Integrated Genomic and Transcriptomic Analysis Revealed Mutation Patterns of De-differentiated Liposarcoma and Leiomyosarcoma

doi:10.21203/rs.3.rs-21114/v1

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Research article

Integrated Genomic and Transcriptomic Analysis Revealed Mutation Patterns of De-differentiated Liposarcoma and Leiomyosarcoma

https://doi.org/10.21203/rs.3.rs-21114/v1

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published 28 Oct, 2020

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Background Treating patients with advanced sarcomas is challenging due to great histologic diversity among different subtypes. Leiomyosarcoma (LMS) and liposarcoma (LPS) are the most common histologic types of soft tissue sarcoma (STS). Although both LMS and de-differentiated LPS (DDLPS) are relatively aggressive and have a high rate of recurrence, they have significant differences in histology, biology, and clinical behaviors.

Methods 32 STS (20 LMS and 12 DDLPS) samples and matched peripheral blood were collected from Zhongshan Hospital, Fudan University with patient consents. WES was conducted on all the tumor/blood pairs. Somatic single nucleotide variations (SNV) and indel were identified using Genome Analysis Toolkits (GATK). Recurrent somatic copy-number alterations (SCNA) were called using GISTIC2.0. RNA-Seq was further conducted, then their chromosome rearrangement was revealed using STAR-Fusion. Next, differential gene expression analysis and pathway enrichment analysis were performed using DeSeq2 and NetwokAnalyst. Stromal and immunological landscape profiling was conducted using MCP-counter.

Results WES identified different genetic variation patterns in LMS and DDLPS. Amplification of oncogenes is prominent in DDLPS, while loss of function mutation and deletion of tumor suppressors mostly occur in LMS. A focal amplification affecting chromosome 12q13–15 region which encodes MDM2, CDK4 and HMGA2, is notable in DDLPS. Mutations in tumor suppressors such as TP53, ATRX, PTEN, and RB1 are identified in LMS. RNA-seq confirmed overexpression of MDM2, CDK4 and HMGA2 in DDLPS and down-regulation of TP53 and RB1 in LMS. It also revealed different fusion patterns between LMS and DDLPS. The rate of gene fusion is significantly higher in DDLPS than in LMS (p=0.007) and fusion transcripts involving chromosome 12 can only be found in DDLPS. Stromal and immunological profiling indicate that microenvironmental signatures are highly correlated with histologic types, and DDLPS have more endothelial cell and fibroblast content than LMS.

Conclusions Our analysis revealed recurrent genetic variation in LMS and DDLPS. DDLPS harbor frequent fusion events and may produce neoepitopes, which can be targeted to produce safer CAR-T (Chimeric Antigen Receptor T) cells for immunotherapy. Additionally, our study suggested the tumor microenvironment of sarcoma can potentially be correlated to patients' clinical outcomes and used to guide personalized treatment.

Cancer Biology

Oncology

Whole exome sequencing

Sarcoma

De-differentiated liposarcoma

Leiomyosarcoma

Tumor microenvironment

Immune infiltration

Although a rare malignant tumor, STS has a great diversity. Its crude incidence is 2.91 per 100,000 people and it accounts for 1.05% of overall cancer incidents in China [1]. It has more that 50 recognized subtypes[2] and this situation has made the accurate diagnosis of STS and treatment accordingly a very difficult task. Among the most frequent STS subtypes are LPS and LMS, which make up 5.75% and 5.97% of all STS cases respectively[1]. LMS and a subtype of LPS, DDLPS are the most aggressive among all STS and have a high rate of recurrence. However, they differ significantly in histology, biology, and clinical behavior[3]. For example, DDLPS tends to recur locally, while LMS has a higher chance of distant recurrence. In order to understand the key genetic and molecular differences that distinct LMS and DDLPS, therefore to gain insight about their origins and enlighten differential treatment, we performed WES and transcriptomic analyses of LMS and DDLPS. We found that in both LMS and DDLPS, the somatic mutation rate is low. When compared with LMS, DDLPS has a higher ratio of monocyte infiltration (p = 0.05), and more chromosomal instability (CIN) events including SCNA and gene fusion. Previous study indicated that immune infiltration in tumor is prognostic for anticancer therapies[4, 5]. Genomics and transcriptome based individualized therapy in sarcoma have shown a survival benefit[6]. The results of our analysis can potentially be correlated to the response of LMS and DDLPS to chemotherapy, targeted therapy, or immunotherapy, and can be used to develop biomarkers of therapeutic choice.

Patients and specimens

20 LMS and 12 DDLPS samples and matched peripheral blood were collected at Zhongshan Hospital, Fudan University with patient consent and the study protocol was reviewed and approved by the Ethics Committee of Zhongshan Hospital. H&E stained histological sections were reviewed by an expert pathologist to confirm subtype diagnosis and tumor content, which is above 20% tumor cells in all samples. The cytogenetic, IHC, and pathologic lab data as well as clinical treatment and outcome records were manually curated from patient electronic medical records.

WES

Tumor DNA was extracted using the MagMAX_FFPE_DNA_RNA_Ultra kit from FFPE tissues, and the paired germline DNA was extracted using the ThermoFisher Maxwell RSC blood DNA kit (cat# AS1400, Promega) from the peripheral blood. DNA was sheared with a Covaris L220 sonicator and hybridized to the probes in the Agilent SureSelect XT Human All Exon V7 kit for exome enrichment. Captured exome DNA was PCR amplified, end-repaired, and attached to the adapters and barcode using the SureSelect XT HS and Low Input Library Preparation Kit for ILM (Pre PCR) kit according to manufacturer’s specification to prepare the sequencing libraries. The libraries were sequenced on an Illumina NovaSeq-6000 Sequencing System. 150 × 150-bp paired-end reads were generated and the image analysis and base calling were done using the Illumina CAVSAVR program with default parameters. Mutation analysis and visualization were performed using the R v3.3.3, Bioconductor v3.4[7] (http://www.Bioconductor.org), and MAFtools[8]. Mutation data in maf format was generated using VCF2MAF v1.6.16[9]. Control-FREEC is selected to detect CNAs on the individual sample level using WES data[10]. For WES data, the window size was set to 500, and the step size was set to 250 as recommended by the software authors. Recurrent focal and broad CNAs were identified by GISTIC2.0[11].

Rna-seq

RNA was purified from FFPE tissue using the MagMAX_FFPE_DNA_RNA_Ultra kit, retrotranscribed using the NEBNext RNA First Strand Synthesis Module (cat# E7525S, NEB) and NEBNext mRNA Second Strand Synthesis Module (cat# E6111S, NEB) for cDNA Synthesis. Libraries are prepared from the cDNA using the SureSelect XT HS and Low Input Library Preparation Kit for ILM (Pre PCR) following manufacturer’s instructions. The libraries were sequenced on an Illumina NovaSeq-6000 Sequencing System following Illumina-provided protocols for 150 × 150 paired-end sequencing.

Differential Gene Expression Analysis

RNAseq reads were aligned using StringTie2 (version 1.3.5)[12] and a count matrix was generated. The raw read counts were further normalized by the upper quartile counts and the list of genes expressed differentially between LMS and DDLPS was generated using DESeq2[13].

Tumor Mutational Burden And Microsatellite Status

Tumor mutational burden (TMB) was calculated from the total number of nonsynonymous somatic mutation using a published algorithm [14]. All autosomal microsatellite tracts containing 1–5 bp repeating subunits in length and comprising five or more repeats in GRCh37/hg19 were identified using MISA (http://pgrc.ipk-gatersleben.de/misa/misa.html) [15]. An MSI (Microsatellite Instability) score (number of unstable microsatellite sites / total valid sites) of ≥ 3.5% was defined as microsatellite instability-high (MSI-H) and < 3.5% as microsatellite instability-low (MSI-L) as previously described [16].

Pathway Analysis

Biological pathway analysis was done with the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis [17] and Gene Set Enrichment Analysis (GSEA) [18] softwares using the above gene expression data and visualized using the NetworkAnalyst (http://www.networkanalyst.ca) software [19].

Rna Fusion Analysis

Sequencing reads were mapped to hg19 with STAR (version 020201) [20] and assembled with StringTie2 (version 1.3.5)[12] using the default parameters. STAR-FUSION 1.8.0[21] was used to identify fusion genes with the standard settings, except a custom filter of at least 3 positive reads per event was applied to increase the calling stringency.

Immune classification of STS

Microenvironment Cell Populations-counter (MCP-counter) method was used to profile tumor microenvironment infiltrating immune cells with the RNA-seq derived Transcripts Per Million (TPM) matrix across all samples generated in the stringtie2 pipeline[22]. Abundance scores for eight immune populations (T cells, CD8+ T cells, cytotoxic lymphocytes, natural killer cells, B cell lineage, monocytic lineage, myeloid dendritic cells and neutrophils), and two stromal populations (endothelial cells and fibroblasts) are calculated using signatures composition defined previously[22]. Unsupervised clustering of samples in each cohort was performed based on the metagene Z-score for the included populations of MCP-counter[22].

Statistical analysis

Statistical analysis was performed with R (version 3.6.3) or SPSS (version 25). Unless stated otherwise, unpaired t-test was used in all group comparisons. A result of P < 0.05 was considered significant.

In the hope of improving STS diagnosis and treatment, 32 STS, including 20 LMS and 12 DDLPS samples and the matched peripheral blood collected at Zhongshan Hospital, Fudan University were subjected to WES analysis. The patient characteristics are indicated in Table 1.

Table 1

Clinical information, TMB and MSI status of patients.
	All	LMS	DDLPS
Sample size	32	20	12
Age ( years)
Median	51	49.5	54
Range	16–70	16–65	37–70
Gender (%)
Male	12 (37.5%)	3 (15%)	9 (75%)
Female	20 (62.5%)	17 (85%)	3 (25%)
TMB
Median	82.5	91.5	7.25
Range	42–563	55–563	42–158
MSI (%)
High	1 (3.1%)	1 (5%)	0 (0%)
Low	31 (96.9%)	19 (95%)	12 (100%)

Top Mutated Genes

We first investigated the mutation pattern of the patients. Overall, the tumor mutation burden (TMB) of STS is relatively low compared with other cancers[3]. The average TMB of the 20 LMS is significantly higher than that of the 12 DDLPS (162.35, vs. 123.58, P = 0.028). The top 20 genes in mutation frequency in the 32 STS samples are MUC16, PABPC3, MUC4, MUC6, TP53, AHNAK2, FLG, MUC17, MUC3A, DCHS1, HRNR, KRTAP10-6, NBPF10, MUC12, TTN, NBPF1, BIRC5, FLG2, PABPC1, and TPSAB1 (Fig. 1). Among them, TP53 (mutation frequency 34%) is a well-known tumor suppressor. The other genes are not listed in oncoKB (https://www.oncokb.org/cancerGenes) as common cancer genes. However, MUC16 is a type I transmembrane protein and is known to promote cancer cell proliferation and inhibit anti-cancer immune responses in multiple cancer types[23–25]. In a transgenic mouse model, p53 mutant animals which over-expressed MUC16 carboxy-terminal fragment developed more spontaneous tumors (sarcomas) when comparing to the animals which did not overexpress the MUC16 fragment [26].

When the samples are divided to LMS and DDLPS, the top20 genes with the highest difference in mutation frequency between the two groups are indicated in Fig. 2. Among these genes, TP53, FLG, AHNAK, ATRX, CSPG4, MKI67, PCMTD1and POTEJ is enriched in LMS, while HERC2, C12orf55, DNAJC16, PTPRQ, TIAM1, ARID1A, FLT4, FRS2, KDM6B, PLK1, PRKDC, and SLC2A3 (Fig. 2). TP53 mutations can only be found in the LMS group, with a mutation rate of 55%, which is similar to that of recently reported in TCGA LMS (50%)[3]. HERC2 belongs to E3 ubiquitin protein ligases, and can modulate p53 activity through regulating p53 oligomerization independent of MDM2 [27]. Interestingly, HERC2 mutation can only be found in the DDLPS group in our cohort (mutation rate 42% for DDLPS), probably because the mutations that affect p53 tetramerization disrupt the HERC2-p53 interaction, and therefore HERC2 mutations are redundant in LMS with mutant TP53.

Co-occurrent And Exclusive Mutations

We did somatic mutation interaction analysis using the somaticInteractions function in MAFtools. The results are shown in Fig. 3, and the full list of genes can be found in Table S1. TP53 is mutually exclusive with BRD9 (P < 0.1) and co-occurs with Filaggrin (FLG) (P < 0.1). BRD9 is a subunit of the human BAF (SWI/SNF) nucleosome remodeling complex[28] and has emerged as an attractive therapeutic target in cancer. Its bromodomain is highly homologues with that of BRD7 and the later is reported to interact with p53 and be required for p53 function[29]. Whether mutant BRD9 in STS functions through impairment of the p53 pathway is to be determined. FLG is a highly mutated driver gene. Its mutations are also found in several other cancer types such as non-melanoma skin cancer, head and neck cancer, lung cancer, colorectal cancer, uterine cancer, prostate cancer etc.[30]. There is no reported synergic interaction between FLG and TP53 mutations in cancer yet, including STS. The above somatic interaction analysis may provide hints for exploring genes with unspecified functions in STS.

Scna Results In Lms And Ddlps

A lack of general recurrent mutation in the LMS and DDLPS samples promoted us to look into SCNA in these tumors. Several recent studies have explored the molecular basis of sarcoma and identified multiple recurrent genomic alterations. To further reveal the somatic copy number alterations in Chinese sarcoma patients, we used GISTIC2.0 to detect SCNA in our cohort. The analysis revealed that chromosomal loss affecting tumor suppressor genes such as TP53, RB1, and PTEN, is a hallmark of LMS. On the other hand, focal amplification affecting chromosome 12q13–15 region which encodes MDM2 (12q15), CDK4 (12q14.1) and HMGA2 (12q14.3), is a feature of DDLPS (Fig. 4). Co-amplification of MDM2 and CDK4 occurs in more than 90% DDLPS. Such co-amplification combined with TP53 inactivation results in cell proliferation, and is thought to be the initiating factor to drive fat tumorigenesis[31].

Amplified and deleted genes detected by GISTIC 2.0 are listed in supplement Table S2-S5. In LMS, 96 genes are significantly amplified, while as much as 4532 genes are significantly deleted (confidence 0.95), indicating deletion is much more frequent than amplification. In DDLPS 1089 genes are significantly amplified and no genes are significantly deleted. Both DDLPS and LMS belong to complex-karyotype tumor in genetic classification of STS[32]. In DDLPS, amplification of oncogenes such as MDM2, CDK4, HMGA2 and JUN are more commonly seen, while deletion of tumor suppressor such as TP53, RB1, and PTEN are enriched in LMS, indicating genomic diversity of copy-number alterations of DDLPS and LMS.

Rna-seq Revealed Genes With Differential Expression

Eight each of the LMS and DDLPS samples that have high RNA quality were further subjected to RNA-Seq analysis. The significance threshold was set at adjusted P-value < 0.05 and Log2 fold change (LogFC) > 1. In total 2396 genes have significantly different expression levels in the two tumors (Table S6). The genes with the most variability were Z-score normalized and visualized in heatmap in Fig. 5. Unsupervised clustering was performed on all the samples to examine the discriminant effect of these genes. We found that all the LMS clustered together, and so did the DDLPS samples. MDM2 ranks the highest, with logFC = 4.12 (DDLPS over LMS) and adjusted P-value = 1.73E-52, consistent with previous studies [3, 33], indicating that the massive amplification of MDM2 in DDLPS is further enhanced at the mRNA level. JUN is upregulated in DDLPS and it has been known to play important roles in blocking adipocytic differentiation (logFC = 1.45, adjusted P-value = 0.0396). Correlation between SCNA and RNA expression is positive in all DDLPS in MDM2 and CDK4 (Fig. 6).

Pathway Analysis Of Genes With Differential Expression

To investigate the pathways affected by the significantly changed genes between LMS and DDLPS, Gene Set Enrichment Analysis (GSEA) was performed [17] [18] using NetworkAnalyst (https://www.networkanalyst.ca/) (Table 2 and Table S7). Pathways with negative EnrichmentScore are enriched in LMS, while pathways with positive EnrichmentScore are enriched in DDLPS. The pathways apparently enriched in LMS such as “Calcium signaling pathway", "Vascular smooth muscle contraction", and “Linoleic acid metabolism”(EnrichmentScores are − 0.37, -0.44, and − 0.60 respectively and P-values are 0.0002 for all) are probably due to the difference of the tissue origin rather than tumorgenesis [34, 35]. Therefore, the appearance of the above pathways in the enrichment should be interpreted cautiously. GSEA analysis revealed that in DDLPS, E3 ubiquitin ligase including MDM2 are up-regulated compared with that in LMS, indicating ubiquitin mediated proteolysis may play more important roles in DDLPS. Notably, GSEA revealed differential regulation of the spliceosome related genes between LMS and DDLPS (EnrichmentScore = 0.53, P = 0.003), which may result in different sets of neoantigens in the two tumors due to alternative splicing. This result may affect antigen selection in the two tumors used in CAR-T cell immunotherapy [36].

Table 2

Result of GSEA in LMS compare with DDLPS
	Name	Total	Hits	EnrichmentScore	Pval	Padj
1	Calcium signaling pathway	68	58	-0.37	0.00017	0.0028
2	Vascular smooth muscle contraction	30	22	-0.44	0.000017	0.0028
3	Linoleic acid metabolism	30	26	-0.60	0.00018	0.0028
4	Ascorbate and aldarate metabolism	34	28	-0.67	0.00018	0.0028
5	Chronic myeloid leukemia	33	27	0.53	0.000022	0.0028
6	p53 signaling pathway	31	27	0.50	0.000022	0.0028
7	Spliceosome	27	22	0.52	0.000022	0.0028
8	AGE-RAGE signaling pathway in diabetic complications	18	16	0.47	0.000023	0.0028
9	Ubiquitin mediated proteolysis	27	25	0.47	0.000023	0.0028
10	Osteoclast differentiation	44	39	0.50	0.000023	0.0028

Rna-seq Revealed Distinct Fusion Patterns Between Ddlps And Lms

In the RNA-seq analysis, we identified four fusion transcripts in three LMS samples (out of 8 that undergone RNA-seq analysis) and 36 fusion transcripts in all the 8 DDLPS samples combined (P = 0.007). There is no recurrent fusion transcript identified. Fusion transcripts involving chromosome 12 are only found in DDLPS, including both interchromosomal and intrachromosomal rearrangements (Fig. 7A). MDM2 and RAB3IP are the most common fusion partners, and both locate in chromosome 12. There is also significant correlation between MDM2/CDK4 amplification and chromosome 12 rearrangement (P < 0.001, Fig. 7B). Gene fusions can occur as a result of translocation, interstitial deletion, or chromosomal inversion during DNA replication, which are more common in DDLPS than in LMS as indicated above. Therefore, it is unsurprised to see gene fusions more frequent in DDPLS, especially in chromosome 12 where ring or giant marker chromosomes occur [3, 37]. Fusion proteins generated from the identified fusion transcripts may generate neoepitopes, which can be targeted to produce safer CAR-T cells for immunotherapy [38].

LMS and DDLPS have different Profiles of Tumor Infiltrating Immune Cells

To investigate whether immune response plays different roles in LMS and DDLPS, MCP-counter was used to estimate the ratio of tumor infiltrating immune cells between the two tumors (Fig. 8) [22]. Among the eight immune populations (T cells, CD8 + T cells, cytotoxic lymphocytes, natural killer cells, B cell lineage, monocytic lineage, myeloid dendritic cells and neutrophils) and two stromal populations (endothelial cells and fibroblasts) we analyzed, DDLPS have higher fibroblasts signature score (P = 0.002) and higher endothelial cell signature score (P = 0.047) compared with LMS. Increased endothelial cell signature score are proved to be associated with high density of CD34 + endothelial cells and enhanced endothelial-driven angiogenesis in STS. The high fibroblasts signature score for DDLPS is consistent with the mesenchymal origin of STS. Previous TCGA study suggested that CD8 + T cells are higher in DDLPS compared with LMS (P < 0.01), but no such significance was observed in the current study (P = 0.76) probably due to its small sample size.

Unsupervised clustering analysis of tumors by Z-score standardized immune infiltration scores divide sixteen samples into two immune subtypes with different profiles. Most LMS samples were classified to immune class A, while most DDLPS samples were classified to immune class B. Previous studies revealed that STS immune subtypes are associated with response rate to PD1 blockade[22]. We cannot conduct survival analysis for the current cohort right now due to the relatively short follow-up time, but these factors can be potentially used for stratification in future study.

WES analysis revealed LMS and DDPLS carry distinct mutagenic patterns, including SNVs and gross genomic instability. Chromosome rearrangement in DDLPS samples, particularly in chr12, is correlated with both gene amplification and fusion. These events may explain the altered gene expression patterns in the two types of sarcoma, which in turn are counted for the phenotypic and clinical difference between them.

The average TMB of the 20 LMS is significantly higher than that of the 12 DDLPS (162 vs. 124, P = 0.028). One LMS sample is MSI-H (TMB = 563), and this patient has the highest TMB among all the 32 samples undergone WES. TMB is an emerging clinical biomarker of response to ICIs for multiple tumors. Higher TMB has been shown to correlate with better clinical efficacy of ICI therapy in NSCLC[39, 40], small cell lung cancer [41], melanoma[42, 43], and colorectal cancer [44]. However, based on previous results, high TMB do not warrant response to ICIs in sarcoma[45]. Therefore, at the current stage, the association between TMB and response to ICIs, while very promising, has not been confirmed in sarcoma.

Both LMS and DDLPS belong to complex-karyotype STSs with an unbalanced karyotype and several genomic aberrations, and complex-karyotype STSs are more likely to respond to ICIs than simple STSs[46, 47]. Several Phase II studies of ICI therapies have been conducted in sarcoma, including SARC028[48], Alliance A091401[46] and PEMBROSARC[49]. Results from these studies revealed that although LMS have higher TMB than DDLPS, the response rate of ICIs in LMS is lower than in DDLPS. For both LMS and DDLPS, the response rate of ICIs in an unselected population is low, and immunological landscape profiling can provide hints for identification of patients who are likely to respond and for tailored immunotherapies. Immunological landscape profiling can also aid interpretation of the efficacy results. Using MCP-counter and RNA-seq results, we simultaneously quantified multiple cell populations. Results from unsupervised clustering of MCP-counter Z-scores define two distinct immune classes. Class A was highly associated with LMS, featuring low immune cell infiltration, low fibroblast and low endothelial cells. Class B was highly associated with DDLPS, have higher fibroblast and endothelial cells infiltration than LMS. Class A is generally considered “cold” tumors with lower response rate to ICIs[50, 51]. For class B tumors, unsupervised clustering defined two subgroups based on immune cell infiltrations, B1 and B2. Class B1 tumors have high fibroblast and endothelial cells, low immune cell infiltration. Class B2 have high fibroblast, high endothelial cells, and also high immune cell infiltration. In colon cancer, researchers reported that immune and stromal classification was associated with molecular subtypes and predictive of patient's prognosis[52]. We need more evidence regarding whether class B1 and B2 predict prognosis of STS patients. Dedicated prospective trial evaluating chemotherapy, targeted therapy, or immunotherapy would illustrate better the roles of immunological landscape profiling in STS treatment. Integrated molecular data derived from the current study also provided hints for developing new biomarkers to classify patients into subgroups.

In spite of the above findings, several limitations remain in this study. Firstly, a relatively small number of each histologic subtype was examined. Since STS are extremely rare, it makes it hard to obtain more samples to increase the statistic power of the analysis. Secondly, immune cell infiltration is derived solely from RNA-seq data. Due to the retrospective design of the current study, we can do not have enough sample for clinical validation using IHC-P or flow-cytometry, thus we intended to do this in future studies.

LMS and DDPLS are distinct as supported by our molecular data of mutation pattern, gross genomic instability and tumor infiltrating immune cell profiles. Chromosome rearrangement may result in gene amplification and fusion, which are displayed the most in chr12 in DDLPS. These observations are consistent to the altered gene expression patterns in the two types of sarcoma. Neoantigens produced by gene fusion may open up new avenues for immunotherapy in STS. Using MCP-counter profiling of transcriptomic data, we quantified immune and stromal infiltration of the two subtypes and found a significant correlation with histologic types. Our data also provide hints for personalized STS immunotherapy.

soft-tissue sarcoma (STS)

whole exome sequencing (WES)

liposarcoma (LPS)

de-differentiated LPS (DDLPS)

leiomyosarcoma (LMS)

CAR-T (Chimeric Antigen Receptor T )

single nucleotide variations (SNV)

somatic copy-number alteration (SCNA)

copy-number alteration (CNA)

Genome Analysis Toolkits (GATK)

chromosomal instability (CIN)

Microsatellite Instability (MSI)

Transcripts Per Million (TPM)

Encyclopedia of Genes and Genomes (KEGG)

Gene Set Enrichment Analysis (GSEA)

tumor mutation burden (TMB)

immune checkpoint inhibitor (ICI)

Ethics Approval And Consent To Participate

All procedures performed involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. Informed consents were obtained from all individual participants included in the study.

Consent For Publication

Not applicable.

Availability Of Data And Materials

The data analyzed in this article is available from the corresponding author on reasonable request.

Competing interests

The authors declare that they have no competing financial and non-financial interests.

Funding

This study was funded by Shanghai Municipal Health Commission Scientific Research Project, Grant Number: 20194Y0459

Authors’ contributions

WSL, HXT, CLZ: study design; RYZ, LCT: sample collection; HG: statistical analysis and manuscript writing; CD: molecular genetic studies; GW: sequence alignment; HY, QWL, XG, ZMW: clinical information management; YW, FS, WQL: contributed to the interpretation of the results; YHZ, YZ: design, supervision, and manuscript writing. All authors read and approved the final manuscript.

Acknowledgement

We thank all patients who participated in this study. We also thank our colleagues at GenomiCare Biotechnology for providing supports in sample preparation, sequencing and bioinformatic analysis.

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Integrated Genomic and Transcriptomic Analysis Revealed Mutation Patterns of De-differentiated Liposarcoma and Leiomyosarcoma

Status:

Journal Publication

Version 1

Abstract

Figures

Background

Methods

Patients and specimens

WES

Rna-seq

Differential Gene Expression Analysis

Tumor Mutational Burden And Microsatellite Status

Pathway Analysis

Rna Fusion Analysis

Immune classification of STS

Statistical analysis

Results

Top Mutated Genes

Co-occurrent And Exclusive Mutations

Scna Results In Lms And Ddlps

Rna-seq Revealed Genes With Differential Expression

Pathway Analysis Of Genes With Differential Expression

Rna-seq Revealed Distinct Fusion Patterns Between Ddlps And Lms

Discussion

Conclusion

Abbreviations

Declaration

References

Supplementary Files

Status:

Journal Publication

Version 1