Xue-Qin Yang

Patterns of cancer invasion revealed by QDs-based quantitative multiplexed imaging of tumor microenvironment

Tumor growth and progression depends on their microenvironment, which undergoes constant co-evolution because of the dynamic tumor-stormal interactions. Such co-evolution has long been under appreciated due to the lack of appropriate technology platforms to simultaneously reveal these complex interactions. Here we report on a quantum dots based multiplexed imaging and spectrum analysis technology to simultaneously study major components of tumor stroma, including type IV collagen, tumor angiogenesis, macrophages infiltration and tissue destructive proteolytic enzyme matrix metalloproteinase 9. The new technology revealed a panoramic picture of the tempo-spatial co-evolution of tumor cells and their stroma at the architecture level. Four patterns of tumor invasion with distinctive co-evolution features were identified as Washing pattern, Ameba-like pattern, Polarity pattern and Linear pattern. This quantum dots based multiplexed technology could help gain new insight into the complex process of tumor invasion, and formulate new anti-cancer strategies.

Quantum dots-based molecular classification of breast cancer by quantitative spectroanalysis of hormone receptors and HER2

The emerging molecular breast cancer (BC) classification based on key molecules, including hormone receptors (HRs), and human epidermal growth factor receptor 2 (HER2) has been playing an important part of clinical practice guideline. The current molecular classification mainly based on their fingerprints, however, could not provide enough essential information for treatment decision making. The molecular information on both patterns and quantities could be more helpful to heterogeneities understanding for BC personalized medicine. Here we conduct quantitative determination of HRs and HER2 by quantum dots (QDs)-based quantitative spectral analysis, which had excellent consistence with traditional method. Moreover, we establish a new molecular classification system of BC by integrating the quantitative information of HER2 and HRs, which could better reveal BC heterogeneity and identify 5 molecular subtypes with different 5-year prognosis. Furthermore, the emerging 5 molecular subtypes based on simple quantitative molecules information could be as informative as multi-genes analysis in routine practice, and might help formulate a more personalized comprehensive therapy strategy and prognosis prediction.

Quantum dots-based double-color imaging of HER2 positive breast cancer invasion.

It has been well recognized that human epidermal growth factor receptor 2 (HER2) level in breast cancer (BC) is closely related to the malignant biologic behaviors of the tumor, including invasion and metastasis. Yet, there has been a lack of directly observable evidence to support such notion. Here we report a quantum dots (QDs)-based double-color imaging technique to simultaneously show the HER2 level on BC cells and the type IV collagen in the tumor matrix. In benign breast tumor, the type IV collagen was intact. With the increasing of HER2 expression level, there has been a progressive decrease in type IV collagen around the cancer nest. At HER2 (3+) expression level, there has virtually been a total destruction of type IV collagen. Moreover, HER2 (3+) BC cells also show direct invasion into the blood vessels. This novel imaging method provides direct observable evidence to support the theory that the HER2 expression level is directly related to BC invasion.

Quantum dot-based quantitative immunofluorescence detection and spectrum analysis of epidermal growth factor receptor in breast cancer tissue arrays

Background The epidermal growth factor receptor (EGFR) is a promising therapeutic target in cancer, but its clinical value in breast cancer remains controversial. Our previous studies have found that quantitative analysis of biomarkers with quantum dot-based nanotechnology had better detection performance than conventional immunohistochemistry. The present study was undertaken to investigate the prognostic value of EGFR in breast cancer using quantum dot-based quantitative spectral analysis. Methods EGFR expression in 65 breast cancer specimens was detected by immunohistochemistry and quantum dot-immunohistochemistry, and comparisons were made between the two methods. EGFR expression in tissue microarrays of 240 breast cancer patients was then detected by quantum dot-immunohistochemistry and spectral analysis. The prognostic value of EGFR immunofluorescence area (EGFR area) for five-year recurrence-free survival was investigated. Results The same antigen localization, high correlation of staining rates (r = 0.914), and high agreement of measurement (κ = 0.848) of EGFR expression in breast cancer were found by quantum dot-immunohistochemistry and immunohistochemistry. The EGFR area showed significant differences by tumor grade, lymph node status, HER2 status, and hormone receptor status (all P < 0.05). Patients in the large EGFR area (≥30.51) group had a significantly higher five-year recurrence rate (47.2% versus 27.4%, P = 0.002) and worse five-year recurrence-free survival (log-rank test, P = 0.0015) than those in the small EGFR area (<30.51) group. In the subgroups, EGFR area was an independent prognosticator in the HER2-positive and lymph node-positive subgroups. Conclusion Quantum dot-based quantitative detection demonstrates the prognostic value of EGFR area in the HER2-positive and lymph node-positive subgroups of invasive breast cancer.

Subtype classification for prediction of prognosis of breast cancer from a biomarker panel: correlations and indications

Background Hormone receptors, including the estrogen receptor and progesterone receptor, human epidermal growth factor receptor 2 (HER2), and other biomarkers like Ki67, epidermal growth factor receptor (EGFR, also known as HER1), the androgen receptor, and p53, are key molecules in breast cancer. This study evaluated the relationship between HER2 and hormone receptors and explored the additional prognostic value of Ki67, EGFR, the androgen receptor, and p53. Methods Quantitative determination of HER2 and EGFR was performed in 240 invasive breast cancer tissue microarray specimens using quantum dot (QD)-based nanotechnology. We identified two subtypes of HER2, ie, high total HER2 load (HTH2) and low total HER2 load (LTH2), and three subtypes of hormone receptor, ie, high hormone receptor (HHR), low hormone receptor (LHR), and no hormone receptor (NHR). Therefore, breast cancer patients could be divided into five subtypes according to HER2 and hormone receptor status. Ki67, p53, and the androgen receptor were determined by traditional immunohistochemistry techniques. The relationship between hormone receptors and HER2 was investigated and the additional value of Ki67, EGFR, the androgen receptor, and p53 for prediction of 5-year disease-free survival was assessed. Results In all patients, quantitative determination showed a statistically significant (P<0.001) negative correlation between HER2 and the hormone receptors and a significant positive correlation (P<0.001) between the estrogen receptor and the progesterone receptor (r=0.588), but a significant negative correlation (P<0.001, r=−0.618) with the HHR subtype. There were significant differences between the estrogen receptor, progesterone receptor, and HER2 subtypes with regard to total HER2 load and hormone receptor subtypes. The rates of androgen receptor and p53 positivity were 46.3% and 57.0%, respectively. Other than the androgen receptor, differences in expression of Ki67, EGFR, and p53 did not achieve statistical significance (P>0.05) between the five subtypes. EGFR and Ki67 had prognostic significance for 5-year disease-free survival in univariate analysis, but the androgen receptor and p53 did not. Multivariate analysis identified that EGFR expression had predictive significance for 5-year disease-free survival in hormone-receptor positive patients and in those with the lymph node-positive breast cancer subtype. Conclusion Hormone receptor expression was indeed one of the molecular profiles in the subtypes identified by quantitative HER2 and vice versa. EGFR status may provide discriminative prognostic information in addition to HER2 and hormone receptor status, and should be integrated into routine practice to help formulate more specific prediction of the prognosis and appropriate individualized treatment.