Single cell lineage tracing reveals clonal dynamics of anti-EGFR therapy resistance in triple negative breast cancer
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ABSTRACT:
SUBMITTER: Pellecchia S
PROVIDER: S-EPMC11008053 | biostudies-literature | 2024 Jan
REPOSITORIES: biostudies-literature
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Project description:This SuperSeries is composed of the SubSeries listed below.
2024-03-25 | GSE228382 | GEO
Project description:We investigate non-genomic mechanisms determining cellular response to EGFR inhibitors in triple negative breast cancer (TNBC). We integrate methods for cellular barcoding and single-cell transcriptomics to enable cell lineage tracing and explore the subclonal evolution of adaptation in an established preclinical model of TNBC in response to incremental concentrations of Afatinib, a second generation EGFR-TKI that irreversibly inhibits both EGFR and HER2. Retrospective lineage tracing data analysis uncovered a pre-existing subpopulation of rare Afatinib-tolerant cells displaying distinct biological features, such as elevated mRNA levels of the IGFBP2 gene. Furthermore, we investigated temporal coordination of transcriptional programs in drug resistant clones with high replication fitness by reordering cells along a pseudotime trajectory. Interestingly, it revealed the activation of biological processes, such as fatty acid metabolism, which have previously been linked to EGFR-TKIs resistance mechanisms.
2024-03-25 | GSE228154 | GEO
Project description:Single cell lineage tracing reveals clonal dynamics of anti-EGFR therapy resistance in triple negative breast cancer
| PRJNA948402 | ENA
Project description:Single cell lineage tracing reveals clonal dynamics of anti-EGFR therapy resistance in triple negative breast cancer.
| PRJNA949598 | ENA
Project description:Colon cancers are composed of phenotypically heterogeneous tumor cell subpopulations with variable expression of putative stem cell and differentiation antigens. While in normal colonic mucosa, clonal repopulation occurs along differentiation gradients from crypt base toward crypt apex, the clonal architecture of colon cancer and the relevance of tumor cell subpopulations for clonal outgrowth are poorly understood. Using a multicolor lineage tracing approach in colon cancer xenografts that reflect primary colon cancer architecture, we here demonstrate that clonal outgrowth is mainly driven by tumor cells located at the leading tumor edge with clonal axis formation toward the tumor center. While our findings are compatible with lineage outgrowth in a cancer stem cell model, they suggest that in colorectal cancer tumor cell position may be more important for clonal outgrowth than tumor cell phenotype.
| S-EPMC5681634 | biostudies-literature
Project description:Tumour cells are subjected to evolutionary selection pressures during progression from initiation to metastasis. We analysed the clonal evolution of squamous skin carcinomas induced by DMBA/TPA treatment using the K5CreER-Confetti mouse and stage-specific lineage tracing. We show that benign tumours are polyclonal, but only one population contains the Hras driver mutation. Thus, benign papillomas are monoclonal in origin but recruit neighbouring epithelial cells during growth. Papillomas that never progress to malignancy retain several distinct clones, whereas progression to carcinoma is associated with a clonal sweep. Newly generated clones within carcinomas demonstrate intratumoural invasion and clonal intermixing, often giving rise to metastases containing two or more distinct clones derived from the matched primary tumour. These data demonstrate that late-stage tumour progression and dissemination are governed by evolutionary selection pressures that operate at a multicellular level and, therefore, differ from the clonal events that drive initiation and the benign-malignant transition.
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Project description:Developmental origins of dendritic cells (DCs) including conventional DCs (cDCs, comprising cDC1 and cDC2 subsets) and plasmacytoid DCs (pDCs) remain unclear. We studied DC development in unmanipulated adult mice using inducible lineage tracing combined with clonal DNA "barcoding" and single-cell transcriptome and phenotype analysis (CITE-seq). Inducible tracing of Cx3cr1+ hematopoietic progenitors in the bone marrow showed that they simultaneously produce all DC subsets including pDCs, cDC1s, and cDC2s. Clonal tracing of hematopoietic stem cells (HSCs) and of Cx3cr1+ progenitors revealed clone sharing between cDC1s and pDCs, but not between the two cDC subsets or between pDCs and B cells. Accordingly, CITE-seq analyses of differentiating HSCs and Cx3cr1+ progenitors identified progressive stages of pDC development including Cx3cr1+ Ly-6D+ pro-pDCs that were distinct from lymphoid progenitors. These results reveal the shared origin of pDCs and cDCs and suggest a revised scheme of DC development whereby pDCs share clonal relationship with cDC1s.
| S-EPMC9344860 | biostudies-literature
Project description:Triple-negative breast cancer, characterized by aggressive growth and high intratumor heterogeneity, presents a significant clinical challenge. Here, we use a lineage-tracing system, ClonMapper, which couples heritable clonal identifying tags with single-cell RNA-sequencing (scRNA-seq), to better elucidate the response to doxorubicin in a model of TNBC. We demonstrate that, while there is a dose-dependent reduction in overall clonal diversity, there is no pre-existing resistance signature among surviving clones. Separately, we found the existence of two transcriptomically distinct clonal subpopulations that remain through the course of treatment. Among clones persisting across multiple samples we identified divergent phenotypes, suggesting a response to treatment independent of clonal identity. Finally, a subset of clones harbor novel changes in expression following treatment. The clone and sample specific responses to treatment identified herein highlight the need for better personalized treatment strategies to overcome tumor heterogeneity.
2025-03-18 | GSE291679 | GEO
Project description:Cancer cells adapt to treatment, leading to the emergence of clones that are more aggressive and resistant to anti-cancer therapies. We have a limited understanding of the development of treatment resistance as we lack technologies to map the evolution of cancer under the selective pressure of treatment. To address this, we developed a hierarchical, dynamic lineage tracing method called FLARE (Following Lineage Adaptation and Resistance Evolution). We use this technique to track the progression of acute myeloid leukemia (AML) cell lines through exposure to Cytarabine (AraC), a front-line treatment in AML, in vitro and in vivo. We map distinct cellular lineages in murine and human AML cell lines predisposed to AraC persistence and/or resistance via the upregulation of cell adhesion and motility pathways. Additionally, we highlight the heritable expression of immunoproteasome 11S regulatory cap subunits as a potential mechanism aiding AML cell survival, proliferation, and immune escape in vivo. Finally, we validate the clinical relevance of these signatures in the TARGET-AML cohort, with a bisected response in blood and bone marrow. Our findings reveal a broad spectrum of resistance signatures attributed to significant cell transcriptional changes. To our knowledge, this is the first application of dynamic lineage tracing to unravel treatment response and resistance in cancer, and we expect FLARE to be a valuable tool in dissecting the evolution of resistance in a wide range of tumor types.
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Project description:MotivationLineage tracing and trajectory inference from single-cell RNA-sequencing data hold tremendous potential for uncovering the genetic programs driving development and disease. Single cell datasets are thought to provide an unbiased view on the diverse cellular architecture of tissues. Sampling bias, however, can skew single cell datasets away from the cellular composition they are meant to represent.ResultsWe demonstrate a novel form of sampling bias, caused by a statistical phenomenon related to repeated sampling from a growing, heterogeneous population. Relative growth rates of cells influence the probability that they will be sampled in clones observed across multiple time points. We support our probabilistic derivations with a simulation study and an analysis of a real time-course of T-cell development. We find that this bias can impact fate probability predictions, and we explore how to develop trajectory inference methods which are robust to this bias.Availability and implementationSource code for the simulated datasets and to create the figures in this manuscript is freely available in python at https://github.com/rbonhamcarter/simulate-clones. A python implementation of the extension of the LineageOT method is freely available at https://github.com/rbonhamcarter/LineageOT/tree/multi-time-clones.
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