Facing Adiposity in personalizing Treatment of Breast Cancer Patients

Project Information

FAT-BC

Grant agreement ID: 101003153

DOI

10.3030/101003153

EC signature date 22 February 2021

Start date 1 March 2021

End date 31 August 2026

Funded under

• EXCELLENT SCIENCE - European Research Council (ERC)

Total cost

€ 1 998 620,00

EU contribution

€ 1 998 620,00

Coordinated by

KATHOLIEKE UNIVERSITEIT LEUVEN
Belgium

Periodic Reporting for period 3 - FAT-BC (Facing Adiposity in personalizing Treatment of Breast Cancer Patients)

Reporting period: 2024-03-01 to 2025-08-31

Summary of the context and overall objectives of the project

One out of 8 women develops breast cancer (BC) in her lifetime and 1 out of 2 is overweight or obese in industrialized countries. While heavier women have an increased risk of developing BC and heavier BC patients present with worse disease characteristics, BC is so far still treated regardless of patient adiposity because of the limited knowledge accumulated so far.
Our project, called FAT-BC, is articulated around 4 main FATaxes in order to better understand the connection between adiposity and BC. First, within FATomics, we made important steps to elucidate molecular differences according to body mass index (BMI) by the reanalysis of large publicly available early breast cancer databases. We show that the landscape of somatic driver mutations found in the tumour differs according to BMI, with the clinically actionable PIK3CA mutation for example being less frequent in obese patients with ER+/HER2- breast cancer. We also show that somatic mutations found in breast tumours of obese patients more often arise from age-related mutational signatures, highlighting how adiposity might induce tumour initiation in the breast tissue through processes mimicking the effect of aging. When looking at gene expression profiles then, bulk RNA sequencing data, we already showed consistently higher expression of pro-inflammatory signatures in obese as compared to lean patients. Secondly, in FATlas, we are complementing these findings at the single cell level by creating a comprehensive atlas of BC according to various patient and mammary adiposity measures for the two most common BC histological subtypes. Within the tumor microenvironment, special attention will be given to the mammary fat cells, the adipocytes, and how these help tumor cells to grow and affect treatment efficacy. Thirdly, thanks to FATshare, the data collected in FATlas will be shared with the scientific community through a webtool and data-platform. Finally, using samples from a prospective in-house clinical FATrial, we will investigate whether adiposity-associated features are associated with anti-proliferative response to exercise and endocrine therapy.
FAT-BC should lead to the identification of potential strategies to tailor BC treatment according to adiposity, a still unmet clinical need in the context of personalized medicine.

Work performed from the beginning of the project to the end of the period covered by the report and main results achieved so far

Our project, called FAT-BC, is articulated around 4 main FATaxes to better understand the connection between adiposity and BC. First, within FATomics, we made important steps to elucidate molecular differences according to body mass index (BMI) by the reanalysis of large publicly available early breast cancer databases. We show that the landscape of somatic driver mutations found in the tumour differs according to BMI, with the clinically actionable PIK3CA mutation for example being less frequent in obese patients with ER+/HER2- breast cancer. We also show that somatic mutations found in breast tumours of obese patients more often arise from age-related mutational signatures, highlighting how adiposity might induce tumour initiation in the breast tissue through processes mimicking the effect of aging. When looking at gene expression profiles then, bulk RNA sequencing data, we already showed consistently higher expression of pro-inflammatory signatures in obese as compared to lean patients. BMI should therefore be further investigated as a factor influencing tumor heterogeneity and considered for BC treatment tailoring. Secondly, in FATlas, we are complementing these findings at the single cell level by creating a comprehensive atlas of BC according to various patient and mammary adiposity measures for the two most common BC histological subtypes. To do so, we have set-up a unique prospective trial including early breast cancer patients planned for breast surgery. We are collecting tissue and blood together with diverse clinic-pathological data including as well lifestyle habits and bioimpedance measurements. So far, 106 patients were recruited. Special attention will be given to the mammary fat cells, the adipocytes, and how these help tumor cells to grow and affect treatment efficacy. Results from the correlation analyses of adiposity measures and our preliminary analyses of single-cell data are almost complete for NST ER+ BC cohort. Thirdly, thanks to FATshare, the data collected in FATlas will be shared with the scientific community through a webtool and data-platform. Finally, using samples from a prospective in-house clinical FATrial, we will investigate whether adiposity-associated features are associated with anti-proliferative response to exercise and endocrine therapy.

Progress beyond the state of the art and expected potential impact (including the socio-economic impact and the wider societal implications of the project so far)

So far, none of the large initiatives that aimed at characterizing BC using the omics technologies tried to link the molecular features of the tumor to patient adiposity. Our FATomics datasets therefore represent to the best of our knowledge the largest series with available BMI, molecular and outcome data. We were able to demonstrate, with actual patient data, that there were differences in the biology of human BC associated with BMI at the genomic and transcriptomic levels. This should therefore be further investigated as a factor influencing tumor heterogeneity and considered for BC treatment tailoring. Importantly, this study is one of the first to explore the single-cell approach for studying the interplay between obesity and BC and was able to demonstrate it is indeed an advantageous strategy to be used in future research.
FATlas is the first BC atlas delivering single-cell data at the transcriptomic and spatially at the protein level, together with extensive adiposity, endocrine and immune measurements.
FATshare will be the first webtool to share and allow interactive interrogation of single-cell, additional molecular and clinical data from BC patients together with various measures of adiposity. Similarly, to previous initiatives such as TCGA or ICGC for which data is easily queried and shared via cbioportal, we expect that this data-sharing will allow many researchers across the world to develop additional hypotheses and generate complementary results.
Preoperative endocrine treatment (ET) decreases tumor proliferation in HR+ BC and this is associated with favorable long-term prognosis. Of interest, we recently observed that this effect was more important in patients with overweight and obesity. Exercise as a potential adjunct to anti-cancer treatment has received more attention lately. Exercise prehabilitation during active treatment is currently recommended by the ASCO guidelines to mitigate side effects of the systemic and local cancer treatment. While preclinical models show an anti-proliferative effect of exercise, there has so far been no randomized clinical trial assessing the antiproliferative effect in the preoperative setting in postmenopausal patients with overweight or obesity and HR+ BC. Our FATrial will be the first to evaluate whether adding exercise to ET could increase the proportion of patients where a complete cell cycle arrest (CCCA, defined by KI67 marker ≤2.7%) is observed. This trial will also offer the unique opportunity to investigate treatment-associated changes in the tumor cells and cells from the tumor microenvironment according to various measures of adiposity.
FAT-BC should lead to the identification of potential strategies to tailor BC treatment according to adiposity, a still unmet clinical need in the context of personalized medicine.