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.

Lung cancer screening: identifying the high risk cohort

Low dose computed tomography (LDCT) is a viable screening tool for early lung cancer detection and mortality reduction. In practice, the success of any lung cancer screening programme will depend on successful identification of individuals at high risk in order to maximise the benefit-harm ratio. Risk prediction models incorporating multiple risk factors have been recognised as a method of identifying individuals at high risk of developing lung cancer. Identification of individuals at high risk will facilitate early diagnosis, reduce overall costs and also improve the current poor survival from lung cancer. This review summarises the current methods utilised in identifying high risk cohorts for lung cancer as proposed by the Liverpool Lung Project (LLP) risk model, Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening Trial risk models and the prediction model for lung cancer death using quintiles. In addition, the cost-effectiveness of CT screening and future perspective for selecting high risk individuals is discussed.

LLPi: Liverpool Lung Project Risk Prediction Model for Lung Cancer Incidence

Identification of high-risk individuals will facilitate early diagnosis, reduce overall costs, and also improve the current poor survival from lung cancer. The Liverpool Lung Project prospective cohort of 8,760 participants ages 45 to 79 years, recruited between 1998 and 2008, was followed annually through the hospital episode statistics until January 31, 2013. Cox proportional hazards models were used to identify risk predictors of lung cancer incidence. C-statistic was used to assess the discriminatory accuracy of the models. Models were internally validated using the bootstrap method. During mean follow-up of 8.7 years, 237 participants developed lung cancer. Age [hazard ratio (HR), 1.04; 95% confidence interval (CI), 1.02-1.06], male gender (HR, 1.48; 95% CI, 1.10-1.98), smoking duration (HR, 1.04; 95% CI, 1.03-1.05), chronic obstructive pulmonary disease (HR, 2.43; 95% CI, 1.79-3.30), prior diagnosis of malignant tumor (HR, 2.84; 95% CI, 2.08-3.89), and early onset of family history of lung cancer (HR, 1.68; 95% CI, 1.04-2.72) were associated with the incidence of lung cancer. The LLPi risk model had a good calibration (goodness-of-fit χ(2) 7.58, P = 0.371). The apparent C-statistic was 0.852 (95% CI, 0.831-0.873) and the optimism-corrected bootstrap resampling C-statistic was 0.849 (95% CI, 0.829-0.873). The LLPi risk model may assist in identifying individuals at high risk of developing lung cancer in population-based screening programs.

Factors associated with dropout in a lung cancer high‑risk cohort--the Liverpool lung project

In long-term longitudinal cohort studies the dropout of participants occurring as a result of withdrawal or lost to follow-up may have greater impact on the effect estimates, if characteristics of participants who drop out and those still active in the study differ significantly. The study aimed to investigate factors associated with dropout in a 5-year follow-up of individuals at 'high‑risk' of lung cancer. We studied 'high‑risk' group of 1,486 individuals aged 45-79 selected from the Liverpool Lung Prospective (LLP) cohort study using a strategy reflecting only age, smoking duration and history of pulmonary disease. Study subjects were recalled annually from 2005-2009 for follow-up collection of specimens and questionnaire data. The dropout rate over the follow-up time was investigated using the Kaplan‑Meier survival curve and the Cox proportional hazard model. Dropout rate was 31% after an average of 3 annual visits. Female gender hazard ratio (HR) 1.35 (95% CI 1.09-1.66), current smoking 1.26 (1.02-1.57), prior diagnosis of malignant disease 0.54 (0.36-0.79), home visits 0.67 (0.48-0.94) and systolic blood pressure 1.46 (1.10-1.94) were significantly associated with the dropout rate. Nearly 40% of individuals selected into the 'high‑risk' group by the old criteria were low risk with predicted 5-year absolute risk of less than 2.5%. In conclusion, follow-up of individuals is feasible within the LLP, but may be prone to selective withdrawal attributable to patient's state of health and mobility. We recommend future design of 'high‑risk' follow‑up studies to consider home visit as a useful strategy to encourage continued participation.

Identification and validation of an anthracycline/cyclophosphamide-based chemotherapy response assay in breast cancer

Background

There is no method routinely used to predict response to anthracycline and cyclophosphamide–based chemotherapy in the clinic; therefore patients often receive treatment for breast cancer with no benefit. Loss of the Fanconi anemia/BRCA (FA/BRCA) DNA damage response (DDR) pathway occurs in approximately 25% of breast cancer patients through several mechanisms and results in sensitization to DNA-damaging agents. The aim of this study was to develop an assay to detect DDR-deficient tumors associated with loss of the FA/BRCA pathway, for the purpose of treatment selection.

Methods

DNA microarray data from 21 FA patients and 11 control subjects were analyzed to identify genetic processes associated with a deficiency in DDR. Unsupervised hierarchical clustering was then performed using 60 BRCA1/2 mutant and 47 sporadic tumor samples, and a molecular subgroup was identified that was defined by the molecular processes represented within FA patients. A 44-gene microarray-based assay (the DDR deficiency assay) was developed to prospectively identify this subgroup from formalin-fixed, paraffin-embedded samples. All statistical tests were two-sided.

Results

In a publicly available independent cohort of 203 patients, the assay predicted complete pathologic response vs residual disease after neoadjuvant DNA-damaging chemotherapy (5-fluorouracil, anthracycline, and cyclophosphamide) with an odds ratio of 3.96 (95% confidence interval [Cl] =1.67 to 9.41; P = .002). In a new independent cohort of 191 breast cancer patients treated with adjuvant 5-fluorouracil, epirubicin, and cyclophosphamide, a positive assay result predicted 5-year relapse-free survival with a hazard ratio of 0.37 (95% Cl = 0.15 to 0.88; P = .03) compared with the assay negative population.

Conclusions

A formalin-fixed, paraffin-embedded tissue-based assay has been developed and independently validated as a predictor of response and prognosis after anthracycline/cyclophosphamide–based chemotherapy in the neoadjuvant and adjuvant settings. These findings warrant further validation in a prospective clinical study.

A microRNA-based prediction algorithm for diagnosis of non-small lung cell carcinoma in minimal biopsy material

BACKGROUND

Diagnosis is jeopardised when limited biopsy material is available or histological quality compromised. Here we developed and validated a prediction algorithm based on microRNA (miRNA) expression that can assist clinical diagnosis of lung cancer in minimal biopsy material to improve clinical management.

METHODS

Discovery utilised Taqman Low Density Arrays (754 miRNAs) in 20 non-small cell lung cancer (NSCLC) tumour/normal pairs. In an independent set of 40 NSCLC patients, 28 miRNA targets were validated using qRT-PCR. A prediction algorithm based on eight miRNA targets was validated blindly in a third independent set of 47 NSCLC patients. The panel was also tested in formalin-fixed paraffin-embedded (FFPE) specimens from 20 NSCLC patients. The genomic methylation status of highly deregulated miRNAs was investigated by pyrosequencing.

RESULTS

In the final, frozen validation set the panel had very high sensitivity (97.5%), specificity (96.3%) and ROC-AUC (0.99, P=10(-15)). The panel provided 100% sensitivity and 95% specificity in FFPE tissue (ROC-AUC=0.97 (P=10(-6))). DNA methylation abnormalities contribute little to the deregulation of the miRNAs tested.

CONCLUSION

The developed prediction algorithm is a valuable potential biomarker for assisting lung cancer diagnosis in minimal biopsy material. A prospective validation is required to measure the enhancement of diagnostic accuracy of our current clinical practice.

DNA methylation biomarkers offer improved diagnostic efficiency in lung cancer

The exceptional high mortality of lung cancer can be instigated to a high degree by late diagnosis. Despite the plethora of studies on potential molecular biomarkers for lung cancer diagnosis, very few have reached clinical implementation. In this study, we developed a panel of DNA methylation biomarkers and validated their diagnostic efficiency in bronchial washings from a large retrospective cohort. Candidate targets from previous high-throughput approaches were examined by pyrosequencing in an independent set of 48 lung tumor/normal paired. Ten promoters were selected and quantitative methylation-specific PCR (qMSP) assays were developed and used to screen 655 bronchial washings from the Liverpool Lung Project (LLP) subjects divided into training (194 cases and 214 controls) and validation (139 cases and 109 controls) sets. Three statistical models were used to select the optimal panel of markers and to evaluate the performance of the discriminatory algorithms. The final logit regression model incorporated hypermethylation at p16, TERT, WT1, and RASSF1. The performance of this 4-gene methylation signature in the validation set showed 82% sensitivity and 91% specificity. In comparison, cytology alone in this set provided 43% sensitivity at 100% specificity. The diagnostic efficiency of the panel did not show any biases with age, gender, smoking, and the presence of a nonlung neoplasm. However, sensitivity was predictably higher in central (squamous and small cell) than peripheral (adenocarcinomas) tumors, as well as in stage 2 or greater tumors. These findings clearly show the impact of DNA methylation-based assays in the diagnosis of cytologically occult lung neoplasms. A prospective trial is currently imminent in the LLP study to provide data on the enhancement of diagnostic accuracy in a clinical setting, including by additional markers.

Increased risk of lung cancer in individuals with a family history of the disease: a pooled analysis from the International Lung Cancer Consortium

BACKGROUND AND METHODS

Familial aggregation of lung cancer exists after accounting for cigarette smoking. However, the extent to which family history affects risk by smoking status, histology, relative type and ethnicity is not well described. This pooled analysis included 24 case-control studies in the International Lung Cancer Consortium. Each study collected age of onset/interview, gender, race/ethnicity, cigarette smoking, histology and first-degree family history of lung cancer. Data from 24,380 lung cancer cases and 23,305 healthy controls were analysed. Unconditional logistic regression models and generalised estimating equations were used to estimate odds ratios and 95% confidence intervals.

RESULTS

Individuals with a first-degree relative with lung cancer had a 1.51-fold increase in the risk of lung cancer, after adjustment for smoking and other potential confounders (95% CI: 1.39, 1.63). The association was strongest for those with a family history in a sibling, after adjustment (odds ratios (OR) = 1.82, 95% CI: 1.62, 2.05). No modifying effect by histologic type was found. Never smokers showed a lower association with positive familial history of lung cancer (OR = 1.25, 95% CI: 1.03, 1.52), slightly stronger for those with an affected sibling (OR = 1.44, 95% CI: 1.07, 1.93), after adjustment.

CONCLUSIONS

The occurrence of lung cancer among never smokers and similar magnitudes of the effect of family history on lung cancer risk across histological types suggests familial aggregation of lung cancer is independent of those risks associated with cigarette smoking. While the role of genetic variation in the aetiology of lung cancer remains to be fully characterised, family history assessment is immediately available and those with a positive history represent a higher risk group.

Predictive accuracy of the Liverpool Lung Project risk model for stratifying patients for computed tomography screening for lung cancer: a case-control and cohort validation study

BACKGROUND

External validation of existing lung cancer risk prediction models is limited. Using such models in clinical practice to guide the referral of patients for computed tomography (CT) screening for lung cancer depends on external validation and evidence of predicted clinical benefit.

OBJECTIVE

To evaluate the discrimination of the Liverpool Lung Project (LLP) risk model and demonstrate its predicted benefit for stratifying patients for CT screening by using data from 3 independent studies from Europe and North America.

DESIGN

Case-control and prospective cohort study.

SETTING

Europe and North America.

PATIENTS

Participants in the European Early Lung Cancer (EUELC) and Harvard case-control studies and the LLP population-based prospective cohort (LLPC) study.

MEASUREMENTS

5-year absolute risks for lung cancer predicted by the LLP model.

RESULTS

The LLP risk model had good discrimination in both the Harvard (area under the receiver-operating characteristic curve [AUC], 0.76 [95% CI, 0.75 to 0.78]) and the LLPC (AUC, 0.82 [CI, 0.80 to 0.85]) studies and modest discrimination in the EUELC (AUC, 0.67 [CI, 0.64 to 0.69]) study. The decision utility analysis, which incorporates the harms and benefit of using a risk model to make clinical decisions, indicates that the LLP risk model performed better than smoking duration or family history alone in stratifying high-risk patients for lung cancer CT screening.

LIMITATIONS

The model cannot assess whether including other risk factors, such as lung function or genetic markers, would improve accuracy. Lack of information on asbestos exposure in the LLPC limited the ability to validate the complete LLP risk model.

CONCLUSION

Validation of the LLP risk model in 3 independent external data sets demonstrated good discrimination and evidence of predicted benefits for stratifying patients for lung cancer CT screening. Further studies are needed to prospectively evaluate model performance and evaluate the optimal population risk thresholds for initiating lung cancer screening.

Abstract 4133: MicroRNAs for early detection of lung cancer

Introduction: Early detection of lung cancer by screening of high risk populations (identified by epidemiological and life-style factors) has the potential to save many lives. However, effective screening is reliant on minimally invasive techniques, such as CT screening, bronchioalveolar lavage (BAL) and blood tests, and the identification of suitable biomarkers. CT screening is effective in reducing mortality, but generates a large proportion of indeterminate nodules that must be further characterised. MicroRNAs (miRNA) have great potential as biomarkers due to their tissue-specific and cancer-specific expression patterns. We have identified tumour-specific miRNAs for non-small cell lung cancer (NSCLC), using a combination of screening on TaqMan microRNA TLDA cards and validation with qRTPCR assays, with the aim of utilising these as biomarkers in the early detection setting. Methods: Our sample group consisted of 31 frozen samples from 20 Liverpool Lung Project (LLP) NSCLC patients, including 10 adenocarcinomas (Ad), 10 squamous cell carcinomas (SCC) & matched normal tissue. Two further validation sets consisted of equal numbers of Ad and SCC tumour/normal pairs (124 in total). MiRNA was prepared from tumour and normal specimens using Qiagen MicroRNeasy kits. Reverse transcription and pre-amplification was performed using Applied Biosystems MegaPlex Pools and miRNAs were quantified on a 7900HT Real-Time PCR System with TaqMan Array Human MiRNA Card Set v3.0 (covering 754 human miRNAs). Ct values were exported using SDS v2.3 data and RQ Manager software and further analysed in Bioconductor. Validation qRTPCR was performed with individual miRNA assays, following reverse transcription with MegaPlex pools. Results: When Benjamin-Hoechst-adjusted-p value &lt;0.05 was used as a cut-off, of the 754 miRNA targets, 68 miRNAs were upregulated and 8 were downregulated with &gt;4.0 fold-change in the cancer group. A subset of 22 miRNAs including miR-34a, miR-96, let-7g and miR-183 was identified with the greatest expression in tumours. Differential expression of all 22 miRNAs was confirmed in an independent set of 24 tumour/normal pairs. Using these 22 validated miRNAs we performed discriminative modelling and identified a model based on just 8 markers that gave a specificity of 100% and a sensitivity of 98%. This panel was validated, with 97% specificity and 91% sensitivity, in a 2nd independent sample set containing 48 tumours and paired normal samples. Conclusion: A number of miRNAs was identified that showed good discriminatory power individually, but greatest sensitivity and specificity when combined as an 8 member panel. The lung cancer specific miRNAs we have identified provide a potential source of early detection biomarkers. Their applicability to minimally-invasive samples is being evaluated in a range of samples including plasma and bronchial lavage.

Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 4133. doi:1538-7445.AM2012-4133

Abstract 1898: The Liverpool Lung Project risk model: An independent validation and clinical utility in primary care

Background: The results from a recent CT screening trial for lung cancer showed evidence of mortality reduction in the screened arm of the study. The long term success of future National Screening Programme as an early diagnosis tool may be dependent upon identifying populations at sufficient risk of developing the disease such that the benefit:harm ratio of the intervention can be maximised. Risk prediction models can play an important role in risk stratification by providing an estimate of individual's risk of developing a disease at a future time, but require validation in independent populations before they can be successfully generalised. Within the Liverpool Lung Project (LLP), we have developed a risk prediction model for estimating individual's 5-year absolute risk of lung cancer. The model was based on five epidemiological risk factors namely smoking duration, prior diagnosis of pneumonia, family history of lung cancer, occupational exposure to asbestos and prior diagnosis of other cancer. We present here an independent validation and clinical utility of the model using data from two case-control studies and a cohort population from Europe and North America.

Method: The 5-year absolute risk of lung cancer was estimated for subjects in the Harvard and European Early Lung Cancer (EUELC) case-control and LLP prospective cohort studies. The model's performance was assessed through its predictive accuracy (discrimination and calibration) and clinical utility. The area under the receiver-operator characteristic curve (AUC) measures the model's discriminatory power while calibration was assessed by comparing the observed and the expected lung cancer cases in the cohort population. The clinical relevance of the model was examined using the decision and relative utility curves.

Results: There was an evidence of good discriminations in Harvard (AUC = 0.76) and LLP cohort (c-index = 0.77, 0.82) with no significant difference in discrimination by age, gender and smoking status. In general, the model calibration indicated an underestimation of absolute risk; this showed improvement with high absolute risks. The application of the model was associated with reasonably good clinical utility across the three datasets as demonstrated by its superior ‘net benefit’ against any other alternative strategy.

Conclusion: The LLP risk model demonstrates good performance and evidence of clinical usefulness in three independent settings and can serve as an adjunct tool for clinicians or used in primary care for referring patients for early detection and prevention intervention.

Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 1898. doi:10.1158/1538-7445.AM2011-1898

Comparison of discriminatory power and accuracy of three lung cancer risk models

Background:

Three lung cancer (LC) models have recently been constructed to predict an individual's absolute risk of LC within a defined period. Given their potential application in prevention strategies, a comparison of their accuracy in an independent population is important.

Methods:

We used data for 3197 patients with LC and 1703 cancer-free controls recruited to an ongoing case–control study at the Harvard School of Public Health and Massachusetts General Hospital. We estimated the 5-year LC risk for each risk model and compared the discriminatory power, accuracy, and clinical utility of these models.

Results:

Overall, the Liverpool Lung Project (LLP) and Spitz models had comparable discriminatory power (0.69), whereas the Bach model had significantly lower power (0.66; P=0.02). Positive predictive values were highest with the Spitz models, whereas negative predictive values were highest with the LLP model. The Spitz and Bach models had lower sensitivity but better specificity than did the LLP model.

Conclusion:

We observed modest differences in discriminatory power among the three LC risk models, but discriminatory powers were moderate at best, highlighting the difficulty in developing effective risk models.

The potential for using risk models in future lung cancer screening trials

Computed tomography screening for early diagnosis of lung cancer is one of the more potentially useful strategies, aside from smoking cessation programmes, for reducing mortality and improving the current poor survival from this disease. The long-term success of lung cancer screening will be dependent upon identifying populations at sufficient risk in order to maximise the benefit-to-harm ratio of the intervention. Risk prediction models could potentially play a major role in the selection of high-risk individuals who would benefit most from screening intervention programmes for the early detection of lung cancer. Improvements of developed lung cancer risk prediction models (through incorporation of objective clinical factors and genetic and molecular biomarkers for precise and accurate estimation of risks), demonstration of their clinical usefulness in decision making, and their use in future screening programmes are the focus of current research.

Incorporation of a genetic factor into an epidemiologic model for prediction of individual risk of lung cancer: the Liverpool Lung Project

The Liverpool Lung Project (LLP) has previously developed a risk model for prediction of 5-year absolute risk of lung cancer based on five epidemiologic risk factors. SEZ6L, a Met430IIe polymorphic variant found on 22q12.2 region, has been previously linked with an increased risk of lung cancer in a case-control population. In this article, we quantify the improvement in risk prediction with addition of SEZ6L to the LLP risk model. Data from 388 LLP subjects genotyped for SEZ6L single-nucleotide polymorphism (SNP) were combined with epidemiologic risk factors. Multivariable conditional logistic regression was used to predict 5-year absolute risk of lung cancer with and without this SNP. The improvement in the model associated with the SEZ6L SNP was assessed through pairwise comparison of the area under the receiver operating characteristic curve and the net reclassification improvements (NRI). The extended model showed better calibration compared with the baseline model. There was a statistically significant modest increase in the area under the receiver operating characteristic curve when SEZ6L was added into the baseline model. The NRI also revealed a statistically significant improvement of around 12% for the extended model; this improvement was better for subjects classified into the two intermediate-risk categories by the baseline model (NRI, 27%). Our results suggest that the addition of SEZ6L improved the performance of the LLP risk model, particularly for subjects whose initial absolute risks were unable to discriminate into "low-risk" or "high-risk" group. This work shows an approach to incorporate genetic biomarkers in risk models for predicting an individual's lung cancer risk.

Abstract A127: Comparison of discriminatory power and accuracy of three lung cancer risk models

Background: Since lung cancer only occurs in a small fraction of long-term smokers, there is a need to develop risk prediction models to identify high-risk subgroups. Three lung cancer models, constructed using clinical and epidemiological variables, predicted absolute risk of lung cancer: one based on a cohort of patients recruited for the CARET study, and two constructed from case-control studies conducted in Houston, Texas and Liverpool, England. Given their potential application to primary chemo-prevention strategies and screening trials, it is important to compare the accuracy of these three models in an independent population.

Methods: We used data for 3197 lung cancer patients and 1703 cancer-free controls recruited to an ongoing case-control study of lung cancer at Harvard School of Public Health and Massachusetts General Hospital (Boston, MA). We estimated 5-year lung cancer risk for each risk model and compared the discriminatory power, as measured by the area under the the receiver-operator characteristic curve, accuracy, as measured by the positive predictive value and negative predictive value, and clinical utility of these models, as measured with scaled rectangles.

Results: Overall, the discriminatory power for the Liverpool Lung Project (LLP) (AUC = 0.69, 95% CI = 0.67–0.71) and Spitz models (AUC = 0.69, 95%CI = 0.66–0.71) were comparable, while the Bach model had significantly lower power (AUC =0.66, 95% CI = 0.64–0.69; P=0.02). Positive predictive values were highest with the Spitz model (0.882) compared to 0.809 for the Bach model and 0.759 for the LLP model. In contrast, the negative predictive values were highest for the LLP model (0.560) compared to 0.450 for the Spitz model and 0.447 for the Bach. The Spitz and Bach models had lower sensitivity but higher specificity compared to the LLP model. For instance, 26.6% of all lung cancer cases have a five-year absolute risk of lung cancer ≥ 2.5% for the Spitz model compared to 66.7% of all cases for the LLP model. However, only 5.6% of all healthy controls have a five-year absolute risk of lung cancer ≥ 2.5% with the Spitz model compared to 33.4% of all controls for the LLP model.

Conclusion: We observed modest differences in discriminatory among the three lung cancer risk models. The level of the discriminatory powers of these three lung cancer risk models was moderate at best, which highlights the difficulty in developing effective risk models. There is considerable room for improvement in model performance by incorporating additional risk factors, such as genetic risk factors, to increase discriminatory power and accuracy, while still maintaining clinical utility.

Citation Information: Cancer Prev Res 2010;3(1 Suppl):A127.

The UK Childhood Cancer Study: maternal occupational exposures and childhood leukaemia and lymphoma

Risks of childhood leukaemia and lymphoma were investigated for specific work-related exposures of mothers in the UK Childhood Cancer Study. Interviews with parents of 1881 leukaemia and lymphoma cases (0-14 years) and 3742 controls collected job histories recording exposure to eight specific agents. Exposure was (1) self-reported and (2) reviewed, based mainly on exposure probability and exposure level. Completeness, consistency and sufficiency evaluated data quality. Of all job exposures which were self-reported as exposed, 33% cases and 34% controls remained classified as exposed after review, with the remainder designated as partially exposed or unexposed. No review of underreporting of exposure was made. Data quality was 'good' for 26% of cases and 24% of controls. For self-reported exposure, significant risks of acute lymphoblastic leukaemia (ALL) were observed for solvents and petrol in all time windows. For reviewed exposure, solvents remained significant for ALL during pregnancy and postnatally. Restricting analyses to good-quality information removed all significant results. Refinement of exposure assessment revealed misclassification of self-reported exposures and data quality influenced risk assessment. Maternal exposure to solvents should further be investigated. These findings must invoke caution in the interpretation of risks reliant on self-reported occupational data.