Development and validation of nomograms predicting survival in Chinese patients with triple negative breast cancer

Improving Prognostic Value in Invasive Triple Negative Breast Cancer Through a Combined Nomogram Approach

OBJECTIVES

To investigate the potential prognostic value of ultrasound (US) features in conjunction with pathological markers and to develop a preliminary working model for predicting poor outcomes in patients with invasive triple-negative breast cancer (TNBC).

METHODS

From January 2012 to December 2018, we enrolled 209 TNBC patients treated with standard therapy, systematically gathered data on US parameters, stromal tumor-infiltrating lymphocytes (TILs), lymphovascular invasion (LVI) status, and other relevant information, and recorded follow-up data. A nomogram combining AJCC staging with US score, stromal TILs, and LVI was constructed and validated to predict poor outcomes, defined as recurrence or death, in patients with invasive TNBC.

RESULTS

The US score of 4 was best related to poor outcomes in patients with TNBC (HR 3.87, P = .015). In the training set, the nomogram had a considerably greater prognostic value [area under the curve (AUC), 0.74 vs. 0.64, P = .045] than AJCC staging alone, and it was comparable to that of the validation set (AUC, 0.71 vs. 0.63, P = .804). An acceptable consistency between the nomogram-predicted and actual survival probabilities was found both in the training and validation sets, with Brier scores of 0.15 and 0.13, respectively.

CONCLUSIONS

The incorporation of AJCC stage with US score, stromal TILs, and LVI improved the model performance for predicting poor outcomes in patients with invasive TNBC compared to routine AJCC staging alone.

Development and validation of a nomogram for predicting rapid relapse in triple-negative breast cancer patients treated with neoadjuvant chemotherapy

Background

Triple-negative breast cancer (TNBC) accounts for disproportionately poor outcomes in breast cancer, driven by a subset of rapid-relapse TNBC (rrTNBC) with marked chemoresistance, rapid metastatic spread, and poor survival. This study aimed to develop and validate a nomogram based on clinicopathological characteristics to predict rapid relapse in TNBC patients treated with neoadjuvant chemotherapy (NAC) first.

Methods

The clinicopathological data of 504 TNBC patients treated with NAC first in Tianjin Medical University Cancer Hospital were analyzed retrospectively, with 109 rapid relapsed patients, and 395 non-rapid relapsed patients, respectively. Based on clinicopathologic characteristics, and follow-up data were analyzed. The independent predictors of clinicopathological characteristics were identified by logistic regression analysis and then used to build a nomogram. The concordance index (C-index), the area under the curve (AUC) of receiver operating characteristic (ROC), and calibration plots were used to evaluate the performance of the model.

Results

Univariate and multivariate logistic regression analyses showed that age at diagnosis (age≥50 years, OR = 0.325,95% CI:0.137-0.771), Nodal staging (N3 staging, OR = 13.669,95% CI:3.693-50.592),sTIL expression levels (sTIL intermediate expression, OR = 0.272,95% CI:0.109-0.678; sTIL high expression, OR = 0.169,95% CI:0.048-0.594), and NAC response (ORR, OR = 0.059,95% CI:0.024-0.143) were independent predictors of rapid relapse in TNBC patients treated with NAC firstly. Among these independent predictors, age ≥ 50 years, sTIL intermediate expression, sTIL high expression, and ORR in NAC were independent protective factors for rapid relapse in TNBC NAC patients. N3 staging was an independent risk factor for rapid relapse in TNBC NAC patients. The ROC curve, calibration curve, and decision curve analysis were used to validate the model. The C-Index of the training sets and validation sets were 0.938 and 0.910, respectively. The Brier scores of the training sets and validation sets were 0.076 and 0.097, respectively.

Conclusion

This study developed and verified a nomogram for predicting rapid relapse in TNBC NAC patients, and the predictive model had high discrimination and accuracy.

Development and validation of a nomogram for predicting cancer-specific survival in small-bowel adenocarcinoma patients using the SEER database

BACKGROUND

Small bowel adenocarcinoma (SBA) is a rare gastrointestinal malignancy forwhich survival is hampered by late diagnosis, complex responses to treatment, and poor prognosis. Accurate prognostic tools are crucial for optimizing treatment strategies and improving patient outcomes. This study aimed to develop and validate a nomogram based on the Surveillance, Epidemiology, and End Results (SEER) database to predict cancer-specific survival (CSS) in patients with SBA and compare it to traditional American Joint Committee on Cancer (AJCC) staging.

METHODS

We analyzed data from 2,064 patients diagnosed with SBA between 2010 and 2020 from the SEER database. Patients were randomly assigned to training and validation cohorts (7:3 ratio). Kaplan‒Meier survival analysis, Cox multivariate regression, and nomograms were constructed for analysis of 3-year and 5-year CSS. The performance of the nomograms was evaluated using Harrell's concordance index (C-index), the area under the receiver operating characteristic (ROC) curve, calibration curves, decision curve analysis (DCA), net reclassification improvement (NRI), and integrated discrimination improvement (IDI).

RESULTS

Multivariate Cox regression identified sex, age at diagnosis, marital status, tumor site, pathological grade, T stage, N stage, M stage, surgery, retrieval of regional lymph nodes (RORLN), and chemotherapy as independent covariates associated with CSS. In both the training and validation cohorts, the developed nomograms demonstrated superior performance to that of the AJCC staging system, with C-indices of 0.764 and 0.759, respectively. The area under the curve (AUC) values obtained by ROC analysis for 3-year and 5-year CSS prediction significantly surpassed those of the AJCC model. The nomograms were validated using calibration and decision curves, confirming their clinical utility and superior predictive accuracy. The NRI and IDI indicated the enhanced predictive capability of the nomogram model.

CONCLUSION

The SEER-based nomogram offers a significantly superior ability to predict CSS in SBA patients, supporting its potential application in clinical decision-making and personalized approaches to managing SBA to improve survival outcomes.

Specific mortality in patients with diffuse large B-cell lymphoma: a retrospective analysis based on the surveillance, epidemiology, and end results database

BACKGROUND

The full potential of competing risk modeling approaches in the context of diffuse large B-cell lymphoma (DLBCL) patients has yet to be fully harnessed. This study aims to address this gap by developing a sophisticated competing risk model specifically designed to predict specific mortality in DLBCL patients.

METHODS

We extracted DLBCL patients' data from the SEER (Surveillance, Epidemiology, and End Results) database. To identify relevant variables, we conducted a two-step screening process using univariate and multivariate Fine and Gray regression analyses. Subsequently, a nomogram was constructed based on the results. The model's consistency index (C-index) was calculated to assess its performance. Additionally, calibration curves and receiver operator characteristic (ROC) curves were generated to validate the model's effectiveness.

RESULTS

This study enrolled a total of 24,402 patients. The feature selection analysis identified 13 variables that were statistically significant and therefore included in the model. The model validation results demonstrated that the area under the receiver operating characteristic (ROC) curve (AUC) for predicting 6-month, 1-year, and 3-year DLBCL-specific mortality was 0.748, 0.718, and 0.698, respectively, in the training cohort. In the validation cohort, the AUC values were 0.747, 0.721, and 0.697. The calibration curves indicated good consistency between the training and validation cohorts.

CONCLUSION

The most significant predictor of DLBCL-specific mortality is the age of the patient, followed by the Ann Arbor stage and the administration of chemotherapy. This predictive model has the potential to facilitate the identification of high-risk DLBCL patients by clinicians, ultimately leading to improved prognosis.

Prognosis of Patients With Triple-negative Breast Cancer: A Population-based Study From SEER Database

BACKGROUND

Triple-negative breast cancer (TNBC) was a particularly aggressive subtype of breast cancer associated with poor prognosis. This retrospective study was conducted to investigate the clinical features, prognostic factors, and benefits of surgery of patients with TNBC.

METHODS

From 2010 to 2015, 33654 female patients with TNBC were obtained from the Surveillance, Epidemiology, and End Results (SEER) database. The patients were randomly divided into the training and validation cohorts. Univariate and multivariable cox regression were performed to identify prognostic factors, based on which a nomogram was constructed. Validation of the nomogram was assessed by concordance index (c-index) and calibration curves. Survival curves were plotted according to metastatic burdens and risk groups differentiated by nomogram.

RESULTS

Patients of younger age (<65 years old), white race, married status, lower grade, lower TNM stage and primary tumor surgery tended to have better outcome. The C-index and calibration curves displayed high discrimination in the training and validation sets (C-index 0.794 and 0.793, respectively), indicating suitable external performance of the nomogram model. Patients of bone-only metastases as well as bone and liver metastases showed superior cancer-specific survival (CSS) time if surgery of primary tumor was performed. Besides, patients of all risk groups showed better CSS when receiving surgery.

CONCLUSION

This study provided population-based prognostic analysis in patients with TNBC and constructed a predicting nomogram with a robust discrimination. The findings of potential benefit of surgery to CSS would shed some lights on the treatment tactics of patients with TNBC.

The predictive value of neutrophil-to-lymphocyte ratio for overall survival and pathological complete response in breast cancer patients receiving neoadjuvant chemotherapy

Purpose

Previous studies have reported that neutrophil-to-lymphocyte ratio (NLR) at pre-treatment was predictive for overall survival (OS) and pathologic complete response (pCR) in breast cancer (BC) patients receiving neoadjuvant chemotherapy (NAC). This study aims to explore the predictive role of both pre- and post-NLR for OS as well as longitudinal NLR kinetics towards pCR in BC patients undergoing NAC.

Methods

We retrospectively included 501 BC patients who received NAC from 2009 to 2018. NLR at pre-, mid (every two cycles of NAC)-, and post-treatment were collected. Overall, 421 patients were included in the survival analysis. These patients were randomly divided into a training cohort (n = 224) and a validation cohort (n = 197). A multivariable Cox model was built using all significant factors in the multivariable analysis from the training cohort. The performance of the model was verified in the validation cohort by the concordance index (C-index). Longitudinal analysis for pCR prediction of NLR was performed using a mixed-effects regression model among 176 patients who finished eight cycles of NAC.

Results

The median follow-up time was 43.2 months for 421 patients. In the training cohort, multivariable analysis revealed that ER status, clinical node stage, pCR, pre-NLR, and post-NLR (all p < 0.05) were independent predictors of OS. The OS nomogram was established based on these parameters. The C-indexes of the nomogram were 0.764 and 0.605 in the training and validation cohorts, respectively. In the longitudinal analysis, patients who failed to achieve pCR experienced an augment of NLR during NAC while NLR remained stable among patients with pCR. Pre-NLR tended to be significantly associated with OS in patients of HER2 overexpressing and TNBC subtypes (all p < 0.05), but not in Luminal A and Luminal B subtypes.

Conclusions

This study demonstrated the prognostic value of both pre-NLR and post-NLR on clinical outcomes in BC patients receiving NAC. A novel nomogram was established to predict OS. Non-pCR patients developed increased NLRs during NAC. Routine assessment of NLR may be a simple and affordable tool to predict prognosis for BC patients receiving NAC.

Evaluation of machine learning algorithms for the prognosis of breast cancer from the Surveillance, Epidemiology, and End Results database

INTRODUCTION

Many researchers used machine learning (ML) to predict the prognosis of breast cancer (BC) patients and noticed that the ML model had good individualized prediction performance.

OBJECTIVE

The cohort study was intended to establish a reliable data analysis model by comparing the performance of 10 common ML algorithms and the the traditional American Joint Committee on Cancer (AJCC) stage, and used this model in Web application development to provide a good individualized prediction for others.

METHODS

This study included 63145 BC patients from the Surveillance, Epidemiology, and End Results database.

RESULTS

Through the performance of the 10 ML algorithms and 7th AJCC stage in the optimal test set, we found that in terms of 5-year overall survival, multivariate adaptive regression splines (MARS) had the highest area under the curve (AUC) value (0.831) and F1-score (0.608), and both sensitivity (0.737) and specificity (0.772) were relatively high. Besides, MARS showed a highest AUC value (0.831, 95%confidence interval: 0.820-0.842) in comparison to the other ML algorithms and 7th AJCC stage (all P < 0.05). MARS, the best performing model, was selected for web application development (https://w12251393.shinyapps.io/app2/).

CONCLUSIONS

The comparative study of multiple forecasting models utilizing a large data noted that MARS based model achieved a much better performance compared to other ML algorithms and 7th AJCC stage in individualized estimation of survival of BC patients, which was very likely to be the next step towards precision medicine.

Characteristics of recurrence, predictors for relapse and prognosis of rapid relapse triple-negative breast cancer

Background

Triple-negative breast cancer (TNBC) patients who recur at different times are associated with distinct biological characteristics and prognoses. Research on rapid-relapse TNBC (RR-TNBC) is sparse. In this study, we aimed to describe the characteristics of recurrence, predictors for relapse, and prognosis in rrTNBC patients.

Methods

Clinicopathological data of 1584 TNBC patients from 2014 to 2016 were retrospectively reviewed. The characteristics of recurrence were compared between patients with RR-TNBC and slow relapse TNBC(SR-TNBC). All TNBC patients were randomly divided into a training set and a validation set to find predictors for rapid relapse. The multivariate logistic regression model was used to analyze the data of the training set. C-index and brier score analysis for predicting rapid relapse in the validation set was used to evaluate the discrimination and accuracy of the multivariate logistic model. Prognostic measurements were analyzed in all TNBC patients.

Results

Compared with SR-TNBC patients, RR-TNBC patients tended to have a higher T staging, N staging, TNM staging, and low expression of stromal tumor-infiltrating lymphocytes (sTILs). The recurring characteristics were prone to appear as distant metastasis at the first relapse. The first metastatic site was apt to visceral metastasis and less likely to have chest wall or regional lymph node metastasis. Six predictors (postmenopausal status, metaplastic breast cancer,≥pT3 staging,≥pN1 staging, sTIL intermediate/high expression, and Her2 [1+]) were used to construct the predictive model of rapid relapse in TNBC patients. The C-index and brier score in the validation set was 0.861 and 0.095, respectively. This suggested that the predictive model had high discrimination and accuracy. The prognostic data for all TNBC patients showed that RR-TNBC patients had the worst prognosis, followed by SR-TNBC patients.

Conclusion

RR-TNBC patients were associated with unique biological characteristics and worse outcomes compared to non-RR-TNBC patients.

Nomogram for predicting overall survival in Chinese triple-negative breast cancer patients after surgery

BACKGROUND

There are few nomograms for the prognosis of Chinese patients with triple-negative breast cancer (TNBC).

AIM

To construct and validate a nomogram for overall survival (OS) of Chinese TNBC patients after surgery.

METHODS

This study used the data of SEER*stat 8.3.5 and selected Chinese patients with TNBC operated on between 2010 and 2015. Univariate and multivariate Cox proportional hazard regression models were used. The identified variables were integrated to form a predictive nomogram and risk stratification model; it was assessed with C-indexes and calibration curves.

RESULTS

The median and maximal OS of the 336 patients was 39 and 83 mo, respectively. The multivariate analysis showed that age (P = 0.043), marital status (P = 0.040), tumor localization (P = 0.030), grade (P = 0.035), T classification (P = 0.012), and N classification (P = 0.002) were independent prognostic factors. The six variables were combined to construct a 1-, 3- and 5-year OS nomogram. The C-indexes of the nomogram to predict OS were 0.766 and compared to the seventh edition staging system, which was higher (0.766 vs 0.707, P < 0.001). In order to categorize patients into different prognostic groups, a risk stratification model was created. There was a significant difference between the Kaplan-Meier curves of the entire cohort and each disease stage according to the nomogram.

CONCLUSION

The nomogram provided prognostic superiority over the traditional tumor, node and metastasis system. It could help clinicians make individual OS or risk predictions for Chinese TNBC patients after surgery.

A Nomogram Based on Hematological Parameters and Clinicopathological Characteristics for Predicting Local-Regional Recurrence After Breast-Conserving Therapy

Objectives

The aim of this study was to identify the factors for local-regional recurrence (LRR) after breast-conserving therapy (BCT). We established a practical nomogram to predict the likelihood of LRR after BCT based on hematological parameters and clinicopathological features.

Methods

A retrospective analysis was performed on 2,085 consecutive breast cancer patients who received BCT in Shandong Cancer Hospital from 2006 to 2016, including 1,460 patients in the training cohort and 625 patients in the validation cohort. Univariate and multivariate analyses were performed based on hematological parameters (fibrinogen, platelets, mean platelet volume, neutrophils, monocytes, and lymphocytes) and clinicopathological characteristics to identify the independent factors for LRR. Subsequently, a nomogram for predicting LRR was established by logistic regression analysis. The nomogram was validated in 625 patients in the validation cohort.

Results

During the median follow-up period of 66 months, 44 (3.01%) patients in the training cohort and 19 (3.04%) patients in the validation cohort suffered from LRR. Multivariate analysis showed six independent factors related to LRR, including molecular subtype, pathological N stage, re-resection, radiotherapy or not, platelet count*MPV*fibrinogen (PMF), and neutrophil count/lymphocyte count ratio (NLR). Six variables were entered into logistic regression to establish the nomogram for predicting LRR. The nomogram of LRR showed excellent discrimination and prediction accuracy. The area under the receiver operating characteristic curve (AUC) was 0.89 (p < 0.001, 95% CI = 0.83, 0.95) in the training cohort and 0.88 (p < 0.001, 95% CI = 0.8, 0.96) in the validation cohort. Calibration curves for the prediction model in the training and validation cohorts both demonstrated satisfactory consistency between the nomogram-predicted and actual LRR.

Conclusion

The combination of hematological parameters and clinicopathological characteristics can predict LRR after BCT. The predictive nomogram based on preoperative and postoperative indicators of BCT might serve as a practical tool for individualized prognostication. More prospective studies should be performed to verify the model.

Survival outcome assessment for triple-negative breast cancer: a nomogram analysis based on integrated clinicopathological, sonographic, and mammographic characteristics

Objective

This study aimed to incorporate clinicopathological, sonographic, and mammographic characteristics to construct and validate a nomogram model for predicting disease-free survival (DFS) in patients with triple-negative breast cancer (TNBC).

Methods

Patients diagnosed with TNBC at our institution between 2011 and 2015 were retrospectively evaluated. A nomogram model was generated based on clinicopathological, sonographic, and mammographic variables that were associated with 1-, 3-, and 5-year DFS determined by multivariate logistic regression analysis in the training set. The nomogram model was validated according to the concordance index (C-index) and calibration curves in the validation set.

Results

A total of 636 TNBC patients were enrolled and divided into training cohort (n = 446) and validation cohort (n = 190). Clinical factors including tumor size > 2 cm, axillary dissection, presence of LVI, and sonographic features such as angular/spiculated margins, posterior acoustic shadows, and presence of suspicious lymph nodes on preoperative US showed a tendency towards worse DFS. The multivariate analysis showed that no adjuvant chemotherapy (HR = 6.7, 95% CI: 2.6, 17.5, p < 0.0005), higher axillary tumor burden (HR = 2.7, 95% CI: 1.0, 7.1, p = 0.045), and ≥ 3 malignant features on ultrasound (HR = 2.4, CI: 1.1, 5.0, p = 0.021) were identified as independent prognostic factors associated with poorer DFS outcomes. In the nomogram, the C-index was 0.693 for the training cohort and 0.694 for the validation cohort. The calibration plots also exhibited excellent consistency between the nomogram-predicted and actual survival probabilities in both the training and validation cohorts.

Conclusions

Clinical variables and sonographic features were correlated with the prognosis of TNBCs. The nomogram model based on three variables including no adjuvant chemotherapy, higher axillary tumor load, and more malignant sonographic features showed good predictive performance for poor survival outcomes of TNBC.

Key Points

• The absence of adjuvant chemotherapy, heavy axillary tumor load, and malignant-like sonographic features can predict DFS in patients with TNBC.

• Mammographic features of TNBC could not predict the survival outcomes of patients with TNBC.

• The nomogram integrating clinicopathological and sonographic characteristics is a reliable predictive model for the prognostic outcome of TNBC.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00330-022-08910-4.

Distant Metastasis Pattern and Prognostic Prediction Model of Colorectal Cancer Patients Based on Big Data Mining

Aims

This study aimed to investigate the distant metastasis pattern from newly diagnosed colorectal cancer (CRC) and also construct and validate a prognostic nomogram to predict both overall survival (OS) and cancer-specific survival (CSS) of CRC patients with distant metastases.

Methods

Primary CRC patients who were initially diagnosed from 2010 to 2016 in the SEER database were included in the analysis. The independent risk factors affecting the OS, CSS, all-cause mortality, and CRC-specific mortality of the patients were screened by the Cox regression and Fine-Gray competitive risk model. The nomogram models were constructed to predict the OS and CSS of the patients. The reliability and accuracy of the prediction model were evaluated by consistency index (C-index) and calibration curve. The gene chip GSE41258 was downloaded from the GEO database, and differentially expressed genes (DEGs) were screened by the GEO2R online tool (p < 0.05, |logFC|>1.5). The Kyoto Encyclopedia of Genes and Genomes (KEGG) Pathway and Gene Ontology (GO) annotation and String website were used for enrichment analysis and protein-protein interaction (PPI) analysis of DEGs, respectively, and Cytoscape software was used to construct PPI network and screen function modules and hub genes.

Results

A total of 57,835 CRC patients, including 47,823 without distant metastases and 10,012 (17.31%) with metastases, were identified. Older age, unmarried status, poorly differentiated or undifferentiated grade, right colon site, larger tumor size, N2 stage, more metastatic sites, and elevated carcinoembryonic antigen (CEA) might lead to poorer prognosis (all p < 0.01). The independent risk factors of OS and CSS were included to construct a prognosis prediction model for predicting OS and CSS in CRC patients with distant metastasis. C-index and calibration curve of the training group and validation group showed that the models had acceptable predictive performance and high calibration degree. Furthermore, by comparing CRC tissues with and without liver metastasis, 158 DEGs and top 10 hub genes were screened. Hub genes were mainly concentrated in liver function and coagulation function.

Conclusion

The big data in the public database were counted and transformed into a prognostic evaluation tool that could be applied to the clinic, which has certain clinical significance for the formulation of the treatment plan and prognostic evaluation of CRC patients with distant metastasis.

Establishment and Validation of Prognostic Nomograms Based on Serum Copper Level for Patients With Early-Stage Triple-Negative Breast Cancer

Background: Altered copper levels have been observed in several cancers, but studies on the relationship between serum copper and early-stage triple-negative breast cancer (TNBC) remain scare. We sought to establish a predictive model incorporating serum copper levels for individualized survival predictions. Methods: We retrospectively analyzed clinicopathological information and baseline peripheric blood samples of patients diagnosed with early-stage TNBC between September 2005 and October 2016 at Sun Yat-sen University Cancer Center. The optimal cut-off point of serum copper level was determined using maximally selected log-rank statistics. Kaplan-Meier curves were used to estimate survival probabilities. Independent prognostic indicators associated with survival were identified using multivariate Cox regression analysis, and subsequently, prognostic nomograms were established to predict individualized disease-free survival (DFS) and overall survival (OS). The nomograms were validated in a separate cohort of 86 patients from the original randomized clinical trial SYSUCC-001 (SYSUCC-001 cohort). Results: 350 patients were eligible in this study, including 264 in the training cohort and 86 in the SYSUCC-001 cohort. An optimal cut-off value of 21.3 μmol/L of serum copper was determined to maximally divide patients into low- and high-copper groups. After a median follow-up of 87.1 months, patients with high copper levels had significantly worse DFS (p = 0.002) and OS (p < 0.001) than those with low copper levels in the training cohort. Multivariate Cox regression analysis revealed that serum copper level was an independent factor for DFS and OS. Further, prognostic models based on serum copper were established for individualized predictions. These models showed excellent discrimination [C-index for DFS: 0.689, 95% confidence interval (CI): 0.621-0.757; C-index for OS: 0.728, 95% CI: 0.654-0.802] and predictive calibration, and were validated in the SYSUCC-001 cohort. Conclusion: Serum copper level is a potential predictive biomarker for patients with early-stage TNBC. Predictive nomograms based on serum copper might be served as a practical tool for individualized prognostication.

A Novel Prognostic Model Based on the Serum Iron Level for Patients With Early-Stage Triple-Negative Breast Cancer

The dysregulation of iron homeostasis has been explored in malignancies. However, studies focusing on the association between the serum iron level and prognosis of patients with early-stage triple-negative breast cancer (TNBC) are scarce. Accordingly, in current study, 272 patients with early-stage TNBC treated at Sun Yat-sen University Cancer Center (SYSUCC) between September 2005 and October 2016 were included as a training cohort, another 86 patients from a previous randomized trial, SYSUCC-001, were analyzed as a validation cohort (SYSUCC-001 cohort). We retrospectively collected their clinicopathological data and tested the serum iron level using blood samples at the diagnosis. In the training cohort, patients were divided into low-iron and high-iron groups according to the serum iron level cut-off of 17.84 μmol/L determined by maximally selected rank statistics. After a median follow-up of 87.10 months, patients with a low iron had a significantly longer median disease-free survival (DFS) of 89.13 [interquartile range (IQR): 66.88-117.38] months and median overall survival (OS) of 92.85 (IQR: 68.83-117.38) months than those in the high-iron group (median DFS: 75.25, IQR: 39.76-105.70 months, P = 0.015; median OS: 77.17, IQR: 59.38-110.28 months, P = 0.015). Univariate and multivariate Cox analysis demonstrated the serum iron level to be an independent predictor for DFS and OS. Then, a prognostic nomogram incorporating the serum iron level, T stage and N stage was developed for individualized prognosis predictions. It had good discriminative ability with a C-index of DFS (0.729; 95% CI 0.666-0.792) and OS (0.739; 95% CI 0.666-0.812), respectively. Furtherly, we validated the predictive model in the SYSUCC-001 cohort, which also showed excellent predictive performance with a C-index of DFS (0.735; 95% CI 0.614-0.855) and OS (0.722; 95% CI 0.577-0.867), respectively. All these suggested that the serum iron level might be a potential prognostic biomarker for patients with early-stage TNBC, the predictive model based on it might be served as a practical tool for individualized survival predictions.

Development of prognostic nomograms using institutional data for patients with triple-negative breast cancer

Background: Triple-negative breast cancer (TNBC) is an aggressive disease. Nomograms can predict prognosis of patients with TNBC. Methods: A total of 745 eligible TNBC patients were recruited and randomly divided into training and validation groups. Endpoints were disease-free survival and overall survival. Concordance index, area under the curve and calibration curves were used to analyze the predictive accuracy and discriminative ability of nomograms. Results: Based on the training cohort, neutrophil-to-lymphocyte ratio, positive lymph nodes, tumor size and tumor-infiltrating lymphocytes were used to construct a nomogram for disease-free survival. In addition, age was added to the overall survival nomogram. Conclusion: The current study developed and validated well-calibrated nomograms for predicting disease-free survival and overall survival in patients with TNBC.

Triple-negative breast cancer outcomes: Does AJCC 8th staging improve chemotherapy decision-making

PURPOSE

To investigate the effect of the 8th American Joint Committee on Cancer (AJCC) pathological prognostic staging on chemotherapy decision-making for triple-negative breast cancer (TNBC) patients with T1-2N0M0 disease.

METHODS

Patients diagnosed with T1-2N0M0 TNBC were retrieved from the Surveillance, Epidemiology, and End Results program. Statistical methods including Kaplan-Meier survival curve, receiver operating characteristics curve, and Cox proportional hazard model.

RESULTS

We identified 12,156 patients, including 9371 (77.1%) patients who received chemotherapy. Overall, 57.4% of patients (n = 6975) were upstaged after being reassigned by the 8th AJCC staging. However, the 8th staging of AJCC did not have a greater prognostic value compared to the 7th staging (P = 0.064). The receipt of chemotherapy significantly improved the breast cancer-specific survival for stage T1c and T2 tumors (P < 0.001), but not for stage T1a (P = 0.188) and T1b (P = 0.376) tumors. Using AJCC 8th staging, chemotherapy benefit was only found in stage IIA patients (P = 0.002), but not for stage IA (P = 0.653) and IB (P = 0.492) patients. There were 9564 patients with stage T1c and T2 diseases and 4979 patients with 8th AJCC stage IIA disease. Therefore, approximately half of patients (47.9%, n = 4585) may be safe to omit chemotherapy using the AJCC 8th staging compared to the current chemotherapy recommendation for T1-2N0M0 TNBC.

CONCLUSION

The 8th AJCC staging system did not demonstrate the superior discriminatory ability of prognostic stratification than the 7th AJCC staging system in T1-2N0M0 TNBC. However, this new AJCC staging could more accurately predict the chemotherapy benefit, thereby enabling more patients to avoid unnecessary chemotherapy.

Biomarkers of response to camrelizumab combined with apatinib: an analysis from a phase II trial in advanced triple-negative breast cancer patients

PURPOSE

We recently reported results of a phase II trial that camrelizumab plus apatinib induced an objective response rate (ORR) at 43.3% in advanced triple-negative breast cancer (TNBC). This study presents analysis of potential biomarkers.

METHODS

TILs, CD8⁺ T cells and PD-1/PD-L1 expression were evaluated in tumor samples by immunohistochemistry. 59 Cytokines/chemokines, growth factors, or checkpoint-related proteins, blood immune cell subpopulations were analyzed in blood samples by multiplexed bead immunoassays or flow cytometry. Correlation between biomarkers and clinical outcomes including ORR, progression-free survival (PFS), and overall survival (OS) was analyzed.

RESULTS

28 Patients had biopsies and blood collected. Baseline TILs were significantly associated with longer PFS (P = 0.035). An increase of tumor-infiltrating CD8⁺ T cells > 15% during therapy was associated with higher ORR (P = 0.040). Patients with lower baseline plasma levels of HGF or IL-8 were more likely to respond to treatment (P = 0.005 or 0.001, respectively), and showed a longer PFS and OS. Patients with a decrease of IL-8, or an increase of TIM-3 or CD152 during treatment responded more to treatment (P = 0.008, 0.040, or 0.014, respectively). Responders had a higher baseline CD4⁺ T cells and B cell proportions in blood than non-responders (P = 0.002 and 0.030, respectively).

CONCLUSION

Higher baseline TILs or a greater increase of tumor-infiltrating CD8⁺ T cells during therapy, lower baseline plasma HGF/IL-8, a decrease of plasma IL-8, an increase of plasma TIM-3/CD152 during therapy, higher baseline CD4⁺ T cells or B cells proportion in blood are potential biomarkers for combinational anti-angiogenesis and immunotherapy in advanced TNBC patients.

Nomograms predict survival of patients with small bowel adenocarcinoma: a SEER-based study

BACKGROUND AND OBJECTIVE

Small bowel adenocarcinoma (SBA) is a rare malignant tumor with a poor prognosis. Most patients with SBA are diagnosed with advanced-stage disease. Due to the lack of randomized controlled trials and prospective studies, it is difficult to predict the prognosis of patients with SBA. Thus, this study aimed to establish a prognostic nomogram for evaluating the prognosis of SBA patients.

METHODS

The clinical features and follow-up data of all patients diagnosed with SBA during 2004-2016 were summarized from the Surveillance, Epidemiology, and End Results (SEER) database. We separated these patients into training and validation groups. Multivariate Cox regression analyses were performed to identify independent prognostic variables for predicting cancer-specific survival (CSS) and overall survival (OS). According to the independent risk factors, we established nomograms and used the calibration curves to evaluate the accuracy.

RESULTS

The data of 3301 patients with SBA were collected from the SEER database. The multivariate analysis showed that age, marital status, tumor site, grade, TNM stage and surgical history were associated with CSS and OS (P < 0.05). Based on these results, we established nomograms of CSS and OS that can predict the 3- and 5-year survival rates of SBA patients (C-index > 0.7). The calibration curves showed that the predicted survival was very close to the actual survival.

CONCLUSION

We analyzed the independent risk factors for prognosis of SBA patients, and established nomograms to predict the 3- and 5-year survival rates of OS and CSS. These new prognostic tools can help clinicians to predict the survival of patients with SBA, further to guide treatment strategy.

Quality of life among breast cancer survivors with triple negative breast cancer--role of hope, self-efficacy and social support

PURPOSE

To investigate quality of life status and its influence factors among Chinese triple negative breast cancer patients, especially the role of hope, self-efficacy and social support.

METHOD

121 triple negative breast cancer patients were recruited from March to June 2019. Data was collected by demographic and clinical characteristics questionnaire and instruments assessing hope, social support, self-efficacy and quality of life. Descriptive statistics, independent samples t-tests or one-way analysis of variance, Pearson correlation analyses and multiple regression analyses were applied.

RESULTS

Hope, social support and self-efficacy were all positively correlated with quality of life (P < 0.001). Multiple regression analyses indicated hope, income, cancer stage, self-efficacy, and social support as indicators of quality of life, explaining 56.2% of the response variation (P < 0.001).

CONCLUSIONS

Quality of life of triple negative breast cancer patients need to be improved. Income, hope, self-efficacy and social support are positive predictors, and cancer stage are negative predictors of quality of life. Supportive programs and interventions targeting on increasing levels of hope, self efficacy and social support should be considered while caring for this group.