Early Identification of Residual Disease After Neuroendocrine Tumor Resection Using a Liquid Biopsy Multigenomic mRNA Signature (NETest).


Journal

Annals of surgical oncology
ISSN: 1534-4681
Titre abrégé: Ann Surg Oncol
Pays: United States
ID NLM: 9420840

Informations de publication

Date de publication:
Nov 2021
Historique:
received: 09 10 2020
accepted: 31 03 2021
pubmed: 20 5 2021
medline: 21 10 2021
entrez: 19 5 2021
Statut: ppublish

Résumé

Surgery is the only cure for neuroendocrine tumors (NETs), with R0 resection being critical for successful tumor removal. Early detection of residual disease is key for optimal management, but both imaging and current biomarkers are ineffective post-surgery. NETest, a multigene blood biomarker, identifies NETs with >90% accuracy. We hypothesized that surgery would decrease NETest levels and that elevated scores post-surgery would predict recurrence. This was a multicenter evaluation of surgically treated primary NETs (n = 153). Blood sampling was performed at day 0 and postoperative day (POD) 30. Follow-up included computed tomography/magnetic resonance imaging (CT/MRI), and messenger RNA (mRNA) quantification was performed by polymerase chain reaction (PCR; NETest score: 0-100; normal ≤20). Statistical analyses were performed using the Mann-Whitney U-test, Chi-square test, Kaplan-Meier survival, and area under the receiver operating characteristic curve (AUROC), as appropriate. Data are presented as mean ± standard deviation. The NET cohort (n = 153) included 57 patients with pancreatic cancer, 62 patients with small bowel cancer, 27 patients with lung cancer, 4 patients with duodenal cancer, and 3 patients with gastric cancer, while the surgical cohort comprised patients with R0 (n = 102) and R1 and R2 (n = 51) resection. The mean follow-up time was 14 months (range 3-68). The NETest was positive in 153/153 (100%) samples preoperatively (mean levels of 68 ± 28). In the R0 cohort, POD30 levels decreased from 62 ± 28 to 22 ± 20 (p < 0.0001), but remained elevated in 30% (31/102) of patients: 28% lung, 29% pancreas, 27% small bowel, and 33% gastric. By 18 months, 25/31 (81%) patients with a POD30 NETest >20 had image-identifiable recurrence. An NETest score of >20 predicted recurrence with 100% sensitivity and correlated with residual disease (Chi-square 17.1, p < 0.0001). AUROC analysis identified an AUC of 0.97 (p < 0.0001) for recurrence-prediction. In the R1 (n = 29) and R2 (n = 22) cohorts, the score decreased (R1: 74 ± 28 to 45 ± 24, p = 0.0012; R2: 72 ± 24 to 60 ± 28, p = non-significant). At POD30, 100% of NETest scores were elevated despite surgery (p < 0.0001). The preoperative NETest accurately identified all NETs (100%). All resections decreased NETest levels and a POD30 NETest score >20 predicted radiologically recurrent disease with 94% accuracy and 100% sensitivity. R0 resection appears to be ineffective in approximately 30% of patients. NET mRNA blood levels provide early objective genomic identification of residual disease and may facilitate management.

Identifiants

pubmed: 34008138
doi: 10.1245/s10434-021-10021-1
pii: 10.1245/s10434-021-10021-1
doi:

Substances chimiques

Biomarkers, Tumor 0
RNA, Messenger 0

Types de publication

Journal Article Multicenter Study

Langues

eng

Sous-ensembles de citation

IM

Pagination

7506-7517

Commentaires et corrections

Type : CommentIn

Informations de copyright

© 2021. Society of Surgical Oncology.

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Auteurs

Irvin M Modlin (IM)

Yale University School of Medicine, New Haven, CT, USA. imodlin@irvinmodlin.com.

Mark Kidd (M)

Wren Laboratories, Branford, CT, USA.

Kjell Oberg (K)

University Hospital, Uppsala, Sweden.

Massimo Falconi (M)

IRCCS San Raffaele Scientific Institute, Milan, Italy.

Pier Luigi Filosso (PL)

University of Torino, Torino, Italy.

Andrea Frilling (A)

Imperial College London, London, UK.

Anna Malczewska (A)

Medical University of Silesia, Katowice, Poland.

Ronald Salem (R)

Yale University School of Medicine, New Haven, CT, USA.

Christos Toumpanakis (C)

Royal Free Hospital, London, UK.

Faidon-Marios Laskaratos (FM)

Royal Free Hospital, London, UK.

Stefano Partelli (S)

IRCCS San Raffaele Scientific Institute, Milan, Italy.

Matteo Roffinella (M)

University of Torino, Torino, Italy.

Claudia von Arx (C)

Imperial College London, London, UK.

Beata Kos Kudla (BK)

Medical University of Silesia, Katowice, Poland.

Lisa Bodei (L)

Memorial Sloan Kettering Cancer Center, New York, NY, USA.

Ignat A Drozdov (IA)

Wren Laboratories, Branford, CT, USA.

Alexandra Kitz (A)

Wren Laboratories, Branford, CT, USA.

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