Risk factors for skeletal-related events in patients with bone metastasis from breast cancer undergoing treatment with zoledronate

Prognostic factors analysis and nomogram construction of breast cancer patients lung metastases and bone metastases

Objective

To investigate the clinicopathological factors influencing lung and bone metastasis in breast cancer, and to further construct a nomogram model for predicting the risk of lung and bone metastasis in breast cancer patients at various time points, followed by a prognostic analysis.

Methods

The retrospective analysis included 200 patients with breast cancer, among whom 51 had lung metastases and 57 had bone metastases. The remaining 92 patients without metastases served as the control group. Baseline characteristics were analyzed using the chi-square test; COX univariate and multivariate analyses were applied to explore the influencing factors. A nomogram was constructed to predict the risk of individuals developing lung or bone metastasis at 1, 3, and 5 years. The predictive model was further validated by ROC curves and calibration curves, and decision curves were plotted to assess the clinical application value of the model.

Results

Analysis revealed that age, BMI, tumor size, lymph node status, ER, PR, HER-2, and Ki67 significantly influenced lung metastasis (P < 0.05), while age, BMI, tumor size, lymph node status, ER, PR, and Ki67 significantly impacted bone metastasis (P < 0.05). The nomogram indicated that HER-2 negativity elevated the risk of breast cancer lung metastases. ROC curves were plotted for 1, 3, and 5 years, with AUC values and 95 % confidence intervals of 0.803 (67.42-93.15), 0.831 (75.93-90.29), and 0.854 (78.43-92.34) in the lung metastasis group, and 0.754 (55.15-95.66), 0.753 (64.91-85.71), and 0.777 (68.64-86.67) in the bone metastasis group, respectively. These results suggest that the model has a superior predictive efficacy and a high degree of predictive reliability. Additionally, the calibration curve demonstrated that the model is well-fitted, and the decision curve indicated that the model possesses clinical utility in practice.

Conclusion

Age, BMI, tumor size, lymph node status, ER, PR, and Ki67 significantly influence lung and bone metastasis in breast cancer. The nomogram developed in this study can evaluate the risk of lung or bone metastasis for individuals at 1, 3, and 5 years, predict prognosis, guide clinical individualized treatment, and bring more benefits, further improving the quality of life for patients. It demonstrates good predictive ability and clinical value.

Key message

The nomogram model constructed in this study can predict prognosis, guide clinical individualized treatment, and bring more benefits, further improving the quality of life for patients. It possesses good predictive ability and holds certain clinical predictive value.

A Comparison of the Efficacy and Safety of Denosumab and Zoledronic Acid in Patients with Bone Metastatic Breast Cancer Receiving CDK4/6 Inhibitor Therapy

Background and Objectives: Bone metastases in patients can cause significant quality-of-life declines due to skeletal-related events (SREs). SRE is defined as the occurrence of radiotherapy for bone pain, pathologic fracture, bone surgery, spinal cord compression, or hypercalcemia. Bone-modifying agents (BMAs), such as denosumab and zoledronic acid, are crucial in reducing the frequency and severity of SREs. The inhibition of cyclin-dependent kinase 4/6 (CDK4/6) inhibitors has emerged as the standard treatment for hormone receptor-positive metastatic breast cancer, demonstrating significant improvements in survival outcomes. This study aims to compare the effectiveness of denosumab and zoledronic acid in preventing SRE in patients receiving CDK4/6 inhibitors with endocrine therapy. Materials and Methods: This retrospective study included 328 patients diagnosed with bone metastatic breast cancer receiving first-line CDK4/6 inhibitor therapy (palbociclib or ribociclib). Patients were assigned to receive either subcutaneous denosumab or intravenous zoledronic acid every 4 weeks. Time to the first skeletal-related event post bone-modifying agent initiation, SRE incidence, and the safety data were evaluated. The data were analyzed using independent samples t-tests, chi-square tests, and Kaplan-Meier methods for time-to-event data. Results: In the denosumab group, the median time to the first skeletal-related event was significantly longer than in the zoledronic acid group (44.55 months and 29.16 months, respectively). Denosumab treatment was associated with a statistically significant reduction in the risk of developing the first SRE after bone-modifying agent initiation compared to zoledronic acid (HR: 0.56, p = 0.001). Additionally, ECOG PS and the number of metastatic bone sites were identified as independent prognostic factors for time to the first SRE. The safety profile was consistent with previous studies reported in the literature. Conclusions: Our study demonstrated that when used with CDK4/6 inhibitors, denosumab is associated with a delay in SREs and a lower SRE incidence than zoledronic acid in patients with bone metastases. These findings support the efficacy of denosumab in preventing SREs and suggest that CDK4/6 inhibitors may have distinct effects on the bone microenvironment, particularly when combined with denosumab.

Factors associated with skeletal-related events in patients with bone metastatic melanoma: A retrospective study of 481 patients

OBJECTIVES

Metastatic bone disease is estimated to develop in up to 17% of patients with melanoma, compromising skeleton integrity resulting in skeletal-related events (SREs), which impair quality of life and reduce survival. The objective of the study was to investigate (1) the proportion of melanoma patients developing SREs following diagnosis of bone metastasis and (2) the predictors for SREs in this patient cohort.

METHODS

Four hundred and eighty-one patients with bone metastatic melanoma from two tertiary centers in the United States from 2008 to 2018 were included. The primary outcome was 90-day and 1-year occurrence of a SRE, including pathological fractures of bones, cord compression, hypercalcemia, radiotherapy, and surgery. Fine-Gray regression analysis was performed for overall SREs and pathological fracture, with death as a competing risk.

RESULTS

By 1-year, 52% (258/481) of patients experienced SREs, and 28% (137/481) had a pathological fracture. At 90-day, lytic lesions, bone pain, elevated calcium and absolute lymphocyte, and decreased albumin and hemoglobin were associated with higher SRE risk. The same factors, except for decreased hemoglobin, were shown to predict development of SREs at 1-year.

CONCLUSION

The high incidence of SREs and pathological fractures warrants vigilance using the identified factors in this study and preventative measures during clinical oncological care.

Value of CT-based radiomics in evaluating the response of bone metastases to systemic drug therapy in breast cancer patients

BACKGROUND

This study aimed to investigate the value of nonenhanced computed tomography (CT)-based radiomics in determining disease progression in breast cancer patients with bone marrow metastases and to develop a model for assessing treatment efficacy.

METHODS

A total of 134 breast cancer patients with bone metastases were enrolled from three hospitals. Nonenhanced CT was performed after two cycles of drug treatment. The images were categorized into an invalid and a valid group according to disease progression status. The largest osteolytic lesions' maximum cross-sections in the CT images were selected as regions of interest (ROIs) for feature extraction. Variance threshold, SelectKBest, and least absolute shrinkage and selection operator (LASSO) were used to reduce feature dimensionality. K-nearest neighbor algorithm (KNN), support vector machine (SVM), extreme gradient boosting (XGBoost), random forest (RF), logistic regression (LR), and decision tree (DT) algorithms were trained to establish radiomics models. Receiver operating characteristic (ROC) curves were generated to evaluate the diagnostic performance of the models.

RESULTS

The KNN classifier demonstrated the best performance compared to the random grouping method. In the validation group, the area under the ROC curve (AUC) was 0.810. In the cross-validation method, the RF classifier showed the best performance with an AUC of 0.84.

CONCLUSION

Nonenhanced CT-based radiomics provides a promising method for evaluating the efficacy of systemic drug therapy in breast cancer patients with osteolytic bone metastases.

The patient generated-subjective global assessment (PG-SGA) and ECOG performance status are associated with mortality in patients hospitalized with breast cancer

AIM

This study evaluated the association between risk of malnutrition and performance status, and mortality in hospitalized breast cancer patients.

METHODS

Prospective cohort study with hospitalized breast cancer patients evaluated at a referral Cancer Center. The Risk of malnutrition was assessed by the Patient-Generated Subjective Global Assessment (PG-SGA) and performance status was determined using the Eastern Cooperative Oncology Group Performance Status Scale (ECOG PS). Logistic regression was used to analyze the factors associated with death, using the odds ratio (OR) with a 95% confidence interval (CI) as an effect measure.

RESULTS

A total of 195 woman were included, with a mean age of 56.3 (±12.6) years. Patients with an overall PG-SGA score ≥18 (OR: 2.11; 95% CI: 1.03-4.62) and ECOG PS ≥ 3 (OR: 3.34; 95% CI: 1.48-7.52) had a higher occurrence of death during hospitalization, regardless of age or disease stage. The concomitant presence of these two factors improved the accuracy of the association (OR: 5.32; 95% CI: 3.11-9.76) and showed good predictive accuracy (C-statistics: 0.77).

CONCLUSION

Nutritional risk and poor performance status were associated with a higher occurrence of death in women with breast cancer. The use of these two indicators improves their predictive accuracy for mortality.

Surgical Efficacy and Prognosis of 54 Cases of Spinal Metastases from Breast Cancer

OBJECTIVE

To analyze the efficacy and complications of spinal metastasis surgery for breast cancer; to understand the survival and the influencing factors; and to verify the predictive ability of the currently used spinal metastasis cancer survival prediction scoring system on 1 year postoperative survival.

METHODS

A retrospective study was conducted of 54 patients with spinal metastases from breast cancer who underwent open surgery after multidisciplinary consultation in our hospital from January 2017 to October 2020. Patient demographic-related variables, breast cancer-related variables, spinal disorder-related variables, and treatment-related variables were collected. Survival curves were plotted using the Kaplan-Meier method, 1-way tests were performed using the log-rank method for factors that might affect prognosis, and candidate variables were included in the Cox model for multifactor analysis. The Tomita score, modified Tokuhashi score, modified Bauer score, and modified Katagiri score were examined by plotting the subject operating characteristic curve and calculating the area under the curve. The area under the curve was used to test the predictive ability of the SORG (Skeletal Oncology Research Group) original version, SORG line graph version, and SORG Web version for 1-year postoperative survival in patients with spinal metastases from breast cancer.

RESULTS

The average age was 51.3 ± 8.6 years in 54 patients. Twenty-one patients underwent vertebral body debulking surgery, 32 patients underwent palliative canal decompression, and 1 patient underwent vertebral en bloc resection, with an operative time of 229.3 ± 87.6 minutes and intraoperative bleeding of 1018.1 ± 931.1 mL. Postoperatively, the patient experienced significant pain relief and gradual recovery from nerve injury. Major surgical complications included cerebrospinal fluid leakage, secondary spinal cord injury, spinal tumor progression, and broken fixation. The mean survival was 32.2 months, including a 6-month survival of 90.7%, a 1-year survival of 77.8%, and a 2-year survival of 60.3%. Univariate analysis showed that preoperation with neurologic deficits, hormone-insensitive type, with brain metastases were potential risk factors for poor prognosis. Multifactorial analysis showed that hormone-insensitive type and concomitant brain metastasis were independent risk factors associated with poor prognosis. The SORG Web version had good ability to predict 1-year postoperative survival in patients with spinal metastases from breast cancer.

CONCLUSIONS

Spinal metastasis from breast cancer has good surgical efficacy, low postoperative recurrence rate, and relatively long survival after surgery. Patients with hormone-insensitive type, with brain metastasis, have a poor prognosis, and SORG Web version can predict patients' 1-year survival more accurately.

Risk factors associated with skeletal-related events following discontinuation of denosumab treatment among patients with bone metastases from solid tumors: A real-world machine learning approach

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This study investigated SRE risk factors after densomuab treatment discontinuation.

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An unbiased machine learning approach was developed to evaluate >60 variables.

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Prior SREs and short denosumab treatment duration were primary risk factors.

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The results can guide denosumab persistence decisions and improve patient outcomes.

Background

Clinical practice guidelines recommend the use of bone-targeting agents for preventing skeletal-related events (SREs) among patients with bone metastases from solid tumors. The anti-RANKL monoclonal antibody denosumab is approved for the prevention of SREs in patients with bone metastases from solid tumors. However, real-world data are lacking on the impact of individual risk factors for SREs, specifically in the context of denosumab discontinuation.

Purpose

We aim to identify risk factors associated with SRE incidence following denosumab discontinuation using a machine learning approach to help profile patients at a higher risk of developing SREs following discontinuation of denosumab treatment.

Methods

Using the Optum PanTher Electronic Health Record repository, patients diagnosed with incident bone metastases from primary solid tumors between January 1, 2007, and September 1, 2019, were evaluated for inclusion in the study. Eligible patients received ≥ 2 consecutive 120 mg denosumab doses on a 4-week (± 14 days) schedule with a minimum follow-up of ≥ 1 year after the last denosumab dose, or an SRE occurring between days 84 and 365 after denosumab discontinuation. Extreme gradient boosting was used to develop an SRE risk prediction model evaluated on a test dataset. Multiple variables associated with patient demographics, comorbidities, laboratory values, treatments, and denosumab exposures were examined as potential factors for SRE risk using Shapley Additive Explanations (SHAP). Univariate analyses on risk factors with the highest importance from pooled and tumor-specific models were also conducted.

Results

A total of 1,414 adult cancer patients (breast: 40%, prostate: 30%, lung: 13%, other: 17%) were eligible, of whom 1,133 (80%) were assigned to model training and 281 (20%) to model evaluation. The median age at inclusion was 67 (range, 19–89) years with a median duration of denosumab treatment of 253 (range, 88–2,726) days; 490 (35%) patients experienced ≥ 1 SRE 83 days after denosumab discontinuation. Meaningful model performance was evaluated by an area under the receiver operating curve score of 77% and an F1 score of 62%; model precision was 60%, with 63% sensitivity and 78% specificity. SHAP identified several significant factors for the tumor-agnostic and tumor-specific models that predicted an increased SRE risk following denosumab discontinuation, including prior SREs, shorter denosumab treatment duration, ≥ 4 clinic visits per month with at least one hospitalization (all-cause) event from the baseline period up to discontinuation of denosumab, younger age at bone metastasis, shorter time to denosumab initiation from bone metastasis, and prostate cancer.

Conclusion

This analysis showed a higher cumulative number of SREs, prior SREs relative to denosumab initiation, a higher number of hospital visits, and a shorter denosumab treatment duration as significant factors that are associated with an increased SRE risk after discontinuation of denosumab, in both the tumor-agnostic and tumor-specific models. Our machine learning approach to SRE risk factor identification reinforces treatment guidance on the persistent use of denosumab and has the potential to help clinicians better assess a patient’s need to continue denosumab treatment and improve patient outcomes.

Establishment and Validation of Novel Clinical Prognosis Nomograms for Luminal A Breast Cancer Patients with Bone Metastasis

PURPOSE

Overall survival (OS) and cancer-specific survival (CSS) of luminal A breast cancer (BC) patients with bone metastasis remain poor and vary dramatically from person to person. Our goal was to build two universally applicable nomograms to accurately predict OS and CSS for luminal A patients with bone metastasis.

METHODS

The data were collected from the Surveillance, Epidemiology, and End Results (SEER) database for luminal A BC patients with bone metastasis between 2010 and 2015. Univariate and multivariate Cox regression analyses were to assess and identify independent risk factors of OS and CSS. Integrating all significant predictors, nomograms and risk group stratification model was developed. The performance of the nomogram was validated with concordance index (C-index), calibration plots, and decision curve analyses (DCA) for discriminative ability, calibration, and clinical utility, respectively.

RESULTS

3171 luminal A BC patients with bone metastasis were included. Through univariate and multivariate Cox regression analyses, 12 variables were identified as both independent OS- and CSS-related factors, including age, race, primary site, histology grade, tumor size, surgery, brain metastasis, liver metastasis, lung metastasis, estrogen receptor status, progesterone receptor status, and insurance. Our nomograms for 1-, 3-, and 5-year survival were based on those significant prognostic factors to develop. The C-indexes of OS- and CSS-nomograms in the training cohort were 0.701 and 0.704, respectively. Similar results were obtained in the validation cohort. The calibration curves and DCA presented satisfactory calibration and clinical utility.

CONCLUSION

Two nomograms have good discrimination, calibration, and clinical utility, can accurately and effectively predict the prognosis of patients, and may benefit for clinical decision-making. In high-risk patients, more aggressive therapy and closer surveillance should be considered.