High somatic mutation and neoantigen burden are correlated with decreased progression-free survival in multiple myeloma

A Miller, Y Asmann, L Cattaneo, E Braggio, J Keats, D Auclair, S Lonial, MMRF CoMMpass Network, S J Russell, A K Stewart, A Miller, Y Asmann, L Cattaneo, E Braggio, J Keats, D Auclair, S Lonial, MMRF CoMMpass Network, S J Russell, A K Stewart

Abstract

Tumor-specific mutations can result in immunogenic neoantigens, both of which have been correlated with responsiveness to immune checkpoint inhibitors in highly mutagenic cancers. However, early results of single-agent checkpoint inhibitors in multiple myeloma (MM) have been underwhelming. Therefore, we sought to understand the relationship between mutation and neoantigen landscape of MM patients and responsiveness to therapies. Somatic mutation burden, neoantigen load, and response to therapy were determined using interim data from the MMRF CoMMpass study (NCT01454297) on 664 MM patients. In this population, the mean somatic and missense mutation loads were 405.84(s=608.55) and 63.90(s=95.88) mutations per patient, respectively. There was a positive linear relationship between mutation and neoantigen burdens (R2=0.862). The average predicted neoantigen load was 23.52(s=52.14) neoantigens with an average of 9.40(s=26.97) expressed neoantigens. Survival analysis revealed significantly shorter progression-free survival (PFS) in patients with greater than average somatic missense mutation load (N=163, 0.493 vs 0.726 2-year PFS, P=0.0023) and predicted expressed neoantigen load (N=214, 0.555 vs 0.729 2-year PFS, P=0.0028). This pattern is maintained when stratified by disease stage and cytogenetic abnormalities. Therefore, high mutation and neoantigen load are clinically relevant risk factors that negatively impact survival of MM patients under current standards of care.

Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Patient-specific mutational load. (a) Distribution of individual patient missense, nonsense, and frameshift somatic mutation counts for the 664 patient cohort. Box represents 25–75% of patients. Bolded line marks the median mutation count. (b) Histogram of natural log-transformed somatic missense mutation count shows log transformation normalizes the distribution of missense mutation frequency in patients.
Figure 2
Figure 2
Correlation between somatic missense mutation load and predicted neoantigen load. (a) Example of predicted neoantigen-binding affinities relative to wild-type binding affinities in a patient with near average mutation load (missense mutation load=64). Predicted binding affinity (IC50) for every 8/9/10-mer peptide containing somatic missense mutation compared to the predicted binding affinity of the consensus non-mutated peptide. (b) Predicted neoantigen-binding affinities relative to wild-type binding affinities for the same patient after limiting the x axis to 500 nM. The red line indicates the accepted cutoff for potential neoantigens. Points above the red line are those with mutant peptide-binding affinity IC50<500 nM and wild-type peptide-binding affinity IC50>500 nM, and are considered potential neoantigens. (c) Missense mutation load vs number of potential neoantigens with predicted binding affinity IC50<500 nM and consensus wild-type-binding affinity IC50>500 nM for all multiple myeloma test cohort patients (linear regression analysis R2=0.862). (d) Magnified view of (c) using decreased x- and y axis limits to show missense mutation vs predicted neoantigen load in test cohort. (e) Histogram of natural log-transformed predicted neoantigen count (mutant peptide-binding affinity IC50<500 nM and wild-type peptide-binding affinity IC50>500 nM) shows log transformation normalizes the distribution of predicted neoantigen frequency in patients. (f) Histogram of natural log-transformed predicted expressed neoantigen count (mutant peptide-binding affinity IC50<500 nM, wild-type peptide-binding affinity IC50>500 nM, RNAseq counts ⩾1) shows log transformation normalizes the distribution of predicted expressed neoantigen frequency in patients.
Figure 3
Figure 3
Survival in multiple myeloma patients stratified by mutation and neoantigen load. Kaplan–Meier survival curves for 664 multiple myeloma test cohort patients showing (a) length of overall survival (days) and (b) length of progression-free survival (days) after induction therapy. Univariate Kaplan–Meier survival comparison of progression-free survival (days) after induction therapy in multiple myeloma test cohort patients with (c) below average (low) or above average (high) somatic mutation load, (d) below average (low) or above average (high) neoantigens with predicted binding affinity IC50<500 nM and consensus wild-type binding affinity IC50>500 nM, (e) below average (low) or above average (high) expressed (RNAseq counts ≥1) neoantigens with predicted binding affinity IC50<500 nM and consensus wild-type binding affinity IC50>500 nM, and (f) both high somatic mutation load and high expressed neoantigen load vs those with either low somatic mutation load, low expressed neoantigen load, or both. (g)Cox-proportional hazard rate and corresponding forest plot associated with progression-free survival of multiple myeloma test cohort patients based on mutation burden and neoantigen burden thresholds as Cox analysis variables.
Figure 4
Figure 4
Survival in bortezomib-treated and IMID-treated multiple myeloma patients stratified by mutation and neoantigen load. Kaplan–Meier survival curves for patients from test cohort receiving bortezomib as part of first-line therapy showing length of progression-free survival (days) after induction therapy in patients with (a) below average (low) or above average (high) somatic mutation load, (b) below average (low) or above average (high) expressed (RNAseq counts ⩾1) neoantigens with predicted binding affinity IC50<500 nM and consensus wild-type-binding affinity IC50>500 nM. Kaplan–Meier survival curves for patients from test cohort receiving IMIDs (lanalidomide, pomalidomide, thalidomide) as part of first-line therapy showing length of progression-free survival (days) after induction therapy in patients with (a) below average (low) or above average (high) somatic mutation load, (b) below average (low) or above average (high) expressed (RNAseq counts ⩾1) neoantigens with predicted binding affinity IC50<500 nM and consensus wild-type-binding affinity IC50>500 nM.
Figure 5
Figure 5
Relationship between somatic missense mutation burden, predicted neoantigen load, ISS stage and survival. (a) Kaplan–Meier survival curves showing length of progression-free survival (days) after induction therapy in multiple myeloma test cohort patients separated by ISS disease stage. Boxplots showing distribution of (b) missense mutation load and (c) predicted expressed neoantigen load in patients with ISS stage I, stage II, or stage III disease. (d) Kaplan–Meier survival curves showing length of progression-free survival (days) after induction therapy in multiple myeloma test cohort patients separated by ISS disease stage and below average (low) or above average (high) somatic missense mutation burden. (e) Kaplan–Meier survival curves showing length of progression-free survival (days) after induction therapy in multiple myeloma test cohort patients separated by ISS disease stage and below average (low) or above average (high) expressed neoantigen load.
Figure 6
Figure 6
Cox-proportional hazard rate and corresponding forest plot associated with progression-free survival of multiple myeloma test cohort patients based on univariate and multivariate comparisons of disease stage, cytogenetic abnormality, and mutation and neoantigen burden as Cox analysis variables.
Figure 7
Figure 7
Relationship between somatic missense mutation burden, predicted neoantigen load, cytogenetic MM subtype, and survival. (a) Kaplan–Meier survival curves showing length of progression-free survival (days) after induction therapy in multiple myeloma test cohort patients with high-risk (t(4:14), t(14:16), t(14:20) translocations, or deletion 17p) or low-risk cytogenetic abnormalities. Boxplots showing distribution of (b) missense mutation load (P=0.005) and (c) predicted expressed neoantigen load in patients with high-risk or low-risk cytogenetic abnormalities (P=0.018). (d) Kaplan–Meier survival curves showing length of progression-free survival (days) after induction therapy in multiple myeloma test cohort patients with low-risk or high-risk cytogenetic abnormalities and below average (low) or above average (high) somatic missense mutation burden. (e) Kaplan–Meier survival curves showing length of progression-free survival (days) after induction therapy in multiple myeloma test cohort patients low-risk or high-risk cytogenetic abnormalities with below average (low) or above average (high) expressed neoantigen load.

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