UNASSIGNED: Recent studies have shown that the triglyceride glucose index (TyG) and cystatin C (CysC) are closely related to cardiovascular disease, but there is limited research on the prognosis of patients with acute coronary syndrome (ACS) after percutaneous coronary intervention (PCI). The aim of this study was to explore the predictive value of the combination of the TyG index and CysC in predicting major adverse cardiovascular events (MACEs) in ACS patients who underwent PCI.
UNASSIGNED: This retrospective study included 319 ACS patients who underwent PCI. The clinical endpoint was the occurrence of MACEs, including all-cause mortality, heart failure, non-fatal myocardial infarction, target vessel revascularization, and angina requiring hospitalization. Patients were classified into MACEs (65 cases) and non-MACEs (254 cases) groups. Univariate factor and multivariate analysis were used to identify predictors of MACEs. The receiver operating curve (ROC) of the prediction model of MACEs was determined. Additionally, the net reclassification improvement and integrated discrimination improvement indexes were calculated to further assess the additional predictive value of the risk factors for MACEs. Subgroup and interaction analysis between the TyG index combined with CysC and MACEs were conducted in various subgroups. Patients were stratified according to the optimal cutoff point value of the TyG index and the CysC determined by ROC curve analysis. The Kaplan-Meier analysis method was used to construct a survival curve 1 year after PCI.
UNASSIGNED: During a median follow-up period of 14 months, 65 (20.38%) patients had experienced at least one primary endpoint event. Multivariate logistic regression analysis indicated that the TyG index and CysC were independently associated with an increased risk of MACEs after PCI (OR, 2.513, 95% CI 1.451-4.351, P= 0.001; and OR, 4.741, 95% CI 1.344-16.731, P=0.016, respectively). The addition of the TyG index and CysC to the baseline risk model had the strongest incremental effect for predicting MACEs in terms of the C-statistic from 0.789 (95% CI 0.723-0.855, P<0.001) to 0.799 (95% CI 0.733-0.865, P<0.001). Furthermore, Kaplan-Meier analysis demonstrated that a TyG index greater than 9.325 and a CysC value greater than 1.065 mg/ml were significantly associated with an increased risk of MACEs (log-rank, all P < 0.01).
UNASSIGNED: The TyG index predicts MACEs after PCI in patients with ASC independent of known cardiovascular risk factors. Adjustment of the CysC by the TyG index further improves the predictive ability for MACEs in patients with ACS undergoing PCI. Thus, both of them are expected to become new prognostic indicators for MACEs in patients with ACS after PCI.

UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: A series of questions articulating essential criteria for extrapolation.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.

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.

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.

BACKGROUND: Immunotherapy has improved survival rates in cancer patients, but identifying those who will respond to treatment remains a challenge. Recent advances in proteomic technologies have enabled the identification and quantification of nearly all expressed proteins in a single experiment. Integration of mass spectrometry with other high-throughput technologies has paved the way for comprehensive and systematic analysis of the plasma proteome in cancer, facilitating early diagnosis and personalized treatment. In this context, the objective of our study was to investigate the predictive and prognostic value of plasma proteome analysis using the SWATH-MS (Sequential Window Acquisition of All Theoretical Mass Spectra) strategy in newly diagnosed NSCLC patients who received pembrolizumab therapy.
METHODS: For this purpose, 64 newly diagnosed advanced NSCLC patients treated with pembrolizumab therapy were enrolled and blood samples were collected from all patients before and during therapy. In total 171 blood samples were collected, and plasma samples were analysed employing SWATH-MS strategy. Next, we compared the plasma protein expression of metastatic NSCLC patients prior to receiving pembrolizumab treatment and divided the cohort into two groups in order to identify a proteomic signature that allow us to predict immunotherapy response.
RESULTS: Proteomic analyses by SWATH-MS strategy allow us to identified 324 differentially expressed proteins between responder and non-responder patients. In addition, we developed a predictive model and found a combination of seven proteins, including ATG9A, DCDC2, HPS5, FIL1L, LZTL1, PGTA, and SPTN2, with stronger predictive value than PD-L1 expression alone. Additionally, survival analyses showed that low levels of ATG9A, DCDC2, and HPS5 were associated with longer progression-free survival (PFS) and overall survival (OS), while low levels of SPTN2 were associated with worse OS.
CONCLUSIONS: Our work highlights the potential value of proteomic technologies to detect predictive biomarkers in blood samples of NSCLC patients. These analyses shed light on the correlation between the response to immunotherapy in patients with NSCLC and the set of 7 proteins.

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.

This study aimed to explore advances in biomarkers related to anti-angiogenic therapy in patients with non-small cell lung cancer (NSCLC), thereby enhancing treatment selection, advancing personalized and precision medicine to improve treatment outcomes and patient survival rates. This article reviews key discoveries in predictive biomarkers for anti-angiogenic therapy in NSCLC in recent years, such as (1) liquid biopsy predictive biomarkers: studies have identified activated circulating endothelial cells (aCECs) via liquid biopsy as potential predictive biomarkers for the efficacy of anti-angiogenic therapy; (2) imaging biomarkers: advanced imaging technologies, such as dynamic contrast-enhanced integrated magnetic resonance positron emission tomography (MR-PET), are used to assess tumor angiogenesis in patients with NSCLC and evaluate the clinical efficacy of anti-angiogenic drugs; (3) genetic predictive biomarkers: research has explored polymorphisms of Vascular Endothelial Growth Factor Receptor-1 (VEGFR-1) and vascular endothelial growth factor-A (VEGF-A), as well as how plasma levels of VEGF-A can predict the outcomes and prognosis of patients with non-squamous NSCLC undergoing chemotherapy combined with bevacizumab. Despite progress in identifying biomarkers related to anti-angiogenic therapy, several challenges remain, including limitations in clinical trials, heterogeneity in NSCLC, and technical hurdles. Future research will require extensive clinical validation and in-depth mechanistic studies to fully exploit the potential of these biomarkers for personalized treatment.

Small biopsies of lung are routinely obtained by many methods, including several that result in cytologic specimens. Because lung cancer is often diagnosed at a stage for which primary resection is not an option, it is critical that all diagnostic, predictive, and prognostic information be derived from such small biopsy specimens. As the number of available diagnostic and predictive markers expands, cytopathologists must familiarize themselves with current requirements for specimen acquisition, handling, results reporting, and molecular and other ancillary testing, all of which are reviewed here.

Over the last decade, cancer diagnostics has undergone a notable transformation with increasing complexity. Minimally invasive diagnostic tests, driven by advanced imaging and early detection protocols, are redefining patient care and reducing the need for more invasive procedures. Modern cytopathologists now safeguard patient samples for vital biomarker and molecular testing. In this article, we explore ancillary testing modalities and the role of biomarkers in organ-specific contexts, underscoring the transformative impact of precision medicine. Finally, the advent of more than 80 Food and Drug Administration-approved predictive biomarkers signals a new era, guiding cancer care toward personalized and targeted strategies.

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.