Evidence map›Paper›PMID 42016158›Full record

ArticleCancer management and research2026

Inflammatory and Nutritional Biomarkers Predict Response to Neoadjuvant Dual Anti-HER2 Therapy in HER2-Positive Breast Cancer: A Retrospective Cohort Study.

Hayriye Şahinli, Galip Can Uyar, Enes Yeşilbaş

Abstract read
In one paragraph

Article in Cancer management and research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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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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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Hayriye ŞahinliDepartment of Medical Oncology, Ankara Etlik City Hospital, Ankara, Turkey.ORCID 0000-0002-1561-9346
Galip Can UyarDepartment of Medical Oncology, Ankara Etlik City Hospital, Ankara, Turkey.ORCID 0000-0002-0698-777X
Enes YeşilbaşDepartment of Medical Oncology, Ankara Etlik City Hospital, Ankara, Turkey.ORCID 0000-0002-0947-702X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: Predicting response to neoadjuvant therapy (NAT) remains a clinical challenge in patients with HER2-positive breast cancer (BC). Systemic inflammatory and immune-nutritional biomarkers have emerged as potential predictors of treatment response; however, their value in patients receiving dual anti-HER2 therapy is not well defined. Patients and Methods: This retrospective study included patients with HER2-positive BC treated with neoadjuvant dual anti-HER2 therapy between January 2023 and February 2025. A total of 136 patients were included. Pathological complete response (pCR) and radiological response assessed by positron emission tomography/computed tomography (PET/CT) were the primary outcomes. Routinely available inflammatory and immune-nutritional indices, including lymphocyte-to-monocyte ratio (LMR), systemic immune-inflammation index (SII), and C-reactive protein-to-albumin ratio (CAR), were evaluated using receiver operating characteristic analysis and multivariable logistic regression. Exploratory RF models were constructed to contextualize regression-based findings, with feature importance assessed using permutation importance and the Gini index. These machine learning analyses were conducted as exploratory, hypothesis-generating tools to support and contextualize regression-based findings rather than to establish standalone predictive models. Results: Among 136 patients, 74% had locally advanced disease; pCR was achieved in 52.9%, and radiological response in 84.6%. Higher LMR (≥2.98) was independently associated with increased odds of pCR, whereas elevated SII and CAR were associated with reduced response. For radiological response, LMR, CAR, baseline CA 15-3 levels, and intermediate Ki-67 expression (20-30%) remained independently associated with outcomes. Exploratory machine-learning analyses consistently identified inflammatory and immune-nutritional biomarkers among the most influential predictors. Conclusion: Routinely available systemic inflammatory and immune-nutritional biomarkers, particularly LMR, SII, and CAR, are independently associated with pathological and radiological response to neoadjuvant dual anti-HER2 therapy in HER2-positive BC. These findings support the potential role of host-related biomarkers in treatment response prediction, pending prospective validation.

Indexed as

breast cancerHER2-positiveinflammationneoadjuvant therapypathological complete response

Identifiers

PMID42016158
PMCPMC13094565

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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.