Shallow Whole Genome Sequencing of Circulating Tumor DNA Is Predictive of Outcome in Diffuse Large B-Cell Lymphoma
Notice bibliographique
Résumé
Introduction Patients with diffuse large B-cell lymphoma (DLBCL) have heterogeneous outcomes, which necessitates the discovery of novel prognostic biomarkers. Plasma from peripheral blood is a minimally invasive source of circulating tumor DNA (ctDNA). Prior studies using targeted DNA sequencing have revealed that pre-treatment ctDNA levels are predictive of outcome in DLBCL (Kurtz et al. 2018, etc.). However, such assays are expensive, and there remains a need for cost-effective methods to reliably estimate tumor burden in plasma. To this end, we evaluated the clinical utility of ctDNA in patients with newly diagnosed DLBCL using shallow whole genome sequencing (sWGS) - a low-cost method to estimate tumor burden without prior knowledge of tumor mutations. Methods sWGS (intended coverage 0.1-1X) was performed on plasma from peripheral blood samples of patients with newly diagnosed DLBCL at our institution. ichorCNA was used to estimate tumor fraction in plasma from sWGS (Adalsteinsson et al. 2017). FDG PET-CT scan was performed for disease burden assessment. Targeted DNA sequencing (hybridization capture) for recurrently mutated genes in lymphoma was performed on plasma samples; ctDNA concentration was expressed as log haploid genome equivalents per mL of plasma. The Fisher's exact test, Mann Whitney test and Pearson's correlation were used as appropriate to assess association between variables. Results We performed sWGS on pre-treatment plasma samples from 53 patients with newly diagnosed DLBCL (median age 62 years). The median follow-up time of the cohort was 2.6 years (range: 0.02 - 4.8 years). The majority (47/53, 89%) of patients were treated with curative-intent chemoimmunotherapy regimens including R-CHOP (n=42), R-CHOP/HD-MTX (n=4) and DA-EPOCH-R (n=1). FDG PET-CT data from time of diagnosis was available for 50 (94%) patients. High tumor fraction, as stratified by the median value (0.07, range: 0 - 0.61), in pre-treatment plasma was associated with established measures of tumor burden, including advanced-stage disease (Ann Arbor stage III/IV; 69% vs. 22%; P < 0.001), elevated serum lactate dehydrogenase (median 351 vs. 250 U/L; P < 0.001), increased tumor size (maximum diameter; median 7 vs. 4 cm; P < 0.001), and >1 extranodal site (38% vs. 7%; P = 0.009). Tumor fraction in pre-treatment plasma was correlated with total lesion glycolysis assessed by FDG PET-CT (R = 0.51, P < 0.001). Targeted DNA sequencing was performed in pre-treatment plasma samples for 50 (94%) patients, of which the majority (47/50, 94%) had detectable mutation(s) in plasma. Tumor fraction quantified using sWGS was concordant with ctDNA concentration as determined by targeted DNA sequencing (R = 0.75, P < 0.001). Among patients treated with curative-intent chemoimmunotherapy regimens, high tumor fraction in pre-treatment plasma, as determined by sWGS, was associated with inferior progression-free survival (PFS, 2-year PFS 53% vs. 94%, P < 0.001) and overall survival (OS, 2-year OS 60% vs. 97%, P = 0.003). In multivariable analysis, high cell-free tumor fraction remained a predictor for inferior PFS but not OS (PFS: P = 0.037, OS: P = 0.102), independent of International Prognostic Index scoring and total lesion glycolysis. Conclusions Tumor fraction in plasma quantified using sWGS is correlated with established measures of tumor burden in DLBCL and orthogonal measures of tumor burden in plasma. Importantly, high tumor fraction is associated with inferior outcomes in patients with newly diagnosed DLBCL. Our study highlights the potential clinical utility of sWGS performed on ctDNA as a minimally invasive assay for DLBCL risk assessment.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».