Molecular features of untreated breast cancer and initial metastatic event inform clinical decision-making and predict outcome: long-term results of ESOPE, a single-arm prospective multicenter study.


Journal

Genome medicine
ISSN: 1756-994X
Titre abrégé: Genome Med
Pays: England
ID NLM: 101475844

Informations de publication

Date de publication:
15 03 2021
Historique:
received: 30 11 2020
accepted: 25 02 2021
entrez: 16 3 2021
pubmed: 17 3 2021
medline: 21 1 2022
Statut: epublish

Résumé

Prognosis evaluation of advanced breast cancer and therapeutic strategy are mostly based on clinical features of advanced disease and molecular profiling of the primary tumor. Very few studies have evaluated the impact of metastatic subtyping during the initial metastatic event in a prospective study. The genomic landscape of metastatic breast cancer has mostly been described in very advanced, pretreated disease, limiting the findings transferability to clinical use. We developed a multicenter, single-arm, prospective clinical trial in order to address these issues. Between November 2010 and September 2013, 123 eligible patients were included. Patients at the first, untreated metastatic event were eligible. All matched primary tumors and metastatic samples were centrally reviewed for pathological typing. Targeted and whole-exome sequencing was applied to matched pairs of frozen tissue. A multivariate overall survival analysis was performed (median follow-up 64 months). Per central review in 84 patients (out of 130), we show that luminal A breast tumors are more prone to subtype switching. By combining targeted sequencing of a 91 gene panel (n = 67) and whole-exome sequencing (n = 30), a slight excess of mutations is observed in the metastases. Luminal A breast cancer has the most heterogeneous mutational profile and the highest number of mutational signatures, when comparing primary tumor and the matched metastatic tissue. Tumors with a subtype change have more mutations that are private. The metastasis-specific mutation load is significantly higher in late than in de novo metastases. The most frequently mutated genes were TP53 and PIK3CA. The most frequent metastasis-specific druggable genes were PIK3CA, PTEN, KDR, ALK, CDKN2A, NOTCH4, POLE, SETD2, SF3B1, and TSC2. Long-term outcome is driven by a combination of tumor load and metastasis biology. Profiling of the first, untreated, metastatic event of breast cancer reveals a profound heterogeneity mostly in luminal A tumors and in late metastases. Based on this profiling, we can derive information relevant to prognosis and therapeutic intervention, which support current guidelines recommending a biopsy at the first metastatic relapse. The trial was registered at ClinicalTrials.gov ( NCT01956552 ).

Sections du résumé

BACKGROUND
Prognosis evaluation of advanced breast cancer and therapeutic strategy are mostly based on clinical features of advanced disease and molecular profiling of the primary tumor. Very few studies have evaluated the impact of metastatic subtyping during the initial metastatic event in a prospective study. The genomic landscape of metastatic breast cancer has mostly been described in very advanced, pretreated disease, limiting the findings transferability to clinical use.
METHODS
We developed a multicenter, single-arm, prospective clinical trial in order to address these issues. Between November 2010 and September 2013, 123 eligible patients were included. Patients at the first, untreated metastatic event were eligible. All matched primary tumors and metastatic samples were centrally reviewed for pathological typing. Targeted and whole-exome sequencing was applied to matched pairs of frozen tissue. A multivariate overall survival analysis was performed (median follow-up 64 months).
RESULTS
Per central review in 84 patients (out of 130), we show that luminal A breast tumors are more prone to subtype switching. By combining targeted sequencing of a 91 gene panel (n = 67) and whole-exome sequencing (n = 30), a slight excess of mutations is observed in the metastases. Luminal A breast cancer has the most heterogeneous mutational profile and the highest number of mutational signatures, when comparing primary tumor and the matched metastatic tissue. Tumors with a subtype change have more mutations that are private. The metastasis-specific mutation load is significantly higher in late than in de novo metastases. The most frequently mutated genes were TP53 and PIK3CA. The most frequent metastasis-specific druggable genes were PIK3CA, PTEN, KDR, ALK, CDKN2A, NOTCH4, POLE, SETD2, SF3B1, and TSC2. Long-term outcome is driven by a combination of tumor load and metastasis biology.
CONCLUSIONS
Profiling of the first, untreated, metastatic event of breast cancer reveals a profound heterogeneity mostly in luminal A tumors and in late metastases. Based on this profiling, we can derive information relevant to prognosis and therapeutic intervention, which support current guidelines recommending a biopsy at the first metastatic relapse.
TRIAL REGISTRATION
The trial was registered at ClinicalTrials.gov ( NCT01956552 ).

Identifiants

pubmed: 33722295
doi: 10.1186/s13073-021-00862-6
pii: 10.1186/s13073-021-00862-6
pmc: PMC7962302
doi:

Banques de données

ClinicalTrials.gov
['NCT01956552']

Types de publication

Journal Article Multicenter Study Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

44

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Auteurs

Céline Callens (C)

Genetics Department, Institut Curie, PSL Research University, Paris, France.

Keltouma Driouch (K)

Genetics Department, Institut Curie, PSL Research University, Paris, France.

Anaïs Boulai (A)

Genetics Department, Institut Curie, PSL Research University, Paris, France.

Zakia Tariq (Z)

Genetics Department, Institut Curie, PSL Research University, Paris, France.

Aurélie Comte (A)

Department of Medical Oncology, Institut Curie, PSL Research University, 26 rue d'Ulm, 75005, Paris, France.

Frédérique Berger (F)

Department of Biostatistics, Institut Curie, Saint-Cloud, France.

Lisa Belin (L)

Department of Biostatistics, Institut Curie, Saint-Cloud, France.

Ivan Bièche (I)

Genetics Department, Institut Curie, PSL Research University, Paris, France.

Vincent Servois (V)

Imaging Department, Institut Curie, PSL Research University, Paris, France.

Patricia Legoix (P)

Institut Curie Genomics of Excellence (ICGex) Platform, Institut Curie Research Center, PSL Research University, Paris, France.

Virginie Bernard (V)

Institut Curie Genomics of Excellence (ICGex) Platform, Institut Curie Research Center, PSL Research University, Paris, France.

Sylvain Baulande (S)

Institut Curie Genomics of Excellence (ICGex) Platform, Institut Curie Research Center, PSL Research University, Paris, France.

Walid Chemlali (W)

Genetics Department, Institut Curie, PSL Research University, Paris, France.

François-Clément Bidard (FC)

Department of Medical Oncology, Institut Curie, PSL Research University, 26 rue d'Ulm, 75005, Paris, France.

Virginie Fourchotte (V)

Surgery Department, Institut Curie, Paris, France.

Anne Vincent- Salomon (AV)

Pathology and Tumor Biology Department, Institut Curie, PSL Research University, Paris, France.

Etienne Brain (E)

Medical Oncology, Institut Curie, Saint-Cloud, France.

Rosette Lidereau (R)

Genetics Department, Institut Curie, PSL Research University, Paris, France.

Thomas Bachelot (T)

Centre Léon Bérard, Lyon, France.

Mahasti Saghatchian (M)

Gustave Roussy Cancer Campus, Villejuif, France.

Mario Campone (M)

Institut de Cancérologie de l'Ouest Nantes, Nantes, France.

Sylvie Giacchetti (S)

Hôpital Saint Louis, Breast diseases center, Paris, France.

Brigitte Sigal Zafrani (BS)

Pathology and Tumor Biology Department, Institut Curie, PSL Research University, Paris, France.

Paul Cottu (P)

Department of Medical Oncology, Institut Curie, PSL Research University, 26 rue d'Ulm, 75005, Paris, France. paul.cottu@curie.fr.

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