DAGM: A novel modelling framework to assess the risk of HER2-negative breast cancer based on germline rare coding mutations

Abstract

Background: Breast cancers can be divided into HER2-negative and HER2-positive subtypes according to different status of HER2 gene. Despite extensive studies connecting germline mutations with possible risk of HER2-negative breast cancer, the main category of breast cancer, it remains challenging to obtain accurate risk assessment and to understand the potential underlying mechanisms.

Methods: We developed a novel framework named Damage Assessment of Genomic Mutations (DAGM), which projects rare coding mutations and gene expressions into Activity Profiles of Signalling Pathways (APSPs).

Findings: We characterized and validated DAGM framework at multiple levels. Based on an input of germline rare coding mutations, we obtained the corresponding APSP spectrum to calculate the APSP risk score, which was capable of distinguish HER2-negative from HER2-positive cases. These findings were validated using breast cancer data from TCGA (AUC = 0.7). DAGM revealed that HER2 signalling pathway was up-regulated in germline of HER2-negative patients, and those with high APSP risk scores had exhibited immune suppression. These findings were validated using RNA sequencing, phosphoproteome analysis, and CyTOF. Moreover, using germline mutations, DAGM could evaluate the risk for HER2-negative breast cancer, not only in women carrying BRCA1/2 mutations, but also in those without known disease-associated mutations.

Interpretation: The DAGM can facilitate the screening of subjects at high risk of HER2-negative breast cancer for primary prevention. This study also provides new insights into the potential mechanisms of developing HER2-negative breast cancer. The DAGM has the potential to be applied in the prevention, diagnosis, and treatment of HER2-negative breast cancer.

Funding: This work was supported by the National Key Research and Development Program of China (grant no. 2018YFC0910406 and 2018AAA0103302 to CZ); the National Natural Science Foundation of China (grant no. 81202076 and 82072939 to MY, 81871513 to KW); the Guangzhou Science and Technology Program key projects (grant no. 2014J2200007 to MY, 202002030236 to KW); the National Key R&D Program of China (grant no. 2017YFC1309100 to CL); Shenzhen Science and Technology Planning Project (grant no. JCYJ20170817095211560 574 to YN); and the Natural Science Foundation of Guangdong Province (grant no. 2017A030313882 to KW and S2013010012048 to MY); Hefei National Laboratory for Physical Sciences at the Microscale (grant no. KF2020009 to GN); and RGC General Research Fund (grant no. 17114519 to YQS).

Keywords: Activity profiles of signalling pathways (APSP); Damage assessment of genomic mutations (DAGM); Germline rare coding mutations; HER2 signalling pathway; HER2-negative breast cancer; Immune suppression; Risk assessment.

Conflict of interest statement

Declaration of Competing Interest The authors declare that they have no competing interests.

Copyright © 2021 The Authors. Published by Elsevier B.V. All rights reserved.

Figures

Fig. 1

Workflow of the DAGM framework. The DAGM framework mainly composed of three steps. In the first step (grey colour), driving force (DF) and global driving force (GDF) are established for future analysis based on the rare coding mutations and gene expression from the COSMIC Cell Lines project. When obtaining the germline rare coding mutations of a subject, we calculated the combined effect of all these mutations (Step 2). In the last step, the activity profile of signalling pathways (APSPs) is evaluated for this subject.

Fig. 2

Germline APSP spectrum distinguishes HER2-negative breast cancer patients from HER2-positive patients. (a) Heatmap of the germline APSPs of 60 pathways for 721 subjects. Each row represents one pathway and each column represents one subject. (b) Hierarchical clustering dendrogram. HER2-negative subtype (Luminal B (HER2-negative), Luminal A, and TNBC) and HER2-positive subtype (ERBB2-positive and Luminal B (HER2-positive)) were well separated by hierarchical clustering with Euclidean distance as the distance measure. (c) Receiver operating characteristic (ROC) curve for distinguishing HER2-negative breast cancer patients from HER2-positive patients by APSPs. Area under the curve (AUC) was 0.77. (d) Receiver operating characteristic (ROC) curve for distinguishing HER2-negative breast cancer patients from controls by APSPs. Area under the curve (AUC) was 0.76.

Fig. 3

Upregulated HER2 signalling pathway in germlines of Her2-negative breast cancer patients. (a) Difference of activity profiles of signalling pathways (APSPs) between breast cancer patients and controls. To test the significance of the difference, P-values were calculated by permutation (1,000,000 times). The FDR corrected P-values were also calculated to correct the multiple comparisons. The asterisk denotes FDR corrected P-value 

Fig. 4

Identification of HER2-negative breast cancer using germline APSP risk score. (a) The boxplot of APSPs for eight pathway panels. The panel of immune system is at the top of three significantly down-regulated panels (labelled by a star) that were then used to calculate APSP risk scores. (b) The distribution of APSP risk scores of HER2-negative breast cancer patients (upper panel), controls (middle panel) and HER2-positive breast cancer patients (bottom panel). The APSP risk scores in HER2-negative breast cancer patients were significantly higher than those in the controls (T-test, P-value = 3.96e-27) and HER2-positive breast cancer patients (T-test, P-value = 4.88e-22). (c) The APSP risk scores were significantly higher in HER2-negative breast cancer patients with or without BRCA1/2 mutations than the control subjects. (d) Receiver operating characteristic (ROC) curve for APSP risk score in distinguishing HER2-negative breast cancer patients from HER2-positive patients. Area under the curve (AUC) was 0.79. (e) Receiver operating characteristic (ROC) curve for APSP risk score in distinguishing HER2-negative breast cancer patients from controls. Area under the curve (AUC) was 0.74.

Fig. 5

Enhanced immune suppression and risk stratification of HER2-negative breast cancer. Mass cytometry analysis revealed percentage of exhausted CD8+ T cells in female subjects of low and high APSP risk scores. (b) Mass cytometry analysis revealed percentage of exhausted CD8+ T cells in TNBC patients and control subjects. (c) Stratification of HER2-negative patients (red dots) as well as control subjects (green dots) into six groups, G1 to G6, according to the different standard deviations of APSP risk scores from their mean value. G1 and G6 included subject with APSP risk score two standard deviations lower and higher than the mean value, respectively. G2 and G5 included subject with APSP risk score between one and two standard deviations lower and higher than the mean value, respectively. G3 and G4 included subject with APSP risk score within one standard deviation lower and higher than the mean value, respectively. (d) Odds ratios for HER2 negative breast cancer display exponential distribution when the subjects were stratified into six groups (G1 to G6) as in (c). The grey circles represent the sample sizes of each group.