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Item does not contain fulltext, Obesity is associated with an increased risk of developing breast cancer (BC) and worse prognosis in BC patients, yet its impact on BC biology remains understudied in humans. This study investigates how the biology of untreated primary BC differs according to patients' body mass index (BMI) using data from >2,000 patients. We identify several genomic alterations that are differentially prevalent in overweight or obese patients compared to lean patients. We report evidence supporting an ageing accelerating effect of obesity at the genetic level. We show that BMI-associated differences in bulk transcriptomic profile are subtle, while single cell profiling allows detection of more pronounced changes in different cell compartments. These analyses further reveal an elevated and unresolved inflammation of the BC tumor microenvironment associated with obesity, with distinct characteristics contingent on the estrogen receptor status. Collectively, our analyses imply that obesity is associated with an inflammaging-like phenotype. We conclude that patient adiposity may play a significant role in the heterogeneity of BC and should be considered for BC treatment tailoring.

Purpose: Gene expression (GE) profiling for breast cancer classification and prognostication has become increasingly used in clinical diagnostics. GE profiling requires a reasonable tumor cell percentage and high-quality RNA. As a consequence, a certain amount of samples drop out. If tumor characteristics are different between samples included and excluded from GE profiling, this can lead to bias. Therefore, we assessed whether patient and tumor characteristics differ between tumors suitable or unsuitable for generating GE profiles in breast cancer. Methods: In a consecutive cohort of 738 breast cancer patients who received neoadjuvant chemotherapy at the Netherlands Cancer Institute, GE profiling was performed. We compared tumor characteristics and treatment outcome between patients included and excluded from GE profiling. Results were validated in an independent cohort of 812 patients treated with primary surgery. Results: GE analysis could be performed in 53% of the samples. Patients with tumor GE profiles more often had high-grade tumors [odds ratio 2.57 (95%CI 1.77–3.72), p < 0.001] and were more often lymph node positive [odds ratio 1.50 (95%CI 1.03–2.19), p = 0.035] compared to the group for which GE profiling was not possible. In the validation cohort, tumors suitable for gene expression analysis were more often high grade. Conclusions: In our gene expression studies, tumors suitable for GE profiling had more often an unfavorable prognostic profile. Due to selection of samples with a high tumor percentage, we automatically select for tumors with specific features, i.e., tumors with a higher grade and lymph node involvement. It is important to be aware of this phenomenon when performing gene expression analysis in a research or clinical context.

Contains fulltext : 136740.pdf (publisher's version ) (Open Access), BACKGROUND: Clinical decision-making in patients with early stage breast cancer requires adequate risk estimation by medical oncologists. This survey evaluates the agreement among oncologists on risk estimations and adjuvant systemic treatment (AST) decisions and the impact of adding the 70-gene signature to known clinico-pathological factors. METHODS: Twelve medical oncologists assessed 37 breast cancer cases (cT1-3N0M0) and estimated their risk of recurrence (high or low) and gave a recommendation for AST. Cases were presented in two written questionnaires sent 4weeks apart. Only the second questionnaire included the 70-gene signature result. RESULTS: The level of agreement among oncologists in risk estimation (kappa=0.57) and AST recommendation (kappa=0.57) was 'moderate' in the first questionnaire. Adding the 70-gene signature result significantly increased the agreement in risk estimation to 'substantial' (kappa=0.61), while agreement in AST recommendations remained 'moderate' (kappa=0.56). Overall, the proportion of high risk was reduced with 7.4% (range: 6.9-22.9%; p<0.001) and the proportion of chemotherapy that was recommended was reduced with 12.2% (range: 5.4-29.5%; p<0.001). CONCLUSION: Oncologists' risk estimations and AST recommendations vary greatly. Even though the number of participating oncologists is low, our results underline the need for a better standardisation tool in clinical decision-making, in which integration of the 70-gene signature may be helpful in certain subgroups to provide patients with individualised, but standardised treatment.