Targeted dual degradation of HER2 and EGFR obliterates oncogenic signaling, overcomes therapy resistance, and inhibits metastatic lesions in HER2-positive breast cancer models.

AIMS: Human epidermal growth factor receptor 2 (HER2) is an oncogenic receptor tyrosine kinase amplified in approximately 20% of breast cancer (BC). HER2-targeted therapies are the linchpin of treating HER2-positive BC. However, drug resistance is common, and the main resistance mechanism is unknown. We tested the hypothesis that drug resistance results mainly from inadequate or lack of inhibition of HER2 and its family member epidermal growth factor receptor (EGFR). METHODS: We used clinically relevant cell and tumor models to assess the impact of targeted degradation of HER2 and EGFR on trastuzumab resistance. Trastuzumab is the most common clinically used HER2 inhibitor. Targeted degradation of HER2 and EGFR was achieved using recombinant human protein PEPDG278D, which binds to the extracellular domains of the receptors. siRNA knockdown was used to assess the relative importance of EGFR and HER2 in trastuzumab resistance. RESULTS: Both HER2 and EGFR are overexpressed in all trastuzumab-resistant HER2-positive BC cell and tumor models and that all trastuzumab-resistant models are highly vulnerable to targeted degradation of HER2 and EGFR. Degradation of HER2 and EGFR induced by PEPDG278D causes extensive inhibition of oncogenic signaling in trastuzumab-resistant HER2-positive BC cells. This is accompanied by strong growth inhibition of cultured cells, orthotopic patient-derived xenografts, and metastatic lesions in the brain and lung of trastuzumab-resistant HER2-positive BC. siRNA knockdown indicates that eliminating both HER2 and EGFR is necessary to maximize therapeutic outcome. CONCLUSIONS: This study unravels the therapeutic vulnerability of trastuzumab-resistant HER2-positive BC and shows that an agent that targets the degradation of both HER2 and EGFR is highly effective in overcoming drug resistance in this disease. The findings provide new insights and innovations for advancing treatment of drug-resistant HER2-positive breast cancer that remains an unmet problem.

NeuroDiag: Software for Automated Diagnosis of Parkinson's Disease Using Handwriting.

OBJECTIVE: A change in handwriting is an early sign of Parkinson's disease (PD). However, significant inter-person differences in handwriting make it difficult to identify pathological handwriting, especially in the early stages. This paper reports the testing of NeuroDiag, a software-based medical device, for the automated detection of PD using handwriting patterns. NeuroDiag is designed to direct the user to perform six drawing and writing tasks, and the recordings are then uploaded onto a server for analysis. Kinematic information and pen pressure of handwriting are extracted and used as baseline parameters. NeuroDiag was trained based on 26 PD patients in the early stage of the disease and 26 matching controls. METHODS: Twenty-three people with PD (PPD) in their early stage of the disease, 25 age-matched healthy controls (AMC), and 7 young healthy controls were recruited for this study. Under the supervision of a consultant neurologist or their nurse, the participants used NeuroDiag. The reports were generated in real-time and tabulated by an independent observer. RESULTS: The participants were able to use NeuroDiag without assistance. The handwriting data was successfully uploaded to the server where the report was automatically generated in real-time. There were significant differences in the writing speed between PPD and AMC (P<0.001). NeuroDiag showed 86.96% sensitivity and 76.92% specificity in differentiating PPD from those without PD. CONCLUSION: In this work, we tested the reliability of NeuroDiag in differentiating between PPD and AMC for real-time applications. The results show that NeuroDiag has the potential to be used to assist neurologists and for telehealth applications. Clinical and Translational Impact Statement - This pre-clinical study shows the feasibility of developing a community-wide screening program for Parkinson's disease using automated handwriting analysis software, NeuroDiag.