Population-Based Study on Cancer Subtypes, Guideline-Concordant Adjuvant Therapy, and Survival Among Women With Stage I-III Breast Cancer

Effect of Previous Cancer History on Survival of Patients with Different Subtypes of Breast Cancer

Patients with a previous cancer history (PCA) are routinely excluded from most clinical trials, which may limit the accuracy and universality of clinical trials. We aimed to explore the association between PCA and survival of patients with different molecular subtypes of breast cancer. Patients diagnosed with breast cancer from the Surveillance, Epidemiology, and End Results (SEER) database between 2010 and 2015 were included in this retrospective cohort study. The primary outcome was overall survival (OS), which was calculated from date of diagnosis to date of death or censor date during this period. The relationship between PCA and OS of patients with different molecular subtypes of breast cancer was analyzed by the Kaplan-Meier curves and multivariate Cox proportional-hazards model. A total of 35,640 primary breast cancer patients were included, and 2,038 (5.72%) patients had a PCA. Female genital system cancer (491 cases, 24.09%) was the largest proportion type of previous cancer, and HER2-positive (24,754 cases, 69.46%) breast cancer was the most common subtype. Patients with previous female genital/endocrine system cancer history and other cancers history were associated with a poorer OS in overall patients, and in patients with triple-negative and HER2-positive subtypes (P < 0.05). In patients with Luminal A and Luminal B subtypes, previous other cancers history was related to poor OS (P < 0.05), while female genital/endocrine system cancer history may not influence the OS (P > 0.05). Subgroup analyses presented that PCA was related to poor OS in patients aged 40-64 years and ≥65 years (P <0.05), while prognosis in patients aged 18-40 years may not be influenced by PCA (P > 0.05). The impact of PCA on the prognosis of breast cancer patients was related to molecular type, patient age, and type of PCA. In clinical trials of breast cancer, the exclusion criteria for PCA patients may be modified according to the above variables.

Impact of prognostic factor distributions on mortality disparities for socioeconomically disadvantaged cancer patients

PURPOSE

We aimed to assess whether differences in the distributions of prognostic factors explain reported mortality disparities between urban safety-net and Surveillance, Epidemiology, and End Results (SEER) cancer populations.

METHODS

We used data from SEER and a safety-net cancer center in Texas. Eligible patients were adults aged ≤64 years and diagnosed with first primary female breast, colorectal, or lung cancer between 2008 and 2016. We estimated crude and adjusted risk differences (RD) in 3- and 5-year all-cause mortality (1- and 3-year for lung cancer), where adjustment was based on entropy balancing weights that standardized the distribution of sociodemographic and tumor characteristics between the two populations.

RESULTS

Our study populations comprised 1914 safety-net patients and 389,709 SEER patients. For breast cancer, the crude 3- and 5-year mortality RDs between safety-net and SEER populations were 7.7% (95% confidence limits [CL]: 4.3%, 11%) and 11% (95% CL: 6.7%, 16%). Adjustment for measured prognostic factors reduced the mortality RDs (3-year adjusted RD = 0.049%, 95% CL: -2.6%, 2.6%; 5-year adjusted RD = 5.6%, 95% CL: -0.83%, 12%). We observed similar patterns for colorectal and lung cancer albeit less magnitude.

CONCLUSIONS

Sociodemographic and tumor characteristics may largely explain early mortality disparities between safety-net and SEER populations but not late mortality disparities.

An immune cell infiltration-based immune score model predicts prognosis and chemotherapy effects in breast cancer

Background: Immune cells have essential auxiliary functions and influence clinical outcomes in cancer, with high immune infiltration being associated with improved clinical outcomes and better response to treatment in breast cancer (BC). However, studies to date have not fully considered the tumor-infiltrating immune cell (TIIC) landscape in tumors. This study investigated potential biomarkers based on TIICs to improve prognosis and treatment effect in BC.

Results: We enrolled 5112 patients for analysis and used cell type identification by estimating relative subsets of RNA transcripts (CIBERSORT), a new computational algorithm, to quantify 22 TIICs in primary BC. From the results of univariate Cox regression, 12 immune cells were determined to be significantly related to the overall survival (OS) of BC patients. Furthermore, least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analyses were applied to construct an immune prognostic model based on six potential biomarkers. By dividing patients into low- and high-risk groups, a significant distinction in OS was found in the training cohort, with 20-year survival rates of 42.6% and 26.3%, respectively. Applying a similar protocol to validation and test cohorts, we found that OS was significantly shorter in the high-risk group than in the low-risk group, regardless of the molecular subtype of BC. Using the immune score model to predict the effect of BC patients to chemotherapy, the survival advantage for the low-risk group was evident among those who received chemotherapy, regardless of the chemotherapy regimen. In evaluating the predictive value of the nomogram, a decision curve showed better predictive accuracy than the standard tumor-node-metastasis (TNM) staging system.

Conclusion: The immune cell infiltration-based immune score model can be effectively and efficiently used to predict the prognosis of BC patients as well as the effect of chemotherapy.