Identification of a Proteomic Signature for Predicting Immunotherapy Response in Patients With Metastatic Non-Small Cell Lung Cancer.

Immunotherapy has improved survival rates in patients with cancer, but identifying those who will respond to treatment remains a challenge. Advances in proteomic technologies have enabled the identification and quantification of nearly all expressed proteins in a single experiment. Integrating mass spectrometry with high-throughput technologies has facilitated comprehensive analysis of the plasma proteome in cancer, facilitating early diagnosis and personalized treatment. In this context, our study aimed to investigate the predictive and prognostic value of plasma proteome analysis using the SWATH-MS (Sequential Window Acquisition of All Theoretical Mass Spectra) strategy in newly diagnosed patients with non-small cell lung cancer (NSCLC) receiving pembrolizumab therapy. We enrolled 64 newly diagnosed patients with advanced NSCLC treated with pembrolizumab. Blood samples were collected from all patients before and during therapy. A total of 171 blood samples were analyzed using the SWATH-MS strategy. Plasma protein expression in metastatic NSCLC patients prior to receiving pembrolizumab was analyzed. A first cohort (discovery cohort) was employed to identify a proteomic signature predicting immunotherapy response. Thus, 324 differentially expressed proteins between responder and non-responder patients were identified. In addition, we developed a predictive model and found a combination of seven proteins, including ATG9A, DCDC2, HPS5, FIL1L, LZTL1, PGTA, and SPTN2, with stronger predictive value than PD-L1 expression alone. Additionally, survival analyses showed an association between the levels of ATG9A, DCDC2, SPTN2 and HPS5 with progression-free survival (PFS) and/or overall survival (OS). Our findings highlight the potential of proteomic technologies to detect predictive biomarkers in blood samples from NSCLC patients, emphasizing the correlation between immunotherapy response and the idenfied protein set.

From Inhalation to Neurodegeneration: Air Pollution as a Modifiable Risk Factor for Alzheimer's Disease.

Air pollution, a growing concern for public health, has been linked to various respiratory and cardiovascular diseases. Emerging evidence also suggests a link between exposure to air pollutants and neurodegenerative diseases, particularly Alzheimer's disease (AD). This review explores the composition and sources of air pollutants, including particulate matter, gases, persistent organic pollutants, and heavy metals. The pathophysiology of AD is briefly discussed, highlighting the role of beta-amyloid plaques, neurofibrillary tangles, and genetic factors. This article also examines how air pollutants reach the brain and exert their detrimental effects, delving into the neurotoxicity of air pollutants. The molecular mechanisms linking air pollution to neurodegeneration are explored in detail, focusing on oxidative stress, neuroinflammation, and protein aggregation. Preclinical studies, including in vitro experiments and animal models, provide evidence for the direct effects of pollutants on neuronal cells, glial cells, and the blood-brain barrier. Epidemiological studies have reported associations between exposure to air pollution and an increased risk of AD and cognitive decline. The growing body of evidence supporting air pollution as a modifiable risk factor for AD underscores the importance of considering environmental factors in the etiology and progression of neurodegenerative diseases, in the face of worsening global air quality.

Utility of polygenic risk scores to aid in the diagnosis of rheumatic diseases.

Rheumatic diseases (RDs) are characterized by autoimmunity and autoinflammation and are recognized as complex due to the interplay of multiple genetic, environmental, and lifestyle factors in their pathogenesis. The rapid advancement of genome-wide association studies (GWASs) has enabled the identification of numerous single nucleotide polymorphisms (SNPs) associated with RD susceptibility. Based on these SNPs, polygenic risk scores (PRSs) have emerged as promising tools for quantifying genetic risk in this disease group. This chapter reviews the current status of PRSs in assessing the risk of RDs and discusses their potential to improve the accuracy of the diagnosis of these complex diseases through their ability to discriminate among different RDs. PRSs demonstrate a high discriminatory capacity for various RDs and show potential clinical utility. As GWASs continue to evolve, PRSs are expected to enable more precise risk stratification by integrating genetic, environmental, and lifestyle factors, thereby refining individual risk predictions and advancing disease management strategies.

Unveiling the Significance of HLA and KIR Diversity in Underrepresented Populations.

Human leukocyte antigen (HLA) molecules and their relationships with natural killer (NK) cells, specifically through their interaction with killer-cell immunoglobulin-like receptors (KIRs), exhibit robust associations with the outcomes of diverse diseases. Moreover, genetic variations in HLA and KIR immune system genes offer limitless depths of complexity. In recent years, a surge of high-powered genome-wide association studies (GWASs) utilizing single nucleotide polymorphism (SNP) arrays has occurred, significantly advancing our understanding of disease pathogenesis. Additionally, advances in HLA reference panels have enabled higher resolution and more reliable imputation, allowing for finer-grained evaluation of the association between sequence variations and disease risk. However, it is essential to note that the majority of these GWASs have focused primarily on populations of Caucasian and Asian origins, neglecting underrepresented populations in Latin America and Africa. This omission not only leads to disparities in health care access but also restricts our knowledge of novel genetic variants involved in disease pathogenesis within these overlooked populations. Since the KIR and HLA haplotypes prevalent in each population are clearly modelled by the specific environment, the aim of this review is to encourage studies investigating HLA/KIR involvement in infection and autoimmune diseases, reproduction, and transplantation in underrepresented populations.

Cell-free DNA methylation profile potential in the diagnosis of lung squamous cell carcinoma.

Background: Aberrant methylation plays an essential role in early cancer development. In this study, we investigated methylation patterns in lung squamous cell carcinoma (LUSC) and matched non-tumor tissue and plasma samples to evaluate the potential of these patterns in the diagnosis of LUSC. Methods: The study group included 49 patients with stage I-III LUSC. We collected resected tumor tissue, paired peritumoral tissue, distant normal tissue, and corresponding plasma samples. A bespoke lung cancer bisulfite sequencing panel was used to profile the methylation level. Another 48 healthy volunteers provided control plasma samples. Results: Peritumoral and distant normal tissues presented similar methylation signatures, distinct from those in tumor tissue samples. A comparison of methylation profiles led to the identification of 871 tumor-specific differentially methylated blocks, including 847 hypermethylated and 24 hypomethylated blocks (adjusted P value <0.05). All top-ranked blocks were tumor-related. Tissue samples were analyzed for field cancerization to identify progressively aggravating aberrant methylations during tumor initiation and development. The analysis revealed that 221 blocks presented a stepwise increase in methylation levels, while seven blocks presented a stepwise decrease in methylation pattern as the sampling drew nearer to the tumor. The malignant contaminated ratio (MCR) confirmed the presence of distinct methylation patterns between tumor and peritumoral tissue samples. We then constructed a diagnostic panel using a combined diagnostic score of cell-free DNA (cfDNA) that showed high sensitivity and specificity. The healthy controls had a significantly lower combined diagnostic score (cd-score) than LUSC patients. Additionally, based on the methylation profiles, LUSC could be classified into two subgroups, C1 and C2. The methylation profile of the C2 group was not distinct from the healthy controls, which had a significantly lower cd-score than did the C1 group. Conclusions: LUSC-specific methylation patterns could potentially discriminate between peritumoral tissue, distant normal tumor tissue, and tumor tissues. This preliminary study also supported the potential utility of cfDNA methylation analysis in diagnosing LUSC.

HLA alleles: important pieces to the COVID-19 puzzle.

Research on human leukocyte antigen (HLA) molecules in coronavirus disease 2019 (COVID-19) raised high expectations but has yielded limited results. Augusto et al.'s recent study in Nature unveils a strong association of HLA-B*15:01 with asymptomatic COVID-19, representing an important contribution to genetics in COVID-19.

HLA-A*11:01 and HLA-C*04:01 are associated with severe COVID-19.

We analyzed the association between HLA polymorphisms and susceptibility to SARS-CoV-2 infection and disease severity. Genotyping data from a total of 9373 COVID-19-positive cases from the Spanish Coalition to Unlock Research on Host Genetics on COVID-19 (SCOURGE) consortium and 5943 population controls were included in the study. We found an association of the alleles HLA-B*14:02 and HLA-C*08:02 with a lower risk to COVID-19 infection (p = 0.006, OR = 0.84, 95% CI = [0.75-0.95], p = 0.024, OR = 0.86, 95% CI = [0.78-0.95], respectively). We also found the alleles HLA-A*11:01 and HLA-C*04:01 associated with disease severity (p = 0.033, OR = 1.16, 95% CI = [1.04-1.31], p = 0.045, OR = 1.14, 95% CI = [1.05-1.25], respectively). These results suggest that an effective presentation of viral peptides by HLA class I alleles involve a faster infection clearance, decreasing the susceptibility and severity of COVID-19.

Bioinformatics analysis and single-cell RNA sequencing: elucidating the ubiquitination pathways and key enzymes in lung adenocarcinoma.

Background: Lung adenocarcinoma (LUAD) is a prevalent subtype of lung cancer associated with high mortality rates. We aimed to utilize single-cell multiomics analysis to identify the key molecules involved in ubiquitination modification, which plays a role in LUAD development and progression. Methods: We use a systematic approach to analyze LUAD-related single-cell and bulk transcriptome datasets from Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. Single-cell RNA sequencing (scRNA-seq) data were normalized, clustered, and annotated with the Seurat package in R. InferCNV was used to distinguish malignant from epithelial cells, and AUCell evaluated the area under the curve (AUC) score of ubiquitination-related enzymes. Survival and differential analyses identified significant molecular markers associated with ubiquitination. PSMD14 expression was confirmed using reverse-transcription quantitative polymerase chain reaction (RT-qPCR) and Western blot assays, and its knockdown cell lines were assessed for effects on cellular processes and tumor formation in mice. PSMD14's interacting proteins were predicted, and its impact on AGR2 protein half-life and ubiquitination was evaluated. Rescue experiments involving PSMD14 overexpression and AGR2 silencing assessed their impact on malignant behaviors. Results: By means of single-cell sequencing analysis, we probed the ubiquitination modification landscape in the LUAD microenvironment. Malignant cells had elevated scores for enzymes and ubiquitin-binding domains compared to normal epithelial cells, with 53 ubiquitination-related molecules showing prognostic disparities. FGR, PSMD14, and ZBTB16 were identified as genes with prognostic significance, with PSMD14 showing higher expression in epithelial and malignant cells. Two missense mutation sites were identified in PSMD14, which had a high copy number amplification ratio and positive correlation with messenger RNA (mRNA) expression. PSMD14 expression and tumor stage were found to be independent prognostic factors, and interfering with PSMD14 expression reduced the malignant behavior of LUAD cells. PSMD14 was found to bind to AGR2 protein and reduce its ubiquitination, leading to increased AGR2 stability. Knockdown of AGR2 inhibited the enhancement of cell viability, invasion, and migration resulting from PSMD14 overexpression. Conclusions: This study examined ubiquitination modifications in LUAD using sequencing data, identifying PSMD14's critical role in malignancy regulation and its potential as a prognostic and therapeutic biomarker. These insights enhance understanding of LUAD mechanisms and treatment.

Somatic mutations in tumor and plasma of locoregional recurrent and/or metastatic head and neck cancer using a next-generation sequencing panel: A preliminary study.

BACKGROUND: We explore the utility of TruSight Tumor 170 panel (TST170) for detecting somatic mutations in tumor and cfDNA from locoregional recurrent and/or metastatic head and neck squamous cell carcinoma (HNSCC). METHODS: Targeted NGS of tumor DNA and plasma cfDNA was performed using TST170 panel. In addition, a set of somatic mutations previously described in HNSCC were selected for validating in tumor, plasma, and saliva by digital droplet PCR. RESULTS: The TST170 panel identified 13 non-synonymous somatic mutations, of which five were detected in tumoral tissue, other five in plasma cfDNA, and three in both tissue and plasma cfDNA. Of the eight somatic mutations identified in tissue, three were also identified in plasma cfDNA, showing an overall concordance rate of 37.5%. CONCLUSIONS: This preliminary study shows the possibility to detect somatic mutations in tumor and plasma of HNSCC patients using a single assay that would facilitate the clinical implementation of personalized medicine in the clinic.

Plasma cell-free DNA and circulating tumor cells as prognostic biomarkers in small cell lung cancer patients.

Background: Lack of biomarkers for treatment selection and monitoring in small cell lung cancer (SCLC) patients with the limited therapeutic options, result in poor outcomes. Therefore, new prognostic biomarkers are needed to improve their management. The prognostic value of cell-free DNA (cfDNA) and circulating tumor cells (CTCs) have been less explored in SCLC. Methods: We quantified cfDNA in 46 SCLC patients at different times during first-line of chemotherapy or chemo-immunotherapy. Moreover, CTCs were analyzed in 21 patients before therapy onset using CellSearch® system. The possible association between both biomarkers and patients' outcomes was investigated in order to develop a prognostic model. Results: High cfDNA levels before therapy were associated with shorter progression-free survival (PFS) and overall survival (OS). Furthermore, cfDNA levels at 3 weeks and at progression disease were also associated with patients' outcomes. Multivariate analyses confirmed the independence of cfDNA levels as a prognostic biomarker. Finally, the three-risk category prognostic model developed included Eastern Cooperative Oncology Group Performance Status (ECOG PS), gender and baseline cfDNA levels was associated with a higher risk of progression and death. Conclusions: We confirmed the prognostic utility of cfDNA quantitative analysis in SCLC patients before and during therapy. Our novel risk prognostic model in clinical practice will allow to identify patients who could benefit with actual therapies.

Pharmacogenetic Variation and Its Clinical Relevance in a Latin American Rural Population.

Latin-American populations have been largely underrepresented in genomic studies of drug response and disease susceptibility. In this paper, we present a genome-wide Chilean dataset from Talca based on the Illumina Global Screening Array. This let us to compare the frequency of gene variants involved in response to drugs among our population and others, taking data from the 1000 Genomes Project. We found four single-nucleotide polymorphisms with low prevalence in Chileans when compared with African, Amerindian, East and South Asian, and European populations: rs2819742 (RYR2), rs2631367 (SLC22A5), rs1063320 (HLA-G), and rs1042522 (TP53). Moreover, two markers showed significant differences between lower and higher proportion of Mapuche ancestry groups: rs1719247 (located in an intergenic region in chromosome 15; p-value = 6.17 × 10-5, Bonferroni corrected p-value = 0.02) and rs738409 (A nonsynonymous gene variant in the PNPLA3 gene; p-value = 9.02 × 10-5, Bonferroni corrected p-value = 0.04). All of these polymorphisms have been shown to be associated with diverse pathologies, such as asthma, cancer, or chronic hepatitis B, or to be involved in a different response to drugs, such as metformin, HMG-CoA reductase inhibitors, or simvastatin. The present work provides a pharmacogenetic landscape of an understudied Latin American rural population and supports the notion that pharmacogenetic studies in admixed populations should consider ancestry for a higher accuracy of the results. Our study stresses the relevance of the pharmacogenomic research to provide guidance for a better choice of the best treatment for each individual in a population with admixed ancestry.

Advances of Genomic Medicine in Psoriatic Arthritis.

Psoriatic arthritis (PsA) is a common type of inflammatory arthritis found in up to 40% of patients with psoriasis. Although early diagnosis is important for reducing the risk of irreversible structural damage, there are no adequate screening tools for this purpose, and there are no clear markers of predisposition to the disease. Much evidence indicates that PsA disorder is complex and heterogeneous, where genetic and environmental factors converge to trigger inflammatory events and the development of the disease. Nevertheless, the etiologic events that underlie PsA are complex and not completely understood. In this review, we describe the existing data in PsA in order to highlight the need for further research in this disease to progress in the knowledge of its pathobiology and to obtain early diagnosis tools for these patients.

Personalized Medicine in Autoimmune Diseases.

Autoimmune diseases are multifactorial disorders caused by both genetic and environmental factors and without a known cure [...].