Malignant Pleural Mesothelioma: an investigation on relevant cancer genes and potentially repositionable drugs
Bibliographic record
Abstract
Malignant pleural mesothelioma (MPM) is a very aggressive cancer originating from the pleural membrane and mainly due to inhalation of asbestos. As a consequence of the past exposure to asbestos in industrialized countries and the present use in developing ones, as Canada, China, India, Kazakhstan, Russia and Thailand, the current incidence of MPM, is still high (1-6/100.000) and it is expected to further increase. The prognosis of MPM is poor with a median overall survival of less than one year from the time of diagnosis. The non-specific symptoms and the lack of accurate biomarkers do not allow a sufficiently early diagnosis for a radical treatment of the disease. To date, few pharmacological treatment options are available and effective cures are missing. Thus, the identification of novel diagnostic and prognostic biomarkers, as well as therapeutic targets is urgently needed. In the current project, we moved onto two different paths. On one side, we focused on a group of deregulated genes in MPM, as ASS1, EIF4G1, GALNT7, GLUT1, IGF2BP3, ITGA4, RAN, SLC16A1, SLC16A3, SOD1 and THBS2. In order to investigate their role in tumorigenesis of MPM we performed an RNA interference-based screen on four MPM cell lines (Mero-14, Mero-25, IST-Mes2 and NCI-H28,) and one non-malignant mesothelial cell line (MeT-5A). The screening was followed by a phenotypic study in vitro. We analysed changes in proliferation rates, caspases 3/7 activity, migration abilities and clonogenicity. In particular, for SLC16A1 and SLC16A3, we further evaluated their expression in vivo on a series of 135 MPM samples, through tissue microarray immunohistochemistry (IHC). On the other side, in the light of the lack of an efficient therapeutical treatment, we aimed to identify novel active compounds to be repositioned in the MPM therapeutic management. Thus, we screened an FDA-approved drug library on the panel of MPM and non-MPM cells above mentioned. We identified two putative drugs, as the antimetabolite Fludarabine and the bisphosphonate Risedronic Acid. Although these results are preliminary and require deepen evaluations, overall, they suggest that repurposing of these two compounds could represent a promising approach for MPM treatment.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".