Deciphering the Abnormally Mutated Molecular Processes in Chronic Lymphocytic Leukemia: Identification of Clinically Relevant Mutation Subtypes

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Deciphering the Abnormally Mutated Molecular Processes in Chronic Lymphocytic Leukemia: Identification of Clinically Relevant Mutation Subtypes

Popis výsledku v původním jazyce

Chronic lymphocytic leukemia (CLL) is the most common form of adult leukemia in the Western world with a highly variable clinical course. Although the CLL genetic landscape has been well-described, patient stratification based on mutation profiles remains elusive mainly due to the heterogeneity of data. Mutations in some driver genes (e.g. TP53 and ATM mutations) are associated with worse clinical outcome whereas, in other instances, reports of prognostic relevance are inconsistent (e.g. NOTCH1 and SF3B1 mutations). Many driver genes cluster in specific signaling pathways, however, in a significant proportion of patients, no known recurrent mutation is found. Here, we aimed to identify clinically relevant patients&apos; subtypes base on their abnormally mutated molecular processes. Mutation data are inherently sparse which makes clustering challenging. Firstly, we attempted to decrease the heterogeneity of somatic mutation data limited to protein-coding regions by mapping mutated genes to the respective biological processes. We tested our approach on the sequencing data gathered by the International Cancer Genome Consortium for 506 CLL patients. We applied ensemble clustering on the pathway mutation score and extracted abnormal molecular pathways with a machine learning approach. We identified four clusters differing in pathway mutational profiles and time to first treatment. Among the most important signatures for the identified groups, biological processes previously described as recurrently mutated in CLL appeared (DNA-damage response, RNA processing, and inflammatory pathways). Additionally, processes known to play a vital role in CLL biology but without previously described mutated components in CLL, such as calcium signaling, were identified. Interestingly, common CLL drivers such as ATM or TP53 were associated with particular subtypes, while others like NOTCH1 or SF3B1 were not. Since many mutations in non-coding regions can have a detrimental effect on the proteins, we decided to employ the CADD score that combines multiple diverse genomic features such as evolutionary constraint, epigenetic measurements, and functional predictions into one metric. Subsequently, we calculated pathway mutation score based on CADD. Moreover, this approach enabled us to combine somatic and germinal mutation profiles. Using the merged somatic and germline data from both coding and non-coding regions, we discovered novel disease subtypes. Currently, we are evaluating their clinical and biological relevance. Finally, we leveraged the knowledge and computational approaches developed while exploring publicly available datasets on a local patient cohort from the University Hospital Brno. We collected samples before and after particular therapies from 52 CLL patients with known clinical courses and different scenarios of TP53 gene mutation expansion. We identified distinct mutated pathways (chromatin SWI/SNF complex, genome integrity, and RTK signaling) characteristic for defined patient groups, and co-occurring and mutually exclusive mutations. Then, we clustered patients based on their pathway mutation score and found clusters enriched with predefined particular groups. Our results aid the understanding of mutational patterns in CLL, which is necessary for the accurate use of available treatment regimens and for the design of suitable diagnostic panels.

Název v anglickém jazyce

Deciphering the Abnormally Mutated Molecular Processes in Chronic Lymphocytic Leukemia: Identification of Clinically Relevant Mutation Subtypes

Popis výsledku anglicky

Chronic lymphocytic leukemia (CLL) is the most common form of adult leukemia in the Western world with a highly variable clinical course. Although the CLL genetic landscape has been well-described, patient stratification based on mutation profiles remains elusive mainly due to the heterogeneity of data. Mutations in some driver genes (e.g. TP53 and ATM mutations) are associated with worse clinical outcome whereas, in other instances, reports of prognostic relevance are inconsistent (e.g. NOTCH1 and SF3B1 mutations). Many driver genes cluster in specific signaling pathways, however, in a significant proportion of patients, no known recurrent mutation is found. Here, we aimed to identify clinically relevant patients&apos; subtypes base on their abnormally mutated molecular processes. Mutation data are inherently sparse which makes clustering challenging. Firstly, we attempted to decrease the heterogeneity of somatic mutation data limited to protein-coding regions by mapping mutated genes to the respective biological processes. We tested our approach on the sequencing data gathered by the International Cancer Genome Consortium for 506 CLL patients. We applied ensemble clustering on the pathway mutation score and extracted abnormal molecular pathways with a machine learning approach. We identified four clusters differing in pathway mutational profiles and time to first treatment. Among the most important signatures for the identified groups, biological processes previously described as recurrently mutated in CLL appeared (DNA-damage response, RNA processing, and inflammatory pathways). Additionally, processes known to play a vital role in CLL biology but without previously described mutated components in CLL, such as calcium signaling, were identified. Interestingly, common CLL drivers such as ATM or TP53 were associated with particular subtypes, while others like NOTCH1 or SF3B1 were not. Since many mutations in non-coding regions can have a detrimental effect on the proteins, we decided to employ the CADD score that combines multiple diverse genomic features such as evolutionary constraint, epigenetic measurements, and functional predictions into one metric. Subsequently, we calculated pathway mutation score based on CADD. Moreover, this approach enabled us to combine somatic and germinal mutation profiles. Using the merged somatic and germline data from both coding and non-coding regions, we discovered novel disease subtypes. Currently, we are evaluating their clinical and biological relevance. Finally, we leveraged the knowledge and computational approaches developed while exploring publicly available datasets on a local patient cohort from the University Hospital Brno. We collected samples before and after particular therapies from 52 CLL patients with known clinical courses and different scenarios of TP53 gene mutation expansion. We identified distinct mutated pathways (chromatin SWI/SNF complex, genome integrity, and RTK signaling) characteristic for defined patient groups, and co-occurring and mutually exclusive mutations. Then, we clustered patients based on their pathway mutation score and found clusters enriched with predefined particular groups. Our results aid the understanding of mutational patterns in CLL, which is necessary for the accurate use of available treatment regimens and for the design of suitable diagnostic panels.

Druh

O - Ostatní výsledky

CEP obor

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OECD FORD obor

30204 - Oncology

Projekt

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Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2021

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů