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).

Breast cancer patients exhibit diverse responses to CDK4/6 inhibitor (CDK4/6i)-based therapies, and identifying eligible patients remains a challenge. Artificial intelligence (AI) has demonstrated the potential to address complex clinical problems. Here, we applied a novel AI-based approach, named as CDK4/6i Response Model (CRM), which combined a previously published method and a scoring model based on random forest algorithm for evaluating breast cancer patients' sensitivity to CDK4/6i-based therapies. To train the CRM, we transformed the genomic data of 980 breast cancer patients from the TCGA database into signaling pathway activity profiles (APSP) by utilizing the modified Damage Assessment of Genomic Mutations (DAGM) algorithm. To mimic the mechanism of action of CDK4/6 inhibitors, a scoring model was then trained to classify the HR+/HER2- and HR-/HER2- breast cancer molecular subtypes by the differential APSP features between the two, which reasonably reflected the potential role played by CDK4/6 molecules in HR+/HER2- breast cancer cells. The effectiveness of the CRM's ability was verified by accurately classifying HR+/HER2- and HR-/HER2- breast cancer patients in a separate local patient cohort (n = 343) in Guangdong, China. Significantly, the scores were observed to be distinct (p = 0.025) between CDK4/6i-treated patients with different responses. Furthermore, breast cancer patients belonging to different subtypes were grouped into five distinct populations based on the scores assigned by the CRM. The results showed not only the heterogenetic responses across subtypes but also more than half of HR+/HER2+ patients might be benefited from CDK4/6i-based treatment. The CRM empowered us to conduct in-silico clinical trials (ICT) on different types of cancer patients responding to CDK4/6i-based therapies. In this study, we performed twin ICT of previously disclosed clinical trials (NCT02246621,NCT02079636,NCT03155997,NCT02513394,NCT02675231), and observed concerted results as the real-world clinical outcomes. These findings show the potential of CRM as a companion diagnostic for CDK4/6i-based therapies and demonstrate promising applications by ICT to guide pan-cancer treatment using CDK4/6 inhibitors in the clinical ends.

BackgroundBreast cancers can be divided into HER2-negative and HER2-positive subtypes according to the 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 accurately assess its potential risk and to understand the potential mechanisms.MethodsWe 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).FindingsWe 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 the HER2 signalling pathway was up-regulated in the germline of HER2-negative patients, and those with high APSP risk scores had suppressed immunity. These findings were validated using RNA sequencing, phosphoproteome analysis, and CyTOF. Moreover, using germline mutations, DAGM could evaluate the risk of developing HER2-negative breast cancer, not only in women carrying BRCA1/2 mutations, but also in those without known disease-associated mutations.InterpretationThe 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.FundingThis 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); and the Natural Science Foundation of Guangdong Province (grant no. 2017A030313882 to KW)Research in contextEvidence before this studyThe majority of hereditary breast cancers are caused by BRCA1/2 mutations, and the presence of these mutations is strongly associated with an increased risk of breast cancer. Meanwhile, BRCA1/2 gene mutations are rarely found in sporadic breast cancers and only account for a modest percentage of all breast cancer patients. Polygenic risk score (PRS), a widely-used approach for stratifying individuals according to their risk of a certain kind of complex disease, has been used to predict subjects at high risk for breast cancer. However, relying on SNPs from genome-wide association studies (GWAS) without including gene expressions or pathway activities, PRS is not very suitable for cross-population prediction and describes disease risk in terms of genomic mutations without alluding to the underlying pathogenic mechanism(s). Therefore, there is still an urgent need for a population-independent comprehensive method to accurately assess the risk of breast cancer and to gain insights on potential mechanism(s).Added value of this studyWhen subjecting germline rare coding mutations (gRCMs) to DAGM framework, which results in the corresponding APSP and APSP risk score. Both APSP and APSP risk score can identify HER2-negative from HER2-positive breast cancers. These findings suggest HER2-negative breast cancer does not develop accidentally, but rather is defined by a genomic evolutionary strategy. Furthermore, this study also revealed the up-regulation of HER2 signalling pathway in germlines of HER2-negative breast cancers and the immune suppression in subjects with high APSP risk score, shedding new light on the potential mechanisms of developing HER2-negative breast cancer. Moreover, our APSP risk score was able to relatively accurately evaluate the risk of developing HER2-negative breast cancer for each female, including not only BRCA1/2 carriers, but also non-carriers.Implications of all the available evidenceThe present study suggests that HER2 signalling pathway activity, as an aggressive factor, contribute to the development of different types of breast cancers, either via the combined effects of multiple germline mutations in HER2-negative germlines or via amplifying the gene itself in HER2-positive tumour cells. This provides a theoretical basis for the prevention, diagnosis, and treatment of breast cancers. At the same time, the study provides preliminary methods for assessing the relative risk of HER2-negative breast cancer for females with or without BRCA1/2 mutations. Finally, our findings provide a new perspective and theoretical basis for identifying high-risk female subjects, based on the high APSP risk score, for early screening and prevention of HER2-negative breast cancer.