A nomogram based on clinicopathological features and serological indicators predicting breast pathologic complete response of neoadjuvant chemotherapy in breast cancer

TP53 Gene Polymorphism at Codon 72 as a Response Predictor for Neoadjuvant Chemotherapy

Introduction

Breast cancer is the most prevalent cancer in women worldwide, and neoadjuvant chemotherapy is a favored method for achieving pathologic complete response (pCR). The TP53 gene is involved in inducing the response to chemotherapy drugs.

Objectives

The present study sought to correlate polymorphism variants at codon 72 with pCR to neoadjuvant chemotherapy.

Casuistry and Methods

The study was conducted in the state of Sergipe, in northeastern Brazil. A total of 206 patients with a histopathological diagnosis of breast cancer who underwent neoadjuvant chemotherapy from 2019 to 2022 were included. DNA samples were collected for the evaluation of TP53 polymorphism at codon 72. A prospective evaluation of the cases was conducted to verify the surgical pathologic response after chemotherapy; the Response Evaluation Criteria in Solid Tumors (RECIST) were used. The study was approved by the University of São Paulo Ethics and Research Committee.

Results

Of the 168 patients, 44.6% were Arg72Arg, 17.3% were Pro72Pro, and 38.0% were Arg72Pro; pCR was achieved in 21.4% of the patients; 10.1% had progressive disease, 13.7% had stable disease, and 54.2% had a partial pathologic response. The only predictor of pCR in multivariate regression was immunohistochemistry (p < 0.001). In the multivariate analysis, Arg72Pro and Pro72Pro increased the odds of the patient evolving with stable disease. This study was innovative in demonstrating a predictor of stable disease in response to neoadjuvant chemotherapy.

Conclusion

TP53 polymorphism at codon 72 is not a predictor of pCR, but it can be a predictor of stable disease.

Systematic Review of Nomograms Used for Predicting Pathological Complete Response in Early Breast Cancer

Pathological complete response (pCR) is an important surrogate outcome to assess the effects of neoadjuvant chemotherapy (NAC). Nomograms to predict pCR have been developed with local data to better select patients who are likely to benefit from NAC; however, they were never critically reviewed regarding their internal and external validity. The purpose of this systematic review was to critically appraise nomograms published in the last 20 years (2010-2022). Articles about nomograms were searched in databases, such as PubMed/MEDLINE, Embase and Cochrane. A total of 1120 hits were found, and seven studies were included for analyses. No meta-analysis could be performed due to heterogeneous reports on outcomes, including the definition of pCR and subtypes. Most nomograms were developed in Asian centers, and nonrandomized retrospective cohorts were the most common sources of data. The most common subtype included in the studies was triple negative (50%). There were articles that included HER2+ (>80%). In one study, scholars performed additional validation of the nomogram using DFS and OS as outcomes; however, there was a lack of clarity on how such endpoints were measured. Nomograms to predict pCR cannot be extrapolated to other settings due to local preferences/availability of NAC. The main gaps identified in this review are also opportunities for future nomogram research and development.

Predictive miRNAs Patterns in Blood of Breast Cancer Patients Demonstrating Resistance Towards Neoadjuvant Chemotherapy

Objective

The effect of chemotherapy in patients with breast cancer (BC) is uncertain. This study attempted to analyze serum microRNAs (miRNAs) in NAC resistant and sensitive BC patients and develop a miRNA-based nomogram model. To further help clinicians make treatment decisions for hormone receptor-positive patients.

Methods

A total of 110 BC patients with NAC were recruited and assigned in sensitive and resistant group, and 4 sensitive patients and 3 resistant patients were subjected to high-throughput sequencing. The functions of their target genes were analyzed by GO and KEGG. Five BC-related reported miRNAs were selected for expression pattern measurement by RT-qPCR and multivariate logistic analysis. The nomogram model was developed using R 4.0.1, and its predictive efficacy, consistency and clinical application value in development and validation groups were evaluated using ROC, calibration and decision curves.

Results

There were 44 differentially-expressed miRNAs in resistant BC patients. miR-3646, miR-4741, miR-6730-3p, miR-6831-5p and miR-8485 were candidate for resistance diagnosis in BC. Logistic multiple regression analysis showed that miR-4741 (or = 0.30, 95% CI = 0.08-0.63, P = 0.02) and miR-6831-5p (or = 0.48, 95% CI = 0.24-0.78, P = 0.01) were protective factors of BC resistance. The ROC curves showed a sensitivity of 0.884 and 0.750 for miR-4741 and miR-6831-5P as markers of resistance, suggesting that they can be used as independent risk factors for BC resistance. The other 3 miRNAs can be used as calibration factors to establish the risk prediction model of resistance in BC. In risk model, the prediction accuracy of resistance of BC is about 78%. 5-miRNA signature diagnostic models can help clinicians provide personalized treatment for NAC resistance BC patients to improve patient survival.

Conclusion

MiR-4741 and miR-6831-5p are independent risk factors for breast cancer resistance. This study constructed a nomogram model of NAC resistance in BC based on 5 differentially-expressed serum miRNAs.

Tumor volume reduction after induction chemotherapy with gemcitabine plus cisplatin in nasopharyngeal carcinoma

OBJECTIVES

To evaluate the tumor volume reduction after induction chemotherapy (IC) with gemcitabine plus cisplatin (GP) and to build prediction models for tumor volume reduction in nasopharyngeal carcinoma (NPC).

METHODS

NPC patients who received GP IC were retrospectively enrolled. The gross tumor volume of the nasopharynx and lymph nodes (GTVnx and GTVnd) were contoured before and after IC. Univariate and multivariate analyses were performed to identify associated factors. Nomogram models were constructed to predict the possibility of tumor volume reduction.

RESULTS

A total of 192 patients were enrolled. The mean relative volume reduction for GTVnx and GTVnd was 29.66% and 31.75%, respectively. The volume reduction of GTVnx and GTVnd had a weak association (r = 0.229, p < 0.001). For GTVnx volume reduction, pre-treatment neutrophil count (p = 0.043), lymphocyte count (p = 0.026), LDH level (p = 0.005), and BMI (p = 0.020) were independently associated factors. For GTVnd volume reduction, pre-treatment EBV-DNA (p = 0.029), GTVnd volume (p < 0.001), eosinophil count (p = 0.043), NLR (p = 0.039), LDH level (p = 0.026), and serum potassium level (p = 0.027) were independently associated factors. For the GTVnx nomogram model, areas under the receiver-operating characteristic curve (AUC) were 0.702 and 0.698 for the training and validation cohorts, respectively. For the GTVnd nomogram model, the AUC was 0.872 and 0.758 for the training and validation cohorts, respectively.

CONCLUSIONS

Tumor volumes reduce significantly after GP induction chemotherapy. Nomogram models for predicting the possibility of tumor volume reduction are built.