Abstract:SummaryUndifferentiated sarcomas (USARCs) of adults are diverse, rare, and aggressive soft tissue cancers. Recent sequencing efforts have confirmed that USARCs exhibit one of the highest burdens of structural aberrations across human cancer. Here, we sought to unravel the molecular basis of the structural complexity in USARCs by integrating DNA sequencing, ploidy analysis, gene expression, and methylation profiling. We identified whole genome duplication as a prevalent and pernicious force in USARC tumorigenes… Show more
“…Currently, SigProfilerMatrixGenerator supports only classifications of small mutational events (i.e., single base substitutions, doublet base substitutions, and small insertions and deletions) as we have previously demonstrated that these classifications generalize across all types of human cancer [14]. While classifications for large mutational events (e.g., copy-number changes and structural rearrangements) have been explored by us and others [29, 32, 33] such classifications have been restricted to individual cancer types and it is unclear whether they will generalize in a pan-tissue setting. Fig.…”
Background
Cancer genomes are peppered with somatic mutations imprinted by different mutational processes. The mutational pattern of a cancer genome can be used to identify and understand the etiology of the underlying mutational processes. A plethora of prior research has focused on examining mutational signatures and mutational patterns from single base substitutions and their immediate sequencing context. We recently demonstrated that further classification of small mutational events (including substitutions, insertions, deletions, and doublet substitutions) can be used to provide a deeper understanding of the mutational processes that have molded a cancer genome. However, there has been no standard tool that allows fast, accurate, and comprehensive classification for all types of small mutational events.
Results
Here, we present SigProfilerMatrixGenerator, a computational tool designed for optimized exploration and visualization of mutational patterns for all types of small mutational events. SigProfilerMatrixGenerator is written in Python with an R wrapper package provided for users that prefer working in an R environment. SigProfilerMatrixGenerator produces fourteen distinct matrices by considering transcriptional strand bias of individual events and by incorporating distinct classifications for single base substitutions, doublet base substitutions, and small insertions and deletions. While the tool provides a comprehensive classification of mutations, SigProfilerMatrixGenerator is also faster and more memory efficient than existing tools that generate only a single matrix.
Conclusions
SigProfilerMatrixGenerator provides a standardized method for classifying small mutational events that is both efficient and scalable to large datasets. In addition to extending the classification of single base substitutions, the tool is the first to provide support for classifying doublet base substitutions and small insertions and deletions. SigProfilerMatrixGenerator is freely available at
https://github.com/AlexandrovLab/SigProfilerMatrixGenerator
with an extensive documentation at
https://osf.io/s93d5/wiki/home/
.
Electronic supplementary material
The online version of this article (10.1186/s12864-019-6041-2) contains supplementary material, which is available to authorized users.
“…Currently, SigProfilerMatrixGenerator supports only classifications of small mutational events (i.e., single base substitutions, doublet base substitutions, and small insertions and deletions) as we have previously demonstrated that these classifications generalize across all types of human cancer [14]. While classifications for large mutational events (e.g., copy-number changes and structural rearrangements) have been explored by us and others [29, 32, 33] such classifications have been restricted to individual cancer types and it is unclear whether they will generalize in a pan-tissue setting. Fig.…”
Background
Cancer genomes are peppered with somatic mutations imprinted by different mutational processes. The mutational pattern of a cancer genome can be used to identify and understand the etiology of the underlying mutational processes. A plethora of prior research has focused on examining mutational signatures and mutational patterns from single base substitutions and their immediate sequencing context. We recently demonstrated that further classification of small mutational events (including substitutions, insertions, deletions, and doublet substitutions) can be used to provide a deeper understanding of the mutational processes that have molded a cancer genome. However, there has been no standard tool that allows fast, accurate, and comprehensive classification for all types of small mutational events.
Results
Here, we present SigProfilerMatrixGenerator, a computational tool designed for optimized exploration and visualization of mutational patterns for all types of small mutational events. SigProfilerMatrixGenerator is written in Python with an R wrapper package provided for users that prefer working in an R environment. SigProfilerMatrixGenerator produces fourteen distinct matrices by considering transcriptional strand bias of individual events and by incorporating distinct classifications for single base substitutions, doublet base substitutions, and small insertions and deletions. While the tool provides a comprehensive classification of mutations, SigProfilerMatrixGenerator is also faster and more memory efficient than existing tools that generate only a single matrix.
Conclusions
SigProfilerMatrixGenerator provides a standardized method for classifying small mutational events that is both efficient and scalable to large datasets. In addition to extending the classification of single base substitutions, the tool is the first to provide support for classifying doublet base substitutions and small insertions and deletions. SigProfilerMatrixGenerator is freely available at
https://github.com/AlexandrovLab/SigProfilerMatrixGenerator
with an extensive documentation at
https://osf.io/s93d5/wiki/home/
.
Electronic supplementary material
The online version of this article (10.1186/s12864-019-6041-2) contains supplementary material, which is available to authorized users.
“…Early transcriptomic studies [8 , 10] have identified a subgroup of inflammatory MFH/UPS, corroborated by most recent studies, albeit non-specifically dedicated to UPS. [11 , 24] Together, these data bring light to modest results obtained with checkpoint inhibitors in unselected STS patients to date. The PEMBROSARC study assessing pembrolizumab and metronomic cyclophosphamide has reported negative results in unselected STS.…”
Section: Discussionmentioning
confidence: 80%
“…who reported significant enrichment for immune-related pathways in a subset of undifferentiated sarcomas (US) characterized by higher mutational burden, as well as another subset of US characterized by a high rearrangement level. [24] Several studies have investigated how underlying genomic alterations may drive the composition of the tumor micro-environnement in solid tumors. Recent publications in other diseases have showed correlation between mutation burden and cytotoxic immune infiltrates, but also association of high level of CNA with low immune infiltrates and worse response to immune checkpoint blockade [19 , 20 , 28 , 29].…”
Background
Undifferentiated pleomorphic sarcoma (UPS) is the most frequent, aggressive and less-characterized sarcoma subtype. This study aims to assess UPS molecular characteristics and identify specific therapeutic targets.
Methods
High-throughput technologies encompassing immunohistochemistry, RNA-sequencing, whole exome-sequencing, mass spectrometry, as well as radiomics were used to characterize three independent cohorts of 110, 25 and 41 UPS selected after histological review performed by an expert pathologist. Correlations were made with clinical outcome. Cell lines and xenografts were derived from human samples for functional experiments.
Findings
CD8 positive cell density was independently associated with metastatic behavior and prognosis. RNA-sequencing identified two main groups: the group A, enriched in genes involved in development and stemness, including FGFR2, and the group B, strongly enriched in genes involved in immunity. Immune infiltrate patterns on tumor samples were highly predictive of gene expression classification, leading to call the group B ‘
immune-high’
and the group A ‘
immune-low’.
This molecular classification and its prognostic impact were confirmed on an independent cohort of UPS from TCGA. Copy numbers alterations were significantly more frequent in immune-low UPS. Proteomic analysis identified two main proteomic groups that highly correlated with the two main transcriptomic groups. A set of nine radiomic features from conventional MRI sequences provided the basis for a radiomics signature that could select immune-high UPS on their pre-therapeutic imaging. Finally,
in vitro
and
in vivo
anti-tumor activity of FGFR inhibitor JNJ-42756493 was selectively shown in cell lines and patient-derived xenograft models derived from immune-low UPS.
Interpretation
Two main disease entities of UPS, with distinct immune phenotypes, prognosis, molecular features and MRI textures, as well as differential sensitivity to specific anticancer agents were identified. Immune-high UPS may be the best candidates for immune checkpoint inhibitors, whereas this study provides rational for assessing FGFR inhibition in immune-low UPS.
Funding
This work was partly founded by a grant from La Ligue.
“…There is also emerging evidence that extraction of copy number signatures from a common cancer, such as high grade serous ovarian carcinoma, provides more robust prognostic information than pathological grading or the analysis of single gene mutations [25]. In sarcomas, copy number signatures have proved useful in understanding the evolutionary trajectories of the genomically complex cancers [26]. In a meta-analysis study of >5000 samples of 12 cancer types, some patterns of somatic copy number alterations were associated with reduced expression of cytotoxic immune signatures [27].…”
Section: Bioinformatics Analysis For Multi-omic Profiling Of Mpnstsmentioning
The Genomics of Malignant Peripheral Nerve Sheath Tumor (GeM) Consortium is an international collaboration focusing on multi-omic analysis of malignant peripheral nerve sheath tumors (MPNSTs), the most aggressive tumor associated with neurofibromatosis type 1 (NF1). Here we present a summary of current knowledge gaps, a description of our consortium and the cohort we have assembled, and an overview of our plans for multi-omic analysis of these tumors. We propose that our analysis will lead to a better understanding of the order and timing of genetic events related to MPNST initiation and progression. Our ten institutions have assembled 96 fresh frozen NF1-related (63%) and sporadic MPNST specimens from 86 subjects with corresponding clinical and pathological data. Clinical data have been collected as part of the International MPNST Registry. We will characterize these tumors with bulk whole genome sequencing, RNAseq, and DNA methylation profiling. In addition, we will perform multiregional analysis and temporal sampling, with the same methodologies, on a subset of nine subjects with NF1-related MPNSTs to assess tumor heterogeneity and cancer evolution. Subsequent multi-omic analyses of additional archival specimens will include deep exome sequencing (500×) and high density copy number arrays for both validation of results based on fresh frozen tumors, and to assess further tumor heterogeneity and evolution. Digital pathology images are being collected in a cloud-based platform for consensus review. The result of these efforts will be the largest MPNST multi-omic dataset with correlated clinical and pathological information ever assembled.
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