Transcriptomic and metabolomic changes might predict frailty in SAMP8 mice.

Frailty is a geriatric, multi-dimensional syndrome that reflects multisystem physiological change and is a transversal measure of reduced resilience to negative events. It is characterized by weakness, frequent falls, cognitive decline, increased hospitalization and dead and represents a risk factor for the development of Alzheimer's disease (AD). The fact that frailty is recognized as a reversible condition encourages the identification of earlier biomarkers to timely predict and prevent its occurrence. SAMP8 (Senescence-Accelerated Mouse Prone-8) mice represent the most appropriate preclinical model to this aim and were used in this study to carry transcriptional and metabolic analyses in the brain and plasma, respectively, upon a characterization at cognitive, motor, structural, and neuropathological level at 2.5, 6, and 9 months of age. At 2.5 months, SAMP8 mice started displaying memory deficits, muscle weakness, and motor impairment. Functional alterations were associated with a neurodevelopmental deficiency associated with reduced neuronal density and glial cell loss. Through transcriptomics, we identified specific genetic signatures well distinguishing SAMP8 mice at 6 months, whereas plasma metabolomics allowed to segregate SAMP8 mice from SAMR1 already at 2.5 months of age by detecting constitutively lower levels of acylcarnitines and lipids in SAMP8 at all ages investigated correlating with functional deficits and neuropathological signs. Our findings suggest that specific genetic alterations at central level, as well as metabolomic changes in plasma, might allow to early assess a frail condition leading to dementia development, which paves the foundation for future investigation in a clinical setting.

Biomarkers to predict efficacy of immune checkpoint inhibitors in colorectal cancer patients: a systematic review and meta-analysis.

Immune checkpoint inhibitors (ICIs) are approved to treat colorectal cancer (CRC) with mismatch-repair gene deficiency, but the response rate remains low. Value of current biomarkers to predict CRC patients' response to ICIs is unclear due to heterogeneous study designs and small sample sizes. Here, we aim to assess and quantify the magnitude of multiple biomarkers for predicting the efficacy of ICIs in CRC patients. We systematically searched MEDLINE, Embase, the Cochrane Library, and Web of Science databases (to June 2023) for clinical studies examining biomarkers for efficacy of ICIs in CRC patients. Random-effect models were performed for meta-analysis. We pooled odds ratio (OR) and hazard ratio (HR) with 95% confidence interval (CI) for biomarkers predicting response rate and survival. 36 studies with 1867 patients were included in systematic review. We found that a lower pre-treatment blood neutrophil-to-lymphocyte ratio (n=4, HR 0.37, 95%CI 0.21-0.67) predicts good prognosis, higher tumor mutation burden (n=10, OR 4.83, 95%CI 2.16-10.78) predicts response to ICIs, and liver metastasis (n=16, OR 0.32, 95%CI 0.16-0.63) indicates resistance to ICIs, especially when combined with VEGFR inhibitors. But the predictive value of tumor PD-L1 expression (n=9, OR 1.01, 95%CI 0.48-2.14) was insignificant in CRC. Blood neutrophil-to-lymphocyte ratio, tumor mutation burden, and liver metastasis, but not tumor PD-L1 expression, function as significant biomarkers to predict efficacy of ICIs in CRC patients. These findings help stratify CRC patients suitable for ICI treatments, improving efficacy of immunotherapy through precise patient management. (PROSPERO, CRD42022346716).

CD161+CD127+CD8+ T cell subsets can predict the efficacy of anti-PD-1 immunotherapy in non-small cell lung cancer with diabetes mellitus.

The role of CD161+CD127+CD8+ T cells in non-small cell lung cancer (NSCLC) patients with diabetes remains unexplored. This study determined the prevalence, phenotype, and function of CD8+ T cell subsets in NSCLC with diabetes. We recruited NSCLC patients (n = 436) treated with anti-PD-1 immunotherapy as first-line treatment. The progression-free survival (PFS), overall survival (OS), T cells infiltration, and peripheral blood immunological characteristics were analyzed in NSCLC patients with or without diabetes. NSCLC patients with diabetes exhibited shorter PFS and OS (p = 0.0069 and p = 0.012, respectively) and significantly lower CD8+ T cells infiltration. Mass cytometry by time-of-flight (CyTOF) showed a higher percentage of CD161+CD127+CD8+ T cells among CD8+T cells in NSCLC with diabetes before anti-PD-1 treatment (p = 0.0071) than that in NSCLC without diabetes and this trend continued after anti-PD-1 treatment (p = 0.0393). Flow cytometry and multiple-immunofluorescence confirmed that NSCLC with diabetes had significantly higher CD161+CD127+CD8+ T cells to CD8+T cells ratios than NSCLC patients without diabetes. The RNA-sequencing analysis revealed immune-cytotoxic genes were reduced in the CD161+CD127+CD8+ T cell subset compared to CD161+CD127-CD8+ T cells in NSCLC with diabetes. CD161+CD127+CD8+ T cells exhibited more T cell-exhausted phenotypes in NSCLC with diabetes. NSCLC patients with diabetes with ≥ 6.3% CD161+CD127+CD8+ T cells to CD8+T cells ratios showed worse PFS. These findings indicate that diabetes is a risk factor for NSCLC patients who undergo anti-PD-1 immunotherapy.CD161+CD127+CD8+ T cells could be a key indicator of a poor prognosis in NSCLC with diabetes. Our findings would help in advancing anti-PD-1 therapy in NSCLC patients with diabetes.

Metabolic Vulnerabilities in Cancer: A New Therapeutic Strategy.

Cancer metabolism is now a key area for therapeutic intervention, targeting unique metabolic reprogramming crucial for tumor growth and survival. This article reviews the therapeutic potential of addressing metabolic vulnerabilities through glycolysis and glutaminase inhibitors, which disrupt cancer cell metabolism. Challenges such as tumor heterogeneity and adaptive resistance are discussed, with strategies including personalized medicine and predictive biomarkers to enhance treatment efficacy. Additionally, integrating diet and lifestyle changes with metabolic targeting underscores a holistic approach to improving therapy outcomes. The article also examines the benefits of incorporating these strategies into standard care, highlighting the potential for more tailored, safer treatments. In conclusion, exploiting metabolic vulnerabilities promises a new era in oncology, positioning metabolic targeting at the forefront of personalized cancer therapy and transforming patient care.

Development of an algorithm combining blood-based biomarkers, fecal immunochemical test, and age for population-based colorectal cancer screening.

BACKGROUND AND AIMS: Implementation of screening modalities have reduced the burden of colorectal cancer (CRC), but high false positive rates pose a major problem for colonoscopy capacity. We aimed to create a tailored screening algorithm that expands the fecal immunochemical test (FIT) with a blood specimen and current age to improve selection of individuals for diagnostic colonoscopy. METHODS: In this prospective multi-center study, eight blood-based biomarkers (CEA, Ferritin, hsCRP, HE4, Cyfra21-1, Hepsin, IL-8 and OPG) were investigated in 1,977 FIT positive individuals from the Danish national CRC screening program undergoing follow-up colonoscopy. Specimens were analyzed on ARCHITECT i2000®, ARCHITECT c8000® or Luminex xMAP® machines. FIT analyses and blood-based biomarker data were combined with clinical data (i.e., age and colonoscopy findings) in a cross-validated logistic regression model (algorithm) benchmarked against a model solely using the FIT result (FIT model) applying different cutoffs for FIT positivity. RESULTS: The cohort included individuals with CRC (n = 240), adenomas (n = 938) or no neoplastic lesions (n = 799). The cross-validated algorithm combining the eight biomarkers, quantitative FIT result and age performed superior to the FIT model in discriminating CRC versus non-CRC individuals (AUC 0.77 versus 0.67, p < 0.001). When discriminating individuals with either CRC or high- or medium-risk adenomas versus low-risk adenomas or clean colorectum, the AUCs were 0.68 versus 0.64 for the algorithm and FIT model, respectively. CONCLUSIONS: The algorithm presented here can improve patient allocation to colonoscopy, reducing colonoscopy burden without compromising cancer and adenomas detection rates or vice versa.

Artificial Intelligence and Machine Learning in Predicting the Response to Immunotherapy in Non-small Cell Lung Carcinoma: A Systematic Review.

Non-small cell lung carcinoma (NSCLC) is a prevalent and aggressive form of lung cancer, with a poor prognosis for metastatic disease. Immunotherapy, particularly immune checkpoint inhibitors (ICIs), has revolutionized the management of NSCLC, but response rates are highly variable. Identifying reliable predictive biomarkers is crucial to optimize patient selection and treatment outcomes. This systematic review aimed to evaluate the current state of artificial intelligence (AI) and machine learning (ML) applications in predicting the response to immunotherapy in NSCLC. A comprehensive literature search identified 19 studies that met the inclusion criteria. The studies employed diverse AI/ML techniques, including deep learning, artificial neural networks, support vector machines, and gradient boosting methods, applied to various data modalities such as medical imaging, genomic data, clinical variables, and immunohistochemical markers. Several studies demonstrated the ability of AI/ML models to accurately predict immunotherapy response, progression-free survival, and overall survival in NSCLC patients. However, challenges remain in data availability, quality, and interpretability of these models. Efforts have been made to develop interpretable AI/ML techniques, but further research is needed to improve transparency and explainability. Additionally, translating AI/ML models from research settings to clinical practice poses challenges related to regulatory approval, data privacy, and integration into existing healthcare systems. Nonetheless, the successful implementation of AI/ML models could enable personalized treatment strategies, improve treatment outcomes, and reduce unnecessary toxicities and healthcare costs associated with ineffective treatments.

A novel algorithm to differentiate between primary lung tumors and distant liver metastasis in lung cancers using an exosome based multi gene biomarker panel.

The lack of non-invasive methods for detection of early metastasis is a crucial reason for the poor prognosis of lung cancer (LC) liver metastasis (LM) patients. In this study, the goal was to identify circulating biomarkers based on a biomarker model for the early diagnosis and monitoring of patients with LCLM. An 8-gene panel identified in our previous study was validated in CTC, cfRNA and exosomes isolated from primary lung cancer with & without metastasis. Further multivariate analysis including PCA & ROC was performed to determine the sensitivity and specificity of the biomarker panel. Model validation cohort (n = 79) was used to verify the stability of the constructed predictive model. Further, clinic-pathological factors, survival analysis and immune infiltration correlations were also performed. In comparison to our previous tissue data, exosomes demonstrated a good discriminative value with an AUC of 0.7247, specificity (72.48%) and sensitivity (96.87%) for the 8-gene panel. Further individual gene patterns led us to a 5- gene panel that showed an AUC of 0.9488 (p = < 0.001) and 0.9924 (p = < 0.001) respectively for tissue and exosomes. Additionally, on validating the model in a larger cohort a risk score was obtained (RS > 0.2) for prediction of liver metastasis with an accuracy of 95%. Survival analysis and immune filtration markers suggested that four exosomal markers were independently associated with poor overall survival. We report a novel blood-based exosomal biomarker panel for early diagnosis, monitoring of therapeutic response, and prognostic evaluation of patients with LCLM.

Immunotherapy in MMR-d/MSI-H recurrent/metastatic endometrial cancer.

INTRODUCTION: The advent of immunotherapy with immune checkpoint inhibitors (ICIs) has revolutionized the management of mismatch repair deficient (MMR-d)/microsatellite instability-high (MSI-H) endometrial cancer (EC). Initially investigated as monotherapy in phase I-II clinical trials for recurrent disease, immunotherapy demonstrated remarkable activity, yielding overall response rates (ORR) ranging from 27% to 58%. Based on these promising findings, phase III trials have explored the integration of immunotherapy into first-line treatment regimens for advanced/recurrent EC in combination with chemotherapy or other agents such as tyrosine kinase inhibitors (TKIs), resulting in improved ORR, progression-free survival, and overall survival compared to the standard chemotherapy regimen of paclitaxel and carboplatin. As a result, the incorporation of ICIs with standard platinum-based chemotherapy is becoming a new standard of care in MMR-d/MSI-H EC. AREAS COVERED: This review synthesizes literature from PubMed, Embase databases, and recent congress abstracts on gynecological cancers. It covers MMR-d/MSI-H EC incidence, molecular diagnostics, clinical trial outcomes, predictive biomarkers for ICIs, patient profiles likely to benefit, resistance mechanisms, and the future of immunotherapy in this setting. EXPERT OPINION: By offering a comprehensive overview, this review delineates the pivotal role of ICIs in the management of MMR-d/MSI-H EC.

Changes In Serum CXCL13 Levels Are Associated With Outcomes of Colorectal Cancer Patients Undergoing First-Line Oxaliplatin-Based Treatment.

Metastatic colorectal cancer (mCRC) currently lacks reliable biomarkers for precision medicine, particularly for chemotherapy-based treatments. This study examines the behavior of 11 CXC chemokines in the blood of 104 mCRC patients undergoing first-line oxaliplatin-based treatment to pinpoint predictive and prognostic markers. Serum samples were collected before treatment, at response evaluation (EVAR), and at disease progression or last follow-up. Chemokines were assessed in all samples using a Luminex® custom panel. CXCL13 levels increased at EVAR in responders, while in non-responders it decreased. Increasing levels of CXCL13 at EVAR, independently correlated with improved progression-free survival (PFS) and overall survival (OS). Nanostring® analysis in primary tumor samples showed CXCL13 gene expression's positive correlation not only with gene profiles related to an immunogenic tumor microenvironment, increased B cells and T cells (mainly CD8+) but also with extended OS. In silico analysis using RNAseq data from liver metastases treated or not with neoadjuvant oxaliplatin-based combinations, and deconvolution analysis using the MCP-counter algorithm, confirmed CXCL13 gene expression's association with increased immune infiltration, improved OS, and Tertiary Lymphoid Structures (TLSs) gene signatures, especially in neoadjuvant-treated patients. CXCL13 analysis in serum from 36 oxaliplatin-treated patients from the METIMMOX study control arm, reported similar findings. In conclusion, the increase of CXCL13 levels in peripheral blood and its association with the formation of TLSs within the metastatic lesions, emerges as a potential biomarker indicative of the therapeutic efficacy in mCRC patients undergoing oxaliplatin-based treatment.

Multitarget Fluorescence In Situ Hybridization Diagnostic Applications in Tumors.

Multitarget fluorescence in situ hybridization (mFISH) is a technique that allows the detection of multiple target sequences on the same sample using spectrally distinct fluorophore labels. The mFISH approach is currently a useful assay in the oncologic field for the detection of predictive, prognostic, and diagnostic biomarkers. In this chapter, we summarize the application of mFISH in the identification of target genetic aberrations in formalin-fixed, paraffin-embedded (FFPE) tissue samples of several tumor types. We discuss the mFISH protocols in FFPE samples, the innovative multitarget probes used, and the critical issues related to their interpretation.

PD-1/PD-L1 axis: implications in immune regulation, cancer progression, and translational applications.

The PD-1/PD-L1 axis is a complex signaling pathway that has an important role in the immune system cells. Programmed cell death protein 1 (PD-1) acts as an immune checkpoint on the T lymphocytes, B lymphocytes, natural killer (NK), macrophages, dendritic cells (DCs), monocytes, and myeloid cells. Its ligand, the programmed cell death 1 ligand (PD-L1), is expressed in the surface of the antigen-presenting cells (APCs). The binding of both promotes the downregulation of the T cell response to ensure the activation to prevent the onset of chronic immune inflammation. This axis in the tumor microenvironment (TME) performs a crucial role in the tumor progression and the escape of the tumor by neutralizing the immune system, the engagement of PD-L1 with PD-1 in the T cell causes dysfunctions, neutralization, and exhaustion, providing the tumor mass production. This review will provide a comprehensive overview of the functions of the PD-1/PD-L1 system in immune function, cancer, and the potential therapeutic implications of the PD-1/PD-L1 pathway for cancer management.

Immune biomarker evaluation of sequential tyrosine kinase inhibitor and nivolumab monotherapies in renal cell carcinoma: the phase I TRIBE trial.

Background: Predictive biomarkers for immune checkpoint blockade in the second-line treatment of metastatic renal cell carcinoma (mRCC) are lacking. Materials and methods: Patients with histologically confirmed RCC who started nivolumab after at least 4 months of tyrosine kinase inhibitors (TKIs) were recruited for this study. Serial tissue and blood samples were collected for immune biomarker evaluation. The primary endpoint was to determine the association of specific T-cell subsets with clinical outcomes tested using Wilcoxon rank sum for clinical benefit rate (CBR) and log-rank test for progression-free survival (PFS). Results: Twenty patients were included in this trial with a median age of 64 years and followed-up for a median of 12 months. The median PFS for patients who received TKI was 13.8 months, while for those subsequently treated with nivolumab following TKI therapy, the median PFS was 2.6 months. CBR of nivolumab was 20% with two partial responses. Functionally active programmed cell death protein 1+ CD4+ T cells were enriched in non-responders (q = 0.003) and associated with worse PFS on nivolumab (P = 0.04). Responders showed a significant reduction in the effector CD4+T-cell (TEF) fraction compared to non-responders at 3 months on nivolumab (0.40 versus 0.80, P = 0.0005). CD127+CD4+ T cells were enriched in patients who developed immune-related adverse effects (q = 0.003). Using in-house validated multiplex immunohistochemistry for six markers, we measured tumour-associated immune cell densities in tissue samples. Responders to nivolumab showed a significantly higher mean of immune cell densities in tissue samples compared to non-responders (346 versus 87 cells/mm2, P = 0.04). Conclusions: In this small study, analysis of tissue-based and peripheral blood immune cell subsets predicted clinical outcomes of nivolumab. Further studies are warranted with larger populations to validate these observations.

Preterm birth, a consequence of immune deviation mediated hyperinflammation.

Preterm birth represents a multifaceted syndrome with intricacies still present in our comprehension of its etiology. In the context of a semi-allograft, the prosperity from implantation to pregnancy to delivery hinges on the establishment of a favorable maternal-fetal immune microenvironment and a successful trilogy of immune activation, immune tolerance and then immune activation transitions. The occurrence of spontaneous preterm birth could be related to abnormalities within the immune trilogy, stemming from deviation in maternal and fetal immunity. These immune deviations, characterized by insufficient immune tolerance and early immune activation, ultimately culminated in an unsustainable pregnancy. In this review, we accentuated the role of both innate and adaptive immune reason in promoting spontaneous preterm birth, reviewed the risk of preterm birth from vaginal microbiome mediated by immune changes and the potential of vaginal microbiomes and metabolites as a new predictive marker, and discuss the changes in the role of progesterone and its interaction with immune cells in a preterm birth population. Our objective was to contribute to the growing body of knowledge in the field, shedding light on the immunologic reason of spontaneous preterm birth and effective biomarkers for early prediction, providing a roadmap for forthcoming investigations.

Circulating Blood Prognostic Biomarker Signatures for Hemorrhagic Cerebral Cavernous Malformations (CCMs).

Cerebral cavernous malformations (CCMs) are a neurological disorder characterized by enlarged intracranial capillaries in the brain, increasing the susceptibility to hemorrhagic strokes, a major cause of death and disability worldwide. The limited treatment options for CCMs underscore the importance of prognostic biomarkers to predict the likelihood of hemorrhagic events, aiding in treatment decisions and identifying potential pharmacological targets. This study aimed to identify blood biomarkers capable of diagnosing and predicting the risk of hemorrhage in CCM1 patients, establishing an initial set of circulating biomarker signatures. By analyzing proteomic profiles from both human and mouse CCM models and conducting pathway enrichment analyses, we compared groups to identify potential blood biomarkers with statistical significance. Specific candidate biomarkers primarily associated with metabolism and blood clotting pathways were identified. These biomarkers show promise as prognostic indicators for CCM1 deficiency and the risk of hemorrhagic stroke, strongly correlating with the likelihood of hemorrhagic cerebral cavernous malformations (CCMs). This lays the groundwork for further investigation into blood biomarkers to assess the risk of hemorrhagic CCMs.

Algorithms for predicting COVID outcome using ready-to-use laboratorial and clinical data.

The pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is an emerging crisis affecting the public health system. The clinical features of COVID-19 can range from an asymptomatic state to acute respiratory syndrome and multiple organ dysfunction. Although some hematological and biochemical parameters are altered during moderate and severe COVID-19, there is still a lack of tools to combine these parameters to predict the clinical outcome of a patient with COVID-19. Thus, this study aimed at employing hematological and biochemical parameters of patients diagnosed with COVID-19 in order to build machine learning algorithms for predicting COVID mortality or survival. Patients included in the study had a diagnosis of SARS-CoV-2 infection confirmed by RT-PCR and biochemical and hematological measurements were performed in three different time points upon hospital admission. Among the parameters evaluated, the ones that stand out the most are the important features of the T1 time point (urea, lymphocytes, glucose, basophils and age), which could be possible biomarkers for the severity of COVID-19 patients. This study shows that urea is the parameter that best classifies patient severity and rises over time, making it a crucial analyte to be used in machine learning algorithms to predict patient outcome. In this study optimal and medically interpretable machine learning algorithms for outcome prediction are presented for each time point. It was found that urea is the most paramount variable for outcome prediction over all three time points. However, the order of importance of other variables changes for each time point, demonstrating the importance of a dynamic approach for an effective patient's outcome prediction. All in all, the use of machine learning algorithms can be a defining tool for laboratory monitoring and clinical outcome prediction, which may bring benefits to public health in future pandemics with newly emerging and reemerging SARS-CoV-2 variants of concern.

BRAFV600E-Mutant Metastatic Colorectal Cancer: Current Evidence, Future Directions, and Research Priorities.

BRAFV600E-mutant metastatic colorectal cancer represents a distinct molecular phenotype known for its aggressive biological behavior, resistance to standard therapies, and poor survival rates. Improved understanding of the biology of the BRAF oncogene has led to the development of targeted therapies that have paved the way for a paradigm shift in managing this disease. However, despite significant recent advancements, responses to targeted therapies are short-lived, and several challenges remain. In this review, we discuss how progress in treating BRAFV600E-mutant metastatic colorectal cancer has been made through a better understanding of its unique biological and clinical features. We provide an overview of the evidence to support current treatment approaches and discuss critical areas of need and future research strategies that hold the potential to refine clinical practice further. We also discuss some challenging aspects of managing this disease, particularly the complexity of acquired resistance mechanisms that develop under the selective pressure of targeted therapies and rational strategies being investigated to overcome them.

Pre-treatment amino acids and risk of paclitaxel-induced peripheral neuropathy in SWOG S0221.

BACKGROUND: Chemotherapy-induced peripheral neuropathy (CIPN) is a treatment-limiting and debilitating neurotoxicity of many commonly used anti-cancer agents, including paclitaxel. The objective of this study was to confirm the previously found inverse association between pre-treatment blood concentrations of histidine and CIPN occurrence and examine relationships of other amino acids with CIPN severity. METHODS: Pre-treatment serum concentrations of 20 amino acids were measured in the SWOG S0221 (NCT00070564) trial of patients with early-stage breast cancer receiving paclitaxel. The associations between amino acids and CIPN severity were tested in regression analysis adjusted for paclitaxel schedule, age, self-reported race, and body mass index with Bonferroni correction. The network of metabolic pathways of amino acids was analyzed using over-representation analysis. The partial correlation network of amino acids was evaluated using a debiased sparse partial correlation algorithm. RESULTS: In the primary analysis, histidine concentration was not associated with CIPN occurrence (odds ratio (OR) = 0.97 [0.83, 1.13], p = 0.72). In secondary analyses, higher concentrations of four amino acids, glutamate (ß = 0.58 [0.23, 0.93], p = 0.001), phenylalanine (ß = 0.54 [0.19, 0.89], p = 0.002), tyrosine (ß = 0.57 [0.23, 0.91], p = 0.001), and valine (ß = 0.58 [0.24, 0.92], p = 0.001) were associated with more severe CIPN, but none of these associations retained significance after adjustment. In the over-representation analysis, no amino acid metabolic pathways were significantly enriched (all FDR > 0.05). In the network of enriched pathways, glutamate metabolism had the highest centrality. CONCLUSIONS: This analysis showed that pre-treatment serum amino acid concentrations are not strongly predictive of CIPN severity. Prospectively designed studies that assess non-amino acid metabolomics predictors are encouraged.

Characterization of Natural Killer Cell Profile in a Cohort of Infected Pregnant Women and Their Babies and Its Relation to CMV Transmission.

Human cytomegalovirus (CMV) is a common herpesvirus causing lifelong latent infection in most people and is a primary cause of congenital infection worldwide. Given the role of NK cells in the materno-fetal barrier, we investigated peripheral blood NK cell behavior in the context of CMV infection acquired during pregnancy. We analyzed the NK phenotype and CD107a surface mobilization on PBMCs from CMV-transmitting and non-transmitting mothers and newborns with or without congenital infection. NK cells from non-transmitting mothers showed the typical phenotype of CMV-adaptive NK cells, characterized by higher levels of NKG2C, CD57, and KIRs, with reduced NKG2A, compared to transmitting ones. A significantly higher percentage of DNAM-1+, PD-1+, and KIR+NKG2A-CD57+PD-1+ CD56dim cells was found in the non-transmitting group. Accordingly, NK cells from congenital-CMV (cCMV)-infected newborns expressed higher levels of NKG2C and CD57, with reduced NKG2A, compared to non-congenital ones. Furthermore, they showed a significant expansion of CD56dim cells co-expressing NKG2C and CD57 or with a memory-like (KIR+NKG2A-CD57+NKG2C+) phenotype, as well as a significant reduction of the CD57-NKG2C- population. Degranulation assays showed a slightly higher CD107a geomean ratio in NK cells of mothers who were non-transmitting compared to those transmitting the virus. Our findings demonstrate that both CMV-transmitting mothers and cCMV newborns show a specific NK profile. These data can guide studies on predicting virus transmission from mothers and congenital infection in infants.

Predictive and Prognostic Relevance of Tumor-Infiltrating Immune Cells: Tailoring Personalized Treatments against Different Cancer Types.

TIICs are critical components of the TME and are used to estimate prognostic and treatment responses in many malignancies. TIICs in the tumor microenvironment are assessed and quantified by categorizing immune cells into three subtypes: CD66b+ tumor-associated neutrophils (TANs), FoxP3+ regulatory T cells (Tregs), and CD163+ tumor-associated macrophages (TAMs). In addition, many cancers have tumor-infiltrating M1 and M2 macrophages, neutrophils (Neu), CD4+ T cells (T-helper), CD8+ T cells (T-cytotoxic), eosinophils, and mast cells. A variety of clinical treatments have linked tumor immune cell infiltration (ICI) to immunotherapy receptivity and prognosis. To improve the therapeutic effectiveness of immune-modulating drugs in a wider cancer patient population, immune cells and their interactions in the TME must be better understood. This study examines the clinicopathological effects of TIICs in overcoming tumor-mediated immunosuppression to boost antitumor immune responses and improve cancer prognosis. We successfully analyzed the predictive and prognostic usefulness of TIICs alongside TMB and ICI scores to identify cancer's varied immune landscapes. Traditionally, immune cell infiltration was quantified using flow cytometry, immunohistochemistry, gene set enrichment analysis (GSEA), CIBERSORT, ESTIMATE, and other platforms that use integrated immune gene sets from previously published studies. We have also thoroughly examined traditional limitations and newly created unsupervised clustering and deconvolution techniques (SpatialVizScore and ProTICS). These methods predict patient outcomes and treatment responses better. These models may also identify individuals who may benefit more from adjuvant or neoadjuvant treatment. Overall, we think that the significant contribution of TIICs in cancer will greatly benefit postoperative follow-up, therapy, interventions, and informed choices on customized cancer medicines.