JCO Clinical Cancer Informatics最新文献

Purpose: Immune effector cell-associated hematotoxicity (ICAHT) is a major cause of nonrelapse mortality after chimeric antigen receptor (CAR) T-cell therapy. We hypothesized that unsupervised time-series clustering could better identify archetypal patterns of early hematotoxicity compared to the early ICAHT (eICAHT) grading system.

Methods: We applied unsupervised k-means time-series clustering based on Euclidean distances to longitudinal absolute neutrophil count (ANC) data from days +0 through +30 post-CAR T-cell infusion in 691 patients treated at our center (training set: n = 483, 70%; test set: n = 208, 30%).

Results: Within our training set, we identified an optimal cluster solution based on four ANC recovery clusters, which were labeled as very good, good, poor, and very poor. We trained a random forest (RF) model including the top five most important features (day +3, +4, +5, +26, and +27 ANC values) to predict the cluster assignments. Within our test set, we applied the RF model to predict cluster assignments. Compared with the eICAHT criteria, the RF-predicted clusters were more compact and better separated (Dunn index: 0.078 v 0.034; average silhouette width: 0.12 v 0.010). In addition, the RF model identified patients in the good recovery cluster with intermediate overall survival (hazard ratio [HR], 1.70 [95% CI, 1.05 to 2.74]; P = .029; reference, very good), which was not captured by grade 2 eICAHT (HR, 1.37 [95% CI, 0.80 to 2.35]; P = .25; reference, grade 0-1).

Conclusion: Unsupervised time-series clustering identified distinct and clinically relevant patterns of hematotoxicity after CAR T-cell therapy. We trained and tested an RF model that accurately predicted cluster assignments using only five features. Predictions can be generated using our online web application.

Purpose: This study examined real-world overall survival (rwOS) in patients with advanced or metastatic non-small cell lung cancer (a/mNSCLC) treated with combination immunotherapy (IO) and platinum chemotherapy in first line (1L), followed by second-line or beyond (2L+) non-IO, nonplatinum chemotherapy and explored the association between real-world duration of response (rwDOR) to 1L treatment and rwOS on the 2L+ treatment.

Methods: This study used two US-based data sets: ConcertAI Patient360 NSCLC data set (ConcertAI) and the Flatiron Health Research Database (FHRD), and included adults with a/mNSCLC diagnosed from January 1, 2018, to March 31, 2023 (data cutoff: March 31, 2024). Kaplan-Meier and multivariate Cox regression analyses estimated rwOS for the index regimen by rwDOR to 1L.

Results: Patients with rwDOR ≤6 (≈60%) v >6 months (≈40%) in 1L were similar across the 596 ConcertAI patients and 1,094 FHRD patients. Across the ConcertAI data set/FHRD, 52.6%/55.7% of patients achieved complete/partial response as real-world best overall response to 1L combination IO and platinum chemotherapy and 17.8%/19.1% had stable disease. The median rwOS on 2L+ treatment was 8.3 v 5.2 months (P = .001; ConcertAI) and 8.3 v 5.1 months (P < .001; FHRD) for patients with 1L rwDOR >6 v ≤6 months. The adjusted hazard ratio for patients with 1L rwDOR >6 v ≤6 months was 0.74 (95% CI, 0.61 to 0.90; P = .002) and 0.76 (95% CI, 0.67 to 0.88; P < .001) in the ConcertAI data set and FHRD, respectively.

Conclusion: Our findings demonstrate that patients with rwDOR ≥6 months on 1L combination IO and platinum chemotherapy exhibit longer rwOS on subsequent treatments. This emphasizes the need for 1L treatments that extend DOR and delay the onset of acquired resistance, which remains an unmet need for approximately 60% of patients who do not achieve a sustained response in clinical practice.

Purpose: Clinical research in pancreatic cancer (PC) has been limited because of a lack of granular data in national data sets. An electronic health record (EHR)-based data set specifically designed for PC has immense potential to advance research. This study describes the creation of an EHR-based data commons for patients with PC.

Methods: We generated an index cohort of adult patients at our institution diagnosed with PC (International Classification of Diseases for Oncology, codes C25.0-25.9) between January 1, 2010, and December 31, 2023. To develop the Pancreatic Cancer Data Commons (PCDC), we linked six data sources: (1) institutional EHR data, (2) cancer-specific data from the North American Association of Central Cancer Registries, (3) surgical outcomes from the National Surgical Quality Improvement Program, (4) community-level data from the American Community Survey, (5) national mortality data from Obituary.com, and (6) genomic data from the cBioPortal for Cancer Genomics. We evaluated the feasibility of using the Observational Medical Outcomes Partnership common data model. The data set is stored on a cloud-based, Health Insurance Portability and Accountability Act-secure, and National Institute of Standards and Technology-compliant server.

Results: The PCDC currently includes data of 3,542 unique patients. The mean age at diagnosis is 66.6 ± 11.7 years; 53.3% is male, and 92.2% is White. Linkage to six national data sets increased the completeness of cancer-specific data from 31.3% to 71.6%. Most patients presented at stage IV (43.6%), followed by stage I (22.6%). As of the latest update, 1,074 (30.3%) patients were still alive.

Conclusion: The PCDC is a centralized resource that solves a gap in PC research. The ability to securely link and analyze protected patient data is a strategic step toward enhancing clinical research and optimizing care for patients with PC. Our future work includes expanding the PCDC to multiple centers using common data models.

Purpose: The Pediatric Relapse Prediction and Risk Evaluation for Acute Lymphoblastic Leukemia (PREPARE-ALL) tool aims to predict relapse in pediatric ALL by integrating clinical expertise with artificial intelligence and machine learning (ML), particularly Extreme Gradient Boosting (XGBoost). PREPARE-ALL demonstrates that multicenter, protocol-driven clinical and laboratory data can be used through ML to generate reproducible relapse predictions with greater sensitivity than individual clinician assessments.

Methods: PREPARE-ALL was developed using data from the ICiCLe ALL-14 pretrial cohort across five centers, incorporating 33 clinical and laboratory features.

Results: Among 2,252 patients enrolled in the study, 565 (25.1%) relapsed. Using an 80:20 train-test split, XGBoost achieved a sensitivity of 68.5% (245/447 relapses detected). Additional metrics included a positive predictive value of 31.3%, a negative predictive value of 82.8%, an accuracy of 54.8%, and a specificity of 50.3%. Key predictors of relapse included high hyperdiploidy and BCR-ABL1 fusion positive, positive measurable residual disease status at the end of induction, sex, age, highest presenting WBC, and final risk group. Three clinicians scored the validation data set; the developed model achieved a higher recall (68.5%) compared with clinical judgment (approximately 31%-36%).

Conclusion: PREPARE-ALL identifies twice as many relapses as clinicians and serves as a practical decision-support tool for early relapse triage and treatment planning, enabling timely therapeutic adjustments and improved outcomes in pediatric ALL.

Purpose: Health-related quality of life (HRQoL) is a valuable counterpart to other end points in oncology trials. However, when analyzing the effect of treatment on HRQoL, results are often mixed. Cancer and its treatment can have direct and indirect effects on symptoms such as pain, nausea, and fatigue. These, in turn, can affect the patients' physical and social functioning and ultimately their HRQoL. This causal cascade requires methodologies, such as structural equation modeling, to estimate direct, indirect, and total effects of symptoms on more distal HRQoL outcomes.

Methods: Data were obtained from two multicenter, randomized trials available from Project Data Sphere, LLC. One trial included patients with non-small cell lung cancer; the other included patients with metastatic breast cancer. Structural equation models were evaluated; models included symptom items from the European Organization for Research and Treatment of Cancer (EORTC) core questionnaire, QLQ-C30 (version 3). A causal cascade of individual symptoms at two time points was hypothesized to affect fatigue, which, in turn, had direct and indirect effects on the functional and HRQoL measures.

Results: Results showed a logical, causal ordering among EORTC QLQ-C30 outcomes. In the final model, the effects of most symptoms on functional domains and the quality of life (QoL) scale were through their direct effect on fatigue, which, in turn, affected patient functioning and HRQoL.

Conclusion: Results strongly suggest that when examining the effects of symptoms on EORTC QLQ-C30 functional scales or HRQoL, symptoms would generally not show direct effects on these more distal outcomes. The results from the two clinical trials demonstrated that anticipating a causal cascade from specific symptoms to functional domains and HRQoL would yield more meaningful results.

Purpose: Somatic next-generation sequencing (NGS) panels are widely used in precision oncology to detect clinically actionable genomic alterations. However, interpreting diverse DNA and RNA alterations remains challenging because of the complexity of tumor-only data and the limitations of current pipelines, which are often proprietary, noncustomizable, or lack visual reports to support clinical interpretation. We present ClinBioNGS, an open-source, panel-agnostic bioinformatics pipeline for the comprehensive analysis of somatic NGS cancer panels in both clinical and translational settings.

Materials and methods: ClinBioNGS is a modular, fully containerized workflow implemented in Nextflow. It supports integrated analysis of DNA and RNA data, including multicaller small variant detection, copy number alteration (CNA) profiling, gene fusion and splice variant identification, and evaluation of complex genomic biomarkers such as tumor mutational burden and microsatellite instability. Variants are annotated and prioritized using established clinical frameworks. The results are compiled in a self-contained interactive HTML report with dynamic tables and informative visualizations to facilitate clinical interpretation. Validation included SEQC2 reference data sets across six commercial panels, and benchmarking was performed on 2,024 clinical tumor samples analyzed with three commercial platforms.

Results: ClinBioNGS achieved high accuracy in SEQC2 validation, with precision (0.987-1.000), recall (0.920-0.997), and F1 scores (0.956-0.999) across diverse panels. In a clinical benchmarking with real-world data, the pipeline demonstrated high concordance with commercial solutions for small variants (97%), CNAs (89%), and RNA alterations (94%), while also identifying additional high-confidence alterations missed by vendor pipelines.

Conclusion: ClinBioNGS provides a robust, flexible, and transparent solution for standardized analysis of somatic NGS cancer panels. It supports reproducible, clinically oriented interpretation of genomic data and is freely available for noncommercial research-use only at GitHub.

Purpose: A core clinical task is to synthesize fragmented patient data into a coherent summary to support decision making. However, electronic health record (EHR) inefficiencies burden clinicians and contribute to their cognitive overload and burnout. This study evaluated the impact of a large language model (LLM)-enabled clinical decision support (LLM-CDS) platform compared with a simulated EHR (SimEPR) on workflow efficiency and user experience in generating accurate clinical summaries during tumor board preparation and explored its applicability to consultation preparation, referrals, treatment planning, and patient communication.

Methods: In a remote, within-participant simulation, 26 oncologists from the United Kingdom, United States, Spain, and Singapore reviewed synthetic breast cancer cases and created comprehensive summaries for tumor board discussions using both LLM-CDS and SimEPR. LLM-CDS provided editable LLM-generated summaries; SimEPR required manual composition. Time to task completion was recorded. An independent reviewer assessed summary quality based on completeness, correctness, and conciseness. Participants also completed surveys on usability, cognitive load, and feature acceptability.

Results: LLM-CDS significantly reduced the summary completion time compared with SimEPR (6:55 v 8:47 minutes; P < .001). Summary completeness was rated higher with LLM-CDS (mean score, 3.93 v 3.13), whereas correctness and conciseness were similar. Overall, 87% of participants would recommend LLM-CDS and 96% would anticipate time savings. The system usability scale score for LLM-CDS was 65.7. Although perceived cognitive load was lower with LLM-CDS, the difference was not statistically significant. The LLM summary was the most valued, with 92% finding it useful for the tumor board and consultation preparation.

Conclusion: The LLM-CDS platform improved the efficiency and completeness of clinical summarization. Strong user acceptance and anticipated time savings underscore the potential for streamlining a range of oncology workflows.

Purpose: Shallow whole-genome sequencing (sWGS) is a cost-effective approach for detecting genome wide copy number profiles in tumor samples. In metastatic castration-resistant prostate cancer (mCRPC), recognizing homologous recombination deficiency (HRD) and tandem duplication (TD) genomic profiles may contribute to improved treatment choices such as poly (ADP-ribose) polymerase inhibitors. This study aims to determine the minimum sequencing depth and tumor content (TC) required to accurately identify these clinically significant genomic profiles using sWGS.

Materials and methods: Whole-genome sequencing (WGS) data from 168 tumor and matched normal biopsies from 155 patients with mCRPC were mixed in silico to generate a set of 3,360 mixtures with varying TCs (original, 20%, 10%, 5%, 3%) and sequencing depths (original, 5×, 2×, 1×, 0.1×). Copy number variations (CNVs) were analyzed using ichorCNA and WisecondorX at different window sizes.

Results: An average sequencing depth of 1× at 20% TC was found to be sufficient to detect CNVs with high sensitivity (>0.85) and high specificity (>0.95). For HRD and TD profile detection, ichorCNA at a 50 Kb window size was optimal and a reliable detection of HRD profiles was achieved with a very strong correlation of R = 0.88 (P < 2.2e-16). Detection of TD profiles also remained accurate at these parameters with a strong correlation of R = 0.72 (P < 2.2e-16), although the median length of duplication events increased at lower depths. TC estimation by ichorCNA strongly correlated with full-depth WGS of diploid genomes.

Conclusion: In this study, through in silico simulations of WGS data, we demonstrate that the genomic scars of two druggable genomic profiles, HRD and TD, can be reliably detected in mCRPC with 1× average sequencing depth and ≥20% TC. Further research is required to correlate these markers with outcome of specific treatments using sWGS.

Purpose: To evaluate the appropriateness of a cure model when analyzing right-censored end points of a randomized clinical trial (RCT) in malignancy in the presence of long-term survivors. We aim to derive how the ratio estimation of censored cured subjects (RECeUS), previously proposed for a homogeneous population, could be extended for use in RCTs.

Methods: Based on the RECeUS method, four decision rules were considered to assess the appropriateness of a cure model. They considered the eligibility conditions to be met: in both arms, in at least one randomized arm, in the entire sample, or when only considering an average of the conditions, respectively. A simulation study was performed to evaluate their performance and the impact of the link function when considering the appropriateness of cure models. We also illustrate the method using two real data examples from two RCTs conducted in patients with acute leukemia and COVID-19 disease.

Results: Simulation results show that the best decision rule that can be applied in all considered treatment effect scenarios might be to check the criteria in at least one randomized arm. Regardless of the rules, the cure model appeared to be appropriate in both RCT data.

Conclusion: When analyzing survival data from RCTs, the appropriateness of a cure model could be considered in the face of a plateau shape of the survival curves. To ensure that the presence of such a plateau in the survival curves is a reliable indicator of the presence of cured patients in the population, the RECeUS method should be used in each randomized arm separately, with criteria met in at least one randomized arm.