Ulrike Koller, Birgit Siepmann, Verena Braun, Julia Geulen, Karen Herold, Karin Greulich-Bode, Birgit Hiller, Susanne Weg-Remers
[Knowledge facilitates health: the "Fit in Health" program for strengthening the health literacy of students]
Bundesgesundheitsblatt, Gesundheitsforschung, Gesundheitsschutz
study aims identify precise biomarkers breast cancer improve patient outcomes addressing limitations traditional staging predicting treatment responses analysis encompassed data 7000 breast cancer patients across 14 datasets included inhouse clinical data singlecell data 8 patients totaling 43766 cells utilized integrative approach applying 10 machine learning algorithms 54 unique combinations analyze 100 existing breast cancer signatures immunohistochemistry assays performed empirical validation study also investigated potential immunotherapies chemotherapies research identified five consistent glutamine metabolic reprogramming gmrrelated genes multicenter cohorts forming foundation novel gmrmodel model demonstrated superior accuracy predicting recurrence mortality risks compared existing clinical molecular features patients classified highrisk model exhibited poorer outcomes ihc validation 30 patients reinforced findings suggesting models broad applicability intriguingly model indicates differential therapeutic response lowrisk patients may benefit immunotherapy whereas highrisk patients showed sensitivity specific chemotherapies like bi2536 ispinesib gmrmodel marks significant leap forward breast cancer prognosis personalization treatment strategies offering vital insights effective management diverse breast cancer patient populations