molecular portraits of tumor specimens, much attention has been directed toward identifying clinically distinct subtypes of cancer. Subtyping of tumors can also be accomplished with the goal of identifying distinct etiologies. We demonstrate the use of new methodologies to identify genes that distinguish etiologically heterogeneous subtypes of breast cancer using data from the case-control Cancer and Steroid Hormone Study. Tumor specimens were evaluated using a breast cancer expression panel of 196 genes. Using a statistical measure that distinguishes the degree of etiologic heterogeneity in tumor subtypes, each gene is ranked on the basis of its ability to distinguish etiologically distinct subtypes. This is accomplished independently using case-control comparisons and by examining the concordance odds ratios in double primaries. The estrogen receptor gene, and others in this pathway with expression levels that correlated strongly with estrogen receptor levels, demonstrate high degrees of etiologic heterogeneity in both methods. Our results are consistent with a growing literature that confirms the distinct etiologies of breast cancers classified on the basis of estrogen receptor expression levels. This proof-of-principle project demonstrates the viability of new strategies to identify genomic features that distinguish subtypes of cancer from an etiologic perspective. [ABSTRACT FROM AUTHOR]

Background The etiologic heterogeneity of cancer has traditionally been investigated by comparing risk factor frequencies within candidate sub-types, defined for example by histology or by distinct tumor markers of interest. Increasingly tumors are being profiled for molecular features much more extensively. This greatly expands the opportunities for defining distinct sub-types. In this article we describe an exploratory analysis of the etiologic heterogeneity of clear cell kidney cancer. Data are available on the primary known risk factors for kidney cancer, while the tumors are characterized on a genome-wide basis using expression, methylation, copy number and mutational profiles. Methods We use a novel clustering strategy to identify sub-types. This is accomplished independently for the expression, methylation and copy number profiles. The goals are to identify tumor sub-types that are etiologically distinct, to identify the risk factors that define specific subtypes, and to endeavor to characterize the key genes that appear to represent the principal features of the distinct sub-types. Results The analysis reveals strong evidence that gender represents an important factor that distinguishes disease sub-types. The sub-types defined using expression data and methylation data demonstrate considerable congruence and are also clearly correlated with mutations in important cancer genes. These sub-types are also strongly correlated with survival. The complexity of the data presents many analytical challenges including, prominently, the risk of false discovery. Conclusions Genomic profiling of tumors offers the opportunity to identify etiologically distinct subtypes, paving the way for a more refined understanding of cancer etiology. [ABSTRACT FROM AUTHOR]

Cancer has traditionally been studied using the disease site of origin as the organizing framework. However, recent advances in molecular genetics have begun to challenge this taxonomy, as detailed molecular profiling of tumors has led to discoveries of subsets of tumors that have profiles that possess distinct clinical and biological characteristics. This is increasingly leading to research that seeks to investigate whether these subtypes of tumors have distinct etiologies. However, research in this field has been opportunistic and anecdotal, typically involving the comparison of distributions of individual risk factors between tumors classified on the basis of candidate tumor characteristics. The purpose of this article is to place this area of investigation within a more general conceptual and analytic framework, with a view to providing more efficient and practical strategies for designing and analyzing epidemiologic studies to investigate etiologic heterogeneity. We propose a formal definition of etiologic heterogeneity and show how classifications of tumor subtypes with larger etiologic heterogeneities inevitably possess greater disease risk predictability overall. We outline analytic strategies for estimating the degree of etiologic heterogeneity among a set of subtypes and for choosing subtypes that optimize the heterogeneity, and we discuss technical challenges that require further methodologic research. We illustrate the ideas by using a pooled case-control study of breast cancer classified by expression patterns of genes known to define distinct tumor subtypes. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Contemporary searches for new risk factors frequently involve genome-wide explorations of very large numbers of candidate risk variants. Given that diseases can often be classified into subtypes that possess evidence of etiologic heterogeneity, the question arises as to whether or not a search for new risk factors would be improved by looking separately within subtypes. Etiologic risk heterogeneity inevitably increases the signal in at least one of the subtypes, but this advantage may be offset by smaller sample sizes and the increased chances of false discovery. In this article, the authors show that only a relatively modest degree of etiologic heterogeneity is necessary for the subtyping strategies to have improved statistical power. In practice, effective exploitation of etiologic heterogeneity requires strong evidence that the subtypes selected are likely to exhibit substantial heterogeneity. Further, defining the subtypes that demonstrate the most heterogeneous profiles is important for optimizing the search for new risk factors. The concepts are illustrated by using data from a breast cancer study in which results are available separately for estrogen receptor-positive (ER+) and -negative (ER−) tumors. [ABSTRACT FROM AUTHOR]

The article presents a study on the interaction of CDKN2A and sun exposure to the etiology of melanoma. It states that 3,624 melanoma patients from nine centers from four countries were recruited for the study to identify how the two factors might increase the risk of melanoma. The study found no evidence suggesting the influence of CDKN2A mutational status and sun exposure on the risk of melanoma.

Many major genes have been identified that strongly influence the risk of cancer. However, there are typically many different mutations that can occur in the gene, each of which may or may not confer increased risk. It is critical to identify which specific mutations are harmful, and which ones are harmless, so that individuals who learn from genetic testing that they have a mutation can be appropriately counseled. This is a challenging task, since new mutations are continually being identified, and there is typically relatively little evidence available about each individual mutation. In an earlier article, we employed hierarchical modeling (, Statistics in Medicine , 1973-1992) using the pseudo-likelihood and Gibbs sampling methods to estimate the relative risks of individual rare variants using data from a case-control study and showed that one can draw strength from the aggregating power of hierarchical models to distinguish the variants that contribute to cancer risk. However, further research is needed to validate the application of asymptotic methods to such sparse data. In this article, we use simulations to study in detail the properties of the pseudo-likelihood method for this purpose. We also explore two alternative approaches: pseudo-likelihood with correction for the variance component estimate as proposed by (1996, Journal of the American Statistical Association , 1007-1016) and a hybrid pseudo-likelihood approach with Bayesian estimation of the variance component. We investigate the validity of these hierarchical modeling techniques by looking at the bias and coverage properties of the estimators as well as at the efficiency of the hierarchical modeling estimates relative to that of the maximum likelihood estimates. The results indicate that the estimates of the relative risks of very sparse variants have small bias, and that the estimated 95% confidence intervals are typically anti-conservative, though the actual coverage rates are generally above 90%. The widths of the confidence intervals narrow as the residual variance in the second-stage model is reduced. The results also show that the hierarchical modeling estimates have shorter confidence intervals relative to estimates obtained from conventional logistic regression, and that these relative improvements increase as the variants become more rare. [ABSTRACT FROM AUTHOR]

This article reports on a study that looked at the risk variation in people who have the BRCA1 and BRCA 2 genes that are thought to be a contributory cause to getting breast cancer. Breast cancer victims with the gene mutations who were related to people with the disease were studied. The characteristics of the probands and this group were compared to see variations between the relatives and families. The study found significant variations in breast cancer risk among carriers of the BRCA1 and the BRCA2 genes.

Cancer investigators frequently conduct studies to examine tumor samples from pairs of apparently independent primary tumors with a view to determine whether they share a “clonal” origin. The genetic fingerprints of the tumors are compared using a panel of markers, often representing loss of heterozygosity (LOH) at distinct genetic loci. In this article we evaluate candidate significance tests for this purpose. The relevant information is derived from the observed correlation of the tumors with respect to the occurrence of LOH at individual loci, a phenomenon that can be evaluated using Fisher's exact test. Information is also available from the extent to which losses at the same locus occur on the same parental allele. Data from these combined sources of information can be evaluated using a simple adaptation of Fisher's exact test. The test statistic is the total number of loci at which concordant mutations occur on the same parental allele, with higher values providing more evidence in favor of a clonal origin for the two tumors. The test is shown to have high power for detecting clonality for plausible models of the alternative (clonal) hypothesis, and for reasonable numbers of informative loci, preferably located on distinct chromosomal arms. The method is illustrated using studies to identify clonality in contralateral breast cancer. Interpretation of the results of these tests requires caution due to simplifying assumptions regarding the possible variability in mutation probabilities between loci, and possible imbalances in the mutation probabilities between parental alleles. Nonetheless, we conclude that the method represents a simple, powerful strategy for distinguishing independent tumors from those of clonal origin. [ABSTRACT FROM AUTHOR]

Cyclin-dependent kinase inhibitor type 2A (CDKN2A) has been identified as a major melanoma susceptibility gene based on the presence of germline mutations in high-risk melanoma families. In this study, we sought to identify and characterize the spectrum of CDKN2A mutations affecting p16 inhibitor of cyclin-dependent kinase type 4 (INK4a) in individuals with melanoma using a population-based study design. DNA samples from 1189 individuals with incident multiple primary melanoma (MPM) and 2424 with incident single primary melanoma unselected for family history of melanoma were available for screening of CDKN2A (p16INK4a) mutations. Variants were classified for functional impact based on intragenic position, existing functional data, sequence, and structural analysis. The impact of individual mutations and functional groupings was assessed by comparing frequencies in cases of MPM versus cases with a single first primary melanoma, and by comparing the reported incidence rates in first-degree relatives. Our results show that mutations occur infrequently in these high-risk groups, and that they occur mainly in exons 1α and 2. Rare coding variants with putative functional impact are observed to increase substantially the risk of melanoma. With the exception of the variant in position −34 of CDKN2A of known functional consequence, the remaining rare variants in the non-coding region have no apparent impact on risk.Journal of Investigative Dermatology (2007) 127, 1234–1243. doi:10.1038/sj.jid.5700689; published online 11 January 2007 [ABSTRACT FROM AUTHOR]

Background: Germline mutations in the CDKN2A gene have been linked to melanoma incidence in many families with multiple cases of the disease. Previous studies of multiple-case families have indicated that the lifetime risk (i.e., penetrance) of melanoma in CDKN2A mutation carriers is very high, ranging from 58% in Europe to 91% in Australia by age 80 years. In this study, we examined lifetime melanoma risk among CDKN2A mutation carriers using carriers who were identified in a population-based study of melanoma. Methods: Probands for the study were incident case patients with either first or subsequent melanoma who were identified in nine geographic regions in Australia, Canada, the United States, and Italy. A total of 3626 probands (53% participation rate) with adequate DNA for analysis were recruited and genotyped for CDKN2A mutations. From the 3550 probands whose DNA could be amplified by polymerase chain reaction of CDKN2A exons 1α, 2, and 3 and surrounding regions, 65 mutation carriers were identified. Melanoma histories in first-degree relatives of these probands were used to calculate the lifetime risk in CDKN2A mutation carriers using the kin-cohort method. Results: The risk of melanoma in CDKN2A mutation carriers was approximately 14% (95% CI = 8% to 22%) by age 50 years, 24% (95% CI = 15% to 34%) by age 70 years, and 28% (95% CI = 18% to 40%) by age 80 years. Eighteen probands had three or more first-degree relatives with melanoma, but only one was a carrier of a CDKN2A mutation. Conclusions: CDKN2A mutation carriers in the general population have a much lower risk of melanoma than that suggested by estimates obtained from multiple-case families. The preponderance of familial clustering of melanoma occurs in families without identifiable mutations in CDKN2A. [ABSTRACT FROM AUTHOR]

Background: Estimating the lifetime risk associated with (i.e., the penetrance of) genetic abnormalities that predispose individuals to cancer is important for genetic counseling. (Penetrance may be estimated from the degree of familial aggregation of cancer, that is, the extent to which cancers cluster in families.) Early penetrance studies of BRCA1 and BRCA2 mutations used high-risk families with multiple cases of breast cancer, a study design that led to very high penetrance estimates. However, such studies were subject to potential ascertainment biases. To offset such biases, recent studies have used data from family members of probands ascertained from population-based incident cases of cancer. The use of case probands is, however, also subject to bias because all risk factors are over-represented in case patients. To draw attention to this problem, literature on the penetrance of breast cancer in BRCA1 and BRCA2 carriers is reviewed.Methods: A theory is presented to show that the use of case probands is itself biased, leading to inflated penetrance estimates. The strategy is unbiased only if all carriers share an identical risk. Any unexplained heterogeneity of risk caused by unknown genetic or shared environmental factors within families leads to an inflated estimate of penetrance.Results: Eight published studies using population-based methods are reviewed. All but one of the family-based studies used case probands.Conclusions: Penetrance estimates from case proband studies must be inflated if other factors influence breast cancer risk in addition to the specific genetic abnormality. Thus, women with such genetic abnormalities and a strong family history of breast cancer are likely to possess a much higher risk for breast cancer than women with such abnormalities but without a strong family history. Methodologic techniques to improve the prediction of cancer risk are needed. [ABSTRACT FROM AUTHOR]

In the face of the apparent knowledge that the cancer burden is essentially fully "explainable" on the basis of known environmental risks, this article addresses the conceptual and empirical basis of the continued search for new risk factors. It proposes that the extent of the aggregation of cancer within individuals in the population--that is, the occurrence of second primary cancers--is a crucial statistic in this context. A study of the incidence of second primary melanoma suggests that the bulk of the risk variation in this disease cannot be explained by known risk factors. The implications of these ideas for research strategy and for public health policy are discussed. (Am J Public Health. 2001 ;91:360-364) [ABSTRACT FROM AUTHOR]

Context: Hospitals that treat a relatively high volume of patients for selected surgical oncology procedures report lower surgical in-hospital mortality rates than hospitals with a low volume of the procedures, but the reports do not take into account length of stay or adjust for case mix.Objective: To determine whether hospital volume was inversely associated with 30-day operative mortality, after adjusting for case mix.Design and Setting: Retrospective cohort study using the Surveillance, Epidemiology, and End Results (SEER)-Medicare linked database in which the hypothesis was prospectively specified. Surgeons determined in advance the surgical oncology procedures for which the experience of treating a larger volume of patients was most likely to lead to the knowledge or technical expertise that might offset surgical fatalities.Patients: All 5013 patients in the SEER registry aged 65 years or older at cancer diagnosis who underwent pancreatectomy, esophagectomy, pneumonectomy, liver resection, or pelvic exenteration, using incident cancers of the pancreas, esophagus, lung, colon, and rectum, and various genitourinary cancers diagnosed between 1984 and 1993.Main Outcome Measure: Thirty-day mortality in relation to procedure volume, adjusted for comorbidity, patient age, and cancer stage.Results: Higher volume was linked with lower mortality for pancreatectomy (P=.004), esophagectomy (P<.001), liver resection (P=.04), and pelvic exenteration (P=.04), but not for pneumonectomy (P=.32). The most striking results were for esophagectomy, for which the operative mortality rose to 17.3% in low-volume hospitals, compared with 3.4% in high-volume hospitals, and for pancreatectomy, for which the corresponding rates were 12.9% vs 5.8%. Adjustments for case mix and other patient factors did not change the finding that low volume was strongly associated with excess mortality.Conclusions: These data support the hypothesis that when complex surgical oncologic procedures are provided by surgical teams in hospitals with specialty expertise, mortality rates are lower. [ABSTRACT FROM AUTHOR]

A distribution-free permutation test procedure is proposed for comparing receiver operating characteristic curves based on continuous data from a paired design. The method tests the hypothesis that the two curves are identical for all operating points, unlike previously proposed methods which test the equivalence of the areas under the curves. The new test is shown by simulation to have very similar operating characteristics to the standard method based on comparisons of the areas when the curves are parallel, but markedly superior power when the curves cross, that is when the curves are different but have similar areas. The prospects of generalising the approach to unpaired experiments and to comparisons of ordinal rating data are discussed. [ABSTRACT FROM AUTHOR]

Gives background information on Marianne Berwick's and other scientists research on the detection of skin cancer via self-examination. Research methodology; Who are at risk; Perception on the progression of melanoma; How data was collected; Analysis of data; Results; Self-examination's impact on early detection.

Proposes a stochastic framework for evaluating the individual and collective impact of cancer and its application to data on the incidence of melanoma. Etiologic relevance of second primary cancer; Construction of a mathematical framework to gauge the relative contribution of individual risk factors; Application to a completed case-control study of melanoma.

Case-control studies increasingly have been used to evaluate the impact of cancer screening strategies. In this context the intent of the screening test has been to reduce cancer mortality by early detection of cancers, permitting curative therapy in some patients who would die of the disease if diagnosis were delayed until the disease was detected clinically. This phenomenon is known as secondary prevention. In an analysis of a case-control study of skin self-examination (SSE) in reducing mortality from melanoma, it was recognized that the exposure (SSE) may encourage the removal of precancerous nevi (moles), thereby reducing cancer incidence (primary prevention). This article describes an analytic strategy for obtaining separate estimates of the primary and secondary preventive impact of the screening practice. The method is focused primarily on resolving the problem of lead-time bias, caused by the artifactual advancement of the time of diagnosis in cases detected by screening. [ABSTRACT FROM AUTHOR]

A random effects model is used to analyze meta-analyses containing both controlled and uncontrolled studies A noniterative estimator of the treatment effect is derived using least squares, with the between-study variance substituted by an empirical Bayes estimator The estimator is shown to be strongly consistent when the between-study variance is known, and the variance estimator is also shown to be strongly consistent The estimator is shown to have desirable heuristic properties. [ABSTRACT FROM AUTHOR]

The potential magnitude of publication bias has been examined with a consecutive sample of published cancer clinical trials. The analysis is based on the premise that the magnitude of the true treatment effect is unrelated to design features of the study, in particular sample size. This assumption permits an analysis based only on published studies. Three primary endpoints are examined: overall patient survival, disease-free survival, and tumor response rate. There are striking trends for each endpoint, with small studies appearing to possess large treatment effects and large studies possessing relatively small effects. It is believed that these differences are primarily due to publication bias. The bias is very large: Absolute differences observed were 41% for overall survival, 79% for disease-free survival, and 17% for response rates. Other study features have been examined that might be associated with bias, or that might be responsible for the striking trends regarding sample size. The results indicate that absence of randomization leads to significant bias, and studies conducted in a single institution are somewhat more prone to bias than multiinstitutional studies, though the trends are less consistent in the latter case. No strong trend was observed for journal type. Nevertheless, none of these variables could account for the strong effect of sample size. Sensitivity analyses of the results were conducted and alternative models were considered. These analyses generally support the contention that the magnitude of the bias due to sample size cannot be explained by alternative factors. An implication of this study is that the results of small published studies are typically unreliable, even taking into account the fact that such trials are imprecise due to sampling variation. [ABSTRACT FROM AUTHOR]

Presents a reflection on the alpha-tocopherol Beta-carotene trial for cancer prevention in the U.S. Reason behind the cancellation of the trial; Ethical considerations for large anti-cancer trials; Assessment on the effects of Beta-carotene on overall mortality.

We studied records of 351 hypertensive patients cared for by 30 internists in private office practice. We correlated the use of outpatient diagnostic tests with personal characteristics of the prescribing physicians. Doctors trained in medical schools with academic orientations used more tests than other physicians, Patterns, of use were not strongly related to the number of years since medical school graduation, or physicians "intolerance of ambiguity" as measured by a standard psychological instrument. These findings suggest that certain types of training may predispose physicians to be high testers. (Am J Public Health 1984; 74: 1271-1273) [ABSTRACT FROM AUTHOR]

Model selection in high-dimensional settings has received substantial attention in recent years, however, similar advancements in the low-dimensional setting have been lacking. In this article, we introduce a new variable selection procedure for low to moderate scale regressions (n>p). This method repeatedly splits the data into two sets, one for estimation and one for validation, to obtain an empirically optimized threshold which is then used to screen for variables to include in the final model. In an extensive simulation study, we show that the proposed variable selection technique enjoys superior performance compared with candidate methods (backward elimination via repeated data splitting, univariate screening at 0.05 level, adaptive LASSO, SCAD), being amongst those with the lowest inclusion of noisy predictors while having the highest power to detect the correct model and being unaffected by correlations among the predictors. We illustrate the methods by applying them to a cohort of patients undergoing hepatectomy at our institution. [ABSTRACT FROM AUTHOR]

In this article the author focuses on the gold-standard endpoint for cancer clinical trials. It further discusses the requirement of the U.S. Food and Drug Administartion that improvement in either survival or meaningful clinical benefit, such as amelioration of major symptoms, is needed to justify marketing approval of a new drug.

A letter to the editor is presented in response to the article "Case-Control and Case-Only Designs With Genotype and Family History Data: Estimating Relative-Risk, Residual Familial Aggregation and Cumulative Risk," by N. Chatterjee, Z. Kalayliouglu, J. Shih and M. Gail in a 2006 issue.

Determines the incidence and mortality rates of cancer in the U.S. Causes of death; Growth in the use of cancer-directed surgery; Classification of cancer mortality.

Summary The Clonality R package is a practical tool to assess the clonal relatedness of two tumors from the same patient. We have previously presented its functionality for testing tumors using loss of heterozygosity data or copy number arrays. Since then somatic mutation data have been more widely available through next generation sequencing and we have developed new methodology for comparing the tumors' mutational profiles. We thus extended the package to include these two new methods for comparing tumors as well as the mutational frequency estimation from external data required for their implementation. The first method is a likelihood ratio test that is readily available on a patient by patient basis. The second method employs a random-effects model to estimate both the population and individual probabilities of clonal relatedness from a group of patients with pairs of tumors. The package is available on Bioconductor. Availability and implementation Bioconductor (http://bioconductor.org/packages/release/bioc/html/Clonality.html). Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Simple Summary: Melanoma is the most severe type of skin cancer, and the risk of developing melanoma and of dying of melanoma varies between women and men. This study investigated whether widely studied genetic single nucleotide polymorphisms (SNPs) in two oncogenes known to promote cell proliferation explain these sex differences by testing such variants in a large cohort of 3663 melanoma patients. Formal analyses demonstrated that females, but not males, who carried the variant MDM4-rs4245739*C were more likely to develop a new primary melanoma, and those with the variant MDM2-rs2279744*G were less likely to succumb to the disease. We also identified a number of additional variants, often co-inherited with the tested SNPs, which modify how these and other genes work locally in the skin, as well as in distal organs where melanoma tends to spread or invade during the progression of the disease. MDM2-SNP309 (rs2279744), a common genetic modifier of cancer incidence in Li-Fraumeni syndrome, modifies risk, age of onset, or prognosis in a variety of cancers. Melanoma incidence and outcomes vary by sex, and although SNP309 exerts an effect on the estrogen receptor, no consensus exists on its effect on melanoma. MDM2 and MDM4 restrain p53-mediated tumor suppression, independently or together. We investigated SNP309, an a priori MDM4-rs4245739, and two coinherited variants, in a population-based cohort of 3663 primary incident melanomas. Per-allele and per-haplotype (MDM2_SNP309-SNP285; MDM4_rs4245739-rs1563828) odds ratios (OR) for multiple-melanoma were estimated with logistic regression models. Hazard ratios (HR) for melanoma death were estimated with Cox proportional hazards models. In analyses adjusted for covariates, females carrying MDM4-rs4245739*C were more likely to develop multiple melanomas (ORper-allele = 1.25, 95% CI 1.03–1.51, and Ptrend = 0.03), while MDM2-rs2279744*G was inversely associated with melanoma-death (HRper-allele = 0.63, 95% CI 0.42–0.95, and Ptrend = 0.03). We identified 16 coinherited expression quantitative loci that control the expression of MDM2, MDM4, and other genes in the skin, brain, and lungs. Our results suggest that MDM4/MDM2 variants are associated with the development of subsequent primaries and with the death of melanoma in a sex-dependent manner. Further investigations of the complex MDM2/MDM4 motif, and its contribution to the tumor microenvironment and observed associations, are warranted. [ABSTRACT FROM AUTHOR]

PURPOSE: Genomic classification of melanoma has thus far focused on the mutational status of BRAF , NRAS , and NF1. The clinical utility of this classification remains limited, and the landscape of alterations in other oncogenic signaling pathways is underexplored. METHODS: Using primary samples from the InterMEL study, a retrospective cohort of cases with specimens collected from an international consortium with participating institutions throughout the United States and Australia, with oversampling of cases who ultimately died of melanoma, we examined mutual exclusivity and co-occurrence of genomic alterations in 495 stage II/III primary melanomas across 11 cancer pathways. Somatic mutation and copy number alterations were analyzed from next-generation sequencing using a clinical sequencing panel. RESULTS: Mutations in the RTK-RAS pathway were observed in 81% of cases. Other frequently occurring pathways were TP53 (31%), Cell Cycle (30%), and PI3K (18%). These frequencies are generally lower than was observed in The Cancer Genome Atlas, where the specimens analyzed were predominantly obtained from metastases. Overall, 81% of the cases had at least one targetable mutation. The RTK-RAS pathway was the only pathway that demonstrated strong and statistically significant mutual exclusivity. However, this strong mutual exclusivity signal was evident only for the three common genes in the pathway (BRAF , NRAS , and NF1). Analysis of co-occurrence of different pathways exhibited no positive significant trends. However, interestingly, a high frequency of cases with none of these pathways represented was observed, 8.4% of cases versus 4.0% expected (P <.001). A higher frequency of RTK-RAS singletons (with no other pathway alteration) was observed compared with The Cancer Genome Atlas. Clonality analyses suggest strongly that both the cell cycle and RTK-RAS pathways represent early events in melanogenesis. CONCLUSION: Our results confirm the dominance of mutations in the RTK-RAS pathway. The presence of many mutations in several well-known, actionable pathways suggests potential avenues for targeted therapy in these early-stage cases. [ABSTRACT FROM AUTHOR]

This article reports that the U.S. Food and Drug Administration has had the authority to approve new drugs for sale in the United States on the basis of their demonstrated safety and efficacy since 1962. Since that time the agency has regularly come under attack by those who believe that access to drugs should be governed by market forces rather than the scientific evaluation of the balance of their benefits and risks. This view has now entered the political domain in the form of a bill before the U.S. Senate, entitled the "Access, Compassion, Care and Ethics for Seriously Ill Patients Act." This bill, if it becomes law will permit marketing of drugs for serious illnesses on the basis of results from non-human studies and a Phase I trial, a trial solely designed to establish a tolerable dose but not designed to assess whether the drug is actually effective or to develop an understanding of its risks. This bill has caused much consternation in the scientific community. The vast majority of drugs that enter Phase I testing turn out to be either ineffective or toxic. Consequently, if the bill becomes law, the market could be flooded with ineffective and possibly harmful agents.

Background: Recent studies of surgery for cancer have demonstrated variations in outcomes among hospitals and among surgeons. We sought to examine variations in morbidity after radical prostatectomy for prostate cancer. Methods: We used the Surveillance, Epidemiology, and End Results–Medicare linked data base to evaluate health-related outcomes after radical prostatectomy. The rates of postoperative complications, late urinary complications (strictures or fistulas 31 to 365 days after the procedure), and long-term incontinence (more than 1 year after the procedure) were inferred from the Medicare claims records of 11,522 patients who underwent prostatectomy between 1992 and 1996. These rates were analyzed in relation to hospital volume and surgeon volume (the number of procedures performed at individual hospitals and by individual surgeons, respectively). Results: Neither hospital volume nor surgeon volume was significantly associated with surgery-related death. Significant trends in the relation between volume and outcome were observed with respect to postoperative complications and late urinary complications. Postoperative morbidity was lower in very-high-volume hospitals than in low-volume hospitals (27 percent vs. 32 percent, P=0.03) and was also lower when the prostatectomy was performed by very-high-volume surgeons than when it was performed by low-volume surgeons (26 percent vs. 32 percent, P<0.001). The rates of late urinary complications followed a similar pattern. Results for long-term preservation of continence were less clear-cut. In a detailed analysis of the 159 surgeons who had a high or very high volume of procedures, wide surgeon-to-surgeon variations in these clinical outcomes were observed, and they were much greater than would be predicted on the basis of chance or observed variations in the case mix. Conclusions: In men undergoing prostatectomy, the rates of postoperative and late urinary complications are significantly reduced if the procedure is performed in a high-volume hospital and by a surgeon who performs a high number of such procedures. (N Engl J Med 2002;346:1138-44.) [ABSTRACT FROM AUTHOR]

A letter to the editor is presented in response to an article focusing on cancer, genes and the environment, by P. Lichtenstein and others in the July 13, 2000 issue.

Two main classes of methodology have been developed for addressing the analytical intractability of generalized linear mixed models: likelihood-based methods and Bayesian methods. Likelihood-based methods such as the penalized quasi-likelihood approach have been shown to produce biased estimates especially for binary clustered data with small clusters sizes. More recent methods using adaptive Gaussian quadrature perform well but can be overwhelmed by problems with large numbers of random effects, and efficient algorithms to better handle these situations have not yet been integrated in standard statistical packages. Bayesian methods, although they have good frequentist properties when the model is correct, are known to be computationally intensive and also require specialized code, limiting their use in practice. In this article, we introduce a modification of the hybrid approach of Capanu and Begg, 2011, Biometrics 67, 371-380, as a bridge between the likelihood-based and Bayesian approaches by employing Bayesian estimation for the variance components followed by Laplacian estimation for the regression coefficients. We investigate its performance as well as that of several likelihood-based methods in the setting of generalized linear mixed models with binary outcomes. We apply the methods to three datasets and conduct simulations to illustrate their properties. Simulation results indicate that for moderate to large numbers of observations per random effect, adaptive Gaussian quadrature and the Laplacian approximation are very accurate, with adaptive Gaussian quadrature preferable as the number of observations per random effect increases. The hybrid approach is overall similar to the Laplace method, and it can be superior for data with very sparse random effects. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

In constructing predictive models, investigators frequently assess the incremental value of a predictive marker by comparing the ROC curve generated from the predictive model including the new marker with the ROC curve from the model excluding the new marker. Many commentators have noticed empirically that a test of the two ROC areas often produces a non-significant result when a corresponding Wald test from the underlying regression model is significant. A recent article showed using simulations that the widely used ROC area test produces exceptionally conservative test size and extremely low power. In this article, we demonstrate that both the test statistic and its estimated variance are seriously biased when predictions from nested regression models are used as data inputs for the test, and we examine in detail the reasons for these problems. Although it is possible to create a test reference distribution by resampling that removes these biases, Wald or likelihood ratio tests remain the preferred approach for testing the incremental contribution of a new marker. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Background: We have observed that the area under the receiver operating characteristic curve (AUC) is increasingly being used to evaluate whether a novel predictor should be incorporated in a multivariable model to predict risk of disease. Frequently, investigators will approach the issue in two distinct stages: first, by testing whether the new predictor variable is significant in a multivariable regression model; second, by testing differences between the AUC of models with and without the predictor using the same data from which the predictive models were derived. These two steps often lead to discordant conclusions. Discussion: We conducted a simulation study in which two predictors, X and X*, were generated as standard normal variables with varying levels of predictive strength, represented by means that differed depending on the binary outcome Y. The data sets were analyzed using logistic regression, and likelihood ratio and Wald tests for the incremental contribution of X* were performed. The patient-specific predictors for each of the models were then used as data for a test comparing the two AUCs. Under the null, the size of the likelihood ratio and Wald tests were close to nominal, but the area test was extremely conservative, with test sizes less than 0.006 for all configurations studied. Where X* was associated with outcome, the area test had much lower power than the likelihood ratio and Wald tests. Summary: Evaluation of the statistical significance of a new predictor when there are existing clinical predictors is most appropriately accomplished in the context of a regression model. Although comparison of AUCs is a conceptually equivalent approach to the likelihood ratio and Wald test, it has vastly inferior statistical properties. Use of both approaches will frequently lead to inconsistent conclusions. Nonetheless, comparison of receiver operating characteristic curves remains a useful descriptive tool for initial evaluation of whether a new predictor might be of clinical relevance. [ABSTRACT FROM AUTHOR]

In a recent article Begg et al. (2007, Biometrics 63, 522–530) proposed a statistical test to determine whether or not a diagnosed second primary tumor is biologically independent of the original primary tumor, by comparing patterns of allelic losses at candidate genetic loci. The proposed concordant mutations test is a conditional test, an adaptation of Fisher's exact test, that requires no knowledge of the marginal mutation probabilities. The test was shown to have generally good properties, but is susceptible to anticonservative bias if there is wide variation in mutation probabilities between loci, or if the individual mutation probabilities of the parental alleles for individual patients differ substantially from each other. In this article, a likelihood ratio test is derived in an effort to address these validity issues. This test requires prespecification of the marginal mutation probabilities at each locus, parameters for which some information will typically be available in the literature. In simulations this test is shown to be valid, but to be considerably less efficient than the concordant mutations test for sample sizes (numbers of informative loci) typical of this problem. Much of the efficiency deficit can be recovered, however, by restricting the allelic imbalance parameter estimate to a prespecified range, assuming that this parameter is in the prespecified range. [ABSTRACT FROM AUTHOR]

In this article we address whether studies of care rendered to patients prior to their death (“studies of decedents”) produce an accurate portrait of care provided to patients who are dying. Studies of decedents typically analyze the care provided to patients over a defined interval antecedent to death. Studies of dying patients analyze care provided to patients subsequent to the time that their terminal status is perceived. We address whether 2 fundamental differences between studies of decedents and studies of the dying—the ways that subjects are identified and the time periods that are examined—lead to differences in interpretation of study results. Using examples from population-based cohorts of individuals with cancer, we show that both the differences in subject selection and time period introduce very substantial biases into studies of decedents. We conclude that studying care received prior to death can lead to invalid conclusions about the quality or type of care provided to dying patients. [ABSTRACT FROM AUTHOR]

Gene–disease association studies based on case–control designs may often be used to identify candidate polymorphisms (markers) conferring disease risk. If a large number of markers are studied, genotyping all markers on all samples is inefficient in resource utilization. Here, we propose an alternative two-stage method to identify disease-susceptibility markers. In the first stage all markers are evaluated on a fraction of the available subjects. The most promising markers are then evaluated on the remaining individuals in Stage 2. This approach can be cost effective since markers unlikely to be associated with the disease can be eliminated in the first stage. Using simulations we show that, when the markers are independent and when they are correlated, the two-stage approach provides a substantial reduction in the total number of marker evaluations for a minimal loss of power. The power of the two-stage approach is evaluated when a single marker is associated with the disease, and in the presence of multiple disease-susceptibility markers. As a general guideline, the simulations over a wide range of parametric configurations indicate that evaluating all the markers on 50% of the individuals in Stage 1 and evaluating the most promising 10% of the markers on the remaining individuals in Stage 2 provides near-optimal power while resulting in a 45% decrease in the total number of marker evaluations. [ABSTRACT FROM AUTHOR]

Estimation of the association between haplotypes and disease from a case-control study is considered. Assuming a single 'disease haplotype' leads to the increased risk, attention focuses on the relative risks associated with a single copy, or two copies of the disease haplotype, relative to individuals with no copies. In this setting, case frequencies of the haplotype pairs are in Hardy-Weinberg Equilibrium (HWE) only if the combined influence of the two copies of the disease haplotype on risk is multiplicative. Thus, imputation cannot rely on the assumption of HWE for cases. A method is presented for obtaining estimates of the relative risks, making use of the EM algorithm and the assumption of HWE only for controls. The method accounts for the additional variation in the estimates due to the imputation of expected frequencies of haplotype pairs from ambiguous genotypes. A simulation study shows that the resulting confidence intervals have nominal coverage, and that the methods based on the assumption of HWE for both cases and controls can lead to bias.. [ABSTRACT FROM AUTHOR]

Genome-wide association studies (GWAS) and candidate pathway studies have identified low-penetrant genetic variants associated with cutaneous melanoma. We investigated the association of melanoma-risk variants with primary melanoma tumor prognostic characteristics and melanoma-specific survival. The Genes, Environment, and Melanoma Study enrolled 3285 European origin participants with incident invasive primary melanoma. For each of 47 melanoma-risk single nucleotide polymorphisms (SNPs), we used linear and logistic regression modeling to estimate, respectively, the per allele mean changes in log of Breslow thickness and odds ratios for presence of ulceration, mitoses, and tumor-infiltrating lymphocytes (TILs). We also used Cox proportional hazards regression modeling to estimate the per allele hazard ratios for melanoma-specific survival. Passing the false discovery threshold (p = 0.0026) were associations of IRF4 rs12203592 and CCND1 rs1485993 with log of Breslow thickness, and association of TERT rs2242652 with presence of mitoses. IRF4 rs12203592 also had nominal associations (p < 0.05) with presence of mitoses and melanomaspecific survival, as well as a borderline association (p = 0.07) with ulceration. CCND1 rs1485993 also had a borderline association with presence of mitoses (p = 0.06). MX2 rs45430 had nominal associations with log of Breslow thickness, presence of mitoses, and melanoma-specific survival. Our study indicates that further research investigating the associations of these genetic variants with underlying biologic pathways related to tumor progression is warranted. [ABSTRACT FROM AUTHOR]

Background Evidence is emerging that melanoma has distinct etiologic pathways and subtypes, characterized by factors like anatomic site. For example, older men have more head and neck melanomas, and younger women more leg melanomas. Family history is a strong risk factor and familial risk genes have been identified. We therefore aimed to investigate familial aggregation of melanoma from an etiologic heterogeneity perspective, to determine whether melanoma subtypes aggregate within families. Methods Using population-level linked health data, we examined the extent to which melanomas in first-degree relatives (FDRs) of cases shared the same body site of occurrence. FDR-pairs diagnosed with melanoma were identified using the Western Australian Cancer Registry and genealogical Family Connections System. Site was categorized as head/neck, trunk/arms, or legs. We identified 1,006 pairs of tumours from 677 family pairs and used Chi-Squared tests to evaluate familial aggregation by site. Degrees of etiologic heterogeneity of individual site-pairs were characterized by site concordance odds ratios. Results Familial aggregation by site was statistically significant (P = 0.01). However, only the site pairings of head/neck versus trunk/arms showed strong evidence of familial concordance (OR = 1.7, 95% CI 1.1-1.7). Trends were broadly similar in same-sex pairs. Conclusions Results show modest evidence of familial aggregation of melanoma by site, with overall evidence of aggregation but inconsistent patterns between sites. Key messages Strong etiologic differences in incidence between anatomic sites for age and sex may be more strongly influenced by non-genetic factors, such as familial patterns of sun exposure. [ABSTRACT FROM AUTHOR]