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Added Value of Whole-Exome and Transcriptome Sequencing for Clinical Molecular Screenings of Advanced Cancer Patients With Solid Tumors

Koeppel, Florence, PhD*; Bobard, Alexandre, PhD; Lefebvre, Céline, PhD; Pedrero, Marion, PhD; Deloger, Marc, PhD§; Boursin, Yannick, MSc§; Richon, Catherine*; Chen-Min-Tao, Romy, MSc§; Robert, Guillaume, MSc§; Meurice, Guillaume, PhD§; Rouleau, Etienne, PharmD, PhD*∥; Michiels, Stefan, PhD; Massard, Christophe, MD, PhD#**; Scoazec, Jean-Yves, MD, PhD; Solary, Eric, MD, PhD††‡‡; Soria, Jean-Charles, MD, PhD‡#**; André, Fabrice, MD, PhD‡#**; Lacroix, Ludovic, PharmD, PhD*∥

doi: 10.1097/PPO.0000000000000322
Original Article

Comprehensive genomic profiling using high-throughput sequencing brings a wealth of information, and its place in the clinical setting has been increasingly prominent. This review emphasizes the utility of whole-exome sequencing (WES) and transcriptome sequencing (RNAseq) in patient care and clinical research, based on published reports as well as our experience with the MOSCATO-01 (MOlecular Screening for CAncer Treatment Optimization) molecular triage trial at Gustave Roussy Cancer Center. In this trial, all contributive samples of patients with advanced solid tumors were analyzed prospectively with targeted gene sequencing (TGS) and comparative genomic hybridization. In addition, 92 consecutive metastatic patients with contributive biopsies were sequenced for WES and RNAseq and compared with TGS and comparative genomic hybridization. Whole-exome sequencing allowed the reporting of additional variants in relevant genes in 38% of patients. Mutation detection sensitivity of WES was 95% compared with TGS. Additional information derived from WES and RNAseq could influence clinical decision, including fusion transcripts, expression levels, allele-specific expression, alternate transcripts, RNA-based pathogen diagnostic, tumor mutation load, mutational signatures, expression signatures, HLA genotyping, and neoepitope prediction. The current challenge is to be able to process the large-scale data from these comprehensive genome-wide technologies in an efficient way.

From the *Genomic Core–Molecular Biopathology Unit and Biological Resource Center, AMMICA, INSERM US23/CNRS;

SIRIC-SOCRATE 2.0/IHU Cancer–Molecular Medicine in Oncology, Gustave Roussy;

INSERM U981, Gustave Roussy, Université Paris XI;

§Bioinformatic Core, AMMICA, INSERM US23/CNRS UMS3655, Gustave Roussy;

Department of Medical Biology and Pathology, Gustave Roussy;

Biostatistics and Epidemiology Unit, Gustave Roussy, Paris-Saclay University, CESP, INSERM, Paris-Sud University Faculty of Medicine

#Drug Development Department (DITEP), Gustave Roussy, and Paris-Sud University Faculty of Medicine

**Department of Medical Oncology, Gustave Roussy;

††INSERM U1170, Gustave Roussy, Paris-Sud University Faculty of Medicine, Paris-Saclay University; and

‡‡Department of Hematology, Gustave Roussy, Villejuif, France.

Conflicts of Interest and Source of Funding: The authors have disclosed that they have no significant relationships with, or financial interest in, any commercial companies pertaining to this article. This work was supported by grant INCa-DGOS-INSERM-6043.

Reprints: Florence Koeppel, PhD, Institut de Cancérologie Gustave Roussy, 114 rue Edouard Vaillant, 94805 Villejuif cedex, France. E-mail:

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