A novel nomogram for predicting locoregional recurrence risk in breast cancer patients treated with neoadjuvant chemotherapy and mastectomy

Predicting Additional Metastases in Axillary Lymph Node Dissection After Neoadjuvant Chemotherapy: Ratio of Positive/Total Sentinel Nodes

BACKGROUND/OBJECTIVES

The aim of the study was to determine the clinical value of the sentinel lymph node ratio (SLN-R) in predicting additional positive lymph nodes during axillary lymph node dissection (ALND) in breast cancer patients following neoadjuvant chemotherapy (NAC).

METHODS

A cross-sectional study was performed at a single institution evaluating data from 1521 BC patients. Inclusion criteria comprised cT1/cT4, cN0/cN1 status with positive post-NAC axillary staging by SLN/TAD, respectively, and subsequent ALND.

RESULTS

The study included 118 patients, divided into two groups based on the presence or absence of additional node metastasis at ALND: 39 in the residual disease group (RD) and 79 in the non-residual disease group (nRD). Univariate logistic regression analysis of SLN-R was conducted to assess its predictive value, yielding an odds ratio (OR) of 7.79 (CI 1.92-29.5, p = 0.003). An SLN-R cut-off point of <0.35 was identified using ROC curve analysis, with a false-negative rate of 10.2%, as a predictor for no additional metastasis at ALND following post-NAC SLN/TAD positivity.

CONCLUSIONS

The study concludes that SLN-R is a valuable predictor for determining the omission of ALND in cases where SLN/TAD is positive after NAC. This metric, in combination with other clinical variables, could help develop a nomogram to spare patients from ALND.

A nomogram based on inflammation and nutritional biomarkers for predicting the survival of breast cancer patients

Background

We aim to develop a new prognostic model that incorporates inflammation, nutritional parameters and clinical-pathological features to predict overall survival (OS) and disease free survival (DFS) of breast cancer (BC) patients.

Methods

The study included clinicopathological and follow-up data from a total of 2857 BC patients between 2013 and 2021. Data were randomly divided into two cohorts: training (n=2001) and validation (n=856) cohorts. A nomogram was established based on the results of a multivariate Cox regression analysis from the training cohorts. The predictive accuracy and discriminative ability of the nomogram were evaluated by the concordance index (C-index) and calibration curve. Furthermore, decision curve analysis (DCA) was performed to assess the clinical value of the nomogram.

Results

A nomogram was developed for BC, incorporating lymphocyte, platelet count, hemoglobin levels, albumin-to-globulin ratio, prealbumin level and other key variables: subtype and TNM staging. In the prediction of OS and DFS, the concordance index (C-index) of the nomogram is statistically greater than the C-index values obtained using TNM staging alone. Moreover, the time-dependent AUC, exceeding the threshold of 0.7, demonstrated the nomogram's satisfactory discriminative performance over different periods. DCA revealed that the nomogram offered a greater overall net benefit than the TNM staging system.

Conclusion

The nomogram incorporating inflammation, nutritional and clinicopathological variables exhibited excellent discrimination. This nomogram is a promising instrument for predicting outcomes and defining personalized treatment strategies for patients with BC.

The impact of the HER2-low status on conditional survival in patients with breast cancer

Introduction

With recent advances in breast cancer (BC) treatment, the disease-free survival (DFS) of patients is increasing and the risk factors for recurrence and metastasis are changing. However, a dynamic approach to assessing the risk of recurrent metastasis in BC is currently lacking. This study aimed to develop a dynamically changing prediction model for recurrent metastases based on conditional survival (CS) analysis.

Methods

Clinical and pathological data from patients with BC who underwent surgery at the Affiliated Hospital of Qingdao University between August 2011 and August 2022 were retrospectively analysed. The risk of recurrence and metastasis in patients with varying survival rates was calculated using CS analysis, and a risk prediction model was constructed.

Results

A total of 4244 patients were included in this study, with a median follow-up of 83.16 ± 31.59 months. Our findings suggested that the real-time DFS of patients increased over time, and the likelihood of DFS after surgery correlated with the number of years of prior survival. We explored different risk factors for recurrent metastasis in baseline patients, 3-year, and 5-year disease-free survivors, and found that low HER2 was a risk factor for subsequent recurrence in patients with 5-year DFS. Based on this, conditional nomograms were developed. The nomograms showed good predictive ability for recurrence and metastasis in patients with BC.

Conclusion

Our study showed that the longer patients with BC remained disease-free, the greater their chances of remaining disease-free again. Predictive models for recurrence and metastasis risk based on CS analysis can help improve the confidence of patients fighting cancer and help doctors personalise treatment and follow-up plans.

Proposing a novel molecular subtyping scheme for predicting distant recurrence-free survival in breast cancer post-neoadjuvant chemotherapy with close correlation to metabolism and senescence

Background

High relapse rates remain a clinical challenge in the management of breast cancer (BC), with distant recurrence being a major driver of patient deterioration. To optimize the surveillance regimen for distant recurrence after neoadjuvant chemotherapy (NAC), we conducted a comprehensive analysis using bioinformatics and machine learning approaches.

Materials and methods

Microarray data were retrieved from the GEO database, and differential expression analysis was performed with the R package 'Limma'. We used the Metascape tool for enrichment analyses, and 'WGCNA' was utilized to establish co-expression networks, selecting the soft threshold power with the 'pickSoftThreshold' algorithm. We integrated ten machine learning algorithms and 101 algorithm combinations to identify key genes associated with distant recurrence in BC. Unsupervised clustering was performed with the R package 'ConsensusCluster Plus'. To further screen the key gene signature of residual cancer burden (RCB), multiple knockdown studies were analyzed with the Genetic Perturbation Similarity Analysis (GPSA) database. Single-cell RNA sequencing (scRNA-seq) analysis was conducted through the Tumour Immune Single-cell Hub (TISCH) database, and the XSum algorithm was used to screen candidate small molecule drugs based on the Connectivity Map (CMAP) database. Molecular docking processes were conducted using Schrodinger software. GMT files containing gene sets associated with metabolism and senescence were obtained from GSEA MutSigDB database. The GSVA score for each gene set across diverse samples was computed using the ssGSEA function implemented in the GSVA package.

Results

Our analysis, which combined Limma, WGCNA, and machine learning approaches, identified 16 RCB-relevant gene signatures influencing distant recurrence-free survival (DRFS) in BC patients following NAC. We then screened GATA3 as the key gene signature of high RCB index using GPSA analysis. A novel molecular subtyping scheme was developed to divide patients into two clusters (C1 and C2) with different distant recurrence risks. This molecular subtyping scheme was found to be closely associated with tumor metabolism and cellular senescence. Patients in cluster C2 had a poorer DRFS than those in cluster C1 (HR: 4.04; 95% CI: 2.60-6.29; log-rank test p < 0.0001). High GATA3 expression, high levels of resting mast cell infiltration, and a high proportion of estrogen receptor (ER)-positive patients contributed to better DRFS in cluster C1. We established a nomogram based on the N stage, RCB class, and molecular subtyping. The ROC curve for 5-year DRFS showed excellent predictive value (AUC=0.91, 95% CI: 0.95-0.86), with a C-index of 0.85 (95% CI: 0.81-0.90). Entinostat was identified as a potential small molecule compound to reverse high RCB after NAC. We also provided a comprehensive review of the EDCs exposures that potentially impact the effectiveness of NAC among BC patients.

Conclusion

This study established a molecular classification scheme associated with tumor metabolism and cancer cell senescence to predict RCB and DRFS in BC patients after NAC. Furthermore, GATA3 was identified and validated as a key gene associated with BC recurrence.

A Novel Albumin-Related Nutrition Biomarker Predicts Breast Cancer Prognosis in Neoadjuvant Chemotherapy: A Two-Center Cohort Study

BACKGROUND

Recently, there has been a growing focus on the prognostic significance of nutrition-related biomarkers. We attempted to explore the association between a novel albumin-related nutrition marker called "lymphocyte × albumin (LA)" and disease-free survival (DFS) in breast cancer patients undergoing neoadjuvant chemotherapy (NAC).

METHODS

In total, 711 non-metastatic breast cancer patients who underwent NAC at two medical centers were retrospectively analyzed. We performed least absolute shrinkage and selection operator (LASSO) Cox regression analysis as well as multivariate Cox regression analyses to identify the variables associated with DFS and to establish a predictive nomogram.

RESULTS

The nomogram incorporated four variables based on the multivariate analysis of DFS in the training cohort: LA, ypN stage, ypT stage, and hormone receptor status. In comparison with the traditional TNM staging system, the nomogram demonstrated superior discrimination, calibration ability, and clinical usefulness in both the training set and internal and external validation sets. Furthermore, patients stratified into different risk groups resulted in significant differences in DFS.

CONCLUSIONS

LA is an independent prognostic biomarker, and LA-based prognostic nomogram offers a more precise assessment of DFS for breast cancer patients treated with NAC, potentially serving as a valuable tool for personalized prognostic predictions.

Development and validation of a network calculator model for safety and efficacy after pancreaticoduodenectomy in the elderly patients with pancreatic head cancer

BACKGROUND

Benefiting from increased life expectancy and improved perioperative management, more elderly patients with pancreatic head cancer (PHC) underwent pancreaticoduodenectomy (PD). However, individualized predictive models for the safety and efficacy of PD is still lacking. this study aimed to developed three safety- and efficacy-related risk calculators for elderly (> = 65 years) PHC patients.

METHODS

This study was designed with two research cohorts, namely, the training cohort and the validation cohort, and comprises four general steps: (1) Risk factors were analyzed for the incidence of postoperative complications, cancer-specific survival (CSS), and overall survival (OS) in the training cohort (N = 271) using logistic and Cox-regression analysis. (2) Nomograms were then plotted based on the above results. (3) The accuracy of the developed nomogram models was then verified with the validation cohort (N = 134) data using consistency index (C-index) and calibration curves. (4) We then evaluated the efficacy of these nomograms using decision curve analysis (DCA) in both the training and validation cohorts, and ultimately constructed three online calculators based on these nomograms.

RESULTS

We identified ASA, diabetes, smoking, and lymph node invasion as predisposing risk factors for postoperative complications, and the predictive factors that affected both OS and CSS were ASA, diabetes, BMI, CA19-9 level, and tumor diameter. By integrating the above risk factors, we constructed three nomograms on postoperative complication, CSS, and OS. The C-index for complication, CSS, and OS were 0.824, 0.784, and 0.801 in the training cohort and 0.746, 0.718, and 0.708 in the validation cohort. Moreover, the validation curves and DCA demonstrated good calibration and robust compliance in both training and validation cohorts. We then developed three web calculators (https://caiming.shinyapps.io/CMCD/, https://caiming.shinyapps.io/CMCSS/, and https://caiming.shinyapps.io/CMOS/) to facilitate the use of the nomograms.

CONCLUSIONS

The calculators demonstrated promising performance as an tool for predicting the safety and efficacy of PD in elderly PHC patients.

Development and validation of novel risk prediction models of breast cancer based on stanniocalcin‐1 level

PURPOSE

The function of stanniocalcin-1 (STC-1) in the oncogenesis and progression of tumors has been extensively studied. The purpose of this study was to investigate the relationship between secreted STC-1 and prognosis in patients with breast cancer (BC) and to determine whether STC-1 could be a key prognostic factor in BC.

METHODS

The STC-1 level was measured by ELISA and clinical data from 1210 female patients with BC were used to develop and validate nomograms. We then verified the models through the plotting of ROC curves and calibration curves, calculating the C-index, and performing decision curve analyses (DCA).

RESULTS

The level of STC-1 in the peripheral plasma was significantly correlated with the T stage, N stage, clinical stage, grade, hormone receptors, HER-2 status, and tumor subtype. Cox regression analyses revealed that estrogen receptor(ER) status, N stage, and STC-1 level were risk factors for overall survival (OS), whereas T stage, N stage, and STC-1 level were independent prognostic factors for distant disease-free survival (DDFS) and disease-free survival (DFS). Both the ROC curve and the C-index confirmed the high resolution of these models, while the DCA identified the feasibility of their practical application. In addition, the calibration curves indicated good consistency between the predicted and actual survival rates.

CONCLUSION

Nomograms were created based on STC-1 levels for 3-, 5-, and 7-year OS, DDFS, and DFS of patients with BC respectively. As a key prognostic factor for BC, peripheral blood STC-1 level can be used clinically as a liquid biopsy indicator.