Divergent Modes of Tumor Evolution Underlie Histological Transformation and Early Progression of Follicular Lymphoma
Notice bibliographique
Résumé
Abstract Introduction: Follicular lymphoma (FL) remains a significant clinical burden as it is an incurable disease and most patients will eventually suffer from disease progression. Two clinical events are associated with poor outcomes for patients with FL: (1) histological transformation (TFL) of their original FL into a high-grade, aggressive lymphoma subtype (2-3% of patients per year) and (2) early disease progression (PFL) where patients experience treatment failure within 2 years of receiving therapy (20% of patients). Despite recent high-throughput sequencing studies, the nature of tumor clonal dynamics leading to TFL or PFL is poorly understood and it is unknown if similar, or contrasting, modes of selection underpin these FL clinical events. Materials & Methods:We assembled a study cohort consisting of 21 patients: 15 experiencing TFL and 6 PFL. For each TFL and PFL patient, we obtained primary biopsies (T1; taken at the time of the initial FL diagnosis), biopsies at transformation/progression (T2) and matched normal samples. We performed whole genome sequencing on each specimen and identified single point mutations and copy number alterations using MutationSeq and TITAN, respectively. We compared T1 to T2 somatic mutation profiles and identified mutations associated with extinction of T1 clones and expansion of T2 clones. To validate these patterns, we selected 192 positions from each patient for deep-targeted sequencing validation (~10733X) in their T1, T2, and normal samples. We applied a statistical model (PyClone) to estimate cancer cell fraction (CCF) of each validated mutation. These CCF estimates were used to construct clonal phylogenies (Citup) and infer clonal dynamic patterns during their evolutionary histories. The Wright-Fisher model of genetic drift was used to model tumor evolution. Results: Temporal analysis of mutational burden revealed that mutational burden was significantly higher in T2 (8162 mutations ± 2146) than in T1 (6373 ± 2630) tumors for both TFL and PFL patients (Wilcox P < 0.001). This was independent of time interval between sampling (Spearman R2 = 0.029, P = 0.456). Mutation variant allelic fraction (VAF) distributions revealed that all distributions showed evidence of shared clonal ancestry between T1 and T2 tumors accompanied by substantial numbers of T1 and T2-specific mutations. We selected ≥ 192 mutations per patient from these distributions and performed deep-targeted amplicon sequencing, validating 96.3% of mutations and acquiring precise VAFs to infer clonal dynamics. In 13 of 15 TFL patients (87%), we observed dramatic clonal dynamics, characteristic of T2 tumors dominated by clones (or phylogenetic lineages) that were absent or extremely rare in T1 tumors (< 1% CCF). Digital droplet PCR was used to confirm the existence of both scenarios (confirming a clone as rare as 2 out of approximately 105 cells). Tumor evolution modeling demonstrated that this mode of evolution was driven through positive selection for mutations that confer fitness advantages and not by genetic drift. In contrast, PFL patients exhibited markedly different patterns of clonal dynamics compared to TFL patients. 4 of 6 PFL patients (67%) harbored readily detectable clones at T1, which expanded to full clonal prevalence during treatment with immuno-chemotherapy. Tumor evolution modeling demonstrated that this mode of evolution could be explained under neutral evolutionary dynamics (drift). Conclusions: We have shown that histological transformation and early progression manifest through divergent modes of tumor evolution. As the transformation phenotype may arise after diagnosis, more frequent monitoring of these patients will be required to determine the exact timing of the evolutionary inflection point that elicits transformation. In comparison, prediction of early treatment resistance should be achievable through comprehensive characterization of the genetic and clonal composition at diagnosis; this would ultimately identify patients who may benefit from upfront alternative therapies without the need to first endure predictable early treatment failure. Disclosures Sehn: roche/genentech: Consultancy, Honoraria; amgen: Consultancy, Honoraria; seattle genetics: Consultancy, Honoraria; abbvie: Consultancy, Honoraria; TG therapeutics: Consultancy, Honoraria; celgene: Consultancy, Honoraria; lundbeck: Consultancy, Honoraria; janssen: Consultancy, Honoraria. Connors:Millennium Takeda: Research Funding; Seattle Genetics: Research Funding; F Hoffmann-La Roche: Research Funding; Bristol Myers Squib: Research Funding; NanoString Technologies: Research Funding. Scott:Janssen: Consultancy; Celgene: Consultancy; Roche: Honoraria; BC Cancer Agency: Patents & Royalties: Inventor on a patent licensed to NanoString Technologies.
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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 | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
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 ».