Molecular Variables Research Articles

Prevention and treatment of transformation of myeloproliferative neoplasms to acute myeloid leukemia.

Philadelphia-chromosome negative (Ph-neg) myeloproliferative neoplasms (MPNs) are hematopoietic stem disorders with a risk of progression to the accelerated-phase (AP) or blastphase (BP) that is influenced by clinical, pathologic, cytogenetic, and molecular variables. Overall survival is limited in MPN-AP/BP with current treatment approaches, particularly in those patients that cannot receive an allogeneic hematopoietic stem cell transplant (allo-HCT). In addition, long-term survival with allo-HCT is predominantly seen in chronic-phase MPNs which suggests that the ideal time for intervention may be before MPNs evolve to AP/BP. Over the course of this review we will focus on the risk factors for progression to MPN-AP/BP, identification of high-risk chronic-phase MPNs, potential early-intervention strategies, and considerations around the timing of allo-HCT. We will also summarize current survival outcomes in MPN-AP/BP, discuss the uncertainty around how to best gauge response to therapy, and outline clinical trial considerations for this patient population. Lastly, we will highlight future directions in the management of high-risk MPNs.

Meta-Analysis and DIA-MS-Based Proteomic Investigation of COPD Patients and Asymptomatic Smokers in the Indian Population.

Chronic obstructive pulmonary disease (COPD) is India's second largest cause of death and is largely caused by smoking. Asymptomatic smokers develop COPD due to genetic, environmental, and molecular variables, making early screening crucial. Data-independent acquisition mass spectrometry (DIA-MS) based-proteomics offers an unbiased method to analyze proteomic profiles. This study is the first to use DIA-based proteomics to analyze individual serum samples from three distinct male cohorts: healthy individuals (n = 10), asymptomatic smokers (n = 10), and COPD patients (n = 10). This comprehensive approach identified 667 proteins with a 1% false discovery rate. Differentially expressed proteins included 40 in the normal versus asymptomatic comparison, 88 in the COPD versus normal comparison, and 40 in the COPD versus asymptomatic comparison. Among them, protein-associated genes such as PRDX6, ELANE, PRKCSH, PRTN3, and MNDA could help differentiate COPD from asymptomatic smokers, while ELANE, H3-3A, IGHE, SLC4A1, and SERPINA11 could differentiate COPD from healthy subjects. Pathway enrichment and protein-protein interaction analyses revealed significant alterations in hemostasis, immune system functions, fibrin clot formation, and post-translational protein modifications. Key proteins were validated using a parallel reaction monitoring assay. DIA data are available via ProteomeXchange with identifier PXD055242. Our findings reveal key protein classifiers in COPD patients, asymptomatic smokers, and healthy individuals, helping clinicians understand disease pathobiology and improve disease management and quality of life.

Synergism of Computational Simulation Technique and Machine Learning Algorithm for Prediction of Anticorrosion Properties of Some Antipyrine Derivatives.

This study aimed to predict the selected antipyrine compounds' inhibitory efficiencies and anticorrosion properties in a hydrochloric acid (HCl) environment. Molecular descriptors and input variables were obtained using density functional theory (DFT), and the variance inflation factor (VIF) was employed to reduce redundant variables, leading to the selection of seven quantum chemical descriptors as input variables. Using machine learning techniques such as K-nearest neighbor (KNN) and artificial neural network (ANN), a predictive model was built for 39 antipyrine compounds with known corrosion inhibition efficiencies for carbon and low alloy steel in hydrochloric acid solutions. The models' predictive capability was assessed using cross-validation, with the ANN model showing superior performance, achieving a coefficient of determination (R2) value of 0.715 compared to 0.548 for the KNN model. Performance metrics such as the mean square error (MSE), mean absolute error (MAE), and root-mean-square error (RMSE) further confirmed the superiority of the ANN model over the KNN model. The corrosion inhibition efficiencies (CIEs) of the selected antipyrine compounds ranged from 68.78 to 99.79%, with compound A1 demonstrating the highest CIE of 99.79% and compound A3 the lowest, as evaluated by the ANN model. Analysis of Fukui index parameters obtained from the Mulliken population analysis suggested that the nucleophilic and electrophilic sites play a crucial role in the interactions between the inhibitor and the metal atom through electron donor-acceptor interactions. Moreover, the energy of adsorption (Eads) in kcal·mol-1 decreased in the order of A1 (-187.8) > A2 (-132.0) > A2 (-84.4), with the high negative value of Eads indicating strong and spontaneous adsorption. Further analysis using radial distribution functions and molecular dynamics simulations revealed that inhibitor A1 exhibited predominantly chemisorption, inhibitor A2 showed a mixed type, and inhibitor A3 demonstrated predominantly physisorption, aligning well with the results of the predictive studies.

Longitudinal liquid biopsy predicts clinical benefit from immunotherapy in advanced non-small cell lung cancer

High heterogeneity in clinical benefit characterizes the use of immune checkpoint inhibitors (ICIs) in non-small cell lung cancer (NSCLC). We prospectively enrolled 113 advanced NSCLC patients treated with ICIs and performed liquid biopsy at the time of ICI start (T1), after 3 weeks (T2) and at the time of radiological evaluation (T3). Molecular variables were associated with outcome endpoints: cfDNA quantification, its dynamic change (∆T2–T1), variant allele frequency (VAF) of the gene with the highest frequency detected at baseline with NGS (maxVAF) and its dynamic change (∆T2–T1). At multivariate analysis, PD-L1 negativity, T1 cfDNA, cfDNA increase (∆T2–T1), and T2 VAF were significantly associated with shorter progression-free survival (PFS); PD-L1 negativity, squamous histology, T1 cfDNA, cfDNA (∆T2–T1) increase, and T2 maxVAF affected overall survival (OS). Among high PD-L1 expressing patients treated in first-line, elevated T2 maxVAF and cfDNA increase (∆T2–T1) correlated with worse PFS; higher T2 maxVAF and cfDNA increase (∆T2–T1) with worse OS. Derived integrated models were used to build nomograms and categorize different risk groups.

Polymorph sampling with coupling to extended variables: enhanced sampling of polymorph energy landscapes and free energy perturbation of polymorph ensembles.

A novel approach to computationally enhance the sampling of molecular crystal structures is proposed and tested. This method is based on the use of extended variables coupled to a Monte Carlo based crystal polymorph generator. Inspired by the established technique of quasi-random sampling of polymorphs using the rigid molecule constraint, this approach represents molecular clusters as extended variables within a thermal reservoir. Polymorph unit-cell variables are generated using pseudo-random sampling. Within this framework, a harmonic coupling between the extended variables and polymorph configurations is established. The extended variables remain fixed during the inner loop dedicated to polymorph sampling, enforcing a stepwise propagation of the extended variables to maintain system exploration. The final processing step results in a polymorph energy landscape, where the raw structures sampled to create the extended variable trajectory are re-optimized without the thermal coupling term. The foundational principles of this approach are described and its effectiveness using both a Metropolis Monte Carlo type algorithm and modifications that incorporate replica exchange is demonstrated. A comparison is provided with pseudo-random sampling of polymorphs for the molecule coumarin. The choice to test a design of this algorithm as relevant for enhanced sampling of crystal structures was due to the obvious relation between molecular structure variables and corresponding crystal polymorphs as representative of the inherent vapor to crystal transitions that exist in nature. Additionally, it is shown that the trajectories of extended variables can be harnessed to extract fluctuation properties that can lead to valuable insights. A novel thermodynamic variable is introduced: the free energy difference between ensembles of Z' = 1 and Z' = 2 crystal polymorphs.

Molecular characteristics of advanced colorectal cancer and multi-hit PIK3CA mutations.

Approximately 20% of patients living with colorectal cancer (CRC) have activating mutations in their tumors in the PIK3CA oncogene. Two or more activating mutations (multi-hit) for the PIK3CA allele increase PI3K⍺ signaling compared to single-point mutations, resulting in exceptional response to PI3K⍺ inhibition. We aimed to identify the prevalence of PIK3CA multi-hit mutations in metastatic CRC to identify patients who may benefit from PI3K inhibitors. The Foundation Medicine database (Boston, MA, USA) was analyzed for patients with CRC who underwent genomic profiling on tumor DNA isolated during routine clinical care from 2013 to 2021. Molecular and clinical variables were abstracted for patients with PIK3CA mutations. We identified 49051 patients with CRC who underwent Foundation Medicine testing. 710/41154 (1.7%) patients had multi-hit PIK3CA mutations, of which 53% were male (n = 448) with a median age of 60. Microsatellite status was available for 697 patients with multi-hit PIK3CA and 17.6% (123/697) were microsatellite instability-high. Clinically relevant mutations in KRAS and BRAFV600E were seen in 459/710 (64.7%) and 65/710 (9.1%), respectively. The 4 most common PIK3CA variants were H1047R (9.8%), E545K (9.2%), E542K (9.0%), and R88Q (7.1%). The most common variant pair was E542K-E545K (4.7%). Multi-hit mutations in PIK3CA are seen in 1.7% of advanced CRC, a meaningful prevalence given the high burden of CRC worldwide, and may represent a subset of patients that have enhanced sensitivity to PI3K inhibition. Future investigation regarding the clinical utility of PI3K inhibitors is warranted in multi-hit PIK3CA CRC.

Exploring transmission dynamics of the Sarcoptes scabiei mite in humans by combining molecular typing and epidemiological variables, the Netherlands 2016–2023

BackgroundScabies, an infestation of the mite Sarcoptes scabiei, has seen an increase in clinical diagnoses in the Netherlands since 2011. This study aimed to analyse PCR-positive S. scabiei skin samples through partial genome sequencing and to link findings to patient epidemiological characteristics.MethodsSkin samples were collected from individuals in the Netherlands between January 2016 and January 2023. On the PCR-positive S. scabiei skin samples, partial mitochondrial cytochrome c oxidase subunit 1 gene (cox1) sequencing was performed to assess genetic variability. Epidemiological information was collected through interviews. We examined associations between cox1 subtypes, epidemiological factors and treatment outcomes.ResultsSequencing results were obtained from 128 patients, with epidemiological information available for 55 (43%) of these patients. Fifteen distinct cox1 subtypes were identified. Subtype 01 was most prevalent (45%) and present across all age groups and social settings. The remaining subtypes were less common and not consistently found in all contexts. Five clusters were identified, each with identical cox1 subtypes. Comparative analysis with GenBank sequences revealed genetic similarities with strains from Australia, the USA and China, suggesting the global distribution and transmission of specific subtypes. A substantial proportion (73%) of patients with scabies required multiple treatments to eradicate the infestation, with no subtype-related differences.ConclusionsThis is the first study linking S. scabiei sequencing results to patient epidemiological data. Several subtypes clustered in specific geographic regions and social contexts, underscoring localised transmission patterns. Further research with larger sample sizes is needed to enhance our understanding of the transmission of this mite. This study provides valuable insights that will strengthen scabies control efforts.Graphical

Predicting HER2 Status Associated with Breast Cancer Aggressiveness Using Four Machine Learning Models.

Breast cancer (BC) is a heterogeneous disease with various biological and clinical subtypes. HER2 status (human epidermal growth factor receptor 2) is a crucial biomarker, associated with aggressive tumor behavior and poor prognosis. Advanced algorithmic models can aid in predicting cancer growth and metastasis, serving as valuable clinical tools for classification and treatment. Effective treatment strategies in oncology rely on accurate decision-making and early identification of factors associated with positive outcomes. Breast cancer (BC) presents challenges in understanding its contributing factors and establishing precise diagnostic methods. Our research introduces a novel method utilizing machine learning (ML) techniques to explore the relationship between various clinical and molecular variables focusing on predicting the status of the human epidermal growth factor receptor 2 (HER2), a key aggressiveness biomarker in BC. This objective aligns with leveraging artificial intelligence (AI) to support decision-making and address diagnostic considerations during treatment. Four ML models, namely logistic regression, random forest, LightGBM, and CatBoost, were implemented and evaluated using Python. The dataset was compiled by extracting medical records of BC patients, covering the period from 2018 to 2020. The model's predictive performance was evaluated using accuracy, precision, recall, and F1-score as performance metrics. The models achieved varying accuracies between 86.36 and 95.45%. The logistic regression model achieved an accuracy of 90.90% while the random forest and LightGBM models achieved an accuracy of 86.36%. The CatBoost model outperformed others with a greater accuracy of 95.45%, indicating its superior predictive capabilities for HER2 status. The ML models demonstrated potential in predicting HER2 status, enabling early detection and facilitating personalized treatment strategies. Our findings emphasize the significance of AI and ML techniques in improving BC outcomes and guiding decision-making. Further research is required to explore the broader applications of ML in predicting comprehensive BC outcomes in diverse healthcare settings and among heterogeneous populations.

Mild traumatic brain injury gives rise to chronic depression-like behavior and associated alterations in glutamatergic protein expression

Mild traumatic brain injury (mTBI) is known to result in chronic somatic, cognitive, and emotional symptoms. Depression is commonly reported among individuals suffering from persistent concussion symptoms; however, the underlying mechanisms are not understood. The glutamatergic system has recently been linked with mTBI and depression due to reports of similar changes in expression of glutamatergic proteins. Using a closed-head controlled cortical impact (cCCI) model in adult male rats (n = 8/group), this study investigated the emergence of self-care deficits and changes in social interaction behaviors at four, eight and twelve weeks post-injury. Western blotting was used to assess associated changes in expression of glutamate transporters and N-methyl-D-aspartate (NMDA) receptor subunits at twelve weeks. Splash test results revealed deficits in self-care behaviors beginning at eight weeks, which continued through twelve weeks in the injury group. Injured animals also exhibited decreased preference for social novelty at four weeks and loss of desire for social interaction as a whole by twelve weeks. GluN1 was increased in injured animals compared to shams in the frontal cortex and amygdala, while decreased GLT-1 was observed in the hippocampus. Linear regression was performed to evaluate relationships between behavioral and molecular variables; the results suggested that injury affects these relationships in a region-dependent manner. Together, these results suggest that the development of chronic depression-like behavior was associated with changes in glutamatergic protein expression. Deeper investigations into how injury influences glutamatergic synaptic protein expression are needed, as this has the potential to affect circuit-level neurotransmission that drives depression-like behavior following mTBI.

Synthetic Data Generation in Hematology - Paving the Way for OMOP and FHIR Integration.

This study advances the utility of synthetic study data in hematology, particularly for Acute Myeloid Leukemia (AML), by facilitating its integration into healthcare systems and research platforms through standardization into the Observational Medical Outcomes Partnership (OMOP) and Fast Healthcare Interoperability Resources (FHIR) formats. In our previous work, we addressed the need for high-quality patient data and used CTAB-GAN+ and Normalizing Flow (NFlow) to synthesize data from 1606 patients across four multicenter AML clinical trials. We published the generated synthetic cohorts, that accurately replicate the distributions of key demographic, laboratory, molecular, and cytogenetic variables, alongside patient outcomes, demonstrating high fidelity and usability. The conversion to the OMOP format opens avenues for comparative observational multi-center research by enabling seamless combination with related OMOP datasets, thereby broadening the scope of AML research. Similarly, standardization into FHIR facilitates further developments of applications, e.g. via the SMART-on-FHIR platform, offering realistic test data. This effort aims to foster a more collaborative research environment and facilitate the development of innovative tools and applications in AML care and research.

Key Players in the Complex Pathophysiology of Obesity: A Cross-Talk Between the Obesogenic Genes and Unraveling the Metabolic Pathway of Action of Capsaicin and Orange Peel.

Obesity is a widespread prevailing health concern with multifactorial causes. Among the various defined molecular targets associated with obesity, peroxisome proliferator activated receptor gamma, leptin, ghrelin, and adiponectin play crucial roles in fundamental processes including energy balance, adipose tissue biology, and metabolic health, making them particularly significant in the study of obesity.Capsaicin and orange peel exhibit promising anti-obesity properties through their thermogenic, metabolic, and anti-inflammatory effects. Potential pathways for therapeutic approaches in the management of obesity are provided by these targets. The lipid-lowering and anti-obesity benefits of specific plant species have been highlighted in Asian medicine. Due to the potential anti-obesity qualities, capsaicin, which is derived from chilli peppers, and orange peel extract has been focused in this review. Capsaicin causes apoptosis in preadipocytes and adipocytes and suppresses adipogenesis. Citrus fruits area significant source of bioactive substances, primarily flavonoids. Due to their ability to reduce adipocyte development and cellular lipid content, citrus polyphenols are helpful in the control of obesity. This extensive analysis offers insights into new treatment approaches for the prevention and management of obesity and metabolic syndrome by examining the interactions of molecular variables in obesity as well as the possible anti-obesity advantages of capsaicin and orange peel extract.

A New Tissue Engineering Strategy to Promote Tendon-bone Healing: Regulation of Osteogenic and Chondrogenic Differentiation of Tendon-derived Stem Cells.

In the field of sports medicine, repair surgery for anterior cruciate ligament (ACL) and rotator cuff (RC) injuries are remarkably common. Despite the availability of relatively effective treatment modalities, outcomes often fall short of expectations. This comprehensive review aims to thoroughly examine current strategies employed to promote tendon-bone healing and analyze pertinent preclinical and clinical research. Amidst ongoing investigations, tendon-derived stem cells (TDSCs), which have comparatively limited prior exploration, have garnered increasing attention in the context of tendon-bone healing, emerging as a promising cell type for regenerative therapies. This review article delves into the potential of combining TDSCs with tissue engineering methods, with ACL reconstruction as the main focus. It comprehensively reviews relevant research on ACL and RC healing to address the issues of graft healing and bone tunnel integration. To optimize tendon-bone healing outcomes, our emphasis lies in not only reconstructing the original microstructure of the tendon-bone interface but also achieving proper bone tunnel integration, encompassing both cartilage and bone formation. In this endeavor, we thoroughly analyze the transcriptional and molecular regulatory variables governing TDSCs differentiation, incorporating a retrospective analysis utilizing single-cell sequencing, with the aim of unearthing relevant signaling pathways and processes. By presenting a novel strategy rooted in TDSCs-driven osteogenic and chondrogenic differentiation for tendon-bone healing, this study paves the way for potential future research avenues and promising therapeutic applications. It is anticipated that the findings herein will contribute to advancing the field of tendon-bone healing and foster the exploration of TDSCs as a viable option for regenerative therapies in the future.

CASTE DEVELOPMENT IN HYMENOPTERA, A MOLECULAR VIEW FOR FARMING IMPROVEMENT OF STINGLESS BEES

<p><strong><span lang="EN-US">Background</span></strong><span lang="EN-US">: The evolution and genetic elements for the development of the caste is an enigma in Hymenoptera, as well as the total impact that the genetic elements given in the food of the larvae have on the development of bees. During the last decades, micro RNAs (miRNAs) from nurse bee secretions in larval food have been found to constitute an additional element in the regulatory control of caste determination. Furthermore, social differentiation is a complex developmental process influenced by genetic, epigenetic, and environmental factors. <strong>Objective:</strong> To examine and summarize miRNAs as a regulatory component of larval food with an effect on caste development in eusocial hymenoptera. <strong>Methodology. </strong>The sequences of miRNAs expressed in eusocial bees were obtained from miRBase.org. MUSCLE V.3 enabled alignment and correction was carried out using Fast.Dist. The TreeDyn software was used to obtain the phylogenetic trees. <strong>Results:</strong> The role of diet and the relationship with microRNAs in caste differentiation and regulation may be part of a general mechanism that involves selective plant genetic information necessary for insect development. <strong>Implications:</strong> The in vitro cultivation of stingless bees is a practice that is already carried out in some species of commercial importance; However, studies that demonstrate how development, differentiation and regulation of gene expression would be shaping the differentiation of different castes are still gaps that must begin to be addressed. In vitro breeding would require the study of environmental and molecular variables that would modulate the expression of the castes. <strong>Conclusion</strong>: The biogenesis, regulation and functions of microRNA in Eukarya are still obscure, however, some light has been shed on the molecular basis of caste differentiation in eusocial bees modulated by 2 miRN transfer pathways; endogenous and exogenous.</span></p>

Tumor Subtype Classification Tool for HPV-associated Head and Neck Cancers.

Molecular subtypes of HPV-associated Head and Neck Squamous Cell Carcinoma (HNSCC), named IMU (immune strong) and KRT (highly keratinized), are well-recognized and have been shown to have distinct mechanisms of carcinogenesis, clinical outcomes, and potentially differing optimal treatment strategies. Currently, no standardized method exists to subtype a new HPV+ HNSCC tumor. Our paper introduces a machine learning-based classifier and webtool to reliably subtype HPV+ HNSCC tumors using the IMU/KRT paradigm and highlights the importance of subtype in HPV+ HNSCC. To develop a robust, accurate machine learning-based classification tool that standardizes the process of subtyping HPV+ HNSCC, and to investigate the clinical, demographic, and molecular features associated with subtype in a meta-analysis of four patient cohorts. We conducted RNA-seq on 67 HNSCC FFPE blocks from University of Michigan hospital. Combining this with three publicly available datasets, we utilized a total of 229 HPV+ HNSCC RNA-seq samples. All participants were HPV+ according to RNA expression. An ensemble machine learning approach with five algorithms and three different input training gene sets were developed, with final subtype determined by majority vote. Several additional steps were taken to ensure rigor and reproducibility throughout. The classifier was trained and tested using 84 subtype-labeled HPV+ RNA-seq samples from two cohorts: University of Michigan (UM; n=18) and TCGA-HNC (n=66). The classifier robustness was validated with two independent cohorts: 83 samples from the HPV Virome Consortium and 62 additional samples from UM. We revealed 24 of 39 tested clinicodemographic and molecular variables significantly associated with subtype. The classifier achieved 100% accuracy in the test set. Validation on two additional cohorts demonstrated successful separation by known features of the subtypes. Investigating the relationship between subtype and 39 molecular and clinicodemographic variables revealed IMU is associated with epithelial-mesenchymal transition (p=2.25×10-4), various immune cell types, and lower radiation resistance (p=0.0050), while KRT is more highly keratinized (p=2.53×10-8), and more likely female than IMU (p=0.0082). This study provides a reliable classifier for subtyping HPV+ HNSCC tumors as either IMU or KRT based on bulk RNA-seq data, and additionally, improves our understanding of the HPV+ HNSCC subtypes.

Combining Molecular and Traditional Prognostic Factors: A Holistic Approach to Breast Cancer Prognostication.

Breast cancer is a heterogeneous disease with various morphologies and molecular features, and it is the second leading cause of cancer death in women in developed countries. According to the literature, we currently lack both prognostic biomarkers and therapeutic targets. The most important prognostic factors are disease stage and Nottingham grade. We conducted a retrospective analysis involving 273 patients with BC who underwent neoadjuvant therapy before proceeding to curative surgical treatment between 1 January 2014 and 31 December 2023. Pathological procedures were conducted at the Department of Pathology, Emergency County Hospital of Targu Mureș, Romania. A statistical analysis was performed. Regarding the relationship between Nottingham grade and Ki67, grade I was associated with a Ki67 of less than 14. The relationship between tumor grade and luminal was similar (p = 0.0001): Grade I was associated with luminal A. Regarding TNM stage, it was statistically significantly correlated with TILs (p = 0.01) and RCB (p = 0.0001). Stages III and IV were associated with a high RCB and poor prognosis. Regarding the prognostic value, Nottingham grade 3 and TNM stages III and IV were correlated with low overall survival and disease-free survival, with poor prognosis, and, among the molecular variables, RCB played the most important prognostic role.

Clinical and Molecular Variables Associated With Early Progression to Checkpoint Inhibitors in MSI-High Metastatic Colorectal Cancer: A Retrospective Cohort Study

BackgroundAbout one third of patients with deficient mismatch repair/microsatellite instability-high metastatic colorectal cancer (dMMR/MSI-H mCRC) experience primary resistance or early progression on immune checkpoint inhibitors (ICI), while others benefit from exceptionally long-lasting responses. In this single-centre retrospective study, we aimed to identify variables associated with improved overall survival (OS) as well as early disease progression. MethodsAll dMMR/MSI-H mCRC patients treated with ICI between 2014 and 2022 were included. Baseline patient demographics, tumour characteristics as well response and outcome data were recorded. OS was estimated using the Kaplan–Meier method. Uni- and multivariate cox regression analysis was used to identify parameters associated with improved OS. Clinicopathological factors associated with early progression (≤ 12 months after treatment initiation) were assessed using uni- and multivariate logistic regression analysis. ResultsAbout 84 ICI-treated dMMR/MSI-H mCRC patients were included. Progressive disease occurred in 37 (44%) patients, but only in 11 (19%) patients with disease control at 12 months. Median OS was 80 months and improved outcome was associated with a lower neutrophile-to-lymphocyte ratio (NLR) (P = .004) and the presence of immune-related adverse events (irAEs) (P = .015). Early progression was associated with poor performance status (P = .036), a higher blood CRP level (P = .033) and absence of irAEs (P = .002). ConclusionDisease progression in ICI-treated dMMR/MSI-H mCRC rarely occurs in patients experiencing disease control for at least 12 months. Performance status, presence of immune-related adverse events, CRP levels, CEA levels and NLR can be helpful to identify those patients that may benefit from ICI treatment, guiding clinicians in therapeutic decisions.

Predicting Benefit From FOLFOXIRI Plus Bevacizumab in Patients With Metastatic Colorectal Cancer.

Patient outcomes may differ from randomized trial averages. We aimed to predict benefit from FOLFOXIRI versus infusional fluorouracil, leucovorin, and oxaliplatin/fluorouracil, leucovorin, and irinotecan (FOLFOX/FOLFIRI), both plus bevacizumab, in patients with metastatic colorectal cancer (mCRC). A Cox model with prespecified clinical, molecular, and laboratory variables was developed in 639 patients from the TRIBE2 trial for predicting 2-year mortality. Data from the CHARTA (n = 232), TRIBE1 (n = 504), and CAIRO5 (liver-only mCRC, n = 287) trials were used for external validation and heterogeneity of treatment effects (HTE) analysis. This involves categorizing patients into risk groups and assessing treatment effects across these groups. Performance was assessed by the C-index and calibration plots. The C-for-benefit was calculated to assess evidence for HTE. The c-for-benefit is specifically designed for HTE analysis. Like the commonly known c-statistic, it summarizes the discrimination of a model. Values over 0.5 indicate evidence for HTE. In TRIBE2, the overoptimism-corrected C-index was 0.66 (95% CI, 0.63 to 0.69). At external validation, the C-index was 0.69 (95% CI, 0.64 to 0.75), 0.68 (95% CI, 0.64 to 0.72), and 0.65 (95% CI, 0.65 to 0.66), in CHARTA, TRIBE1, and CAIRO5, respectively. Calibration plots indicated slight underestimation of mortality. The c-for-benefit indicated evidence for HTE in CHARTA (0.56, 95% CI, 0.48 to 0.65), but not in TRIBE1 (0.49, 95% CI, 0.44 to 0.55) and CAIRO5 (0.40, 95% CI, 0.32 to 0.48). Although 2-year mortality could be reasonably estimated, the HTE analysis showed that clinically available variables did not reliably identify which patients with mCRC benefit from FOLFOXIRI versus FOLFOX/FOLFIRI, both plus bevacizumab, across the three studies.

LGG-44. CLINICAL AND MOLECULAR TUMOR CHARACTERISTICS OF ‘HIGHER RISK’ PEDIATRIC LOW-GRADE GLIOMAS

Abstract BACKGROUND CNS WHO grade 1 pediatric low-grade gliomas (pLGGs) have an overall excellent survival. A small but relevant proportion of these patients, however, suffer from severe disease burden reflected in recurrent progression, metastasis or even death. To unravel clinicomolecular tumor characteristics that identify these ‘pLGG-higher risk’ patients we retrospectively analyzed clinical and molecular data of 223 patients from our center. METHODS All tumors were classified using histology, DNA methylation profiling and whole transcriptome analysis (RNA sequencing), with whole exome or panel DNA sequencing in a subset of cases, revealing a driver alteration in 100% of cases. Progression of disease was defined as continuous tumor growth upon treatment or need for multiple treatments. The effect of clinical and molecular variables on progression free survival was analyzed with the univariate and multivariate cox proportional hazard model. RESULTS The median follow-up of the entire cohort was 3,4 years. In univariate analyses the risk of progression significantly decreased with age (in years) at diagnosis (HR 0.92,p<0.001). For tumor location using hemispheric tumors as reference, chiasm/suprasellar LGGs showed a significantly higher risk of progression (HR 2.96,p<0.001). Type of molecular driver and sex was not associated with progression. In a multivariable model including age, location and driver, there was no longer a significant effect on progression for age but the effect of chiasm/suprasellar location remained intact. Furthermore, NF1 (HR 0.37,p0.02), and posterior fossa LGG (HR 0.24,p0.03) were both significantly associated with a lower risk of progression. CONCLUSIONS Our multivariate results show that the ‘pLGG-higher risk’ patients were characterized by a tumor in the chiasm/suprasellar location, while molecular driver (at least so far) did not affect risk of progression. Future work will focus on extending the cohort and integrating other multiomic data enabling to compose a prediction model for higher risk pLGG.

HGG-13. INFANT-TYPE HEMISPHERIC GLIOMA (IHG): AN INDIVIDUAL PATIENT DATA META-ANALYSIS

Abstract BACKGROUND Infant-type hemispheric gliomas (IHG) are epigenetically distinct pediatric high-grade gliomas characterized by fusions in receptor tyrosine kinase (RTK) genes. METHODS We performed a methodical literature search, including 30 publications (22 case reports), to identify patients who met the diagnostic criteria of IHG based on the 2021 WHO Classification of CNS Tumors. Individual patient data were obtained from published literature and/or via the authors of the publications. Survival analysis was conducted using the Kaplan-Meier method, and multivariate analysis was performed to investigate the effect of clinical and molecular variables on outcomes. RESULTS Hundred-fifty-five previously reported and one unpublished IHG were identified: 131 (84%) had fusions in RTK genes, of which ALK was most prevalent (62/131), followed by NTRK1/2/3 (30/131), ROS1 (30/131), and MET (9/131). Twenty-five patients, with either no identified RTK fusion (6/156) or not assessable fusion (19/156), had methylation scores ≥ 0.9 for IHG (using the Molecular Neuropathology brain tumor classifier versions ≥ 11b4). Surgery followed by adjuvant chemotherapy in 69% (67/97) was the most common primary treatment used in our cohort. The 3-year EFS and OS were 55% (95%CI: 45-67) and 81 % (95%CI: 73-89). 41 patients with relapsed or progressive tumors received various second-line treatments including surgery, chemotherapy, radiotherapy, and targeted therapy. Based on multivariate analysis, complete resection resulted in better EFS (p = 0.05) and OS (p = 0.009), and the presence and type of RTK gene fusion were not associated with clinical outcomes. CONCLUSION Our results show that despite favorable OS, patients with IHG often show early progression, indicating that the primary optimal treatment for IHG is yet to be established. Our analysis further indicates that achieving a safe, complete resection may play an important role in treating these patients. A comprehensive analysis of the salvage regimen is required to understand their role in OS.

Overall survival prediction and risk stratification in patients with neuroblastoma through machine learning in the large multi-institutional PRIMAGE cohort.

10054 Background: Neuroblastoma (NB) is the most prevalent solid cancer in childhood in which imaging plays a pivotal role at every step of the patient's journey. This investigation aimed to develop a machine learning model using clinical, molecular (MycN amplification), and magnetic resonance (MR) radiomics features at diagnosis to predict patient’s overall survival (OS) and improve their risk stratification. Methods: A database comprising clinical, molecular, and International Neuroblastoma Risk Group (INRG) staging on 513 patients with accessible MR imaging (discovery cohort) was employed for model training, validation, and testing. An additional 22 patients from non-discovery cohort hospitals served as an external validation group. Tumor segmentations of NB were manually and semi-automatically performed on corresponding T2-weighted MR images by an experienced radiologist. In total, 107 radiomics features were extracted and harmonized across MR scan manufacturers and magnetic field strengths using the nested ComBat methodology to correct both batch effects. These radiomics features, combined with clinical and molecular data, were utilized as input for the models. A nested cross-validation approach was employed for model development to determine the optimal preprocessing, machine learning algorithm, and model configuration. Results: The discovery cohort yielded a C-index of 0.788 ± 0.049 in the test partitions, with a random survival forest (RSF) exhibiting the best performance. In the validation cohort, a C-index of 0.934 was achieved. Interpretability analysis identified lesion heterogeneity, size, and molecular variables as crucial factors in OS prediction. The model demonstrated superior predictive performance and patient stratification compared to conventional staging systems in both cohorts. Conclusions: The RSF predictive model exhibited high performance, emphasizing the significant contribution of radiomics features and alignment with established clinical and molecular variables. The model stratified NB patients into low-, intermediate-, and high-risk categories and suggests that radiomics features potentially could improve current risk stratification systems. Additional external validation is warranted as this may present new evidence for enhancing patient care and clinical decision-making for NB patients.