Biology subjects

Doroshow, J. H.

Publications and source records attributed to Doroshow, J. H..

8 recordsLinked to original sources

bioRxiv2024

Combinations of RAS pathway inhibitors with targeted agents are active in spheroids of patient-derived cells with oncogenic KRAS variants from multiple cancer types

The KRAS gene is among the most frequently altered genes in cancer and the KRAS protein was long deemed undruggable. Recent strategies to target oncogenic KRAS have included both direct inhibition of the KRAS protein and indirect inhibition of its activity by targeting upstream and downstream signaling pathway mediators. A high-throughput screen of multi-cell type tumor spheroids was designed to identify active combinations of targeted small molecules and KRAS pathway inhibitors. Inhibitors of the non-receptor protein tyrosine phosphatase SHP2 and the guanine nucleotide exchange factor SOS1 were tested to evaluate indirect upstream pathway inhibition, while sotorasib directly inhibited the KRAS G12C variant. As single agents, sotorasib and the SHP2 inhibitor batoprotafib (TNO155) exhibited selectivity towards spheroids with KRAS G12C, whereas the SOS1 inhibitor BI-3406 showed varying activity across KRAS variants. Vertical inhibition of the RAS/MEK/ERK pathway by targeting SHP2 or SOS1 and the downstream kinases MEK (trametinib) or ERK (temuterkib) was highly effective. Inhibition of upstream tyrosine receptor kinases with nintedanib in combination with batoprotafib or BI-3406 was also effective, and in combination with sotorasib, demonstrated synergy in spheroids harboring KRAS G12C. Dual inhibition of the RAS/MEK/ERK and PI3K/AKT/mTOR pathways with batoprotafib or sotorasib with either the mTORC1/2 inhibitor sapanisertib or the AKT inhibitor ipatasertib demonstrated combination activity, primarily in spheroids harboring KRAS G12C. Combination of the BCL-2 inhibitor venetoclax with sotorasib, batoprotafib or BI-3406 resulted in additive and synergistic cytotoxicity. Lastly, concurrent inhibition of the KRAS pathway with sotorasib and batoprotafib demonstrated combination activity in spheroids containing KRAS G12C. SIGNIFICANCEKRAS variants are oncogenic drivers for a range of human cancers. Multiple combinations of small molecule agents that target RAS signaling were screened and reduced the viability of multi-cell type spheroid models for a variety of human solid tumors. Combinations warranting further testing were identified.

cancer biology↗

bioRxiv2024

Drug combinations with apoptosis pathway targeted agents alrizomadlin, pelcitoclax, and dasminapant in multi-cell type tumor spheroids

Apoptosis, or programmed cell death, plays a critical role in maintaining tissue homeostasis by eliminating damaged or abnormal cells. Dysregulation of apoptosis pathways is a hallmark of cancer, allowing malignant cells to evade cell death and proliferate uncontrollably. Targeting apoptosis pathways has emerged as a promising therapeutic strategy in cancer treatment, aiming to restore the balance between cell survival and death. In this context, the MDM2 inhibitor alrizomadlin, the Bcl-2/Bcl-xL inhibitor pelcitoclax, and the IAP family inhibitor dasminapant were evaluated both individually and in combination with standard of care and investigational anticancer small molecules with a spheroid model of solid tumors. The multi-cell type tumor spheroids were grown from endothelial cells and mesenchymal stem cells combined with human malignant cells that were either established or patient-derived cell lines from the NCI Patient- Derived Models Repository. The malignant cell lines were derived from a range of solid tumors including uterine carcinosarcoma, synovial sarcoma, rhabdomyosarcoma, soft tissue sarcoma, malignant fibrous histiocytoma, malignant peripheral nerve sheath tumor (MPNST), pancreas, ovary, colon, breast, and small cell lung cancer. Interactions were observed from combinations of the apoptosis pathway targeted agents. Additionally, interactions were observed from combinations of the apoptosis pathway targeted agents with other agents, including PARP inhibitors, the XPO1 inhibitor eltanexor, and the PI3K inhibitor copanlisib. Enhanced activity was also observed from combinations of the apoptosis pathway targeted agents with MAPK pathway targeted agents, including the MEK inhibitor cobimetinib as well as adagrasib and MRTX1133, which specifically target the KRAS G12C and G12D variants, respectively. SIGNIFICANCEMulti-cell type tumor spheroids grown from normal and patient-derived malignant cell lines were screened to identify potentially efficacious combinations with the investigational agents alrizomadlin, pelcitoclax or dasminapant that target cell death pathways. This article highlights novel combinations with FDA approved drugs including eltanexor, cobimetinib and copanlisib.

cancer biology↗

bioRxiv2024

Data Imbalance in Drug Response Prediction - Multi-Objective Optimization Approach in Deep Learning Setting

Drug response prediction (DRP) methods tackle the complex task of associating the effectiveness of small molecules with the specific genetic makeup of the patient. Anti-cancer DRP is a particularly challenging task requiring costly experiments as underlying pathogenic mechanisms are broad and associated with multiple genomic pathways. The scientific community has exerted significant efforts to generate public drug screening datasets, giving a path to various machine learning (ML) models that attempt to reason over complex data space of small compounds and biological characteristics of tumors. However, the data depth is still lacking compared to computer vision or natural language processing domains, limiting current learning capabilities. To combat this issue and increase the generalizability of the DRP models, we are exploring strategies that explicitly address the imbalance in the DRP datasets. We reframe the problem as a multi-objective optimization across multiple drugs to maximize deep learning model performance. We implement this approach by constructing Multi-Objective Optimization Regularized by Loss Entropy (MOORLE) loss function and plugging it into a Deep Learning model. We demonstrate the utility of proposed drug discovery methods and make suggestions for further potential application of the work to promote equitable outcomes in the healthcare field. Availabilityhttps://github.com/AlexandrNP/MOORLE Contactonarykov@anl.gov

cancer biology↗

bioRxiv2022

A pan-cancer PDX histology image repository with genomic and pathological annotations for deep learning analysis

Patient-derived xenografts (PDXs) model human intra-tumoral heterogeneity in the context of the intact tissue of immunocompromised mice. Histological imaging via hematoxylin and eosin (H&E) staining is performed on PDX samples for routine assessment and, in principle, captures the complex interplay between tumor and stromal cells. Deep learning (DL)-based analysis of large human H&E image repositories has extracted inter-cellular and morphological signals correlated with disease phenotype and therapeutic response. Here, we present an extensive, pan-cancer repository of nearly 1,000 PDX and paired human progenitor H&E images. These images, curated from the PDXNet consortium, are associated with genomic and transcriptomic data, clinical metadata, pathological assessment of cell composition, and, in several cases, detailed pathological annotation of tumor, stroma, and necrotic regions. We demonstrate that DL can be applied to these images to classify tumor regions and to predict xenograft-transplant lymphoproliferative disorder, the unintended outgrowth of human lymphocytes at the transplantation site. This repository enables PDX-specific, investigations of cancer biology through histopathological analysis and contributes important model system data that expand on existing human histology repositories. We expect the PDXNet Image Repository to be valuable for controlled digital pathology analysis, both for the evaluation of technical issues such as stain normalization and for development of novel computational methods based on spatial behaviors within cancer tissues.

bioinformatics↗

bioRxiv2022

Exogenous DNA upregulates DUOX2 expression and function in human pancreatic cancer cells by activating the cGAS-STING signaling pathway

Pro-inflammatory cytokines upregulate the expression of the H2O2-producing NADPH oxidase dual oxidase 2 (DUOX2) which, when elevated, adversely affects survival from pancreatic ductal adenocarcinoma (PDAC). Because the cGAS-STING pathway is known to initiate pro-inflammatory cytokine expression following uptake of exogenous DNA, we examined whether activation of cGAS-STING could play a role in the generation of reactive oxygen species by PDAC cells. Here, we found that a variety of exogenous DNA species markedly increased the production of cGAMP, the phosphorylation of TBK1 and IRF3, and the translocation of phosphorylated IRF3 into the nucleus, leading to a significant, IRF3-dependent enhancement of DUOX2 expression, and a significant flux of H2O2 in PDAC cells. However, unlike the canonical cGAS-STING pathway, DNA-related DUOX2 upregulation was not mediated by NF-{kappa}B; and although exogenous IFN-{beta} significantly increased Stat1/2-associated DUOX2 expression, intracellular IFN-{beta} signaling that followed cGAMP or DNA exposure did not itself increase DUOX2 levels. Finally, DUOX2 upregulation subsequent to cGAS-STING activation was accompanied by the enhanced, normoxic expression of HIF-1 as well as DNA double strand cleavage, suggesting that cGAS-STING signaling may support the development of an oxidative, pro-angiogenic microenvironment that could contribute to the inflammation-related genetic instability of pancreatic cancer.

cancer biology↗

bioRxiv2022

Dexamethasone Inhibits Cytokine-Induced, DUOX2-Related VEGF-A Expression and DNA damage in Human Pancreatic Cancer Cells and Growth of Pancreatic Cancer Xenografts

Previously, we demonstrated that pro-inflammatory cytokines enhance dual oxidase 2 (DUOX2)-dependent production of reactive oxygen species by human pancreatic ductal carcinoma (PDAC) cells, and that DUOX2 expression is significantly increased in patients with early stages of PDAC. In genetically-engineered mouse models of PDAC, dexamethasone (Dex) decreases formation of pancreatic intraepithelial neoplasia (PanIn) foci as well as PDAC invasiveness. Herein, we report that Dex, in a concentration- and time-dependent fashion, inhibited pro-inflammatory cytokine (IFN-{gamma}/LPS/IL-17A/IL-4)-mediated enhancement of DUOX2 expression in BxPC-3, CFPAC-1, and AsPC-1 human PDAC cell lines, as well as DUOX2-induced DNA damage. The inhibitory effects of Dex were abolished by pre-treatment with the Dex antagonist RU-486. Examination of the human DUOX2 promoter in silico revealed a putative negative glucocorticoid receptor (GR) binding element (IRnGRE). Western analysis, using nuclear extracts from Dex-treated PDAC cells, demonstrated that Dex activated the glucocorticoid receptor in PDAC cell nuclei in the presence of certain co-repressors, such as NCoR-1/2 and histone deacetylases (HDAC1, 2, and 3). Dex produced no anti-proliferative effects on PDAC cells in vitro. However, Dex significantly decreased the growth of BxPC-3 xenografts while decreasing inflammatory and immune cell infiltration of the microenvironment, as well as the mRNA expression of DUOX2 and VEGF-A, in BxPC-3 tumors. In contrast, Dex had no effect on the growth of xenografts developed from MIA-PaCa cells that are unresponsive to pro-inflammatory cytokines in culture. In summary, these studies suggest that suppression of inflammation-related DUOX2 expression by Dex could diminish the oxidative milieu supporting PDAC growth and development.

pharmacology and toxicology↗

bioRxiv2021

PDXNet Portal: Patient-Derived Xenograft model, data, workflow, and tool discovery

We created the PDX Network (PDXNet) Portal (https://portal.pdxnetwork.org/) to centralize access to the National Cancer Institute-funded PDXNet consortium resources (i.e., PDX models, sequencing data, treatment response data, and bioinformatics workflows), to facilitate collaboration among researchers, and to make resources easily available for research. The portal includes sections for resources, analysis results, metrics for PDXNet activities, data processing protocols, and training materials for processing PDX data. The initial portal release highlights PDXNet model and data resources, including 334 new models across 33 cancer types. Tissue samples of these models were deposited in the NCIs Patient-Derived Model Repository (PDMR) for public access. These models have 2,822 associated sequencing files from 873 samples across 307 patients, which are hosted on the Cancer Genomics Cloud powered by Seven Bridges and the NCI Cancer Data Service for long-term storage and access with dbGaP permissions. The portal also includes results from standardized analysis workflows on PDXNet sequencing files and PDMR data (2,594 samples from 463 patients across 78 disease types). These 15 analysis workflows for whole-exome and RNA-Seq data are freely available, robust, validated, and standardized. The model and data lists will grow substantially over the next two years and will be continuously updated as new data are available. PDXNet models support multi-agent treatment studies, determination of sensitivity and resistance mechanisms, and preclinical trials. The PDXNet portal is a centralized location for these data and resources, which we expect to be of significant utility for the cancer research community.

cancer biology↗

bioRxiv2019

Systematic Establishment of Robustness and Standards in Patient-Derived Xenograft Experiments and Analysis

Patient-Derived Xenografts (PDXs) are tumor-in-mouse models for cancer. PDX collections, such as those supported by the NCI PDXNet program, are powerful resources for preclinical therapeutic testing. However, variations in experimental design and analysis procedures have limited interpretability. To determine the robustness of PDX studies, the PDXNet tested temozolomide drug response for three pre-validated PDX models (sensitive, resistant, and intermediate) across four blinded PDX Development and Trial Centers (PDTCs) using independently selected SOPs. Each PDTC was able to correctly identify the sensitive, resistant, and intermediate models, and statistical evaluations were concordant across all groups. We also developed and benchmarked optimized PDX informatics pipelines, and these yielded robust assessments across xenograft biological replicates. These studies show that PDX drug responses and sequence results are reproducible across diverse experimental protocols. Here we share the range of experimental procedures that maintained robustness, as well as standardized cloud-based workflows for PDX exome-seq and RNA-Seq analysis and for evaluating growth.

cancer biology↗