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Omics · study · 2026

A Unique System of Paired PDX Models to Investigate the Progression of Potentially Lethal to Lethal Prostate Tumors at a Multi-omic Level

Listed in NCBI GEO

Prostate cancer (PCa) exhibits substantial heterogeneity in aggressiveness among patients, with significant lethality in its advanced metastatic stages.

Description

Defining the molecular mechanisms underlying rapid disease progression remains a pressing unmet need. Here, we present a comparative analysis of a longitudinally paired patient-derived xenograft model of aggressive PCa progression before and after therapeutic intervention.

Using multi-omic analysis, we characterized the MDA PCa 177-B (androgen receptor [AR]-negative, basal) and MDA PCa 189-1 (AR-positive, luminal) models, which exhibit distinct transcriptomic profiles yet originate from a shared clonal lineage. Targeted and single-cell DNA sequencing revealed that these models share 71.5% of copy number variations, tracing back to a common ancestral cell population. Targeted bisulfite sequencing of both models revealed epigenetic marks unique for aggressive disease compared to normal/benign prostate.

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Next, through single-cell RNA sequencing, we identified a subset of cells exhibiting a low differentiation profile, present in both PDX models and characterized by a unique molecular signature associated with adaptive immune responses and metastasis-related pathways. Further integrative analysis using data from a broad spectrum of disease samples demonstrated that, within this pair of aggressive PDX models, these low-differentiation clusters are exclusive to stem-like tumor cell populations.

These findings support the hypothesis that a continuum of epigenetic, transcriptomic, and phenotypic transitions occurs during the progression of PCa from its potentially lethal to lethal stages. We suggest that this continuum can be captured through well-characterized, longitudinal PDX models derived from the same patient. Our multi-omic approach underscores the value of such models in uncovering the dynamic and adaptive mechanisms of PCa aggressiveness.

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Life Sciences
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Cancer 75% · Disease 75% · Longitudinal study 65% · RNA sequencing 75% · Sequencing 75% · Single-cell RNA sequencing 75%
Provenance · 1 source records, 13 field assertions
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NCBI GEOGSE29391113 d agoJSON v1
FieldAssertionExtractorEvidence
access_levelsource · NCBI GEOconnector:ncbi_geo@1.0.0
concepts[disease].local:disease:cancerenrichment · NCBI GEOkeyword-concept-rules@1.0.0title+description (75%)
concepts[disease].local:disease:diseaseenrichment · NCBI GEOkeyword-concept-rules@1.0.0title+description (75%)
concepts[field].local:field:life-sciencesmapping · NCBI GEOconnector:ncbi_geo@1.0.0
concepts[method].geo_series_type:expression-profiling-by-high-throughput-sequencingsource · NCBI GEOconnector:ncbi_geo@1.0.0/gdstype
concepts[method].local:method:longitudinal-studyenrichment · NCBI GEOkeyword-concept-rules@1.0.0title+description (65%)
concepts[modality].local:modality:rna-seqenrichment · NCBI GEOkeyword-concept-rules@1.0.0title+description (75%)
concepts[modality].local:modality:sequencingenrichment · NCBI GEOkeyword-concept-rules@1.0.0title+description (75%)
concepts[modality].local:modality:single-cell-rna-seqenrichment · NCBI GEOkeyword-concept-rules@1.0.0title+description (75%)
concepts[organism].NCBITaxon:9606source · NCBI GEOconnector:ncbi_geo@1.0.0/taxon
descriptionsource · NCBI GEOconnector:ncbi_geo@1.0.0/summary
publication_datesource · NCBI GEOconnector:ncbi_geo@1.0.0
titlesource · NCBI GEOconnector:ncbi_geo@1.0.0/title