Histomorphological Factors Predicting the Response to Neoadjuvant Chemotherapy in Triple-Negative Breast Cancer

Pre-Treatment Prediction of Breast Cancer Response to Neoadjuvant Chemotherapy Using Intratumoral and Peritumoral Radiomics from T2-Weighted and Contrast-Enhanced T1-Weighted MRI

(1) Background: Neoadjuvant chemotherapy (NAC) is an integral part of breast cancer management, and response to NAC is an important prognostic factor associated with improved survival outcomes. However, the current standard for response assessment relies on post-surgical histopathological analysis, which limits early therapeutic decision-making and treatment personalization. This study aimed to develop and evaluate a machine learning model that integrates pre-treatment MRI radiomics and clinical features to predict response to NAC in breast cancer patients. (2) Methods: In this study, a machine learning model was developed to predict breast cancer response to NAC using pre-treatment magnetic resonance imaging (MRI) radiomics and clinical data. Radiomic features were extracted from contrast-enhanced T1-weighted (CE-T1) and T2-weighted (T2) MRI sequences using both intratumoral and peritumoral segmentations. Furthermore, this study uniquely examined two response assessment criteria: (1) pathologic complete response (pCR) versus non-pCR, and (2) clinical response versus non-response. A total of 254 patients with biopsy-confirmed breast cancer who completed NAC were included. Radiomic features (n = 400) and clinical features (n = 7) were analyzed to build a predictive model employing the XGBoost classifier. Performance was measured in terms of accuracy, precision, sensitivity, specificity, F1-score, and AUC. (3) Results: The integration of radiomic features with clinical data significantly enhanced the predictive performance. For pCR and non-pCR prediction, the combined features model achieved an accuracy of 80% and AUC of 0.85, outperforming both the clinical features model (Accuracy = 68%, AUC = 0.81) and radiomic features model (Accuracy = 66%, AUC = 0.60). Similarly, for the clinical response and non-response prediction, the combined features model achieved an Accuracy of 74% and AUC of 0.75, outperforming both the clinical features model (Accuracy = 63%, AUC = 0.68) and radiomic features model (Accuracy = 66%, AUC = 0.57). (4) Conclusions: These findings highlight the synergistic effect of integrating clinical data and MRI-based radiomics to improve pre-treatment NAC response prediction, which has the potential to enable more precise and personalized treatment strategies.

Clinical value of SUVpeak-to-tumor centroid distance on FDG PET/CT for predicting neoadjuvant chemotherapy response in patients with breast cancer

BACKGROUND

Since it has been found that the maximum metabolic activity of a cancer lesion shifts toward the lesion edge during cancer progression, normalized distances from the hot spot of radiotracer uptake to tumor centroid (NHOC) and tumor perimeter (NHOP) have been suggested as novel F-18 fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) parameters that can reflect cancer aggressiveness. This study aimed to investigate whether NHOC and NHOP parameters could predict pathological response to neoadjuvant chemotherapy (NAC) and progression-free survival (PFS) in breast cancer patients.

METHODS

This study retrospectively enrolled 135 female patients with breast cancer who underwent pretreatment FDG PET/CT and received NAC and subsequent surgical resection. From PET/CT images, normalized distances of maximum SUV and peak SUV-to-tumor centroid (NHOCmax and NHOCpeak) and -to-tumor perimeter (NHOPmax and NHOPpeak) were measured, in addition to conventional PET/CT parameters.

RESULTS

Of 135 patients, 32 (23.7%) achieved pathological complete response (pCR), and 34 (25.2%) had events during follow-up. In the receiver operating characteristic (ROC) curve analysis, NHOCmax showed the highest area under the ROC curve value (0.710) for predicting pCR, followed by NHOCpeak (0.694). In the multivariate logistic regression analysis, NHOCmax, NHOCpeak, and NHOPmax were independent predictors for pCR (p < 0.05). In the multivariate survival analysis, NHOCpeak (p = 0.026) was an independent predictor for PFS along with metabolic tumor volume, with patients having higher NHOCpeak showing worse PFS.

CONCLUSION

NHOCpeak on pretreatment FDG PET/CT could be a potential imaging parameter for predicting NAC response and survival in patients with breast cancer.

Predictive value of tumor-infiltrating lymphocytes for neoadjuvant therapy response in triple-negative breast cancer: A systematic review and meta-analysis

BACKGROUND

The association between tumor-infiltrating lymphocyte (TIL) levels and the response to neoadjuvant therapy (NAT) in patients with triple-negative breast cancer (TNBC) remains unclear.

AIM

To investigate the predictive potential of TIL levels for the response to NAT in TNBC patients.

METHODS

A systematic search of the National Center for Biotechnology Information PubMed database was performed to collect relevant published literature prior to August 31, 2023. The correlation between TIL levels and the NAT pathologic complete response (pCR) in TNBC patients was assessed using a systematic review and meta-analysis. Subgroup analysis, sensitivity analysis, and publication bias analysis were also conducted.

RESULTS

A total of 32 studies were included in this meta-analysis. The overall meta-analysis results indicated that the pCR rate after NAT treatment in TNBC patients in the high TIL subgroup was significantly greater than that in patients in the low TIL subgroup (48.0% vs 27.7%) (risk ratio 2.01; 95% confidence interval 1.77-2.29; P < 0.001, I 2 = 56%). Subgroup analysis revealed that the between-study heterogeneity originated from differences in study design, TIL level cutoffs, and study populations. Publication bias could have existed in the included studies. The meta-analysis based on different NAT protocols revealed that all TNBC patients with high levels of TILs had a greater rate of pCR after NAT treatment in all protocols (all P ≤ 0.01), and there was no significant between-protocol difference in the statistics among the different NAT protocols (P = 0.29). Additionally, sensitivity analysis demonstrated that the overall results of the meta-analysis remained consistent when the included studies were individually excluded.

CONCLUSION

TILs can serve as a predictor of the response to NAT treatment in TNBC patients. TNBC patients with high levels of TILs exhibit a greater NAT pCR rate than those with low levels of TILs, and this predictive capability is consistent across different NAT regimens.

Deep learning-based risk stratification of preoperative breast biopsies using digital whole slide images

BACKGROUND

Nottingham histological grade (NHG) is a well established prognostic factor in breast cancer histopathology but has a high inter-assessor variability with many tumours being classified as intermediate grade, NHG2. Here, we evaluate if DeepGrade, a previously developed model for risk stratification of resected tumour specimens, could be applied to risk-stratify tumour biopsy specimens.

METHODS

A total of 11,955,755 tiles from 1169 whole slide images of preoperative biopsies from 896 patients diagnosed with breast cancer in Stockholm, Sweden, were included. DeepGrade, a deep convolutional neural network model, was applied for the prediction of low- and high-risk tumours. It was evaluated against clinically assigned grades NHG1 and NHG3 on the biopsy specimen but also against the grades assigned to the corresponding resection specimen using area under the operating curve (AUC). The prognostic value of the DeepGrade model in the biopsy setting was evaluated using time-to-event analysis.

RESULTS

Based on preoperative biopsy images, the DeepGrade model predicted resected tumour cases of clinical grades NHG1 and NHG3 with an AUC of 0.908 (95% CI: 0.88; 0.93). Furthermore, out of the 432 resected clinically-assigned NHG2 tumours, 281 (65%) were classified as DeepGrade-low and 151 (35%) as DeepGrade-high. Using a multivariable Cox proportional hazards model the hazard ratio between DeepGrade low- and high-risk groups was estimated as 2.01 (95% CI: 1.06; 3.79).

CONCLUSIONS

DeepGrade provided prediction of tumour grades NHG1 and NHG3 on the resection specimen using only the biopsy specimen. The results demonstrate that the DeepGrade model can provide decision support to identify high-risk tumours based on preoperative biopsies, thus improving early treatment decisions.

Construction and validation of a prognostic nutritional index-based nomogram for predicting pathological complete response in breast cancer: a two-center study of 1,170 patients

Background

Pathological complete response (pCR) after neoadjuvant chemotherapy (NAC) is associated with favorable outcomes in breast cancer patients. Identifying reliable predictors for pCR can assist in selecting patients who will derive the most benefit from NAC. The prognostic nutritional index (PNI) serves as an indicator of nutritional status and systemic immune competence. It has emerged as a prognostic biomarker in several malignancies; however, its predictive value for pCR in breast cancer remains uncertain. The objective of this study is to assess the predictive value of pretreatment PNI for pCR in breast cancer patients.

Methods

A total of 1170 patients who received NAC in two centers were retrospectively analyzed. The patients were divided into three cohorts: a training cohort (n=545), an internal validation cohort (n=233), and an external validation cohort (n=392). Univariate and multivariate analyses were performed to assess the predictive value of PNI and other clinicopathological factors. A stepwise logistic regression model for pCR based on the smallest Akaike information criterion was utilized to develop a nomogram. The C-index, calibration plots and decision curve analysis (DCA) were used to evaluate the discrimination, calibration and clinical value of the model.

Results

Patients with a high PNI (≥53) had a significantly increased pCR rate (OR 2.217, 95% CI 1.215-4.043, p=0.009). Tumor size, clinical nodal status, histological grade, ER, Ki67 and PNI were identified as independent predictors and included in the final model. A nomogram was developed as a graphical representation of the model, which incorporated the PNI and five other factors (AIC=356.13). The nomogram demonstrated satisfactory calibration and discrimination in the training cohort (C-index: 0.816, 95% CI 0.765-0.866), the internal validation cohort (C-index: 0.780, 95% CI 0.697-0.864) and external validation cohort (C-index: 0.714, 95% CI 0.660-0.769). Furthermore, DCA indicated a clinical net benefit from the nomogram.

Conclusion

The pretreatment PNI is a reliable predictor for pCR in breast cancer patients. The PNI-based nomogram is a low-cost, noninvasive tool with favorable predictive accuracy for pCR, which can assist in determining individualized treatment strategies for breast cancer patients.

Predicting Response of Triple-Negative Breast Cancer to Neoadjuvant Chemotherapy Using a Deep Convolutional Neural Network-Based Artificial Intelligence Tool

PURPOSE

Achieving a pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) is associated with improved patient outcomes in triple-negative breast cancer (TNBC). Currently, there are no validated predictive biomarkers for the response to NAC in TNBC. We developed and validated a deep convolutional neural network-based artificial intelligence (AI) model to predict the response of TNBC to NAC.

MATERIALS AND METHODS

Whole-slide images (WSIs) of hematoxylin and eosin-stained core biopsies from 165 (pCR in 60 and non-pCR in 105) and 78 (pCR in 31 and non-pCR in 47) patients with TNBC were used to train and validate the model. The model extracts morphometric features from WSIs in an unsupervised manner, thereby generating clusters of morphologically similar patterns. Downstream ranking of clusters provided regions of interest and morphometric scores; a low score close to zero and a high score close to one represented a high or low probability of response to NAC.

RESULTS

The predictive ability of AI score for the entire cohort of 78 patients with TNBC ascertained by receiver operating characteristic analysis demonstrated an area under the curve (AUC) of 0.75. The AUC for stages I, II, and III disease were 0.88, 0.73, and 0.74, respectively. Using a cutoff value of 0.35, the positive predictive value of the AI score for pCR was 73.7%, and the negative predictive value was 76.2% for non-pCR patients.

CONCLUSION

To our knowledge, this study is the first to demonstrate the use of an AI tool on digitized hematoxylin and eosin-stained tissue images to predict the response to NAC in patients with TNBC with high accuracy. If validated in subsequent studies, these results may serve as an ancillary aid for individualized therapeutic decisions in patients with TNBC.

Federated learning for predicting histological response to neoadjuvant chemotherapy in triple-negative breast cancer

Triple-negative breast cancer (TNBC) is a rare cancer, characterized by high metastatic potential and poor prognosis, and has limited treatment options. The current standard of care in nonmetastatic settings is neoadjuvant chemotherapy (NACT), but treatment efficacy varies substantially across patients. This heterogeneity is still poorly understood, partly due to the paucity of curated TNBC data. Here we investigate the use of machine learning (ML) leveraging whole-slide images and clinical information to predict, at diagnosis, the histological response to NACT for early TNBC women patients. To overcome the biases of small-scale studies while respecting data privacy, we conducted a multicentric TNBC study using federated learning, in which patient data remain secured behind hospitals' firewalls. We show that local ML models relying on whole-slide images can predict response to NACT but that collaborative training of ML models further improves performance, on par with the best current approaches in which ML models are trained using time-consuming expert annotations. Our ML model is interpretable and is sensitive to specific histological patterns. This proof of concept study, in which federated learning is applied to real-world datasets, paves the way for future biomarker discovery using unprecedentedly large datasets.

Predictive and prognostic values of tumor infiltrating lymphocytes in breast cancers treated with neoadjuvant chemotherapy: A meta-analysis

BACKGROUND

This meta-analysis assessed the predictive and prognostic value of tumor infiltrating lymphocytes (TILs) in neoadjuvant chemotherapy (NACT) treated breast cancer and an optimal threshold for predicting pathologic complete response (pCR).

METHODS

A systematic search of PubMed, EMBASE and Web of Science electronic databases was conducted to identify eligible studies published before April 2022. Either a fixed or random effects model was applied to estimate the pooled hazard ratio (HR) and odds ratio (OR) for prognosis and predictive values of TILs in breast cancer patients treated with NACT. The study is registered with PROSPERO (CRD42020221521).

RESULTS

A total of 29 published studies were eligible. Increased levels of TILs predicted response to NACT in HER2 positive breast cancer (OR = 2.54 95%CI, 1.50-4.29) and triple negative breast cancer (TNBC) (OR = 3.67, 95%CI, 1.93-6.97), but not for hormone receptor (HR) positive breast cancer (OR = 1.68, 95 %CI, 0.67-4.25). A threshold of 20% of H & E-stained TILs was associated with prediction of pCR in both HER2 positive breast cancer (P = 0.035) and TNBC (P = 0.001). Moreover, increased levels of TILs (either iTILs or sTILs) were associated with survival benefit in HER2-positive breast cancer and TNBC. However, an increased level of TILs was not a prognostic factor for survival in HR positive breast cancer (pooled HR = 0.64, 95%CI: 0.03-14.1, P = 0.78).

CONCLUSIONS

Increased levels of TILs were associated with increased rates of response to NACT and improved prognosis for the molecular subtypes of TNBC and HER2-positive breast cancer, but not for patients with HR positive breast cancer. A threshold of 20% TILs was the most powerful outcome prognosticator of pCR.

Efficacy and prognostic factors of neoadjuvant chemotherapy for triple-negative breast cancer

BACKGROUND

Breast cancer mainly occurs in young and premenopausal women; its incidence is increasing annually. Patients with triple-negative breast cancer (TNBC) have relatively high recurrence and transfer rates during the operation and 3 years after postoperative adjuvant chemotherapy. Currently, the treatment for patients with TNBC is mainly based on a comprehensive combination of surgery and chemotherapy. Therefore, identifying additional effective treatments to improve patient prognosis is important.

AIM

To explore and discuss the effects and prognostic factors of neoadjuvant chemotherapy in TNBC.

METHODS

In total, 118 patients diagnosed with TNBC from January 2016 to January 2020 in our hospital were selected and divided into the observation (n = 60) and control (n = 58) groups according to therapeutic regimen. The control group received routine chemotherapy, and the observation group received neoadjuvant chemotherapy. The therapeutic effects of the two groups were observed, and the survival of patients was followed up.

RESULTS

The karyopherin A2 (KPNA2)-positive and SRY-related HMG box-2 (SOX2)-positive expression rates of patients with TNBC with intravascular tumor thrombus and tumor-node-metastasis (TNM) stage IV were 92.00% and 91.67% and 96.00% and 95.83%, respectively, which were significantly higher than those of patients with no intravascular tumor thrombus and TNM stage III (P < 0.05). KPNA2 was positively associated with SOX2 expression (r_s = 0.514, P < 0.50). The short-term curative effect of the observation group was better than that of the control group (P < 0.05), and the total effective rate was 58.33%. After treatment, carcinoembryonic antigen, cancer antigen (CA) 19-9, and CA125 Levels in the observation group were 11.40 ± 2.32 mg/L, 19.92 ± 3.42 kU/L, and 54.30 ± 12.28 kU/L, respectively, which were significantly lower than those in the control group (P < 0.05). The median survival time of the observation group was 33 mo (95%CI: 31.21-34.79), which was significantly longer than that of the control group (P < 0.05). TNM stage, degree of differentiation, lymph node metastasis, KPNA2 and SOX2 expressions, and treatment plan were prognostic factors of TNBC (relative risk = 1.575, 1.380, 1.366, 1.433, 1.411, and 0.581, respectively, P < 0.05).

CONCLUSION

Neoadjuvant chemotherapy for TNBC treatment can achieve good curative effects. TNM stage, differentiation degree, lymph node metastasis, KPNA2 and SOX2 expressions, and treatment plan are prognostic factors of TNBC.

Construction and Validation of a Serum Albumin-to-Alkaline Phosphatase Ratio-Based Nomogram for Predicting Pathological Complete Response in Breast Cancer

Background

Breast cancer patients who achieve pathological complete response (pCR) after neoadjuvant chemotherapy (NAC) have favorable outcomes. Reliable predictors for pCR help to identify patients who will benefit most from NAC. The pretreatment serum albumin-to-alkaline phosphatase ratio (AAPR) has been shown to be a prognostic predictor in several malignancies, but its predictive value for pCR in breast cancer is still unknown. This study aims to investigate the predictive role of AAPR in breast cancer patients and develop an AAPR-based nomogram for pCR rate prediction.

Methods

A total of 780 patients who received anthracycline and taxane-based NAC from January 2012 to March 2018 were retrospectively analyzed. Univariate and multivariate analyses were performed to assess the predictive value of AAPR and other clinicopathological factors. A nomogram was developed and calibrated based on multivariate logistic regression. A validation cohort of 234 patients was utilized to further validate the predictive performance of the model. The C-index, calibration plots and decision curve analysis (DCA) were used to evaluate the discrimination, calibration and clinical value of the model.

Results

Patients with a lower AAPR (<0.583) had a significantly reduced pCR rate (OR 2.228, 95% CI 1.246-3.986, p=0.007). Tumor size, clinical nodal status, histological grade, PR, Ki67 and AAPR were identified as independent predictors and included in the final model. The nomogram was used as a graphical representation of the model. The nomogram had satisfactory calibration and discrimination in both the training cohort and validation cohort (the C-index was 0.792 in the training cohort and 0.790 in the validation cohort). Furthermore, DCA indicated a clinical net benefit from the nomogram.

Conclusions

Pretreatment serum AAPR is a potentially valuable predictor for pCR in breast cancer patients who receive NAC. The AAPR-based nomogram is a noninvasive tool with favorable predictive accuracy for pCR, which helps to make individualized treatment strategy decisions.

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

A single tumor marker is not enough to predict the breast pathologic complete response (bpCR) after neoadjuvant chemotherapy (NAC) in breast cancer patients. We aimed to establish a nomogram based on multiple clinicopathological features and routine serological indicators to predict bpCR after NAC in breast cancer patients. Data on clinical factors and laboratory indices of 130 breast cancer patients who underwent NAC and surgery in First Affiliated Hospital of Xi'an Jiaotong University from July 2017 to July 2019 were collected. Multivariable logistic regression analysis identified 11 independent indicators: body mass index, carbohydrate antigen 125, total protein, blood urea nitrogen, cystatin C, serum potassium, serum phosphorus, platelet distribution width, activated partial thromboplastin time, thrombin time, and hepatitis B surface antibodies. The nomogram was established based on these indicators. The 1000 bootstrap resampling internal verification calibration curve and the GiViTI calibration belt showed that the model was well calibrated. The Brier score of 0.095 indicated that the nomogram had a high accuracy. The area under the curve (AUC) of receiver operating characteristic (ROC) curve was 0.941 (95% confidence interval: 0.900-0.982) showed good discrimination of the model. In conclusion, this nomogram showed high accuracy and specificity and did not increase the economic burden of patients, thereby having a high clinical application value.

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.

Analysis of tumor nuclear features using artificial intelligence to predict response to neoadjuvant chemotherapy in high-risk breast cancer patients

PURPOSE

Neoadjuvant chemotherapy (NAC) is used to treat patients with high-risk breast cancer. The tumor response to NAC can be classified as either a pathological partial response (pPR) or pathological complete response (pCR), defined as complete eradication of invasive tumor cells, with a pCR conferring a significantly lower risk of recurrence. Predicting the response to NAC, however, remains a significant clinical challenge. The objective of this study was to determine if analysis of nuclear features on core biopsies using artificial intelligence (AI) can predict response to NAC.

METHODS

Fifty-eight HER2-positive or triple-negative breast cancer patients were included in this study (pCR n = 37, pPR n = 21). Multiple deep convolutional neural networks were developed to automate tumor detection and nuclear segmentation. Nuclear count, area, and circularity, as well as image-based first- and second-order features including mean pixel intensity and correlation of the gray-level co-occurrence matrix (GLCM-COR) were determined.

RESULTS

In univariate analysis, the pCR group had fewer multifocal/multicentric tumors, higher nuclear intensity, and lower GLCM-COR compared to the pPR group. In multivariate binary logistic regression, tumor multifocality/multicentricity (OR = 0.14, p = 0.012), nuclear intensity (OR = 1.23, p = 0.018), and GLCM-COR (OR = 0.96, p = 0.043) were each independently associated with likelihood of achieving a pCR, and the model was able to successful classify 79% of cases (62% for pPR and 89% for pCR).

CONCLUSION

Analysis of tumor nuclear features using digital pathology/AI can significantly improve models to predict pathological response to NAC.

Novel biomarkers and prediction model for the pathological complete response to neoadjuvant treatment of triple-negative breast cancer

Objective: To develop and validate a prediction model for the pathological complete response (pCR) to neoadjuvant chemotherapy (NCT) of triple-negative breast cancer (TNBC). Methods: We systematically searched Gene Expression Omnibus, ArrayExpress, and PubMed for the gene expression profiles of operable TNBC accessible to NCT. Molecular heterogeneity was detected with hierarchical clustering method, and the biological profiles of differentially expressed genes were investigated by Gene Ontology, Kyoto Encyclopedia of Genes and Genomes analyses, and Gene Set Enrichment Analysis (GSEA). Next, machine-learning algorithms including random-forest analysis and least absolute shrinkage and selection operator (LASSO) analysis were synchronously performed and, then, the intersected proportion of significant genes was undergone binary logistic regression to fulfill variables selection. The predictive response score (pRS) system was built as the product of the gene expression and coefficient obtained from the logistic analysis. Last, the cohorts were randomly divided in a 7:3 ratio into training cohort and validation cohort for the introduction of a robust model, and a nomogram was constructed with the independent predictors for pCR rate. Results: A total of 217 individuals from four cohort datasets (GSE32646, GSE25065, GSE25055, GSE21974) with complete clinicopathological information were included. Based on the microarray data, a six-gene panel (ATP4B, FBXO22, FCN2, RRP8, SMERK2, TET3) was identified. A robust nomogram, adopting pRS and clinical tumor size stage, was established and the performance was successively validated by calibration curves and receiver operating characteristic curves with the area under curve 0.704 and 0.756, respectively. Results of GSEA revealed that the biological processes including apoptosis, hypoxia, mTORC1 signaling and myogenesis, and oncogenic features of EGFR and RAF were in proactivity to attribute to an inferior response. Conclusions: This study provided a robust prediction model for pCR rate and revealed potential mechanisms of distinct response to NCT in TNBC, which were promising and warranted to further validate in the perspective.

Patient Selection for Clinical Trials Eliminating Surgery for HER2-Positive Breast Cancer Treated with Neoadjuvant Systemic Therapy

BACKGROUND

Patients with epidermal growth factor receptor 2-positive (HER2+) breast cancer and pathologic complete response (pCR) after neoadjuvant systemic therapy (NST) may be candidates for nonoperative clinical trials if residual invasive and in situ disease are eradicated.

METHODS

This study analyzed 280 patients with clinical T1-2N0-1 HER2+ breast cancer who underwent NST followed by surgical resection to determine key characteristics of patients with pCR in the breast and lymph nodes compared with those with residual disease.

RESULTS

Of the 280 patients, 102 (36.4%) had pCR in the breast and lymph nodes after NST, and 50 patients (17.9%) had residual ductal carcinoma in situ (DCIS) in the breast only. For 129 patients (46.1%), DCIS was present on the pretreatment biopsy, and NST failed to eradicate the DCIS component in 64.3%. Patients with residual disease were more likely to have hormone receptor-positive (HR+) tumors than those with negative tumors (73.4% vs. 50.8%; p < 0.0001). Radiologic response (odds ratio [OR], 5.62; p = 0.002) and HR+ status (OR, 2.56; p < 0.0001) were predictive of residual disease. Combined imaging methods after NST had a sensitivity of 97.1% and a negative predictive value of 70.6% for detection of residual disease. Patients with invasive disease and DCIS shown on the pretreatment core biopsy were less likely than those without DCIS to achieve pCR in the breast (31% vs. 43%; p  = 0.038).

CONCLUSION

The study results delineate and identify unique characteristics associated with HER2+ breast cancers that are important in selecting patients for inclusion in clinical trials assessing nonoperative management after NST, and the low negative predictive value of imaging mandates image-guided biopsy for selection.

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.

Platelet-to-lymphocyte ratio as a predictive factor of complete pathologic response to neoadjuvant chemotherapy in breast cancer

Response to neoadjuvant chemotherapy in breast cancer patients is of prognostic value in determining short- and mid-term outcomes. Inflammatory biomarkers, such as platelet-to-lymphocyte ratio (PLR) and neutrophil to lymphocyte ratio (NLR), have been proposed as predictive factors of response to neoadjuvant chemotherapy. Currently, there are no studies in Colombian patients reporting the role of inflammatory biomarkers as response predictors in patients receiving neoadjuvant chemotherapy. Therefore, in this study we performed a cross-sectional study and analyzed the association between inflammatory biomarkers and pCR (pathological complete response) in patients diagnosed with breast cancer–of different molecular subtypes- and treated with neoadjuvant chemotherapy. A total of 288 patients were included in the study, with a median age of 51 years old. Disease was locally advanced in 83% of the participants, and 77.7% had compromised lymph nodes. In our cohort, the most frequent tumor molecular subtype was luminal B/Her2- (27.8%) followed by triple negative [TN] (21.5%), luminal B/Her2+ (19.8%), Her2-enriched (16%) and luminal A (13.5%). PLR was not associated with age, menopausal status, baseline tumor size, histologic grade, axillary lymph node involvement, disease stage, estrogen receptor status, or Ki67; however, complete pathological response was significantly higher in the low PLR group (PLR<150) compared with the high PLR group (35.1% Vs. 22.2%, p = 0.03). In addition, Her2-enriched tumors achieved the highest pCR rates (65%), followed by TN (34%) tumors. Our results suggest that breast cancer patients with low platelet-to-lymphocyte ratio (PLR <150), treated with neoadjuvant chemotherapy achieve higher complete pathological response, independently of primary tumor molecular subtype.

Predictive value of tumor-infiltrating lymphocytes to pathological complete response in neoadjuvant treated triple-negative breast cancers

BACKGROUND

Triple-negative breast cancers (TNBCs) are a group of heterogeneous diseases with various morphology, prognosis, and treatment response. Therefore, it is important to identify valuable biomarkers to predict the therapeutic response and prognosis for TNBCs. Tumor-infiltrating lymphocytes (TILs) may have predictive value to pathological complete response (pCR) in neoadjuvant treated TNBCs. However, absence of standardized methodologies for TILs measurement has limited its evaluation and application in practice. In 2014, the International TILs Working Group formulated the recommendations of pathologic evaluation for TILs in breast cancers.

METHODS

To evaluate the predictive value of TILs scored by methods recommended by International TILs Working Group 2014, we performed a retrospective study of TILs in 166 core needle biopsy specimens of primary invasive TNBCs with neoadjuvant chemotherapy (NAC) in a Chinese population. Intratumoral TILs (iTILs) and stromal TILs (sTILs) were scored respectively. The associations between TILs and pCR were analyzed.

RESULTS

Both sTILs (p = 0.0001) and iTILs (P = 0.001) were associated with pCR in univariate logistic regression analysis. Multivariate logistic regression analysis indicated that both sTILs (P = 0.006) and iTILs (P = 0.04) were independent predictors for pCR. Receiver operating characteristics (ROC) curve analysis was used to identify the optimal thresholds of TILs. TNBCs with more than 20% sTILs (P = 0.001) or with more than 10% iTILs (P = 0.003) were associated with higher pCR rates in univariate analysis. Multivariate analysis showed that a 20% threshold of sTILs (P = 0.005) was an independent predictive factor for pCR.

CONCLUSIONS

Our study indicated that TILs scored by recommendations of International TILs Working Group 2014 in pre-NAC core needle biopsy specimens was significantly correlated with pCR in TNBCs, higher TILs scores predicting higher pCR rate. Both sTILs and iTILs were independent predictors for pCR in TNBCs. A 20% threshold for sTILs may be feasible to predict pCR to NAC in TNBCs.

Baseline factors predicting a response to neoadjuvant chemotherapy with implications for non-surgical management of triple-negative breast cancer

BACKGROUND

Patients with triple-negative breast cancer (TNBC) and a pathological complete response (pCR) after neoadjuvant chemotherapy may be suitable for non-surgical management. The goal of this study was to identify baseline clinicopathological variables that are associated with residual disease, and to evaluate the effect of neoadjuvant chemotherapy on both the invasive and ductal carcinoma in situ (DCIS) components in TNBC.

METHODS

Patients with TNBC treated with neoadjuvant chemotherapy followed by surgical resection were identified. Patients with a pCR were compared with those who had residual disease in the breast and/or lymph nodes. Clinicopathological variables were analysed to determine their association with residual disease.

RESULTS

Of the 328 patients, 36·9 per cent had no residual disease and 9·1 per cent had residual DCIS only. Patients with residual disease were more likely to have malignant microcalcifications (P = 0·023) and DCIS on the initial core needle biopsy (CNB) (P = 0·030). Variables independently associated with residual disease included: DCIS on CNB (odds ratio (OR) 2·46; P = 0·022), T2 disease (OR 2·40; P = 0·029), N1 status (OR 2·03; P = 0·030) and low Ki-67 (OR 2·41; P = 0·083). Imaging after neoadjuvant chemotherapy had an accuracy of 71·7 (95 per cent c.i. 66·3 to 76·6) per cent and a negative predictive value of 76·9 (60·7 to 88·9) per cent for identifying residual disease in the breast and lymph nodes. Neoadjuvant chemotherapy did not eradicate the DCIS component in 55 per cent of patients.

CONCLUSION

The presence of microcalcifications on imaging and DCIS on initial CNB are associated with residual disease after neoadjuvant chemotherapy in TNBC. These variables can aid in identifying patients with TNBC suitable for inclusion in trials evaluating non-surgical management after neoadjuvant chemotherapy.