ArchetypeShift: An R Package Integrating KEGG-Informed Pathway Analysis and IPA-Derived Functional Predictions for Validation of Single-Cell Archetypes
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ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE334564 | GEO | 2026/06/30
REPOSITORIES: GEO
Similar Datasets
Project description:ArchetypeShift: An R Package Integrating KEGG-Informed Pathway Analysis and IPA-Derived Functional Predictions for Validation of Single-Cell Archetypes
| PRJNA1475458 | ENA
Project description:The classic cancer evolution model posits that driver mutations sweep the population sequentially as the complete set of hallmarks are assembled by the neoplastic clone. However, recent work has challenged this model revealing that most tumors contain highly complex dynamics with genetic diversity reflecting distinct clonal architectures. The functional and phenotypic heterogeneity also has been shown to have a crucial influence on the fate of the tumor5–10. However, it is not well understood how distinct tumor clonal populations coexist and function. Here, we study tumor architecture at the level of individual cells by sampling a zebrafish melanoma tumor over time and space. We found that cancer transcriptional programs can be classified to three archetypes, each exploiting the existing neural crest, mature melanocytes, and stress modules, and distinct intra-tumor locations. Strikingly, these archetypes are conserved in human melanoma. Further, we found that the cancer cells are comprised of two distinct clones, where one expresses a unique archetype. Over time, we found that the cells of this clone adapt by exhibiting a more similar profile to the corresponding archetype.
2020-09-09 | GSE115140 | GEO
Project description:Multiple myeloma (MM) is a plasma cell malignancy characterized by substantial genomic and biological heterogeneity, limiting disease classification and development of more targeted therapies. Here, we present a population-level single-cell atlas of MM and detailed clinical annotations from 342 patients representing MM disease and treatment continuum. Our analysis identifies five distinct malignant transcriptional archetypes (MM1–MM5), and a proliferative module variably expressed across archetypes. This proliferation signature emerges as a context-dependent program with independent prognostic value across archetypes. Combined archetype classification with proliferation scoring defines therapeutic resistance, genomic instability, and clinical outcomes. External validation using the CoMMpass cohort confirms the biological robustness and prognostic relevance of this stratification across platforms. Leveraging the resolution of our single-cell atlas, we applied a computational pipeline for therapeutic target discovery based on malignancy, cell-type specificity and tissue restriction. This approach highlights FCRL2 as a promising MM-specific therapeutic target, with selective expression in malignant plasma and B cells at both transcriptomic and protein levels. This integrative analysis provides a clinically actionable blueprint for patient-specific stratification and precision medicine in plasma cell malignancies.
2026-07-29 | GSE305970 | GEO
Project description:Multiple myeloma (MM) is a plasma cell malignancy characterized by substantial genomic and biological heterogeneity, limiting disease classification and development of more targeted therapies. Here, we present a population-level single-cell atlas of MM and detailed clinical annotations from 342 patients representing MM disease and treatment continuum. Our analysis identifies five distinct malignant transcriptional archetypes (MM1–MM5), and a proliferative module variably expressed across archetypes. This proliferation signature emerges as a context-dependent program with independent prognostic value across archetypes. Combined archetype classification with proliferation scoring defines therapeutic resistance, genomic instability, and clinical outcomes. External validation using the CoMMpass cohort confirms the biological robustness and prognostic relevance of this stratification across platforms. Leveraging the resolution of our single-cell atlas, we applied a computational pipeline for therapeutic target discovery based on malignancy, cell-type specificity and tissue restriction. This approach highlights FCRL2 as a promising MM-specific therapeutic target, with selective expression in malignant plasma and B cells at both transcriptomic and protein levels. This integrative analysis provides a clinically actionable blueprint for patient-specific stratification and precision medicine in plasma cell malignancies.
2026-07-21 | GSE305967 | GEO
Project description:Multiple myeloma (MM) is a plasma cell malignancy characterized by substantial genomic and biological heterogeneity, limiting disease classification and development of more targeted therapies. Here, we present a population-level single-cell atlas of MM and detailed clinical annotations from 342 patients representing MM disease and treatment continuum. Our analysis identifies five distinct malignant transcriptional archetypes (MM1–MM5), and a proliferative module variably expressed across archetypes. This proliferation signature emerges as a context-dependent program with independent prognostic value across archetypes. Combined archetype classification with proliferation scoring defines therapeutic resistance, genomic instability, and clinical outcomes. External validation using the CoMMpass cohort confirms the biological robustness and prognostic relevance of this stratification across platforms. Leveraging the resolution of our single-cell atlas, we applied a computational pipeline for therapeutic target discovery based on malignancy, cell-type specificity and tissue restriction. This approach highlights FCRL2 as a promising MM-specific therapeutic target, with selective expression in malignant plasma and B cells at both transcriptomic and protein levels. This integrative analysis provides a clinically actionable blueprint for patient-specific stratification and precision medicine in plasma cell malignancies.
2026-07-21 | GSE305968 | GEO
Project description:Multiple myeloma (MM) is a plasma cell malignancy characterized by substantial genomic and biological heterogeneity, limiting disease classification and development of more targeted therapies. Here, we present a population-level single-cell atlas of MM and detailed clinical annotations from 342 patients representing MM disease and treatment continuum. Our analysis identifies five distinct malignant transcriptional archetypes (MM1–MM5), and a proliferative module variably expressed across archetypes. This proliferation signature emerges as a context-dependent program with independent prognostic value across archetypes. Combined archetype classification with proliferation scoring defines therapeutic resistance, genomic instability, and clinical outcomes. External validation using the CoMMpass cohort confirms the biological robustness and prognostic relevance of this stratification across platforms. Leveraging the resolution of our single-cell atlas, we applied a computational pipeline for therapeutic target discovery based on malignancy, cell-type specificity and tissue restriction. This approach highlights FCRL2 as a promising MM-specific therapeutic target, with selective expression in malignant plasma and B cells at both transcriptomic and protein levels. This integrative analysis provides a clinically actionable blueprint for patient-specific stratification and precision medicine in plasma cell malignancies.
2026-07-21 | GSE305969 | GEO
Project description:Lung adenocarcinoma (LUAD), the most common form of lung cancer, is a heterogeneous disease with highly variable histopathologic features and clinical outcomes. LUAD premalignant lesions (PMLs) are localized proliferative lesions of atypical pneumocytes that proliferate along the alveolar walls. In this study, we characterized the molecular heterogeneity across PMLs, regardless of histologic designation, and identified four unique groups or “archetypes” of PMLs using bulk RNA and exome sequencing data from laser captured microdissected PMLs, normal, and tumor tissue from LUAD resection cases. The archetypes were defined based on expression of recurrent gene co-expression modules discovered using the LUAD PMLs we profiled and three publicly available datasets. One PML archetype, termed “proliferation”, was associated with increased expression of genes involved in cell proliferation, a pro-tumor immune environment, and enrichment for EGFR driver mutations. Another archetype, termed “normal-like”, was associated with similar features to normal tissue, an anti-tumor immune response, and lacked enrichment for driver mutations compared to other archetypes. We projected the PML archetypes into independent gene expression datasets profiling LUAD and found that tumors closest to the proliferation archetype were enriched for multiple features of aggressive LUAD, including high tumor grade and lymph node invasion, and had significantly lower disease-free survival. Proliferation archetype PMLs may represent a subset of lesions with more aggressive features that could improve patient stratification and suggest novel targets for lung cancer interception.
2026-04-24 | GSE319666 | GEO
Project description:Bone represents congenial soil for metastatic seeds and is frequently affected by metastasis of multiple cancer types. The histological and molecular characteristics of bone metastases (BMs) are diverse but poorly understood. Herein, we performed single-cell RNA-seq on 34 BMs from 6 cancer types and identified 3 ecosystem archetypes characterized by enrichment of macrophages/osteoclasts (Mφ-OC), regulatory/exhausted T cells (Treg-Tex), and monocytes (Mono), respectively. Breast cancer BMs are mostly the Mφ-OC archetype driven by the osteolytic vicious cycle, whereas kidney cancers BMs mainly belong to the Treg-Tex archetype that lacks osteoclasts. Lung cancers BMs evenly distributed across all archetypes. Further analyses revealed parallel mechanisms of immunosuppression and bone remodeling. Elevated estrogen signaling distinguishes macrophages in the Mφ-OC subtype, which was investigated in a companion study. Together, we elucidated that divergent mechanisms toward bone colonization and that BMs of different origins can adopt the same mechanism through convergent evolution or adaptation.
2025-05-27 | GSE266330 | GEO
Project description:Molecular phenotyping of biopsies affords opportunities for increased precision and improved disease classification to address the limitations of conventional histologic diagnostic systems. We applied archetypal analysis, an unsupervised method similar to cluster analysis, to microarray data from 1208 prospectively collected kidney transplant biopsies from 13 centers. Seven machine learning-generated cross-validated classifier scores per biopsy were used as input for the archetypal analysis. Six archetypes representing extreme phenotypes were generated: no rejection; T cell-mediated rejection (TCMR); three phenotypes associated with antibody-mediated rejection (ABMR) - early-stage, fully-developed, and late-stage; and mixed rejection (TCMR plus early-stage ABMR). Each biopsy was assigned six scores, one for each archetype, that together represent a probabilistic assessment of that biopsy based on its rejection-related molecular properties. Viewed as clusters, the archetypes were similar to existing histologic Banff categories, but there was 32% disagreement, much of it probably reflecting the “noise” in the current histologic assessment system. Graft survival was worst for fully-developed and late-stage ABMR and was better predicted by molecular archetype scores than histologic diagnoses. The results provide a system for precision molecular assessment of biopsies and a new standard for recalibrating conventional diagnostic systems. (ClinicalTrials.gov NTC1299168) We applied archetypal analysis, an unsupervised method similar to cluster analysis, to microarray data from 1208 prospectively collected kidney transplant biopsies from 13 centers. This dataset is part of the TransQST collection.
2017-07-10 | E-GEOD-98320 | biostudies-arrayexpress
Project description:Molecular phenotyping of biopsies affords opportunities for increased precision and improved disease classification to address the limitations of conventional histologic diagnostic systems. We applied archetypal analysis, an unsupervised method similar to cluster analysis, to microarray data from 1208 prospectively collected kidney transplant biopsies from 13 centers. Seven machine learning-generated cross-validated classifier scores per biopsy were used as input for the archetypal analysis. Six archetypes representing extreme phenotypes were generated: no rejection; T cell-mediated rejection (TCMR); three phenotypes associated with antibody-mediated rejection (ABMR) - early-stage, fully-developed, and late-stage; and mixed rejection (TCMR plus early-stage ABMR). Each biopsy was assigned six scores, one for each archetype, that together represent a probabilistic assessment of that biopsy based on its rejection-related molecular properties. Viewed as clusters, the archetypes were similar to existing histologic Banff categories, but there was 32% disagreement, much of it probably reflecting the “noise” in the current histologic assessment system. Graft survival was worst for fully-developed and late-stage ABMR and was better predicted by molecular archetype scores than histologic diagnoses. The results provide a system for precision molecular assessment of biopsies and a new standard for recalibrating conventional diagnostic systems. (ClinicalTrials.gov NTC1299168)
2017-07-10 | GSE98320 | GEO