Advancing Pharmacogenomics from Single-Gene to Preemptive Testing.

Pharmacogenomic testing can be an effective tool to enhance medication safety and efficacy. Pharmacogenomically actionable medications are widely used, and approximately 90-95% of individuals have an actionable genotype for at least one pharmacogene. For pharmacogenomic testing to have the greatest impact on medication safety and clinical care, genetic information should be made available at the time of prescribing (preemptive testing). However, the use of preemptive pharmacogenomic testing is associated with some logistical concerns, such as consistent reimbursement, processes for reporting preemptive results over an individual's lifetime, and result portability. Lessons can be learned from institutions that have implemented preemptive pharmacogenomic testing. In this review, we discuss the rationale and best practices for implementing pharmacogenomics preemptively.

New trends on personalized sunscreens.

BACKGROUND/PURPOSE: Nowadays, there are emerging trends in customized and personalized photoprotection, focusing on the innovative approaches to enhance sun protection efficacy tailored to individual needs. METHODS: We conducted an electronic search of the following databases: MEDLINE, EMBASE, Cochrane Database of Systematic Reviews, Cochrane Skin Group Specialised Skin Register, and TESEO. Specific search terms related to personalized photoprotection and the variables of age, genetic predisposition, skin phototype, photodermatosis, and physiological conditions such as pregnancy, as well as lifestyle habits were used. RESULTS/CONCLUSION: The article highlights the challenges and opportunities in adopting personalized photoprotection strategies, aiming to promote skin health and prevent the harmful effects of UV radiation in the era of precision medicine.

A Survey on Blood Pressure Measurement Technologies: Addressing Potential Sources of Bias.

Regular blood pressure (BP) monitoring in clinical and ambulatory settings plays a crucial role in the prevention, diagnosis, treatment, and management of cardiovascular diseases. Recently, the widespread adoption of ambulatory BP measurement devices has been predominantly driven by the increased prevalence of hypertension and its associated risks and clinical conditions. Recent guidelines advocate for regular BP monitoring as part of regular clinical visits or even at home. This increased utilization of BP measurement technologies has raised significant concerns regarding the accuracy of reported BP values across settings. In this survey, which focuses mainly on cuff-based BP monitoring technologies, we highlight how BP measurements can demonstrate substantial biases and variances due to factors such as measurement and device errors, demographics, and body habitus. With these inherent biases, the development of a new generation of cuff-based BP devices that use artificial intelligence (AI) has significant potential. We present future avenues where AI-assisted technologies can leverage the extensive clinical literature on BP-related studies together with the large collections of BP records available in electronic health records. These resources can be combined with machine learning approaches, including deep learning and Bayesian inference, to remove BP measurement biases and provide individualized BP-related cardiovascular risk indexes.

Costimulatory molecule expression profile as a biomarker to predict prognosis and chemotherapy response for patients with small cell lung cancer.

Owing to the paucity of specimens, progress in identifying prognostic and therapeutic biomarkers for small cell lung cancer (SCLC) has been stagnant for decades. Considering that the costimulatory molecules are essential elements in modulating immune responses and determining therapeutic response, we systematically revealed the expression landscape and identified a costimulatory molecule-based signature (CMS) to predict prognosis and chemotherapy response for SCLCs for the first time. We found T cell activation was restrained in SCLCs, and costimulatory molecules exhibited widespread abnormal genetic alterations and expression. Using a LASSO Cox regression model, the CMS was built with a training cohort of 77 cases, which successfully divided patients into high- or low-risk groups with significantly different prognosis and chemotherapy benefit (both P < 0.001). The CMS was well validated in an independent cohort containing 131 samples with qPCR data. ROC and C-index analysis confirmed the superior predictive performance of the CMS in comparison with other clinicopathological parameters from different cohorts. Importantly, the CMS was confirmed as a significantly independent prognosticator for clinical outcomes and chemotherapy response in SCLCs through multivariate Cox analysis. Further analysis revealed that low-risk patients were characteristic by an activated immune phenotype with distinct expression of immune checkpoints. In summary, we firstly uncovered the expression heterogeneity of costimulatory molecules in SCLC and successfully constructed a novel predictive CMS. The identified signature contributed to more accurate patient stratification and provided robust prognostic value in estimating survival and the clinical response to chemotherapy, allowing optimization of treatment and prognosis management for patients with SCLC.

Implementation of genomic medicine for rare disease in a tertiary healthcare system: Mayo Clinic Program for Rare and Undiagnosed Diseases (PRaUD).

BACKGROUND: In the United States, rare disease (RD) is defined as a condition that affects fewer than 200,000 individuals. Collectively, RD affects an estimated 30 million Americans. A significant portion of RD has an underlying genetic cause; however, this may go undiagnosed. To better serve these patients, the Mayo Clinic Program for Rare and Undiagnosed Diseases (PRaUD) was created under the auspices of the Center for Individualized Medicine (CIM) aiming to integrate genomics into subspecialty practice including targeted genetic testing, research, and education. METHODS: Patients were identified by subspecialty healthcare providers from 11 clinical divisions/departments. Targeted multi-gene panels or custom exome/genome-based panels were utilized. To support the goals of PRaUD, a new clinical service model, the Genetic Testing and Counseling (GTAC) unit, was established to improve access and increase efficiency for genetic test facilitation. The GTAC unit includes genetic counselors, genetic counseling assistants, genetic nurses, and a medical geneticist. Patients receive abbreviated point-of-care genetic counseling and testing through a partnership with subspecialty providers. RESULTS: Implementation of PRaUD began in 2018 and GTAC unit launched in 2020 to support program expansion. Currently, 29 RD clinical indications are included in 11 specialty divisions/departments with over 142 referring providers. To date, 1152 patients have been evaluated with an overall solved or likely solved rate of 17.5% and as high as 66.7% depending on the phenotype. Noteworthy, 42.7% of the solved or likely solved patients underwent changes in medical management and outcome based on genetic test results. CONCLUSION: Implementation of PRaUD and GTAC have enabled subspecialty practices advance expertise in RD where genetic counselors have not historically been embedded in practice. Democratizing access to genetic testing and counseling can broaden the reach of patients with RD and increase the diagnostic yield of such indications leading to better medical management as well as expanding research opportunities.

Prognostic role of CRP-independent inflammatory patterns in patients undergoing primary percutaneous interventions.

BACKGROUND: Despite the key pathophysiological role of inflammation in the development of coronary artery disease (CAD), the evaluation of inflammatory status has not been clearly established in patients presenting with acute coronary syndrome (ACS). The aim of this study is to evaluate the prevalence of CRP-independent inflammatory patterns in patients referred for primary percutaneous coronary intervention (pPCI) and to determine their one-year relationship with adverse clinical outcomes. METHODS: We carried out a single-centre, observational study consecutively enrolling all patients presenting at a large-volume PCI hub with a diagnosis of ST-segment elevation myocardial infarction (STEMI) and treated with pPCI. Systemic immune-inflammatory index (SII) was calculated at admission and discharge. According to different SII trajectories patients were divided into four patterns: 'persistent-low', 'down-sloping', 'up-sloping' and 'persistent-high' patterns. The primary endpoint was a composite of all-cause of death and myocardial infarction (MI) at a one-year follow-up. RESULTS: Among the total 2353 subjects enrolled, 44% of them belonged to 'persistent-low', 31% to 'down-sloping', 4% to 'up-sloping' and 21% to 'persistent-high' pattern. The primary endpoint was observed in 8% of patients with a 'persistent-low', 12% with a 'down-sloping', 27% with an 'up-sloping' and 25% with a persistent-high pattern (p = 0.001). After multivariate analysis, 'up-sloping' (OR: 3.2 [1.59-3.93]; p = 0.001) and 'persistent-high' (OR: 4.1 [3.03-4.65]; p = 0.001) patterns emerged as independent predictors of one-year adverse events. CONCLUSIONS: 'Persistent-high' and 'up-sloping' CRP-independent inflammatory patterns in patients undergoing primary PCI are associated with an increased risk of adverse events at one-year follow-up. The prognostic value of these inflammatory patterns might be helpful to individualize potential therapeutic targets.

Relative sparsity for medical decision problems.

Existing statistical methods can estimate a policy, or a mapping from covariates to decisions, which can then instruct decision makers (eg, whether to administer hypotension treatment based on covariates blood pressure and heart rate). There is great interest in using such data-driven policies in healthcare. However, it is often important to explain to the healthcare provider, and to the patient, how a new policy differs from the current standard of care. This end is facilitated if one can pinpoint the aspects of the policy (ie, the parameters for blood pressure and heart rate) that change when moving from the standard of care to the new, suggested policy. To this end, we adapt ideas from Trust Region Policy Optimization (TRPO). In our work, however, unlike in TRPO, the difference between the suggested policy and standard of care is required to be sparse, aiding with interpretability. This yields "relative sparsity," where, as a function of a tuning parameter, λ $$ \lambda $$ , we can approximately control the number of parameters in our suggested policy that differ from their counterparts in the standard of care (eg, heart rate only). We propose a criterion for selecting λ $$ \lambda $$ , perform simulations, and illustrate our method with a real, observational healthcare dataset, deriving a policy that is easy to explain in the context of the current standard of care. Our work promotes the adoption of data-driven decision aids, which have great potential to improve health outcomes.

Population Pharmacokinetics and Pharmacogenetics Analyses of Dasatinib in Chinese Patients with Chronic Myeloid Leukemia.

AIMS: Dasatinib, a second-generation tyrosine kinase inhibitor of BCR-ABL 1, used for first-line treatment of Philadelphia chromosome-positive chronic myeloid leukemia (CML), exhibits high pharmacokinetic (PK) variability. However, its PK data in Chinese patients with CML remains rarely reported to date. Thus, we developed a population pharmacokinetic (PPK) model of dasatinib in Chinese patients and identified the covariate that could explain the individual variability of PK for optimal individual administration. METHODS: PPK modeling for dasatinib was performed based on 754 plasma concentrations obtained from 140 CML patients and analysis of various genetic and physicochemical parameters. Modeling was performed with nonlinear mixed-effects (NLME) using Phoenix NLME. The finally developed model was evaluated using internal and external validation. Monte Carlo simulations were used to predict drug exposures at a steady state for various dosages. RESULTS: The PK of dasatinib were well described by a two-compartment with a log-additive residual error model. Patients in the current study had a relatively low estimate of CL/F (126 L/h). A significant association was found between the covariate of age and CL/F of dasatinib, which was incorporated into the final model. None of the genetic factors was confirmed as a significant covariate for dasatinib. The results of external validation with 140 samples from 36 patients were acceptable. Simulation results showed significantly higher exposures in elderly patients. CONCLUSIONS: This study's findings suggested that low-dose dasatinib would be better suited for Chinese patients, and the dosage can be appropriately reduced according to the increase of age, especially for the elderly.

Ethical principles, opportunities and constraints in clinical proteomics.

Recent advances in MS-based proteomics have vastly increased the quality and scope of biological information that can be derived from human samples. These advances have rendered current workflows increasingly applicable in biomedical and clinical contexts. As proteomics is poised to take an important role in the clinic, associated ethical responsibilities increase in tandem with the impact on the health, privacy, and well-being of individuals. Here we conducted and report a systematic literature review of ethical issues in clinical proteomics. We add our perspectives from a background of bioethics, the results of our accompanying paper extracting individual-sensitive results from patient samples, and the literature addressing similar issues in genomics. The spectrum of potential issues ranges from patient re-identification to incidental findings of clinical significance. The latter can be divided into actionable and unactionable findings. Some of these have the potential to be employed in discriminatory or privacy-infringing ways. However, incidental findings may also have great positive potential. A plasma proteome profile, for instance, could inform on the general health or disease status of an individual regardless of the narrow diagnostic question that prompted it. We suggest that early discussion of ethical issues in clinical proteomics is important to ensure that eventual regulations reflect the considered judgment of the community as well as to anticipate opportunities and problems that may arise as the technology matures further.

Comprehensive analyses of N6 -methyladenosine-related long noncoding RNA profiles with prognosis, chemotherapy response, and immune landscape in small cell lung cancer.

Small cell lung cancer (SCLC) is the most devastating subtype of lung cancer with no clinically available prognostic biomarkers. N6 -methyladenosine (m6 A) and noncoding RNAs play critical roles in cancer development and treatment response. However, little is known about m6 A-related long noncoding RNAs (lncRNAs) in SCLC. We used 206 limited-stage SCLC (LS-SCLC) samples from two cohorts to undertake the first and most comprehensive exploration of the m6 A-related lncRNA profile in SCLC and constructed a relevant prognostic signature. In total, 289 m6 A-related lncRNAs were screened out. We then built a seven-lncRNA-based signature in the training cohort with 48 RNA sequencing data using univariate and multivariate Cox regression models. The signature was well validated in an independent cohort containing 158 cases with quantitative PCR data. In both cohorts, the signature divided patients into high- and low-risk groups with significantly different survival rates (both p < 0.001). Our signature predicted chemotherapy survival benefit in patients with LS-SCLC. Receiver operating characteristic and C-index analyses indicated that the signature was better at predicting prognosis and chemotherapy benefit than other clinicopathologic features. Moreover, the signature was identified as an independent predictor of prognosis and chemotherapy response in different cohorts. Furthermore, functional analysis showed that multiple activated immune-related pathways were enriched in the low-risk group. Additionally, the signature was also closely related to various immune checkpoints and inflammatory responses. We generated the first clinically available m6 A-related lncRNA signature to predict prognosis and chemotherapy benefit in patients with LS-SCLC. Our findings could help optimize the clinical management of patients with LS-SCLC and inform future therapeutic targets for SCLC.

Implementation of preemptive DNA sequence-based pharmacogenomics testing across a large academic medical center: The Mayo-Baylor RIGHT 10K Study.

PURPOSE: The Mayo-Baylor RIGHT 10K Study enabled preemptive, sequence-based pharmacogenomics (PGx)-driven drug prescribing practices in routine clinical care within a large cohort. We also generated the tools and resources necessary for clinical PGx implementation and identified challenges that need to be overcome. Furthermore, we measured the frequency of both common genetic variation for which clinical guidelines already exist and rare variation that could be detected by DNA sequencing, rather than genotyping. METHODS: Targeted oligonucleotide-capture sequencing of 77 pharmacogenes was performed using DNA from 10,077 consented Mayo Clinic Biobank volunteers. The resulting predicted drug response-related phenotypes for 13 genes, including CYP2D6 and HLA, affecting 21 drug-gene pairs, were deposited preemptively in the Mayo electronic health record. RESULTS: For the 13 pharmacogenes of interest, the genomes of 79% of participants carried clinically actionable variants in 3 or more genes, and DNA sequencing identified an average of 3.3 additional conservatively predicted deleterious variants that would not have been evident using genotyping. CONCLUSION: Implementation of preemptive rather than reactive and sequence-based rather than genotype-based PGx prescribing revealed nearly universal patient applicability and required integrated institution-wide resources to fully realize individualized drug therapy and to show more efficient use of health care resources.

Pharmacogenomics for the efficacy and side effects of antihistamines.

Antihistamines, especially H1 antihistamines, are widely used in the treatment of allergic diseases such as urticaria and allergic rhinitis, mainly for reversing elevated histamine and anti-allergic effects. Antihistamines are generally safe, but some patients experience adverse reactions, such as cardiotoxicity, central inhibition and anticholinergic effects. There are also individual differences in antihistamine efficacy in clinical practice. The concept of individualized medicine has been deeply rooted in people's minds since it was put forward. Pharmacogenomics is the study of the role of inheritance in individual variations in drug response. In recent decades, pharmacogenomics has been developing rapidly, which provides new ideas for individualized medicine. Polymorphisms in the genes encoding metabolic enzymes, transporters and target receptors have been shown to affect the efficacy of antihistamines. In addition, recent evidence suggests that gene polymorphisms influence urticaria susceptibility and antihistamine therapy. Here, we summarize current reports in this area, aiming to contribute to future research in antihistamines and clinical guidance for antihistamines use in individualized medicine.

Factors associated with increased health-related quality-of-life benefits in hereditary transthyretin amyloidosis polyneuropathy patients treated with inotersen.

INTRODUCTION/AIMS: Hereditary transthyretin amyloidosis with polyneuropathy (ATTRv-PN) is a genetic condition associated with significant morbidity and mortality. In this study we aimed to identify patient subgroups exhibiting the greatest health-related quality of life (HRQL) benefit from inotersen treatment. METHODS: We examined data from the inotersen phase 2/3 randomized, controlled trial for ATTRv-PN, NEURO-TTR (NCT01737398, 66 weeks). LASSO regression models predicted changes in Norfolk QoL-DN total score (TQoL, range -4 to 136; higher scores indicate poorer HRQL) from baseline in the inotersen and placebo arm, respectively. Individualized efficacy scores (ES) were calculated as differences between predicted change scores had patients received inotersen vs placebo. Patients were ranked by ES to define the greatest-benefit subpopulation (top 50%). Characteristics of the top 50% and bottom 50% of patients were compared. RESULTS: The overall mean ± standard deviation TQoL change was -0.20 ± 19.13 for inotersen (indicating no change) and 10.77 ± 21.13 for placebo (indicating deterioration). Within the highest-benefit patients, mean TQoL change was -11.03 ± 17.06 (improvement) for inotersen and 11.24 ± 22.97 (deterioration) for placebo (P < .001). Compared with the overall population, patients in the greatest-benefit subpopulation were younger, more likely to have polyneuropathy disability (PND) scores 1 or 2, less likely to have received prior tafamidis or diflunisal treatment, and more likely to have Val30Met mutations and higher (worse) baseline TQoL. CONCLUSIONS: Patients who were younger and/or at earlier polyneuropathy stages experienced greater HRQL benefits from inotersen over 66 weeks. These findings underscore the need for early diagnosis and treatment initiation, especially among more severely affected patients in early stages of ATTRv-PN.

Guidance Document: Validation of a High-Performance Liquid Chromatography-Tandem Mass Spectrometry Immunopeptidomics Assay for the Identification of HLA Class I Ligands Suitable for Pharmaceutical Therapies.

For more than two decades naturally presented, human leukocyte antigen (HLA)-restricted peptides (immunopeptidome) have been eluted and sequenced using liquid chromatography-tandem mass spectrometry (LC-MS/MS). Since, identified disease-associated HLA ligands have been characterized and evaluated as potential active substances. Treatments based on HLA-presented peptides have shown promising results in clinical application as personalized T cell-based immunotherapy. Peptide vaccination cocktails are produced as investigational medicinal products under GMP conditions. To support clinical trials based on HLA-presented tumor-associated antigens, in this study the sensitive LC-MS/MS HLA class I antigen identification pipeline was fully validated for our technical equipment according to the current US Food and Drug Administration (FDA) and European Medicines Agency (EMA) guidelines.The immunopeptidomes of JY cells with or without spiked-in, isotope labeled peptides, of peripheral blood mononuclear cells of healthy volunteers as well as a chronic lymphocytic leukemia and a bladder cancer sample were reliably identified using a data-dependent acquisition method. As the LC-MS/MS pipeline is used for identification purposes, the validation parameters include accuracy, precision, specificity, limit of detection and robustness.

Measurement of Hepatic CYP3A4 and 2D6 Activity Using Radioiodine-Labeled O-Desmethylvenlafaxine.

Drug metabolizing enzyme activity is affected by various factors such as drug-drug interactions, and a method to quantify drug metabolizing enzyme activity in real time is needed. In this study, we developed a novel radiopharmaceutical for quantitative imaging to estimate hepatic CYP3A4 and CYP2D6 activity. Iodine-123- and 125-labeled O-desmethylvenlafaxine (123/125I-ODV) was obtained with high labeling and purity, and its metabolism was found to strongly involve CYP3A4 and CYP2D6. SPECT imaging in normal mice showed that the administered 123I-ODV accumulated early in the liver and was excreted into the gallbladder, as evaluated by time activity curves. In its biological distribution, 125I-ODV administered to mice accumulated early in the liver, and only the metabolite of 125I-ODV was quickly excreted into the bile. In CYP3A4- and CYP2D6-inhibited model mice, the accumulation in bile decreased more than in normal mice, indicating inhibition of metabolite production. These results indicated that imaging and quantifying the accumulation of radioactive metabolites in excretory organs will aid in determining the dosages of various drugs metabolized by CYP3A4 and CYP2D6 for individualized medicine. Thus, 123/125I-ODV has the potential to direct, comprehensive detection and measurement of hepatic CYP3A4 and CYP2D6 activity by a simple and less invasive approach.

[Precision medicine in pediatric neurology exemplified by the new treatment forms]. / Präzisionsmedizin in der Kinderneurologie am Beispiel der neuen Therapien.

BACKGROUND: In recent years the possibilities for molecular diagnostics and treatment of rare childhood diseases have greatly improved. The first gene-modifying drugs have now been approved, leading to a new era of precision treatment in pediatric neurology. OBJECTIVE: This article describes the dynamic developments of precision medicine in pediatric neurology in the areas of prevention, diagnostics and targeted treatment. DISCUSSION: The paradigm shift as a result of precision medicine is based on a treatment approach focused more strongly on the individual and the corresponding unique characteristics. Modern methods of genetic and molecular diagnostics are used to accurately describe and characterize affected children, complemented by a precise description of the clinical phenotype. Nevertheless, the success of the best individual treatment strategy derived from this information is often dependent on the time of diagnosis. Therefore, methods for disease prevention, particularly newborn screening programs, become increasingly more important to achieve the best possible success of novel therapies even before the onset of disease symptoms. In addition to a precise stratification of therapies, special attention should be paid in the future to the consideration of the individual perspective of patients and parents/guardians. Furthermore, a normative framework for a quality-ensured application of gene-modifying therapies in the German healthcare system must be created.

Evaluation of the Efficacy and Complications of Uterine Artery Embolization in Comparison with Laparotomy-Myomectomy in the Treatment of Uterine Myomas: A Randomized Clinical Trial.

Background: Due to the high prevalence of uterine fibroids or leiomyomas in women of reproductive age and the many treatment options for myomas, finding the best treatment is a challenge for surgeons. Therefore, this study aimed at evaluating the efficacy and safety of 2 treatment options surgical interventions and uterine artery embolization (UAE) in patients with uterine myoma. Methods: The present study was a single-blind randomized clinical trial. The study population included all women with uterine myoma. Hence, 80 patients were divided into 2 groups of 40. The first group underwent laparotomy-myomectomy and the second underwent UAE. These patients were evaluated for clinical symptoms, menstrual disorders, estimated blood loss per menstrual cycle, and pain and complication on the 10th day, and at 2, 6, and 12 months after the intervention. The data were analyzed with SPSS software (Version 25) using an independent samples t test, a repeated measure analysis, and a chi-square test. Results: Ten days, 2, 6, and 12 months after the intervention, there was no significant difference between the 2 approaches in terms of their decreasing effects on per menstrual cycle blood loss (p > 0.05), respectively. After 10 days and 2 months, the pain intensity in the embolization group was higher than laparotomy group (p = 0.045, 0.060), respectively. The pain intensity was also not significantly different between the 2 groups after 6 months and 1 year (p > 0.05), respectively. Also, the frequency of fever was higher in the embolization group (p = 0.745). However, the documented post-procedural complications indicated that hemoglobin level declined post-operation (p > 0.050). Conclusion: The results showed no significant difference between the 2 groups in terms of post-procedural mensuration blood loss or pain intensity and the incidence of menstrual disorders within 1 year. It seems that there is no significant difference between the 2 groups and it may be possible to use the UAE depending on the patient's condition.

Drilling for Insight: Forecasting Phenotype from Genotype.

The manifestation of disease can vary substantially from person to person. Yet, much of the emphasis of genomics in individualized medicine has been on linking genetic variants to broad disease categories. A new approach takes a first step towards predicting detailed phenotypic information from disease-causative variants.

m6A regulator expression profile predicts the prognosis, benefit of adjuvant chemotherapy, and response to anti-PD-1 immunotherapy in patients with small-cell lung cancer.

BACKGROUND: Small cell lung cancer (SCLC) is lethal and possesses limited therapeutic options. Platinum-based chemotherapy-with or without immune checkpoint inhibitors (anti-PDs)-is the current first-line therapy for SCLCs; however, its associated outcomes are heterogeneous. N6-methyladenosine (m6A) is a novel and decisive factor in tumour progression, chemotherapy resistance, and immunotherapy response. However, m6A modification in SCLC remains poorly understood. METHODS: We systematically explored the molecular features and clinical significance of m6A regulators in SCLC. We then constructed an m6A regulator-based prognostic signature (m6A score) based on our examination of 256 cases with limited-stage SCLC (LS-SCLC) from three different cohorts-including an independent cohort that contained 150 cases with qPCR data. We additionally evaluated the relationships between the m6A score and adjuvant chemotherapy (ACT) benefits and the patients' responses to anti-PD-1 treatment. Immunohistochemical (IHC) staining and the HALO digital pathological platform were used to calculate CD8+ T cell density. RESULTS: We observed abnormal somatic mutations and expressions of m6A regulators. Using the LASSO Cox model, a five-regulator-based (G3BP1, METTL5, ALKBH5, IGF2BP3, and RBM15B) m6A score was generated from the significant regulators to classify patients into high- and low-score groups. In the training cohort, patients with high scores had shorter overall survival (HR, 5.19; 2.75-9.77; P < 0.001). The prognostic accuracy of the m6A score was well validated in two independent cohorts (HR 4.6, P = 0.006 and HR 3.07, P < 0.001). Time-dependent ROC and C-index analyses found the m6A score to possess superior predictive power than other clinicopathological parameters. A multicentre multivariate analysis revealed the m6A score to be an independent prognostic indicator. Additionally, patients with low scores received a greater survival benefit from ACT, exhibited more CD8+ T cell infiltration, and were more responsive to cancer immunotherapy. CONCLUSIONS: Our results, for the first time, affirm the significance of m6A regulators in LS-SCLC. Our multicentre analysis found that the m6A score was a reliable prognostic tool for guiding chemotherapy and immunotherapy selections for patients with SCLC.

An immune-related lncRNA signature predicts prognosis and adjuvant chemotherapeutic response in patients with small-cell lung cancer.

BACKGROUND: Patients with small-cell lung cancer (SCLC) are burdened by limited treatment options and the disease's dismal prognosis. Long non-coding RNAs (lncRNAs) are essential regulators of genetic alteration and are actively involved in tumor immunity. However, few studies have examined interactions between immune genes and lncRNAs in SCLC. METHODS: Immune-related lncRNA (irlncRNA) expression profiles and their clinical significance were explored. We enrolled 227 patients with SCLC, including 79 cases from GSE65002 and 148 cases from a validation cohort with corresponding qPCR data. The least absolute shrinkage and selection operator (LASSO) model was applied to identify prognostic irlncRNAs for an irlncRNA-based SCLC signature. We additionally investigated the potential mechanisms and immune landscape of the signature using bioinformatics methods. RESULTS: An irlncRNA signature including 8 irlncRNAs (ENOX1-AS1, AC005162, LINC00092, RPL34-AS1, AC104135, AC015971, AC126544, AP001189) was established for patients with SCLC in the training cohort. Low-risk patients were more likely to benefit from chemotherapy and achieve a favorable prognosis. The signature was also well-validated in the validation cohort and various clinical subgroups. Compared to other clinical parameters, the irlncRNA signature exhibited superior predictive performance for chemotherapy response and prognosis. The signature was as an independent prognostic factor in the training and validation cohorts. Interestingly, low-risk patients showed an activated immune phenotype. CONCLUSION: We constructed the first irlncRNA-based signature for chemotherapy efficacy and outcome prediction. The irlncRNA signature is a reliable and robust prognostic classifier that could be useful for clinical management and determination of potential chemotherapy benefit for patients with SCLC.