Young age at diagnosis is associated with better prognosis in stage IV breast cancer

The effect of BMI on survival outcome of breast cancer patients: a systematic review and meta-analysis

OBJECTIVE

The main goal of the present research is to explore the potential link of body mass index (BMI) with different survival metrics in breast cancer patients. Our aim is to offer the latest and most thorough meta-analysis, assessing the strength and reliability of the connection that BMI has with prognostic indicators in this disease.

PATIENTS AND METHODS

As of January 2024, we conducted a systematic literature search across PubMed, Embase, Web of Science, and the Cochrane Library databases. Our search aimed to identify studies examining BMI as an exposure factor, with breast cancer patients constituting the study population, and utilizing adjusted hazard ratio (HR) as the data type of interest.

RESULTS

The evidence synthesis incorporated a total of 61 eligible articles involving 201,006 patients. Being underweight posed a risk factor for overall survival (OS) in breast cancer patients compared to normal weight (HR 1.15, 95% CI 0.98-1.35; P = 0.08). Overweight or obesity, in comparison to normal weight, was a risk factor for OS (HR 1.18, 95% CI 1.14-1.23; P < 0.00001), disease-free survival (DFS) (HR 1.11, 95% CI 1.08-1.13; P < 0.00001), relapse-free survival (RFS) (HR 1.14, 95% CI 1.06-1.22; P = 0.03), and breast cancer-specific survival (BCSS) (HR 1.18, 95% CI 1.11-1.26; P < 0.00001), but not for progression-free survival (PFS) (HR 0.91, 95% CI 0.76-1.10; P = 0.33). Notably, in subgroup analyses, overweight patients achieved prolonged PFS (HR 0.80, 95% CI 0.64-0.99; P = 0.04), and compared to the obese population, the overweight cohort exhibited a significant difference in OS (HR 1.11, 95% CI 1.05-1.16; P < 0.00001) and DFS (HR 1.06, 95% CI 1.03-1.10; P = 0.0004), with a considerably stronger association. Furthermore, compared to HER- patients, HER + patients exhibited a greater predictive value for OS (HR 1.23, 95% CI 1.10-1.37; P = 0.0004), RFS (HR 1.30, 95% CI 1.03-1.64; P < 0.00001), and DFS (HR 1.10, 95% CI 1.03-1.17; P = 0.003).

CONCLUSIONS

The results of our meta-analysis reveal a notable association between BMI and various survival measures in breast cancer prognosis. These findings provide a solid basis for predicting breast cancer outcomes and implementing more effective therapeutic approaches.

A Population-Based Research Utilized a Risk Stratification Model to Forecast the Overall Survival of Young Women With Diagnosed Stage IV Breast Cancer

BACKGROUND

The goal of this study is to develop a risk prediction model for estimating overall survival (OS) in young females diagnosed with stage IV breast cancer.

METHODS

The clinical information was retrieved from the Surveillance, Epidemiology, and End Results (SEER) database between 2010 and 2015. To identify the dependent risk factors, we utilized the Cox proportional hazards regression model in both single and multivariate analyses. We then created a new nomogram to predict the 1-, 3-, and 5-year overall survival probability for these patients based on the identified risk factors.

RESULTS

Six hundred seventy-six patients who met the eligibility requirements were stochastically partitioned into training (n = 475) and validation (n = 201) groups in a 7:3 ratio. Histology, breast subtype, T classification, brain metastasis, bone metastasis, liver metastasis, and surgery were identified as independent prognostic factors for cancer. To predict the 1-, 3-, and 5-year overall survival (OS) probabilities, all of these independent factors were incorporated into nomograms. Our nomogram demonstrated a favorable discriminatory power, as evidenced by a C-index of 0.737 (95% CI: 0.708-0.766) and 0.717 (95% CI: 0.664-0.770) for the training and validation cohorts, respectively. The calibration curves showed satisfactory consistency in both cohorts. Using this nomogram, we developed a risk stratification model that categorized patients into low-, intermediate-, and high-risk groups.

CONCLUSION

The prediction model was more precisely to predict the OS of young females with stage IV breast cancer and could enable individualized risk estimation that could be conducive to physicians exploring therapeutic strategies for effectiveness.

Establishment and Verification of Prognostic Nomograms for Young Women With Breast Cancer Bone Metastasis

Purpose

The prognosis of patients with metastatic breast cancer usually varies greatly among individuals. At present, the application of nomogram is very popular in metastatic tumors. The present study was conducted to identify independent survival predictors and construct nomograms among young women with breast cancer bone metastasis (BCBM).

Patients and Methods

We searched the Surveillance, Epidemiology, and End Results (SEER) database to identify young women diagnosed with BCBM between 2010 and 2016. We first analyzed the potential risk factors of overall survival (OS) and cancer-specific survival (CSS) by applying univariate Cox regression analysis. Then we conducted multivariate Cox analysis to identify independent survival predictors. Based on significant independent predictors, we developed and validated novel prognostic nomograms by using the R version 4.1.0 software.

Results

We finally identified 715 eligible young women with BCBM for survival analysis, of which 358 patients were in the training set, and 357 patients in the validation set. Approximately four-fifths of patients are between 31 and 40 years old. The 5-year OS and CSS rates of this research population were 41.9 and 43.3%, respectively. Multivariate analysis revealed seven independent predictors of both OS and CSS, including race, tumor subtype, tumor size, surgical treatment, brain metastasis, liver metastasis, and lung metastasis. Based on these predictors, we developed and validated OS and CSS nomograms. The C-index of the OS nomogram reached 0.728 and 0.73 in the training and validation sets, respectively. The C-index of the CSS nomogram reached 0.743 and 0.695 in the training and validation sets, respectively. Meanwhile, high quality calibration plots were revealed in both OS and CSS nomograms.

Conclusion

The current novel nomograms can provide an individualized survival evaluation of young women with BCBM and instruct clinicians to treat them appropriately.

Clinicopathological features and BRCA1 and BRCA2 mutation status in a prospective cohort of young women with breast cancer

BACKGROUND

Breast cancer in young women is more likely to have higher risk features and be associated with germline BRCA1/BRCA2 mutations. We present the clinicopathologic features of breast cancers in a prospective cohort of young women, and associations between surrogate molecular subtype and BRCA1/BRCA2 mutation status.

METHODS

Histopathological features, biomarker status, tumour stage and BRCA status were collected. Invasive tumours were categorised as luminal A-like (ER + and/or PR + , HER2-, grade 1/2), luminal B-like (ER + and/or PR + , HER2 + , or ER + and/or PR + , HER2-, and grade 3), HER2-enriched (ER/PR-, HER2 + ) or triple-negative.

RESULTS

In all, 57.3% (654/1143) of invasive tumours were high grade. In total, 32.9% were luminal A-like, 42.4% luminal B-like, 8.3% HER2-enriched, and 16.4% triple-negative. Among different age groups, there were no differences in molecular phenotype, stage, grade or histopathology. 11% (131) of tumours were from BRCA mutation carriers; 64.1% BRCA1 (63.1% triple-negative), and 35.9% BRCA2 (55.3% luminal B-like).

DISCUSSION

The opportunity to provide comparisons across young age groups, BRCA mutation status, surrogate molecular phenotype, and the identification of more aggressive hormone receptor-positive phenotypes in this population provides direction for future work to further understand and improve disparate outcomes for young women with luminal B-like cancers, particularly BRCA2-associated cancers, with potential implications for tailored prevention and treatment.