Cox, Angela

1512 Background: Tobacco smoking profoundly impacts LC risk; however, data are limited as to what extent SC prior to diagnosis impacts LC overall survival (OS) and lung cancer specific survival (LCSS). LC screening offers a possible teachable moment, but there is uncertainty of SC benefits after a lifetime of smoking. We use the ILCCO database to answer if SC prior to LC dx is associated with better OS and LCSS, considering time since smoking cessation (TSSC). Methods: Using individual data, analysis was performed on 17 ILCCO studies with available TSSC to estimate survival using univariable analysis and models of stage-adjusted and cumulative smoking-adjusted multivariable analysis. Adjusted Hazard Ratios (aHR) from Cox models, cubic spline smooth curves and Kaplan-Meier curves were created. Sensitivity analysis was performed for TSSC and LCSS on 13 studies. Results: Of 34649 patients, 14322 (41%) were current smokers 14273 (41%) ex-smokers and 6054 (18%) never smokers at diagnosis. We confirmed that ex-smokers (aHR 0.88 CI 0.86-0.91) and never smokers (aHR 0.76 CI 0.73-0.8) improved OS compared to current smokers. Amongst ex-smokers, < 2y TSSC (aHR 0.88 CI 0.82-0.94), 2-5y TSSC (aHR 0.83 CI 0.77-0.90) and > 5y TSSC (aHR 0.8 CI 0.76-0.84) had improved OS compared to CS. Sensitivity analysis showed a trend towards improved LCSS survival for < 2y TSSC (aHR 0.95 CI 0.86-1.05) and 2-5y TSSC (aHR 0.93 CI 0.83-1.04), whereas > 5y TSSC significantly improved LCSS by 15% (aHR 0.85 CI 0.78-0.92). To mimic the LC screening participants, in analysis of > 30 pack-years individuals, associations were strikingly strong: < 2y TSSC had improved OS by 14% (aHR 0.86 CI 0.80-0.93); 2-5y TSSC by 17% (aHR 0.83 CI 0.76-0.90); and > 5 TSSC by 22% (aHR 0.78 CI 0.74-0.83), compared to current smokers; for < 30 packs-years, a trend towards better OS was observed for < 2y TSSC (aHR 0.95 CI 0.92-1.02) and 2-5y TSSC (aHR 0.86 CI 0.74-1.01), whereas > 5y TSSC improved OS by 23% (aHR 0.77 CI 0.72-0.82). Conclusions: Among ex-smokers, the risk of overall death was reduced by 12% on < 2y TSSC, 17% on 2-5y TSSC and 20% > 5y TSSC, whereas for LCSS, the benefit was significant only for > 5y TSCC, compared to current smokers at time of diagnosis. Here we demonstrate that convincing screening participants to quit smoking at any point of their trajectory, even just prior to dx such as < 2y TSSC, improved OS, and LCSS benefit was present beyond 5y of quitting. These relationships are independent of pack-years, age, across all stages and other prognostic variables.

Abstract We evaluated the joint associations between a new 313-variant PRS (PRS313) and questionnaire-based breast cancer risk factors for women of European ancestry, using 72 284 cases and 80 354 controls from the Breast Cancer Association Consortium. Interactions were evaluated using standard logistic regression and a newly developed case-only method for breast cancer risk overall and by estrogen receptor status. After accounting for multiple testing, we did not find evidence that per-standard deviation PRS313 odds ratio differed across strata defined by individual risk factors. Goodness-of-fit tests did not reject the assumption of a multiplicative model between PRS313 and each risk factor. Variation in projected absolute lifetime risk of breast cancer associated with classical risk factors was greater for women with higher genetic risk (PRS313 and family history) and, on average, 17.5% higher in the highest vs lowest deciles of genetic risk. These findings have implications for risk prevention for women at increased risk of breast cancer.

AbstractPrevious transcriptome‐wide association studies (TWAS) have identified breast cancer risk genes by integrating data from expression quantitative loci and genome‐wide association studies (GWAS), but analyses of breast cancer subtype‐specific associations have been limited. In this study, we conducted a TWAS using gene expression data from GTEx and summary statistics from the hitherto largest GWAS meta‐analysis conducted for breast cancer overall, and by estrogen receptor subtypes (ER+ and ER−). We further compared associations with ER+ and ER− subtypes, using a case‐only TWAS approach. We also conducted multigene conditional analyses in regions with multiple TWAS associations. Two genes, STXBP4 and HIST2H2BA, were specifically associated with ER+ but not with ER– breast cancer. We further identified 30 TWAS‐significant genes associated with overall breast cancer risk, including four that were not identified in previous studies. Conditional analyses identified single independent breast‐cancer gene in three of six regions harboring multiple TWAS‐significant genes. Our study provides new information on breast cancer genetics and biology, particularly about genomic differences between ER+ and ER− breast cancer.

AbstractFemale Hodgkin lymphoma (HL) patients treated with chest radiotherapy (RT) have a very high risk of breast cancer. The contribution of genetic factors to this risk is unclear. We therefore examined 211 155 germline single-nucleotide polymorphisms (SNPs) for gene-radiation interaction on breast cancer risk in a case-only analysis including 327 breast cancer patients after chest RT for HL and 4671 first primary breast cancer patients. Nine SNPs showed statistically significant interaction with RT on breast cancer risk (false discovery rate, &lt;20%), of which 1 SNP in the PVT1 oncogene attained the Bonferroni threshold for statistical significance. A polygenic risk score (PRS) composed of these SNPs (RT-interaction-PRS) and a previously published breast cancer PRS (BC-PRS) derived in the general population were evaluated in a case-control analysis comprising the 327 chest-irradiated HL patients with breast cancer and 491 chest-irradiated HL patients without breast cancer. Patients in the highest tertile of the RT-interaction-PRS had a 1.6-fold higher breast cancer risk than those in the lowest tertile. Remarkably, we observed a fourfold increased RT-induced breast cancer risk in the highest compared with the lowest decile of the BC-PRS. On a continuous scale, breast cancer risk increased 1.4-fold per standard deviation of the BC-PRS, similar to the effect size found in the general population. This study demonstrates that genetic factors influence breast cancer risk after chest RT for HL. Given the high absolute breast cancer risk in radiation-exposed women, these results can have important implications for the management of current HL survivors and future patients.

Abstract Introduction: Genome-wide association studies over the past 10 years have identified over 100 common variants associated with breast cancer risk. The association of these polymorphisms varies according to estrogen receptor status of the tumor, with some associated primarily with ER+ or ER- disease, or both subtypes. However, ER expression in breast cancer cells is continuous. The aim of this study was to establish whether the association between polygenic risk scores and ER expression in breast cancer is continuous in nature. Methods: Data were taken from the international Breast Cancer Association Consortium (BCAC). Germline genotype data for 75 SNPs previously reported to be associated with breast cancer were available for 4,999 breast cancer cases for which ER expression data were also available. Automated ER expression on a continuous scale was measured using two image analysis algorithms: Astrogrid and Ariol. A third form of ER expression score, the manual Allred ordinal, was used. Genotype data were used to calculate polygenic risk scores using the equation: PRS = ∑ βx where β is the log-odds of the odds ratio (OR) of breast cancer associated with that single nucleotide polymorphism (SNP), and x is the number of variant alleles at a SNP locus. Five different PRSs were calculated: i) SNPs associated with both ER+ and ER- disease (PRS-A, n= 74) ii) SNPs associated with ER+ disease at P&lt;0.01 (PRS-B, n= 65) iii) SNPs associated with ER- disease at P&lt;0.01 (PRS-C, n= 43) iv) SNPs associated with ER+ but not ER- disease (PRS-D, n= 30) v) SNPs associated with ER- but not ER+ disease (PRS-E, n = 8) The ER-specific weights for the PRS were taken from previous OR estimates. Linear regression models adjusted for study were used to test for a relationship between the five PRSs and ER expression measured on the Ariol, Astrogrid and Allred scales. Results: There was a strong positive correlation found between PRS-B against Astrogrid (β=0.0176, P=4.06x10-4), a weak negative correlation found with PRS-C (β=-0.012, P=0.019) and a weak positive correlation for PRS-A (β=0.012, P=0.0144). PRS-D and E showed far stronger association with Astrogrid than PRS-B and C, respectively (β=0.0186 and -0.017, P=1.97x10-4 and 8.1x10-4). There was a qualitatively similar association when the PRSs were correlated with Ariol and Allred, apart from the stronger correlations with PRS-B (β=91.2 and 0.0853, P=5.52x10-4 and 1.33x10-3). Conclusion: We have shown a robust association between PRS and ER expression in the directions we predicted. This suggests that the continuous variation in ER expression is biologically plausible and linked to genetic polymorphism. The weakening of association with PRS-A and the stronger associations with PRS-D and E show that having SNPs that are associated with both ER subtypes in the same PRS weakens significance, and suggests that subtype-specific PRSs may be improved on by using SNPs exclusive to the respective subtype. Citation Format: Anamay H. Shetty, Raza Ali, Fiona Blows, Carlos Caldas, Nicholas Inard, Javier Benítez, Jenny Chang-Claude, Georgia Chenevix-Trench, Fergus Couch, Angela Cox, Peter Devilee, Douglas Easton, Montserrat Garcia-Closas, Arto Mannermaa, Heli Nevanlinna, Marjanka Schmidt, Paul Pharoah. Patient level polygenic risk scores and continuous estrogen receptor expression in breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 2211.

AbstractEpidemiological, biological, and molecular data suggest links between endometriosis and endometrial cancer, with recent epidemiological studies providing evidence for an association between a previous diagnosis of endometriosis and risk of endometrial cancer. We used genetic data as an alternative approach to investigate shared biological etiology of these two diseases. Genetic correlation analysis of summary level statistics from genomewide association studies (GWAS) using LD Score regression revealed moderate but significant genetic correlation (rg = 0.23, P = 9.3 × 10−3), and SNP effect concordance analysis provided evidence for significant SNP pleiotropy (P = 6.0 × 10−3) and concordance in effect direction (P = 2.0 × 10−3) between the two diseases. Cross‐disease GWAS meta‐analysis highlighted 13 distinct loci associated at P ≤ 10−5 with both endometriosis and endometrial cancer, with one locus (SNP rs2475335) located within PTPRD associated at a genomewide significant level (P = 4.9 × 10−8, OR = 1.11, 95% CI = 1.07–1.15). PTPRD acts in the STAT3 pathway, which has been implicated in both endometriosis and endometrial cancer. This study demonstrates the value of cross‐disease genetic analysis to support epidemiological observations and to identify biological pathways of relevance to multiple diseases.