Exploring potential molecular resistance and clonal evolution in advanced HER2-positive gastric cancer under trastuzumab therapy.
Qi XuXiaoqing XuHaimeng TangJunrong YanJingjing LiHua BaoXue WuYang ShaoCong LuoHaimin WenJianying JinJie-Er YingPublished in: Oncogenesis (2023)
HER2-positive gastric cancer (GC) makes up 15-20% of all GC incidences, and targeted therapy with trastuzumab is the standard of treatment. However, the mechanisms of resistance to trastuzumab are still not fully understood and presents a significant challenge in clinical practice. In this study, whole exome sequencing (WES) was performed on paired tumor tissues before trastuzumab treatment (at baseline) and at progressive disease (PD) in 23 GC patients. Clinicopathological and molecular features that may be associated with primary and/or acquired resistance to trastuzumab were identified. Lauren classification of intestinal type was associated with a more prolonged progression-free survival (PFS) than diffuse type (HR = 0.29, P = 0.019). Patients with low tumor mutation burden (TMB) showed significantly worse PFS, while high chromosome instability (CIN) was correlated with prolonged OS (HR = 0.27; P = 0.044). Patients who responded to treatment had a higher CIN than nonresponders, and a positive trend towards increasing CIN was observed as response improved (P = 0.019). In our cohort, the most common genes to acquire mutations are AURKA, MYC, STK11, and LRP6 with four patients each. We also discovered an association between clonal branching pattern and survival, with an extensive clonal branching pattern being more closely related to a shorter PFS than other branching patterns (HR = 4.71; P = 0.008). We identified potential molecular and clinical factors that provide insight regarding potential association to trastuzumab resistance in advanced HER2-positive GC patients.
Keyphrases
- end stage renal disease
- epidermal growth factor receptor
- chronic kidney disease
- newly diagnosed
- prognostic factors
- free survival
- clinical practice
- machine learning
- peritoneal dialysis
- gene expression
- patient reported outcomes
- risk assessment
- metastatic breast cancer
- risk factors
- mesenchymal stem cells
- deep learning
- transcription factor
- multiple sclerosis
- dna methylation
- bone marrow
- smoking cessation
- copy number