Dataset Information

Histological Transformation and Progression in Follicular Lymphoma: a Clonal Evolution Study

ABSTRACT: Background: Follicular lymphoma (FL) is an indolent, yet incurable B-cell malignancy. A subset of patients experience increased mortality rate driven by two distinct clinical end points: histological transformation and early progression after immuno-chemotherapy. The nature of tumor clonal dynamics leading to these clinical endpoints is poorly understood and previously determined genetic alterations do not explain the majority of transformed cases or accurately predict early progressive disease. We contend that detailed knowledge of the expansion patterns of specific cell populations plus their associated mutations would provide insight into therapeutic strategies and disease biology over the timecourse of FL clinical histories. Methods and Findings: Using a combination of whole genome sequencing, targeted deep sequencing and digital droplet PCR on matched diagnostic and relapse specimens, we deciphered the constituent clonal populations in n=15 transformation patients and n=6 progressors, and measured their change in abundance over time. We observed widely divergent patterns of clonal dynamics in transformed cases relative to progressed cases. Transformation specimens were generally composed of clones that were rare or absent in diagnostic specimens, consistent with dramatic clonal expansions that came to dominate the transformation specimen. This pattern was independent of time to transformation and treatment modality. By contrast, progression specimens were composed of clones that were already present in the diagnostic specimens and exhibited only moderate clonal dynamics, even in the presence of immuno-chemotherapy. Analysis of somatic mutations impacting 94 genes was undertaken in a validation cohort consisting of 395 samples from 277 patients in order to decipher disrupted biology in the two clinical endpoints. We found 12 genes were more commonly mutated in transformed samples than in preceding FL, including TP53, B2M, CCND3, GNA13, S1PR2 and P2RY8. Moreover, 10 genes were more commonly mutated in diagnostic specimens of early progressors including TP53, BTG1, MKI67 and XBP1. Conclusions: Our results illuminate contrasting modes of evolution shaping the clinical histories of transformation and progression. They have implication for interpretation of evolutionary dynamics in the context of treatment-induced selective pressures, and indicate that transformation and progression will require different clinical management strategies.

PROVIDER: EGAS00001001709 | EGA |

REPOSITORIES: EGA

ACCESS DATA

Similar Datasets

Project description:Background: In a previous study of patients with assumed benign IgA nephropathy (IgAN), we have shown that 18.6% develop a progressive clinical course, which was not predicted by any known clinical nor histopathological parameters (Knoop 2017). We aim to differentiate between future progressors and non-progressors with assumed benign IgAN by using glomerular transcriptomics from diagnostic kidney biopsies and to investigate whether this can identify drugs to be used in those patients. Methods: We included adult progressors (n=15) and patients with clinical remission (non-progressors, n=21) from our previously reported IgAN cohort. Glomerular cross-sections were obtained through laser-capture microdissection from 10µm archival kidney biopsy sections for mRNA sequencing, using the SMARTer Stranded Total RNA-Seq kit – Pico Input Mammalian (Clontech Laboratories, California, USA). Key results were validated in external cohorts and on the protein level with immunohistochemistry in the discovery cohort and an internal validation cohort. We also compared our transcriptomic findings with an external genome-wide association study (GWAS) dataset (Kiryluk 2021). Results: For the statistical analyses, we studied 8 progressors and 9 non-progressors. Based on the 1,240 differentially expressed glomerular genes from the initial kidney biopsies, we were able to identify future IgAN progressors and non-progressors using a two-component classifier. The classifier predicted disease progression with 88% accuracy, 75% sensitivity and 100% specificity (AUC=0.875), more accurate than the recommended International IgAN Prediction Tool in this specific patient cohort. Among our differentially expressed genes, 10 were also found in the GWAS dataset (Kiryluk 2021). We identified 45 drug targets in the DrugBank database, including angiotensinogen, a target of sparsentan, currently investigated as therapy in IgAN (NCT03762850 and NCT04663204). The findings on upregulation of angiotensinogen, which was independent of blood pressure, were validated in an internal IgAN validation cohort. Interestingly, complement activation did not play a major role in our data. Our work suggests that LXR, FXR and macrophage activation pathways could be critically involved in the inhibition of the progression of low-risk ccRCC. Furthermore, a 10-component classifier could support an early identification of apparently low-risk ccRCC patients.

Dataset Information

Histological Transformation and Progression in Follicular Lymphoma: a Clonal Evolution Study

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure