Prognostic Impact of Immunoglobulin Kappa C (IGKC) in Early Breast Cancer

We studied the prognostic impact of tumor immunoglobulin kappa C (IGKC) mRNA expression as a marker of the humoral immune system in the FinHer trial patient population, where 1010 patients with early breast cancer were randomly allocated to either docetaxel-containing or vinorelbine-containing adjuvant chemotherapy. HER2-positive patients were additionally allocated to either trastuzumab or no trastuzumab. Hormone receptor-positive patients received tamoxifen. IGKC was evaluated in 909 tumors using quantitative real-time polymerase chain reaction, and the influence on distant disease-free survival (DDFS) was examined using univariable and multivariable Cox regression and Kaplan–Meier estimates. Interactions were analyzed using Cox regression. IGKC expression, included as continuous variable, was independently associated with DDFS in a multivariable analysis also including age, molecular subtype, grade, and pT and pN stage (HR 0.930, 95% CI 0.870–0.995, p = 0.034). An independent association with DDFS was also found in a subset analysis of triple-negative breast cancers (TNBC) (HR 0.843, 95% CI 0.724–0.983, p = 0.029), but not in luminal (HR 0.957, 95% CI 0.867–1.056, p = 0.383) or HER2-positive (HR 0.933, 95% CI 0.826–1.055, p = 0.271) cancers. No significant interaction between IGKC and chemotherapy or trastuzumab administration was detected (Pinteraction = 0.855 and 0.684, respectively). These results show that humoral immunity beneficially influences the DDFS of patients with early TNBC

BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License

Antagonism Between Saturated and Unsaturated Fatty Acids in ROS Mediated Lipotoxicity in Rat Insulin-Producing Cells

Background/Aims: Elevated levels of non-esterified fatty acids (NEFAs) are under suspicion to mediate β-cell dysfunction and β-cell loss in type 2 diabetes, a phenomenon known as lipotoxicity. Whereas saturated fatty acids show a strong cytotoxic effect upon insulin-producing cells, unsaturated fatty acids are not toxic and can even prevent toxicity. Experimental evidence suggests that oxidative stress mediates lipotoxicity and there is evidence that the subcellular site of ROS formation is the peroxisome. However, the interaction between unsaturated and saturated NEFAs in this process is unclear. Methods: Toxicity of rat insulin-producing cells after NEFA incubation was measured by MTT and caspase assays. NEFA induced H2O2 formation was quantified by organelle specific expression of the H2O2 specific fluorescence sensor protein HyPer. Results: The saturated NEFA palmitic acid had a significant toxic effect on the viability of rat insulin-producing cells. Unsaturated NEFAs with carbon chain lengths >14 showed, irrespective of the number of double bonds, a pronounced protection against palmitic acid induced toxicity. Palmitic acid induced H2O2 formation in the peroxisomes of insulin-producing cells. Oleic acid incubation led to lipid droplet formation, but in contrast to palmitic acid induced neither an ER stress response nor peroxisomal H2O2 generation. Furthermore, oleic acid prevented palmitic acid induced H2O2 production in the peroxisomes. Conclusion: Thus unsaturated NEFAs prevent deleterious hydrogen peroxide generation during peroxisomal β-oxidation of long-chain saturated NEFAs in rat insulin-producing cells

Prognostic Impact of LAG-3 mRNA Expression in Early Breast Cancer

Background: Monoclonal antibodies against PD-1 or PD-L1 have been established in clinical practice for the treatment of both early and advanced/metastatic triple-negative breast cancer. Beyond the established immune checkpoints (ICPs) (PD-1 and CTLA-4), additional ICPs, such as lymphocyte activation gene-3 (LAG-3), are subject of current research. In the present retrospective gene-expression analysis, we evaluated the prognostic significance of LAG-3 in 461 patients with early breast cancer. In addition, we examined whether there was a correlation between the different ICP and CD8 expressions. Methods: Using microarray-based gene-expression analysis, we examined the prognostic significance of LAG-3 mRNA expression for metastasis-free survival (MFS) in the whole cohort of 461 breast cancer patients and among different molecular subtypes. Correlations were analyzed using Spearman’s rho correlation coefficient. Results: In the whole cohort, LAG-3 expression had no significant impact on MFS (p = 0.712, log-rank). In the subgroup analyses, there was a trend that a higher LAG-3 expression was associated with a favorable outcome in the luminal B (p = 0.217), basal-like (p = 0.370) and HER2 (p = 0.089) subtypes, although significance was not reached. In contrast, in a multivariate Cox regression analysis, adjusted for age, tumor size, axillary nodal status, histological grade of differentiation and proliferation marker Ki-67, LAG-3 showed a significant influence on MFS (HR 0.574; 95% CI 0.369–0.894; p = 0.014). High LAG-3 significantly correlated with CD8 (ρ = 0.571; p < 0.001). Conclusions: LAG-3 expression had an independent impact on MFS. In addition to PD-1 and PD-L1, further immune checkpoints, such as LAG-3, could serve as therapeutic targets in breast cancer

Prognostic Significance of Interferon-γ and Its Signaling Pathway in Early Breast Cancer Depends on the Molecular Subtypes

Interferons are crucial for adaptive immunity and play an important role in the immune landscape of breast cancer. Using microarray-based gene expression analysis, we examined the subtype-specific prognostic significance of interferon-γ (IFN-γ) as a single gene as well as an IFN-γ signature covering the signaling pathway in 461 breast cancer patients. Prognostic significance of IFN-γ, as well as the IFN-γ signature for metastasis-free survival (MFS), were examined using Kaplan–Meier as well as univariate and multivariate Cox regression analyses in the whole cohort and in different molecular subtypes. The independent prognostic significance of IFN-γ as a single gene was limited to basal-like breast cancer (hazard ratio (HR) 2.779, 95% confidence interval (95% CI) 1.117–6.919, p = 0.028). In contrast, the IFN-γ-associated gene signature was an independent prognostic factor in the whole cohort (HR 2.287, 95% CI 1.410–3.633, p < 0.001) as well as in the basal-like (HR 3.458, 95% CI 1.154–10.359, p = 0.027) and luminal B (HR 2.690, 95% CI 1.416–5.112, p = 0.003) molecular subtypes. These results underline the subtype-dependent prognostic influence of the immune system in early breast cancer