Impact of post-operative transoral robotic surgery hemorrhage on adjuvant treatment delays in patients with oropharyngeal squamous cell carcinoma.

Objectives: Transoral robotic surgery (TORS) for the treatment for oropharyngeal squamous cell carcinoma (SCC) carries a risk of post-operative hemorrhage. Increased time from surgery to completion of adjuvant therapy has been associated with decreased survival. Our objective was to assess for adjuvant treatments delays in patients with post-operative bleeding.

Robust identification of perturbed cell types in single-cell RNA-seq data.

Single-cell transcriptomics has emerged as a powerful tool for understanding how different cells contribute to disease progression by identifying cell types that change across diseases or conditions. However, detecting changing cell types is challenging due to individual-to-individual and cohort-to-cohort variability and naive approaches based on current computational tools lead to false positive findings. To address this, we propose a computational tool, scDist, based on a mixed-effects model that provides a statistically rigorous and computationally efficient approach for detecting transcriptomic differences.

IGSF8 is an innate immune checkpoint and cancer immunotherapy target.

Antigen presentation defects in tumors are prevalent mechanisms of adaptive immune evasion and resistance to cancer immunotherapy, whereas how tumors evade innate immunity is less clear. Using CRISPR screens, we discovered that IGSF8 expressed on tumors suppresses NK cell function by interacting with human KIR3DL2 and mouse Klra9 receptors on NK cells. IGSF8 is normally expressed in neuronal tissues and is not required for cell survival in vitro or in vivo.

Psychometric properties of EQ-5D-5L for use in patients with Graves' disease.

Background: The EQ-5D-5 L is a commonly used generic measure of health. This study aimed to evaluate the psychometric properties of the EQ-5D-5 L in patients with Graves' disease (GD).

Methods: A prospective cohort of patients with GD recruited at three public hospitals in Hong Kong completed the EQ-5D-5 L and ThyPRO-39 questionnaires at baseline, 1-month, and 6-month follow-ups.

Including Community Partners in the Development and Adaptation of Intervention Strategies to Prevent Initiation or Escalation of Opioid Misuse.

Current literature lacks clear examples of how to engage with communities in the development of opioid misuse interventions for diverse populations and across various settings. The National Institutes of Health (NIH) Helping to End Addiction Long-term Initiative (HEAL) Prevention Cooperative (HPC) research projects work collaboratively with communities to develop and adapt their opioid misuse interventions to increase both feasibility and sustainability. Ten HPC projects were selected to receive NIH funding and are required to have partnerships with communities where their intervention is being conducted.

Tutorial: integrative computational analysis of bulk RNA-sequencing data to characterize tumor immunity using RIMA.

RNA-sequencing (RNA-seq) has become an increasingly cost-effective technique for molecular profiling and immune characterization of tumors. In the past decade, many computational tools have been developed to characterize tumor immunity from gene expression data. However, the analysis of large-scale RNA-seq data requires bioinformatics proficiency, large computational resources and cancer genomics and immunology knowledge.

CCX559 is a potent, orally-administered small molecule PD-L1 inhibitor that induces anti-tumor immunity.

The interaction of PD-L1 with PD-1 is a major immune checkpoint that limits effector T cell function against cancer cells; monoclonal antibodies that block this pathway have been approved in multiple tumor indications. As a next generation therapy, small molecule inhibitors of PD-L1 have inherent drug properties that may be advantageous for certain patient populations compared to antibody therapies. In this report we present the pharmacology of the orally-available, small molecule PD-L1 inhibitor CCX559 for cancer immunotherapy.

Transfer learning enables predictions in network biology.

Mapping gene networks requires large amounts of transcriptomic data to learn the connections between genes, which impedes discoveries in settings with limited data, including rare diseases and diseases affecting clinically inaccessible tissues. Recently, transfer learning has revolutionized fields such as natural language understanding and computer vision by leveraging deep learning models pretrained on large-scale general datasets that can then be fine-tuned towards a vast array of downstream tasks with limited task-specific data. Here, we developed a context-aware, attention-based deep learning model, Geneformer, pretrained on a large-scale corpus of about 30 million single-cell transcriptomes to enable context-specific predictions in settings with limited data in network biology.

Efficient and accurate KIR and HLA genotyping with massively parallel sequencing data.

Killer cell immunoglobulin like receptor (KIR) genes and human leukocyte antigen (HLA) genes play important roles in innate and adaptive immunity. They are highly polymorphic and cannot be genotyped with standard variant calling pipelines. Compared with HLA genes, many KIR genes are similar to each other in sequences and may be absent in the chromosomes.

MetaTiME integrates single-cell gene expression to characterize the meta-components of the tumor immune microenvironment.

Recent advances in single-cell RNA sequencing have shown heterogeneous cell types and gene expression states in the non-cancerous cells in tumors. The integration of multiple scRNA-seq datasets across tumors can indicate common cell types and states in the tumor microenvironment (TME). We develop a data driven framework, MetaTiME, to overcome the limitations in resolution and consistency that result from manual labelling using known gene markers.

Effect of the COVID-19 pandemic on surgery for indeterminate thyroid nodules (THYCOVID): a retrospective, international, multicentre, cross-sectional study.

Background: Since its outbreak in early 2020, the COVID-19 pandemic has diverted resources from non-urgent and elective procedures, leading to diagnosis and treatment delays, with an increased number of neoplasms at advanced stages worldwide. The aims of this study were to quantify the reduction in surgical activity for indeterminate thyroid nodules during the COVID-19 pandemic; and to evaluate whether delays in surgery led to an increased occurrence of aggressive tumours.

Methods: In this retrospective, international, cross-sectional study, centres were invited to participate in June 22, 2022; each centre joining the study was asked to provide data from medical records on all surgical thyroidectomies consecutively performed from Jan 1, 2019, to Dec 31, 2021.

Addressing Tumor Heterogeneity by Sensitizing Resistant Cancer Cells to T cell-Secreted Cytokines.

Unlabelled: Tumor heterogeneity is a major barrier to cancer therapy, including immunotherapy. Activated T cells can efficiently kill tumor cells following recognition of MHC class I (MHC-I)-bound peptides, but this selection pressure favors outgrowth of MHC-I-deficient tumor cells. We performed a genome-scale screen to discover alternative pathways for T cell-mediated killing of MHC-I-deficient tumor cells.

Discovery of Targets for Immune-Metabolic Antitumor Drugs Identifies Estrogen-Related Receptor Alpha.

Unlabelled: Drugs that kill tumors through multiple mechanisms have the potential for broad clinical benefits. Here, we first developed an in silico multiomics approach (BipotentR) to find cancer cell-specific regulators that simultaneously modulate tumor immunity and another oncogenic pathway and then used it to identify 38 candidate immune-metabolic regulators. We show the tumor activities of these regulators stratify patients with melanoma by their response to anti-PD-1 using machine learning and deep neural approaches, which improve the predictive power of current biomarkers.

Machine Learning Modeling of Protein-intrinsic Features Predicts Tractability of Targeted Protein Degradation.

Targeted protein degradation (TPD) has rapidly emerged as a therapeutic modality to eliminate previously undruggable proteins by repurposing the cell's endogenous protein degradation machinery. However, the susceptibility of proteins for targeting by TPD approaches, termed "degradability", is largely unknown. Here, we developed a machine learning model, model-free analysis of protein degradability (MAPD), to predict degradability from features intrinsic to protein targets.

Combined Shear Wave Elastography and EU TIRADS in Differentiating Malignant and Benign Thyroid Nodules.

Although multimodal ultrasound approaches have been suggested to potentially improve the diagnosis of thyroid cancer; the diagnostic utility of the combination of SWE and malignancy-risk stratification systems remains vague due to the lack of standardized criteria. The purpose of the study was to assess the diagnostic value of the combination of grey scale ultrasound assessment using EU TIRADS and shear wave elastography. 121 patients (126 nodules−81 benign; 45 malignant) underwent grey scale ultrasound and SWE imaging of nodules between 0.

Machine learning on syngeneic mouse tumor profiles to model clinical immunotherapy response.

Most patients with cancer are refractory to immune checkpoint blockade (ICB) therapy, and proper patient stratification remains an open question. Primary patient data suffer from high heterogeneity, low accessibility, and lack of proper controls. In contrast, syngeneic mouse tumor models enable controlled experiments with ICB treatments.

Hippo Signaling Pathway Regulates Cancer Cell-Intrinsic MHC-II Expression.

MHC-II is known to be mainly expressed on the surface of antigen-presenting cells. Evidence suggests MHC-II is also expressed by cancer cells and may be associated with better immunotherapy responses. However, the role and regulation of MHC-II in cancer cells remain unclear.