Biology subjects

Loipfinger, S.

Publications and source records attributed to Loipfinger, S..

3 recordsLinked to original sources

bioRxiv2026

Targeting ZC3H12C improves T cell persistence and antitumor function in adoptive T cell therapy

Adoptive T cell therapy (ACT) has achieved remarkable clinical responses in hematologic malignancies but remains limited by progressive T cell dysfunction under chronic antigen stimulation. Here, we identify ZC3H12C as a conserved feature of dysfunctional T cells and show that its disruption enhances the durability and antitumor activity of engineered T cells. By integrating single-cell chromatin accessibility and transcriptomic profiling of human tumor-infiltrating lymphocytes (TILs), we identified the ZC3H12C locus as selectively remodeled in exhausted T cells. ZC3H12C induction is largely absent across acute T cell activation contexts, indicating regulation that is specific to chronic antigen-driven dysfunction. Genetic disruption of ZC3H12C improves T cell expansion, cytotoxicity, and expression of effector molecules during repeated in vitro stimulation, translating into enhanced tumor control in vivo across both T cell receptor (TCR) and chimeric antigen receptor (CAR) T cell therapy platforms. Improved efficacy is observed in hematologic, solid, and metastatic tumor models and is accompanied by increased T cell persistence. Further, ZC3H12C is enriched in clinical pre-infusion CAR T cell products associated with non-response. Together, these findings identify ZC3H12C as a T cell dysfunction-specific target to improve ACT performance.

immunology↗

bioRxiv2025

PROTRIDER: Protein abundance outlier detection from mass spectrometry-based proteomics data with a conditional autoencoder

Structured abstractO_ST_ABSMotivationC_ST_ABSDetection of gene regulatory aberrations enhances our ability to interpret the impact of inherited and acquired genetic variation for rare disease diagnostics and tumor characterization. While numerous methods for calling RNA expression outliers from RNA-sequencing data have been proposed, the establishment of protein expression outliers from mass spectrometry data is lacking. ResultsHere, we propose and assess various modeling approaches to call protein expression outliers across three datasets from rare disease diagnostics and oncology. We use as independent evidence the enrichment for outlier calls in matched RNA-seq samples and the enrichment for rare variants likely disrupting protein expression. We show that controlling for hidden confounders and technical covariates, while simultaneously modeling the occurrence of missing values, is largely beneficial and can be achieved using conditional autoencoders. Moreover, we find that the differences between experimental and fitted log-transformed intensities by such models exhibit heavy tails that are poorly captured with the Gaussian distribution and report stronger statistical calibration when instead using the Students t-distribution. Our resulting method, PROTRIDER, outperformed baseline approaches based on raw log-intensities Z-scores or on differential expression analysis with limma. The application of PROTRIDER reveals significant enrichments of AlphaMissense pathogenic variants in protein expression outliers. Overall, PROTRIDER provides a method to confidently identify aberrantly expressed proteins applicable to rare disease diagnostics and cancer proteomics. Availability and ImplementationPROTRIDER is freely available at github.com/gagneurlab/PROTRIDER and also available on Zenodo under the DOI zenodo.15569781. ContactJulien Gagneur: gagneur at in.tum.de

bioinformatics↗

bioRxiv2024

Transcriptional pattern enriched for synaptic signaling is associated with shorter survival of patients with high-grade serous ovarian cancer

BackgroundBulk transcriptomic analyses of high-grade serous ovarian cancer (HGSOC) so far have not uncovered potential drug targets, possibly because subtle, disease-relevant transcriptional patterns are overshadowed by dominant, non-relevant ones. Our aim was to uncover disease-outcome-related patterns in HGSOC transcriptomes that may reveal novel drug targets. MethodUsing consensus-independent component analysis, we dissected 678 HGSOC transcriptomes of systemic therapy naive patients--sourced from public repositories--into statistically independent transcriptional components (TCs). To enhance c-ICAs robustness, we added 447 transcriptomes from non-serous histotypes, low-grade serous, and non- cancerous ovarian tissues. Cox regression and survival tree analysis were performed to determine the association between TC activity and overall survival (OS). Finally, we determined the activity of the OS-associated TCs in 11 publicly available spatially resolved ovarian cancer transcriptomes. ResultsWe identified 374 TCs, capturing prominent and subtle transcriptional patterns linked to specific biological processes. Six TCs, age, and tumor stage stratified patients with HGSOC receiving platinum-based chemotherapy into ten distinct OS groups. Three TCs were linked to copy-number alterations affecting expression levels of genes involved in replication, apoptosis, proliferation, immune activity, and replication stress. Notably, the TC identifying patients with the shortest OS captured a novel transcriptional pattern linked to synaptic signaling, which was active in tumor regions within all spatially resolved transcriptomes. ConclusionThe association between a synaptic signaling-related TC and OS supports the emerging role of neurons and their axons as cancer hallmark-inducing constituents of the tumor microenvironment. These constituents might offer a novel drug target for patients with HGSOC.

cancer biology↗