@article {807,
	title = {HMGA1 regulates trabectedin sensitivity in advanced soft-tissue sarcoma (STS): A Spanish Group for Research on Sarcomas (GEIS) study.},
	journal = {Cell Mol Life Sci},
	volume = {81},
	year = {2024},
	month = {2024 May 17},
	pages = {219},
	abstract = {<p>HMGA1 is a structural epigenetic chromatin factor that has been associated with tumor progression and drug resistance. Here, we reported the prognostic/predictive value of HMGA1 for trabectedin in advanced soft-tissue sarcoma (STS) and the effect of inhibiting HMGA1 or the mTOR downstream pathway in trabectedin activity. The prognostic/predictive value of HMGA1 expression was assessed in a cohort of 301 STS patients at mRNA (n = 133) and protein level (n = 272), by HTG EdgeSeq transcriptomics and immunohistochemistry, respectively. The effect of HMGA1 silencing on trabectedin activity and gene expression profiling was measured in leiomyosarcoma cells. The effect of combining mTOR inhibitors with trabectedin was assessed on cell viability in vitro studies, whereas in vivo studies tested the activity of this combination. HMGA1 mRNA and protein expression were significantly associated with worse progression-free survival of trabectedin and worse overall survival in STS. HMGA1 silencing sensitized leiomyosarcoma cells for trabectedin treatment, reducing the spheroid area and increasing cell death. The downregulation of HGMA1 significantly decreased the enrichment of some specific gene sets, including the PI3K/AKT/mTOR pathway. The inhibition of mTOR, sensitized leiomyosarcoma cultures for trabectedin treatment, increasing cell death. In in vivo studies, the combination of rapamycin with trabectedin downregulated HMGA1 expression and stabilized tumor growth of 3-methylcholantrene-induced sarcoma-like models. HMGA1 is an adverse prognostic factor for trabectedin treatment in advanced STS. HMGA1 silencing increases trabectedin efficacy, in part by modulating the mTOR signaling pathway. Trabectedin plus mTOR inhibitors are active in preclinical models of sarcoma, downregulating HMGA1 expression levels and stabilizing tumor growth.</p>},
	keywords = {Animals, Antineoplastic Agents, Alkylating, Cell Line, Tumor, Drug Resistance, Neoplasm, Female, Gene Expression Regulation, Neoplastic, HMGA1a Protein, Humans, Leiomyosarcoma, Mice, Prognosis, Sarcoma, Signal Transduction, TOR Serine-Threonine Kinases, Trabectedin, Xenograft Model Antitumor Assays},
	issn = {1420-9071},
	doi = {10.1007/s00018-024-05250-y},
	author = {Moura, David S and Mondaza-Hernandez, Jose L and Sanchez-Bustos, Paloma and Pe{\~n}a-Chilet, Maria and Cordero-Varela, Juan A and Lopez-Alvarez, Maria and Carrillo-Garcia, Jaime and Martin-Ruiz, Marta and Romero-Gonzalez, Pablo and Renshaw-Calderon, Marta and Ramos, Rafael and Marcilla, David and Alvarez-Alegret, Ramiro and Agra-Pujol, Carolina and Izquierdo, Francisco and Ortega-Medina, Luis and Martin-Davila, Francisco and Hernandez-Leon, Carmen Nieves and Romagosa, Cleofe and Salgado, Maria Angeles Vaz and Lavernia, Javier and Bagu{\'e}, Silvia and Mayodormo-Aranda, Empar and Alvarez, Rosa and Valverde, Claudia and Martinez-Trufero, Javier and Castilla-Ramirez, Carolina and Gutierrez, Antonio and Dopazo, Joaquin and Hindi, Nadia and Garcia-Foncillas, Jesus and Martin-Broto, Javier}
}
@article {612,
	title = {Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen.},
	journal = {Nat Commun},
	volume = {10},
	year = {2019},
	month = {2019 06 17},
	pages = {2674},
	abstract = {<p>The effectiveness of most cancer targeted therapies is short-lived. Tumors often develop resistance that might be overcome with drug combinations. However, the number of possible combinations is vast, necessitating data-driven approaches to find optimal patient-specific treatments. Here we report AstraZeneca{\textquoteright}s large drug combination dataset, consisting of 11,576 experiments from 910 combinations across 85 molecularly characterized cancer cell lines, and results of a DREAM Challenge to evaluate computational strategies for predicting synergistic drug pairs and biomarkers. 160 teams participated to provide a comprehensive methodological development and benchmarking. Winning methods incorporate prior knowledge of drug-target interactions. Synergy is predicted with an accuracy matching biological replicates for >60\% of combinations. However, 20\% of drug combinations are poorly predicted by all methods. Genomic rationale for synergy predictions are identified, including ADAM17 inhibitor antagonism when combined with PIK3CB/D inhibition contrasting to synergy when combined with other PI3K-pathway inhibitors in PIK3CA mutant cells.</p>},
	keywords = {ADAM17 Protein, Antineoplastic Combined Chemotherapy Protocols, Benchmarking, Biomarkers, Tumor, Cell Line, Tumor, Computational Biology, Datasets as Topic, Drug Antagonism, Drug Resistance, Neoplasm, Drug Synergism, Genomics, Humans, Molecular Targeted Therapy, mutation, Neoplasms, pharmacogenetics, Phosphatidylinositol 3-Kinases, Phosphoinositide-3 Kinase Inhibitors, Treatment Outcome},
	issn = {2041-1723},
	doi = {10.1038/s41467-019-09799-2},
	author = {Menden, Michael P and Wang, Dennis and Mason, Mike J and Szalai, Bence and Bulusu, Krishna C and Guan, Yuanfang and Yu, Thomas and Kang, Jaewoo and Jeon, Minji and Wolfinger, Russ and Nguyen, Tin and Zaslavskiy, Mikhail and Jang, In Sock and Ghazoui, Zara and Ahsen, Mehmet Eren and Vogel, Robert and Neto, Elias Chaibub and Norman, Thea and Tang, Eric K Y and Garnett, Mathew J and Veroli, Giovanni Y Di and Fawell, Stephen and Stolovitzky, Gustavo and Guinney, Justin and Dry, Jonathan R and Saez-Rodriguez, Julio}
}
