Deciphering resistance mechanisms in cancer: final report of MATCH-R study with a focus on molecular drivers and PDX development.


Journal

Molecular cancer
ISSN: 1476-4598
Titre abrégé: Mol Cancer
Pays: England
ID NLM: 101147698

Informations de publication

Date de publication:
04 Oct 2024
Historique:
received: 08 07 2024
accepted: 20 09 2024
medline: 4 10 2024
pubmed: 4 10 2024
entrez: 4 10 2024
Statut: epublish

Résumé

Understanding the resistance mechanisms of tumor is crucial for advancing cancer therapies. The prospective MATCH-R trial (NCT02517892), led by Gustave Roussy, aimed to characterize resistance mechanisms to cancer treatments through molecular analysis of fresh tumor biopsies. This report presents the genomic data analysis of the MATCH-R study conducted from 2015 to 2022 and focuses on targeted therapies. The study included resistant metastatic patients (pts) who accepted an image-guided tumor biopsy. After evaluation of tumor content (TC) in frozen tissue biopsies, targeted NGS (10 < TC < 30%) or Whole Exome Sequencing and RNA sequencing (TC > 30%) were performed before and/or after the anticancer therapy. Patient-derived xenografts (PDX) were established by implanting tumor fragments into NOD scid gamma mice and amplified up to five passages. A total of 1,120 biopsies were collected from 857 pts with the most frequent tumor types being lung (38.8%), digestive (16.3%) and prostate (14.1%) cancer. Molecular targetable driver were identified in 30.9% (n = 265/857) of the patients, with EGFR (41.5%), FGFR2/3 (15.5%), ALK (11.7%), BRAF (6.8%), and KRAS (5.7%) being the most common altered genes. Furthermore, 66.0% (n = 175/265) had a biopsy at progression on targeted therapy. Among resistant cases, 41.1% (n = 72/175) had no identified molecular mechanism, 32.0% (n = 56/175) showed on-target resistance, and 25.1% (n = 44/175) exhibited a by-pass resistance mechanism. Molecular profiling of the 44 patients with by-pass resistance identified 51 variants, with KRAS (13.7%), PIK3CA (11.8%), PTEN (11.8%), NF2 (7.8%), AKT1 (5.9%), and NF1 (5.9%) being the most altered genes. Treatment was tailored for 45% of the patients with a resistance mechanism identified leading to an 11 months median extension of clinical benefit. A total of 341 biopsies were implanted in mice, successfully establishing 136 PDX models achieving a 39.9% success rate. PDX models are available for EGFR (n = 31), FGFR2/3 (n = 26), KRAS (n = 18), ALK (n = 16), BRAF (n = 6) and NTRK (n = 2) driven cancers. These models closely recapitulate the biology of the original tumors in term of molecular alterations and pharmacological status, and served as valuable models to validate overcoming treatment strategies. The MATCH-R study highlights the feasibility of on purpose image guided tumor biopsies and PDX establishment to characterize resistance mechanisms and guide personalized therapies to improve outcomes in pre-treated metastatic patients.

Sections du résumé

BACKGROUND BACKGROUND
Understanding the resistance mechanisms of tumor is crucial for advancing cancer therapies. The prospective MATCH-R trial (NCT02517892), led by Gustave Roussy, aimed to characterize resistance mechanisms to cancer treatments through molecular analysis of fresh tumor biopsies. This report presents the genomic data analysis of the MATCH-R study conducted from 2015 to 2022 and focuses on targeted therapies.
METHODS METHODS
The study included resistant metastatic patients (pts) who accepted an image-guided tumor biopsy. After evaluation of tumor content (TC) in frozen tissue biopsies, targeted NGS (10 < TC < 30%) or Whole Exome Sequencing and RNA sequencing (TC > 30%) were performed before and/or after the anticancer therapy. Patient-derived xenografts (PDX) were established by implanting tumor fragments into NOD scid gamma mice and amplified up to five passages.
RESULTS RESULTS
A total of 1,120 biopsies were collected from 857 pts with the most frequent tumor types being lung (38.8%), digestive (16.3%) and prostate (14.1%) cancer. Molecular targetable driver were identified in 30.9% (n = 265/857) of the patients, with EGFR (41.5%), FGFR2/3 (15.5%), ALK (11.7%), BRAF (6.8%), and KRAS (5.7%) being the most common altered genes. Furthermore, 66.0% (n = 175/265) had a biopsy at progression on targeted therapy. Among resistant cases, 41.1% (n = 72/175) had no identified molecular mechanism, 32.0% (n = 56/175) showed on-target resistance, and 25.1% (n = 44/175) exhibited a by-pass resistance mechanism. Molecular profiling of the 44 patients with by-pass resistance identified 51 variants, with KRAS (13.7%), PIK3CA (11.8%), PTEN (11.8%), NF2 (7.8%), AKT1 (5.9%), and NF1 (5.9%) being the most altered genes. Treatment was tailored for 45% of the patients with a resistance mechanism identified leading to an 11 months median extension of clinical benefit. A total of 341 biopsies were implanted in mice, successfully establishing 136 PDX models achieving a 39.9% success rate. PDX models are available for EGFR (n = 31), FGFR2/3 (n = 26), KRAS (n = 18), ALK (n = 16), BRAF (n = 6) and NTRK (n = 2) driven cancers. These models closely recapitulate the biology of the original tumors in term of molecular alterations and pharmacological status, and served as valuable models to validate overcoming treatment strategies.
CONCLUSION CONCLUSIONS
The MATCH-R study highlights the feasibility of on purpose image guided tumor biopsies and PDX establishment to characterize resistance mechanisms and guide personalized therapies to improve outcomes in pre-treated metastatic patients.

Identifiants

pubmed: 39363320
doi: 10.1186/s12943-024-02134-4
pii: 10.1186/s12943-024-02134-4
doi:

Substances chimiques

Biomarkers, Tumor 0

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

221

Informations de copyright

© 2024. The Author(s).

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Auteurs

Damien Vasseur (D)

Medical Biology and Pathology Department, Gustave Roussy, Villejuif, France.
AMMICa UAR3655/US23, Gustave Roussy, Villejuif, France.

Ludovic Bigot (L)

Université Paris-Saclay, Gustave Roussy, Inserm U981, Villejuif, France.

Kristi Beshiri (K)

Département d'Innovation Thérapeutique (DITEP), Gustave Roussy, Villejuif, France.

Juan Flórez-Arango (J)

Université Paris-Saclay, Gustave Roussy, Inserm U981, Villejuif, France.

Francesco Facchinetti (F)

Université Paris-Saclay, Gustave Roussy, Inserm U981, Villejuif, France.

Antoine Hollebecque (A)

Département d'Innovation Thérapeutique (DITEP), Gustave Roussy, Villejuif, France.
Département de Médecine Oncologique, Gustave Roussy, Villejuif, France.

Lambros Tselikas (L)

Department of Interventional Radiology, BIOTHERIS, Gustave Roussy, Université Paris-Saclay, Villejuif, France.

Mihaela Aldea (M)

Département de Médecine Oncologique, Gustave Roussy, Villejuif, France.

Felix Blanc-Durand (F)

Département de Médecine Oncologique, Gustave Roussy, Villejuif, France.

Anas Gazzah (A)

Département d'Innovation Thérapeutique (DITEP), Gustave Roussy, Villejuif, France.

David Planchard (D)

Département de Médecine Oncologique, Gustave Roussy, Villejuif, France.

Ludovic Lacroix (L)

Medical Biology and Pathology Department, Gustave Roussy, Villejuif, France.
AMMICa UAR3655/US23, Gustave Roussy, Villejuif, France.

Noémie Pata-Merci (N)

AMMICa UAR3655/US23, Gustave Roussy, Villejuif, France.

Catline Nobre (C)

Université Paris-Saclay, Gustave Roussy, Inserm U981, Villejuif, France.

Alice Da Silva (A)

Université Paris-Saclay, Gustave Roussy, Inserm U981, Villejuif, France.

Claudio Nicotra (C)

Département d'Innovation Thérapeutique (DITEP), Gustave Roussy, Villejuif, France.

Maud Ngo-Camus (M)

Département d'Innovation Thérapeutique (DITEP), Gustave Roussy, Villejuif, France.

Floriane Braye (F)

Université Paris-Saclay, Gustave Roussy, Inserm U981, Villejuif, France.

Sergey I Nikolaev (SI)

Université Paris-Saclay, Gustave Roussy, Inserm U981, Villejuif, France.

Stefan Michiels (S)

Université Paris-Saclay, CESP, InsermVillejuif, France.
Office of Biostatistics and Epidemiology, Gustave Roussy, Villejuif, France.

Gérôme Jules-Clement (G)

Bioinformatics Core Facility, Gustave Roussy, Université Paris-Saclay, CNRS UMS 3655, Inserm US23, Villejuif, France.

Ken André Olaussen (KA)

Université Paris-Saclay, Gustave Roussy, Inserm U981, Villejuif, France.

Fabrice André (F)

Université Paris-Saclay, Gustave Roussy, Inserm U981, Villejuif, France.
Département de Médecine Oncologique, Gustave Roussy, Villejuif, France.

Jean-Yves Scoazec (JY)

Medical Biology and Pathology Department, Gustave Roussy, Villejuif, France.
AMMICa UAR3655/US23, Gustave Roussy, Villejuif, France.

Fabrice Barlesi (F)

Département de Médecine Oncologique, Gustave Roussy, Villejuif, France.

Santiago Ponce (S)

Université Paris-Saclay, Gustave Roussy, Inserm U981, Villejuif, France.
Département de Médecine Oncologique, Gustave Roussy, Villejuif, France.

Jean-Charles Soria (JC)

Université Paris-Saclay, Gustave Roussy, Inserm U981, Villejuif, France.
Département de Médecine Oncologique, Gustave Roussy, Villejuif, France.

Benjamin Besse (B)

Université Paris-Saclay, Gustave Roussy, Inserm U981, Villejuif, France.
Département de Médecine Oncologique, Gustave Roussy, Villejuif, France.

Yohann Loriot (Y)

Université Paris-Saclay, Gustave Roussy, Inserm U981, Villejuif, France. yohann.loriot@gustaveroussy.fr.
Département d'Innovation Thérapeutique (DITEP), Gustave Roussy, Villejuif, France. yohann.loriot@gustaveroussy.fr.
Département de Médecine Oncologique, Gustave Roussy, Villejuif, France. yohann.loriot@gustaveroussy.fr.

Luc Friboulet (L)

Université Paris-Saclay, Gustave Roussy, Inserm U981, Villejuif, France. luc.friboulet@gustaveroussy.fr.

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