A precision oncology approach to the pharmacological targeting of mechanistic dependencies in neuroendocrine tumors

Abstract

We introduce and validate a new precision oncology framework for the systematic prioritization of drugs targeting mechanistic tumor dependencies in individual patients. Compounds are prioritized on the basis of their ability to invert the concerted activity of master regulator proteins that mechanistically regulate tumor cell state, as assessed from systematic drug perturbation assays. We validated the approach on a cohort of 212 gastroenteropancreatic neuroendocrine tumors (GEP-NETs), a rare malignancy originating in the pancreas and gastrointestinal tract. The analysis identified several master regulator proteins, including key regulators of neuroendocrine lineage progenitor state and immunoevasion, whose role as critical tumor dependencies was experimentally confirmed. Transcriptome analysis of GEP-NET-derived cells, perturbed with a library of 107 compounds, identified the HDAC class I inhibitor entinostat as a potent inhibitor of master regulator activity for 42% of metastatic GEP-NET patients, abrogating tumor growth in vivo. This approach may thus complement current efforts in precision oncology.

A Modular Master Regulator Landscape Determines the Impact of Genetic Alterations on the Transcriptional Identity of Cancer Cells

“…This was independently validated by assessing statistically significant overlap of MR proteins in proliferation related MR-Blocks with Achilles' project dependencies, suggesting that MR-Blocks associated with other hallmarks (e.g., immunoevasion or migration) may be critical to tumor survival and progression in vivo. Second, they may redirect the search for new cancer drugs development, from the development of inhibitors of signaling proteins that only indirectly affect MR activity and whose effect can be easily bypassed by alternative mutations, to direct MR protein activity inhibitors inducing Tumor Checkpoint collapse, which was shown to abrogate tumor viability in vivo, see for instance (Alvarez et al, 2018;Califano and Alvarez, 2017). This is especially relevant because, over the last decade, regulatory proteins are relinquishing their status as undruggable targets, for instance as a result of novel covalent inhibitors targeting protein cystines (Singh et al, 2011) or via activation of degron mechanisms (Gan et al, 2015).…”

“…In addition, we provide both the Tumor Checkpoint MRs for the 112 tumor subtypes identified by the analysis as well as the MRs in the 24 MR-Blocks. These represent a comprehensive new collection of candidate tumor dependencies and therapeutic targets and outcome/drug-sensitivity biomarkers, several of which have been validated in previous studies, see for instance (Alvarez et al, 2018;Aytes et al, 2014b;Bisikirska et al, 2016;Carro et al, 2010;Rajbhandari et al, 2018b;Walsh et al, 2017). Given the pan-cancer nature of this work, in the following sections we will use different tumor types to highlight key advantages and novel findings made possible by the MOMA framework.…”

“…The Oncotecture hypothesis, which represents the cancer-specific counterpart of the Omnigene Hypothesis in human genetics (Boyle et al, 2017), is supported by a wealth of experimental evidence, from prostate cancer (Aytes et al, 2014b) and breast cancer (Rodriguez-Barrueco et al, 2015;Walsh et al, 2017), to glioblastoma (Carro et al, 2010), neuroblastoma (Rajbhandari et al, 2018a), and neuroendocrine tumors (Alvarez et al, 2018), see (Califano and Alvarez, 2017) for a comprehensive overview, but has not yet been comprehensively and systematically assessed across multiple tumor types.…”

Long intergenic noncoding RNA profiles of pheochromocytoma and paraganglioma: A novel prognostic biomarker

“…Transcriptomic profiles of three types of gastro‐entero‐pancreatic neuroendocrine cancers were collected from the Gene Expression Omnibus with series accession no. GSE98894 . A separate validation cohort of transcriptomic profiles of 40 PCPG samples with molecular subtyping annotations, was provided by Dr. Richard Tothill .…”

“…GSE98894. 31 A separate validation cohort of transcriptomic profiles of 40 PCPG samples with molecular subtyping annotations, was provided by Dr. Richard Tothill. 15 The raw gene expression count data for all tumor samples were processed with the R package edgeR and converted to log counts per million (logCPM) before unsupervised clustering analysis between tumor samples or use in machine-learning algorithms.…”