Transfusion of packed red blood cells (pRBCs) saves lives, but iron overload limits survival of chronically transfused patients. Quality control methods, which involve entering pRBC units and removing them from the blood supply, reveal that hemoglobin (38.5-79.9 g) and heme iron (133.42-276.89 mg) vary substantially between pRBCs. Yet, neither hemoglobin nor iron content can be quantified for individual clinically used pRBCs leading to rules of thumb for pRBC transfusions. Keeping their integrity, the authors seek to predict hemoglobin/iron content of any given pRBC unit applying eight machine learning models on 6,058 pRBCs. Based on thirteen features routinely collected during blood donation, production and quality control testing, the model with best trade-off between performance and complexity in hemoglobin/iron content prediction is identified. Validation of this model in an independent cohort of 2637 pRBCs confirms an adjusted R 2 > 0.9 corresponding to a mean absolute prediction error of ≤1.43 g hemoglobin/4.96 mg iron (associated standard deviation: ≤1.13 g hemoglobin/3.92 mg iron). Such unprecedented precise prediction enables reliable pRBC dosing per pharmaceutically active agent, and monitoring iron uptake in patients and individual iron loss in donors. The model is implemented in a free open source web application to facilitate clinical application.
Estrogen (ER) and/or progesterone (PR) receptor-positive, early breast cancer benefits from targeted therapy via long-term estrogen deprivation. Valid treatment options include the selective ER modulator tamoxifen (TAM) that interferes with estrogen-binding at the ER, and aromatase inhibitors (AI) that block the enzyme aromatase to prevent the conversion of androgens to estrogen. Both treatment principles are in clinical use however fail in about one third of the patients. The choice of endocrine treatment is currently not well supported by predictive tumor markers. Gene expression signatures covering critical breast cancer pathways were tested to predict TAM and AI associated outcomes in a prospectively collected postmenopausal, hormone-receptor positive, early breast cancer cohort (IKP211; 1200 patients, median follow up 5.5 years; DRKS00000605). RNA was extracted from formalin-fixed paraffin-embedded tumor sections of 631 patients and subjected to gene expression profiling with 770 genes across 23 key breast cancer pathways/processes (NanoString®BC360 panel) including the prognostic PAM50 signature for intrinsic subtype classification. Signatures were measured with the nCounter Digital Analyzer system (Nanostring) and revealed 60% Luminal A, 31% Luminal B, 6% HER2 enriched and 3% Basal-like tumor subtypes. Predefined signature scores (Nanostring) or single gene expression scores were analyzed in relation to breast cancer recurrence-free survival (EFS). PAM50 subtype designation and its derived Genomic Risk Score (ROR) were strongly associated with EFS of patients treated with AI (Log Rank P<0.05 all comparisons) but not in patients treated with mono-TAM. Preliminary data show that higher levels of FOXA1 and androgen receptor (AR) gene expression are both associated with longer EFS in patients with AI therapy. Likewise, increased (mutant-like) p53 signaling was prognostic for shorter EFS (Log Rank P<0.05). These relations were not observed in patients treated with TAM or with a switch treatment regimen. A trend for longer EFS linked to an increased anti-tumor immune activity, as deduced by cytotoxic cell abundance and activity, was observed in a subgroup of mainly TAM treated patients. Our preliminary data suggest that gene expression signatures pertaining to FOXA1 and AR signaling, p53 and anti-tumor immune activity may define subgroups of patients with different outcomes and may aid in future personalized treatment concepts for hormone-sensitive postmenopausal early breast cancer. Citation Format: Werner Schroth, Reiner Hoppe, Florian Büttner, Stefan Winter, Siarhei Kandabarau, Jörg Kumbrink, Heather A. Brauer, Peter Fritz, Matthias Schwab, Thomas Mürdter, Hiltrud Brauch. Gene expression signatures for the prediction of endocrine treatment outcome in early-stage luminal breast cancer patients [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 464.
Purpose Although p53 is rarely mutated in ccRCC, its overexpression has been linked to poor prognosis. The current study sought to elucidate the unique role of p53 in ccRCC with genomic, proteomic, and functional analyses. Materials and Methods Data from the Cancer Genome Atlas (TCGA) were evaluated for genomic and proteomic characteristics of p53; a tissue micro array (TMA) study was carried out to evaluate the association of p53 and phosphorylated p53 (pp53) with clinical outcome. Mechanistic in vitro experiments were performed to confirm a pro-apoptotic loss of p53 in ccRCC and p53 isoforms as well as posttranslational modifications of p53 where assessed to provide possible reasons for a functional inhibition of p53 in ccRCC. Results A low somatic mutation rate of p53 could be confirmed. Although mRNA levels were correlated with poor prognosis and clinicopathological features, there was no monotonous association of mRNA levels with survival outcome. Higher p53 protein levels could be confirmed as poor prognostic features. In vitro, irradiation of ccRCC cell lines markedly induced levels of p53 and of activated (phosphorylated) p53. However, irradiated ccRCC cells demonstrated similar proliferation, migration, and p53 transcriptional activity like non-irradiated controls indicating a functional inhibition of p53. p53 isoforms and could not be correlated with clinical outcome of ccRCC patients. Conclusions p53 is rarely mutated but the wildtype p53 is functionally inhibited in ccRCC. To investigate mechanisms that underly functional inhibition of p53 may provide attractive therapeutic targets in ccRCC.
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