Profiling of circulating tumor DNA and tumor tissue for treatment selection in patients with advanced and refractory carcinoma: a prospective, two-stage phase II Individualized Cancer Treatment trial.

circulating tumor DNA molecular profiling progression-free survival ratio refractory cancer

Journal

Therapeutic advances in medical oncology
ISSN: 1758-8340
Titre abrégé: Ther Adv Med Oncol
Pays: England
ID NLM: 101510808

Informations de publication

Date de publication:
2021
Historique:
received: 28 07 2020
accepted: 17 12 2020
entrez: 15 3 2021
pubmed: 16 3 2021
medline: 16 3 2021
Statut: epublish

Résumé

Molecular profiling (MP) represents an opportunity to match patients to a targeted therapy and when tumor tissue is unavailable, circulating tumor deoxyribonucleic acid (ctDNA) can be harnessed as a non-invasive analyte for this purpose. We evaluated the success of a targeted therapy selected by profiling of ctDNA and tissue in patients with advanced and refractory carcinoma. A blood draw as well as an optional tissue biopsy were obtained for MP. Whole-genome sequencing and a cancer hotspot panel were performed, and publicly available databases were used to match the molecular profile to targeted treatments. The primary endpoint was the progression-free survival (PFS) ratio (PFS on MP-guided therapy/PFS on the last evidence-based therapy), whereas the success of the targeted therapy was defined as a PFS ratio ⩾1.2. To test the impact of molecular profile-treatment matching strategies, we retrospectively analyzed selected cases Interim analysis of 24 patients yielded informative results from 20 patients (83%). A potential tumor-specific drug could be matched in 11 patients (46%) and eight (33%) received a matched treatment. Median PFS in the matched treatment group was 61.5 days [interquartile range (IQR) 49.8-71.0] compared with 81.5 days (IQR 68.5-117.8) for the last evidence-based treatment, resulting in a median PFS ratio of 0.7 (IQR 0.6-0.9). Hence, as no patient experienced a PFS ratio ⩾1.2, the study was terminated. Except for one case, the CureMatch analysis identified either a two-drug or three-drug combination option. Our study employed a histotype-agnostic approach to harness molecular profiling data from both ctDNA and metastatic tumor tissue. The outcome results indicate that more innovative approaches to study design and matching algorithms are necessary to achieve improved patient outcomes.

Sections du résumé

BACKGROUND BACKGROUND
Molecular profiling (MP) represents an opportunity to match patients to a targeted therapy and when tumor tissue is unavailable, circulating tumor deoxyribonucleic acid (ctDNA) can be harnessed as a non-invasive analyte for this purpose. We evaluated the success of a targeted therapy selected by profiling of ctDNA and tissue in patients with advanced and refractory carcinoma.
PATIENTS AND METHODS METHODS
A blood draw as well as an optional tissue biopsy were obtained for MP. Whole-genome sequencing and a cancer hotspot panel were performed, and publicly available databases were used to match the molecular profile to targeted treatments. The primary endpoint was the progression-free survival (PFS) ratio (PFS on MP-guided therapy/PFS on the last evidence-based therapy), whereas the success of the targeted therapy was defined as a PFS ratio ⩾1.2. To test the impact of molecular profile-treatment matching strategies, we retrospectively analyzed selected cases
RESULTS RESULTS
Interim analysis of 24 patients yielded informative results from 20 patients (83%). A potential tumor-specific drug could be matched in 11 patients (46%) and eight (33%) received a matched treatment. Median PFS in the matched treatment group was 61.5 days [interquartile range (IQR) 49.8-71.0] compared with 81.5 days (IQR 68.5-117.8) for the last evidence-based treatment, resulting in a median PFS ratio of 0.7 (IQR 0.6-0.9). Hence, as no patient experienced a PFS ratio ⩾1.2, the study was terminated. Except for one case, the CureMatch analysis identified either a two-drug or three-drug combination option.
CONCLUSIONS CONCLUSIONS
Our study employed a histotype-agnostic approach to harness molecular profiling data from both ctDNA and metastatic tumor tissue. The outcome results indicate that more innovative approaches to study design and matching algorithms are necessary to achieve improved patient outcomes.

Identifiants

pubmed: 33717225
doi: 10.1177/1758835920987658
pii: 10.1177_1758835920987658
pmc: PMC7923987
doi:

Types de publication

Journal Article

Langues

eng

Pagination

1758835920987658

Informations de copyright

© The Author(s), 2021.

Déclaration de conflit d'intérêts

Conflict of interest statement: EH and MRS have an unrelated sponsored research agreement with Servier within CANCER-ID, a project funded by the Innovative Medicines Joint Undertaking (IMI JU), EH receives funding from Freenome, South San Francisco, CA, and PreAnalytiX, Hombrechtikon, Switzerland. EH received honoraria from Roche for advisory boards.

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Auteurs

Jakob M Riedl (JM)

Division of Oncology, Department of Internal Medicine, Medical University of Graz, Graz, Austria.

Samantha O Hasenleithner (SO)

Institute of Human Genetics, Diagnostic and Research Center for Molecular BioMedicine, Medical University of Graz, Graz, Austria.

Gudrun Pregartner (G)

Institute for Medical Informatics, Statistics and Documentation, Medical University of Graz, Graz, Austria.

Lukas Scheipner (L)

Division of Oncology, Department of Internal Medicine, Medical University of Graz, Graz, Austria.

Florian Posch (F)

Division of Oncology, Department of Internal Medicine, Medical University of Graz, Graz, Austria.

Karin Groller (K)

Division of Oncology, Department of Internal Medicine, Medical University of Graz, Graz, Austria.

Karl Kashofer (K)

Institute of Pathology, Diagnostic and Research Center for Molecular BioMedicine, Medical University of Graz, Graz, Austria.

Stephan W Jahn (SW)

Institute of Pathology, Diagnostic and Research Center for Molecular BioMedicine, Medical University of Graz, Graz, Austria.

Thomas Bauernhofer (T)

Division of Oncology, Department of Internal Medicine, Medical University of Graz, Graz, Austria.

Martin Pichler (M)

Division of Oncology, Department of Internal Medicine, Medical University of Graz, Graz, Austria.

Herbert Stöger (H)

Division of Oncology, Department of Internal Medicine, Medical University of Graz, Graz, Austria.

Andrea Berghold (A)

Institute for Medical Informatics, Statistics and Documentation, Medical University of Graz, Graz, Austria.

Gerald Hoefler (G)

Institute of Pathology, Diagnostic and Research Center for Molecular BioMedicine, Medical University of Graz, Graz, Austria.

Michael R Speicher (MR)

Institute of Human Genetics, Diagnostic and Research Center for Molecular BioMedicine, Medical University of Graz, Graz, Austria.

Ellen Heitzer (E)

Diagnostic and Research Institute of Human Genetics, Diagnostic and Research Center for Molecular BioMedicine, Medical University of Graz, Auenbruggerplatz 2, Graz 8036, Austria.

Armin Gerger (A)

Division of Oncology, Department of Internal Medicine, Medical University of Graz, Graz, Austria.

Classifications MeSH