From proteomic analysis to potential therapeutic targets: functional profile of two lung cancer cell lines, A549 and SW900, widely studied in pre-clinical research

Lung cancer is a serious health problem and the leading cause of cancer death worldwide. The standard use of cell lines as in vitro pre-clinical models to study the molecular mechanisms that drive tumorigenesis and access drug sensitivity/effectiveness is of undisputable importance. Label-free mass spectrometry and bioinformatics were employed to study the proteomic profiles of two representative lung cancer cell lines and to unravel the specific biological processes. Adenocarcinoma A549 cells were enriched in proteins related to cellular respiration, ubiquitination, apoptosis and response to drug/hypoxia/oxidative stress. In turn, squamous carcinoma SW900 cells were enriched in proteins related to translation, apoptosis, response to inorganic/organic substances and cytoskeleton organization. Several proteins with differential expression were related to cancer transformation, tumor resistance, proliferation, migration, invasion and metastasis. Combined analysis of proteome and interactome data highlighted key proteins and suggested that adenocarcinoma might be more prone to PI3K/Akt/mTOR and topoisomerase IIα inhibitors, and squamous carcinoma to Ck2 inhibitors. Moreover, ILF3 overexpression in adenocarcinoma, and PCNA and NEDD8 in squamous carcinoma shows them as promising candidates for therapeutic purposes. This study highlights the functional proteomic differences of two main subtypes of lung cancer models and hints several targeted therapies that might assist in this type of cancer.publishe

Publisher Correction:Network inference from glycoproteomics data reveals new reactions in the IgG glycosylation pathway

Correction to: Nature Communications (2017) 8:1231. doi:10.1038/s41467-017-01525-

Cancer Biomarker Discovery: The Entropic Hallmark

Background: It is a commonly accepted belief that cancer cells modify their transcriptional state during the progression of the disease. We propose that the progression of cancer cells towards malignant phenotypes can be efficiently tracked using high-throughput technologies that follow the gradual changes observed in the gene expression profiles by employing Shannon's mathematical theory of communication. Methods based on Information Theory can then quantify the divergence of cancer cells' transcriptional profiles from those of normally appearing cells of the originating tissues. The relevance of the proposed methods can be evaluated using microarray datasets available in the public domain but the method is in principle applicable to other high-throughput methods. Methodology/Principal Findings: Using melanoma and prostate cancer datasets we illustrate how it is possible to employ Shannon Entropy and the Jensen-Shannon divergence to trace the transcriptional changes progression of the disease. We establish how the variations of these two measures correlate with established biomarkers of cancer progression. The Information Theory measures allow us to identify novel biomarkers for both progressive and relatively more sudden transcriptional changes leading to malignant phenotypes. At the same time, the methodology was able to validate a large number of genes and processes that seem to be implicated in the progression of melanoma and prostate cancer. Conclusions/Significance: We thus present a quantitative guiding rule, a new unifying hallmark of cancer: the cancer cell's transcriptome changes lead to measurable observed transitions of Normalized Shannon Entropy values (as measured by high-throughput technologies). At the same time, tumor cells increment their divergence from the normal tissue profile increasing their disorder via creation of states that we might not directly measure. This unifying hallmark allows, via the the Jensen-Shannon divergence, to identify the arrow of time of the processes from the gene expression profiles, and helps to map the phenotypical and molecular hallmarks of specific cancer subtypes. The deep mathematical basis of the approach allows us to suggest that this principle is, hopefully, of general applicability for other diseases

Abstract P6-06-05: RadiotypeDx: Identification and validation of a radiation sensitivity signature in human breast cancer

Abstract Purpose: An unmet clinical need in breast cancer (BC) management is the identification of which patients will respond to radiation therapy (RT). We hypothesized that the integration of post-RT clonogenic survival data with gene expression data across a large spectrum of BC cell lines would generate a BC-specific RT sensitivity signature predictive for RT response in BC patients and allow identification of patients with tumors refractive to conventional therapy. Methods: Using clonogenic survival assays, we identified the range of surviving fraction (SF) after 2 Gy of RT across 21 BC cell lines. Using SF as a continuous variable, the RT sensitivity score (RSS) was correlated to gene expression using a Spearman correlation method on an individual gene basis. Genes were selected for the signature based on positive or negative correlation with a p-value &amp;lt;0.05 and FDR of &amp;lt;0.01. Unsupervised hierarchical clustering identified differences in gene expression across resistant and sensitive cell lines to generate a radiation sensitivity (RS) signature. This signature was trained and validated in a separate human breast tumor dataset (185 pts) containing early stage, node-negative patients treated with surgery and RT alone without adjuvant chemotherapy to assess the predictive effect of RS signature on recurrence risk after RT. Gene function and potentially actionable targets from the signature were validated using clongenic survival and DNA damage assays. Results: Clonogenic survival identifies a range of radiation sensitivity in human BCC lines (SF 77%-17%) with no significant correlation (r value &amp;lt;0.3) to the intrinsic BC subtype. Using Spearmans correlation method, a total of 126 genes were identified as being associated with radiation sensitivity (72 positively correlated, 54 negatively correlated). Unsupervised hierarchical expression discriminates gene expression patterns in the RT resistant and RT sensitive cell lines and is enriched for genes involved in cell cycle arrest and DNA damage response (enrichment p-value 5.0 E-22). Knockdown of genes associated with the radioresistance signature identifies previously unreported radiation resistance genes, including TACC1 and RND3 with enhancement ratios of 1.25 and 1.37 in BCC lines. Application of this RS signature to an independent breast cancer dataset with clinical outcomes validates the signature and accurately identifies patients with decreased rates of recurrence compared to patients with high expression of the radioresistant signature (p-value &amp;lt;0.0001, misclassification error rate .31, 12/13 patients with locoregional recurrence accurately identified). Conclusion: In this study, we derive a human BC-specific RT sensitivity signature (RadiotypeDx) with biologic relevance from preclinical studies and validate this signature for prediction of recurrence in an independent clinical dataset. The signature is not correlated to the intrinsic subtypes of human breast cancer and thus provides useful information beyond traditional breast cancer subtyping. By identifying patients with tumors refractory to standard RT, this signature has the potential to allow for personalization of radiotherapy, particularly in patients for whom treatment intensification is needed. Citation Information: Cancer Res 2013;73(24 Suppl): Abstract nr P6-06-05.</jats:p