Biology subjects

Czernilofsky, F.

Publications and source records attributed to Czernilofsky, F..

4 recordsLinked to original sources

bioRxiv2023

A single-cell multi-omic and spatial atlas of nodal B-cell lymphomas reveals B-cell maturation drives intratumor heterogeneity

Intratumor heterogeneity underpins cancer pathogenesis and evolution, although it is typically considered independent from the differentiation processes that drive physiological cell-type diversity. As cancer types and subtypes arise from different cell types, we investigated whether cellular differentiation influences intratumor heterogeneity. Nodal B-cell non-Hodgkin lymphomas are a diverse set of cancers originating from different stages of B-cell maturation. Through single-cell transcriptome and surface epitope profiling (CITE-Seq) of diffuse large B-cell, mantle cell, follicular, and marginal zone lymphomas in addition to reactive lymph nodes from 51 patients, we found multiple B-cell maturation states within tumors. Intratumor maturation states emerged from the same clone, revealing divergent differentiation from a shared cell of origin. Maturation state composition varied across subtypes and tumors, which encompassed mixed cell-of-origin diagnostic subtypes. Through highly multiplexed immunohistochemistry (CODEX) of samples from 19 of these patients, we found that intratumor maturation states inhabited distinct spatial niches, displaying cellular interactions and regulatory networks typical of their maturation states while harboring different genetic variants. By deconvoluting intratumor maturation states from a microarray dataset of 507 patients, we identified risk groups within diagnoses with striking differences in survival, including IgM memory-enriched germinal center B-cell (M = 1.9 vs >10 years, p = 0.00039) and activated B-cell (M = 2.4 vs 9.6 years, p = 0.016) diffuse large B-cell lymphoma, and dark zone-enriched follicular lymphoma (M = 8.6 vs 13 years; p = 0.0019). Our findings reveal cellular differentiation remains plastic in B-cell lymphomas, driving tumor variation, evolution, and response. Key PointsO_LICellular differentiation remains plastic in B-cell lymphomas, driving tumor variation, evolution, and response. C_LIO_LIIntratumor maturation states occupy unique immune niches, harbor distinct genetic variants, and are tied to different survival outcomes. C_LI

cancer biology↗

bioRxiv2022

Multimodal and spatially resolved profiling identifies distinct patterns of T-cell infiltration in nodal B-cell lymphoma entities

T-cell-engaging immunotherapies have improved the treatment of nodal B-cell lymphoma, but responses vary highly. Future improvements of such therapies require better understanding of the variety of lymphoma-infiltrating T-cells. We employed single-cell RNA and T-cell receptor sequencing alongside quantification of surface proteins, flow cytometry and multiplexed immunofluorescence on 101 lymph nodes from healthy controls, and patients with diffuse large B-cell, mantle cell, follicular, or marginal zone lymphoma. This multimodal resource revealed entity-specific quantitative and spatial aberrations of the T-cell microenvironment. Clonal PD1+ TCF7- but not PD1+ TCF7+ cytotoxic T-cells converged into terminally exhausted T-cells, the proportions of which were variable across entities and linked to inferior prognosis. In follicular and marginal zone lymphoma, we observed expansion of follicular helper and IKZF3+ regulatory T-cells, which were clonally related and inversely associated with tumor grading. Overall, we portray lymphoma-infiltrating T-cells with unprecedented comprehensiveness and decipher both beneficial and adverse dimensions of T-cell response.

cancer biology↗

bioRxiv2022

Pre-analytical processing of plasma and serum samples for combined proteome and metabolome analysis

Metabolomic and proteomic analyses of human plasma and serum samples harbour the power to advance our understanding of disease biology. Pre-analytical factors may contribute to variability and bias in the detection of analytes, especially when multiple labs are involved, caused by sample handling, processing time, and differing operating procedures. To better understand the impact of pre-analytical factors that are relevant to implement a unified proteomic and metabolomic approach in a clinical setting, we assessed the influence of temperature, sitting times, and centrifugation speed on the plasma and serum metabolomes and proteomes from six healthy volunteers. We used targeted metabolic profiling (497 metabolites) and data-independent acquisition (DIA) proteomics (572 proteins) on the same samples generated with well-defined pre-analytical conditions to evaluate criteria for pre-analytical SOPs for plasma and serum samples. Time and temperature showed the strongest influence on the integrity of plasma and serum proteome and metabolome. While rapid handling and low temperatures (4{degrees}C) are imperative for metabolic profiling, the analysed proteome showed variability when exposed to temperatures of 4{degrees}C for more than 2 hours, highlighting the need for compromises in a combined analysis. We formalised a quality control scoring system to objectively rate sample stability and tested this score using external data sets from other pre-analytical studies. Stringent and harmonised standard operating procedures (SOPs) are required for pre-analytical sample handling when combining proteomics and metabolomics of clinical samples to yield robust and interpretable data on a longitudinal scale and across different clinics. To ensure an adequate level of practicability in a clinical routine for metabolomics and proteomics studies we suggest to keep blood samples up to 2 hours on ice (4{degrees}C) prior to snap-freezing as a compromise between stability and operability. Finally, we provide the methodology as an open source R package allowing the systematic scoring of proteomics and metabolomics datasets to assess the stability of plasma and serum samples.

biochemistry↗

bioRxiv2022

Proteogenomics refines the molecular classification of chronic lymphocytic leukemia

Cancer heterogeneity at the proteome level may explain differences in therapy response and prognosis beyond the currently established genomic and transcriptomic based diagnostics. The relevance of proteomics for disease classifications remains to be established in clinically heterogeneous cancer entities such as chronic lymphocytic leukemia (CLL). Here, we characterized the proteome and transcriptome in-depth alongside genetic and ex-vivo drug response profiling in a clinically well annotated CLL discovery cohort (n= 68). Unsupervised clustering of the proteome data revealed six subgroups. Five of these proteomic groups were associated with genetic features, while one group was only detectable at the proteome level. This new group was characterized by accelerated disease progression, high spliceosomal protein abundances associated with aberrant splicing, and low B cell receptor signaling protein abundances (ASB-CLL). We developed classifiers to identify ASB-CLL based on its characteristic proteome or splicing signature in two independent cohorts (n= 165, n= 169) and confirmed that ASB-CLL comprises about 20 % of CLL patients. The inferior overall survival observed in ASB-CLL was independent of both TP53- and IGHV mutation status. Our multi-omics analysis refines the classification of CLL and highlights the potential of proteomics to improve cancer patient stratification beyond genetic and transcriptomic profiling. Single sentence summaryWe performed the largest proteogenomic analysis of CLL, linked proteomic profiles to clinical outcomes, and discovered a new poor outcome subgroup (ASB-CLL).

cancer biology↗