Nomograms to predict pathologic complete response and metastasis-free survival after preoperative chemotherapy for breast cancer

Preoperative systemic inflammation response index: Clinicopathologic predictor of pathological complete response in HER2-positive breast cancer patients receiving neoadjuvant systemic therapy

BACKGROUND

Multiple pretreatment systemic inflammatory markers (SIMs) have been reported as predictors of pathological complete response (pCR) after neoadjuvant systemic therapy (NST) in patients with breast cancer (BC). However, the most significant SIM remains to be conclusively identified, and variations among different molecular subtypes remain unknown. The objective of the study was to identify the most significant SIM in patients with human epidermal growth factor receptor 2 (HER2) positive BC, to construct a pCR-predictive nomogram combining it with other clinicopathologic factors, and to evaluate its prognostic value on survival.

METHODS

We retrospectively reviewed the findings for 240 patients with stage I-III HER2-positive BC who underwent NST and subsequent surgery at Kaohsiung and Taichung Veterans General Hospital from 2011 to 2021. Clinicopathologic factors were analyzed by stepwise logistic regression with backward selection. The data were used to construct a nomogram plot for determining the pCR probability. Kaplan-Meier curves and log-rank test were used to evaluate disease-free survival (DFS) and overall survival (OS).

RESULTS

Among the pretreatment SIMs, only the systemic inflammation response index (SIRI) was significantly related to pCR, with an optimal cutoff value of 1.27 × 10 9 /L. Stepwise logistic analyses indicated that clinical N stage, HER2 immunohistochemistry score, hormone receptor status, targeted therapy regimen, and SIRI were independent predictors of pCR, with an area under the curve of 0.722. The Hosmer-Lemeshow test and calibration curve revealed that the predictive ability was a good fit to actual observations. A nomogram was constructed based on the logistic model. The external validation of the model also revealed satisfactory discrimination and calibration. Kaplan-Meier analysis showed that patients with SIRI <1.27 had longer DFS and OS.

CONCLUSION

Pretreatment SIRI <1.27 is predictive of pCR, DFS, and OS in HER2-positive BC. Our nomogram could efficiently predict pCR and facilitate clinical decision-making before neoadjuvant treatment.

Prediction of prognosis of patients with hepatocellular carcinoma based on immune-related score

BACKGROUND

Immune-related scores are currently used for prognostic evaluation and as an immunotherapy reference in various cancers. However, the relationship between immune-related score and hepatocellular carcinoma (HCC) prognosis has not yet been investigated. This study aimed to explore the clinical application value of immune-related score for predicting HCC prognosis-related indicators including disease-free survival (DFS) and overall survival (OS), and to construct a clinical nomogram prediction model related to verification.

METHODS

This study included 284 HCC patients who were selected from the Cancer Genome Atlas (TCGA) database and linked to the immune-related score downloaded from the public platform. A Cox proportional hazards regression model was used to estimate the adjusted risk ratio, and a nomogram was constructed based on multivariate analysis results and clinical significance. The model was internally verified by bootstrap. The performance of the prediction model was evaluated using the C-index and calibration curves.

RESULTS

Patients were divided into three subgroups according to the immune-related score level. Compared with patients in the low immune-related score group, the DFS of patients in the medium and high immune-related score groups was significantly prolonged (HR: 0.53, 95% CI: 0.32-0.87; HR: 0.37, 95% CI: 0.21-0.63, respectively). The OS of patients in the medium and high immune-related score groups was also significantly prolonged (HR: 0.43, 95% CI: 0.20-0.95, p = 0.038; HR: 0.29, 95% CI: 0.14-0.58, p < 0.001, respectively). The C-indexes for predicting DFS and OS were 0.687 (95% CI: 0.665-0.700) and 0.743 (95% CI: 0.709-0.776), respectively. The calibration curves of 3-year and 5-year DFS and OS showed that the results predicted by the nomogram were in good agreement with the actual observations.

CONCLUSIONS

Moderate/high-grade immune-related score was significantly associated with better DFS and OS in HCC patients. In addition, a nomogram for prognosis estimation can help clinicians predict the survival status of patients.

Development and External Validation of a Machine Learning Model to Predict Pathological Complete Response After Neoadjuvant Chemotherapy in Breast Cancer

PURPOSE

Several predictive models have been developed to predict the pathological complete response (pCR) after neoadjuvant chemotherapy (NAC); however, few are broadly applicable owing to radiologic complexity and institution-specific clinical variables, and none have been externally validated. This study aimed to develop and externally validate a machine learning model that predicts pCR after NAC in patients with breast cancer using routinely collected clinical and demographic variables.

METHODS

The electronic medical records of patients with advanced breast cancer who underwent NAC before surgical resection between January 2017 and December 2020 were reviewed. Patient data from Seoul National University Bundang Hospital were divided into training and internal validation cohorts. Five machine learning techniques, including gradient boosting machine (GBM), support vector machine, random forest, decision tree, and neural network, were used to build predictive models, and the area under the receiver operating characteristic curve (AUC) was compared to select the best model. Finally, the model was validated using an independent cohort from Seoul National University Hospital.

RESULTS

A total of 1,003 patients were included in the study: 287, 71, and 645 in the training, internal validation, and external validation cohorts, respectively. Overall, 36.3% of the patients achieved pCR. Among the five machine learning models, the GBM showed the highest AUC for pCR prediction (AUC, 0.903; 95% confidence interval [CI], 0.833-0.972). External validation confirmed an AUC of 0.833 (95% CI, 0.800-0.865).

CONCLUSION

Commonly available clinical and demographic variables were used to develop a machine learning model for predicting pCR following NAC. External validation of the model demonstrated good discrimination power, indicating that routinely collected variables were sufficient to build a good prediction model.

Prediction of Tumor Progression During Neoadjuvant Chemotherapy and Survival Outcome in Patients With Triple-Negative Breast Cancer

OBJECTIVE

To investigate the association of clinical, pathologic, and magnetic resonance imaging (MRI) variables with progressive disease (PD) during neoadjuvant chemotherapy (NAC) and distant metastasis-free survival (DMFS) in patients with triple-negative breast cancer (TNBC).

MATERIALS AND METHODS

This single-center retrospective study included 252 women with TNBC who underwent NAC between 2010 and 2019. Clinical, pathologic, and treatment data were collected. Two radiologists analyzed the pre-NAC MRI. After random allocation to the development and validation sets in a 2:1 ratio, we developed models to predict PD and DMFS using logistic regression and Cox proportional hazard regression, respectively, and validated them.

RESULTS

Among the 252 patients (age, 48.3 ± 10.7 years; 168 in the development set; 84 in the validation set), PD was occurred in 17 patients and 9 patients in the development and validation sets, respectively. In the clinical-pathologic-MRI model, the metaplastic histology (odds ratio [OR], 8.0; P = 0.032), Ki-67 index (OR, 1.02; P = 0.044), and subcutaneous edema (OR, 30.6; P = 0.004) were independently associated with PD in the development set. The clinical-pathologic-MRI model showed a higher area under the receiver-operating characteristic curve (AUC) than the clinical-pathologic model (AUC: 0.69 vs. 0.54; P = 0.017) for predicting PD in the validation set. Distant metastases occurred in 49 patients and 18 patients in the development and validation sets, respectively. Residual disease in both the breast and lymph nodes (hazard ratio [HR], 6.0; P = 0.005) and the presence of lymphovascular invasion (HR, 3.3; P < 0.001) were independently associated with DMFS. The model consisting of these pathologic variables showed a Harrell's C-index of 0.86 in the validation set.

CONCLUSION

The clinical-pathologic-MRI model, which considered subcutaneous edema observed using MRI, performed better than the clinical-pathologic model for predicting PD. However, MRI did not independently contribute to the prediction of DMFS.

EXPRESSION OF ALU REPEAT IN BLOOD PLASMA OF PATIENTS WITH BREAST CANCER DURING NEOADJUVANT CHEMOTHERAPY: AN EXPLORATORY STUDY

BACKGROUND

Locally advanced breast cancer (LABC) rates are unusually high in developing countries. There is a need for the identification of predictive biomarkers for the selection of patients who could benefit from neoadjuvant chemotherapy (NAC).

AIM

As the expression of ALU repeat is increased in cancer and has not been assessed in liquid biopsy of cancer patients, our goal was to assess ALU expression in the blood plasma of LABC patients during NAC.

PATIENTS AND METHODS

Plasma samples drawn at baseline and at the end of the fourth cycle of chemotherapy were used to determine the plasma levels of ALU-RNA by quantitative real-time PCR.

RESULTS

ALU expression from baseline to the fourth cycle of NAC increased from a median relative level of 1870 to 3370 in the whole group (p = 0.03). The increase in ALU-RNA levels in the course of NAC was more pronounced in premenopausal women and in patients with hormone-positive tumors. In patients with complete response to NAC, baseline ALU expression was higher than that in those with partial response.

CONCLUSION

This exploratory study provides evidence that plasma ALU-RNA levels are modulated by the menopausal status and hormone receptor status of breast cancer patients and pre-therapeutic ALU-RNA levels might be useful in predicting the response to chemotherapy in a neoadjuvant setting.

Patients achieved pCR during neoadjuvant chemotherapy had better outcome than adjuvant chemotherapy setting in breast cancer: A comparative study

PURPOSE

Pathological complete response(pCR) during neoadjuvant chemotherapy(NAC) has been proposed as a predictor for better prognosis in breast cancer. However, few studies compare the outcomes of patients receiving NAC and adjuvant chemotherapy(AC).

METHODS

We retrospectively matched the patients who received NAC(N = 462) and AC(N = 462) by age, time of diagnosis, and primary clinical stage using the propensity score match in breast cancer patients treated in Sir Run Run Shaw Hospital with the median follow up of 67 months. Death from breast cancer and recurrence were used as endpoints. A multivariable Cox models were used to estimate the hazard ratios for breast-cancer specific survival (BCSS) and DFS. A multivariable logistic regression model was simulated to predict pCR.

RESULTS

In patients who received NAC, 18.0%(83/462) patients achieved pCR, while the rest of the patients did not. pCR subgroup demonstrated significant better BCSS and DFS than patients receiving AC(BCSS: HR = 0.39, 95% CI:0.12-0.93, P = 0.03; DFS: HR = 0.16, 95%CI 0.009-0.73, P = 0.013) and non-pCR patients(BCSS: HR = 0.32, 95%CI 0.10-0.77, P = 0.008; DFS: HR = 0.12, 95%CI 0.007-0.55, P = 0.002). Patients who received AC demonstrated insignificant survival compared to non-pCR patients(BCSS: HR= 0.82, 95%CI 0.62-1.10, P = 0.19; DFS: HR = 0.75, 95%CI 0.53-1.07, P = 0.12). Patients with AC had significant better DFS than non-pCR patients(HR = 0.33, 95% CI 0.10-0.94, P = 0.04) in luminal B Her2+ patients. More NAC cycles(>2), TNBC, lower cT stage, and mixed histology indicate higher possibility of pCR(AUC = 0.89).

CONCLUSION

pCR patients with NAC indicated better prognosis than patients receiving AC or non-pCR patients from NAC. The timing of chemotherapy may need carefully pondering in luminal B Her2+ patients.

Development and Validation of Nomograms to Predict Survival in Patients Undergoing Complete Cytoreduction and Hyperthermic Intraperitoneal Chemotherapy for Pseudomyxoma Peritonei of Appendiceal Origin

Importance

Pseudomyxoma peritoni, a rare condition characterized by mucinous ascites and peritoneal deposits, mainly originates from a ruptured mucinous appendix tumor and is considered an indolent disease but can progress and become fatal. Optimal treatment to improve cure and survival rates involves complete cytoreductive surgery (CCRS) and hyperthermic intraperitoneal chemotherapy (HIPEC). Accurate predictive models are useful in supporting and informing treatment strategies and stratifying patient follow-up.

Objective

To evaluate the prognostic significance of clinically important variables and generate validated nomograms to predict overall (OS) and disease-free survival (DFS) following CCRS and hyperthermic intraperitoneal HIPEC for pseudomyxoma peritonei (PMP) of appendiceal origin.

Design, Setting, and Participants

This retrospective study used prospectively collected data on patients who had cytoreductive surgery (CRS) and HIPEC in a single institution between 1994 and 2018. The cohort was randomly allocated into development (70%) and validation (30%) sets. Univariate and multivariate analyses were performed with Cox proportional hazards regression.

Main Outcomes and Measures

A prediction model was developed with significant prognostic factors identified by multivariate analysis. The model's prognostic performance was evaluated with the concordance index (C index). The nomogram was calibrated by comparing the predicted and observed probabilities.

Results

Of 2637 CRS and HIPEC operations, 1102 patients (female, 64.4%; median age [IQR], 57.0 [48.0-66.0] years) (41.8%) had CCRS for PMP of appendiceal origin. Elevated tumor markers, peritoneal carcinomatosis index, gastrectomy, and tumor grade were independent predictive factors for DFS. Gender, age, elevated tumor makers, peritoneal carcinomatosis index, and tumor grade influenced OS. The nomograms were generated with respective prognostic factors. The nomograms showed good performance in predicting survival. Median OS of the cohort was 16.5 years (95% CI, 13.7-19.2) with a 5-year probability of survival of 80.2%. The median DFS was 10.3 years (95% CI, 7.2- 13.3) and the 5-year probability of recurrence-free survival was 60.5%.

Conclusions and Relevance

Clinically important independent predictors for survival and recurrence were selected to develop the nomograms for OS and DFS. These 2 nomograms are user friendly and useful tools for patient management with clinical trial design applications.

A novel nomogram model of breast cancer-based imaging for predicting the status of axillary lymph nodes after neoadjuvant therapy

This study is aimed to develop and validate a novel nomogram model that can preoperatively predict axillary lymph node pathological complete response (pCR) after NAT and avoid unnecessary axillary lymph node dissection (ALND) for breast cancer patients. A total of 410 patients who underwent NAT and were pathologically confirmed to be axillary lymph node positive after breast cancer surgery were included. They were divided into two groups: patients with axillary lymph node pCR and patients with residual node lesions after NAT. Then the nomogram prediction model was constructed by univariate and multivariate logistic regression. The result of multivariate logistic regression analysis showed that molecular subtypes, molybdenum target (MG) breast, computerized tomography (CT) breast, ultrasound (US) axilla, magnetic resonance imaging (MRI) axilla, and CT axilla (all p < 0.001) had a significant impact on the evaluation of axillary lymph node status after NAT. The nomogram score appeared that AUC was 0.832 (95% CI 0.786-0.878) in the training cohort and 0.947 (95% CI 0.906-0.988) in the validation cohort, respectively. The decision curve represented that the nomogram has a positive predictive ability, indicating its potential as a practical clinical tool.

Prediction nomogram development and validation for postoperative radiotherapy in the management of oral squamous cell carcinoma

BACKGROUND

Predictive nomograms are useful tools to guide clinicians in estimating disease course. Oral squamous cell carcinoma (OSCC) patients would benefit from an interactive prediction calculator that defines their levels of survival-risk specific to their tumors to guide the use of postoperative radiotherapy (PORT).

METHODS

Patients with OSCC surgically treated with curative intent at four Head and Neck Cancer Centres were recruited retrospectively for development and validation of nomograms. Predictor variables include PORT, age, T and N classification, surgical margins, perineural invasion, and lymphovascular invasion. Outcomes were disease-free, disease-specific, and overall survivals over 5 years.

RESULTS

1296 patients with OSCC were in training cohort for nomogram analysis. Algorithms were developed to show relative benefit of PORT in survivals for higher-risk patients. External validation on 1212 patients found the nomogram to be robust with favorable discrimination and calibration.

CONCLUSION

The proposed calculator can assist clinicians and patients in the decision-making process for PORT.

Nomogram Prediction Model of Hypernatremia on Mortality in Critically Ill Patients

Objective

To investigate the value of hypernatremia in the intensive care unit (ICU) for the risk prediction of mortality in severe patients.

Methods

Clinical data of critically ill patients admitted to the ICU of Beijing Friendship Hospital, were collected for retrospective analysis. Univariate and multivariate logistic regression analyses were employed to analyze the influencing factors. Nomograms predicting the mortality were constructed with R software and validated with repeated sampling.

Results

A total of 442 cases were eligible for this study. Hypernatremia within 48 hours of ICU admission, change in sodium concentration (CNa+) within 48 hours, septic shock, APACHE II score, hyperlactatemia within 48 hours, use of continuous renal replacement therapy (CRRT) within 48 hours, and the use of mechanical ventilation (MV) within 48 hours of ICU admission were all identified as independent risk factors for death within 28 days of ICU admission. These predictors were included in a nomogram of 28-day mortality in severe patients, which was constructed using R software.

Conclusion

The nomogram could predict the individualized risk of 28-day mortality based on the above factors. The model has better discrimination and accuracy and has high clinical application value.

Development of a nomogram for predicting pathological complete response in luminal breast cancer patients following neoadjuvant chemotherapy

Background

Given the low chance of response to neoadjuvant chemotherapy (NACT) in luminal breast cancer (LBC), the identification of predictive factors of pathological complete response (pCR) represents a challenge. A multicenter retrospective analysis was performed to develop and validate a predictive nomogram for pCR, based on pre-treatment clinicopathological features.

Methods

Clinicopathological data from stage I-III LBC patients undergone NACT and surgery were retrospectively collected. Descriptive statistics was adopted. A multivariate model was used to identify independent predictors of pCR. The obtained log-odds ratios (ORs) were adopted to derive weighting factors for the predictive nomogram. The receiver operating characteristic analysis was applied to determine the nomogram accuracy. The model was internally and externally validated.

Results

In the training set, data from 539 patients were gathered: pCR rate was 11.3% [95% confidence interval (CI): 8.6-13.9] (luminal A-like: 5.3%, 95% CI: 1.5-9.1, and luminal B-like: 13.1%, 95% CI: 9.8-13.4). The optimal Ki67 cutoff to predict pCR was 44% (area under the curve (AUC): 0.69; p < 0.001). Clinical stage I-II (OR: 3.67, 95% CI: 1.75-7.71, p = 0.001), Ki67 ⩾44% (OR: 3.00, 95% CI: 1.59-5.65, p = 0.001), and progesterone receptor (PR) <1% (OR: 2.49, 95% CI: 1.15-5.38, p = 0.019) were independent predictors of pCR, with high replication rates at internal validation (100%, 98%, and 87%, respectively). According to the nomogram, the probability of pCR ranged from 3.4% for clinical stage III, PR > 1%, and Ki67 <44% to 53.3% for clinical stage I-II, PR < 1%, and Ki67 ⩾44% (accuracy: AUC, 0.73; p < 0.0001). In the validation set (248 patients), the predictive performance of the model was confirmed (AUC: 0.7; p < 0.0001).

Conclusion

The combination of commonly available clinicopathological pre-NACT factors allows to develop a nomogram which appears to reliably predict pCR in LBC.

The biomarkers related to immune infiltration to predict distant metastasis in breast cancer patients

Background: The development of distant metastasis (DM) results in poor prognosis of breast cancer (BC) patients, however, it is difficult to predict the risk of distant metastasis. Methods: Differentially expressed genes (DEGs) were screened out using GSE184717 and GSE183947. GSE20685 were randomly assigned to the training and the internal validation cohort. A signature was developed according to the results of univariate and multivariate Cox regression analysis, which was validated by using internal and external (GSE6532) validation cohort. Gene set enrichment analysis (GSEA) was used for functional analysis. Finally, a nomogram was constructed and calibration curves and concordance index (C-index) were compiled to determine predictive and discriminatory capacity. The clinical benefit of this nomogram was revealed by decision curve analysis (DCA). Finally, we explored the relationships between candidate genes and immune cell infiltration, and the possible mechanism. Results: A signature containing CD74 and TSPAN7 was developed according to the results of univariate and multivariate Cox regression analysis, which was validated by using internal and external (GSE6532) validation cohort. Mechanistically, the signature reflect the overall level of immune infiltration in tissues, especially myeloid immune cells. The expression of CD74 and TSPAN7 is heterogeneous, and the overexpression is positively correlated with the infiltration of myeloid immune cells. CD74 is mainly derived from myeloid immune cells and do not affect the proportion of CD8+T cells. Low expression levels of TSPAN7 is mainly caused by methylation modification in BC cells. This signature could act as an independent predictive factor in patients with BC (p = 0.01, HR = 0.63), and it has been validated in internal (p = 0.023, HR = 0.58) and external (p = 0.0065, HR = 0.67) cohort. Finally, we constructed an individualized prediction nomogram based on our signature. The model showed good discrimination in training, internal and external cohort, with a C-index of 0.742, 0.801, 0.695 respectively, and good calibration. DCA demonstrated that the prediction nomogram was clinically useful. Conclusion: A new immune infiltration related signature developed for predicting metastatic risk will improve the treatment and management of BC patients.

Nomogram for Early Prediction of Pathological Complete Response to Neoadjuvant Chemotherapy in Breast Cancer Combining Both Clinicopathological and Imaging Indicators

To construct a nomogram for early prediction of pathological complete response (pCR) in patients with breast cancer (BC) after neoadjuvant chemotherapy (NAC). A total of 257 patients with BC from the fourth hospital of Hebei Medical University were included in the study. The patients were divided into training (n = 128) and validation groups (n = 129). Variables were screened using univariate and multivariate logistic regression analyses, and the nomogram model was set up based on the training group. The training and validation groups were validated using the receiver operating characteristic (ROC) curves and calibration plots. The diagnostic value of the nomogram was evaluated using decision curve analysis (DCA). Indicators such as hormone receptor status, clinical TNM stage, and change rate in apparent diffusion coefficient of breast magnetic resonance imaging after two NAC cycles were used for nomogram construction. The calibration plots showed high consistency between nomogram-predicted and actual pCR probabilities in the training and validation groups. The areas under the curve of the ROC curve with discrimination ability were 0.942 and 0.921 in the training and validation groups, respectively. This showed an excellent discrimination ability of our nomogram for pCR prediction. Further, DCA showed favorable diagnostic value in our model. The nomogram may be instructive to clinicians for early prediction of pCR and helpful to adjust the treatment program on time in neoadjuvant management.

SDF-1 expression and tumor-infiltrating lymphocytes identify clinical subtypes of triple-negative breast cancer with different responses to neoadjuvant chemotherapy and survival

Background

In this study, we investigated the prediction and prognostic value of SDF-1 for triple-negative breast cancer (TNBC) patients who underwent neoadjuvant chemotherapy (NAC) following standard radical surgery.

Methods

A total of 303 TNBC patients were included in this study. The NAC regimen was weekly paclitaxel plus carboplatin (PC) for all patients. SDF-1 and CXCR4 expression were measured at baseline and surgery via enzyme-linked immunosorbent assay (ELISA) and immunohistochemistry (IHC), respectively. Correlations between variables and treatment response were studied, and Cox proportional hazards regression analysis was implemented for prognostic evaluation.

Results

Of the 303 patients, 103 (34.0%) experienced pathological complete response (pCR) after completion of NAC. Serum SDF-1 expression before NAC was significantly correlated with the abundance of TILs. A higher pCR rate was more likely to be observed in patients with lower serum SDF-1 levels before NAC (P=0.001, OR=0.997, 95% CI: 0.996-0.999) and higher levels of TILs (P=0.005). In the multivariate survival model for nonpCR patients, serum SDF-1 expression at surgery served as an independent prognostic value for survival (high level, HR=1.980, 95% CI: 1.170-3.350, low level was used as a reference; P=0.011). Additionally, the predictive and prognostic value of serum SDF-1 expression was significant in patients with high abundance of TILs but not in patients with low abundance of TILs.

Conclusions

This study contributes to the clarification of the value of serum SDF-1 to predict pCR and survival for TNBC patients who underwent NAC. This new serum marker, together with TILs, might help identify clinical subtypes of TNBC with different treatment responses and survival and play an important role in tailoring and modifying the NAC strategy for advanced TNBCs in the future.

Nomogram Prediction Model of Serum Chloride and Sodium Ions on the Risk of Acute Kidney Injury in Critically Ill Patients

Purpose

This study aims to investigate the effect of serum chloride and sodium ions on AKI occurrence in ICU patients, and further constructs a prediction model containing these factors to explore the predictive value of these ions in AKI.

Methods

The clinical information of patients admitted to ICU of Beijing Friendship Hospital Affiliated to Capital Medical University was collected for retrospective analysis. Logistic regression analysis was used to analyzing the influencing factors. A nomogram for predicting AKI risk was constructed with R software and validated by repeated sampling. Afterwards, the effectiveness and accuracy of the model were tested and evaluated.

Results

A total of 446 cases met the requirements of this study, of which 178 developed AKI during their stay in ICU, with an incidence rate of 39.9%. Hypernatremia, heart failure, sepsis, APACHE II score, and initial creatinine value and BE value at ICU admission before the diagnosis of AKI were identified as independent risk factors for developing AKI during ICU stay. These predictors were incorporated into the nomogram of AKI risk in critically ill patients, which was constructed by using R software. Receiver operating characteristic curve analysis was further used and showed that the area under the curve of the model was 0.7934 (95% CI 0.742–0.8447), indicating that the model had an ideal value. Finally, further evaluated its clinical effectiveness. The clinical effect curve and decision curve showed that most areas of the decision curve of this model were greater than 0, indicating that this model owned a certain clinical effectiveness.

Conclusion

The nomogram based on hypernatremia, heart failure, sepsis, APACHE II score, and initial creatinine and BE value in ICU can predict the individualized risk of AKI with satisfactory distinguishability and accuracy.

Luminal A Breast Cancer: How Feasible is Omitting Axillary Dissection Without Neoadjuvant Therapy

Background. Luminal A breast cancer has a good prognosis and the criteria for adjuvant and neoadjuvant chemotherapy (NAC) are not clear. The aim of this study was to present our results of upfront surgery and long-term survival in luminal A tumors as well as the rates of protection from axillary dissection. Material and Methods. 271 Luminal A breast cancer patients who had operated at our center were evaluated retrospectively. In patients with 2 or less sentinel lymph node (SLN) positivity who did not receive neoadjuvant therapy and underwent breast-conserving surgery, axillary lymph node dissection was omitted (OAD). Axillary lymph node dissection (ALND) was performed in patients with positive SLN who did not meet these criteria (axillary dissection after sentinel/ADAS). Results. While Sentinel Lymph Node Biopsy (SLNB) was performed in 212 (77.9%) patients, SLNB + Axillary Dissection (AD) was performed in 58 (21.3%), and direct axillary dissection was performed in 1 (0.8%) patient. OAD was applied to 18 (23.6%) of the positive patients. Discussion/Conclusions. ALND rates are still strikingly high in luminal A breast cancer treatment, despite the disease’s milder clinical course. In order to avoid complications of axillary dissection, patients should be considered for NAC as much as possible. Novel neoadjuvant or other therapy options are also required.

Predicting Pathological Complete Response in Neoadjuvant Dual Blockade With Trastuzumab and Pertuzumab in HER2 Gene Amplified Breast Cancer

Background

Dual-targeted therapy is the standard treatment for human epidermal growth factor receptor 2 (HER2)-positive breast cancer, and effective biomarkers to predict the response to neoadjuvant trastuzumab and pertuzumab treatment need further investigation. Here, we developed a predictive model to evaluate the dual-targeted neoadjuvant treatment efficacy in HER2 gene-amplified breast cancer.

Method

This retrospective study included 159 HER2-amplified patients with locally advanced breast cancer who received neoadjuvant trastuzumab, pertuzumab, and chemotherapy. The correlation between clinicopathological factors and pathological complete response (pCR, in the breast and axilla) was evaluated. Patients were randomly assigned into the training set (n=110) and the testing set (n=49). We used an independent cohort (n=65) for external validation. We constructed our predictive nomogram model with the results of risk variables associated with pCR identified in the multivariate logistic analysis. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve, decision curve analysis, and calibration curves were employed to assess the nomogram's performance.

Results

We revealed that the HER2/CEP17 ratio (p=0.001), CD8 levels (p=0.005), and histological grade (p=0.007) were independent indicators for pCR in dual-targeted neoadjuvant treatment after multivariate adjustment. The combined prediction efficacy of the three indicators was significantly higher than that of each single indicator alone. The AUCs were 0.819, 0.773, and 0.744 in the training, testing, and external validation sets, respectively.

Conclusions

The HER2/CEP17 ratio, CD8 levels, and histological grade were significantly correlated with pCR in dual-targeted neoadjuvant treatment. The combined model using these three markers provided a better predictive value for pCR than the HER2/CEP17 ratio, CD8 levels, and the histological grade alone, which showed that an immunological effect partially mediates the predictive impact of neoadjuvant treatment.

Tumor-Infiltrating Lymphocytes Improve Magee Equation-Based Prediction of Pathologic Complete Response in HR-Positive/HER2-Negative Breast Cancer

OBJECTIVES

Magee equation 3 (ME3) is predictive of the pathologic complete response (pCR) to neoadjuvant chemotherapy (NAC) in patients with hormone receptor (HR)-positive, human epidermal growth factor receptor 2 (HER2)-negative breast cancer but with insufficient predictive performance. This study was designed to improve predictive ability by combining ME3 with additional clinicopathologic markers.

METHODS

We retrospectively enrolled 460 patients with HR-positive/HER2-negative breast cancer from 2 centers. We obtained baseline characteristics, the ME3 score, and the number of stromal tumor-infiltrating lymphocytes (sTILs). After performing a logistic regression analysis, a predictive nomogram was built and validated externally.

RESULTS

ME3 score (adjusted odds ratio [OR], 1.14 [95% confidence interval (CI), 1.10-1.17]; P < .001) and TILs (adjusted OR, 5.21 [95% CI, 3.33-8.14]; P < .001) were independently correlated with pCR. The nomogram (named ME3+) was established using ME3 and sTILs, and it demonstrated an area under the curve of 0.816 and 0.862 in internal and external validation, respectively, outperforming the ME3 score alone. sTILs and ME3 scores were also found to be positively correlated across the entire cohort (P < .001).

CONCLUSIONS

The combination of sTILs and ME3 score potentially shows better performance for predicting pCR than ME3 alone. Larger validations are required for widespread application of ME3+ nomogram in NAC settings for HR-positive/HER2-negative breast cancer.

A longitudinal study on psychological burden of medical students during COVID-19 outbreak and remission period in China

BACKGROUND AND OBJECTIVES

Due to their professional characteristics and future career orientation, medical students have a deeper understanding of COVID-19 and enact disease prevention and control measures, which may cause psychological burden. We aimed to assess the psychological impact during the COVID-19 outbreak period(OP) and remission period(RP) among medical students.

METHODS

We surveyed the medical students in Shantou University Medical College twice-during the OP and the RP, surveying psychological burden of COVID-19 lockdowns and its associated factors. 1069 respondents were recruited in OP and 1511 participants were recruited in RP. We constructed nomograms to predict the risk of psychological burden using risk factors that were screened through univariate analysis of the surveyed data set.

RESULTS

There was a statistically significant longitudinal increment in psychological burden from OP to RP, and stress as well as cognition in psychological distress were the most dominant ones. Common impact factors of the depression, anxiety and stress included frequency of outdoor activities, mask-wearing adherence, self-perceived unhealthy status and exposure to COVID-19. In addition, the high frequency of handwashing was a protective factor for depression and anxiety. The C-index was 0.67, 0.74 and 0.72 for depression, anxiety and stress, respectively.

CONCLUSION

The psychological impact of COVID-19 was worse during the RP than during the OP. Thus, it's necessary to continue to emphasize the importance of mental health in medical students during the pandemic and our proposed nomograms can be useful tools for screening high-risk groups for psychological burden risk in medical students.

Multimodal deep learning models for the prediction of pathologic response to neoadjuvant chemotherapy in breast cancer

The achievement of the pathologic complete response (pCR) has been considered a metric for the success of neoadjuvant chemotherapy (NAC) and a powerful surrogate indicator of the risk of recurrence and long-term survival. This study aimed to develop a multimodal deep learning model that combined clinical information and pretreatment MR images for predicting pCR to NAC in patients with breast cancer. The retrospective study cohort consisted of 536 patients with invasive breast cancer who underwent pre-operative NAC. We developed a deep learning model to fuse high-dimensional MR image features and the clinical information for the pretreatment prediction of pCR to NAC in breast cancer. The proposed deep learning model trained on all datasets as clinical information, T1-weighted subtraction images, and T2-weighted images shows better performance with area under the curve (AUC) of 0.888 as compared to the model using only clinical information (AUC = 0.827, P < 0.05). Our results demonstrate that the multimodal fusion approach using deep learning with both clinical information and MR images achieve higher prediction performance compared to the deep learning model without the fusion approach. Deep learning could integrate pretreatment MR images with clinical information to improve pCR prediction performance.

Development, verification, and comparison of a risk stratification model integrating residual cancer burden to predict individual prognosis in early-stage breast cancer treated with neoadjuvant therapy

Background

A favorable model for predicting disease-free survival (DFS) and stratifying prognostic risk in breast cancer (BC) treated with neoadjuvant chemotherapy (NAC) is lacking. The aim of the current study was to formulate an excellent model specially for predicting prognosis in these patients.

Patients and methods

Between January 2012 and December 2015, 749 early-stage BC patients who received NAC in Xijing hospital were included. Patients were randomly assigned to a training cohort (n = 563) and an independent cohort (n = 186). A prognostic model was created and subsequently validated. Predictive performance and discrimination were further measured and compared with other models.

Results

Clinical American Joint Committee on Cancer stage, grade, estrogen receptor expression, human epidermal growth factor receptor 2 (HER2) status and treatment, Ki-67 expression, lymphovascular invasion, and residual cancer burden were identified as independent prognostic variables for BC treated with NAC. The C-index of the model consistently outperformed other available models as well as single independent factors with 0.78, 0.80, 0.75, 0.82, and 0.77 in the training cohort, independent cohort, luminal BC, HER2-positive BC, and triple-negative BC, respectively. With the optimal cut-off values (280 and 360) selected by X-tile, patients were categorized as low-risk (total points ≤280), moderate-risk (280 < total points ≤ 360), and high-risk (total points >360) groups presenting significantly different 5-year DFS of 89.9%, 56.9%, and 27.7%, respectively.

Conclusions

In patients with BC, the first model including residual cancer burden index was demonstrated to predict the survival of individuals with favorable performance and discrimination. Furthermore, the risk stratification generated by it could determine the risk level of recurrence in whole early-stage BC cohort and subtype-specific cohorts, help tailor personalized intensive treatment, and select comparable study cohort in clinical trials.

•

Establishing the first risk stratification nomogram for BC treated with NAC and validate its performance in BC cohorts.

•

Incorporating residual cancer burden index into predictive nomogram for the first time.

•

Predictive model can be utilized to predict DFS for all early-stage BC treated with NAC.

•

Performing a continuous rather than categorized model to predict individual survival.

•

The risk stratification can be used to select comparable population in trial design.

Can We Reliably Identify the Pathological Outcomes of Neoadjuvant Chemotherapy in Patients with Breast Cancer? Development and Validation of a Logistic Regression Nomogram Based on Preoperative Factors

BACKGROUND

Pathological responses of neoadjuvant chemotherapy (NCT) are associated with survival outcomes in patients with breast cancer. Previous studies constructed models using out-of-date variables to predict pathological outcomes, and lacked external validation, making them unsuitable to guide current clinical practice.

OBJECTIVE

The aim of this study was to develop and validate a nomogram to predict the objective remission rate (ORR) of NCT based on pretreatment clinicopathological variables.

METHODS

Data from 110 patients with breast cancer who received NCT were used to establish and calibrate a nomogram for pathological outcomes based on multivariate logistic regression. The predictive performance of this model was further validated using a second cohort of 55 patients with breast cancer. Discrimination of the prediction model was assessed using an area under the receiver operating characteristic curve (AUC), and calibration was assessed using calibration plots. The diagnostic odds ratio (DOR) was calculated to further evaluate the performance of the nomogram and determine the optimal cut-off value.

RESULTS

The final multivariate regression model included age, NCT cycles, estrogen receptor, human epidermal growth factor receptor 2 (HER2), and lymphovascular invasion. A nomogram was developed as a graphical representation of the model and showed good calibration and discrimination in both sets (an AUC of 0.864 and 0.750 for the training and validation cohorts, respectively). Finally, according to the Youden index and DORs, we assigned an optimal ORR cut-off value of 0.646.

CONCLUSION

We developed a nomogram to predict the ORR of NCT in patients with breast cancer. Using the nomogram, for patients who are operable and whose ORR is < 0.646, we believe that the benefits of NCT are limited and these patients can be treated directly using surgery.

Serum HER2 levels predict treatment efficacy and prognosis in patients with HER2-positive breast cancer undergoing neoadjuvant treatment

Background

Controversy remains regarding the predictive and prognostic value of serum human epidermal growth factor receptor 2 (HER2) in breast cancer. The purpose of this retrospective study was to determine the clinical utility and efficacy of serum HER2 (sHER2) in predicting treatment response and prognosis in patients with HER2-positive breast cancer undergoing neoadjuvant chemotherapy and trastuzumab treatment.

Methods

A total of 309 HER2-positive breast cancer patients diagnosed at Fudan University Shanghai Cancer Center from July 2015 to January 2019 were analyzed. Baseline sHER2 levels were obtained for all patients and sHER2 levels were collected after 2 cycles of treatment in 208 patients. A sHER2 level ≥15 ng/mL was regarded as "high expression" and sHER2 <15 ng/mL was regarded as "low expression". Outcome measures of treatment efficacy and prognosis were pathological complete response (pCR) and invasive disease-free survival (iDFS), respectively.

Results

In patients with high baseline sHER2, more were ER-negative (P=0.029), had larger tumor size (P=0.006), more advanced clinical stage (P=0.002), higher Miller-Payne grade (P=0.024) and higher likelihood of iDFS events (P=0.015). Patients with high sHER2 levels after 2 cycles of treatment had lower pCR rates (P=0.038), higher Miller-Payne grade (P=0.013) and higher likelihood of iDFS events (P=0.003). Kaplan-Meier analysis showed significant differences in iDFS between patients with high and low sHER2 levels at baseline (P=0.019) and after 2 cycles of treatment (P=0.000). Further analyses according to cancer subtypes found baseline sHER2 to be significantly correlated with the iDFS of Luminal B patients (p=0.002), while sHER2 levels after 2 cycles of treatment was significantly correlated with the iDFS of HER2-enriched patients (P=0.000). Univariate analysis showed significant association between iDFS and tumor size (P=0.026), lymph node status (P=0.008), clinical stage (P=0.031), baseline sHER2 (P=0.024), overall tumor response (P=0.011), pCR (P=0.043) and Miller-Payne grade (P=0.001). Multivariate analysis found Miller-Payne grade (P=0.037) to be significantly associated with iDFS.

Conclusions

Our results demonstrate the clinical value of sHER2 in a population of Chinese breast cancer patients, suggesting that sHER2 levels after 2 cycles of neoadjuvant therapy may be more predictive of treatment outcomes and that the prognostic value of sHER2 may be time point and subtype dependent.

Factors Affecting Pathologic Complete Response Following Neoadjuvant Chemotherapy in Breast Cancer: Development and Validation of a Predictive Nomogram

Background There is an increasing need to develop a more accurate prediction model for pathologic complete response (pCR) after neoadjuvant chemotherapy (NAC) in breast cancer. Purpose To develop a nomogram based on MRI and clinical-pathologic variables to predict pCR. Materials and Methods In this single-center retrospective study, consecutive women with stage II-III breast cancer who underwent NAC followed by surgery between January 2011 and December 2017 were considered for inclusion. The women were divided into a development cohort between January 2011 and September 2015 and a validation cohort between October 2015 and December 2017. Clinical-pathologic data were collected, and mammograms and MRI scans obtained before and after NAC were analyzed. Logistic regression analyses were performed to identify independent variables associated with pCR in the development cohort from which the nomogram was created. Nomogram performance was assessed with the area under the receiver operating characteristic curve (AUC) and calibration slope. Results A total of 359 women (mean age, 49 years ± 10 [standard deviation]) were in the development cohort and 351 (49 years ± 10) in the validation cohort. Hormone receptor negativity (odds ratio [OR], 3.1; 95% CI: 1.4, 7.1; P = .006), high Ki-67 index (OR, 1.05; 95% CI: 1.03, 1.07; P < .001), and post-NAC MRI variables, including small tumor size (OR, 0.6; 95% CI: 0.4, 0.9; P = .03), low lesion-to-background parenchymal signal enhancement ratio (OR, 0.2; 95% CI: 0.1, 0.6; P = .004), and absence of enhancement in the tumor bed (OR, 3.8; 95% CI: 1.4, 10.5; P = .009) were independently associated with pCR. The nomogram incorporating these variables showed good discrimination (AUC, 0.90; 95% CI: 0.86, 0.94) and calibration abilities (calibration slope, 0.91; 95% CI: 0.69, 1.13) in the independent validation cohort. Conclusion A nomogram incorporating hormone receptor status, Ki-67 index, and MRI variables showed good discrimination and calibration abilities in predicting pathologic complete response. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Imbriaco and Ponsiglione in this issue.

Nomogram-derived prediction of pathologic complete response (pCR) in breast cancer patients treated with neoadjuvant chemotherapy (NCT)

BACKGROUND

Previous research results on the predictive factors of neoadjuvant chemotherapy (NCT) efficacy in breast cancer are inconsistent, suggesting that the ability of a single factor to predict efficacy is insufficient. Combining multiple potential efficacy-related factors to build a model may improve the accuracy of prediction. This study intends to explore the clinical and biological factors in breast cancer patients receiving NCT and to establish a nomogram that can predict the pathologic complete response (pCR) rate of NCT.

METHODS

We selected 165 breast cancer patients receiving NCT from July 2017 to May 2019. Using pretreatment biopsy materials, immunohistochemical studies to assess estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER-2), and Ki-67 expression. The correlation between biological markers and pCR was analyzed. These predictors were used to develop a binary logistic regression model with cross-validation and to show the established predictive model with a nomogram.

RESULTS

The nomogram for pCR based on lymphovascular invasion, anemia (hemoglobin≤120 g/L), ER, Ki67 expression levels and NCT regimen had good discrimination performance (area under the curve [AUC], 0.758; 95% confidence interval [CI], 0.675-0.841) and calibration coordination. According to the Hosmer-Lemeshow test, the calibration chart showed satisfactory agreement between the predicted and observed probabilities. The final prediction accuracy of cross-validation was 76%.

CONCLUSIONS

We developed a nomogram based on multiple clinical and biological covariations that can provide an early prediction of NCT response and can help to quickly assess the individual benefits of NCT.

Genome-wide analysis of therapeutic response uncovers molecular pathways governing tamoxifen resistance in ER+ breast cancer

Background

Prioritization of breast cancer patients based on the risk of resistance to tamoxifen plays a significant role in personalized therapeutic planning and improving disease course and outcomes.

Methods

In this work, we demonstrate that a genome-wide pathway-centric computational framework elucidates molecular pathways as markers of tamoxifen resistance in ER+ breast cancer patients. In particular, we associated activity levels of molecular pathways with a wide spectrum of response to tamoxifen, which defined markers of tamoxifen resistance in patients with ER+ breast cancer.

Findings

We identified five biological pathways as markers of tamoxifen failure and demonstrated their ability to predict the risk of tamoxifen resistance in two independent patient cohorts (Test cohort1: log-rank p-value = 0.02, adjusted HR = 3.11; Test cohort2: log-rank p-value = 0.01, adjusted HR = 4.24). We have shown that these pathways are not markers of aggressiveness and outperform known markers of tamoxifen response. Furthermore, for adoption into clinic, we derived a list of pathway read-out genes and their associated scoring system, which assigns a risk of tamoxifen resistance for new incoming patients.

Interpretation

We propose that the identified pathways and their read-out genes can be utilized to prioritize patients who would benefit from tamoxifen treatment and patients at risk of tamoxifen resistance that should be offered alternative regimens.

Funding

This work was supported by the Rutgers SHP Dean's research grant, Rutgers start-up funds, Libyan Ministry of Higher Education and Scientific Research, and Katrina Kehlet Graduate Award from The NJ Chapter of the Healthcare Information Management Systems Society.

A Novel Six-Gene Signature for Prognosis Prediction in Ovarian Cancer

Ovarian cancer (OC) is the most malignant tumor in the female reproductive tract. Although abundant molecular biomarkers have been identified, a robust and accurate gene expression signature is still essential to assist oncologists in evaluating the prognosis of OC patients. In this study, samples from 367 patients in The Cancer Genome Atlas (TCGA) database were subjected to mRNA expression profiling. Then, we used a gene set enrichment analysis (GSEA) to screen genes correlated with epithelial–mesenchymal transition (EMT) and assess their prognostic power with a Cox proportional regression model. Six genes (TGFBI, SFRP1, COL16A1, THY1, PPIB, BGN) associated with overall survival (OS) were used to construct a risk assessment model, after which the patients were divided into high-risk and low-risk groups. The six-gene signature was an independent prognostic biomarker of OS for OC patients based on the multivariate Cox regression analysis. In addition, the six-gene model was validated with samples from the Gene Expression Omnibus (GEO) database. In summary, we established a six-gene signature relevant to the prognosis of OC, which might become a therapeutic tool with clinical applications in the future.

Construction of Nomograms for Predicting Pathological Complete Response and Tumor Shrinkage Size in Breast Cancer

Purpose

Pathological complete response (pCR) is the goal of neoadjuvant chemotherapy (NAC) for the HER2-positive and triple-negative subtypes of breast cancer and is related to survival benefit; however, luminal breast cancer is not sensitive to NAC, and the size of tumor shrinkage is a more meaningful clinical indicator for the luminal breast cancer subtype. We wanted to use a nomogram or formula to develop and implement a series of prediction models for pCR or tumor shrinkage size.

Patients and Methods

We developed a prediction model in a primary cohort consisting of 498 patients with invasive breast cancer, and the data were gathered from July 2016 to September 2018. The endpoint was pCR and tumor shrinkage size. In the primary cohort, the HER2-positive cohort, and the triple-negative cohort, multivariate logistic regression analysis was used to screen the significant clinical features and clinicopathological features to develop nomograms. In the luminal group, multivariate linear regression analysis was used to test the risk factors that affect tumor shrinkage size. The area under the receiver operating characteristic curve (AUC) and calibration curves were adopted to evaluate and analyze the discrimination and calibration ability of nomograms. Furthermore, we also performed internal validation and independent validation in the primary cohort.

Results

ER status, KI67 status, HER2 status, number of NAC cycles, and tumor size were independent predictive factors of pCR in the primary cohort. These indicators had good discrimination and calibration in the primary and validation cohorts (AUC: 0.873, 0.820). The nomogram for HER2-positive and triple-negative breast cancer (TNBC) had an AUC of 0.820 and 0.785, respectively. Both the HER2 positive and TNBC nomogram calibration curves indicated significant agreement. Moreover, the luminal subtype prediction model was Y (tumor shrinkage size) = -0.576 × (age at diagnosis) + 2.158 × (number of NAC cycles) + 0.233 × (pre-NAC tumor size) + 51.662.

Conclusion

Utilizing this predictive model will enable us to identify patients at high probability for pCR after NAC. Clinicians can stratify these patients and make individualized and personalized recommendations for therapy.

A comparison of complete pathologic response rates following neoadjuvant chemotherapy among South African breast cancer patients with and without concurrent HIV infection

PURPOSE

Among patients diagnosed with breast cancer (BC), women also living with HIV (WLWH) have worse survival than women without HIV. Chronic HIV infection may interfere with the effectiveness of BC treatment, contributing to this disparity. We attempted to determine the impact of HIV infection on response to neoadjuvant chemotherapy (NACT) among South African women with BC.

METHODS

We evaluated women from the South African Breast Cancer and HIV Outcomes cohort study who had stage I-III disease, initiated NACT, underwent definitive breast surgery, and had available surgical pathology reports. We compared pathologic complete response (pCR) rates among women with and without HIV infection, using multivariable logistic regression to control for differences in tumor characteristics. We also evaluated the impact of HIV infection on pCR within subgroups based on patient and tumor factors.

RESULTS

Of 715 women, the 173 (24.2%) WLWH were less likely to achieve pCR than women without HIV (8.7% vs 16.4%, [odds ratio (OR) 0.48, 95% confidence interval (95% CI) 0.27-0.86]). WLWH continued to have lower likelihood of achieving pCR on multivariable analysis (OR 0.52, 95% CI 0.28-0.98). A similar pattern was seen within subgroups, although HIV infection appeared to affect pCR more in ER/PR-positive BC (OR 0.24, 95% CI 0.08-0.71) than in ER/PR-negative BC (OR 0.94, 95% CI 0.39-2.29).

CONCLUSION

WLWH were less like to achieve pCR following NACT for BC than women without HIV. This reduced response to systemic therapy may contribute to the poorer BC outcomes seen in WLWH.

Development and Validation of a Nomogram for Individually Predicting Pathologic Complete Remission After Preoperative Chemotherapy in Chinese Breast Cancer: A Population-Based Study

PURPOSE

To explore the independent predictors of pathologic complete remission response (pCR) for Chinese patients with breast cancer (BC) after preoperative chemotherapy and to develop an individualized nomogram for predicting the probability of pCR.

PATIENTS AND METHODS

The clinicopathologic data of clinical stage I-III BC patients who received preoperative chemotherapy in Xijing Hospital were retrospectively analyzed. A total of 689 BC patients diagnosed in 2015-2017 were included in the training set to develop a nomogram. A separate cohort of 357 patients in the same center was regarded as a validation set for externally examining the performance of the model. The area under the receiver operating characteristic curve and calibration curve were used to verify the predictive performance of the nomogram.

RESULTS

Multivariate logistic regression analysis showed that independent predictors of pCR were menopause status at diagnosis, family history of BC, initial tumor size, estrogen receptor status, HER2/neu (human epidermal growth factor receptor 2) status, and Ki-67 expression. On the basis of these factors, a nomogram was developed using R software. Our nomogram had good discrimination in the training and validation set (area under the receiver operating characteristic curve, 0.762 and 0.768, respectively). The calibration curves further confirmed that the model performs well.

CONCLUSION

Menopause status and family history of BC were independent predictors of pCR after preoperative chemotherapy for the first time. The nomogram can accurately predict pCR rate in BC, which may provide some guidelines for breast surgery options and patient counseling.

Factor analysis-derived cognitive profile predicting early dementia conversion in PD

OBJECTIVES

To investigate which baseline neuropsychological profile predicts the risk of developing dementia in early-stage Parkinson disease (PD).

METHODS

We retrospectively reviewed detailed medical records of 350 drug-naive patients with early-stage PD (follow-up >3 years) who underwent a detailed neuropsychological test at initial assessment. Factor analysis was conducted to determine cognitive profiles that yielded 4 cognitive function factors: factor 1, visual memory/visuospatial; factor 2, verbal memory; factor 3, frontal/executive; and factor 4, attention/working memory/language. Subsequently, we assessed the effect of these cognitive function factors on the risk for dementia conversion. We also constructed a nomogram to calculate the risk for developing dementia over a 5-year follow-up period based on these cognitive profiles.

RESULTS

Cox regression analysis demonstrated that a higher composite score of factor 1 (hazard ratio [HR] 0.558, 95% confidence interval [CI] 0.427-0.730), factor 2 (HR 0.768, 95% CI 0.596-0.991), and factor 3 (HR 0.425, 95% CI 0.305-0.593) was associated with a lower risk for dementia conversion, while factor 3 had the most predictive power. The nomogram had a fair ability (Heagerty integrated area under the curve 0.763) to estimate the risk for dementia conversion within 5 years. The composite scores of factor 3 contributed more to the occurrence of dementia in PD than those of the other cognitive function factors.

CONCLUSIONS

These findings suggest that these factor analysis-derived cognitive profiles can be used to predict dementia conversion in early-stage PD. In addition, frontal/executive dysfunction contributes most to the occurrence of dementia in PD.

Prognostic nomogram based on immune scores for laryngeal squamous cell cancer

PURPOSE

Immune scores have been used as a prognostic factor for various types of cancer. However, the association between immune scores and the prognosis of laryngeal squamous cell cancer (LSCC) has not yet been investigated. This study aimed to explore the prognostic significance of immune scores and construct a clinical nomogram to predict the survival of patients with LSCC.

METHODS

The clinicopathological characteristics and immune scores of 102 patients with LSCC were obtained from TCGA database and a nomogram was developed. C-index and calibration curves were applied to assess the performance of the model.

RESULTS

Patients with higher immune scores had significantly better overall survival (OS). The prognostic nomogram presented a good performance in survival prediction.

CONCLUSIONS

High immune scores are correlated with improved OS in patients with LSCC. In addition, the nomogram developed for this study may assist clinicians in the prognostic evaluation of patients with LSCC.

Using association signal annotations to boost similarity network fusion

MOTIVATION

Recent technology developments have made it possible to generate various kinds of omics data, which provides opportunities to better solve problems such as disease subtyping or disease mapping using more comprehensive omics data jointly. Among many developed data-integration methods, the similarity network fusion (SNF) method has shown a great potential to identify new disease subtypes through separating similar subjects using multi-omics data. SNF effectively fuses similarity networks with pairwise patient similarity measures from different types of omics data into one fused network using both shared and complementary information across multiple types of omics data.

RESULTS

In this article, we proposed an association-signal-annotation boosted similarity network fusion (ab-SNF) method, adding feature-level association signal annotations as weights aiming to up-weight signal features and down-weight noise features when constructing subject similarity networks to boost the performance in disease subtyping. In various simulation studies, the proposed ab-SNF outperforms the original SNF approach without weights. Most importantly, the improvement in the subtyping performance due to association-signal-annotation weights is amplified in the integration process. Applications to somatic mutation data, DNA methylation data and gene expression data of three cancer types from The Cancer Genome Atlas project suggest that the proposed ab-SNF method consistently identifies new subtypes in each cancer that more accurately predict patient survival and are more biologically meaningful.

AVAILABILITY AND IMPLEMENTATION

The R package abSNF is freely available for downloading from https://github.com/pfruan/abSNF.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

[Comparing prediction performances of 18F-FDG PET and CGFL/Curie nomogram to predict pathologic complete response after neoadjuvant chemotherapy for HER2-positive breast cancers]

OBJECTIVES

The aim of this study was to compare the value of ¹⁸F-fluorodesoxyglucose positron emission tomography (¹⁸F-FDG PET/CT) with CGFL/Curie nomogram to predict a pathologic complete response (pCR) after neoadjuvant chemotherapy (NAC) in women with human epidermal growth factor 2 (HER2)-positive breast cancer treated by trastuzumab.

METHODS

Fifty-one women with HER2-positive breast cancer treated with trastuzumab plus taxane-based NAC were retrospectively included from January 2005 to December 2015. For ¹⁸F-FDG PET/CT, the analyzed predictor was the maximum standardized uptake value of the primary tumor and axillary nodes after the first course of NAC (PET₂.SUV_max). pCR was defined by no residual infiltrative tumor but in situ tumor was accepted. Accuracy of CGFL/Curie nomogram and PET₂.SUV_max was evaluated measuring sensitivity (Se), specificity (Sp), positive predictive value (PPV) and negative predictive value (NPV). Combined prediction was evaluated testing predictor's associations.

RESULTS

For CGFL/Curie nomogram's performances, Se, Sp, PPV and NPV were respectively: 76% (95%CI: 58-90%), 57% (95%CI: 43-66%), 55% (95%CI: 42-65), 77% (95%CI: 59-90%). For PET₂.SUV_max's performances, Se, Sp, PPV and NPV were respectively: 67% (95%CI: 48-81%), 77% (95%CI: 64-97%), 67% (95%CI: 48-82%), 77% (95%CI: 64-87%). ROC curves for these predictors were similar; the areas under the curve were 0.6 (95%CI: 0.56-0.64) for PET₂.SUV_max and 0.55 (95%CI: 0.50-0.59) for CGFL/Curie nomogram. Combined prediction was efficient with Se at 80%, VPN at 76%, Sp at 78% and VPP at 81%.

CONCLUSIONS

CGFL/Curie nomogram and PET₂.SUV_max were two efficient predictors of pCR in patients with HER2-positive breast cancer. Combined prediction has an improved accuracy.

Validation of a Novel Nomogram for Prediction of Local Relapse after Surgery for Invasive Breast Carcinoma

BACKGROUND

Around 7% of women who undergo breast-conserving surgery (BCS) or mastectomy are at risk of developing ipsilateral breast tumor recurrence (IBTR). When assessing risks that, like that of IBTR, depend on multiple clinicopathological variables, nomograms are the predictive tools of choice. In this study, two independent nomograms were constructed to estimate the individualized risk of IBTR after breast surgery.

PATIENTS AND METHODS

In this retrospective study, 18,717 consecutive patients with primary invasive breast cancer were enrolled. The training set used for building the nomograms comprised 15,124 patients (11,627 treated with BCS and 3497 with mastectomy), while the validation set included 3593 women (2565 BCS and 1028 mastectomy). Median follow-up time was 8 years in the training set and 6 years in the validation set. Multivariable Cox proportional hazards regression was used to identify independent factors for IBTR. Two separated nomograms were constructed on multivariate models for BCS and mastectomy.

RESULTS

The factors that associated with IBTR after either BCS or mastectomy were identified. The two multivariable models were used to build nomograms for the prediction of IBTR 1 year, 5 years, and 10 years after BCS or after mastectomy. Five-year and 10-year IBTR rates in the BCS training set were equal to 3.50% and 7.00%, respectively, and to 5.39% and 7.94% in the mastectomy training set. The nomograms were subsequently validated with c-index values of 0.77 and 0.69 in the BCS and mastectomy validation sets, respectively.

CONCLUSIONS

The nomograms presented in this study provide clinicians and patients with a valuable decision-making tool for choosing between different treatment options for invasive breast cancer.

Nomogram for prediction of pathologic complete remission using biomarker expression and endoscopic finding after preoperative chemoradiotherapy in rectal cancer

Objective

The aim of this study is to develop a nomogram for prediction of pathologic complete remission (pCR) after preoperative chemoradiotherapy (CRT) for rectal cancer.

Methods

mRNA expression levels of seven molecular markers [p53, p21, Ki-67, vascular endothelial growth factor (VEGF), CD133, CD24, CD44] were measured by reverse transcriptase polymerase chain reaction (RT-PCR) in 120 rectal cancers. Endoscopic findings of clinical complete remission (cCR) and biologic variables were used to construct nomogram in the training group (n=80), which was validated in the validation group (n=40).

Results

mRNA expression levels of four markers (p53, p21, Ki67, CD133) correlated with pCR (24/80, 30.0%) in the training group. Low expression of p53 and/or high expression of p21, Ki67 and CD133 showed greater pCR rate. pCR was shown in 18 (69.2%) of 26 cases showing endoscopic cCR in the training group. Higher pCR rate was demonstrated in lower tumor location than middle tumor (19/49, 38.8% vs. 5/31, 16.1%). A nomogram for prediction of pCR was developed from the multivariate prediction model using these six variables, which showed good discrimination ability in the training group [area under the curve (AUC)=0.945] and validation group (AUC=0.922). The calibration plot showed good agreement between actual and predicted pCR in both patient groups.

Conclusions

Nomogram for assessment of pCR can be useful for making treatment decisions after CRT according to predicted responses.

Nomograms Predict Survival in Patients with Anaplastic Thyroid Carcinoma

Background

Anaplastic thyroid carcinoma (ATC) is a very rare, highly lethal malignant cancer. Our aim in this study was to develop nomograms that predict survival in ATC patients.

Material/Methods

ATC incidence and mortality were assessed via joinpoint regression analysis of 567 ATC patients selected from the Surveillance, Epidemiology, and End Results 18 Registries Research database. Predictive models were established via univariate and multivariate Cox regression analysis of potential risk factors and used to produce nomograms. Performance of the nomograms in terms of discrimination ability and calibration was evaluated by determining the concordance index (C-index) and by generating calibration plots, respectively.

Results

The incidence and mortality rates for ATC increased from 2000 to 2015 according to the collected data (p<0.05). Two nomograms were constructed based on 2 predictive models: nomogram 1 considered age, tumor size, and metastasis (all before surgery), and nomogram 2 considered age, tumor size, metastasis, surgery, and extrathyroidal extension (all after surgery). Both nomogram 1 (C-index, 0.6803; 95% confidence interval, 0.6517–0.7089) and nomogram 2 (C-index, 0.7064; 95% confidence interval, 0.6783–0.7345) had good discrimination ability. The validated C-index values were 0.6783 and 0.7029 for nomogram 1 and 2, respectively. The observed values were in agreement with the calibration curves.

Conclusions

Nomogram 1 can assist in preoperative prediction of survival time in ATC patients, whereas nomogram 2 can provide additional outcome-related information.

A prognostic eight-lncRNA expression signature in predicting recurrence of ER-positive breast cancer receiving endocrine therapy

Long noncoding RNAs (lncRNAs) have the main role in the tumorigenesis of breast cancer. In the present study, lncRNA expression profiling was collected to identify a lncRNA expression signature from the Gene Expression Omnibus database. An eight-lncRNA signature was established to predict the survival of patients with estrogen receptor (ER)-positive breast cancer receiving endocrine therapy. Patients were separated into a low-risk group and a high-risk group based on this signature. Patients in high-risk group have worse survival compared to those in low-risk group using Kaplan-Meier curve analysis with log-rank test. Receiver operating characteristic analysis suggested good diagnostic efficiency of the eight-lncRNA signature. When adjusting the clinical features, including age, grade, lymph node status, and tumor size, this signature was independently associated with the relapse-free survival. The prognostic value of the lncRNA prognostic model was then validated in validation sets. When validated in a cohort of patients treated with neoadjuvant chemotherapy and endocrine therapy, this signature demonstrated good performance as well. Besides, we have built a nomogram that integrated the conventional clinicopathological features and the eight-lncRNA-based signature. To sum up, our results indicated that the eight-lncRNA prognostic model was a reliable tool to group patients at high and low risk of disease relapse. This signature may have possible implication in prognostic evaluations of patients with ER-positive breast cancer receiving endocrine therapy.

A prognostic 10-lncRNA expression signature for predicting the risk of tumour recurrence in breast cancer patients

Breast cancer is one of the most frequently diagnosed malignancies and a leading cause of cancer death among females. Multiple molecular alterations are observed in breast cancer. LncRNA transcripts were proved to play important roles in the biology of tumorigenesis. In this study, we aimed to identify lncRNA expression signature that can predict breast cancer patient survival. We developed a 10‐lncRNA signature‐based risk score which was used to separate patients into high‐risk and low‐risk groups. Patients in the low‐risk group had significantly better survival than those in the high‐risk group. Receiver operating characteristic analysis indicated that this signature exhibited excellent diagnostic efficiency for 1‐, 3‐ and 5‐year disease‐relapse events. Moreover, multivariate Cox regression analysis demonstrated that this 10‐lncRNA signature was an independent risk factor when adjusting for several clinical signatures such as age, tumour size and lymph node status. The prognostic value of risk scores was validated in the validation set. In addition, a nomogram was established and the calibration plots analysis indicated the good performance and clinical utility of the nomogram. In conclusion, our results demonstrated that this 10‐lncRNA signature effectively grouped patients at low and high risk of disease recurrence.

Potential Benefits of Neoadjuvant Chemotherapy in Clinically Node-Positive Luminal Subtype- Breast Cancer

Purpose

Neoadjuvant chemotherapy (NAC) is less effective for luminal breast cancer because luminal breast cancer has a lower rate of pathological complete response (pCR) after NAC than human epidermal growth factor receptor 2 (HER2)-type and triple-negative breast cancer (TNBC). We investigated the efficacy of NAC and the predictive factors of a better response in luminal breast cancer.

Methods

Between 2010 and 2016, we retrieved data of 244 patients with clinically node-positive breast cancer who were treated with NAC followed by surgery from a prospectively collected database. We classified breast cancer into luminal HER2⁻ and non-luminal HER2⁻ breast cancer (luminal HER2⁺, HER2⁺, and TNBC types). We analyzed each subtype with respect to surgical outcomes, response to NAC, and determined variables associated with surgical outcomes and response in patients with luminal HER2⁻ breast cancer.

Results

The total, breast, and axillary pCR rates were significantly lower in 114 patients with luminal HER2⁻ breast cancer than in those with other subtypes (7.9%, 12.3%, and 22.8%, respectively). However, breast-conserving surgery (BCS) conversion and tumor response rates did not significantly differ between patients with luminal HER2⁻ and those with non-luminal HER2⁻ breast cancer (p = 0.836 and p = 0.180, respectively). In the multivariate analysis, high tumor response rate (≥ 46.4%) was significantly associated with an increased BCS conversion rate. In the subgroup analysis of luminal HER2⁻ breast cancer, the multivariate analysis showed that higher Ki67 expression and axilla pCR and BCS conversion rates were significantly associated with tumor response to NAC.

Conclusion

Despite the low pCR rate, the tumor response and BCS conversion rates after NAC of luminal HER2⁻ breast cancer were similar to those of other subtypes. NAC has the potential benefit of reducing the size of breast cancer, thereby increasing the BCS conversion rate in luminal HER2⁻ breast cancer.

Nomogram Identifies Age as the Most Important Predictor of Overall Survival in Oral Cavity Squamous Cell Cancer After Primary Surgery

Our goal was to determine the most important predictors and construct a nomogram for overall survival (OS) in oral cavity squamous cell cancer (OCSCC) treated with primary surgery followed by observation, adjuvant radiation or chemoradiation. Multivariable analysis was performed using Cox Proportional Hazard model of 9258 OCSCC patients from Surveillance, Epidemiology and End Results Program (SEER) database treated with surgery from 2003 to 2009. Potential predictors of OS were age, gender, race, tobacco use, oral cancer sub-sites, pathologic tumor stage and grade, pathologic nodal stage, extra-capsular invasion, clinical levels IV and V involvement, and adjuvant treatment selection. Weighted propensity scores for treatment were used to balance observed baseline characteristics between three treatment groups in order to reduce bias. Following primary surgery, patients underwent observation (56%), radiation alone (31%) or chemoradiation (13%). All tested predictors were statistically significant and included in our final nomogram. Most important predictor of OS was age, followed by pathologic tumor stage. SEER based-survival nomogram for OCSCC patients differs from published models derived from patients treated in a single or few academic treatment centers. An unexpected finding of patient age being the best OS predictor suggests that this factor may be more critical for the outcome than previously anticipated.

A Nomogram to Predict the Pathologic Complete Response of Neoadjuvant Chemotherapy in Triple-Negative Breast Cancer Based on Simple Laboratory Indicators

BACKGROUND

Triple-negative breast cancer (TNBC) patients who achieve a pathologic complete response (pCR) after neoadjuvant chemotherapy (NAC) have better prognoses.

OBJECTIVE

This study aimed to develop an intuitive nomogram based on simple laboratory indexes to predict the pCR of standard NAC in TNBC patients.

METHODS

A total of 80 TNBC patients who received eight cycles of thrice-weekly standard NAC (anthracycline and cyclophosphamide followed by taxane) and subsequently underwent surgery in Zhejiang Cancer Hospital were retrospectively enrolled, and data on their pretreatment clinical features and multiple simple laboratory indexes were collected. The optimal cut-off values of the laboratory indexes were determined by the Youden index using receiver operating characteristic (ROC) curve analyses. Forward stepwise logistic regression (likelihood ratio) analysis was applied to identify predictive factors for a pCR of NAC. A nomogram was then developed according to the logistic model, and internally validated using the bootstrap resampling method.

RESULTS

pCR was achieved in 39 (48.8%) patients after NAC. Multivariate analysis identified four independent indicators: clinical tumor stage, lymphocyte to monocyte ratio, fibrinogen level, and D-dimer level. The nomogram established based on these factors showed its discriminatory ability, with an area under the curve (AUC) of 0.803 (95% confidence interval 0.706-0.899) and a bias-corrected AUC of 0.771. The calibration curve and Hosmer-Lemeshow test showed that the predictive ability of the nomogram was a good fit to actual observation.

CONCLUSIONS

The nomogram proposed in the present study exhibited a sufficient discriminatory ability for predicting pCR of NAC in TNBC patients.

Evaluation of Breast and Axillary Lymph Node Specimens in Breast Cancer Patients Treated With Neoadjuvant Systemic Therapy

Breast and axillary lymph node specimens from breast cancer patients treated with neoadjuvant systemic therapy are being encountered by pathologists with increasing frequency. Evaluation of these specimens presents challenges that differ from those encountered during the examination of other types of breast specimens. This article reviews the key issues regarding the gross and microscopic evaluation of post-neoadjuvant systemic therapy breast and lymph node specimens, and emphasizes the importance of accurate specimen evaluation in assessing treatment response.

LncRNA Profile Study Reveals a Three-LncRNA Signature Associated With the Pathological Complete Response Following Neoadjuvant Chemotherapy in Breast Cancer

Background

The purpose of this study is to develop an effective but concise long non-coding RNA (lncRNA) expression signature that can predict response to neoadjuvant chemotherapy for breast cancer (BC) patients.

Methods

lncRNA expression profiling in 1102 BC patients from Gene Expression Omnibus datasets was analyzed using lncRNA-mining approach. The association between lncRNA signature and pathological complete response (pCR) was analyzed using logistic regression model in the training set (GSE25066, n = 488). Validation was performed in independent testing datasets, GSE20194, GSE20271, GSE22093, and GSE23988 (n = 614). Bonferroni method was employed for multiple testing corrections. Cell proliferation assay and Western blot assay were performed to evaluate the cell viability and protein expression level, respectively.

Results

Three lncRNAs (AK291479, U79293, and BC032585) have been identified to be significantly associated with pCR in the training dataset (Bonferroni p-value < 0.05). Expression signature with these lncRNAs was predictive of pCR in the training (AUC = 0.74) and testing (AUC = 0.72) datasets. Weighted gene co-expression network analysis and gene functional annotation suggest that the three lncRNAs were involved in cell cycle process. To confirm the functional significance of the identified lncRNAs, BC032585 was selectively silenced using RNA interference. Knockdown of BC032585 lncRNA significantly promoted cell resistance to multiple anticancer-drugs through upregulating MDR1 expression in breast cancer cells.

Conclusion

These results suggest that lncRNAs such as BC032585 might be involved in chemotherapeutic response in breast cancer patients, and the three-lncRNA signature identified in the present study may serve as a useful biomarker for the selection of responsive breast cancer patients in neoadjuvant chemotherapy.

Nomogram Predicting Locoregional Recurrence to Assist Decision-Making of Postmastectomy Radiation Therapy in Patients With T1-2N1 Breast Cancer

PURPOSE

Postmastectomy radiation therapy (PMRT) had heterogeneous effects on survival outcome of patients with T1-2N1 breast cancer. A reliable model to estimate individuals' risk of locoregional recurrence (LRR) and the potential benefit derived from PMRT is needed.

METHODS AND MATERIALS

We retrospectively analyzed 1141 patients with T1-2N1 breast cancer who underwent mastectomy between January 2001 and December 2012. Based on the Fine and Gray competing risks regression in 623 unirradiated patients, a nomogram predicting LRR was conducted for risk quantification. Decision tree analysis was performed for patient grouping. The impact of PMRT was evaluated among 3 subgroups.

RESULTS

With a median follow-up of 74.9 months, the 5-year cumulative incidence of LRR, distant recurrence (DR) and breast cancer mortality (BCM) were 3.9%, 8.8%, and 6.0%, respectively, for the entire cohort. Based on nomogram scores, patients were classified into 3 risk groups in decision tree analysis. In the high-risk group, PMRT was found to be associated with a 12.7% risk reduction of 5-year LRR, 9.2% risk reduction of 5-year DR, and 7.0% risk reduction of 5-year BCM, whereas it was not significantly associated with LRR, DR, or BCM in low- and intermediate-risk groups.

CONCLUSIONS

The nomogram performed individualized risk quantification of LRR in patients with T1-2N1 breast cancer. A newly identified patient subgroup with high risk of LRR were found to derive survival benefit from PMRT.

Locoregional recurrence-associated factors and risk-adapted postmastectomy radiotherapy for breast cancer staged in cT1-2N0-1 after neoadjuvant chemotherapy

Objective

In order to identify risk factors associated with locoregional recurrence (LRR) and assess the role of postmastectomy radiotherapy (PMRT) in early breast cancer (BC), managed with neoadjuvant chemotherapy (NAC) and mastectomy, a retrospective analysis of BC diagnosed with clinical stage T1-2N0-1 was conducted.

Patients and methods

A total of 217 patients were included in this analysis. The median age was 50 years (24-72 years). The clinical stage distributions were cT1 in 15 cases, cT2 in 202, cN0 in 53, and cN1 in 161 cases. All patients were treated with NAC and mastectomy, and 128 patients received PMRT.

Results

With a median follow-up time of 61 months, the 5-year cumulative LRR rate was 12%. Multivariate analysis demonstrated that pathological N stage, lymph-vascular invasion, and histological grade were independent prognostic factors associated with LRR. A nomogram model based on these factors was established, based on which the patients were deeply stratified into low- and high-risk group. In the low-risk group, radiotherapy did not decrease LRR (3.3% in PMRT group, 1.7% in no PMRT group, P=0.192). While in the high-risk group, PMRT significantly decreased LRR (21.8% in PMRT group, 42.2% in no PMRT group, P=0.031).

Conclusion

Lymph-vascular invasion, histological grade, as well as pathological N stage were important prognostic factors associated with LRR in BC patients staged in cT1-2N0-1, who were managed with NAC and mastectomy. In our cohort, not only clinical and pathological stage information but also other risk factors were taken into consideration when adjuvant PMRT was recommended. In the high-risk subgroup, PMRT significantly improved the prognosis.

Predictive and prognostic value of Matrix metalloproteinase (MMP) - 9 in neoadjuvant chemotherapy for triple-negative breast cancer patients

Background

This study aimed to investigate the clinical utility of serum and histological MMP-9 detection during neoadjuvant chemotherapy (NAC) for triple-negative breast cancer (TNBC).

Methods

A total of 303 TNBC patients who underwent weekly paclitaxel plus carboplatin treatments followed by surgical resection were included in this study. Enzyme-linked immunosorbent assay (ELISA) was used to detect the serum level of Matrix metalloproteinase-9 (sMMP-9) at baseline and prior to surgery. Immunohistochemistry was used to detect histological MMP-9 (hMMP-9) expression in patients with residual tumors after NAC. The value of MMP-9 to predict the response to NAC and patient survival was studied.

Results

Of the 303 patients, 103 (34.0%) patients experienced pathological complete response (pCR) after completion of NAC. Univariate and multivariate analyses revealed that the relative change in sMMP-9, rather than sMMP-9 at baseline or surgery, had a remarkable predictive value for pCR. Each 1 ng/ml decrease in sMMP-9 after NAC was shown to result in a 0.3% increase in pCR rate. Additionally, in survival analyses, hMMP-9 expression in residual tumors was independently correlated with disease-free survival for non-pCR responders (P < 0.001).

Conclusions

Our findings indicate that monitoring serum MMP-9 and detection of histological MMP-9 could help identify TNBC patients who will respond to NAC and will display varying risks of disease relapse. MMP-9 may serve as a predictive and prognostic biomarker for tailoring and modifying the NAC strategy for TNBC.

Prediction of postoperative disease-free survival and brain metastasis for HER2-positive breast cancer patients treated with neoadjuvant chemotherapy plus trastuzumab using a machine learning algorithm

PURPOSE

This study aimed to develop mathematical tools to predict the likelihood of recurrence after neoadjuvant chemotherapy (NAC) plus trastuzumab in patients with human epidermal growth factor receptor 2 (HER2)-positive breast cancer.

METHODS

Data of 776 patients from a multicenter retrospective cohort study were collected. All patients had HER2-positive breast cancer and received NAC plus trastuzumab between 2001 and 2010. Two mathematical tools using a machine learning method were developed to predict the likelihood of disease-free survival (DFS) (DFS model) and brain metastasis (BM) (BM model) within 5 years after surgery. For validation, bootstrap analyses were conducted. The area under the receiver operating characteristics curve (AUC) was calculated to examine the discrimination.

RESULTS

The AUC values were 0.785 (95% CI 0.740-0.831, P < 0.001) for the DFS model and 0.871 (95% CI 0.830-0.912, P < 0.001) for the BM model. Patients with low-risk DFS or BM events, as predicted by the models, showed better 5-year DFS and BM rates than those with high-risk DFS or BM events (89% vs. 61% for the DFS model, P < 0.001; 99% vs. 87% for the BM model, P < 0.001). These models maintained discrimination abilities in both luminal and non-luminal subtypes, providing prognostic information independent of pathological response. Bootstrap validation confirmed the high generalization abilities of the models.

CONCLUSIONS

The DFS and BM models have a high accuracy to predict prognosis among HER2-positive patients treated with NAC plus trastuzumab. Our models can help optimize adjuvant therapy and postoperative surveillance.

Monitoring Serum VEGF in Neoadjuvant Chemotherapy for Patients with Triple-Negative Breast Cancer: A New Strategy for Early Prediction of Treatment Response and Patient Survival

BACKGROUND

This study aimed to investigate the clinical utility of serum biomarker changes during neoadjuvant chemotherapy (NAC) for triple-negative breast cancer (TNBC).

METHODS

A total of 303 patients with TNBC were included in this study. Serum samples were taken at three time points during NAC: baseline, prior to the third cycle, and prior to surgery. Luminex multibiomarker panel for 29 serum biomarkers was used to detect their correlation with NAC response. The predictive and prognostic value of each selected biomarker was then studied.

RESULTS

Vascular endothelial growth factor (VEGF) was the only biomarker that correlated with treatment response, with a decreasing trend in pCR patients relative to non-pCR patients (p < .001). Univariable and multivariable analyses revealed that the relative change in VEGF prior to the third cycle of NAC had a remarkable predictive value for both pCR and pathological nonresponse with high sensitivity and specificity. VEGF was also independently correlated with disease-free survival.

CONCLUSION

Our findings indicate that monitoring serum VEGF could help identify patients with different responses at an early time point of NAC and at varying risk of disease relapse. Serum VEGF may also serve as an alternative to traditional response-evaluating methodologies in tailoring and modifying the NAC strategy for both operable and advanced TNBCs.

IMPLICATIONS FOR PRACTICE

Neoadjuvant chemotherapy (NAC) followed by definitive surgery is a standard of care for locally advanced breast cancer. The identification of sensitive responders to neoadjuvant therapy is highly significant for breast cancer, especially triple-negative breast cancer (TNBC). Results of this study indicate that the monitoring of serum vascular endothelial growth factor (VEGF) could identify patients with favorable or poor responses at an early time point of NAC. Furthermore, the prediction power of VEGF was better than traditional response-evaluating methods. VEGF might serve as a complement or alternative to traditional imaging-based response-evaluating methodologies in tailoring systemic treatment strategies for both operable and advanced TNBCs.