Dataset Information

CtDNA-based detection of molecular residual disease in stage I-III non-small cell lung cancer patients treated with definitive radiotherapy.

ABSTRACT:

Background

Sensitive and reliable biomarkers for early detection of recurrence are needed to improve post-definitive radiation risk stratification, disease management, and outcomes for patients with unresectable early-stage or locally advanced non-small cell lung cancer (NSCLC) who are treated with definitive radiation therapy (RT). This prospective, multistate single-center, cohort study investigated the association of circulating tumor DNA (ctDNA) status with recurrence in patients with unresectable stage I-III NSCLC who underwent definitive RT.

Methods

A total of 70 serial plasma samples from 17 NSCLC patients were collected before, during, and after treatment. A personalized, tumor-informed ctDNA assay was used to track a set of up to 16 somatic, single nucleotide variants in the associated patient's plasma samples.

Results

Pre-treatment ctDNA detection rate was 82% (14/17) and varied based on histology and stage. ctDNA was detected in 35% (6/17) of patients at the first post-RT timepoint (median of 1.66 months following the completion of RT), all of whom subsequently developed clinical progression. At this first post-RT time point, patients with ctDNA-positivity had significantly worse progression-free survival (PFS) [hazard ratio (HR): 24.2, p=0.004], and ctDNA-positivity was the only significant prognostic factor associated with PFS (HR: 13.4, p=0.02) in a multivariate analysis. All patients who developed clinical recurrence had detectable ctDNA with an average lead time over radiographic progression of 5.4 months, and post-RT ctDNA positivity was significantly associated with poor PFS (p<0.0001).

Conclusion

Personalized, longitudinal ctDNA monitoring can detect recurrence early in patients with unresectable NSCLC patients undergoing curative radiation and potentially risk-stratify patients who might benefit most from treatment intensification.

SUBMITTER: Lebow ES

PROVIDER: S-EPMC10546425 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

ctDNA-based detection of molecular residual disease in stage I-III non-small cell lung cancer patients treated with definitive radiotherapy.

Lebow Emily S ES Shaverdian Narek N Eichholz Jordan E JE Kratochvil Leah B LB McCune Megan M Murciano-Goroff Yonina R YR Jee Justin J Eng Juliana J Chaft Jamie E JE Kris Mark G MG Kalashnikova Ekaterina E Feeney Jordan J Scalise Carly Bess CB Sudhaman Sumedha S Palsuledesai Charuta C CC Malhotra Meenakshi M Krainock Michael M Sethi Himanshu H Aleshin Alexey A Liu Minetta C MC Shepherd Annemarie F AF Wu Abraham J AJ Simone Charles B CB Gelblum Daphna Y DY Johnson Kaylie A KA Rudin Charles M CM Gomez Daniel R DR Razavi Pedram P Reis-Filho Jorge S JS Isbell James M JM Li Bob T BT Rimner Andreas A

Frontiers in oncology 20230919

<h4>Background</h4>Sensitive and reliable biomarkers for early detection of recurrence are needed to improve post-definitive radiation risk stratification, disease management, and outcomes for patients with unresectable early-stage or locally advanced non-small cell lung cancer (NSCLC) who are treated with definitive radiation therapy (RT). This prospective, multistate single-center, cohort study investigated the association of circulating tumor DNA (ctDNA) status with recurrence in patients wit ...[more]

PMID: 37795442

Similar Datasets

Project description:BackgroundIdentification of residual disease in patients with localized non-small cell lung cancer (NSCLC) following treatment with curative intent holds promise to identify patients at risk of relapse. New methods can detect circulating tumour DNA (ctDNA) in plasma to fractional concentrations as low as a few parts per million, and clinical evidence is required to inform their use.Patients and methodsWe analyzed 363 serial plasma samples from 88 patients with early-stage NSCLC (48.9%/28.4%/22.7% at stage I/II/III), predominantly adenocarcinomas (62.5%), treated with curative intent by surgery (n = 61), surgery and adjuvant chemotherapy/radiotherapy (n = 8), or chemoradiotherapy (n = 19). Tumour exome sequencing identified somatic mutations and plasma was analyzed using patient-specific RaDaR™ assays with up to 48 amplicons targeting tumour-specific variants unique to each patient.ResultsctDNA was detected before treatment in 24%, 77% and 87% of patients with stage I, II and III disease, respectively, and in 26% of all longitudinal samples. The median tumour fraction detected was 0.042%, with 63% of samples <0.1% and 36% of samples <0.01%. ctDNA detection had clinical specificity >98.5% and preceded clinical detection of recurrence of the primary tumour by a median of 212.5 days. ctDNA was detected after treatment in 18/28 (64.3%) of patients who had clinical recurrence of their primary tumour. Detection within the landmark timepoint 2 weeks to 4 months after treatment end occurred in 17% of patients, and was associated with shorter recurrence-free survival [hazard ratio (HR): 14.8, P <0.00001] and overall survival (HR: 5.48, P <0.0003). ctDNA was detected 1-3 days after surgery in 25% of patients yet was not associated with disease recurrence. Detection before treatment was associated with shorter overall survival and recurrence-free survival (HR: 2.97 and 3.14, P values 0.01 and 0.003, respectively).ConclusionsctDNA detection after initial treatment of patients with early-stage NSCLC using sensitive patient-specific assays has potential to identify patients who may benefit from further therapeutic intervention.

Project description:BackgroundSeveral studies have suggested that lung tissue heterogeneity is associated with overall survival (OS) in lung cancer. However, the quantitative relationship between the two remains unknown. The purpose of this study is to investigate the prognostic value of whole lung-based and tumor-based radiomics for OS in LA-NSCLC treated with definitive radiotherapy.MethodsA total of 661 patients with LA-NSCLC treated with definitive radiotherapy in combination with chemotherapy were enrolled in this study, with 292 patients in the training set, 57 patients from the same hospital from January to December 2017 as an independent test set (test-set-1), 83 patients from a multi-institutional prospective clinical trial data set (RTOG0617) as test-set-2, and 229 patients from a Dutch radiotherapy center as test-set-3. Tumor-based radiomic features and whole lung-based radiomic features were extracted from primary tumor and whole lungs (excluding the primary tumor) delineations in planning CT images. Feature selection of radiomic features was done by the least absolute shrinkage (LASSO) method embedded with a Cox proportional hazards (CPH) model with 5-fold cross-internal validation, with 1000 bootstrap samples. Radiomics prognostic scores (RS) were calculated by CPH regression based on selected features. Three models based on a tumor RS, and a lung RS separately and their combinations were constructed. The Harrell concordance index (C-index) and calibration curves were used to evaluate the discrimination and calibration performance. Patients were stratified into high and low risk groups based on median RS, and a log-rank test was performed.ResultsThe discrimination ability of lung- and tumor-based radiomics model was similar in terms of C-index, 0.69 vs. 0.68 in training set, 0.68 vs. 0.66 in test-set-1, 0.61 vs. 0.62 in test-set-2, 0.65 vs. 0.64 in test-set-3. The combination of tumor- and lung-based radiomics model performed best, with C-index of 0.71 in training set, 0.70 in test-set-1, 0.69 in test-set-2, and 0.68 in test-set-3. The calibration curve showed good agreement between predicted values and actual values. Patients were well stratified in training set, test-set-1 and test-set-3. In test-set-2, it was only whole lung-based RS that could stratify patients well and tumor-based RS performed bad.ConclusionLung- and tumor-based radiomic features have the power to predict OS in LA-NSCLC. The combination of tumor- and lung-based radiomic features can achieve optimal performance.