Serum HER2 as a response indicator to various chemotherapeutic agents in tissue HER2 positive metastatic breast cancer

HER2 amplification level is not a prognostic factor for HER2-positive breast cancer with trastuzumab-based adjuvant treatment: a systematic review and meta-analysis

Background

Trastuzumab-based therapy is a standard, targeted treatment for HER2-positive breast cancer in the adjuvant setting. However, patients do not benefit equally from it and the association between HER2 amplification level and patients' survival remains controversial. A systematic review and meta-analysis was conducted by incorporating all available evidence to evaluate the association between disease free survival (DFS) and HER2 amplification level.

Results

Three cohort studies involving 1360 HER2-positive breast cancer patients stratified by HER2 amplification magnitude were eligible for meta-analysis. The combined HRs for DFS were 1.05 (95% CI: 0.80−1.36, p = 0.74) and 0.97 (95% CI: 0.73−1.29, p = 0.83) for HER2 gene copy number (GCN) and HER2/CEP 17 ratio. No evidence of heterogeneity or public bias was found.

Methods

Databases including PubMed, Embase, Web of Science, and Cochrane Central Register of Controlled Trials (CENTRAL), were searched for eligible literature. HER2 amplification level was evaluated by fluorescence in situ hybridization (FISH) in terms of gene copy number (GCN) and HER2/CEP17 ratio. Hazard ratios (HRs) for DFS with 95% confidence interval (CI) according to the amplification level of HER2 were extracted. The outcomes were synthesized based on a fixed-effects model.

Conclusions

HER2 amplification level is not a prognostic factor for HER2-positive breast cancer with trastuzumab-based targeted therapy in the clinical adjuvant setting.

Serum HER2 levels are increased in cats with mammary carcinomas and predict tissue HER2 status

HER2 is overexpressed in about 30% of feline mammary carcinomas (FMC) and in 15-30% of breast cancers. Women with HER2-positive breast tumors are associated with shorter survival. This study aimed to optimize the detection and quantification of serum HER2 (sHER2) in cats and to evaluate its potential in diagnosing cats with mammary carcinomas (MC) overexpressing HER2. A prospective study was conducted in 60 queens showing MC and 20 healthy animals. Pre-operative serum samples were collected for sHER2 quantification using two immunoassays: ELISA and Dot blot assay. sHER2 levels were compared with tissue HER2 status assessed by immunohistochemistry. Queens with FMC showed significantly higher mean levels of sHER2 by both ELISA and Dot blot assay. A significant difference in the sHER2 levels was also found between cats with HER2-positive MC and those with low-expressing HER2 MC. A significant correlation between sHER2 levels and tumor HER2 status was also found, particularly when ELISA was used (r = 0.58, p < 0.0001). The value of 10 ng/ml was proposed as the optimal cutoff for both immunoassays by ROC analysis. Like in humans, sHER2 levels are increased in cats with MC HER2-positive, strongly suggesting that evaluation of sHER2 levels can be very useful in feline oncology. The results show that ELISA and Dot blot assay can replace the immunohistochemistry technique, due to their efficacy and lower costs for diagnostic purposes and for monitoring the response to anti-HER2 therapies in cats.

The Significance of Serum HER2 Levels at Diagnosis on Intrinsic Subtype-Specific Outcome of Operable Breast Cancer Patients

Purpose

This study evaluated the association of serum HER2 (sHER2) levels at diagnosis with clinicopathologic parameters and disease free survival (DFS) in operable breast cancer patients according to intrinsic subtype.

Methods

The sHER2 levels were measured using a chemiluminescence immunoassay. The HER2 status in all tumor tissues was determined by immunohistochemistry, and confirmed in equivocal cases by fluorescence in situ.

Results

There were 436 consecutive stage I-III breast cancer patients with sHER2 result at diagnosis between Nov 2004 and Dec 2011. High sHER2 levels (≥ 15 ng/ml) were reported in 52 patients (11.9%) and HER2 overexpression in tumor tissue was observed in 111 patients (25.5%). High sHER2 levels were associated significantly with advanced stage (P < 0.001), mastectomy (P = 0.012), neoadjuvant chemotherapy (P < 0.001), anti-HER2 therapy (P < 0.001) and hormone therapy (P = 0.022). The patients with high sHER2 levels had a worse DFS (P < 0.001). In multivariate analysis, high sHER2 levels were associated significantly with worse DFS (HR = 2.25, 95% CI 1.27–3.99, P = 0.005). High sHER2 levels were associated with worse DFS in the HR+/HER2-, HR+/HER2+ and HR-/HER2+ subtypes (P = 0.043, 0.003 and 0.041, respectively).

Conclusions

These results show that the sHER2 level at diagnosis is a useful prognostic factor in patients with operable breast cancer, especially in the HR+/HER2-, HR+/HER2+ and HR-/HER2+ subtypes.

Classification of Cancer Primary Sites Using Machine Learning and Somatic Mutations

An accurate classification of human cancer, including its primary site, is important for better understanding of cancer and effective therapeutic strategies development. The available big data of somatic mutations provides us a great opportunity to investigate cancer classification using machine learning. Here, we explored the patterns of 1,760,846 somatic mutations identified from 230,255 cancer patients along with gene function information using support vector machine. Specifically, we performed a multiclass classification experiment over the 17 tumor sites using the gene symbol, somatic mutation, chromosome, and gene functional pathway as predictors for 6,751 subjects. The performance of the baseline using only gene features is 0.57 in accuracy. It was improved to 0.62 when adding the information of mutation and chromosome. Among the predictable primary tumor sites, the prediction of five primary sites (large intestine, liver, skin, pancreas, and lung) could achieve the performance with more than 0.70 in F-measure. The model of the large intestine ranked the first with 0.87 in F-measure. The results demonstrate that the somatic mutation information is useful for prediction of primary tumor sites with machine learning modeling. To our knowledge, this study is the first investigation of the primary sites classification using machine learning and somatic mutation data.

A comparative study of disease genes and drug targets in the human protein interactome

BACKGROUND

Disease genes cause or contribute genetically to the development of the most complex diseases. Drugs are the major approaches to treat the complex disease through interacting with their targets. Thus, drug targets are critical for treatment efficacy. However, the interrelationship between the disease genes and drug targets is not clear.

RESULTS

In this study, we comprehensively compared the network properties of disease genes and drug targets for five major disease categories (cancer, cardiovascular disease, immune system disease, metabolic disease, and nervous system disease). We first collected disease genes from genome-wide association studies (GWAS) for five disease categories and collected their corresponding drugs based on drugs' Anatomical Therapeutic Chemical (ATC) classification. Then, we obtained the drug targets for these five different disease categories. We found that, though the intersections between disease genes and drug targets were small, disease genes were significantly enriched in targets compared to their enrichment in human protein-coding genes. We further compared network properties of the proteins encoded by disease genes and drug targets in human protein-protein interaction networks (interactome). The results showed that the drug targets tended to have higher degree, higher betweenness, and lower clustering coefficient in cancer Furthermore, we observed a clear fraction increase of disease proteins or drug targets in the near neighborhood compared with the randomized genes.

CONCLUSIONS

The study presents the first comprehensive comparison of the disease genes and drug targets in the context of interactome. The results provide some foundational network characteristics for further designing computational strategies to predict novel drug targets and drug repurposing.

Serum HER2 level measured by dot blot: a valid and inexpensive assay for monitoring breast cancer progression

Human epidermal growth factor receptor 2 (HER2) is one of the most important prognostic and predictive factors for breast cancer patients. Recently, serum HER2 ECD level of patients detected by enzyme-linked immunoabsorbent assay (ELISA) has been shown to predict tumor HER2 status and reveal its association with tumor progression, recurrence and poor prognosis. In this study, we established a new method, dot blot assay, to measure the serum HER2 level in breast cancer patients and further to evaluate the clinical value for monitoring breast cancer progression. We found that the serum HER2 level measured by dot blot assay was significantly correlated with tissue HER2 status in breast cancer patients (P = 0.001), and also significantly correlated with HER2 level measured by ELISA (P = 1.06×10⁻¹¹). Compared with ELISA method, the specificity and sensitivity of dot blot assay were 95.3% and 65.0%, respectively. The serum HER2 levels of patients with grade III or ER-negative were higher than those with grade I–II (P = 0.004) and ER-positive (P = 0.033), respectively. Therefore, the novel dot blot method to detect serum HER2 level is a valid and inexpensive assay with potential application in monitoring breast cancer progression in clinical situations.

In vivo activity of novel anti-ErbB2 antibody chA21 alone and with Paclitaxel or Trastuzumab in breast and ovarian cancer xenograft models

It was well studied that ErbB2 (HER2/p185(her2/neu)) overexpression in human malignant cancers correlates with poor prognosis and chemo-resistance. Although Trastuzumab (Herceptin) has been widely used in patients with ErbB2-overexpressing metastatic breast cancer, many patients either do not respond to Trastuzumab therapy or progress within 1 year of initiating Trastuzumab treatment. Previously, we reported a novel tumor-inhibitory antibody chA21, which recognized ErbB2 extracellular domain with an epitope distinct from other tumor-inhibitory anti-ErbB2 antibodies. Here, we report that chA21 combined with Paclitaxel or Trastuzumab significantly enhances the tumor-inhibition effects on ErbB2-overexpressing breast and ovarian cancer in xenograft mice. Moreover, the study reveals that the effects by chA21 to cause an enhanced inhibition on cancer cell proliferation and angiogenesis was highly associated with the intrinsic ability of chA21 to down-regulate ErbB2 receptor, inhibit downstream MAPK and PI3K-AKT signal transduction and activate natural killer cells. Our findings show that chA21 may represent a unique anti-ErbB2 antibody with potentials as therapeutic candidate alone or combination with other anti-ErbB2 reagents in cancer therapy.