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. 2024 Dec 14;25(24):13418.
doi: 10.3390/ijms252413418.

Whole-Exome Sequencing, Mutational Signature Analysis, and Outcome in Multiple Myeloma-A Pilot Study

Lorenz Oelschläger  1 , Axel Künstner  2   3 , Friederike Frey  1 , Theo Leitner  1 , Lisa Leypoldt  4 , Niklas Reimer  2   3 , Niklas Gebauer  1 , Lorenz Bastian  3   5 , Katja Weisel  4 , Verena-Wilbeth Sailer  3   6 , Christoph Röcken  7 , Wolfram Klapper  7 , Björn Konukiewitz  7 , Eva Maria Murga Penas  8 , Michael Forster  9 , Natalie Schub  5 , Helal M M Ahmed  1 , Jutta Kirfel  3   6 , Nikolas Christian Cornelius von Bubnoff  1 , Hauke Busch  2   3 , Cyrus Khandanpour  1
Affiliations

Whole-Exome Sequencing, Mutational Signature Analysis, and Outcome in Multiple Myeloma-A Pilot Study

Lorenz Oelschläger et al. Int J Mol Sci. .

Abstract

The complex and heterogeneous genomic landscape of multiple myeloma (MM) and many of its clinical and prognostic implications remains to be understood. In other cancers, such as breast cancer, using whole-exome sequencing (WES) and molecular signatures in clinical practice has revolutionized classification, prognostic prediction, and patient management. However, such integration is still in its early stages in MM. In this study, we analyzed WES data from 35 MM patients to identify potential mutational signatures and driver mutations correlated with clinical and cytogenetic characteristics. Our findings confirm the complex mutational spectrum and its impact on previously described ontogenetic and epigenetic pathways. They show TYW1 as a possible new potential driver gene and find no significant associations of mutational signatures with clinical findings. Further studies are needed to strengthen the role of mutational signatures in the clinical context of patients with MM to improve patient management.

Keywords: multiple myeloma; somatic signatures; whole-exome sequencing.

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Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
Oncoplot displaying potential driver genes inferred by MutSigCV (p < 0.001, n = 35). Bar plots refer to individual tumor burden (upper bar plot in mutations per megabase), −log10 p values retrieved from MutSigCV (left), and the number of samples harboring mutations in a given gene (right). Different classes of mutations are color-coded, and additional covariates are shown below (Revised International Scoring System (R-ISS)).
Figure 2
Figure 2
Oncogenic pathways are affected by mutations found in the cohort. (A) Heatmap showing the individual sample contributions to affected pathways and the frequency of affected pathways in percentage; (B) bar graphs showing the fraction of genes mutated in a particular pathway.
Figure 3
Figure 3
Somatic interactions between mutated genes selected by MutSigCV (p < 0.001). Higher co-occurrence of gene mutations is shown in red, while blue refers to mutually exclusive mutations. Gene names on the left and upper side with the number of affected patients in the cohort; p-values for statistical significance marked with (p < 0.05) or * (p < 0.01).
Figure 4
Figure 4
COSMIC single-base substitution (SBS) signatures found in the analyzed cohort. Bar graphs show the color-coded proportion of somatic signatures per individual sample.
Figure 5
Figure 5
Kaplan–Meier curve differences in progression-free survival with mutational status of KRAS.
See this image and copyright information in PMC

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