DENDRO: genetic heterogeneity profiling and subclone detection by single-cell RNA sequencing
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ABSTRACT:
ORGANISM(S): Mus musculus
PROVIDER: GSE139248 | GEO | 2019/11/10
REPOSITORIES: GEO
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Project description:Intratumoral heterogeneity (ITH) arises from distinct subclonal expansion following genetic or epigenetic alterations, and profoundly influences how tumors response to their immune microenvironment. Tumor progression trees based on single-cell mutational profiles have made it possible to trace subclonal evolution; however, conventional tree-building methods can incorporate only a limited number of cells and mutations, restricting their application to larger single-cell data. To investigate the effect of ITH on the therapeutic response of melanoma, we have developed Trisicell (https://trisicell.rtfd.io), a computational toolkit for scalable inference of mutational ITH through assessment of single-cell genomic variant data. By applying Trisicell to genetically matched mouse melanoma datasets, we found that expressed mutations are sufficient to effectively drive subclonal evolution. On single-cell, full-length RNA sequencing data of mouse melanoma from preclinical immune checkpoint blockade (ICB) studies, the analysis showed that the subtree-seeding mutations in the trees identified distinct subclones associated with a specific developmental state and neural crest lineage markers. Using the tree to trace cell lineages, we found that neoantigens depleted by ICB were predominantly expressed in minor subclones, suggesting that post-treatment recurrence is driven by immunoediting. Moreover, these neoantigens were enriched with those derived from frameshift mutations and mutated nuclear genes. Importantly, recurrently mutated genes in ICB-responding human melanoma exhibited the same features. We next used Trisicell to analyze single-cell, full-length RNA data of brain metastases (BM) from melanoma patients treated with ICB, and discovered that relapsing BM from ICB-responding patients exhibited subclones that also expressed a higher fraction of frameshift mutations and were associated with elevated levels of infiltrated T cells. Notably, they also exhibited more mutated HLA genes and expressed high level of the novel immune checkpoint HLA-G, a putative local immunosuppressive mechanism. In summary, applying Trisicell to single-cell transcriptomic analyses allowed us to identify novel features of ICB-responding melanoma neoantigens and distinct mechanisms for adapting to immunotherapy at different sites. These results have important implications for both melanoma evolution and target identification for immunotherapies, including ICB and cancer vaccines. This is the first study to trace subclonal evolution of neoantigens at single-cell resolution, demonstrating that Trisicell and our datasets represent important resources for the field.
2024-10-25 | GSE215960 | GEO
Project description:Although scRNA-seq is now ubiquitously adopted in studies of intratumor heterogeneity, detection of somatic mutations and inference of clonal membership from scRNA-seq is currently unreliable. We propose DENDRO, an analysis method for scRNA-seq data that clusters single cells into genetically distinct subclones and reconstructs the phylogenetic tree relating the subclones. DENDRO utilizes transcribed point mutations and accounts for technical noise and expression stochasticity. We benchmark DENDRO and demonstrate its application on simulation data and real data from three cancer types. In particular, on a mouse melanoma model in response to immunotherapy, DENDRO delineates the role of neoantigens in treatment response.
| S-EPMC6961311 | biostudies-literature
Project description:DENDRO: genetic heterogeneity profiling and subclone detection by single-cell RNA sequencing
| PRJNA578900 | ENA
Project description:BackgroundAlthough both copy number variations (CNVs) and single nucleotide variations (SNVs) detected by single-cell RNA sequencing (scRNA-seq) are used to study intratumor heterogeneity and detect clonal groups, a software that integrates these two types of data in the same cells is unavailable.ResultsWe developed Clonal Architecture with Integration of SNV and CNV (CAISC), an R package for scRNA-seq data analysis that clusters single cells into distinct subclones by integrating CNV and SNV genotype matrices using an entropy weighted approach. The performance of CAISC was tested on simulation data and four real datasets, which confirmed its high accuracy in sub-clonal identification and assignment, including subclones which cannot be identified using one type of data alone. Furthermore, integration of SNV and CNV allowed for accurate examination of expression changes between subclones, as demonstrated by the results from trisomy 8 clones of the myelodysplastic syndromes (MDS) dataset.ConclusionsCAISC is a powerful tool for integration of CNV and SNV data from scRNA-seq to identify clonal clusters with better accuracy than obtained from a single type of data. CAISC allows users to interactively examine clonal assignments.
| S-EPMC8939069 | biostudies-literature
Project description:Intratumor heterogeneity as a clinical challenge becomes most evident after several treatment lines, when multidrug-resistant subclones accumulate. To address this challenge, the characterization of resistance mechanisms at the subclonal level is key to identify common vulnerabilities. In this study, we integrate whole-genome sequencing, single-cell (sc) transcriptomics (scRNA sequencing), and chromatin accessibility (scATAC sequencing) together with mitochondrial DNA mutations to define subclonal architecture and evolution for longitudinal samples from 15 patients with relapsed or refractory multiple myeloma. We assess transcriptomic and epigenomic changes to resolve the multifactorial nature of therapy resistance and relate it to the parallel occurrence of different mechanisms: (1) preexisting epigenetic profiles of subclones associated with survival advantages, (2) converging phenotypic adaptation of genetically distinct subclones, and (3) subclone-specific interactions of myeloma and bone marrow microenvironment cells. Our study showcases how an integrative multiomics analysis can be applied to track and characterize distinct multidrug-resistant subclones over time for the identification of molecular targets against them.
2023-08-01 | GSE235783 | GEO
Project description:Whole Genome Sequencing of the murine breast cancer cell line 4T1 and of the murine melanoma cell line B16-ova was carried out with the aim of identifying somatic mutations. We also ran deep Mass Spectrometry proteomics analysis on the same cell lines, aiming to determine which somatic mutations carry over to the protein expression level. Further, we tested these cancer specific protein epitopes (putative neoantigens) for immunogenicity using mouse models. Finally, the putative neoantigens that showed good immunogenic potential were used in tumor growth control experiments with mice engrafted with the two tumor cell lines. In these experiments we tested whether cancer vaccines based on individual neoantigen peptides (MHC-I) restricted the growth of the tumor compared to adequate controls. The overall aim of the project is to validate the ability of our multi-omics/bioinformatics pipeline to identify and deliver neoantigens that can be used to suppress tumor growth. File names Sample names P10859_101_S1_L001_R1_001_BHKWV3CCXY 4T1_S1_L001_R1_001_BHKWV3CCXY P10859_101_S1_L001_R2_001_BHKWV3CCXY 4T1_S1_L001_R2_001_BHKWV3CCXY P10859_101_S1_L002_R1_001_BHKWV3CCXY 4T1_S1_L002_R1_001_BHKWV3CCXY P10859_101_S1_L002_R2_001_BHKWV3CCXY 4T1_S1_L002_R2_001_BHKWV3CCXY P10859_102_S2_L003_R1_001_BHKWV3CCXY B16-OVA_S2_L003_R1_001_BHKWV3CCXY P10859_102_S2_L003_R2_001_BHKWV3CCXY B16-OVA_S2_L003_R2_001_BHKWV3CCXY P10859_102_S2_L004_R1_001_BHKWV3CCXY B16-OVA_S2_L004_R1_001_BHKWV3CCXY P10859_102_S2_L004_R2_001_BHKWV3CCXY B16-OVA_S2_L004_R2_001_BHKWV3CCXY
2024-01-31 | E-MTAB-13635 | biostudies-arrayexpress
Project description:RNA Sequencing of the murine breast cancer cell line 4T1 and of the murine melanoma cell line B16-ova was carried out with the aim of establishing the complete transcriptome of these cell lines. Because these are cancer cells and we expect a lot of aberrant splicing, we carried out de novo assembly (genome guided) of the transcripts. We also ran deep Mass Spectrometry proteomics analysis on the same cell lines, aiming to determine which aberrant transcripts carry over to the protein expression level. Further, we tested these cancer specific protein epitopes (putative neoantigens) for immunogenicity using mouse models. Finally, the putative neoantigens that showed good immunogenic potential were used in tumor growth control experiments with mice engrafted with the two tumor cell lines. In these experiments we tested whether cancer vaccines based on individual neoantigen peptides (MHC-I) restricted the growth of the tumor compared to adequate controls. The overall aim of the project is to validate the ability of our multi-omics/bioinformatics pipeline to identify and deliver neoantigens that can be used to suppress tumor growth.
2024-06-30 | E-MTAB-14130 | biostudies-arrayexpress
Project description:Syngeneic grafts of the D4M.3A.3 (parental) mouse melanoma cell line (derived from a Tyr::CreER;BrafCA;Ptenlox/lox mouse) in C56BL/6 mice model poorly immunogenic, low neoantigen human melanomas. The D3UV2 (UV2) cell line was derived by serial UVB irradiation and single cell cloning. The addition of UVB-induced putative neoantigens sensitizes UV2 syngeneic melanoma grafts to immune checkpoint inhibitors and triggers epitope skewing to tumor-lineage self-antigens, a phenomenon that can be successfully mimicked in parental melanomas through treatment combinations such as anti-PD-1 with ablative fractional photothermolysis and imiquimod. Our mouse models were used to characterize gene expression changes between neoantigen rich and neoantigen poor melanomas, and with immunotherapy.
2021-02-16 | GSE159344 | GEO
Project description:Chronic sun-damaged (CSDhigh) melanoma represents 10-20% of cutaneous melanomas and is characterized by infrequent BRAF V600E mutations and high mutational load. However, the order of genetic events, or the extent of intra-tumor heterogeneity (ITH) in CSDhigh melanoma is still unknown. Ultra-deep targeted sequencing of 40 cancer-associated genes was performed in 73 in situ or invasive CMM, including 23 CSDhigh cases. In addition, we performed whole-exome and RNA sequencing on multiple regions of primary tumor and multiple in-transit metastases from one CSDhigh melanoma patient. We found no significant difference in mutation frequency in melanoma-related genes or in mutational load between in situ and invasive CSDhigh lesions while this difference was observed in CSDlow lesions. In addition, increased frequency of BRAF V600K, NF1 and TP53 mutations (P < 0.01, Fisher’s Exact Test) was found in CSDhigh melanomas. Sequencing of multiple specimens from one CSDhigh patient revealed strikingly limited ITH with > 95% shared mutations. Our results provide evidence that CSDhigh and CSDlow melanomas are distinct molecular entities that progress via different genetic routes.
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