SUMMARY Unresectable glioblastoma (GBM) cells in the invading tumor edge can act as seeds for recurrence. The molecular and phenotypic properties of these cells remain elusive. Here, we report that the invading edge and tumor core have two distinct types of glioma stem-like cells (GSCs) that resemble proneural (PN) and mesenchymal (MES) subtypes, respectively. Upon exposure to ionizing radiation (IR), GSCs, initially enriched for a CD133 + PN signature, transition to a CD109 + MES subtype in a C/EBP-β-dependent manner. Our gene expression analysis of paired cohorts of patients with primary and recurrent GBMs identified a CD133-to-CD109 shift in tumors with an MES recurrence. Patient-derived CD133 − /CD109 + cells are highly enriched with clonogenic, tumor-initiating, and radiation-resistant properties, and silencing CD109 significantly inhibits these phenotypes. We also report a conserved regulation of YAP/TAZ pathways by CD109 that could be a therapeutic target in GBM.

SummaryATG4B stimulates autophagy by promoting autophagosome formation through reversible modification of ATG8. We identify ATG4B as a substrate of mammalian sterile20-like kinase (STK) 26/MST4. MST4 phosphorylates ATG4B at serine residue 383, which stimulates ATG4B activity and increases autophagic flux. Inhibition of MST4 or ATG4B activities using genetic approaches or an inhibitor of ATG4B suppresses autophagy and the tumorigenicity of glioblastoma (GBM) cells. Furthermore, radiation induces MST4 expression, ATG4B phosphorylation, and autophagy. Inhibiting ATG4B in combination with radiotherapy in treating mice with intracranial GBM xenograft markedly slows tumor growth and provides a significant survival benefit. Our work describes an MST4-ATG4B signaling axis that influences GBM autophagy and malignancy, and whose therapeutic targeting enhances the anti-tumor effects of radiotherapy.

We introduce PubMedQA, a novel biomedical question answering (QA) dataset collected from PubMed abstracts. The task of Pub-MedQA is to answer research questions with yes/no/maybe (e.g.: Do preoperative statins reduce atrial fibrillation after coronary artery bypass grafting?) using the corresponding abstracts. PubMedQA has 1k expert-annotated, 61.2k unlabeled and 211.3k artificially generated QA instances. Each PubMedQA instance is composed of (1) a question which is either an existing research article title or derived from one, (2) a context which is the corresponding abstract without its conclusion, (3) a long answer, which is the conclusion of the abstract and, presumably, answers the research question, and (4) a yes/no/maybe answer which summarizes the conclusion. Pub-MedQA is the first QA dataset where reasoning over biomedical research texts, especially their quantitative contents, is required to answer the questions. Our best performing model, multi-phase fine-tuning of BioBERT with long answer bag-of-word statistics as additional supervision, achieves 68.1% accuracy, compared to single human performance of 78.0% accuracy and majority-baseline of 55.2% accuracy, leaving much room for improvement. PubMedQA is publicly available at https://pubmedqa.github.io.

Head and neck squamous cell carcinoma (HNSCC) is characterized by complex relations between stromal, epithelial, and immune cells within the tumor microenvironment (TME). To enable the development of more efficacious therapies, we aim to study the heterogeneity, signatures of unique cell populations, and cell-cell interactions of non-immune and immune cell populations in 6 human papillomavirus (HPV)+ and 12 HPV– HNSCC patient tumor and matched peripheral blood specimens using single-cell RNA sequencing. Using this dataset of 134,606 cells, we show cell type-specific signatures associated with inflammation and HPV status, describe the negative prognostic value of fibroblasts with elastic differentiation specifically in the HPV+ TME, predict therapeutically targetable checkpoint receptor-ligand interactions, and show that tumor-associated macrophages are dominant contributors of PD-L1 and other immune checkpoint ligands in the TME. We present a comprehensive single-cell view of cell-intrinsic mechanisms and cell-cell communication shaping the HNSCC microenvironment.

Precision oncology involves identifying drugs that will effectively treat a tumor and then prescribing an optimal clinical treatment regimen. However, most first-line chemotherapy drugs do not have biomarkers to guide their application. For molecularly targeted drugs, using the genomic status of a drug target as a therapeutic indicator has limitations. In this study, machine learning methods (e.g., deep learning) were used to identify informative features from genome-scale omics data and to train classifiers for predicting the effectiveness of drugs in cancer cell lines. The methodology introduced here can accurately predict the efficacy of drugs, regardless of whether they are molecularly targeted or nonspecific chemotherapy drugs. This approach, on a per-drug basis, can identify sensitive cancer cells with an average sensitivity of 0.82 and specificity of 0.82; on a per-cell line basis, it can identify effective drugs with an average sensitivity of 0.80 and specificity of 0.82. This report describes a data-driven precision medicine approach that is not only generalizable but also optimizes therapeutic efficacy. The framework detailed herein, when successfully translated to clinical environments, could significantly broaden the scope of precision oncology beyond targeted therapies, benefiting an expanded proportion of cancer patients. .

Two hallmarks of glioblastoma multiforme, the most common malignant brain cancer in humans, are aggressive growth and the ability of single glioma cells to disperse throughout the brain. These characteristics render tumors resistant to current therapies and account for the poor prognosis of patients. Although it is known that oncogenic signaling caused by overexpression of genes such as PDGFRA is responsible for robust glioma growth and cell infiltration, the mechanisms underlying glioblastoma malignancy remain largely elusive. Here, we report that PDGFRα signaling in glioblastomas leads to Src-dependent phosphorylation of the guanine nucleotide exchange factor Dock180 at tyrosine 1811 (Dock180 Y1811 ) that results in activation of the GTPase Rac1 and subsequent cell growth and invasion. In human glioma cells, knockdown of Dock180 and reversion with an RNAi-resistant Dock180 Y1811F abrogated, whereas an RNAi-resistant Dock180 WT rescued, PDGFRα-promoted glioma growth, survival, and invasion. Phosphorylation of Dock180 Y1811 enhanced its association with CrkII and p130 Cas , causing activation of Rac1 and consequent cell motility. Dock180 also associated with PDGFRα to promote cell migration. Finally, phosphorylated Dock180 Y1811 was detected in clinical samples of gliomas and various types of human cancers, and coexpression of phosphorylated Dock180 Y1811 , phosphorylated Src Y418 , and PDGFRα was predictive of extremely poor prognosis of patients with gliomas. Taken together, our findings provide insight into PDGFRα-stimulated gliomagenesis and suggest that phosphorylated Dock180 Y1811 contributes to activation of Rac1 in human cancers with PDGFRA amplification.

Glioblastoma (GBM) is a lethal disease with no effective therapies available. We previously observed upregulation of the TAM (Tyro-3, Axl, and Mer) receptor tyrosine kinase family member AXL in mesenchymal GBM and showed that knockdown of AXL induced apoptosis of mesenchymal, but not proneural, glioma sphere cultures (GSC). In this study, we report that BGB324, a novel small molecule inhibitor of AXL, prolongs the survival of immunocompromised mice bearing GSC-derived mesenchymal GBM-like tumors. We show that protein S (PROS1), a known ligand of other TAM receptors, was secreted by tumor-associated macrophages/microglia and subsequently physically associated with and activated AXL in mesenchymal GSC. PROS1-driven phosphorylation of AXL (pAXL) induced NFκB activation in mesenchymal GSC, which was inhibited by BGB324 treatment. We also found that treatment of GSC-derived mouse GBM tumors with nivolumab, a blocking antibody against the immune checkpoint protein PD-1, increased intratumoral macrophages/microglia and activation of AXL. Combinatorial therapy with nivolumab plus BGB324 effectively prolonged the survival of mice bearing GBM tumors. Clinically, expression of AXL or PROS1 was associated with poor prognosis for patients with GBM. Our results suggest that the PROS1-AXL pathway regulates intrinsic mesenchymal signaling and the extrinsic immune microenvironment, contributing to the growth of aggressive GBM tumors. These findings suggest that development of combination treatments of AXL and immune checkpoint inhibitors may provide benefit to patients with GBM. .

BackgroundA living cell has a complex, hierarchically organized signaling system that encodes and assimilates diverse environmental and intracellular signals, and it further transmits signals that control cellular responses, including a tightly controlled transcriptional program. An important and yet challenging task in systems biology is to reconstruct cellular signaling system in a data-driven manner. In this study, we investigate the utility of deep hierarchical neural networks in learning and representing the hierarchical organization of yeast transcriptomic machinery.ResultsWe have designed a sparse autoencoder model consisting of a layer of observed variables and four layers of hidden variables. We applied the model to over a thousand of yeast microarrays to learn the encoding system of yeast transcriptomic machinery. After model selection, we evaluated whether the trained models captured biologically sensible information. We show that the latent variables in the first hidden layer correctly captured the signals of yeast transcription factors (TFs), obtaining a close to one-to-one mapping between latent variables and TFs. We further show that genes regulated by latent variables at higher hidden layers are often involved in a common biological process, and the hierarchical relationships between latent variables conform to existing knowledge. Finally, we show that information captured by the latent variables provide more abstract and concise representations of each microarray, enabling the identification of better separated clusters in comparison to gene-based representation.ConclusionsContemporary deep hierarchical latent variable models, such as the autoencoder, can be used to partially recover the organization of transcriptomic machinery.