A persistent neutrophil-associated immune signature characterizes post-COVID-19 pulmonary sequelae.

Interstitial lung disease and associated fibrosis occur in a proportion of individuals who have recovered from severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection through unknown mechanisms. We studied individuals with severe coronavirus disease 2019 (COVID-19) after recovery from acute illness. Individuals with evidence of interstitial lung changes at 3 to 6 months after recovery had an up-regulated neutrophil-associated immune signature including increased chemokines, proteases, and markers of neutrophil extracellular traps that were detectable in the blood. Similar pathways were enriched in the upper airway with a concomitant increase in antiviral type I interferon signaling. Interaction analysis of the peripheral phosphoproteome identified enriched kinases critical for neutrophil inflammatory pathways. Evaluation of these individuals at 12 months after recovery indicated that a subset of the individuals had not yet achieved full normalization of radiological and functional changes. These data provide insight into mechanisms driving development of pulmonary sequelae during and after COVID-19 and provide a rational basis for development of targeted approaches to prevent long-term complications.

Computational Analysis of Cholangiocarcinoma Phosphoproteomes Identifies Patient-Specific Drug Targets.

Cholangiocarcinoma is a form of hepatobiliary cancer with an abysmal prognosis. Despite advances in our understanding of cholangiocarcinoma pathophysiology and its genomic landscape, targeted therapies have not yet made a significant impact on its clinical management. The low response rates of targeted therapies in cholangiocarcinoma suggest that patient heterogeneity contributes to poor clinical outcome. Here we used mass spectrometry-based phosphoproteomics and computational methods to identify patient-specific drug targets in patient tumors and cholangiocarcinoma-derived cell lines. We analyzed 13 primary tumors of patients with cholangiocarcinoma with matched nonmalignant tissue and 7 different cholangiocarcinoma cell lines, leading to the identification and quantification of more than 13,000 phosphorylation sites. The phosphoproteomes of cholangiocarcinoma cell lines and patient tumors were significantly correlated. MEK1, KIT, ERK1/2, and several cyclin-dependent kinases were among the protein kinases most frequently showing increased activity in cholangiocarcinoma relative to nonmalignant tissue. Application of the Drug Ranking Using Machine Learning (DRUML) algorithm selected inhibitors of histone deacetylase (HDAC; belinostat and CAY10603) and PI3K pathway members as high-ranking therapies to use in primary cholangiocarcinoma. The accuracy of the computational drug rankings based on predicted responses was confirmed in cell-line models of cholangiocarcinoma. Together, this study uncovers frequently activated biochemical pathways in cholangiocarcinoma and provides a proof of concept for the application of computational methodology to rank drugs based on efficacy in individual patients. SIGNIFICANCE: Phosphoproteomic and computational analyses identify patient-specific drug targets in cholangiocarcinoma, supporting the potential of a machine learning method to predict personalized therapies.

Drug ranking using machine learning systematically predicts the efficacy of anti-cancer drugs.

Artificial intelligence and machine learning (ML) promise to transform cancer therapies by accurately predicting the most appropriate therapies to treat individual patients. Here, we present an approach, named Drug Ranking Using ML (DRUML), which uses omics data to produce ordered lists of >400 drugs based on their anti-proliferative efficacy in cancer cells. To reduce noise and increase predictive robustness, instead of individual features, DRUML uses internally normalized distance metrics of drug response as features for ML model generation. DRUML is trained using in-house proteomics and phosphoproteomics data derived from 48 cell lines, and it is verified with data comprised of 53 cellular models from 12 independent laboratories. We show that DRUML predicts drug responses in independent verification datasets with low error (mean squared error < 0.1 and mean Spearman's rank 0.7). In addition, we demonstrate that DRUML predictions of cytarabine sensitivity in clinical leukemia samples are prognostic of patient survival (Log rank p < 0.005). Our results indicate that DRUML accurately ranks anti-cancer drugs by their efficacy across a wide range of pathologies.

Rituximab and obinutuzumab differentially hijack the B cell receptor and NOTCH1 signaling pathways.

The anti-CD20 monoclonal antibodies rituximab and obinutuzumab differ in their mechanisms of action, with obinutuzumab evoking greater direct B cell death. To characterize the signaling processes responsible for improved B cell killing by obinutuzumab, we undertook a phosphoproteomics approach and demonstrate that rituximab and obinutuzumab differentially activate pathways downstream of the B cell receptor. Although both antibodies induce strong ERK and MYC activation sufficient to promote cell-cycle arrest and B cell death, obinutuzumab exceeds rituximab in supporting apoptosis induction by means of aberrant SYK phosphorylation. In contrast, rituximab elicits stronger anti-apoptotic signals by activating AKT, by impairing pro-apoptotic BAD, and by releasing membrane-bound NOTCH1 to up-regulate pro-survival target genes. As a consequence, rituximab appears to reinforce BCL2-mediated apoptosis resistance. The unexpected complexity and differences by which rituximab and obinutuzumab interfere with signaling pathways essential for lymphoma pathogenesis and treatment provide important impetus to optimize and personalize the application of different anti-CD20 treatments.

Genome instability is a consequence of transcription deficiency in patients with bone marrow failure harboring biallelic ERCC6L2 variants.

Biallelic variants in the ERCC excision repair 6 like 2 gene (ERCC6L2) are known to cause bone marrow failure (BMF) due to defects in DNA repair and mitochondrial function. Here, we report on eight cases of BMF from five families harboring biallelic variants in ERCC6L2, two of whom present with myelodysplasia. We confirm that ERCC6L2 patients' lymphoblastoid cell lines (LCLs) are hypersensitive to DNA-damaging agents that specifically activate the transcription coupled nucleotide excision repair (TCNER) pathway. Interestingly, patients' LCLs are also hypersensitive to transcription inhibitors that interfere with RNA polymerase II (RNA Pol II) and display an abnormal delay in transcription recovery. Using affinity-based mass spectrometry we found that ERCC6L2 interacts with DNA-dependent protein kinase (DNA-PK), a regulatory component of the RNA Pol II transcription complex. Chromatin immunoprecipitation PCR studies revealed ERCC6L2 occupancy on gene bodies along with RNA Pol II and DNA-PK. Patients' LCLs fail to terminate transcript elongation accurately upon DNA damage and display a significant increase in nuclear DNA-RNA hybrids (R loops). Collectively, we conclude that ERCC6L2 is involved in regulating RNA Pol II-mediated transcription via its interaction with DNA-PK to resolve R loops and minimize transcription-associated genome instability. The inherited BMF syndrome caused by biallelic variants in ERCC6L2 can be considered as a primary transcription deficiency rather than a DNA repair defect.

Approaches to identify kinase dependencies in cancer signalling networks.

Cells integrate extracellular signals into appropriate responses through a complex network of biochemical reactions driven by the activity of protein and lipid kinases, among other proteins. In order to understand this complexity, new approaches, both experimental and computational, have recently been developed with the aim to identify regulatory kinases and infer their activation status in the context of their signalling network. Here, we review such approaches with particular focus on those based on phosphoproteomics. Integration of kinase activity measurements inferred from phosphoproteomics data with other 'omics' datasets is starting to be used to identify regulatory nodes in biochemical networks. These methodologies may in the future be used to identify patient-specific targets and thus advance personalised cancer medicine.

Exome sequencing identifies MPL as a causative gene in familial aplastic anemia.

The primary cause of aplastic anemia remains unknown in many patients. The aim of this study was to clarify the genetic cause of familial aplastic anemia. Genomic DNA of an affected individual from a multiplex consanguineous family was hybridized to a Nimblegen exome library before being sequenced on a GAIIx genome analyzer. Once the disease causing homozygous mutation had been confirmed in the consanguineous family, this gene was then analyzed for mutation in 33 uncharacterized index cases of aplastic anemia (<13 years) using denaturing HPLC. Abnormal traces were confirmed by direct sequencing. Exome sequencing identified a novel homozygous nonsense mutation in the thrombopoietin receptor gene MPL. An additional novel homozygous MPL mutation was identified in the screen of 33 aplastic anemia patients. This study shows for the first time a link between homozygous MPL mutations and familial aplastic anemia. It also highlights the important role of MPL in trilineage hematopoiesis.