Identification Of Predictive Biomarkers Research Articles

GeM-LR: Discovering predictive biomarkers for small datasets in vaccine studies.

Despite significant progress in vaccine research, the level of protection provided by vaccination can vary significantly across individuals. As a result, understanding immunologic variation across individuals in response to vaccination is important for developing next-generation efficacious vaccines. Accurate outcome prediction and identification of predictive biomarkers would represent a significant step towards this goal. Moreover, in early phase vaccine clinical trials, small datasets are prevalent, raising the need and challenge of building a robust and explainable prediction model that can reveal heterogeneity in small datasets. We propose a new model named Generative Mixture of Logistic Regression (GeM-LR), which combines characteristics of both a generative and a discriminative model. In addition, we propose a set of model selection strategies to enhance the robustness and interpretability of the model. GeM-LR extends a linear classifier to a non-linear classifier without losing interpretability and empowers the notion of predictive clustering for characterizing data heterogeneity in connection with the outcome variable. We demonstrate the strengths and utility of GeM-LR by applying it to data from several studies. GeM-LR achieves better prediction results than other popular methods while providing interpretations at different levels.

From Genomic Exploration to Personalized Treatment: Next-Generation Sequencing in Oncology

Next-generation sequencing (NGS) has revolutionized personalized oncology care by providing exceptional insights into the complex genomic landscape. NGS offers comprehensive cancer profiling, which enables clinicians and researchers to better understand the molecular basis of cancer and to tailor treatment strategies accordingly. Targeted therapies based on genomic alterations identified through NGS have shown promise in improving patient outcomes across various cancer types, circumventing resistance mechanisms and enhancing treatment efficacy. Moreover, NGS facilitates the identification of predictive biomarkers and prognostic indicators, aiding in patient stratification and personalized treatment approaches. By uncovering driver mutations and actionable alterations, NGS empowers clinicians to make informed decisions regarding treatment selection and patient management. However, the full potential of NGS in personalized oncology can only be realized through bioinformatics analyses. Bioinformatics plays a crucial role in processing raw sequencing data, identifying clinically relevant variants, and interpreting complex genomic landscapes. This comprehensive review investigates the diverse NGS techniques, including whole-genome sequencing (WGS), whole-exome sequencing (WES), and single-cell RNA sequencing (sc-RNA-Seq), elucidating their roles in understanding the complex genomic/transcriptomic landscape of cancer. Furthermore, the review explores the integration of NGS data with bioinformatics tools to facilitate personalized oncology approaches, from understanding tumor heterogeneity to identifying driver mutations and predicting therapeutic responses. Challenges and future directions in NGS-based cancer research are also discussed, underscoring the transformative impact of these technologies on cancer diagnosis, management, and treatment strategies.

A single-cell perspective on immunotherapy for pancreatic cancer: from microenvironment analysis to therapeutic strategy innovation.

Pancreatic cancer remains one of the most lethal malignancies, with conventional treatment options providing limited efficacy. Recent advancements in immunotherapy have offered new hope, yet the unique tumor microenvironment (TME) of pancreatic cancer poses significant challenges to its successful application. This review explores the transformative impact of single-cell technology on the understanding and treatment of pancreatic cancer. By enabling high-resolution analysis of cellular heterogeneity within the TME, single-cell approaches have elucidated the complex interplay between various immune and tumor cell populations. These insights have led to the identification of predictive biomarkers and the development of innovative, personalized immunotherapeutic strategies. The review discusses the role of single-cell technology in dissecting the intricate immune landscape of pancreatic cancer, highlighting the discovery of T cell exhaustion profiles and macrophage polarization states that influence treatment response. Moreover, it outlines the potential of single-cell data in guiding the selection of immunotherapy drugs and optimizing treatment plans. The review also addresses the challenges and prospects of translating these single-cell-based innovations into clinical practice, emphasizing the need for interdisciplinary research and the integration of artificial intelligence to overcome current limitations. Ultimately, the review underscores the promise of single-cell technology in driving therapeutic strategy innovation and improving patient outcomes in the battle against pancreatic cancer.

The Journey of Antibody-Drug Conjugates: Lessons Learned from 40 Years of Development.

Antibody-drug conjugates (ADC) represent one of the most rapidly expanding treatment modalities in oncology, with 11 ADCs approved by the FDA and more than 210 currently being tested in clinical trials. Spanning over 40 years, ADC clinical development has enhanced our understanding of the multifaceted mechanisms of action for this class of therapeutics. In this article, we discuss key insights into the toxicity, efficacy, stability, distribution, and fate of ADCs. Furthermore, we highlight ongoing challenges related to their clinical optimization, the development of rational sequencing strategies, and the identification of predictive biomarkers. Significance: The development and utilization of ADCs have allowed for relevant improvements in the prognosis of multiple cancer types. Concomitantly, the rise of ADCs in oncology has produced several challenges, including the prediction of their activity, their utilization in sequence, and minimization of their side effects, that still too often resemble those of the cytotoxic molecule that they carry. In this review, we retrace 40 years of development in the field of ADCs and delve deep into the mechanisms of action of these complex therapeutics and reasons behind the many achievements and failures observed in the field to date.

Acute Sarcopenia: Mechanisms and Management.

Acute sarcopenia refers to the swift decline in muscle function and mass following acute events such as illness, surgery, trauma, or burns that presents significant challenges in hospitalized older adults. narrative review to describe the mechanisms and management of acute sarcopenia. The prevalence of acute sarcopenia ranges from 28% to 69%, likely underdiagnosed due to the absence of muscle mass and function assessments in most clinical settings. Systemic inflammation, immune-endocrine dysregulation, and anabolic resistance are identified as key pathophysiological factors. Interventions include early mobilization, resistance exercise, neuromuscular electrical stimulation, and nutritional strategies such as protein supplementation, leucine, β-hydroxy-β-methyl-butyrate, omega-3 fatty acids, and creatine monohydrate. Pharmaceuticals show variable efficacy. Future research should prioritize serial monitoring of muscle parameters, identification of predictive biomarkers, and the involvement of multidisciplinary teams from hospital admission to address sarcopenia. Early and targeted interventions are crucial to improve outcomes and prevent long-term disability associated with acute sarcopenia.

BioPred: An R package for biomarkers analysis in precision medicine.

The R package BioPred offers a suite of tools for subgroup and biomarker analysis in precision medicine. Leveraging Extreme Gradient Boosting (XGBoost) along with propensity score weighting and A-learning methods, BioPred facilitates the optimization of individualized treatment rules (ITR) to streamline subgroup identification. BioPred also enables the identification of predictive biomarkers and obtaining their importance rankings. Moreover, the package provides graphical plots tailored for biomarker analysis. This tool enables clinical researchers seeking to enhance their understanding of biomarkers and patient population in drug development. The package is available at CRAN and https://github.com/deeplearner0731/BioPred. Supplementary data are available at Bioinformatics online.

Peripheral blood lymphocyte subpopulations as predictive biomarkers for first-line programmed death 1 inhibitors efficacy in esophageal squamous cell carcinoma: A retrospective study.

Esophageal cancer (EC) poses a significant global health burden, necessitating effective treatment strategies. Immune checkpoint inhibitors have emerged as a promising therapeutic option for EC, but the identification of predictive biomarkers remains crucial for optimizing patient outcomes. We conducted a retrospective analysis of medical records from advanced esophageal squamous cell carcinoma patients treated with first-line programmed death 1 inhibitors. Peripheral blood lymphocyte subpopulations were evaluated using flow cytometry, while hematological tests provided data on neutrophil, lymphocyte, and monocyte counts. Cox regression and logistic regression analyses were employed to explore the association between lymphocyte subpopulations, baseline characteristics, and progression-free survival (PFS). Among the 100 initially included patients, 70 met eligibility criteria. Multivariate Cox regression analysis revealed a significant association between high CD16+CD56+ lymphocyte proportions and longer PFS, independent of other clinical variables. Similarly, a high CD4+/CD8+ ratio was correlated with prolonged PFS. Kaplan-Meier survival curves supported these findings. Logistic regression analysis indicated no significant differences in the CD4+/CD8+ ratio and CD16+CD56+ lymphocytes concerning baseline characteristics, suggesting their potential as independent prognostic markers. Our study highlights the predictive value of peripheral blood CD16+CD56+ lymphocytes and the CD4+/CD8+ ratio for the efficacy of programmed death 1 inhibitors in advanced esophageal squamous cell carcinoma patients. These findings underscore the importance of peripheral blood biomarkers in guiding personalized immunotherapy strategies and improving outcomes for EC patients.

Hematological Parameters at Baseline: A Novel Prognostic Factor for Cervical Cancer Patients Undergoing Concurrent Chemoradiotherapy in South India.

Introduction In cervical cancer treatment, neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and albumin-globulin ratio (AGR) are being studied as potential prognostic markers for predicting the effectiveness of concurrent chemoradiotherapy (CCRT). This study aims to investigate the relationship between these biomarkers and survival outcomes in cervical cancer patients undergoing CCRT. Materials and methods This retrospective study was conducted at Amrita Institute of Medical Sciences between January 2016 and December 2019. It included patients at any stage who received definitive CCRT and were followed for at least two years post-treatment. Patients who had initial surgery and those lost to follow-up were excluded. Results The study included 123 patients with a median age of 68. Most patients had stage IIB (39%) and squamous cell carcinoma (76.4%). With a median follow-up of 56 months, the five-yearoverall survival (OS) was 66.8%, progression-free survival (PFS) was 94%, and recurrence-free survival (RFS) was 81.2%.AGR (p = 0.001), NLR (p = 0.0001), and PLR (p = 0.001) were found to be significantly associated with OS, NLR (p = 0.002) and AGR (p = 0.001) significantly affected RFS, while only PLR (p = 0.02) significantly affected PFSon univariate analysis. NLRsignificantly impacted OS (p = 0.003) and RFS(p = 0.03) on multivariate analysis. Conclusion The results of our study showed that increased NLR and elevated levels of albumin indicate a higher likelihood of mortality. Furthermore, a higher NLR was linked to an increased probability of recurrence in patients with cervical cancer who received primary treatment with CCRT. Therefore, the identification of predictive biomarkers could significantly improve the assessment of progression risk, aiding in the selection of the most suitable treatment and personalized therapy.

Plasma proteomics in children with new-onset type 1 diabetes identifies new potential biomarkers of partial remission

Partial remission (PR) occurs in only half of people with new-onset type 1 diabetes (T1D) and corresponds to a transient period characterized by low daily insulin needs, low glycemic fluctuations and increased endogenous insulin secretion. While identification of people with newly-onset T1D and significant residual beta-cell function may foster patient-specific interventions, reliable predictive biomarkers of PR occurrence currently lack. We analyzed the plasma of children with new-onset T1D to identify biomarkers present at diagnosis that predicted PR at 3 months post-diagnosis. We first performed an extensive shotgun proteomic analysis using Liquid Chromatography-Tandem-Mass-Spectrometry (LCMS/MS) on the plasma of 16 children with new-onset T1D and quantified 98 proteins significantly correlating with Insulin-Dose Adjusted glycated hemoglobin A1c score (IDAA1C). We next applied a series of both qualitative and statistical filters and selected protein candidates that were associated to pathophysiological mechanisms related to T1D. Finally, we translationally verified several of the candidates using single-shot targeted proteomic (PRM method) on raw plasma. Taken together, we identified plasma biomarkers present at diagnosis that may predict the occurrence of PR in a single mass-spectrometry run. We believe that the identification of new predictive biomarkers of PR and β-cell function is key to stratify people with new-onset T1D for β-cell preservation therapies.

Causal Association between Circulating Metabolites and Dementia: A Mendelian Randomization Study.

The causal association of circulating metabolites with dementia remains uncertain. We assessed the causal association of circulating metabolites with dementia utilizing Mendelian randomization (MR) methods. We performed univariable MR analysis to evaluate the associations of 486 metabolites with dementia, Alzheimer's disease (AD), and vascular dementia (VaD) risk. For secondary validation, we replicated the analyses using an additional dataset with 123 metabolites. We observed 118 metabolites relevant to the risk of dementia, 59 of which were lipids, supporting the crucial role of lipids in dementia pathogenesis. After Bonferroni adjustment, we identified nine traits of HDL particles as potential causal mediators of dementia. Regarding dementia subtypes, protective effects were observed for epiandrosterone sulfate on AD (OR = 0.60, 95% CI: 0.48-0.75) and glycoproteins on VaD (OR = 0.89, 95% CI: 0.83-0.95). Bayesian model averaging MR (MR-BMA) analysis was further conducted to prioritize the predominant metabolites for dementia risk, which highlighted the mean diameter of HDL particles and the concentration of very large HDL particles as the predominant protective factors against dementia. Moreover, pathway analysis identified 17 significant and 2 shared metabolic pathways. These findings provide support for the identification of promising predictive biomarkers and therapeutic targets for dementia.

Precision therapeutics and emerging strategies for HR-positive metastatic breast cancer.

Anti-oestrogen-based therapies, often combined with a CDK4/6 inhibitor, are the current standard-of-care first-line therapy for patients with advanced-stage hormone receptor-positive (HR+) breast cancer. Resistance to anti-oestrogen agents inevitably occurs, mediated by oestrogen receptor (ER)-dependent or ER-independent mechanisms that drive tumour progression. Emerging endocrine therapies include, but are not limited to, next-generation oral ER degraders and proteolysis targeting chimeras, which might be particularly effective in patients with ESR1-mutant breast cancer. Furthermore, cancers harbouring driver alterations in oncogenic signalling pathways, including AKT and PI3K, might be susceptible to novel combination strategies involving targeted inhibitors. Next-generation CDK2/4 inhibitors are an area of active clinical investigation, and efforts are ongoing to evaluate the role of sequential CDK inhibition. Approved and emerging antibody-drug conjugates exploiting novel target antigens have also demonstrated promising clinical activity. These novel agents, as well as further identification and characterization of predictive biomarkers, will hopefully continue to improve clinical outcomes, reduce the incidence of toxicities, and limit the extent of overtreatment in this population. In this Review, we describe the evolving treatment paradigm for patients with metastatic HR+ breast cancer in light of the growing armamentarium of drugs and biomarkers that will helpto shape the future therapeutic landscape. These strategies are expected to involve tumour molecular profiling to enable the delivery of precision medicine.

Emerging biomarkers for immunotherapy response in biliary tract cancers: a comprehensive review of immune checkpoint inhibitor strategies.

Biliary tract cancers (BTCs) have rising incidence and mortality rates. Chemotherapy's limited efficacy has led to exploring new treatments like immunotherapy. which offers modest benefits. Moreover, the identification of reliable predictive biomarkers for immune checkpoint therapy in BTCs remains elusive, hindering personalized treatment strategies. This review provides an overview of the current landscape of emerging biomarkers for immunotherapy response in BTCs. We discuss the incremental benefits of combination therapy and the evolving role of immunotherapy in managing advanced BTC. Additionally, we highlight the need for robust predictive biomarkers to optimize treatment outcomes and foster a more individualized approach to patient care. We aim to identify promising research avenues and strategies to enhance therapeutic efficacy and patient survival in BTCs.

Amniotic fluid proteomic analysis identifies IL1RL1, APOE, and NECTIN4 as new biomarkers for preterm birth

BackgroundDespite extensive research, the identification of effective biomarkers for early prediction of preterm birth (PTB) continues to be a challenging endeavor. This study aims to identify amniotic fluid (AF) protein biomarkers useful for the early diagnosis of PTB.MethodsWe initially identified the protein expression profiles in the AF of women with PTB (n = 22) and full-term birth (FTB, n = 22), from the First People’s Hospital of Yunnan Province who underwent amniocentesis from November 2019 to February 2020, using mass spectrometry employing the data-independent acquisition (DIA) technique, and then analyzed differentially expressed proteins (DEPs). Subsequently, the least absolute shrinkage and selection operator (LASSO) and random forest analysis were employed to further screen the key proteins for PTB biomarker identification. The receiver operating characteristic (ROC) analysis, calibration plots, and decision curve analyses (DCA) were utilized to assess the discrimination and calibration of the key biomarkers.ResultsA total of 25 DEPs were identified between the PTB and FTB groups, comprising 13 up-regulated and 12 down-regulated proteins. Three key protein biomarkers for early PTB diagnosis were identified: IL1RL1 (interleukin-1 receptor-like 1), APOE (apolipoprotein E), and NECTIN4 (nectin cell adhesion molecule 4). The results of the ROC analysis showed that the area under the curve (AUC) of the three proteins combined as a biomarker for early diagnosis of PTB was 0.913 (95% CI: 0.823-1.000), with a sensitivity of 0.864 and a specificity of 0.955, both superior to those of the individual biomarkers. Bootstrap internal validation revealed a concordance index (C-index) of 0.878, with a sensitivity of 0.812 and a specificity of 0.773, indicating the robust predictive performance of these biomarkers.ConclusionsWe identified three previously unexplored yet potentially useful protein biomarkers in AF for early PTB diagnosis: IL1RL1, APOE, and NECTIN4.

Effects of pesticide exposure on the expression of selected genes in normal and cancer samples: Identification of predictive biomarkers for risk assessment

Pesticides pivotal in controlling pests, can represent a threat for human health. Regulatory agencies constantly monitor their harmful effects, regulating their use. Several studies support a positive association between long-term exposure to pesticides and chronic pathologies, such as cancer. Geno-toxicological biomonitoring has proven to be valuable to assess genetic risks associated with exposure to pesticides, representing a promising tool to improve preventive measures and identify workers at higher risk. In this study, a differential gene expression analysis of 70 candidate genes deregulated upon pesticide exposure, was performed in 10 GEO human gene expression DataSets. It was found that six genes (PMAIP1, GCLM, CD36, SQSTM1, ABCC3, NR4A2) had significant AUC predictive values. Also, CD36 was upregulated in non-transformed cell samples and healthy workers, but downregulated in cancer cells. Further validation in larger groups of workers will corroborate the importance of the identified candidates as biomarkers of exposure/effect.

Epigenetic modification of PHLDA2 is associated with tumor microenvironment and unfavorable outcome of immune checkpoint inhibitor-based therapies in clear cell renal cell carcinoma

BackgroundA substantial proportion of patients with metastatic clear cell renal cell carcinoma (ccRCC) cannot derive benefit from immune checkpoint inhibitor (ICI) plus anti-angiogenic agent combination therapy, making identification of predictive biomarkers an urgent need. The members of pleckstrin homology-like domain family A (PHLDA) play critical roles in multiple cancers, whereas their roles in ccRCC remain unknown.MethodsTranscriptomic, clinical, genetic alteration and DNA methylation data were obtained for integrated analyses from TCGA database. RNA sequencing was performed on 117 primary tumors and 79 normal kidney tissues from our center. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analysis, gene set enrichment analysis were performed to explore transcriptomic features. Data from three randomized controlled trials (RCT), including CheckMate025, IMmotion151, JAVELIN101, were obtained for validation.ResultsMembers of PHLDA family were dysregulated in pan-cancer. Elevated PHLDA2 expression was associated with adverse clinicopathologic parameters and worse prognosis in ccRCC. Aberrant DNA hypomethylation contributed to up-regulation of PHLDA2. An immunosuppressive microenvironment featured by high infiltrates of Tregs and cancer-associated fibroblasts, was observed in ccRCC with higher PHLDA2 expression. Utilizing data from three RCTs, the association of elevated PHLDA2 expression with poor therapeutic efficacy of ICI plus anti-angiogenic combination therapy was confirmed.ConclusionsOur study revealed that elevated PHLDA2 expression regulated by DNA hypomethylation was correlated with poor prognosis and immunosuppressive microenvironment, and highlighted the role of PHLDA2 as a robust biomarker for predicting therapeutic efficacy of ICI plus anti-angiogenic agent combination therapy in ccRCC, which expand the dimension of precision medicine.

Unveiling Bladder Cancer Prognostic Insights by Integrating Patient-Matched Sample and CpG Methylation Analysis.

Bladder cancer prognosis remains a pressing clinical challenge, necessitating the identification of novel biomarkers for precise survival prediction and improved quality of life outcomes. This study proposes a comprehensive strategy to uncover key prognostic biomarkers in bladder cancer using DNA methylation analysis and extreme survival pattern observations in matched pairs of cancer and adjacent normal cells. Unlike traditional approaches that overlook cancer heterogeneity by analyzing entire samples, our methodology leverages patient-matched samples to account for this variability. Specifically, DNA methylation profiles from adjacent normal bladder tissue and bladder cancer tissue collected from the same individuals were analyzed to pinpoint critical methylation changes specific to cancer cells while mitigating confounding effects from individual genetic differences. Utilizing differential threshold settings for methylation levels within cancer-associated pathways enabled the identification of biomarkers that significantly impact patient survival. Our analysis identified distinct survival patterns associated with specific CpG sites, underscoring these sites' pivotal roles in bladder cancer outcomes. By hypothesizing and testing the influence of methylation levels on survival, we pinpointed CpG biomarkers that profoundly affect the prognosis. Notably, CpG markers, such as cg16269144 (PRKCZ), cg16624272 (PTK2), cg11304234, and cg26534425 (IL18), exhibited critical methylation thresholds that correlate with patient mortality. This study emphasizes the importance of tailored approaches to enhancing prognostic accuracy and refining therapeutic strategies for bladder cancer patients. The identified biomarkers pave the way for personalized prognostication and targeted interventions, promising advancements in bladder cancer management and patient care.

SEOM–GEICO clinical guideline on epithelial ovarian cancer (2023)

In recent years, the incorporation of new strategies to the therapeutic armamentarium has completely changed the outcomes of epithelial ovarian cancer (EOC). The identification of new predictive and prognostic biomarkers has also enabled the selection of those patients more likely to respond to targeted agents. Nevertheless, EOC is still a highly lethal disease and resistance to many of these new agents is common. The objective of this guideline is to summarize the most relevant strategies to manage EOC, to help the clinician throughout the challenging diagnostic and therapeutic processes and to provide evidence-based recommendations.

Identification of Predictive Biomarkers of Lameness in Transition Dairy Cows.

The aim of this study was to identify with a high level of confidence metabolites previously identified as predictors of lameness and understand their biological relevance by carrying out pathway analyses. For the dairy cattle sector, lameness is a major challenge with a large impact on animal welfare and farm economics. Understanding metabolic alterations during the transition period associated with lameness before the appearance of clinical signs may allow its early detection and risk prevention. The annotation with high confidence of metabolite predictors of lameness and the understanding of interactions between metabolism and immunity are crucial for a better understanding of this condition. Using liquid chromatography-tandem mass spectrometry (LC-MS/MS) with authentic standards to increase confidence in the putative annotations of metabolites previously determined as predictive for lameness in transition dairy cows, it was possible to identify cresol, valproic acid, and gluconolactone as L1, L2, and L1, respectively which are the highest levels of confidence in identification. The metabolite set enrichment analysis of biological pathways in which predictors of lameness are involved identified six significant pathways (p < 0.05). In comparison, over-representation analysis and topology analysis identified two significant pathways (p < 0.05). Overall, our LC-MS/MS analysis proved to be adequate to confidently identify metabolites in urine samples previously found to be predictive of lameness, and understand their potential biological relevance, despite the challenges of metabolite identification and pathway analysis when performing untargeted metabolomics. This approach shows potential as a reliable method to identify biomarkers that can be used in the future to predict the risk of lameness before calving. Validation with a larger cohort is required to assess the generalization of these findings.

P-789 Trophoblast organoids as a 3D model for the study of oxygen concentration during in vitro preimplantation embryo culture

Abstract Study question To determine if trophoblast organoids could represent a relevant robust model to study the impact of oxygen concentration on human preimplantation embryo development. Summary answer Transcriptomic analysis of trophoblast organoids cultured in monophasic (5%) or biphasic (5-2%) O2 concentration identified 1,184 RNAs differentially expressed. What is known already Our team has previously demonstrated that a biphasic (5-2%) O2 concentration strategy during preimplantation embryo culture (from Day 0 to Day 6) significantly improved blastulation and live birth rates in in vitro fertilization (IVF). We also identified differentially expressed RNAs between monophasic and biphasic O2 strategies through whole transcriptome analysis on human embryos donated for research. However, the number of embryos donated to research is limited, they are all genetically different, and their use iethically controversial, representing a significative drawback to experimentation. Latest studies on trophoblast organoids indicate that they could represent a relevant model for studying preimplantation embryo development. Study design, size, duration A prospective monocentric study approved by the Institutional Review Board (I.R.B.) was conducted between February 2022 and September 2023: four early first-trimester human placentas were collected after surgical abortions and used for derivation of organoids in a 3D culture system. The protocol for organoid culture is described in the study of Sheridan and al, 2020. Organoids were cultured with either a monophasic or a biphasic oxygen concentration strategy. Participants/materials, setting, methods Trophoblast organoids were either exposed to 5% oxygen concentration during 5 days or to 5% O2 for 3 days and to 2% O2 for 2 days. Total RNA extraction of 21 organoids for each condition was performed. After whole transcriptome analysis using human Clariom D arrays (ThermoFisher Scientific®), microarray data were normalized and differentially expressed RNAs were identified using the Transcriptome Analysis Console software; the genetic network was generated with Ingenuity Pathway Analysis (Qiagen®). Main results and the role of chance Transcriptomic analysis of trophoblast organoids cultured in monophasic (5%) or biphasic (5-2%) O2 concentration identified 1,184 RNAs that were differentially expressed depending on the O2 strategy (fold change &amp;gt;2 or &amp;lt;-2, p &amp;lt; 0.05), with most of them (75.1%, n = 889/1,184) being downregulated in the biphasic (5-2%) O2 group. IPA analysis highlighted downregulation of transcripts involved in cell survival and differentiation as well as upregulation of transcripts involved in embryo angiogenesis and vascularization of extraembryonic tissue. The transcript HIF1a was upregulated in organoids cultured in biphasic strategy compared to monophasic strategy (fold change 2, p = 0.0129). Comparison between differentially expressed transcripts in monophasic and biphasic strategies highlighted 22 transcripts in common between organoids and embryos donated to research from our previous study. Limitations, reasons for caution Our study was limited by a low sample size. Wider implications of the findings This innovative model of trophoblast organoids could help us to overcome the rarity as well as the genetic/epigenetic heterogeneity associated with IVF human embryos donated to the research, thus leading to the realization of large-scale experimentations and promoting the identification of new non-invasive predictive biomarkers for IVF. Trial registration number not applicable

Extensive-stage small-cell lung cancer in patients receiving atezolizumab plus carboplatin–etoposide: stratification of outcome based on a composite score that combines gene expression profiling and immune characterization of microenvironment

PurposeSmall-cell lung cancer (SCLC) is an aggressive disease with a dismal prognosis. The addition of immune checkpoints inhibitors to standard platinum-based chemotherapy in first-line setting achieves a durable benefit only...