ABSTRACT Thyroid function has been suggested to be associated with breast cancer risk, but not much is known about thyroid hormones as a potential prognostic factor for breast cancer. This study investigated the relation between thyroid hormones in breast cancer patients and mortality. A total of 2000 breast cancer patients from the Swedish SCAN‐B cohort were followed for 9 years. Blood samples were collected at the time of diagnosis, before surgery, and analysed for free and total T3 and T4. All‐cause mortality and recurrent disease were compared between quartiles (Q) using Cox proportional hazard analysis adjusted for prognostic factors, yielding hazard ratios (HR) with 95% confidence intervals (CIs). During follow‐up, 310 deaths and 167 recurrences occurred. Free T3 was associated with a lower mortality; the combined group Q2 to Q4 had an adjusted HR of 0.73 (0.58–0.93) as compared to Q1. Moreover, a high ratio of free T3/T4 was associated with a low mortality in Q2, Q3 and Q4, respectively. High levels of free and total T4 were associated with a high risk of all‐cause mortality; the crude HR for free T4 Q4 vs. Q1 was 1.53 (1.14–2.07) and the corresponding HR for total T4 was 1.63 (1.20–2.22). These results did not remain in the adjusted analysis. No clear associations were found regarding recurrent disease. We conclude that high levels of free T3, and a high free T3/T4 ratio were associated with a lower risk of all‐cause mortality and may be a marker of a favourable prognosis.
Annie Brange, Ylva Heyman, Kamil Demircan et al.· Cancer Medicine· 0 citations
The development and validation of prognostic and predictive biomarkers in breast cancer is limited by the availability of well-annotated datasets linking tumor molecular features to treatment response and survival outcomes. To address this need, we generated an extensive mouse models dataset comprised of 26 immunocompetent mammary tumor models spanning diverse genetic backgrounds, epithelial-mesenchymal states, the basal-luminal axis, and distinct immune microenvironments. For each model, survival was measured under no treatment, immune checkpoint inhibition (ICI), and carboplatin/paclitaxel chemotherapy, and RNA-sequencing was performed on baseline tumors and on 7-day on-treatment samples for both regimens. Baseline murine tumor gene expression features were used to train a machine learning Elastic Net model that predicted survival outcomes on multiple human breast cancer datasets with performance comparable to that of existing prognostic assays. Models trained for ICI benefit, using either the untreated or 7-day ICI treated samples, predicted ICI benefit on human ICI treated datasets, with the 7-day treated tumor model showing better performance. A predictor of carboplatin/paclitaxel response developed from the murine mammary tumor data performed well in mice but did not generalize to human chemotherapy cohorts. Finally, comparison of multiple computational approaches, including XGBoost, random forests, and support vector regression, showed that all methods successfully predicted survival outcomes, with Elastic Net offering the best performance and interpretability. These results indicate conserved cancer biology between mouse and human tumors for prognosis and ICI response and establish a large preclinical dataset with linked phenotypic and genomic data as a resource for biomarker discovery.
Matthew D. Sutcliffe, Kevin R Mott, Tulay Yilmaz-Swenson et al.· Cancer Research· 0 citations
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