TNBC Dataset: Multi-Modal Insights

Updated 7 July 2025

TNBC Dataset is a comprehensive collection of diverse data modalities characterizing triple-negative breast cancer, defined by the absence of ER, PR, and HER2 expression.
It integrates genomic, histopathology, imaging, and immunoprofiling data to enable robust model development and reliable biomarker identification.
Researchers leverage these datasets for treatment stratification, outcome prediction, and validating advanced computational methodologies in aggressive breast cancer.

Triple-Negative Breast Cancer (TNBC) datasets are foundational to the paper of TNBC, an aggressive breast cancer subtype defined by the absence of estrogen receptor (ER), progesterone receptor (PR), and HER2 expression. These datasets encompass a variety of data modalities—including histopathology, imaging, transcriptomics, and immunoprofiling—facilitating the development of robust predictive models, biomarker discovery, and treatment stratification for TNBC. This article provides a detailed overview of TNBC datasets across multiple methodological dimensions.

1. Composition and Modalities of TNBC Datasets

TNBC datasets incorporate diverse data types tailored to address specific biological and clinical research questions:

Genomic and Transcriptomic Data: The Cancer Genome Atlas (TCGA) Breast Invasive Carcinoma (BRCA) RNA-Seq dataset contains expression profiles for up to 57,251 genes across over 1,200 samples, with specialized analysis on 19,688 protein-coding genes for TNBC cases (1807.01510). Patient subtyping labels are curated from clinical immunohistochemistry (IHC) and FISH testing. Robust statistical methods have identified both previously known and novel TNBC gene biomarkers from these high-dimensional datasets.
Digital Histopathology: Large whole-slide images (WSIs) of H&E-stained breast cancer biopsies are segmented into tiled patches for computational efficiency. Several datasets annotate histological subtypes, nuclei boundaries, and cellular classes—including extended TNBC datasets with nuclei classified into nine types and mitotic state (2207.10950).
Hyperspectral Imaging: Micro-FTIR datasets offer high-resolution spectra from breast tissue, with comprehensive labeling for cancer type, molecular subtype (including TNBC), and biomarker levels. Such datasets support deep learning models that integrate both spatial and spectral features (2310.15094, 2310.15099).
Radiomics and MRI: Cohorts such as the Saha et al. dataset and Duke-Breast-Cancer-MRI dataset supply multiparametric MRI scans, with lesion segmentation, clinical metadata, and outcome annotations (2401.04149, 2504.01692). These resources allow radiomic feature extraction and assessment of feature stability under segmentation variability.
Immunophenotyping and Tumor Microenvironment Profiling: Multiplexed immunohistochemistry (mIHC) and spatial RNA-scope fluorescence datasets characterize local immune cell infiltration (e.g., CD8+ T cells, CD163+ macrophages, NK cells), which are crucial for understanding immune response and treatment efficacy (2307.03308, 2505.14730).
Clinical Outcomes and Demographics: SEER-based datasets link molecular subtype and demographic factors (e.g., age, ethnicity) to survival outcomes and recurrence rates (2401.08712).

2. Preprocessing, Annotation, and Quality Control

Successful use of TNBC datasets depends on rigorous preprocessing and annotation protocols:

Tissue Segmentation and Labeling: Automated approaches based on clustering (e.g., K-means) and software like QuPath allow for the identification and isolation of tissue regions and key histologies, with background and non-tissue removed from hyperspectral and digital pathology images (2310.15099, 2411.09766).
Nuclei and Cell Annotation: Datasets may include up to 4,056 nuclei per 50 WSIs with further enrichment for nuclear class or mitotic state. Annotation pipelines often rely on software such as CellCognition, with expert pathology review for ground truth validation (2207.10950). The SHIDC-BC-Ki-67 dataset uses Gaussian modeling around labeled cell centers to create density map ground truths for deep detection models (2010.04713).
Quality Control of Segmentations: Nuclear segmentation datasets implement patch-level and WSI-level quality control. Instance Dice scores, mean absolute error percentages (MAE%), and manual ground truthing are standard. The inclusion criterion may require that at least 80% of patches in a WSI meet minimum precision and recall (2002.07913).
Radiomics and MRI Harmonization: Preprocessing includes z-score normalization, fixed-bin count discretization, and ComBat harmonization to address scanner-related biases. Segmentation masks are often varied to assess feature and model robustness under annotation uncertainty (2504.01692).

3. Computational and Statistical Methodologies

Analyses of TNBC datasets leverage statistical, machine learning, and deep learning techniques:

Robust Statistical Modeling: Robust sparse logistic regression (adapting the least trimmed squares approach) is used to mitigate the influence of outlier labels and high-dimensional noise in gene expression data. Outlier detection extends to cellwise approaches, such as the DDC algorithm, which identifies misclassified or aberrant sample cells (1807.01510).
Machine Learning for Prognosis and Subtyping: Deep learning frameworks—such as MxNet, 1D and 2D convolutional neural networks (CaReNet-V1, CaReNet-V2), and attention-based multiple instance learning—classify TNBC versus other subtypes and predict outcomes from patch-level and spectral data (1809.08534, 2310.15094, 2310.15099, 2505.14730).
Graph and Transformer Models: The NACNet architecture captures the spatial arrangement of tumor microenvironment constituents by constructing graph representations of histology-labeled tiles and propagating features via transformer-attended graph convolutional networks (2411.09766).
Radiomics Feature Selection and Stability: Logistic regression with L₁ penalty, ANOVA-based screening, and explainability via SHapley Additive exPlanations (SHAP) are standard for feature selection. Feature stability with respect to segmentation is assessed using Intraclass Correlation Coefficient (ICC), Pearson correlation, and derived reliability scores (2504.01692).

4. Key Findings and Clinical Implications

Analysis of TNBC datasets has yielded findings with direct clinical relevance:

Molecular Biomarkers: Integration of robust statistics and transcriptomics has identified a compact 36-gene TNBC signature, including 14 novel candidate biomarkers for diagnosis and therapeutic targeting. Network analysis reveals regulatory features—such as the FOXA1–AGR2 interaction—that may inform treatment development (1807.01510).
Imaging and Immune Biomarkers: Prognostic models associate high ratios of CD8+ T cells or favorable spatial arrangement of immune and tumor features with better outcomes. Explainable AI demonstrates that optimal fractions of CD4+ T cells (>0.041, ≤0.061) and B cells (>0.018) are linked to improved survival (2104.12021).
Prediction of Chemotherapy Response: Deep learning models using H&E or hyperspectral images, sometimes augmented by immune profiling, achieve AUCs up to 0.86–0.90 for prediction of neoadjuvant chemotherapy response, suggesting potential for early therapy stratification (2505.14730, 2411.09766, 2310.15094).
Radiomics and Segmentation Robustness: The predictive capability of MRI-based radiomic models for TNBC is robust to significant segmentation variability; highly stable features (by ICC) are not always the most predictive, challenging feature-selection conventions (2504.01692).
Demographic and Survival Patterns: While TNBC patients exhibit worse prognosis than non-TNBC, analyses show that within TNBC, younger age (<30 years) does not confer a statistically significant difference in survival, suggesting a dominant role for tumor biology over age (2401.08712).

5. Biomarkers and Features for Prognostication

Tables below summarize selected key gene and immune biomarkers from TNBC datasets:

Modality	Biomarkers/Features	Application
Transcriptomics	CT83, FZD9, SRSF12, HORMAD1, FOXC1, PODN, JAM3...	Subtype diagnosis, target discovery (1807.01510)
Immunoprofiling	PD-L1, CD8+ T cells, CD163+ macrophages, B cells	Response prediction, survival inference (2104.12021, 2505.14730)
Morphology	Nuclear shape, size, mitosis occurrence	Aggressiveness, classification (2207.10950, 2002.07913)
Hyperspectral	Amide I/III, collagen- and adenine-associated bands	Subtype separation, biochemical insight (2310.15094)
Radiomics	GLCM texture, peritumoral intensity, shape metrics	Subtype prediction, feature robustness (2504.01692)

6. Practical Implications, Limitations, and Future Directions

Integration and Standardization: Publicly available, well-annotated TNBC datasets (e.g., from TCGA, SHIDC-BC-Ki-67, and open micro-FTIR resources) enable reproducible benchmarking and comparative evaluation of computational models.
Interpretability: The convergence of computational attention (e.g., Grad-CAM) and immune/cellular biomarker analysis elucidates biological underpinnings of predictive models. This enhances trust and translational potential.
Limitations: Dataset-specific limitations include small sample sizes in high-resolution nuclei annotation (2207.10950), potential batch effects or selection biases in imaging datasets, and the risk that conventional feature stability metrics might exclude useful predictors (2504.01692).
Future Directions: Ongoing priorities include expanding datasets across institutions and ancestries, reducing annotation burden via semi-supervised learning, integrating multiomics for deeper biological insight, and validating digital twin approaches for individualized therapy planning (2212.04270, 2411.09766).

7. Representative TNBC Datasets and Access

Notable TNBC-related datasets, their modality, and application context:

Dataset/Resource	Modality	Use Case
TCGA-BRCA (public)	RNA-Seq, clinical	Subtype labeling, biomarker discovery
Segmented Nuclei in H&E (2002.07913)	Histopathology (nuclei)	Morphology extraction, computational pathology
SHIDC-BC-Ki-67 (2010.04713)	IHC cell/density maps	Ki-67, TILs automated scoring
TNBC nuclei w/class annotation (2207.10950)	H&E nuclei+class	Segmentation/classification, mitotic index
Saha et al. / Duke MRI (2401.04149, 2504.01692)	Multiparametric MRI	Radiomics-based stratification
Micro-FTIR images (2310.15094, 2310.15099)	Hyperspectral imaging	Deep learning subtype identification

Researchers can access these datasets through resources such as TCGA, The Cancer Imaging Archive (TCIA), and referenced GitHub or institutional repositories, as detailed in the individual studies.

The breadth and diversity of TNBC datasets underpin a data-rich research environment for understanding the molecular, morphological, and clinical landscape of this challenging breast cancer subtype and serve as vital platforms for developing, validating, and interpreting novel computational methodologies for prognosis and personalized care.