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Enregistrement W4389232111 · doi:10.1182/blood-2023-182906

Evaluation of a Prognostic 23-Gene Expression Panel in a Set of 390 Multiethnic Hodgkin Lymphoma Patients

2023· article· en· W4389232111 sur OpenAlexaff
Anthony Colombo, Jia Y. Wan, Aixiang Jiang, Jose Aparicio, Esther Lam, Tomohiro Aoki, Joo Y. Song, Sheeja T. Pullarkat, Chun Chao, Juan Manuel Mejía‐Aranguré, Brenda Y. Hernandez, Pamela B. Allen, Christopher R. Flowers, Sophia Wang, Juanita Evans, Owen Chan, Jakub Svoboda, Anja Mottok, Leon Bernal‐Mizrachi, Megan S. Lim, David W. Scott, Imran Siddiqi, David V. Conti, Christian Steidl, Wendy Cozen

Notice bibliographique

RevueBlood · 2023
Typearticle
Langueen
DomaineMedicine
ThématiqueLymphoma Diagnosis and Treatment
Établissements canadiensBC Cancer AgencySpinal Cord Injury BC
Organismes subventionnairesnon disponible
Mots-clésMedicineOncologyInternal medicineLymphomaEpidemiologyDiffuse large B-cell lymphomaNodular sclerosisPathologyHodgkin lymphoma

Résumé

récupéré en direct d'OpenAlex

Classic Hodgkin lymphoma (CHL) is a curable lymphoma with unique epidemiological features, including rare neoplastic cells, and an adolescent/young adult age (AYA) incidence peak higher in females, correlated with socioeconomic status. We previously reported that RNA expression measured in formalin-fixed paraffin-embedded (FFPE) tissues using a panel of 23 immune response genes was associated with 5-year overall survival (OS) in predominantly White/European advanced staged CHL patients treated with ABVD chemotherapy (Scott et al., 2013). Here, we conducted a study to evaluate if the 23-gene expression panel predicted 5-year OS in a real-world set of FFPE tumor tissue from racially/ethnically diverse populations in the U.S. and Mexico from the Multi- Ethnic Study of Hodgkin Lymphoma (MESH). Methods: We collected tumor blocks from 891 patients from 9 hospitals and 2 Residual Tissue Repositories in the U.S. and Mexico. Demographic and clinical data, including survival by month, age at diagnosis, birth year, race/ethnicity, sex, histology were abstracted. Patients were diagnosed from 1978 to 2018. CHL diagnoses and histological subtypes were validated by histopathology review. RNA was extracted and NanoString gene expression profiling was performed using 250-gene codeset including the published 23-gene expression (GE) panel (Scott et. al., 2013). 390 cases passed QC after applying a modified normalization protocol (Chan et.al., 2017). The multiethnic risk index was derived from a linear equation of the log 2 GE in MESH multiplied by the established regression coefficients (Scott et al., 2013). Next, we examined a dichotomized score using a cut-off value at the 75 th percentile. Both were tested for association with 5-year OS using a Cox proportional hazards model using R software 4.3.0 version adjusting for age as a continuous and categorical variable (15-39 and 40+ years), type of hospital that provided the samples, sex, EBV tumor status (assigned using EBER2), race/ethnicity, and histology. The 5-year OS was computed by right-censoring any patient that survived longer than 5 years. Proportionality assumptions were tested (using an alpha level of 0.05). Stratified models by categorical age and race/ethnicity were adjusted for patient demographical variables and effect heterogeneity was examined using a t-test. Result: The analytic set comprised 47% females and 44% Hispanic, 24% White, 20% Black, 9% Asian, and 3% Pacific Islander/Native Hawaiians. 62% were 15-39 years and 32% 40+ years at diagnosis. The multivariate analysis, excluding age, indicated an association between the risk index and 5-year OS (HR =1.41 (1.08, 1.93), p=0.01). However, including age attenuated the association (HR=1.18 (0.89, 1.57), p=0.25). When stratified by age categories, the continuous risk index was associated with 5-year OS only in the older age group: 15-39 years (HR = 0.89 (0.54, 1.47), p=0.65), and >40 years (HR = 1.47 (1.08, 2.01), p=0.02), indicating effect heterogeneity by age groups (p=0.09). We did not observe significant effect heterogeneity by EBV status (p=0.6), race/ethnicity (p=0.72), nor sex (p=0.33). When the dichotomized score was examined, the model was attenuated after adjusting for age, but stratification analysis showed an association in the older adults (HR= 2.8 (1.42, 5.51), p<0.01) with significant effect heterogeneity across age (p=0.02) (Figure 1). Evaluating the association between individual genes and OS, we found effect heterogeneity by age; two genes from the 23-gene panel indicated protective effects in the AYA but hazardous in older adults: B2M (p effect heterogeneity' =0.048) and APOL6 (p'=0.059). Individual genes with heterogeneity of association across race/ethnicity included RNF144B (p' =0.0021) with increased hazards in Whites compared to Blacks, whereas WDR83 (p'=0.018), PRF1 (p'=0.0018), LYZ (p'=0.0028) had higher hazards in Blacks compared to Whites, and RAPGEF2 (p'=0.036) had higher hazards in Hispanics compared to Whites. Conclusion: The risk index and dichotomized score derived from the previously developed 23-gene panel was significantly associated with OS among >40 year age group, with evidence of age effect heterogeneity. We detected 2 genes with an association that varied by age, and five across race/ethnicity, suggesting the importance of including diverse racial/ethnic and age groups in CHL patient populations when developing predictive models.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,001
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,002
Score d'incertitude au seuil0,004

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0000,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

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.

Tête enseignante Opus0,074
Tête enseignante GPT0,318
Écart entre enseignants0,244 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2023
Routes d'admission1
Résumé présentoui

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