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Integrated Multi-Omics Reveals Cellular States and Microenvironmental Remodeling in Coexisting DCIS and IDC

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…

By the Truvace desk1 min read1 sourcePeer-reviewedTRV-2026-1224· cite & verify
Integrated Multi-Omics Reveals Cellular States and Microenvironmental Remodeling in Coexisting DCIS and IDC
Case TCGA-AN-A046 slide 01Z-00-DX1 from the TCGA-BRCA project by Unknown photographer. CC0 · http://creativecommons.org/publicdomain/zero/1.0/deed.en

In brief

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.

Main points

  1. Study integrated spatial transcriptomics, single-cell RNA sequencing, and single-cell DNA sequencing on coexisting DCIS and IDC samples.
  2. Identified candidate genes MGP, PLAT, and SERPINA3 potentially limiting progression from DCIS to IDC.
  3. Malignant epithelial meta-programs differed, with development-associated MP1 enriched in DCIS and cell cycle-related MP5 enriched in IDC.
  4. Microenvironment differed, with Mph_SPP1 and iCAFs_HOPX enriched in DCIS and Mph_PRDM1 and tCAFs_BNIP3 predominating in IDC.

The gain

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.

The 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

  1. Peer-reviewedAdvanced Science2026-09-27

The debate