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OBJECTIVES: To determine the role of keratin 17 (K17) as a predictive biomarker for response to chemotherapy by defining thresholds of K17 expression based on immunohistochemical tests that could be used to optimize therapeutic intervention for patients with pancreatic ductal adenocarcinoma (PDAC). METHODS: We profiled K17 expression, a hallmark of the basal molecular subtype of PDAC, by immunohistochemistry in 2 cohorts of formalin-fixed, paraffin-embedded PDACs (n = 305). We determined a K17 threshold of expression to optimize prognostic stratification according to the lowest Akaike information criterion and explored the potential relationship between K17 and chemoresistance by multivariate predictive analyses. RESULTS: Patients with advanced-stage, low K17 PDACs treated using 5-fluorouracil (5-FU)-based chemotherapeutic regimens had 3-fold longer survival than corresponding cases treated with gemcitabine-based chemotherapy. By contrast, PDACs with high K17 did not respond to either regimen. The predictive value of K17 was independent of tumor mutation status and other clinicopathologic variables. CONCLUSIONS: The detection of K17 in 10% or greater of PDAC cells identified patients with shortest survival. Among patients with low K17 PDACs, 5-FU-based treatment was more likely than gemcitabine-based therapies to extend survival.

Programmed cell death protein-1 (PD-1)/programmed death ligand-1 (PD-L1) immune checkpoint blockade as a breakthrough in cancer immunotherapy has shown unprecedented positive outcomes in the clinic. However, the overall effectiveness of PD-L1 antibody is less than expected. An increasing number of studies have demonstrated that PD-L1 is widely distributed and expressed not only on the cell membrane but also on the inside of the cells as well as on the extracellular vesicles secreted by tumour cells. Both endogenous and exogenous PD-L1 play significant roles in influencing the therapeutic effect of anti-tumour immunity. Herein, we mainly focused on the distribution and function of PD-L1 and further summarized the potential targeted therapeutic strategies. More importantly, in addition to taking the overall expression abundance of PD-L1 as a predictive indicator for selecting corresponding PD-1/PD-L1 monoclonal antibodies (mAbs), we also proposed that personalized combination therapies based on the different distribution of PD-L1 are worth attention to achieve more efficient and effective therapeutic outcomes in cancer patients. LINKED ARTICLES: This article is part of a themed issue on Cancer Microenvironment and Pharmacological Interventions. To view the other articles in this section visit http://onlinelibrary.wiley.com/doi/10.1111/bph.v181.2/issuetoc.

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. [ABSTRACT FROM AUTHOR]

Background: Immune checkpoint inhibitors (ICIs) have revolutionized the treatment landscape for non-small cell lung cancer (NSCLC). The variability in patient responses necessitates a blood-based, multi-cohort gene signature to predict ICI response in NSCLC. Methods: We performed transcriptomic profiling of peripheral blood mononuclear cell (PBMC) and buffy coat (BC) samples from three independent cohorts of NSCLC patients treated with ICIs: a retrospective cohort (PMBCR, n = 59), a retrospective validation cohort (BC, n = 44), and a prospective validation cohort (PBMCP, n = 42). We identified a 5-gene signature (UQCRB, NDUFA3, CDKN2D, FMNL1-DT, and APOL3) predictive of ICI response and validated its clinical utility in the prospective PBMCP cohort. Response was evaluated using RECIST criteria, and patients were followed up for progression-free survival (PFS) and overall survival (OS). Results: In the prospective PBMCP cohort, the 5-gene signature demonstrated high accuracy in stratifying patients into responders and non-responders (AUC = 0.89, 95% CI: 0.80–0.99). Predicted responders exhibited significantly longer PFS compared to predicted non-responders (median: 13.8 months vs. 4.2 months, HR = 0.21, 95% CI: 0.07–0.58, p = 0.005). Conclusion: Our study confirms a 5-gene signature as a key biomarker for ICI response in NSCLC, enhancing treatment precision. [ABSTRACT FROM AUTHOR]

Introduction: Immune checkpoint inhibitors (ICIs) have transformed advanced gastric cancer treatment, yet patient responses vary, highlighting the need for effective biomarkers. Common markers, such as programmed cell death ligand-1 (PD-L1), microsatellite instability/mismatch repair (MSI/MMR), tumor mutational burden, tumor-infiltrating lymphocytes, and Epstein–Barr virus, face sampling challenges and high costs. This study seeks practical, minimally invasive biomarkers to enhance patient selection and improve outcomes. Methods: This multicenter retrospective study analyzed 617 patients with advanced gastric or gastroesophageal junction cancer treated with programmed cell death protein-1 (PD-1)/PD-L1 inhibitors from January 2019 to March 2023. Clinical data and peripheral blood marker data were collected before and after treatment. The primary endpoints were overall survival (OS) and progression-free survival (PFS); the secondary endpoints included the objective response rate (ORR) and disease control rate (DCR). Least absolute shrinkage and selection operator (LASSO)-Cox and LASSO logistic regression analyses identified independent factors for OS, PFS, and ORR. Predictive nomograms were validated using receiver operating characteristic (ROC) curves, areas under the curve (AUCs), C-indices, and calibration curves, with clinical utility assessed via decision curve analysis (DCA), net reclassification improvement (NRI), and integrated discrimination improvement (IDI). Results: OS-related factors included treatment line, T stage, ascites, pretreatment indirect bilirubin (pre-IBIL), posttreatment CA125, CA199, CA724, and the PLR. PFS-related factors included treatment lines, T stage, metastatic sites, pre-IBIL, posttreatment globulin (GLOB), CA125, and CA199 changes. ORR-related factors included treatment line, T stage, N stage, liver metastasis, pretreatment red cell distribution width-to-platelet ratio (RPR), CA125, and CA724 changes. The nomograms showed strong predictive performance and clinical utility. Conclusions: Early treatment, lower T stage, the absence of ascites, and lower pre-IBIL, post-CA125, CA199, CA724, and PLR correlate with better OS. Factors for improved PFS include early treatment, lower T stage, fewer metastatic sites, and lower pre-IBIL, post-GLOB, and post-CA125 levels. Nomogram models can help identify patients who may benefit from immunotherapy, providing valuable clinical guidance. [ABSTRACT FROM AUTHOR]

Pre-clinical data suggest that mutations in the MEN1, DAXX, and/or ATRX genes may potentially increase radiation efficacy in cancer cells. Herein, we explore the association between response to peptide receptor radionuclide therapy (PRRT) and those mutations in patients with gastroenteropancreatic neuroendocrine tumors (GEP-NETs). We analyzed tissue-based next generation sequencing (NGS) assay results and clinicopathologic data from 28 patients with GEP-NETs treated with PRRT. Findings were correlated with progression-free survival (PFS) and objective response rate (ORR). Patients with mutations in MEN1, DAXX, and/or ATRX (n = 13) had a longer median PFS (26.47 vs 12.13 months; P = 0.014) than wild-type (n = 15) patients when adjusted for surgery prior to PRRT, tumor grade, and presence of TP53 mutation. Alterations in MEN1 along with a concurrent mutation in either DAXX or ATRX (n = 6) trended toward longer PFS compared to patients without concurrent mutations (31.53 vs 17.97 months; P = 0.09). ORR was higher in patients with a mutation in MEN1, DAXX, or ATRX (41.67% vs 15.38%). In pancreatic NET patients, these target mutations also showed a longer PFS (28.43 vs 9.83 months; P = 0.04). TP53 alterations showed a shorter PFS than wild-type cases (11.17 vs 20.47 months; P = 0.009). Mutations in MEN1/DAXX/ATRX are associated with improved PFS in patients with GEP-NETs receiving PRRT and might be used as a biomarker for treatment response. [ABSTRACT FROM AUTHOR]

Background: Total knee arthroplasty (TKA) is an effective treatment for knee osteoarthritis; however, early readmissions due to complications are common. This study assessed the ability of the hemoglobin-to-red cell distribution width ratio (HRR), hemoglobin-to-monocyte ratio (HMR), and hemoglobin-to-leukocyte ratio (HLR) to predict readmission within 14 days after TKA. Methods: Data from the Chang Gung Medical Research Database (CGRD) from 2014 to 2022 were retrospectively analyzed. Patients ≥ 20 years old who underwent primary TKA were eligible for inclusion. Patients with incomplete data on the indices of interest or follow-up < 14 days were excluded. Patient demographic, clinical, and comorbidity data were collected. Logistic regression was utilized to determine the associations between HRR, HMR, and HLR and 14-day readmission. Results: Data from 1,137 patients were analyzed. Multivariable analysis revealed that a higher HMR was significantly associated with lower 14-day readmission risk (adjusted OR [aOR] = 0.72, 95% confidence interval [CI]: 0.51–0.997), an HMR ≥ 2.18 (optimal cutoff value) was predictive of a significantly lower 14-day readmission risk (aOR = 0.61, 95% CI: 0.39–0.96). The composite indicator, HRR-HMR-HLR score, derived from the 3 indices assessed, was significantly associated with a lower 14-day readmission risk (score 2 vs. score 0: aOR = 0.51, 95% CI: 0.27–0.98; score 3 vs. score 0: aOR = 0.37, 95% CI: 0.17–0.82). Conclusions: High HMR and the HRR-HMR-HLR score are independently associated with a lower 14-day readmission risk after TKA. Implementing these indices into clinical practice may enhance postoperative management. [ABSTRACT FROM AUTHOR]

The 25th Annual Western Canadian Gastrointestinal Cancer Consensus Conference (WCGCCC) was held in Winnipeg, Manitoba, on 26–27 October 2023. The WCGCCC is an interactive multidisciplinary conference that was attended by healthcare professionals from across Western Canada (British Columbia, Alberta, and Manitoba) who are involved in the care of patients with gastrointestinal cancer. Surgical, medical, and radiation oncologists; pathologists; oncology nurses; pharmacists; and a family physician in oncology (FPO) participated in presentation and discussion sessions for the purpose of developing the recommendations presented here. This consensus statement addresses current issues in the management of gastroesophageal cancers. [ABSTRACT FROM AUTHOR]

Background/Objectives: Prior research has identified a significant association between heart disease and metabolic-dysfunction-associated steatotic liver disease (MASLD); however, the underlying mechanisms are unclear. This study aimed to identify predictive biomarkers associated with grade I left ventricular diastolic dysfunction (LVDD) in patients with type 2 diabetes mellitus (T2DM). Methods: This single-center, cross-sectional study evaluated 73 T2DM patients for grade 1 LVDD and MASLD using 2D echocardiography, tissue analysis, spectral color Doppler, and Fibromax. Results: This study analyzed 50 patients (mean age 58.0 ± 11.3 years) with a median diabetes duration of 7 years, abdominal obesity (mean body mass index (BMI) 34.4 ± 5.9 kg/m2), and a mean HbA1c of 7.9 ± 1.5%. The prevalence of grade I LVDD, fibrosis, mild steatosis, moderate-to-severe liver steatosis, mild MASLD, and moderate MASLD was 54%, 44%, 14%, 80%, 43%, and 34%, respectively. Regression analysis revealed that grade 1 LVDD was positively associated with age, Fibrotest, α2-macroglobulin, epicardiac adipose tissue (EAT), and negatively associated with lateral s′, E wave, E/e′, E/A, medium E′, and septal e′ (p < 0.05 for all). α2-macroglobulin > 1.92 g/L (area under the receiver operating characteristic curve (AUROC) = 0.782, sensitivity 70.4%, specificity 81.2%) and fibrotest score > 0.11 (AUROC 0.766, sensitivity 92.6%, specificity 56.2%) were significant predictors of grade I LVDD. Conclusions: Although the underlying mechanisms remain unclear, innovative non-invasive biomarkers, such as α2-macroglobulin or fibrotest, could concurrently indicate liver stiffness and the likelihood of grade I LVDD, an early, asymptomatic HF stage in T2DM patients. [ABSTRACT FROM AUTHOR]

Head and neck squamous cell carcinoma (HNSCC) is the sixth most common cancer worldwide, characterized by high aggressiveness and frequent metastasis to regional lymph nodes. Despite advances in therapy, including checkpoint inhibitor immunotherapy, surgery, radiotherapy, and chemotherapy, survival rates for patients with advanced HNSCC remain unsatisfactory. This article presents the latest research on predictive biomarkers such as PD-L1, PD-1, CTLA-4, p53, and HPV, which may enhance treatment efficacy and improve clinical outcomes for patients. The clinical value of these biomarkers, their limitations, and their potential application in HNSCC therapy are emphasized. Special attention is given to immunotherapy, which shows promising results in treating this type of cancer through the modulation of the immune response. The review's findings highlight the need for further research on new biomarkers to develop more personalized and effective therapeutic strategies for HNSCC patients. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Small cell lung cancer (SCLC) is a highly malignant and poor-prognosis cancer, with most cases diagnosed at the extensive stage (ES). Amidst a landscape marked by limited progress in treatment modalities for ES-SCLC over the past few decades, the integration of immune checkpoint inhibitors (ICIs) with platinum-based chemotherapy has provided a milestone approach for improving prognosis, emerging as the new standard for initial therapy in ES-SCLC. However, only a minority of SCLC patients can benefit from ICIs, which frequently come with varying degrees of immune-related adverse events (irAEs). Therefore, it is crucial to investigate predictive biomarkers to screen potential beneficiaries of ICIs, mitigate the risk of side effects, and improve treatment precision. This review summarized potential biomarkers for predicting ICI response in ES-SCLC, with a primary focus on markers sourced from tumor tissue or peripheral blood samples. The former mainly included PD-L1 expression, tumor mutational burden (TMB), along with cellular or molecular components related to the tumor microenvironment (TME) and antigen presentation machinery (APM), molecular subtypes of SCLC, and inflammatory gene expression profiles. Circulating biomarkers predominantly comprised circulating tumor DNA (ctDNA), circulating tumor cells (CTCs), cytokines, plasma autoantibodies, inflammation-related parameters, and blood TMB. We synthesized and analyzed the research progress of these potential markers. Notably, investigations into PD-L1 expression and TMB have been the most extensive, exhibiting preliminary predictive efficacy in salvage immunotherapy; however, consistent conclusions have yet to be reached across studies. Additionally, novel predictive markers developed based on TME composition, APM, transcriptomic and genomic features provide promising tools for precision immunotherapy. Circulating biomarkers offer the advantages of convenience, non-invasiveness, and a comprehensive reflection of tumor molecular characteristics. They may serve as alternative options for predicting immunotherapy efficacy in SCLC. However, there is a scarcity of studies, and the significant heterogeneity in research findings warrants attention. [ABSTRACT FROM AUTHOR]

Background: Advances in targeted therapy development and tumor sequencing technology are reclassifying cancers into smaller biomarker-defined diseases. Randomized controlled trials (RCTs) are often impractical in rare diseases, leading to calls for single-arm studies to be sufficient to inform clinical practice based on a strong biological rationale. However, without RCTs, favorable outcomes are often attributed to therapy but may be due to a more indolent disease course or other biases. When the clinical benefit of targeted therapy in a common cancer is established in RCTs, this benefit may extend to rarer cancers sharing the same biomarker. However, careful consideration of the appropriateness of extending the existing trial evidence beyond specific cancer types is required. A framework for extrapolating evidence for biomarker-targeted therapies to rare cancers is needed to support transparent decision-making. Objectives: To construct a framework outlining the breadth of criteria essential for extrapolating evidence for a biomarker-targeted therapy generated from RCTs in common cancers to different rare cancers sharing the same biomarker. Design: A series of questions articulating essential criteria for extrapolation. Methods: The framework was developed from the core topics for extrapolation identified from a previous scoping review of methodological guidance. Principles for extrapolation outlined in guidance documents from the European Medicines Agency, the US Food and Drug Administration, and Australia's Medical Services Advisory Committee were incorporated. Results: We propose a framework for assessing key assumptions of similarity of the disease and treatment outcomes between the common and rare cancer for five essential components: prognosis of the biomarker-defined cancer, biomarker test analytical validity, biomarker actionability, treatment efficacy, and safety. Knowledge gaps identified can be used to prioritize future studies. Conclusion: This framework will allow systematic assessment, standardize regulatory, reimbursement and clinical decision-making, and facilitate transparent discussions between key stakeholders in drug assessment for rare biomarker-defined cancers. [ABSTRACT FROM AUTHOR]

Predictive biomarkers for immune checkpoint inhibitors (ICIs) in solid tumors such as melanoma, hepatocellular carcinoma (HCC), colorectal cancer (CRC), non-small cell lung cancer (NSCLC), endometrial carcinoma, renal cell carcinoma (RCC), or urothelial carcinoma (UC) include programmed cell death ligand 1 (PD-L1) expression, tumor mutational burden (TMB), defective deoxyribonucleic acid (DNA) mismatch repair (dMMR), microsatellite instability (MSI), and the tumor microenvironment (TME). Over the past decade, several types of ICIs, including cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) inhibitors, anti-programmed cell death 1 (PD-1) antibodies, anti-programmed cell death ligand 1 (PD-L1) antibodies, and anti-lymphocyte activation gene-3 (LAG-3) antibodies have been studied and approved by the Food and Drug Administration (FDA), with ongoing research on others. Recent studies highlight the critical role of the gut microbiome in influencing a positive therapeutic response to ICIs, emphasizing the importance of modeling factors that can maintain a healthy microbiome. However, resistance mechanisms can emerge, such as increased expression of alternative immune checkpoints, T-cell immunoglobulin (Ig), mucin domain-containing protein 3 (TIM-3), LAG-3, impaired antigen presentation, and alterations in the TME. This review aims to synthesize the data regarding the interactions between microbiota and immunotherapy (IT). Understanding these mechanisms is essential for optimizing ICI therapy and developing effective combination strategies. [ABSTRACT FROM AUTHOR]

Patient response after treatment of renal cell cancer (RCC) with systemic agents, which include various drug categories, is generally poor and unpredictable. In this context, the ideal drug administration includes tools to predict the sensitivity of the disease to therapy. The aim of this study was to systematically summarize the reports on the predictive value of the methylation status in the systemic therapy of RCC. Only original articles reporting on the association of promoter methylation with the response of patients or cell lines to systemic agents were included in this review. We applied PRISMA recommendations to the structure and methodology of this systematic review. Our literature search concluded with 31 articles conducted on RCC cell lines and patient tissues. The majority of the studies demonstrated a methylation-dependent response to systemic agents. This correlation suggests that the methylation pattern can be used as a predictive tool in the management of RCC with various classes of systemic agents. However, although methylation biomarkers show promise for predicting response, the evidence of such correlation is still weak. More studies on the gene methylation pattern in patients under systemic therapy and its correlation with different degrees of response are needed. [ABSTRACT FROM AUTHOR]

Abstract Background Immune checkpoint inhibitors (ICIs) have revolutionized the treatment landscape for non-small cell lung cancer (NSCLC). The variability in patient responses necessitates a blood-based, multi-cohort gene signature to predict ICI response in NSCLC. Methods We performed transcriptomic profiling of peripheral blood mononuclear cell (PBMC) and buffy coat (BC) samples from three independent cohorts of NSCLC patients treated with ICIs: a retrospective cohort (PMBCR, n = 59), a retrospective validation cohort (BC, n = 44), and a prospective validation cohort (PBMCP, n = 42). We identified a 5-gene signature (UQCRB, NDUFA3, CDKN2D, FMNL1-DT, and APOL3) predictive of ICI response and validated its clinical utility in the prospective PBMCP cohort. Response was evaluated using RECIST criteria, and patients were followed up for progression-free survival (PFS) and overall survival (OS). Results In the prospective PBMCP cohort, the 5-gene signature demonstrated high accuracy in stratifying patients into responders and non-responders (AUC = 0.89, 95% CI: 0.80–0.99). Predicted responders exhibited significantly longer PFS compared to predicted non-responders (median: 13.8 months vs. 4.2 months, HR = 0.21, 95% CI: 0.07–0.58, p = 0.005). Conclusion Our study confirms a 5-gene signature as a key biomarker for ICI response in NSCLC, enhancing treatment precision.

Abstract Endometrial carcinoma (EC) is the most common gynecologic malignancy in high-income countries, with its incidence and mortality rising globally, particularly in countries undergoing rapid socioeconomic transitions. Over the past decade, the management of EC has shifted towards molecular classification and biomarker-driven therapies. This shift began in 2013 with the discovery of the 4 prognostic and molecular EC subgroups by The Cancer Genome Atlas (TCGA). Following the TCGA discovery, the Proactive Molecular Risk Classifiers for Endometrial Cancer (ProMisE) and the TransPORTEC initiative have provided 4 pragmatic molecular classifiers by combining next-generation sequencing (NGS) and surrogate immunohistochemical markers to TCGA’s categorization: Mismatch Repair Deficient (MMRd); p53-abnormal (p53abn); No Specific Molecular Profile (NSMP); and POLE-mutant (POLEmut). These subgroups not only provide insights into the biological behavior of EC but also have strong clinical relevance and prognostic implications. In line with these advancements, the World Health Organization (WHO) endorsed molecular classification in 2020, advocating for its integration into EC pathology reports. In 2023, the Federation of International Gynecology and Obstetrics (FIGO) proposed a new staging system that integrates histological parameters and molecular profiles into routine pathology practice. The use of complete molecular classification surrogates in all EC cases, whenever resources permit, is intended to promote prognostic risk-group stratification, data collection, Lynch Syndrome (LS) screening, and potentially influence adjuvant and systemic treatment decisions, as well as predict the efficacy of Immune Checkpoint Inhibitors (ICI). Consequently, the evolving landscape of predictive biomarkers in EC has gained importance in daily oncology practice, profoundly changing the role of pathologists. Pathologists are now crucial in interpreting molecular information and participating as members of multidisciplinary teams in therapeutic decisions. This review article aims to emphasize the importance of molecular classification in EC and encourage pathologists to become familiar with the use of predictive biomarkers in their daily anatomical pathology practice.

Abstract Background Total knee arthroplasty (TKA) is an effective treatment for knee osteoarthritis; however, early readmissions due to complications are common. This study assessed the ability of the hemoglobin-to-red cell distribution width ratio (HRR), hemoglobin-to-monocyte ratio (HMR), and hemoglobin-to-leukocyte ratio (HLR) to predict readmission within 14 days after TKA. Methods Data from the Chang Gung Medical Research Database (CGRD) from 2014 to 2022 were retrospectively analyzed. Patients ≥ 20 years old who underwent primary TKA were eligible for inclusion. Patients with incomplete data on the indices of interest or follow-up

Abstract Small cell lung cancer (SCLC) is a highly malignant and poor-prognosis cancer, with most cases diagnosed at the extensive stage (ES). Amidst a landscape marked by limited progress in treatment modalities for ES-SCLC over the past few decades, the integration of immune checkpoint inhibitors (ICIs) with platinum-based chemotherapy has provided a milestone approach for improving prognosis, emerging as the new standard for initial therapy in ES-SCLC. However, only a minority of SCLC patients can benefit from ICIs, which frequently come with varying degrees of immune-related adverse events (irAEs). Therefore, it is crucial to investigate predictive biomarkers to screen potential beneficiaries of ICIs, mitigate the risk of side effects, and improve treatment precision. This review summarized potential biomarkers for predicting ICI response in ES-SCLC, with a primary focus on markers sourced from tumor tissue or peripheral blood samples. The former mainly included PD-L1 expression, tumor mutational burden (TMB), along with cellular or molecular components related to the tumor microenvironment (TME) and antigen presentation machinery (APM), molecular subtypes of SCLC, and inflammatory gene expression profiles. Circulating biomarkers predominantly comprised circulating tumor DNA (ctDNA), circulating tumor cells (CTCs), cytokines, plasma autoantibodies, inflammation-related parameters, and blood TMB. We synthesized and analyzed the research progress of these potential markers. Notably, investigations into PD-L1 expression and TMB have been the most extensive, exhibiting preliminary predictive efficacy in salvage immunotherapy; however, consistent conclusions have yet to be reached across studies. Additionally, novel predictive markers developed based on TME composition, APM, transcriptomic and genomic features provide promising tools for precision immunotherapy. Circulating biomarkers offer the advantages of convenience, non-invasiveness, and a comprehensive reflection of tumor molecular characteristics. They may serve as alternative options for predicting immunotherapy efficacy in SCLC. However, there is a scarcity of studies, and the significant heterogeneity in research findings warrants attention.

Shivani Srivastava,1,&ast; Anuradha Tyagi,2,&ast; Vishakha Anand Pawar,3,&ast; Nawaid Hussain Khan,4,&ast; Kavita Arora,5,6 Chaitenya Verma,7,8 Vinay Kumar9 1Department of Pathology, School of Medicine, Yale University, New Haven, CT, USA; 2Department of cBRN, Institute of Nuclear Medicine and Allied Science, Delhi, India; 3The University of Texas MD Anderson Cancer Center, Houston, TX, USA; 4Faculty of Medicine, Ala-Too International University, Bishkek, Kyrgyz Republic; 5Advanced Instrumentation Research Facility, Jawaharlal Nehru University, New Delhi, India; 6School of Computational & Integrative Sciences, Jawaharlal Nehru University, New Delhi, India; 7Department of Pathology, Wexner Medical Center, Ohio State University, Columbus, OH, USA; 8Department of Biotechnology, SSET, Sharda University, Greater Noida, 201306, India; 9Pennsylvania State University Hershey Medical Center, 500 University Dr, Heshey, PA, USA&ast;These authors contributed equally to this workCorrespondence: Chaitenya Verma; Vinay Kumar, Email chaitenya999@gmail.com; vinayktyagi07@gmail.comAbstract: The CAR-T cell therapy has marked the dawn of new era in the cancer therapeutics and cell engineering techniques. The review emphasizes on the challenges that obstruct the therapeutic efficiency caused by cell toxicities, immunosuppressive tumor environment, and decreased T cell infiltration. In the interest of achieving the overall survival (OS) and event-free survival (EFS) of patients, the conceptual background of potential target selection and various CAR-T cell design techniques are described which can minimize the off-target effects, reduce toxicity, and thus increase the resilience of CAR-T cell treatment in the haematological malignancies as well as in solid tumors. Furthermore, it delves into cutting-edge technologies like gene editing and synthetic biology, providing new opportunities to enhance the functionality of CAR-T cells and overcome mechanisms of immune evasion. This review provides a comprehensive understanding of the complex and diverse aspects of CAR-T cell-based gene treatments, including both scientific and clinical aspects. By effectively addressing the obstacles and utilizing the capabilities of cutting-edge technology, CAR-T cell therapy shows potential in fundamentally changing immunotherapy and reshaping the approach to cancer treatment.Keywords: CAR-T cells, immune checkpoint inhibitors, gene therapies, gene editing, predictive biomarkers

Abstract 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 = 0.2) 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.

Abstract Aims Predicting mortality in severe AL cardiac amyloidosis is challenging due to elevated biomarker levels and limited thresholds for stratifying severe cardiac damage. Methods and results This prospective, observational, cohort study included de novo, confirmed cardiac AL amyloidosis patients at the Henri Mondor National Reference Centre. The goal was to identify predictors of mortality to enhance prognostic stratification and improve informed decision‐making regarding therapy. Over the 12‐year study period, among the 233 patients included, 133 were NYHA III‐IV and 179 Mayo 2004 III. The independent predictors for mortality identified were hsTnT, NT‐proBNP, cardiac output, and conjugated bilirubin. A novel prognostic, conditional stratification, Mondor amyloidosis cardiac staging (MACS) was developed with biomarker cut‐off values for Stage 1: hsTnT ≤ 107 ng/L and NT‐proBNP ≤ 3867 ng/L (n = 77; 33%); for stage 2 NT‐proBNP > 3867 ng/L (n = 72; 30%). For stage 3, if troponin >107 ng/L, regardless of NT‐proBNP then CB 4 μmol/L, was added (n = 41; 17.5%) and stage 4: CB > 4 μmol/L (n = 43; 18.5%). The median overall survival was 8 months 95% CI [2–24]. At 1 year, 102 (44%) patients died and the Kaplan–Meier median survival with MACS Stage 1 was not reached, while stage 2 was 15.2 months (95% CI [11–18]) and stage 3, 6.6 months (95% CI [1–13]). Notably, among European stage II patients, 17.1%, n = 8 were MACS stage 3 and European stage IIIb 21.4% (n = 23) were MACS stage 4. Importantly, among European stage IIIb patients 42.2% (n = 29) were classified MACS stage 4 and 12.5% n = 9 were only MACS stage 2. Conclusions The Mondor prognostic staging system, including conjugate bilirubin may significantly improve prognostic stratification for patients with severe cardiac amyloidosis.

Ovarian carcinoma (OC) still represents an insidious and fatal malignancy, and few significant results have been obtained in the last two decades to improve patient survival. Novel targeted therapies such as poly (ADP-ribose) polymerase inhibitors (PARPi) have been successfully introduced in the clinical management of OC, but not all patients will benefit, and drug resistance almost inevitably occurs. The identification of patients who are likely to respond to PARPi-based therapies relies on homologous recombination deficiency (HRD) tests, as this condition is associated with response to these treatments. This review summarizes the genomic and functional HRD assays currently used in clinical practice and those under evaluation, the clinical implications of HRD testing in OC, and their current pitfalls and limitations. Special emphasis will be placed on the functional HRD assays under development and the use of machine learning and artificial intelligence technologies as novel strategies to overcome the current limitations of HRD tests for a better-personalized treatment to improve patient outcomes. [ABSTRACT FROM AUTHOR]

Simple Summary: Although the high mutation burden in small-cell lung cancer (SCLC) suggests that it should be responsive to immune checkpoint blockade (ICB), relatively few patients have shown durable clinical benefit (DCB). To identify biomarkers of responses, biopsies from 35 patients treated with ICB (21 first-line and 14 second-line) were subjected to transcriptomic analysis and gene signatures were studied. Patients with DCB were compared to those with no clinical benefit (NCB). The response to ICB in the second-line, but not the first-line, setting was associated with gene signatures of inflammation, antigen presentation, interferon responses, and increased CD8 T cells. Our data suggest that responses to ICB in the second-line setting can be predicted by the baseline inflammatory state of the tumor; however, this strong association with inflammation was not observed in the first-line setting, likely because chemotherapy alters the immune milieu allowing a response to ICB. Although immune checkpoint blockade (ICB) is currently approved for the treatment of extensive-stage small-cell lung cancer (SCLC) in combination with chemotherapy, relatively few patients have demonstrated durable clinical benefit (DCB) to these therapies. Biomarkers predicting responses are needed. Biopsies from 35 SCLC patients treated with ICB were subjected to transcriptomic analysis; gene signatures were assessed for associations with responses. Twenty-one patients were treated with ICB in the first-line setting in combination with platinum-based chemotherapy; fourteen patients were treated in the second-line setting with ICB alone. DCB after ICB in SCLC in the second-line setting (3 of 14 patients) was associated with statistically higher transcriptomic levels of genes associated with inflammation (p = 0.003), antigen presentation machinery (p = 0.03), interferon responses (p < 0.05), and increased CD8 T cells (p = 0.02). In contrast, these gene signatures were not significantly different in the first-line setting. Our data suggest that responses to ICB in SCLC in the second-line setting can be predicted by the baseline inflammatory state of the tumor; however, this strong association with inflammation was not seen in the first-line setting. We postulate that chemotherapy alters the immune milieu allowing a response to ICB. Other biomarkers will be needed to predict responses in first-line therapy patients. [ABSTRACT FROM AUTHOR]

Hepatocellular carcinoma (HCC) is the most common liver cancer and is among the leading causes of cancer-related death worldwide. There is no reliable biomarker for the early diagnosis of HCC. Circulating microRNAs (miRNAs) have attracted attention as potential biomarkers of disease. By small-RNA next-generation sequencing, the analysis of serum miRNAs led to the identification of molecular signatures able to discriminate advanced HCC from early HCC (n = 246); advanced HCC from CIRRHOSIS (n = 299); advanced HCC from HEALTHY (n = 320); HEALTHY from early HCC (n = 343); and HEALTHY from CIRRHOSIS (n = 414). Cirrhotic patients and early HCC patients exhibited similar serum miRNA profiles, yet a small number of miRNAs (n = 57) were able to distinguish these two classes of patients. A second objective of the study was to identify serum miRNAs capable of predicting the response to therapy in patients with advanced HCC. All patients were treated with sorafenib as first-line therapy: 24 were nonresponsive and 24 responsive. Analysis of circulating miRNAs revealed a 54 miRNAs signature able to separate the two subgroups. This study suggested that circulating miRNAs could be useful biomarkers for monitoring patients with liver diseases ranging from cirrhosis to advanced HCC and possibly predicting susceptibility to first-line treatment based on sorafenib. [ABSTRACT FROM AUTHOR]

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 (β = 0.58 [0.23, 0.93], p = 0.001), phenylalanine (β = 0.54 [0.19, 0.89], p = 0.002), tyrosine (β = 0.57 [0.23, 0.91], p = 0.001), and valine (β = 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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. 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. By offering a comprehensive overview, this review delineates the pivotal role of ICIs in the management of MMR-d/MSI-H EC. [ABSTRACT FROM AUTHOR]

Mismatch repair proficient (MMRp) tumors of colorectal origin are one of the prevalent yet unpredictable clinical challenges. Despite earnest efforts, optimal treatment modalities have yet to emerge for this class. The poor prognosis and limited actionability of MMRp are ascribed to a low neoantigen burden and a desert-like microenvironment. This review focuses on the critical roadblocks orchestrated by an immune evasive mechanistic milieu in the context of MMRp. The low density of effector immune cells, their weak spatiotemporal underpinnings, and the high-handedness of the IL-17-TGF-b signaling are intertwined and present formidable challenges for the existing therapies. Microbiome niche decorated by Fusobacterium nucleatum alters the metabolic program to maintain an immunosuppressive state. We also highlight the evolving strategies to repolarize and reinvigorate this microenvironment. Reconstruction of anti-tumor chemokine signaling, rational drug combinations eliciting T cell activation, and reprograming the maladapted microbiome are exciting developments in this direction. Alternative vulnerability of other DNA damage repair pathways is gaining momentum. Integration of liquid biopsy and ex vivo functional platforms provide precision oncology insights. We illustrated the perspectives and changing landscape of MMRp-CRC. The emerging opportunities discussed in this review can turn the tide in favor of fighting the treatment dilemma for this elusive cancer. [ABSTRACT FROM AUTHOR]

The early and accurate identification of predictive biomarkers for antiviral treatment efficacy remains a significant clinical challenge, particularly in the management of chronic hepatitis B (CHB). This study aimed to assess whether the plasma metabolome could reliably predict the success of antiviral therapy in CHB patients. We conducted a retrospective analysis on 56 treatment-naive CHB patients at the First Affiliated Hospital of Zhejiang University from December 2013 to March 2016. Patients who underwent a 48-week treatment regimen of entecavir (ETV) and interferon-alpha (IFN-α) were randomly assigned to either a discovery cohort (n=29) or a validation cohort (n=27). Based on the outcome of the treatment, patients were classified as HBeAg seroconversion group (High responders, Hrp) or the non-remission group (Low responder, Lrp). Our methodology involved an untargeted analysis of the amine/phenol and carboxylic acid submetabolomes in the CHB patients under treatment, utilizing chemical isotope labeling (CIL) techniques with liquid chromatography-mass spectrometry (LC-MS). Several metabolites were identified as having significant diagnostic potential for distinguishing Hrp from Lrp, with areas under the receiver operating characteristic curve (AUC) exceeding those typical clinical indicators. Notably, four metabolites, namely 2-methyl-3-ketovaleric acid, 2-ketohexanoic acid, 6-oxo-1,4,5,6-tetrahydronicotinic acid, and α-ketoisovaleric acid, demonstrated exceptionally high sensitivity and specificity in both cohorts, nearing 100%. In contrast, the clinical indicators, including HBcAb, log(HBsAg), and HBeAb, demonstrated lower and inconsistent sensitivity and specificity between the discovery and validation cohorts. Using HBcAb as a marker, the sensitivity was 87.5% with 76.9% specificity in the discovery cohort; however, the sensitivity dropped to 46.7% with 91.7% specificity in the validation cohort. Using log(HBsAg), the sensitivity was 84.6% with 69.2% specificity in the discovery cohort, compared to 85.7% sensitivity and 83.3% specificity in the validation cohort. For HBeAb, the separation of Hrp and Lrp had a sensitivity of 87.5% with 69.2% specificity in the discovery cohort, while the validation cohort showed 86.7% sensitivity and 91.7% specificity. [ABSTRACT FROM AUTHOR]

Melanoma, a malignant skin cancer arising from melanocytes, exhibits rapid metastasis and a high mortality rate, especially in advanced stages. Current treatment modalities, including surgery, radiation, and immunotherapy, offer limited success, with immunotherapy using immune checkpoint inhibitors (ICIs) being the most promising. However, the high mortality rate underscores the urgent need for robust, non-invasive biomarkers to predict patient response to adjuvant therapies. The immune microenvironment of melanoma comprises various immune cells, which influence tumor growth and immune response. Melanoma cells employ multiple mechanisms for immune escape, including defects in immune recognition and epithelial-mesenchymal transition (EMT), which collectively impact treatment efficacy. Single-cell analysis technologies, such as single-cell RNA sequencing (scRNA-seq), have revolutionized the understanding of tumor heterogeneity and immune microenvironment dynamics. These technologies facilitate the identification of rare cell populations, co-expression patterns, and regulatory networks, offering deep insights into tumor progression, immune response, and therapy resistance. In the realm of biomarker discovery for melanoma, single-cell analysis has demonstrated significant potential. It aids in uncovering cellular composition, gene profiles, and novel markers, thus advancing diagnosis, treatment, and prognosis. Additionally, tumor-associated antibodies and specific genetic and cellular markers identified through single-cell analysis hold promise as predictive biomarkers. Despite these advancements, challenges such as RNA-protein expression discrepancies and tumor heterogeneity persist, necessitating further research. Nonetheless, single-cell analysis remains a powerful tool in elucidating the mechanisms underlying therapy response and resistance, ultimately contributing to the development of personalized melanoma therapies and improved patient outcomes. [ABSTRACT FROM AUTHOR]

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). [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Aims: Predicting mortality in severe AL cardiac amyloidosis is challenging due to elevated biomarker levels and limited thresholds for stratifying severe cardiac damage. Methods and results: This prospective, observational, cohort study included de novo, confirmed cardiac AL amyloidosis patients at the Henri Mondor National Reference Centre. The goal was to identify predictors of mortality to enhance prognostic stratification and improve informed decision‐making regarding therapy. Over the 12‐year study period, among the 233 patients included, 133 were NYHA III‐IV and 179 Mayo 2004 III. The independent predictors for mortality identified were hsTnT, NT‐proBNP, cardiac output, and conjugated bilirubin. A novel prognostic, conditional stratification, Mondor amyloidosis cardiac staging (MACS) was developed with biomarker cut‐off values for Stage 1: hsTnT ≤ 107 ng/L and NT‐proBNP ≤ 3867 ng/L (n = 77; 33%); for stage 2 NT‐proBNP > 3867 ng/L (n = 72; 30%). For stage 3, if troponin >107 ng/L, regardless of NT‐proBNP then CB 4 μmol/L, was added (n = 41; 17.5%) and stage 4: CB > 4 μmol/L (n = 43; 18.5%). The median overall survival was 8 months 95% CI [2–24]. At 1 year, 102 (44%) patients died and the Kaplan–Meier median survival with MACS Stage 1 was not reached, while stage 2 was 15.2 months (95% CI [11–18]) and stage 3, 6.6 months (95% CI [1–13]). Notably, among European stage II patients, 17.1%, n = 8 were MACS stage 3 and European stage IIIb 21.4% (n = 23) were MACS stage 4. Importantly, among European stage IIIb patients 42.2% (n = 29) were classified MACS stage 4 and 12.5% n = 9 were only MACS stage 2. Conclusions: The Mondor prognostic staging system, including conjugate bilirubin may significantly improve prognostic stratification for patients with severe cardiac amyloidosis. [ABSTRACT FROM AUTHOR]

Immunotherapy represents a significant and essential component of renal carcinoma therapy (RCC), but the selection of an optimal regimen for an individual patient remains unclear. Despite significant improvements in therapeutic options for RCC, predictive biomarkers for immunotherapeutic agents remain elusive. Neopterin is a biomarker of cell-mediated immune response, with concentrations increased in different disorders, including cancer. High neopterin levels herald, in general, a poor prognosis. This review briefly overviews the contemporary clinical data on biomarkers in metastatic RCC therapy, focusing on neopterin. Elevated neopterin levels have been observed in tumors of different primary locations. Research indicates that neopterin may serve as a potential biomarker for assessing the inflammatory status associated with certain cancers. However, it is necessary to interpret neopterin levels in the context of a comprehensive clinical evaluation, as elevated neopterin alone is not specific to cancer and can be influenced by other factors, including comorbid conditions. Neopterin has also been identified as a prognostic biomarker. An increasing neopterin level in serum and urine is associated with advanced cancer, but the role as a potential predictor of response to immunotherapy has yet to be established. A reliable biomarker for optimal therapy selection in metastatic RCC is still putative. [ABSTRACT FROM AUTHOR]

Thromboembolic diseases including arterial and venous thrombosis are common causes of morbidity and mortality globally. Thrombosis frequently recurs and can also complicate many inflammatory conditions through the process of 'thrombo-inflammation,' as evidenced during the COVID-19 pandemic. Current candidate biomarkers for thrombosis prediction, such as D-dimer, have poor predictive efficacy. This limits our capacity to tailor anticoagulation duration individually and may expose lower risk individuals to undue bleeding risk. Global coagulation assays, such as the Overall Haemostatic Potential (OHP) assay, that investigate fibrin generation and fibrinolysis, may provide a more accurate and functional assessment of hypercoagulability. We present a review of fibrin's critical role as a central modulator of thrombotic risk. The results of our studies demonstrating the OHP assay as a predictive biomarker in venous thromboembolism, chronic renal disease, diabetes mellitus, post-thrombotic syndrome, and COVID-19 are discussed. As a comprehensive and global measurement of fibrin generation and fibrinolytic capacity, the OHP assay may be a valuable addition to future multi-modal predictive tools in thrombosis. [ABSTRACT FROM AUTHOR]

IntroductionThis study investigates the correlation between the total burden of Cerebral Small Vessel Disease (CSVD) and Subjective Cognitive Decline (SCD) in patients with Parkinson’s disease (PD).MethodsA cross-sectional design was employed, involving 422 patients with PD. Demographic and clinical data were collected. Brain magnetic resonance imaging (MRI) was conducted to identify CSVD markers. SCD was assessed using the Cognitive Complaints Inventory (CCI).ResultsLogistic regression analyses revealed that the total burden of CSVD and specific imaging markers, including Deep White Matter Hyperintensities (DWMH), Periventricular Hyperintensities (PVH), and Enlarged Perivascular Spaces (EPVS), were significant predictors of SCD. The total burden of CSVD demonstrated the highest predictive accuracy for SCD in PD patients.DiscussionThe findings suggest that the total burden of CSVD, as measured by MRI, could serve as a potential biomarker for early identification of cognitive decline in PD, highlighting the importance of considering vascular factors in the early detection of cognitive changes in PD.

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.

IntroductionImmune checkpoint inhibitors (ICIs) have transformed advanced gastric cancer treatment, yet patient responses vary, highlighting the need for effective biomarkers. Common markers, such as programmed cell death ligand-1 (PD-L1), microsatellite instability/mismatch repair (MSI/MMR), tumor mutational burden, tumor-infiltrating lymphocytes, and Epstein–Barr virus, face sampling challenges and high costs. This study seeks practical, minimally invasive biomarkers to enhance patient selection and improve outcomes.MethodsThis multicenter retrospective study analyzed 617 patients with advanced gastric or gastroesophageal junction cancer treated with programmed cell death protein-1 (PD-1)/PD-L1 inhibitors from January 2019 to March 2023. Clinical data and peripheral blood marker data were collected before and after treatment. The primary endpoints were overall survival (OS) and progression-free survival (PFS); the secondary endpoints included the objective response rate (ORR) and disease control rate (DCR). Least absolute shrinkage and selection operator (LASSO)-Cox and LASSO logistic regression analyses identified independent factors for OS, PFS, and ORR. Predictive nomograms were validated using receiver operating characteristic (ROC) curves, areas under the curve (AUCs), C-indices, and calibration curves, with clinical utility assessed via decision curve analysis (DCA), net reclassification improvement (NRI), and integrated discrimination improvement (IDI).ResultsOS-related factors included treatment line, T stage, ascites, pretreatment indirect bilirubin (pre-IBIL), posttreatment CA125, CA199, CA724, and the PLR. PFS-related factors included treatment lines, T stage, metastatic sites, pre-IBIL, posttreatment globulin (GLOB), CA125, and CA199 changes. ORR-related factors included treatment line, T stage, N stage, liver metastasis, pretreatment red cell distribution width-to-platelet ratio (RPR), CA125, and CA724 changes. The nomograms showed strong predictive performance and clinical utility.ConclusionsEarly treatment, lower T stage, the absence of ascites, and lower pre-IBIL, post-CA125, CA199, CA724, and PLR correlate with better OS. Factors for improved PFS include early treatment, lower T stage, fewer metastatic sites, and lower pre-IBIL, post-GLOB, and post-CA125 levels. Nomogram models can help identify patients who may benefit from immunotherapy, providing valuable clinical guidance.

IntroductionLiver cancer, particularly Hepatocellular carcinoma (HCC), remains a significant global health concern due to its high prevalence and heterogeneous nature. Despite the existence of approved drugs for HCC treatment, the scarcity of predictive biomarkers limits their effective utilization. Integrating diverse data types to revolutionize drug response prediction, ultimately enabling personalized HCC management.MethodIn this study, we developed multiple supervised machine learning models to predict treatment response. These models utilized classifiers such as logistic regression (LR), k-nearest neighbors (kNN), neural networks (NN), support vector machines (SVM), and random forests (RF) using a comprehensive set of molecular, biochemical, and immunohistochemical features as targets of three drugs: Pantoprazole, Cyanidin 3-glycoside (Cyan), and Hesperidin. A set of performance metrics for the complete and reduced models were reported including accuracy, precision, recall (sensitivity), specificity, and the Matthews Correlation Coefficient (MCC).Results and DiscussionNotably, (NN) achieved the best prediction accuracy where the combined model using molecular and biochemical features exhibited exceptional predictive power, achieving solid accuracy of 0.9693 ∓ 0.0105 and average area under the ROC curve (AUC) of 0.94 ∓ 0.06 coming from three cross-validation iterations. Also, found seven molecular features, seven biochemical features, and one immunohistochemistry feature as promising biomarkers of treatment response. This comprehensive method has the potential to significantly advance personalized HCC therapy by allowing for more precise drug response estimation and assisting in the identification of effective treatment strategies.

Advanced biliary tract cancers (BTCs) have gained notoriety among gastrointestinal tumours for their comparatively high incidence of actionable alterations and their compelling benefit from targeted therapies matched to these alterations. Such successes are exemplified by BTC-specific approvals of fibroblast growth factor receptor (FGFR) inhibitors for tumours with FGFR2 rearrangements, as well as mutant isocitrate dehydrogenase 1 inhibitors. Nevertheless, there is a clear absence of therapeutic benefit in a subset of patients despite their tumours fulfilling the current molecular criteria for treatment with these drugs. This results in inefficient management of patients with otherwise bleak prognosis, as well as considerable financial burden. Even among responders, the duration of response is limited, a clinical observation that could be considered unusual as these inhibitors typically target driver genes hypothesised to be responsible for tumour formation. However, BTCs exhibit oncogenic addiction to signalling networks rather than individual genes, and by extension, therapeutic response is dependent on these signalling networks rather than simply the status of the specific target gene. Primary resistance is mediated by co-occurring genetic (DNA) and non-genetic (transcriptional, translational, post-translational) alterations in members of signalling networks that are upstream, downstream, or in parallel pathways to the target alteration. Refining the molecular criteria to select patients is a necessary next step, by incorporating co-occurrence of resistance biomarkers as individual parameters or into predictors of treatment benefit. Characterising the molecular bases of resistance to targeted therapies will fuel next-generation combination treatments, maximising the catchment of responders and enhancing the duration of response.

Epithelial-mesenchymal transition (EMT) has been identified as a driver of therapy resistance, particularly in esophageal adenocarcinoma (EAC), where transforming growth factor beta (TGF-β) can induce this process. Inhibitors of TGF-β may counteract the occurrence of mesenchymal, resistant tumor cell populations following chemo(radio)therapy and improve treatment outcomes in EAC. Here, we aimed to identify predictive biomarkers for the response to TGF-β targeting. In vitro approximations of neoadjuvant treatment were applied to publicly available primary EAC cell lines. TGF-β inhibitors fresolimumab and A83–01 were employed to inhibit EMT, and mesenchymal markers were quantified via flow cytometry to assess efficacy. Our results demonstrated a robust induction of mesenchymal cell states following chemoradiation, with TGF-β inhibition leading to variable reductions in mesenchymal markers. The cell lines were clustered into responders and non-responders. Genomic expression profiles were obtained through RNA-seq analysis. Differentially expressed gene (DEG) analysis identified 10 positively- and 23 negatively-associated hub genes, which were bioinformatically identified. Furthermore, the correlation of DEGs with response to TGF-β inhibition was examined using public pharmacogenomic databases, revealing 9 positively associated and 11 negatively associated DEGs. Among these, ERBB2, EFNB1, and TNS4 were the most promising candidates. Our findings reveal a distinct gene expression pattern associated with the response to TGF-β inhibition in chemo(radiated) EAC. The identified DEGs and predictive markers may assist patient selection in clinical studies investigating TGF-β targeting.

Abstract Background The intravesical instillation of the paclitaxel-hyaluronan conjugate ONCOFID-P-B™ in patients with bacillus Calmette-Guérin (BCG)-unresponsive bladder carcinoma in situ (CIS; NCT04798703 phase I study), induced 75 and 40% of complete response (CR) after 12 weeks of intensive phase and 12 months of maintenance phase, respectively. The aim of this study was to provide a detailed description of the tumor microenvironment (TME) of ONCOFID-P-B™-treated BCG-unresponsive bladder CIS patients enrolled in the NCT04798703 phase I study, in order to identify predictive biomarkers of response. Methods The composition and spatial interactions of tumor-infiltrating immune cells and the expression of the most relevant hyaluronic acid (HA) receptors on cancer cells, were analyzed in biopsies from the 20 patients enrolled in the NCT04798703 phase I study collected before starting ONCOFID-P-B™ therapy (baseline), and after the intensive and the maintenance phases. Clinical data were correlated with cell densities, cell distribution and cell interactions. Associations between immune populations or HA receptors expression and outcome were analyzed using univariate Cox regression and log-rank analysis. Results In baseline biopsies, patients achieving CR after the intensive phase had a lower density of intra-tumoral CD8+ cytotoxic T lymphocytes (CTL), but also fewer interactions between CTL and macrophages or T-regulatory cells, as compared to non-responders (NR). NR expressed higher levels of the HA receptors CD44v6, ICAM-1 and RHAMM. The intra-tumoral macrophage density was positively correlated with the expression of the pro-metastatic and aggressive variant CD44v6, and the combined score of intra-tumoral macrophage density and CD44v6 expression had an AUC of 0.85 (95% CI 0.68–1.00) for patient response prediction. Conclusions The clinical response to ONCOFID-P-B™ in bladder CIS likely relies on several components of the TME, and the combined evaluation of intra-tumoral macrophages density and CD44v6 expression is a potentially new predictive biomarker for patient response. Overall, our data allow to advance a potential rationale for combinatorial treatments targeting the immune infiltrate such as immune checkpoint inhibitors, to make bladder CIS more responsive to ONCOFID-P-B™ treatment.

Abstract Background Immune Checkpoint Inhibitors (ICIs) lead to durable response and a significant increase in long-term survival in patients with advanced malignant melanoma (MM) and Non-Small Cell Lung Cancer (NSCLC). The identification of serum cytokines that can predict their activity and efficacy, and their sex interaction, could improve treatment personalization. Methods In this prospective study, we enrolled immunotherapy-naïve patients affected by advanced MM and NSCLC treated with ICIs. The primary endpoint was to dissect the potential sex correlations between serum cytokines (IL-1β, IL-2, IL-4, IL-5, IL-6, IL-8, IL-10, GM-CSF, MCP-1, TNF-ɑ, IP-10, VEGF, sPD-L1) and the objective response rate (ORR). Secondly, we analyzed biomarker changes during treatment related to ORR, disease control rate (DCR), progression free survival (PFS) and overall survival (OS). Blood samples, collected at baseline and during treatment until disease progression (PD) or up to 2 years, were analyzed using Luminex xMAP or ELLA technologies. Results Serum samples from 161 patients (98 males/63 females; 92 MM/69 NSCLC) were analyzed for treatment response. At baseline, IL-6 was significantly lower in females (F) versus males (M); lower levels of IL-4 in F and of IL-6 in both sexes significantly correlated with a better ORR, while higher IL-4 and TNF-ɑ values were predictive of a lower ORR in F versus M. One hundred and sixty-five patients were evaluable for survival analysis: at multiple Cox regression, an increased risk of PD was observed in F with higher baseline values of IL-4, sPD-L1 and IL-10, while higher IL-6 was a negative predictor in males. In males, higher levels of GM-CSF predict a longer survival, whereas higher IL-1β predicts a shorter survival. Regardless of sex, high baseline IL-8 values were associated with an increased risk of both PD and death, and high IL-6 levels only with shorter OS. Conclusions Serum IL-1β, IL-4, IL-6, IL-10, GM-CSF, TNF-ɑ, and sPD-L1 had a significant sex-related predictive impact on ORR, PFS and OS in melanoma and NSCLC patients treated with ICIs. These results will potentially pave the way for new ICI combinations, designed according to baseline and early changes of these cytokines and stratified by sex.

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. [ABSTRACT FROM AUTHOR]

Simple Summary: Immune checkpoint inhibitors (ICI) have the potential to induce serious and unpredictable immune-related adverse events (irAEs), the underlying mechanisms of which remain incompletely understood. In this study, we investigated the relationship between irAEs and the expression of IFN-inducible chemokines and cytokines in patients with solid tumours treated with PD-(L)1 inhibitors. We analysed plasma levels of various IFN-related cytokines at different time points in patients categorized by irAE development and severity. We found that patients with serious irAEs showed significant increases in CXCL9, CXCL10, CXCL11, IL-18 and IL-10 at the onset of the irAE compared to patients with mild irAEs and those without irAEs. Additionally, IL-18 emerged as a promising predictive biomarker for serious irAE development. In summary, this study provides valuable insights into the immune responses associated with irAEs and proposes potential predictive markers for their severity. Immune checkpoint inhibitors (ICI) have the potential to trigger unpredictable immune-related adverse events (irAEs), which can be severe. The underlying mechanisms of these events are not fully understood. As PD-L1 is upregulated by IFN, the heightened immune activation resulting from PD-1/PD-L1 inhibition may enhance the IFN response, triggering the expression of IFN-inducible genes and contributing to irAE development and its severity. In this study, we investigated the interplay between irAEs and the expression of IFN-inducible chemokines and cytokines in 134 consecutive patients with solid tumours treated with PD-(L)1 inhibitors as monotherapy or in combination with chemotherapy or other immunotherapy agents. We compared the plasma levels of IFN-associated cytokines (CXCL9/10/11, IL-18, IL-10, IL-6 and TGFβ) at various time points (at baseline, at the onset of irAE and previous to irAE onset) in three patient groups categorized by irAE development and severity: patients with serious irAEs, mild irAEs and without irAEs after PD-(L)1 inhibitors. No differences were observed between groups at baseline. However, patients with serious irAEs exhibited significant increases in CXCL9/10/11, IL-18 and IL-10 levels at the onset of the irAE compared to baseline. A network analysis and correlation patterns highlighted a robust relationship among these chemokines and cytokines at serious-irAE onset. Combining all of the analysed proteins in a cluster analysis, we identified a subgroup of patients with a higher incidence of serious irAEs affecting different organs or systems. Finally, an ROC analysis and a decision tree model proposed IL-18 levels ≥ 807 pg/mL and TGFβ levels ≤ 114 pg/mL as predictors for serious irAEs in 90% of cases. In conclusion, our study elucidates the dynamic changes in cytokine profiles associated with serious irAE development during treatment with PD-(L)1 inhibitors. The study's findings offer valuable insights into the intricate IFN-induced immune responses associated with irAEs and propose potential predictive markers for their severity. [ABSTRACT FROM AUTHOR]

Simple Summary: This review summarizes the pivotal role of tumor-infiltrating immune cells (TIICs) within the tumor microenvironment (TME) and their impact on cancer prognosis and treatment response. By analyzing TIICs alongside tumor mutation burden (TMB) and immune checkpoint inhibitor (ICI) scores, this study reveals insights into cancer's immune landscapes. Understanding TIICs' influence enables tailored cancer treatments, aiding postoperative care, therapy decisions, and personalized medicine choices. We effectively examined the predictive and prognostic value of TIICs alongside TMB and ICI scores in identifying the diverse immunological environments of cancer. Several approaches discussed in this review provide more accurate predictions of patient outcomes and treatment responses. These models can help identify individuals who may derive greater benefit from adjuvant or neoadjuvant treatment. In summary, we believe that the major contribution of TIICs in cancer will have a substantial positive impact on postoperative follow-up, therapy, interventions, and the ability to make educated decisions regarding personalized cancer treatments. This comprehensive study underscores the significant role of TIICs in combating tumor-mediated immunosuppression and fostering antitumor immune responses, promising improved cancer prognosis and therapeutic outcomes. 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. [ABSTRACT FROM AUTHOR]

Prediction of women at high risk of preeclampsia is important for prevention and increased surveillance of the disease. Current prediction models need improvement, particularly with regard to late-onset preeclampsia. Preeclampsia shares pathophysiological entities with cardiovascular disease; thus, cardiovascular biomarkers may contribute to improving prediction models. In this nested case-control study, we explored the predictive importance of mid-pregnancy cardiovascular biomarkers for subsequent preeclampsia. We included healthy women with singleton pregnancies who had donated blood in mid-pregnancy (~ 18 weeks' gestation). Cases were women with subsequent preeclampsia (n = 296, 10% of whom had early-onset preeclampsia [< 34 weeks]). Controls were women who had healthy pregnancies (n = 333). We collected data on maternal, pregnancy, and infant characteristics from medical records. We used the Olink cardiovascular II panel immunoassay to measure 92 biomarkers in the mid-pregnancy plasma samples. The Boruta algorithm was used to determine the predictive importance of the investigated biomarkers and first-trimester pregnancy characteristics for the development of preeclampsia. The following biomarkers had confirmed associations with early-onset preeclampsia (in descending order of importance): placental growth factor (PlGF), matrix metalloproteinase (MMP-12), lectin-like oxidized LDL receptor 1, carcinoembryonic antigen-related cell adhesion molecule 8, serine protease 27, pro-interleukin-16, and poly (ADP-ribose) polymerase 1. The biomarkers that were associated with late-onset preeclampsia were BNP, MMP-12, alpha-L-iduronidase (IDUA), PlGF, low-affinity immunoglobulin gamma Fc region receptor II-b, and T cell surface glycoprotein. Our results suggest that MMP-12 is a promising novel preeclampsia biomarker. Moreover, BNP and IDUA may be of value in enhancing prediction of late-onset preeclampsia. [ABSTRACT FROM AUTHOR]