Evidence map›Paper›PMID 41286068›Full record

ArticleScientific reports2025

Expression of carcinoma ecotypes in the tumor microenvironment predicts response to neoadjuvant therapy in early-stage breast cancer.

Karama Asleh, Gillian Bethune, Paola Marcato

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

3 authors.

Karama AslehDepartment of Pathology and Laboratory Medicine, Dalhousie University, Halifax, NS, Canada. karama.asleh@dal.ca.
Gillian BethuneDepartment of Pathology and Laboratory Medicine, Dalhousie University, Halifax, NS, Canada.
Paola MarcatoDepartment of Pathology and Laboratory Medicine, Dalhousie University, Halifax, NS, Canada. paola.marcato@dal.ca.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Immunotherapy is associated with modest pathologic complete response (pCR) rates in early-stage breast cancer, and a subset of patients still achieves a pCR after neoadjuvant chemotherapy (NAC) only. Identifying biomarkers in the complex tumor ecosystem which define the subsets of patients who achieve pCR benefit on immunotherapy versus not is of a critical need. Transcriptomic data for patients enrolled in the two neoadjuvant immunotherapy arms of 'pembrolizumab' (n = 69) and 'durvalumab' (n = 71), and the chemotherapy arm 'control' (n = 210) of the I-SPY2 breast cancer clinical trial were included. Using a machine learning algorithm for tumor ecosystem-based classification, we deconvoluted transcriptomic data into the established 10 multicellular organization systems known as 'Ecotypes'. We found that the most pro-inflammatory carcinoma ecotype (CE)9 predicts pCR in the 'pembrolizumab' arm (OR = 2.07; 95% CI 1.35-3.8; adjusted P value = 0.01), in the 'control' arm (OR = 1.89; 95% CI 1.36-2.63; adjusted P value = 0.002), and in the 'durvalumab' arm (OR = 1.66; 95%CI 1.18-2.34; adjusted P value = 0.03). In contrast, the basal-enriched ecotype CE2 was the most significant predictor of pCR in the 'durvalumab' arm, which included the addition of olaparib (OR = 3.22; 95%CI 2.25-4.60; adjusted P value < 0.0001), but not in NAC (OR = 1.18; 95%CI 0.81-1.72; adjusted P value = 0.94). Our findings suggest that CE9 could identify early-stage breast cancer patients who achieve a pCR after neoadjuvant therapy and may have a good prognosis. Whether CE9 patients could still be considered for immunotherapy or be candidates for de-escalation strategies in the neoadjuvant setting requires further investigation in future studies with link to survival outcomes. In contrast, CE2 tumors would benefit from the combination of immunotherapy with olaparib. Integrating tumor ecosystem-based patient classification could guide effective clinical management in early-stage breast cancer.

Indexed as

Breast NeoplasmsNeoadjuvant TherapyTumor MicroenvironmentBiomarkers, TumorFemaleGene Expression Regulation, NeoplasticHumansImmunotherapyMiddle AgedNeoplasm StagingTreatment OutcomeBiomarkers, TumorCarcinoma ecotypeCell stateEarly-stage breast cancerImmunotherapyMachine learningNeoadjuvant therapyPathologic complete responsePredictive biomarkersTranscriptomicsTumor microenvironment

Identifiers

PMID41286068
PMCPMC12750013

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LicenceCC BY-NC-ND
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.