METTL13 methylation of eEF1A increases translational output to promote tumorigenesis

Increased protein synthesis plays an etiologic role in diverse cancers. Here, we demonstrate that METTL13 (methyltransferase-like 13) dimethylation of eEF1A (eukaryotic elongation factor 1A) lysine 55 (eEF1AK55me2) is utilized by Ras-driven cancers to increase translational output and promote tumorigenesis in vivo. METTL13-catalyzed eEF1A methylation increases eEF1A's intrinsic GTPase activity in vitro and protein production in cells. METTL13 and eEF1AK55me2 levels are upregulated in cancer and negatively correlate with pancreatic and lung cancer patient survival. METTL13 deletion and eEF1AK55me2 loss dramatically reduce Ras-driven neoplastic growth in mouse models and in patient-derived xenografts (PDXs) from primary pancreatic and lung tumors. Finally, METTL13 depletion renders PDX tumors hypersensitive to drugs that target growth-signaling pathways. Together, our work uncovers a mechanism by which lethal cancers become dependent on the METTL13-eEF1AK55me2 axis to meet their elevated protein synthesis requirement and suggests that METTL13 inhibition may constitute a targetable vulnerability of tumors driven by aberrant Ras signaling.We thank Pal Falnes, Jerry Pelletier, and Julien Sage for helpful discussion, Lauren Brown and William Devine for SDS-1-021, and members of the Gozani and Mazur laboratories for critical reading of the manuscript. This work was supported in part by grants from the NIH to S.M.C. (K99CA190803), M.P.K. (5K08CA218690-02), J.A.P. (R35GM118173), M.C.B. (1DP2HD084069-01), J.S. (1R35GM119721), I.T. (R01CA202021), P.K.M. (R00CA197816, P50CA070907, and P30CA016672), and O.G. (R01GM079641). J.E.E. received support from Stanford ChEM-H, and A.M. was supported by the MD Anderson Moonshot Program. I.T. is a Junior 2 Research Scholar of the Fonds de Recherche du Quebec - Sante (FRQ-S). P.K.M. is supported by the Neuroendocrine Tumor Research Foundation and American Association for Cancer Research and is the Andrew Sabin Family Foundation Scientist and CPRIT scholar (RR160078). S.H. is supported by a Deutsche Forschungsgemeinschaft Postdoctoral Fellowship. J.W.F. is supported by 5T32GM007276. (K99CA190803 - NIH; 5K08CA218690-02 - NIH; R35GM118173 - NIH; 1DP2HD084069-01 - NIH; 1R35GM119721 - NIH; R01CA202021 - NIH; R00CA197816 - NIH; P50CA070907 - NIH; P30CA016672 - NIH; R01GM079641 - NIH; Stanford ChEM-H; MD Anderson Moonshot Program; Neuroendocrine Tumor Research Foundation; American Association for Cancer Research; Deutsche Forschungsgemeinschaft Postdoctoral Fellowship; 5T32GM007276)Supporting documentationAccepted manuscrip

A Semantic Web Management Model for Integrative Biomedical Informatics

Data, data everywhere. The diversity and magnitude of the data generated in the Life Sciences defies automated articulation among complementary efforts. The additional need in this field for managing property and access permissions compounds the difficulty very significantly. This is particularly the case when the integration involves multiple domains and disciplines, even more so when it includes clinical and high throughput molecular data.The emergence of Semantic Web technologies brings the promise of meaningful interoperation between data and analysis resources. In this report we identify a core model for biomedical Knowledge Engineering applications and demonstrate how this new technology can be used to weave a management model where multiple intertwined data structures can be hosted and managed by multiple authorities in a distributed management infrastructure. Specifically, the demonstration is performed by linking data sources associated with the Lung Cancer SPORE awarded to The University of Texas MD Anderson Cancer Center at Houston and the Southwestern Medical Center at Dallas. A software prototype, available with open source at www.s3db.org, was developed and its proposed design has been made publicly available as an open source instrument for shared, distributed data management.The Semantic Web technologies have the potential to addresses the need for distributed and evolvable representations that are critical for systems Biology and translational biomedical research. As this technology is incorporated into application development we can expect that both general purpose productivity software and domain specific software installed on our personal computers will become increasingly integrated with the relevant remote resources. In this scenario, the acquisition of a new dataset should automatically trigger the delegation of its analysis

Prognostic impact of vitamin B6 metabolism in lung cancer

Patients with non-small cell lung cancer (NSCLC) are routinely treated with cytotoxic agents such as cisplatin. Through a genome-wide siRNA-based screen, we identified vitamin B6 metabolism as a central regulator of cisplatin responses in vitro and in vivo. By aggravating a bioenergetic catastrophe that involves the depletion of intracellular glutathione, vitamin B6 exacerbates cisplatin-mediated DNA damage, thus sensitizing a large panel of cancer cell lines to apoptosis. Moreover, vitamin B6 sensitizes cancer cells to apoptosis induction by distinct types of physical and chemical stress, including multiple chemotherapeutics. This effect requires pyridoxal kinase (PDXK), the enzyme that generates the bioactive form of vitamin B6. In line with a general role of vitamin B6 in stress responses, low PDXK expression levels were found to be associated with poor disease outcome in two independent cohorts of patients with NSCLC. These results indicate that PDXK expression levels constitute a biomarker for risk stratification among patients with NSCLC.publishedVersio

State-of-the-Art of Profiling Immune Contexture in the Era of Multiplexed Staining and Digital Analysis to Study Paraffin Tumor Tissues

Multiplexed platforms for multiple epitope detection have emerged in the last years as very powerful tools to study tumor tissues. These revolutionary technologies provide important visual techniques for tumor examination in formalin-fixed paraffin-embedded specimens to improve the understanding of the tumor microenvironment, promote new treatment discoveries, aid in cancer prevention, as well as allowing translational studies to be carried out. The aim of this review is to highlight the more recent methodologies that use multiplexed staining to study simultaneous protein identification in formalin-fixed paraffin-embedded tumor tissues for immune profiling, clinical research, and potential translational analysis. New multiplexed methodologies, which permit the identification of several proteins at the same time in one single tissue section, have been developed in recent years with the ability to study different cell populations, cells by cells, and their spatial distribution in different tumor specimens including whole sections, core needle biopsies, and tissue microarrays. Multiplexed technologies associated with image analysis software can be performed with a high-quality throughput assay to study cancer specimens and are important tools for new discoveries. The different multiplexed technologies described in this review have shown their utility in the study of cancer tissues and their advantages for translational research studies and application in cancer prevention and treatments

Pathological Response and Immune Biomarker Assessment in Non-Small-Cell Lung Carcinoma Receiving Neoadjuvant Immune Checkpoint Inhibitors

Lung cancer is the leading cause of cancer incidence and mortality worldwide. Adjuvant and neoadjuvant chemotherapy have been used in the perioperative setting of non-small-cell carcinoma (NSCLC); however, the five-year survival rate only improves by about 5%. Neoadjuvant treatment with immune checkpoint inhibitors (ICIs) has become significant due to improved survival in advanced NSCLC patients treated with immunotherapy agents. The assessment of pathology response has been proposed as a surrogate indicator of the benefits of neaodjuvant therapy. An outline of recommendations has been published by the International Association for the Study of Lung Cancer (IASLC) for the evaluation of pathologic response (PR). However, recent studies indicate that evaluations of immune-related changes are distinct in surgical resected samples from patients treated with immunotherapy. Several clinical trials of neoadjuvant immunotherapy in resectable NSCLC have included the study of biomarkers that can predict the response of therapy and monitor the response to treatment. In this review, we provide relevant information on the current recommendations of the assessment of pathological responses in surgical resected NSCLC tumors treated with neoadjuvant immunotherapy, and we describe current and potential biomarkers to predict the benefits of neoadjuvant immunotherapy in patients with resectable NSCLC

Artificial Intelligence in Lung Cancer Pathology Image Analysis

Objective: Accurate diagnosis and prognosis are essential in lung cancer treatment selection and planning. With the rapid advance of medical imaging technology, whole slide imaging (WSI) in pathology is becoming a routine clinical procedure. An interplay of needs and challenges exists for computer-aided diagnosis based on accurate and efficient analysis of pathology images. Recently, artificial intelligence, especially deep learning, has shown great potential in pathology image analysis tasks such as tumor region identification, prognosis prediction, tumor microenvironment characterization, and metastasis detection. Materials and Methods: In this review, we aim to provide an overview of current and potential applications for AI methods in pathology image analysis, with an emphasis on lung cancer. Results: We outlined the current challenges and opportunities in lung cancer pathology image analysis, discussed the recent deep learning developments that could potentially impact digital pathology in lung cancer, and summarized the existing applications of deep learning algorithms in lung cancer diagnosis and prognosis. Discussion and Conclusion: With the advance of technology, digital pathology could have great potential impacts in lung cancer patient care. We point out some promising future directions for lung cancer pathology image analysis, including multi-task learning, transfer learning, and model interpretation