High throughput profiling of undifferentiated pleomorphic sarcomas identifies two main subgroups with distinct immune profile, clinical outcome and sensitivity to targeted therapies.


Journal

EBioMedicine
ISSN: 2352-3964
Titre abrégé: EBioMedicine
Pays: Netherlands
ID NLM: 101647039

Informations de publication

Date de publication:
Dec 2020
Historique:
received: 19 04 2020
revised: 01 10 2020
accepted: 29 10 2020
pubmed: 1 12 2020
medline: 25 8 2021
entrez: 30 11 2020
Statut: ppublish

Résumé

Undifferentiated pleomorphic sarcoma (UPS) is the most frequent, aggressive and less-characterized sarcoma subtype. This study aims to assess UPS molecular characteristics and identify specific therapeutic targets. High-throughput technologies encompassing immunohistochemistry, RNA-sequencing, whole exome-sequencing, mass spectrometry, as well as radiomics were used to characterize three independent cohorts of 110, 25 and 41 UPS selected after histological review performed by an expert pathologist. Correlations were made with clinical outcome. Cell lines and xenografts were derived from human samples for functional experiments. CD8 positive cell density was independently associated with metastatic behavior and prognosis. RNA-sequencing identified two main groups: the group A, enriched in genes involved in development and stemness, including FGFR2, and the group B, strongly enriched in genes involved in immunity. Immune infiltrate patterns on tumor samples were highly predictive of gene expression classification, leading to call the group B 'immune-high' and the group A 'immune-low'. This molecular classification and its prognostic impact were confirmed on an independent cohort of UPS from TCGA. Copy numbers alterations were significantly more frequent in immune-low UPS. Proteomic analysis identified two main proteomic groups that highly correlated with the two main transcriptomic groups. A set of nine radiomic features from conventional MRI sequences provided the basis for a radiomics signature that could select immune-high UPS on their pre-therapeutic imaging. Finally, in vitro and in vivo anti-tumor activity of FGFR inhibitor JNJ-42756493 was selectively shown in cell lines and patient-derived xenograft models derived from immune-low UPS. Two main disease entities of UPS, with distinct immune phenotypes, prognosis, molecular features and MRI textures, as well as differential sensitivity to specific anticancer agents were identified. Immune-high UPS may be the best candidates for immune checkpoint inhibitors, whereas this study provides rational for assessing FGFR inhibition in immune-low UPS. This work was partly founded by a grant from La Ligue.

Sections du résumé

BACKGROUND BACKGROUND
Undifferentiated pleomorphic sarcoma (UPS) is the most frequent, aggressive and less-characterized sarcoma subtype. This study aims to assess UPS molecular characteristics and identify specific therapeutic targets.
METHODS METHODS
High-throughput technologies encompassing immunohistochemistry, RNA-sequencing, whole exome-sequencing, mass spectrometry, as well as radiomics were used to characterize three independent cohorts of 110, 25 and 41 UPS selected after histological review performed by an expert pathologist. Correlations were made with clinical outcome. Cell lines and xenografts were derived from human samples for functional experiments.
FINDINGS RESULTS
CD8 positive cell density was independently associated with metastatic behavior and prognosis. RNA-sequencing identified two main groups: the group A, enriched in genes involved in development and stemness, including FGFR2, and the group B, strongly enriched in genes involved in immunity. Immune infiltrate patterns on tumor samples were highly predictive of gene expression classification, leading to call the group B 'immune-high' and the group A 'immune-low'. This molecular classification and its prognostic impact were confirmed on an independent cohort of UPS from TCGA. Copy numbers alterations were significantly more frequent in immune-low UPS. Proteomic analysis identified two main proteomic groups that highly correlated with the two main transcriptomic groups. A set of nine radiomic features from conventional MRI sequences provided the basis for a radiomics signature that could select immune-high UPS on their pre-therapeutic imaging. Finally, in vitro and in vivo anti-tumor activity of FGFR inhibitor JNJ-42756493 was selectively shown in cell lines and patient-derived xenograft models derived from immune-low UPS.
INTERPRETATION CONCLUSIONS
Two main disease entities of UPS, with distinct immune phenotypes, prognosis, molecular features and MRI textures, as well as differential sensitivity to specific anticancer agents were identified. Immune-high UPS may be the best candidates for immune checkpoint inhibitors, whereas this study provides rational for assessing FGFR inhibition in immune-low UPS.
FUNDING BACKGROUND
This work was partly founded by a grant from La Ligue.

Identifiants

pubmed: 33254023
pii: S2352-3964(20)30507-7
doi: 10.1016/j.ebiom.2020.103131
pmc: PMC7708794
pii:
doi:

Substances chimiques

Biomarkers, Tumor 0

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

103131

Informations de copyright

Copyright © 2020. Published by Elsevier B.V.

Déclaration de conflit d'intérêts

Declaration of Competing Interests JA reports personal fees from AstraZeneca, Roche, BMS, Bayer and MSD outside the submitted work. SS reports employment by AstraZeneca outside the submitted work. TH reports employment by Nantomics and AstraZeneca outside the submitted work, and patents use from Nantomics relevant to this work. AI reports grants and personal fees from Bayer, Roche, grants from MSD, AstraZeneca, Pharmamar, personal fees from Springworks, Ipsen, and non-financial support from Merck outside the submitted work. The other authors declare no conflict of interest.

Auteurs

Maud Toulmonde (M)

Medical Oncology Department, Institut Bergonié, Bordeaux, France; University of Bordeaux, Bordeaux, France.

Carlo Lucchesi (C)

Bioinformatics Department, Institut Bergonié, Bordeaux, France; INSERM U1218, Bordeaux, France.

Stéphanie Verbeke (S)

INSERM U1218, Bordeaux, France; Research Department, Institut Bergonié, Bordeaux, France.

Amandine Crombe (A)

University of Bordeaux, Bordeaux, France; Radiology Department, Institut Bergonié, Bordeaux, France.

Julien Adam (J)

Pathology Department, Gustave Roussy, Villejuif, France.

Damien Geneste (D)

Bioinformatics Department, Institut Bergonié, Bordeaux, France; INSERM U1218, Bordeaux, France.

Vanessa Chaire (V)

INSERM U1218, Bordeaux, France; Research Department, Institut Bergonié, Bordeaux, France.

Audrey Laroche-Clary (A)

Bioinformatics Department, Institut Bergonié, Bordeaux, France; Research Department, Institut Bergonié, Bordeaux, France.

Raul Perret (R)

Pathology Department, Institut Bergonié, Bordeaux, France.

François Bertucci (F)

Centre de Recherche en Cancérologie de Marseille (CRCM), CNRS UMR725, INSERM U1068, Institut Paoli-Calmettes, Aix-Marseille Université, Marseille, France.

Frederic Bertolo (F)

Bioinformatics Department, Institut Bergonié, Bordeaux, France; INSERM U1218, Bordeaux, France.

Laurence Bianchini (L)

Laboratory of solid tumor genetics, Université Côte d'Azur (UCA), CNRS UMR7284, INSERM U1081, Institute for Research on Cancer and Aging, Nice (IRCAN), Nice, France.

Bérengère Dadone-Montaudie (B)

Laboratory of solid tumor genetics, Université Côte d'Azur (UCA), CNRS UMR7284, INSERM U1081, Institute for Research on Cancer and Aging, Nice (IRCAN), Nice, France.

Todd Hembrough (T)

Nantomics, Rockville, MD, United States.

Steve Sweet (S)

Nantomics, Rockville, MD, United States.

Yeoun Jin Kim (YJ)

Nantomics, Rockville, MD, United States.

Fabiola Cecchi (F)

Nantomics, Rockville, MD, United States.

François Le Loarer (F)

Pathology Department, Institut Bergonié, Bordeaux, France.

Antoine Italiano (A)

Medical Oncology Department, Institut Bergonié, Bordeaux, France; University of Bordeaux, Bordeaux, France; INSERM U1218, Bordeaux, France. Electronic address: a.italiano@bordeaux.unicancer.fr.

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Classifications MeSH