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TRUVACE RECORD VERSION
record: TRV-2026-1224
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-09-30T06:56:59.002345Z
status: published
lens: g_space
sector: health
headline: Integrated Multi-Omics Reveals Cellular States and Microenvironmental Remodeling in Coexisting DCIS and IDC
dek: Ductal carcinoma in situ (DCIS) is a non-invasive precursor of invasive ductal carcinoma (IDC), yet the biological mechanisms underlying the transition from DCIS to IDC remain incompletely understood. Here, we integrate spatial transcriptomics, single-cell RNA sequencing, and single-cell DNA sequencing on coexisting DCIS and IDC samples to characterize cellular and microenvironmental alterations. Integrated analyses reveal differential molecular characterizations between coexisting DCIS and IDC and identify cand…
gain_title: Researchers built and externally validated a machine learning model that identifies DCIS at high risk of progressing to invasive disease, enabling more precise risk stratification and clinical management.
problem_title: (none)
trace_subject: (none)
gain_reading: Researchers built and externally validated a machine learning model that identifies DCIS at high risk of progressing to invasive disease, enabling more precise risk stratification and clinical management.
gain_evidence: We then construct a machine learning model to identify DCIS at high risk of progression, which is externally validated across independent bulk RNA-sequencing cohorts (mean AUC = 0.903). | with the potential to inform more precise risk stratification and clinical management for DCIS progression.
problem_reading: (none)
problem_evidence: (none)
quick_read: Researchers analyzed coexisting ductal carcinoma in situ and invasive ductal carcinoma using spatial transcriptomics, single-cell RNA sequencing, and single-cell DNA sequencing to map how malignant cells and their microenvironment change at invasion.

The work matters because DCIS management depends on predicting who will progress; a validated machine learning model that flags high-risk DCIS could guide treatment decisions, though the source presents it as potential for future stratification rather than current clinical deployment, and generalizability beyond the studied cohorts remains to be established.
limitation: 
tag: Evidence-backed gain
key_points: Study integrated spatial transcriptomics, single-cell RNA sequencing, and single-cell DNA sequencing on coexisting DCIS and IDC samples. | Identified candidate genes MGP, PLAT, and SERPINA3 potentially limiting progression from DCIS to IDC. | Malignant epithelial meta-programs differed, with development-associated MP1 enriched in DCIS and cell cycle-related MP5 enriched in IDC. | Microenvironment differed, with Mph_SPP1 and iCAFs_HOPX enriched in DCIS and Mph_PRDM1 and tCAFs_BNIP3 predominating in IDC.
rundown: The team profiled coexisting DCIS and IDC from the same samples using three omics layers to compare cellular states and microenvironmental remodeling during invasion.

Differential analysis highlighted distinct transcriptional meta-programs and immune and fibroblast subsets between the two stages, and nominated progression-limiting genes.

The resulting machine learning classifier for high-risk DCIS was tested in independent bulk RNA-sequencing cohorts, achieving a mean AUC of 0.903 by the September 2026 publication date.
sources:
- peer_reviewed | Advanced Science | https://doi.org/10.1002/advs.77897 | 2026-09-27
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