Cluster analyses of the TCGA and a TMA dataset using the coexpression of HSP27 and CRYAB improves alignment with clinical-pathological parameters of breast cancer and suggests different epichaperome influences for each sHSP.

Our cluster analysis of the Cancer Genome Atlas for co-expression of HSP27 and CRYAB in breast cancer patients identified three patient groups based on their expression level combination (high HSP27 + low CRYAB; low HSP27 + high CRYAB; similar HSP27 + CRYAB). Our analyses also suggest that there is a statistically significant inverse relationship between HSP27 and CRYAB and known clinicopathological markers in breast cancer. Screening an unbiased 248 breast cancer patient tissue microarray (TMA) for the protein expression of HSP27 and phosphorylated HSP27 (HSP27-82pS) with CRYAB also identified three patient groups based on HSP27 and CRYAB expression levels. TMA24 also had recorded clinical-pathological parameters, such as ER and PR receptor status, patient survival, and TP53 mutation status. High HSP27 protein levels were significant with ER and PR expression. HSP27-82pS associated with the best patient survival (Log Rank test). High CRYAB expression in combination with wild-type TP53 was significant for patient survival, but a different patient outcome was observed when mutant TP53 was combined with high CRYAB expression. Our data suggest that HSP27 and CRYAB have different epichaperome influences in breast cancer, but more importantly evidence the value of a cluster analysis that considers their coexpression. Our approach can deliver convergence for archival datasets as well as those from recent treatment and patient cohorts and can align HSP27 and CRYAB expression to important clinical-pathological features of breast cancer.

Identification of a 17-protein signature in the serum of lung cancer patients.

Early detection of lung cancer may potentially help to improve the outcome of this fatal disease. Currently, no satisfactory laboratory tests are available to screen for this type of cancer. The aim of this study was to improve diagnostic procedures for lung cancer through the discovery of serum biomarkers using SELDI-TOF MS (surface-enhanced laser desorption/ionization time-of-flight mass spectrometry). Mass spectrometric profiling was applied to the serum of a total of 139 lung cancer patients and 158 healthy individuals for developing a prognostic signature. For validation, two separate groups were employed, comprising of 126 and 50 individuals, respectively. Informative regions of mass spectra were identified by forming protein mass clusters and identifying predictive clusters in a logistic regression model. A total of 17 differential predictive protein mass clusters were identified in patients with metastatic lung cancer disease compared to healthy individuals. These clusters provide for a robust risk prediction model. The sensitivity and specificity of this model was estimated to be 87.3 and 81.9%, respectively, for the first validation set, and 96.0 and 66.7%, respectively, for a second validation set of patients with early disease (stages I and II). A differential 11.5/11.7 kDa double-peak could be identified as serum amyloid alpha (SAA) by peptide mapping. Our data suggest that the SELDI-TOF MS technology in combination with a careful statistical analysis appears to be a useful experimental platform which delivers a rapid insight into the proteome of body fluids and may guide to identify novel biomarkers for lung cancer disease.