Proteogenomics 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. This is the transcriptomics data of the discovery cohort.

Type: Other
Archiver: European Genome-Phenome Archive (EGA)

1 Dataset 2 Publications

Click on a Dataset ID in the table below to learn more, and to find out who to contact about access to these data

Dataset ID	Description	Technology	Samples
EGAD00001008320	This dataset includes Illumina RNA Sequencing Data for 59 chronic lymphocytic leukemia patient samples. 57 samples are single end, 2 samples are paired end sequencing.		59

Publications	Citations
Proteogenomics refines the molecular classification of chronic lymphocytic leukemia. Herbst SA, Vesterlund M, Helmboldt AJ, Jafari R, Siavelis I, Stahl M, Schitter EC, Liebers N, Brinkmann BJ, Czernilofsky F, Roider T, Bruch PM, Iskar M, Kittai A, Huang Y, Lu J, Richter S, Mermelekas G, Umer HM, Knoll M, Kolb C, Lenze A, Cao X, Österholm C, Wahnschaffe L, Herling C, Scheinost S, Ganzinger M, Mansouri L, Kriegsmann K, Kriegsmann M, Anders S, Zapatka M, Del Poeta G, Zucchetto A, Bomben R, Gattei V, Dreger P, Woyach J, Herling M, Müller-Tidow C, Rosenquist R, Stilgenbauer S, Zenz T, Huber W, Tausch E, Lehtiö J, Dietrich S. Nat Commun 13: 2022 6226	7
Functional analysis of structural variants in single cells using Strand-seq. Jeong H, Grimes K, Rauwolf KK, Bruch PM, Rausch T, Hasenfeld P, Benito E, Roider T, Sabarinathan R, Porubsky D, Herbst SA, Erarslan-Uysal B, Jann JC, Marschall T, Nowak D, Bourquin JP, Kulozik AE, Dietrich S, Bornhauser B, Sanders AD, Korbel JO. Nat Biotechnol 41: 2023 832-844	8

Publications

Citations

Proteogenomics refines the molecular classification of chronic lymphocytic leukemia.
Herbst SA, Vesterlund M, Helmboldt AJ, Jafari R, Siavelis I, Stahl M, Schitter EC, Liebers N, Brinkmann BJ, Czernilofsky F, Roider T, Bruch PM, Iskar M, Kittai A, Huang Y, Lu J, Richter S, Mermelekas G, Umer HM, Knoll M, Kolb C, Lenze A, Cao X, Österholm C, Wahnschaffe L, Herling C, Scheinost S, Ganzinger M, Mansouri L, Kriegsmann K, Kriegsmann M, Anders S, Zapatka M, Del Poeta G, Zucchetto A, Bomben R, Gattei V, Dreger P, Woyach J, Herling M, Müller-Tidow C, Rosenquist R, Stilgenbauer S, Zenz T, Huber W, Tausch E, Lehtiö J, Dietrich S. Nat Commun 13: 2022 6226

Functional analysis of structural variants in single cells using Strand-seq.
Jeong H, Grimes K, Rauwolf KK, Bruch PM, Rausch T, Hasenfeld P, Benito E, Roider T, Sabarinathan R, Porubsky D, Herbst SA, Erarslan-Uysal B, Jann JC, Marschall T, Nowak D, Bourquin JP, Kulozik AE, Dietrich S, Bornhauser B, Sanders AD, Korbel JO. Nat Biotechnol 41: 2023 832-844