A Network of Conserved Synthetic Lethal Interactions for Exploration of Precision Cancer Therapy.

An emerging therapeutic strategy for cancer is to induce selective lethality in a tumor by exploiting interactions between its driving mutations and specific drug targets. Here we use a multi-species approach to develop a resource of synthetic lethal interactions relevant to cancer therapy. First, we screen in yeast ∼169,000 potential interactions among orthologs of human tumor suppressor genes (TSG) and genes encoding drug targets across multiple genotoxic environments. Guided by the strongest signal, we evaluate thousands of TSG-drug combinations in HeLa cells, resulting in networks of conserved synthetic lethal interactions. Analysis of these networks reveals that interaction stability across environments and shared gene function increase the likelihood of observing an interaction in human cancer cells. Using these rules, we prioritize ∼10(5) human TSG-drug combinations for future follow-up. We validate interactions based on cell and/or patient survival, including topoisomerases with RAD17 and checkpoint kinases with BLM.

Ruxolitinib reverses checkpoint inhibition by reducing programmed cell death ligand-1 (PD-L1) expression and increases anti-tumour effects of T cells in multiple myeloma.

Multiple myeloma (MM) tumour cells evade host immunity through a variety of mechanisms, which may potentially include the programmed cell death ligand-1 (PD-L1):programmed cell death protein-1 (PD-1) axis. This interaction contributes to the immunosuppressive bone marrow (BM) microenvironment, ultimately leading to reduced effector cell function. PD-L1 is overexpressed in MMBM and is associated with the resistance to immune-based approaches for treating MM. Ruxolitinib (RUX), an inhibitor of the Janus kinase (JAK) family of protein tyrosine kinases, is approved for myeloproliferative diseases. We investigated the effects of RUX alone or in combination with anti-MM agents on the expression of PD-L1 and T-cell cytotoxicity in MM. We showed that the expression of the PD-L1 gene was markedly increased in BM mononuclear cells from patients with MM with progressive disease versus those in complete remission. Furthermore, RUX treatment resulted in a concentration-dependent reduction of PD-L1 gene expression in the MM tumour cells cultured alone or co-cultured with stromal cells compared with untreated cells. The results also demonstrated that RUX increased MM cell apoptosis in the presence of interleukin-2-stimulated T cells to a similar degree as the treatment with anti-PD-1 or anti-PD-L1 antibodies. In summary, these results indicate that RUX can block PD-L1 expression resulting in augmentation of anti-MM effects of T cells.

pyNBS: a Python implementation for network-based stratification of tumor mutations.

Summary: We present pyNBS: a modularized Python 2.7 implementation of the network-based stratification (NBS) algorithm for stratifying tumor somatic mutation profiles into molecularly and clinically relevant subtypes. In addition to release of the software, we benchmark its key parameters and provide a compact cancer reference network that increases the significance of tumor stratification using the NBS algorithm. The structure of the code exposes key steps of the algorithm to foster further collaborative development. Availability and implementation: The package, along with examples and data, can be downloaded and installed from the URL https://github.com/idekerlab/pyNBS.

BDNF Val66Met polymorphism and clinical response to antipsychotic treatment in schizophrenia and schizoaffective disorder patients: a meta-analysis.

Brain-derived neurotrophic factor (BDNF) plays an important role in dopaminergic and serotonergic neurotransmission by modulating dopaminergic neuron differentiation and establishment. Multiple studies have analyzed the functional BDNF Val66Met variant in relation to antipsychotic response in schizophrenia (SCZ) patients, yielding mixed results. A meta-analysis was thus performed to examine the relationship between this variant and symptom improvement during antipsychotic treatment. Searches using PubMed, Web of Science, and PsycInfo until October 2017 yielded 11 studies that met inclusion criteria (total n = 3774). These studies investigated the BDNF Val66Met variant and antipsychotic response in patients with SCZ or schizoaffective disorder. Responders to antipsychotics were defined using the original criteria applied in each study. Effect sizes were computed using odds ratios, which were pooled according to the Mantel-Haenszel method. The BDNF Val66Met variant was not associated with the total number of responders and non-responders (p > 0.05) under dominant, recessive, or allelic models. Secondary analyses stratifying for individuals of each ethnicity and drug type also revealed no significant associations. Our findings suggest that the BDNF Val66Met variant is not associated with response to antipsychotics in individuals with SCZ. However, considering the current sample size, small effects cannot be ruled out. Moreover, recent studies have suggested that Val66Met forms haplotypes with other BDNF variants. Future studies should examine the Val66Met variant in conjunction with these other variants in relation to antipsychotic response. Moreover, since illness duration appears to influence BDNF levels in SCZ patients, future studies should aim to control for this potential confounding factor in response analyses.

Predictive modeling of in vivo response to gemcitabine in pancreatic cancer.

A clear contradiction exists between cytotoxic in-vitro studies demonstrating effectiveness of Gemcitabine to curtail pancreatic cancer and in-vivo studies failing to show Gemcitabine as an effective treatment. The outcome of chemotherapy in metastatic stages, where surgery is no longer viable, shows a 5-year survival <5%. It is apparent that in-vitro experiments, no matter how well designed, may fail to adequately represent the complex in-vivo microenvironmental and phenotypic characteristics of the cancer, including cell proliferation and apoptosis. We evaluate in-vitro cytotoxic data as an indicator of in-vivo treatment success using a mathematical model of tumor growth based on a dimensionless formulation describing tumor biology. Inputs to the model are obtained under optimal drug exposure conditions in-vitro. The model incorporates heterogeneous cell proliferation and death caused by spatial diffusion gradients of oxygen/nutrients due to inefficient vascularization and abundant stroma, and thus is able to simulate the effect of the microenvironment as a barrier to effective nutrient and drug delivery. Analysis of the mathematical model indicates the pancreatic tumors to be mostly resistant to Gemcitabine treatment in-vivo. The model results are confirmed with experiments in live mice, which indicate uninhibited tumor proliferation and metastasis with Gemcitabine treatment. By extracting mathematical model parameter values for proliferation and death from monolayer in-vitro cytotoxicity experiments with pancreatic cancer cells, and simulating the effects of spatial diffusion, we use the model to predict the drug response in-vivo, beyond what would have been expected from sole consideration of the cancer intrinsic resistance. We conclude that this integrated experimental/computational approach may enhance understanding of pancreatic cancer behavior and its response to various chemotherapies, and, further, that such an approach could predict resistance based on pharmacokinetic measurements with the goal to maximize effective treatment strategies.

Chloroquine-mediated cell death in metastatic pancreatic adenocarcinoma through inhibition of autophagy.

CONTEXT: Cells in the interior of pancreatic tumors are believed to undergo continual autophagy to maintain homeostasis during unregulated growth in hypoxia caused by impaired vascularity. We hypothesize that treating metastatic cells with chloroquine, an inhibitor of autophagy, in hypoxia will decrease cell viability and induce cell death. DESIGN: MiaPaCa2 (non-metastatic) and S2VP10 (metastatic) cell lines were treated with 25 and 50 µM chloroquine for 24 and 48 hours in normoxia and hypoxia (5-10% O2). Viability was measured using ATPlite™. After treatment, the cell stress was analyzed, and protein was lysed and quantified using the Bradford assay. Autophagy-associated protein levels were determined by Western blot. RESULTS: S2VP10 cells treated for 48 hours with 50 µM chloroquine in hypoxia had 24% viability compared to normoxia control, with loss of 10% viability caused by low O2 alone. MiaPaCa2 cells under these conditions had 60% viability compared to normoxia control, with loss of 25% viability caused by low O2 alone. Analysis of cell stress pathways and dosimetry of Western blot data suggest that chloroquine inhibits the autophagy pathway in the metastatic S2VP10 cells. CONCLUSION: Autophagy blockage with chloroquine or similar-acting drugs may serve as a viable therapy for highly metastatic pancreatic cancers.

A comprehensive meta-analysis and systematic review of breath analysis in detection of COVID-19 through Volatile organic compounds.

BACKGROUND: The COVID-19 pandemic had profound global impacts on daily lives, economic stability, and healthcare systems. Diagnosis of COVID-19 infection via RT-PCR was crucial in reducing spread of disease and informing treatment management. While RT-PCR is a key diagnostic test, there is room for improvement in the development of diagnostic criteria. Identification of volatile organic compounds (VOCs) in exhaled breath provides a fast, reliable, and economically favorable alternative for disease detection. METHODS: This meta-analysis analyzed the diagnostic performance of VOC-based breath analysis in detection of COVID-19 infection. A systematic review of twenty-nine papers using the grading criteria from Newcastle-Ottawa Scale (NOS) and PRISMA guidelines was conducted. RESULTS: The cumulative results showed a sensitivity of 0.92 (95 % CI, 90 %-95 %) and a specificity of 0.90 (95 % CI 87 %-93 %). Subgroup analysis by variant demonstrated strong sensitivity to the original strain compared to the Omicron and Delta variant in detection of SARS-CoV-2 infection. An additional subgroup analysis of detection methods showed eNose technology had the highest sensitivity when compared to GC-MS, GC-IMS, and high sensitivity-MS. CONCLUSION: Overall, these results support the use of breath analysis as a new detection method of COVID-19 infection.

Detection of COVID-19 by quantitative analysis of carbonyl compounds in exhaled breath.

COVID-19 has caused a worldwide pandemic, creating an urgent need for early detection methods. Breath analysis has shown great potential as a non-invasive and rapid means for COVID-19 detection. The objective of this study is to detect patients infected with SARS-CoV-2 and even the possibility to screen between different SARS-CoV-2 variants by analysis of carbonyl compounds in breath. Carbonyl compounds in exhaled breath are metabolites related to inflammation and oxidative stress induced by diseases. This study included a cohort of COVID-19 positive and negative subjects confirmed by reverse transcription polymerase chain reaction between March and December 2021. Carbonyl compounds in exhaled breath were captured using a microfabricated silicon microreactor and analyzed by ultra-high-performance liquid chromatography-mass spectrometry (UHPLC-MS). A total of 321 subjects were enrolled in this study. Of these, 141 (85 males, 60.3%) (mean ± SD age: 52 ± 15 years) were COVID-19 (55 during the alpha wave and 86 during the delta wave) positive and 180 (90 males, 50%) (mean ± SD age: 45 ± 15 years) were negative. Panels of a total of 34 ketones and aldehydes in all breath samples were identified for detection of COVID-19 positive patients. Logistic regression models indicated high accuracy/sensitivity/specificity for alpha wave (98.4%/96.4%/100%), for delta wave (88.3%/93.0%/84.6%) and for all COVID-19 positive patients (94.7%/90.1%/98.3%). The results indicate that COVID-19 positive patients can be detected by analysis of carbonyl compounds in exhaled breath. The technology for analysis of carbonyl compounds in exhaled breath has great potential for rapid screening and detection of COVID-19 and for other infectious respiratory diseases in future pandemics.

Association between echocardiographic features, troponin levels, and survival time in hospitalized COVID-19 patients with cardiovascular events.

Introduction: This study aims to explore the predictive roles of echocardiographic parameters and biomarkers in determining outcomes among hospitalized COVID-19 patients experiencing cardiovascular events. Methods: A retrospective cohort study was conducted involving 49 COVID-19 patients who encountered cardiovascular events during hospitalization and underwent echocardiography. Our findings revealed notable associations between echocardiographic parameters and survival time. Results: A decrease in left ventricular ejection fraction (LVEF) of 10% was linked to a 20% reduction in survival time (TR: 0.80, 95% CI: 0.67 - 0.96, p = .017). Similarly, an increase in left ventricular (LV) volume by 10 mL was associated with a 9% decrease in survival time (TR: 0.91, 95% CI: 0.84 - 0.98, p = .011). Moreover, an increase in left atrial (LA) volume by 10 mL corresponded to an 8% decrease in survival time (TR: 0.92, 95% CI: 0.86 - 0.99, p = .026). Additionally, each 1 cm increase in right ventricular (RV) diameter was linked to a 22% reduction in survival time (TR: 0.78, 95% CI: 0.61 - 0.99, p = .043). Furthermore, a 10 mL increase in right atrial (RA) volume was associated with a 12% decrease in survival time (TR: 0.88, 95% CI: 0.78 - 0.98, p = .017). Notably, a tenfold rise in troponin levels was linked to a 33% decrease in survival time (TR: 0.67, 95% CI: 0.48 - 0.93, p = .014). Conclusions: Our study emphasizes the significant associations between various echocardiographic parameters and troponin levels with reduced survival time among COVID-19 patients experiencing cardiovascular events. These findings highlight the potential utility of echocardiography and troponin assessment in predicting outcomes and guiding management strategies in this patient population.

Different clinical characteristics and outcomes of adult hospitalized SARS-CoV-2 pneumonia patients complicated by cardiovascular events during the first, delta and omicron waves of COVID-19.

Background: The effects of SARS-CoV-2 have varied between significant waves of hospitalization. Research question: Are cardiovascular complications different among the first, delta and omicron waves of hospitalized COVID-19 pneumonia patients? Study design and methods: This was a multi-centre retrospective study of patients hospitalized with SARS-CoV-2 pneumonia: 632 were hospitalized during the first wave (March-July 2020), 1013 during the delta wave (September 2020-March 2021), and 323 during the omicron wave (January 2022-July 2022). Patients were stratified by wave and occurrence of cardiovascular events. Results: Among all hospitalized patients with cardiovascular events, patients in the omicron wave were younger (62.4 ± 14 years) than patients in the first wave (67.4 ± 7.8 years) and the delta wave (66.9 ± 12.6 years) and had a higher proportion of non-Hispanic White people than in the first wave (78.6% vs. 61.7%). For COVID-19 patients who suffered from cardiovascular events, the omicron wave patients had significantly higher neutrophil/lymphocyte ratio, white blood cell and platelet counts when compared to the first wave. Omicron wave patients had significantly lower albumin and B-type natriuretic peptide levels (only 5.8% of the first wave and 14.6% of the delta wave) when compared to either the first wave or delta wave patients. In COVID-19 patients who suffered cardiovascular events during hospitalization, mortality rate in the omicron wave (26.8%) was significantly lower than the first wave (48.3%), time to mortality for non-survivors of COVID-19 patients who suffered cardiovascular events was significantly longer in the omicron wave (median 16 days) than in the first wave (median 10 days). Conclusions: Younger and white patients were affected with cardiovascular complications more often by the omicron variant. Despite higher neutrophil/lymphocyte ratio and WBC counts, the omicron patients with cardiovascular events showed lower heart injuries, lower mortality and longer time to mortality for non-survivors when compared to the first and delta waves.

Inhibition of autophagy with chloroquine is effective in melanoma.

BACKGROUND: Cancer cells adapt to the stress resulting from accelerated cell growth and a lack of nutrients by activation of the autophagy pathway. Two proteins that allow cell growth in the face of metabolic stress and hypoxia are hypoxia-inducible factor-1α (HIF-1α) and heat shock protein 90 (Hsp 90). We hypothesize that chloroquine (CQ), an antimalarial drug that inhibits autophagosome function, in combination with either echinomycin, a HIF-1α inhibitor, or 17-dimethylaminoethylamino-17-dimethoxygeldanamycin (17-DMAG), an Hsp 90 inhibitor, will result in cytotoxicity in melanoma. MATERIALS AND METHODS: Multiple human melanoma cell lines (BRAF wild-type and mutant) were tested in vitro with CQ in combination with echinomycin or 17-DMAG. These treatments were performed in hypoxic (5% O2) and normoxic (18% O2) conditions. Mechanism of action was determined through Western blot of autophagy-associated proteins HIF-1α and Hsp 90. RESULTS: Chloroquine, echinomycin, and 17-DMAG each induced cytotoxicity in multiple human melanoma cell lines, in both normoxia and hypoxia. Chloroquine combined with echinomycin achieved synergistic cytotoxicity under hypoxic conditions in multiple melanoma cell lines (BRAF wild-type and mutant). Western blot analysis indicated that echinomycin reduced HIF-1α levels, both alone and in combination with CQ. Changes in LC3 flux indicated inhibition of autophagy at the level of the autophagosome by CQ therapy. CONCLUSIONS: Targeting autophagy with the antimalarial drug CQ may be an effective cancer therapy in melanoma. Sensitivity to chloroquine is independent of BRAF mutational status. Combining CQ with the HIF-1α inhibitor echinomycin improves cytotoxicity in hypoxic conditions.

The cost of anti-Asian racism during the COVID-19 pandemic.

Anti-Chinese sentiment increased during the COVID-19 pandemic, presenting as a considerable spike in overt violence and hatred directed at Asian American individuals. However, it is less clear how subtle patterns of consumer discrimination, which are difficult to directly observe yet greatly impact Asian American livelihoods, changed through the pandemic. Here we examine this in the context of restaurants-ubiquitous small businesses that sell goods that are closely entwined with ethnicity. Using a series of surveys, online search trends and consumer traffic data, we find that Asian restaurants experienced an 18.4% decrease in traffic (estimated US$7.42 billion lost revenue in 2020) relative to comparable non-Asian restaurants, with greater decreases in areas with higher levels of support for Donald Trump. Our findings are consistent with the roles of collective blame, out-group homogeneity and ethnic misidentification in explaining how anti-China rhetoric can harm the Asian American community, underlining the importance of avoiding racism and stigmatization in political and public health communications.

Interferon signaling promotes tolerance to chromosomal instability during metastatic evolution in renal cancer.

Molecular routes to metastatic dissemination are critical determinants of aggressive cancers. Through in vivo CRISPR-Cas9 genome editing, we generated somatic mosaic genetically engineered models that faithfully recapitulate metastatic renal tumors. Disruption of 9p21 locus is an evolutionary driver to systemic disease through the rapid acquisition of complex karyotypes in cancer cells. Cross-species analysis revealed that recurrent patterns of copy number variations, including 21q loss and dysregulation of the interferon pathway, are major drivers of metastatic potential. In vitro and in vivo genomic engineering, leveraging loss-of-function studies, along with a model of partial trisomy of chromosome 21q, demonstrated a dosage-dependent effect of the interferon receptor genes cluster as an adaptive mechanism to deleterious chromosomal instability in metastatic progression. This work provides critical knowledge on drivers of renal cell carcinoma progression and defines the primary role of interferon signaling in constraining the propagation of aneuploid clones in cancer evolution.

Leveraging weak supervision to perform named entity recognition in electronic health records progress notes to identify the ophthalmology exam.

OBJECTIVE: To develop deep learning models to recognize ophthalmic examination components from clinical notes in electronic health records (EHR) using a weak supervision approach. METHODS: A corpus of 39,099 ophthalmology notes weakly labeled for 24 examination entities was assembled from the EHR of one academic center. Four pre-trained transformer-based language models (DistilBert, BioBert, BlueBert, and ClinicalBert) were fine-tuned to this named entity recognition task and compared to a baseline regular expression model. Models were evaluated on the weakly labeled test dataset, a human-labeled sample of that set, and a human-labeled independent dataset. RESULTS: On the weakly labeled test set, all transformer-based models had recall > 0.93, with precision varying from 0.815 to 0.843. The baseline model had lower recall (0.769) and precision (0.682). On the human-annotated sample, the baseline model had high recall (0.962, 95 % CI 0.955-0.067) with variable precision across entities (0.081-0.999). Bert models had recall ranging from 0.771 to 0.831, and precision >=0.973. On the independent dataset, precision was 0.926 and recall 0.458 for BlueBert. The baseline model had better recall (0.708, 95 % CI 0.674-0.738) but worse precision (0.399, 95 % CI -0.352-0.451). CONCLUSION: We developed the first deep learning system to recognize eye examination components from clinical notes, leveraging a novel opportunity for weak supervision. Transformer-based models had high precision on human-annotated labels, whereas the baseline model had poor precision but higher recall. This system may be used to improve cohort and feature identification using free-text notes.Our weakly supervised approach may help amass large datasets of domain-specific entities from EHRs in many fields.

Age-dependent cognitive impairment, hydrocephalus and leukocyte infiltration in transgenic mice with endothelial expression of human EPHX2.

Soluble epoxide hydrolase (sEH) is upregulated in microvascular endothelium of human brain with vascular cognitive impairment (VCI). Transgenic endothelial expression of human sEH in mice (Tie2hsEH) induces endothelial dysfunction (ED), a pathogenetic mechanism of VCI. We sought to determine if endothelial upregulation of sEH is sufficient to cause cognitive impairment, and if cognitive impairment due to chronic hypoperfusion induced by unilateral common carotid artery occlusion (CCAO) is exacerbated in Tie2hsEH mice. Behavioral performance was assessed by the open field, rotarod, novel object, Morris water maze and fear conditioning tests. Cerebral blood flow and brain morphology were evaluated by MRI, and inflammatory changes investigated using immunohistochemistry and flow cytometry. We demonstrate that transgenic endothelial expression of sEH is sufficient to induce cognitive impairment, associated with leukocyte infiltration, brain atrophy and accelerated, age-dependent ventriculomegaly, identifying ED and sEH upregulation as potential underlying mechanisms and therapeutic targets for VCI.

Loss of ARID1A Promotes Epithelial-Mesenchymal Transition and Sensitizes Pancreatic Tumors to Proteotoxic Stress.

Cellular dedifferentiation is a key mechanism driving cancer progression. Acquisition of mesenchymal features has been associated with drug resistance, poor prognosis, and disease relapse in many tumor types. Therefore, successful targeting of tumors harboring these characteristics is a priority in oncology practice. The SWItch/Sucrose non-fermentable (SWI/SNF) chromatin remodeling complex has also emerged as a critical player in tumor progression, leading to the identification of several SWI/SNF complex genes as potential disease biomarkers and targets of anticancer therapies. AT-rich interaction domain-containing protein 1A (ARID1A) is a component of SWI/SNF, and mutations in ARID1A represent one of the most frequent molecular alterations in human cancers. ARID1A mutations occur in approximately 10% of pancreatic ductal adenocarcinomas (PDAC), but whether these mutations confer a therapeutic opportunity remains unclear. Here, we demonstrate that loss of ARID1A promotes an epithelial-mesenchymal transition (EMT) phenotype and sensitizes PDAC cells to a clinical inhibitor of HSP90, NVP-AUY922, both in vitro and in vivo. Although loss of ARID1A alone did not significantly affect proliferative potential or rate of apoptosis, ARID1A-deficient cells were sensitized to HSP90 inhibition, potentially by promoting the degradation of intermediate filaments driving EMT, resulting in cell death. Our results describe a mechanistic link between ARID1A defects and a quasi-mesenchymal phenotype, suggesting that deleterious mutations in ARID1A associated with protein loss exhibit potential as a biomarker for patients with PDAC who may benefit by HSP90-targeting drugs treatment. SIGNIFICANCE: This study identifies ARID1A loss as a promising biomarker for the identification of PDAC tumors that are potentially responsive to treatment with proteotoxic agents.

High-Throughput Prediction of MHC Class I and II Neoantigens with MHCnuggets.

Computational prediction of binding between neoantigen peptides and major histocompatibility complex (MHC) proteins can be used to predict patient response to cancer immunotherapy. Current neoantigen predictors focus on in silico estimation of MHC binding affinity and are limited by low predictive value for actual peptide presentation, inadequate support for rare MHC alleles, and poor scalability to high-throughput data sets. To address these limitations, we developed MHCnuggets, a deep neural network method that predicts peptide-MHC binding. MHCnuggets can predict binding for common or rare alleles of MHC class I or II with a single neural network architecture. Using a long short-term memory network (LSTM), MHCnuggets accepts peptides of variable length and is faster than other methods. When compared with methods that integrate binding affinity and MHC-bound peptide (HLAp) data from mass spectrometry, MHCnuggets yields a 4-fold increase in positive predictive value on independent HLAp data. We applied MHCnuggets to 26 cancer types in The Cancer Genome Atlas, processing 26.3 million allele-peptide comparisons in under 2.3 hours, yielding 101,326 unique predicted immunogenic missense mutations (IMM). Predicted IMM hotspots occurred in 38 genes, including 24 driver genes. Predicted IMM load was significantly associated with increased immune cell infiltration (P < 2 × 10-16), including CD8+ T cells. Only 0.16% of predicted IMMs were observed in more than 2 patients, with 61.7% of these derived from driver mutations. Thus, we describe a method for neoantigen prediction and its performance characteristics and demonstrate its utility in data sets representing multiple human cancers.

Allosteric SHP2 Inhibitor, IACS-13909, Overcomes EGFR-Dependent and EGFR-Independent Resistance Mechanisms toward Osimertinib.

Src homology 2 domain-containing phosphatase (SHP2) is a phosphatase that mediates signaling downstream of multiple receptor tyrosine kinases (RTK) and is required for full activation of the MAPK pathway. SHP2 inhibition has demonstrated tumor growth inhibition in RTK-activated cancers in preclinical studies. The long-term effectiveness of tyrosine kinase inhibitors such as the EGFR inhibitor (EGFRi), osimertinib, in non-small cell lung cancer (NSCLC) is limited by acquired resistance. Multiple clinically identified mechanisms underlie resistance to osimertinib, including mutations in EGFR that preclude drug binding as well as EGFR-independent activation of the MAPK pathway through alternate RTK (RTK-bypass). It has also been noted that frequently a tumor from a single patient harbors more than one resistance mechanism, and the plasticity between multiple resistance mechanisms could restrict the effectiveness of therapies targeting a single node of the oncogenic signaling network. Here, we report the discovery of IACS-13909, a specific and potent allosteric inhibitor of SHP2, that suppresses signaling through the MAPK pathway. IACS-13909 potently impeded proliferation of tumors harboring a broad spectrum of activated RTKs as the oncogenic driver. In EGFR-mutant osimertinib-resistant NSCLC models with EGFR-dependent and EGFR-independent resistance mechanisms, IACS-13909, administered as a single agent or in combination with osimertinib, potently suppressed tumor cell proliferation in vitro and caused tumor regression in vivo. Together, our findings provide preclinical evidence for using a SHP2 inhibitor as a therapeutic strategy in acquired EGFRi-resistant NSCLC. SIGNIFICANCE: These findings highlight the discovery of IACS-13909 as a potent, selective inhibitor of SHP2 with drug-like properties, and targeting SHP2 may serve as a therapeutic strategy to overcome tumor resistance to osimertinib.

A Fast and Flexible Framework for Network-Assisted Genomic Association.

We present an accessible, fast, and customizable network propagation system for pathway boosting and interpretation of genome-wide association studies. This system-NAGA (Network Assisted Genomic Association)-taps the NDEx biological network resource to gain access to thousands of protein networks and select those most relevant and performative for a specific association study. The method works efficiently, completing genome-wide analysis in under 5 minutes on a modern laptop computer. We show that NAGA recovers many known disease genes from analysis of schizophrenia genetic data, and it substantially boosts associations with previously unappreciated genes such as amyloid beta precursor. On this and seven other gene-disease association tasks, NAGA outperforms conventional approaches in recovery of known disease genes and replicability of results. Protein interactions associated with disease are visualized and annotated in Cytoscape, which, in addition to standard programmatic interfaces, allows for downstream analysis.

Predictive Signatures Inform the Effective Repurposing of Decitabine to Treat KRAS-Dependent Pancreatic Ductal Adenocarcinoma.

Mutated KRAS protein is a pivotal tumor driver in pancreatic cancer. However, despite comprehensive efforts, effective therapeutics that can target oncogenic KRAS are still under investigation or awaiting clinical approval. Using a specific KRAS-dependent gene signature, we implemented a computer-assisted inspection of a drug-gene network to in silico repurpose drugs that work like inhibitors of oncogenic KRAS. We identified and validated decitabine, an FDA-approved drug, as a potent inhibitor of growth in pancreatic cancer cells and patient-derived xenograft models that showed KRAS dependency. Mechanistically, decitabine efficacy was linked to KRAS-driven dependency on nucleotide metabolism and its ability to specifically impair pyrimidine biosynthesis in KRAS-dependent tumors cells. These findings also showed that gene signatures related to KRAS dependency might be prospectively used to inform on decitabine sensitivity in a selected subset of patients with KRAS-mutated pancreatic cancer. Overall, the repurposing of decitabine emerged as an intriguing option for treating pancreatic tumors that are addicted to mutant KRAS, thus offering opportunities for improving the arsenal of therapeutics for this extremely deadly disease. SIGNIFICANCE: Decitabine is a promising drug for cancer cells dependent on RAS signaling.