A nomogram for predicting pathological complete response in patients with human epidermal growth factor receptor 2 negative breast cancer

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

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

Pre- and Post-Neoadjuvant Clinicopathological Parameters Can Help in the Prognosis and the Prediction of Response in HER2+ and Triple Negative Breast Cancer

Neoadjuvant treatment (NAT) is one of the most widely used options for HER2+ and triple negative (TN) early breast cancer (BC). Since around half of the patients treated with NAT do not achieve a pathologically complete response (pCR), biomarkers to predict resistance are urgently needed. The correlation of clinicopathological factors with pCR was studied in 150 patients (HER2 = 81; TN = 69) and pre- and post-NAT differences in tumour biomarkers were compared. Low estrogen receptor (ER) expression, high tumour-infiltrating lymphocytes (TILs) and low cT-stage were associated with pCR in HER2+ tumours (p = 0.022; p = 0.032 and p = 0.005, respectively). Furthermore, ER expression was also associated with residual cancer burden (RCB; p = 0.046) in the HER2+ subtype. Similarly, pre-NAT, low progesterone receptor expression (PR; 1-10%) was associated with higher RCB (p < 0.001) in TN tumours. Only clinical and pathological T-stage (cpT-stage) had prognostic capacity in HER2+ tumours, whereas pre-NAT cpT-stage and post-NAT TILs had this capacity for the prognosis of TN tumours. We conclude that ER and PR expression may help predict response to NAT in HER2 and TN BC and should be taken into account in residual tumours. Also, changes observed in the phenotype after NAT suggest the need to reevaluate biomarkers in surviving residual tumour cells.

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.

Multi-omic machine learning predictor of breast cancer therapy response

Breast cancers are complex ecosystems of malignant cells and the tumour microenvironment1. The composition of these tumour ecosystems and interactions within them contribute to responses to cytotoxic therapy2. Efforts to build response predictors have not incorporated this knowledge. We collected clinical, digital pathology, genomic and transcriptomic profiles of pre-treatment biopsies of breast tumours from 168 patients treated with chemotherapy with or without HER2 (encoded by ERBB2)-targeted therapy before surgery. Pathology end points (complete response or residual disease) at surgery3 were then correlated with multi-omic features in these diagnostic biopsies. Here we show that response to treatment is modulated by the pre-treated tumour ecosystem, and its multi-omics landscape can be integrated in predictive models using machine learning. The degree of residual disease following therapy is monotonically associated with pre-therapy features, including tumour mutational and copy number landscapes, tumour proliferation, immune infiltration and T cell dysfunction and exclusion. Combining these features into a multi-omic machine learning model predicted a pathological complete response in an external validation cohort (75 patients) with an area under the curve of 0.87. In conclusion, response to therapy is determined by the baseline characteristics of the totality of the tumour ecosystem captured through data integration and machine learning. This approach could be used to develop predictors for other cancers.

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.

Quantitative Identification of Maize Lodging-Causing Feature Factors Using Unmanned Aerial Vehicle Images and a Nomogram Computation

Maize (zee mays L.) is one of the most important grain crops in China. Lodging is a natural disaster that can cause significant yield losses and threaten food security. Lodging identification and analysis contributes to evaluate disaster losses and cultivates lodging-resistant maize varieties. In this study, we collected visible and multispectral images with an unmanned aerial vehicle (UAV), and introduce a comprehensive methodology and workflow to extract lodging features from UAV imagery. We use statistical methods to screen several potential feature factors (e.g., texture, canopy structure, spectral characteristics, and terrain), and construct two nomograms (i.e., Model-1 and Model-2) with better validation performance based on selected feature factors. Model-2 was superior to Model-1 in term of its discrimination ability, but had an over-fitting phenomenon when the predicted probability of lodging went from 0.2 to 0.4. The results show that the nomogram could not only predict the occurrence probability of lodging, but also explore the underlying association between maize lodging and the selected feature factors. Compared with spectral features, terrain features, texture features, canopy cover, and genetic background, canopy structural features were more conclusive in discriminating whether maize lodging occurs at the plot scale. Using nomogram analysis, we identified protective factors (i.e., normalized difference vegetation index, NDVI and canopy elevation relief ratio, CRR) and risk factors (i.e., Hcv1) related to maize lodging, and also found a problem of terrain spatial variability that is easily overlooked in lodging-resistant breeding trials.

[Predictive factors of conservative breast surgery after neoadjuvant chemotherapy for breast cancer]

OBJECTIVES

The aim of our study was to evaluate the existence of predictive factors of conservative breast surgery after neoadjuvant chemotherapy (NAC) for breast cancer.

METHODS

We included all women with invasive breast cancer who received NAC and underwent breast surgery between January 2007 and December 2013 in our institution. Univariable and multivariable analyses were performed to determine the association between clinical and histological factors and conservative breast surgery.

RESULTS

During the study period, 229 women were included of whom 73 had breast conservative surgery (32%). At univariable analysis, significant predictive factors were age (OR 0.97 [CI 95% 0.95-0.99], P=0.02), radiological size (OR 0.97 [CI 95% 0.96-0.99], P<0.001), multifocality (OR 0.53 [CI 95% 0.27-1.05], P=0.06), breast inflammation (OR 0.15 [CI 95% 0.07-0.32], P<0.001) and the type of hormone receptors (P=0.12). In multivariable analysis, all these factors but age were significant factors and thus considered as independent predictive factors.

CONCLUSION

This work permitted to identify independent predictive factors of breast conservative surgery after NAC for breast cancer that will be included in a risk scoring system that we aim to evaluate prospectively.

Clinical tumor stage is the most important predictor of pathological complete response rate after neoadjuvant chemotherapy in breast cancer patients

BACKGROUND

Pathological complete response (pCR) is the ultimate response in breast cancer patients treated with neoadjuvant chemotherapy (NCT). It might be a surrogate outcome for disease-free survival (DFS) and overall survival (OS). We studied the effect of clinical tumor stage (cT-stage) on tumor pCR and the effect of pCR per cT-stage on 5-year OS and DFS.

METHODS

Using the Netherlands Cancer Registry, all primary invasive breast cancer patients treated with NCT from 2005 until 2008 were identified. Univariable logistic regression analysis was performed to evaluate the effect of cT-stage on pCR, stepwise logistic regression analysis to correct for potential confounders and Kaplan-Meier survival analyses to calculate OS and DFS after five years.

RESULTS

In 2366 patients, overall pCR rate was 21%. For cT1, cT2, cT3, and cT4, pCR rates were 31, 22, 18, and 17%, respectively. Lower cT-stage (cT1-2 vs cT3-4) was a significant independent predictor of higher pCR rate (p < 0.001, OR 3.15). Furthermore, positive HER2 status (p < 0.001, OR 2.30), negative estrogen receptor status (p = 0.062, OR 1.69), and negative progesterone receptor status (p = 0.008, OR 2.27) were independent predictors of pCR. OS and DFS were up to 20% higher in patients with cT2-4 tumors with pCR versus patients without pCR. DFS was also higher for cT1 tumors with pCR.

CONCLUSIONS

The most important predictor of pCR in breast cancer patients is cT-stage: lower cT-stages have significantly higher pCR rates than higher cT-stages. Patients with cT2-4 tumors achieving pCR have higher OS and DFS compared to patients not achieving pCR.