SPINT2 is hypermethylated in both IDH1 mutated and wild-type glioblastomas, and exerts tumor suppression via reduction of c-Met activation

CRUX, a platform for visualising, exploring and analysing cancer genome cohort data

To better understand how tumours develop, identify prognostic biomarkers and find new treatments, researchers have generated vast catalogues of cancer genome data. However, these datasets are complex, so interpreting their important features requires specialized computational skills and analytical tools, which presents a significant technical challenge. To address this, we developed CRUX, a platform for exploring genomic data from cancer cohorts. CRUX enables researchers to perform common analyses including cohort comparisons, biomarker discovery, survival analysis, and to create visualisations including oncoplots and lollipop charts. CRUX simplifies cancer genome analysis in several ways: (i) it has an easy-to-use graphical interface; (ii) it enables users to create custom cohorts, as well as analyse precompiled public and private user-created datasets; (iii) it allows analyses to be run locally to address data privacy concerns (though an online version is also available) and (iv) it makes it easy to use additional specialized tools by exporting data in the correct formats. We showcase CRUX's capabilities with case studies employing different types of cancer genome analysis, demonstrating how it can be used flexibly to generate valuable insights into cancer biology. CRUX is freely available at https://github.com/CCICB/CRUX and https://ccicb.shinyapps.io/crux (DOI: 10.5281/zenodo.8015714).

Catechol-O-methyl transferase suppresses cell invasion and interplays with MET signaling in estrogen dependent breast cancer

Catechol-O-methyl transferase (COMT) is involved in detoxification of catechol estrogens, playing cancer-protective role in cells producing or utilizing estrogen. Moreover, COMT suppressed migration potential of breast cancer (BC) cells. To delineate COMT role in metastasis of estrogen receptor (ER) dependent BC, we investigated the effect of COMT overexpression on invasion, transcriptome, proteome and interactome of MCF7 cells, a luminal A BC model, stably transduced with lentiviral vector carrying COMT gene (MCF7-COMT). 2D and 3D assays revealed that COMT overexpression associates with decreased cell invasion (p < 0.0001 for Transwell assay, p < 0.05 for spheroid formation). RNA-Seq and LC-DIA-MS/MS proteomics identified genes associated with invasion (FTO, PIR, TACSTD2, ANXA3, KRT80, S100P, PREX1, CLEC3A, LCP1) being downregulated in MCF7-COMT cells, while genes associated with less aggressive phenotype (RBPMS, ROBO2, SELENBP, EPB41L2) were upregulated both at transcript (|log2FC|> 1, adj. p < 0.05) and protein (|log2FC|> 0.58, q < 0.05) levels. Importantly, proteins driving MET signaling were less abundant in COMT overexpressing cells, and pull-down confirmed interaction between COMT and Kunitz-type protease inhibitor 2 (SPINT2), a negative regulator of MET (log2FC = 5.10, q = 1.04-7). In conclusion, COMT may act as tumor suppressor in ER dependent BC not only by detoxification of catechol estrogens but also by suppressing cell invasion and interplay with MET pathway.

Biomarkers, Master Regulators and Genomic Fabric Remodeling in a Case of Papillary Thyroid Carcinoma

Publicly available (own) transcriptomic data have been analyzed to quantify the alteration in functional pathways in thyroid cancer, establish the gene hierarchy, identify potential gene targets and predict the effects of their manipulation. The expression data have been generated by profiling one case of papillary thyroid carcinoma (PTC) and genetically manipulated BCPAP (papillary) and 8505C (anaplastic) human thyroid cancer cell lines. The study used the genomic fabric paradigm that considers the transcriptome as a multi-dimensional mathematical object based on the three independent characteristics that can be derived for each gene from the expression data. We found remarkable remodeling of the thyroid hormone synthesis, cell cycle, oxidative phosphorylation and apoptosis pathways. Serine peptidase inhibitor, Kunitz type, 2 (SPINT2) was identified as the Gene Master Regulator of the investigated PTC. The substantial increase in the expression synergism of SPINT2 with apoptosis genes in the cancer nodule with respect to the surrounding normal tissue (NOR) suggests that SPINT2 experimental overexpression may force the PTC cells into apoptosis with a negligible effect on the NOR cells. The predictive value of the expression coordination for the expression regulation was validated with data from 8505C and BCPAP cell lines before and after lentiviral transfection with DDX19B.

Multi-platform Affinity Proteomics Identify Proteins Linked to Metastasis and Immune Suppression in Ovarian Cancer Plasma

A central reason behind the poor clinical outcome of patients with high-grade serous carcinoma (HGSC) of the ovary is the difficulty in reliably detecting early occurrence or recurrence of this malignancy. Biomarkers that provide reliable diagnosis of this disease are therefore urgently needed. Systematic proteomic methods that identify HGSC-associated molecules may provide such biomarkers. We applied the antibody-based proximity extension assay (PEA) platform (Olink) for the identification of proteins that are upregulated in the plasma of OC patients. Using binders targeting 368 different plasma proteins, we compared 20 plasma samples from HGSC patients (OC-plasma) with 20 plasma samples from individuals with non-malignant gynecologic disorders (N-plasma). We identified 176 proteins with significantly higher levels in OC-plasma compared to N-plasma by PEA (p < 0.05 by U-test; Benjamini-Hochberg corrected), which are mainly implicated in immune regulation and metastasis-associated processes, such as matrix remodeling, adhesion, migration and proliferation. A number of these proteins have not been reported in previous studies, such as BCAM, CDH6, DDR1, N2DL-2 (ULBP2), SPINT2, and WISP-1 (CCN4). Of these SPINT2, a protease inhibitor mainly derived from tumor cells within the HGSC microenvironment, showed the highest significance (p < 2 × 10⁻⁷) similar to the previously described IL-6 and PVRL4 (NECTIN4) proteins. Results were validated by means of the aptamer-based 1.3 k SOMAscan proteomic platform, which revealed a high inter-platform correlation with a median Spearman ρ of 0.62. Likewise, ELISA confirmed the PEA data for 10 out of 12 proteins analyzed, including SPINT2. These findings suggest that in contrast to other entities SPINT2 does not act as a tumor suppressor in HGSC. This is supported by data from the PRECOG and KM-Plotter meta-analysis databases, which point to a tumor-type-specific inverse association of SPINT2 gene expression with survival. Our data also demonstrate that both the PEA and SOMAscan affinity proteomics platforms bear considerable potential for the unbiased discovery of novel disease-associated biomarkers.

The Gene Master Regulators (GMR) Approach Provides Legitimate Targets for Personalized, Time-Sensitive Cancer Gene Therapy

The dynamic and never exactly repeatable tumor transcriptomic profile of people affected by the same form of cancer requires a personalized and time-sensitive approach of the gene therapy. The Gene Master Regulators (GMRs) were defined as genes whose highly controlled expression by the homeostatic mechanisms commands the cell phenotype by modulating major functional pathways through expression correlation with their genes. The Gene Commanding Height (GCH), a measure that combines the expression control and expression correlation with all other genes, is used to establish the gene hierarchy in each cell phenotype. We developed the experimental protocol, the mathematical algorithm and the computer software to identify the GMRs from transcriptomic data in surgically removed tumors, biopsies or blood from cancer patients. The GMR approach is illustrated with applications to our microarray data on human kidney, thyroid and prostate cancer samples, and on thyroid, prostate and blood cancer cell lines. We proved experimentally that each patient has his/her own GMRs, that cancer nuclei and surrounding normal tissue are governed by different GMRs, and that manipulating the expression has larger consequences for genes with higher GCH. Therefore, we launch the hypothesis that silencing the GMR may selectively kill the cancer cells from a tissue.