Personalized Treatment Decisions Research Articles

Methotrexate and cytarabine in adult LCH: High risk, high reward and maintenance free?

Lin et al. report the long‐term follow‐up of a phase II trial involving 95 adult patients with Langerhans cell histiocytosis (LCH), investigating the combination of methotrexate and cytarabine (MA). After a median follow‐up of 6.5 years, the study showed high response rates, with 90% overall response; 55% of patients free from an event such as disease progression, poor response or death; and 93% of patients were alive, though nearly half experienced febrile neutropenia. This prospective study helps fill gaps in understanding adult LCH treatment, indicating that fixed‐duration chemotherapy can yield durable responses despite its associated risks. It emphasizes the importance of personalized treatment decisions, considering both fixed‐duration chemotherapy and continuous targeted agents based on patient and disease‐specific factors.Commentary on: Lin et al. Long term follow‐up of methotrexate and cytarabine in adult patients with Langerhans cell histiocytosis. Br J Haematol 2024 (Online ahead of print). doi: 10.1111/bjh.19830.

Abstract PR003: ctDNA release kinetics and fragmentation to monitor treatment response and resistance in esophageal adenocarcinoma

Abstract Background: Esophageal adenocarcinoma (EAC) has a low 5-year survival rate and limited options for targeted therapies. Circulating tumor (ct)DNA isolated from liquid biopsies could help monitor patients to personalize treatment decisions. However, ctDNA studies in EAC remain sparse, and significant knowledge gaps exist in our understanding of ctDNA emission in response to therapy. The goal of this study was 1) to conduct a multimodal, longitudinal analysis of plasma cell free (cf)DNA in EAC patients and 2) to better understand the biology of ctDNA release during drug treatment. Methods: 1) A total of 189 longitudinal blood samples were collected from 41 EAC patients throughout treatment. cfDNA was isolated from baseline and post-neoadjuvant chemo samples for targeted deep sequencing alongside white blood cell controls. Shallow whole-genome sequencing was conducted on cfDNA for ichorCNA and fragmentomics analysis. 2) To dynamically monitor ctDNA fluctuations during EAC treatment, cfDNA was isolated from all other timepoints for digital (d)PCR quantification of the mutations identified from sequencing. ctDNA release kinetics were studied in vitro using three cell lines (OE19, FLO-1, A549), as well as our newly established cisplatin resistant model of OE19. ctDNA was analyzed by Qubit, dPCR, and Bioanalyzer. Annexin-V/PI flow cytometry was used to assess percentages of apoptotic and necrotic cells to correlate ctDNA to release mechanism. Results: 1) Sequencing has been completed for 21/41 patients and is ongoing for 20/41 patients. In the first 21 patients, somatic mutations were found in potential EAC driver genes, such as KRAS, TP53, ACVR2A, NOTCH1, and APC. Interestingly, in one patient’s cfDNA sample sequenced post neoadjuvant chemo, a novel mutation emerged in FBXW7, representing a potential resistance driver. ichorCNA tumor fraction (TF) values overall were low, and when using the recommended 0.03 TF estimate cutoff, there were significantly larger baseline TF values in metastatic (stage IVB) patients than those with localized disease (stage III) (p<0.05). 2) In vitro viable cancer cell numbers correlated to ctDNA levels, as measured by qubit and dPCR analysis. Additionally, cisplatin and 5-FU chemo treatments led to larger ctDNA release in all cells. ctDNA emission kinetics correlated to cytotoxicity (apoptosis and necrosis as shown through flow cytometry), with higher levels of ctDNA released by cisplatin-sensitive vs. resistant cells (p<0.05). Additionally, cisplatin treatment caused a shift in average ctDNA fragment size, with larger fragments observed during treatment, corresponding to an increased proportion of necrotic cells. Conclusions: This study reveals that multimodal cfDNA analysis can successfully be used in EAC patients to monitor treatment response and potentially identify resistance mechanisms. Moreover, in vitro models demonstrated a correlation of ctDNA emission and fragment length with chemo-cytotoxicity, shedding light on the release kinetics of DNA from cancer cells. Citation Format: Alexandra Bartolomucci, Laura Kienzle, Sarah Tadhg Ferrier, Lisa-Monique Edward, Jeffrey Bruce, Kwang-Bo Joung, Stephenie Prokopec, Wotan Zeng, Abirami Sharma, Kyle Dickinson, Nicholas Bertos, Jonathan Cools-Lartigue, Lorenzo Ferri, Trevor J. Pugh, Julia V. Burnier. ctDNA release kinetics and fragmentation to monitor treatment response and resistance in esophageal adenocarcinoma [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 PR003.

Providing Context to the "Unknown": Patient and Provider Reflections on Connecting Personal Tracking, Patient-Reported Insights, and EHR Data within a Post-COVID Clinic

COVID Long Haul (CLH) is an emerging chronic illness for which the healthcare system continues to seek a common understanding of symptoms, diagnosis, and treatment. CLH experiences can differ drastically, necessitating personalized care plans. Because patients interact with different clinicians during their CLH journey, it becomes important to ensure interoperability and understand clinical relevance of different data that can support clinicians in making appropriate recommendations. We conducted qualitative research where we interviewed 13 patients, conducted a focus group with 8 clinicians, and analyzed care plan follow-up records. We report patient and clinician expectations from and interactions with clinic data. We uncover logistical challenges, personal contexts, and health barriers impacting patient compliance. As researchers embedded in the clinical system, we identify the potential of using multiple patient data streams to support personalized treatment and clinical decisions. We discuss technology design opportunities and provide actionable recommendations for improving clinical workflows and cross-provider collaboration.

Putative mechanisms of primary resistance to EGFR-targeted therapies: A retrospective study

BackgroundsAdvanced lung adenocarcinoma (LUAD) patient with EGFR mutations often experience resistance to first-line epidermal growth factor tyrosine kinase inhibitors (EGFR-TKIs) therapy. Nonetheless, the mechanism and biomarkers of primary resistance remain unclear. Further exploration of independent prognostic factors will help clinicians identify patients who may not respond to EGFR-TKIs and select appropriate treatment strategies. MethodsA retrospective study involving 124 patients with stage IV LUAD harboring a common sensitizing EGFR mutation (exon 19 deletion or L858R mutation) who received EGFR-TKIs as first-line therapy was performed. All participants were tested by DNA-targeted sequencing in baseline samples, and there were 19 patients with progression-free survival (PFS) ≤ 3 months (cohort 1, C1, primary resistance), 22 patients with 3 < PFS < 8 months (cohort 2, C2, poor response) without known mutations associated with resistance, and 83 patients with PFS ≥ 8 months (cohort 3, C3, normal). ResultsThe most commonly mutated genes at baseline in patients prior to treatment within the entire study population. were TP53 (65 %), MYC (19 %), CDKN2A (12 %), MUC16 (12 %) and RBM10 (12 %). The baseline characteristics, except for the proportions of patients with EGFR L858R mutation and exon 19 deletion in C1 plus C2 compared to C3 (p = 0.036), were not significantly different among the cohorts. The frequencies of PIK3C2G, STK11, EPAS1, RARA and BTG2 variation were significantly higher in C1, the primary resistance group. Multivariate Cox analysis revealed that PIK3C2G (HR 15.70 95 % CI 3.24–76.05, p < 0.001), STK11 (HR 17.04, 95 % CI 3.68–78.92, p < 0.001), EPAS1 (HR 11.99, 95 % CI 2.57–56.03, p = 0.002), and BTG2 amplification (HR 9.53, 95 % CI 1.67–54.28, p = 0.011) were significantly associated with shorter PFS. ConclusionsThe genomic landscape varies significantly among patients with LUAD, which should be considered when making personalized treatment decisions. This information could provide insights into molecular changes and their effects on clinical treatment in diverse patients with LUAD harboring sensitizing EGFR mutations.

Histopathological Heterogeneity in Upper Tract Urothelial Carcinoma: A Systematic Review

Upper tract urothelial carcinoma (UTUC) is a rare malignancy with varied histopathological features that influence treatment strategies and patient outcomes. This systematic review comprehensively analyzes the histopathological heterogeneity of UTUC, focusing on its impact on diagnosis, prognosis, and therapeutic implications. A systematic search of PubMed, Google Scholar, NCBI, and ScienceDirect was conducted to identify relevant studies published in the last 20 years. Articles were included if they focused on the histopathological characterization of UTUC. A total of 10 studies were included in the review. Histopathological analysis revealed diverse subtypes, including papillary, micropapillary, nested, plasmacytoid, and sarcomatoid variants, each with distinct features and prognostic implications. UTUC exhibits significant histopathological heterogeneity, which influences tumor behavior and patient outcomes. Integrating histopathological evaluation with molecular profiling can refine risk stratification and guide personalized treatment decisions. Further research is needed to elucidate the complex interplay of these factors and develop targeted therapies for improved management of UTUC.

Heterogeneous associations of traditional atherosclerotic risk factors and long-term events in different arterial territories: the EPIC Norfolk prospective population cohort

Abstract Background Atherosclerotic risk factors have been associated with cardiovascular disease (CVD) events in specific arterial territories. However, these observations are limited by relatively short-term follow-up (i.e. ten years) and a focus on a limited number of outcome events. We therefore investigated associations between atherosclerotic risk factors and territory-specific cardiovascular events in a large cohort with ≥20 years follow-up. Methods We included participants without baseline CVD from the European Prospective Investigation of Cancer (EPIC) Norfolk prospective population cohort. The strengths of associations between CVD risk factors (smoking, low-density lipoprotein cholesterol (LDL-c), and systolic blood pressure (SBP)) and the first occurrence of a CVD event were evaluated using a competing risk-adjusted regression model. CVD events were defined as hospitalisations or death due to ischemic heart disease (IHD), ischemic stroke, haemorrhagic stroke, peripheral arterial disease (PAD) or aortic aneurysm (AA). Patients were followed-up until the first event, non-CVD death (competing risk) or end of study (15-03-2018). The model was adjusted for sex, age, diabetes, HDL-c, kidney function and body mass index. LDL-c and SBP were analysed by comparing highest versus lowest quartiles. Smoking (at baseline) was compared to never smoking. Results We included 23,581 participants (56% women) with a median baseline age of 58 years [interquartile range (IQR) 51-66]. During median follow up of 21.3 years (IQR 19.0-22.8), 26.4% of individuals experienced ≥1 CVD event. The first event of occurrence was IHD in 17.3%, ischemic stroke in 3.6%, haemorrhagic stroke in 1.4%, PAD in 3.1% and AA in 1.4%. LDL-c was strongest associated with IHD [adjusted hazard rate (aHR) 1.7, 95% confidence interval (CI) 1.5-1.9], SBP with ischemic stroke (aHR 1.9, 95%CI 1.5-2.5) and smoking with AA (aHR 4.8, 95%CI 3.5-6.5) (Figure 1). Conclusions There is considerable heterogeneity in the associations between atherosclerotic risk factors and long-term, first, territory-specific cardiovascular events. Knowledge of these differences may assist in personalised treatment decisions and risk communication.

Factors predicting treatment response to biological and targeted synthetic disease-modifying antirheumatic drugs in psoriatic arthritis - a systematic review and meta-analysis.

The therapeutic response of patients with psoriatic arthritis (PsA) varies greatly and is often unsatisfactory. Accordingly, it is essential to individualise treatment selection to minimise long-term complications. This study aimed to identify factors that might predict treatment response to biological and targeted synthetic disease-modifying antirheumatic drugs (bDMARDs and tsDMARDs) in patients with PsA and to outline their potential application using artificial intelligence (AI). Five electronic databases were screened to identify relevant studies. A random-effects meta-analysis was performed for factors that were investigated in at least four studies. Finally, 37 studies with a total of 17,042 patients were included. The most frequently investigated predictors in these studies were sex, age, C-reactive protein (CRP), the Health Assessment Questionnaire (HAQ), BMI, and disease duration. The meta-analysis revealed that male sex (odds ratio (OR) = 2.188, 95% confidence interval (CI) = 1.912-2.503) and higher baseline CRP (1.537, 1.111-2.125) were associated with greater treatment response. Older age (0.982, 0.975-0.99), higher baseline HAQ score (0.483, 0.336-0.696), higher baseline DAPSA score (0.789, 0.663-0.938), and higher baseline tender joint count (TJC) (0.97, 0.945-0.996) were negatively correlated with the response to therapy. The other factors were not statistically significant but might be of clinical importance in the context of a complex AI test battery. Further studies are needed to validate these findings and identify novel factors that could guide personalised treatment decisions for PsA patients, in particular in developing AI applications. In accordance with the latest medical developments, decision-support tools based on supervised learning algorithms have been proposed as a clinical application of these predictors. Key messages • Given the often unsatisfactory and unpredictable therapeutic response in patients with Psoriatic Arthritis (PsA), treatment selection must be highly individualized. • A systematic literature review was conducted to identify the most reliable predictors of treatment response to biologic and targeted synthetic disease-modifying antirheumatic drugs in PsA patients. • The potential integration of these predictors into AI tools for routine clinical practice is discussed.

Pharmacogenetic DPYD allele variant frequencies: A comprehensive analysis across an ancestrally diverse Iranian population.

Cancer treatment has improved over the past decades, but many cancer patients still experience adverse drug reactions (ADRs). Pharmacogenomics (PGx), known as personalized treatment, is a pillar of precision medicine that aims to optimize the efficacy and safety of medications by studying the germline variations. Germline variations in the DPYD lead to significant ADRs. The present cross-sectional study aims to evaluate the allele frequency of the DPYD gene variations in the Iranian population to provide insights into personalized treatment decisions in the Iranian population. The allele frequency of 51 pharmacogenetic variations in the clinically relevant DPYD was assessed in a representative sample set of 1142 unrelated Iranian individuals and subpopulations of different ethnic groups who were genotyped using the Infinium Global Screening Array-24 BeadChip. The genotyping assay revealed eight pharmacogenetic variants including DPYD rs1801265 (c.85T > C; DPYD*9A), rs2297595 (c.496A > G), rs1801158 (c.1601G > A; DPYD*4), rs1801159 (c.1627A > G; DPYD*5), rs1801160 (c.2194G > A; DPYD*6), rs17376848 (c.1896T > C), rs56038477 (c.1236G > A; HapB3), and rs75017182 (c.1129-5923C > G; HapB3) with minor allele frequency (MAF) ≥ 1%. The results of the study reveal significant genetic variations among Iranian population that could significantly influence clinical decision-making. These variants, with their potential to explain the substantial variability in drug response phenotypes among different populations, shed light on a crucial aspect of pharmacogenomics. These findings not only provide valuable insights but also inspire the design and implementation of future pharmacogenomic clinical trials, motivating further research in this crucial area.

Artificial Intelligence and Advanced Technology in Glaucoma: A Review.

Glaucoma is a leading cause of irreversible blindness worldwide, necessitating precise management strategies tailored to individual patient characteristics. Artificial intelligence (AI) holds promise in revolutionizing the approach to glaucoma care by providing personalized interventions. This review explores the current landscape of AI applications in the personalized management of glaucoma patients, highlighting advancements, challenges, and future directions. A systematic search of electronic databases, including PubMed, Scopus, and Web of Science, was conducted to identify relevant studies published up to 2024. Studies exploring the use of AI techniques in personalized management strategies for glaucoma patients were included. The review identified diverse AI applications in glaucoma management, ranging from early detection and diagnosis to treatment optimization and prognosis prediction. Machine learning algorithms, particularly deep learning models, demonstrated high accuracy in diagnosing glaucoma from various imaging modalities such as optical coherence tomography (OCT) and visual field tests. AI-driven risk stratification tools facilitated personalized treatment decisions by integrating patient-specific data with predictive analytics, enhancing therapeutic outcomes while minimizing adverse effects. Moreover, AI-based teleophthalmology platforms enabled remote monitoring and timely intervention, improving patient access to specialized care. Integrating AI technologies in the personalized management of glaucoma patients holds immense potential for optimizing clinical decision-making, enhancing treatment efficacy, and mitigating disease progression. However, challenges such as data heterogeneity, model interpretability, and regulatory concerns warrant further investigation. Future research should focus on refining AI algorithms, validating their clinical utility through large-scale prospective studies, and ensuring seamless integration into routine clinical practice to realize the full benefits of personalized glaucoma care.

Considerations for the application of polygenic scores to clinical care of individuals with substance use disorders.

Substance use disorders (SUDs) are highly prevalent and associated with excess morbidity, mortality, and economic costs. Thus, there is considerable interest in the early identification of individuals who may be more susceptible to developing SUDs and in improving personalized treatment decisions for those who have SUDs. SUDs are known to be influenced by both genetic and environmental factors. Polygenic scores (PGSs) provide a single measure of genetic liability that could be used as a biomarker in predicting disease development, progression, and treatment response. Although PGSs are rapidly being integrated into clinical practice, there is little information to guide clinicians in their responsible use and interpretation. In this Review, we discuss the potential benefits and pitfalls of the use of PGSs in the clinical care of SUDs, highlighting current research. We also provide suggestions for important considerations prior to implementing the clinical use of PGSs and recommend future directions for research.

Choosing the optimal therapy for metastatic renal cell carcinoma: Insights from a real-world comparative study.

The evolution of combination therapies integrating immune checkpoint inhibitors has revolutionized the first-line treatment of metastatic renal cell carcinoma (mRCC). Although these therapies are clinically approved, direct comparisons between dual immune checkpoint inhibitors (IOIO) and immune checkpoint inhibitors combined with tyrosine kinase inhibitors (IOTKI) in clinical trials are lacking. This gap creates uncertainties in selecting the most appropriate treatment based on patient-specific factors. This study employed the inverse probability of treatment weighting (IPTW) method to analyze progression-free survival (PFS) and overall survival (OS) for patients with mRCC receiving IOIO or IOTKI treatment regimens. A total of 171 patients were analyzed after applying inclusion criteria and propensity scoring. The study found no significant differences in PFS and OS between the two treatment modalities in the IPTW cohort. However, subgroup analyses revealed that IOTKI therapy was associated with better PFS and OS for patients without bone metastases and better OS for patients with a body mass index (BMI) over 25. IOIO therapy showed better OS for patients with a BMI below 18.5. Both IOIO and IOTKI therapies were effective. Therapy selection could be better tailored to patient characteristics by including factors such as the presence of bone metastases and BMI. This study enhances understanding of how patient-specific factors interact with different treatment modalities, potentially guiding more personalized treatment decisions in clinical practice for mRCC.

Acute Coronary Syndrome in Elderly Patients: How to Tackle Them?

Elderly patients diagnosed with acute coronary syndromes (ACS) represent a growing demographic population. These patients typically present more comorbidities and experience poorer outcomes compared to younger patients. Furthermore, they are less frequently subjected to revascularization procedures and are less likely to receive evidence-based medications in both the short and long-term periods. Assessing frailty is crucial in elderly patients with ACS because it can influence management decisions, as well as risk stratification and prognosis. Indeed, treatment decisions should consider geriatric syndromes, frailty, polypharmacy, sarcopenia, nutritional deficits, prevalence of comorbidities, thrombotic risk, and, at the same time, an increased risk of bleeding. Rigorous clinical assessments, clear revascularization criteria, and tailored approaches to antithrombotic therapy are essential for guiding personalized treatment decisions in these individuals. Assessing frailty helps healthcare providers identify patients who may benefit from targeted interventions to improve their outcomes and quality of life. Elderly individuals who experience ACS remain significantly underrepresented and understudied in randomized controlled trials. For this reason, the occurrence of ACS in the elderly continues to be a particularly complex issue in clinical practice, and one that clinicians increasingly have to address, given the general ageing of populations. This review aims to address the complex aspects of elderly patients with ACS to help clinicians make therapeutic decisions when faced with such situations.

Real-world molecular testing patterns in primary advanced or recurrent endometrial cancer (pA/rEC) in the US.

395 Background: Targeted therapy can improve outcomes in women with pA/rEC, but real-world biomarker testing rates are unknown. This study estimated the real-world proportion of patients receiving biomarker testing (within ≤6 months of advanced/recurrent diagnosis) and characterized testing according to sociodemographic and clinical characteristics. Methods: We used the US Flatiron Health electronic health record–derived deidentified database. Records from 1/1/2013 to 8/31/2023 for women ≥18 years old with advanced/recurrent EC were searched to identify testing for DNA mismatch repair/microsatellite instability (MMR/MSI), HER2, and estrogen or progesterone receptors (ER/PR); a subsample dataset from 4/1/2013 to 11/30/2022 was analyzed for additional biomarkers. The subsample has information on additional biomarker testing and was used to evaluate the frequency of testing for these biomarkers. Testing rates were evaluated using descriptive statistics; characteristics associated with testing were identified by multivariate logistic regression. Results: Among persons meeting eligibility criteria (n=2982), the mean age was 65 years; the majority were non-Hispanic White (56%), had commercial insurance (79%), and received care in a community setting (73%). The subsample (n=509) had similar characteristics to the full cohort. Between 2013-2022, 73% (n=2165) of patients received testing for any biomarker. Testing rates for individual biomarkers are shown in the Table. From 2013 to 2021, testing increased for MMR/MSI (17% to 81%), ER/PR (45% to 62%), and HER2 (15% to 43%). The likelihood of testing was higher among patients with primary advanced EC (OR, 1.45; 95% CI, 1.03-2.06), endometrioid carcinoma histology (1.39; 1.01-1.93), commercial insurance (1.51; 1.18-1.93), at an academic facility (1.70; 1.37-2.11), and with no prior surgery (2.29; 1.83-2.86) relative to other attributes. Conclusions: Molecular testing has increased over time, indicating a continuous trend toward personalized treatment decisions. Several factors associated with an increased likelihood of biomarker testing were identified. Understanding these factors can inform approaches to increase access to molecular testing and increase testing rates. Biomarker testing rates, 2013-2022. Biomarker, n (%) 2013 2021 Overall MMR/MSI a 35 (15) 238 (80) 1604 (54) HER2 a 31 (14) 121 (41) 612 (21) ER/PR a 99 (43) 177 (59) 1465 (49) p53/ TP53 b 13 (36) 31 (74) 260 (51) TMB b 3 (8) 14 (33) 91 (18) CTNNB1 b 4 (11) 12 (29) 88 (17) ARID1A b 4 (11) 15 (36) 91 (18) CDK4/6 b 5 (14) 14 (33) 110 (22) POLE b 4 (11) 17 (40) 110 (22) ARID1A, AT-rich interactive domain-containing protein 1A; CDK4/6, cyclin-dependent kinases 4 and 6; CTNNB1, catenin beta-1; POLE, polymerase-epsilon; TMB, tumor mutation burden. a 2013, n=228; 2021, n=298; overall, n=2982. b Rates from subsample dataset (4/1/2013 to 11/30/2022); 2013, n=36; 2021, n=42; overall, n=509.

Ensemble transformer-based multiple instance learning to predict pathological subtypes and tumor mutational burden from histopathological whole slide images of endometrial and colorectal cancer

In endometrial cancer (EC) and colorectal cancer (CRC), in addition to microsatellite instability, tumor mutational burden (TMB) has gradually gained attention as a genomic biomarker that can be used clinically to determine which patients may benefit from immune checkpoint inhibitors. High TMB is characterized by a large number of mutated genes, which encode aberrant tumor neoantigens, and implies a better response to immunotherapy. Hence, a part of EC and CRC patients associated with high TMB may have higher chances to receive immunotherapy. TMB measurement was mainly evaluated by whole-exome sequencing or next-generation sequencing, which was costly and difficult to be widely applied in all clinical cases. Therefore, an effective, efficient, low-cost and easily accessible tool is urgently needed to distinguish the TMB status of EC and CRC patients. In this study, we present a deep learning framework, namely Ensemble Transformer-based Multiple Instance Learning with Self-Supervised Learning Vision Transformer feature encoder (ETMIL-SSLViT), to predict pathological subtype and TMB status directly from the H&E stained whole slide images (WSIs) in EC and CRC patients, which is helpful for both pathological classification and cancer treatment planning. Our framework was evaluated on two different cancer cohorts, including an EC cohort with 918 histopathology WSIs from 529 patients and a CRC cohort with 1495 WSIs from 594 patients from The Cancer Genome Atlas. The experimental results show that the proposed methods achieved excellent performance and outperforming seven state-of-the-art (SOTA) methods in cancer subtype classification and TMB prediction on both cancer datasets. Fisher’s exact test further validated that the associations between the predictions of the proposed models and the actual cancer subtype or TMB status are both extremely strong (p<0.001). These promising findings show the potential of our proposed methods to guide personalized treatment decisions by accurately predicting the EC and CRC subtype and the TMB status for effective immunotherapy planning for EC and CRC patients.

Decoding the immune microenvironment: unveiling CD8 + T cell-related biomarkers and developing a prognostic signature for personalized glioma treatment

BackgroundGliomas are aggressive brain tumors with poor prognosis. Understanding the tumor immune microenvironment (TIME) in gliomas is essential for developing effective immunotherapies. This study aimed to identify TIME-related biomarkers in glioma using bioinformatic analysis of RNA-seq data.MethodsIn this study, we employed weighted gene co-expression network analysis (WGCNA) on bulk RNA-seq data to identify TIME-related genes. To identify prognostic genes, we performed univariate Cox regression and least absolute shrinkage and selection operator (LASSO) regression analyses. Based on these genes, we constructed a prognostic signature and delineated risk groups. To validate the prognostic signature, external validation was conducted.ResultsCD8 + T cell infiltration was strongly correlated with glioma patient prognosis. We identified 115 CD8 + T cell-related genes through integrative analysis of bulk-seq data. CDCA5, KIF11, and KIF4A were found to be significant immune-related genes (IRGs) associated with overall survival in glioma patients and served as independent prognostic factors. We developed a prognostic nomogram that incorporated these genes, age, gender, and grade, providing a reliable tool for clinicians to predict patient survival probabilities. The nomogram’s predictions were supported by calibration plots, further validating its accuracy.ConclusionIn conclusion, our study identifies CD8 + T cell infiltration as a strong predictor of glioma patient outcomes and highlights the prognostic value of genes. The developed prognostic nomogram, incorporating these genes along with clinical factors, provides a reliable tool for predicting patient survival probabilities and has important implications for personalized treatment decisions in glioma.

Development and validation of a prognostic nomogram for predicting liver metastasis in thyroid cancer: a study based on the surveillance, epidemiology, and end results database

This study aimed to create a prognostic nomogram to predict the risk of liver metastasis (LM) in thyroid cancer (TC) patients and assess survival outcomes for those with LM. Data were collected from the SEER database, covering TC patients from 2010 to 2020, totaling 110,039 individuals, including 142 with LM. Logistic regression and stepwise regression based on the Akaike information criterion (AIC) identified significant factors influencing LM occurrence: age, histological type, tumor size, bone metastasis, lung metastasis, and T stage (p < 0.05). A nomogram was constructed using these factors, achieving a Cindex of 0.977, with ROC curve analysis showing an area under the curve (AUC) of 0.977. For patients with TCLM, follicular TC, medullary TC, papillary TC, and examined regional nodes were associated with better prognosis (p < 0.001, HR < 1), while concurrent brain metastasis indicated poorer outcomes (HR = 2.747, p = 0.037). In conclusion, this nomogram effectively predicts LM risk and evaluates prognosis for TCLM patients, aiding clinicians in personalized treatment decisions

Advancements and Challenges in Biomarkers for Colorectal Cancer Detection: A Comprehensive Review

This study provides an overview of the current landscape of biomarkers for colorectal cancer (CRC) detection, focusing on genetic, proteomic, circulating microRNA (miRNA), and metabolomic biomarkers. CRC remains a significant global health challenge, ranking among the most prevalent cancers worldwide and being a leading cause of cancer-related deaths. Despite advancements in screening methods such as colonoscopy, sigmoidoscopy, and fecal occult blood tests (FOBT), the asymptomatic nature of early-stage CRC often results in late diagnoses, negatively impacting patient outcomes. Genetic biomarkers like APC, KRAS, TP53, and microsatellite instability (MSI) play critical roles in CRC pathogenesis and progression. These biomarkers, detectable through polymerase chain reaction, next-generation sequencing, and other advanced techniques, guide early detection and personalized treatment decisions. Proteomic biomarkers such as CEA, CA 19-9, and novel signatures offer insights into CRC’s physiological changes and disease status, aiding prognosis and treatment response assessments through enzyme-linked immunosorbent assay and mass spectrometry. Circulating miRNAs, including miR-21 and miR-92a, present promising non-invasive biomarkers that can be detected in blood and stool samples, reflecting CRC presence, progression, and therapeutic response. Metabolomic biomarkers, encompassing amino acids, lipids, and TCA cycle intermediates, provide further insights into CRC-associated metabolic alterations, which are crucial for early detection and treatment monitoring using mass spectrometry and nuclear magnetic resonance. Despite these advancements, challenges such as biomarker validation, standardization, and clinical utility remain. Future research directions include integrating multi-omics approaches and leveraging technologies like liquid biopsies and AI for enhanced biomarker discovery and clinical application. By addressing these challenges and advancing research in biomarker development, CRC screening and management could potentially be revolutionized, improving patient outcomes and reducing the global burden of this disease.

The additional value of myocardial T1ρ mapping to T1 and T2 mapping for predicting subsequent cancer therapy-related cardiac dysfunction in breast cancer patients who received anthracyclines with/without trastuzumab

ObjectivesWe aimed to describe changes in parameters derived from myocardial T1ρ, T1, and T2 mapping and assess whether incorporating T1ρ mapping improves the predictive performance of T1 and T2 mapping for subsequent cancer therapy-related cardiac dysfunction (CTRCD) in breast cancer patients treated with anthracyclines with/without trastuzumab. MethodsFrom March 2021 to May 2023, 82 participants with breast cancer treated with anthracyclines with/without trastuzumab were prospectively recruited. Cardiac magnetic resonance was performed at baseline, 3 and 6 months in relation to baseline. T1ρ, T1 and T2 values were measured and compared by repeated measures analyses of variance. Logistic regression and receiver operating characteristic analysis were used to assess the performance in predicting subsequent CTRCD. ResultsNineteen (23.17 %) participants developed CTRCD. T1ρ and T1 values progressively increased over time (all p < 0.001), while T2 values increased at 3 (p < 0.001 and p = 0.002, respectively) and 6 months (all p < 0.001) compared to baseline in both the CTRCD (+) and CTRCD (−) groups. The changes in T1ρ (OR, 3.892, p = 0.003) and T1 (OR, 1.082, p = 0.002) from baseline to 3 months were associated with subsequent CTRCD. The combination of the changes in T1ρ and T1 from baseline to 3 months obtained an improved area under the curve of 0.853. ConclusionT1ρ, T1 and T2 increased after treatment of anthracyclines with/without trastuzumab. Myocardial T1ρ mapping provides additional predictive value to T1 mapping for subsequent CTRCD in breast cancer patients who received anthracyclines with/without trastuzumab. Clinical relevance statementMyocardial T1ρ mapping offers additional predictive value to T1 and T2 mapping for subsequent CTRCD in breast cancer patients who received anthracyclines with/without trastuzumab. This may facilitate accurate prediction of cardiotoxicity and personalized treatment decision making in breast cancer.

Development and validation of a TRIM27-based nomogram for predicting metachronous liver metastasis and prognosis in postoperative colorectal cancer patients

BackgroundColorectal cancer ranks among the most prevalent malignancies globally. Accurate prediction of metachronous liver metastasis is crucial for optimizing postoperative management. Tripartite motif-containing protein 27 (TRIM27), an E3 ubiquitin ligase, is implicated in diverse cellular functions and tumorigenesis.MethodsThis study aimed to develop and validate a TRIM27-based nomogram for prognostication in colorectal cancer patients. Transcriptome sequencing of five paired tumor and normal tissue samples identified TRIM27 as a potential prognostic biomarker. Immunohistochemistry was employed to assess TRIM27 expression in colorectal cancer cohorts from two institutions.ResultsTRIM27 expression correlated significantly with both the prognosis of colorectal cancer patients and the occurrence of metachronous liver metastasis. A nomogram incorporating TRIM27 and clinical factors was constructed and demonstrated robust predictive accuracy in an independent validation cohort.ConclusionThe TRIM27-based nomogram is a valuable prognostic tool for predicting prognosis and metachronous liver metastasis in colorectal cancer patients, aiding in personalized treatment decisions.

MultiSCRIPT-Cycle 1—a pragmatic trial embedded within the Swiss Multiple Sclerosis Cohort (SMSC) on neurofilament light chain monitoring to inform personalized treatment decisions in multiple sclerosis: a study protocol for a randomized clinical trial

BackgroundTreatment decisions for persons with relapsing–remitting multiple sclerosis (RRMS) rely on clinical and radiological disease activity, the benefit-harm profile of drug therapy, and preferences of patients and physicians. However, there is limited evidence to support evidence-based personalized decision-making on how to adapt disease-modifying therapy treatments targeting no evidence of disease activity, while achieving better patient-relevant outcomes, fewer adverse events, and improved care. Serum neurofilament light chain (sNfL) is a sensitive measure of disease activity that captures and prognosticates disease worsening in RRMS. sNfL might therefore be instrumental for a patient-tailored treatment adaptation. We aim to assess whether 6-monthly sNfL monitoring in addition to usual care improves patient-relevant outcomes compared to usual care alone.MethodsPragmatic multicenter, 1:1 randomized, platform trial embedded in the Swiss Multiple Sclerosis Cohort (SMSC). All patients with RRMS in the SMSC for ≥ 1 year are eligible. We plan to include 915 patients with RRMS, randomly allocated to two groups with different care strategies, one of them new (group A) and one of them usual care (group B). In group A, 6-monthly monitoring of sNfL will together with information on relapses, disability, and magnetic resonance imaging (MRI) inform personalized treatment decisions (e.g., escalation or de-escalation) supported by pre-specified algorithms. In group B, patients will receive usual care with their usual 6- or 12-monthly visits. Two primary outcomes will be used: (1) evidence of disease activity (EDA3: occurrence of relapses, disability worsening, or MRI activity) and (2) quality of life (MQoL-54) using 24-month follow-up. The new treatment strategy with sNfL will be considered superior to usual care if either more patients have no EDA3, or their health-related quality of life increases. Data collection will be embedded within the SMSC using established trial-level quality procedures.DiscussionMultiSCRIPT aims to be a platform where research and care are optimally combined to generate evidence to inform personalized decision-making in usual care. This approach aims to foster better personalized treatment and care strategies, at low cost and with rapid translation to clinical practice.Trial registrationClinicalTrials.gov NCT06095271. Registered on October 23, 2023