Biology subjects

Fehrmann, R. S. N.

Publications and source records attributed to Fehrmann, R. S. N..

4 recordsLinked to original sources

bioRxiv2025

The landscape of tertiary lymphoid structures in endometrial cancer revealed through harmonized multi-level transcriptomics

Tertiary lymphoid structures (TLSs) are ectopic lymphoid tissues that form within tumors. TLSs are associated with improved prognosis and better responses to immune checkpoint inhibitors (ICI) in cancer. However, the contribution of genetic, immune, and (micro)environmental factors to TLS formation and function remain incompletely understood. Using harmonized bulk and single-cell RNA-sequencing coupled with spatial transcriptomics, we present a comprehensive characterization of tumor-associated TLSs in endometrial cancer (EC). Our integrated datasets reveal the cellular architecture of TLSs, their organizing principles--including chemokine-receptor gradients--and TLS activity compared to paired tumor-draining lymph nodes, as well as spatial preferences in IgA/IgG plasma cell and T cell distribution. We identified a consensus set of transcriptional components that independently predict the presence of TLS in EC. Furthermore, we demonstrate that clinical neoadjuvant ICI in EC patients shifts the cellular balance in favor of immunogenic CXCL13+ PDCD1+ T cells, providing a mechanistic insight into ICI-induced TLSs formation. Our data offers a systems-level understanding of TLSs and a roadmap for their therapeutic exploitation.

immunology↗

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↗

bioRxiv2021

MYC promotes immune-suppression in TNBC via inhibition of IFN signaling

Immune checkpoint inhibitor (ICI) treatment has thus far shown limited efficacy in triple-negative breast cancer (TNBC) patients, presumably due to sparse or unresponsive tumor-infiltrating lymphocytes. We reveal a strong correlation between MYC expression and loss of immune signatures in human TNBC. In mouse models of BRCA1-proficient and -deficient TNBC, MYC overexpression dramatically decreased lymphocyte infiltration in tumors, along with immune signature loss. Likewise, MYC overexpression suppressed inflammatory signaling induced by BRCA1/2 inactivation in human TNBC cell lines. Moreover, MYC overexpression prevented the recruitment and activation of lymphocytes in co-cultures with human and mouse TNBC models. Chromatin immunoprecipitation (ChIP)-sequencing revealed that MYC directly binds promoters of multiple interferon-signaling genes, which were downregulated upon MYC expression. Finally, MYC overexpression suppressed induction of interferon signaling and tumor growth inhibition by a Stimulator of Interferon Genes (STING) agonist. Together, our data reveal that MYC suppresses innate immunity and facilitates immune escape, explaining the poor immunogenicity of MYC-overexpressing TNBCs. Statement of SignificanceMYC suppresses recruitment and activation of immune cells in TNBC by repressing the transcription of interferon genes. These findings provide a mechanistic rationale for the association of high MYC expression levels with immune exclusion in human TNBCs, which might underlie the relatively poor response of many TNBCs to ICI.

cancer biology↗

bioRxiv2020

Robust and independent metabolic transcriptional components in 34,494 patient-derived samples and cell lines: a resource for translational cancer research

Patient-derived expression profiles of cancers can provide insight into transcriptional changes that underlie reprogrammed metabolism in cancer. These profiles represent the average expression pattern of all heterogeneous tumor and non-tumor cells present in biopsies of tumor lesions. Therefore, subtle transcriptional footprints of metabolic processes can be concealed by other biological processes and experimental artifacts. We, therefore, performed consensus Independent Component Analyses (c-ICA) with 34,494 bulk expression profiles of patient-derived tumor biopsies, non-cancer tissues, and cell lines. c-ICA enabled us to create a transcriptional metabolic landscape in which many robust metabolic transcriptional components and their activation score in individual samples were defined. Here we demonstrate how this landscape can be used to explore associations between the metabolic transcriptome and drug sensitivities, patient outcomes, and the composition of the immune tumor microenvironment. The metabolic landscape can be explored at http://www.themetaboliclandscapeofcancer.com.

bioinformatics↗