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The aryl hydrocarbon receptor (AhR) is a ligand-activated transcription factor that regulates genes of drug transporters and metabolic enzymes to detoxify small molecule xenobiotics. It has a complex role in cancer biology, influencing both the progression and suppression of tumors by modulating malignant properties of tumor cells and anti-tumor immunity, depending on the specific tumor type and developmental stage. This has led to the discovery and development of selective AhR modulators, including BAY 2416964 which is currently in clinical trials. To identify small molecule anticancer agents that might be combined with AhR antagonists for cancer therapy, a high-throughput combination screen was performed using multi-cell type tumor spheroids grown from malignant cells, endothelial cells, and mesenchymal stem cells. The AhR selective antagonists BAY 2416964, GNF351, and CH-223191 were tested individually and in combination with twenty-five small molecule anticancer agents. As single agents, BAY 2416964 and CH-223191 showed minimal activity, whereas GNF351 reduced the viability of some spheroid models at concentrations greater than 1 µM. The activity of most combinations aligned well with the single agent activity of the combined agent, without apparent contributions from the AhR antagonist. All three AhR antagonists sensitized tumor spheroids to TAK-243, an E1 ubiquitin-activating enzyme inhibitor. These combinations were active in spheroids containing bladder, breast, ovary, kidney, pancreas, colon, and lung tumor cell lines. The AhR antagonists also potentiated pevonedistat, a selective inhibitor of the NEDD8-activating enzyme E1 regulatory subunit, in several tumor spheroid models. In contrast, the AhR antagonists did not enhance the cytotoxicity of the proteasome inhibitor bortezomib.

ABSTRACTHistone deacetylases (HDACs) and sirtuins (SIRTs) are important epigenetic regulators of cancer pathways. There is a limited understanding of how transcriptional regulation of their genes is affected by chemotherapeutic agents, and how such transcriptional changes affect tumour sensitivity to drug treatment. We investigated the concerted transcriptional response of HDAC and SIRT genes to 15 approved antitumor agents in the NCI-60 cancer cell line panel. Antitumor agents with diverse mechanisms of action induced upregulation or downregulation of multiple HDAC and SIRT genes. HDAC5 was upregulated by dasatinib and erlotinib in the majority of the cell lines. Tumour cell line sensitivity to kinase inhibitors was associated with upregulation of HDAC5, HDAC1, and several SIRT genes. We confirmed changes in HDAC and SIRT expression in independent datasets. We also experimentally validated the upregulation of HDAC5 mRNA and protein expression by dasatinib in the highly sensitive IGROV1 cell line. HDAC5 was not upregulated in the UACC-257 cell line resistant to dasatinib. The effects of cancer drug treatment on expression of HDAC and SIRT genes may influence chemosensitivity and may need to be considered during chemotherapy.

Background: Ten-eleven-translocation (TET) 2 is a member of the TET family of proteins (TET1-3). DNMT1 gene deletion confers resistance to DNA methyltransferase (DNMT) inhibitors in colorectal, breast, and ovarian cancer cells. Currently, the effect of DNMT1 gene status on TET2 phenotype following DNMT inhibitor treatment is unclear in human malignancies. Methods: Human colorectal carcinoma HCT116 cells (DNMT+/+) and their isogenic DNMT1 knockout (DNMT1–/–) counterpart were treated with DNMT inhibitors. Expression of TET2 and tumor suppressor (p16ink4A and p15ink4B) proteins were examined by Western blot. Apoptosis and CDKN2A promoter demethylation following drug treatment were detected by Annexin-V apoptosis assay and methylation-specific PCR. Results: TET2 expression was robustly increased in DNMT1−/− cells by 0.5 µM and 5 µM decitabine and azacitidine treatment. Augmentation of TET2 expression was accompanied by re-expression of p16ink4A and p15ink4B proteins and CDKN2A promoter demethylation. TET2 upregulation and tumor suppressor re-expression were associated with resistance conferred by DNMT1 deletion. Treatment with 5-aza-4′-thio-2′-deoxycytidine at a low 0.5 µM dose only upregulated TET2 and reduced CDKN2A promoter methylation, and re-expression of p16ink4A in DNMT1−/− cells. DNMT inhibitors showed minimal effects on TET2 upregulation and re-expression of tumor suppressor proteins in cells with intact DNMT1. Conclusions: DNMT1 gene deletion made cancer cells prone to TET2 upregulation and activation of tumor suppressor expression upon DNMT inhibitor challenge. TET2 augmentation is concomitant with resistance to DNMT inhibitors in a DNMT1-deleted state.

Abstract Role of DNA damage and demethylation on anticancer activity of DNA methyltransferase inhibitors (DNMTi) remains undefined. We report the effects of DNMT1 gene deletion/disruption (DNMT1 −/− ) on anticancer activity of a class of DNMTi in vitro, in vivo and in human cancers. The gene deletion markedly attenuated cytotoxicity and growth inhibition mediated by decitabine, azacitidine and 5-aza-4′-thio-2′-deoxycytidine (aza-T-dCyd) in colon and breast cancer cells. The drugs induced DNA damage that concurred with DNMT1 inhibition, subsequent G2/M cell-cycle arrest and apoptosis, and upregulated p21 in DNMT1 +/+ versus DNMT1 −/− status, with aza-T-dCyd the most potent. Tumor growth and DNMT1 were significantly inhibited, and p21 was upmodulated in mice bearing HCT116 DNMT1+/+ xenograft and bladder PDX tumors. DNMT1 gene deletion occurred in ~ 9% human colon cancers and other cancer types at varying degrees. Decitabine and azacitidine demethylated CDKN2A/CDKN2B genes in DNMT1 +/+ and DNMT1 −/− conditions and increased histone-H3 acetylation with re-expression of p16INK4A/p15INK4B in DNMT1 −/− state. Thus, DNMT1 deletion confers resistance to DNMTi, and their anti-cancer activity is determined by DNA damage effects. Patients with DNMT1 gene deletions may not respond to DNMTi treatment.

Abstract Background Several new generation CDK4/6 inhibitors have been developed and approved for breast cancer therapy in combination with endocrine therapeutics. Application of these inhibitors either alone or in combination in other solid tumors has been proposed, but no imaging biomarkers of response have been reported in non-breast cancer animal models. The purpose of this study was to evaluate 3'-[18F]fluoro-3'-deoxythymidine ([18F]FLT) Positron Emission Tomography (PET) as in vivo biomarker of response to palbociclib in a non-breast cancer model. Methods Twenty-four NSG mice bearing patient derived xenografts (PDX) of a well-characterized bladder tumor were randomized into 4 treatment groups: vehicle (n = 6); palbociclib (n = 6); temozolomide (n = 6); and palbociclib plus temozolomide (n = 6) and treated with two cycles of therapy or vehicle. Tumor uptake of [18F]FLT was determined by micro-PET/CT at baseline, 3 days, and 9 days post initiation of therapy. Following the second cycle of therapy, the mice were maintained until their tumors reached a size requiring humane termination. Results [18F]FLT uptake decreased significantly in the palbociclib and combination arms (p = 0.0423 and 0.0106 respectively at day 3 and 0.0012 and 0.0031 at day 9) with stable tumor volume. In the temozolomide arm [18F]FLT uptake increased with day 9 uptake significantly different than baseline (p = 0.0418) and progressive tumor growth was observed during the treatment phase. All groups exhibited progressive disease after day 22, 10 days following cessation of therapy. Conclusion Significant decreases in [18F]FLT uptake as early as three days post initiation of therapy with palbociclib, alone or in combination with temozolomide, in this bladder cancer model correlates with an absence of tumor growth during therapy that persists until day 18 for the palbociclib group and day 22 for the combination group (6 days and 10 days) following cessation of therapy. These results support early modulation of [18F]FLT as an in vivo biomarker predictive of palbociclib therapy response in a non-breast cancer model.

Growing cancer cells effectively evade most programs of regulated cell death, particularly apoptosis. This necessitates a search for alternative therapeutic modalities to cause cancer cell's demise, among them – ferroptosis. One of the obstacles to using pro-ferroptotic agents to treat cancer is the lack of adequate biomarkers of ferroptosis. Ferroptosis is accompanied by peroxidation of polyunsaturated species of phosphatidylethanolamine (PE) to hydroperoxy- (-OOH) derivatives, which act as death signals. We demonstrate that RSL3-induced death of A375 melanoma cells in vitro was fully preventable by ferrostatin-1, suggesting their high susceptibility to ferroptosis. Treatment of A375 cells with RSL3 caused a significant accumulation of PE-(18:0/20:4-OOH) and PE-(18:0/22:4-OOH), the biomarkers of ferroptosis, as well as oxidatively truncated products - PE-(18:0/hydroxy-8-oxo-oct-6-enoic acid (HOOA) and PC-(18:0/HOOA). A significant suppressive effect of RSL3 on melanoma growth was observed in vivo (utilizing a xenograft model of inoculation of GFP-labeled A375 cells into immune-deficient athymic nude mice). Redox phospholipidomics revealed elevated levels of 18:0/20:4-OOH in RSL3-treated group vs controls. In addition, PE-(18:0/20:4-OOH) species were identified as major contributors to the separation of control and RSL3-treated groups, with the highest variable importance in projection predictive score. Pearson correlation analysis revealed an association between tumor weight and contents of PE-(18:0/20:4-OOH) (r = −0.505), PE-18:0/HOOA (r = −0.547) and PE 16:0-HOOA (r = −0.503). Thus, LC-MS/MS based redox lipidomics is a sensitive and precise approach for the detection and characterization of phospholipid biomarkers of ferroptosis induced in cancer cells by radio- and chemotherapy.

Patient-derived xenografts (PDXs) are an appealing platform for preclinical drug studies. A primary challenge in modeling drug response prediction (DRP) with PDXs and neural networks (NNs) is the limited number of drug response samples. We investigate multimodal neural network (MM-Net) and data augmentation for DRP in PDXs. The MM-Net learns to predict response using drug descriptors, gene expressions (GE), and histology whole-slide images (WSIs). We explore whether combining WSIs with GE improves predictions as compared with models that use GE alone. We propose two data augmentation methods which allow us training multimodal and unimodal NNs without changing architectures with a single larger dataset: 1) combine single-drug and drug-pair treatments by homogenizing drug representations, and 2) augment drug-pairs which doubles the sample size of all drug-pair samples. Unimodal NNs which use GE are compared to assess the contribution of data augmentation. The NN that uses the original and the augmented drug-pair treatments as well as single-drug treatments outperforms NNs that ignore either the augmented drug-pairs or the single-drug treatments. In assessing the multimodal learning based on the MCC metric, MM-Net outperforms all the baselines. Our results show that data augmentation and integration of histology images with GE can improve prediction performance of drug response in PDXs.

The tumor suppressor gene DLC1 can be downregulated in cancers through genetic and non-genetic mechanisms. Here, the authors show cytoplasmic EZH2 can directly methylate and destabilize the DLC1 protein, and EZH2 inhibition can increase the DLC1 protein half-life at least 20-fold.

Patient-derived xenograft models (PDX) have been extensively used to study the molecular and clinical features of cancers. Here the authors present a cohort of 536 PDX models from 25 cancers, as well as their genomic and evolutionary profiles and their suitability for clinical trials.

Abstract Background In order to correctly decode phenotypic information from RNA-sequencing (RNA-seq) data, careful selection of the RNA-seq quantification measure is critical for inter-sample comparisons and for downstream analyses, such as differential gene expression between two or more conditions. Several methods have been proposed and continue to be used. However, a consensus has not been reached regarding the best gene expression quantification method for RNA-seq data analysis. Methods In the present study, we used replicate samples from each of 20 patient-derived xenograft (PDX) models spanning 15 tumor types, for a total of 61 human tumor xenograft samples available through the NCI patient-derived model repository (PDMR). We compared the reproducibility across replicate samples based on TPM (transcripts per million), FPKM (fragments per kilobase of transcript per million fragments mapped), and normalized counts using coefficient of variation, intraclass correlation coefficient, and cluster analysis. Results Our results revealed that hierarchical clustering on normalized count data tended to group replicate samples from the same PDX model together more accurately than TPM and FPKM data. Furthermore, normalized count data were observed to have the lowest median coefficient of variation (CV), and highest intraclass correlation (ICC) values across all replicate samples from the same model and for the same gene across all PDX models compared to TPM and FPKM data. Conclusion We provided compelling evidence for a preferred quantification measure to conduct downstream analyses of PDX RNA-seq data. To our knowledge, this is the first comparative study of RNA-seq data quantification measures conducted on PDX models, which are known to be inherently more variable than cell line models. Our findings are consistent with what others have shown for human tumors and cell lines and add further support to the thesis that normalized counts are the best choice for the analysis of RNA-seq data across samples.

Abstract Background Motivated by the size and availability of cell line drug sensitivity data, researchers have been developing machine learning (ML) models for predicting drug response to advance cancer treatment. As drug sensitivity studies continue generating drug response data, a common question is whether the generalization performance of existing prediction models can be further improved with more training data. Methods We utilize empirical learning curves for evaluating and comparing the data scaling properties of two neural networks (NNs) and two gradient boosting decision tree (GBDT) models trained on four cell line drug screening datasets. The learning curves are accurately fitted to a power law model, providing a framework for assessing the data scaling behavior of these models. Results The curves demonstrate that no single model dominates in terms of prediction performance across all datasets and training sizes, thus suggesting that the actual shape of these curves depends on the unique pair of an ML model and a dataset. The multi-input NN (mNN), in which gene expressions of cancer cells and molecular drug descriptors are input into separate subnetworks, outperforms a single-input NN (sNN), where the cell and drug features are concatenated for the input layer. In contrast, a GBDT with hyperparameter tuning exhibits superior performance as compared with both NNs at the lower range of training set sizes for two of the tested datasets, whereas the mNN consistently performs better at the higher range of training sizes. Moreover, the trajectory of the curves suggests that increasing the sample size is expected to further improve prediction scores of both NNs. These observations demonstrate the benefit of using learning curves to evaluate prediction models, providing a broader perspective on the overall data scaling characteristics. Conclusions A fitted power law learning curve provides a forward-looking metric for analyzing prediction performance and can serve as a co-design tool to guide experimental biologists and computational scientists in the design of future experiments in prospective research studies.

Abstract Background Challenges remain on the selection of patients who potentially respond to a class of drugs that target epigenetics for cancer treatment. This study aims to investigate TET2/DNMT3A mutations and antitumor activity of a novel epigenetic agent in multiple human cancer cell lines and animal models. Methods Seventeen cancer cell lines and multiple xenograft models bearing representative human solid tumors were subjected to 4′-thio-2′-deoxycytidine (T-dCyd) or control treatment. Gene mutations in cell lines were examined by whole exome and/or Sanger sequencing. Specific gene expression was measured in cells and xenograft tumor samples by Western blotting and immunohistochemistry. TET2/DNMT3A mutation status in 47,571 human tumor samples was analyzed at cBioPortal for Cancer Genomics. Results Cell survival was significantly inhibited by T-dCyd in breast BT549, lung NCI-H23, melanoma SKMEL5 and renal ACHN cancer lines harboring deleterious TET2 and nonsynonymous DNMT3A mutations compared to 13 lines without such mutation pattern (P = 0.007). The treatment upregulated p21 and induced cell cycle arrest in NCI-H23 cells, and dramatically inhibited their xenograft tumor growth versus wildtype models. T-dCyd administrations led to a significant p21 increase and near eradication of tumor cells in the double-mutant xenografts by histological evaluation. TET2/DNMT3A was co-mutated in human lung, breast, skin and kidney cancers and frequently in angioimmunoblastic and peripheral T cell lymphomas and several types of leukemia. Conclusions Cell and animal models with concurrent mutations in TET2 and DNMT3A were sensitive to T-dCyd treatment. The mutations were detectable in human solid tumors and frequently occur in some hematological malignancies.

Abstract Convolutional neural networks (CNNs) have been successfully used in many applications where important information about data is embedded in the order of features, such as speech and imaging. However, most tabular data do not assume a spatial relationship between features, and thus are unsuitable for modeling using CNNs. To meet this challenge, we develop a novel algorithm, image generator for tabular data (IGTD), to transform tabular data into images by assigning features to pixel positions so that similar features are close to each other in the image. The algorithm searches for an optimized assignment by minimizing the difference between the ranking of distances between features and the ranking of distances between their assigned pixels in the image. We apply IGTD to transform gene expression profiles of cancer cell lines (CCLs) and molecular descriptors of drugs into their respective image representations. Compared with existing transformation methods, IGTD generates compact image representations with better preservation of feature neighborhood structure. Evaluated on benchmark drug screening datasets, CNNs trained on IGTD image representations of CCLs and drugs exhibit a better performance of predicting anti-cancer drug response than both CNNs trained on alternative image representations and prediction models trained on the original tabular data.

The small animal imaging Digital Imaging and Communications in Medicine (DICOM) acquisition context structured report (SR) was developed to incorporate pre-clinical data in an established DICOM format for rapid queries and comparison of clinical and non-clinical datasets. Established terminologies (i.e., anesthesia, mouse model nomenclature, veterinary definitions, NCI Metathesaurus) were utilized to assist in defining terms implemented in pre-clinical imaging and new codes were added to integrate the specific small animal procedures and handling processes, such as housing, biosafety level, and pre-imaging rodent preparation. In addition to the standard DICOM fields, the small animal SR includes fields specific to small animal imaging such as tumor graft (i.e., melanoma), tissue of origin, mouse strain, and exogenous material, including the date and site of injection. Additionally, the mapping and harmonization developed by the Mouse-Human Anatomy Project were implemented to assist co-clinical research by providing cross-reference human-to-mouse anatomies. Furthermore, since small animal imaging performs multi-mouse imaging for high throughput, and queries for co-clinical research requires a one-to-one relation, an imaging splitting routine was developed, new Unique Identifiers (UID’s) were created, and the original patient name and ID were saved for reference to the original dataset. We report the implementation of the small animal SR using MRI datasets (as an example) of patient-derived xenograft mouse models and uploaded to The Cancer Imaging Archive (TCIA) for public dissemination, and also implemented this on PET/CT datasets. The small animal SR enhancement provides researchers the ability to query any DICOM modality pre-clinical and clinical datasets using standard vocabularies and enhances co-clinical studies.

Abstract Background Spontaneously metastatic xenograft models of cancer are infrequent and the few that exist are resource intensive. In xenografts, caliper measurements can be used to determine primary tumor burden and response to therapy but in metastatic disease models determination of the presence of metastatic disease, metastatic burden, and response to therapy are difficult, often requiring serial necropsy. In this study we characterized the development of visceral metastases in a patient derived xenograft model (PDXM) using in vivo imaging. Results We identified and characterized the previously unreported development of spontaneous liver and bone metastasis in a known patient derived xenograft, bladder xenograft BL0293F, developed by Jackson Laboratories and the University of California at Davis and available from the National Cancer Institute Patient-Derived Models Repository [1]. Among FDG-PET/CT, contrast-enhanced MRI and non-contrast MRI, non-contrast T2w MRI was the most effective and efficient imaging technique. On non-contrast T2 weighted MRI, hepatic metastases were observed in over 70% of animals at 52 days post tumor implantation without resection of the xenograft and in 100% of animals at day 52 following resection of the xenograft. In a group of animals receiving one cycle of effective chemotherapy, no animals demonstrated metastasis by imaging, confirming the utility of this model for therapy evaluation. There was good agreement between pathologic grade and extent of involvement observed on MRI T2w imaging. Conclusion PDX BL0293F is a reliable visceral organ (liver) metastatic model with high penetrance in both non-aggravated and post excisional situations, providing a reliable window for therapy intervention prior to required excision of the xenograft. The imaging characteristics of this model are highly favorable for non-clinical research studies of metastatic disease when used in conjunction with non-contrast T2 weighted MRI.

Abstract Background The National Cancer Institute drug pair screening effort against 60 well-characterized human tumor cell lines (NCI-60) presents an unprecedented resource for modeling combinational drug activity. Results We present a computational model for predicting cell line response to a subset of drug pairs in the NCI-ALMANAC database. Based on residual neural networks for encoding features as well as predicting tumor growth, our model explains 94% of the response variance. While our best result is achieved with a combination of molecular feature types (gene expression, microRNA and proteome), we show that most of the predictive power comes from drug descriptors. To further demonstrate value in detecting anticancer therapy, we rank the drug pairs for each cell line based on model predicted combination effect and recover 80% of the top pairs with enhanced activity. Conclusions We present promising results in applying deep learning to predicting combinational drug response. Our feature analysis indicates screening data involving more cell lines are needed for the models to make better use of molecular features.

Three patient-derived NSCLC lines and three well-established NSCLC lines with varied EGFR gene status were compared for expression of EGFR protein, proliferation and epithelial and mesenchymal markers in monolayer, simple spheroid and complex spheroid cultures. The effects of diverse culture conditions and exposure time on the response of the six NSCLC lines to the EGFR inhibitors erlotinib, afatinib, lapatinib, and osimertinib were examined. The clinical Cmax was used as the test concentration to determine whether cells were responsive or resistant to each agent. Among the patient-derived lines, LG0703-F948, which has an EGFR L858R mutation, was responsive to each of the four EGFR inhibitor when grown as spheroids but resistant when grown in monolayer. The HCC827 line, which carries an EGFR E746-A750 deletion, was responsive to each of the four EGFR inhibitors when grown as spheroids or monolayers. NCI-H1975 cells which have an EGFR T790M mutation and an EGFR L858R mutation, were sensitive to osimertinib when propagated as spheroids but not when grown in monolayer. The results suggest that the expression of cell surface targets and response to drugs targeting cell surface proteins varies depending upon cell culture format. These findings may help to explain, in part, the concordance or discordance between cell culture and in vivo findings in experimental systems.

Abstract C57Bl6 (B6) mice devoid of glutathione peroxidases 1 and 2 (Gpx1/2‐DKO) develop ileitis after weaning. We previously showed germ‐free Gpx1/2‐DKO mice of mixed B6.129 background did not develop ileocolitis. Here, we examine the composition of the ileitis provoking microbiota in B6 Gpx1/2‐DKO mice. DNA was isolated from the ileum fecal stream and subjected to high‐throughput sequencing of the V3 and V4 regions of the 16S rRNA gene to determine the abundance of operational taxonomic units (OTUs). We analyzed the role of bacteria by comparing the microbiomes of the DKO and pathology‐free non‐DKO mice. Mice were treated with metronidazole, streptomycin, and vancomycin to alter pathology and correlate the OTU abundances with pathology levels. Principal component analysis based on Jaccard distance of abundance showed 3 distinct outcomes relative to the source Gpx1/2‐DKO microbiome. Association analyses of pathology and abundance of OTUs served to rule out 7–11 of 24 OTUs for involvement in the ileitis. Collections of OTUs were identified that appeared to be linked to ileitis in this animal model and would be classified as commensals. In Gpx1/2‐DKO mice, host oxidant generation from NOX1 and DUOX2 in response to commensals may compromise the ileum epithelial barrier, a role generally ascribed to oxidants generated from mitochondria, NOX2 and endoplasmic reticulum stress in response to presumptive pathogens in IBD. Elevated oxidant levels may contribute to epithelial cell shedding, which is strongly associated with progress toward inflammation in Gpx1/2‐DKO mice and predictive of relapse in IBD by allowing leakage of microbial components into the submucosa.

Reactive oxygen formation plays a mechanistic role in the cardiotoxicity of doxorubicin, a chemotherapeutic agent that remains an important component of treatment programs for breast cancer and hematopoietic malignancies. To examine the role of doxorubicin-induced reactive oxygen species (ROS) in drug-related cardiac apoptosis, murine embryonic fibroblast cell lines were derived from the hearts of glutathione peroxidase 1 (Gpx-1) knockout mice. Cells from homozygous Gpx-1 knockout mice and parental animals were propagated with (Se+) and without (Se-) 100 nM sodium selenite. Activity levels of the peroxide detoxifying selenoprotein glutathione peroxidase (GSHPx) were marginally detectable (

Recent studies suggest that of the molecules postulated to function as inhibitors of the NADPH oxidase family of enzymes iodonium analogs known to broadly interfere with flavin dehydrogenase function demonstrate mechanistic validity as NADPH oxidase poisons. In recent work, we have produced a series of novel iodonium compounds as putative inhibitors of these oxidases. To evaluate the potential utility of two novel molecules with favorable chemical properties, NSC 740104 and NSC 751140, we compared effects of these compounds to the two standard inhibitors of this class, diphenyleneiodonium and di-2-thienyliodonium, with respect to antiproliferative, cell cycle, and gene expression effects in human colon cancer cells that require the function of NADPH oxidase 1. Both new agents blocked NADPH oxidase-related reactive oxygen production, inhibited tumor cell proliferation, produced a G1/S block in cell cycle progression, and inhibited NADPH oxidase 1 expression at the mRNA and protein levels at low nM concentrations in a fashion similar to or better than the parent molecules. These studies suggest that NSC 740104 and NSC 751140 should be developed further as mechanistic tools to better understand the role of NADPH oxidase inhibition as an approach to the development of novel therapeutic agents for colon cancer.

NADPH oxidase 4 (NOX4) is a redox active, membrane-associated protein that contributes to genomic instability, redox signaling, and radiation sensitivity in human cancers based on its capacity to generate H2O2 constitutively. Most studies of NOX4 in malignancy have focused on the evaluation of a small number of tumor cell lines and not on human tumor specimens themselves; furthermore, these studies have often employed immunological tools that have not been well characterized. To determine the prevalence of NOX4 expression across a broad range of solid tumors, we developed a novel monoclonal antibody that recognizes a specific extracellular region of the human NOX4 protein, and that does not cross-react with any of the other six members of the NOX gene family. Evaluation of 20 sets of epithelial tumors revealed, for the first time, high levels of NOX4 expression in carcinomas of the head and neck (15/19 patients), esophagus (12/18 patients), bladder (10/19 patients), ovary (6/17 patients), and prostate (7/19 patients), as well as malignant melanoma (7/15 patients) when these tumors were compared to histologically-uninvolved specimens from the same organs. Detection of NOX4 protein upregulation by low levels of TGF-β1 demonstrated the sensitivity of this new probe; and immunofluorescence experiments found that high levels of endogenous NOX4 expression in ovarian cancer cells were only demonstrable associated with perinuclear membranes. These studies suggest that NOX4 expression is upregulated, compared to normal tissues, in a well-defined, and specific group of human carcinomas, and that its expression is localized on intracellular membranes in a fashion that could modulate oxidative DNA damage.

Mice deficient in glutathione peroxidase (GPx)-1 and -2 (GPx1-/-GPx2-/- double knockout or DKO mice) develop very-early-onset (VEO) ileocolitis, suggesting that lack of defense against reactive oxygen species (ROS) renders susceptibility to intestinal inflammation. Two members of ROS-generating NADPH oxidase family, NOX1 and DUOX2, are highly inducible in the intestinal epithelium. Previously, we reported that Nox1 deficiency ameliorated the pathology in DKO mice (Nox1-TKO). The role of Duox2 in ileocolitis of the DKO mice is evaluated here in Duoxa-TKO mice by breeding DKO mice with Duoxa-/- mice (Duoxa-TKO), which do not have Duox2 activity. Similar to Nox1-TKO mice, Duoxa-TKO mice no longer have growth retardation, shortened intestine, exfoliation of crypt epithelium, crypt abscesses and depletion of goblet cells manifested in DKO mice by 35 days of age. Unlike Nox1-TKO mice, Duoxa-TKO mice still have rampant crypt apoptosis, elevated proliferation, partial loss of Paneth cells and diminished crypt density. Treating DKO mice with NOX inhibitors (di-2-thienyliodonium/DTI and thioridazine/THZ) and an antioxidant (mitoquinone/MitoQ) significantly reduced gut pathology. Furthermore, in the inflamed human colon, DUOX protein expression is highly elevated in the apical, lateral and perinuclear membrane along the whole length of gland. Taken together, we conclude that exfoliation of crypt epithelium, but not crypt apoptosis, is a major contributor to inflammation. Both Nox1 and Duox2 induce exfoliation of crypt epithelium, but only Nox1 induces apoptosis. NOX1 and DUOX2 may be potential therapeutic targets for treating ileocolitis in human patients suffering inflammatory bowel disease (IBD). Keywords: Glutathione peroxidase, NADPH oxidase, Dual oxidase-2, Very-early-onset inflammatory-bowel-disease, Double-knockout mouse

The application of data science in cancer research has been boosted by major advances in three primary areas: (1) Data: diversity, amount, and availability of biomedical data; (2) Advances in Artificial Intelligence (AI) and Machine Learning (ML) algorithms that enable learning from complex, large-scale data; and (3) Advances in computer architectures allowing unprecedented acceleration of simulation and machine learning algorithms. These advances help build in silico ML models that can provide transformative insights from data including: molecular dynamics simulations, next-generation sequencing, omics, imaging, and unstructured clinical text documents. Unique challenges persist, however, in building ML models related to cancer, including: (1) access, sharing, labeling, and integration of multimodal and multi-institutional data across different cancer types; (2) developing AI models for cancer research capable of scaling on next generation high performance computers; and (3) assessing robustness and reliability in the AI models. In this paper, we review the National Cancer Institute (NCI) -Department of Energy (DOE) collaboration, Joint Design of Advanced Computing Solutions for Cancer (JDACS4C), a multi-institution collaborative effort focused on advancing computing and data technologies to accelerate cancer research on three levels: molecular, cellular, and population. This collaboration integrates various types of generated data, pre-exascale compute resources, and advances in ML models to increase understanding of basic cancer biology, identify promising new treatment options, predict outcomes, and eventually prescribe specialized treatments for patients with cancer.

Background & Aims: Defects in intestinal innate defense systems predispose patients to inflammatory bowel disease (IBD). Reactive oxygen species (ROS) generated by nicotinamide-adenine dinucleotide phosphate (NADPH) oxidases in the mucosal barrier maintain gut homeostasis and defend against pathogenic attack. We hypothesized that molecular genetic defects in intestinal NADPH oxidases might be present in children with IBD. Methods: After targeted exome sequencing of epithelial NADPH oxidases NOX1 and DUOX2 on 59 children with very early onset inflammatory bowel disease (VEOIBD), the identified mutations were validated using Sanger Sequencing. A structural analysis of NOX1 and DUOX2 variants was performed by homology in silico modeling. The functional characterization included ROS generation in model cell lines and in in vivo transduced murine crypts, protein expression, intracellular localization, and cell-based infection studies with the enteric pathogens Campylobacter jejuni and enteropathogenic Escherichia coli. Results: We identified missense mutations in NOX1 (c.988G>A, p.Pro330Ser; c.967G>A, p.Asp360Asn) and DUOX2 (c.4474G>A, p.Arg1211Cys; c.3631C>T, p.Arg1492Cys) in 5 of 209 VEOIBD patients. The NOX1 p.Asp360Asn variant was replicated in a male Ashkenazi Jewish ulcerative colitis cohort. Patients with both NOX1 and DUOX2 variants showed abnormal Paneth cell metaplasia. All NOX1 and DUOX2 variants showed reduced ROS production compared with wild-type enzymes. Despite appropriate cellular localization and comparable pathogen-stimulated translocation of altered oxidases, cells harboring NOX1 or DUOX2 variants had defective host resistance to infection with C. jejuni. Conclusions: This study identifies the first inactivating missense variants in NOX1 and DUOX2 associated with VEOIBD. Defective ROS production from intestinal epithelial cells constitutes a risk factor for developing VEOIBD. Keywords: Inflammatory Bowel Disease, NADPH Oxidase, NOX1, DUOX2, Reactive Oxygen Species, VEOIBD