STAG2 is a clinically relevant tumor suppressor in pancreatic ductal adenocarcinoma

Background Pancreatic ductal adenocarcinoma (PDA) is a highly lethal cancer characterized by complex aberrant genomes. A fundamental goal of current studies is to identify those somatic events arising in the variable landscape of PDA genomes that can be exploited for improved clinical outcomes. Methods We used DNA content flow sorting to identify and purify tumor nuclei of PDA samples from 50 patients. The genome of each sorted sample was profiled by oligonucleotide comparative genomic hybridization and targeted resequencing of STAG2. Transposon insertions within STAG2 in a KRASG12D-driven genetically engineered mouse model of PDA were screened by RT-PCR. We then used a tissue microarray to survey STAG2 protein expression levels in 344 human PDA tumor samples and adjacent tissues. Univariate Kaplan Meier analysis and multivariate Cox Regression analysis were used to assess the association of STAG2 expression relative to overall survival and response to adjuvant therapy. Finally, RNAi-based assays with PDA cell lines were used to assess the potential therapeutic consequence of STAG2 expression in response to 18 therapeutic agents. Results STAG2 is targeted by somatic aberrations in a subset (4%) of human PDAs. Transposon-mediated disruption of STAG2 in a KRASG12D genetically engineered mouse model promotes the development of PDA and its progression to metastatic disease. There was a statistically significant loss of STAG2 protein expression in human tumor tissue (Wilcoxon-Rank test) with complete absence of STAG2 staining observed in 15 (4.3%) patients. In univariate Kaplan Meier analysis nearly complete STAG2 positive staining (>95% of nuclei positive) was associated with a median survival benefit of 6.41 months (P = 0.031). The survival benefit of adjuvant chemotherapy was only seen in patients with a STAG2 staining of less than 95% (median survival benefit 7.65 months; P = 0.028). Multivariate Cox Regression analysis showed that STAG2 is an independent prognostic factor for survival in pancreatic cancer patients. Finally, we show that RNAi-mediated knockdown of STAG2 selectively sensitizes human PDA cell lines to platinum-based therapy. Conclusions Based on these iterative findings we propose that STAG2 is a clinically significant tumor suppressor in PDA

Novel Brain-Penetrant, Small-Molecule Tubulin Destabilizers for the Treatment of Glioblastoma

Glioblastoma (GB) is the most lethal brain cancer in adults, with a 5-year survival rate of 5%. The standard of care for GB includes maximally safe surgical resection, radiation, and temozolomide (TMZ) therapy, but tumor recurrence is inevitable in most GB patients. Here, we describe the development of a blood–brain barrier (BBB)-penetrant tubulin destabilizer, RGN3067, for the treatment of GB. RGN3067 shows good oral bioavailability and achieves high concentrations in rodent brains after oral dosing (Cmax of 7807 ng/mL (20 μM), Tmax at 2 h). RGN3067 binds the colchicine binding site of tubulin and inhibits tubulin polymerization. The compound also suppresses the proliferation of the GB cell lines U87 and LN-18, with IC50s of 117 and 560 nM, respectively. In four patient-derived GB cell lines, the IC50 values for RGN3067 range from 148 to 616 nM. Finally, in a patient-derived xenograft (PDX) mouse model, RGN3067 reduces the rate of tumor growth compared to the control. Collectively, we show that RGN3067 is a BBB-penetrant small molecule that shows in vitro and in vivo efficacy and that its design addresses many of the physicochemical properties that prevent the use of microtubule destabilizers as treatments for GB and other brain cancers

Abstract A142: Drug combination screen to identify novel synergy with Minnelide

Abstract Triptolide, which was initially extracted from a Chinese medicinal herb, demonstrates potent antitumor, anti-inflammatory, and immunosuppressive properties, yet it is clinically limited due to its poor solubility, bioavailability, and toxicity. Minnelide, a water-soluble analog of triptolide, has potent antiproliferative activity. It is currently in Phase I/II clinical trials for Acute Myeloid Leukemia (AML) and 7 solid tumor indications, including advanced gastrointestinal tumors, pancreatic cancer, and colorectal cancer. To overcome the potential drug resistance and to improve the drug potency as well as to minimize the drug toxicity, we conducted a high throughput screening (HTS) for drug synergy, combining Minnelide with compounds from the LOPAC and/or Prestwick libraries in 3 different cancer cell lines: BxPC3 (pancreatic), HT29 (colon) and SNU5 (gastric). LOPAC and Prestwick libraries consist of drugs approved by FDA and/or drugs that have reached clinical trial stages, representing 2,274 unique compounds in total. After quality control evaluation and data normalization, hits were selected if greater than 20% inhibition was achieved when combined compared with Minnelide alone. A total of 43, 54, and 47 hits were identified for each cell line, representing an average hit rate of 2.1%. We further prioritized the hits by selecting those that were common in two or more cell lines, which resulted in 9 compounds from Prestwick library and 20 compounds from LOPAC library (Harmane is in common between the two libraries). Only 17 compounds were available for purchase for hits confirmation, which was conducted in a similar manner as in primary HTS, except that drugs were tested in a dose-dependent manner instead of a single concentration done in HTS, a duplicated biological run was performed, and a total of 9 cell lines were screened, with 3 cell lines for each tumor type (pancreatic, colon and gastric). IC50 and Combination Index (CI) values for each drug combination were calculated for each cell line. A fold change of 2 or more for the IC50 values and CI value 50% of the cell lines tested, with Harmane and Harmine demonstrating synergy in all 9 cell lines tested. In addition, CGS-15943, Amiloride, Naphthalimide, and Idazoxan indicated good synergy with Minnelide in at least 6 out of 9 cell lines. Interestingly, Phenamil, an irreversible inhibitor of amiloride-sensitive Na+ channels, showed synergy in all cell lines tested for pancreatic and colon cancers, but not in gastric cancer cell lines. Overall, our study shed light on potential drug combinations with Minnlide for clinical use. Citation Format: Nanyun Tang, Chris Sereduk, Ashok Saluja, Mohana Velagapudi, Haiyong Han, Daniel D Von Hoff, Hongwei Holly Yin. Drug combination screen to identify novel synergy with Minnelide [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference on Molecular Targets and Cancer Therapeutics; 2019 Oct 26-30; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2019;18(12 Suppl):Abstract nr A142. doi:10.1158/1535-7163.TARG-19-A14

Trisulfide Bond‐Mediated Molecular Phototheranostic Platform for “Activatable” NIR‐II Imaging‐Guided Enhanced Gas/Chemo‐Hypothermal Photothermal Therapy

Abstract Tumor microenvironment (TME)‐triggered phototheranostic platform offers a feasible strategy to improve cancer diagnosis accuracy and minimize treatment side effects. Developing a stable and biocompatible molecular phototheranostic platform for TME‐activated second near‐infrared (NIR‐II) fluorescence imaging‐guided multimodal cascade therapy is a promising strategy for creating desirable anticancer agents. Herein, a new NIR‐II fluorescence imaging‐guided activatable molecular phototheranostic platform (IR‐FEP‐RGD‐S‐S‐S‐Fc) is presented for actively targeted tumor imaging and hydrogen sulfide (H2S) gas‐enhanced chemodynamic‐hypothermal photothermal combined therapy (CDT/HPTT). It is revealed for the first time that the coupling distance between IR‐FE and ferrocene is proportional to the photoinduced electron transfer (PET), and the aqueous environment is favorable for PET generation. The part of Cyclic‐RGDfK (cRGDfk) peptides can target the tumor and benefit the endocytosis of nanoparticles. The high‐concentration glutathione (GSH) in the TME will separate the fluorescence molecule and ferrocene by the GSH‐sensitive trisulfide bond, realizing light‐up NIR‐II fluorescence imaging and a cascade of trimodal synergistic CDT/HPTT/gas therapy (GT). In addition, the accumulation of hydroxyl radicals (•OH) and down‐regulation of glutathione peroxidase 4 (GPX4) can produce excessive harmful lipid hydroperoxides, ultimately leading to ferroptosis

Ponatinib Shows Potent Antitumor Activity in Small Cell Carcinoma of the Ovary Hypercalcemic Type (SCCOHT) through Multikinase Inhibition

Purpose: Small cell carcinoma of the ovary, hypercalcemic type (SCCOHT) is a rare, aggressive ovarian cancer in young women that is universally driven by loss of the SWI/SNF ATPase subunits SMARCA4 and SMARCA2. A great need exists for effective targeted therapies for SCCOHT.Experimental Design: To identify underlying therapeutic vulnerabilities in SCCOHT, we conducted high-throughput siRNA and drug screens. Complementary proteomics approaches profiled kinases inhibited by ponatinib. Ponatinib was tested for efficacy in two patient-derived xenograft (PDX) models and one cell-line xenograft model of SCCOHT.Results: The receptor tyrosine kinase (RTK) family was enriched in siRNA screen hits, with FGFRs and PDGFRs being overlapping hits between drug and siRNA screens. Of multiple potent drug classes in SCCOHT cell lines, RTK inhibitors were only one of two classes with selectivity in SCCOHT relative to three SWI/SNF wild-type ovarian cancer cell lines. We further identified ponatinib as the most effective clinically approved RTK inhibitor. Reexpression of SMARCA4 was shown to confer a 1.7-fold increase in resistance to ponatinib. Subsequent proteomic assessment of ponatinib target modulation in SCCOHT cell models confirmed inhibition of nine known ponatinib target kinases alongside 77 noncanonical ponatinib targets in SCCOHT. Finally, ponatinib delayed tumor doubling time 4-fold in SCCOHT-1 xenografts while reducing final tumor volumes in SCCOHT PDX models by 58.6% and 42.5%.Conclusions: Ponatinib is an effective agent for SMARCA4-mutant SCCOHT in both in vitro and in vivo preclinical models through its inhibition of multiple kinases. Clinical investigation of this FDA-approved oncology drug in SCCOHT is warranted. Clin Cancer Res; 24(8); 1932-43. ©2018 AACR

Frequent somatic mutations in MAP3K5 and MAP3K9 in metastatic melanoma identified by exome sequencing

We sequenced eight melanoma exomes to identify new somatic mutations in metastatic melanoma. Focusing on the mitogen-activated protein (MAP) kinase kinase kinase (MAP3K) family, we found that 24% of melanoma cell lines have mutations in the protein-coding regions of either MAP3K5 or MAP3K9. Structural modeling predicted that mutations in the kinase domain may affect the activity and regulation of these protein kinases. The position of the mutations and the loss of heterozygosity of MAP3K5 and MAP3K9 in 85% and 67% of melanoma samples, respectively, together suggest that the mutations are likely to be inactivating. In in vitro kinase assays, MAP3K5 1780F and MAP3K9 W333* variants had reduced kinase activity. Overexpression of MAP3K5 or MAP3K9 mutants in HEK293T cells reduced the phosphorylation of downstream MAP kinases. Attenuation of MAP3K9 function in melanoma cells using siRNA led to increased cell viability after temozolomide treatment, suggesting that decreased MAP3K pathway activity can lead to chemoresistance in melanoma

Beyond the imitation game: Quantifying and extrapolating the capabilities of language models

Language models demonstrate both quantitative improvement and new qualitative capabilities with increasing scale. Despite their potentially transformative impact, these new capabilities are as yet poorly characterized. In order to inform future research, prepare for disruptive new model capabilities, and ameliorate socially harmful effects, it is vital that we understand the present and near-future capabilities and limitations of language models. To address this challenge, we introduce the Beyond the Imitation Game benchmark (BIG-bench). BIG-bench currently consists of 204 tasks, contributed by 442 authors across 132 institutions. Task topics are diverse, drawing problems from linguistics, childhood development, math, common-sense reasoning, biology, physics, social bias, software development, and beyond. BIG-bench focuses on tasks that are believed to be beyond the capabilities of current language models. We evaluate the behavior of OpenAI's GPT models, Google-internal dense transformer architectures, and Switch-style sparse transformers on BIG-bench, across model sizes spanning millions to hundreds of billions of parameters. In addition, a team of human expert raters performed all tasks in order to provide a strong baseline. Findings include: model performance and calibration both improve with scale, but are poor in absolute terms (and when compared with rater performance); performance is remarkably similar across model classes, though with benefits from sparsity; tasks that improve gradually and predictably commonly involve a large knowledge or memorization component, whereas tasks that exhibit "breakthrough" behavior at a critical scale often involve multiple steps or components, or brittle metrics; social bias typically increases with scale in settings with ambiguous context, but this can be improved with prompting

Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models

Language models demonstrate both quantitative improvement and new qualitative capabilities with increasing scale. Despite their potentially transformative impact, these new capabilities are as yet poorly characterized. In order to inform future research, prepare for disruptive new model capabilities, and ameliorate socially harmful effects, it is vital that we understand the present and near-future capabilities and limitations of language models. To address this challenge, we introduce the Beyond the Imitation Game benchmark (BIG-bench). BIG-bench currently consists of 204 tasks, contributed by 442 authors across 132 institutions. Task topics are diverse, drawing problems from linguistics, childhood development, math, common-sense reasoning, biology, physics, social bias, software development, and beyond. BIG-bench focuses on tasks that are believed to be beyond the capabilities of current language models. We evaluate the behavior of OpenAI's GPT models, Google-internal dense transformer architectures, and Switch-style sparse transformers on BIG-bench, across model sizes spanning millions to hundreds of billions of parameters. In addition, a team of human expert raters performed all tasks in order to provide a strong baseline. Findings include: model performance and calibration both improve with scale, but are poor in absolute terms (and when compared with rater performance); performance is remarkably similar across model classes, though with benefits from sparsity; tasks that improve gradually and predictably commonly involve a large knowledge or memorization component, whereas tasks that exhibit "breakthrough" behavior at a critical scale often involve multiple steps or components, or brittle metrics; social bias typically increases with scale in settings with ambiguous context, but this can be improved with prompting.Comment: 27 pages, 17 figures + references and appendices, repo: https://github.com/google/BIG-benc