Prognostic factors and survival prediction in HER2-positive breast cancer with bone metastases: A retrospective cohort study

Factors Associated With Short- and Long-Term Survival in Metastatic HER2-Positive Breast Cancer

BACKGROUND

We sought to evaluate prognostic factors in human epidermal growth factor receptor 2 (HER2)-positive metastatic breast cancer (MBC) and their relationship with short- and long-term overall survival (OS).

METHODS

Using the Surveillance, Epidemiology, and End Results (SEER) database, we evaluated patients with de novo HER2-positive MBC diagnosed from 2010 to 2018. Univariate analyses were performed to determine effect of each variable on OS. Significant variables were included in a multivariate Cox model for OS. Univariate and multivariate logistic regression were used to evaluate the association of each variable with short- (<2 years) and long- (≥5 years) term OS.

RESULTS

Overall, 5576 patients were included. Median follow up was 48 months (interquartile range 25-73 months), and median OS was 41 months. The proportion alive at 2, 5, and 8 years was 63.3% (95% confidence interval [CI] 62.0%-64.7%), 37.8% (95% CI, 36.2%-39.4%), and 26.8% (95% CI, 24.8%-28.9%), respectively. Factors associated with short-term OS were older age; Black race; nonductal nonlobular; brain, liver, or lung metastases; estrogen/progesterone receptor (ER/PR)-negative disease, and lower income (all P < .04). Number of metastatic organ sites was not significant. Factors associated with long-term OS were younger age, White race, fewer metastatic organ sites, ER/PR-positive disease, and higher income (all P < .02). Specific organ sites were not significant.

CONCLUSIONS

In this cohort with de novo HER2-positive MBC, OS improved significantly over the study period. We identified patient-specific and tumor-specific factors that were associated with short- and long-term survival in HER2-positive MBC.

Development of prognostic models for HER2-positive metastatic breast cancer in females: a retrospective population-based study

BACKGROUND

This study aimed to construct, evaluate, and validate nomograms for breast cancer-specific survival (BCSS) and overall survival (OS) prediction in patients with HER2- overexpressing (HER2+) metastatic breast cancer (MBC).

METHODS

The Surveillance, Epidemiology, and End Results (SEER) database was used to select female patients diagnosed with HER2 + MBC between 2010 and 2015. These patients were distributed into training and validation groups (7:3 ratio). Variables were screened using univariate and multivariate Cox regression analyses, and BCSS and OS nomograms were constructed to determine one-, three-, and five-year survival probabilities. The nomograms were evaluated and validated using the concordance index (C-index), time-dependent receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis. Stratification was evaluated using Kaplan-Meier curves and log-rank tests based on optimal total score cut-off values. We published web-based versions of these nomograms for clinical use.

RESULTS

A total of 2,151 eligible patients were randomized into training (n = 1,505) and validation (n = 646) groups. Independent prognostic factors of BCSS and OS included: age; marital status; race; oestrogen receptor status; surgery; chemotherapy; and bone, brain, liver, and lung metastases. The C-indices for the BCSS and OS training groups were 0.707 and 0.702, respectively. The ROC, calibration, and decision curves demonstrated the strength of the nomograms. According to cut-off values, patients were categorized into low-, intermediate-, and high-risk groups, with significant differences in survival outcomes between them.

CONCLUSION

We constructed predictive nomograms and stratified risk to assess the prognosis of patients with HER2 + MBC, which could help inform therapeutic decisions.

TRIAL REGISTRATION

Not applicable.

A network dynamic nomogram for predicting overall survival and cancer-specific survival in patients with breast cancer liver metastases: an analysis based on the SEER database

The liver stands out as one of the most frequent sites for distant metastasis in breast cancer cases. However, effective risk stratification tools for patients with breast cancer liver metastases (BCLM) are still lacking. We identified BCLM patients from the SEER database spanning from 2010 to 2016. After meticulously filtering out cases with incomplete data, a total of 3179 patients were enrolled and randomly divided into training and validation cohorts at a ratio of 2:1. Leveraging comprehensive patient data, we constructed a nomogram through rigorous evaluation of a Cox regression model. Validation of the nomogram was conducted using a range of statistical measures, including the concordance index (C-index), calibration curves, time-dependent receiver operating characteristic curves, and decision curve analysis (DCA). Both univariable and multivariable analyses revealed significant associations between OS and CSS in BCLM patients and 14 variables, including age, race, and tumor stage, among others. Utilizing these pertinent variables, we formulated nomograms for OS and CSS prediction. Subsequent validation involved rigorous assessment using time-dependent ROC curves, decision curve analysis, C-index evaluations, and calibration curves. Our web-based dynamic nomogram represents a valuable tool for efficiently analyzing the clinical profiles of BCLM patients, thereby aiding in informed clinical decision-making processes.

Prognostic nomograms for young breast cancer: A retrospective study based on the SEER and METABRIC databases

Background

Young breast cancer (YBC) is a subset of breast cancer that is often more aggressive, but less is known about its prognosis. In this study, we aimed to generate nomograms to predict the overall survival (OS) and breast cancer-specific survival (BCSS) of YBC patients.

Methods

Data of women diagnosed with YBC between 2010 and 2020 were obtained from the Surveillance, Epidemiology, and End Results (SEER) database. The patients were randomly allocated into a training cohort (n = 15,227) and internal validation cohort (n = 6,526) at a 7:3 ratio. With the Cox regression models, significant prognostic factors were identified and used to construct 3-, 5-, and 10-year nomograms of OS and BCSS. Data from the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) database were used as an external validation cohort (n = 90).

Results

We constructed nomograms incorporating 10 prognostic factors for OS and BCSS. These nomograms demonstrated strong predictive accuracy for OS and BCSS in the training cohort, with C-indexes of 0.806 and 0.813, respectively. The calibration curves verified that the nomograms have good prediction accuracy. Decision curve analysis demonstrated their practical clinical value for predicting YBC patient survival rates. Additionally, we provided dynamic nomograms to improve the operability of the results. The risk stratification ability assessment also showed that the OS and BCSS rates of the low-risk group were significantly better than those of the high-risk group.

Conclusions

Here, we generated and validated more comprehensive and accurate OS and BCSS nomograms than models previously developed for YBC. These nomograms can help clinicians evaluate patient prognosis and make clinical decisions.

Development and Validation of a Prognostic Nomogram for Breast Cancer Patients With Multi-Organ Metastases: An Analysis of the Surveillance, Epidemiology, and End Results Program Database

BACKGROUND

Multi-organ metastases represent a substantial life-threatening risk for breast cancer (BC) patients. Nonetheless, the current dearth of assessment tools for patients with multi-organ metastatic BC adversely impacts their evaluation.

METHODS

We conducted a retrospective analysis of BC patients with multi-organ metastases using data from the SEER database from 2010 to 2019. The patients were randomly allocated into a training cohort and a validation cohort in a 7:3 ratio. Univariate COX regression analysis, the LASSO, and multivariate Cox regression analyses were performed to identify independent prognostic factors in the training set. Based on these factors, a nomogram was constructed to estimate overall survival (OS) probability for BC patients with multi-organ metastases. The performance of the nomogram was evaluated using C-indexes, ROC curves, calibration curves, decision curve analysis (DCA) curves, and the risk classification system for validation.

RESULTS

A total of 3626 BC patients with multi-organ metastases were included in the study, with 2538 patients in the training cohort and 1088 patients in the validation cohort. Age, grade, metastasis location, surgery, chemotherapy, and subtype were identified as significant independent prognostic factors for OS in BC patients with multi-organ metastases. A nomogram for predicting 1-year, 3-year, and 5-year OS was constructed. The evaluation metrics, including C-indexes, ROC curves, calibration curves, and DCA curves, demonstrated the excellent predictive performance of the nomogram. Additionally, the risk grouping system effectively stratified BC patients with multi-organ metastases into distinct prognostic categories.

CONCLUSION

The developed nomogram showed high accuracy in predicting the survival probability of BC patients with multi-organ metastases, providing valuable information for patient counseling and treatment decision making.

Development and Validation of a Prognostic Nomogram Model for HER2-Positive Male Breast Cancer Patients

BACKGROUND

HER2-positive male breast cancer (MBC) is a rare condition that has a poor prognosis. The purpose of this study was to establish a nomogram model for predicting the prognosis of HER2-positive MBC patients.

METHODS

240 HER2-positive MBC patients from 2004 to 2015 were retrieved from the surveillance, epidemiology, and end results (SEER) database. All HER2-positive MBC patients were divided randomly into training (n = 144) and validation cohorts (n = 96) according to a ratio of 6:4. Univariate and multivariate Cox regression analyses were used to determine the prognostic factors associated with HER2-positive MBC patients. A clinical prediction model was constructed to predict the overall survival of these patients. The nomogram model was assessed by using receiver operating characteristics (ROC) curves, calibration plots and decision curve analysis (DCA).

RESULTS

The Cox regression analysis showed that T-stage, M-stage, surgery and chemotherapy were independent risk factors for the prognosis of HER2-positive MBC patients. The model could also accurately predict the Overall survival (OS) of the patients. In the training and validation cohorts, the C indexes of the OS nomograms were 0.746 (0.677-0.815) and 0.754 (0.679-0.829), respectively. Calibration curves and DCA verified the reliability and accuracy of the clinical prediction model.

CONCLUSION

In conclusion, the predictive model constructed had good clinical utility and can help the clinician to select appropriate treatment strategies for HER2-positive MBC patients.

Machine Learning-Based Prediction of 1-Year Survival Using Subjective and Objective Parameters in Patients With Cancer

PURPOSE

Palliative care is recommended for patients with cancer with a life expectancy of <12 months. Machine learning (ML) techniques can help in predicting survival outcomes among patients with cancer and may help distinguish who benefits the most from palliative care support. We aim to explore the importance of several objective and subjective self-reported variables. Subjective variables were collected through electronic psycho-oncologic and palliative care self-assessment screenings. We used these variables to predict 1-year mortality.

MATERIALS AND METHODS

Between April 1, 2020, and March 31, 2021, a total of 265 patients with advanced cancer completed a patient-reported outcome tool. We documented objective and subjective variables collected from electronic health records, self-reported subjective variables, and all clinical variables combined. We used logistic regression (LR), 20-fold cross-validation, decision trees, and random forests to predict 1-year mortality. We analyzed the receiver operating characteristic (ROC) curve-AUC, the precision-recall curve-AUC (PR-AUC)-and the feature importance of the ML models.

RESULTS

The performance of clinical nonpatient variables in predictions (LR reaches 0.81 [ROC-AUC] and 0.72 [F1 score]) are much more predictive than that of subjective patient-reported variables (LR reaches 0.55 [ROC-AUC] and 0.52 [F1 score]).

CONCLUSION

The results show that objective variables used in this study are much more predictive than subjective patient-reported variables, which measure subjective burden. These findings indicate that subjective burden cannot be reliably used to predict survival. Further research is needed to clarify the role of self-reported patient burden and mortality prediction using ML.

Risk factors, prognostic factors, and nomograms for synchronous brain metastases of solid tumors: a population-based study

In previous literatures, we found that similar studies on the short-term prognosis of synchronous brain metastases (S-BM) from other systems are rare. Our aim was to evaluate the early mortality rate of patients with S-BM from the Surveillance, Epidemiology, and End Result (SEER) database and explore the risk factors for early mortality (≤ 1 year). We used Kaplan-Meier (KM) curves to evaluate early mortality in patients with S-BM from the SEER database. Logistic regression analyses were used to identify significant independent prognostic factors in patients with a follow-up time > 12 months. And the meaningful factors were used to construct a nomogram of overall early death. The receiver operating characteristic (ROC) curve was used to test the predictive ability of the model, while the decision curve analysis (DCA) curve was used to validate the clinical application ability of the model. A total of 47,284 patients were used for univariate and multivariate logistic regression analysis to screen variables to constructing a nomogram. In the all-cause early mortality specific model, the area under the ROC (AUC) curve of the training set was 0.764 (95% confidence interval (CI): 0.758-0.769), and the AUC of the validation set was 0.761 (95% CI: 0.752-0.770). The DCA calibration curves of the training set and validation set indicate that the 1-year early mortality rate predicted by this model is consistent with the actual situation. We found that the 1-year early mortality rate was 76.4%. We constructed a validated nomogram using these covariates to effectively predict 1-year early mortality in patients with S-BM. This nomogram can help clinical workers screen high-risk patients to develop more reasonable treatment plans.

Predicting survival of patients with bone metastasis of unknown origin

Purpose

Bone metastasis of unknown origin is a rare and challenging situation, which is infrequently reported. Therefore, the current study was performed to analyze the clinicopathologic features and risk factors of survival among patients with bone metastasis of unknown origin.

Patients and methods

We retrospectively analyzed the clinical data for patients with bone metastasis of unknown origin between 2010 and 2016 based on the Surveillance, Epidemiology, and End Results (SEER) database. Overall survival (OS) and cancer-specific survival (CSS) were first analyzed by applying univariable Cox regression analysis. Then, we performed multivariable analysis to confirm independent survival predictors.

Results

In total, we identified 1224 patients with bone metastasis of unknown origin for survival analysis, of which 704 males (57.5%) and 520 females (42.5%). Patients with bone metastasis of unknown origin had a 1-year OS rate of 14.50% and CSS rate of 15.90%, respectively. Race, brain metastasis, liver metastasis, radiotherapy, and chemotherapy were significant risk factors of OS on both univariable and multivariable analyses (p <0.05). As for CSS, both univariable and multivariable analyses revealed that no brain metastasis, no liver metastasis, radiotherapy, and chemotherapy were associated with increased survival (p <0.05).

Conclusion

Patients with bone metastasis of unknown origin experienced an extremely poor prognosis. Radiotherapy and chemotherapy were beneficial for prolonging the survival of those patients.

Tailoring Potential Natural Compounds for the Treatment of Luminal Breast Cancer

Breast cancer (BC) is the most diagnosed cancer worldwide, mainly affecting the epithelial cells from the mammary glands. When it expresses the estrogen receptor (ER), the tumor is called luminal BC, which is eligible for endocrine therapy with hormone signaling blockade. Hormone therapy is essential for the survival of patients, but therapeutic resistance has been shown to be worrying, significantly compromising the prognosis. In this context, the need to explore new compounds emerges, especially compounds of plant origin, since they are biologically active and particularly promising. Natural products are being continuously screened for treating cancer due to their chemical diversity, reduced toxicity, lower side effects, and low price. This review summarizes natural compounds for the treatment of luminal BC, emphasizing the activities of these compounds in ER-positive cells. Moreover, their potential as an alternative to endocrine resistance is explored, opening new opportunities for the design of optimized therapies.

Human Epidermal Growth Factor Receptor 2/Human Epidermal Growth Factor Receptor 3 PET Imaging: Challenges and Opportunities

Human epidermal growth factor receptor 2 (HER2) and HER3 provide actionable targets for both therapy and imaging in breast cancer. Further, clinical trials have shown the prognostic impact of receptor status discordance in breast cancer. Intra- and intertumoral heterogeneity of both HER and hormone receptor expression contributes to inherent errors in tissue sampling, and single biopsies are incapable of identifying discordance in biomarker expression. Numerous PET radiopharmaceuticals have been developed to evaluate (or target for therapy) HER2 and HER3 expression. This review seeks to inform on challenges and opportunities in HER2 and HER3 PET imaging in both clinical and preclinical settings.

Recent Trends in Synchronous Brain Metastasis Incidence and Mortality in the United States: Ten-Year Multicenter Experience

BACKGROUND

Large epidemiological studies describing the trends in incidence rates and mortality of synchronous brain metastases (SBMs) are lacking. The study aimed to provide a comprehensive understanding of the changes in the incidence and mortality of SBMs over the previous ten years.

METHODS

Trends in the incidence of solid malignancies outside of the CNS in patients with SBMs and incidence-based mortality rates were assessed using data from the Surveillance, Epidemiology, and End Results database. Joinpoint analyses were used to calculate annual percent changes (APCs) and 95% CIs.

RESULTS

Between 2010 and 2019, 66,655 patients, including 34,821 (52.24%) men and 31,834 (47.76%) women, were found to have SBMs, and 57,692 deaths occurred over this period. Lung cancer SBMs, melanoma SBMs, and breast cancer SBMs were ranked in the top three, having the highest age-standardized incidence rates. The incidence of SBMs decreased significantly with an APC of -0.6% from 2010 to 2019, while the APC was 1.2% for lung cancer SBMs, 2.5% for melanoma SBMs, and 0.6% for breast cancer SBMs. The SBM mortality first experienced a rapid increase (APC = 28.6%) from 2010 to 2012 and then showed a significant decline at an APC of -1.8% from 2012 to 2019. Lung cancer SBMs showed similar trends, while melanoma SBM and breast cancer SBM mortality increased continuously.

CONCLUSIONS

SBMs incidence (2010-2019) and incidence-based mortality (2012-2019) declined significantly. These findings can advance our understanding of the prevalence of SBMs.

Development and Validation of Prognostic Nomogram for Elderly Breast Cancer: A Large-Cohort Retrospective Study

Purpose

Our research aims to study the bone metastatic patterns and prognostic outcomes in elderly breast cancer (BC) and to develop elder-specific nomograms.

Methods

We downloaded the data of BC patients between 2010 and 2016 from the Surveillance, Epidemiology, and End Results database. The differences in clinical features and prognosis between young (age < 65) and elderly (age ≥ 65) BC patients were compared. The univariate and multivariate Cox analyses were used to determine the overall survival (OS)- and cancer-specific survival (CSS)-related variables and establish two nomograms of BC patients with bone metastasis (BCBM). The receiver operating characteristic (ROC) curve with area under the curve (AUC), calibration curve, decision curve analysis (DCA), and Kaplan–Meier survival curve were selected to evaluate nomograms.

Results

A total of 230,177 BC patients were enrolled in our research, including 142,025 young and 88,152 elderly patients. The prognosis of elderly BCBM patients was significantly worse than young patients. Age, race, breast subtype, tumor size, tumor grade, brain metastasis, liver metastasis, surgery, and chemotherapy were independent prognostic variables for elderly BCBM patients, including OS and CSS. The AUC values at 12, 18, and 24 months were 0.750, 0.751, and 0.739 for OS nomogram and 0.759, 0.762, and 0.752 for CSS nomogram in the training cohort, which were higher than the AUC values of all single independent prognostic variables. The survival curve showed a distinct prognosis between low-, median- and high-risk groups (p < 0.001). Finally, calibration curves and DCA indicated that both nomograms have favorable performance.

Conclusion

Elderly and young patients presented with different bone metastatic frequencies, clinical features, and prognostic outcomes. Two elder-specific nomograms incorporating nine clinical variables were established and validated to be a valuable predictor for elderly BCBM patients.

Prognostic factors and survival prediction in HER2-positive breast cancer with bone metastases: A retrospective cohort study

BACKGROUND

Bone is the most common metastatic site of breast cancer. The developmental pattern of bone metastasis differs in different molecular subtypes. The prognostic factors of HER2-positive breast cancer with bone metastases require further investigation. The goal of this retrospective study was to identify the clinical features and prognostic factors for HER2-positive patients with bone metastases.

METHODS

A total of 34,084 HER2-positive breast cancer cases and 1204 cases of bone metastases from the Surveillance, Epidemiology, and End Results (SEER) database from 2010 to 2015 were analyzed to identify clinical characteristics and prognostic factors. A nomogram was constructed based on the Cox proportional hazards regression model. The C-index, calibration curve, and receiver operating characteristic (ROC) were utilized for model validation.

RESULTS

In the HER2-positive breast cancer total population (34,084 cases), 6.2% developed metastatic diseases. Bone metastases accounted for 3.5% of the entire cohort and 56.7% of all metastatic cases. Univariate and multivariate Cox regression analyses identified seven prognostic factors for predicting cancer-specific survival (CSS) for HER2-positive breast cancer patients with bone metastases, including age, brain metastases, liver metastases, lung metastases, PR status, surgery, and chemotherapy. The C-index of the nomogram was 0.74 vs. 0.78 (for 3-year CSS) and 0.77 vs. 0.81 (for 5-year CSS) in the model and validation cohorts, respectively. The AUCs were 0.74 vs. 0.78 (for 3-year CSS) and 0.77 vs. 0.81 (for 5-year CSS) in the model and validation cohorts, respectively. The calibration curves indicated favorable agreement between the actual observations and the predictions.

CONCLUSION

Our study provided population-based clinical features and prognostic factors for HER2-positive breast cancer patients with bone metastases and we constructed a prognostic nomogram with reliable accuracy.