Biology subjects

Diolaiti, D.

Publications and source records attributed to Diolaiti, D..

3 recordsLinked to original sources

bioRxiv2021

Pre-clinical validation of an RNA-based precision oncology platform for patient-therapy alignment in a diverse set of human malignancies resistant to standard treatments

Predicting tumor sensitivity to antineoplastics remains an elusive challenge, with no methods demonstrating predictive power. Joint analysis of tumors--from patients with distinct malignancies who had progressed on multiple lines of therapy--and drug perturbation transcriptional profiles predicted sensitivity to 28 of 350 drugs, 26 of which (93%) were confirmed in low-passage, patient-derived xenograft (PDX) models. Drugs were prioritized based on their ability to either invert the activity of individual Master Regulator proteins, with available high-affinity inhibitors, or of the modules they comprise (Tumor-Checkpoints), based on de novo mechanism of action analysis. Of 138 PDX mice enrolled in 16 single and 18 multi-protein treatment arms, a disease control rate (DCR) of 68% and 91 %, and an objective response rate (ORR) of 12% and 17%, were achieved respectively, compared to 6% and 0% in the negative controls arm, with multi-protein drugs achieving significantly more durable responses. Thus, these approaches may effectively complement and expand current precision oncology approaches, as also illustrated by a case study.

systems biology↗

bioRxiv2021

Identification and validation of a non-genetically encoded vulnerability to XPO1 inhibition in malignant rhabdoid tumors - expanding patient-driven discovery beyond the Nof1.

Malignant rhabdoid tumors (MRTs) are rare, aggressive pediatric solid tumors, characterized by a 22q11 deletion that inactivates the SMARCB1 gene. Outcomes remain poor despite multimodality treatment. MRTs are among the most genomically stable cancers and lack therapeutically targetable genetic mutations. We utilized metaVIPER, an extension of the Virtual Inference of Protein-activity by Enriched Regulon (VIPER) algorithm, to computationally infer activated druggable proteins in the tumor of an eight month old patient and then expanded the analysis to TCGA and TARGET cohorts. In vitro studies were performed on a panel of MRT and atypical teratoid/rhabdoid tumor cell lines. Two patient-derived xenograft (PDX) mouse models of MRT were used for in vivo efficacy studies. MetaVIPER analysis from the patients tumor identified significantly high inferred activity of nuclear export protein Exportin-1 (XPO1). Expanded metaVIPER analysis of TCGA and TARGET cohorts revealed consistent elevations in XPO1 inferred activity in MRTs compared to other cancer types. All MRT cell lines demonstrated baseline activation of XPO1. MRT cell lines demonstrated in vitro sensitivity to the XPO1 inhibitor, selinexor which led to cell cycle arrest and induction of apoptosis. Targeted inhibition of XPO1 in patient-derived xenograft models of MRT using selinexor resulted in abrogation of tumor growth. Selinexor demonstrates efficacy in preclinical models of MRT. These results support investigation of selinexor in a phase II study in children with MRT and illustrate the importance of an N-of-1 approach in driving discovery beyond the single patient. Statement of Translational RelevanceWe describe the patient-driven discovery of XPO1 activation as a non-genetically encoded vulnerability in MRTs. The application of metaVIPER analysis to tumors lacking actionable oncogenic alterations represents a novel approach for identifying potential therapeutic targets and biomarkers of response. Our preclinical validation of selinexor confirms XPO1 inhibition as a promising therapeutic strategy for the treatment of MRT.

cancer biology↗

bioRxiv2019

Single-cell based elucidation of molecularly-distinct glioblastoma states and drug sensitivity

Glioblastoma heterogeneity and plasticity remain controversial, with proposed subtypes representing the average of highly heterogeneous admixtures of independent transcriptional states. Single-cell, protein-activity-based analysis allowed full quantification of >6,000 regulatory and signaling proteins, thus providing a previously unattainable single-cell characterization level. This helped identify four novel, molecularly distinct subtypes that successfully harmonize across multiple GBM datasets, including previously published bulk and single-cell profiles and single cell profiles from seven orthotopic PDX models, representative of prior subtype diversity. GBM is thus characterized by the plastic coexistence of single cells in two mutually-exclusive developmental lineages, with additional stratification provided by their proliferative potential. Consistently, all previous subtypes could be recapitulated by single-cell mixtures drawn from newly identified states. Critically, drug sensitivity was predicted and validated as highly state-dependent, both in single-cell assays from patient-derived explants and in PDX models, suggesting that successful treatment requires combinations of multiple drugs targeting these distinct tumor states.\n\nSignificanceWe propose a new, 4-subtype GBM classification, which harmonizes across bulk and single-cell datasets. Single-cell mixtures from these subtypes effectively recapitulate all prior classifications, suggesting that the latter are a byproduct of GBM heterogeneity. Finally, we predict single-cell level activity of three clinically-relevant drugs, and validate them in patient-derived explant.

systems biology↗