bims-eabrec 2023-02-26 papers

Issue of 2023‒02‒26
three papers selected by
Rakesh Kumar
Swami Rama University

Comput Methods Programs Biomed. 2023 Feb 03. pii: S0169-2607(23)00068-8. [Epub ahead of print]231 107401

Mechanistic modeling of metastatic relapse in early breast cancer to investigate the biological impact of prognostic biomarkers.

Célestin Bigarré, François Bertucci, Pascal Finetti, Gaëtan Macgrogan, Xavier Muracciole, Sébastien Benzekry.

BACKGROUND AND OBJECTIVE: Estimating the risk of metastatic relapse is a major challenge to decide adjuvant treatment options in early-stage breast cancer (eBC). To date, distant metastasis-free survival (DMFS) analysis mainly relies on classical, agnostic, statistical models (e.g., Cox regression). Instead, we propose here to derive mechanistic models of DMFS.METHODS: The present series consisted of eBC patients who did not receive adjuvant systemic therapy from three datasets, composed respectively of 692 (Bergonié Institute), 591 (Paoli-Calmettes Institute, IPC), and 163 (Public Hospital Marseille, AP-HM) patients with routine clinical annotations. The last dataset also contained expression of three non-routine biomarkers. Our mechanistic model of DMFS relies on two mathematical parameters that represent growth (α) and dissemination (μ). We identified their population distributions using mixed-effects modeling. Critically, we propose a novel variable selection procedure allowing to: (i) identify the association of biological parameters with either α, μ or both, and (ii) generate an optimal candidate model for DMFS prediction.
RESULTS: We found that Ki67 and Thymidine Kinase-1 were associated with α, and nodal status and Plasminogen Activator Inhibitor-1 with μ. The predictive performances of the model were excellent in calibration but moderate in discrimination, with c-indices of 0.72 (95% CI [0.48, 0.95], AP-HM), 0.63 ([0.44, 0.83], Bergonié) and 0.60 (95% CI [0.54, 0.80], IPC).
CONCLUSIONS: Overall, we demonstrate that our novel method combining mechanistic and advanced statistical modeling is able to unravel the biological roles of clinicopathological parameters from DMFS data.

Keywords: Breast cancer; Mathematical model; Mechanistic modeling; Metastasis; Metastasis-free survival

DOI: https://doi.org/10.1016/j.cmpb.2023.107401

Br J Cancer. 2023 Feb 24.

Alternative methods to measure breast density in younger women.

Rachel Lloyd, Sarah Pirikahu, Jane Walter, Gemma Cadby, Ellie Darcey, Dilukshi Perera, Martha Hickey, Christobel Saunders, Karol Karnowski, David D Sampson, John Shepherd, Lothar Lilge, Jennifer Stone.

BACKGROUND: Breast density is a strong and potentially modifiable breast cancer risk factor. Almost everything we know about breast density has been derived from mammography, and therefore, very little is known about breast density in younger women aged <40. This study examines the acceptability and performance of two alternative breast density measures, Optical Breast Spectroscopy (OBS) and Dual X-ray Absorptiometry (DXA), in women aged 18-40.METHODS: Breast tissue composition (percent water, collagen, and lipid content) was measured in 539 women aged 18-40 using OBS. For a subset of 169 women, breast density was also measured via DXA (percent fibroglandular dense volume (%FGV), absolute dense volume (FGV), and non-dense volume (NFGV)). Acceptability of the measurement procedures was assessed using an adapted validated questionnaire. Performance was assessed by examining the correlation and agreement between the measures and their associations with known determinants of mammographic breast density.
RESULTS: Over 93% of participants deemed OBS and DXA to be acceptable. The correlation between OBS-%water + collagen and %FGV was 0.48. Age and BMI were inversely associated with OBS-%water + collagen and %FGV and positively associated with OBS-%lipid and NFGV.
CONCLUSIONS: OBS and DXA provide acceptable and viable alternative methods to measure breast density in younger women aged 18-40 years.

DOI: https://doi.org/10.1038/s41416-023-02201-5

Sci Transl Med. 2023 Feb 22. 15(684): eade1857

Obesity promotes breast epithelium DNA damage in women carrying a germline mutation in BRCA1 or BRCA2.

Obesity, defined as a body mass index (BMI) ≥ 30, is an established risk factor for breast cancer among women in the general population after menopause. Whether elevated BMI is a risk factor for women with a germline mutation in BRCA1 or BRCA2 is less clear because of inconsistent findings from epidemiological studies and a lack of mechanistic studies in this population. Here, we show that DNA damage in normal breast epithelia of women carrying a BRCA mutation is positively correlated with BMI and with biomarkers of metabolic dysfunction. In addition, RNA sequencing showed obesity-associated alterations to the breast adipose microenvironment of BRCA mutation carriers, including activation of estrogen biosynthesis, which affected neighboring breast epithelial cells. In breast tissue explants cultured from women carrying a BRCA mutation, we found that blockade of estrogen biosynthesis or estrogen receptor activity decreased DNA damage. Additional obesity-associated factors, including leptin and insulin, increased DNA damage in human BRCA heterozygous epithelial cells, and inhibiting the signaling of these factors with a leptin-neutralizing antibody or PI3K inhibitor, respectively, decreased DNA damage. Furthermore, we show that increased adiposity was associated with mammary gland DNA damage and increased penetrance of mammary tumors in Brca1+/- mice. Overall, our results provide mechanistic evidence in support of a link between elevated BMI and breast cancer development in BRCA mutation carriers. This suggests that maintaining a lower body weight or pharmacologically targeting estrogen or metabolic dysfunction may reduce the risk of breast cancer in this population.

DOI: https://doi.org/10.1126/scitranslmed.ade1857