Immune Gene Signature as a Predictor of CDK4/6 Inhibitor Response in HR+/HER2– Breast Cancer

Immune Gene Signature as a Predictor of CDK4/6 Inhibitor Response in HR+/HER2– Breast Cancer | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Immune Gene Signature as a Predictor of CDK4/6 Inhibitor Response in HR+/HER2– Breast Cancer Eudald Felip, Edurne Garcia-Vidal, Sara Cabrero-de las Heras, and 17 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6129690/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Cyclin-dependent kinase 4/6 inhibitors (CDK4/6i) are a standard treatment for hormone receptor-positive (HR+)/human epidermal growth factor receptor 2–negative (HER2–) advanced breast cancer (ABC). However, reliable predictive biomarkers for treatment efficacy remain an unmet clinical need. Methods: A cohort of HR+/HER2– ABC patients (n=100) treated with CDK4/6i was characterized from both a clinical and molecular perspective. Pre-treatment tumor biopsies underwent transcriptomic profiling using the nCounter Breast 360™ panel. Gene set enrichment and pathway analyses were employed to identify differentially expressed genes (DEGs) and associated pathways across efficacy groups. Correlations between clinical, transcriptomic, and treatment outcomes were assessed using logistic and Cox regression models. The NeoPalAna dataset served as an external validation cohort. Results: A clinical stratification algorithm, integrating known determinants of CDK4/6i efficacy from pivotal trials, enabled the classification of patients into two balanced efficacy groups. Transcriptomic analysis revealed an overexpression of immune-related signatures in poor responders (14/18), notably the interferon-gamma (IFN-γ) signature, which remained independently associated with progression-free survival (PFS) in multivariate analyses. DEG analysis and unsupervised consensus clustering further delineated immune function as a key determinant of treatment response, accurately classifying 90% of first-line responders (19/21; p=0.004) based on immune gene expression. A refined transcriptomic analysis identified KIMA, a 9-gene immune signature, as significantly enriched in patients with poor responses across both first-line and later treatment lines (p=0.0048 and p=0.0022, respectively). Elevated KIMA expression was independently correlated with inferior PFS and overall survival (OS) in multivariate Cox regression analyses (p=0.033 and p=0.034). Receiver operating characteristic (ROC) curve analysis, as measured by the area under the curve (AUC), confirmed the superior predictive performance of KIMA compared to the predefined BC360™ immune signature. Finally, KIMA was validated in the NeoPalAna cohort of patients receiving neoadjuvant palbociclib (p=0.026). Conclusions: These findings highlight the pivotal role of the immune microenvironment in modulating CDK4/6i efficacy. The KIMA signature emerges as a novel and robust predictive biomarker, offering a refined tool for tailoring therapeutic strategies in HR+/HER2– breast cancer. Its integration into clinical decision-making frameworks could enhance patient stratification and optimize treatment outcomes. breast cancer CDK4/6 inhibitor clinical efficacy immune-based biomarker tumor transcriptomics tumor immunity Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 INTRODUCTION CDK4/6 inhibitors (CDK4/6i) combined with endocrine therapy have become the standard treatment for estrogen receptor-positive and HER2-negative (ER+/HER2-) advanced breast cancer (ABC); significantly improving progression-free survival (PFS) and overall survival (OS) across both endocrine-sensitive and resistant cases. Their benefits extend to the adjuvant setting, where they reduce the risk of recurrence in high-risk, early-stage ER+/HER2- breast cancer, further solidifying their critical role in the therapeutic landscape of ER+/HER2- ABC ( 1 – 6 ). Despite the significant improvements achieved with CDK4/6i, approximately 10% of patients exhibit primary resistance, and nearly all patients eventually develop acquired resistance, leading to treatment failure( 7 ). Clinical factors, such as metastatic site, disease-free survival, and previous therapies before CDK4/6i, may influence the response rate and the development of resistance. Importantly, resistance to CDK4/6i arises not only from genomic aberrations, such as point mutations, insertions/deletions, or amplifications but also from changes in gene expression ( 8 ). This suggests that specific gene signatures or differentially expressed genes could predict resistance or efficacy to CDK4/6i ( 8 ). To date, several biomarkers associated with CDK4/6i resistance have been proposed, including cell cycle regulators, oncogenic kinase pathway genes ( 9 ), gene expression patterns and genes involved in modulating the tumor microenvironment which significantly influences the antitumor response to CDK4/6i ( 10 , 11 ). Despite these advances, currently there is not a precise biomarker/s that is able to identify patients of limited benefit from CDK4/6i treatment. Distinct preclinical and clinical evidence indicate that the effect of CDK4/6i extends beyond cell cycle inhibition, suggesting a broader role for CDK4/6 kinases in cellular processes, including modulation of the antitumor immune response ( 9 , 10 ). Both cyclins and CDKs are essential for the development and function of immune cells ( 12 ), with different immune cell types relying on specific cyclins and CDKs for development, expansion, and activation. CDK4/6i have been shown to induce specific immunological changes that alter the tumor microenvironment and the balance of tumor-immune interactions ( 13 ). The primary effects of this modulation include (i) enhanced intra-tumoral infiltration by immune cells, (ii) increased antigen presentation, (iii) modifications in cytokine expression within the tumor microenvironment, and (iv) upregulation of co-inhibitory molecules such as PD-L1 ( 10 , 14 – 17 ). These immunomodulatory effects suggest CDK4/6i may reprogram immune responses within tumors, creating opportunities for new therapeutic intervention. Although promising preclinical results have been observed with combinations of CDK4/6i and immune checkpoint inhibitors, such as anti-PD-1 monoclonal antibodies, clinical trials have not yet shown significant efficacy improvements and have reported increased toxicity ( 18 ). Optimizing these combination strategies will require a deeper understanding of the immunological changes induced by CDK4/6i, emphasizing the need for translational research to design safe and effective combination therapies. Here, we conducted a single-center prospective study to gain insights into the clinical and tumor characteristics determining CDK4/6i efficacy in ABC. Through standardized tissue-based transcriptomic analysis, we identified and characterized immunological and molecular pathways associated with the different treatment outcomes, reinforcing the pivotal role of the immune system in modulating CDK4/6i efficacy. Our findings reveal a novel 9-gene immune-based signature, the KIMA signature, which predicts CDK4/6i efficacy in breast cancer patients. This signature offers a practical tool for integration into routine clinical practice, enabling personalized treatment strategies and improving therapeutic decision-making. MATERIALS AND METHODS Clinical cohort and study design A prospective observational study was designed to include patients with ER+/HER2- ABC who started treatment with CDK4/6i in the Catalan Institute of Oncology (ICO) of Hospital Germans Trias i Pujol (HUGTIP), Badalona, Spain. Between May 2018 and April 2022, a total of 100 patients who started treatment with any of the three approved CDK4/6i (palbociclib, ribociclib, or abemaciclib) in any line of therapy for metastatic disease were enrolled, according to CDK4/6i approval status, or indications throughout the study period (2018–2022). The cohort's detailed clinical information and disease evolution were extracted from medical records (Table 1 ). Surplus 63 tumor blocks from clinical practice were collected for subsequent analysis in 55 patients, including paired samples from primary tumor and metastasis in 8 cases. All participants provided written informed consent specific to the study and tumor banking by the ethical protocols of Hospital Germans Trias i Pujol. The study was reviewed and approved by the Institution's ethical committee from the Hospital. Table 1 Clinical characteristics of the study cohort (n = 100), stratified also depending on the CDK4/6i administered. Cohort (n = 100) Abemaciclib (n = 19) Palbociclib (n = 60) Ribociclib (n = 21) p-value Age, average [IQR] 62.3 [51.6; 69.8] 63.5 [54.3; 68.5] 62.3 [53.0; 69.9] 55.3 [48.1; 70.7] 0.659 Year of treatment initiation: n (%) < 0.001 2018 36 (36.0%) 5 (26.3%) 22 (36.7%) 9 (42.9%) 2019 26 (26.0%) 2 (10.5%) 22 (36.7%) 2 (9.5%) 2020 16 (16.0%) 5 (26.3%) 11 (18.3%) 0 (0.0%) 2021 16 (16.0%) 6 (31.6%) 3 (5.0%) 7 (33.3%) 2022 6 (6%) 1 (5.3%) 2 (3.3%) 3 (14.3%) Treatment line, n (%) 0.080 1 60 (60.0%) 8 (42.1%) 35 (58.3%) 17 (81.0%) 2 13 (13.0%) 2 (10.5%) 9 (15%) 2 (9.5%) > 2 27 (27.0%) 9 (47.4%) 16 (26.7%) 2 (9.5%) Hormone-therapy, n (%) 0.043 Tamoxife 9 (9.0%) 2 (10.5%) 3 (5.0%) 4 (19.0%) Aromatase Inhibitor 53 (53.0%) 10 (52.6%) 29 (48.3%) 14 (66.7%) Fulvestrant 38 (38.0%) 7 (36.8%) 28 (46.7%) 3 (14.3%) Treatment with analogue LH-RH, n (%) : 0.002 Yes 19 (19.0%) 2 (10.5%) 7 (11.7%) 10 (47.6%) No 81 (81.0%) 17 (89.5%) 53 (88.3%) 11 (52.4%) De novo metastasis , n (%) 0.309 Yes 29 (29%) 7 (36.8%) 14 (23.3%) 8 (38.1%) No 71 (71%) 12 (63.2%) 46 (76.7%) 13 (61.9%) Hormone sensitivity, n (%) 0.019 De novo metastatic debut 14 (14%) 1 (5.3%) 6 (10%) 7 (33.3%) 1L and relapse > 12m post-end HT adjuvant 29 (29%) 5 (26.3%) 17 (28.3%) 8 (38.2%) 2L or relapse during HT or 2L metastatic line 27 (27%) 9 (47.4%) 16 (26.7) 2 (9.5%) Previous chemotherapy for advanced BC, n (%) 0.077 Yes 23 (23%) 8 (42.1%) 12 (20%) 3 (14.3%) No 77 (77%) 11 (57.9%) 48 (80%) 18 (85.7%) Immunohistochemistry ER expression, median, [IQR] 90.0 [87.5; 95.0] 90.0 [80.0; 99.5] 90.0 [90.0; 95.0] 90.0 [90.0; 95.0] 0.950 PR < 20 expression, n (%) 41.0 (41%) 7 (36.8%) 29 (48.3%) 5 (23.8%) 0.133 HER2 , n (%): 0.841 HER2 negative 47 (47.0%) 8 (42.1%) 29 (48.3%) 10 (47.6%) HER2 low 46 (46.0%) 8 (42.2%) 28 (46.6%) 10 (47.6%) No information 7 (7.0%) 3 (15.8%) 3 (5.0%) 1 (4.8%) n, number of patients; HT, hormone therapy; AI, aromatase inhibidors; L, number of treatment line; m, months; ER, estrogen receptors; PR, progesterone receptors; IQR, interquartile range; M1, metastasis; BC, breast cancer; p-value was considered significant if Chi-Square < 0,05 (in red). Patient stratification based on clinical variables for functional analysis A clinical stratification approach for patients included was performed based on anticipated treatment efficacy, encompassing both hormone-sensitive and hormone-resistant populations of ABC, aimed at optimizing functional analysis and minimizing cohort heterogeneity. Different cut-off points were established for each situation, considering the diverse populations included and the progression-free survival (PFS) reported in the pivotal studies for the distinct CDK4/6i ( 1 , 3 , 5 , 6 , 19 , 20 ). This approach aimed to reduce the heterogeneity within the cohort and identify patients who derived maximum benefit from CDK4/6i, irrespective of treatment line or prior therapies (Supplementary Fig. 1). The definition of each classification group was as follows: Group 1: Hormone-sensitive patients with treatment-naive metastatic disease or a > 12 months relapsing time after completing adjuvant endocrine therapy (ET). Categories: Good efficacy (PFS ≥ 24 months) vs. Bad efficacy (PFS < 24). Group 2: Hormone-resistant patients with progression during adjuvant ET or within the first year after completion or after ≥ 1 line of hormone therapy (no previous chemotherapy). Categories: Good efficiency (PFS ≥ 12 months) vs. Bad efficiency (PFS < 12 months). Group 3: Patients with prior chemotherapy for metastatic disease. Categories: Good efficiency (PFS ≥ 7 months) vs. Bad efficiency (PFS < 7 months). Reanalysis of patient characteristics confirmed that the stratified groups were homogeneous in age and showed no significant differences in other clinical variables. This supports the robustness and simplicity of the dichotomous categorization for subsequent analyses (Supplementary Table 1). Tumor biopsies and RNA extraction Tumor samples were collected retrospectively, including primary tumors and metastatic biopsies obtained before the initiation of treatment. The Tumor Biobank and the Pathology Department of the Germans Trias i Pujol Hospital facilitated the selection and handling of these samples. For each sample, a pathologist identified areas enriched with tumor cells (minimum 40%) using hematoxylin-eosin-stained slides, ensuring the integrity of the selected regions for downstream analyses. RNA extraction was performed from 6–10-micron tumor slices using the protocol established in the RNeasy FFPE Kit (Qiagen, Venlo, Netherlands). The quality of the RNA was determined using NanoDrop One (ThermoScientific) and Bioanalyzer 2100. Samples with a 260/280 ratio between 1.7 and 2.3 were included. RNA degradation was tested through the DV200 parameter, considering suitable samples with values over 40% for analysis. Samples that did not meet these requirements were discarded. Gene expression and bioinformatic analysis 250ng of total RNA (50ng/µL) was used to analyze gene expression using the Breast Cancer 360™ (BC360™) panel on the multiplexed digital nCounter® platform (NanoString Technologies, Inc., Seattle, WA, USA). BC360 TM panel includes 758 genes relevant to breast cancer, including those with established roles in tumor biology, the immune response, or the tumor microenvironment, and 18 housekeeping genes. Transcript counts were log2-transformed and normalized to internal controls and housekeeping gene expression. For each sample, normalized data was used to determine correlation scores for the four Prosigna® intrinsic subtype signatures and assign intrinsic subtype according to published methods (NanoString Technologies, Inc.) ( 21 , 22 ). Gene Set Enrichment Analysis (GSEA) was performed using the expression of the 776 genes from the BC360 TM panel. Briefly, patients were stratified by efficacy group, and the mean expression for each gene was calculated within each group. Then, for each gene, the mean expression value of the good efficacy group was subtracted from that of the bad efficacy group. These differential expression values were then organized in decreasing order and used as a ranked input for a GSEA Pre-Ranked analysis with the Hallmark gene set database (h.all.v2024.1.Hs.symbols) from Molecular Signatures Database (MSigDB).( 23 – 25 ). Single gene expression analysis from first-line patients was used to select a putative predictive gene signature. Immune-related genes with at least a 50% significant expression (p-value ≤ 0.1) difference between the groups were selected, resulting in a list of 9 upregulated ( CXCL10, OAS3, STAT1, CD27, TIGIT, IL2RA, FOXP3, TAP1, TAP2 ) and 5 downregulated ( HLA-DQA1, HLA-DQB1, CXCL8, TNFSF10, IL1B ) genes in the bad efficacy group. The protein interaction map of the selected upregulated genes was generated with QIAGEN IPA (QIAGEN Inc., https://digitalinsights.qiagen.com/IPA ) ( 26 ). Intermediate proteins and connections between the nodes are the automated result of the Grow and Path Explorer tools using the IPA database. Diseases, cell functions and biomarkers nodes were manually added according to their relevance with our study scope and the number of connections with the signature members. Statistical Analysis Quantitative variables were represented using medians and ranges for clinical descriptive analysis, while qualitative variables were expressed with absolute frequencies and percentages. PFS was defined as the time (in months) from the initiation of treatment with CDK4/6i until treatment discontinuation due to disease progression or death from any cause. OS was the time (in months) calculated from the date of treatment initiation until death from any cause or the date of censoring at the last time the subject was known to be alive in an intention-to-treat population. Median PFS and OS were estimated using the Kaplan-Meier method and analyzed using the log-rank test, with statistical significance considered at a p-value < 0.05. The Kruskal-Wallis or Mann-Whitney U tests were applied for independent quantitative variables, and the chi-square test for qualitative variables, with a significance threshold of p-value < 0.05. For survival analysis, Cox proportional hazards regression models were used to assess the prognostic and predictive relevance of intrinsic subtype and normalized gene/signature scores for PFS and OS. Hazard ratios (HR) and their associated 95% confidence intervals (CI) were reported. Scores relevant in univariate analyses (p < 0.1) were further investigated through multiple logistic regression. For the scatter plots representing the mean expression of gene signatures, each dot represents an individual patient. Data distribution for each plot was assessed with the Shapiro–Wilk test. When data was normally distributed, the significance was assessed by unpaired t-test analysis with Welch’s correction. Otherwise, the Mann-Whitney U test was performed. The receiver operating characteristic (ROC) curves and area under the curve (AUC) values were calculated using the pROC package in RStudio (v4.5.1) to assess the predictive capacity of gene signatures. The optimal threshold value determined by this package was applied to classify patients into high or low-expression groups for each signature. Kaplan-Meier survival curves, scatter plots, GSEA plot and the representation of ROC curves were generated using GraphPad Prism (v.10.3.1), while RStudio was used for statistical analysis of clinical variables and for representing heatmaps (using the pHeatmap package) and volcano plots (using the ggplot2 package). We used Principal Component Analysis (PCA) to explore dimensionality reduction and potential correlation between the genes from the KIMA signature. The Elbow method was used to choose the number of principal components on the screen plot. For this analysis, the Jamovi project 2024 (v2.6) was used. RESULTS Clinical characteristics of patients treated with CDK4/6 inhibitors From March 2018 to April 2022, 100 patients initiating CDK4/6i therapy for ABC were enrolled. Of these, 91 patients had adequate follow-up for efficacy assessment at the analysis time. 63 tumor samples were collected before CDK4/6i initiation, obtained from 55 patients, including 39 primary tumors and 24 metastases, with paired biopsies available for 8 patients (Fig. 1 A). As of the analysis data cut-off on July 31, 2023, 25 patients (25%) remained on CDK4/6i treatment, 26 patients (26%) had changed to other oncologic therapies, and the remaining 49 patients (49%) were deceased. Notably, 8 patients sustained a benefit to CDK4/6i + ET for over four years (Fig. 1 B). Patient demographic and clinical characteristics are summarized in Table 1 . Most patients were treated with palbociclib (60%), followed by abemaciclib (19%) and ribociclib (21%). This cohort was heterogeneous: the majority (60%) received first-line treatment for ABC, 27% had received multiple therapy lines (two or more), and 25% had previously received chemotherapy for ABC. Regarding hormonal sensitivity, 43% were deemed hormone-sensitive, either experiencing recurrence more than 12 months after adjuvant hormonal treatment (29%) or receiving treatment for de novo ABC (14%). This heterogeneity extended to metastatic involvement, with 44% having visceral disease (62% with hepatic involvement) and 27% showing exclusive bone metastases. To streamline the assessment of patient-specific characteristics, we categorized patients into two groups based on progression-free survival data from pivotal trials: good and bad efficacy groups. These categories considered (i) treatment regimen, (ii) line of therapy, (iii) hormonal sensitivity, and (iv) prior chemotherapy for ABC ( 3 , 5 , 6 , 19 , 27 , 28 ) (Supplementary Fig. 1). This approach allowed the independent classification of 91 patients into the two efficacy groups, whereas 9 cases (9/100) could not be assessed due to inadequate follow-up or treatment discontinuation after toxicity. The classification resulted in 57% (52/91) of patients categorized as good efficacy and 43% (39/91) as bad efficacy. As expected, comparing the two efficacy groups revealed statistically significant differences in PFS. Specifically, patients in the good efficacy group achieved a median PFS of 31 months, compared to 5.7 months in the bad efficacy group (Fig. 1 C). Notably, no significant differences were observed between the two groups considering treatment line number, metastatic involvement (visceral or bone-only), or hormone sensitivity (Supplementary Table 1). This uniformity in clinical characteristics across efficacy groups supports the hypothesis that additional biological factors contribute to CDK4/6i efficacy in ABC patients. Clinical Characteristics and Intrinsic Tumor Subtypes as Determinants of CDK4/6i Treatment Efficacy A comprehensive evaluation of clinical and tumor-specific characteristics that may influence CDK4/6i efficacy in ABC was performed. Variability in CDK4/6i agents was attributed to external factors such as approval status in Spain, evolving clinical evidence, and changes in indications throughout the study period (2018–2022; Table 1 ). Survival analysis across the cohort indicated a median PFS of 14 months (Interquartile Range [IQR]: 5.6–26.56) and a median OS of 42.8 months (IQR: 20.33–47.30) (Supplementary Figs. 2A and 2B). However, the OS was limited by the relatively short follow-up period. As expected, first-line therapy resulted in significantly longer PFS than later lines (median PFS of 30.4 vs. 9.3 months, p = 0.001; Supplementary Fig. 2C). Hormone sensitivity at CDK4/6i initiation also strongly impacted in PFS (45.8 vs. 14 months for sensitive vs. resistant cases). Additionally, exclusive bone involvement (PFS of 30.4 vs. 24 months), and absence of liver metastasis (PFS of 35.9 vs. 24 months) showed a trend towards longer PFS (Supplementary Figs. 2D–F). The influence of intrinsic tumor subtypes on CDK4/6i efficacy was also assessed using the PAM50 gene expression signature of 55 patients with available tissue (31 primary tumors and 24 metastatic lesions), presenting similar clinical characteristics to the entire cohort (Supplementary Table 2). Luminal subtypes predominated (38% luminal A and 47% luminal B), with non-luminal subtypes comprising only 15% of cases (11% HER2-enriched and 4% basal) (Supplementary Fig. 3A). Notably, metastatic lesions showed a shift in subtype distribution with fewer luminal A and increased HER2-enriched and basal cases compared to primary tumors (p = 0.034). Among patients receiving CDK4/6i as first-line therapy (n = 31), luminal A tumors exhibited a median PFS of 35.9 months, compared to 21.55 months in HER2-enriched cases (Supplementary Fig. 3B), although the differences were not statistically significant. These findings suggest that intrinsic subtypes may influence CDK4/6i efficacy but also highlight the existence of additional factors contributing to treatment outcomes, underscoring the complexity of predictive factors in ABC. Exploring BC360™ Transcriptomic Signatures as Determinants of CDK4/6i Efficacy To identify potential tumor prognostic and predictive factors influencing the efficacy of CDK4/6i, we performed a transcriptomic analysis using the BC360™ panel on the cohort of 47 patients with available tumor samples and enough clinical follow-up. We compared the expression levels of the BC360™ gene signatures between the good and bad efficacy groups. Differential expression analysis identified 19 differentially expressed gene signatures (p < 0.1), most upregulated in the bad efficacy group (18/19) (Supplementary Table 3). Among the overexpressed signatures, we identified substantial enrichment in immune-related pathways, including IDO1, TIGIT, Treg, TIS, CD8, PD-1, inflammatory chemokines, IFN-gamma, PD-L1, PD-L2, and macrophages (Fig. 2 A). In contrast, the only downregulated signature was differentiation (Fig. 2 A). To further assess the clinical impact of these signatures on patient outcome, survival analysis was performed in patients receiving CDK4/6i as first-line therapy (n = 31) to minimize potential biases from prior treatments. Univariate Cox survival analysis showed an association between progression-free survival (PFS) and expression levels of IFN-gamma, PD-L2, TIGIT, Macrophages, and IDO1 signatures (Supplementary Table 4). Multivariate Cox analysis on the five relevant signatures showed that only the increased expression levels of the IFN-gamma signature were independently associated with the poor efficacy group (Fig. 2 B). Then, ROC curve analysis was used to determine the optimal threshold for categorizing patients based on their levels of IFN-gamma signature expression. Using this threshold, patients with high IFN-gamma expression had a significantly worse median PFS (15 months) compared to the low-expression group (not reached (NR)) (Fig. 2 C), supporting previous observations on the negative impact of high IFN-gamma signature expression on PFS. Identification and Functional Characterization of the 9-Gene KIMA Signature To further elucidate the immunologic features determining CDK4/6i efficacy, we analyzed differences in single gene expression from the BC360™ panel in first-line patients. Among the 758 genes included in the BC360™, 43 displayed significant differential expression between good and poor efficacy patients (p < 0.05) (Fig. 3 A), including overexpression of immune-related genes like CXCL10 or ISG15 in the bad efficacy group (Supplementary Fig. 4B). Unsupervised consensus clustering based on the expression of the 43 differentially expressed genes (DEGs) showed limited ability to stratify patients based on the CDK4/6i clinical efficacy (Supplementary Fig. 4A). Further analysis using Gene set enrichment analysis (GSEA) with the Hallmark gene set database confirmed significant enrichment of immune-related pathways in the poor efficacy group, including IFN-gamma response, allograft rejection, inflammatory response, and IL6/JAK/STAT signaling, alongside cell cycle regulation alterations. Conversely, patients from the good efficacy group displayed an upregulation in estrogen response pathways (Supplementary Fig. 4C). Based on these findings, we focused on immune-related genes for further evaluation. Among the 43 DEGs, 14 were linked to immune function and showed at least a 50% significant difference in expression between efficacy groups (p-value < 0.1). Unsupervised consensus clustering according to the expression of these 14 immune-related genes significantly improved patient selection, clustering together 90% (19/21) of patients from the good efficacy group (Fig. 3 B). These 14 genes were further classified into two groups based on elevated or downregulated expression in the poor efficacy groups: the 9 upregulated genes ( CXCL10, OAS3, STAT1, CD27, TIGIT, IL2RA, FOXP3, TAP1, TAP2 ) and the 5 downregulated genes ( HLA-DQA1, HLA-DQB1, CXCL8, TNFSF10, IL1B ). As expected, the mean expression of the 9-gene signature in first-line patients was significantly higher in the bad efficacy group (p = 0.0048) (Fig. 3 B, higher scatter plot) while the 5-gene signature was significatively lower (p = 0.0044) (Fig. 3 B, lower scatter plot) compared to the good efficacy group. Survival analysis revealed that high expression levels of the 9-gene set correlated with significantly shorter PFS and OS compared to the low-expression patients (PFS of 15 months vs NR; p = 0.016 and OS of 29.9 months vs NR; p = 0.027) (Fig. 3 C). By contrast, the downregulated 5-gene set showed no significant association with PFS or OS in either the first-line setting or across all treatment lines (Supplementary Figs. 5A and 5C). Further evaluation of both signatures, identified the 9-gene set as critical for CDK4/6i efficacy and thus we defined it as the “Key IMmune Activation” (KIMA) signature (Fig. 3 D). To understand if all 9 genes of the KIMA signature were needed in our signature, we performed a principal component analysis (PCA). PCA confirmed the relevance of each individual gene within the KIMA signature, indicating that all nine genes of the KIMA signature contributed substantially to an overall signature variance of 63% (Supplementary Fig. 6). Furthermore, the connections between the genes of the KIMA signature were analyzed, revealing several inter-gene relationships, with STAT1 emerging as a central regulatory element, underscoring the interconnected roles of these genes within the immune network. Additional direct and indirect connections between the genes and other nodes of interest defined in the IPA package were also found, such as Breast or ovarian cancer, Neoplasia of cells, Systemic autoimmune syndrome, Cell cycle progression, Immune evasion by tumor, Refractory hormone receptor-positive HER2 negative breast cancer, and Biomarkers (BM) for breast cancer efficacy (Supplementary Fig. 4D), supporting its role as putative biomarkers of efficacy in breast cancer. Then, the predictive value of the KIMA signature was further evaluated in all the patients, irrespective of the line of treatment. Similarly to first-line patients, patients from the poor efficacy group presented significantly higher expression levels of the KIMA signature, independent of the treatment line (Fig. 4 A, p = 0.0022). ROC curve analysis confirmed the robust predictive performance of the KIMA signature, with an AUC of 0.787 (Fig. 4 B). Finally, in the survival analysis, elevated KIMA expression was consistently associated with poorer PFS and OS outcomes (Fig. 4 C), further supporting the robustness of the KIMA signature to predict CDK4/6i efficacy. Interestingly, compared to the NanoString BC360™ IFN-gamma signature, KIMA demonstrated slightly better predictive accuracy for treatment efficacy across the whole cohort, obtaining a higher AUC value (0.787 vs 0.728) when representing ROC curves (Fig. 4 D), underscoring its potential as a reliable biomarker for CDK4/6i efficacy. The KIMA Signature predicts CDK4/6i efficacy in early neoadjuvant BC Currently, the use of CDK4/6i inhibitors in early BC cases is being studied in several clinical trials and its approval is warranted in the near future. To validate the predictive capacity of the 9-gene KIMA signature, we assessed its performance in the NeoPalAna study (NCT01723774). This phase II neoadjuvant trial enrolled patients with stage II or III ER + breast cancer. Participants were treated with anastrozole for four weeks, followed by the addition of palbociclib for four 28-day cycles prior to surgery (NeoPalAna: Neoadjuvant Palbociclib plus Anastrozole in ER + breast cancer). The study demonstrated that the palbociclib-based regimens significantly increased the complete cell cycle arrest rate compared to anastrozole alone ( 29 ). Notably, the trial also revealed a subset of patients exhibiting intrinsic resistance to the combination of palbociclib and anastrozole, underscoring the heterogeneity in treatment response ( 29 ). Consistent with findings from our primary cohort, palbociclib-resistant patients in the NeoPalAna study exhibited significantly higher KIMA signature expression than sensitive patients (Fig. 5A, p = 0.0055). Moreover, KIMA signature demonstrated a strong predictive capacity in distinguishing between sensitive and resistant patients, achieving an AUC of 0.82 in the ROC analysis (Fig. 5B). Overall, these findings confirm the robustness and applicability of the KIMA signature as a predictive transcriptomic signature for the CDK4/6i therapeutic efficacy. DISCUSSION CDK4/6i have significantly improved clinical outcomes in ER+/HER2- ABC and early-stage breast cancer (EBC); but response variability and occurrence of relapses remain significant challenges. Currently, reliable biomarkers are lacking in identifying patients most likely to benefit, thereby minimizing toxicities and reducing healthcare costs. Therefore, raising cost-effectiveness concerns on the universal use of CDK4/6i limits access in some patients. Here, we describe and validate the KIMA signature as a reliable immune-based transcriptomic efficacy biomarker in a prospective real-world cohort. The clinical characteristics of our study cohort are closely aligned with those of key pivotal trials, highlighting the reliability and clinical relevance applicability of our findings. The cohort median PFS (14 months) is in line with the MONARCH-2 and MONALEESA-3 trials (16.9 and 14.6 months, respectively) and exceeds PALOMA-3 (9.5 months), which included more heavily pretreated and hormone-resistant patients ( 1 , 3 , 5 , 6 , 11 , 20 , 21 , 30 , 31 ). Indeed, as expected, both hormone sensitivity and metastatic site significantly influenced PFS. Specifically, patients with hormone-resistant disease and hepatic metastasis exhibited shorter PFS, consistent with findings from larger trials ( 19 , 27 , 32 , 33 ). Similarly, intrinsic subtypes influenced outcomes in CDK4/6i-treated patients ( 13 , 34 , 35 ), luminal subtypes (85% in our cohort) showed better prognosis and longer PFS, while HER2-enriched subtypes were linked to shorter PFS ( 11 ). Overall, the clinical characteristics and outcomes of our cohort not only replicate findings from pivotal clinical trials but also reflect the heterogeneity observed in real-world clinical practice in an ER+/HER2- ABC. This strengthens the applicability of our results to routine clinical care. Since CD4/6i approval, the molecular mechanisms driving tumor resistance have remained incompletely understood. Current insights are predominantly derived from studies on key cell cycle regulators, largely using single-agent experimental models or cancer cell lines. Many proposed biomarkers identified in preclinical studies, such as CCND1 amplification, p16 loss, or alterations in CDK4, CDK6, CDK7, CDK9, and CCNE1-CDK2 fail to translate consistently to clinical settings ( 36 – 42 ). Furthermore, clinically actionable genetic alterations, such as RB1 deletions or mutations, have been implicated as a resistance mechanism—evident in the PALOMA-3 trial where ctDNA analysis identified these mutations in patients treated with palbociclib—. However, their rarity (1–4% detection rate) limits their clinical utility despite their mechanistic relevance ( 27 , 43 – 47 ). Several pieces of evidence show that the impact of CDK4/6i is far beyond cell cycle control, including the modulation of the antitumor immune response. Transcriptomic analysis of CDK4/6 knockouts in breast cancer revealed that CDK4 regulates inflammatory cytokine signaling, while CDK6 influences DNA replication and repair, highlighting their distinct roles in tumor biology. Transcriptomic analysis is essential for understanding the molecular mechanisms underlying cancer progression and treatment responses. In this sense, these findings underscore its critical role in uncovering the multifaceted effects of CDK4/6i( 48 ) on tumor-intrinsic and immune-mediated mechanisms. Prospective studies, including ours, have employed advanced transcriptomic panels such as the BC360™ panel to identify predictive biomarkers and immune-related features. Findings from the KENDO trial underscored the significance of intrinsic tumor biology, such as PAM50 subtypes and risk of recurrence scores (ROR-P), alongside immunological components like tumor-infiltrating lymphocytes (TILs) and tertiary lymphoid structures (TLS). These features play a pivotal role in guiding therapeutic strategies. Interestingly, CD24 was identified in the KENDO trial as a potential therapeutic target, while mRNA-based markers, including CD19 and CXCL13, showed promise as standardized predictors of TLS presence ( 49 ). In our analysis, leveraging the BC360™ panel, we identified a 9-gene "Key Immune Activation" (KIMA) signature comprising immune-related genes upregulated in the poor efficacy group. The KIMA signature provides a wide comprehensive characterization of the tumor immune microenvironment by incorporating genes related to immunosuppressive elements, including T regs markers (FOXP3 and IL2RA) and TIGIT or CD27, as well as TAP1, which is involved in antigen presentation. By integrating these additional markers, the KIMA signature demonstrated strong predictive power, correlating significantly shorter PFS and overall survival (OS) in patients with high expression levels, offering broader insights into immune presence and activity within the tumor microenvironment. This composition suggests that the tumor microenvironment in the bad efficacy group has enhanced immunosuppressive characteristics, likely driven by chronic IFN-gamma signaling via STAT1. While IFN-γ is crucial for activating cellular immunity, its chronic signaling can paradoxically promote pro-tumorigenic effects. Persistent IFN-γ signaling has been linked to immune evasion through the upregulation of immune checkpoint molecules, T-cell exhaustion, and the recruitment of immunosuppressive elements such as regulatory T cells (Tregs), M2-like macrophages, and cancer-associated fibroblasts within the tumor microenvironment ( 50 , 51 ). Indeed, CDK4/6i, such as palbociclib, have been shown to induce type III interferon production, stimulate T-cell activation, and influence tumor-intrinsic pathways ( 50 ). A recent study employing a custom RNA panel of 192 genes revealed that treatment with palbociclib significantly increased complete cell cycle arrest, as evidenced by a pronounced reduction in proliferation markers like Ki67. In addition to these antiproliferative effects, palbociclib upregulated immune-related genes, suggesting a beneficial impact on the tumor immune microenvironment( 52 ). This dual mechanism—combining tumor proliferation inhibition with immune modulation—highlights the potential of CDK4/6i to influence both cellular and immune-mediated anti-tumor responses. Notably, alterations in the interferon-gamma (IFN-γ) signature have been linked to resistance to palbociclib, emphasizing the importance of immune pathways in shaping therapeutic outcomes( 35 ). Despite these findings, no definitive predictive biomarker for CDK4/6 inhibitors has been validated for clinical use, underscoring the need for further investigation. As research progresses, integrating transcriptomic data with immunological profiling promises to optimize therapeutic strategies and improve outcomes for cancer patients. The robustness of the KIMA signature was further supported by principal component analysis, which confirmed the contribution of all nine genes to the signature’s variance. Additionally, network analysis highlighted STAT1 as a central regulatory element, emphasizing the immune pathways' interconnected nature. More importantly, the central role of STAT1, underscores the potential of targeting the JAK/STAT pathway to reprogram the tumor microenvironment, enhancing CDK4/6i efficacy by promoting a more favorable immune response. Indeed, the JAK/STAT pathway mediates cytokine and growth factor signaling, frequently dysregulated in cancers such as myeloproliferative neoplasms and certain solid tumors. Some JAK inhibitors have shown efficacy in hematologic malignancies, while STAT-targeting agents are investigated for tumors with hyperactive JAK/STAT signaling. These inhibitors can reprogram the tumor microenvironment, enhancing anti-tumor immunity and improving responses to therapies like CDK4/6 inhibitors ( 53 , 54 ). Our data confirms the interplay between CDK4/6i and the immune system, highlighting the importance of an active immune response for optimal therapeutic outcomes. The RIBBECA study, for instance, demonstrated that CDK4/6i bolsters pre-existing adaptive immune responses rather than inducing de novo immune activation, emphasizing the need for a proficient immune system ( 55 ). These findings suggest that the KIMA signature captures a comprehensive immune activation profile predictive of CDK4/6i resistance. Given their dual role as immunomodulators and enhancers of immune responses in line with present data, CDK4/6i have been evaluated with immunotherapies, yielding mixed clinical results despite promising preclinical findings ( 16 ). For instance, a phase Ib study of abemaciclib plus pembrolizumab showed increased hepatic toxicity and pneumonitis without added PFS or OS benefit, limiting its feasibility( 56 ). In contrast, the PACE study demonstrated that adding avelumab to palbociclib and fulvestrant improved PFS without increasing toxicity( 57 ). Furthermore, in heavily pretreated ER + ABC patients, pembrolizumab with or without palbociclib highlighted the role of effector memory T cells in enhancing the immune response to CDK4/6i( 58 ). However, none of the studies have been successful enough to result in practice-changing strategies. The putative combination with JAK/STAT inhibitors may be interesting to test from our data. Finally, we tested the predictive capacity of the KIMA signature in the NeoPalAna study, a neoadjuvant trial evaluating palbociclib combined with anastrozole in early-stage breast cancer. Consistent with findings in the ABC cohort, KIMA signature expression was significantly higher in resistant cases. The signature achieved an area under the curve (AUC) of 0.82 in distinguishing between sensitive and resistant patients, underscoring its utility across different treatment settings. CDK4/6i are now a key part of adjuvant treatment for high-risk early breast cancer( 59 , 60 ). However, their expanded application raises cost-effectiveness concerns that could affect approval and reimbursement. In this regard, the KIMA signature aims to shed light on the molecular mechanisms involved, providing valuable insights to optimize the adjuvant treatment of breast cancer patients and improve therapeutic outcomes and equitable access. In summary, our study underscores the critical role of immune profiling in optimizing CDK4/6i efficacy in ER+/HER2- patients. By identifying the KIMA signature as a robust predictive biomarker, we provide a foundation for identifying patients with bad efficacy under CDK4/6i and which could be needed immune interventions to improve treatment efficacy. The interplay between immune pathways and CDK4/6i response highlights opportunities to refine therapeutic approaches, particularly through selective modulation of immunosuppressive components within the tumor microenvironment. These findings open avenues for further investigation into immune-related predictors of response and highlight the potential for sequential or combined modular treatment strategies tailored to individual immune profiles, ultimately aiming to improve patient outcomes in a real-world setting. Declarations Acknowledgements We sincerely thank all patients for their invaluable and selfless participation in this study. We also thank the Tumor Biobank of the IGTP/Hospital Germans Trias i Pujol for their excellent work managing samples. Additionally, we would like to thank the Oncology Day Hospital team, including nursing and administrative staff, for their unwavering support and dedication throughout this project. Funding This work has been funded by Instituto de Salud Carlos III (ISCIII) through the project PI21/00642 (Co-funded by European Regional Development Fund/European Social Fund) "Investing in your future"), through the project InMaM funded by the “Plan Complementario de Biotecnología Aplicada a la Salut” coordinated by IBEC in the framework of recovery, transformation and resilience Plan (C17, Resilience Plan C17.I1) funded by the European Union – NextGeneration UE and by Pfizer (Pfizer Independent Research Grant). EF, MB and ABP are fellows from ISCIII (JR23/00044, CM22/00101 and CD21/00054 respectively). Author’s contributions E.F. and E.G-V. substantially contributed to the conception, methodology, patient data extraction, biological analysis, formal analysis, and writing. S.C-H, M.B. and A.B-P. substantially contributed to the biological analysis. E.B. and M.M. substantially contributed to the conception, conceptualization, methodology planification, formal analysis, writing and review, and funding acquisition. All authors have read, opined, corrected and agreed to the published version of the manuscript. Ethics approval and consent to participate This research was approved by the Ethics Committee of Hospital Germans Trias I Pujol, and written informed consent was obtained from all patients before they were enrolled in the research project. Consent for publication All authors have read the manuscript and provided their consent for the submission. Competing interests M.R. declares a consulting and advisory role for GSK, AstraZeneca, and MSD; and research funding from Pfizer, Clovis, GSK, AstraZeneca, and MSD. R.M declares a consulting and advisory role for Merck, MSD, Roche, AstraZeneca, BEM, the speaker’s bureau for Merck, MSD, BMS, and Roche. M.M. declares a consulting and advisory role for Novartis, Pfizer, Pier Fabre, and Roche; research funding from Roche, Eisai, and AstraZeneca; and travel expenses from Roche. E.F declares research funding from Pfizer, being invited as speaker for Pfizer and Novartis, and travel expenses from Roche, Lilly, Pfizer and Novartis. A.P. declares being invited as speaker for GSK, Eisai, and Lilly, travel expenses and congress assistance from Lilly, Gilead, Dr. Reddys, and Pfizer. A.L-P. declares being invited as speaker for Eisai, Lilly, and Novartis, and travel expenses from Roche, Gilead, and Novartis. B.C. declares being invited as speaker for BMS, Merck, and MSD. Training grants from BMS, Merck, and MSD. Advisory board: BMS, Merck, and MSD. V.Q. declares being invited as speaker for AstraZeneca, Novartis, Pfizer, and Roche. Advisory board for Roche. Educational activities from GSK, Lilly, and Pfizer and travel expenses from Pfizer and Roche. I.T. declares being invited as speaker for Astra Zeneca. Training grants from Novartis, Lilly, ROCHE, and MSD. A.F-D. declares being invited as speaker for MSD and Angelini Pharma; and travel expenses from MSD, Lilly, Roche, Merck, and BMS. M.B. declares advisory funding from Eisai, AstraZeneca, Pfizer, Novartis, and travel expenses from Novartis and AstraZeneca. The rest of the authors declare no potential conflict of interest. Availability of data and materials The datasets supporting this article's conclusions are included within the article (and its Additional files) and available from the corresponding author on reasonable request. Transcriptomic data and associated clinical information have been deposited in GEO (GSE265870). References Finn RS, Martin M, Rugo HS, Jones S, Im SA, Gelmon K, et al. Palbociclib and Letrozole in Advanced Breast Cancer. N Engl J Med. 2016;375(20):1925–36. Hortobagyi GN, Stemmer SM, Burris HA, Yap YS, Sonke GS, Paluch-Shimon S, et al. Ribociclib as First-Line Therapy for HR-Positive, Advanced Breast Cancer. N Engl J Med. 2016;375(18):1738–48. Goetz MP, Toi M, Campone M, Sohn J, Paluch-Shimon S, Huober J, et al. MONARCH 3: Abemaciclib As Initial Therapy for Advanced Breast Cancer. J Clin Oncol. 2017;35(32):3638–46. Turner NC, Ro J, André F, Loi S, Verma S, Iwata H, et al. Palbociclib in Hormone-Receptor-Positive Advanced Breast Cancer. N Engl J Med. 2015;373(3):209–19. Slamon DJ, Neven P, Chia S, Fasching PA, De Laurentiis M, Im SA, et al. Phase III Randomized Study of Ribociclib and Fulvestrant in Hormone Receptor-Positive, Human Epidermal Growth Factor Receptor 2-Negative Advanced Breast Cancer: MONALEESA-3. J Clin Oncol. 2018;36(24):2465–72. Sledge GW Jr., Toi M, Neven P, Sohn J, Inoue K, Pivot X, et al. MONARCH 2: Abemaciclib in Combination With Fulvestrant in Women With HR+/HER2- Advanced Breast Cancer Who Had Progressed While Receiving Endocrine Therapy. J Clin Oncol. 2017;35(25):2875–84. McCartney A, Migliaccio I, Bonechi M, Biagioni C, Romagnoli D, De Luca F, et al. Mechanisms of Resistance to CDK4/6 Inhibitors: Potential Implications and Biomarkers for Clinical Practice. Front Oncol. 2019;9:666. Lloyd MR, Spring LM, Bardia A, Wander SA. Mechanisms of Resistance to CDK4/6 Blockade in Advanced Hormone Receptor-positive, HER2-negative Breast Cancer and Emerging Therapeutic Opportunities. Clin Cancer Res. 2022;28(5):821–30. Goel S, Decristo MJ, McAllister SS, Zhao JJ. CDK4/6 Inhibition in Cancer: Beyond Cell Cycle Arrest. Trends Cell Biol. 2018;28(11):911–25. Goel S, DeCristo MJ, Watt AC, BrinJones H, Sceneay J, Li BB, et al. CDK4/6 inhibition triggers anti-tumour immunity. Nature. 2017;548(7668):471–5. Prat A, Chaudhury A, Solovieff N, Paré L, Martinez D, Chic N, et al. Correlative Biomarker Analysis of Intrinsic Subtypes and Efficacy Across the MONALEESA Phase III Studies. J Clin Oncol. 2021;39(13):1458–67. Laphanuwat P, Jirawatnotai S. Immunomodulatory Roles of Cell Cycle Regulators. Front Cell Dev Biol. 2019;7:23. Petroni G, Formenti SC, Chen-Kiang S, Galluzzi L. Immunomodulation by anticancer cell cycle inhibitors. Nat Rev Immunol. 2020;20(11):669–79. Coppe JP, Desprez PY, Krtolica A, Campisi J. The senescence-associated secretory phenotype: the dark side of tumor suppression. Annu Rev Pathol. 2010;5:99–118. Schaer DA, Beckmann RP, Dempsey JA, Huber L, Forest A, Amaladas N, et al. The CDK4/6 Inhibitor Abemaciclib Induces a T Cell Inflamed Tumor Microenvironment and Enhances the Efficacy of PD-L1 Checkpoint Blockade. Cell Rep. 2018;22(11):2978–94. Deng J, Wang ES, Jenkins RW, Li S, Dries R, Yates K, et al. CDK4/6 Inhibition Augments Antitumor Immunity by Enhancing T-cell Activation. Cancer Discov. 2018;8(2):216–33. Zhang J, Bu X, Wang H, Zhu Y, Geng Y, Nihira NT, et al. Cyclin D-CDK4 kinase destabilizes PD-L1 via cullin 3-SPOP to control cancer immune surveillance. Nature. 2018;553(7686):91–5. Jerusalem G, Prat A, Salgado R, Reinisch M, Saura C, Ruiz-Borrego M, et al. Neoadjuvant nivolumab + palbociclib + anastrozole for oestrogen receptor-positive/human epidermal growth factor receptor 2-negative primary breast cancer: Results from CheckMate 7A8. Breast. 2023;72:103580. Cristofanilli M, Turner NC, Bondarenko I, Ro J, Im S-A, Masuda N, et al. Fulvestrant plus palbociclib versus fulvestrant plus placebo for treatment of hormone-receptor-positive, HER2-negative metastatic breast cancer that progressed on previous endocrine therapy (PALOMA-3): final analysis of the multicentre, double-blind, phase 3 randomised controlled trial. Lancet Oncol. 2016;17(4):425–39. Hortobagyi GN, Stemmer SM, Burris HA, Yap YS, Sonke GS, Paluch-Shimon S, et al. Updated results from MONALEESA-2, a phase III trial of first-line ribociclib plus letrozole versus placebo plus letrozole in hormone receptor-positive, HER2-negative advanced breast cancer. Ann Oncol. 2018;29(7):1541–7. Sørlie T, Perou CM, Tibshirani R, Aas T, Geisler S, Johnsen H, et al. Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications. Proc Natl Acad Sci U S A. 2001;98(19):10869–74. Prat A, Parker JS, Fan C, Perou CM. PAM50 assay and the three-gene model for identifying the major and clinically relevant molecular subtypes of breast cancer. Breast Cancer Res Treat. 2012;135(1):301–6. Liberzon A, Birger C, Thorvaldsdóttir H, Ghandi M, Mesirov Jill P, Tamayo P. The Molecular Signatures Database Hallmark Gene Set Collection. Cell Syst. 2015;1(6):417–25. Liberzon A, Subramanian A, Pinchback R, Thorvaldsdóttir H, Tamayo P, Mesirov JP. Molecular signatures database (MSigDB) 3.0. Bioinformatics. 2011;27(12):1739–40. Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A. 2005;102(43):15545–50. Krämer A, Green J, Pollard J Jr., Tugendreich S. Causal analysis approaches in Ingenuity Pathway Analysis. Bioinformatics. 2014;30(4):523–30. Finn RS, Crown JP, Lang I, Boer K, Bondarenko IM, Kulyk SO, et al. The cyclin-dependent kinase 4/6 inhibitor palbociclib in combination with letrozole versus letrozole alone as first-line treatment of oestrogen receptor-positive, HER2-negative, advanced breast cancer (PALOMA-1/TRIO-18): a randomised phase 2 study. Lancet Oncol. 2015;16(1):25–35. Janni W, Alba E, Bachelot T, Diab S, Gil-Gil M, Beck TJ, et al. First-line ribociclib plus letrozole in postmenopausal women with HR+, HER2- advanced breast cancer: Tumor response and pain reduction in the phase 3 MONALEESA-2 trial. Breast Cancer Res Treat. 2018;169(3):469–79. Ma CX, Gao F, Luo J, Northfelt DW, Goetz M, Forero A, et al. NeoPalAna: Neoadjuvant Palbociclib, a Cyclin-Dependent Kinase 4/6 Inhibitor, and Anastrozole for Clinical Stage 2 or 3 Estrogen Receptor-Positive Breast Cancer. Clin Cancer Res. 2017;23(15):4055–65. Cristofanilli M, Rugo HS, Im SA, Slamon DJ, Harbeck N, Bondarenko I, et al. Overall Survival with Palbociclib and Fulvestrant in Women with HR+/HER2- ABC: Updated Exploratory Analyses of PALOMA-3, a Double-blind, Phase III Randomized Study. Clin Cancer Res. 2022;28(16):3433–42. Martin M, Zielinski C, Ruiz-Borrego M, Carrasco E, Turner N, Ciruelos EM, et al. Palbociclib in combination with endocrine therapy versus capecitabine in hormonal receptor-positive, human epidermal growth factor 2-negative, aromatase inhibitor-resistant metastatic breast cancer: a phase III randomised controlled trial-PEARL. Ann Oncol. 2021;32(4):488–99. Zhong B, Zhang J, Wu J, Sun L, Li S, Zeng X, et al. Efficacy and safety of palbociclib plus endocrine therapy for patients with HR+/HER2 – advanced breast cancer in real-world clinical practice. Annals Translational Med. 2022;10(6):362. Knudsen ES, Schultz E, Hamilton D, Attwood K, Edge S, O’Connor T, et al. Real-World Experience with CDK4/6 Inhibitors for Metastatic HR+/HER2 – Breast Cancer at a Single Cancer Center. Oncologist. 2022;27(8):646–54. Lu YSME, Azim H et al. Primary results from the randomized phase II RIGHT Choice trial of premenopausal patients with aggressive HR+/HER2 – advanced breast cancer treated with ribociclib + endocrine therapy vs physician’s choice combination chemotherapy. Presented at SABCS 2022 December 6–10, 2022.2022. De Angelis C, Fu X, Cataldo ML, Nardone A, Pereira R, Veeraraghavan J, et al. Activation of the IFN Signaling Pathway is Associated with Resistance to CDK4/6 Inhibitors and Immune Checkpoint Activation in ER-Positive Breast Cancer. Clin Cancer Res. 2021;27(17):4870–82. Qie S, Diehl JA. Cyclin D1, cancer progression, and opportunities in cancer treatment. J Mol Med (Berl). 2016;94(12):1313–26. Araki K, Miyoshi Y. Mechanism of resistance to endocrine therapy in breast cancer: the important role of PI3K/Akt/mTOR in estrogen receptor-positive, HER2-negative breast cancer. Breast Cancer. 2018;25(4):392–401. Dean JL, McClendon AK, Hickey TE, Butler LM, Tilley WD, Witkiewicz AK, et al. Therapeutic response to CDK4/6 inhibition in breast cancer defined by ex vivo analyses of human tumors. Cell Cycle. 2012;11(14):2756–61. Martin LA, Pancholi S, Ribas R, Gao Q, Simigdala N, Nikitorowicz J, et al. Abstract P3-03-09: Resistance to palbociclib depends on multiple targetable mechanisms highlighting the potential of drug holidays and drug switching to improve therapeutic outcome. Cancer Res. 2017;77:P3–03. Garriga J, Bhattacharya S, Calbó J, Marshall RM, Truongcao M, Haines DS, et al. CDK9 is constitutively expressed throughout the cell cycle, and its steady-state expression is independent of SKP2. Mol Cell Biol. 2003;23(15):5165–73. Herrera-Abreu MT, Palafox M, Asghar U, Rivas MA, Cutts RJ, Garcia-Murillas I, et al. Early Adaptation and Acquired Resistance to CDK4/6 Inhibition in Estrogen Receptor-Positive Breast Cancer. Cancer Res. 2016;76(8):2301–13. Álvarez-Fernández M, Malumbres M. Mechanisms of Sensitivity and Resistance to CDK4/6 Inhibition. Cancer Cell. 2020;37(4):514–29. Finn RS, Liu Y, Zhu Z, Martin M, Rugo HS, Diéras V, et al. Biomarker Analyses of Response to Cyclin-Dependent Kinase 4/6 Inhibition and Endocrine Therapy in Women with Treatment-Naïve Metastatic Breast Cancer. Clin Cancer Res. 2020;26(1):110–21. Turner NC, Liu Y, Zhu Z, Loi S, Colleoni M, Loibl S, et al. Cyclin E1 Expression and Palbociclib Efficacy in Previously Treated Hormone Receptor–Positive Metastatic Breast Cancer. J Clin Oncol. 2019;37(14):1169–78. O'Leary B, Cutts RJ, Huang X, Hrebien S, Liu Y, André F, et al. Circulating Tumor DNA Markers for Early Progression on Fulvestrant With or Without Palbociclib in ER + Advanced Breast Cancer. J Natl Cancer Inst. 2021;113(3):309–17. Gong X, Litchfield LM, Webster Y, Chio LC, Wong SS, Stewart TR, et al. Genomic Aberrations that Activate D-type Cyclins Are Associated with Enhanced Sensitivity to the CDK4 and CDK6 Inhibitor Abemaciclib. Cancer Cell. 2017;32(6):761–e766. Dean JL, Thangavel C, McClendon AK, Reed CA, Knudsen ES. Therapeutic CDK4/6 inhibition in breast cancer: key mechanisms of response and failure. Oncogene. 2010;29(28):4018–32. Dai M, Boudreault J, Wang N, Poulet S, Daliah G, Yan G, et al. Differential Regulation of Cancer Progression by CDK4/6 Plays a Central Role in DNA Replication and Repair Pathways. Cancer Res. 2021;81(5):1332–46. Schettini F, Palleschi M, Mannozzi F, Brasó-Maristany F, Cecconetto L, Galván P, et al. CDK4/6-Inhibitors Versus Chemotherapy in Advanced HR+/HER2-Negative Breast Cancer: Results and Correlative Biomarker Analyses of the KENDO Randomized Phase II Trial. Oncologist. 2024;29(5):e622–34. Jorgovanovic D, Song M, Wang L, Zhang Y. Roles of IFN-γ in tumor progression and regression: a review. Biomark Res. 2020;8:49. Scirocchi F, Scagnoli S, Botticelli A, Di Filippo A, Napoletano C, Zizzari IG, et al. Immune effects of CDK4/6 inhibitors in patients with HR(+)/HER2(-) metastatic breast cancer: Relief from immunosuppression is associated with clinical response. EBioMedicine. 2022;79:104010. Pernas S, Sanfeliu E, Villacampa G, Salvador J, Perelló A, González X, et al. Palbociclib and letrozole for hormone receptor-positive HER2-negative breast cancer with residual disease after neoadjuvant chemotherapy. NPJ Breast Cancer. 2024;10(1):101. Wei XH, Liu YY. Potential applications of JAK inhibitors, clinically approved drugs against autoimmune diseases, in cancer therapy. Front Pharmacol. 2023;14:1326281. Rah B, Rather RA, Bhat GR, Baba AB, Mushtaq I, Farooq M, et al. JAK/STAT Signaling: Molecular Targets, Therapeutic Opportunities, and Limitations of Targeted Inhibitions in Solid Malignancies. Front Pharmacol. 2022;13:821344. Peuker CA, Yaghobramzi S, Grunert C, Keilholz L, Gjerga E, Hennig S, et al. Treatment with ribociclib shows favourable immunomodulatory effects in patients with hormone receptor-positive breast cancer-findings from the RIBECCA trial. Eur J Cancer. 2022;162:45–55. Rugo HS, Kabos P, Beck JT, Jerusalem G, Wildiers H, Sevillano E, et al. Abemaciclib in combination with pembrolizumab for HR+, HER2- metastatic breast cancer: Phase 1b study. NPJ Breast Cancer. 2022;8(1):118. Mayer EL, Ren Y, Wagle N, Mahtani R, Ma C, DeMichele A et al. PACE: A Randomized Phase II Study of Fulvestrant, Palbociclib, and Avelumab After Progression on Cyclin-Dependent Kinase 4/6 Inhibitor and Aromatase Inhibitor for Hormone Receptor-Positive/Human Epidermal Growth Factor Receptor-Negative Metastatic Breast Cancer. J Clin Oncol. 2024:JCO2301940. Egelston C, Guo W, Yost S, Lee JS, Rose D, Avalos C et al. Pre-existing effector T-cell levels and augmented myeloid cell composition denote response to CDK4/6 inhibitor palbociclib and pembrolizumab in hormone receptor-positive metastatic breast cancer. J Immunother Cancer. 2021;9(3). Harbeck N, Rastogi P, Martin M, Tolaney SM, Shao ZM, Fasching PA, et al. Adjuvant abemaciclib combined with endocrine therapy for high-risk early breast cancer: updated efficacy and Ki-67 analysis from the monarchE study. Ann Oncol. 2021;32(12):1571–81. Hortobagyi GN, Lacko A, Sohn J, Cruz F, Ruiz Borrego M, Manikhas A et al. A phase III trial of adjuvant ribociclib plus endocrine therapy versus endocrine therapy alone in patients with HR-positive/HER2-negative early breast cancer: final invasive disease-free survival results from the NATALEE trial. Ann Oncol. 2024. Additional Declarations No competing interests reported. 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d'Oncologia","correspondingAuthor":false,"prefix":"","firstName":"Iris","middleName":"","lastName":"Teruel","suffix":""},{"id":423314394,"identity":"793eb888-8377-4629-8098-766a55ba999c","order_by":8,"name":"Angelica Ferrando-Díez","email":"","orcid":"","institution":"Institut Català d'Oncologia","correspondingAuthor":false,"prefix":"","firstName":"Angelica","middleName":"","lastName":"Ferrando-Díez","suffix":""},{"id":423314395,"identity":"37ae7e39-f25d-4f07-8643-be075bbe3997","order_by":9,"name":"Anna Pous","email":"","orcid":"","institution":"Institut Català d'Oncologia","correspondingAuthor":false,"prefix":"","firstName":"Anna","middleName":"","lastName":"Pous","suffix":""},{"id":423314396,"identity":"784611ea-cb9e-4d1b-bf8e-7db56eeead56","order_by":10,"name":"Assumpció Lopez-Paradís","email":"","orcid":"","institution":"Institut Català d'Oncologia","correspondingAuthor":false,"prefix":"","firstName":"Assumpció","middleName":"","lastName":"Lopez-Paradís","suffix":""},{"id":423314398,"identity":"06a50de4-0e67-45a4-a864-685910c4bf0b","order_by":11,"name":"Laia Boronat","email":"","orcid":"","institution":"Institut Català d'Oncologia","correspondingAuthor":false,"prefix":"","firstName":"Laia","middleName":"","lastName":"Boronat","suffix":""},{"id":423314399,"identity":"98a57ded-cdd1-47cb-9434-5bcec0439ac5","order_by":12,"name":"Marga Romeo","email":"","orcid":"","institution":"Institut Català d'Oncologia","correspondingAuthor":false,"prefix":"","firstName":"Marga","middleName":"","lastName":"Romeo","suffix":""},{"id":423314400,"identity":"cf87e151-c2a9-46ad-9667-73c3c2a47f11","order_by":13,"name":"Ricard Mesía","email":"","orcid":"","institution":"Institut Català d'Oncologia","correspondingAuthor":false,"prefix":"","firstName":"Ricard","middleName":"","lastName":"Mesía","suffix":""},{"id":423314401,"identity":"c2d8cede-761c-49af-96c4-4a7559cf0840","order_by":14,"name":"Pedro Luis Fernandez","email":"","orcid":"","institution":"Hospital Universitari Germans Trias i Pujol, Universitat Autònoma de Barcelona","correspondingAuthor":false,"prefix":"","firstName":"Pedro","middleName":"Luis","lastName":"Fernandez","suffix":""},{"id":423314404,"identity":"effeb727-b0ed-432c-aadb-6c26c3fea520","order_by":15,"name":"Bonaventura Clotet","email":"","orcid":"","institution":"Hospital Universitari Germans Trias i Pujol, Universitat Autònoma de Barcelona","correspondingAuthor":false,"prefix":"","firstName":"Bonaventura","middleName":"","lastName":"Clotet","suffix":""},{"id":423314405,"identity":"326aab83-9c10-4c97-a436-47d7101b353a","order_by":16,"name":"Eva Riveira-Muñoz","email":"","orcid":"","institution":"Hospital Universitari Germans Trias i Pujol, Universitat Autònoma de Barcelona","correspondingAuthor":false,"prefix":"","firstName":"Eva","middleName":"","lastName":"Riveira-Muñoz","suffix":""},{"id":423314406,"identity":"5b6765b8-961f-4862-bdad-5ca27d590b8b","order_by":17,"name":"Anna Martínez-Cardús","email":"","orcid":"","institution":"Hospital Universitari Germans Trias i Pujol, Universitat Autònoma de Barcelona","correspondingAuthor":false,"prefix":"","firstName":"Anna","middleName":"","lastName":"Martínez-Cardús","suffix":""},{"id":423314407,"identity":"9ff1d74f-3c61-49e8-9d51-da59cd28936d","order_by":18,"name":"Ester Ballana","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA80lEQVRIie2QsWrDMBBAzwiURUWrjAv5BZVAtta/csJDlw7ZY6jBkC4lXgPtR7QUMl8R2Euha4YMCoHMzhLSpVRzKSLZOugNp+UevBNAJPIfUZCQ869kfiCAOEUB8puQ1mcrmk7tklntCMv1cNRdbHtX3lzqjpLNMaCkz60mbHdXSzsYK2wLkT4iG4Xy9Aq9wm2ytJyDqZiQgDwLheWr257w2+ZvNWe9qe4Fl27wFQrT6k6TmVnzwjgoU1khFfLgv6n1x4TM3BYLH+Zv6US62NRZSJFPD6+uP9jrpmnZ/lhOc/1ZvO9DYX+RVGcKkUgkEvnND8okT5FppffhAAAAAElFTkSuQmCC","orcid":"","institution":"Hospital Universitari Germans Trias i Pujol, Universitat Autònoma de Barcelona","correspondingAuthor":true,"prefix":"","firstName":"Ester","middleName":"","lastName":"Ballana","suffix":""},{"id":423314408,"identity":"0fafdb4d-b8dc-4b08-9985-246714cac178","order_by":19,"name":"Mireia Margelí","email":"","orcid":"","institution":"Hospital Universitari Germans Trias i Pujol, Universitat Autònoma de Barcelona","correspondingAuthor":false,"prefix":"","firstName":"Mireia","middleName":"","lastName":"Margelí","suffix":""}],"badges":[],"createdAt":"2025-02-28 15:08:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6129690/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6129690/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":78254599,"identity":"3a177aa0-bb79-4932-b0f5-f405bd5fdd19","added_by":"auto","created_at":"2025-03-11 10:26:56","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":89678,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCohort characteristics and patient follow-up.\u003c/strong\u003e (A) Flowchart detailing the study plan. Among all participants enrolled, 91 patients had sufficient follow-up to evaluate the clinical efficacy, and the transcriptomic analysis in tissue biopsies was performed in 55 of them. (B) Swimmer Plot showing individual CDK4/6i treatment duration (blue bars), subsequent follow-up treatments (red bars), and survival status. Triangles denote progression to CDK4/6i, and squares: treatment stopped due to toxicity. (C) Kaplan-Meier analysis of progression-free survival (PFS) of good and bad efficacy groups to CDK4/6i treatment, according to the clinical classification developed. HR, Hazard Ratio. p-value and HR determination were calculated using the log-rank test.\u003c/p\u003e","description":"","filename":"Slide1.png","url":"https://assets-eu.researchsquare.com/files/rs-6129690/v1/95b98d7c5e21c541791e5f69.png"},{"id":78254598,"identity":"504867f8-8a5a-447d-8f09-a4f007adc5a3","added_by":"auto","created_at":"2025-03-11 10:26:56","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":62975,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe expression of immune-based BC360TM gene signatures influences the efficacy of CDK4/6i treatment.\u003c/strong\u003e (A) Bar plot of the top 20 differentially enriched genes (DEGs) in the poor response group compared to the good response group in the first-line setting (n=31), based on Log2 gene expression (Log2FC). Significantly down- or up-regulated DEG are highlighted in blue or red. (B) Multivariate survival analysis of the five relevant BC360\u003csup\u003eTM \u003c/sup\u003esignatures in univariate analysis (p\u0026lt;0.1, Supplementary Table 4) in first-line patients (n=31). HR, Hazard Ratio; CI, Confidence Interval. (C) Kaplan-Meier analysis describing the impact of IFN-gamma signature in patients' PFS, stratified by high vs. low expression (cut-off based on the optimal threshold calculated on the ROC curve). The log-rank test was used for p-value calculation and HR determination.\u003c/p\u003e","description":"","filename":"Slide2.png","url":"https://assets-eu.researchsquare.com/files/rs-6129690/v1/45980f222899f62848cd20a2.png"},{"id":78254603,"identity":"c71d0612-eff6-4b59-b35b-5077af4c4499","added_by":"auto","created_at":"2025-03-11 10:26:56","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":174977,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA 9-immune-gene signature predicts efficacy to CDK4/6i treatment in first-line patients.\u003c/strong\u003e (A) Volcano plot of differentially expressed genes (DEGs) in the bad response group compared to the good response group in first-line patients (n=31). Significantly (p\u0026lt;0.05) upregulated and downregulated genes are depicted in red and blue, respectively. (B) Heatmap (left) and scatter plots with mean±SEM (right) depicting the expression levels of the 14 selected immune-related genes among the first-line patients (n=31). In the heatmap, clusters were unsupervised using hierarchical clustering with Euclidean distances for rows (genes) and columns (patients). Gene expression values were row-scaled (z-score normalization) to highlight relative expression patterns across samples. Scatter plots represent the mean expression of the 9-gene KIMA (upper plot) and 5-gene NEGIM (lower plot) signatures in each of the patients. Significance was calculated with an unpaired t test with Welch’s correction. **, p\u0026lt;0.01. (C) Kaplan-Meier survival curves showing progression free survival (PFS) and overall survival (OS) of first-line patients split according to high vs. low expression (cut-off based on the optimal threshold calculated on the ROC curve) of the KIMA signature. The log-rank test was used for p-value calculation and hazard ratio (HR) determination. (D) Kaplan-Meier survival curves for PFS and OS in first-line patients stratified across four groups based on the combined evaluation of high and low expression of the KIMA and NEGIM signatures. Statistical differences between the groups were assessed using the log-rank test. Median survival months (m) are shown on each curve respectively.\u003c/p\u003e","description":"","filename":"Slide3.png","url":"https://assets-eu.researchsquare.com/files/rs-6129690/v1/1855c268687af25cac0656f7.png"},{"id":78254605,"identity":"5b2ff3bd-a7c0-471a-9c8d-0135f62995c3","added_by":"auto","created_at":"2025-03-11 10:26:56","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":209036,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe KIMA signature predicts efficacy in patients from all lines of treatment.\u003c/strong\u003e (A) Scatter plot with mean±SEM comparing the mean expression of the KIMA gene signature between good and bad efficacy groups regardless of the treatment line number (n=47). Significance was calculated with an unpaired t test with Welch’s correction. **, p\u0026lt;0.01. (B) ROC (Receiver Operating Characteristic) curve depicting the true and false positive rates of the KIMA signature in the entire cohort (n=47). The AUC (Area Under the Curve) value represents the discrimination rate of the KIMA signature to classify the patients correctly. (C) Kaplan-Meier survival curves showing PFS and OS of all patients in the cohort, split according to high vs. low expression (cut-off based on the optimal threshold calculated on the ROC curve) of the KIMA signature. The log-rank test was used for p-value calculation and HR determination. (D) ROC curves and AUC values comparison between KIMA and the BC360\u003csup\u003eTM\u003c/sup\u003e’s IFN-gamma signatures.\u003c/p\u003e","description":"","filename":"Slide4.png","url":"https://assets-eu.researchsquare.com/files/rs-6129690/v1/4fb7efeb741710adec448d94.png"},{"id":78254783,"identity":"e55da28b-c62d-4e11-8a75-bfb2bca712e6","added_by":"auto","created_at":"2025-03-11 10:34:56","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":27083,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eValidation. Relative Expression ofPOSIM0.1 Signature in other cohorts\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Slide5.png","url":"https://assets-eu.researchsquare.com/files/rs-6129690/v1/36e1f54c157c00648865b24b.png"},{"id":78255963,"identity":"b8182951-5717-4790-b24d-c5f01fee8dda","added_by":"auto","created_at":"2025-03-11 10:43:08","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1890578,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6129690/v1/803315e0-94ac-4356-a776-b02902d77ddc.pdf"},{"id":78254784,"identity":"20ead1f3-a1e8-412c-b0c7-b2e17db4150c","added_by":"auto","created_at":"2025-03-11 10:34:56","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":41964,"visible":true,"origin":"","legend":"","description":"","filename":"suplementarytablestextfigsfeb25.docx","url":"https://assets-eu.researchsquare.com/files/rs-6129690/v1/a5abc4c37093f9dc4dce8e88.docx"},{"id":78254604,"identity":"a06c8d17-35f8-48c1-9afd-af6d0cd963eb","added_by":"auto","created_at":"2025-03-11 10:26:56","extension":"pptx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":1199047,"visible":true,"origin":"","legend":"","description":"","filename":"Felipetalsuppfigs.pptx","url":"https://assets-eu.researchsquare.com/files/rs-6129690/v1/b5e500dabd16a48df778e80a.pptx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Immune Gene Signature as a Predictor of CDK4/6 Inhibitor Response in HR+/HER2– Breast Cancer","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eCDK4/6 inhibitors (CDK4/6i) combined with endocrine therapy have become the standard treatment for estrogen receptor-positive and HER2-negative (ER+/HER2-) advanced breast cancer (ABC); significantly improving progression-free survival (PFS) and overall survival (OS) across both endocrine-sensitive and resistant cases. Their benefits extend to the adjuvant setting, where they reduce the risk of recurrence in high-risk, early-stage ER+/HER2- breast cancer, further solidifying their critical role in the therapeutic landscape of ER+/HER2- ABC (\u003cspan additionalcitationids=\"CR2 CR3 CR4 CR5\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite the significant improvements achieved with CDK4/6i, approximately 10% of patients exhibit primary resistance, and nearly all patients eventually develop acquired resistance, leading to treatment failure(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Clinical factors, such as metastatic site, disease-free survival, and previous therapies before CDK4/6i, may influence the response rate and the development of resistance. Importantly, resistance to CDK4/6i arises not only from genomic aberrations, such as point mutations, insertions/deletions, or amplifications but also from changes in gene expression (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). This suggests that specific gene signatures or differentially expressed genes could predict resistance or efficacy to CDK4/6i (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). To date, several biomarkers associated with CDK4/6i resistance have been proposed, including cell cycle regulators, oncogenic kinase pathway genes (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e), gene expression patterns and genes involved in modulating the tumor microenvironment which significantly influences the antitumor response to CDK4/6i (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Despite these advances, currently there is not a precise biomarker/s that is able to identify patients of limited benefit from CDK4/6i treatment.\u003c/p\u003e \u003cp\u003eDistinct preclinical and clinical evidence indicate that the effect of CDK4/6i extends beyond cell cycle inhibition, suggesting a broader role for CDK4/6 kinases in cellular processes, including modulation of the antitumor immune response (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Both cyclins and CDKs are essential for the development and function of immune cells (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e), with different immune cell types relying on specific cyclins and CDKs for development, expansion, and activation. CDK4/6i have been shown to induce specific immunological changes that alter the tumor microenvironment and the balance of tumor-immune interactions (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). The primary effects of this modulation include (i) enhanced intra-tumoral infiltration by immune cells, (ii) increased antigen presentation, (iii) modifications in cytokine expression within the tumor microenvironment, and (iv) upregulation of co-inhibitory molecules such as PD-L1 (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan additionalcitationids=\"CR15 CR16\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). These immunomodulatory effects suggest CDK4/6i may reprogram immune responses within tumors, creating opportunities for new therapeutic intervention. Although promising preclinical results have been observed with combinations of CDK4/6i and immune checkpoint inhibitors, such as anti-PD-1 monoclonal antibodies, clinical trials have not yet shown significant efficacy improvements and have reported increased toxicity (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). Optimizing these combination strategies will require a deeper understanding of the immunological changes induced by CDK4/6i, emphasizing the need for translational research to design safe and effective combination therapies.\u003c/p\u003e \u003cp\u003eHere, we conducted a single-center prospective study to gain insights into the clinical and tumor characteristics determining CDK4/6i efficacy in ABC. Through standardized tissue-based transcriptomic analysis, we identified and characterized immunological and molecular pathways associated with the different treatment outcomes, reinforcing the pivotal role of the immune system in modulating CDK4/6i efficacy. Our findings reveal a novel 9-gene immune-based signature, the KIMA signature, which predicts CDK4/6i efficacy in breast cancer patients. This signature offers a practical tool for integration into routine clinical practice, enabling personalized treatment strategies and improving therapeutic decision-making.\u003c/p\u003e"},{"header":"MATERIALS AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eClinical cohort and study design\u003c/h2\u003e \u003cp\u003eA prospective observational study was designed to include patients with ER+/HER2- ABC who started treatment with CDK4/6i in the Catalan Institute of Oncology (ICO) of Hospital Germans Trias i Pujol (HUGTIP), Badalona, Spain. Between May 2018 and April 2022, a total of 100 patients who started treatment with any of the three approved CDK4/6i (palbociclib, ribociclib, or abemaciclib) in any line of therapy for metastatic disease were enrolled, according to CDK4/6i approval status, or indications throughout the study period (2018\u0026ndash;2022). The cohort's detailed clinical information and disease evolution were extracted from medical records (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Surplus 63 tumor blocks from clinical practice were collected for subsequent analysis in 55 patients, including paired samples from primary tumor and metastasis in 8 cases. All participants provided written informed consent specific to the study and tumor banking by the ethical protocols of Hospital Germans Trias i Pujol. The study was reviewed and approved by the Institution's ethical committee from the Hospital.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClinical characteristics of the study cohort (n\u0026thinsp;=\u0026thinsp;100), stratified also depending on the CDK4/6i administered.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCohort\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;100)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAbemaciclib\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;19)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePalbociclib\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;60)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRibociclib\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;21)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003ep-value\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge, average [IQR]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e62.3 [51.6; 69.8]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e63.5 [54.3; 68.5]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e62.3 [53.0; 69.9]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e55.3 [48.1; 70.7]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.659\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eYear of treatment initiation: n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e\u0026lt;\u0026thinsp;0.001\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36 (36.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5 (26.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22 (36.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9 (42.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26 (26.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2 (10.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22 (36.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2 (9.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16 (16.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5 (26.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11 (18.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16 (16.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6 (31.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (5.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7 (33.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1 (5.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (3.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3 (14.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTreatment line, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.080\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60 (60.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8 (42.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35 (58.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17 (81.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13 (13.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2 (10.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9 (15%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2 (9.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27 (27.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9 (47.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16 (26.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2 (9.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHormone-therapy, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e0.043\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTamoxife\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9 (9.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2 (10.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (5.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4 (19.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAromatase Inhibitor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e53 (53.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10 (52.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29 (48.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14 (66.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFulvestrant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38 (38.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7 (36.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28 (46.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3 (14.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTreatment with analogue LH-RH, n (%)\u003c/b\u003e:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e0.002\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19 (19.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2 (10.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7 (11.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10 (47.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e81 (81.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17 (89.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e53 (88.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11 (52.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDe novo\u003c/b\u003e \u003cb\u003emetastasis\u003c/b\u003e, \u003cb\u003en (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.309\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29 (29%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7 (36.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14 (23.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8 (38.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e71 (71%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12 (63.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e46 (76.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13 (61.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHormone sensitivity, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e0.019\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDe novo \u003cem\u003emetastatic debut\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14 (14%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1 (5.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 (10%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7 (33.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1L and relapse\u0026thinsp;\u0026gt;\u0026thinsp;12m post-end HT adjuvant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29 (29%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5 (26.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17 (28.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8 (38.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2L or relapse during HT or \u0026lt;\u0026thinsp;12m post-ending HT adjuvant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30 (30%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4 (21.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21 (35%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4 (19%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;2L metastatic line\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27 (27%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9 (47.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16 (26.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2 (9.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePrevious chemotherapy for advanced BC, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.077\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23 (23%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8 (42.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12 (20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3 (14.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e77 (77%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11 (57.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e48 (80%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18 (85.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eImmunohistochemistry\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eER expression, median, [IQR]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e90.0 [87.5; 95.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e90.0 [80.0; 99.5]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e90.0 [90.0; 95.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e90.0 [90.0; 95.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.950\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePR\u0026thinsp;\u0026lt;\u0026thinsp;20 expression, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41.0 (41%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7 (36.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29 (48.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5 (23.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.133\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHER2\u003c/b\u003e, n (%):\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.841\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHER2 negative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e47 (47.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8 (42.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29 (48.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10 (47.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHER2 low\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46 (46.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8 (42.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28 (46.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10 (47.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo information\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (7.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3 (15.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (5.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 (4.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003en, number of patients; HT, hormone therapy; AI, aromatase inhibidors; L, number of treatment line; m, months; ER, estrogen receptors; PR, progesterone receptors; IQR, interquartile range; M1, metastasis; BC, breast cancer; p-value was considered significant if Chi-Square\u0026thinsp;\u0026lt;\u0026thinsp;0,05 (in red).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003ePatient stratification based on clinical variables for functional analysis\u003c/h3\u003e\n\u003cp\u003eA clinical stratification approach for patients included was performed based on anticipated treatment efficacy, encompassing both hormone-sensitive and hormone-resistant populations of ABC, aimed at optimizing functional analysis and minimizing cohort heterogeneity. Different cut-off points were established for each situation, considering the diverse populations included and the progression-free survival (PFS) reported in the pivotal studies for the distinct CDK4/6i (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). This approach aimed to reduce the heterogeneity within the cohort and identify patients who derived maximum benefit from CDK4/6i, irrespective of treatment line or prior therapies (Supplementary Fig.\u0026nbsp;1). The definition of each classification group was as follows:\u003c/p\u003e \u003cp\u003eGroup 1: Hormone-sensitive patients with treatment-naive metastatic disease or a\u0026thinsp;\u0026gt;\u0026thinsp;12 months relapsing time after completing adjuvant endocrine therapy (ET). Categories: Good efficacy (PFS\u0026thinsp;\u0026ge;\u0026thinsp;24 months) vs. Bad efficacy (PFS\u0026thinsp;\u0026lt;\u0026thinsp;24).\u003c/p\u003e \u003cp\u003eGroup 2: Hormone-resistant patients with progression during adjuvant ET or within the first year after completion or after \u0026ge;\u0026thinsp;1 line of hormone therapy (no previous chemotherapy). Categories: Good efficiency (PFS\u0026thinsp;\u0026ge;\u0026thinsp;12 months) vs. Bad efficiency (PFS\u0026thinsp;\u0026lt;\u0026thinsp;12 months).\u003c/p\u003e \u003cp\u003eGroup 3: Patients with prior chemotherapy for metastatic disease. Categories: Good efficiency (PFS\u0026thinsp;\u0026ge;\u0026thinsp;7 months) vs. Bad efficiency (PFS\u0026thinsp;\u0026lt;\u0026thinsp;7 months).\u003c/p\u003e \u003cp\u003eReanalysis of patient characteristics confirmed that the stratified groups were homogeneous in age and showed no significant differences in other clinical variables. This supports the robustness and simplicity of the dichotomous categorization for subsequent analyses (Supplementary Table\u0026nbsp;1).\u003c/p\u003e\n\u003ch3\u003eTumor biopsies and RNA extraction\u003c/h3\u003e\n\u003cp\u003eTumor samples were collected retrospectively, including primary tumors and metastatic biopsies obtained before the initiation of treatment. The Tumor Biobank and the Pathology Department of the Germans Trias i Pujol Hospital facilitated the selection and handling of these samples. For each sample, a pathologist identified areas enriched with tumor cells (minimum 40%) using hematoxylin-eosin-stained slides, ensuring the integrity of the selected regions for downstream analyses.\u003c/p\u003e \u003cp\u003eRNA extraction was performed from 6\u0026ndash;10-micron tumor slices using the protocol established in the RNeasy FFPE Kit (Qiagen, Venlo, Netherlands). The quality of the RNA was determined using NanoDrop One (ThermoScientific) and Bioanalyzer 2100. Samples with a 260/280 ratio between 1.7 and 2.3 were included. RNA degradation was tested through the DV200 parameter, considering suitable samples with values over 40% for analysis. Samples that did not meet these requirements were discarded.\u003c/p\u003e\n\u003ch3\u003eGene expression and bioinformatic analysis\u003c/h3\u003e\n\u003cp\u003e250ng of total RNA (50ng/\u0026micro;L) was used to analyze gene expression using the Breast Cancer 360\u0026trade; (BC360\u0026trade;) panel on the multiplexed digital nCounter\u0026reg; platform (NanoString Technologies, Inc., Seattle, WA, USA). BC360 \u003csup\u003eTM\u003c/sup\u003e panel includes 758 genes relevant to breast cancer, including those with established roles in tumor biology, the immune response, or the tumor microenvironment, and 18 housekeeping genes. Transcript counts were log2-transformed and normalized to internal controls and housekeeping gene expression. For each sample, normalized data was used to determine correlation scores for the four Prosigna\u0026reg; intrinsic subtype signatures and assign intrinsic subtype according to published methods (NanoString Technologies, Inc.) (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGene Set Enrichment Analysis (GSEA) was performed using the expression of the 776 genes from the BC360 \u003csup\u003eTM\u003c/sup\u003e panel. Briefly, patients were stratified by efficacy group, and the mean expression for each gene was calculated within each group. Then, for each gene, the mean expression value of the good efficacy group was subtracted from that of the bad efficacy group. These differential expression values were then organized in decreasing order and used as a ranked input for a GSEA Pre-Ranked analysis with the Hallmark gene set database (h.all.v2024.1.Hs.symbols) from Molecular Signatures Database (MSigDB).(\u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSingle gene expression analysis from first-line patients was used to select a putative predictive gene signature. Immune-related genes with at least a 50% significant expression (p-value\u0026thinsp;\u0026le;\u0026thinsp;0.1) difference between the groups were selected, resulting in a list of 9 upregulated (\u003cem\u003eCXCL10, OAS3, STAT1, CD27, TIGIT, IL2RA, FOXP3, TAP1, TAP2\u003c/em\u003e) and 5 downregulated (\u003cem\u003eHLA-DQA1, HLA-DQB1, CXCL8, TNFSF10, IL1B\u003c/em\u003e) genes in the bad efficacy group. The protein interaction map of the selected upregulated genes was generated with QIAGEN IPA (QIAGEN Inc., \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://digitalinsights.qiagen.com/IPA\u003c/span\u003e\u003cspan address=\"https://digitalinsights.qiagen.com/IPA\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). Intermediate proteins and connections between the nodes are the automated result of the Grow and Path Explorer tools using the IPA database. Diseases, cell functions and biomarkers nodes were manually added according to their relevance with our study scope and the number of connections with the signature members.\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eQuantitative variables were represented using medians and ranges for clinical descriptive analysis, while qualitative variables were expressed with absolute frequencies and percentages. PFS was defined as the time (in months) from the initiation of treatment with CDK4/6i until treatment discontinuation due to disease progression or death from any cause. OS was the time (in months) calculated from the date of treatment initiation until death from any cause or the date of censoring at the last time the subject was known to be alive in an intention-to-treat population. Median PFS and OS were estimated using the Kaplan-Meier method and analyzed using the log-rank test, with statistical significance considered at a p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05. The Kruskal-Wallis or Mann-Whitney U tests were applied for independent quantitative variables, and the chi-square test for qualitative variables, with a significance threshold of p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003cp\u003eFor survival analysis, Cox proportional hazards regression models were used to assess the prognostic and predictive relevance of intrinsic subtype and normalized gene/signature scores for PFS and OS. Hazard ratios (HR) and their associated 95% confidence intervals (CI) were reported. Scores relevant in univariate analyses (p\u0026thinsp;\u0026lt;\u0026thinsp;0.1) were further investigated through multiple logistic regression.\u003c/p\u003e \u003cp\u003eFor the scatter plots representing the mean expression of gene signatures, each dot represents an individual patient. Data distribution for each plot was assessed with the Shapiro\u0026ndash;Wilk test. When data was normally distributed, the significance was assessed by unpaired t-test analysis with Welch\u0026rsquo;s correction. Otherwise, the Mann-Whitney U test was performed.\u003c/p\u003e \u003cp\u003eThe receiver operating characteristic (ROC) curves and area under the curve (AUC) values were calculated using the pROC package in RStudio (v4.5.1) to assess the predictive capacity of gene signatures. The optimal threshold value determined by this package was applied to classify patients into high or low-expression groups for each signature. Kaplan-Meier survival curves, scatter plots, GSEA plot and the representation of ROC curves were generated using GraphPad Prism (v.10.3.1), while RStudio was used for statistical analysis of clinical variables and for representing heatmaps (using the pHeatmap package) and volcano plots (using the ggplot2 package). We used Principal Component Analysis (PCA) to explore dimensionality reduction and potential correlation between the genes from the KIMA signature. The Elbow method was used to choose the number of principal components on the screen plot. For this analysis, the Jamovi project 2024 (v2.6) was used.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eClinical characteristics of patients treated with CDK4/6 inhibitors\u003c/h2\u003e \u003cp\u003eFrom March 2018 to April 2022, 100 patients initiating CDK4/6i therapy for ABC were enrolled. Of these, 91 patients had adequate follow-up for efficacy assessment at the analysis time. 63 tumor samples were collected before CDK4/6i initiation, obtained from 55 patients, including 39 primary tumors and 24 metastases, with paired biopsies available for 8 patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAs of the analysis data cut-off on July 31, 2023, 25 patients (25%) remained on CDK4/6i treatment, 26 patients (26%) had changed to other oncologic therapies, and the remaining 49 patients (49%) were deceased. Notably, 8 patients sustained a benefit to CDK4/6i\u0026thinsp;+\u0026thinsp;ET for over four years (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003ePatient demographic and clinical characteristics are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Most patients were treated with palbociclib (60%), followed by abemaciclib (19%) and ribociclib (21%). This cohort was heterogeneous: the majority (60%) received first-line treatment for ABC, 27% had received multiple therapy lines (two or more), and 25% had previously received chemotherapy for ABC. Regarding hormonal sensitivity, 43% were deemed hormone-sensitive, either experiencing recurrence more than 12 months after adjuvant hormonal treatment (29%) or receiving treatment for de novo ABC (14%). This heterogeneity extended to metastatic involvement, with 44% having visceral disease (62% with hepatic involvement) and 27% showing exclusive bone metastases.\u003c/p\u003e \u003cp\u003eTo streamline the assessment of patient-specific characteristics, we categorized patients into two groups based on progression-free survival data from pivotal trials: good and bad efficacy groups. These categories considered (i) treatment regimen, (ii) line of therapy, (iii) hormonal sensitivity, and (iv) prior chemotherapy for ABC (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e) (Supplementary Fig.\u0026nbsp;1). This approach allowed the independent classification of 91 patients into the two efficacy groups, whereas 9 cases (9/100) could not be assessed due to inadequate follow-up or treatment discontinuation after toxicity. The classification resulted in 57% (52/91) of patients categorized as good efficacy and 43% (39/91) as bad efficacy.\u003c/p\u003e \u003cp\u003eAs expected, comparing the two efficacy groups revealed statistically significant differences in PFS. Specifically, patients in the good efficacy group achieved a median PFS of 31 months, compared to 5.7 months in the bad efficacy group (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). Notably, no significant differences were observed between the two groups considering treatment line number, metastatic involvement (visceral or bone-only), or hormone sensitivity (Supplementary Table\u0026nbsp;1). This uniformity in clinical characteristics across efficacy groups supports the hypothesis that additional biological factors contribute to CDK4/6i efficacy in ABC patients.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eClinical Characteristics and Intrinsic Tumor Subtypes as Determinants of CDK4/6i Treatment Efficacy\u003c/h3\u003e\n\u003cp\u003eA comprehensive evaluation of clinical and tumor-specific characteristics that may influence CDK4/6i efficacy in ABC was performed. Variability in CDK4/6i agents was attributed to external factors such as approval status in Spain, evolving clinical evidence, and changes in indications throughout the study period (2018\u0026ndash;2022; Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Survival analysis across the cohort indicated a median PFS of 14 months (Interquartile Range [IQR]: 5.6\u0026ndash;26.56) and a median OS of 42.8 months (IQR: 20.33\u0026ndash;47.30) (Supplementary Figs.\u0026nbsp;2A and 2B). However, the OS was limited by the relatively short follow-up period. As expected, first-line therapy resulted in significantly longer PFS than later lines (median PFS of 30.4 vs. 9.3 months, p\u0026thinsp;=\u0026thinsp;0.001; Supplementary Fig.\u0026nbsp;2C). Hormone sensitivity at CDK4/6i initiation also strongly impacted in PFS (45.8 vs. 14 months for sensitive vs. resistant cases). Additionally, exclusive bone involvement (PFS of 30.4 vs. 24 months), and absence of liver metastasis (PFS of 35.9 vs. 24 months) showed a trend towards longer PFS (Supplementary Figs.\u0026nbsp;2D\u0026ndash;F).\u003c/p\u003e \u003cp\u003eThe influence of intrinsic tumor subtypes on CDK4/6i efficacy was also assessed using the PAM50 gene expression signature of 55 patients with available tissue (31 primary tumors and 24 metastatic lesions), presenting similar clinical characteristics to the entire cohort (Supplementary Table\u0026nbsp;2). Luminal subtypes predominated (38% luminal A and 47% luminal B), with non-luminal subtypes comprising only 15% of cases (11% HER2-enriched and 4% basal) (Supplementary Fig.\u0026nbsp;3A). Notably, metastatic lesions showed a shift in subtype distribution with fewer luminal A and increased HER2-enriched and basal cases compared to primary tumors (p\u0026thinsp;=\u0026thinsp;0.034). Among patients receiving CDK4/6i as first-line therapy (n\u0026thinsp;=\u0026thinsp;31), luminal A tumors exhibited a median PFS of 35.9 months, compared to 21.55 months in HER2-enriched cases (Supplementary Fig.\u0026nbsp;3B), although the differences were not statistically significant. These findings suggest that intrinsic subtypes may influence CDK4/6i efficacy but also highlight the existence of additional factors contributing to treatment outcomes, underscoring the complexity of predictive factors in ABC.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eExploring BC360\u0026trade; Transcriptomic Signatures as Determinants of CDK4/6i Efficacy\u003c/h2\u003e \u003cp\u003eTo identify potential tumor prognostic and predictive factors influencing the efficacy of CDK4/6i, we performed a transcriptomic analysis using the BC360\u0026trade; panel on the cohort of 47 patients with available tumor samples and enough clinical follow-up. We compared the expression levels of the BC360\u0026trade; gene signatures between the good and bad efficacy groups. Differential expression analysis identified 19 differentially expressed gene signatures (p\u0026thinsp;\u0026lt;\u0026thinsp;0.1), most upregulated in the bad efficacy group (18/19) (Supplementary Table\u0026nbsp;3). Among the overexpressed signatures, we identified substantial enrichment in immune-related pathways, including IDO1, TIGIT, Treg, TIS, CD8, PD-1, inflammatory chemokines, IFN-gamma, PD-L1, PD-L2, and macrophages (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). In contrast, the only downregulated signature was differentiation (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo further assess the clinical impact of these signatures on patient outcome, survival analysis was performed in patients receiving CDK4/6i as first-line therapy (n\u0026thinsp;=\u0026thinsp;31) to minimize potential biases from prior treatments. Univariate Cox survival analysis showed an association between progression-free survival (PFS) and expression levels of IFN-gamma, PD-L2, TIGIT, Macrophages, and IDO1 signatures (Supplementary Table\u0026nbsp;4). Multivariate Cox analysis on the five relevant signatures showed that only the increased expression levels of the IFN-gamma signature were independently associated with the poor efficacy group (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). Then, ROC curve analysis was used to determine the optimal threshold for categorizing patients based on their levels of IFN-gamma signature expression. Using this threshold, patients with high IFN-gamma expression had a significantly worse median PFS (15 months) compared to the low-expression group (not reached (NR)) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC), supporting previous observations on the negative impact of high IFN-gamma signature expression on PFS.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eIdentification and Functional Characterization of the 9-Gene KIMA Signature\u003c/h2\u003e \u003cp\u003eTo further elucidate the immunologic features determining CDK4/6i efficacy, we analyzed differences in single gene expression from the BC360\u0026trade; panel in first-line patients.\u003c/p\u003e \u003cp\u003eAmong the 758 genes included in the BC360\u0026trade;, 43 displayed significant differential expression between good and poor efficacy patients (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA), including overexpression of immune-related genes like \u003cem\u003eCXCL10\u003c/em\u003e or \u003cem\u003eISG15\u003c/em\u003e in the bad efficacy group (Supplementary Fig.\u0026nbsp;4B). Unsupervised consensus clustering based on the expression of the 43 differentially expressed genes (DEGs) showed limited ability to stratify patients based on the CDK4/6i clinical efficacy (Supplementary Fig.\u0026nbsp;4A).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFurther analysis using Gene set enrichment analysis (GSEA) with the Hallmark gene set database confirmed significant enrichment of immune-related pathways in the poor efficacy group, including IFN-gamma response, allograft rejection, inflammatory response, and IL6/JAK/STAT signaling, alongside cell cycle regulation alterations. Conversely, patients from the good efficacy group displayed an upregulation in estrogen response pathways (Supplementary Fig.\u0026nbsp;4C). Based on these findings, we focused on immune-related genes for further evaluation.\u003c/p\u003e \u003cp\u003eAmong the 43 DEGs, 14 were linked to immune function and showed at least a 50% significant difference in expression between efficacy groups (p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.1). Unsupervised consensus clustering according to the expression of these 14 immune-related genes significantly improved patient selection, clustering together 90% (19/21) of patients from the good efficacy group (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). These 14 genes were further classified into two groups based on elevated or downregulated expression in the poor efficacy groups: the 9 upregulated genes (\u003cem\u003eCXCL10, OAS3, STAT1, CD27, TIGIT, IL2RA, FOXP3, TAP1, TAP2\u003c/em\u003e) and the 5 downregulated genes (\u003cem\u003eHLA-DQA1, HLA-DQB1, CXCL8, TNFSF10, IL1B\u003c/em\u003e). As expected, the mean expression of the 9-gene signature in first-line patients was significantly higher in the bad efficacy group (p\u0026thinsp;=\u0026thinsp;0.0048) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB, higher scatter plot) while the 5-gene signature was significatively lower (p\u0026thinsp;=\u0026thinsp;0.0044) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB, lower scatter plot) compared to the good efficacy group. Survival analysis revealed that high expression levels of the 9-gene set correlated with significantly shorter PFS and OS compared to the low-expression patients (PFS of 15 months vs NR; p\u0026thinsp;=\u0026thinsp;0.016 and OS of 29.9 months vs NR; p\u0026thinsp;=\u0026thinsp;0.027) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). By contrast, the downregulated 5-gene set showed no significant association with PFS or OS in either the first-line setting or across all treatment lines (Supplementary Figs.\u0026nbsp;5A and 5C).\u003c/p\u003e \u003cp\u003eFurther evaluation of both signatures, identified the 9-gene set as critical for CDK4/6i efficacy and thus we defined it as the \u0026ldquo;Key IMmune Activation\u0026rdquo; (KIMA) signature (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). To understand if all 9 genes of the KIMA signature were needed in our signature, we performed a principal component analysis (PCA). PCA confirmed the relevance of each individual gene within the KIMA signature, indicating that all nine genes of the KIMA signature contributed substantially to an overall signature variance of 63% (Supplementary Fig.\u0026nbsp;6). Furthermore, the connections between the genes of the KIMA signature were analyzed, revealing several inter-gene relationships, with \u003cem\u003eSTAT1\u003c/em\u003e emerging as a central regulatory element, underscoring the interconnected roles of these genes within the immune network. Additional direct and indirect connections between the genes and other nodes of interest defined in the IPA package were also found, such as Breast or ovarian cancer, Neoplasia of cells, Systemic autoimmune syndrome, Cell cycle progression, Immune evasion by tumor, Refractory hormone receptor-positive HER2 negative breast cancer, and Biomarkers (BM) for breast cancer efficacy (Supplementary Fig.\u0026nbsp;4D), supporting its role as putative biomarkers of efficacy in breast cancer.\u003c/p\u003e \u003cp\u003eThen, the predictive value of the KIMA signature was further evaluated in all the patients, irrespective of the line of treatment. Similarly to first-line patients, patients from the poor efficacy group presented significantly higher expression levels of the KIMA signature, independent of the treatment line (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA, p\u0026thinsp;=\u0026thinsp;0.0022). ROC curve analysis confirmed the robust predictive performance of the KIMA signature, with an AUC of 0.787 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFinally, in the survival analysis, elevated KIMA expression was consistently associated with poorer PFS and OS outcomes (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC), further supporting the robustness of the KIMA signature to predict CDK4/6i efficacy. Interestingly, compared to the NanoString BC360\u0026trade; IFN-gamma signature, KIMA demonstrated slightly better predictive accuracy for treatment efficacy across the whole cohort, obtaining a higher AUC value (0.787 vs 0.728) when representing ROC curves (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD), underscoring its potential as a reliable biomarker for CDK4/6i efficacy.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eThe KIMA Signature predicts CDK4/6i efficacy in early neoadjuvant BC\u003c/h2\u003e \u003cp\u003eCurrently, the use of CDK4/6i inhibitors in early BC cases is being studied in several clinical trials and its approval is warranted in the near future. To validate the predictive capacity of the 9-gene KIMA signature, we assessed its performance in the NeoPalAna study (NCT01723774). This phase II neoadjuvant trial enrolled patients with stage II or III ER\u0026thinsp;+\u0026thinsp;breast cancer. Participants were treated with anastrozole for four weeks, followed by the addition of palbociclib for four 28-day cycles prior to surgery (NeoPalAna: Neoadjuvant Palbociclib plus Anastrozole in ER\u0026thinsp;+\u0026thinsp;breast cancer).\u003c/p\u003e \u003cp\u003eThe study demonstrated that the palbociclib-based regimens significantly increased the complete cell cycle arrest rate compared to anastrozole alone (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). Notably, the trial also revealed a subset of patients exhibiting intrinsic resistance to the combination of palbociclib and anastrozole, underscoring the heterogeneity in treatment response (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). Consistent with findings from our primary cohort, palbociclib-resistant patients in the NeoPalAna study exhibited significantly higher KIMA signature expression than sensitive patients (Fig.\u0026nbsp;5A, p\u0026thinsp;=\u0026thinsp;0.0055).\u003c/p\u003e \u003cp\u003eMoreover, KIMA signature demonstrated a strong predictive capacity in distinguishing between sensitive and resistant patients, achieving an AUC of 0.82 in the ROC analysis (Fig.\u0026nbsp;5B). Overall, these findings confirm the robustness and applicability of the KIMA signature as a predictive transcriptomic signature for the CDK4/6i therapeutic efficacy.\u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eCDK4/6i have significantly improved clinical outcomes in ER+/HER2- ABC and early-stage breast cancer (EBC); but response variability and occurrence of relapses remain significant challenges. Currently, reliable biomarkers are lacking in identifying patients most likely to benefit, thereby minimizing toxicities and reducing healthcare costs. Therefore, raising cost-effectiveness concerns on the universal use of CDK4/6i limits access in some patients. Here, we describe and validate the KIMA signature as a reliable immune-based transcriptomic efficacy biomarker in a prospective real-world cohort.\u003c/p\u003e \u003cp\u003eThe clinical characteristics of our study cohort are closely aligned with those of key pivotal trials, highlighting the reliability and clinical relevance applicability of our findings. The cohort median PFS (14 months) is in line with the MONARCH-2 and MONALEESA-3 trials (16.9 and 14.6 months, respectively) and exceeds PALOMA-3 (9.5 months), which included more heavily pretreated and hormone-resistant patients (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). Indeed, as expected, both hormone sensitivity and metastatic site significantly influenced PFS. Specifically, patients with hormone-resistant disease and hepatic metastasis exhibited shorter PFS, consistent with findings from larger trials (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). Similarly, intrinsic subtypes influenced outcomes in CDK4/6i-treated patients (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e), luminal subtypes (85% in our cohort) showed better prognosis and longer PFS, while HER2-enriched subtypes were linked to shorter PFS (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Overall, the clinical characteristics and outcomes of our cohort not only replicate findings from pivotal clinical trials but also reflect the heterogeneity observed in real-world clinical practice in an ER+/HER2- ABC. This strengthens the applicability of our results to routine clinical care.\u003c/p\u003e \u003cp\u003eSince CD4/6i approval, the molecular mechanisms driving tumor resistance have remained incompletely understood. Current insights are predominantly derived from studies on key cell cycle regulators, largely using single-agent experimental models or cancer cell lines. Many proposed biomarkers identified in preclinical studies, such as CCND1 amplification, p16 loss, or alterations in CDK4, CDK6, CDK7, CDK9, and CCNE1-CDK2 fail to translate consistently to clinical settings (\u003cspan additionalcitationids=\"CR37 CR38 CR39 CR40 CR41\" citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e). Furthermore, clinically actionable genetic alterations, such as RB1 deletions or mutations, have been implicated as a resistance mechanism\u0026mdash;evident in the PALOMA-3 trial where ctDNA analysis identified these mutations in patients treated with palbociclib\u0026mdash;. However, their rarity (1\u0026ndash;4% detection rate) limits their clinical utility despite their mechanistic relevance (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan additionalcitationids=\"CR44 CR45 CR46\" citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSeveral pieces of evidence show that the impact of CDK4/6i is far beyond cell cycle control, including the modulation of the antitumor immune response. Transcriptomic analysis of CDK4/6 knockouts in breast cancer revealed that CDK4 regulates inflammatory cytokine signaling, while CDK6 influences DNA replication and repair, highlighting their distinct roles in tumor biology. Transcriptomic analysis is essential for understanding the molecular mechanisms underlying cancer progression and treatment responses. In this sense, these findings underscore its critical role in uncovering the multifaceted effects of CDK4/6i(\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e) on tumor-intrinsic and immune-mediated mechanisms. Prospective studies, including ours, have employed advanced transcriptomic panels such as the BC360\u0026trade; panel to identify predictive biomarkers and immune-related features. Findings from the KENDO trial underscored the significance of intrinsic tumor biology, such as PAM50 subtypes and risk of recurrence scores (ROR-P), alongside immunological components like tumor-infiltrating lymphocytes (TILs) and tertiary lymphoid structures (TLS). These features play a pivotal role in guiding therapeutic strategies. Interestingly, CD24 was identified in the KENDO trial as a potential therapeutic target, while mRNA-based markers, including CD19 and CXCL13, showed promise as standardized predictors of TLS presence (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e). In our analysis, leveraging the BC360\u0026trade; panel, we identified a 9-gene \"Key Immune Activation\" (KIMA) signature comprising immune-related genes upregulated in the poor efficacy group. The KIMA signature provides a wide comprehensive characterization of the tumor immune microenvironment by incorporating genes related to immunosuppressive elements, including T regs markers (FOXP3 and IL2RA) and TIGIT or CD27, as well as TAP1, which is involved in antigen presentation. By integrating these additional markers, the KIMA signature demonstrated strong predictive power, correlating significantly shorter PFS and overall survival (OS) in patients with high expression levels, offering broader insights into immune presence and activity within the tumor microenvironment. This composition suggests that the tumor microenvironment in the bad efficacy group has enhanced immunosuppressive characteristics, likely driven by chronic IFN-gamma signaling via STAT1.\u003c/p\u003e \u003cp\u003eWhile IFN-γ is crucial for activating cellular immunity, its chronic signaling can paradoxically promote pro-tumorigenic effects. Persistent IFN-γ signaling has been linked to immune evasion through the upregulation of immune checkpoint molecules, T-cell exhaustion, and the recruitment of immunosuppressive elements such as regulatory T cells (Tregs), M2-like macrophages, and cancer-associated fibroblasts within the tumor microenvironment (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e). Indeed, CDK4/6i, such as palbociclib, have been shown to induce type III interferon production, stimulate T-cell activation, and influence tumor-intrinsic pathways (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e). A recent study employing a custom RNA panel of 192 genes revealed that treatment with palbociclib significantly increased complete cell cycle arrest, as evidenced by a pronounced reduction in proliferation markers like Ki67. In addition to these antiproliferative effects, palbociclib upregulated immune-related genes, suggesting a beneficial impact on the tumor immune microenvironment(\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e). This dual mechanism\u0026mdash;combining tumor proliferation inhibition with immune modulation\u0026mdash;highlights the potential of CDK4/6i to influence both cellular and immune-mediated anti-tumor responses. Notably, alterations in the interferon-gamma (IFN-γ) signature have been linked to resistance to palbociclib, emphasizing the importance of immune pathways in shaping therapeutic outcomes(\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite these findings, no definitive predictive biomarker for CDK4/6 inhibitors has been validated for clinical use, underscoring the need for further investigation. As research progresses, integrating transcriptomic data with immunological profiling promises to optimize therapeutic strategies and improve outcomes for cancer patients.\u003c/p\u003e \u003cp\u003eThe robustness of the KIMA signature was further supported by principal component analysis, which confirmed the contribution of all nine genes to the signature\u0026rsquo;s variance. Additionally, network analysis highlighted STAT1 as a central regulatory element, emphasizing the immune pathways' interconnected nature. More importantly, the central role of STAT1, underscores the potential of targeting the JAK/STAT pathway to reprogram the tumor microenvironment, enhancing CDK4/6i efficacy by promoting a more favorable immune response. Indeed, the JAK/STAT pathway mediates cytokine and growth factor signaling, frequently dysregulated in cancers such as myeloproliferative neoplasms and certain solid tumors. Some JAK inhibitors have shown efficacy in hematologic malignancies, while STAT-targeting agents are investigated for tumors with hyperactive JAK/STAT signaling. These inhibitors can reprogram the tumor microenvironment, enhancing anti-tumor immunity and improving responses to therapies like CDK4/6 inhibitors (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e). Our data confirms the interplay between CDK4/6i and the immune system, highlighting the importance of an active immune response for optimal therapeutic outcomes. The RIBBECA study, for instance, demonstrated that CDK4/6i bolsters pre-existing adaptive immune responses rather than inducing de novo immune activation, emphasizing the need for a proficient immune system (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e). These findings suggest that the KIMA signature captures a comprehensive immune activation profile predictive of CDK4/6i resistance.\u003c/p\u003e \u003cp\u003eGiven their dual role as immunomodulators and enhancers of immune responses in line with present data, CDK4/6i have been evaluated with immunotherapies, yielding mixed clinical results despite promising preclinical findings (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). For instance, a phase Ib study of abemaciclib plus pembrolizumab showed increased hepatic toxicity and pneumonitis without added PFS or OS benefit, limiting its feasibility(\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e). In contrast, the PACE study demonstrated that adding avelumab to palbociclib and fulvestrant improved PFS without increasing toxicity(\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e). Furthermore, in heavily pretreated ER\u0026thinsp;+\u0026thinsp;ABC patients, pembrolizumab with or without palbociclib highlighted the role of effector memory T cells in enhancing the immune response to CDK4/6i(\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e). However, none of the studies have been successful enough to result in practice-changing strategies. The putative combination with JAK/STAT inhibitors may be interesting to test from our data.\u003c/p\u003e \u003cp\u003eFinally, we tested the predictive capacity of the KIMA signature in the NeoPalAna study, a neoadjuvant trial evaluating palbociclib combined with anastrozole in early-stage breast cancer. Consistent with findings in the ABC cohort, KIMA signature expression was significantly higher in resistant cases. The signature achieved an area under the curve (AUC) of 0.82 in distinguishing between sensitive and resistant patients, underscoring its utility across different treatment settings. CDK4/6i are now a key part of adjuvant treatment for high-risk early breast cancer(\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e). However, their expanded application raises cost-effectiveness concerns that could affect approval and reimbursement. In this regard, the KIMA signature aims to shed light on the molecular mechanisms involved, providing valuable insights to optimize the adjuvant treatment of breast cancer patients and improve therapeutic outcomes and equitable access. In summary, our study underscores the critical role of immune profiling in optimizing CDK4/6i efficacy in ER+/HER2- patients. By identifying the KIMA signature as a robust predictive biomarker, we provide a foundation for identifying patients with bad efficacy under CDK4/6i and which could be needed immune interventions to improve treatment efficacy. The interplay between immune pathways and CDK4/6i response highlights opportunities to refine therapeutic approaches, particularly through selective modulation of immunosuppressive components within the tumor microenvironment. These findings open avenues for further investigation into immune-related predictors of response and highlight the potential for sequential or combined modular treatment strategies tailored to individual immune profiles, ultimately aiming to improve patient outcomes in a real-world setting.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe sincerely thank all patients for their invaluable and selfless participation in this study. We also thank the Tumor Biobank of the IGTP/Hospital Germans Trias i Pujol for their excellent work managing samples. Additionally, we would like to thank the Oncology Day Hospital team, including nursing and administrative staff, for their unwavering support and dedication throughout this project.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work has been funded by Instituto de Salud Carlos III (ISCIII) through the project PI21/00642 (Co-funded by European Regional Development Fund/European Social Fund) \u0026quot;Investing in your future\u0026quot;), through the project InMaM funded by the \u0026ldquo;Plan Complementario de Biotecnolog\u0026iacute;a Aplicada a la Salut\u0026rdquo; coordinated by IBEC in the framework of recovery, transformation and resilience Plan (C17, Resilience Plan C17.I1) funded by the European Union \u0026ndash; NextGeneration UE and by Pfizer (Pfizer Independent Research Grant). EF, MB and ABP are fellows from ISCIII (JR23/00044, CM22/00101 and CD21/00054 respectively).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor\u0026rsquo;s contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eE.F. and E.G-V. substantially contributed to the conception, methodology, patient data extraction, biological analysis, formal analysis, and writing. S.C-H, M.B. and A.B-P. substantially contributed to the biological analysis. E.B. and M.M. substantially contributed to the conception, conceptualization, methodology planification, formal analysis, writing and review, and funding acquisition. All authors have read, opined, corrected and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was approved by the Ethics Committee of Hospital Germans Trias I Pujol, and written informed consent was obtained from all patients before they were enrolled in the research project.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors have read the manuscript and provided their consent for the submission.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eM.R. declares a consulting and advisory role for GSK, AstraZeneca, and MSD; and research funding from Pfizer, Clovis, GSK, AstraZeneca, and MSD. \u0026nbsp;R.M declares a consulting and advisory role for Merck, MSD, Roche, AstraZeneca, BEM, the speaker\u0026rsquo;s bureau for Merck, MSD, BMS, and Roche. M.M. declares a consulting and advisory role for Novartis, Pfizer, Pier Fabre, and Roche; research funding from Roche, Eisai, and AstraZeneca; and travel expenses from Roche. E.F declares research funding from Pfizer, being invited as speaker for Pfizer and Novartis, and travel expenses from Roche, Lilly, Pfizer and Novartis. A.P. declares being invited as speaker for GSK, Eisai, and Lilly, travel expenses and congress assistance from Lilly, Gilead, Dr. Reddys, and Pfizer. \u0026nbsp;A.L-P. declares being invited as speaker for Eisai, Lilly, and Novartis, and travel expenses from Roche, Gilead, and Novartis. B.C. declares being invited as speaker for BMS, Merck, and MSD. Training grants from BMS, Merck, and MSD. Advisory board: BMS, Merck, and MSD. V.Q. declares being invited as speaker for AstraZeneca, Novartis, Pfizer, and Roche. Advisory board for Roche. Educational activities from GSK, Lilly, and Pfizer and travel expenses from Pfizer and Roche. I.T. declares being invited as speaker for Astra Zeneca. Training grants from Novartis, Lilly, ROCHE, and MSD. A.F-D. declares being invited as speaker for MSD and Angelini Pharma; and travel expenses from MSD, Lilly, Roche, Merck, and BMS. M.B. declares advisory funding from Eisai, AstraZeneca, Pfizer, Novartis, and travel expenses from Novartis and AstraZeneca. The rest of the authors declare no potential conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets supporting this article\u0026apos;s conclusions are included within the article (and its Additional files) and available from the corresponding author on reasonable request. Transcriptomic data and associated clinical information have been deposited in GEO (GSE265870).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eFinn RS, Martin M, Rugo HS, Jones S, Im SA, Gelmon K, et al. Palbociclib and Letrozole in Advanced Breast Cancer. N Engl J Med. 2016;375(20):1925\u0026ndash;36.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHortobagyi GN, Stemmer SM, Burris HA, Yap YS, Sonke GS, Paluch-Shimon S, et al. Ribociclib as First-Line Therapy for HR-Positive, Advanced Breast Cancer. N Engl J Med. 2016;375(18):1738\u0026ndash;48.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGoetz MP, Toi M, Campone M, Sohn J, Paluch-Shimon S, Huober J, et al. MONARCH 3: Abemaciclib As Initial Therapy for Advanced Breast Cancer. J Clin Oncol. 2017;35(32):3638\u0026ndash;46.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTurner NC, Ro J, Andr\u0026eacute; F, Loi S, Verma S, Iwata H, et al. Palbociclib in Hormone-Receptor-Positive Advanced Breast Cancer. N Engl J Med. 2015;373(3):209\u0026ndash;19.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSlamon DJ, Neven P, Chia S, Fasching PA, De Laurentiis M, Im SA, et al. Phase III Randomized Study of Ribociclib and Fulvestrant in Hormone Receptor-Positive, Human Epidermal Growth Factor Receptor 2-Negative Advanced Breast Cancer: MONALEESA-3. J Clin Oncol. 2018;36(24):2465\u0026ndash;72.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSledge GW Jr., Toi M, Neven P, Sohn J, Inoue K, Pivot X, et al. MONARCH 2: Abemaciclib in Combination With Fulvestrant in Women With HR+/HER2- Advanced Breast Cancer Who Had Progressed While Receiving Endocrine Therapy. J Clin Oncol. 2017;35(25):2875\u0026ndash;84.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcCartney A, Migliaccio I, Bonechi M, Biagioni C, Romagnoli D, De Luca F, et al. Mechanisms of Resistance to CDK4/6 Inhibitors: Potential Implications and Biomarkers for Clinical Practice. Front Oncol. 2019;9:666.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLloyd MR, Spring LM, Bardia A, Wander SA. Mechanisms of Resistance to CDK4/6 Blockade in Advanced Hormone Receptor-positive, HER2-negative Breast Cancer and Emerging Therapeutic Opportunities. Clin Cancer Res. 2022;28(5):821\u0026ndash;30.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGoel S, Decristo MJ, McAllister SS, Zhao JJ. CDK4/6 Inhibition in Cancer: Beyond Cell Cycle Arrest. Trends Cell Biol. 2018;28(11):911\u0026ndash;25.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGoel S, DeCristo MJ, Watt AC, BrinJones H, Sceneay J, Li BB, et al. CDK4/6 inhibition triggers anti-tumour immunity. Nature. 2017;548(7668):471\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePrat A, Chaudhury A, Solovieff N, Par\u0026eacute; L, Martinez D, Chic N, et al. Correlative Biomarker Analysis of Intrinsic Subtypes and Efficacy Across the MONALEESA Phase III Studies. J Clin Oncol. 2021;39(13):1458\u0026ndash;67.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLaphanuwat P, Jirawatnotai S. Immunomodulatory Roles of Cell Cycle Regulators. Front Cell Dev Biol. 2019;7:23.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePetroni G, Formenti SC, Chen-Kiang S, Galluzzi L. Immunomodulation by anticancer cell cycle inhibitors. Nat Rev Immunol. 2020;20(11):669\u0026ndash;79.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCoppe JP, Desprez PY, Krtolica A, Campisi J. The senescence-associated secretory phenotype: the dark side of tumor suppression. Annu Rev Pathol. 2010;5:99\u0026ndash;118.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchaer DA, Beckmann RP, Dempsey JA, Huber L, Forest A, Amaladas N, et al. The CDK4/6 Inhibitor Abemaciclib Induces a T Cell Inflamed Tumor Microenvironment and Enhances the Efficacy of PD-L1 Checkpoint Blockade. Cell Rep. 2018;22(11):2978\u0026ndash;94.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDeng J, Wang ES, Jenkins RW, Li S, Dries R, Yates K, et al. CDK4/6 Inhibition Augments Antitumor Immunity by Enhancing T-cell Activation. Cancer Discov. 2018;8(2):216\u0026ndash;33.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang J, Bu X, Wang H, Zhu Y, Geng Y, Nihira NT, et al. Cyclin D-CDK4 kinase destabilizes PD-L1 via cullin 3-SPOP to control cancer immune surveillance. Nature. 2018;553(7686):91\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJerusalem G, Prat A, Salgado R, Reinisch M, Saura C, Ruiz-Borrego M, et al. Neoadjuvant nivolumab\u0026thinsp;+\u0026thinsp;palbociclib\u0026thinsp;+\u0026thinsp;anastrozole for oestrogen receptor-positive/human epidermal growth factor receptor 2-negative primary breast cancer: Results from CheckMate 7A8. Breast. 2023;72:103580.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCristofanilli M, Turner NC, Bondarenko I, Ro J, Im S-A, Masuda N, et al. Fulvestrant plus palbociclib versus fulvestrant plus placebo for treatment of hormone-receptor-positive, HER2-negative metastatic breast cancer that progressed on previous endocrine therapy (PALOMA-3): final analysis of the multicentre, double-blind, phase 3 randomised controlled trial. Lancet Oncol. 2016;17(4):425\u0026ndash;39.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHortobagyi GN, Stemmer SM, Burris HA, Yap YS, Sonke GS, Paluch-Shimon S, et al. Updated results from MONALEESA-2, a phase III trial of first-line ribociclib plus letrozole versus placebo plus letrozole in hormone receptor-positive, HER2-negative advanced breast cancer. Ann Oncol. 2018;29(7):1541\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eS\u0026oslash;rlie T, Perou CM, Tibshirani R, Aas T, Geisler S, Johnsen H, et al. Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications. Proc Natl Acad Sci U S A. 2001;98(19):10869\u0026ndash;74.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePrat A, Parker JS, Fan C, Perou CM. PAM50 assay and the three-gene model for identifying the major and clinically relevant molecular subtypes of breast cancer. Breast Cancer Res Treat. 2012;135(1):301\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiberzon A, Birger C, Thorvaldsd\u0026oacute;ttir H, Ghandi M, Mesirov Jill P, Tamayo P. The Molecular Signatures Database Hallmark Gene Set Collection. Cell Syst. 2015;1(6):417\u0026ndash;25.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiberzon A, Subramanian A, Pinchback R, Thorvaldsd\u0026oacute;ttir H, Tamayo P, Mesirov JP. Molecular signatures database (MSigDB) 3.0. Bioinformatics. 2011;27(12):1739\u0026ndash;40.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSubramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A. 2005;102(43):15545\u0026ndash;50.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKr\u0026auml;mer A, Green J, Pollard J Jr., Tugendreich S. Causal analysis approaches in Ingenuity Pathway Analysis. Bioinformatics. 2014;30(4):523\u0026ndash;30.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFinn RS, Crown JP, Lang I, Boer K, Bondarenko IM, Kulyk SO, et al. The cyclin-dependent kinase 4/6 inhibitor palbociclib in combination with letrozole versus letrozole alone as first-line treatment of oestrogen receptor-positive, HER2-negative, advanced breast cancer (PALOMA-1/TRIO-18): a randomised phase 2 study. Lancet Oncol. 2015;16(1):25\u0026ndash;35.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJanni W, Alba E, Bachelot T, Diab S, Gil-Gil M, Beck TJ, et al. First-line ribociclib plus letrozole in postmenopausal women with HR+, HER2- advanced breast cancer: Tumor response and pain reduction in the phase 3 MONALEESA-2 trial. Breast Cancer Res Treat. 2018;169(3):469\u0026ndash;79.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMa CX, Gao F, Luo J, Northfelt DW, Goetz M, Forero A, et al. NeoPalAna: Neoadjuvant Palbociclib, a Cyclin-Dependent Kinase 4/6 Inhibitor, and Anastrozole for Clinical Stage 2 or 3 Estrogen Receptor-Positive Breast Cancer. Clin Cancer Res. 2017;23(15):4055\u0026ndash;65.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCristofanilli M, Rugo HS, Im SA, Slamon DJ, Harbeck N, Bondarenko I, et al. Overall Survival with Palbociclib and Fulvestrant in Women with HR+/HER2- ABC: Updated Exploratory Analyses of PALOMA-3, a Double-blind, Phase III Randomized Study. Clin Cancer Res. 2022;28(16):3433\u0026ndash;42.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMartin M, Zielinski C, Ruiz-Borrego M, Carrasco E, Turner N, Ciruelos EM, et al. Palbociclib in combination with endocrine therapy versus capecitabine in hormonal receptor-positive, human epidermal growth factor 2-negative, aromatase inhibitor-resistant metastatic breast cancer: a phase III randomised controlled trial-PEARL. Ann Oncol. 2021;32(4):488\u0026ndash;99.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhong B, Zhang J, Wu J, Sun L, Li S, Zeng X, et al. Efficacy and safety of palbociclib plus endocrine therapy for patients with HR+/HER2\u0026thinsp;\u0026ndash;\u0026thinsp;advanced breast cancer in real-world clinical practice. Annals Translational Med. 2022;10(6):362.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKnudsen ES, Schultz E, Hamilton D, Attwood K, Edge S, O\u0026rsquo;Connor T, et al. Real-World Experience with CDK4/6 Inhibitors for Metastatic HR+/HER2\u0026thinsp;\u0026ndash;\u0026thinsp;Breast Cancer at a Single Cancer Center. Oncologist. 2022;27(8):646\u0026ndash;54.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLu YSME, Azim H et al. Primary results from the randomized phase II RIGHT Choice trial of premenopausal patients with aggressive HR+/HER2\u0026thinsp;\u0026ndash;\u0026thinsp;advanced breast cancer treated with ribociclib\u0026thinsp;+\u0026thinsp;endocrine therapy vs physician\u0026rsquo;s choice combination chemotherapy. Presented at SABCS 2022 December 6\u0026ndash;10, 2022.2022.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDe Angelis C, Fu X, Cataldo ML, Nardone A, Pereira R, Veeraraghavan J, et al. Activation of the IFN Signaling Pathway is Associated with Resistance to CDK4/6 Inhibitors and Immune Checkpoint Activation in ER-Positive Breast Cancer. Clin Cancer Res. 2021;27(17):4870\u0026ndash;82.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eQie S, Diehl JA. Cyclin D1, cancer progression, and opportunities in cancer treatment. J Mol Med (Berl). 2016;94(12):1313\u0026ndash;26.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAraki K, Miyoshi Y. Mechanism of resistance to endocrine therapy in breast cancer: the important role of PI3K/Akt/mTOR in estrogen receptor-positive, HER2-negative breast cancer. Breast Cancer. 2018;25(4):392\u0026ndash;401.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDean JL, McClendon AK, Hickey TE, Butler LM, Tilley WD, Witkiewicz AK, et al. Therapeutic response to CDK4/6 inhibition in breast cancer defined by ex vivo analyses of human tumors. Cell Cycle. 2012;11(14):2756\u0026ndash;61.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMartin LA, Pancholi S, Ribas R, Gao Q, Simigdala N, Nikitorowicz J, et al. Abstract P3-03-09: Resistance to palbociclib depends on multiple targetable mechanisms highlighting the potential of drug holidays and drug switching to improve therapeutic outcome. Cancer Res. 2017;77:P3\u0026ndash;03.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGarriga J, Bhattacharya S, Calb\u0026oacute; J, Marshall RM, Truongcao M, Haines DS, et al. CDK9 is constitutively expressed throughout the cell cycle, and its steady-state expression is independent of SKP2. Mol Cell Biol. 2003;23(15):5165\u0026ndash;73.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHerrera-Abreu MT, Palafox M, Asghar U, Rivas MA, Cutts RJ, Garcia-Murillas I, et al. Early Adaptation and Acquired Resistance to CDK4/6 Inhibition in Estrogen Receptor-Positive Breast Cancer. Cancer Res. 2016;76(8):2301\u0026ndash;13.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e\u0026Aacute;lvarez-Fern\u0026aacute;ndez M, Malumbres M. Mechanisms of Sensitivity and Resistance to CDK4/6 Inhibition. Cancer Cell. 2020;37(4):514\u0026ndash;29.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFinn RS, Liu Y, Zhu Z, Martin M, Rugo HS, Di\u0026eacute;ras V, et al. Biomarker Analyses of Response to Cyclin-Dependent Kinase 4/6 Inhibition and Endocrine Therapy in Women with Treatment-Na\u0026iuml;ve Metastatic Breast Cancer. Clin Cancer Res. 2020;26(1):110\u0026ndash;21.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTurner NC, Liu Y, Zhu Z, Loi S, Colleoni M, Loibl S, et al. Cyclin E1 Expression and Palbociclib Efficacy in Previously Treated Hormone Receptor\u0026ndash;Positive Metastatic Breast Cancer. J Clin Oncol. 2019;37(14):1169\u0026ndash;78.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eO'Leary B, Cutts RJ, Huang X, Hrebien S, Liu Y, Andr\u0026eacute; F, et al. Circulating Tumor DNA Markers for Early Progression on Fulvestrant With or Without Palbociclib in ER\u0026thinsp;+\u0026thinsp;Advanced Breast Cancer. J Natl Cancer Inst. 2021;113(3):309\u0026ndash;17.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGong X, Litchfield LM, Webster Y, Chio LC, Wong SS, Stewart TR, et al. Genomic Aberrations that Activate D-type Cyclins Are Associated with Enhanced Sensitivity to the CDK4 and CDK6 Inhibitor Abemaciclib. Cancer Cell. 2017;32(6):761\u0026ndash;e766.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDean JL, Thangavel C, McClendon AK, Reed CA, Knudsen ES. Therapeutic CDK4/6 inhibition in breast cancer: key mechanisms of response and failure. Oncogene. 2010;29(28):4018\u0026ndash;32.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDai M, Boudreault J, Wang N, Poulet S, Daliah G, Yan G, et al. Differential Regulation of Cancer Progression by CDK4/6 Plays a Central Role in DNA Replication and Repair Pathways. Cancer Res. 2021;81(5):1332\u0026ndash;46.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchettini F, Palleschi M, Mannozzi F, Bras\u0026oacute;-Maristany F, Cecconetto L, Galv\u0026aacute;n P, et al. CDK4/6-Inhibitors Versus Chemotherapy in Advanced HR+/HER2-Negative Breast Cancer: Results and Correlative Biomarker Analyses of the KENDO Randomized Phase II Trial. Oncologist. 2024;29(5):e622\u0026ndash;34.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJorgovanovic D, Song M, Wang L, Zhang Y. Roles of IFN-γ in tumor progression and regression: a review. Biomark Res. 2020;8:49.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eScirocchi F, Scagnoli S, Botticelli A, Di Filippo A, Napoletano C, Zizzari IG, et al. Immune effects of CDK4/6 inhibitors in patients with HR(+)/HER2(-) metastatic breast cancer: Relief from immunosuppression is associated with clinical response. EBioMedicine. 2022;79:104010.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePernas S, Sanfeliu E, Villacampa G, Salvador J, Perell\u0026oacute; A, Gonz\u0026aacute;lez X, et al. Palbociclib and letrozole for hormone receptor-positive HER2-negative breast cancer with residual disease after neoadjuvant chemotherapy. NPJ Breast Cancer. 2024;10(1):101.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWei XH, Liu YY. Potential applications of JAK inhibitors, clinically approved drugs against autoimmune diseases, in cancer therapy. Front Pharmacol. 2023;14:1326281.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRah B, Rather RA, Bhat GR, Baba AB, Mushtaq I, Farooq M, et al. JAK/STAT Signaling: Molecular Targets, Therapeutic Opportunities, and Limitations of Targeted Inhibitions in Solid Malignancies. Front Pharmacol. 2022;13:821344.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePeuker CA, Yaghobramzi S, Grunert C, Keilholz L, Gjerga E, Hennig S, et al. Treatment with ribociclib shows favourable immunomodulatory effects in patients with hormone receptor-positive breast cancer-findings from the RIBECCA trial. Eur J Cancer. 2022;162:45\u0026ndash;55.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRugo HS, Kabos P, Beck JT, Jerusalem G, Wildiers H, Sevillano E, et al. Abemaciclib in combination with pembrolizumab for HR+, HER2- metastatic breast cancer: Phase 1b study. NPJ Breast Cancer. 2022;8(1):118.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMayer EL, Ren Y, Wagle N, Mahtani R, Ma C, DeMichele A et al. PACE: A Randomized Phase II Study of Fulvestrant, Palbociclib, and Avelumab After Progression on Cyclin-Dependent Kinase 4/6 Inhibitor and Aromatase Inhibitor for Hormone Receptor-Positive/Human Epidermal Growth Factor Receptor-Negative Metastatic Breast Cancer. J Clin Oncol. 2024:JCO2301940.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEgelston C, Guo W, Yost S, Lee JS, Rose D, Avalos C et al. Pre-existing effector T-cell levels and augmented myeloid cell composition denote response to CDK4/6 inhibitor palbociclib and pembrolizumab in hormone receptor-positive metastatic breast cancer. J Immunother Cancer. 2021;9(3).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHarbeck N, Rastogi P, Martin M, Tolaney SM, Shao ZM, Fasching PA, et al. Adjuvant abemaciclib combined with endocrine therapy for high-risk early breast cancer: updated efficacy and Ki-67 analysis from the monarchE study. Ann Oncol. 2021;32(12):1571\u0026ndash;81.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHortobagyi GN, Lacko A, Sohn J, Cruz F, Ruiz Borrego M, Manikhas A et al. A phase III trial of adjuvant ribociclib plus endocrine therapy versus endocrine therapy alone in patients with HR-positive/HER2-negative early breast cancer: final invasive disease-free survival results from the NATALEE trial. Ann Oncol. 2024.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"breast cancer, CDK4/6 inhibitor, clinical efficacy, immune-based biomarker, tumor transcriptomics, tumor immunity","lastPublishedDoi":"10.21203/rs.3.rs-6129690/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6129690/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u0026nbsp;\u003c/strong\u003eCyclin-dependent kinase 4/6 inhibitors (CDK4/6i) are a standard treatment for hormone receptor-positive (HR+)/human epidermal growth factor receptor 2–negative (HER2–) advanced breast cancer (ABC). However, reliable predictive biomarkers for treatment efficacy remain an unmet clinical need.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u0026nbsp;\u003c/strong\u003eA cohort of HR+/HER2– ABC patients (n=100) treated with CDK4/6i was characterized from both a clinical and molecular perspective. Pre-treatment tumor biopsies underwent transcriptomic profiling using the nCounter Breast 360™ panel. Gene set enrichment and pathway analyses were employed to identify differentially expressed genes (DEGs) and associated pathways across efficacy groups. Correlations between clinical, transcriptomic, and treatment outcomes were assessed using logistic and Cox regression models. The NeoPalAna dataset served as an external validation cohort.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u0026nbsp;\u003c/strong\u003eA clinical stratification algorithm, integrating known determinants of CDK4/6i efficacy from pivotal trials, enabled the classification of patients into two balanced efficacy groups. Transcriptomic analysis revealed an overexpression of immune-related signatures in poor responders (14/18), notably the interferon-gamma (IFN-γ) signature, which remained independently associated with progression-free survival (PFS) in multivariate analyses. DEG analysis and unsupervised consensus clustering further delineated immune function as a key determinant of treatment response, accurately classifying 90% of first-line responders (19/21; p=0.004) based on immune gene expression. A refined transcriptomic analysis identified KIMA, a 9-gene immune signature, as significantly enriched in patients with poor responses across both first-line and later treatment lines (p=0.0048 and p=0.0022, respectively). Elevated KIMA expression was independently correlated with inferior PFS and overall survival (OS) in multivariate Cox regression analyses (p=0.033 and p=0.034). Receiver operating characteristic (ROC) curve analysis, as measured by the area under the curve (AUC), confirmed the superior predictive performance of KIMA compared to the predefined BC360™ immune signature. Finally, KIMA was validated in the NeoPalAna cohort of patients receiving neoadjuvant palbociclib (p=0.026).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u0026nbsp;\u003c/strong\u003eThese findings highlight the pivotal role of the immune microenvironment in modulating CDK4/6i efficacy. The KIMA signature emerges as a novel and robust predictive biomarker, offering a refined tool for tailoring therapeutic strategies in HR+/HER2– breast cancer. Its integration into clinical decision-making frameworks could enhance patient stratification and optimize treatment outcomes.\u003c/p\u003e","manuscriptTitle":"Immune Gene Signature as a Predictor of CDK4/6 Inhibitor Response in HR+/HER2– Breast Cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-11 10:26:49","doi":"10.21203/rs.3.rs-6129690/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5012d614-5dfb-4de4-8c38-12302e66942c","owner":[],"postedDate":"March 11th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-03-11T10:26:51+00:00","versionOfRecord":[],"versionCreatedAt":"2025-03-11 10:26:49","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6129690","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6129690","identity":"rs-6129690","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}