Air Pollution and Lung Cancer: A Review by International Association for the Study of Lung Cancer Early Detection and Screening Committee

INTRODUCTION

The second leading cause of lung cancer is air pollution. Air pollution and smoking are synergistic. Air pollution can worsen lung cancer survival.

METHODS

The Early Detection and Screening Committee of the International Association for the Study of Lung Cancer formed a working group to better understand issues in air pollution and lung cancer. These included identification of air pollutants, their measurement, and proposed mechanisms of carcinogenesis. The burden of disease and the underlying epidemiologic evidence linking air pollution to lung cancer in individuals who never and ever smoked were summarized to quantify the problem, assess risk prediction models, and develop recommended actions.

RESULTS

The number of estimated attributable lung cancer deaths has increased by nearly 30% since 2007 as smoking has decreased and air pollution has increased. In 2013, the International Agency for Research on Cancer classified outdoor air pollution and particulate matter with aerodynamic diameter less than 2.5 microns in outdoor air pollution as carcinogenic to humans (International Agency for Research on Cancer group 1) and as a cause of lung cancer. Lung cancer risk models reviewed do not include air pollution. Estimation of cumulative exposure to air pollution exposure is complex which poses major challenges with accurately collecting long-term exposure to ambient air pollution for incorporation into risk prediction models in clinical practice.

CONCLUSIONS

Worldwide air pollution levels vary widely, and the exposed populations also differ. Advocacy to lower sources of exposure is important. Health care can lower its environmental footprint, becoming more sustainable and resilient. The International Association for the Study of Lung Cancer community can engage broadly on this topic.

Lung adenocarcinoma promotion by air pollutants

A complete understanding of how exposure to environmental substances promotes cancer formation is lacking. More than 70 years ago, tumorigenesis was proposed to occur in a two-step process: an initiating step that induces mutations in healthy cells, followed by a promoter step that triggers cancer development1. Here we propose that environmental particulate matter measuring ≤2.5 μm (PM2.5), known to be associated with lung cancer risk, promotes lung cancer by acting on cells that harbour pre-existing oncogenic mutations in healthy lung tissue. Focusing on EGFR-driven lung cancer, which is more common in never-smokers or light smokers, we found a significant association between PM2.5 levels and the incidence of lung cancer for 32,957 EGFR-driven lung cancer cases in four within-country cohorts. Functional mouse models revealed that air pollutants cause an influx of macrophages into the lung and release of interleukin-1β. This process results in a progenitor-like cell state within EGFR mutant lung alveolar type II epithelial cells that fuels tumorigenesis. Ultradeep mutational profiling of histologically normal lung tissue from 295 individuals across 3 clinical cohorts revealed oncogenic EGFR and KRAS driver mutations in 18% and 53% of healthy tissue samples, respectively. These findings collectively support a tumour-promoting role for  PM2.5 air pollutants  and provide impetus for public health policy initiatives to address air pollution to reduce disease burden.

Design and methodological considerations for biomarker discovery and validation in the Integrative Analysis of Lung Cancer Etiology and Risk (INTEGRAL) Program

The Integrative Analysis of Lung Cancer Etiology and Risk (INTEGRAL) program is an NCI-funded initiative with an objective to develop tools to optimize low-dose CT (LDCT) lung cancer screening. Here, we describe the rationale and design for the Risk Biomarker and Nodule Malignancy projects within INTEGRAL. The overarching goal of these projects is to systematically investigate circulating protein markers to include on a panel for use (i) pre-LDCT, to identify people likely to benefit from screening, and (ii) post-LDCT, to differentiate benign versus malignant nodules. To identify informative proteins, the Risk Biomarker project measured 1161 proteins in a nested-case control study within 2 prospective cohorts (n = 252 lung cancer cases and 252 controls) and replicated associations for a subset of proteins in 4 cohorts (n = 479 cases and 479 controls). Eligible participants had a current or former history of smoking and cases were diagnosed up to 3 years following blood draw. The Nodule Malignancy project measured 1078 proteins among participants with a heavy smoking history within four LDCT screening studies (n = 425 cases diagnosed up to 5 years following blood draw, 430 benign-nodule controls, and 398 nodule-free controls). The INTEGRAL panel will enable absolute quantification of 21 proteins. We will evaluate its performance in the Risk Biomarker project using a case-cohort study including 14 cohorts (n = 1696 cases and 2926 subcohort representatives), and in the Nodule Malignancy project within five LDCT screening studies (n = 675 cases, 680 benign-nodule controls, and 648 nodule-free controls). Future progress to advance lung cancer early detection biomarkers will require carefully designed validation, translational, and comparative studies.

Abstract 2232: Preliminary results for a novel single extracellular vesicle assay for early lung cancer: The power of co-localized detection of surface biomarkers

Introduction: Screening for lung cancer (LC), the leading cause of cancer deaths, with helical computerized tomography lowers mortality but uptake is poor. Investigations into new approaches such as using circulating tumor cells and circulating tumor DNA for LC detection have soared in the last decade. However, the low abundance of these targets has limited the performance of these approaches as screening tools. We hypothesize that co-localization of biomarkers on the surface of individual extracellular vesicles (EVs), which are shed into the circulation by cancer cells, may lead to development of a blood test for early stage LC. We evaluated the potential of our approach in detecting early stage LC in clinical samples.

Methods: EVs were purified from plasma using size-exclusion chromatography and immunoaffinity capture, and biomarkers co-localized on the EV surface were detected with proximity ligation qPCR. We used antibody combinations comprising 1 capture antibody and 2 oligonucleotide-tagged detection antibodies, recognizing 1, 2 or 3 unique biomarkers. We evaluated this approach by testing plasma samples from early stage I/II lung adenocarcinoma (LUAD) patients (15 smokers, 19 non-smokers), late stage III/IV LUAD patients (16 smokers, 18 non-smokers), and healthy donors (34 smokers, 33 non-smokers). Samples were from one vendor, processed using a standardized protocol. LUAD samples were sourced from a cancer research center and healthy samples from a primary care facility. PCR cycle threshold (Ct) values were generated for each combination and data was evaluated using univariate analysis.

Results: Combinations recognizing 3 biomarkers were better in detecting all stages of LUAD (AUC=0.83, 95% CI 0.77-0.90), as compared to combinations recognizing 2 biomarkers (AUC=0.71, 95% CI 0.63-0.80) or 1 biomarker (AUC=0.50, 95% CI 0.35-0.55), demonstrating greater accuracy with an increasing number of co-localized biomarkers. In detecting LUAD (all stages) at a specificity of 0.80 (95% CI 0.69-0.88), sensitivity improved as the number of co-localized biomarkers increased from 1 (0.08, 95% CI 0.03-0.18) to 2 (0.60, 95% CI 0.48-0.72) to 3 (0.76, 95% CI 0.65-0.86). In detecting early stage I/II LUAD, the most effective combination used 3 biomarkers (STn, MUC1, CEACAM6) and had a sensitivity of 0.56 (95% CI 0.38-0.73).

Conclusions: These preliminary data highlight the potential of detecting biomarkers co-localized on the surface of single EVs as an effective tool for early stage LC detection, and the benefit of using 3 biomarkers simultaneously. Despite inherent challenges associated with commercial samples, our finding that detection of co-localized EV surface biomarkers distinguished LUAD is promising. Additional studies with LC cohorts beyond LUAD are underway to refine combinations and independently validate our assay for early stage LC detection.

Citation Format: Daniel P. Salem, Laura T. Bortolin, Sanchari Banerjee, Kelly M. Biette, Delaney M. Byrne, Anthony D. Couvillon, Peter A. Duff, Jonian Grosha, Daniel Gusenleitner, MacKenzie Sadie King, Christopher R. Sedlak, Ibukunoluwapo O. Zabroski, Karen Copeland, Emily S. Winn-Deen, Eric K. Huang, Christine D. Berg, Joseph C. Sedlak. Preliminary results for a novel single extracellular vesicle assay for early lung cancer: The power of co-localized detection of surface biomarkers [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2232.

Management of Lung Cancer Screening Results Based on Individual Prediction of Current and Future Lung Cancer Risks

OBJECTIVES

We propose a risk-tailored approach for management of lung cancer screening results. This approach incorporates individual risk factors and low-dose computed tomography (LDCT) image features into calculations of immediate and next-screen (1-y) risks of lung cancer detection, which in turn can recommend short-interval imaging or 1-year or 2-year screening intervals.

METHODS

We first extended the "LCRAT+CT" individualized risk calculator to predict lung cancer risk after either a negative or abnormal LDCT screen result. To develop the abnormal screen portion, we analyzed 18,129 abnormal LDCT results in the National Lung Screening Trial (NLST), including lung cancers detected immediately (n = 649) or at the next screen (n = 235). We estimated the potential impact of this approach among NLST participants with any screen result (negative or abnormal).

RESULTS

Applying the draft National Health Service (NHS) England protocol for lung screening to NLST participants referred 76% of participants to a 2-year interval, but delayed diagnosis for 40% of detectable cancers. The Lung Cancer Risk Assessment Tool+Computed Tomography (LCRAT+CT) risk model, with a threshold of less than 0.95% cumulative lung cancer risk, would also refer 76% of participants to a 2-year interval, but would delay diagnosis for only 30% of cancers, a 25% reduction versus the NHS protocol. Alternatively, LCRAT+CT, with a threshold of less than 1.7% cumulative lung cancer risk, would also delay diagnosis for 40% of cancers, but would refer 85% of participants for a 2-year interval, a 38% further reduction in the number of required 1-year screens beyond the NHS protocol.

CONCLUSIONS

Using individualized risk models to determine management in lung cancer screening could substantially reduce the number of screens or increase early detection.

Abstract PR-13: Potential effect on racial/ethnic disparities of removing racial/ethnic variables from risk models: The example of lung-cancer screening

Background: Some uses of “race correction” in clinical algorithms and prediction models unfairly reduce access to care, resulting in calls to remove racial/ethnic variables from all models and algorithms. However, for models that are based on unbiased, high-quality, and plentiful data, removing racial/ethnic variables may reduce prediction accuracy for minorities. We model racial/ethnic disparities in screening eligibility from augmenting USPSTF-2021 guidelines (ages 50-80, ≥20 pack-years, ≤15 quit-years) to also include individuals selected by an NCCN-recommended risk model that includes race (PLCOM2012) versus the same model with race/ethnicity removed (PLCOM2012_NoRace). Methods: We used previously published methodology to model the performance of lung cancer screening using 6915 ever-smokers ages 50-80 from the US-representative 2015 National Health Interview Survey (NHIS). Individuals were considered eligible for screening if they are eligible by USPSTF-2021 guidelines or by PLCOM2012 (“USPSTF+PLCOM2012”), versus being eligible by USPSTF-2021 or PLCOM2012_NoRace (“USPSTF+PLCOM2012_NoRace”). Both models used the NCCN-recommended ≥1.3% 6-year risk-threshold for eligibility. We evaluated model accuracy (average percent over/under-estimation) by race/ethnicity, estimated the proportion of life-years gainable achieved by each eligible cohort (LYG), and evaluated the LYG disparity (difference in LYG between whites and each minority). Results: USPSTF+PLCOM2012 and USPSTF+PLCOM2012_NoRace identified similar numbers of minorities as eligible for screening (~2.7 million). However, USPSTF+PLCOM2012_NoRace selected 125% more Hispanic-Americans and 31% less African-Americans. LYG disparities decreased using USPSTF+PLCOM2012_NoRace versus USPSTF+PLCOM2012 for Hispanic Americans (LYG: 33% to 29%). However, LYG disparities for African Americans increased (LYG: 16% to 18%). PLCOM2012 underestimated lung cancer risk by 49% for Hispanic-Americans, whereas PLCOM2012_NoRace performed well (4% overestimation). However, PLCOM2012underestimated risk in African-Americans by only 6%, PLCOM2012_NoRace underestimated risk in African-Americans by 36%. Conclusion: The model that was most accurate for a minority group was projected to reduce disparities the most for that group. Removing race from the PLCOM2012 model substantially underestimated risk for African-Americans and may increase disparities. Inexplicably, PLCOM2012 substantially underestimated risk in Hispanic-Americans despite including race/ethnicity, which was alleviated by removing race/ethnicity. Great care must be taken when removing racial/ethnic variables from models, because this will assign minorities risk estimates that may be largely, or entirely, based on the majority population.

Citation Format: Corey D. Young, Li C. Cheung, Christine D. Berg, Patricia Rivera, Hilary A. Robbins, Anil K. Chaturvedi, Hormuzd A. Katki, Rebecca Landy. Potential effect on racial/ethnic disparities of removing racial/ethnic variables from risk models: The example of lung-cancer screening [abstract]. In: Proceedings of the AACR Virtual Conference: 14th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2021 Oct 6-8. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2022;31(1 Suppl):Abstract nr PR-13.

USPSTF2013 versus PLCOm2012 lung cancer screening eligibility criteria (International Lung Screening Trial): interim analysis of a prospective cohort study

BACKGROUND

Lung cancer is a major health problem. CT lung screening can reduce lung cancer mortality through early diagnosis by at least 20%. Screening high-risk individuals is most effective. Retrospective analyses suggest that identifying individuals for screening by accurate prediction models is more efficient than using categorical age-smoking criteria, such as the US Preventive Services Task Force (USPSTF) criteria. This study prospectively compared the effectiveness of the USPSTF2013 and PLCOm2012 model eligibility criteria.

METHODS

In this prospective cohort study, participants from the International Lung Screening Trial (ILST), aged 55-80 years, who were current or former smokers (ie, had ≥30 pack-years smoking history or ≤15 quit-years since last permanently quitting), and who met USPSTF2013 criteria or a PLCOm2012 risk threshold of at least 1·51% within 6 years of screening, were recruited from nine screening sites in Canada, Australia, Hong Kong, and the UK. After enrolment, patients were assessed with the USPSTF2013 criteria and the PLCOm2012 risk model with a threshold of at least 1·70% at 6 years. Data were collected locally and centralised. Main outcomes were the comparison of lung cancer detection rates and cumulative life expectancies in patients with lung cancer between USPSTF2013 criteria and the PLCOm2012 model. In this Article, we present data from an interim analysis. To estimate the incidence of lung cancers in individuals who were USPSTF2013-negative and had PLCOm2012 of less than 1·51% at 6 years, ever-smokers in the Prostate Lung Colorectal and Ovarian Cancer Screening Trial (PLCO) who met these criteria and their lung cancer incidence were applied to the ILST sample size for the mean follow-up occurring in the ILST. This trial is registered at ClinicalTrials.gov, NCT02871856. Study enrolment is almost complete.

FINDINGS

Between June 17, 2015, and Dec 29, 2020, 5819 participants from the International Lung Screening Trial (ILST) were enrolled on the basis of meeting USPSTF2013 criteria or the PLCOm2012 risk threshold of at least 1·51% at 6 years. The same number of individuals was selected for the PLCOm2012 model as for the USPSTF2013 criteria (4540 [78%] of 5819). After a mean follow-up of 2·3 years (SD 1·0), 135 lung cancers occurred in 4540 USPSTF2013-positive participants and 162 in 4540 participants included in the PLCOm2012 of at least 1·70% at 6 years group (cancer sensitivity difference 15·8%, 95% CI 10·7-22·1%; absolute odds ratio 4·00, 95% CI 1·89-9·44; p<0·0001). Compared to USPSTF2013-positive individuals, PLCOm2012-selected participants were older (mean age 65·7 years [SD 5·9] vs 63·3 years [5·7]; p<0·0001), had more comorbidities (median 2 [IQR 1-3] vs 1 [1-2]; p<0·0001), and shorter life expectancy (13·9 years [95% CI 12·8-14·9] vs 14·8 [13·6-16·0] years). Model-based difference in cumulative life expectancies for those diagnosed with lung cancer were higher in those who had PLCOm2012 risk of at least 1·70% at 6 years than individuals who were USPSTF2013-positive (2248·6 years [95% CI 2089·6-2425·9] vs 2000·7 years [1841·2-2160·3]; difference 247·9 years, p=0·015).

INTERPRETATION

PLCOm2012 appears to be more efficient than the USPSTF2013 criteria for selecting individuals to enrol into lung cancer screening programmes and should be used for identifying high-risk individuals who benefit from the inclusion in these programmes.

FUNDING

Terry Fox Research Institute, The UBC-VGH Hospital Foundation and the BC Cancer Foundation, the Alberta Cancer Foundation, the Australian National Health and Medical Research Council, Cancer Research UK and a consortium of funders, and the Roy Castle Lung Cancer Foundation for the UK Lung Screen Uptake Trial.

Using Prediction Models to Reduce Persistent Racial and Ethnic Disparities in the Draft 2020 USPSTF Lung Cancer Screening Guidelines

We examined whether draft 2020 United States Preventive Services Task Force (USPSTF) lung cancer screening recommendations "partially ameliorate racial disparities in screening eligibility" compared with the 2013 guidelines, as claimed. Using data from the 2015 National Health Interview Survey, USPSTF-2020 increased eligibility by similar proportions for minorities (97.1%) and Whites (78.3%). Contrary to the intent of USPSTF-2020, the relative disparity (differences in percentages of model-estimated gainable life-years from National Lung Screening Trial-like screening by eligible Whites vs minorities) actually increased from USPSTF-2013 to USPSTF-2020 (African Americans: 48.3%-33.4% = 15.0% to 64.5%-48.5% = 16.0%; Asian Americans: 48.3%-35.6% = 12.7% to 64.5%-45.2% = 19.3%; Hispanic Americans: 48.3%-24.8% = 23.5% to 64.5%-37.0% = 27.5%). However, augmenting USPSTF-2020 with high-benefit individuals selected by the Life-Years From Screening with Computed Tomography (LYFS-CT) model nearly eliminated disparities for African Americans (76.8%-75.5% = 1.2%) and improved screening efficiency for Asian and Hispanic Americans, although disparities were reduced only slightly (Hispanic Americans) or unchanged (Asian Americans). The draft USPSTF-2020 guidelines increased the number of eligible minorities vs USPSTF-2013 but may inadvertently increase racial and ethnic disparities. LYFS-CT could reduce disparities in screening eligibility by identifying ineligible people with high predicted benefit regardless of race and ethnicity.

Modeled Reductions in Late-stage Cancer with a Multi-Cancer Early Detection Test

BACKGROUND

Cancer is the second leading cause of death globally, with many cases detected at a late stage when prognosis is poor. New technologies enabling multi-cancer early detection (MCED) may make "universal cancer screening" possible. We extend single-cancer models to understand the potential public health effects of adding a MCED test to usual care.

METHODS

We obtained data on stage-specific incidence and survival of all invasive cancers diagnosed in persons aged 50-79 between 2006 and 2015 from the US Surveillance, Epidemiology, and End Results (SEER) program, and combined this with published performance of a MCED test in a state transition model (interception model) to predict diagnostic yield, stage shift, and potential mortality reductions. We model long-term (incident) performance, accou.

Abstract PO-247: Use of prediction models to reduce racial/ethnic disparities in eligibility for lung-cancer screening

Background: For the same age and smoking history as whites, minorities have substantially different lung-cancer risk. However, current US Preventive Services Task Force (USPSTF) lung-cancer screening recommendations make no allowance for race/ethnicity and may induce health disparities. Incorporating individualized prediction-models into USPSTF guidelines may reduce racial/ethnic disparities in lung-cancer screening eligibility. We examine whether expanding current USPSTF lung cancer screening eligibility to include ever-smokers whose risk (calculated by an individualized prediction model) exceeded a threshold would reduce racial/ethnic disparities induced by current USPSTF guidelines. Methods: We used the US- representative 2015 National Health Interview Survey to examine screening eligibility. We identified the thresholds for each of 5 models: lung-cancer risk (Bach, PLCOM2012 and LCRAT models), lung-cancer death risk (LCDRAT model), and life- years gained by attending screening (LYFS-CT model), which select the same number of ever-smokers aged 50-80yrs as USPSTF guidelines. We defined 5 cohorts of ever- smokers as eligible for screening if they were eligible by each screening model or USPSTF guidelines. Among each race/ethnicity, we calculated the number eligible for screening, proportion of preventable lung-cancer deaths prevented (LCD sensitivity), proportion of gainable life-years gained (LYG sensitivity) and screening effectiveness (the number needed to screen to prevent one lung-cancer death). Results: USPSTF criteria performed best for whites (20% eligible, preventing 55% of preventable lung- cancer deaths). Asian-Americans had the least effective screening (NNS=419), only 13% of African-Americans were eligible despite having the most effective screening (NNS=135), and Hispanic-Americans had the lowest percentages eligible (9%) and deaths preventable (30%). Augmenting USPSTF criteria with LCDRAT or LYFS-CT prediction-models nearly equalized the performance of screening for African- Americans with that of whites, doubling the number of African-Americans eligible and increasing the number of preventable deaths and life-years gained by nearly 80%, although at a 25% loss in effectiveness. Prediction-models improved all screening metrics for Asian-Americans and Hispanic-Americans. However models estimated risk more accurately for whites than minorities. Conclusions: Augmenting USPSTF criteria with the LCDRAT or LYFS-CT prediction-models nearly eliminated the white/African-American disparity. All screening metrics were substantially improved for Asian/Hispanic-Americans.

Citation Format: Rebecca Landy, Corey D. Young, Martin Skarzynski, Li C. Cheung, Christine D. Berg, M. Patricia Rivera, Hilary A. Robbins, Anil K. Chaturvedi, Hormuzd A. Katki. Use of prediction models to reduce racial/ethnic disparities in eligibility for lung-cancer screening [abstract]. In: Proceedings of the AACR Virtual Conference: Thirteenth AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2020 Oct 2-4. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2020;29(12 Suppl):Abstract nr PO-247.

Statistical approaches using longitudinal biomarkers for disease early detection: A comparison of methodologies

Early detection of clinical outcomes such as cancer may be predicted using longitudinal biomarker measurements. Tracking longitudinal biomarkers as a way to identify early disease onset may help to reduce mortality from diseases like ovarian cancer that are more treatable if detected early. Two disease risk prediction frameworks, the shared random effects model (SREM) and the pattern mixture model (PMM) could be used to assess longitudinal biomarkers on disease early detection. In this article, we studied the discrimination and calibration performances of SREM and PMM on disease early detection through an application to ovarian cancer, where early detection using the risk of ovarian cancer algorithm (ROCA) has been evaluated. Comparisons of the above three approaches were performed via analyses of the ovarian cancer data from the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial. Discrimination was evaluated by the time-dependent receiver operating characteristic curve and its area, while calibration was assessed using calibration plot and the ratio of observed to expected number of diseased subjects. The out-of-sample performances were calculated via using leave-one-out cross-validation, aiming to minimize potential model overfitting. A careful analysis of using the biomarker cancer antigen 125 for ovarian cancer early detection showed significantly improved discrimination performance of PMM as compared with SREM and ROCA, nevertheless all approaches were generally well calibrated. Robustness of all approaches was further investigated in extensive simulation studies. The improved performance of PMM relative to ROCA is in part due to the fact that the biomarker measurements were taken at a yearly interval, which is not frequent enough to reliably estimate the changepoint or the slope after changepoint in cases under ROCA.

Identification of Candidates for Longer Lung Cancer Screening Intervals Following a Negative Low-Dose Computed Tomography Result

Lengthening the annual low-dose computed tomography (CT) screening interval for individuals at lowest risk of lung cancer could reduce harms and improve efficiency. We analyzed 23 328 participants in the National Lung Screening Trial who had a negative CT screen (no ≥4-mm nodules) to develop an individualized model for lung cancer risk after a negative CT. The Lung Cancer Risk Assessment Tool + CT (LCRAT+CT) updates "prescreening risk" (calculated using traditional risk factors) with selected CT features. At the next annual screen following a negative CT, risk of cancer detection was reduced among the 70% of participants with neither CT-detected emphysema nor consolidation (median risk = 0.2%, interquartile range [IQR] = 0.1%-0.3%). However, risk increased for the 30% with CT emphysema (median risk = 0.5%, IQR = 0.3%-0.8%) and the 0.6% with consolidation (median = 1.6%, IQR = 1.0%-2.5%). As one example, a threshold of next-screen risk lower than 0.3% would lengthen the interval for 57.8% of screen-negatives, thus averting 49.8% of next-screen false-positives among screen-negatives but delaying diagnosis for 23.9% of cancers. Our results support that many, but not all, screen-negatives might reasonably lengthen their CT screening interval.

Risk of Prostate Cancer-related Death Following a Low PSA Level in the PLCO Trial

Longer-than-annual screening intervals have been suggested to improve the balance of benefits and harms in prostate cancer screening. Many researchers, societies, and guideline committees have suggested that screening intervals could depend on the prostate-specific antigen (PSA) result. We analyzed data from men (N = 33,897) ages 55-74 years with a baseline PSA test in the intervention arm of the Prostate, Lung, Colorectal and Ovarian Cancer Screening trial (United States, 1993-2001). We estimated 5- and 10-year risks of aggressive cancer (Gleason ≥8 and/or stage III/IV) and 15-year risks of prostate cancer-related mortality for men with baseline PSA ≤ 0.5 ng/mL (N = 4,862), ≤1 ng/mL (N = 15,110), and 1.01-2.5 ng/mL (N = 12,422). A total of 217 men died from prostate cancer through 15 years, although no men with PSA ≤ 1 ng/mL died from prostate cancer within 5 years [95% confidence interval (CI), 0.00%-0.03%]. The 5-year incidence of aggressive disease was low (0.08%; 95% CI, 0.03%-0.12%) for men with PSA ≤ 1 ng/mL, and higher for men with baseline PSA 1.01-2.5 ng/mL (0.51%; 95% CI, 0.38%-0.74%). No men aged ≥65 years with PSA ≤ 0.5 ng/mL died from prostate cancer within 15 years (95% CI, 0.00%-0.32%), and their 10-year incidence of aggressive disease was low (0.25%; 95% CI, 0.00%-0.53%). Compared with white men, black men with PSA ≤ 1 ng/mL had higher 10-year rates of aggressive disease (1.6% vs. 0.4%; P < 0.01). Five-year screening intervals may be appropriate for the 45% of men with PSA ≤ 1 ng/mL. Men ages ≥65 years with PSA ≤ 0.5 ng/mL could consider stopping screening. Substantial risk disparities suggest appropriate screening intervals could depend on race/ethnicity.

Life-Gained-Based Versus Risk-Based Selection of Smokers for Lung Cancer Screening

BACKGROUND

Although risk-based selection of ever-smokers for screening could prevent more lung cancer deaths than screening according to the U.S. Preventive Services Task Force (USPSTF) guidelines, it preferentially selects older ever-smokers with shorter life expectancies due to comorbidities.

OBJECTIVE

To compare selection of ever-smokers for screening based on gains in life expectancy versus lung cancer risk.

DESIGN

Cohort analyses and model-based projections.

SETTING

U.S. population of ever-smokers aged 40 to 84 years.

PARTICIPANTS

130 964 National Health Interview Survey participants, representing about 60 million U.S. ever-smokers during 1997 to 2015.

INTERVENTION

Annual computed tomography (CT) screening for 3 years versus no screening.

MEASUREMENTS

Estimated number of lung cancer deaths averted and life-years gained after development of a mortality model.

RESULTS

Using the calibrated and validated mortality model in U.S. ever-smokers aged 40 to 84 years and selecting 8.3 million ever-smokers to match the number selected by the USPSTF criteria in 2013 to 2015, the analysis estimated that life-gained-based selection would increase the total life expectancy from CT screening (633 400 vs. 607 800 years) but prevent fewer lung cancer deaths (52 600 vs. 55 000) compared with risk-based selection. The 1.56 million persons selected by the life-gained-based strategy but not the risk-based strategy were younger (mean age, 59 vs. 75 years) and had fewer comorbidities (mean, 0.75 vs. 3.7).

LIMITATION

Estimates are model-based and assume implementation of lung cancer screening with short-term effectiveness similar to that from trials.

CONCLUSION

Life-gained-based selection could maximize the benefits of lung cancer screening in the U.S. population by including ever-smokers who have both high lung cancer risk and long life expectancy.

PRIMARY FUNDING SOURCE

Intramural Research Program of the National Cancer Institute, National Institutes of Health.

Pragmatic randomised clinical trial of proton versus photon therapy for patients with non-metastatic breast cancer: the Radiotherapy Comparative Effectiveness (RadComp) Consortium trial protocol

INTRODUCTION

A broad range of stakeholders have called for randomised evidence on the potential clinical benefits and harms of proton therapy, a type of radiation therapy, for patients with breast cancer. Radiation therapy is an important component of curative treatment, reducing cancer recurrence and extending survival. Compared with photon therapy, the international treatment standard, proton therapy reduces incidental radiation to the heart. Our overall objective is to evaluate whether the differences between proton and photon therapy cardiac radiation dose distributions lead to meaningful reductions in cardiac morbidity and mortality after treatment for breast cancer.

METHODS

We are conducting a large scale, multicentre pragmatic randomised clinical trial for patients with breast cancer who will be followed longitudinally for cardiovascular morbidity and mortality, health-related quality of life and cancer control outcomes. A total of 1278 patients with non-metastatic breast cancer will be randomly allocated to receive either photon or proton therapy. The primary outcomes are major cardiovascular events, defined as myocardial infarction, coronary revascularisation, cardiovascular death or hospitalisation for unstable angina, heart failure, valvular disease, arrhythmia or pericardial disease. Secondary endpoints are urgent or unanticipated outpatient or emergency room visits for heart failure, arrhythmia, valvular disease or pericardial disease. The Radiotherapy Comparative Effectiveness (RadComp) Clinical Events Centre will conduct centralised, blinded adjudication of primary outcome events.

ETHICS AND DISSEMINATION

The RadComp trial has been approved by the institutional review boards of all participating sites. Recruitment began in February 2016. Current version of the protocol is A3, dated 08 November 2018. Dissemination plans include presentations at scientific conferences, scientific publications, stakeholder engagement efforts and presentation to the public via lay media outlets.

TRIAL REGISTRATION NUMBER

NCT02603341.

Insights for Management of Ground-Glass Opacities From the National Lung Screening Trial

BACKGROUND

In the National Lung Screening Trial (NLST), screen-detected cancers that would not have been identified by the Lung Computed Tomographic Screening Reporting and Data System (Lung-RADS) nodule management guidelines were frequently ground-glass opacities (GGOs). Lung-RADS suggests that GGOs with diameter less than 20 mm return for annual screening, and GGOs greater than or equal to 20 mm receive 6-month follow-up. We examined whether this 20-mm threshold gives consistent management of GGOs compared with solid nodules.

METHODS

First, we calculated diameter-specific malignancy probabilities for GGOs and solid nodules in the NLST. Using the solid-nodule malignancy risks as benchmarks, we suggested risk-based management categories for GGOs based on their probability of malignancy. Second, we compared lung-cancer mortality between GGOs and solid nodules in the same risk-based category.

RESULTS

Using the Lung-RADS v1.0 classifications, malignancy probability is higher for GGOs than solid nodules within the same category. A risk-based classification of GGOs would assign annual screening for GGOs 4 to 5 mm (0.4% malignancy risk); 6-month follow-up for GGOs 6 to 7 mm (1.1%), 8 to 14 mm (3.0%), and 15 to 19 mm (5.2%); and 3-month follow-up for greater than or equal to 20 mm (10.9%). This reclassification would have assigned similarly fatal cancers to 3-month follow-up (hazard ratio = 2.0 for lung-cancer death in GGOs versus solid-nodule cancers, 95% confidence interval: 0.4-8.7), but for 6-month follow-up, mortality was lower in GGO cancers (hazard ratio = 0.18, 95% confidence interval: 0.05-0.67).

CONCLUSIONS

If Lung-RADS categories for GGOs were based on malignancy probability, then 6- to 19-mm GGOs would receive 6-month follow-up and greater than or equal to 20-mm GGOs would receive 3-month follow-up. Such risk-based management for GGOs could improve the sensitivity of Lung-RADS, especially for large GGO cancers. However, small GGO cancers were less aggressive than their solid-nodule counterparts.

Overall and Multiphasic Findings of the Prostate, Lung, Colorectal and Ovarian (PLCO) Randomized Cancer Screening Trial

BACKGROUND

Screening tests are typically evaluated for a single disease, but multiple tests for multiple diseases are performed in practice. The Prostate, Lung, Colorectal and Ovarian (PLCO) cancer screening trial assessed testing for four cancers simultaneously and can be viewed as a multiphasic cancer intervention. This paper presents overall and multiphasic findings of this trial.

METHODS

The PLCO trial was a randomized multi-center trial conducted at ten screening centers in the US. Participants were 76,682 men and 78,215 women ages 55 - 74 and free of the target cancers at trial entry. Screening tests were PSA and digital rectal examination for prostate cancer, chest x-ray for lung cancer, flexible sigmoidoscopy for colorectal cancer, CA125 and transvaginal ultrasound for ovarian cancer. Outcomes and harms of screening were assessed including compliance, test results, incidence, mortality, false positives and overdiagnosis.

RESULTS

Screening compliance was 82%, 72,820 (8%) of 906,064 exams were positive, the overall PPV was 4.2% and the cancer detection rate was 3.38/1000. A mortality reduction was observed only for colorectal cancer (RR 0.72, 95% CI 0.61 - 0.85) with no effect on all-cause mortality. Ninety-six percent of positive exams were falsely positive and there was a suggestion of overdiagnosis of prostate and possibly ovarian cancers. Multiphasic testing resulted in 7374 men and 2748 women experiencing multiple false positive results from multiple types of tests.

CONCLUSION

Multiphasic cancer screening led to reduced mortality for one target cancer and imposed a burden on the health care system that included substantial false positives and likely overdiagnosis.

Abstract IA08: Individualized risk based lung cancer screening: The way forward

The United States Preventive Services Task Force (USPSTF) recommends annual low-dose computed tomography (CT) lung-cancer screening, as does the Center for Medicare and Medicaid Services (CMS) and many major medical organizations. The USPSTF criteria, similar to those used for entry into the definitive National Lung Screening Trial, are for persons aged 55-80 years, who currently smoke or quit within the past 15 years, and who have accumulated at least 30 pack-years of cigarette smoking. Risk calculators as compared to micro-simulation modeling as used for the USPSTF can more specifically account for demographic, clinical, and smoking characteristics and personalize risk. The USPSTF criteria may miss smokers at high risk for lung cancer who would have been selected for CT screening by individual risk calculators. Recently, the National Comprehensive Cancer Network guidelines for lung cancer screening added consideration of use of risk models to select ever-smokers for screening. Online risk tools are available to assist individuals and health care providers as to whether lung cancer screening may be helpful.

Several lung cancer risk models are available, and comparisons across them have been done. In this presentation, a comparison we performed of the statistical predictive properties of 8 published risk models in 2 large prospective U.S. cohorts will be presented. Identifying a consensus cost-effective risk threshold to define screening eligibility is crucial. Another issue is how best to account for competing morbidities that might make screening less useful. Perhaps a life-years gained approach could help to adjust for this. Additionally, risk models require accurate risk factor data, which are rarely available in electronic health records and may require special collection. It also poses challenges in the health care delivery setting given its complexity.

To better capture high-risk smokers and prevent premature deaths from lung cancer, eligibility for lung cancer screening should be based on exceeding a cost-effective risk threshold that balances CT screening benefits and harms, using a lung cancer risk tool validated in the U.S. population.

Citation Format: Christine D. Berg. Individualized risk based lung cancer screening: The way forward [abstract]. In: Proceedings of the Fifth AACR-IASLC International Joint Conference: Lung Cancer Translational Science from the Bench to the Clinic; Jan 8-11, 2018; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2018;24(17_Suppl):Abstract nr IA08.

Implications of Nine Risk Prediction Models for Selecting Ever-Smokers for Computed Tomography Lung Cancer Screening

BACKGROUND

Lung cancer screening guidelines recommend using individualized risk models to refer ever-smokers for screening. However, different models select different screening populations. The performance of each model in selecting ever-smokers for screening is unknown.

OBJECTIVE

To compare the U.S. screening populations selected by 9 lung cancer risk models (the Bach model; the Spitz model; the Liverpool Lung Project [LLP] model; the LLP Incidence Risk Model [LLPi]; the Hoggart model; the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial Model 2012 [PLCOM2012]; the Pittsburgh Predictor; the Lung Cancer Risk Assessment Tool [LCRAT]; and the Lung Cancer Death Risk Assessment Tool [LCDRAT]) and to examine their predictive performance in 2 cohorts.

DESIGN

Population-based prospective studies.

SETTING

United States.

PARTICIPANTS

Models selected U.S. screening populations by using data from the National Health Interview Survey from 2010 to 2012. Model performance was evaluated using data from 337 388 ever-smokers in the National Institutes of Health-AARP Diet and Health Study and 72 338 ever-smokers in the CPS-II (Cancer Prevention Study II) Nutrition Survey cohort.

MEASUREMENTS

Model calibration (ratio of model-predicted to observed cases [expected-observed ratio]) and discrimination (area under the curve [AUC]).

RESULTS

At a 5-year risk threshold of 2.0%, the models chose U.S. screening populations ranging from 7.6 million to 26 million ever-smokers. These disagreements occurred because, in both validation cohorts, 4 models (the Bach model, PLCOM2012, LCRAT, and LCDRAT) were well-calibrated (expected-observed ratio range, 0.92 to 1.12) and had higher AUCs (range, 0.75 to 0.79) than 5 models that generally overestimated risk (expected-observed ratio range, 0.83 to 3.69) and had lower AUCs (range, 0.62 to 0.75). The 4 best-performing models also had the highest sensitivity at a fixed specificity (and vice versa) and similar discrimination at a fixed risk threshold. These models showed better agreement on size of the screening population (7.6 million to 10.9 million) and achieved consensus on 73% of persons chosen.

LIMITATION

No consensus on risk thresholds for screening.

CONCLUSION

The 9 lung cancer risk models chose widely differing U.S. screening populations. However, 4 models (the Bach model, PLCOM2012, LCRAT, and LCDRAT) most accurately predicted risk and performed best in selecting ever-smokers for screening.

PRIMARY FUNDING SOURCE

Intramural Research Program of the National Institutes of Health/National Cancer Institute.

Factors Associated With Small Aggressive Non-Small Cell Lung Cancers in the National Lung Screening Trial: A Validation Study

Background

A small proportion of non-small cell lung cancers (NSCLCs) have been observed to spread to distant lymph nodes (N3) or metastasize (M1) or both, while the primary tumor is small (≤3 cm, T1). These small aggressive NSCLCs (SA-NSLSC) are important as they are clinically significant, may identify unique biologic pathways, and warrant aggressive follow-up and treatment. This study identifies factors associated with SA-NSCLC and attempts to validate a previous finding that women with a family history of lung cancer are at particularly elevated risk of SA-NSCLC.

Methods

This study used a case-case design within the National Cancer Institute's National Lung Screening Trial (NLST) cohort. Case patients and "control" patients were selected based on TNM staging parameters. Case patients (n = 64) had T1 NSCLCs that were N3 or M1 or both, while "control" patients (n = 206) had T2 or T3, N0 to N2, and M0 NSCLCs. Univariate and multivariable logistic regression were used to identify factors associated with SA-NSCLC.

Results

In bootstrap bias-corrected multivariable logistic regression models, small aggressive adenocarcinomas were associated with a positive history of emphysema (odds ratio [OR] = 5.15, 95% confidence interval [CI] = 1.63 to 23.00) and the interaction of female sex and a positive family history of lung cancer (OR = 6.55, 95% CI = 1.06 to 50.80).

Conclusions

Emphysema may play a role in early lung cancer progression. Females with a family history of lung cancer are at increased risk of having small aggressive lung adenocarcinomas. These results validate previous findings and encourage research on the role of female hormones interacting with family history and genetic factors in lung carcinogenesis and progression.

The efficacy of prostate-specific antigen screening: Impact of key components in the ERSPC and PLCO trials

BACKGROUND

The European Randomized Study of Screening for Prostate Cancer (ERSPC) demonstrated that prostate-specific antigen (PSA) screening significantly reduced prostate cancer mortality (rate ratio, 0.79; 95% confidence interval, 0.69-0.91). The US Prostate, Lung, Colorectal, and Ovarian (PLCO) trial indicated no such reduction but had a wide 95% CI (rate ratio for prostate cancer mortality, 1.09; 95% CI, 0.87-1.36). Standard meta-analyses are unable to account for key differences between the trials that can impact the estimated effects of screening and the trials' point estimates.

METHODS

The authors calibrated 2 microsimulation models to individual-level incidence and mortality data from 238,936 men participating in the ERSPC and PLCO trials. A cure parameter for the underlying efficacy of screening was estimated by the models separately for each trial. The authors changed step-by-step major known differences in trial settings, including enrollment and attendance patterns, screening intervals, PSA thresholds, biopsy receipt, control arm contamination, and primary treatment, to reflect a more ideal protocol situation and differences between the trials.

RESULTS

Using the cure parameter estimated for the ERSPC, the models projected 19% to 21% and 6% to 8%, respectively, prostate cancer mortality reductions in the ERSPC and PLCO settings. Using this cure parameter, the models projected a reduction of 37% to 43% under annual screening with 100% attendance and biopsy compliance and no contamination. The cure parameter estimated for the PLCO trial was 0.

CONCLUSIONS

The observed cancer mortality reduction in screening trials appears to be highly sensitive to trial protocol and practice settings. Accounting for these differences, the efficacy of PSA screening in the PLCO setting is not necessarily inconsistent with ERSPC results. Cancer 2018;124:1197-206. © 2017 American Cancer Society.

Body mass index and breast cancer survival: a Mendelian randomization analysis

Background

There is increasing evidence that elevated body mass index (BMI) is associated with reduced survival for women with breast cancer. However, the underlying reasons remain unclear. We conducted a Mendelian randomization analysis to investigate a possible causal role of BMI in survival from breast cancer.

Methods

We used individual-level data from six large breast cancer case-cohorts including a total of 36 210 individuals (2475 events) of European ancestry. We created a BMI genetic risk score (GRS) based on genotypes at 94 known BMI-associated genetic variants. Association between the BMI genetic score and breast cancer survival was analysed by Cox regression for each study separately. Study-specific hazard ratios were pooled using fixed-effect meta-analysis.

Results

BMI genetic score was found to be associated with reduced breast cancer-specific survival for estrogen receptor (ER)-positive cases [hazard ratio (HR) = 1.11, per one-unit increment of GRS, 95% confidence interval (CI) 1.01-1.22, P = 0.03). We observed no association for ER-negative cases (HR = 1.00, per one-unit increment of GRS, 95% CI 0.89-1.13, P = 0.95).

Conclusions

Our findings suggest a causal effect of increased BMI on reduced breast cancer survival for ER-positive breast cancer. There is no evidence of a causal effect of higher BMI on survival for ER-negative breast cancer cases.

Outcomes from ovarian cancer screening in the PLCO trial: Histologic heterogeneity impacts detection, overdiagnosis and survival

AIM

A mortality benefit from screening for ovarian cancer has never been demonstrated. The aim of this study was to evaluate the screening outcomes for different histologic subtypes of ovarian cancers.

METHODS

Women in the screening arm of the Prostate, Lung, Colorectal and Ovarian Screening Trial underwent CA-125 and transvaginal ultrasound annually for 3-5 years. We compared screening test characteristics (including overdiagnosis) and outcomes by tumour type (type II versus other) and study arm (screening versus usual care).

RESULTS

Of 78,215 women randomised, 496 women were diagnosed with ovarian cancer. Of the tumours that were characterised (n = 413; 83%), 74% (n = 305) were type II versus 26% other (n = 108). Among screened patients, 70% of tumours were type II compared to 78% in usual care (p = 0.09). Within the screening arm, 29% of type II tumours were screen detected compared to 54% of the others (p < 0.01). The sensitivity of screening was 65% for type II tumours versus 86% for other types (p = 0.02). 15% of type II screen-detected tumours were stage I/II, compared to 81% of other tumours (p < 0.01). The overdiagnosis rate was lower for type II compared to other tumours (28.2% versus 72.2%; p < 0.01). Ovarian cancer-specific survival was worse for type II tumours compared to others (p < 0.01). Survival was similar for type II (p = 0.74) or other types (p = 0.32) regardless of study arm.

CONCLUSIONS

Test characteristics of screening for ovarian cancer differed for type II tumours compared to other ovarian tumours. Type II tumours were less likely to be screen diagnosed, early stage at diagnosis or overdiagnosed.

Reconciling the Effects of Screening on Prostate Cancer Mortality in the ERSPC and PLCO Trials

BACKGROUND

The ERSPC (European Randomized Study of Screening for Prostate Cancer) found that screening reduced prostate cancer mortality, but the PLCO (Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial) found no reduction.

OBJECTIVE

To evaluate whether effects of screening on prostate cancer mortality relative to no screening differed between the ERSPC and PLCO.

DESIGN

Cox regression of prostate cancer death in each trial group, adjusted for age and trial. Extended analyses accounted for increased incidence due to screening and diagnostic work-up in each group via mean lead times (MLTs), which were estimated empirically and using analytic or microsimulation models.

SETTING

Randomized controlled trials in Europe and the United States.

PARTICIPANTS

Men aged 55 to 69 (ERSPC) or 55 to 74 (PLCO) years at randomization.

INTERVENTION

Prostate cancer screening.

MEASUREMENTS

Prostate cancer incidence and survival from randomization; prostate cancer incidence in the United States before screening began.

RESULTS

Estimated MLTs were similar in the ERSPC and PLCO intervention groups but were longer in the PLCO control group than the ERSPC control group. Extended analyses found no evidence that effects of screening differed between trials (P = 0.37 to 0.47 [range across MLT estimation approaches]) but strong evidence that benefit increased with MLT (P = 0.0027 to 0.0032). Screening was estimated to confer a 7% to 9% reduction in the risk for prostate cancer death per year of MLT. This translated into estimates of 25% to 31% and 27% to 32% lower risk for prostate cancer death with screening as performed in the ERSPC and PLCO intervention groups, respectively, compared with no screening.

LIMITATION

The MLT is a simple metric of screening and diagnostic work-up.

CONCLUSION

After differences in implementation and settings are accounted for, the ERSPC and PLCO provide compatible evidence that screening reduces prostate cancer mortality.

PRIMARY FUNDING SOURCE

National Cancer Institute.

Abstract 5298: Lung cancer risk and scarring on imaging and histology in the National Lung Screening Trial

Background: The contribution of pulmonary scars to lung cancer development and the degree to which lung cancers cause a scarring response are unclear. Also unknown is how lung scarring impacts lung cancer screening.

Methods: We evaluated associations between scarring and lung cancer in the National Lung Screening Trial (NLST), a lung cancer screening trial among current or former, heavy smokers, 55-74 years-old. Baseline scarring (presence vs. absence) on screening low dose computed tomography (LDCT) scan was assessed at baseline (T0). Associations of T0 scarring with screen-detected lung cancers and with interval-detected lung cancers missed on screening within 3 years of T0 screen were analyzed using multinomial logistic regression. Cox proportional hazards models were used to analyze the relationship between T0 scarring and incident lung cancers diagnosed &gt;3 years after T0. Regression models included age, sex, race, smoking history, chronic obstructive pulmonary disease, history of pneumonia, and family history of lung cancer. A thoracic pathologist (first author) evaluated lung cancer pathology slides from the Lung Screening Study (LSS) subset of NLST for scar grade (none, sparse, dense) and maturity (none, immature, intermediate, mature). Associations between T0 scarring on LDCT and histological scarring were examined by logistic regression.

Results: NLST’s LDCT arm enrolled 26,722 participants (65% from the LSS). T0 scars were present in 132 (22%) screen-detected, 12 (29%) interval-detected, and 94 (26%) incident lung cancer cases. T0 scarring did not increase or decrease screen-detection of cancers [odds ratio (OR) 95% CI: 1.03 (0.84-1.26)]. However, scarring might increase the chance of an interval-detected cancer [OR (95% CI): 1.54 (0.76-3.12)]. After screening stopped, T0 scarring was associated with increased incident lung cancer risk [hazard ratio (HR) (95% CI): 1.27 (1.00-1.62); P=0.048]. Pathology slides were available for 258 (38%) lung cancers in LSS. Lung scarring was found in 172 (67%) of these cancers with 58 (22%) being characterized as mature scars. On microscopic review, scars were found in 80 (66%) ADC, 46 (82%) squamous cell carcinomas, and 20 (51%) bronchioloalveolar carcinomas. Microscopic scarring tended to be more frequent among cases with T0 scarring than those without T0 scarring (75% vs. 64%; P=0.10) [OR (95% CI): 1.89 (0.98-3.86)].

Conclusion: The association between T0 scarring and incident lung cancer over a period of more than 3 years is consistent with an etiologic contribution of scarring to development of lung cancer. The relationship between T0 scarring and scarring on microscopic evaluation suggests that scarring preceded the cancer, further supporting an etiologic relationship. Finally, the borderline association of T0 scarring and interval cancers suggests that scarring may decrease the sensitivity of screening.

Citation Format: Alison L. Van Dyke, Christine D. Berg, Neil E. Caporaso, Hormuzd A. Katki, Anil K. Chaturvedi, Eric A. Engels. Lung cancer risk and scarring on imaging and histology in the National Lung Screening Trial [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 5298. doi:10.1158/1538-7445.AM2017-5298

Abstract 5291: Effect of screening CT results and features on lung cancer risk prediction within the National Lung Screening Trial

BACKGROUND: In the course of screening, individual risk of disease evolves based on screening results. We calculated how individual lung cancer risk changes based on screening CT findings using data from the National Lung Screening Trial (NLST), which conducted annual screening for 3 years.

METHODS: We calculated lung cancer risks by combining CT findings with individual predicted 1-year “pre-screening risk.” Pre-screening risk r(x) was predicted in the absence of screening using a validated risk model (Katki et al., JAMA 2016) with covariates (x): age, education, sex, race, smoking intensity/duration/quit-years, body mass index, family history of lung cancer, and self-reported emphysema. We used log-binomial regression to calculate the risk of an “interval” lung cancer (within 1 year of a negative screen) or a “screen-detected” cancer detected at the next annual screen. For each, covariates included log-transformed 1-year pre-screening risk and CT findings including classification as negative or false-positive and other specific features.

RESULTS: The median 1-year pre-screening risk at the first NLST screen was 0.32% (interquartile range 0.19-0.53%). Among CT-negatives, risk over the next year was substantially reduced as r(x)1.32 (median interval cancer risk 0.05%), but risk at the next screen reverted to pre-screening risk as r(x)1. Risk at the next screen was higher for those whose CT noted either emphysema (r(x)0.95, median risk 0.53%) or consolidation (r(x)0.76, median risk 1.6%).

Among CT-false-positives, overall risk at the next screen increased as r(x)0.74 (median risk 1.5%). Risk was higher among those with nodule(s) that were larger, had spiculated margins (median risk 4.1%), were located in the upper lobes (median risk 1.4%), or grew during the most recent screening interval (median risk 7.9%), while nodules with smooth margins indicated lower risk (median risk 0.71%). Those with a smooth-margins nodule and no risk-increasing factors essentially reverted to their pre-screening risk at the next screen as r(x)1.01 (median risk 0.29%), as if they had screened negative. Overall, only the immediately prior screen result, and not earlier screens, predicted lung cancer risk (all p&gt;0.2). Exponents were similar for each interval and at each screen (all p&gt;0.07).

CONCLUSIONS: CT-negatives experienced reduced lung cancer risk over the next year, but reverted to their pre-screening risk at the next screen. CT-false-positives experienced substantially increased lung cancer detection at the next annual screen, with most risks exceeding 1%. These risk increases were explained by specific CT features including nodule size, location, margins, and growth.

Citation Format: Hilary A. Robbins, Christine D. Berg, Li C. Cheung, Anil K. Chaturvedi, Hormuzd A. Katki. Effect of screening CT results and features on lung cancer risk prediction within the National Lung Screening Trial [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 5291. doi:10.1158/1538-7445.AM2017-5291

A re-analysis of the prostate, lung, colorectal, and ovarian (PLCO) cancer screening trial accounting for ovarian cancer (OVCA) heterogeneity

5564

Background: A mortality benefit from screening for OVCA has not been demonstrated, but screening efficacy could differ for histologic subtypes. We re-analyzed PLCO evaluating whether OVCA detection and outcomes were affected by the heterogeneous biologic behavior of this disease. Methods: Type 2 tumors (moderately/poorly differentiated serous and adenocarcinoma) were compared to all other tumor (OT) types (low grade serous and endometrioid, clear cell, other, low malignancy potential) (LMP). We examined differences in the distribution of tumor types and stage by study arm and method of diagnosis [screen detected (SD) and interval detected (ID) (i.e. assigned to screening but diagnosed between screening tests)]. Stage distribution and survival were analyzed. Results: Among the entire PLCO population, 531 women were diagnosed with OVCA during the study; 282 (53%) in the screening arm and 249 (47%) in the usual care arm. Of the tumors able to be characterized (n=408; 77%), 74% (n=300) were Type 2 and 26% OT (n=108). In the screening arm, 70% of tumors diagnosed were Type 2 compared to 78% in usual care (p=0.07). Overall, survival was significantly better for OT tumors compared to Type 2 tumors (p<0.01) but there was no difference in survival by study arm for either tumor type separately (Type 2: p=0.50; OT: p=0.23). Within the screening arm, 30% of Type 2 tumors were SD compared to 54% of OT tumors (p=0.02) (see Table). Only 15% of Type 2 SD tumors were Stage I/II, compared to 82% of SD OT tumors (p<0.01). Stage at diagnosis was similar among Type 2 patients whether they were SD or ID (p=0.56) and there was no difference in survival (p=0.56). Conclusions: A significant difference in tumor types by study arm was not observed. However, within the screening arm, Type 2 tumors were less likely to be SD or Stage I/II compared to OT tumors. Survival for Type 2 tumors was similar regardless of method of diagnosis. [Table: see text]

Data sharing in clinical trials: An experience with two large cancer screening trials

Paul Pinsky of the US National Cancer Institute and colleagues describe the implementation and outcomes of web-based data sharing from the PLCO and NLST cancer screening trials.

Abstract IA18: Development and validation of risk models to select ever-smokers for CT lung-cancer screening

The US Preventive Services Task Force (USPSTF) recommends computed-tomography (CT) lung-cancer screening for ever-smokers ages 55-80 years who smoked at least 30 pack-years with no more than 15 years since quitting. However, selecting ever-smokers for screening using individualized lung-cancer risk calculations may be more effective and efficient than current USPSTF recommendations. We compare of modeled outcomes from risk-based CT lung-screening strategies versus USPSTF recommendations. We developed empirical risk models for lung-cancer incidence and death in the absence of CT screening using data on ever-smokers from the Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial (PLCO; 1993-2009) control group. Covariates included age, education, sex, race, smoking intensity/duration/quit-years, Body Mass Index, family history of lung-cancer, and self-reported emphysema. Models were validated in the chest radiography groups of the PLCO and the National Lung Screening Trial (NLST; 2002-2009), with additional validation of the death model in the National Health Interview Survey (NHIS; 1997-2001), a representative sample of the US. Models applied to US ever-smokers ages 50-80 (NHIS 2010-2012) to estimate outcomes of risk-based selection for annual CT lung-screening for 3 years, assuming screening for all ever-smokers yields the percent changes in lung-cancer detection and death observed in the NLST. Lung-cancer incidence and death risk models were well-calibrated in PLCO and NLST. The lung-cancer death model calibrated and discriminated well for US ever-smokers ages 50-80 (NHIS 1997-2001: Estimated/Observed=0.94, 95%CI=0.84-1.05; AUC=0.78, 95%CI=0.76-0.80). Under USPSTF recommendations, the models estimated 9.0 million US ever-smokers would qualify for lung-cancer screening and 46,488 (95%CI=43,924-49,053) lung-cancer deaths were estimated as screen-avertable over 5 years (estimated NNS=194, 95%CI=187-201). In contrast, risk-based selection screened the same number of ever-smokers (9.0 million) at highest 5-year lung-cancer risk (≥1.9%), was estimated to avert 20% more deaths (55,717; 95%CI=53,033-58,400) and was estimated to reduce the estimated NNS by 17% (NNS=162, 95%CI=157-166). Among a cohort of US ever-smokers age 50-80 years, application of a risk-based model for CT screening for lung cancer compared with a model based on USPSTF recommendations was estimated to be associated with a greater number of lung-cancer deaths prevented over 5 years along with a lower NNS to prevent 1 lung-cancer death.

Citation Format: Hormuzd A. Katki, Stephanie A. Kovalchik, Christine D. Berg, Li C. Cheung, Anil K. Chaturvedi. Development and validation of risk models to select ever-smokers for CT lung-cancer screening. [abstract]. In: Proceedings of the AACR Special Conference: Improving Cancer Risk Prediction for Prevention and Early Detection; Nov 16-19, 2016; Orlando, FL. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2017;26(5 Suppl):Abstract nr IA18.

Extended mortality results for ovarian cancer screening in the PLCO trial with median 15years follow-up

BACKGROUND

The Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening Trial originally reported no mortality benefit of ovarian cancer screening after a median of 12.4years of follow-up. The UKCTOCS screening trial failed to show a statistically significant mortality reduction in the primary analysis but reported an apparent increased mortality benefit in trial years 7-14 compared to 0-7. Here we report an updated analysis of PLCO with extended mortality follow-up.

METHODS

Participants were randomized from 1993 to 2001 at ten U.S. centers to an intervention or usual care arm. Intervention arm women were screened for ovarian cancer with annual trans-vaginal ultrasound (TVU) (4years) and CA-125 (6years), with a fixed cutoff at 35U/mL for CA-125. The original follow-up period was for up to 13years (median follow-up 12.4years); in this analysis follow-up for mortality was extended by up to 6years.

RESULTS

39,105 (intervention) and 39,111 (usual care) women were randomized, of which 34,253 and 34,304, respectively, had at least one ovary at baseline. Median follow-up was 14.7years in each arm and maximum follow-up 19.2years in each arm. A total of 187 (intervention) and 176 (usual care) deaths from ovarian cancer were observed, for a risk-ratio of 1.06 (95% CI: 0.87-1.30). Risk-ratios were similar for study years 0-7 (RR=1.04), 7-14 (RR=1.06) and 14+ (RR=1.09). The risk ratio for all-cause mortality was 1.01 (95% CI: 0.97-1.05). Ovarian cancer specific survival was not significantly different across trial arms (p=0.16).

CONCLUSION

Extended follow-up of PLCO indicated no mortality benefit from screening for ovarian cancer with CA-125 and TVU.

Breast Cancer Risk From Modifiable and Nonmodifiable Risk Factors Among White Women in the United States

IMPORTANCE

An improved model for risk stratification can be useful for guiding public health strategies of breast cancer prevention.

OBJECTIVE

To evaluate combined risk stratification utility of common low penetrant single nucleotide polymorphisms (SNPs) and epidemiologic risk factors.

DESIGN, SETTING, AND PARTICIPANTS

Using a total of 17 171 cases and 19 862 controls sampled from the Breast and Prostate Cancer Cohort Consortium (BPC3) and 5879 women participating in the 2010 National Health Interview Survey, a model for predicting absolute risk of breast cancer was developed combining information on individual level data on epidemiologic risk factors and 24 genotyped SNPs from prospective cohort studies, published estimate of odds ratios for 68 additional SNPs, population incidence rate from the National Cancer Institute-Surveillance, Epidemiology, and End Results Program cancer registry and data on risk factor distribution from nationally representative health survey. The model is used to project the distribution of absolute risk for the population of white women in the United States after adjustment for competing cause of mortality.

EXPOSURES

Single nucleotide polymorphisms, family history, anthropometric factors, menstrual and/or reproductive factors, and lifestyle factors.

MAIN OUTCOMES AND MEASURES

Degree of stratification of absolute risk owing to nonmodifiable (SNPs, family history, height, and some components of menstrual and/or reproductive history) and modifiable factors (body mass index [BMI; calculated as weight in kilograms divided by height in meters squared], menopausal hormone therapy [MHT], alcohol, and smoking).

RESULTS

The average absolute risk for a 30-year-old white woman in the United States developing invasive breast cancer by age 80 years is 11.3%. A model that includes all risk factors provided a range of average absolute risk from 4.4% to 23.5% for women in the bottom and top deciles of the risk distribution, respectively. For women who were at the lowest and highest deciles of nonmodifiable risks, the 5th and 95th percentile range of the risk distribution associated with 4 modifiable factors was 2.9% to 5.0% and 15.5% to 25.0%, respectively. For women in the highest decile of risk owing to nonmodifiable factors, those who had low BMI, did not drink or smoke, and did not use MHT had risks comparable to an average woman in the general population.

CONCLUSIONS AND RELEVANCE

This model for absolute risk of breast cancer including SNPs can provide stratification for the population of white women in the United States. The model can also identify subsets of the population at an elevated risk that would benefit most from risk-reduction strategies based on altering modifiable factors. The effectiveness of this model for individual risk communication needs further investigation.

The Prostate, Lung, Colorectal and Ovarian Cancer (PLCO) Screening Trial Pathology Tissue Resource

BACKGROUND

Pathology tissue specimens with associated epidemiologic and clinical data are valuable for cancer research. The Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening Trial undertook a large-scale effort to create a public resource of pathology tissues from PLCO participants who developed a cancer during the trial.

METHODS

Formalin-fixed paraffin-embedded tissue blocks were obtained from pathology laboratories on a loan basis for central processing of tissue microarrays, with additional free-standing tissue cores collected for nucleic acid extraction.

RESULTS

Pathology tissue specimens were obtained for prostate cancer (n = 1,052), lung cancer (n = 434), colorectal cancer (n = 675) and adenoma (n = 658), ovarian cancer and borderline tumors (n = 212), breast cancer (n = 870), and bladder cancer (n = 204). The process of creating this resource was complex, involving multidisciplinary teams with expertise in pathology, epidemiology, information technology, project management, and specialized laboratories.

CONCLUSIONS

Creating the PLCO tissue resource required a multistep process, including obtaining medical records and contacting pathology departments where pathology materials were stored after obtaining necessary patient consent and authorization. The potential to link tissue biomarkers to prospectively collected epidemiologic information, screening and clinical data, and matched blood or buccal samples offers valuable opportunities to study etiologic heterogeneity, mechanisms of carcinogenesis, and biomarkers for early detection and prognosis.

IMPACT

The methods and protocols developed for this effort, and the detailed description of this resource provided here, will be useful for those seeking to use PLCO pathology tissue specimens for their research and may also inform future tissue collection efforts in other settings. Cancer Epidemiol Biomarkers Prev; 25(12); 1635-42. ©2016 AACR.

Development and Validation of Risk Models to Select Ever-Smokers for CT Lung Cancer Screening

IMPORTANCE

The US Preventive Services Task Force (USPSTF) recommends computed tomography (CT) lung cancer screening for ever-smokers aged 55 to 80 years who have smoked at least 30 pack-years with no more than 15 years since quitting. However, selecting ever-smokers for screening using individualized lung cancer risk calculations may be more effective and efficient than current USPSTF recommendations.

OBJECTIVE

Comparison of modeled outcomes from risk-based CT lung-screening strategies vs USPSTF recommendations.

DESIGN, SETTING, AND PARTICIPANTS

Empirical risk models for lung cancer incidence and death in the absence of CT screening using data on ever-smokers from the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial (PLCO; 1993-2009) control group. Covariates included age; education; sex; race; smoking intensity, duration, and quit-years; body mass index; family history of lung cancer; and self-reported emphysema. Model validation in the chest radiography groups of the PLCO and the National Lung Screening Trial (NLST; 2002-2009), with additional validation of the death model in the National Health Interview Survey (NHIS; 1997-2001), a representative sample of the United States. Models were applied to US ever-smokers aged 50 to 80 years (NHIS 2010-2012) to estimate outcomes of risk-based selection for CT lung screening, assuming screening for all ever-smokers, yield the percent changes in lung cancer detection and death observed in the NLST.

EXPOSURES

Annual CT lung screening for 3 years beginning at age 50 years.

MAIN OUTCOMES AND MEASURES

For model validity: calibration (number of model-predicted cases divided by number of observed cases [estimated/observed]) and discrimination (area under curve [AUC]). For modeled screening outcomes: estimated number of screen-avertable lung cancer deaths and estimated screening effectiveness (number needed to screen [NNS] to prevent 1 lung cancer death).

RESULTS

Lung cancer incidence and death risk models were well calibrated in PLCO and NLST. The lung cancer death model calibrated and discriminated well for US ever-smokers aged 50 to 80 years (NHIS 1997-2001: estimated/observed = 0.94 [95%CI, 0.84-1.05]; AUC, 0.78 [95%CI, 0.76-0.80]). Under USPSTF recommendations, the models estimated 9.0 million US ever-smokers would qualify for lung cancer screening and 46,488 (95% CI, 43,924-49,053) lung cancer deaths were estimated as screen-avertable over 5 years (estimated NNS, 194 [95% CI, 187-201]). In contrast, risk-based selection screening of the same number of ever-smokers (9.0 million) at highest 5-year lung cancer risk (≥1.9%) was estimated to avert 20% more deaths (55,717 [95% CI, 53,033-58,400]) and was estimated to reduce the estimated NNS by 17% (NNS, 162 [95% CI, 157-166]).

CONCLUSIONS AND RELEVANCE

Among a cohort of US ever-smokers aged 50 to 80 years, application of a risk-based model for CT screening for lung cancer compared with a model based on USPSTF recommendations was estimated to be associated with a greater number of lung cancer deaths prevented over 5 years, along with a lower NNS to prevent 1 lung cancer death.

Lung cancer screening: promise and pitfalls

The results of the National Lung Screening Trial (NLST) have provided the medical community and American public with considerable optimism about the potential to reduce lung cancer mortality with imaging-based screening. Designed as a randomized trial, the NLST has provided the first evidence of screening benefit by showing a 20% reduction in lung cancer mortality and a 6.7% reduction in all-cause mortality with low dose helical computed tomography (LDCT) screening relative to chest X-ray. The major harms of LDCT screening include the potential for radiation-induced carcinogenesis; high false-positivity rates in individuals without lung cancer, and overdiagnosis. Following the results of the NLST, the National Comprehensive Cancer Network (NCCN) published the first of multiple lung cancer screening guidelines under development by major medical organizations. These recommendations amalgamated screening cohorts, practices, interpretations, and diagnostic follow-up based on the NLST and other published studies to provide guidance for the implementation of LDCT screening. There are major areas of opportunity to optimize implementation. These include standardizing practices in the screening setting, optimizing risk profiles for screening and for managing diagnostic evaluation in individuals with indeterminate nodules, developing interdisciplinary screening programs in conjunction with smoking cessation, and approaching all stakeholders systematically to ensure the broadest education and dissemination of screening benefits relative to risks. The incorporation of validated biomarkers of risk and preclinical lung cancer can substantially enhance the effectiveness screening programs.

ABO blood group alleles and prostate cancer risk: Results from the breast and prostate cancer cohort consortium (BPC3)

BACKGROUND

ABO blood group has been associated with risk of cancers of the pancreas, stomach, ovary, kidney, and skin, but has not been evaluated in relation to risk of aggressive prostate cancer.

METHODS

We used three single nucleotide polymorphisms (SNPs) (rs8176746, rs505922, and rs8176704) to determine ABO genotype in 2,774 aggressive prostate cancer cases and 4,443 controls from the Breast and Prostate Cancer Cohort Consortium (BPC3). Unconditional logistic regression was used to calculate age and study-adjusted odds ratios and 95% confidence intervals for the association between blood type, genotype, and risk of aggressive prostate cancer (Gleason score ≥8 or locally advanced/metastatic disease (stage T3/T4/N1/M1).

RESULTS

We found no association between ABO blood type and risk of aggressive prostate cancer (Type A: OR = 0.97, 95%CI = 0.87-1.08; Type B: OR = 0.92, 95%CI =n0.77-1.09; Type AB: OR = 1.25, 95%CI = 0.98-1.59, compared to Type O, respectively). Similarly, there was no association between "dose" of A or B alleles and aggressive prostate cancer risk.

CONCLUSIONS

ABO blood type was not associated with risk of aggressive prostate cancer.

Identifying post-menopausal women at elevated risk for epithelial ovarian cancer

OBJECTIVE

We developed and validated a hybrid risk classifier combining serum markers and epidemiologic risk factors to identify post-menopausal women at elevated risk for invasive fallopian tube, primary peritoneal, and ovarian epithelial carcinoma.

METHODS

To select epidemiologic risk factors for use in the classifier, Cox proportional hazards analyses were conducted using 74,786 Women's Health Initiative (WHI) Observational Study (OS) participants. To construct a combination classifier, 210 WHI OS cases and 536 matched controls with serum marker measurements were analyzed; validation employed 143 cases and 725 matched controls from the WHI Clinical Trial (CT) with similar data.

RESULTS

Analyses identified a combination risk classifier composed of two elevated-risk groups: 1) women with CA125 or HE4 exceeding a 98% specificity threshold; and 2) women with intact fallopian tubes, prior use of menopausal hormone therapy for at least two years, and either a first degree relative with breast or ovarian cancer or a personal history of breast cancer. In the WHI OS population, it classified 13% of women as elevated risk, identifying 30% of ovarian cancers diagnosed up to 7.8years post-enrollment (Hazard Ratio [HR]=2.6, p<0.001). In the WHI CT validation population, it classified 8% of women as elevated risk, identifying 31% of cancers diagnosed within 7years of enrollment (HR=4.6, p<0.001).

CONCLUSION

CA125 and HE4 contributed significantly to a risk prediction classifier combining serum markers with epidemiologic risk factors. The hybrid risk classifier may be useful to identify post-menopausal women who would benefit from timely surgical intervention to prevent epithelial ovarian cancer.

Genome-wide association analysis of more than 120,000 individuals identifies 15 new susceptibility loci for breast cancer

Genome-wide association studies (GWAS) and large-scale replication studies have identified common variants in 79 loci associated with breast cancer, explaining ∼14% of the familial risk of the disease. To identify new susceptibility loci, we performed a meta-analysis of 11 GWAS, comprising 15,748 breast cancer cases and 18,084 controls together with 46,785 cases and 42,892 controls from 41 studies genotyped on a 211,155-marker custom array (iCOGS). Analyses were restricted to women of European ancestry. We generated genotypes for more than 11 million SNPs by imputation using the 1000 Genomes Project reference panel, and we identified 15 new loci associated with breast cancer at P < 5 × 10(-8). Combining association analysis with ChIP-seq chromatin binding data in mammary cell lines and ChIA-PET chromatin interaction data from ENCODE, we identified likely target genes in two regions: SETBP1 at 18q12.3 and RNF115 and PDZK1 at 1q21.1. One association appears to be driven by an amino acid substitution encoded in EXO1.

Leveraging biospecimen resources for discovery or validation of markers for early cancer detection

Validation of early detection cancer biomarkers has proven to be disappointing when initial promising claims have often not been reproducible in diagnostic samples or did not extend to prediagnostic samples. The previously reported lack of rigorous internal validity (systematic differences between compared groups) and external validity (lack of generalizability beyond compared groups) may be effectively addressed by utilizing blood specimens and data collected within well-conducted cohort studies. Cohort studies with prediagnostic specimens (eg, blood specimens collected prior to development of clinical symptoms) and clinical data have recently been used to assess the validity of some early detection biomarkers. With this background, the Division of Cancer Control and Population Sciences (DCCPS) and the Division of Cancer Prevention (DCP) of the National Cancer Institute (NCI) held a joint workshop in August 2013. The goal was to advance early detection cancer research by considering how the infrastructure of cohort studies that already exist or are being developed might be leveraged to include appropriate blood specimens, including prediagnostic specimens, ideally collected at periodic intervals, along with clinical data about symptom status and cancer diagnosis. Three overarching recommendations emerged from the discussions: 1) facilitate sharing of existing specimens and data, 2) encourage collaboration among scientists developing biomarkers and those conducting observational cohort studies or managing healthcare systems with cohorts followed over time, and 3) conduct pilot projects that identify and address key logistic and feasibility issues regarding how appropriate specimens and clinical data might be collected at reasonable effort and cost within existing or future cohorts.

Vitamin D-associated genetic variation and risk of breast cancer in the breast and prostate cancer cohort consortium (BPC3)

BACKGROUND

Two recent genome-wide association studies (GWAS) identified SNPs in or near four genes related to circulating 25-hydroxyvitamin D [25(OH)D] concentration. To examine the hypothesized inverse relationship between vitamin D status and breast cancer, we studied the associations between SNPs in these genes and breast cancer risk in a large pooled study of 9,456 cases and 10,816 controls from six cohorts.

METHODS

SNP markers localized to each of four genes (GC, CYP24A1, CYP2R1, and DHCR7) previously associated with 25(OH)D were genotyped and examined both individually and as a 4-SNP polygenic score. Logistic regression was used to estimate the associations between the genetic variants and risk of breast cancer.

RESULTS

We found no association between any of the four SNPs or their polygenic score and breast cancer risk.

CONCLUSIONS

Our findings do not support an association between vitamin D status, as reflected by 25(OH)D-related genotypes, and breast cancer risk.

IMPACT

These findings may contribute to future meta-analyses and scientific review articles, and provide new data about the association between vitamin D-related genes and breast cancer. Cancer Epidemiol Biomarkers Prev; 24(3); 627-30. ©2014 AACR.

Imputation and subset-based association analysis across different cancer types identifies multiple independent risk loci in the TERT-CLPTM1L region on chromosome 5p15.33

Genome-wide association studies (GWAS) have mapped risk alleles for at least 10 distinct cancers to a small region of 63 000 bp on chromosome 5p15.33. This region harbors the TERT and CLPTM1L genes; the former encodes the catalytic subunit of telomerase reverse transcriptase and the latter may play a role in apoptosis. To investigate further the genetic architecture of common susceptibility alleles in this region, we conducted an agnostic subset-based meta-analysis (association analysis based on subsets) across six distinct cancers in 34 248 cases and 45 036 controls. Based on sequential conditional analysis, we identified as many as six independent risk loci marked by common single-nucleotide polymorphisms: five in the TERT gene (Region 1: rs7726159, P = 2.10 × 10(-39); Region 3: rs2853677, P = 3.30 × 10(-36) and PConditional = 2.36 × 10(-8); Region 4: rs2736098, P = 3.87 × 10(-12) and PConditional = 5.19 × 10(-6), Region 5: rs13172201, P = 0.041 and PConditional = 2.04 × 10(-6); and Region 6: rs10069690, P = 7.49 × 10(-15) and PConditional = 5.35 × 10(-7)) and one in the neighboring CLPTM1L gene (Region 2: rs451360; P = 1.90 × 10(-18) and PConditional = 7.06 × 10(-16)). Between three and five cancers mapped to each independent locus with both risk-enhancing and protective effects. Allele-specific effects on DNA methylation were seen for a subset of risk loci, indicating that methylation and subsequent effects on gene expression may contribute to the biology of risk variants on 5p15.33. Our results provide strong support for extensive pleiotropy across this region of 5p15.33, to an extent not previously observed in other cancer susceptibility loci.

Evaluation of the lung cancer risks at which to screen ever- and never-smokers: screening rules applied to the PLCO and NLST cohorts

BACKGROUND

Lung cancer risks at which individuals should be screened with computed tomography (CT) for lung cancer are undecided. This study's objectives are to identify a risk threshold for selecting individuals for screening, to compare its efficiency with the U.S. Preventive Services Task Force (USPSTF) criteria for identifying screenees, and to determine whether never-smokers should be screened. Lung cancer risks are compared between smokers aged 55-64 and ≥ 65-80 y.

METHODS AND FINDINGS

Applying the PLCO(m2012) model, a model based on 6-y lung cancer incidence, we identified the risk threshold above which National Lung Screening Trial (NLST, n = 53,452) CT arm lung cancer mortality rates were consistently lower than rates in the chest X-ray (CXR) arm. We evaluated the USPSTF and PLCO(m2012) risk criteria in intervention arm (CXR) smokers (n = 37,327) of the Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial (PLCO). The numbers of smokers selected for screening, and the sensitivities, specificities, and positive predictive values (PPVs) for identifying lung cancers were assessed. A modified model (PLCOall2014) evaluated risks in never-smokers. At PLCO(m2012) risk ≥ 0.0151, the 65th percentile of risk, the NLST CT arm mortality rates are consistently below the CXR arm's rates. The number needed to screen to prevent one lung cancer death in the 65th to 100th percentile risk group is 255 (95% CI 143 to 1,184), and in the 30th to <65th percentile risk group is 963 (95% CI 291 to -754); the number needed to screen could not be estimated in the <30th percentile risk group because of absence of lung cancer deaths. When applied to PLCO intervention arm smokers, compared to the USPSTF criteria, the PLCO(m2012) risk ≥ 0.0151 threshold selected 8.8% fewer individuals for screening (p<0.001) but identified 12.4% more lung cancers (sensitivity 80.1% [95% CI 76.8%-83.0%] versus 71.2% [95% CI 67.6%-74.6%], p<0.001), had fewer false-positives (specificity 66.2% [95% CI 65.7%-66.7%] versus 62.7% [95% CI 62.2%-63.1%], p<0.001), and had higher PPV (4.2% [95% CI 3.9%-4.6%] versus 3.4% [95% CI 3.1%-3.7%], p<0.001). In total, 26% of individuals selected for screening based on USPSTF criteria had risks below the threshold PLCO(m2012) risk ≥ 0.0151. Of PLCO former smokers with quit time >15 y, 8.5% had PLCO(m2012) risk ≥ 0.0151. None of 65,711 PLCO never-smokers had PLCO(m2012) risk ≥ 0.0151. Risks and lung cancers were significantly greater in PLCO smokers aged ≥ 65-80 y than in those aged 55-64 y. This study omitted cost-effectiveness analysis.

CONCLUSIONS

The USPSTF criteria for CT screening include some low-risk individuals and exclude some high-risk individuals. Use of the PLCO(m2012) risk ≥ 0.0151 criterion can improve screening efficiency. Currently, never-smokers should not be screened. Smokers aged ≥ 65-80 y are a high-risk group who may benefit from screening. Please see later in the article for the Editors' Summary.