Abstract P3-09-18: The association between genomic alterations and body mass index in patients with early breast cancer
Bibliographic record
Abstract
Abstract Background: High body mass index (BMI) is an established risk factor for developing breast cancer (BC), especially estrogen receptor (ER)-positive, and also has been associated with adverse survival. Still, patients with BC are currently treated independently of their BMI given limited understandings of the association between BC biology and patient adiposity. In this study, using retrospective data retrieved from two large BC studies, we aimed to identify genomic alterations of primary BC that are associated with BMI in the most common histological BC subtype - invasive carcinoma of no special type (NST). Patients, Data and Methods: Clinicopathological and genomic alteration data were retrieved from two study cohorts: METABRIC (Pereira et al. 2016) and ICGC (Nik-Zainal et al. 2016), with BMI recorded at the time of diagnosis and represented as either a continuous variable or a categorical variable of three categories - lean, overweight and obese. Stratification according to ER and HER2 status resulted in two focused subgroups: NST ER+/HER2- (n=392) and NST ER-/HER2- (n=152). Mutations classified as oncogenic using a set of predefined criteria were used to determine gene-level mutation status. Copy number alteration (CNA) calls were distinguished into three event types: amplification, hemizygous deletion and homozygous deletion. We used multivariable Firth’s logistic regression models with the presence of a genomic alteration as the response variable, BMI as the predicting variable of interest, and data cohort (METABRIC vs ICGC), age group (≤50 vs >50) and tumor grade (I & II vs III) as covariates, to assess the associations between BMI and recurrent gene-level genomic alterations, including gene mutations and CNAs. In a similar manner, we performed multivariable linear regression analysis, adjusting for age and tumor grade, to evaluate the associations of BMI with mutational signatures (MS) and tumor mutational burden in the ICGC NST subsets where these data are available. Results: Considering BMI as a categorical variable, we observed in the NST ER+/HER2- subgroup that PIK3CA was significantly less frequently mutated in obese compared to lean patients (33% vs 46%, odds ratio (OR) = 0.57 (95% confidence interval = (0.33, 0.97)), p = .039), while PTEN and TBX3 showed an increased frequency in overweight (6% vs 1%, OR = 4.14 (1.1, 22.34), p = .034) and obese (8% vs 1%, OR = 7.41 (1.82, 70.65), p = .008) patients, respectively. Regression analyses with BMI as a continuous variable revealed an increased prevalence of mutations in CDH1 and TBX3 genes as BMI increases by 1kg/m2 (OR = 1.14 (1.05, 1.24), p = .002, and OR = 1.13 (1.04, 1.22), p = .005, respectively) in patients with NST ER+/HER2- BC. No associations between BMI and oncogenic mutations was observed in the NST ER-/HER2- subgroup. Interrogation of gene-level CNAs in both subgroups demonstrated differences according to BMI in the prevalence of CNAs affecting a number of genes, many of which are known or have been presented with evidence to be involved in regulation of or regulated by hallmark pathways of BC, such as the MAPK/ERK, JAK/STAT and Wnt/β-catenin signaling pathways. We report a strong positive association between the single-base substitution signature 1 (SBS1), an age-correlated MS, and both continuous (coefficient (coef) = 18.3 (7.7, 28.9), p < .001) and categorical BMI (obese vs lean, coef = 336.3 (187.9, 484.8), p < .001) in the ICGC NST ER+/HER2- subgroup. Conclusion: This exploratory retrospective study suggests that the genomic profiles of primary BC may differ according to BMI. Clinical implications of these differences, especially the decreased prevalence of PIK3CA mutations in obese patients in the context of alpelisib, warrant further investigation. These results however indicate that patient adiposity should be taken into account in the era of personalized medicine. Citation Format: Ha-Linh Nguyen, Tatjana Geukens, Marion Maetens, Karen Van Baelen, Maxim De Schepper, Sophia Leduc, Edoardo Isnaldi, Sam Aparicio, Ake Borg, Jane Brock, Annegien Broeks, Carlos Caldas, Andrew Green, Hazem Khout, Eyfjörð Jórunn, Stian Knappskog, Savitri Krishnamurthy, Sunil Lakhani, Anita Langerod, John WM Martens, Leigh Murphy, Serena Nik-Zainal, Colin Purdie, Emad Rakha, Andrea Richardson, Anne Salomon, Peter Simpson, Christos Sotiriou, Paul Span, Benita Kiat-Tee Tan, Alastair Thompson, Stefania Tommasi, Marc Van de Vijver, Steven Van Laere, Alain Viari, Giuseppe Floris, Elia Biganzoli, François Richard, Christine Desmedt. The association between genomic alterations and body mass index in patients with early breast cancer [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr P3-09-18.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".