Browse
Submit Data
Databases
API
Help

Dataset Information

23 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Mmsig: a fitting approach to accurately identify somatic mutational signatures in hematological malignancies.

ABSTRACT: Mutational signatures have emerged as powerful biomarkers in cancer patients, with prognostic and therapeutic implications. Wider clinical utility requires access to reproducible algorithms, which allow characterization of mutational signatures in a given tumor sample. Here, we show how mutational signature fitting can be applied to hematological cancer genomes to identify biologically and clinically important mutational processes, highlighting the importance of careful interpretation in light of biological knowledge. Our newly released R package mmsig comes with a dynamic error-suppression procedure that improves specificity in important clinical and biological settings. In particular, mmsig allows accurate detection of mutational signatures with low abundance, such as those introduced by APOBEC cytidine deaminases. This is particularly important in the most recent mutational signature reference (COSMIC v3.1) where each signature is more clearly defined. Our mutational signature fitting algorithm mmsig is a robust tool that can be implemented immediately in the clinic.

SUBMITTER: Rustad EH

PROVIDER: S-EPMC8007623 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

A practical guide for mutational signature analysis in hematological malignancies.

Project description:Analysis of mutational signatures is becoming routine in cancer genomics, with implications for pathogenesis, classification, prognosis, and even treatment decisions. However, the field lacks a consensus on analysis and result interpretation. Using whole-genome sequencing of multiple myeloma (MM), chronic lymphocytic leukemia (CLL) and acute myeloid leukemia, we compare the performance of public signature analysis tools. We describe caveats and pitfalls of de novo signature extraction and fitting approaches, reporting on common inaccuracies: erroneous signature assignment, identification of localized hyper-mutational processes, overcalling of signatures. We provide reproducible solutions to solve these issues and use orthogonal approaches to validate our results. We show how a comprehensive mutational signature analysis may provide relevant biological insights, reporting evidence of c-AID activity among unmutated CLL cases or the absence of BRCA1/BRCA2-mediated homologous recombination deficiency in a MM cohort. Finally, we propose a general analysis framework to ensure production of accurate and reproducible mutational signature data.

| S-EPMC6611883 | biostudies-literature

Mutational signatures of ionizing radiation in second malignancies.

Project description:Ionizing radiation is a potent carcinogen, inducing cancer through DNA damage. The signatures of mutations arising in human tissues following in vivo exposure to ionizing radiation have not been documented. Here, we searched for signatures of ionizing radiation in 12 radiation-associated second malignancies of different tumour types. Two signatures of somatic mutation characterize ionizing radiation exposure irrespective of tumour type. Compared with 319 radiation-naive tumours, radiation-associated tumours carry a median extra 201 deletions genome-wide, sized 1-100 base pairs often with microhomology at the junction. Unlike deletions of radiation-naive tumours, these show no variation in density across the genome or correlation with sequence context, replication timing or chromatin structure. Furthermore, we observe a significant increase in balanced inversions in radiation-associated tumours. Both small deletions and inversions generate driver mutations. Thus, ionizing radiation generates distinctive mutational signatures that explain its carcinogenic potential.

| S-EPMC5027243 | biostudies-literature

Gene expression profiling identifies IRF4-associated molecular signatures in hematological malignancies.

Project description:The lymphocyte-specific transcription factor Interferon (IFN) Regulatory Factor 4 (IRF4) is implicated in certain types of lymphoid and myeloid malignancies. However, the molecular mechanisms underlying its interactions with these malignancies are largely unknown. In this study, we have first profiled molecular signatures associated with IRF4 expression in associated cancers, by analyzing existing gene expression profiling datasets. Our results show that IRF4 is overexpressed in melanoma, in addition to previously reported contexts including leukemia, myeloma, and lymphoma, and that IRF4 is associated with a unique gene expression pattern in each context. A pool of important genes involved in B-cell development, oncogenesis, cell cycle regulation, and cell death including BATF, LIMD1, CFLAR, PIM2, and CCND2 are common signatures associated with IRF4 in non-Hodgkin B cell lymphomas. We confirmed the correlation of IRF4 with LIMD1 and CFLAR in a panel of cell lines derived from lymphomas. Moreover, we profiled the IRF4 transcriptome in the context of EBV latent infection, and confirmed several genes including IFI27, IFI44, GBP1, and ARHGAP18, as well as CFLAR as novel targets for IRF4. These results provide valuable information for understanding the IRF4 regulatory network, and improve our knowledge of the unique roles of IRF4 in different hematological malignancies.

| S-EPMC4160201 | biostudies-literature

XPO1 in B cell hematological malignancies: from recurrent somatic mutations to targeted therapy.

Project description:Many recent publications highlight the large role of the pivotal eukaryotic nuclear export protein exportin-1 (XPO1) in the oncogenesis of several malignancies, and there is emerging evidence that XPO1 inhibition is a key target against cancer. The clinical validation of the pharmacological inhibition of XPO1 was recently achieved with the development of the selective inhibitor of nuclear export compounds, displaying an interesting anti-tumor activity in patients with massive pre-treated hematological malignancies. Recent reports have shown molecular alterations in the gene encoding XPO1 and showed a mutation hotspot (E571K) in the following two hematological malignancies with similar phenotypes and natural histories: primary mediastinal diffuse large B cell lymphoma and classical Hodgkin's lymphoma. Emerging evidence suggests that the mutant XPO1 E571K plays a role in carcinogenesis, and this variant is quantifiable in tumor and plasma cell-free DNA of patients using highly sensitive molecular biology techniques, such as digital PCR and next-generation sequencing. Therefore, it was proposed that the XPO1 E571K variant may serve as a minimal residual disease tool in this setting. To clarify and summarize the recent findings on the role of XPO1 in B cell hematological malignancies, we conducted a literature search to present the major publications establishing the landscape of XPO1 molecular alterations, their impact on the XPO1 protein, their interest as biomarkers, and investigations into the development of new XPO1-targeted therapies in B cell hematological malignancies.

| S-EPMC5307790 | biostudies-literature

Mutational patterns and their correlation to CHIP-related mutations and age in hematological malignancies.

Project description:Acquired somatic mutations are crucial for the development of most cancers. We performed a comprehensive comparative analysis of the mutational landscapes and their correlation with CHIP-related (clonal hematopoiesis of indeterminate potential) mutations and patient age of 122 genes in 3096 cases of 28 different hematological malignancies. Differences were observed regarding (1) the median number of mutations (highest, median n = 4; lowest, n = 0); (2) specificity of certain mutations (high frequencies in atypical chronic myeloid leukemia [aCML; ASXL1, 86%], follicular lymphoma [FL; KMT2D, 87%; CREBBP, 73%], hairy cell lymphoma [BRAF, 100%], lymphoplasmacytic lymphoma [MYD88, 98%; CXCR4, 51%], myeloproliferative neoplasm [MPN; AK2, 68%]); (3) distribution of mutations (broad distribution within/across the myeloid/lymphoid lineage for TET2, ASXL1, DNMT3A, TP53, BCOR, and ETV6); (4) correlation of mutations with patient's age (correlated with older age across entities: TET2, DNMT3A, ASXL1, TP53, EZH2, BCOR, GATA2, and IDH2; younger age: KIT, POT1, RAD21, U2AF2, and WT1); (5) correlation of mutation number per patient with age. Moreover, we observed high frequencies of mutations in RUNX1, SRSF2, IDH2, NRAS, and EZH2 in cases comprising at least 1 DTA (DNMT3A, TET2, ASXL1) mutation, whereas in cases without DTA mutations, TP53, KRAS, WT1, and SF3B1 were more frequent across entities, suggesting differences in pathophysiology. These results give further insight into the complex genetic landscape and the role of DTA mutations in hematological neoplasms and define mutation-driven entities (myelodysplastic syndrome/MPN overlap; secondary acute myeloid) in comparison with entities defined by chromosomal fusions (chronic myeloid leukemia; myeloid/lymphoid neoplasm with eosinophilia).

| S-EPMC8579257 | biostudies-literature

An ensemble approach to accurately detect somatic mutations using SomaticSeq.

Project description:SomaticSeq is an accurate somatic mutation detection pipeline implementing a stochastic boosting algorithm to produce highly accurate somatic mutation calls for both single nucleotide variants and small insertions and deletions. The workflow currently incorporates five state-of-the-art somatic mutation callers, and extracts over 70 individual genomic and sequencing features for each candidate site. A training set is provided to an adaptively boosted decision tree learner to create a classifier for predicting mutation statuses. We validate our results with both synthetic and real data. We report that SomaticSeq is able to achieve better overall accuracy than any individual tool incorporated.

| S-EPMC4574535 | biostudies-literature

A pooled mutational analysis identifies ionizing radiation-associated mutational signatures conserved between mouse and human malignancies.

Project description:Single nucleotide variants (SNVs) identified in cancer genomes can be de-convolved using non-negative matrix factorization (NMF) into discrete trinucleotide-based mutational signatures indicative of specific cancer-causing processes. The stability of NMF-generated mutational signatures depends upon the numbers of variants available for analysis. In this work, we sought to assess whether data from well-controlled mouse models can compensate for scarce human data for some cancer types. High quality sequencing data from radiotherapy-induced cancers is particularly scarce and the mutational processes defining ionizing radiation (IR)-induced mutagenesis in vivo are poorly defined. Here, we combine sequencing data from mouse models of IR-induced malignancies and human IR-induced malignancies. To determine whether the signatures identified from IR-exposed subjects can be differentiated from other mutagenic signatures, we included data from an ultraviolet radiation (UV)-induced human skin cancer and from a mouse model of urethane-induced cancers. NMF distinguished all three mutagens and in the pooled analysis IR was associated with mutational signatures common to both species. These findings illustrate the utility of pooled analysis of mouse and human sequencing data.

| S-EPMC5550450 | biostudies-literature

Improvement in prediction of prostate cancer prognosis with somatic mutational signatures.

Project description:Prostate cancer is a leading male malignancy worldwide, while the prognosis prediction remains quite inaccurate. The study aimed to observe whether there was an association between the prognosis of prostate cancer and genetic mutation profile, and to build an accurate prognostic predictor based on the genetic signatures. The patients diagnosed of prostate cancer from The Cancer Genomic Atlas were used for prognostic stratification, while the somatic gene mutation profiles were compared between different prognostic groups. The genetic features were further used for training machine-learning models to predict prostate cancer prognosis. No significant gene with somatic mutation rate difference was found between prognostic groups of prostate cancer. Total 43 atypical genes were screened for building a support vector machine model to predict prostate cancer prognosis, with an average accuracy of 66% and 64% for 5-fold cross-validation or training-testing evaluation respectively. When combined with the National Institute for Health and Care Excellence (NICE) features, the model could be further improved, with the 5-fold cross-validation accuracy of ~71%, much better than NICE itself (62%). To our knowledge, for the first time, the research studied the relationship of genome-wide somatic mutations with prostate prognosis, and developed an effective prognostic prediction model with the atypical genetic signatures.

| S-EPMC5665042 | biostudies-literature

Immunotherapy for hematological malignancies.

Project description:Tumor immune tolerance remains a major barrier for effective anti-cancer therapy. A growing number of pathways whereby solid tumors escape immune surveillance have been characterized (1). This progress led us to revisit the "hallmarks of cancer" and brought forward many promising immunotherapies. Every growing bodies of research have brought forward many exciting treatment strategies for hematological cancers like chimeric antigen receptor T cells (CAR-T cells) and immune checkpoint inhibitors. Given the distinct characteristics of the different cancers, some benefited profoundly from such therapies while some remain challenging for scientists and physicians. Here, we discuss the unique aspect of hematological malignancies, and briefly review the history, existing and future of immunotherapies for this group of cancer.

| S-EPMC6709701 | biostudies-literature

Telomerase Activation in Hematological Malignancies.

Project description:Telomerase expression and telomere maintenance are critical for cell proliferation and survival, and they play important roles in development and cancer, including hematological malignancies. Transcriptional regulation of the rate-limiting subunit of human telomerase reverse transcriptase gen (hTERT) is a complex process, and unveiling the mechanisms behind its reactivation is an important step for the development of diagnostic and therapeutic applications. Here, we review the main mechanisms of telomerase activation and the associated hematologic malignancies.

| S-EPMC5039560 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data