Biology subjects

Scaltriti, M.

Publications and source records attributed to Scaltriti, M..

3 recordsLinked to original sources

bioRxiv2020

UDP-glucose pyrophosphorylase 2, a regulator of glycosylation and glycogen, is essential for pancreatic cancer growth

Pancreatic ductal adenocarcinomas (PDACs) have enhanced nutrient uptake requirements and rapid metabolic processing. The enzyme UDP-glucose pyrophosphorylase 2 (UGP2) rests at the convergence of multiple metabolic pathways, however the role of UGP2 in tumor maintenance and cancer metabolism remains unclear. Here, we identify an essential role for UGP2 in the maintenance of PDAC growth in both in vitro and in vivo tumor models. Transcription of UGP2 is directly regulated by the YAP/TEAD complex. Loss of UGP2 leads to decreased intracellular glycogen and defects in N-glycosylation targets important for cell growth including epidermal growth factor receptor (EGFR). In murine xenograft models, knockdown of UGP2 halted tumor growth and repressed expression of EGFR. The critical roles of UGP2 in cancer maintenance, metabolism, and protein glycosylation may offer new avenues of therapy for otherwise intractable PDACs. Impact StatementConvergent findings reveal that UDP-glucose pyrophosphorylase 2 has a central role in growth and metabolism of pancreatic ductal adenocarcinomas, highlighting novel therapeutic possibilities for this deadly cancer.

cancer biology↗

bioRxiv2020

Cell-line-specific network models of ER+ breast cancer identify PI3Kα inhibitor sensitivity factors and drug combinations

Durable control of invasive solid tumors necessitates identifying therapeutic resistance mechanisms and effective drug combinations. A promising approach to tackle the cancer drug resistance problem is to build mechanistic mathematical models of the signaling network of cancer cells, and explicitly model the dynamics of information flow through this network under distinct genetic conditions and in response to perturbations. In this work, we used a network-based mathematical model to identify sensitivity factors and drug combinations for the PI3K inhibitor alpelisib, which was recently approved for ER+ PIK3CA mutant breast cancer. We experimentally validated the model-predicted efficacious combination of alpelisib and BH3 mimetics (e.g. MCL1 inhibitors) in ER+ breast cancer cell lines. We also experimentally validated the reduced sensitivity to alpelisib caused by FOXO3 knockdown, which is a novel potential resistance mechanism. Our experimental results showed cell line-specific sensitivity to the combination of alpelisib and BH3 mimetics, which was driven by the choice of BH3 mimetics. We find that cell lines were sensitive to the addition of either MCL1 inhibitor s63845 alone or in combination with BCL-XL/BCL-2 inhibitor navitoclax, and that the need for the combination of both BH3 mimetics was predicted by the expression of BCL-XL. Based on these results, we developed cell line-specific network models that are able to recapitulate the observed differential response to alpelisib and BH3 mimetics, and also incorporate the most recent knowledge on resistance and response to PI3K inhibitors. Overall, we present an approach for the development, experimental testing, and refining of mathematical models, which we apply to the context of PI3K inhibitor drug resistance in breast cancer. Our approach predicted and validated PI3K inhibitor sensitivity factors (FOXO3 knockdown) and drug combinations (BH3 mimetics), and illustrates that network-based mathematical models can contribute to overcoming the challenge of cancer drug resistance.

cancer biology↗

bioRxiv2019

Personalized Cancer Therapy Prioritization Based on Driver Alteration Co-occurrence Patterns

Identification of actionable genomic vulnerabilities is the cornerstone of precision oncology. Based on a large-scale drug screening in patient derived-xenografts, we uncover connections between driver gene alterations, derive Driver Co-Occurrence (DCO) networks, and relate these to drug sensitivity. Our collection of 53 drug response predictors attained an average balanced accuracy of 58% in a cross-validation setting, which rose to a 66% for the subset of high-confidence predictions. Morevover, we experimentally validated 12 out of 14 de novo predictions in mice. Finally, we adapted our strategy to obtain drug-response models from patients progression free survival data. By revealing unexpected links between oncogenic alterations, our strategy can increase the clinical impact of genomic profiling.

cancer biology↗