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. 2023 Oct 20;7(1):106.
doi: 10.1038/s41698-023-00457-x.

Multicentric pilot study to standardize clinical whole exome sequencing (WES) for cancer patients

Michael Menzel  1   2 , Stephan Ossowski #  3   4   5 , Sebastian Kral #  6   7 , Patrick Metzger #  7   8 , Peter Horak #  2   9   10 , Ralf Marienfeld #  11   12 , Melanie Boerries  7   8   13   14 , Steffen Wolter  6   7 , Markus Ball  1   2 , Olaf Neumann  1   2 , Sorin Armeanu-Ebinger  3   4 , Christopher Schroeder  3   4 , Uta Matysiak  6   7 , Hannah Goldschmid  1   2 , Vincent Schipperges  7   8 , Axel Fürstberger  11   12   15 , Michael Allgäuer  1   2 , Timo Eberhardt  11   12 , Jakob Niewöhner  11 , Andreas Blaumeiser  7   8   14 , Carolin Ploeger  1   2 , Tobias Bernd Haack  3   4 , Timothy Kwang Yong Tay  1   2   16 , Olga Kelemen  3   4 , Thomas Pauli  7   8 , Martina Kirchner  1   2 , Klaus Kluck  1   2 , Alexander Ott  3   4 , Marcus Renner  1   2   9 , Jakob Admard  3   4 , Axel Gschwind  3   4 , Silke Lassmann  6   7 , Hans Kestler  11   12 , Falko Fend  17 , Anna Lena Illert  18   19   20 , Martin Werner  6   7   14 , Peter Möller  11 , Thomas Theodor Werner Seufferlein  12   21 , Nisar Malek  4   22 , Peter Schirmacher  1   2   10 , Stefan Fröhling  2   9   10 , Daniel Kazdal  1   2 , Jan Budczies  23   24   25 , Albrecht Stenzinger  26   27   28
Affiliations

Multicentric pilot study to standardize clinical whole exome sequencing (WES) for cancer patients

Michael Menzel et al. NPJ Precis Oncol. .

Abstract

A growing number of druggable targets and national initiatives for precision oncology necessitate broad genomic profiling for many cancer patients. Whole exome sequencing (WES) offers unbiased analysis of the entire coding sequence, segmentation-based detection of copy number alterations (CNAs), and accurate determination of complex biomarkers including tumor mutational burden (TMB), homologous recombination repair deficiency (HRD), and microsatellite instability (MSI). To assess the inter-institution variability of clinical WES, we performed a comparative pilot study between German Centers of Personalized Medicine (ZPMs) from five participating institutions. Tumor and matched normal DNA from 30 patients were analyzed using custom sequencing protocols and bioinformatic pipelines. Calling of somatic variants was highly concordant with a positive percentage agreement (PPA) between 91 and 95% and a positive predictive value (PPV) between 82 and 95% compared with a three-institution consensus and full agreement for 16 of 17 druggable targets. Explanations for deviations included low VAF or coverage, differing annotations, and different filter protocols. CNAs showed overall agreement in 76% for the genomic sequence with high wet-lab variability. Complex biomarkers correlated strongly between institutions (HRD: 0.79-1, TMB: 0.97-0.99) and all institutions agreed on microsatellite instability. This study will contribute to the development of quality control frameworks for comprehensive genomic profiling and sheds light onto parameters that require stringent standardization.

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Conflict of interest statement

P.H. reports Consulting or Advisory Role: Platomics GmbH, Honoraria: Roche Pharma AG, Trillium GmbH. R.M. reports participation in Advisory Boards of Roche, AstraZenca; research grants from Bristol-Myers-Squibb. C.S. reports an institutional grant from Illumina and research grants from BMS Stiftung Immunonkologie outside the submitted work. M.K.: speaker’s honoraria and travel grants from Veracyte Inc. P.S. reports personal fees for speaker honoraria from Incyte, Roche, Janssen, Novartis, AstraZeneca, Eisai, Leica, grants from Novartis, BMS, AstraZeneca, Illumina, Incyte, and boards from Incyte, Roche, AstraZeneca, BMS, MSD, Amgen, Janssen, Novartis, Bayer, Eisai, outside the submitted work. S.F. reports consulting or advisory board membership: Bayer, Illumina, Roche; honoraria: Amgen, Eli Lilly, PharmaMar, Roche; research funding: AstraZeneca, Pfizer, PharmaMar, Roche; travel or accommodation expenses: Amgen, Eli Lilly, Illumina, PharmaMar, Roche. D.K. reports personal fees for speaker honoraria from AstraZeneca, and Pfizer, personal fees for Advisory Board from Bristol-Myers Squibb, outside the submitted work. S.L. reports research grant from BMS, advisory board/speaker invitation from AstraZeneca, Eli Lily, Roche and Takeda outside of this work. A.I.: speaker’s honoria/ advisory boards Takeda, Roche, Amgen, Janssen, Abbvie, Bayer, Incyte, FiDO outside the submitted work. J.B. reports grants from German Cancer Aid and consulting from MSD, outside the submitted work. A.S. reports grants and personal fees from Bayer, BMS, grants from Chugai and personal fees from Astra Zeneca, MSD, Takeda, Seattle Genetics, Novartis, Illumina, Thermo Fisher, Eli Lily, Takeda, outside the submitted work. A.S. reports participation in Advisory Board/Speaker’s Bureau for Astra Zeneca, AGCT, Bayer, Bristol-Myers Squibb, Eli Lilly, Illumina, Janssen, MSD, Novartis, Pfizer, Roche, Seattle Genetics, Takeda, and Thermo Fisher, grants from Bayer, Bristol-Myers Squibb, and Chugai, outside the submitted work. M.M., S.O., S.L., P.M., P.H., M.B., S.W., M.B., O.N., S.A., U.M., H.G., V.S., A.F., M.A., T.E., J.N., A.B., C.P., T.B.H., T.K., O.K., T.P., K.K., A.O., J.A., A.G., S.K., H.K., F.F., A.L., M.W., P.M., T.S., N.M., S.F. report no conflicts of interest.

Figures

Fig. 1
Fig. 1. Overview on the study design.
Tumor and normal DNA of 30 patients were distributed by DKFZ and NCT Heidelberg to four participating NGS laboratories. Bioinformatic analysis was performed at five participating departments. Additionally, all sequencing data were collected and analyzed with the same bioinformatic pipeline (blue arrows). Therapeutic relevant results from the DKFZ/NCT/DKTK MASTER program were collected and included in the comparative analysis.
Fig. 2
Fig. 2. Inter-institution concordance of the detected somatic variants.
Numbers in brackets refer to the numbers of somatic variants. Most variants were detected by all institutions (52%, red bar), while lower percentages of variants were detected by two to four institutions (22%, orange bar), and by only a single institution (26%, blue bar). Potential causes for discordant variant detection were analyzed and the variant sets were split accordingly.
Fig. 3
Fig. 3. Comparison of the somatic variants detected by each of the institutions with a consensus list including all variants detected by at least three institutions.
a Number of variants (3x consensus) for each of the cases as well as sensitivity and positive predictive value for each of the cases and institutions. Empty boxes (case 29) = no variants detected. b Sensitivity and positive predictive value for each of five institutions in comparison to three different references: consensus list of variants found in at least two institutions (Consensus 2x, triangle), consensus of variants found in at least three institutions (Consensus 3x, square), and TSO500 (circle). c Inter-institution concordance of therapeutic relevant somatic variants (OncoKB levels 1–4) with associated treatments and their OncoKB level. Boxes indicate detected variants and are colored by treatment option, variants marked with “SV” were found as structural variant.
Fig. 4
Fig. 4. Inter-institution concordance of CNA calls.
a Samples were compared between all pairs of institutions and the genomic sequence was split into segments with concordant CN (green), segments with concordant CN after correction for genome duplication (purple), and discordant CN (red). b Inter-institution concordance of amplifications and deletions in the set of oncogenic/likely oncogenic (according to OncoKB) genes. Colored box = CNA detected with number referring to the detected CN with amplifications in red and deletions in blue.
Fig. 5
Fig. 5. Inter-institution concordance of HRD, TMB, and MSI scores.
Left: Distribution of the complex biomarkers scores together with the institution-specific cut-off points. Boxplots show the median, quartiles and whiskers up to 1.5 times of the box size. Right: Pearson correlation of the scores between pairs of institutions (upper triangle). Systematic deviation (in %) between two institutions (lower triangle). Red = systematic higher scores. Blue = systematic lower scores.
Fig. 6
Fig. 6. Comparison of bioinformatic and wet-lab inter-institution variability.
To obtain “SD Bioinformatics”, the data of a single sequencing institution were evaluated by three bioinformatic institutions. To obtain “SD Sequencing” the data of three sequencing institutions were evaluated by a single bioinformatic institution. Outliers are annotated with the sample number. Boxplots show the median, quartiles and whiskers up to 1.5 times of the box size.
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