Personalized Treatment Strategies Research Articles

Recommendations for Research to Develop a Patient‐Centered Clinical Follow‐Up Protocol for Oral Epithelial Dysplasia

ABSTRACTOral epithelial dysplasia (OED) is the primary histological marker for assessing the progression of oral potentially malignant disorders (OPMDs) to cancer. Despite challenges in grading and low inter‐pathologist reproducibility, OED severity remains the key predictor of malignant transformation. However, globally accepted guidelines for OED monitoring are lacking, despite calls for individualized management based on host and lesion characteristics. The proposed research protocol involves acquiring high‐quality intraoral images, assessing oral hygiene and periodontal status, eliminating chronic mechanical irritation and Candida infections, and applying adjunctive diagnostic methods like toluidine blue staining, optical evaluation, and brush cytology. Tailored follow‐up regimens based on individual risk assessments are emphasized, with frequent monitoring for high‐grade dysplasia or patients at higher risk of progression. Therefore, effective OED management should consider the patient's immune status, dietary habits, and oral microbiota, aiming to develop personalized treatment strategies that optimize patient‐centered care.

Isolation and identification of patient-derived liver cancer stem cells and development of personalized treatment strategies

Isolation and identification of patient-derived liver cancer stem cells and development of personalized treatment strategies

The impact of diet on the inflammatory bowel disease course – a literature review

Introduction and Objective. Inflammatory bowel disease (IBD), including ulcerative colitis (UC) and Crohn’s disease (CD), is a chronic gastrointestinal disorder marked by relapsing symptoms. Current treatments mainly involve immunosuppressive therapies, which are effective but may have significant side effects. Diet as an adjunctive treatment is appealing due to its accessibility, low cost, and minimal adverse effects. This review explores the role of diet in managing IBD, focusing on dietary strategies during both active disease and remission. Methods. A literature review was conducted using PubMed and Google Scholar with search terms like "inflammatory bowel disease", "Crohn’s disease", "ulcerative colitis", "diet", "IBD treatment", "anti-inflammatory food" and related variations. Articles published within the last five years were prioritized. Brief description of the state of Knowledge. Ulcerative colitis and Crohn’s disease cause gastrointestinal inflammation. Crohn’s disease symptoms include abdominal pain, fistulas and rectal lesions, while UC is characterized by diarrhea, severe discomfort, and blood in the stool. Certain foods often exacerbate IBD symptoms. Proper nutrition during disease stages can improve patients’ quality of life. Diets high in processed foods, sugar, and red meat may increase IBD risk, while fiber and vitamin D3 are associated with fewer flare-ups. Regular malnutrition screening is essential for all IBD patients. Conclusions. Understanding the dietary components that influence inflammation in IBD could lead to better treatment outcomes and longer-lasting symptom relief. Integrating nutrition into personalized treatment strategies should become standard practice for managing IBD.

A delphi-based model for prognosis of femoral head collapse in osteonecrosis: a multi-factorial approach.

Osteonecrosis of the femoral head (ONFH) is a progressive and debilitating condition characterized by the death of bone tissue due to inadequate blood supply. Despite advances in diagnostic imaging and treatment strategies, predicting the risk of femoral head collapse remains a significant clinical challenge. This study seeks to address this gap by developing a robust prognostic model that integrates clinical, imaging, and laboratory data to improve early diagnosis and guide therapeutic decision-making. We conducted a qualitative systematic review and employed the Delphi method to select key prognostic factors from clinical data, imaging findings, and laboratory indicators. The study included ONFH patients treated from January 2014 to December 2021. We used univariate and multivariate Cox regression analyses to develop a nomogram for predicting the risk of femoral head collapse. The model's performance was evaluated using the concordance index (C-index), calibration plots, and decision curve analysis (DCA). The study included 297 patients (454 hips) with ONFH. Key prognostic factors identified included pain presence (p < 0.001, RR = 0.185, 95% CI: 0.11-0.31), JIC classification (C1: p < 0.001, RR = 0.096, 95% CI: 0.054-0.171; C2: p < 0.001, RR = 0.323, 95% CI: 0.215-0.487), necrotic area (3 < MNAI < 6: p < 0.001, RR = 0.107, 95% CI: 0.061-0.190; MNAI ≥ 6: p < 0.001, RR = 0.466, 95% CI: 0.314-0.692), weight-bearing reduction (p < 0.001, RR = 0.466, 95% CI: 0.323-0.672), preservation of the anterolateral pillar (p < 0.001, RR = 0.223, 95% CI: 0.223-0.473), and subchondral bone fracture on CT (p < 0.001, RR = 0.32, 95% CI: 0.217-0.472). The nomogram demonstrated a high C-index of 0.88, indicating excellent predictive accuracy. Calibration plots showed good agreement between predicted and observed outcomes, and DCA confirmed the model's clinical utility. The prognostic model developed in this study provides a reliable tool for predicting femoral head collapse in ONFH patients. It allows for early identification of high-risk patients, guiding personalized treatment strategies to improve patient outcomes and reduce the need for invasive surgical procedures.

HLA-G high-expressor 3'UTR markers are linked to gastric cancer development and survival.

Gastric cancer ranks fifth in both world prevalence and lethality, with a 5-year survival of less than 30%. HLA-G, a non-classical class I HLA gene, has emerged as a potential marker for cancer susceptibility and prognosis due to its immunomodulatory properties. Its level of expression is regulated by polymorphisms in the 3' untranslated region (3'UTR) polymorphisms, which form various combined haplotypes (UTR-1 to -9). In this study, we examined HLA-G 3'UTR polymorphisms in paired tissue samples from 111 patients with gastric adenocarcinoma and 119 healthy controls. Polymorphism analysis was performed using PCR and Sanger sequencing, followed by statistical analysis using SNPStats software. Survival analysis was conducted using Kaplan-Meier curves and multivariate Cox regression models. High-expressor HLA-G 3'UTR haplotypes (UTR-1 and UTR-6) were significantly associated with gastric cancer susceptibility, indicating a potential role in tumor immune evasion. Additionally, the 14 base pair insertion/deletion polymorphism (14bp I/D) emerged as a prognostic marker, with D/D genotype carriers showing lower survival rates compared to I/D and I/I genotype carriers. Our study highlights the clinical relevance of HLA-G polymorphisms in gastric cancer, suggesting their potential as prognostic markers and therapeutic targets. Further elucidation of HLA-G-related pathways could lead to personalized treatment strategies and improved patient outcomes in gastric cancer.

Functional Outcome Prediction of Acute Ischemic Stroke Based on the Oral and Gut Microbiota.

Although several studies have identified a distinct gut microbiota in individuals with acute ischemic stroke (AIS), there is a limited amount of research that has simultaneously investigated alterations in the oral and intestinal microbiota in AIS patients and their correlation with clinical prognosis. This was a prospective and observational single-center cohort study in which we included 160 AIS patients who were admitted within 24h after a stroke event. We collected oral and rectal swab samples for analysis using 16S rRNA high-throughput sequencing. Our study revealed that patients with unfavorable outcomes after AIS showed early disruptions in their oral and intestinal microbiota. Rectal swabs showed increased levels of facultatively anaerobic bacteria in patients with a poor prognosis, while the oral cavity exhibited higher levels of anaerobic and opportunistic pathogenic bacteria. By employing machine learning analysis, we found that the microbiota composition at both rectal and oral sites could predict early and long-term outcomes. Moreover, patients with a poor prognosis displayed increased oral bacterial colonization in the rectal microbiota and altered interactions between the oral and gut microbiota. This study reveals distinct rectal and oral bacteria that could predict unfavorable outcomes for AIS patients. Monitoring the microbiota of various body sites during the early stages after admission may hold prognostic value and inform personalized treatment strategies. The presence of oral bacteria colonizing the intestines during the acute phase of stroke could serve as an early indication of poor outcomes for AIS patients.

Subtyping colorectal cancer based on septic shock-associated genes: prognosis and immune characteristics

BackgroundSepsis and colorectal cancer (CRC) are leading causes of death. Given their mutual dependence for susceptibility, we used bioinformatics to explore potential connections between septic shock (SS) and CRC.MethodsWe identified 452 co-expressed genes between SS-related differential expression genes (SS-DEGs) and CRC patient-expressed genes (TCGA-CRC genes). CRC samples were categorized into two cluster subgroups through hierarchical clustering. We then compared the prognosis and immune landscapes of the two cluster subgroups through survival analysis, immune microenvironment analysis, and immune therapy response evaluation.ResultsClustering analysis of the 452 CRC patient-expressed SS-DEGs identified two subtypes: SS-like CRC (SL-CRC) and non-SS-like CRC (NSL-CRC). There were no significant differences in overall survival between the CRC subtypes. However, the subtypes displayed significant differences in immune score, stromal score, and ESTIMATE score. Based on immune therapy databases, there were also significant differences in responses to anti-CTLA-4 and anti-PD-1 immune checkpoint inhibitors between the subtypes.ConclusionOur study reveals significant differences in the immune microenvironment and immune therapy responses between SL-CRC and NSL-CRC subtypes. These findings provide a foundation for identifying new therapeutic targets and developing personalized treatment strategies for specific CRC subtypes.

Investigating the diagnostic and prognostic significance of genes related to fatty acid metabolism in hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is one of the most prevalent and lethal cancers worldwide, with death rates increasing by approximately 2-3% per year. The high mortality and poor prognosis of HCC are primarily due to inaccurate early diagnosis and lack of monitoring when liver transplantation is not feasible. Fatty acid (FA) metabolism is a critical metabolic pathway that provides energy and signaling factors in cancer, particularly in HCC, and promotes malignancy. Therefore, it is essential to explore specific FA metabolism-related diagnostic and prognostic signatures that can enable the effective early diagnosis and monitoring of HCC. In this study, we used genes associated with FA metabolism pathway and weighted gene co-expression network analysis (WGCNA) to establish a gene co-expression network and identify hub genes related to HCC (disease WGCNA) and FA clusters (cluster WGCNA). A diagnostic model was constructed using data downloaded from the Gene Expression Omnibus database (GSE25097), and a prognostic model was established using The Cancer Genome Atlas cohort, in which Univariate Cox regression analysis, multivariate Cox risk model, and LASSO Cox regression analysis were applied. The immune infiltration of HCC cells was evaluated using CIBERSORT. The function of the key SLC22A1 gene was experimentally verified in vitro and in vivo. Twelve overlapping genes (CPEB3, ASPDH, DEPDC7, ETFDH, UGT2B7, GYS2, F11, ANXA10, CYP2C8, GLYATL1, C6, and SLC22A1) from disease and cluster WGCNA were identified as key genes and used in the construction of the diagnostic and prognostic models. The RF model had the highest area under the ROC curve (AUC) of 0.994 was identified as the most effective for distinguishing patients with HCC with different features. The top five important genes (C6, UGT2B7, SLC22A1, F11, and CYP2C8) from the RF model were selected as diagnostic genes for further analysis (ROC curves: AUC value = 0.986, 95% confidence interval [95% CI] = 0.967-0.999). Moreover, a risk score formula consisting of four genes (GYS2, F11, ANXA10 and SLC22A1) was established and its independent prognostic ability was further demonstrated (univariate Cox regression analysis: hazard ratio [HR] = 3.664%, 95% CI = 2.033-6.605, P < 0.001; multivariate Cox regression analysis: HR = 2.801%, 95% CI = 1.553-5.049, P < 0.001). Additionally, in vitro and in vivo experiments demonstrated that SLC22A1 inhibits HCC tumor development, suggesting it may be a potential therapeutic target for HCC. These findings indicate a considerable value of specific FA metabolism-related genes in the diagnostic and prognostic evaluation of HCC, which provide novel insights into the disease's management, as well as has potential implications for personalized treatment strategies. However, further investigation of the effects of these model genes on HCC is required.

Analysis of immune status and prognostic model incorporating lactic acid metabolism-associated genes.

Cancer development is intricately linked with metabolic dysregulation, including lactic acid metabolism, which plays a pivotal role in tumor progression and immune evasion. However, its specific implications in gastric adenocarcinoma (STAD) remain unclear. This study introduces a novel methodology to evaluate lactic acid metabolism comprehensively in STAD, aiming to elucidate its prognostic significance and impact on immunotherapy efficacy. Targeted therapies directed at key lactic acid metabolism genes (LMGs) identified within the tumor microenvironment (TME) hold promise for personalized treatment strategies. Lactic acid metabolism patterns were assessed in 415 STAD patients using a panel of 21 LMGs. Cox regression and Lasso regression analyses were employed to develop a predictive risk model based on differentially expressed genes (DEGs). Validation of the model was conducted using independent cohorts from the GEO and TCGA databases, as well as additional datasets focused on immunotherapy responses. Further investigations into TME dynamics of lactic acid metabolism included functional assays targeting SLC16A3, a pivotal gene identified through our analyses. Patients were stratified into distinct risk groups based on their lactic acid metabolism profiles. Low-risk patients exhibited attenuated lactic acid metabolism, correlating with favorable clinical outcomes characterized by prolonged survival and enhanced responsiveness to immunotherapy. Notably, tumor cells within the TME demonstrated heightened levels of active lactic acid metabolism, particularly impacting tumor-infiltrating lymphocytes such as CD8 + T cells and regulatory T cells. Mechanistically, SLC16A3 emerged as a critical regulator promoting STAD cell proliferation, invasion, and migration while modulating the metabolic landscape. This study underscores the prognostic value of a lactic acid metabolism-based model in STAD, providing insights into its potential as a predictive biomarker for patient stratification and therapeutic targeting. The findings highlight SLC16A3 as a promising candidate for therapeutic intervention aimed at modulating lactic acid metabolism in the TME, thereby advancing personalized treatment strategies in gastric cancer management.

Clinical Subtype Trajectories in Sepsis Patients Admitted to the ICU: A Secondary Analysis of an Observational Study

OBJECTIVES: Sepsis is an evolving process and proposed subtypes may change over time. We hypothesized that previously established sepsis subtypes are dynamic, prognostic of outcome, and trajectories are associated with host response alterations. DESIGN: A secondary analysis of two observational critically ill sepsis cohorts: the Molecular diAgnosis and Risk stratification of Sepsis (MARS) and the Medical Information Mart for Intensive Care-IV (MIMIC-IV). SETTING: ICUs in the Netherlands and United States between 2011–2014 and 2008–2019, respectively. PARTICIPANTS: Patient admission fulfilling the Sepsis-3 criteria upon ICU admission adjudicated to one of four previously identified subtypes, comprising 2,416 admissions in MARS and 10,745 in MIMIC-IV. MAIN OUTCOMES AND MEASURES: Subtype stability and the changes per subtype on days 2, 4 and 7 of ICU admission were assessed. Next, the associated between change in clinical subtype and outcome and host response alterations. RESULTS: In MARS, upon ICU admission, 6% (n = 150) of the patient admissions were α-type, 3% (n = 70) β-type, 55% (n = 1317) γ-type, and 36% (n = 879) δ-type; in MIMIC-IV, this was α = 22% (n = 2398), β = 22% (n = 2365), γ = 31% (n = 3296), and δ = 25% (2686). Overall, prevalence of subtypes was stable over days 2, 4, and 7. However, 28–56% (MARS/MIMIC-IV) changed from α on ICU admission to any of the other subtypes on day 2, 33–71% from β, 57–32% from γ, and 50–48% from δ. On day 4, overall subtype persistence was 33–36%. γ or δ admissions remaining in, or transitioning to, subtype γ on days 2, 4, and 7 exhibited lower mortality rates compared with those remaining in, or transitioning to, subtype δ. Longitudinal host response biomarkers reflecting inflammation, coagulation, and endothelial dysfunction were most altered in the δ-δ group, followed by the γ-δ group, independent of the day or biomarker domain. CONCLUSIONS AND RELEVANCE: In two large cohorts, subtype change to δ was associated with worse clinical outcome and more aberrant biomarkers reflecting inflammation, coagulation, and endothelial dysfunction. These findings underscore the importance of monitoring sepsis subtypes and their linked host responses for improved prognostication and personalized treatment strategies.

Improved prognostication of overall survival after radiotherapy in lung cancer patients by an interpretable machine learning model integrating lung and tumor radiomics and clinical parameters.

Accurate prognostication of overall survival (OS) for non-small cell lung cancer (NSCLC) patients receiving definitive radiotherapy (RT) is crucial for developing personalized treatment strategies. This study aims to construct an interpretable prognostic model that combines radiomic features extracted from normal lung and from primary tumor with clinical parameters. Our model aimed to clarify the complex, nonlinear interactions between these variables and enhance prognostic accuracy. We included 661 stage III NSCLC patients from three multi-national datasets: a training set (N = 349), test-set-1 (N = 229), and test-set-2 (N = 83), all undergoing definitive RT. A total of 104 distinct radiomic features were separately extracted from the regions of interest in the lung and the tumor. We developed four predictive models using eXtreme gradient boosting and selected the top 10 features based on the Shapley additive explanations (SHAP) values. These models were the tumor radiomic model (Model-T), lung radiomic model (Model-L), a combined radiomic model (Model-LT), and an integrated model incorporating clinical parameters (Model-LTC). Model performance was evaluated through Harrell's concordance index, Kaplan-Meier survival curves, time-dependent area under the receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis. Interpretability was assessed using the SHAP framework. Model-LTC exhibited superior performance, with notable predictive accuracy (C-index: training set, 0.87; test-set-2, 0.76) and time-dependent AUC above 0.75. Complex nonlinear relationships and interactions were evident among the model's variables. The integration of radiomic and clinical factors within an interpretable framework significantly improved OS prediction. The SHAP analysis provided insightful interpretability, enhancing the model's clinical applicability and potential for aiding personalized treatment decisions.

Machine learning-based prediction of one-year mortality in ischemic stroke patients

Abstract Background Accurate prediction of mortality following an ischemic stroke is essential for tailoring personalized treatment strategies. This study evaluates the effectiveness of machine learning models in predicting one-year mortality after an ischemic stroke. Methods Five machine learning models were trained using data from a national stroke registry, with logistic regression demonstrating the highest performance. The SHapley Additive exPlanations (SHAP) analysis explained the model’s outcomes and defined the influential predictive factors. Results Analyzing 8183 ischemic stroke patients, logistic regression achieved 83% accuracy, 0.89 AUC, and an F1 score of 0.83. Significant predictors included stroke severity, pre-stroke functional status, age, hospital-acquired pneumonia, ischemic stroke subtype, tobacco use, and co-existing diabetes mellitus (DM). Discussion The model highlights the importance of predicting mortality in enhancing personalized stroke care. Apart from pneumonia, all predictors can serve the early prediction of mortality risk which supports the initiation of early preventive measures and in setting realistic expectations of disease outcomes for all stakeholders. The identified tobacco paradox warrants further investigation. Conclusion This study offers a promising tool for early prediction of stroke mortality and for advancing personalized stroke care. It emphasizes the need for prospective studies to validate these findings in diverse clinical settings.

Experimental study of early evaluation of radiosensitivity in mouse models of lung cancers using 89Zr-anti-γH2AX-TAT PET imaging.

Early evaluation of radiation sensitivity in lung cancer patients can facilitate the transition to personalized treatment strategies. To this end, we assessed the capability of 89Zr-anti-γH2AX-TAT microPET imaging in determining the radiosensitivity of lung cancer xenograft models. We prepared and conducted quality control on 89Zr-anti-γH2AX-TAT. The radiosensitivity of human non-small cell lung cancer cells (H460) and adenocarcinoma cells (A549) was analyzed through clonogenic survival experiments. Additionally, the role of γH2AX as a biomarker for radiosensitivity was validated by quantifying γH2AX foci via fluorescence staining. Subsequently, the H460 and A549 xenograft mouse models were subjected to irradiation, followed by 89Zr-anti-γH2AX-TAT microPET imaging. Concurrently, we performed immunofluorescence staining for γH2AX in tumor tissues to establish a correlation between the uptake of 89Zr-anti-γH2AX-TAT and γH2AX expression. The surviving fraction 2Gy (SF2) values of H460 and A549 indicating that A549 adenocarcinoma has higher radiosensitivity. The cell immunofluorescence experiment showed that the repair of γH2AX foci in H460 cells after irradiation was significantly higher than that in A549 cells, which also confirmed that A549 has higher radiosensitivity. The microPET imaging results showed the uptake of 89Zr-anti-γH2AX-TAT in the tumor of the A549 models after radiotherapy was higher than H460 models. The immunofluorescence staining of tumor tissue confirmed that the expression level of γH2AX was higher and the correlation with microPET imaging uptake was good. 89Zr-anti-γH2AX-TAT allows PET imaging of radiosensitivity in lung cancer xenograft models, and is expected to become an early evaluation method for lung cancer radiosensitivity.

Pancreatic Neuroendocrine Neoplasms: Classification and Novel Role of Endoscopic Ultrasound in Diagnosis and Treatment Personalization.

The incidence and prevalence of pancreatic neuroendocrine neoplasms are steadily increasing. These tumors are highly heterogeneous, with treatment options ranging from observation to surgery, and various medical therapies. The choice of treatment is influenced by factors such as tumor stage, grade (proliferative activity), and the presence of hormone-related syndromes. Endoscopic ultrasound (EUS) is becoming increasingly valuable for assessing pancreatic neuroendocrine neoplasms, offering detailed morphological, vascular, and functional information through techniques such as contrast enhancement and elastography. It also allows biopsies that are useful for both histopathological and molecular analyses. These tumors are highly heterogeneous, with treatment options ranging from observation to various medical therapies and surgery. Recent data suggest that small, non-functioning PanNENs with low proliferation rates may be safely monitored, whereas more aggressive or functioning tumors typically require surgery. EUS-guided ablation is a promising alternative for patients with functional pancreatic neuroendocrine neoplasms who are unsuitable for surgery, although randomized trials are needed. In non-resectable pancreatic neuroendocrine neoplasms, treatment options include somatostatin analogs, targeted therapies (e.g., everolimus, sunitinib), chemotherapy, and radioligand therapy. This review discusses key factors in planning personalized treatment strategies for pancreatic neuroendocrine neoplasms.

Factors Inducing Cutaneous Adverse Reactions in Cancer Patients Treated with PD-1 and PD-L1 Inhibitors: A Machine-Learning Algorithm Approach

Immune checkpoint inhibitors (ICIs) show promise in cancer treatment but can lead to immune-related adverse events (irAEs), notably affecting the skin. Understanding the factors behind these skin reactions is crucial for effective management during treatment. Hence, the aim of this study was to uncover associations between patient characteristics and cutaneous adverse reactions among cancer patients undergoing ICI treatment. The study involved 209 cancer patients receiving ICIs. Statistical methods, including the chi-square test, Fisher's exact test, and multivariable logistic regression, were employed to analyze variables such as hypertension, antihistamine use, cancer metastasis, diabetes, and opioid usage. Additionally, machine learning techniques, including logistic regression, elastic net, random forest, and support vector machines (SVM), were utilized to develop predictive models anticipating skin-related adverse events. Results highlighted significant associations between specific patient attributes and the incidence of skin reactions post-ICI treatment. Notably, patients using antihistamines or with cancer metastasis exhibited higher rates of skin adverse reactions, while those with diabetes or using opioids displayed lower incidence rates. Robust performance in forecasting these adverse events was observed, particularly in the predictive models employing logistic regression and elastic net. This pioneering study contributes crucial insights into predictive modeling for ICI-induced skin reactions, emphasizing the importance of personalized treatment strategies. By identifying risk factors and utilizing tailored predictive models, healthcare providers can proactively manage adverse events, optimizing the benefits of ICIs while mitigating potential side effects.

Decoding Acute Myeloid Leukemia: A Clinician’s Guide to Functional Profiling

Acute myeloid leukemia (AML) is a complex clonal disorder characterized by clinical, genetic, metabolomic, and epigenetic heterogeneity resulting in the uncontrolled proliferation of aberrant blood-forming precursor cells. Despite advancements in the understanding of the genetic, metabolic, and epigenetic landscape of AML, it remains a significant therapeutic challenge. Functional profiling techniques, such as BH3 profiling (BP), gene expression profiling (GEP), proteomics, metabolomics, drug sensitivity/resistance testing (DSRT), CRISPR/Cas9, and RNAi screens offer valuable insights into the functional behavior of leukemia cells. BP evaluates the mitochondrial response to pro-apoptotic BH3 peptides, determining a cell’s apoptotic threshold and its reliance on specific anti-apoptotic proteins. This knowledge can pinpoint vulnerabilities in the mitochondria-mediated apoptotic pathway in leukemia cells, potentially informing treatment strategies and predicting therapeutic responses. GEP, particularly RNA sequencing, evaluates the transcriptomic landscape and identifies gene expression alterations specific to AML subtypes. Proteomics and metabolomics, utilizing mass spectrometry and nuclear magnetic resonance (NMR), provide a detailed view of the active proteins and metabolic pathways in leukemia cells. DSRT involves exposing leukemia cells to a panel of chemotherapeutic and targeted agents to assess their sensitivity or resistance profiles and potentially guide personalized treatment strategies. CRISPR/Cas9 and RNAi screens enable systematic disruption of genes to ascertain their roles in leukemia cell survival and proliferation. These techniques facilitate precise disease subtyping, uncover novel biomarkers and therapeutic targets, and provide a deeper understanding of drug-resistance mechanisms. Recent studies utilizing functional profiling have identified specific mutations and gene signatures associated with aggressive AML subtypes, aberrant signaling pathways, and potential opportunities for drug repurposing. The integration of multi-omics approaches, advances in single-cell sequencing, and artificial intelligence is expected to refine the precision of functional profiling and ultimately improve patient outcomes in AML. This review highlights the diverse landscape of functional profiling methods and emphasizes their respective advantages and limitations. It highlights select successes in how these methods have further advanced our understanding of AML biology, identifies druggable targets that have improved outcomes, delineates challenges associated with these techniques, and provides a prospective view of the future where these techniques are likely to be increasingly incorporated into the routine care of patients with AML.

Racial/ethnic differences in survival and treatment response with PD-1/PD-L1 inhibitors in resectable non-small cell lung cancer: a meta-analysis of randomized controlled trials.

The optimal treatment for resectable Non-Small Cell Lung Cancer (NSCLC) remains under investigation, particularly about its effectiveness across different ethnicities. This meta-analysis aims to investigate the potential benefits of adding PD1/PD-L1 inhibitors for treatment, stratified by ethnicity. We searched PubMed, Embase, and Cochrane databases for randomized controlled trials (RCTs) that investigated the use of PD1/PD-L1 inhibitors to treat patients with resectable NSCLC. The outcomes evaluated were disease-free survival/event-free survival (DFS/EFS), major pathological response (MPR), and pathological complete response (pCR). Hazard ratios (HRs) or risk ratios (RRs) with 95% confidence intervals (CIs) were computed for all endpoints using DerSimonian and Laird random-effects models. Statistical analyses were performed with R Software, version 4.2.3. A total of six RCTs comprising 3,827 patients with NSCLC were included. In populations of Asian descent, PD1/PD-L1 significantly improved DFS/EFS (HR 0.59; 95% CI 0.44-0.78), MPR (RR 5.76; 95% CI 3.58-9.28), and pCR (RR 25.00; 95% CI 6.17-101.36). Similarly, patients of European ancestry experienced significantly improved DFS/EFS (HR 0.77; 95% CI 0.65-0.90), MPR (RR 2.75; 95% CI 2.00-3.77), and pCR (RR 4.53; 95% CI 2.69-7.6) with PD1/PD-L1 therapy. Notably, patients with mixed ethnicity also demonstrated significant improvement in MPR (RR 4.05; 95% CI 2.60-6.33) and pCR (RR 8.44; 95% CI 3.75-19.00) when receiving PD1/PD-L1 inhibitors. This comprehensive meta-analysis suggests that incorporating PD1/PD-L1 inhibitors into treatments offers a promising benefit for patients with resectable NSCLC, regardless of ethnicity. Future studies with in-depth molecular characterization of patients can further refine these findings and potentially guide the development of personalized treatment strategies based on individual ethnic backgrounds.

Integrating Genetic Insights, Technological Advancements, Screening, and Personalized Pharmacological Interventions in Childhood Obesity.

Childhood obesity is a significant global health challenge with rising prevalence over the past 50years, affecting both immediate and long-term health outcomes. The increase in prevalence from 0.7% to 5.6% in girls and 0.9% to 7.8% in boys highlights the urgency of addressing this epidemic. By 2025, it is estimated that 206million children and adolescents aged 5-19years will be living with obesity. This review explores the complex interplay of genomics and genetics in pediatric obesity, transitioning from monogenic and polygenic obesity to epigenetics, and incorporating advancements in omics technologies. The evolutionary purpose of adiposity, systemic evaluation of hyperphagia, and the role of various genetic factors are discussed. Technological advancements in genotyping offer new insights and interventions. The integration of genetic screening into clinical practice for early identification and personalized treatment strategies is emphasized.

Abstract B030: Advancing precision management in retinoblastoma via aqueous humor liquid biopsy: A case of TP53 and MDM4 alterations

Abstract Background: Precision medicine has revolutionized cancer treatment, but its application in retinoblastoma (RB) has been limited due to the risks associated with traditional tumor biopsies, which can lead to the spread of cancer outside the eye. Blood plasma has not been effective in intraocular RB management because the blood-brain barrier (BBB) prevents tumor-derived material from entering the bloodstream, and in cases of bilateral RB—occurring in over 40% of patients—each eye may harbor distinct genomic alterations, complicating the use of blood-based biomarkers. Given these challenges, the discovery of tumor-derived genomic material in the aqueous humor (AH) offers a promising and safer alternative—a liquid biopsy that provides localized and specific insights into the tumor's genetic makeup. Although AH liquid biopsies have been valuable in identifying genomic changes in RB, they have yet to guide treatment decisions. This is primarily due to the rarity of the disease, the heterogeneity of treatment modalities, and the unclear definition of in vivo RB subtype classifications. Consequently, translating these genomic insights into actionable, personalized treatment strategies has been challenging. Now, we present the first application of AH-monitored treatment in a 35-month-old male with unilateral RB, characterized by TP53 alteration and ecDNA-MDM4 amplification, highlighting a significant advancement in the precision management of RB. Methods: AH samples were obtained via clear corneal paracentesis and analyzed for copy number alterations and somatic variants using shallow whole-genome sequencing and targeted sequencing. The patient initially received systematic CEV, followed with intravitreal melphalan (IVM) monotherapy. Due to resistance, treatment was escalated to a combination of intravitreal melphalan and topotecan (IVM+IVT), monitored by the AH liquid biopsy genomic findings. Results:The patient initially showed resistance to monotherapy with intravitreal melphalan (IVM), but dual therapy with melphalan and topotecan (IVM+IVT) significantly reduced tumor fraction (TFx) from 81.61% to 26.63% and decreased tumor size. Continued dual therapy and subsequent topotecan monotherapy resulted in complete regression of vitreous seeds and stabilization of the disease, as indicated by a final AH TFx of 0%. At our institution, Children's Hospital Los Angeles (CHLA), the majority of RB cases are treated using IAC or IVM, with dual chemotherapy regimens rarely employed. Conclusions: This case underscores the importance of tailoring treatment protocols to genetic profiles identified through AH liquid biopsy in managing aggressive RB. The successful combination of genomic profiling and personalized therapy has potential to significantly improve clinical outcomes, marking a critical step toward real precision management of RB. Further clinical collaboration is essential to validate and refine protocols for TP53-altered and ecDNA-MDM4-driven RB. Citation Format: Liya Xu, Elaine Huang, Jesse Lee Berry. Advancing precision management in retinoblastoma via aqueous humor liquid biopsy: A case of TP53 and MDM4 alterations [abstract]. In: Proceedings of the AACR Special Conference: Liquid Biopsy: From Discovery to Clinical Implementation; 2024 Nov 13-16; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(21_Suppl):Abstract nr B030.

Abstract B032: Genome-wide 5-hydroxymethylation mapping in cell-free DNA to characterize the epigenetic differences in minimal residual disease of multiple myeloma

Abstract Background: Multiple myeloma (MM), a B-cell neoplasm characterized by the infiltration of malignant plasma cells into the bone marrow, is the second most common blood malignancy in the United States. The molecular pathogenesis of MM involves both genetic and epigenetic alterations. While 60-75% of newly diagnosed patients achieve a complete response or better with current modern combination therapies, almost all patients eventually progress largely due to the persistence or re-emergence of minimal residual disease (MRD). Monitoring for MRD has become essential for evaluating treatment efficacy and predicting long-term outcomes in MM patients. We aimed to investigate whether epigenetic changes, particularly 5- hydroxymethylcytosines (5hmC), at diagnosis are associated with MRD status following treatment. Methods: Utilizing 5hmC-Seal, a highly sensitive chemical labeling technique, we profiled genome-wide 5hmC in circulating cell-free DNA (cfDNA) and bone marrow genomic DNA (gDNA) at baseline and after 8 cycles of treatment (C8) from newly diagnosed MM patients (n=25) enrolled in a clinical trial (NCT01816971). MRD testing was performed on bone marrow samples using the clonoSEQ next generation sequencing assay (limit of detection 10-6). Results: The 5hmC modification levels were summarized across various genomic features, revealing enrichment in gene bodies and enhancer regions, consistent with the known regulatory role of 5hmC. We ranked 5hmC-modified genes at baseline by their variability, and found that the number of overlapping genes between cfDNA and gDNA was significantly higher than expected by permutation analysis. Subsequently, we identified the top 100 most variable 5hmC- modified genes in gDNA, which exhibited a higher Pearson correlation with cfDNA within individuals (mean r=0.90) compared to between individuals (mean r=0.82; p&amp;lt;0.05), suggesting that cfDNA has the potential to reflect gDNA in capturing some MM-related variations. Among the 11 patients with MRD+ at C8, a genome-wide scan of 5hmC in cfDNA comparing baseline and C8 identified 1,614 differentially modified genes (p&amp;lt;0.05) after adjusting for age and sex. [BC1] Furthermore, we identified 459 differentially modified genes comparing baseline 5hmC levels between patients who were MRD+ (n=11) and MRD- (n= 14) at C8 (p&amp;lt;0.05), after adjusting for age and sex. These results highlight the differential epigenetic landscape associated with MRD status. Conclusions: Genome-wide 5hmC profiling of cfDNA from blood revealed a similar epigenetic landscape to gDNA from bone marrow in patients with MM, suggesting that cfDNA may offer a less invasive alternative for assessing epigenetic modifications. Moreover, this profiling identified distinct epigenetic changes associated with the achievement of MRD- negativity. These findings provide a foundation for further investigation into the molecular alterations linked to MM disease biology that could potentially lead to MRD negativity. Future directions will focus on identifying biomarkers that could inform personalized treatment strategies. Citation Format: Zhou Zhang, Bei Wang, Krissana Kowitwanich, John Cursio, Daniel Appelbaum, Chuan He, Wei Zhang, Benjamin Derman, Brian Chiu, Andrzej Jakubowiak. Genome-wide 5-hydroxymethylation mapping in cell-free DNA to characterize the epigenetic differences in minimal residual disease of multiple myeloma [abstract]. In: Proceedings of the AACR Special Conference: Liquid Biopsy: From Discovery to Clinical Implementation; 2024 Nov 13-16; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(21_Suppl):Abstract nr B032.