PAIRWISE: Deep Learning-based Prediction of Effective Personalized Drug Combinations in Cancer.

Combination therapies offer promise for improving cancer treatment efficacy and preventing recurrence. However, identifying optimal drug combinations tailored to specific cancer subtypes and individual patients is extremely challenging due to the vast number of possible combinations and tumor heterogeneity. To address this gap, we take a machine learning approach combining deep learning with transfer learning to incorporate prior scientific knowledge and predict drug synergy based on tumor-specific transcriptome profiles.

Network-driven cancer cell avatars for combination discovery and biomarker identification for DNA damage response inhibitors.

Combination therapy is well established as a key intervention strategy for cancer treatment, with the potential to overcome monotherapy resistance and deliver a more durable efficacy. However, given the scale of unexplored potential target space and the resulting combinatorial explosion, identifying efficacious drug combinations is a critical unmet need that is still evolving. In this paper, we demonstrate a network biology-driven, simulation-based solution, the Simulated Cell™.

Immune Cell Abundance and T-cell Receptor Landscapes Suggest New Patient Stratification Strategies in Head and Neck Squamous Cell Carcinoma.

Unlabelled: Head and neck squamous cell carcinoma (HNSCC) is a molecularly and spatially heterogeneous disease frequently characterized by impairment of immunosurveillance mechanisms. Despite recent success with immunotherapy treatment, disease progression still occurs quickly after treatment in the majority of cases, suggesting the need to improve patient selection strategies. In the quest for biomarkers that may help inform response to checkpoint blockade, we characterized the tumor microenvironment (TME) of 162 HNSCC primary tumors of diverse etiologic and spatial origin, through gene expression and IHC profiling of relevant immune proteins, T-cell receptor (TCR) repertoire analysis, and whole-exome sequencing.

Pan-Cancer Proteomics Analysis to Identify Tumor-Enriched and Highly Expressed Cell Surface Antigens as Potential Targets for Cancer Therapeutics.

The National Cancer Institute's Clinical Proteomic Tumor Analysis Consortium (CPTAC) provides unique opportunities for cancer target discovery using protein expression. Proteomics data from CPTAC tumor types have been primarily generated using a multiplex tandem mass tag (TMT) approach, which is designed to provide protein quantification relative to reference samples. However, relative protein expression data are suboptimal for prioritization of targets within a tissue type, which requires additional reprocessing of the original proteomics data to derive absolute quantitation estimation.

Phase Ia/b, Open-Label, Multicenter Study of AZD4635 (an Adenosine A2A Receptor Antagonist) as Monotherapy or Combined with Durvalumab, in Patients with Solid Tumors.

Purpose: To evaluate AZD4635, an adenosine A2A receptor antagonist, as monotherapy or in combination with durvalumab in patients with advanced solid tumors.

Patients And Methods: In phase Ia (dose escalation), patients had relapsed/refractory solid tumors; in phase Ib (dose expansion), patients had checkpoint inhibitor-naïve metastatic castration-resistant prostate cancer (mCRPC) or colorectal carcinoma, non-small cell lung cancer with prior anti-PD-1/PD-L1 exposure, or other solid tumors (checkpoint-naïve or prior anti-PD-1/PD-L1 exposure). Patients received AZD4635 monotherapy (75-200 mg once daily or 125 mg twice daily) or in combination with durvalumab (AZD4635 75 or 100 mg once daily).

Cancer-driving mutations are enriched in genic regions intolerant to germline variation.

Large reference datasets of protein-coding variation in human populations have allowed us to determine which genes and genic subregions are intolerant to germline genetic variation. There is also a growing number of genes implicated in severe Mendelian diseases that overlap with genes implicated in cancer. We hypothesized that cancer-driving mutations might be enriched in genic subregions that are depleted of germline variation relative to somatic variation.

Knowledge graph-based recommendation framework identifies drivers of resistance in EGFR mutant non-small cell lung cancer.

Resistance to EGFR inhibitors (EGFRi) presents a major obstacle in treating non-small cell lung cancer (NSCLC). One of the most exciting new ways to find potential resistance markers involves running functional genetic screens, such as CRISPR, followed by manual triage of significantly enriched genes. This triage process to identify 'high value' hits resulting from the CRISPR screen involves manual curation that requires specialized knowledge and can take even experts several months to comprehensively complete.

Identification of Intrinsic Drug Resistance and Its Biomarkers in High-Throughput Pharmacogenomic and CRISPR Screens.

High-throughput drug screens in cancer cell lines test compounds at low concentrations, thereby enabling the identification of drug-sensitivity biomarkers, while resistance biomarkers remain underexplored. Dissecting meaningful drug responses at high concentrations is challenging due to cytotoxicity, i.e.

Drug mechanism-of-action discovery through the integration of pharmacological and CRISPR screens.

Low success rates during drug development are due, in part, to the difficulty of defining drug mechanism-of-action and molecular markers of therapeutic activity. Here, we integrated 199,219 drug sensitivity measurements for 397 unique anti-cancer drugs with genome-wide CRISPR loss-of-function screens in 484 cell lines to systematically investigate cellular drug mechanism-of-action. We observed an enrichment for positive associations between the profile of drug sensitivity and knockout of a drug's nominal target, and by leveraging protein-protein networks, we identified pathways underpinning drug sensitivity.

Adenosine Signaling Is Prognostic for Cancer Outcome and Has Predictive Utility for Immunotherapeutic Response.

Purpose: There are several agents in early clinical trials targeting components of the adenosine pathway including A2AR and CD73. The identification of cancers with a significant adenosine drive is critical to understand the potential for these molecules. However, it is challenging to measure tumor adenosine levels at scale, thus novel, clinically tractable biomarkers are needed.

PI3Kα/δ inhibition promotes anti-tumor immunity through direct enhancement of effector CD8 T-cell activity.

PI3K inhibitors with differential selectivity to distinct PI3K isoforms have been tested extensively in clinical trials, largely to target tumor epithelial cells. PI3K signaling also regulates the immune system and inhibition of PI3Kδ modulate the tumor immune microenvironment of pre-clinical mouse tumor models by relieving T-regs-mediated immunosuppression. PI3K inhibitors as a class and PI3Kδ specifically are associated with immune-related side effects.

Network-Based Drug Discovery: Coupling Network Pharmacology with Phenotypic Screening for Neuronal Excitability.

Diseases such as chronic pain with complex etiologies are unlikely to respond to single, target-specific therapeutics but rather require intervention at multiple points within a perturbed disease system. Such approaches are being enabled by the rise of computational methods to identify key points of intervention and by new screening techniques that focus on a relevant condition or phenotype, rather than a specific target. Here we apply an in silico network pharmacology approach to identify small-molecule compounds with the potential to selectively disrupt the structure of a chronic-pain specific disease network, which we validate using a novel phenotypic screen that recapitulates key aspects of neuronal and pain biology by measuring changes in neuronal excitability in native sensory neurons.

Molecular profiling of aged neural progenitors identifies Dbx2 as a candidate regulator of age-associated neurogenic decline.

Adult neurogenesis declines with aging due to the depletion and functional impairment of neural stem/progenitor cells (NSPCs). An improved understanding of the underlying mechanisms that drive age-associated neurogenic deficiency could lead to the development of strategies to alleviate cognitive impairment and facilitate neuroregeneration. An essential step towards this aim is to investigate the molecular changes that occur in NSPC aging on a genomewide scale.

Interpreting transcriptional changes using causal graphs: new methods and their practical utility on public networks.

Background: Inference of active regulatory cascades under specific molecular and environmental perturbations is a recurring task in transcriptional data analysis. Commercial tools based on large, manually curated networks of causal relationships offering such functionality have been used in thousands of articles in the biomedical literature. The adoption and extension of such methods in the academic community has been hampered by the lack of freely available, efficient algorithms and an accompanying demonstration of their applicability using current public networks.

A FOXM1 Dependent Mesenchymal-Epithelial Transition in Retinal Pigment Epithelium Cells.

The integrity of the epithelium is maintained by a complex but regulated interplay of processes that allow conversion of a proliferative state into a stably differentiated state. In this study, using human embryonic stem cell (hESC) derived Retinal Pigment Epithelium (RPE) cells as a model; we have investigated the molecular mechanisms that affect attainment of the epithelial phenotype. We demonstrate that RPE undergo a Mesenchymal-Epithelial Transition in culture before acquiring an epithelial phenotype in a FOXM1 dependent manner.

The pain interactome: connecting pain-specific protein interactions.

Understanding the molecular mechanisms associated with disease is a central goal of modern medical research. As such, many thousands of experiments have been published that detail individual molecular events that contribute to a disease. Here we use a semi-automated text mining approach to accurately and exhaustively curate the primary literature for chronic pain states.

Molecular causes of transcriptional response: a Bayesian prior knowledge approach.

Motivation: The abundance of many transcripts changes significantly in response to a variety of molecular and environmental perturbations. A key question in this setting is as follows: what intermediate molecular perturbations gave rise to the observed transcriptional changes? Regulatory programs are not exclusively governed by transcriptional changes but also by protein abundance and post-translational modifications making direct causal inference from data difficult. However, biomedical research over the last decades has uncovered a plethora of causal signaling cascades that can be used to identify good candidates explaining a specific set of transcriptional changes.

Cataloging the biomedical world of pain through semi-automated curation of molecular interactions.

The vast collection of biomedical literature and its continued expansion has presented a number of challenges to researchers who require structured findings to stay abreast of and analyze molecular mechanisms relevant to their domain of interest. By structuring literature content into topic-specific machine-readable databases, the aggregate data from multiple articles can be used to infer trends that can be compared and contrasted with similar findings from topic-independent resources. Our study presents a generalized procedure for semi-automatically creating a custom topic-specific molecular interaction database through the use of text mining to assist manual curation.

HDAC inhibitors attenuate the development of hypersensitivity in models of neuropathic pain.

Histone deacetylase inhibitors (HDACIs) interfere with the epigenetic process of histone acetylation and are known to have analgesic properties in models of chronic inflammatory pain. The aim of this study was to determine whether these compounds could also affect neuropathic pain. Different class I HDACIs were delivered intrathecally into rat spinal cord in models of traumatic nerve injury and antiretroviral drug-induced peripheral neuropathy (stavudine, d4T).

Causal reasoning on biological networks: interpreting transcriptional changes.

Motivation: The interpretation of high-throughput datasets has remained one of the central challenges of computational biology over the past decade. Furthermore, as the amount of biological knowledge increases, it becomes more and more difficult to integrate this large body of knowledge in a meaningful manner. In this article, we propose a particular solution to both of these challenges.

An integrated transcriptomic and meta-analysis of hepatoma cells reveals factors that influence susceptibility to HCV infection.

Hepatitis C virus (HCV) is a global problem. To better understand HCV infection researchers employ in vitro HCV cell-culture (HCVcc) systems that use Huh-7 derived hepatoma cells that are particularly permissive to HCV infection. A variety of hyper-permissive cells have been subcloned for this purpose.

Selection, optimization, and pharmacokinetic properties of a novel, potent antiviral locked nucleic acid-based antisense oligomer targeting hepatitis C virus internal ribosome entry site.

We have screened 47 locked nucleic acid (LNA) antisense oligonucleotides (ASOs) targeting conserved (>95% homology) sequences in the hepatitis C virus (HCV) genome using the subgenomic HCV replicon assay and generated both antiviral (50% effective concentration [EC(50)]) and cytotoxic (50% cytotoxic concentration [CC(50)]) dose-response curves to allow measurement of the selectivity index (SI). This comprehensive approach has identified an LNA ASO with potent antiviral activity (EC(50) = 4 nM) and low cytotoxicity (CC(50) >880 nM) targeting the 25- to 40-nucleotide region (nt) of the HCV internal ribosome entry site (IRES) containing the distal and proximal miR-122 binding sites. LNA ASOs targeting previously known accessible regions of the IRES, namely, loop III and the initiation codon in loop IV, had poor SI values.