Breast Cancer-Specific Mortality in Small-Sized Tumor with Stage IV Breast Cancer: A Population-Based Study

Role of sex in liver tumor occurrence and clinical outcomes: A comprehensive review

Clinical research on sex-based differences in the manifestations, pathophysiology, and prevalence of several diseases, including those affecting the liver, has expanded considerably in recent years. Increasing evidence suggests that liver diseases develop, progress, and respond to treatment differently depending on the sex. These observations support the concept that the liver is a sexually dimorphic organ in which estrogen and androgen receptors are present, which results in disparities between men and women in liver gene expression patterns, immune responses, and the progression of liver damage, including the propensity to develop liver malignancies. Sex hormones play protective or deleterious roles depending on the patient's sex, the severity of the underlying disease, and the nature of precipitating factors. Moreover, obesity, alcohol consumption, and active smoking, as well as social determinants of liver diseases leading to sex-related inequalities, may interact strongly with hormone-related mechanisms of liver damage. Drug-induced liver injury, viral hepatitis, and metabolic liver diseases are influenced by the status of sex hormones. Available data on the roles of sex hormones and gender differences in liver tumor occurrence and clinical outcomes are conflicting. Here, we critically review the main gender-based differences in the molecular mechanisms associated with liver carcinogenesis and the prevalence, prognosis, and treatment of primary and metastatic liver tumors.

Should the Breast Cancer Staging System be Revised?

OBJECTIVE

The purpose of this study was to determine whether breast cancer patients at stage T2N0 with tumor size ≥4 cm and <4 cm.

METHOD

Patients with T2N0 stage breast cancer diagnosed between 2010 and 2019 were analyzed in 2 groups as <4 cm (T2a) and ≥4 cm (T2b) in the study using the SEER 17 Research Plus database. The patients' clinicopathological characteristics and oncological outcomes were included. Group comparisons of prognostic factors, overall survival (OS), and cancer-specific survival (CSS) were made.

RESULTS

In this study, which involved 70971 patients, the T2a group had higher 5-year OS rate (87.2 ± .2 vs 80.8 ± .5%) and 5-year CSS rate (93.7 ± .1% vs 89.4 ± .4%) than the T2b group (P < .001). Univariate analysis revealed that the overall risk of death was 1.5 times higher in T2b than T2a (HR: 1.533 [95% CI: 1.450-1.622], P < .001), whereas multivariate analysis demonstrated the risk was 1.4 times higher (HR: 1.384 [95% CI: 1.307-1.466], P < .001). The risk of cancer-specific death was 1.7 times higher in univariate analysis (HR: 1.691 [95% CI: 1.561-1.832], P < .001) and 1.4 times higher in multivariate analysis (HR: 1.420 [95% CI: 1.309-1.541], P < .001).

CONCLUSION

Overall survival and BCSS rates in stage T2b breast cancer patients are significantly lower than in T2a patients. Tumor size ≥4 cm in breast cancer is a negative predictor of prognosis.

A novel machine learning prediction model for metastasis in breast cancer

BACKGROUND

Breast cancer (BC) metastasis is the common cause of high mortality. Conventional prognostic criteria cannot accurately predict the BC metastasis risk. The machine learning technologies can overcome the disadvantage of conventional models.

AIM

We developed a model to predict BC metastasis using the random survival forest (RSF) method.

METHODS

Based on demographic data and routine clinical data, we used RSF-recursive feature elimination to identify the predictive variables and developed a model to predict metastasis using RSF method. The area under the receiver operating characteristic curve (AUROC) and Kaplan-Meier survival (KM) analyses were plotted to validate the predictive effect when C-index was plotted to assess the discrimination and Brier scores was plotted to assess the calibration of the predictive model.

RESULTS

We developed a metastasis prediction model comprising three variables (pathological stage, aspartate aminotransferase, and neutrophil count) selected by RSF-recursive feature elimination. The model was reliable and stable when assessed by the AUROC (0.932 in training set and 0.905 in validation set) and KM survival analyses (p < .0001). The C-indexes (0.959) and Brier score (0.097) also validated the good predictive ability of this model.

CONCLUSIONS

This model relies on routine data and examination indicators in real-time clinical practice and exhibits an accurate prediction performance without increasing the cost for patients. Using this model, clinicians can facilitate risk communication and provide precise and efficient individualized therapy to patients with breast cancer.

Incidence and risk factors of lymph node metastasis in breast cancer patients without preoperative chemoradiotherapy and neoadjuvant therapy: analysis of SEER data

Background

Breast cancer (BC) is the leading cause of death in the female reproductive system, often linked to lymph node involvement, indicating poor prognosis. This study investigated lymph node metastasis incidence and risk factors in M0 stage BC patients who hadn't received preoperative chemoradiotherapy or neoadjuvant therapy. We explored the influence of various factors on lymph node metastasis.

Methods

We conducted a retrospective analysis using Surveillance, Epidemiology, and End Results data from BC patients diagnosed between 2010 and 2015. Binary logistic regression and propensity score matching (PSM) assessed significant factors in BC patients without preoperative treatment. We developed predictive nomograms and evaluated model performance using the concordance index, calibration curve, area under the curve, and decision curve analysis.

Results

Among 256,504 eligible BC patients, 25.57% had lymph node metastasis. Multivariate logistic regression revealed associations between lymph node metastasis and younger age, African-American ethnicity, central/nipple location, lobular carcinoma, human epidermal growth factor receptor 2 (HER2)-positive status, grade III classification, and T3 stage. PSM confirmed these findings. Interactions were identified between age, race, primary site, histology, breast subtype, grade, and T stage, all influencing lymph node metastasis.

Conclusions

This retrospective study identified lymph node metastasis in female BC patients with distinct clinicopathological characteristics who received no preoperative treatment. We constructed valuable nomograms, revealing that: (I) young age (<35 years), African-American race, central/nipple location, infiltrating duct carcinoma, HER2 positivity, high histological grade (grade III), and larger tumor size are risk factors for regional lymph node metastasis; (II) lymph node metastasis may not solely represent the invasive nature of triple-negative BC; (III) patients with different BC subtypes in T1c-T2 stages may benefit from individualized neoadjuvant treatment strategies.

Identifying clinicopathological risk factors for regional lymph node metastasis in Chinese patients with T1 breast cancer: a population-based study

Objectives

To analyze clinicopathological risk factors and regular pattern of regional lymph node metastasis (LNM) in Chinese patients with T1 breast cancer and the effect on overall survival (OS) and disease-free survival (DFS).

Materials and methods

Between 1999 and 2020, breast cancer patients meeting inclusion criteria of unilateral, no distant metastatic site, and T1 invasive ductal carcinoma were reviewed. Clinical pathology characteristics were retrieved from medical records. Survival analysis was performed using Kaplan-Meier methods and an adjusted Cox proportional hazards model.

Results

We enrolled 11,407 eligible patients as a discovery cohort to explore risk factors for LNM and 3484 patients with stage T1N0 as a survival analysis cohort to identify the effect of those risk factors on OS and DFS. Compared with patients with N- status, patients with N+ status had a younger age, larger tumor size, higher Ki67 level, higher grade, higher HR+ and HER2+ percentages, and higher luminal B and HER2-positive subtype percentages. Logistic regression indicated that age was a protective factor and tumor size/higher grade/HR+ and HER2+ risk factors for LNM. Compared with limited LNM (N1) patients, extensive LNM (N2/3) patients had larger tumor sizes, higher Ki67 levels, higher grades, higher HR- and HER2+ percentages, and lower luminal A subtype percentages. Logistic regression indicated that HR+ was a protective factor and tumor size/higher grade/HER2+ risk factors for extensive LNM. Kaplan-Meier analysis indicated that grade was a predictor of both OS and DFS; HR was a predictor of OS but not DFS. Multivariate survival analysis using the Cox regression model demonstrated age and Ki67 level to be predictors of OS and grade and HER2 status of DFS in stage T1N0 patients.

Conclusion

In T1 breast cancer patients, there were several differences between N- and N+ patients, limited LNM and extensive LNM patients. Besides, HR+ plays a dual role in regional LNM. In patients without LNM, age and Ki67 level are predictors of OS, and grade and HER2 are predictors of DFS.

Expert Consensus on the Diagnosis and Treatment of Anticancer Drug-Induced Interstitial Lung Disease

Drug-induced interstitial lung disease (DILD) is the most common pulmonary adverse event of anticancer drugs. In recent years, the incidence of anticancer DILD has gradually increased with the rapid development of novel anticancer agents. Due to the diverse clinical manifestations and the lack of specific diagnostic criteria, DILD is difficult to diagnose and may even become fatal if not treated properly. Herein, a multidisciplinary group of experts from oncology, respiratory, imaging, pharmacology, pathology, and radiology departments in China has reached the "expert consensus on the diagnosis and treatment of anticancer DILD" after several rounds of a comprehensive investigation. This consensus aims to improve the awareness of clinicians and provide recommendations for the early screening, diagnosis, and treatment of anticancer DILD. This consensus also emphasizes the importance of multidisciplinary collaboration while managing DILD.

Predictive and Prognostic Value of TRIM58 Protein Expression in Patients with Breast Cancer Receiving Neoadjuvant Chemotherapy

Introduction

Tripartite motif-containing protein (TRIM) family members play crucial roles in carcinogenesis and chemotherapy resistance. In this study, we aimed to determine whether TRIM58 protein expression is related to patient responses to neoadjuvant therapy (NAT) and their survival outcome.

Methods

Immunohistochemistry was performed on female breast cancer samples from biopsies before NAT in Shenzhen Second People's Hospital. Univariate and multivariate logistic regression tests were used to analyze the association between TRIM58 protein expression and pathological complete response (pCR). The Cox proportional hazards model was used to calculate the adjusted hazard ratio (HR) with a 95% confidence interval (95% CI). The Kaplan-Meier plotter database was used to analyze the prognostic value of TRIM58.

Results

High TRIM58 expression was associated with small tumor size in all the patients (n = 58). Multivariate analysis suggested that low TRIM58 expression was an independent predictive factor for higher pCR (odds ratio = 0.06, 95% CI 0.005-0.741, P = 0.028). The Kaplan-Meier Plotter dataset suggested that the TRIM58 high-expression group showed a worse 5-year overall survival than the low-expression group (HR = 1.34, 95% CI 1.07-1.67, P = 0.01). Pathway analysis revealed the potential mechanisms of TRIM58 in chemoresistance.

Discussion

Our study suggests that TRIM58 is a promising biomarker for both neoadjuvant chemosensitivity and long-term clinical outcomes in breast cancer. It may also help to identify candidate responders and determine treatment strategies.

Determining prognostic factors and optimal surgical intervention for early-onset triple-negative breast cancer

Background

Few studies have focused specifically on prognostic factors and optimal surgical intervention for early-onset triple-negative breast cancer (eTNBC), which is characterized by high malignancy and poor prognosis.

Methods

We performed a cohort study with a median follow-up of 31 months using Surveillance, Epidemiology, and End Results (SEER) data of patients diagnosed with stages I–III eTNBC between 2010 and 2016. In addition, we collected cases between 2006 and 2016 from our center as an external validation set. Clinical features, pathologic characteristics and oncologic outcomes were analyzed. Prognostic factors for overall survival (OS) and breast cancer-specific survival (BCSS) were determined by Cox proportional hazards analyses and were incorporated into the prognostic nomogram. Subgroup analysis based on propensity score matching method was conducted to explore the subset of patients that would benefit from breast-conserving therapy (BCT).

Results

Based on SEER dataset, patients with eTNBC were more likely to undergo mastectomy than BCT. On multivariable analysis, patients with better survival outcomes were those not married, uninsured, had higher T and N stage, and had histological type of mixed invasive ductal and lobular carcinoma. The prognostic nomogram based on these variables successfully predicted the 3- and 5-year BCSS (C-index in training cohort, 0.774; in validation cohort from SEER, 0.768; in validation cohort from our center, 0.723). Subgroup analysis illustrated that patients with T1N0M0 or T2-4N+M0 tumors who underwent BCT achieved longer overall survival than those who underwent mastectomy (for T1N0M0, P = 0.022; for T2-4N+M0, P = 0.003); however, the type of surgery did not influence OS among patients with T1N+M0 or T2-4N0M0 tumors (for T1N+M0, P = 0.305; for T2-4N0M0, P = 0.317).

Conclusions

The prognosis of patients with eTNBC is mainly affected by marital status, insurance status, T stage, N stage and histological type. The prognostic nomogram based on these factors is quite reliable. Subgroup analysis suggested that BCT may be a superior option for patients with eTNBC, especially those with T1N0M0 and T2-4N+M0 tumors.

Prognostic Factors and a Nomogram Predicting Survival in Patients with Breast Ductal Carcinoma in situ with Microinvasion: A Population-Based Study

Purpose

Ductal carcinoma in situ with microinvasion (DCISM) can be challenging to balance the risks of overtreatment versus undertreatment. We aim to identify prognostic factors in patients with DCISM and construct a nomogram to predict breast cancer-specific survival (BCSS).

Materials and Methods

A retrospective cohort study of women diagnosed with DCISM from 1988 to 2015 who were identified in the Surveillance, Epidemiology and End Results database. Clinical variables and tumor characteristics were evaluated, and Cox proportional-hazards regression was performed. A nomogram was constructed from the multivariate logistic regression to combine all the prognostic factors to predict the prognosis of DCISM patients at 5 years, 10 years, and 15 years.

Results

We identified 5438 total eligible breast cancer patients with a median and max survival time of 78 and 227 months, respectively. Here, patients with poorer survival outcomes were those diagnosed between 1988 and 2001, African-American race, under 40 years of age, higher tumor N stage, progesterone receptor-negative tumor, and received no surgery. The nomogram was constructed by the seven variables and passed the calibration and validation steps. The area under the receiver operating characteristic (ROC) curve (AUC) of both the training set and the validating set (5-year AUC: 0.77 and 0.88, 10-year AUC: 0.75 and 0.73, 15-year AUC: 0.72 and 0.65). Receiving chemotherapy was associated with a better BCSS (hazard ratio, HR=0.45, 95% confidence interval, 95% CI = 0.23–0.89), especially in patients with estrogen receptor (ER) negative, progesterone receptor (PR) negative (HR = 0.35, 95% CI = 0.13–0.97) and ER+PR-/ER-PR+ DCISM (HR = 0.07, 95% CI = 0.01–0.59).

Conclusion

Our current study is the first to construct nomograms of patients with DCISM which could help physicians identify breast cancer patients that more likely to benefit from more intensive treatment and follow-up. Chemotherapy might benefit patients with ER-PR- and ER+PR-/ER-PR+ DCISM.