Conversion of immunohistochemical markers and breast density are associated with pathological response and prognosis in very young breast cancer patients who fail to achieve a pathological complete response after neoadjuvant chemotherapy

Correlation between postoperative chemotherapy regimen and survival in patients with resectable gastric adenocarcinoma accompanied with vascular cancer thrombus

BACKGROUND

Patients with resectable gastric adenocarcinoma accompanied by vascular cancer thrombus (RGAVCT) have a poor prognosis, with a 5-year survival rate ranging from 18.42%-53.57%. These patients need a reasonable postoperative treatment plan to improve their prognosis.

AIM

To determine the most effective postoperative chemotherapy regimen for patients with RGAVCT.

METHODS

We retrospectively collected the clinicopathological data of 530 patients who underwent radical resection for gastric cancer between January 2017 and January 2022 and who were pathologically diagnosed with gastric adenocarcinoma with a choroidal cancer embolus. Furthermore, we identified the high-risk variables that can influence the prognosis of patients with RGAVCT by assessing the clinical and pathological features of the patients who met the inclusion criteria. We also assessed the significance of survival outcomes using Mantel-Cox univariate and multivariate analyses. The subgroups of patients with stages I, II, and III disease who received single-, dual-, or triple-drug regimens following surgery were analyzed using SPSS 25.0 and the ggplot2 package in R 4.3.0.

RESULTS

In all, 530 eligible individuals with RGAVCT were enrolled in this study. The median overall survival (OS) of patients with RGAVCT was 24 months, and the survival rates were 80.2%, 62.5%, and 42.3% at 12, 24, and 59 months, respectively. Preoperative complications, tumor size, T stage, and postoperative chemotherapy were identified as independent factors that influenced OS in patients with RGAVCT according to the Cox multivariate analysis model. A Kaplan-Meier analysis revealed that chemotherapy had no effect on OS of patients with stage I or II RGAVCT; however, chemotherapy did have an effect on OS of stage III patients. Stage III patients who were treated with chemotherapy consisting of dual- or triple-agent regimens had better survival than those treated with single-agent regimens, and no significant difference was observed in the survival of patients treated with chemotherapy consisting of dual- or triple-agent regimens.

CONCLUSION

For patients with stage III RGAVCT, a dual-agent regimen of postoperative chemotherapy should be recommended rather than a triple-agent treatment, as the latter is associated with increased frequency of adverse events.

Prediction of Disease-Free Survival in Breast Cancer using Deep Learning with Ultrasound and Mammography: A Multicenter Study

BACKGROUND

Breast cancer is a leading cause of cancer morbility and mortality in women. The possibility of overtreatment or inappropriate treatment exists, and methods for evaluating prognosis need to be improved.

MATERIALS AND METHODS

Patients (from January 2013 to December 2018) were recruited and divided into a training group and a testing group. All patients were followed for more than 3 years. Patients were divided into a disease-free group and a recurrence group based on follow up results at 3 years. Ultrasound (US) and mammography (MG) images were collected to establish deep learning models (DLMs) using ResNet50. Clinical data, MG, and US characteristics were collected to select independent prognostic factors using a cox proportional hazards model to establish a clinical model. DLM and independent prognostic factors were combined to establish a combined model.

RESULTS

In total, 1242 patients were included. Independent prognostic factors included age, neoadjuvant chemotherapy, HER2, orientation, blood flow, dubious calcification, and size. We established 5 models: the US DLM, MG DLM, US + MG DLM, clinical and combined model. The combined model using US images, MG images, and pathological, clinical, and radiographic characteristics had the highest predictive performance (AUC = 0.882 in the training group, AUC = 0.739 in the testing group).

CONCLUSION

DLMs based on the combination of US, MG, and clinical data have potential as predictive tools for breast cancer prognosis.

HER2-positive is an independent indicator for predicting pathological complete response to neoadjuvant therapy and Ki67-changed after neoadjuvant chemotherapy predicts favorable prognosis in Chinese women with locally advanced breast cancer

The growing body of evidence suggests that breast cancer (BC) who achieve pathological complete response (pCR) after neoadjuvant chemotherapy (NAC) may experience a more favorable prognosis. The objective of this study is to investigate the correlation between clinicopathologic parameters of locally advanced breast cancer (LABC) patients and the outcomes of NAC, with the aim of identifying predictive indicators for pCR. Additionally, we seek to examine the conversion of IHC markers in pCR patients following NAC and its impact on the prognosis of BC patients. We conducted a study involving 126 patients with LABC. Clinicopathological parameters associated with pCR were subjected to univariate and multivariate analysis. Kaplan-Meier (KM) curves and the log-rank test were used to compare the statistical difference in prognosis in different groups of patients. Additionally, we used difference and consistency tests to examine the conversion of immunohistochemistry (IHC) markers following NAC. The status of estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2) and molecular subtypes of BC were associated with pCR in the univariate analysis (all P < .05), which may be potential markers to predict pCR. HER2 was identified as an independent factor for predicting pCR in the multivariate analysis. The pCR rate of HER2-positive patients who received NAC combined targeted therapy was higher than that of patients who only received NAC (P = .003). The disease-free survival (DFS) rate of TNBC patients who achieved pCR was significantly higher than that of non-pCR TNBC patients (P = .026). The IHC marker conversion after NAC mainly existed in PR (P = .041). Ki67 expression decreased in the luminal B subtype and increased in the HER2 enriched subtype after NAC (all P < .001). Patients with Ki67 expression change after NAC had longer overall survival (OS) and DFS than unchanged patients (all P < .05). HER2-positive is an independent indicator for predicting pCR, and HE2-positive patients who received NAC combined targeted therapy were favorable to achieving pCR. IHC markers of BC patients exhibit varying degrees of alterations after NAC, and changes in Ki67 expression after NAC could serve as a marker to predict a better prognosis.

Ultrasound Strain Elastography and Contrast-Enhanced Ultrasound in Predicting the Efficacy of Neoadjuvant Chemotherapy for Breast Cancer: A Nomogram Integrating Ki-67 and Ultrasound Features

OBJECTIVES

To explore whether conventional elastography and contrast-enhanced ultrasound (CEUS) combined with histopathology can monitor the efficacy of neoadjuvant chemotherapy (NAC) for breast cancer (BC), and develop a Nomogram prediction model monitoring response to NAC.

METHODS

From February 2010 to November 2015, 91 BC patients who received NAC were recruited. The maximum diameter, stiffness, and CEUS features were assessed. Core biopsy, surgical pathology immunophenotype, and Miller-Payne (MP) evaluation were documented. Univariate and multivariate analysis was performed using receiver operating characteristic (ROC) analysis and logistic regression analysis.

RESULTS

There were 37 cases showing pathological complete response (pCR) and 54 of non-pCR. The changes of maximal diameter were correlated with MP (P < .05). The sensitivity (SEN), specificity (SPE), and area under the ROC curve (AUC) of baseline size predicting pCR were 57.40%, 70.30%, and 0.64 (P = .024). Baseline Ki-67 index of pCR group is significantly higher than that of non-pCR group (P = .029), and the ROC analysis of baseline Ki-67 indicates the SEN, SPE, and AUC of 51.70%, 78.00%, and 0.638 (P = .050). When combined with size, CEUS features, stiffness, and Ki-67 of baseline, the ROC curve shows good performance with SEN, SPE, and AUC of 70.00%, 76.19%, 0.821 (P = .004). Incorporating the change of characteristics into multivariate regression analysis, the results demonstrate excellent performance (SEN 100.00%, SPE 95.24%, AUC 0.986, P = .000).

CONCLUSIONS

The change of the maximum size was correlated with MP score, which can provide reference to predict efficacy of NAC and evaluate residual lesions. When combining with elastography, CEUS, and Ki-67, better performance in predicting pathological response was shown.

Identification of predictors based on drug targets highlights accurate treatment of goserelin in breast and prostate cancer

Goserelin is an effective alternative to surgery or estrogen therapy in prostate cancer palliation, and possibly to ovariectomy in premenopausal breast cancer. However, not all users of goserelin can benefit from it, or some patients are not sensitive to goserelin. The advent of network pharmacology has highlighted the need for accurate treatment and predictive biomarkers. In this study, we successfully to identify 76 potential targets related to the compound of goserelin through network pharmacology approach. We also identified 18 DEGs in breast cancer tissues and 5 DEGs in cells, and 6 DEGs in prostate cancer tissues and 9 DEGs in cells. CRABP2 is the common DEG both in breast and prostate cancer. The risk prediction models constructed with potential prognostic targets of goserelin can successfully predict the prognosis in breast and prostate cancer, especially for very young breast cancer patients. Moreover, seven subgroups in breast cancer and six subgroups in prostate cancer were respectively identified based on consensus clustering using potential prognostic targets of goserelin that significantly influenced survival. The expression of representative genes including CORO1A and ANXA5 in breast and DPP4 in prostate showed strong correlations with clinic-pathological factors. Taken together, the novel signature can facilitate identification of new biomarkers which sensitive to goserelin, increase the using accuracy of goserelin and clarify the classification of disease molecular subtypes in breast and prostate cancer.