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This work demonstrates an efficient way to prepare chiral propargylic alcohols by asymmetric addition of terminal Zn-acetylide to aldehydes catalyzed by bifunctional zinc-based complexes. The corresponding products with moderate to good yields and enantioselectivities were obtained in the absence of moisture-sensitive Ti(O i Pr)4. [ABSTRACT FROM AUTHOR]

Despite the critical need, progress in developing cell-free DNA (cfDNA) liquid biopsy biomarkers for the diagnosis and risk stratification of head and neck squamous cell carcinoma (HNSC) has been limited. In this study, we present a comprehensive paired-sample differential methylation region (psDMR) analysis in HNSC, aimed at identifying reliable and HNSC-specific regions for cfDNA biomarker discovery. Traditional DMR analyses often overlook paired-sample information and fail to account for the heterogeneity within HNSC tissues. Our findings reveal a substantial overlap of hypermethylated DMRs across two independent HNSC methylation datasets, demonstrating the robustness and clinical relevance of these regions for cfDNA biomarker development. Furthermore, we identified consistent DMRs in both HNSC and lung squamous cell carcinoma (LSCC), suggesting the potential for pan-squamous cell carcinoma biomarkers. Notably, gene clusters such as HOXD , ZNF , and NKX2 were frequently hypermethylated, providing new insights into the shared epigenetic landscape of HNSC and LSCC. This study underscores the importance of incorporating matched normal tissues in cancer methylome analyses and establishes a foundation for advancing cfDNA-based biomarker discovery across squamous cell carcinomas., Competing Interests: Conflict of Interest Statement: The authors declare that they have no conflict of interest regarding the publication of this paper.

Cancer transcriptomic data are used extensively to interrogate the prognostic value of targeted genes, yet basic scientists and clinicians have predominantly relied on univariable survival analysis for this purpose. This method often fails to capture the full prognostic potential and contextual relevance of the genes under study, inadvertently omitting a group of genes we term univariable missed-opportunity prognostic (UMOP) genes. Recognizing the complexity of revealing multifaceted prognostic implications, especially when extending the analysis to include various covariates and thresholds, we present the Cancer Gene Prognosis Atlas (CGPA). This platform greatly enhances gene-centric biomarker research across cancer types by offering an interactive and user-friendly interface for highly customized, in-depth prognostic analysis. CGPA notably supports data-driven exploration of gene pairs and gene-hallmark relationships, elucidating key composite biological mechanisms like synthetic lethality and immunosuppression. It further expands its capabilities to assess multi-gene panels using both public and user-provided data, facilitating a seamless mechanism-to-machine analysis. Additionally, CGPA features a designated portal for discovering prognostic gene modules using curated cancer immunotherapy data. Ultimately, CGPA's comprehensive, accessible tools allow cancer researchers, including those without statistical expertise, to precisely investigate the prognostic landscape of genes, customizing the model to fit specific research hypotheses and enhancing biomarker discovery and validation through a synergy of mechanistic and data-driven strategies., Competing Interests: Conflict of interest: The authors declare no potential conflicts of interest.

Background: Cancer initiation, progression, and immune evasion depend on the tumor microenvironment (TME). Thus, understanding the TME immune architecture is essential for understanding tumor metastasis and therapy response. This study aimed to create an immune cell states (CSs) atlas using bulk RNA-seq data enriched by eco-type analyses to resolve the complex immune architectures in the TME., Methods: We employed EcoTyper, a machine-learning (ML) framework, to study the real-world prognostic significance of immune CSs and multicellular ecosystems, utilizing molecular data from 1,610 patients with multiple malignancies who underwent immune checkpoint inhibitor (ICI) therapy within the ORIEN Avatar cohort, a well-annotated real-world dataset., Results: Our analysis revealed consistent ICI-specific prognostic TME carcinoma ecotypes (CEs) (including CE1, CE9, CE10) across our pan-cancer dataset, where CE1 being more lymphocyte-deficient and CE10 being more proinflammatory. Also, the analysis of specific immune CSs across different cancers showed consistent CD8+ and CD4+ T cell CS distribution patterns. Furthermore, survival analysis of the ORIEN ICI cohort demonstrated that ecotype CE9 is associated with the most favorable survival outcomes, while CE2 is linked to the least favorable outcomes. Notably, the melanoma-specific prognostic EcoTyper model confirmed that lower predicted risk scores are associated with improved survival and better response to immunotherapy. Finally, de novo discovery of ecotypes in the ORIEN ICI dataset identified Ecotype E3 as significantly associated with poorer survival outcomes., Conclusion: Our findings offer important insights into refining the patient selection process for immunotherapy in real-world practice and guiding the creation of novel therapeutic strategies to target specific ecotypes within the TME., Competing Interests: Conflicts of Interest: IE, MLC, JC, API, WSD, GJW, and PH declare no conflicts of interest. VS declares receiving research funding from Eli Lilly and Company and Crinetics Pharmaceuticals. Consulting for GE Health. Advisory Board member of Exelixis. Receiving speaking fees from Lantheus Pharmaceuticals. MM declares contracted research grants with Merck, Taiho, and the National Comprehensive Cancer Network. IP declares Program Leader (1.2 cal months) for NIH/NCI Cancer Center Support Grant (P30CA016056); consulting with Nouscom, Iovance, Nektar, and Regeneron; stock owner with Ideaya, Inc. HC declares advisory board/consultant: Best Doctors/Teladoc, Orbus Therapeutics, Bristol Meyers Squibb, Regeneron, Novocure, and PPD/Chimerix; research funding (site PI/institutional contract): Orbus, GCAR, Array BioPharma, Karyopharm Therapeutics, Nuvation Bio, Bayer, Bristol Meyer Squib, and Sumitomo Dainippon Pharma. To date, S.M.A. is a member of ASCO COI. A.A.T.declares contracted research grants with institution from Bristol Myers Squib, Genentech-Roche, Regeneron, Sanofi-Genzyme, Nektar, Clinigen, Merck, Acrotech, Pfizer, Checkmate, OncoSec, Scholar Rock, InflaRx GmbH, Agenus; personal consultant/advisory board fees from Bristol Myers Squibb, Merck, Easai, Instil Bio, Clinigin, Regeneron, Sanofi-Genzyme, Novartis, Partner Therapeutics, Genentech/Roche, BioNTech, Concert AI, AstraZeneca outside the submitted work.

The search for prognostic biomarkers capable of predicting patient outcomes, by analyzing gene expression in tissue samples and other molecular profiles, remains largely on single-gene-based or global-gene-search approaches. Gene-centric approaches, while foundational, fail to capture the higher-order dependencies that reflect the activities of co-regulated processes, pathway alterations, and regulatory networks, all of which are crucial in determining the patient outcomes in complex diseases like cancer. Here, we introduce GPS-Net, a computational framework that fills the gap in efficiently identifying prognostic modules by incorporating the holistic pathway structures and the network of gene interactions. By innovatively incorporating advanced multiple kernel learning techniques and network-based regularization, the proposed method not only enhances the accuracy of biomarker and pathway identification but also significantly reduces computational complexity, as demonstrated by extensive simulation studies. Applying GPS-Net, we identified key pathways that are predictive of patient outcomes in a cancer immunotherapy study. Overall, our approach provides a novel framework that renders genome-wide pathway-level prognostic analysis both feasible and scalable, synergizing both mechanism-driven and data-driven for precision genomics., Competing Interests: Declaration of interest The authors have no conflicts of interest to declare.

Liquid biopsy analysis of cell-free DNA (cfDNA) has revolutionized cancer research by enabling non-invasive assessment of tumor-derived genetic and epigenetic changes. In this study, we conducted a comprehensive paired-sample differential methylation analysis (psDMR) on reprocessed methylation data from two large datasets, CPTAC and TCGA, to identify and validate differentially methylated regions (DMRs) as potential cfDNA biomarkers for head and neck squamous cell carcinoma (HNSC). Our hypothesis is that the paired sample test provides a more suitable and powerful approach for the analysis of heterogeneous cancers like HNSC. The psDMR analysis revealed a significant number of overlapped hypermethylated DMRs between two datasets, indicating the reliability and relevance of these regions for cfDNA methylation biomarker discovery. We identified several candidate genes, including CALCA , ALX4 , and HOXD9 , which have been previously established as liquid biopsy methylation biomarkers in various cancer types. Furthermore, we demonstrated the efficacy of targeted region analysis using cfDNA methylation data from oral cavity squamous cell carcinoma and nasopharyngeal carcinoma patients, further validating the utility of psDMR analysis in prioritizing cfDNA methylation biomarkers. Overall, our study contributes to the development of cfDNA-based approaches for early cancer detection and monitoring, expanding our understanding of the epigenetic landscape of HNSC, and providing valuable insights for liquid biopsy biomarker discovery not only in HNSC and other cancer types.

Identifying novel and reliable prognostic biomarkers for predicting patient survival outcomes is essential for deciding personalized treatment strategies for diseases such as cancer. Numerous feature selection techniques have been proposed to address the high-dimensional problem in constructing prediction models. Not only does feature selection lower the data dimension, but it also improves the prediction accuracy of the resulted models by mitigating overfitting. The performances of these feature selection methods when applied to survival models, on the other hand, deserve further investigation. In this paper, we construct and compare a series of prediction-oriented biomarker selection frameworks by leveraging recent machine learning algorithms, including random survival forests, extreme gradient boosting, light gradient boosting and deep learning-based survival models. Additionally, we adapt the recently proposed prediction-oriented marker selection (PROMISE) to a survival model (PROMISE-Cox) as a benchmark approach. Our simulation studies indicate that boosting-based approaches tend to provide superior accuracy with better true positive rate and false positive rate in more complicated scenarios. For demonstration purpose, we applied the proposed biomarker selection strategies to identify prognostic biomarkers in different modalities of head and neck cancer data., (© The Author(s) 2023. Published by Oxford University Press on behalf of NAR Genomics and Bioinformatics.)