2.1 Integrative analysis of lncRNA expression and immune cell composition in breast cancer immunotherapy response

In this investigation, we included seven breast cancer patients undergoing immunotherapy at Sun Yat-sen Memorial Hospital of Sun Yat-sen University (SYSMH). Employing transcriptome sequencing, we analyzed lncRNA expression profiles in patients with distinct treatment responses to chemotherapy combined with immunotherapy (three responders vs. four nonresponders). A meticulous screening identified 198 lncRNAs associated with diverse treatment responses (Figure S1A,B). Subsequently, we applied unsupervised clustering using deep learning to the breast cancer samples of 925 patients in The Cancer Genome Atlas Program (TCGA) with sequenced lncRNAs, establishing a lncRNA-based breast cancer immunophenotyping system (Figures 1A,B and S1C). The clustering revealed three distinct lncRNA-based clusters, namely, lncRNA-cluster 1 (369 patients), lncRNA-cluster 2 (334 patients), and lncRNA-cluster 3 (222 patients).

Building on previous findings⁹ associating lncRNA and CD8⁺ T cells with breast cancer classification and response to immunotherapy, we extended investigation to explore correlations between lncRNA and other components of the TME. Employing single sample gene set enrichment analysis (ssGSEA) analysis, we identified 28 immune cell types in the TME in the 925 patients. Unsupervised clustering on the TCGA cohort revealed predominant separation into two immune-cell clusters (Figure S1D).

2.2 Deciphering tumor metabolism via lncRNA-immune cell interplay

A previous study demonstrated the prognostic value between T-cell infiltration and lncRNA signature.⁹ The individuals showing both an active immune response and significant immune-cell infiltration had higher expression levels of immune molecules than those with a functional immune response but lower levels of immune-cell infiltration; this delineation identified individuals within the “immune-active” and “immune-exclusion” tumor groups, respectively. Furthermore, the patients exhibiting low expression of immunotherapy-associated lncRNAs were stratified based on their immune-cell infiltration levels into the “immune-dysfunctional” and “immune-desert” groups.

Utilizing the aforementioned lncRNA profiles in conjunction with immune status, we developed a two-dimensional index classifying the patients into four subtypes as an extension of our former studies, which demonstrated distinct metabolic signatures. Hence, the amalgamation of “lncRNA-cluster 1 and immune cell cluster 1” characterized the “immune-active class” subtype. Either “lncRNA-cluster 2 and immune cell cluster 1” or “lncRNA-cluster 1 and immune cell cluster 2” represented the “immune-exclusion class” subtype. Next, “lncRNA-cluster 2 and immune cell cluster 2” or “lncRNA-cluster 3 and immune cell cluster 1” identified the “immune-dysfunctional” subtype. Finally, “lncRNA-cluster 3 and immune cell cluster 2” delineated the “immune-desert” subtype.

To uncover the underlying mediation between lncRNA–immune subtypes and immune status, we conducted gene set enrichment analysis (GSEA) pathway analyses across the four subtypes (Kruskal–Wallis test, p < 0.001; Figure 1C–F). Intriguingly, distinct metabolic statuses differentiated these subtypes. The “immuno–fatty acid (iFA)” subtype, denoting activation of FA metabolism, was attributed to the “immune-active class” subtype. In contrast, the “immuno–amino acid (iAA)” subtype associated with the “immune-exclusion class” displayed enrichment in AA metabolism. The “immuno–glucose (iGlu)” subtype, indicative of the “immune-dysfunctional” tumors, exhibited enriched Glu metabolism. Last, the “immune-desert” subtype revealed upregulated folate and pterin metabolism, which was identified as “immuno–folate (iFolate)” subtype.

Crucially, OS concerning immunotherapy showcased a significant variance across these subtypes. The immuno–FA subtype manifested the most substantial OS benefit from immunotherapy (Tarone-Ware test, p = 0.00039; Figure 2A), implying a potential link between Fatty acid metabolism and patient prognosis. The immuno–Glu subtype had the shortest OS, indicating a potential correlation between folate and pterin metabolism and poorer prognosis. Sankey plotting revealed a predominant association of the immuno-FA and immuno-AA tumor subtypes with Lumina A and Lumina B subtypes according to PAM50 subtypes. Conversely, the immuno-Glu and immuno-Folate subtypes predominantly correlated with TNBC, aligning with later-stage tumors and poorer prognostic outcomes (Figure 2B).

Furthermore, we depicted somatic mutation landscapes across the four subtypes through OncoPrint analysis. Notably, PIK3CA mutations were predominant in the immuno–FA (54%; Figures S3A and S5A) and immuno–AA (41%; Figures S3B and S5B) subtypes, while TP53 mutations were more prevalent in the other two subtypes, especially in the immuno–folate subtype (82%; Figures S3C,D and S4). This finding suggests divergent genetic heterogeneity driving these subtypes and potentially contributing to the heterogeneity in the TME and metabolic states.

2.3 Distinct metabolic profiles, immune landscapes, and functional traits in tumor subsets

We further analyzed the distribution of immune cells in these four subtypes. We found that high infiltration of some cells (activated CD8+ T cells, natural killer [NK] cells, central memory CD4⁺ T cells, central memory CD8⁺ T cells) was involved in immuno–FA subtype, highlighting the correlation between immune-active state and FA metabolism state (Figures 3A and S6). However, we found that in immuno–Glu subtype that also identified as immune-dysfunctional group, high infiltration of some cells (myeloid-derived suppressor cells [MDSCs], T follicular helper cells, type 1 T helper cells, macrophages) was also involved, suggesting that dysfunction of the glucose metabolism may be involved in the immune-dysfunctional state (Figure S6). These results confirmed the correlation underlying the four metabolism subtypes and immune status. However, some of the antitumor effective immune cells were both high in immuno–FA and immuno–Glu subtype, while patients in immuno–Glu subtype showed poorer prognosis than immuno–FA and immuno–AA, even when fewer immune cells demonstrated high level in immuno–AA. This phenomenon also indicated that level of immune cells infiltration could not show credible immune state and demonstrated that subtypes with higher dimension is necessary.

Next, we investigated the relationship between immune checkpoint sets and immune genes in the four metabolism subtypes. The samples in immuno–FA subtype showed higher immune checkpoints gene expression than the samples in immuno–AA subtype, suggesting a benefit of immunotherapy (Figure S7). The samples in immuno–FA subtype were associated with high expression levels of CD48, CD27, and TNFRSF14. The samples in immuno–Glu subtype were associated with high BLTA, PDCCD1, and CD244 expression (Figure 3B). As for the expression of other immune-related genes, the patients in immuno–FA subtype also had a higher STC2, PGR, and IL6ST expression (Figure 3B). Moreover, the samples in immuno–folate subtype had worse prognosis and lower immune gene expression than those in the other three clusters (Figure S7).

Furthermore, we employed GSEA to extend our exploration beyond the primary metabolic pathways, examining additional pathways associated with the distinctive metabolic states identified across the four subtypes. Within immuno–FA subtype, a predominant association was observed with lipid metabolism pathways, such as arachidonic acid metabolism (Figure 3B). Noteworthy within this subtype was the notable appearance of immune-related pathways, notably the IL-6 signaling pathway and complement pathway, hinting at an intricate interplay between metabolism and immune modulation (Figure S7). Conversely, immuno–folate subtype exhibited a distinct enrichment profile primarily linked to pathways critical in tRNA processing and transcriptional processes (Figure 3B). This divergence underscores the diverse biological foundations characterizing these metabolic subtypes, suggesting varying mechanisms governing tumor progression and immune response modulation within these distinct subtypes.

2.4 AI-based pathology model for precise prediction of immune–metabolism subtypes

In light of the intricacies in delineating the immune–metabolic subtypes, we developed a pathology-based AI model, DeepClinMed-IM (deep learning–based clinical immune–metabolic subtypes), for subtype prediction (Figure 1B). Thorough exploration of the hyperparameter space for the transfer learning model, incorporating a linear mapping to connect attention at its penultimate layer using ResNet50, led to the identification of the most optimal configuration. This resulted in accurate predictive performance for categorizing the four subtypes within both the training and the validation cohorts. After performing fivefold cross-validation, the model with best performance for each cohort was identified and presented. For each slide, the model's attention can be visualized, showing captured regions of cells for subtype discrimination. A specific image with summarized regions of interest was demonstrated as Figure 4A.

A thorough stratified analysis was conducted to evaluate the prediction model's performance within each subtype, and yielded consistent and robust results. The receiver operating characteristic (ROC) curves depicted impressive discrimination, showcasing the area under the curve (AUC) values of 0.93 (immuno–FA subtype), 0.89 (immuno–AA subtype), 0.92 (immuno–Glu subtype), and 0.93 (immuno–folate subtype) within the training cohort (Figure 4B). The AUC values remained notably high within the validation cohort, registering at 0.84 for immuno–FA subtype, 0.78 for immuno–AA subtype, 0.87 for immuno–Glu subtype, and 0.86 for immuno–folate subtype (Figure 4C). The successful deployment of this pathology-based AI model underscores its efficacy in accurately predicting immune–metabolism subtypes, demonstrating promising performance and generalizability across distinct cohorts.

2.5 AI-based multimodal model for precise prediction of prognostics

Expanding on the previously mentioned model, we innovatively integrated multimodal inputs, including pathology images, lncRNA data, immune-cell ssGSEA scores, and clinical information seamlessly. This novel addition of a QR played a pivotal role in refining the fusion approach, contributing to a more robust prognostics prediction model named DeepClinMed-PGM (deep learning–based multimodal clinical pathology genomics) (Figure 5). The integration of diverse data types through the enhanced fusion paradigm significantly bolstered the predictive capabilities across all cohorts, showcasing substantial advancements in disease prognosis. Likewise, datasets were randomly split into five equal parts according to the label distribution, and a fivefold Monte Carlo cross-validation was conducted.

The resultant model demonstrated exceptional performance metrics, exhibiting a concordance index of 0.82 in the training cohort (n = 740, sourced from TCGA), 0.83 in the validating cohort (n = 185, also from TCGA), and an impressive 0.90 in the independent testing cohort (n = 95, the SYSMH cohort), and Table S1 shows the clinicopathologic characteristics of the patients in the testing cohort. The survival analysis showcased the model's robust performance in stratifying distinct disease-free survival (DFS) risk profiles across the various cohorts, with hazard ratios (HRs) values of 6.35 and 15.95 observed in the training (n = 740, 95% CI 3.73–10.80, Log-rank test: p < 0.001; Figure 6A) and validation (n = 185, 95% CI 4.80–53.07, Log-rank test: p < 0.001; Figure 6C) datasets from TCGA, respectively. In the independent testing cohort sourced from the SYSMH cohort, the HR value was 18.51 (n = 95, 95% CI 5.21–65.79, Log-rank test: p < 0.001; Figure 6E).

Notably, the AUC values at 1, 3, and 5 years DFS were noteworthy indicators of predictive accuracy. Within the training cohort (n = 740; Figure 6B), the AUC values at 1, 3, and 5 years stood at 0.80, 0.88, and 0.72, respectively, demonstrating the model's robustness over various time frames. Similarly, in the validation cohort (n = 185; Figure 6D), the AUC values at 1, 3, and 5 years were 0.93, 0.81, and 0.79, respectively, confirming the model's consistent predictive power. Importantly, the independent testing cohort (n = 95; Figure 6F) demonstrated compelling AUC values at 1 and 3 years, reaching 0.91 and 0.92 for DFS prediction, respectively.

Next, we compared the ROC curves of single-omics and pathology slide-based models with our DeepClinMed-PGM model for prognostic prediction. While the DeepClinMed-PGM model achieved a concordance index of 0.82–0.90, the single-omics model only showed indices of 0.69 in the training cohort, 0.70 in the validation cohort, and 0.60 in the testing cohort. And in the training cohort (n = 740; Figure S8A), the AUC values at 1, 3, and 5 years were 0.80, 0.88, and 0.72, respectively. The validation cohort (n = 185; Figure S8B) had AUC values of 0.93, 0.81, and 0.79 at 1, 3, and 5 years. The testing cohort (n = 95; Figure S8C) showed AUC values of 0.91 and 0.92 at 1 and 3 years for DFS prediction. These results confirmed that our multimodal data based-model DeepClinMed-PGM showed consistently superior predictive power compared with the single-omics model across all cohorts.

We also analyzed the weights assigned to each feature in our model. Clinical data like clinical stage and T stage showed high positive weights, indicating significant contributions, while age and PAM50 subtype had high negative weights, suggesting an inverse relationship to the model's output (Figure S9). Additionally, immune cells like Type 1 T helper cells and effector memory CD4 T cells had high positive weights, whereas eosinophils and Type 2 T helper cells had high negative weights (Figure S9). This detailed weight distribution provides insights into the roles of various features within the multimodal data-based model framework, highlighting the model's ability to leverage the heterogeneity of multimodal omics for accurate predictions.

4.1 Study design and patients

In this study, we conducted an individual patient-level analysis involving 1,027 patients, utilizing validated whole-slide image (WSI) and RNA-sequencing data in accordance with the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis guideline.⁴² Patients in this study were enrolled from SYSMH, Sun Yat-sen University, and TCGA. The SYSMH cohort comprised 95 breast cancer patients who received chemotherapy and standard treatment between September 2019 and February 2022. Additionally, seven patients who received chemotherapy combined with immunotherapy in SYSMH were enrolled in this study for subtypes construction. And the TCGA cohort comprised 925 patients with breast cancer.

The study proceeded through several phases, initially involving the categorization of all patients into three cohorts. In the construction of immune–metabolic subtypes, RNA-sequencing data from seven patients in the SYSMH cohort who received chemotherapy combined with immunotherapy were accessed and analyzed for subtypes signatures. And RNA-sequencing data from patients with 925 breast cancer in the TCGA cohorts were used for exploring biological characteristics of the subtypes.

As the basis for building both of the AI models in this study, a training cohort and an internal validation cohort were defined, comprising 741 and 184 patients, respectively, diagnosed through TCGA via the Genomic Data Commons (GDC) Data Portal.⁴³ And 95 patients in the SYSMH cohort was defined as an external testing cohort for both of AI models in this study. RNA-sequencing and WSI data from both cohorts were obtained for further analysis. Clinical data from all the patients in both cohorts were accessed, including age, PAM50 subtype, classification of T (tumor), N (nodes), and clinical stage. Based on label distribution, the dataset was randomly divided into five equal parts, and a fivefold Monte Carlo cross-validation was performed.

The inclusion criteria were as follows: (a) female sex, age at least 18 years, and histologically confirmed stage I to III invasive breast cancer; (b) treatment with surgery and pathologically confirmed breast cancer treatment; and (c) availability of WSI and RNA-sequencing data pertaining to breast tumors. The exclusion criteria were as follows: (a) samples exhibiting poor-quality or inadequate pathological results; (b) incomplete data on WSI and RNA-sequencing features or follow-up information; and (c) presence of previous or simultaneous other tumors.

The primary outcome assessed in this study were OS and DFS. OS was defined as the duration from the date of breast cancer diagnosis to the time of death from any cause, or the date of the last follow-up visit, whichever came first. DFS was defined as the duration from the breast cancer surgery to the occurrence of the first relapse at any site, confirmation of metastatic disease, death from any cause other than breast cancer, or the date of the last follow-up visit, whichever came first. The T and N stages were assessed through imaging techniques (magnetic resonance imaging, ultrasonography, or positron emission tomography) or clinical examination, while follow-up procedures adhered to the recommendations outlined in the National Comprehensive Cancer Network and American Joint Committee on Cancer Staging Manual guideline.⁴⁴

Ethical considerations and approval: This study adhered to the principles set forth in the Declaration of Helsinki and received approval from the Ethics Committee of SYSMH, Sun Yat-sen University (Approval Number: SYSKY-2024-363-01). As the study was retrospective in nature and utilized publicly available datasets, the necessity for informed consent from participants was waived by the Ethics Committee.

4.2 Procedures of RNA sequencing

The RNA-sequencing data of 925 patients were acquired from the TCGA database, adhering to the standardized RNA-sequencing analysis procedures outlined in the corresponding guidelines. Additionally, we retrospectively acquired samples from 95 patients at SYSMH and conducted transcriptome RNA-sequencing analysis. RNA was isolated from all tumor tissue samples using the formalin-fixed paraffin-embedded (FFPE) RNeasy kit, followed by the extraction process. Subsequently, RNA quantification and assessment of RNA integrity were conducted to ensure the quality of the extracted RNA. Following RNA isolation, the RNA library preparation included RNA fragmentation, reverse transcription, and addition of adapters for sequencing. The resulting libraries were subjected to amplification and then subjected to high-throughput sequencing. The raw sequencing data were generated thorough preprocessing steps, including quality control to remove low-quality reads and eliminate adapter sequences. After alignment of the cleaned reads to a reference genome, transcript expression levels were quantified through read counting.

Total RNA was extracted from FFPE samples using the QIAGEN FFPE RNeasy kit (QIAGEN GmbH, Hilden, Germany). Tissue sections suspended in QIAzol were vortexed for well mixing. Then, chloroform was added to each sample, vortexed, and transferred into new tubes. The mixing samples were then spun at 12,000×g for 15 min at 4°C, and the upper clear phase containing RNA was transferred. Subsequent extraction was performed using the Qiagen QIAsymphony in accordance with the recommended protocol. Subsequently, RNA quantification was performed using the Qubit Fluorometer (Invitrogen), and RNA integrity was assessed with an Agilent Bioanalyzer 2100 (Agilent Technologies, Santa Clara, CA, USA) utilizing the Agilent RNA 6000 Nano Kit to ensure the quality of the extracted RNA. For amplification, 500 ng of total RNA was utilized in the Ovation FFPE WTA System (NuGEN, San Carlos, CA, USA). Fragmentation and labeling of the amplified RNA were carried out using the NEBNext® Ultra™ II DNA Library Prep Kit (Illumina). The quality and quantity of the resulting libraries were assessed using Qubit (Invitrogen, Carlsbad, CA, USA) and the Agilent Bioanalyzer 2100. Subsequently, all libraries were sequenced on a DNBSEQ-T7RS (MGI) platform with 100-bp paired-end reads. Base call files generated during sequencing were converted using cal2Fastq to the fastq format. Raw data were normalized and further procession using fastp (version 0.20.1).

4.3 Tissue preparation and WSIs patching

A total of 2557 H&E-stained histopathology slides from 925 patients were accessed through TCGA's GDC, and 1557 slides from 95 patients were retrospectively acquired from SYSMH. These FFPE breast tumor slides, obtained 1–4 weeks before chemotherapy or targeted therapy, were digitized. WSIs, with ∼10 gigapixels each, were preprocessed for analysis.

The biopsy tissue in each WSI was segmented using the CLAM WSI analysis toolbox.⁴⁵ The processing pipeline for digitized slides involved automated tissue segmentation. WSIs were downsampled (e.g., 32-fold) and converted from RGB to HSV. A binary mask for tissue regions was created using thresholding on the saturation channel, followed by median blurring and morphological closing. Detected foreground contours were filtered by area threshold. Segmentation masks were available for visual inspection, and a text file with key parameters was generated, allowing for manual adjustments if needed. Postsegmentation, the algorithm extracted 256 × 256 patches from the segmented foreground at user-specified magnification. These patches, along with coordinates and slide metadata, were saved in the hdf5 format. The number of patches varied from hundreds for biopsy slides at ×20 magnification to hundreds of thousands for large resection slides at ×40 magnification.

4.4 Transcriptome sequencing data analysis

Differential expression analysis of microarray data was performed using the limma R package,⁴⁶ and differential expression analysis of RNA-sequencing data was based on popular R packages (www.r-project.org) for analysis to evaluate robustness, namely, the DESeq2⁴⁷ and edgeR.⁴⁸

Unsupervised clustering analysis was used to identify different lncRNA patterns and immune patterns. The patients were divided into different subtypes using the R package “ConsensusClusterPlus” for further analysis.⁴⁹ A total of 1000 iterations were conducted and a resample rate of 80% was defined to ensure the stability of classification, and the cumulative distribution function curve was used to determine the clustering number.

To identify the enriched terms in Gene Ontology and Kyoto Encyclopedia of Genes and Genomes, Gene Set Enrichment Analysis (GSEA) was conducted via the R package “clusterProfiler⁵⁰”. GSEA is a statistical method used to demonstrate significant differences among subtypes and the signaling pathways regulated by lncRNA and immune cells using TCGA data.

The infiltration level of the different immune-cell populations was determined by ssGSEA in the R Bioconductor package Gene Set Variation Analysis using default parameters according to the original study and a previous study.^{51, 52} The ssGSEA algorithm is a rank-based method that defines a score representing the degree of absolute enrichment of a particular gene set in each sample. The ssGSEA scores for most immune-cell populations were obtained using the gene sets. We used hierarchical clustering to identify immune subtypes of breast cancer based on the ssGSEA scores of 28 immune-cell types, including CD56-bright NK cells, effector memory CD4⁺ T cells, eosinophils, CD56-dim NK cells, type 17 T helper cells, activated B cells, monocytes, memory B cells, activated CD4⁺ T cells, type 2 T helper cells, plasmacytoid dendritic cells, neutrophils, macrophages, effector memory CD8⁺ T cells, MDSCs, immature B cells, T follicular helper cells, immature dendritic cells, mast cells, type 1 T helper cells, activated dendritic cells, central memory CD4⁺ T cells, gamma delta T cells, central memory CD8⁺ T cells, regulatory T cells, activated CD8⁺ T cells, and NK T cells.

The exploring mutation signatures of the subtypes, MAF data were obtained from GDC portal and analyzed via the R package “maftools” according to the guideline from Bioconductor. Oncoplots were performed for summarizing mutation landscape. And function “oncodrive” which is based on algorithm oncodriveCLUST,⁵³ was used to identify driver mutation. Clinical enrichment analysis was performed also using “clinicalEnrichment” of this package.

4.5 Developing a pathology-based AI model for lncRNA–metabolism subtype prediction

This study presented a sophisticated model tailored for lncRNA–metabolism class prediction, leveraging a novel bag-of-patches approach. The methodology encompassed the utilization of digitized high-resolution histology slides, organized into bags of patches, as input data for a pathology-based AI model. To enhance the model's performance, a two-step process was adopted: (1) initially, transfer learning was applied with a feature extraction stage, utilizing frozen parameters to ensure stability and efficiency; (2) next, the model underwent fine-tuning with a modified structure, allowing trainable parameters to adapt and optimize performance.

To perform feature extraction, we used a customized CNN based on pretrained ResNet50 architecture trained on ImageNet dataset.^{54, 55} The subsequent modified module comprised an embedding section with a fully connected layer, seamlessly integrating an attention-gated mechanism. The attention-gated module was designed with two linear projections, each utilizing distinct activation functions—rectified linear unit and sigmoid—as the query and key, respectively.⁵⁶ Subsequently, the softmax operation was applied to compute the product of the two outcomes, resulting in attention scores. These scores were then used as multipliers against the attention scores assigned to the entire set of patches.

The resulting high-dimensional features were then passed to the final classifier layer that consisted of a linear layer. This layer processed the aggregated features to produce probabilities for different classifications, providing a robust prediction framework for lncRNA–metabolism subtypes.

4.6 Details of developing pathology-based AI model for lncRNA–metabolism subtype prediction

4.7 Developing a multimodal AI model for survival risk prediction

To predict prognostics, we engineered a cutting-edge multimodal AI model that encompassed the same CNN architecture, an attention-module, concatenation fusion, and a conclusive regressor. This model harmoniously amalgamated intricately tailored nested neural networks designed for pathology images, lncRNA data, immune-cell ssGSEA scores, and clinical information.

To ensure meticulous feature embedding, dedicated encoders played a pivotal role in our model architecture. Initial features extracted from WSIs stemmed from the modified CNN model, capturing inherent intricacies. In optimizing the attention-module's efficiency, we innovatively devised a dynamic query-router (QR). This QR incorporated two multilayer perceptron (MLP) layers, each complemented by exponential linear unit activation functions⁵⁸ and a Gumbel-Softmax layer for the purpose of sampling binary routes.

The input query was first transformed by an MLP layer, then dynamically integrated into postprocessing via another MLP layer, enhancing adaptability. Gumbel-Softmax sampling on the modified query generated binary route probabilities, which masked the input query to produce the final masked version. This process enabled dynamic information routing and adaptive feature selection. Features were then processed through an attention module with the modified query to obtain latent features based on attention scores.

For comprehensive integration and optimal data retention, we used concatenation to merge modality features into a unified representation. This was followed by a fully connected layer that served as a regressor, producing three key values: the status probability (0 or 1) and the time-based risk level score. To refine risk assessment, we calculated a robust risk score by multiplying the maximum status probability with the time-based risk level score, thereby improving model accuracy and reliability.

4.8 Details of developing multimodal AI model for survival risk prediction

4.9 Statistical analysis

Survival curve analysis was based on Kaplan–Meier plots. The results are displayed as HRs and p values from a log-rank test. Kaplan–Meier curves with log-rank tests were used to determine survival differences. Significance thresholds were set at two-sided p values below 0.05 for all conducted analyses. Patient stratification into high- and low-risk groups was achieved using optimal cutoff values identified by the R package survminer. The prognostic or predictive accuracy of the generated signatures was assessed through ROC curve analysis. Sensitivity and specificity were evaluated using the area under the ROC curve (AUC), providing an effective measure to assess the predictive performance of the signatures. These statistical computations were performed using R software (version 4.3.1). WSI processing and feature extraction were performed using Python (version 3.7.7). For WSI processing and segmentation, OpenSlide-Python (1.2.0), OpenSlide (3.0), and OpenCV-Python (version 4.1.1) were employed. PyTorch (1.7.1+cu101) was utilized to train deep learning models on GPUs.