Re: Discriminatory accuracy from single-nucleotide polymorphisms in models to predict breast cancer risk

DNA methylation signatures of childhood trauma predict psychiatric disorders and other adverse outcomes 17 years after exposure

Childhood trauma is robustly linked to a broad range of adverse outcomes with consequences persisting far into adulthood. We conducted a prospective longitudinal study to predict psychiatric disorders and other adverse outcomes from trauma-related methylation changes 16.9 years after trauma exposure in childhood. Methylation was assayed using a sequencing-based approach that provides near-complete coverage of all 28 million sites in the blood methylome. Methylation data involved 673 assays from 489 participants aged 13.6 years (SD = 1.9) with outcomes measures collected at age 30.4 (SD = 2.26). For a subset of 303 participants we also generated methylation data in adulthood. Trauma-related methylation risk scores (MRSs) significantly predicted adult depression, externalizing problems, nicotine dependence, alcohol use disorder, serious medical problems, social problems and poverty. The predictive power of the MRSs was higher than that of reported trauma and could not be explained by the reported trauma, correlations with demographic variables, or a continuity of the predicted health problems from childhood to adulthood. Rather than measuring the occurrence of traumatic events, the MRSs seemed to capture the subject-specific impact of trauma. The majority of predictive sites did not remain associated with the outcomes suggesting the signatures of trauma do not become biologically embedded in the blood methylome. Instead, the long-term effects of trauma therefore seemed more consistent with a developmental mechanism where the initial subject-specific impacts of trauma are magnified over time. The MRSs have the potential to be a novel clinical biomarker for the assessment of trauma-related health risks.

Utility of incorporating a gene-based lung cancer risk test on uptake and adherence in a community-based lung cancer screening pilot study

Based on the results of randomized control trials, screening for lung cancer using computed tomography (CT) is now widely recommended. However, adherence to screening remains an issue outside the clinical trial setting. This study examines the utility of biomarker-based risk assessment on uptake and subsequent adherence in a community screening study.

In a single arm pilot study, current or former smokers > 50 years old with 20 + pack year history were recruited following local advertising. One hundred and fifty seven participants volunteered to participate in the study that offered an optional gene-based lung cancer risk assessment followed by low-dose CT according to a standardised screening protocol.

All 157 volunteers who attended visit 1 underwent the gene-based risk assessment comprising of a clinical questionnaire and buccal swab. Of this group, 154 subsequently attended for CT screening (98%) and were followed prospectively for a median of 2.7 years. A participant’s adherence to screening was influenced by their baseline lung cancer risk category, with overall adherence in those with a positive scan being significantly greater in the “very high” risk group compared to “moderate” and “high” risk categories (71% vs 52%, Odds ratio = 2.27, 95% confidence interval of 1.02–5.05, P = 0.047). Those in the “moderate” risk group were not different to those in the “high” risk group (52% and 52%, P > 0.05).

In this proof-of-concept study, personalised gene-based lung cancer risk assessment was well accepted, associated with a 98% uptake for screening and increased adherence for those in the highest risk group.

Rad51 paralogs and the risk of unselected breast cancer: A case-control study

A case-control study was conducted in which we evaluated the association between genetic variability of DNA repair proteins belonging to the Rad51 family and breast cancer (BrC) risk. In the study, 132 female BrC cases and 189 healthy control females were genotyped for a total of 14 common single nucleotide polymorphisms (SNPs) within Rad51 and Xrcc3. Moreover, our previously reported Rad51C genetic data were involved to explore the nonlinear interactions among SNPs within the three genes and effect of such interactions on BrC risk. The rare rs5030789 genotype (-4601AA) in Rad51 was found to significantly decrease the BrC risk (OR = 0.5, 95% CI: 0.3-1.0, p<0.05). An interaction between this SNP, rs2619679 and rs2928140 (both in Rad51), was found to result in a two three-locus genotypes -4719AA/-4601AA/2972CG and -4719AT/-4601GA/2972CC, both of which were found to increase the risk of BrC (OR = 8.4, 95% CI: 1.8-38.6, p<0.0001), instead. Furthermore, rare Rad51 rs1801320 (135CC) and heterozygous Xrcc3 rs3212057 (10343GA) genotypes were found to respectively increase (OR = 10.6, 95% CI: 1.9-198, p<0.02) and decrease (OR = 0.0, 95% CI: 0.0-NA, p<0.05) the risk of BrC. Associations between these SNPs and BrC risk were further supported by outcomes of employed machine learning analyses. In Xrcc3, the 4541A/9685A haplotype was found to be significantly associated with reduced BrC risk (OR = 0.5; 95% CI: 0.3-0.9; p<0.05). Concluding, our study indicates a complex role of SNPs within Rad51 (especially rs5030789) and Xrcc3 in BrC, although their significance with respect to the disease needs to be further clarified.

Determining Risk of Barrett's Esophagus and Esophageal Adenocarcinoma Based on Epidemiologic Factors and Genetic Variants

BACKGROUND & AIMS

We developed comprehensive models to determine risk of Barrett's esophagus (BE) or esophageal adenocarcinoma (EAC) based on genetic and non-genetic factors.

METHODS

We used pooled data from 3288 patients with BE, 2511 patients with EAC, and 2177 individuals without either (controls) from participants in the international Barrett's and EAC consortium as well as the United Kingdom's BE gene study and stomach and esophageal cancer study. We collected data on 23 genetic variants associated with risk for BE or EAC, and constructed a polygenic risk score (PRS) for cases and controls by summing the risk allele counts for the variants weighted by their natural log-transformed effect estimates (odds ratios) extracted from genome-wide association studies. We also collected data on demographic and lifestyle factors (age, sex, smoking, body mass index, use of nonsteroidal anti-inflammatory drugs) and symptoms of gastroesophageal reflux disease (GERD). Risk models with various combinations of non-genetic factors and the PRS were compared for their accuracy in identifying patients with BE or EAC using the area under the receiver operating characteristic curve (AUC) analysis.

RESULTS

Individuals in the highest quartile of risk, based on genetic factors (PRS), had a 2-fold higher risk of BE (odds ratio, 2.22; 95% confidence interval, 1.89-2.60) or EAC (odds ratio, 2.46; 95% confidence interval, 2.07-2.92) than individual in the lowest quartile of risk based on PRS. Risk models developed based on only demographic or lifestyle factors or GERD symptoms identified patients with BE or EAC with AUC values ranging from 0.637 to 0.667. Combining data on demographic or lifestyle factors with data on GERD symptoms identified patients with BE with an AUC of 0.793 and patients with EAC with an AUC of 0.745. Including PRSs with these data only minimally increased the AUC values for BE (to 0.799) and EAC (to 0.754). Including the PRSs in the model developed based on non-genetic factors resulted in a net reclassification improvement for BE of 3.0% and for EAC of 5.6%.

CONCLUSIONS

We used data from 3 large databases of patients from studies of BE or EAC to develop a risk prediction model based on genetic, clinical, and demographic/lifestyle factors. We identified a PRS that increases discrimination and net reclassification of individuals with vs without BE and EAC. However, the absolute magnitude of improvement is not sufficient to justify its clinical use.

Incorporating epistasis interaction of genetic susceptibility single nucleotide polymorphisms in a lung cancer risk prediction model

Incorporation of genetic variants such as single nucleotide polymorphisms (SNPs) into risk prediction models may account for a substantial fraction of attributable disease risk. Genetic data, from 2385 subjects recruited into the Liverpool Lung Project (LLP) between 2000 and 2008, consisting of 20 SNPs independently validated in a candidate-gene discovery study was used. Multifactor dimensionality reduction (MDR) and random forest (RF) were used to explore evidence of epistasis among 20 replicated SNPs. Multivariable logistic regression was used to identify similar risk predictors for lung cancer in the LLP risk model for the epidemiological model and extended model with SNPs. Both models were internally validated using the bootstrap method and model performance was assessed using area under the curve (AUC) and net reclassification improvement (NRI). Using MDR and RF, the overall best classifier of lung cancer status were SNPs rs1799732 (DRD2), rs5744256 (IL-18), rs2306022 (ITGA11) with training accuracy of 0.6592 and a testing accuracy of 0.6572 and a cross-validation consistency of 10/10 with permutation testing P<0.0001. The apparent AUC of the epidemiological model was 0.75 (95% CI 0.73–0.77). When epistatic data were incorporated in the extended model, the AUC increased to 0.81 (95% CI 0.79–0.83) which corresponds to 8% increase in AUC (DeLong's test P=2.2e-¹⁶); 17.5% by NRI. After correction for optimism, the AUC was 0.73 for the epidemiological model and 0.79 for the extended model. Our results showed modest improvement in lung cancer risk prediction when the SNP epistasis factor was added.

A tutorial on variable selection for clinical prediction models: feature selection methods in data mining could improve the results

OBJECTIVES

Identifying an appropriate set of predictors for the outcome of interest is a major challenge in clinical prediction research. The aim of this study was to show the application of some variable selection methods, usually used in data mining, for an epidemiological study. We introduce here a systematic approach.

STUDY DESIGN AND SETTING

The P-value-based method, usually used in epidemiological studies, and several filter and wrapper methods were implemented to select the predictors of diabetes among 55 variables in 803 prediabetic females, aged ≥ 20 years, followed for 10-12 years. To develop a logistic model, variables were selected from a train data set and evaluated on the test data set. The measures of Akaike information criterion (AIC) and area under the curve (AUC) were used as performance criteria. We also implemented a full model with all 55 variables.

RESULTS

We found that the worst and the best models were the full model and models based on the wrappers, respectively. Among filter methods, symmetrical uncertainty gave both the best AUC and AIC.

CONCLUSION

Our experiment showed that the variable selection methods used in data mining could improve the performance of clinical prediction models. An R program was developed to make these methods more feasible and visualize the results.

Evaluating surrogate endpoints, prognostic markers, and predictive markers: Some simple themes

BACKGROUND

A surrogate endpoint is an endpoint observed earlier than the true endpoint (a health outcome) that is used to draw conclusions about the effect of treatment on the unobserved true endpoint. A prognostic marker is a marker for predicting the risk of an event given a control treatment; it informs treatment decisions when there is information on anticipated benefits and harms of a new treatment applied to persons at high risk. A predictive marker is a marker for predicting the effect of treatment on outcome in a subgroup of patients or study participants; it provides more rigorous information for treatment selection than a prognostic marker when it is based on estimated treatment effects in a randomized trial.

METHODS

We organized our discussion around a different theme for each topic.

RESULTS

"Fundamentally an extrapolation" refers to the non-statistical considerations and assumptions needed when using surrogate endpoints to evaluate a new treatment. "Decision analysis to the rescue" refers to use the use of decision analysis to evaluate an additional prognostic marker because it is not possible to choose between purely statistical measures of marker performance. "The appeal of simplicity" refers to a straightforward and efficient use of a single randomized trial to evaluate overall treatment effect and treatment effect within subgroups using predictive markers.

CONCLUSION

The simple themes provide a general guideline for evaluation of surrogate endpoints, prognostic markers, and predictive markers.

How many longitudinal covariate measurements are needed for risk prediction?

OBJECTIVE

In epidemiologic follow-up studies, many key covariates, such as smoking, use of medication, blood pressure, and cholesterol, are time varying. Because of practical and financial limitations, time-varying covariates cannot be measured continuously, but only at certain prespecified time points. We study how the number of these longitudinal measurements can be chosen cost-efficiently by evaluating the usefulness of the measurements for risk prediction.

STUDY DESIGN AND SETTING

The usefulness is addressed by measuring the improvement in model discrimination between models using different amounts of longitudinal information. We use simulated follow-up data and the data from the Finnish East-West study, a follow-up study, with eight longitudinal covariate measurements carried out between 1959 and 1999.

RESULTS

In a simulation study, we show how the variability and the hazard ratio of a time-varying covariate are connected to the importance of remeasurements. In the East-West study, it is seen that for older people, the risk predictions obtained using only every other measurement are almost equivalent to the predictions obtained using all eight measurements.

CONCLUSION

Decisions about the study design have significant effects on the costs. The cost-efficiency can be improved by applying the measures of model discrimination to data from previous studies and simulations.

Lifetime cumulative risk factors predict cardiovascular disease mortality in a 50-year follow-up study in Finland

BACKGROUND

Systolic blood pressure, total cholesterol and smoking are known predictors of cardiovascular disease (CVD) mortality. Less is known about the effect of lifetime accumulation and changes of risk factors over time as predictors of CVD mortality, especially in very long follow-up studies.

METHODS

Data from the Finnish cohorts of the Seven Countries Study were used. The baseline examination was in 1959 and seven re-examinations were carried out at approximately 5-year intervals. Cohorts were followed up for mortality until the end of 2011. Time-dependent Cox models with regular time-updated risk factors, time-dependent averages of risk factors and latest changes in risk factors, using smoothing splines to discover nonlinear effects, were used to analyse the predictive effect of risk factors for CVD mortality.

RESULTS

A model using cumulative risk factors, modelled as the individual-level averages of several risk factor measurements over time, predicted CVD mortality better than a model using the most recent measurement information. This difference seemed to be most prominent for systolic blood pressure. U-shaped effects of the original predictors can be explained by partitioning a risk factor effect between the recent level and the change trajectory. The change in body mass index predicted the risk although body mass index itself did not.

CONCLUSIONS

The lifetime accumulation of risk factors and the observed changes in risk factor levels over time are strong predictors of CVD mortality. It is important to investigate different ways of using the longitudinal risk factor measurements to take full advantage of them.

The cytokinesis-blocked micronucleus assay as a strong predictor of lung cancer: extension of a lung cancer risk prediction model

BACKGROUND

There is an urgent need to improve lung cancer outcome by identifying and validating markers of risk. We previously reported that the cytokinesis-blocked micronucleus assay (CBMN) is a strong predictor of lung cancer risk. Here, we validate our findings in an independent external lung cancer population and test discriminatory power improvement of the Spitz risk prediction model upon extension with this biomarker.

METHODS

A total of 1,506 participants were stratified into a test set of 995 (527 cases/468 controls) from MD Anderson Cancer Center (Houston, TX) and a validation set of 511 (239 cases/272 controls) from Massachusetts General Hospital (Boston, MA). An epidemiologic questionnaire was administered and genetic instability was assessed using the CBMN assay.

RESULTS

Excellent concordance was observed between the two populations in levels and distribution of CBMN endpoints [binucleated-micronuclei (BN-MN), binucleated-nucleoplasmic bridges (BN-NPB)] with significantly higher mean BN-MN and BN-NPB values among cases (P < 0.0001). Extension of the Spitz model led to an overall improvement in the AUC (95% confidence intervals) from 0.61 (55.5-65.7) with epidemiologic variables to 0.92 (89.4-94.2) with addition of the BN-MN endpoint. The most dramatic improvement was observed with the never-smokers extended model followed by the former and current smokers.

CONCLUSIONS

The CBMN assay is a sensitive and specific predictor of lung cancer risk, and extension of the Spitz risk prediction model led to an AUC that may prove useful in population screening programs to identify the "true" high-risk individuals.

IMPACT

Identifying high-risk subgroups that would benefit from screening surveillance has immense public health significance.

How to interpret a small increase in AUC with an additional risk prediction marker: decision analysis comes through

An important question in the evaluation of an additional risk prediction marker is how to interpret a small increase in the area under the receiver operating characteristic curve (AUC). Many researchers believe that a change in AUC is a poor metric because it increases only slightly with the addition of a marker with a large odds ratio. Because it is not possible on purely statistical grounds to choose between the odds ratio and AUC, we invoke decision analysis, which incorporates costs and benefits. For example, a timely estimate of the risk of later non-elective operative delivery can help a woman in labor decide if she wants an early elective cesarean section to avoid greater complications from possible later non-elective operative delivery. A basic risk prediction model for later non-elective operative delivery involves only antepartum markers. Because adding intrapartum markers to this risk prediction model increases AUC by 0.02, we questioned whether this small improvement is worthwhile. A key decision-analytic quantity is the risk threshold, here the risk of later non-elective operative delivery at which a patient would be indifferent between an early elective cesarean section and usual care. For a range of risk thresholds, we found that an increase in the net benefit of risk prediction requires collecting intrapartum marker data on 68 to 124 women for every correct prediction of later non-elective operative delivery. Because data collection is non-invasive, this test tradeoff of 68 to 124 is clinically acceptable, indicating the value of adding intrapartum markers to the risk prediction model.

Net risk reclassification p values: valid or misleading?

BACKGROUND

The Net Reclassification Index (NRI) and its P value are used to make conclusions about improvements in prediction performance gained by adding a set of biomarkers to an existing risk prediction model. Although proposed only 5 years ago, the NRI has gained enormous traction in the risk prediction literature. Concerns have recently been raised about the statistical validity of the NRI.

METHODS

Using a population dataset of 10000 individuals with an event rate of 10.2%, in which four biomarkers have no predictive ability, we repeatedly simulated studies and calculated the chance that the NRI statistic provides a positive statistically significant result. Subjects for training data (n = 420) and test data (n = 420 or 840) were randomly selected from the population, and corresponding NRI statistics and P values were calculated. For comparison, the change in the area under the receiver operating characteristic curve and likelihood ratio statistics were calculated.

RESULTS

We found that rates of false-positive conclusions based on the NRI statistic were unacceptably high, being 63.0% in the training datasets and 18.8% to 34.4% in the test datasets. False-positive conclusions were rare when using the change in the area under the curve and occurred at the expected rate of approximately 5.0% with the likelihood ratio statistic.

CONCLUSIONS

Conclusions about biomarker performance that are based primarily on a statistically significant NRI statistic should be treated with skepticism. Use of NRI P values in scientific reporting should be halted.

Development and validation of risk models and molecular diagnostics to permit personalized management of cancer

Despite the advances made in cancer management over the past few decades, improvements in cancer diagnosis and prognosis are still poor, highlighting the need for individualized strategies. Toward this goal, risk prediction models and molecular diagnostic tools have been developed, tailoring each step of risk assessment from diagnosis to treatment and clinical outcomes based on the individual's clinical, epidemiological, and molecular profiles. These approaches hold increasing promise for delivering a new paradigm to maximize the efficiency of cancer surveillance and efficacy of treatment. However, they require stringent study design, methodology development, comprehensive assessment of biomarkers and risk factors, and extensive validation to ensure their overall usefulness for clinical translation. In the current study, the authors conducted a systematic review using breast cancer as an example and provide general guidelines for risk prediction models and molecular diagnostic tools, including development, assessment, and validation.

MC1R genotype as a predictor of early-onset melanoma, compared with self-reported and physician-measured traditional risk factors: an Australian case-control-family study

Background

Melanocortin-1 receptor (MC1R) gene variants are very common and are associated with melanoma risk, but their contribution to melanoma risk prediction compared with traditional risk factors is unknown. We aimed to 1) evaluate the separate and incremental contribution of MC1R genotype to prediction of early-onset melanoma, and compare this with the contributions of physician-measured and self-reported traditional risk factors, and 2) develop risk prediction models that include MC1R, and externally validate these models using an independent dataset from a genetically similar melanoma population.

Methods

Using data from an Australian population-based, case-control-family study, we included 413 case and 263 control participants with sequenced MC1R genotype, clinical skin examination and detailed questionnaire. We used unconditional logistic regression to estimate predicted probabilities of melanoma. Results were externally validated using data from a similar study in England.

Results

When added to a base multivariate model containing only demographic factors, MC1R genotype improved the area under the receiver operating characteristic curve (AUC) by 6% (from 0.67 to 0.73; P < 0.001) and improved the quartile classification by a net 26% of participants. In a more extensive multivariate model, the factors that contributed significantly to the AUC were MC1R genotype, number of nevi and previous non-melanoma skin cancer; the AUC was 0.78 (95% CI 0.75-0.82) for the model with self-reported nevi and 0.83 (95% CI 0.80-0.86) for the model with physician-counted nevi. Factors that did not further contribute were sun and sunbed exposure and pigmentation characteristics. Adding MC1R to a model containing pigmentation characteristics and other self-reported risk factors increased the AUC by 2.1% (P = 0.01) and improved the quartile classification by a net 10% (95% CI 1-18%, P = 0.03).

Conclusions

Although MC1R genotype is strongly associated with skin and hair phenotype, it was a better predictor of early-onset melanoma than was pigmentation characteristics. Physician-measured nevi and previous non-melanoma skin cancer were also strong predictors. There might be modest benefit to measuring MC1R genotype for risk prediction even if information about traditional self-reported or clinically measured pigmentation characteristics and nevi is already available.

Personalizing lung cancer prevention through a reverse migration strategy

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

Clinical applications of gene-based risk prediction for lung cancer and the central role of chronic obstructive pulmonary disease

Lung cancer is the leading cause of cancer death worldwide and nearly 90% of cases are attributable to smoking. Quitting smoking and early diagnosis of lung cancer, through computed tomographic screening, are the only ways to reduce mortality from lung cancer. Recent epidemiological studies show that risk prediction for lung cancer is optimized by using multivariate risk models that include age, smoking exposure, history of chronic obstructive pulmonary disease (COPD), family history of lung cancer, and body mass index. It has also been shown that COPD predates lung cancer in 65–70% of cases, conferring a four- to sixfold greater risk of lung cancer compared to smokers with normal lung function. Genome-wide association studies of smokers have identified a number of genetic variants associated with COPD or lung cancer. In a case–control study, where smokers with normal lungs were compared to smokers who had spirometry-defined COPD or histology confirmed lung cancer, several of these variants were shown to overlap, conferring the same susceptibility or protective effects on both COPD and lung cancer (independent of COPD status). In this perspective article, we show how combining clinical data with genetic variants can help identify heavy smokers at the greatest risk of lung cancer. Using this approach, we found that gene-based risk testing helped engage smokers in risk mitigating activities like quitting smoking and undertaking lung cancer screening. We suggest that such an approach could facilitate the targeted selection of smokers for cost-effective life-saving interventions.

Using haplotype analysis to elucidate significant associations between genes and Hodgkin lymphoma

In this study, we estimated the association between the inferred haplotypes in the inflammation, DNA repair, and folate pathways, and developed risk models for Hodgkin lymphoma. The study population consisted of 200 Hodgkin lymphoma cases and 220 controls. A susceptible association was observed on the XPC gene with haplotype CT (rs2228001 and rs2228000), and a protective association was observed on the IL4R gene with haplotype TCA (rs1805012, rs1805015, and rs1801275). These results can provide the necessary tools to identify high-risk individuals after validation in large data sets.

Inclusion of gene-gene and gene-environment interactions unlikely to dramatically improve risk prediction for complex diseases

Genome-wide association studies have identified hundreds of common genetic variants associated with the risk of multifactorial diseases. However, their impact on discrimination and risk prediction is limited. It has been suggested that the identification of gene-gene (G-G) and gene-environment (G-E) interactions would improve disease prediction and facilitate prevention. We conducted a simulation study to explore the potential improvement in discrimination if G-G and G-E interactions exist and are known. We used three diseases (breast cancer, type 2 diabetes, and rheumatoid arthritis) as motivating examples. We show that the inclusion of G-G and G-E interaction effects in risk-prediction models is unlikely to dramatically improve the discrimination ability of these models.

How the genetics of lung cancer may overlap with COPD

Over the last 30years, epidemiological studies have shown that COPD is the single most important risk factor for lung cancer after smoking exposure. Recent genetic studies using genome-wide approaches suggest that the genetic risk factors predisposing smokers to COPD and lung cancer may overlap. The genes identified by these studies suggest that this overlapping genetic susceptibility may be mediated through receptors expressed on the bronchial epithelium that implicate molecular pathways underlying both COPD and lung cancer. Furthermore, it appears that aberrant inflammatory and/or immune-modulatory pathways leading to excess matrix metalloproteinases, growth factors and airway remodelling in COPD may also be promoting malignant transformation of the bronchial epithelium. The process linking inflammation, remodelling and cancer formation is called epithelial-mesenchymal transition. There are several clinical implications arising from the COPD-lung cancer overlap. First, if COPD is a precursor disease to lung cancer then efforts to prevent COPD, might be even more important. Second, if drugs targeting the overlapping molecular pathways can be identified, chemoprevention that reduce the propensity to COPD and lung cancer is an attractive option. Finally, if low-dose computerized tomography can identify treatable lung cancer, gene-based tests of susceptibility might help identify those smokers who should undergo radiological screening.

The BATTLE to personalize lung cancer prevention through reverse migration

Agents can enter clinical development for cancer prevention either initially or after previous development for a different indication, such as arthritis, with both approaches consuming many years of development before an agent is fully evaluated for cancer prevention. We propose the following, third approach: reverse migration, that is, importing agents, targets, and study designs to personalize interventions and concepts developed in advanced cancer to the setting of cancer prevention. Importing these "ready-made" features from therapy will allow reverse migration to streamline preventive agent development. We recently reported the Biomarker-integrated Approaches of Targeted Therapy for Lung Cancer Elimination (BATTLE) trial of personalized lung cancer therapy and now propose the reverse migration development of personalized lung cancer prevention based on the BATTLE model.

Variants in inflammation genes are implicated in risk of lung cancer in never smokers exposed to second-hand smoke

Lung cancer in lifetime never smokers is distinct from that in smokers, but the role of separate or overlapping carcinogenic pathways has not been explored. We therefore evaluated a comprehensive panel of 11,737 single-nucleotide polymorphisms (SNP) in inflammatory-pathway genes in a discovery phase (451 lung cancer cases, 508 controls from Texas). SNPs that were significant were evaluated in a second external population (303 cases, 311 controls from the Mayo Clinic). An intronic SNP in the ACVR1B gene, rs12809597, was replicated with significance and restricted to those reporting adult exposure to environmental tobacco smoke. Another promising candidate was an SNP in NR4A1, although the replication OR did not achieve statistical significance. ACVR1B belongs to the TGFR-β superfamily, contributing to resolution of inflammation and initiation of airway remodeling. An inflammatory microenvironment (second-hand smoking, asthma, or hay fever) is necessary for risk from these gene variants to be expressed. These findings require further replication, followed by targeted resequencing, and functional validation.

Genetic evidence linking lung cancer and COPD: a new perspective

Epidemiological studies indicate that tobacco smoke exposure accounts for nearly 90% of cases of chronic obstructive pulmonary disease (COPD) and lung cancer. However, genetic factors may explain why 10%–30% of smokers develop these complications. This perspective reviews the evidence suggesting that COPD is closely linked to susceptibility to lung cancer and outlines the potential relevance of this observation. Epidemiological studies show that COPD is the single most important risk factor for lung cancer among smokers and predates lung cancer in up to 80% of cases. Genome-wide association studies of lung cancer, lung function, and COPD have identified a number of overlapping “susceptibility” loci. With stringent phenotyping, it has recently been shown that several of these overlapping loci are independently associated with both COPD and lung cancer. These loci implicate genes underlying pulmonary inflammation and apoptotic processes mediated by the bronchial epithelium, and link COPD with lung cancer at a molecular genetic level. It is currently possible to derive risk models for lung cancer that incorporate lung cancer-specific genetic variants, recently identified “COPD-related” genetic variants, and clinical variables. Early studies suggest that single nucleotide polymorphism-based risk stratification of smokers might help better target novel prevention and early diagnostic strategies in lung cancer.

Assessment of clinical validity of a breast cancer risk model combining genetic and clinical information

BACKGROUND

The Gail model is widely used for the assessment of risk of invasive breast cancer based on recognized clinical risk factors. In recent years, a substantial number of single-nucleotide polymorphisms (SNPs) associated with breast cancer risk have been identified. However, it remains unclear how to effectively integrate clinical and genetic risk factors for risk assessment.

METHODS

Seven SNPs associated with breast cancer risk were selected from the literature and genotyped in white non-Hispanic women in a nested case-control cohort of 1664 case patients and 1636 control subjects within the Women's Health Initiative Clinical Trial. SNP risk scores were computed based on previously published odds ratios assuming a multiplicative model. Combined risk scores were calculated by multiplying Gail risk estimates by the SNP risk scores. The independence of Gail risk and SNP risk was evaluated by logistic regression. Calibration of relative risks was evaluated using the Hosmer-Lemeshow test. The performance of the combined risk scores was evaluated using receiver operating characteristic curves. The net reclassification improvement (NRI) was used to assess improvement in classification of women into low (<1.5%), intermediate (1.5%-2%), and high (>2%) categories of 5-year risk. All tests of statistical significance were two-sided.

RESULTS

The SNP risk score was nearly independent of Gail risk. There was good agreement between predicted and observed SNP relative risks. In the analysis for receiver operating characteristic curves, the combined risk score was more discriminating, with area under the curve of 0.594 compared with area under the curve of 0.557 for Gail risk alone (P < .001). Classification also improved for 5.6% of case patients and 2.9% of control subjects, showing an NRI value of 0.085 (P = 1.0 × 10⁻⁵). Focusing on women with intermediate Gail risk resulted in an improved NRI of 0.195 (P = 8.6 × 10⁻⁵).

CONCLUSIONS

Combining validated common genetic risk factors with clinical risk factors resulted in modest improvement in classification of breast cancer risks in white non-Hispanic postmenopausal women. Classification performance was further improved by focusing on women at intermediate risk.

The evolving discipline of molecular epidemiology of cancer

Classical epidemiologic studies have made seminal contributions to identifying the etiology of most common cancers. Molecular epidemiology was conceived of as an extension of traditional epidemiology to incorporate biomarkers with questionnaire data to further our understanding of the mechanisms of carcinogenesis. Early molecular epidemiologic studies employed functional assays. These studies were hampered by the need for sequential and/or prediagnostic samples, viable lymphocytes and the uncertainty of how well these functional data (derived from surrogate lymphocytic tissue) reflected events in the target tissue. The completion of the Human Genome Project and Hapmap Project, together with the unparalleled advances in high-throughput genotyping revolutionized the practice of molecular epidemiology. Early studies had been constrained by existing technology to use the hypothesis-driven candidate gene approach, with disappointing results. Pathway analysis addressed some of the concerns, although the study of interacting and overlapping gene networks remained a challenge. Whole-genome scanning approaches were designed as agnostic studies using a dense set of markers to capture much of the common genome variation to study germ-line genetic variation as risk factors for common complex diseases. It should be possible to exploit the wealth of these data for pharmacogenetic studies to realize the promise of personalized therapy. Going forward, the temptation for epidemiologists to be lured by high-tech 'omics' will be immense. Systems Epidemiology, the observational prototype of systems biology, is an extension of classical epidemiology to include powerful new platforms such as the transcriptome, proteome and metabolome. However, there will always be the need for impeccably designed and well-powered epidemiologic studies with rigorous quality control of data, specimen acquisition and statistical analysis.