Journal: Acta Neuropathologica Communications

Article Title: Variant allelic frequencies of driver mutations can identify gliomas with potentially false-negative MGMT promoter methylation results

doi: 10.1186/s40478-023-01680-0

Figure Lengend Snippet: MGMT promoter methylation trends in pyrosequencing samples . ( A ) Frequency of positive results for all glioma samples above and below the cutoff value of VAF = 0.325. ( B ) Cumulative mean frequency of positive test results as a function of VAF. ( C ) Trends in cumulative mean MGMT promoter pyrosequencing score with increasing VAF. ( D ) Mean MGMT promoter pyrosequencing scores above and below VAF = 0.325. Similar results are shown for each tumor subtype, including IDH-wildtype glioblastoma ( E - H ), IDH-mutant astrocytoma ( I - L ) and IDH-mutant and 1p/19q co-deleted oligodendroglioma ( M - P ). (Horizontal dashed black lines: mean values for cohort; Horizontal solid red lines: MGMT positivity cutoff of 10.0%; Vertical dashed black lines: cutoff values identified by Cutoff Finder; Vertical dashed red lines: cutoff values identified by multi-part linear regression; Panels A, E, I, M: Fisher’s exact test; Panels D, H, L, P: unpaired Student’s T-test; pyroseq: pyrosequencing; *p < 0.05; ****p < 0.0001)

Article Snippet: Fig. 4 Driver mutation VAF , MGMT promoter methylation scores, and tumor cellularity . ( A ) Linear regression of MGMT promoter pyrosequencing score versus driver mutation VAF for all glioma samples. ( B ) Linear regression of MGMT promoter pyrosequencing score versus TERT promoter mutation VAF for GBM. ( C ) Linear regression of MGMT promoter pyrosequencing score versus IDH mutation VAF for IDHmut astrocytoma. ( D ) Linear regression of MGMT promoter pyrosequencing score versus IDH mutation VAF for IDHmut oligodendroglioma. ( E ) Linear regression of microscopically estimated cellularity versus cellularity calculated from driver mutation VAF (2×VAF×100%) for all glioma samples. ( F ) Differences between microscopically estimated cellularity and cellularity calculated from VAF (Y-axis) plotted as a function of VAF (X-axis), for all glioma samples. ( G ) TERT promoter mutation VAF by ddPCR in high versus low cellularity areas of GBM tissue samples. ( H ) MGMT promoter methylation score by ddPCR in high versus low cellularity areas of GBM tissue samples (GBM: IDH-wildtype glioblastoma, IDHmut astrocytoma: IDH-mutant astrocytoma, IDHmut oligodendroglioma: IDH-mutant and 1p/19q co-deleted oligodendroglioma, pyroseq: pyrosequencing, ddPCR: droplet digital PCR) To further assess the relationship between tumor cellularity, driver VAF, and MGMT assay outcomes, we performed additional analyses on 5 IDHwt GBM samples.

Techniques: Methylation, Mutagenesis

Journal: Acta Neuropathologica Communications

Article Title: Variant allelic frequencies of driver mutations can identify gliomas with potentially false-negative MGMT promoter methylation results

doi: 10.1186/s40478-023-01680-0

Figure Lengend Snippet: MGMT promoter methylation trends in DNA methylation array samples . ( A ) Frequency of positive results for all glioma array samples above and below the cutoff of VAF = 0.245. ( B ) Cumulative mean frequency of positive results for all glioma array samples as a function of VAF. ( C ) Frequency of positive results for GBM array samples above and below the cutoff of TERT VAF = 0.325. ( D ) Cumulative mean frequency of positive results in GBM array samples as a function of TERT VAF. ( E ) Frequency of positive results in GBM pyrosequencing samples using MGMT cutoff of 10.0% versus GBM array samples. ( F ) Frequency of positive results in GBM pyrosequencing samples using MGMT cutoff of 7.28% versus GBM array samples. ( G ) Frequency of positive results in GBM pyrosequencing samples with TERT VAF < 0.115, using MGMT cutoff of 7.28%, versus GBM array samples with TERT VAF < 0.325. ( H ) Frequency of positive results in GBM pyrosequencing samples with TERT VAF ≥ 0.115, using MGMT cutoff of 7.28%, versus GBM array samples with TERT VAF ≥ 0.325. ( I ) Frequency of positive results for IDHmut astrocytoma above and below the cutoff of IDH VAF = 0.325 (left), by methylation class match (center), and above and below the cutoff of classifier score = 0.955 (right). ( J ) Cumulative mean frequency of positive results in IDHmut astrocytoma array samples as a function of IDH VAF (Fisher’s exact test for panels A, C, E, F, G, H, I; GBM: IDH-wildtype glioblastoma, IDHmut astrocytoma: IDH-mutant astrocytoma)

Article Snippet: Fig. 4 Driver mutation VAF , MGMT promoter methylation scores, and tumor cellularity . ( A ) Linear regression of MGMT promoter pyrosequencing score versus driver mutation VAF for all glioma samples. ( B ) Linear regression of MGMT promoter pyrosequencing score versus TERT promoter mutation VAF for GBM. ( C ) Linear regression of MGMT promoter pyrosequencing score versus IDH mutation VAF for IDHmut astrocytoma. ( D ) Linear regression of MGMT promoter pyrosequencing score versus IDH mutation VAF for IDHmut oligodendroglioma. ( E ) Linear regression of microscopically estimated cellularity versus cellularity calculated from driver mutation VAF (2×VAF×100%) for all glioma samples. ( F ) Differences between microscopically estimated cellularity and cellularity calculated from VAF (Y-axis) plotted as a function of VAF (X-axis), for all glioma samples. ( G ) TERT promoter mutation VAF by ddPCR in high versus low cellularity areas of GBM tissue samples. ( H ) MGMT promoter methylation score by ddPCR in high versus low cellularity areas of GBM tissue samples (GBM: IDH-wildtype glioblastoma, IDHmut astrocytoma: IDH-mutant astrocytoma, IDHmut oligodendroglioma: IDH-mutant and 1p/19q co-deleted oligodendroglioma, pyroseq: pyrosequencing, ddPCR: droplet digital PCR) To further assess the relationship between tumor cellularity, driver VAF, and MGMT assay outcomes, we performed additional analyses on 5 IDHwt GBM samples.

Techniques: Methylation, DNA Methylation Assay, Mutagenesis

Journal: Acta Neuropathologica Communications

Article Title: Variant allelic frequencies of driver mutations can identify gliomas with potentially false-negative MGMT promoter methylation results

doi: 10.1186/s40478-023-01680-0

Figure Lengend Snippet: Driver mutation VAF , MGMT promoter methylation scores, and tumor cellularity . ( A ) Linear regression of MGMT promoter pyrosequencing score versus driver mutation VAF for all glioma samples. ( B ) Linear regression of MGMT promoter pyrosequencing score versus TERT promoter mutation VAF for GBM. ( C ) Linear regression of MGMT promoter pyrosequencing score versus IDH mutation VAF for IDHmut astrocytoma. ( D ) Linear regression of MGMT promoter pyrosequencing score versus IDH mutation VAF for IDHmut oligodendroglioma. ( E ) Linear regression of microscopically estimated cellularity versus cellularity calculated from driver mutation VAF (2×VAF×100%) for all glioma samples. ( F ) Differences between microscopically estimated cellularity and cellularity calculated from VAF (Y-axis) plotted as a function of VAF (X-axis), for all glioma samples. ( G ) TERT promoter mutation VAF by ddPCR in high versus low cellularity areas of GBM tissue samples. ( H ) MGMT promoter methylation score by ddPCR in high versus low cellularity areas of GBM tissue samples (GBM: IDH-wildtype glioblastoma, IDHmut astrocytoma: IDH-mutant astrocytoma, IDHmut oligodendroglioma: IDH-mutant and 1p/19q co-deleted oligodendroglioma, pyroseq: pyrosequencing, ddPCR: droplet digital PCR)

Article Snippet: Fig. 4 Driver mutation VAF , MGMT promoter methylation scores, and tumor cellularity . ( A ) Linear regression of MGMT promoter pyrosequencing score versus driver mutation VAF for all glioma samples. ( B ) Linear regression of MGMT promoter pyrosequencing score versus TERT promoter mutation VAF for GBM. ( C ) Linear regression of MGMT promoter pyrosequencing score versus IDH mutation VAF for IDHmut astrocytoma. ( D ) Linear regression of MGMT promoter pyrosequencing score versus IDH mutation VAF for IDHmut oligodendroglioma. ( E ) Linear regression of microscopically estimated cellularity versus cellularity calculated from driver mutation VAF (2×VAF×100%) for all glioma samples. ( F ) Differences between microscopically estimated cellularity and cellularity calculated from VAF (Y-axis) plotted as a function of VAF (X-axis), for all glioma samples. ( G ) TERT promoter mutation VAF by ddPCR in high versus low cellularity areas of GBM tissue samples. ( H ) MGMT promoter methylation score by ddPCR in high versus low cellularity areas of GBM tissue samples (GBM: IDH-wildtype glioblastoma, IDHmut astrocytoma: IDH-mutant astrocytoma, IDHmut oligodendroglioma: IDH-mutant and 1p/19q co-deleted oligodendroglioma, pyroseq: pyrosequencing, ddPCR: droplet digital PCR) To further assess the relationship between tumor cellularity, driver VAF, and MGMT assay outcomes, we performed additional analyses on 5 IDHwt GBM samples.

Techniques: Mutagenesis, Methylation, Digital PCR

Journal: Acta Neuropathologica Communications

Article Title: Variant allelic frequencies of driver mutations can identify gliomas with potentially false-negative MGMT promoter methylation results

doi: 10.1186/s40478-023-01680-0

Figure Lengend Snippet: Re-testing pyrosequencing samples with DNA methylation array and droplet digital PCR

Article Snippet: Fig. 4 Driver mutation VAF , MGMT promoter methylation scores, and tumor cellularity . ( A ) Linear regression of MGMT promoter pyrosequencing score versus driver mutation VAF for all glioma samples. ( B ) Linear regression of MGMT promoter pyrosequencing score versus TERT promoter mutation VAF for GBM. ( C ) Linear regression of MGMT promoter pyrosequencing score versus IDH mutation VAF for IDHmut astrocytoma. ( D ) Linear regression of MGMT promoter pyrosequencing score versus IDH mutation VAF for IDHmut oligodendroglioma. ( E ) Linear regression of microscopically estimated cellularity versus cellularity calculated from driver mutation VAF (2×VAF×100%) for all glioma samples. ( F ) Differences between microscopically estimated cellularity and cellularity calculated from VAF (Y-axis) plotted as a function of VAF (X-axis), for all glioma samples. ( G ) TERT promoter mutation VAF by ddPCR in high versus low cellularity areas of GBM tissue samples. ( H ) MGMT promoter methylation score by ddPCR in high versus low cellularity areas of GBM tissue samples (GBM: IDH-wildtype glioblastoma, IDHmut astrocytoma: IDH-mutant astrocytoma, IDHmut oligodendroglioma: IDH-mutant and 1p/19q co-deleted oligodendroglioma, pyroseq: pyrosequencing, ddPCR: droplet digital PCR) To further assess the relationship between tumor cellularity, driver VAF, and MGMT assay outcomes, we performed additional analyses on 5 IDHwt GBM samples.

Techniques: DNA Methylation Assay

Journal: Acta Neuropathologica Communications

Article Title: Variant allelic frequencies of driver mutations can identify gliomas with potentially false-negative MGMT promoter methylation results

doi: 10.1186/s40478-023-01680-0

Figure Lengend Snippet: False negative results in IDH-wildtype glioblastoma with low TERT VAF . ( A ) MGMT promoter methylation results for 12 GBM samples (6 with TERT VAF ≤ 0.10, 6 with TERT VAF ≥ 0.25) comparing initial pyrosequencing methylation scores (left Y-axis, cutoff for positive = 10.0%, horizontal solid red line) to results on re-testing with DNA methylation array (right Y-axis). ( B ) MGMT promoter methylation results for the same 12 GBM samples comparing initial pyrosequencing methylation levels to results on re-testing with ddPCR. (GBM: IDH-wildtype glioblastoma, pos: positive, equiv: equivocal, neg: negative, QNS: quality/quantity of DNA not sufficient for reliable test result, ddPCR: droplet digital PCR)

Article Snippet: Fig. 4 Driver mutation VAF , MGMT promoter methylation scores, and tumor cellularity . ( A ) Linear regression of MGMT promoter pyrosequencing score versus driver mutation VAF for all glioma samples. ( B ) Linear regression of MGMT promoter pyrosequencing score versus TERT promoter mutation VAF for GBM. ( C ) Linear regression of MGMT promoter pyrosequencing score versus IDH mutation VAF for IDHmut astrocytoma. ( D ) Linear regression of MGMT promoter pyrosequencing score versus IDH mutation VAF for IDHmut oligodendroglioma. ( E ) Linear regression of microscopically estimated cellularity versus cellularity calculated from driver mutation VAF (2×VAF×100%) for all glioma samples. ( F ) Differences between microscopically estimated cellularity and cellularity calculated from VAF (Y-axis) plotted as a function of VAF (X-axis), for all glioma samples. ( G ) TERT promoter mutation VAF by ddPCR in high versus low cellularity areas of GBM tissue samples. ( H ) MGMT promoter methylation score by ddPCR in high versus low cellularity areas of GBM tissue samples (GBM: IDH-wildtype glioblastoma, IDHmut astrocytoma: IDH-mutant astrocytoma, IDHmut oligodendroglioma: IDH-mutant and 1p/19q co-deleted oligodendroglioma, pyroseq: pyrosequencing, ddPCR: droplet digital PCR) To further assess the relationship between tumor cellularity, driver VAF, and MGMT assay outcomes, we performed additional analyses on 5 IDHwt GBM samples.

Techniques: Methylation, DNA Methylation Assay, Digital PCR

Journal: Acta Neuropathologica Communications

Article Title: Variant allelic frequencies of driver mutations can identify gliomas with potentially false-negative MGMT promoter methylation results

doi: 10.1186/s40478-023-01680-0

Figure Lengend Snippet: Central hypothesis . MGMT promoter methylation is pathologic, and occurs only in tumor cells. Cellular glioma samples are rich in DNA from tumor cells, whereas paucicellular glioma samples contain a large fraction of DNA from non-tumor cells, which can “dilute” positive methylation signals from tumor cell DNA, leading to false-negative results

Article Snippet: Fig. 4 Driver mutation VAF , MGMT promoter methylation scores, and tumor cellularity . ( A ) Linear regression of MGMT promoter pyrosequencing score versus driver mutation VAF for all glioma samples. ( B ) Linear regression of MGMT promoter pyrosequencing score versus TERT promoter mutation VAF for GBM. ( C ) Linear regression of MGMT promoter pyrosequencing score versus IDH mutation VAF for IDHmut astrocytoma. ( D ) Linear regression of MGMT promoter pyrosequencing score versus IDH mutation VAF for IDHmut oligodendroglioma. ( E ) Linear regression of microscopically estimated cellularity versus cellularity calculated from driver mutation VAF (2×VAF×100%) for all glioma samples. ( F ) Differences between microscopically estimated cellularity and cellularity calculated from VAF (Y-axis) plotted as a function of VAF (X-axis), for all glioma samples. ( G ) TERT promoter mutation VAF by ddPCR in high versus low cellularity areas of GBM tissue samples. ( H ) MGMT promoter methylation score by ddPCR in high versus low cellularity areas of GBM tissue samples (GBM: IDH-wildtype glioblastoma, IDHmut astrocytoma: IDH-mutant astrocytoma, IDHmut oligodendroglioma: IDH-mutant and 1p/19q co-deleted oligodendroglioma, pyroseq: pyrosequencing, ddPCR: droplet digital PCR) To further assess the relationship between tumor cellularity, driver VAF, and MGMT assay outcomes, we performed additional analyses on 5 IDHwt GBM samples.

Techniques: Methylation

Journal: Acta Neuropathologica Communications

Article Title: Variant allelic frequencies of driver mutations can identify gliomas with potentially false-negative MGMT promoter methylation results

doi: 10.1186/s40478-023-01680-0

Figure Lengend Snippet: Patient cohort characteristics

Article Snippet: Fig. 4 Driver mutation VAF , MGMT promoter methylation scores, and tumor cellularity . ( A ) Linear regression of MGMT promoter pyrosequencing score versus driver mutation VAF for all glioma samples. ( B ) Linear regression of MGMT promoter pyrosequencing score versus TERT promoter mutation VAF for GBM. ( C ) Linear regression of MGMT promoter pyrosequencing score versus IDH mutation VAF for IDHmut astrocytoma. ( D ) Linear regression of MGMT promoter pyrosequencing score versus IDH mutation VAF for IDHmut oligodendroglioma. ( E ) Linear regression of microscopically estimated cellularity versus cellularity calculated from driver mutation VAF (2×VAF×100%) for all glioma samples. ( F ) Differences between microscopically estimated cellularity and cellularity calculated from VAF (Y-axis) plotted as a function of VAF (X-axis), for all glioma samples. ( G ) TERT promoter mutation VAF by ddPCR in high versus low cellularity areas of GBM tissue samples. ( H ) MGMT promoter methylation score by ddPCR in high versus low cellularity areas of GBM tissue samples (GBM: IDH-wildtype glioblastoma, IDHmut astrocytoma: IDH-mutant astrocytoma, IDHmut oligodendroglioma: IDH-mutant and 1p/19q co-deleted oligodendroglioma, pyroseq: pyrosequencing, ddPCR: droplet digital PCR) To further assess the relationship between tumor cellularity, driver VAF, and MGMT assay outcomes, we performed additional analyses on 5 IDHwt GBM samples.

Techniques: Pyrosequencing Assay, Methylation, Mutagenesis

Journal: Neuro-Oncology Advances

Article Title: Clinical value of the MGMT promoter methylation score in IDHmt low-grade glioma for predicting benefit from temozolomide treatment

doi: 10.1093/noajnl/vdae224

Figure Lengend Snippet: Association between progression-free survival (PFS) and the MGMTp methylation score. The association of the MGMTp methylation score, based on the MGMT-STP27 procedure, with progression-free survival (PFS; interval from diagnosis) was evaluated by the hazard ratio (confidence interval at 95%) from the cox regression model. The forest plots in A and B correspond to the PFS reported for patients treated in EORTC-22033 with temozolomide (TMZ) or radiotherapy (RT), respectively. , The MGMT-STP27 score was significant in the TMZ treated patients, but not in the patients with RT. The forest plots in C and D visualize the outcome of the Montpellier patients treated with TMZ, with time to progression defined by the next treatment-free survival (NxtTrtFS), and PFS defined by RANO, respectively. The MGMT-STP27 score was significant in the Montpellier cohort when using NxtTrtFS as outcome measure, a trend was observed when using RANO criteria for PFS. * P <.05; ** P <.01.

Article Snippet: The association between the MGMT-STP27 score and NxtTrtFS was confirmed using a pyrosequencing-based MGMTp methylation score (MGMT-PYROscore) obtained for the same samples ( P = .002, ).

Techniques: Methylation, Biomarker Discovery

Journal: Acta Neuropathologica Communications

Article Title: Variant allelic frequencies of driver mutations can identify gliomas with potentially false-negative MGMT promoter methylation results

doi: 10.1186/s40478-023-01680-0

Figure Lengend Snippet: MGMT promoter methylation trends in pyrosequencing samples . ( A ) Frequency of positive results for all glioma samples above and below the cutoff value of VAF = 0.325. ( B ) Cumulative mean frequency of positive test results as a function of VAF. ( C ) Trends in cumulative mean MGMT promoter pyrosequencing score with increasing VAF. ( D ) Mean MGMT promoter pyrosequencing scores above and below VAF = 0.325. Similar results are shown for each tumor subtype, including IDH-wildtype glioblastoma ( E - H ), IDH-mutant astrocytoma ( I - L ) and IDH-mutant and 1p/19q co-deleted oligodendroglioma ( M - P ). (Horizontal dashed black lines: mean values for cohort; Horizontal solid red lines: MGMT positivity cutoff of 10.0%; Vertical dashed black lines: cutoff values identified by Cutoff Finder; Vertical dashed red lines: cutoff values identified by multi-part linear regression; Panels A, E, I, M: Fisher’s exact test; Panels D, H, L, P: unpaired Student’s T-test; pyroseq: pyrosequencing; *p < 0.05; ****p < 0.0001)

Article Snippet: Fig. 5 False negative results in IDH-wildtype glioblastoma with low TERT VAF . ( A ) MGMT promoter methylation results for 12 GBM samples (6 with TERT VAF ≤ 0.10, 6 with TERT VAF ≥ 0.25) comparing initial pyrosequencing methylation scores (left Y-axis, cutoff for positive = 10.0%, horizontal solid red line) to results on re-testing with DNA methylation array (right Y-axis). ( B ) MGMT promoter methylation results for the same 12 GBM samples comparing initial pyrosequencing methylation levels to results on re-testing with ddPCR. (GBM: IDH-wildtype glioblastoma, pos: positive, equiv: equivocal, neg: negative, QNS: quality/quantity of DNA not sufficient for reliable test result, ddPCR: droplet digital PCR)

Techniques: Methylation, Mutagenesis

Journal: Acta Neuropathologica Communications

Article Title: Variant allelic frequencies of driver mutations can identify gliomas with potentially false-negative MGMT promoter methylation results

doi: 10.1186/s40478-023-01680-0

Figure Lengend Snippet: MGMT promoter methylation trends in DNA methylation array samples . ( A ) Frequency of positive results for all glioma array samples above and below the cutoff of VAF = 0.245. ( B ) Cumulative mean frequency of positive results for all glioma array samples as a function of VAF. ( C ) Frequency of positive results for GBM array samples above and below the cutoff of TERT VAF = 0.325. ( D ) Cumulative mean frequency of positive results in GBM array samples as a function of TERT VAF. ( E ) Frequency of positive results in GBM pyrosequencing samples using MGMT cutoff of 10.0% versus GBM array samples. ( F ) Frequency of positive results in GBM pyrosequencing samples using MGMT cutoff of 7.28% versus GBM array samples. ( G ) Frequency of positive results in GBM pyrosequencing samples with TERT VAF < 0.115, using MGMT cutoff of 7.28%, versus GBM array samples with TERT VAF < 0.325. ( H ) Frequency of positive results in GBM pyrosequencing samples with TERT VAF ≥ 0.115, using MGMT cutoff of 7.28%, versus GBM array samples with TERT VAF ≥ 0.325. ( I ) Frequency of positive results for IDHmut astrocytoma above and below the cutoff of IDH VAF = 0.325 (left), by methylation class match (center), and above and below the cutoff of classifier score = 0.955 (right). ( J ) Cumulative mean frequency of positive results in IDHmut astrocytoma array samples as a function of IDH VAF (Fisher’s exact test for panels A, C, E, F, G, H, I; GBM: IDH-wildtype glioblastoma, IDHmut astrocytoma: IDH-mutant astrocytoma)

Article Snippet: Fig. 5 False negative results in IDH-wildtype glioblastoma with low TERT VAF . ( A ) MGMT promoter methylation results for 12 GBM samples (6 with TERT VAF ≤ 0.10, 6 with TERT VAF ≥ 0.25) comparing initial pyrosequencing methylation scores (left Y-axis, cutoff for positive = 10.0%, horizontal solid red line) to results on re-testing with DNA methylation array (right Y-axis). ( B ) MGMT promoter methylation results for the same 12 GBM samples comparing initial pyrosequencing methylation levels to results on re-testing with ddPCR. (GBM: IDH-wildtype glioblastoma, pos: positive, equiv: equivocal, neg: negative, QNS: quality/quantity of DNA not sufficient for reliable test result, ddPCR: droplet digital PCR)

Techniques: Methylation, DNA Methylation Assay, Mutagenesis

Journal: Acta Neuropathologica Communications

Article Title: Variant allelic frequencies of driver mutations can identify gliomas with potentially false-negative MGMT promoter methylation results

doi: 10.1186/s40478-023-01680-0

Figure Lengend Snippet: Driver mutation VAF , MGMT promoter methylation scores, and tumor cellularity . ( A ) Linear regression of MGMT promoter pyrosequencing score versus driver mutation VAF for all glioma samples. ( B ) Linear regression of MGMT promoter pyrosequencing score versus TERT promoter mutation VAF for GBM. ( C ) Linear regression of MGMT promoter pyrosequencing score versus IDH mutation VAF for IDHmut astrocytoma. ( D ) Linear regression of MGMT promoter pyrosequencing score versus IDH mutation VAF for IDHmut oligodendroglioma. ( E ) Linear regression of microscopically estimated cellularity versus cellularity calculated from driver mutation VAF (2×VAF×100%) for all glioma samples. ( F ) Differences between microscopically estimated cellularity and cellularity calculated from VAF (Y-axis) plotted as a function of VAF (X-axis), for all glioma samples. ( G ) TERT promoter mutation VAF by ddPCR in high versus low cellularity areas of GBM tissue samples. ( H ) MGMT promoter methylation score by ddPCR in high versus low cellularity areas of GBM tissue samples (GBM: IDH-wildtype glioblastoma, IDHmut astrocytoma: IDH-mutant astrocytoma, IDHmut oligodendroglioma: IDH-mutant and 1p/19q co-deleted oligodendroglioma, pyroseq: pyrosequencing, ddPCR: droplet digital PCR)

Article Snippet: Fig. 5 False negative results in IDH-wildtype glioblastoma with low TERT VAF . ( A ) MGMT promoter methylation results for 12 GBM samples (6 with TERT VAF ≤ 0.10, 6 with TERT VAF ≥ 0.25) comparing initial pyrosequencing methylation scores (left Y-axis, cutoff for positive = 10.0%, horizontal solid red line) to results on re-testing with DNA methylation array (right Y-axis). ( B ) MGMT promoter methylation results for the same 12 GBM samples comparing initial pyrosequencing methylation levels to results on re-testing with ddPCR. (GBM: IDH-wildtype glioblastoma, pos: positive, equiv: equivocal, neg: negative, QNS: quality/quantity of DNA not sufficient for reliable test result, ddPCR: droplet digital PCR)

Techniques: Mutagenesis, Methylation, Digital PCR

Journal: Acta Neuropathologica Communications

Article Title: Variant allelic frequencies of driver mutations can identify gliomas with potentially false-negative MGMT promoter methylation results

doi: 10.1186/s40478-023-01680-0

Figure Lengend Snippet: False negative results in IDH-wildtype glioblastoma with low TERT VAF . ( A ) MGMT promoter methylation results for 12 GBM samples (6 with TERT VAF ≤ 0.10, 6 with TERT VAF ≥ 0.25) comparing initial pyrosequencing methylation scores (left Y-axis, cutoff for positive = 10.0%, horizontal solid red line) to results on re-testing with DNA methylation array (right Y-axis). ( B ) MGMT promoter methylation results for the same 12 GBM samples comparing initial pyrosequencing methylation levels to results on re-testing with ddPCR. (GBM: IDH-wildtype glioblastoma, pos: positive, equiv: equivocal, neg: negative, QNS: quality/quantity of DNA not sufficient for reliable test result, ddPCR: droplet digital PCR)

Article Snippet: Fig. 5 False negative results in IDH-wildtype glioblastoma with low TERT VAF . ( A ) MGMT promoter methylation results for 12 GBM samples (6 with TERT VAF ≤ 0.10, 6 with TERT VAF ≥ 0.25) comparing initial pyrosequencing methylation scores (left Y-axis, cutoff for positive = 10.0%, horizontal solid red line) to results on re-testing with DNA methylation array (right Y-axis). ( B ) MGMT promoter methylation results for the same 12 GBM samples comparing initial pyrosequencing methylation levels to results on re-testing with ddPCR. (GBM: IDH-wildtype glioblastoma, pos: positive, equiv: equivocal, neg: negative, QNS: quality/quantity of DNA not sufficient for reliable test result, ddPCR: droplet digital PCR)

Techniques: Methylation, DNA Methylation Assay, Digital PCR

Journal: Acta Neuropathologica Communications

Article Title: Variant allelic frequencies of driver mutations can identify gliomas with potentially false-negative MGMT promoter methylation results

doi: 10.1186/s40478-023-01680-0

Figure Lengend Snippet: Central hypothesis . MGMT promoter methylation is pathologic, and occurs only in tumor cells. Cellular glioma samples are rich in DNA from tumor cells, whereas paucicellular glioma samples contain a large fraction of DNA from non-tumor cells, which can “dilute” positive methylation signals from tumor cell DNA, leading to false-negative results

Article Snippet: Fig. 5 False negative results in IDH-wildtype glioblastoma with low TERT VAF . ( A ) MGMT promoter methylation results for 12 GBM samples (6 with TERT VAF ≤ 0.10, 6 with TERT VAF ≥ 0.25) comparing initial pyrosequencing methylation scores (left Y-axis, cutoff for positive = 10.0%, horizontal solid red line) to results on re-testing with DNA methylation array (right Y-axis). ( B ) MGMT promoter methylation results for the same 12 GBM samples comparing initial pyrosequencing methylation levels to results on re-testing with ddPCR. (GBM: IDH-wildtype glioblastoma, pos: positive, equiv: equivocal, neg: negative, QNS: quality/quantity of DNA not sufficient for reliable test result, ddPCR: droplet digital PCR)

Techniques: Methylation

Journal: Acta Neuropathologica Communications

Article Title: Variant allelic frequencies of driver mutations can identify gliomas with potentially false-negative MGMT promoter methylation results

doi: 10.1186/s40478-023-01680-0

Figure Lengend Snippet: Patient cohort characteristics

Article Snippet: Fig. 5 False negative results in IDH-wildtype glioblastoma with low TERT VAF . ( A ) MGMT promoter methylation results for 12 GBM samples (6 with TERT VAF ≤ 0.10, 6 with TERT VAF ≥ 0.25) comparing initial pyrosequencing methylation scores (left Y-axis, cutoff for positive = 10.0%, horizontal solid red line) to results on re-testing with DNA methylation array (right Y-axis). ( B ) MGMT promoter methylation results for the same 12 GBM samples comparing initial pyrosequencing methylation levels to results on re-testing with ddPCR. (GBM: IDH-wildtype glioblastoma, pos: positive, equiv: equivocal, neg: negative, QNS: quality/quantity of DNA not sufficient for reliable test result, ddPCR: droplet digital PCR)

Techniques: Pyrosequencing Assay, Methylation, Mutagenesis

Journal: Acta Neuropathologica Communications

Article Title: Variant allelic frequencies of driver mutations can identify gliomas with potentially false-negative MGMT promoter methylation results

doi: 10.1186/s40478-023-01680-0

Figure Lengend Snippet: Analysis of differentially dissected tumor samples

Article Snippet: Fig. 3 MGMT promoter methylation trends in DNA methylation array samples . ( A ) Frequency of positive results for all glioma array samples above and below the cutoff of VAF = 0.245. ( B ) Cumulative mean frequency of positive results for all glioma array samples as a function of VAF. ( C ) Frequency of positive results for GBM array samples above and below the cutoff of TERT VAF = 0.325. ( D ) Cumulative mean frequency of positive results in GBM array samples as a function of TERT VAF. ( E ) Frequency of positive results in GBM pyrosequencing samples using MGMT cutoff of 10.0% versus GBM array samples. ( F ) Frequency of positive results in GBM pyrosequencing samples using MGMT cutoff of 7.28% versus GBM array samples. ( G ) Frequency of positive results in GBM pyrosequencing samples with TERT VAF < 0.115, using MGMT cutoff of 7.28%, versus GBM array samples with TERT VAF < 0.325. ( H ) Frequency of positive results in GBM pyrosequencing samples with TERT VAF ≥ 0.115, using MGMT cutoff of 7.28%, versus GBM array samples with TERT VAF ≥ 0.325. ( I ) Frequency of positive results for IDHmut astrocytoma above and below the cutoff of IDH VAF = 0.325 (left), by methylation class match (center), and above and below the cutoff of classifier score = 0.955 (right). ( J ) Cumulative mean frequency of positive results in IDHmut astrocytoma array samples as a function of IDH VAF (Fisher’s exact test for panels A, C, E, F, G, H, I; GBM: IDH-wildtype glioblastoma, IDHmut astrocytoma: IDH-mutant astrocytoma) Further analyses suggested that variation in MGMT assay results between pyrosequencing and methylation array for IDHwt GBM may be driven by differing MGMT pyrosequencing cutoff values and divergent results in low-VAF GBMs specifically.

Techniques: Mutagenesis

Journal: Acta Neuropathologica Communications

Article Title: Variant allelic frequencies of driver mutations can identify gliomas with potentially false-negative MGMT promoter methylation results

doi: 10.1186/s40478-023-01680-0

Figure Lengend Snippet: MGMT promoter methylation trends in DNA methylation array samples . ( A ) Frequency of positive results for all glioma array samples above and below the cutoff of VAF = 0.245. ( B ) Cumulative mean frequency of positive results for all glioma array samples as a function of VAF. ( C ) Frequency of positive results for GBM array samples above and below the cutoff of TERT VAF = 0.325. ( D ) Cumulative mean frequency of positive results in GBM array samples as a function of TERT VAF. ( E ) Frequency of positive results in GBM pyrosequencing samples using MGMT cutoff of 10.0% versus GBM array samples. ( F ) Frequency of positive results in GBM pyrosequencing samples using MGMT cutoff of 7.28% versus GBM array samples. ( G ) Frequency of positive results in GBM pyrosequencing samples with TERT VAF < 0.115, using MGMT cutoff of 7.28%, versus GBM array samples with TERT VAF < 0.325. ( H ) Frequency of positive results in GBM pyrosequencing samples with TERT VAF ≥ 0.115, using MGMT cutoff of 7.28%, versus GBM array samples with TERT VAF ≥ 0.325. ( I ) Frequency of positive results for IDHmut astrocytoma above and below the cutoff of IDH VAF = 0.325 (left), by methylation class match (center), and above and below the cutoff of classifier score = 0.955 (right). ( J ) Cumulative mean frequency of positive results in IDHmut astrocytoma array samples as a function of IDH VAF (Fisher’s exact test for panels A, C, E, F, G, H, I; GBM: IDH-wildtype glioblastoma, IDHmut astrocytoma: IDH-mutant astrocytoma)

Article Snippet: Fig. 3 MGMT promoter methylation trends in DNA methylation array samples . ( A ) Frequency of positive results for all glioma array samples above and below the cutoff of VAF = 0.245. ( B ) Cumulative mean frequency of positive results for all glioma array samples as a function of VAF. ( C ) Frequency of positive results for GBM array samples above and below the cutoff of TERT VAF = 0.325. ( D ) Cumulative mean frequency of positive results in GBM array samples as a function of TERT VAF. ( E ) Frequency of positive results in GBM pyrosequencing samples using MGMT cutoff of 10.0% versus GBM array samples. ( F ) Frequency of positive results in GBM pyrosequencing samples using MGMT cutoff of 7.28% versus GBM array samples. ( G ) Frequency of positive results in GBM pyrosequencing samples with TERT VAF < 0.115, using MGMT cutoff of 7.28%, versus GBM array samples with TERT VAF < 0.325. ( H ) Frequency of positive results in GBM pyrosequencing samples with TERT VAF ≥ 0.115, using MGMT cutoff of 7.28%, versus GBM array samples with TERT VAF ≥ 0.325. ( I ) Frequency of positive results for IDHmut astrocytoma above and below the cutoff of IDH VAF = 0.325 (left), by methylation class match (center), and above and below the cutoff of classifier score = 0.955 (right). ( J ) Cumulative mean frequency of positive results in IDHmut astrocytoma array samples as a function of IDH VAF (Fisher’s exact test for panels A, C, E, F, G, H, I; GBM: IDH-wildtype glioblastoma, IDHmut astrocytoma: IDH-mutant astrocytoma) Further analyses suggested that variation in MGMT assay results between pyrosequencing and methylation array for IDHwt GBM may be driven by differing MGMT pyrosequencing cutoff values and divergent results in low-VAF GBMs specifically.

Techniques: Methylation, DNA Methylation Assay, Mutagenesis

Journal: Acta Neuropathologica Communications

Article Title: Variant allelic frequencies of driver mutations can identify gliomas with potentially false-negative MGMT promoter methylation results

doi: 10.1186/s40478-023-01680-0

Figure Lengend Snippet: Driver mutation VAF , MGMT promoter methylation scores, and tumor cellularity . ( A ) Linear regression of MGMT promoter pyrosequencing score versus driver mutation VAF for all glioma samples. ( B ) Linear regression of MGMT promoter pyrosequencing score versus TERT promoter mutation VAF for GBM. ( C ) Linear regression of MGMT promoter pyrosequencing score versus IDH mutation VAF for IDHmut astrocytoma. ( D ) Linear regression of MGMT promoter pyrosequencing score versus IDH mutation VAF for IDHmut oligodendroglioma. ( E ) Linear regression of microscopically estimated cellularity versus cellularity calculated from driver mutation VAF (2×VAF×100%) for all glioma samples. ( F ) Differences between microscopically estimated cellularity and cellularity calculated from VAF (Y-axis) plotted as a function of VAF (X-axis), for all glioma samples. ( G ) TERT promoter mutation VAF by ddPCR in high versus low cellularity areas of GBM tissue samples. ( H ) MGMT promoter methylation score by ddPCR in high versus low cellularity areas of GBM tissue samples (GBM: IDH-wildtype glioblastoma, IDHmut astrocytoma: IDH-mutant astrocytoma, IDHmut oligodendroglioma: IDH-mutant and 1p/19q co-deleted oligodendroglioma, pyroseq: pyrosequencing, ddPCR: droplet digital PCR)

Article Snippet: Fig. 3 MGMT promoter methylation trends in DNA methylation array samples . ( A ) Frequency of positive results for all glioma array samples above and below the cutoff of VAF = 0.245. ( B ) Cumulative mean frequency of positive results for all glioma array samples as a function of VAF. ( C ) Frequency of positive results for GBM array samples above and below the cutoff of TERT VAF = 0.325. ( D ) Cumulative mean frequency of positive results in GBM array samples as a function of TERT VAF. ( E ) Frequency of positive results in GBM pyrosequencing samples using MGMT cutoff of 10.0% versus GBM array samples. ( F ) Frequency of positive results in GBM pyrosequencing samples using MGMT cutoff of 7.28% versus GBM array samples. ( G ) Frequency of positive results in GBM pyrosequencing samples with TERT VAF < 0.115, using MGMT cutoff of 7.28%, versus GBM array samples with TERT VAF < 0.325. ( H ) Frequency of positive results in GBM pyrosequencing samples with TERT VAF ≥ 0.115, using MGMT cutoff of 7.28%, versus GBM array samples with TERT VAF ≥ 0.325. ( I ) Frequency of positive results for IDHmut astrocytoma above and below the cutoff of IDH VAF = 0.325 (left), by methylation class match (center), and above and below the cutoff of classifier score = 0.955 (right). ( J ) Cumulative mean frequency of positive results in IDHmut astrocytoma array samples as a function of IDH VAF (Fisher’s exact test for panels A, C, E, F, G, H, I; GBM: IDH-wildtype glioblastoma, IDHmut astrocytoma: IDH-mutant astrocytoma) Further analyses suggested that variation in MGMT assay results between pyrosequencing and methylation array for IDHwt GBM may be driven by differing MGMT pyrosequencing cutoff values and divergent results in low-VAF GBMs specifically.

Techniques: Mutagenesis, Methylation, Digital PCR

Journal: Acta Neuropathologica Communications

Article Title: Variant allelic frequencies of driver mutations can identify gliomas with potentially false-negative MGMT promoter methylation results

doi: 10.1186/s40478-023-01680-0

Figure Lengend Snippet: Re-testing pyrosequencing samples with DNA methylation array and droplet digital PCR

Article Snippet: Fig. 3 MGMT promoter methylation trends in DNA methylation array samples . ( A ) Frequency of positive results for all glioma array samples above and below the cutoff of VAF = 0.245. ( B ) Cumulative mean frequency of positive results for all glioma array samples as a function of VAF. ( C ) Frequency of positive results for GBM array samples above and below the cutoff of TERT VAF = 0.325. ( D ) Cumulative mean frequency of positive results in GBM array samples as a function of TERT VAF. ( E ) Frequency of positive results in GBM pyrosequencing samples using MGMT cutoff of 10.0% versus GBM array samples. ( F ) Frequency of positive results in GBM pyrosequencing samples using MGMT cutoff of 7.28% versus GBM array samples. ( G ) Frequency of positive results in GBM pyrosequencing samples with TERT VAF < 0.115, using MGMT cutoff of 7.28%, versus GBM array samples with TERT VAF < 0.325. ( H ) Frequency of positive results in GBM pyrosequencing samples with TERT VAF ≥ 0.115, using MGMT cutoff of 7.28%, versus GBM array samples with TERT VAF ≥ 0.325. ( I ) Frequency of positive results for IDHmut astrocytoma above and below the cutoff of IDH VAF = 0.325 (left), by methylation class match (center), and above and below the cutoff of classifier score = 0.955 (right). ( J ) Cumulative mean frequency of positive results in IDHmut astrocytoma array samples as a function of IDH VAF (Fisher’s exact test for panels A, C, E, F, G, H, I; GBM: IDH-wildtype glioblastoma, IDHmut astrocytoma: IDH-mutant astrocytoma) Further analyses suggested that variation in MGMT assay results between pyrosequencing and methylation array for IDHwt GBM may be driven by differing MGMT pyrosequencing cutoff values and divergent results in low-VAF GBMs specifically.

Techniques: DNA Methylation Assay

Journal: Acta Neuropathologica Communications

Article Title: Variant allelic frequencies of driver mutations can identify gliomas with potentially false-negative MGMT promoter methylation results

doi: 10.1186/s40478-023-01680-0

Figure Lengend Snippet: False negative results in IDH-wildtype glioblastoma with low TERT VAF . ( A ) MGMT promoter methylation results for 12 GBM samples (6 with TERT VAF ≤ 0.10, 6 with TERT VAF ≥ 0.25) comparing initial pyrosequencing methylation scores (left Y-axis, cutoff for positive = 10.0%, horizontal solid red line) to results on re-testing with DNA methylation array (right Y-axis). ( B ) MGMT promoter methylation results for the same 12 GBM samples comparing initial pyrosequencing methylation levels to results on re-testing with ddPCR. (GBM: IDH-wildtype glioblastoma, pos: positive, equiv: equivocal, neg: negative, QNS: quality/quantity of DNA not sufficient for reliable test result, ddPCR: droplet digital PCR)

Article Snippet: Fig. 3 MGMT promoter methylation trends in DNA methylation array samples . ( A ) Frequency of positive results for all glioma array samples above and below the cutoff of VAF = 0.245. ( B ) Cumulative mean frequency of positive results for all glioma array samples as a function of VAF. ( C ) Frequency of positive results for GBM array samples above and below the cutoff of TERT VAF = 0.325. ( D ) Cumulative mean frequency of positive results in GBM array samples as a function of TERT VAF. ( E ) Frequency of positive results in GBM pyrosequencing samples using MGMT cutoff of 10.0% versus GBM array samples. ( F ) Frequency of positive results in GBM pyrosequencing samples using MGMT cutoff of 7.28% versus GBM array samples. ( G ) Frequency of positive results in GBM pyrosequencing samples with TERT VAF < 0.115, using MGMT cutoff of 7.28%, versus GBM array samples with TERT VAF < 0.325. ( H ) Frequency of positive results in GBM pyrosequencing samples with TERT VAF ≥ 0.115, using MGMT cutoff of 7.28%, versus GBM array samples with TERT VAF ≥ 0.325. ( I ) Frequency of positive results for IDHmut astrocytoma above and below the cutoff of IDH VAF = 0.325 (left), by methylation class match (center), and above and below the cutoff of classifier score = 0.955 (right). ( J ) Cumulative mean frequency of positive results in IDHmut astrocytoma array samples as a function of IDH VAF (Fisher’s exact test for panels A, C, E, F, G, H, I; GBM: IDH-wildtype glioblastoma, IDHmut astrocytoma: IDH-mutant astrocytoma) Further analyses suggested that variation in MGMT assay results between pyrosequencing and methylation array for IDHwt GBM may be driven by differing MGMT pyrosequencing cutoff values and divergent results in low-VAF GBMs specifically.

Techniques: Methylation, DNA Methylation Assay, Digital PCR