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MULTIPLE MYELOMA, GAMMOPATHIES

Positive selection as the unifying force for clonal evolution in multiple myeloma

Leukemia volume 35, pages 1511–1515 (2021)Cite this article

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Multiple myeloma (MM), the second most common hematologic malignancy in the U.S in 2020, is known for a lengthy progression from premalignant clonal dyscrasia to frank plasma cell malignancy, spanning 30–40 years until clinical detection [1]. Traditionally, Darwinian forces have been thought to positively select sequential advantageous mutational events to shape tumor evolution. Analysis of serial tumor samples chronicling evolution from smoldering to multiple myeloma have been explained by punctuated and/or static evolutionary models [2, 3]. Recently, an alternative neutral evolutionary model has been posited broadly across malignancies [4,5,6]. Neutrality predicates that a tumor will ultimately reach a threshold at which all of the drivers necessary to confer malignant potential will be acquired. Following this clonal, malignant transformation (i.e., the “Big Bang”), the tumor expands neutrally such that further (subclonal) mutations no longer provide an appreciable fitness advantage and thus are not selected for, as the tumor architecture remains in relative equilibrium [4]. Following the “Big Bang,” new mutations adhere to the rules of a simple power-law distribution in which the cumulative number of mutations at a given allelic frequency is inversely proportional to the allelic frequency: M (ƒ) ∝ 1/ƒ [4, 5]. Given recent reports of worse clinical outcomes for purportedly neutral myeloma tumors [7], as well as potential implications for early intervention and other therapeutic considerations, we were motivated to comprehensively characterize the selective forces responsible for shaping the evolutionary trajectory of multiple myeloma.

To quantify the prevalence of neutrality in multiple myeloma, we analyzed the CoMMpass data (version IA13, Multiple Myeloma Research Foundation Personalized Medicine Initiative). Copy number profiles were extracted from low-coverage, long-insert whole genome sequencing (WGS) data and the catalog of single nucleotide variants (SNV) was generated with all variants called by at least two of the three variant callers Seurat, Mutect, and Strelka. For 752 patients with available WGS at baseline, we then utilized the neutralitytestr R package—which calculates the fit of the tumor’s mutational profile and variant frequency to a neutral model via an R2 value (github.com/marcjwilliams1/neutralitytestr). Samples were eligible for analysis if they met criteria dictated by the original neutral evolution study such that they contained ≥12 subclonal mutations within diploid regions with coverage depth ≥10 and at least 3 supporting reads. SNVs were limited to the range of variant allelic frequency (VAF) between 0.12 and 0.24 to select only subclonal mutations per Williams et al. [5]. Application of these criteria left 431 samples (57.3%) eligible for the proposed analysis. Utilization of the neutralitytestr algorithm then yielded 114 (26.5%) neutral and 317 (73.5%) non-neutral tumors. In pairwise association analysis, neutrality was not significantly associated with any known drivers [8], canonical translocations, nor sample purity nor coverage after false discovery rate correction (Supplementary Table S1). To determine whether neutrality affected clinical outcome, we then ran univariate analysis for neutral vs non-neutral tumors and found no significant difference in disease progression (p = 0.528) or overall survival (p = 0.796). This was further confirmed using a cox multivariate model including age, stage (ISS), treatment effect (bortezomib, carfilzomib, and lenalidomide) and high-risk features of deletion or mutation of TP53, 1q gain, and MAF translocations (Supplementary Fig. S1).

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Fig. 1: Subclonal mutations under selection and clonal event acquisition across multiple time windows in purportedly neutral myeloma tumors.
Fig. 2: Model for evolutionary trajectory of multiple myeloma.

Data availability

Sequence files are available at the European Genome-phenome and dbGaP archive under the Accession codes: EGAD00001003309: 67 WGS data from 30 multiple myeloma patients; phs000748.v1.p1: Whole exome sequencing (WXS) and low coverage/long insert WGS sequencing data from 752 newly diagnosed multiple myeloma patients included in this study (CoMMpass trial; IA 13)

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Acknowledgements

This work is supported by the Memorial Sloan Kettering Cancer Center NCI Core Grant (P30 CA 008748). BD is supported by Conquer Cancer, the ASCO Foundation. FM is supported by the American Society of Hematology, the International Myeloma Foundation and The Society of Memorial Sloan Kettering Cancer Center. KHM is supported by the Royal Australasian College of Physicians Dr Helen Rarity McCreanor Travelling Fellowship and the Snowdome Foundation.

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Authors and Affiliations

  1. Myeloma Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Benjamin Diamond, Venkata Yellapantula, Even H. Rustad, Kylee H. Maclachlan & Francesco Maura

  2. Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Marius Mayerhoefer

  3. The Royal Marsden Hospital, London, UK

    Martin Kaiser

  4. The Institute of Cancer Research, London, UK

    Martin Kaiser

  5. NYU Perlmutter Cancer Center, New York, NY, USA

    Gareth Morgan

  6. Sylvester Comprehensive Cancer Center—University of Miami, Miami, FL, USA

    Ola Landgren & Francesco Maura

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  1. Benjamin Diamond
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  2. Venkata Yellapantula
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  8. Ola Landgren
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  9. Francesco Maura
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Contributions

FM and OL designed and supervised the study, collected and analyzed data and wrote the paper. BD collected and analyzed data and wrote the paper. KHM, VY, ER, MM, MK, GM collected and analyzed the data.

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Correspondence to Francesco Maura.

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Conflict of interest

Dr. Landgren’s disclosures:

  • Grant support: NIH, FDA, MMRF, IMF, LLS, Perelman Family Foundation, Rising Tide Foundation, Amgen, Celgene, Janssen, Takeda, Glenmark, Seattle Genetics, Karyopharm

  • Honoraria/ad boards: Adaptive, Amgen, Binding Site, BMS, Celgene, Cellectis, Glenmark, Janssen, Juno, Pfizer

  • Independent Data Monitoring Committee (IDMC): Takeda, Merck, Janssen, Theradex

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Diamond, B., Yellapantula, V., Rustad, E.H. et al. Positive selection as the unifying force for clonal evolution in multiple myeloma. Leukemia 35, 1511–1515 (2021). https://doi.org/10.1038/s41375-021-01130-7

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