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The evolutionary pattern of mutations in glioblastoma reveals therapy-mediated selection

Muscat, Andrea M., Wong, Nicholas C., Drummond, Katharine J., Algar, Elizabeth M., Khasraw, Mustafa, Verhaak, Roel, Field, Kathryn, Rosenthal, Mark A., Ashley, David M.·2018·Oncotarget
DOI 10.18632/oncotarget.23541PMC PMC5814263
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Figure 1 Patterns of mutations in key signaling pathway genes were consistent with previously described traits of primary GBM OncoPrint summarizing the non-synonymous and splice site variants detected by targeted deep sequencing in both diagnostic (open circles) and relapse (closed circles) specimens for each of the 21 patients. Specimens with methylation of MGMT are noted in red.
Figure 1 Patterns of mutations in key signaling pathway genes were consistent with previously described traits of primary GBM OncoPrint summarizing the non-synonymous and splice site variants detected by targeted deep sequencing in both diagnostic (open circles) and relapse (closed circles) specimens for each of the 21 patients. Specimens with methylation of MGMT are noted in red.CC BY · PubMed Central
Figure 2 The burden of mutations was not heavily impacted by treatment with RT and TMZ (A) The number of variants detected overall in untreated and treated patients, presented according to the specimen/s in which they were observed. (B) Box plot showing the mean number of variants by treatment or sample type. Patient 21 was excluded as an outlier from this analysis. (C) Coding region variants defined as non-synonymous and splice site mutations, presented as shared or unique to either diagnosis or relapse specimens. (D) Box plot showing the mean number of non-synonymous and splice site variants by treatment (treated vs untreated) or sample type (diagnosis vs relapse). Patient 21 was excluded from this analysis. (E) The presence of SNVs was analyzed according to the TP53 status of tumor specimens. TP53 wildtype tumors (WT) were compared to TP53 mutant tumors (mut), which were defined as those that contained non-synonymous TP53 variants. SNVs were designated low VAF (< 0.4) or high VAF (≥ 0.4) and were tallied separately for the diagnosis and relapse specimens of each patient (excluding the relapse sample of patient 21). Untreated patients are depicted as open squares and treated patients as closed circles. The mean and 95% confidence intervals are also shown. A two-way analysis of variance was performed using a general linear model and p-values for significant differences (p ≤ 0.001) are displayed.
Figure 2 The burden of mutations was not heavily impacted by treatment with RT and TMZ (A) The number of variants detected overall in untreated and treated patients, presented according to the specimen/s in which they were observed. (B) Box plot showing the mean number of variants by treatment or sample type. Patient 21 was excluded as an outlier from this analysis. (C) Coding region variants defined as non-synonymous and splice site mutations, presented as shared or unique to either diagnosis or relapse specimens. (D) Box plot showing the mean number of non-synonymous and splice site variants by treatment (treated vs untreated) or sample type (diagnosis vs relapse). Patient 21 was excluded from this analysis. (E) The presence of SNVs was analyzed according to the TP53 status of tumor specimens. TP53 wildtype tumors (WT) were compared to TP53 mutant tumors (mut), which were defined as those that contained non-synonymous TP53 variants. SNVs were designated low VAF (< 0.4) or high VAF (≥ 0.4) and were tallied separately for the diagnosis and relapse specimens of each patient (excluding the relapse sample of patient 21). Untreated patients are depicted as open squares and treated patients as closed circles. The mean and 95% confidence intervals are also shown. A two-way analysis of variance was performed using a general linear model and p-values for significant differences (p ≤ 0.001) are displayed.CC BY · PubMed Central
Figure 3 TMZ-induced mutational events were not evident in the majority of samples at recurrence (A) Boxplot representing the distribution of single base substitutions unique to recurrence. The y-axis excludes patient 21 as an outlier with 48 detected variants. The mean is shown as a circular symbol and the asterisks highlight outliers. (B) An individual value plot presenting the spread of single base substitutions occurring uniquely at recurrence and the frequency of patients in which they occur. Substitutions detected in treated patients (including patient 21) and untreated patients are highlighted. (C) Total number of C:G > T:A substitutions and the proportion of each dinucleotide context.
Figure 3 TMZ-induced mutational events were not evident in the majority of samples at recurrence (A) Boxplot representing the distribution of single base substitutions unique to recurrence. The y-axis excludes patient 21 as an outlier with 48 detected variants. The mean is shown as a circular symbol and the asterisks highlight outliers. (B) An individual value plot presenting the spread of single base substitutions occurring uniquely at recurrence and the frequency of patients in which they occur. Substitutions detected in treated patients (including patient 21) and untreated patients are highlighted. (C) Total number of C:G > T:A substitutions and the proportion of each dinucleotide context.CC BY · PubMed Central
Figure 4 Neutral tumor evolution was evident in untreated GBM, however, a shift away from effectively-neutral subclone dynamics was observed after radiochemotherapy (A) Boxplot presenting the estimated purity of samples determined by LUMP analysis. The line of short dashes indicates the 55% purity threshold for inclusion in neutral evolution analyses. (B) The cumulative distribution M(f) of subclonal mutations in primary tumors prior to therapy analyzed collectively was linear with the inverse of their frequency (1/f) (upper panel) and a shift away from linearity and therefore neutral evolution was evident in tumors after treatment at recurrence (lower panel; excludes patient 21 recurrent sample). A stringent R2 > 0.98 defines neutral evolution in these collective analyses. (C) Subclonal mutations of individual samples were assessed and the variation between tumors at diagnosis and relapse are presented for the 10 paired samples analyzed. R2 goodness-of-fit measures for the neutral evolution model decrease from diagnosis to recurrence in 80% of paired samples from the same patient. (D) Boxplot of the R2 goodness-of-fit for the neutral evolution model. Subclonal mutations of individual samples were assessed and the variation between tumors at diagnosis and relapse are presented for all samples analyzed. The line of short dashes indicates the R2 = 0.98 threshold used to distinguish neutral and non-neutral evolution. A two-way analysis of variance was performed using the general linear model for continuous variables and the corresponding p-value is displayed. (E) The cumulative distribution M(f) of subclonal mutations in the primary tumor (upper panel) and relapse tumor (lower panel) of patient 21. A switch from neutral to non-neutral evolutionary dynamics was apparent between tumors at diagnosis and relapse.
Figure 4 Neutral tumor evolution was evident in untreated GBM, however, a shift away from effectively-neutral subclone dynamics was observed after radiochemotherapy (A) Boxplot presenting the estimated purity of samples determined by LUMP analysis. The line of short dashes indicates the 55% purity threshold for inclusion in neutral evolution analyses. (B) The cumulative distribution M(f) of subclonal mutations in primary tumors prior to therapy analyzed collectively was linear with the inverse of their frequency (1/f) (upper panel) and a shift away from linearity and therefore neutral evolution was evident in tumors after treatment at recurrence (lower panel; excludes patient 21 recurrent sample). A stringent R2 > 0.98 defines neutral evolution in these collective analyses. (C) Subclonal mutations of individual samples were assessed and the variation between tumors at diagnosis and relapse are presented for the 10 paired samples analyzed. R2 goodness-of-fit measures for the neutral evolution model decrease from diagnosis to recurrence in 80% of paired samples from the same patient. (D) Boxplot of the R2 goodness-of-fit for the neutral evolution model. Subclonal mutations of individual samples were assessed and the variation between tumors at diagnosis and relapse are presented for all samples analyzed. The line of short dashes indicates the R2 = 0.98 threshold used to distinguish neutral and non-neutral evolution. A two-way analysis of variance was performed using the general linear model for continuous variables and the corresponding p-value is displayed. (E) The cumulative distribution M(f) of subclonal mutations in the primary tumor (upper panel) and relapse tumor (lower panel) of patient 21. A switch from neutral to non-neutral evolutionary dynamics was apparent between tumors at diagnosis and relapse.CC BY · PubMed Central
Figure 5 Interrogation of publically available datasets also indicates a stronger fit to the neutral evolution model in untreated primary tumors (A) Boxplot of the R2 goodness-of-fit for the neutral evolution model, presenting data from cohort 2. The minimum purity level of included samples is categorized below the x-axis and the 93 samples which satisfied inclusion criteria for analysis using purity-adjusted VAFs are compared. The line of short dashes indicates the R2 = 0.98 threshold used to distinguish neutral and non-neutral evolution and the number of samples greater than this threshold out of the total number analyzed in each category is indicated above the dashed line. (B) Boxplot of the R2 goodness-of-fit for the neutral evolution model, presenting data from cohort 3. The line of short dashes indicates the R2 = 0.98 threshold. A two-way analysis of variance was performed using the general linear model for continuous variables and the corresponding p-value is displayed.
Figure 5 Interrogation of publically available datasets also indicates a stronger fit to the neutral evolution model in untreated primary tumors (A) Boxplot of the R2 goodness-of-fit for the neutral evolution model, presenting data from cohort 2. The minimum purity level of included samples is categorized below the x-axis and the 93 samples which satisfied inclusion criteria for analysis using purity-adjusted VAFs are compared. The line of short dashes indicates the R2 = 0.98 threshold used to distinguish neutral and non-neutral evolution and the number of samples greater than this threshold out of the total number analyzed in each category is indicated above the dashed line. (B) Boxplot of the R2 goodness-of-fit for the neutral evolution model, presenting data from cohort 3. The line of short dashes indicates the R2 = 0.98 threshold. A two-way analysis of variance was performed using the general linear model for continuous variables and the corresponding p-value is displayed.CC BY · PubMed Central
Figure 6 Neutral expansion of variants shifts toward non-neutral modes after therapy Pictorial representation of the neutral expansion of subclones and the linear accumulation of variants M(f) with the inverse of their frequency f. An R2 equal to 1.0 depicts precisely neutral evolutionary dynamics. With the applied pressure of therapy subclonal expansion becomes altered and variable. Some resistant clones and clones with treatment-induced mutations experience a growth advantage and expand rapidly, while other treatment-sensitive clones are reduced or eliminated. The allelic frequency of some variants increases at a more rapid rate while others are decreased. Non-neutral modes of subclonal variant expansion dominate.
Figure 6 Neutral expansion of variants shifts toward non-neutral modes after therapy Pictorial representation of the neutral expansion of subclones and the linear accumulation of variants M(f) with the inverse of their frequency f. An R2 equal to 1.0 depicts precisely neutral evolutionary dynamics. With the applied pressure of therapy subclonal expansion becomes altered and variable. Some resistant clones and clones with treatment-induced mutations experience a growth advantage and expand rapidly, while other treatment-sensitive clones are reduced or eliminated. The allelic frequency of some variants increases at a more rapid rate while others are decreased. Non-neutral modes of subclonal variant expansion dominate.CC BY · PubMed Central

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Metadata

Authors
Muscat, A. M., Wong, N. C., Drummond, K. J., Algar, E. M., Khasraw, M., Verhaak, R., Field, K., Rosenthal, M. A., & Ashley, D. M.
Affiliations
  • School of Medicine, Deakin University, Geelong, Victoria, Australia
  • Department of Pediatrics, The University of Melbourne, Parkville, Victoria, Australia
  • Department of Neurosurgery, The Royal Melbourne Hospital, Parkville, Victoria, Australia
  • Center for Cancer Research, Hudson Institute of Medical Research, Clayton, Victoria, Australia
  • The Jackson Laboratory for Genomic Medicine, Farmington, Connecticut, USA
  • Department of Medical Oncology, Peter MacCallum Cancer Center, Melbourne, Victoria, Australia
Venue
Oncotarget
Publisher
Impact Journals, LLC
Published
2017-12-15
Volume
9
Issue
8
Pages
7844-7858
ISSN
1949-2553
DOI
10.18632/oncotarget.23541
PMC
PMC5814263
Cited by
32
References
2
Language
en

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