Abstract We and others have shown that transition and maintenance of biological states is controlled by master regulator proteins, which can be inferred by interrogating tissue-specific regulatory models (interactomes) with transcriptional signatures, using the VIPER algorithm. Yet, some tissues may lack molecular profiles necessary for interactome inference (orphan tissues), or, as for single cells isolated from heterogeneous samples, their tissue context may be undetermined. To address this problem, we introduce metaVIPER, an algorithm designed to assess protein activity in tissue-independent fashion by integrative analysis of multiple, non-tissue-matched interactomes. This assumes that transcriptional targets of each protein will be recapitulated by one or more available interactomes. We confirm the algorithm’s value in assessing protein dysregulation induced by somatic mutations, as well as in assessing protein activity in orphan tissues and, most critically, in single cells, thus allowing transformation of noisy and potentially biased RNA-Seq signatures into reproducible protein-activity signatures.
Regardless of how creative, innovative, and elegant our computational methods, the ultimate proof of an algorithm's worth is the experimentally validated quality of its predictions. Unfortunately, this truism is hard to reduce to practice. Usually, modelers produce hundreds to hundreds of thousands of predictions, most (if not all) of which go untested. In a best-case scenario, a small subsample of predictions (three to ten usually) is experimentally validated, as a quality control step to attest to the global soundness of the full set of predictions. However, whether this small set is even representative of the global algorithm's performance is a question usually left unaddressed. Thus, a clear understanding of the strengths and weaknesses of an algorithm most often remains elusive, especially to the experimental biologists who must decide which tool to use to address a specific problem. In this chapter, we describe the first systematic set of challenges posed to the systems biology community in the framework of the DREAM (Dialogue for Reverse Engineering Assessments and Methods) project. These tests, which came to be known as the DREAM2 challenges, consist of data generously donated by participants to the DREAM project and curated in such a way as to become problems of network reconstruction and whose solutions, the actual networks behind the data, were withheld from the participants. The explanation of the resulting five challenges, a global comparison of the submissions, and a discussion of the best performing strategies are the main topics discussed.
<div>AbstractPurpose:<p>Glioblastoma is the most frequent and lethal primary brain tumor. Development of novel therapies relies on the availability of relevant preclinical models. We have established a panel of 96 glioblastoma patient-derived xenografts (PDX) and undertaken its genomic and phenotypic characterization.</p>Experimental Design:<p>PDXs were established from glioblastoma, IDH-wildtype (<i>n</i> = 93), glioblastoma, IDH-mutant (<i>n</i> = 2), diffuse midline glioma, H3 K27M-mutant (<i>n</i> = 1), and both primary (<i>n</i> = 60) and recurrent (<i>n</i> = 34) tumors. Tumor growth rates, histopathology, and treatment response were characterized. Integrated molecular profiling was performed by whole-exome sequencing (WES, <i>n</i> = 83), RNA-sequencing (<i>n</i> = 68), and genome-wide methylation profiling (<i>n</i> = 76). WES data from 24 patient tumors was compared with derivative models.</p>Results:<p>PDXs recapitulate many key phenotypic and molecular features of patient tumors. Orthotopic PDXs show characteristic tumor morphology and invasion patterns, but largely lack microvascular proliferation and necrosis. PDXs capture common and rare molecular drivers, including alterations of <i>TERT, EGFR, PTEN, TP53, BRAF</i>, and <i>IDH1</i>, most at frequencies comparable with human glioblastoma. However, <i>PDGFRA</i> amplification was absent. RNA-sequencing and genome-wide methylation profiling demonstrated broad representation of glioblastoma molecular subtypes. <i>MGMT</i> promoter methylation correlated with increased survival in response to temozolomide. WES of 24 matched patient tumors showed preservation of most genetic driver alterations, including <i>EGFR</i> amplification. However, in four patient–PDX pairs, driver alterations were gained or lost on engraftment, consistent with clonal selection.</p>Conclusions:<p>Our PDX panel captures the molecular heterogeneity of glioblastoma and recapitulates many salient genetic and phenotypic features. All models and genomic data are openly available to investigators.</p></div>
<p>XLSX - 1231K, Supplementary Table 1: List of genes deleted in 75 to 100% of the end-stage *PTEN tumors Supplementary Table 2: List of genes showing copy number gains in *PTEN end-stage and *PTEN/p53 mouse tumors. Supplementary Table 3: Genes that were deleted in 75-100% of *PTEN end-stage mouse tumors and at least 10% of human tumors within one of the four GBM subtypes mapped to their chromosomal location in the human genome. Supplementary Table 4: List of gene deletions that were specific for one of the four GBM subtypes. Supplementary Table 5: Correlation of proneural-specific genetic alterations and deletions identified by cross-species comparison. Supplementary Table 6: Spearman correlation for Verhaak gene sets show highest correlation for human proneural GBM subtype for 21 dpi *PTEN tumors, end-stage *PTEN tumors and *p53 end-stage tumors. Supplementary Table 7: MR identified from NB versus *PTEN 21 dpi and NB versus *PTEN end-stage mouse MARINas, and human NB versus proneural GBM (TCGA) MARINa.</p>
Abstract Sensitivity to therapeutic agents is impacted both by genetic heterogeneity between patients or among clones within a tumor and by heterogeneity of epigenetic cell states. Prior work demonstrates that human pancreatic tumors comprise malignant cells in a mixture of multiple cell states with distinct genetic dependencies, leading to a reservoir of chemoresistance across the tumor; targeting just one or two malignant cell states is insufficient to achieve durable therapeutic responses. One approach to overcome this challenge is to identify agents with efficacy against malignant cells in multiple cell states. However, in the absence of genetic distinctions between cell states, identifying therapeutic vulnerabilities for each malignant cell state present in a patient’s tumor in real time remains a challenge. OncoTreat is a CLIA certified algorithm that matches drugs to patients based on tumor gene expression profiles. The approach utilizes regulatory network analysis, a systems biology framework that transforms gene expression data into regulatory protein activity profiles, in a manner that facilitates the identification of master transcriptional regulators of cell state. We carried out a Phase 1 clinical trial of the OncoTreat framework to examine feasibility for implementing RNA-based precision medicine in patients with metastatic pancreatic ductal adenocarcinoma (2nd line and beyond). Patient-derived xenografts were established from subjects prior to 1st line therapy, and selected agents matched by OncoTreat were assessed co-clinically while subjects received standard of care regimens, with the potential for subsequent treatment with successful agents upon progression. Two subjects in the study matched to selenexor, an XPO1 inhibitor that is FDA approved for multiple myeloma. Co-clinical studies of selinexor in patient-derived xenograft models generated from the matched subjects showed prolonged survival compared to control regimens. Single-cell RNA sequencing analysis of these tumors found that the activity of master regulators confirmed that the activity of selinexor-responsive master regulators were inverted in response to treatment in vivo, reducing the fraction of malignant cells in the tumors. Strikingly, selenexor treatment was associated with significant inhibition of RAS/MAPK signaling, suggesting a potential novel role for selinexor targeting Ras. Together, these data indicate a potential therapeutic vulnerability for a subset of PDAC patients that can be predicted by a CLIA-certified RNA-based precision medicine platform. Citation Format: Alvaro Curiel-Garcia, Lorenzo Tomassoni, Tanner C. Dalton, Amanda R. Decker-Farrell, Carmine F. Palermo, Daniel R. Ross, Stephen A. Sastra, Urszula N. Wasko, Isabel M. Goncalves, Prabhot Mundi, Basil S. Bakir, Rachael A. Safyan, Hanina Hibshoosh, Gulam A. Manji, Andrea Califano, Kenneth P. Olive. RNA-based precision medicine predicts sensitivity to selinexor in select pancreatic ductal adenocarcinoma patients [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 934.
