https://orcid.org/0000-0002-5618-6057
Figures
Abstract
Neoadjuvant chemotherapy (NAC) is a critical component of breast cancer treatment, but the molecular mechanisms underlying resistance remain poorly understood. This study aimed to identify transcriptomic changes associated with NAC resistance across four breast cancer subtypes: Luminal A, Luminal B/HER2-positive, Luminal B/HER2-negative, and Triple-Negative Breast Cancer (TNBC). RNA-seq analysis was performed on paired pre- and post-NAC breast cancer samples from 32 non-responders. Differentially expressed genes (DEGs) were identified, and functional enrichment analyses were conducted. Protein-protein interaction (PPI) networks were constructed to identify hub genes. Tumor microenvironment (TME) infiltration was estimated using deconvolution algorithms. The results revealed distinct gene expression profiles between pre- and post-NAC samples, with FOS and NR4A1 being common DEGs across all subtypes. Enriched pathways varied among subtypes, including signal transduction, estrogen biosynthesis, extracellular matrix organization, dendritic cell activation, and B cell activation. TME analysis showed increased infiltration of specific immune cell populations after NAC, including CD4 memory T cells, regulatory T cells, neutrophils, macrophages, and mast cells, varying by subtype. These findings suggest that NAC modulates gene expression, cellular activity, and TME interactions, potentially contributing to treatment resistance. Understanding the molecular determinants of NAC resistance is crucial for developing targeted therapeutic strategies and improving outcomes for breast cancer patients.
Citation: Guevara-Nieto HM, Orozco-Castaño CA, Parra-Medina R, Poveda-Garavito JN, Garai J, Zabaleta J, et al. (2025) Molecular determinants of neoadjuvant chemotherapy resistance in breast cancer: An analysis of gene expression and tumor microenvironment. PLoS One 20(10): e0334335. https://doi.org/10.1371/journal.pone.0334335
Editor: UDAYAN BHATTACHARYA, Weill Cornell University, UNITED STATES OF AMERICA
Received: April 15, 2025; Accepted: September 25, 2025; Published: October 14, 2025
Copyright: © 2025 Guevara-Nieto et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: The data generated in this study are publicly available in the Gene Expression Omnibus (GEO) repository under GSE309004.
Funding: Instituto Nacional de Cancerología [grant number: C19010300-403] U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences: [grant number: P20GM121288-06] The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
Introduction
Neoadjuvant chemotherapy (NAC) is a cornerstone of primary breast cancer (BC) treatment. improving surgery outcomes and providing real-time efficacy assessment while offering overall survival (OS) and disease-free survival (DFS) rates comparable to adjuvant therapy. This equivalence allows clinicians to opt for NAC without compromising long-term outcomes [1,2]. Despite its benefits, NAC resistance remains a challenge. Tailored to tumor subtypes, NAC efficacy varies, sometimes reducing tumor size but increasing local recurrence risk [3,4].
Treatment resistance hinders long-term remission as resistant cancer subclones emerge during therapy [5]. Residual tumors often differ from pretreatment ones, with genetic and molecular changes driving resistance. NAC may not fully eliminate cancer but instead select resistant clones and promote metastasis by inducing cellular stress and remodeling the tumor microenvironment (TME) [6–8]. Understanding transcriptomic and molecular alterations in residual disease is crucial to overcoming resistance.
Similar challenges arise in esophageal adenocarcinoma (EAC), where NAC or chemoradiotherapy precedes surgery. Therapy response varies and predicts survival and recurrence. Some tumors are intrinsically resistant, while others mix sensitive and resistant cells [9]. In BC and EAC, most tumors likely harbor resistant subpopulations due to genetic or epigenetic changes, though these alterations are rare and hard to detect pre-treatment [9]. Beyond mutations, factors like hypoxia, cancer stem cell (CSC) phenotypes, and the stromal microenvironment further reduce therapeutic efficacy [10].
Tumor heterogeneity plays a central role in cancer progression and therapy resistance. Studies show tumors exhibit high cellular diversity, driving evolution of drug-resistant clones [10]. NAC resistance may arise from therapy-induced selection of resistant subpopulations, leading to increased cellular plasticity. Resistant cancer cells adapt their transcriptomic signature and interactome, acquiring phenotypes independent of drug-targeted pathways [11]. Genetic mutations contribute to intrinsic resistance, with alterations in key genes like PIK3CA promoting cell survival and proliferation, reducing chemotherapy effectiveness [12]. Cancer stem cells (CSCs) contribute to resistance by repopulating tumors after treatment, exhibiting mechanisms less responsive to standard therapies [12,13]. TME influences resistance through interactions with cancer cells, hypoxia, low pH, and stromal cell-mediated support, diminishing chemotherapy effectiveness [14,15]. Understanding genes, pathways, and molecular interactions involved in drug resistance is crucial for developing precision medicine strategies [16].
A promising approach to overcome NAC resistance involves identifying key genes contributing to resistance through compensatory pathways and protein interaction network alterations. Bioinformatics and systems biology enable comparative analysis of gene expression profiles between NAC-sensitive and NAC-resistant tumors, identifying molecular drivers of resistance. Moreover, hub genes involved in transcriptomic reprogramming and therapy resistance may serve as critical biomarkers for treatment response [17].
Hispanics/Latinos exhibit unique cancer incidence and mortality patterns compared to non-Hispanic Whites (NHW). Hispanic/Latino patients tend to be diagnosed at more advanced stages for several cancers, including breast, colorectal, prostate, and lung cancers [18]. Interestingly, traditional risk factors explain less of the cancer risk in Hispanics than in NHW for certain cancers. These unique patterns warrant further investigation to elucidate the effects of treatment, genetics, environment, and lifestyle on cancer risk in this population. In conclusion, studying Hispanic/Latino populations in cancer research is essential to address health disparities, improve cancer prevention and early detection strategies, and develop culturally appropriate interventions. The genetic admixture of this population offers unique opportunities to identify novel risk factors and advance our understanding of cancer etiology [19,20].
This study aims to characterize transcriptomic alterations driving clonal resistance to NAC in BC. By integrating sequencing data with publicly available RNA-seq datasets and advanced computational analyses, we seek to identify key genes implicated in therapy resistance and explore potential drug repurposing strategies. Deciphering these mechanisms will enhance our understanding of NAC resistance and enable identification of novel biomarkers, ultimately guiding development of more precise and effective therapeutic interventions for patients who do not achieve a complete pathological response.
Materials and methods
Patient population and samples
Female patients diagnosed with (a) locally advanced mammary adenocarcinoma (stage IIB–IIIC) at the National Cancer Institute of Colombia (INC) between September 2013 and March 2021 and (b) candidates for NAC were enrolled in this study. All participants provided written informed consent through an IRB-approved (Institutional Review Board) consent form, in which they were informed of the use of their samples (formalin-fixed paraffin-embedded or FFPE) and medical records for research purposes. This study was approved by the Comité de Ética e Investigaciones (IRB equivalent) of the INC (Protocol C1901300403). This study was conducted in accordance with the principles of the Declaration of Helsinki. Data used in this retrospective study were accessed for research purposes between April 1st 2019 and September 30th 2023. Personal information was anonymized. All data were handled in accordance with institutional and ethical guidelines to ensure participant confidentiality. Most cases included paired pre- and post-treatment samples, except for eight cases in which only sample was available.
Sample breast cancer collection
To explore gene expression profiles associated with NAC resistance, a set of 58 FFPE samples from non-responders was selected. A pathologist evaluated tumor cellularity for each sample, and only those with ≥50% tumor cellularity from hematoxylin-eosin stained pre-treatment (biopsy) and post-treatment (surgical resection) samples were included in the study. Non-response to NAC was defined based on post-chemotherapy pathology reports, where the presence of invasive tumor cells in both the breast and lymph nodes (ypT0/is ypN0) indicated lack of response. Responders, on the other hand, were those who achieved pathological complete response (pCR), characterized by the absence of invasive tumor cells in both the breast and lymph nodes. Four-micron sections were obtained from the FFPE blocks and manually microdissected.
RNA extraction
FFPE samples were deparaffinized with two rounds of 1 ml xylene at 50°C for 3 minutes each, followed by two washes with 100% ethanol. Residual ethanol was removed, and samples were air-dried at room temperature. Total RNA was extracted using the Qiagen All Prep DNA/RNA FFPE Kit (Qiagen, Hilden, Germany), automated with QIAGEN QIAcube and MagMAX® FFPE RNA Ultra Kit (Applied Biosystems®, Foster City, CA, USA), following the manufacturer’s protocol. Extracted nucleic material was frozen at −80ºC. RNA quantity and quality were assessed using a Qubit 3.0 Fluorometer (Life Technologies, CA, USA) and an Agilent 2100 Bioanalyzer (Agilent Technologies). RNA integrity was calculated using the DV200 value with Agilent 2011 Bioanalyzer Expert Software, representing the percentage of RNA fragment ≥200 nucleotides in length.
Library preparation and sequencing
Genomic libraries were prepared using the Illumina TruSeq RNA Exome Library prep kit, following the manufacturer’s protocol with modifications: RNA input was increased to 300ng, samples with DV200 > 30% were chosen, PCR was reduced to 9 cycles, and hybridization time extended to 16 h. These adjustments enhance library diversity and data quality, crucial for FFPE material, which is highly degradable [21]. Libraries were quantified using the Qubit instrument and DNA HS kit (Invitrogen) and assessed with the Agilent 2100 Bioanalyzer using a DNA 1000 chip (pre-capture) or high-sensitivity DNA chip (post-capture). Library concentrations were normalized to 10 nM and pooled for multiplex sequencing. Pooled libraries were denatured, PhiX control added, clustered, and sequenced in a NextSeq500 High-Output flow cell at 2 x 75 bp. Sequencing quality exceeded 90%, with an average read count per sample of 30,105,535.
RNA sequencing data processing, gene expression analysis of DEGs
Genome alignment, quality control were analyzed using Partek Flow (Partek, St. Louis, MO, USA). Raw reads were trimmed at both ends using a minimum quality score of 20 and a minimum read length of 25. Contaminants were removed with Bowtie 2 v2.2.5, and reads were aligned to the human genome (hg38) using STAR 2.7.3a. Aligned reads were quantified using RefSeq Transcripts 96, retaining genes with at least five reads in 80% of the samples. Differential expression was analyzed using DEseq2 after normalization with variant stabilizing transformation (VST). Benjamini and Hochberg correction (FDR = 0.05) and fold-change (FC) values of 2 were used to identify differentially expressed genes (DEGs). Gene expression analyses compared responses to neoadjuvant chemotherapy across intrinsic subtypes: Luminal A, LuminalB/Her2-, LuminalB/Her2 + , and TNBC. Library preparation, sequencing, and analysis were performed at the Translational Genomics Core, Department of Interdisciplinary Oncology, Louisiana State University Health Sciences Center, New Orleans, LA.
Functional enrichment analysis of DEGs
Pathway enrichment analysis was performed to identify pathways in nonresponder patients using the Kyoto Encyclopedia of Genes and Genomes (KEGG) [22], Gene Ontology (GO) [23], and Disease Ontology [24] (DO) with the clusterProfiler [25] R package using DEGs from RNA-Seq data. Benjamini-Hochberg false discovery rate (FDR) <0.05 was chosen as cutoff criterion.
PPI network construction, hub gene selection
A protein-protein interaction (PPI) network for DEGs was constructed using the Search Tool for the Retrieval of Interacting Genes (STRING, version 12.0) database [26] using “Homo sapiens” as reference and using a medium confidence interaction. Experimental evidence and co-expression were selected as active interaction source parameters and full-string network appearance. Modules of PPI network of DEGs with confidence scores greater than or equal to 0.4 were retrieved and imported to Cytoscape3.10 [27]. Cytohubba plug-in in Cytoscape was used to identify the top 10 hub nodes according to maximal clique centrality (MCC) method with neighbors and shortest paths as additional parameters.
Estimation of relative cell type abundance
Abundance scores for cell types in the tumor microenvironment were estimated using enrichment (xCell [28], 64 immune and stromal cells) and deconvolution (CIBERSORTx [29], 22 immune cells) methods on normalized RNA-seq data. xCell scores correlated highly with true cell proportions but could not be directly interpreted as cell fractions [28]. This approach assigns enrichment scores across all samples by integrating the single-sample gene set enrichment analysis and deconvolution methods. All sequenced samples were analyzed with xCell and CIBERSORTx to compare stromal and immune scores for nonresponders and responders using the non-parametric Mann-Whitney U test with FDR correction for multiple comparisons.
Integrating Bootstrap and Jackknife techniques to address small sample sizes
To address the challenges associated with small sample sizes and improve result reproducibility, sensitivity analyses were conducted using bootstrap resampling and jackknife methods. Specifically, 20 bootstrap iterations were performed for each molecular subtype using a sampling scheme consisting of 10 pre-NAC and 10 post-NAC samples randomly selected with replacement. This approach enabled the evaluation of consistency across the molecular subtypes. Differential gene expression analysis was performed in each iteration to identify the top 20 differentially expressed genes consistently observed across the bootstrap samples. To assess the variability and consistency of gene expression, several statistical measures were calculated for each gene. The mean fold change was determined by averaging the fold-change values across all iterations, excluding the missing values. The standard deviation of fold changes was computed to quantify variability in gene expression, and the coefficient of variation was derived by dividing the standard deviation by the absolute value of the mean fold change, providing a relative measure of variability. The number of valid data points for each gene was counted by tallying the non-missing fold-change values, ensuring that missing data were appropriately accounted for. Additionally, the jackknife method was applied to evaluate the bias and variance of the estimates, thereby enhancing the generalizability of the findings [30–33]. The combination of these methods allowed for a robust assessment of gene expression stability across assays. Boxplots and heatmaps were generated to assess the consistency of fold changes, identify genes with stable expression patterns, and minimize the impact of small sample sizes.
Gene expression validation
To validate gene expression findings, publicly available mRNA expression datasets were retrieved from the Gene Expression Omnibus (GEO) database. The keywords “neoadjuvant chemotherapy AND breast cancer AND non-responder” were used in the search, and the results were limited to Homo sapiens as the organism. The inclusion criteria for the search were (1) at least 30 gene expression profiles present; (2) data obtained for the same cancer type and using the same experimental platform; (3) every profile linked with the patient’s clinical history; (4) all cancers treated with at least one common drug or chemotherapy regimen; and (5) available treatment outcomes enabling the classification of every case as either responder or non-responder. Comparative analyses of the pre-NAC and post-NAC samples were performed using the GEO2R web tool. P < 0.05, adjusted to FDR, was chosen as the cut-off criterion.
Results
Clinicopathologic characteristics of BC patients with incomplete response to NAC
Of the 444 patients with locally advanced tumors (Fig 1), 176 were excluded due to incomplete NAC treatment, relocation, or insufficient tissue in their FFPE blocks for RNA extraction. RNA was extracted from FFPE samples of 268 patients who completed the NAC regimen; however, 210 samples did not meet the required minimum RNA concentration and DV200 quality standards necessary for the RNA-seq library preparation. A total of 32 patients who achieved an incomplete pathological response following NAC were included. Table 1 details the clinicopathological characteristics. The median patient age was 53 years. Most patients were overweight or obese, and postmenopausal. The majority were classified as grade II or III, with clinical stage IIIB being the most common. Most patients did not present metastatic nodules or lymphovascular or perineural invasion. Most patients received standard neoadjuvant chemotherapy of anthracyclines and taxanes.
Patients were classified as non-responders if they did not achieve pathological complete response (pCR).
Differences in transcriptome profiles before and after NAC treatment
To identify mRNA biomarkers associated to NAC non-responsiveness, RNA-Seq was used for transcriptome profiling. Unsupervised clustering analysis revealed distinct gene expression patterns between pre- and post-NAC samples across the different molecular subtypes (Fig 2A–D). Differential expression analysis using DESeq2 identified DEGs in post-NAC samples (S1 Table). Unsupervised hierarchical clustering showed that DEGs predominantly segregated pre- and post-NAC samples among Luminal A (40 DEGs, Fig 2A), LuminalB/HER2+ (18 DEGs, Fig 2B), LuminalB/HER2- (342 DEGs, Fig 2C), and TNBC (1385 DEGs, Fig 2D), highlighting the heterogeneous transcriptomic profile among different breast cancer subtypes.
Functional analysis (significantly enriched pathways) of differentially expressed genes in non-responder samples post-NAC and pre-NAC in Luminal A (E), LuminalB/HER2- (F), LuminalB/HER2+ (G), and TNBC (H) subtypes.
Enriched pathways associated with nonresponse to NAC
Gene ontology and KEGG pathway enrichment analyses were conducted on DEGs to identify biological processes, cellular components, and molecular functions (S1A–D Fig), in addition to significantly enriched pathways (Fig 2E–H) on pre- and post-NAC across molecular subtypes. The top 10 enriched biological processes from clusterProfiler analysis under Gene Ontology terms are shown in the dot plot for various comparison groups (Fig 2E–H). Gene set enrichment analysis (GSEA) analysis across all subtypes revealed an upregulation of neutrophil extracellular trap formation, microRNAs in cancer, stem cell pluripotency signaling, and MAPK and Wnt pathways in post-NAC, whereas cytokine-cytokine receptor interaction was highe r pre-NAC (Fig 3A). Key shared enriched terms included apelin, IL-17, MAPK, NF-kappa B, TNF signaling pathways, complement and coagulation cascades (Fig 3B), indicating NAC’s influence on mRNAs for proteins in these pathways.
Venn diagram depicting shared KEGG enriched pathways (B).
Integration of DEGS to identify key signaling pathways
To elucidate signaling pathways associated with NAC resistance, DEGs were integrated into MSigDB hallmark gene set. Comparisons were performed with annotated gene sets associated to cisplatin, docetaxel, and doxorubicin resistance as well as angiogenesis, antigen processing, apoptosis, DNA repair, EMT, interleukin-2, PI3K-AKT-MTOR, WNT beta-catenin, Notch signaling, TNFA, TGF-β, and Hedgehog signaling, and identified several genes shared with our samples across subtypes (Table 2). These shared genes were further classified within gene families such as cytokines, growth factors, transcription factors, cell differentiation markers, protein kinases, translocated cancer genes, oncogenes, and tumor suppressors (Table 3). This classification provides insights into the molecular mechanisms underlying NAC resistance and highlights potential targets for therapeutic intervention.
Intersection of DEGs across molecular subtypes
Intersection analysis, visualized via a upset plot [34] (Fig 4A), identified common DEGs across molecular subtypes. FOS and NR4A1 were present in all subtypes, whereas ZBTB16 was unique to luminal B. FOSB was common in all luminal samples. ATF3, DUSP1, EGR1, and JUN were shared among HER2-negative subtypes (Luminal A, Luminal B/HER2-negative, and TNBC). GEM was common to luminal A and Luminal B/HER2-negative subtypes. Validation using publicly available geoDatabases (GSE18728, GSE28844, GSE28826, and GSE87455) confirmed FOS and NR4A1 presence across all groups, whereas other DEGs were shared among subtypes. Further comparative analysis of pre- and post-treatment sample changes based on gene expression using the Wilcoxon signed-rank test revealed a significant increase in FOS and NR4A1 expression levels in post-treatment tumor samples (p < 0.01, Fig 4B–C). These findings suggest a potential role of these genes in NAC resistance across different BC subtypes. The top 10 nodes (from PPI network analysis) ranked by MCC method were chosen: FOS, NR4A1, and FOSB were hubs found in Luminal A, Luminal B/HER2+ and Luminal B/HER2- analyses. A set of histone genes were among the top 10 hub nodes in TNBC (S2 Table). Luminal A network (Fig 4D) had 12 nodes and 28 edges. Luminal B/HER2-positive network had 10 nodes and 12 edges (Fig 4E). Luminal B/Her2-negative network (Fig 4F) had 54 nodes and 311 edges, whereas TNBC network had 162 nodes and 3038 edges (Fig 4G).
(A) Upset plot showing the number of unique and intersecting genes among different datasets. The black bars represent the number of genes in each intersection, and the connected dots indicate which datasets are included in that intersection. All sample sets have FOS and NR4A1 genes in common. (B) and (C) Paired boxplots of FOS and NR4A1 expression levels (normalized counts) in pre- and post-treatment samples, respectively. The gray lines connect the paired samples. The p-values were calculated using the Wilcoxon signed-rank test. (D-G) Hub genes identified by network analysis using the Cytohubba plug-in, ranked by Maximal Clique Centrality (MCC). The networks correspond to (D) Luminal A, (E) Luminal B/HER2 + , (F) Luminal B/HER2-, and (G) Triple-Negative Breast Cancer (TNBC). The color of the nodes indicates centrality: red for high, orange for high-moderate, yellow for low-moderate, and blue for low.
Tumor microenvironment (TME) infiltration estimation
CIBERSORTx TME analysis provided insights into immune cell composition before and after NAC across molecular subtypes. In Luminal A post-NAC samples, CIBERSORTx revealed elevated levels of CD4 memory resting T cells, CD8 T cells, regulatory T cells (Tregs), and neutrophils, while xCell showed no statistical differences. For Luminal B/HER2 + post-treatment, CIBERSORTx identified increased CD8 T cells, CD4 memory resting T cells, resting mast cells, and neutrophils, with xCell also showing higher neutrophil fractions. In Luminal B/HER2negative, CIBERSORTx detected higher levels of B cell memory, CD4 T memory resting, Macrophages M2, resting mast cells, and neutrophils in post-NAC, corroborated by xCell’s findings of increased neutrophils. In TNBC post-samples, CIBERSORTx showed elevated resting mast cells and memory CD4 resting T cells along with higher levels of common lymphoid and megakaryocyte-erythroid progenitors and type 1 T-helper cells (Fig 5). Overall, memory B cells increased in pre-NAC samples for all subtypes. CD4 memory resting T cells increased in post-NAC samples in all subtypes. CD8 T cells were increased in Luminal A and Luminal B/HER2 + post-NAC samples. Neutrophils were increased in post-NAC samples in Luminal subtypes. Resting mast cells increased in post-NAC in Luminal B/HER2 + , Luminal B/HER2- and TNBC. Mean xCell and CIBERSORTx scores, along with p-values for all TME cell types, are provided in S3 and S4 Tables, respectively. These results highlight the immune landscape alterations associated with NAC response across subtypes.
Luminal A(A), LuminalB/HER2+(B,E), LuminalB/HER2-(C,F), and TNBC (D,G).P < 0.05; as determined by non-parametric U-Mann Whitney between pre-NAC (light green) and post-NAC (pink) samples. No significant differences were found in xCell scores between Luminal A pre-NAC and post-NAC samples.
Bootstrap Analysis of Gene Expression Variability across molecular subtypes
To assess the reproducibility of gene expression across molecular subtypes, a bootstrap resampling approach was employed using a sampling scheme of 10 pre-NAC and 10 post-NAC samples for each molecular subtype. S2 Fig analyzes Luminal A and Luminal B/HER2- subtypes, while S3 Fig focuses on Luminal B/HER2+ and TNBC. The bar plots (Panels A,D) illustrate the fold-change variability (standard deviation, SD) across 20 bootstrap iterations for the top 20 DEGs. A lower SD indicates a more consistent fold change across the iterations, suggesting a more reliable result. The box plots (Panels B,E) visualize the distribution and direction (upregulated or downregulated) of the log2 fold changes for each gene. The heatmaps (Panel 1C, F) provide a visual summary of the gene expression patterns across all bootstrap iterations, with red indicating upregulation and blue indicating downregulation. The analysis highlights specific findings for the FOS and NR4A1 genes, demonstrating their subtype-specific expression patterns. FOS show a consistent and strong upregulation particularly in Luminal B/HER2- and TNBC, with a tight distribution of fold changes in Luminal A and LuminalB/HER2 + . Similarly, the NR4A1 gene exhibits distinct, reliable expression patterns. It is consistently upregulated particularly in Luminal B/HER2- and TNBC, with low fold-change variability as shown by their respective bar plots. These results underscore how the expression of key genes like FOS and NR4A1 varies significantly and consistently between different breast cancer molecular subtypes.
Discussion
Our study identified several DEGs associated with NAC non-responsiveness in BC, notably FOS and NR4A1, upregulated across all molecular subtypes, which is consistent with the existing literature on BC pathogenesis and treatment resistance.
The FOS gene (Fos Proto-Oncogene), particularly c-Fos (FOS gene cellular variant), drives chemotherapy resistance in BC by activating survival pathways, regulating membrane biogenesis, and altering gene expression [35]. As part of the AP-1 complex, c-Fos promotes proliferation and survival, enabling resistance to apoptosis. Sustained JNK (c-Jun N-terminal kinase) activation increases c-Fos expression, helping cancer cells withstand chemotherapy stress [36]. In aggressive BC, GATA3 loss shifts gene expression, enhancing epithelial-mesenchymal transition (EMT) and CSC properties [37], further driving resistance. Additionally, FOS boosts radiation resistance and weakens CD8 + T cell anti-tumor activity in TNBC via PI3K/AKT signaling pathway [38].
NR4A1, a nuclear receptor family member, is involved in carcinogenesis, angiogenesis, apoptosis, DNA repair, proliferation, metabolism, and inflammation [39]. Its role is complex and context-dependent, influencing cellular processes that impact treatment outcomes. Depending on the tumor type, environment, and stimuli, NR4A1 can promote or inhibit malignancy. NR4A1 expression is often downregulated in BC tissues, especially in aggressive subtypes like TNBC, correlating with advanced tumor stage, lymph node metastasis, and poor prognosis [40,41]. Restoring NR4A1 levels in TNBC cell lines inhibits proliferation and invasion, suggesting its tumor suppressor potential [40]. Conversely, NR4A1 exhibits pro-oncogenic activity in solid tumor-derived cell lines and is frequently overexpressed in lung, pancreatic, and colon tumors. High NR4A1 expression is a prognostic indicator of decreased survival [42].
High NR4A1 expression is linked to better survival in BC, suggesting its potential as a NAC response marker [41,43,44]. It regulates apoptosis and cell cycle progression, enhancing therapy sensitivity via ERK signaling pathway [44]. In our study, NR4A1 upregulation in resistant luminal subtypes aligned with a pro-oncogenic role, yet its loss has been associated with NAC resistance [42,45]. Low NR4A1 levels reduce apoptosis, promoting cell survival and chemotherapy failure. Its dual role in cancer suggests resistance mechanisms may vary by molecular context, requiring further investigation.
Enriched pathways vary among subtypes, including signal transduction and estrogen biosynthesis. Signal transduction pathways are differentially activated across BC subtypes. MAPK pathway is frequently activated in ER-positive tumors through growth factor stimulation, whereas TNBC often shows alterations in the JNK signaling pathway during treatment resistance [46,47]. The convergence of these pathways often leads to therapeutic resistance, complicating treatment strategies. The identification of specific enriched pathways among BC subtypes underscores the need for personalized treatment strategies that consider the unique biological characteristics of each subtype. This approach can enhance NAC efficacy and improve patient outcomes.
Research has shown that specific immune cell gene expressions correlate with clinical outcomes following NAC. The expression patterns of immune and stromal cells are crucial for determining tumor response to chemotherapy, suggesting the TME’s vital role in mediating these responses [48,49]. The infiltration of immune cell populations following NAC in BC is complex and varies by subtype and pathological response [50]. While some studies indicate beneficial increases in certain immune cells, others reveal shifts towards immunosuppressive environments hindering anti-tumor responses. Understanding these dynamics is crucial for optimizing treatment strategies and improving patient outcomes in BC management. Few studies have systematically characterized chemotherapy’s effect on the tumor immune milieu, particularly during or after treatment in clinical settings.
Our analysis revealed subtype-specific immune alterations in the TME post-NAC. Luminal A showed increased CD4 memory resting T cells, CD8 T cells, Tregs, and neutrophils. Luminal B/HER2 + had elevated CD8 T cells, CD4 memory resting T cells, resting mast cells, and neutrophils, while Luminal B/HER2- exhibited higher B cell memory, CD4 memory resting T cells, M2 macrophages, resting mast cells, and neutrophils. TNBC showed increased resting mast cells and CD4 memory resting T cells. The X-cell score indicated a decline in principal effector cells. These findings highlight NAC’s role in reshaping the TME, potentially favoring chemotherapy-resistant cells promoting tumor invasiveness [51]. Notably, increased Tregs in Luminal A post-NAC may compromise antitumor responses, promoting resistance and metastasis. A concurrent rise in CD8 + T cells and Tregs suggests an imbalance that could weaken immune-mediated tumor control [51].
Neutrophil elevation across subtypes in our study highlights their dual role in cancer. They adapt to the TME, promoting metastasis and suppressing T cells, aiding tumor survival [52]. Some chemotherapy regimens increase neutrophil infiltration, linked to poor outcomes. IL-17 signaling, enriched in Luminal A and Luminal B/HER2 + , drives resistance by recruiting neutrophils and altering the TME [53,54]. Targeting IL-17 pathways may help overcome resistance and improve treatment efficacy.
Characterizing tumor-associated macrophages (TAMs) pre- and post-therapy reveals their role in treatment resistance and metastasis. We observed increased M2 macrophages in Luminal B/HER2- tumors, aligning with reports of elevated CD163 + macrophages and EGFR expression in tamoxifen-resistant ER + /HER2- tumors [55]. A study on paired pre/post-chemotherapy samples highlighted immunosuppressive macrophage expansion in residual disease, reinforcing their role in resistance [56]. Significant M2 infiltration, marked by high CD68 and CD163, correlates with poor prognosis. Our findings show NAC alters the TME immune landscape, underscoring the need to modulate it to enhance therapy efficacy and overcome resistance.
Understanding NAC resistance is crucial for developing targeted therapies in BC. Resistance arises from intrinsic factors like genetic and epigenetic alterations and tumor heterogeneity [57–59]. Intrinsic factors include genetic and epigenetic alterations and tumor heterogeneity. Pre-existing mutations and epigenetic changes drive inherent resistance, while tumor diversity leads to varied treatment responses, complicating efficacy prediction [60].
TME and cancer stem cells drive resistance by supporting tumor survival and drug evasion. TME influences heterogeneity and therapy response through signaling interactions [60,61], while immune cells, fibroblasts, and endothelial cells secrete factors that promote resistance [62]. Hallmark gene set analysis (Table 2) revealed EMT-related genes across all subtypes. EMT, driven by TME signals, enhances drug resistance by increasing cancer cell migration and invasion [13], emphasizing NAC resistance complexity and potential therapeutic targets.
Our findings identified FOS and NR4A1 as potential biomarkers of NAC resistance in BC. FOS, which plays a role in chemotherapy resistance through the AP-1 complex and JNK activation, can be utilized as a predictive marker for early signs of treatment failure. Monitoring FOS expression could enable early adjustment of treatment regimens, especially in cases of resistance. NR4A1, which has both tumor-suppressing and oncogenic roles, is associated with chemotherapy sensitivity and resistance. Its expression level could potentially predict NAC response, particularly in TNBC, where it is often low. These biomarkers could guide personalized treatment strategies, but further clinical validation is required. FOS and NR4A1 also present the possibility of drug repurposing. Targeting FOS-mediated resistance mechanisms, such as JNK/AP-1 signaling, could involve repurposing MAPK or c-Jun inhibitors to enhance the NAC response. Restoration of NR4A1 function in resistant tumors (e.g., TNBC) or inhibition of its oncogenic function in others could improve treatment response. Examining existing drugs that modulate FOS and NR4A1 could be a quick path to restoring NAC response in BC patients.
Our findings provide valuable insights and directions for future research; however, there are several limitations to consider. First, the sample size and the lack of functional analysis of candidate DEGs limit the depth of our conclusions. Sample size constraints restricted our ability to fully explore the molecular characteristics associated with therapy resistance in this population. Additionally, the study’s analysis of TME relied solely on computational deconvolution algorithms (xCell and CIBERSORTx). As these methods are predictive rather than direct measurements of cellular infiltration, the results should be interpreted with caution. To provide definitive confirmation of the TME composition, future research should employ experimental techniques such as immunohistochemistry or flow cytometry. While this study identifies associations between gene expression changes and TME in relation to NAC resistance, it is crucial to emphasize that these associations do not establish causal relationships. To confirm the mechanistic roles of key hub genes, such as FOS and NR4A1, and the pathways involved in NAC resistance, future functional studies, including in vitro gene knockdown/overexpression experiments and in vivo models, are necessary. While bootstrap and jackknife resampling improved the robustness of our analysis, the small sample size for each molecular subtype limits the generalizability of the findings. We employed 20 bootstrap iterations using a homogenous sampling scheme to evaluate the consistency of our results. However, the potential for bias and reduced precision due to the limited sample size requires caution. The jackknife method helped stabilize the results, but it is also susceptible to small sample size limitations [30,32]. While standard deviation, boxplots, and heatmaps were used to assess fold-change consistency, assay variability and outliers could still influence the outcome. Robust clustering methods, such as those using jackknife distances, can identify co-regulated genes, but assay-specific biases must be carefully considered in any clinical application. Furthermore, our study specifically focuses on a Latin-American cohort, contributing to a more diverse representation in cancer research. However, the absence of comprehensive genetic ancestry data related to treatment response in publicly available cohorts has limited our ability to fully evaluate the ethnic specificity and generalizability of the identified predictive biomarkers. This highlights the significance of our data in addressing this gap. Future studies should incorporate multicenter approaches to validate, strengthen, and broaden our findings, ultimately improving their robustness and applicability across diverse populations and all molecular subtypes. Addressing these limitations in future studies will be essential for translating molecular insights into clinically effective strategies to overcome NAC resistance.
Conclusion
This study provides valuable insights into the molecular mechanisms underlying NAC resistance in breast cancer, highlighting the role of FOS and NR4A1 in treatment response as well as the enrichment of IL-17, MAPK, NF-kappa B, and TNF signaling pathways. The observed changes in immune cell infiltration patterns across molecular subtypes further emphasize TME influence on chemotherapy resistance. Our findings suggest that NAC modulates gene expression and cellular interactions within the TME, potentially contributing to nonresponsiveness and disease recurrence. A deeper understanding of these mechanisms is essential for refining predictive biomarkers and identifying therapeutic targets. Moving forward, future translational research must focus on developing targeted strategies that can mitigate TME-driven resistance, with an emphasis on clinical application. Such strategies have the potential to significantly improve NAC efficacy, leading to more personalized and effective treatment options for breast cancer patients. Ultimately, the integration of these findings into clinical practice could transform therapeutic approaches and improve patient outcomes in the fight against breast cancer.
Supporting information
S1 Table. Differentially Expressed Genes (DEGs) by BC molecular subtype in post-NAC samples compared with Pre-NAC samples.
The table provides the complete list of differentially expressed genes for each subtype, showing the Log2Fold Change (Log2FC) values.
https://doi.org/10.1371/journal.pone.0334335.s001
(XLSX)
S2 Table. Top 10 STRING Network Hub Genes Obtained from CytoHubba Analysis.
Genes are ranked by the Maximum Clique Centrality (MCC) method, including associated neighbor and shortest path data from the protein-protein interaction network.
https://doi.org/10.1371/journal.pone.0334335.s002
(XLSX)
S3 Table. xCell Immune and Stromal Cell Type Abundances.
Mean abundance values (pre- and post-NAC samples) and p-values from xCell cell types (post-NAC samples compared with Pre-NAC samples) across all four molecular subtypes. Significant values (p < 0.05) are highlighted in bold.
https://doi.org/10.1371/journal.pone.0334335.s003
(XLSX)
S4 Table. CIBERSORTx Immune Cell Type Abundances.
Mean abundance values (pre- and post-NAC samples) and p-values from CIBERSORTx cell types (post-NAC samples compared with Pre-NAC samples) across all four molecular subtypes. Significant values (p < 0.05) are highlighted in bold.
https://doi.org/10.1371/journal.pone.0334335.s004
(XLSX)
S1 Fig. Gene Ontology and KEGG Pathway Enrichment Analyses.
Enrichment analyses for differentially expressed genes are shown with bar plots for biological processes (BP), cellular components (CC), and molecular functions (MF) post-NAC compared to pre-NAC samples across Luminal A (A), LuminalB/HER2- (B), LuminalB/HER2+ (C), and TNBC (D) subtypes.
https://doi.org/10.1371/journal.pone.0334335.s005
(TIF)
S2 Fig. Bootstrap Analysis of Gene Expression Variability for Luminal A and Luminal B/HER2- Subtypes.
(A, B, C) show results for Luminal A (n = 20); (D, E, F) show results for Luminal B/HER2- (n = 20). (A, D) Bar plots of fold-change variability (standard deviation, SD) per gene; (B, E) Box plots of fold changes per gene; (C, F) Heatmaps of gene expression data across assays. The analysis demonstrates the consistency of differential expression patterns.
https://doi.org/10.1371/journal.pone.0334335.s006
(TIF)
S3 Fig. Bootstrap Analysis of Gene Expression Variability for Luminal B/HER2+ and TNBC Subtypes.
(A, B, C) show results for Luminal B/HER2+ (n = 20); (D, E, F) show results for TNBC (n = 20). (A, D) Bar plots of fold-change variability (standard deviation, SD) per gene; (B, E) Box plots of fold changes per gene; (C, F) Heatmaps of gene expression data across assays. The analysis demonstrates the consistency of differential expression patterns.
https://doi.org/10.1371/journal.pone.0334335.s007
(TIF)
Acknowledgments
We are grateful to the patients who agreed to participate in the study as well as their families. We thank members of the Department of Pathology, Instituto Nacional de Cancerologia (INC) for allowing access to patient samples, Doctors Juan Carlos Mejia Henao and Patricia Lopez-Correa for providing sample selection, and members of the Genomics Core, Stanley S. Scott Cancer Center, Louisiana State University Health Science Center for assisting with RNA sequencing. Special thanks to the American Association for Cancer Research (AACR), the European Association for Cancer Research (EACR) and the company of Biologists for supporting HMGN with travel grants.
References
- 1. Baskin AS, Huppert LA, Kelil T, Singer L, Mukhtar RA. The neoadjuvant approach to treatment of breast cancer: Multidisciplinary management to improve outcomes. Surg Oncol Insight. 2024;1(2):100059.
- 2. Globus O, Greenhouse I, Sella T, Gal-Yam EN. The neoadjuvant systemic treatment of early breast cancer: a narrative review. Ann Breast Surg. 2023;7:39–39.
- 3. Caparica R, Lambertini M, Pondé N, Fumagalli D, de Azambuja E, Piccart M. Post-neoadjuvant treatment and the management of residual disease in breast cancer: state of the art and perspectives. Ther Adv Med Oncol. 2019;11. pmid:30833989
- 4. Viale G, Fusco N. Pathology after neoadjuvant treatment - How to assess residual disease. Breast. 2022;62 Suppl 1(Suppl 1):S25–8. pmid:34810049
- 5. Schmitt MW, Loeb LA, Salk JJ. The influence of subclonal resistance mutations on targeted cancer therapy. Nat Rev Clin Oncol. 2016;13(6):335–47. pmid:26483300
- 6. Hasim MS, Nessim C, Villeneuve PJ, Vanderhyden BC, Dimitroulakos J. Activating transcription factor 3 as a novel regulator of chemotherapy response in breast cancer. Transl Oncol. 2018;11(4):988–98. pmid:29940414
- 7. Perelmuter VM, Tashireva LA, Savelieva OE, Denisov EV, Kaigorodova EV, Zavyalova MV, et al. Mechanisms behind prometastatic changes induced by neoadjuvant chemotherapy in the breast cancer microenvironment. Breast Cancer (Dove Med Press). 2019;11:209–19. pmid:31308736
- 8. Imyanitov EN, Yanus GA. Neoadjuvant therapy: theoretical, biological and medical consideration. Chin Clin Oncol. 2018;7(6):55. pmid:30509077
- 9. Findlay JM, Castro-Giner F, Makino S, Rayner E, Kartsonaki C, Cross W, et al. Differential clonal evolution in oesophageal cancers in response to neo-adjuvant chemotherapy. Nat Commun. 2016;7:11111. pmid:27045317
- 10. Andre F, Berrada N, Desmedt C. Implication of tumor microenvironment in the resistance to chemotherapy in breast cancer patients. Curr Opin Oncol. 2010;22(6):547–51. pmid:20842030
- 11. Bhat GR, Sethi I, Sadida HQ, Rah B, Mir R, Algehainy N, et al. Cancer cell plasticity: from cellular, molecular, and genetic mechanisms to tumor heterogeneity and drug resistance. Cancer Metastasis Rev. 2024;43(1):197–228. pmid:38329598
- 12. Kinnel B, Singh SK, Oprea-Ilies G, Singh R. Targeted therapy and mechanisms of drug resistance in breast cancer. Cancers (Basel). 2023;15(4):1320. pmid:36831661
- 13. An J, Peng C, Tang H, Liu X, Peng F. New advances in the research of resistance to neoadjuvant chemotherapy in breast cancer. Int J Mol Sci. 2021;22(17):9644. pmid:34502549
- 14. Khalaf K, Hana D, Chou JT-T, Singh C, Mackiewicz A, Kaczmarek M. Aspects of the tumor microenvironment involved in immune resistance and drug resistance. Front Immunol. 2021;12:656364. pmid:34122412
- 15. Dzobo K, Senthebane DA, Dandara C. The tumor microenvironment in tumorigenesis and therapy resistance revisited. Cancers (Basel). 2023;15(2):376. pmid:36672326
- 16. Trédan O, Galmarini CM, Patel K, Tannock IF. Drug resistance and the solid tumor microenvironment. J Natl Cancer Inst. 2007;99(19):1441–54. pmid:17895480
- 17. Saha Detroja T, Detroja R, Mukherjee S, Samson AO. Identifying hub genes associated with neoadjuvant chemotherapy resistance in breast cancer and potential drug repurposing for the development of precision medicine. Int J Mol Sci. 2022;23(20):12628. pmid:36293493
- 18. Fejerman L, Ramirez AG, Nápoles AM, Gomez SL, Stern MC. Cancer epidemiology in hispanic populations: what have we learned and where do we need to make progress? Cancer Epidemiol Biomarkers Prev. 2022;31(5):932–41. pmid:35247883
- 19. Haile RW, John EM, Levine AJ, Cortessis VK, Unger JB, Gonzales M, et al. A review of cancer in U.S. Hispanic populations. Cancer Prev Res (Phila). 2012;5(2):150–63. pmid:22307564
- 20. Avilés-Santa ML, Hsu L, Lam TK, Arteaga SS, Artiles L, Coady S, et al. Funding of Hispanic/Latino health-related research by the National Institutes of Health: an analysis of the portfolio of research program grants on six health topic areas. Front Public Health. 2020;8:330. pmid:33014952
- 21. Wen J, Shao Y, Tatevossian R, Li Y, Ellison DW, Wu G, et al. Abstract 5352: Optimization of library and enrichment procedures for RNASeq using RNA from formalin fixed paraffin embedded tissue. Cancer Research. 2017;77(13_Supplement):5352.
- 22. Kanehisa M, Goto S, Furumichi M, Tanabe M, Hirakawa M. KEGG for representation and analysis of molecular networks involving diseases and drugs. Nucleic Acids Res. 2010;38:D355–60. pmid:19880382
- 23. Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, et al. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet. 2000;25(1):25–9. pmid:10802651
- 24. Osborne JD, Flatow J, Holko M, Lin SM, Kibbe WA, Zhu LJ, et al. Annotating the human genome with Disease Ontology. BMC Genomics. 2009;10 Suppl 1(Suppl 1):S6. pmid:19594883
- 25. Yu G, Wang L-G, Han Y, He Q-Y. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS. 2012;16(5):284–7. pmid:22455463
- 26. Szklarczyk D, Gable AL, Nastou KC, Lyon D, Kirsch R, Pyysalo S, et al. The STRING database in 2021: customizable protein-protein networks, and functional characterization of user-uploaded gene/measurement sets. Nucleic Acids Res. 2021;49(D1):D605–12. pmid:33237311
- 27. Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003;13(11):2498–504. pmid:14597658
- 28. Aran D, Hu Z, Butte AJ. xCell: digitally portraying the tissue cellular heterogeneity landscape. Genome Biol. 2017;18(1):220. pmid:29141660
- 29. Newman AM, Steen CB, Liu CL, Gentles AJ, Chaudhuri AA, Scherer F, et al. Determining cell type abundance and expression from bulk tissues with digital cytometry. Nat Biotechnol. 2019;37(7):773–82. pmid:31061481
- 30.
Taylor DL. Evaluating the sample specificity of discriminant analysis results using the jackknife statistic. New Orleans. 1991. Available from: https://eric.ed.gov/?id=ED328574
- 31. Tukey J. Bias and confidence in not-quite large samples (abstract). Ann Math Stat. 1958;2:614.
- 32. Zhang W, Robbins K, Wang Y, Bertrand K, Rekaya R. A jackknife-like method for classification and uncertainty assessment of multi-category tumor samples using gene expression information. BMC Genomics. 2010;11:273. pmid:20429942
- 33. Efron B, Gong G. A Leisurely Look at the Bootstrap, the Jackknife, and Cross-Validation. Am Stat. 1983;37(1):36–48.
- 34. Conway JR, Lex A, Gehlenborg N. UpSetR: an R package for the visualization of intersecting sets and their properties. Bioinformatics. 2017;33(18):2938–40. pmid:28645171
- 35. Song D, Lian Y, Zhang L. The potential of activator protein 1 (AP-1) in cancer targeted therapy. Front Immunol. 2023;14:1224892. pmid:37483616
- 36. Wang J, Kuiatse I, Lee AV, Pan J, Giuliano A, Cui X. Sustained c-Jun-NH2-kinase activity promotes epithelial-mesenchymal transition, invasion, and survival of breast cancer cells by regulating extracellular signal-regulated kinase activation. Mol Cancer Res. 2010;8(2):266–77. pmid:20145041
- 37. Liu X, Bai F, Wang Y, Wang C, Chan HL, Zheng C, et al. Loss of function of GATA3 regulates FRA1 and c-FOS to activate EMT and promote mammary tumorigenesis and metastasis. Cell Death Dis. 2023;14(6):370. pmid:37353480
- 38. Shu Y, Lan J, Luo H, Fu H, Xiao X, Yang L. FOS-Mediated PLCB1 induces radioresistance and weakens the antitumor effects of CD8+ T cells in triple-negative breast cancer. Mol Carcinog. 2025;64(1):162–75. pmid:39451071
- 39. Mohan HM, Aherne CM, Rogers AC, Baird AW, Winter DC, Murphy EP. Molecular pathways: the role of NR4A orphan nuclear receptors in cancer. Clin Cancer Res. 2012;18(12):3223–8. pmid:22566377
- 40. Wu H, Bi J, Peng Y, Huo L, Yu X, Yang Z, et al. Nuclear receptor NR4A1 is a tumor suppressor down-regulated in triple-negative breast cancer. Oncotarget. 2017;8(33):54364–77. pmid:28903348
- 41. Yousefi H, Fong J, Alahari SK. NR4A family genes: a review of comprehensive prognostic and gene expression profile analysis in breast cancer. Front Oncol. 2022;12:777824. pmid:35547878
- 42. Hedrick E, Lee S-O, Doddapaneni R, Singh M, Safe S. Nuclear receptor 4A1 as a drug target for breast cancer chemotherapy. Endocr Relat Cancer. 2015;22(5):831–40. pmid:26229035
- 43. Guo H, Golczer G, Wittner BS, Langenbucher A, Zachariah M, Dubash TD, et al. NR4A1 regulates expression of immediate early genes, suppressing replication stress in cancer. Mol Cell. 2021;81(19):4041–4058.e15. pmid:34624217
- 44. Kim YC, Kim CY, Oh JH, Kim MH. NR4A1 regulates tamoxifen resistance by suppressing ERK signaling in ER-positive breast cancer. Cells. 2021;10(7):1633. pmid:34209871
- 45. Mohankumar K, Wright G, Kumaravel S, Shrestha R, Zhang L, Abdelrahim M, et al. Bis-indole-derived NR4A1 antagonists inhibit colon tumor and splenic growth and T-cell exhaustion. Cancer Immunol Immunother. 2023;72(12):3985–99. pmid:37847301
- 46. Griffiths JI, Chen J, Cosgrove PA, O’Dea A, Sharma P, Ma C, et al. Serial single-cell genomics reveals convergent subclonal evolution of resistance as early-stage breast cancer patients progress on endocrine plus CDK4/6 therapy. Nat Cancer. 2021;2(6):658–71. pmid:34712959
- 47. Yin L, Duan J-J, Bian X-W, Yu S-C. Triple-negative breast cancer molecular subtyping and treatment progress. Breast Cancer Res. 2020;22(1):61. pmid:32517735
- 48. Dhruba SR, Sahni S, Wang B, Wu D, Rajagopal PS, Schmidt Y, et al. The expression patterns of different cell types and their interactions in the tumor microenvironment are predictive of breast cancer patient response to neoadjuvant chemotherapy. bioRxiv. 2024. pmid:39372749
- 49. Wang Y, Gao P, Hao Z, Chen L, Li X, Jiao Y, et al. The effect of neoadjuvant chemotherapy on the tumor immune microenvironment in gastrointestinal tumors. Front Oncol. 2022;12:1054598. pmid:36439457
- 50. Park YH, Lal S, Lee JE, Choi Y-L, Wen J, Ram S, et al. Chemotherapy induces dynamic immune responses in breast cancers that impact treatment outcome. Nat Commun. 2020;11(1):6175. pmid:33268821
- 51. Urueña C, Lasso P, Bernal-Estevez D, Rubio D, Salazar AJ, Olaya M, et al. The breast cancer immune microenvironment is modified by neoadjuvant chemotherapy. Sci Rep. 2022;12(1):7981. pmid:35562400
- 52. Fridlender ZG, Sun J, Kim S, Kapoor V, Cheng G, Ling L, et al. Polarization of tumor-associated neutrophil phenotype by TGF-beta: “N1” versus “N2” TAN. Cancer Cell. 2009;16(3):183–94. pmid:19732719
- 53. Wu L, Awaji M, Saxena S, Varney ML, Sharma B, Singh RK. IL-17-CXC chemokine receptor 2 axis facilitates breast cancer progression by up-regulating neutrophil recruitment. Am J Pathol. 2020;190(1):222–33. pmid:31654638
- 54. Fabre J, Giustiniani J, Garbar C, Antonicelli F, Merrouche Y, Bensussan A, et al. Targeting the tumor microenvironment: the protumor effects of IL-17 related to cancer type. Int J Mol Sci. 2016;17(9):1433. pmid:27589729
- 55. Xuan Q, Wang J, Nanding A, Wang Z, Liu H, Lian X, et al. Tumor-associated macrophages are correlated with tamoxifen resistance in the postmenopausal breast cancer patients. Pathol Oncol Res. 2014;20(3):619–24. pmid:24414992
- 56. Waks AG, Stover DG, Guerriero JL, Dillon D, Barry WT, Gjini E, et al. The immune microenvironment in hormone receptor-positive breast cancer before and after preoperative chemotherapy. Clin Cancer Res. 2019;25(15):4644–55. pmid:31061067
- 57. Kartal-Yandim M, Adan-Gokbulut A, Baran Y. Molecular mechanisms of drug resistance and its reversal in cancer. Crit Rev Biotechnol. 2016;36(4):716–26. pmid:25757878
- 58. Pan S-T, Li Z-L, He Z-X, Qiu J-X, Zhou S-F. Molecular mechanisms for tumour resistance to chemotherapy. Clin Exp Pharmacol Physiol. 2016;43(8):723–37. pmid:27097837
- 59. Cetin R, Quandt E, Kaulich M. Functional genomics approaches to elucidate vulnerabilities of intrinsic and acquired chemotherapy resistance. Cells. 2021;10(2):260. pmid:33525637
- 60. Khan SU, Fatima K, Aisha S, Malik F. Unveiling the mechanisms and challenges of cancer drug resistance. Cell Commun Signal. 2024;22(1):109. pmid:38347575
- 61. Lei Z-N, Tian Q, Teng Q-X, Wurpel JND, Zeng L, Pan Y, et al. Understanding and targeting resistance mechanisms in cancer. MedComm (2020). 2023;4(3):e265. pmid:37229486
- 62. Salemme V, Centonze G, Avalle L, Natalini D, Piccolantonio A, Arina P, et al. The role of tumor microenvironment in drug resistance: emerging technologies to unravel breast cancer heterogeneity. Front Oncol. 2023;13:1170264. pmid:37265795