Gene expression and copy number profiling of follicular lymphoma biopsies from patients treated with first-line rituximab without chemotherapy.

Follicular lymphoma copy number profiling cox models gene expression profiling whole genome microarrays

Journal

Leukemia & lymphoma
ISSN: 1029-2403
Titre abrégé: Leuk Lymphoma
Pays: United States
ID NLM: 9007422

Informations de publication

Date de publication:
08 Sep 2023
Historique:
pubmed: 8 9 2023
medline: 8 9 2023
entrez: 8 9 2023
Statut: aheadofprint

Résumé

The Nordic Lymphoma Study Group has performed two randomized clinical trials with chemotherapy-free first-line treatment (rituximab +/- interferon) in follicular lymphoma (FL), with 73% of patients alive and 38% without any need of chemotherapy after 10.6 years median follow-up. In order to identify predictive markers, that may also serve as therapeutic targets, gene expression- and copy number profiles were obtained from 97 FL patients using whole genome microarrays. Copy number alterations (CNAs) were identified, e.g. by GISTIC. Cox Lasso Regression and Lasso logistic regression were used to determine molecular features predictive of time to next therapy (TTNT). A few molecular changes were associated with TTNT (e.g. increased expression of INPP5B, gains in 12q23/q24), but were not significant after adjusting for multiple testing. Our findings suggest that there are no strong determinants of patient outcome with respect to GE data and CNAs in FL patients treated with a chemotherapy-free regimen (i.e. rituximab +/- interferon).

Identifiants

pubmed: 37683053
doi: 10.1080/10428194.2023.2240462
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

1-11

Auteurs

E Leich (E)

Institute of Pathology, University of Würzburg, Comprehensive Cancer Center Mainfranken, Würzburg, Germany.

Marianne Brodtkorb (M)

Department of Oncology, Oslo University Hospital, Oslo, Norway.

T Schmidt (T)

Statistical Bioinformatics, Institute of Functional Genomics, University of Regensburg, Regensburg, Germany.

M Altenbuchinger (M)

Statistical Bioinformatics, Institute of Functional Genomics, University of Regensburg, Regensburg, Germany.
Department of Medical Bioinformatics, University Medical Center Göttingen, Göttingen, Germany.

Ole Christian Lingjærde (OC)

Division of Biomedical Informatics, Department of Computer Science, University of Oslo, Norway.

S Lockmer (S)

Division of Hematology, Department of Medicine at Huddinge, Karolinska Institutet, Stockholm, Sweden.

H Holte (H)

Department of Oncology, Oslo University Hospital, Oslo, Norway.

T Nedeva (T)

Institute of Pathology, University of Würzburg, Comprehensive Cancer Center Mainfranken, Würzburg, Germany.

T Grieb (T)

Institute of Pathology, University of Würzburg, Comprehensive Cancer Center Mainfranken, Würzburg, Germany.

B Sander (B)

Department of Laboratory Medicine, Division of Pathology, Karolinska Institutet, Stockholm, Sweden.

C Sundström (C)

Department of Pathology, Uppsala University Hospital, Uppsala University, Uppsala, Sweden.

R Spang (R)

Statistical Bioinformatics, Institute of Functional Genomics, University of Regensburg, Regensburg, Germany.

E Kimby (E)

Division of Hematology, Department of Medicine at Huddinge, Karolinska Institutet, Stockholm, Sweden.

A Rosenwald (A)

Institute of Pathology, University of Würzburg, Comprehensive Cancer Center Mainfranken, Würzburg, Germany.

Classifications MeSH