Background: T helper (Th) cell differentiation is a complex process regulated by multiple factors.Results: PIM kinases promote Th1 differentiation through regulating the expression of genes important for this process.Conclusion: PIM kinases were identified as new regulators of Th1 cell differentiation.Significance: This study provides new insights into the mechanisms controlling Th cell differentiation.

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Background Circulating tumor DNA (ctDNA) analysis has emerged as a promising tool for detecting and profiling longitudinal genomic changes in cancer. While copy-number alterations (CNAs) play a major role in cancers, monitoring treatment effects using copy-number profiles has received limited attention compared to mutations. This is primarily due to the challenge of CNA analysis for real-life tumor-fraction ctDNA samples. Method Our study aimed to investigate the longitudinal copy number profiles of ctDNA and assess the role of ctDNA samples, even with low tumor fractions, in monitoring cancer treatment. We conducted copy-number analysis on 152 plasma samples collected from 29 patients diagnosed with high-grade serous carcinoma (HGSC). Our sequencing panel targeted more than 500 genes, enabling a comprehensive evaluation of genomic changes. Among the patients, 21 individuals had matched tissue and plasma samples collected at different time points, allowing us to assess the concordance between tissue and plasma and to evaluate the sensitivity of our approach using Kendall correlation values. Statistical comparisons were performed using the Wilcoxon rank test to ensure the robustness of the findings. Result Our approach successfully detected concordant CNA profiles in most plasma samples, even with tumor content as low as 3%. Additionally, highly amplified regions were identified in samples with approximately 1% tumor content. Longitudinal analysis revealed changes in CNA profiles in seven out of 11 patients with high tumor-content plasma samples at relapse. These changes included focal acquired or lost copy numbers, whereas the majority of the genome remained stable. Two patients displayed significant changes in their copy-number profile during therapy. Our analysis uncovered ctDNA-detectable subclonal selection resulting from surgery and chemotherapy. Conclusion Overall, our study demonstrated acquired and diminished CNAs at relapse compared with pre-treatment samples, highlighting the dynamic nature of CNA profiles during treatment. These findings provide compelling evidence for the potential of ctDNA analysis in tracking treatment responses and detecting genomic changes associated with relapse. Monitoring CNAs can serve as a complementary tool to understand cancer cell evolution and treatment efficacy.