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Biology subjects

Aburi, M.

Publications and source records attributed to Aburi, M..

2 recordsLinked to original sources

Systematic elucidation and pharmacologic targeting on non-oncogene dependencies in imatinib-resistant gastrointestinal stromal tumor

Treatment of gastrointestinal stromal tumor (GIST) with imatinib and other KIT-targeting drugs has improved outcomes significantly. However, most patients with advanced GIST eventually develop imatinib resistance and succumb to disease. We have developed mutation-agnostic, network-based methodologies to systematically elucidate and pharmacologically target Master Regulator (MR) proteins--critical non-oncogene dependencies--in cancer cells. Unsupervised, MR-based clustering of 34 GIST patient tumor samples produced two clusters, one of which contained all imatinib-resistant tumors. Analysis of 9 single-cell RNA profiles of high-risk GIST revealed that tumors with clinical progression on imatinib harbored large subpopulations enriched for the MR-activity signature of imatinib-resistant tumors, while tumors with resistance-associated mutations but without overt progression showed smaller, variably sized enriched subpopulations. High-throughput profiling of transcriptional responses by two GIST cell lines to FDA-approved and late-stage experimental drugs identified six candidate drugs that reversed the MR activity of imatinib-resistant GIST. Predictions were validated in two imatinib-resistant, patient-derived xenograft (PDX) models. The top prediction, linifanib, induced marked tumor growth inhibition in both PDXs across a wide dose range; selinexor and selumetinib were also effective compared to imatinib. We confirmed in vivo MR-activity reversal by these drugs, but not by ineffective drugs.

cancer biology↗

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↗