Development and validation of nomograms integrating immune-related genomic signatures with clinicopathologic features to improve prognosis and predictive value of triple-negative breast cancer: A gene expression-based retrospective study

A new prognostic model including immune biomarkers, genomic proliferation tumor markers (AURKA and MYBL2) and clinical-pathological features optimizes prognosis in neoadjuvant breast cancer patients

Background

Up to 30% of breast cancer (BC) patients treated with neoadjuvant chemotherapy (NCT) will relapse. Our objective was to analyze the predictive capacity of several markers associated with immune response and cell proliferation combined with clinical parameters.

Methods

This was a single-center, retrospective cohort study of BC patients treated with NCT (2001-2010), in whom pretreatment biomarkers were analyzed: neutrophil-to-lymphocyte ratio (NLR) in peripheral blood, CD3+ tumor-infiltrating lymphocytes (TILs), and gene expression of AURKA, MYBL2 and MKI67 using qRT-PCR.

Results

A total of 121 patients were included. Median followup was 12 years. In a univariate analysis, NLR, TILs, AURKA, and MYBL2 showed prognostic value for overall survival. In multivariate analyses, including hormone receptor, HER2 status, and response to NCT, NLR (HR 1.23, 95% CI 1.01-1.75), TILs (HR 0.84, 95% CI 0.73-0.93), AURKA (HR 1.05, 95% CI 1.00-1.11) and MYBL2 (HR 1.19, 95% CI 1.05-1.35) remained as independent predictor variables.

Conclusion

Consecutive addition of these biomarkers to a regression model progressively increased its discriminatory capacity for survival. Should independent cohort studies validate these findings, management of early BC patients may well be changed.

Identification of a Two-Gene Signature and Establishment of a Prognostic Nomogram Predicting Overall Survival in Diffuse-Type Gastric Cancer

BACKGROUND

It is widely acknowledged that the molecular biological characteristics of diffuse-type gastric cancer are different from intestinal-type gastric cancer. Notwithstanding that significant progress in high-throughput sequencing technology has been made, there is a paucity of effective prognostic biomarkers for diffuse gastric cancer for clinical practice.

METHODS

We downloaded four GEO datasets (GSE22377, GSE38749, GSE47007 and GSE62254) to establish and validate a prognostic two-gene signature for diffuse gastric cancer. The TGCA-STAD dataset was used for external validation. The optimal gene signature was established by using Cox regression analysis. Receiver operating characteristic (ROC) methodology was used to find the best prognostic model. Gene set enrichment analysis was used to analyze the possible signaling pathways of the two genes (MEF2C and TRIM15).

RESULTS

A total of four differently expressed genes (DEGs) (two upregulated and two downregulated) were identified. After a comprehensive analysis, two DEGs (MEF2C and TRIM15) were utilized to construct a prognostic model. A prognostic prediction model was constructed according to T stage, N stage, M stage and the expression of MEF2C and TRIM15. The area under the time-dependent receiver operator characteristic was used to evaluate the performance of the prognosis model in the GSE62254 dataset.

CONCLUSIONS

We demonstrated that MEF2C and TRIM15 might be key genes. We also established a prognostic nomogram based on the two-gene signature that yielded a good performance for predicting overall survival in diffuse-type gastric cancer.

Identification of a novel signature with prognostic value in triple-negative breast cancer through clinico-transcriptomic analysis

Background

Although perceived as a highly aggressive disease, triple-negative breast cancer (TNBC) constitutes heterogeneous features with various outcomes. In this study, we aimed to establish a prognostic signature for patients with TNBC to improve risk stratification.

Methods

Gene expression data were obtained from The Cancer Genome Atlas (TCGA). Differentially expressed genes (DEGs) were detected pairwise between TNBC and other subtypes of samples. Then, TNBC-correlated modules were determined using coexpression network analysis. A gene signature was established based on the prognostic genes in the intersection between DEGs and selected gene modules using least absolute shrinkage and selection operator (LASSO) Cox regression. Finally, a clinico-transcriptomic signature was developed to predict overall survival (OS). Model performance was quantified, and the bootstrap resampling method was used for validation.

Results

The gene signature included 6 messenger RNAs (mRNAs) and a clinical score indicating an increased likelihood of death when used as continuous or categorical predictors. A nomogram was built by integrating the pathological stage and gene signature to predict 2-, 3-, and 5-year OS. The addition of pathological stage increased the concordance index (C-index) compared with pathological stage alone and the gene signature alone. Bootstrap resampling revealed a stable performance of the nomogram.

Conclusions

A 6-mRNA signature was established to inform prognosis for patients with TNBC. Its combination with pathological stage can contribute to improving performance and provide additional supporting evidence for clinical decision-making.

Potential of chimeric antigen receptor (CAR)-redirected immune cells in breast cancer therapies: Recent advances

Despite substantial developments in conventional treatments such as surgery, chemotherapy, radiotherapy, endocrine therapy, and molecular-targeted therapy, breast cancer remains the leading cause of cancer mortality in women. Currently, chimeric antigen receptor (CAR)-redirected immune cell therapy has emerged as an innovative immunotherapeutic approach to ameliorate survival rates of breast cancer patients by eliciting cytotoxic activity against cognate tumour-associated antigens expressing tumour cells. As a crucial component of adaptive immunity, T cells and NK cells, as the central innate immune cells, are two types of pivotal candidates for CAR engineering in treating solid malignancies. However, the biological distinctions between NK cells- and T cells lead to differences in cancer immunotherapy outcomes. Likewise, optimal breast cancer removal via CAR-redirected immune cells requires detecting safe target antigens, improving CAR structure for ideal immune cell functions, promoting CAR-redirected immune cells filtration to the tumour microenvironment (TME), and increasing the ability of these engineered cells to persist and retain within the immunosuppressive TME. This review provides a concise overview of breast cancer pathogenesis and its hostile TME. We focus on the CAR-T and CAR-NK cells and discuss their significant differences. Finally, we deliver a summary based on recent advancements in the therapeutic capability of CAR-T and CAR-NK cells in treating breast cancer.

Research progress on immunotherapy in triple‑negative breast cancer (Review)

Triple-negative breast cancer (TNBC) is a highly heterogeneous and aggressive malignancy. Due to the absence of estrogen receptors and progesterone receptors and the lack of overexpression of human epidermal growth factor receptor 2, TNBC responds poorly to endocrine and targeted therapies. As a neoadjuvant therapy, chemotherapy is usually the only option for TNBC; however, chemotherapy may induce tumor resistance. The emergence of immunotherapy as an adjuvant therapy is expected to make up for the deficiency of chemotherapy. Most of the research on immunotherapies has been performed on advanced metastatic TNBC, which has provided significant clinical benefits. In the present review, possible immunotherapy targets and ongoing immunotherapy strategies were discussed. In addition, progress in research on immune checkpoint inhibitors in early TNBC was outlined.

Radiomics Assessment of the Tumor Immune Microenvironment to Predict Outcomes in Breast Cancer

Background

The immune microenvironment of tumors provides information on prognosis and prediction. A prior validation of the immunoscore for breast cancer (IS_BC) was made on the basis of a systematic assessment of immune landscapes extrapolated from a large number of neoplastic transcripts. Our goal was to develop a non-invasive radiomics-based IS_BC predictive factor.

Methods

Immunocell fractions of 22 different categories were evaluated using CIBERSORT on the basis of a large, open breast cancer cohort derived from comprehensive information on gene expression. The IS_BC was constructed using the LASSO Cox regression model derived from the Immunocell type scores, with 479 quantified features in the intratumoral and peritumoral regions as observed from DCE-MRI. A radiomics signature [radiomics ImmunoScore (RIS)] was developed for the prediction of IS_BC using a random forest machine-learning algorithm, and we further evaluated its relationship with prognosis.

Results

An IS_BC consisting of seven different immune cells was established through the use of a LASSO model. Multivariate analyses showed that the IS_BC was an independent risk factor in prognosis (HR=2.42, with a 95% CI of 1.49–3.93; P<0.01). A radiomic signature of 21 features of the IS_BC was then exploited and validated (the areas under the curve [AUC] were 0.899 and 0.815). We uncovered statistical associations between the RIS signature with recurrence-free and overall survival rates (both P<0.05).

Conclusions

The RIS is a valuable instrument with which to assess the immunoscore, and offers important implications for the prognosis of breast cancer.

Developing an immune signature for triple-negative breast cancer to predict prognosis and immune checkpoint inhibitor response

Aim: We aimed to develop a new signature based on immune-related genes to predict prognosis and response to immune checkpoint inhibitors in patients with triple-negative breast cancer (TNBC). Materials & methods: Single-sample gene set enrichment was used to develop an immune-based prognostic signature (IPRS) for TNBC patients. We conducted multivariate Cox analysis to evaluate the prognosis value of the IPRS. Result: An IPRS based on 66 prognostic genes was developed. Multivariate Cox analysis indicated that the IPRS was an independent factor for prognosis. PD-1, PD-L1, PD-L2 and CTLA4 gene expression was higher in the low-risk group, suggesting IPRS could predict the response to immune checkpoint inhibitors. Conclusion: The IPRS might be a reliable signature to predict TNBC patients' prognosis and response to immune checkpoint inhibitors, but needs prospective validation.

Radiomics features on ultrasound imaging for the prediction of disease-free survival in triple negative breast cancer: a multi-institutional study

OBJECTIVES

To explore the predictive value of radiomics nomogram using pretreatment ultrasound for disease-free survival (DFS) after resection of triple negative breast cancer (TNBC).

METHODS AND MATERIALS

A total of 486 TNBC patients from 3 different institutions were consecutively recruited for this study. They were categorized into the primary cohort (n = 216), as well as the internal validation cohort (n = 108) and external validation cohort (n = 162). In primary cohort, least absolute shrinkage and selection operator logistic regression algorithm was used to select recurrence-related radiomics features extracted from the breast tumor and peritumor regions, and a radiomics signature was constructed derived from the grayscale ultrasound images. A radiomic nomogram integrating independent clinicopathological variables and radiomic signature was established with uni- and multivariate cox regressions. The predictive nomogram was validated using an internal cohort and an independent external cohort regarding abilities of discrimination, calibration and clinical usefulness.

RESULTS

The patients with higher Rad-score had a worse prognostic outcome than those with lower Rad-score in primary cohort and two validation cohorts (All p < 0.05).The radiomics nomogram indicated more effective prognostic performance compared with the clinicopathological model and tumor node metastasis staging system (p < 0.01), with a training C-index of 0.75 (95% confidence interval (CI), 0.71-0.80), an internal validation C-index of 0.73 (95% CI, 0.69-0.78) and an external validation 0.71 (95% CI,0.66-0.76). Moreover, the calibration curves revealed a good consistency for survival prediction of the radiomics model.

CONCLUSIONS

The ultrasound-based radiomics signature was a promising biomarker for risk stratification for TNBC patients. Furthermore, the proposed radiomics modal integrating the optimal radiomics features and clinical data provided individual relapse risk accurately.

ADVANCES IN KNOWLEDGE

The radiomics model integrating radiomic signature and independent clinicopathological variables could improve individual prognostic evaluation and facilitate therapeutic decision-making, which demonstrated the incremental value of the radiomics signature for prognostic prediction in TNBC.

Prognostic significance of the systemic immune-inflammation index in pancreatic carcinoma patients: A meta-analysis

Background: Systemic immune-inflammation index (SII) is a prognostic indicator for several malignancies, including pancreatic carcinoma; however, there is no consensus on its significance. In the current study, a systematic meta-analysis was used to explore the correlation between SII and prognosis in pancreatic carcinoma patients.

Methods: PubMed, Embase and Cochrane Library databases were screened from inception to May 2020. Studies describing the prognostic role of SII in pancreatic carcinoma were then retrieved. The pooled hazard ratio (HR) and 95% confidence interval (CI) was calculated using random- or fixed-effects models to determine the correlation between SII and prognosis.

Results: A total of four studies, comprising 1749 patients, met the inclusion criteria of the study and were therefore included in this meta-analysis. The meta-analysis showed that high SII indicated was correlated with worse overall survival (OS) in patients with pancreatic carcinoma (HR: 1.43, 95% CI: 1.24–1.65, P<0.001). These findings were validated through subgroup analyses, stratified by the American Joint Committee on Cancer (AJCC) stage. In addition, patients with high SII showed poorer cancer-specific survival (HR: 2.32, 95% CI: 1.55–3.48, P<0.001). However, analysis showed no significant correlations between SII and disease-free and relapse-free survival (RFS).

Conclusion: These findings indicate that SII is a potential non-invasive and a promising tool for predicting clinical outcomes of pancreatic carcinoma patients. However, the current research did not explore whether neoadjuvant therapy has an effect on the prognostic value of SII. Further studies using adequate designs and larger sample sizes are required to validate these findings.

Development and Validation of Web-Based Nomograms for Predicting Cause-Specific Mortality in Surgically Resected Nonmetastatic Invasive Breast Cancer: A Population-Based Study

BACKGROUND

This study aims to build nomograms to predict overall survival (OS) and breast cancer-specific death (BCSD) in resected nonmetastatic invasive breast cancer.

PATIENTS AND METHODS

Patients extracted from surveillance, epidemiology, and end results database between 2010 and 2014 were analyzed. Through multivariate Cox regression and Fine and Gray competing risks regression, independent predictive factors were identified and integrated to build nomograms for predicting OS and BCSD. The models were validated by bootstrap resampling and an independent cohort. Additionally, the models' performance was measured by the Harrell's C-index, calibrate curve, and time-dependent receiver operating characteristic (ROC) curves.

RESULTS

In total, 110,180 cases were identified and enrolled in the analysis, with 83,450 in the training cohort and 26,730 in the validation cohort. Several independent predictive factors for OS and BCSD were identified and integrated to construct the nomograms. The C-indexes in the training cohort and validation cohort were 0.759 and 0.772 for predicting OS, and 0.857 and 0.856 for predicting BCSD, respectively. The nomogram models were well calibrated, and the time-dependent ROC curves verified the superiority of our models for clinical usefulness. Significant differences in the OS and BCSD curves were also observed when stratifying patients into several different risk groups. For convenient access, we deployed these proposed nomograms into web-based calculators.

CONCLUSIONS

We established and validated novel nomograms for individualized prediction of OS and BCSD in resected nonmetastatic invasive breast cancer. These nomograms perform better than previous models and could be easily accessed easily by clinicians.

Construction and Validation of an Immune Infiltration-Related Gene Signature for the Prediction of Prognosis and Therapeutic Response in Breast Cancer

Breast cancer patients show significant heterogeneity in overall survival. Current assessment models are insufficient to accurately predict patient prognosis, and models for predicting treatment response are lacking. We evaluated the relationship between various immune cells and breast cancer and confirmed the association between immune infiltration and breast cancer progression. Different bioinformatics and statistical approaches were combined to construct a robust immune infiltration-related gene signature for predicting patient prognosis and responses to immunotherapy and chemotherapy. Our research found that a higher immune infiltration-related risk score (IRS) indicates that the patient has a worse prognosis and is not very sensitive to immunotherapy. In addition, a new nomogram was constructed based on the gene signature and clinicopathological features to improve the risk stratification and quantify the risk assessment of individual patients. Our study might contribute to the optimization of the risk stratification for survival and the personalized management of breast cancer.

Magnolin Inhibits Proliferation and Invasion of Breast Cancer MDA-MB-231 Cells by Targeting the ERK1/2 Signaling Pathway

The aim of this study was to evaluate the effects of Magnolin (MGL) on inhibition of human breast cancer cells, and explore the underlying molecular mechanisms. The viability of the treated cells was assessed with the Cell Counting Kit-8 (CCK-8) assay, and the proliferation was analyzed in terms of EdU uptake, colony formation, and flow cytometry. The in vitro invasion and migration were determined by the transwell and wound healing assays respectively. The mRNA and protein levels of relevant factors was evaluated by quantitative real-time PCR and Western blotting respectively. MGL significantly decreased the viability and promoted apoptosis of MDA-MB-231 cells, along with reducing EdU incorporation rate as well as the colony forming capacity compared to the untreated control cells. In addition, the in vitro invasion and migration were also significantly inhibited by MGL. Furthermore, MGL suppressed the phosphorylation of MEK1/2, extracellular signal-regulated kinase (ERK)1/2 and significantly downregulated the expression of cyclin-dependent kinase 1 (CDK1), the anti-apoptotic B-cell lymphoma 2 (BCL2) and metastasis-associated matrix metalloproteases (MMPs) 2 & 9, and upregulated the cleaved caspases 3 and 9. After ERK was completely inhibited with the small interfering RNA (siRNA), MGL had no effect on these factors, indicating that ERK is essential for MGL action in breast cancer. In conclusion, MGL inhibits proliferation and invasion of and induces apoptosis in breast cancer cells through the ERK pathway.

Development and validation of a prognostic nomogram for patients with triple-negative breast cancer with histology of infiltrating duct carcinoma

Background

The purpose of this study was to develop prognostic nomograms from a cohort of patients with triple-negative breast cancer (TNBC) with histology of infiltrating duct carcinoma (IDC) by correlating their clinical and pathological parameters with the rates of disease-free survival (DFS) and overall survival (OS).

Methods

We retrospectively analyzed TNBC patients with histology of IDC at our institution between 2009 and 2012. Age, family history, menopausal status, surgery type, T stage, N stage, histological grade, vascular invasion, perineural invasion, cytokeratin 5/6 status, Ki-67 expression, and epithelial cadherin (E-cadherin) status were analyzed. Predictors were used in multivariable logistic regression analysis to develop a nomogram to predict DFS and OS rates. The nomograms were then subjected to internal validation, with external validation of the nomogram for predicting OS using separate cohorts of TNBC patients known from the Cancer Genome Atlas (TCGA) database. Using the concordance index (C-index) with calibration curves, the predictive accuracy and discriminative ability were calculated.

Results

A total of 242 eligible TNBC patients were included for analysis. The median follow-up time was 70.73 months. Of the patients, 32.6%, 42.6%, and 24.8% had stage I, II, and III disease, respectively. The 3- and 5-year survival rates were 81.0% and 76.5% for DFS, and 86.5% and 81.1%, for OS, respectively. Age, T stage, N stage, and E-cadherin status were found to be risk factors. The nomograms based on those risk factors accurately predicted the 3- and 5-year survival rates. The C-index was 0.798 and 0.821 for DFS and OS, respectively. Besides, the nomogram for OS showed relatively reliable performance in stratifying different risk groups of patients in training and validation cohorts identified from the TCGA database. The C-index reached 0.843. DFS validation was not completed, as there was insufficient data.

Conclusions

Using clinicopathological information, we produced a prognostic nomogram that accurately predicts the 3- and 5-year DFS and OS for patients with TNBC with histology of IDC. More external confirmation is required.

Novel prognostic nomograms for female patients with breast cancer and bone metastasis at presentation

Background

There is a paucity of literature about prognostic evaluation for patients with breast cancer (BC) and bone metastasis at presentation. To date, little is known about how to accurately predict the prognosis of BC patients with bone metastasis at presentation. Thus, an accurate prediction tool of prognosis in this population is urgently needed. Our goal is to construct novel and prognostic nomograms for BC patients with bone metastasis at presentation.

Methods

We searched Surveillance, Epidemiology, and End Results (SEER) database for BC patients with bone metastasis at presentation between 2010 and 2016. Multivariate analysis was performed to obtain significantly independent variables. Then, novel prognostic nomograms were constructed based on those independent predictors.

Results

Tumor grade, histological type, primary tumor size, tumor subtype, surgery, chemotherapy and number of metastatic organs except bone were recognized as significantly independent variables of both overall survival (OS) and cancer-specific survival (CSS). Then those significant variables were integrated to construct nomograms for 3- and 5-year survival. Calibration plots for the 3- and 5-year survival in training and validation sets showed that the prediction curve was close to a 45 degree slash. The C-indices of OS in training and validation cohorts were 0.705 and 0.678, respectively. Similar results were observed for CSS in training and validation cohorts.

Conclusions

Our proposed nomograms can effectively and accurately predict the prognosis of BC patients with bone metastasis at presentation, which provide a basis for individual treatments for metastatic lesions.

Quadruple negative breast cancer

Quadruple negative breast cancer (QNBC), lacking the expression of ER (estrogen receptor), PR (progesterone receptor), HER2 (human epidermal growth factor receptor-2) and AR (androgen receptor), was regarded as one breast cancer subtype with the worst prognosis. Recently, the molecular features of QNBC are not well understood. Different from AR-positive triple-negative breast cancer, QNBC is insensitive to conventional chemotherapeutic agents and has no efficient treatment targets. However, QNBC has been shown to express unique proteins that may be amenable to use in the development of targeted therapies. Here we reviewed the features of QNBC and proteins that may serve as effective targets for QNBC treatment, such as ACSL4, SKP2, immune checkpoint inhibitors, EGFR, MicroRNA signatures and Engrailed 1.

Bioinformatic profiling of prognosis-related genes in the breast cancer immune microenvironment

In the microenvironment of breast cancer, immune cell infiltration is associated with an improved prognosis. To identify immune-related prognostic markers and therapeutic targets, we determined the lymphocyte-specific kinase (LCK) metagene scores of samples from breast cancer patients in The Cancer Genome Atlas. The LCK metagene score correlated highly with other immune-related scores, as well as with the clinical stage, prognosis and tumor suppressor gene mutation status (BRCA2, TP53, PTEN) of patients in the four breast cancer subtypes. A weighted gene co-expression network analysis was performed to detect representative genes from LCK metagene-related gene modules. In two of these modules, the levels of the co-expressed genes correlated highly with LCK metagene levels, so we conducted an enrichment analysis to discover their functions. We also identified differentially expressed genes in samples with high and low LCK metagene scores. By examining the overlapping results from these analyses, we obtained 115 genes, and found that 22 of them were independent predictors of overall survival in breast cancer patients. These genes were validated for their prognostic and diagnostic value with external data sets and paired tumor and non-tumor tissues. The genes identified herein could serve as diagnostic/prognostic markers and immune-related therapeutic targets in breast cancer.

Characteristics and prognosis of pelvic Ewing sarcoma: a SEER population-based study

Background

The pelvis is one of the primary sites of Ewing sarcoma (ES) and is associated with poorer prognoses than the extremities. Due to the rarity of this disease and limited data available, the prognostic factors of pelvic ES remain controversial. Thus, this study aimed to identify independent prognostic factors, and develop a nomogram for predicting survival rates in patients with pelvic ES.

Methods

Using data provided by the Surveillance, Epidemiology, and End Results (SEER) database, variables including age, sex, race, tumor size, tumor stage, surgery, and radiotherapy were analyzed using the Kaplan–Meier method and Cox proportional hazards regression. Based on the results of multivariate analyses, a nomogram was built to predict the overall survival (OS) of patients with pelvic ES. The performance of the nomogram was evaluated by the concordance index (C-index).

Results

A total of 267 cases diagnosed between 2004 and 2016 were included in the study. Univariate and multivariate analyses showed that patients who were younger, white, had a localized tumor stage, or underwent surgery were associated with improved prognoses, while no significant differences were observed in OS based on sex, tumor size, or radiotherapy. A nomogram was developed and the C-index was 0.728, indicating adequate performance for survival prediction.

Conclusions

Age, race, tumor stage, and surgery were identified as independent prognostic factors for the OS of pelvic ES. The nomogram developed in this study can individually predict 3- and 5-year OS in patients with pelvic ES.

Development and validation of nomograms integrating immune-related genomic signatures with clinicopathologic features to improve prognosis and predictive value of triple-negative breast cancer: A gene expression-based retrospective study

Purpose

Accumulating evidence indicated that triple‐negative breast cancer (TNBC) can stimulate stronger immune responses than other subtypes of breast cancer. We hypothesized that integrating immune‐related genomic signatures with clinicopathologic factors may yield a predictive accuracy exceeding that of the currently available system.

Methods

Ten signatures that reflect specific immunogenic or immune microenvironmental features of TNBC were identified and re‐analyzed using bioinformatic methods. Then, clinically annotated TNBC (n = 711) with the corresponding expression profiles, which predicted a patient's probability of disease‐free survival (DFS) and overall survival (OS), was pooled to evaluate their prognostic values and establish a clinicopathologic‐genomic nomogram. Three and two immune features were, respectively, selected out of 10 immune features to construct nomogram for DFS and OS prediction based on multivariate backward stepwise Cox regression analyses.

Results

By integrating the above immune expression signatures with prognostic clinicopathologic features, clinicopathologic‐genomic nomograms were cautiously constructed, which showed reasonable prediction accuracies (DFS: HR, 1.79; 95% CI, 1.46‐2.18, P < 0.001; AUC, 0.71; OS: HR, 1.96; 95% CI, 1.54‐2.49; P < 0.001; AUC, 0.73). The nomogram showed low‐risk subgroup had higher immune checkpoint molecules (PD‐L1, PD‐1, CTLA‐4, LAG‐3) expression and benefited from radiotherapy (HR, 0.2, 95% CI, 0.05‐0.89; P = 0.034) rather than chemotherapy (HR, 1.26, 95% CI, 0.66‐2.43; P = 0.485).

Conclusions

These findings offer evidence that immune‐related genomic data provide independent and complementary prognostic information for TNBC, and the nomogram might be a practical predictive tool to identify TNBC patients who would benefit from chemotherapy, radiotherapy, and upcoming popularity of immunotherapy.