Project description:All-trans retinoic acid (ATRA) is used in the treatment of Acute Promyelocytic Leukemia (APL) with exceptional results, inducing long-lasting remissions in 78% of the patients. In previous studies (Bolis M et al., Ann Oncol. 2017, 28:611; Centritto F et al., EMBO Mol Med. 2015, 7:950) we demonstrated the therapeutic potential of ATRA in the personalized treatment of breast cancer. The present study is aimed at providing pre-clinical evidence supporting the use of pharmacological strategies based on ATRA in the management of stomach cancer, a very heterogeneous type of tumor, which lacks effective therapeutic options other than surgery. To define the sensitivity of gastric cancer to the anti-proliferative action of ATRA, we used 27 cell-lines, recapitulating the heterogeneity of the tumor. Fourteen of these cell-lines are characterized by a G-DIF phenotype, while the remainders present with a G-INT phenotype. Each cell-line was exposed to increasing concentrations of ATRA and the growth-inhibitory effects of the retinoid were evaluated at 3, 6, and 9 days. The 27 cell-lines were ranked for their sensitivity to the retinoid with the “ATRA-score”, a continuous/quantitative index, which we developed. The results indicate that the cell-lines can be divided into 3 separate groups characterized by high, intermediate and low sensitivity to ATRA. No significant correlation between ATRA-sensitivity and the G-DIF or G-INT phenotype is observed in our panel of cell-lines. To support the results obtained with the cell-lines, we evaluated the anti-proliferative effects exerted by ATRA on short-term tissue slice cultures of primary tumors derived from 13 cases of stomach cancer, which we classified as G-DIF or G-INT following determination of the basal gene-expression profiles with the use of RNA-sequencing studies. The results obtained with the short-term tissue culture model confirm the data generated with the cell-lines. The whole genome RNA-sequencing studies performed in the cell-lines and short-term tissue cultures in basal conditions allowed us to define a model consisting of 42 genes whose expression is quantitatively correlated with ATRA-sensitivity. The model was used to predict the sensitivity of gastric cancer cases to ATRA using the TCGA dataset. The results obtained indicate that approximately 60% of the gastric cancer cases are predicted to be characterized by high/intermediate sensitivity to ATRA, regardless of the G-DIF or G-INT phenotype. In conclusion, our results support the concept that ATRA-based therapeutic strategies are likely to be beneficial in the majority of stomach cancer cases. In addition, we generate a gene-expression tool that may be used for the selection of ATRA-sensitive patients in the clinics.

Project description:Non-small cell lung cancer (NSCLC) cell lines are widely used model systems to study molecular aspects of lung cancer. Comparative and in-depth proteome expression data across many NSCLC cell lines has not been generated yet, but would be of utility for the investigation of candidate targets and markers in oncogenesis. We employed a SILAC reference approach to perform replicate proteome quantifications across 23 distinct NSCLC cell lines. On average, close to 4000 distinct proteins were identified and quantified per cell line. These included many known targets and diagnostic markers, indicating that our proteome expression data represents a useful resource for NSCLC pre-clinical research. To assess proteome diversity within the NSCLC cell line panel, we performed hierarchical clustering and principal component analysis of proteome expression data. Our results indicate that general proteome diversity among NSCLC cell lines supersedes potential effects common to K-Ras or epidermal growth factor receptor (EGFR) oncoprotein expression. However, we observed partial segregation of EGFR or KRAS mutant cell lines for certain principal components, which reflected biological differences according to gene ontology enrichment analyses. Moreover, statistical analysis revealed several proteins that were significantly overexpressed in KRAS or EGFR mutant cell lines. Biological significance Despite enormous progress in molecular characterization and targeted therapy NSCLC represents a major cause for cancer-related deaths. While pre-clinical models such as NSCLC cell lines have been studied on the genomic and transcriptional level, proteome composition is poorly characterized. We conducted quantitative profiling across 23 NSCLC cell lines and studied global proteome diversity in relation to the presence of oncogenic KRAS or EGFR mutations. Notably, in-depth bioinformatics analysis pointed to prominent biological processes as well as up-regulated proteins in KRAS and EGFR mutant cells, highlighting the utility of cancer cell proteomics to identify target or biomarker candidates in the context of specific oncogenic mechanisms.

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Fast proliferating and slowly migrating non-small cell lung cancer cells are vulnerable to decitabine and retinoic acid combinatorial treatment

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