Construction of a risk stratification model integrating ctDNA to predict response and survival in neoadjuvant-treated breast cancer

Abstract

Background: The pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) of breast cancer is closely related to a better prognosis. However, there are no reliable indicators to accurately identify which patients will achieve pCR before surgery, and a model for predicting pCR to NAC is required.

Methods: A total of 269 breast cancer patients in Shandong Cancer Hospital and Liaocheng People's Hospital receiving anthracycline and taxane-based NAC were prospectively enrolled. Expression profiling using a 457 cancer-related gene sequencing panel (DNA sequencing) covering genes recurrently mutated in breast cancer was carried out on 243 formalin-fixed paraffin-embedded tumor biopsies samples before NAC from 243 patients. The unique personalized panel of nine individual somatic mutation genes from the constructed model was used to detect and analyze ctDNA on 216 blood samples. Blood samples were collected at indicated time points including before chemotherapy initiation, after the 1^st NAC and before the 2^nd NAC cycle, during intermediate evaluation, and prior to surgery. In this study, we characterized the value of gene profile mutation and circulating tumor DNA (ctDNA) in combination with clinical characteristics in the prediction of pCR before surgery and investigated the prognostic prediction. The median follow-up time for survival analysis was 898 days.

Results: Firstly, we constructed a predictive NAC response model including five single nucleotide variant (SNV) mutations (TP53, SETBP1, PIK3CA, NOTCH4 and MSH2) and four copy number variation (CNV) mutations (FOXP1-gain, EGFR-gain, IL7R-gain, and NFKB1A-gain) in the breast tumor, combined with three clinical factors (luminal A, Her2 and Ki67 status). The tumor prediction model showed good discrimination of chemotherapy sensitivity for pCR and non-pCR with an AUC of 0.871 (95% CI, 0.797-0.927) in the training set, 0.771 (95% CI, 0.649-0.883) in the test set, and 0.726 (95% CI, 0.556-0.865) in an extra test set. This tumor prediction model can also effectively predict the prognosis of disease-free survival (DFS) with an AUC of 0.749 at 1 year and 0.830 at 3 years. We further screened the genes from the tumor prediction model to establish a unique personalized panel consisting of 9 individual somatic mutation genes to detect and analyze ctDNA. It was found that ctDNA positivity decreased with the passage of time during NAC, and ctDNA status can predict NAC response and metastasis recurrence. Finally, we constructed the chemotherapy prediction model combined with the tumor prediction model and pretreatment ctDNA levels, which has a better prediction effect of pCR with the AUC value of 0.961.

Conclusions: In this study, we established a chemotherapy predictive model with a non-invasive tool that is built based on genomic features, ctDNA status, as well as clinical characteristics for predicting pCR to recognize the responders and non-responders to NAC, and also predicting prognosis for DFS in breast cancer. Adding pretreatment ctDNA levels to a model containing gene profile mutation and clinical characteristics significantly improves stratification over the clinical variables alone.

Keywords: Breast cancer; Neoadjuvant chemotherapy; Prediction model; ctDNA; pCR.

Fig. 1

The study design, sample collections, and patients’ response. A Sample collection at different points. B Condition of enrolled patients. C Number of patients in different groups responding to pCR

Fig. 2

The landscape of clinical and mutational characteristics. A The landscape of highly mutated genes. B Significantly different SNV included in the model in different chemotherapy responses. C Significantly different CNV included in the model in different chemotherapy responses. D Comparing the differences in clinical characteristics of different chemotherapy responses. P values were calculated using Fisher’s exact test. The size of the white dots in B and C represents the size of the samples

Fig. 3

Predicting response and DFS combing mutation characteristics and clinical characteristics. A The ROC curve of predictive model in RCB index system. B Nomogram from stepwise logistic regression for predicting pCR in RCB index system. C The ROC curve of predictive model in MP scoring system. D Nomogram from stepwise logistic regression for predicting pCR in MP scoring system. E Predicting DFS combing important mutation and clinical characteristics. F Kaplan–Meier curves for patients in high- and low-risk groups. Response rate refers to the probability of a patient responding to treatment

Fig. 4

Mutation landscape of ctDNA. A Overview of ctDNA status, clinical characters, and response at baseline (T₀). B Proportion of ctDNA-positive and ctDNA-negative patients at baseline (T₀) according to clinical characteristics. C Proportion of ctDNA-positive and ctDNA-negative patients at different time points. D Comparing the difference of ctDNA fraction at different time points. P values were calculated using one-way analysis of variance

Fig. 5

The association between the dynamic changes of ctDNA and DFS or response in the course of NAC. A Patients with complete ctDNA data for four time points (n = 50) were grouped according to the different patterns of ctDNA clearance or non-clearance. B Sankey plot showing the dynamic changes of patients with complete ctDNA data and DFS data (n = 45). C Sankey plot showing ctDNA dynamics in ctDNA-positive patients at T₀. D DFS in ctDNA-cleared patients and non-cleared patients during NAC. E Kaplan–Meier analysis of DFS stratified based on ctDNA status after NAC (T₃) and response to treatment, RCB means no-pCR

Fig. 6

The prediction effect of pCR and the prognosis by a combination of the prediction model and ctDNA monitoring. A The pCR prediction determined by the chemotherapy predictive model constructed by combining the information from the established tumor prediction model (including DNA mutations and clinical factors), along with the information from ctDNA status. B Different chemotherapy predictive models are established using random forest based on the expression status of ctDNA at different time points of T₀, T₁, and T₂. C The predictive effect for the chemotherapy predictive model on the prognosis of NAC patients. D Kaplan–Meier curves for patients in high- and low-risk group

Fig. 7

The pattern diagram guiding the clinical application of the therapeutic efficacy prediction model