ErbB2 tyrosine kinase inhibitors (TKI) block tyrosine autophosphorylation and activation of the full-length transmembrane ErbB2 receptor (p185ErbB2). In addition to p185ErbB2 truncated forms of ErbB2 exist in breast cancer cell lines and clinical tumors. The contribution of these truncated forms, specifically those expressed in tumor cell nuclei, to the development of therapeutic resistance to ErbB2 TKIs has not been previously demonstrated. Here we show that expression of a 95 kDa tyrosine phosphorylated form of ErbB2, herein referred to as p95L (lapatinib-induced p95) was increased in ErbB2+ breast cancer cells treated with potent ErbB2 TKIs (lapatinib, GW2974). Expressed in tumor cell nuclei, tyrosine phosphorylation of p95L was resistant to inhibition by ErbB2 TKIs. Furthermore, the expression of p95L was increased in ErbB2+ breast cancer models of acquired therapeutic resistance to lapatinib that mimic the clinical setting. Pretreatment with proteasome inhibitors blocked p95L induction in response to ErbB2 TKIs, implicating the role of the proteasome in the regulation of p95L expression. In addition, tyrosine phosphorylated c-terminal fragments of ErbB2, generated by alternate initiation of translation and similar in molecular weight to p95L, were expressed in tumor cell nuclei, where they too were resistant to inhibition by ErbB2 TKIs. When expressed in the nuclei of lapatinib sensitive ErbB2+ breast cancer cells, truncated ErbB2 rendered cells resistant to lapatinib-induced apoptosis. Elucidating the function of nuclear truncated forms of ErbB2, and developing therapeutic strategies to block their expression and/or activation, may enhance the clinical efficacy of ErbB2 TKIs.

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C-reactive protein (CRP) levels vary remarkably with ethnic status. Its distribution and correlates should be investigated across diverse populations, and these were limited in a representative Chinese population. We investigated 3133 participants aged 18–80 years in Shanghai, which were sampled using a randomized, stratified, multi-stage sampling method. The distribution of CRP was highly skewed toward a lower level. The median CRP was 0.55 mg/L (0.61 mg/L in males, 0.51 mg/L in females). Participants living in urban region had higher CRP levels than those in rural region (0.67 vs. 0.46 mg/L). CRP levels showed significant correlation with traditional cardiovascular risk factors, and it was most strongly correlated with body mass index. Multivariate logistic regression analyses indicated that elevated CRP (being in the top 15 percentile of CRP; CRP ≥ 2.09 mg/L) was significantly associated with obesity, hypertension, diabetes, low high-density lipoprotein cholesterol, high low-density lipoprotein cholesterol, high triglycerides and cardiovascular disease history. In conclusion, the distribution of CRP in adult Chinese was comparable with that of many other Asian populations but different from that of Western populations. Metabolic impairment was associated with elevated CRP, and CRP levels should be interpreted in conjunction with the lipid profile.

Background Frailty is a common characteristic of older people with the ageing process. We aimed to develop and validate a dynamic statistical prediction model to calculate the risk of death in people aged ≥65 years, using a longitudinal frailty index (FI). Methods One training dataset and three validation datasets from the Chinese Longitudinal Healthy Longevity Survey (CLHLS) were used in our study. The training dataset and validation datasets 1 to 3 included data from 9,748, 7,459, 9,093 and 6,368 individuals, respectively. We used 35 health deficits to construct the FI and a longitudinal FI based on repeated measurement of FI at every wave of the CLHLS. A joint model was used to build a dynamic prediction model considering both baseline covariates and the longitudinal FI. Areas under time-dependent receiver operating characteristic curves (AUCs) and calibration curves were employed to assess the predictive performance of the model. Results A linear mixed-effects model used time, sex, residence (city, town, or rural), living alone, smoking and alcohol consumption to calculate a subject-specific longitudinal FI. The dynamic prediction model was built using the longitudinal FI, age, residence, sex and an FI–age interaction term. The AUCs ranged from 0.64 to 0.84, and both the AUCs and the calibration curves showed good predictive ability. Conclusions We developed a dynamic prediction model that was able to update predictions of the risk of death as updated measurements of FI became available. This model could be used to estimate the risk of death in individuals aged >65 years.