Prediction of pathological response to neoadjuvant treatment in rectal cancer with a two-protein immunohistochemical score derived from stromal gene-profiling.

BACKGROUND: Preoperative chemoradiotherapy followed by surgical mesorectal resection is the standard of care for locally advanced rectal carcinomas. Yet, predicting that patients will respond to treatment remains an unmet clinical challenge. EXPERIMENTAL DESIGN: Using laser-capture microdissection we isolated RNA from stroma and tumour glands from prospective pre-treatment samples (n = 15). Transcriptomic profiles were obtained hybridising PrimeView Affymetrix arrays. We modelled a carcinoma-associated fibroblast-specific genes filtering data using GSE39396. RESULTS: The analysis of differentially expressed genes of stroma/tumour glands from responder and non-responder patients shows that most changes were associated with the stromal compartment; codifying mainly for extracellular matrix and ribosomal components. We built a carcinoma-associated fibroblast (CAF) specific classifier with genes showing changes in expression according to the tumour regression grade (FN1, COL3A1, COL1A1, MMP2 and IGFBP5). We assessed these five genes at the protein level by means of immunohistochemical staining in a patient's cohort (n = 38). For predictive purposes we used a leave-one-out cross-validated model with a positive predictive value (PPV) of 83.3%. Random Forest identified FN1 and COL3A1 as the best predictors. Rebuilding the leave-one-out cross-validated regression model improved the classification performance with a PPV of 93.3%. An independent cohort was used for classifier validation (n = 36), achieving a PPV of 88.2%. In a multivariate analysis, the two-protein classifier proved to be the only independent predictor of response. CONCLUSION: We developed a two-protein immunohistochemical classifier that performs well at predicting the non-response to neoadjuvant treatment in rectal cancer.

Excessive collagen turnover products are released during colorectal cancer progression and elevated in serum from metastatic colorectal cancer patients.

During cancer progression, the homeostasis of the extracellular matrix becomes imbalanced with an excessive collagen remodeling by matrix metalloproteinases. As a consequence, small protein fragments of degraded collagens are released into the circulation. We have investigated the potential of protein fragments of collagen type I, III and IV as novel biomarkers for colorectal cancer. Specific fragments of degraded type I, III and IV collagen (C1M, C3M, C4M) and type III collagen formation (Pro-C3) were assessed in serum from colorectal cancer patients, subjects with adenomas and matched healthy controls using well-characterized and validated ELISAs. Serum levels of the biomarkers were significantly elevated in colorectal cancer patients compared to subjects with adenomas (C1M, Pro-C3, C3M) and controls (C1M, Pro-C3). When patients were stratified according to their tumour stage, all four biomarkers were able to differentiate stage IV metastatic patients from all other stages. Combination of all markers with age and gender in a logistic regression model discriminated between metastatic and non-metastatic patients with an AUROC of 0.80. The data suggest that the levels of these collagen remodeling biomarkers may be a measure of tumour activity and invasiveness and may provide new clinical tools for monitoring of patients with advanced stage colorectal cancer.

Molecular monitoring of epithelial-to-mesenchymal transition in breast cancer cells by means of Raman spectroscopy.

In breast cancer the presence of cells undergoing the epithelial-to-mesenchymal transition is indicative of metastasis progression. Since metabolic features of breast tumour cells are critical in cancer progression and drug resistance, we hypothesized that the lipid content of malignant cells might be a useful indirect measure of cancer progression. In this study Multivariate Curve Resolution was applied to cellular Raman spectra to assess the metabolic composition of breast cancer cells undergoing the epithelial to mesenchymal transition. Multivariate Curve Resolution analysis led to the conclusion that this transition affects the lipid profile of cells, increasing tryptophan but maintaining a low fatty acid content in comparison with highly metastatic cells. Supporting those results, a Partial Least Square-Discriminant analysis was performed to test the ability of Raman spectroscopy to discriminate the initial steps of epithelial to mesenchymal transition in breast cancer cells. We achieved a high level of sensitivity and specificity, 94% and 100%, respectively. In conclusion, Raman microspectroscopy coupled with Multivariate Curve Resolution enables deconvolution and tracking of the molecular content of cancer cells during a biochemical process, being a powerful, rapid, reagent-free and non-invasive tool for identifying metabolic features of breast cancer cell aggressiveness at first stages of malignancy.

Peroxiredoxin 2 specifically regulates the oxidative and metabolic stress response of human metastatic breast cancer cells in lungs.

Little is known about metastatic pathways that are specific to the lung rather than other organs. We previously showed that antioxidant proteins such as peroxiredoxins were specifically upregulated in lung metastatic breast cancer cells. We hypothesize that cancer cells that live under aerobic conditions, as might be the case in lungs, protect themselves against the damage caused by reactive oxygen species (ROS). To examine this hypothesis, we studied the role of peroxiredoxin-2 (PRDX2) in lung vs bone metastasis formation. A metastatic variant of MDA-MB-435 breast cancer cells that specifically metastasize to lungs (435-L3) was transduced with short hairpin RNAs to specifically silence PRDX2. Conversely, a bone metastatic variant of MDA-MB-231 cells (BO2) was stably transfected to overexpress PRDX2. The 435-L3 cells silenced for PRDX2 were significantly more sensitive to H(2)O(2)-induced oxidative stress than the parental and scrambled transfected cells. BO2/PRDX2 cells produced less ROS than BO2/green fluorescent protein control cells under oxidative stress. Moreover, PRDX2 knockdown inhibited the growth of 435-L3 cells in the lungs, whereas lymph node metastasis remained unaffected. In contrast, PRDX2 overexpression in bone metastatic BO2 breast cancer cells led to drastic inhibition of the skeletal tumor burden and reduction of bone destruction. Furthermore, PRDX2 expression in breast cancer cells was associated with a glucose-dependent phenotype, different from bone metastatic cells. Overall, our results strongly suggest that PRDX2 is a targetable 'metabolic adaptor' driver protein implicated in the selective growth of metastatic cells in the lungs by protecting them against oxidative stress.

Tools for protein-protein interaction network analysis in cancer research

As cancer is a complex disease, the representation of a malignant cell as a protein-protein interaction network (PPIN) and its subsequent analysis can provide insight into the behaviour of cancer cells and lead to the discovery of new biomarkers. The aim of this review is to help life-science researchers without previous computer programming skills to extract meaningful biological information from such networks, taking advantage of easy-to-use, public bioinformatics tools. It is structured in four parts: the first section describes the pipeline of consecutive steps from network construction to biological hypothesis generation. The second part provides a repository of public, user-friendly tools for network construction, visualisation and analysis. Two different and complementary approaches of network analysis are presented: the topological approach studies the network as a whole by means of structural graph theory, whereas the global approach divides the PPIN into sub-graphs, or modules. In section three, some concepts and tools regarding heterogeneous molecular data integration through a PPIN are described. Finally, the fourth part is an example of how to extract meaningful biological information from a colorectal cancer PPIN using some of the described tools (AU)