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Co-authored-by: Jaclyn Taroni <[email protected]>
Co-authored-by: Jo Lynne Rokita <[email protected]>

content/03.results.md

@@ -25,11 +25,11 @@ We followed a similar process in our Manubot-powered [@doi:10.1371/journal.pcbi.
 
 Since 2000, neuro-oncology experts and the WHO have collaborated to iteratively redefine central nervous system (CNS) tumor classifications [@pubmed:11895036; @doi:10.1007/s00401-007-0243-4].
 In 2016 [@doi:10.1007/s00401-016-1545-1], molecular subtypes driven by genetic alterations were integrated into these classifications.
-Since CBTN specimen collection began in 2011 before molecular data were classified, most tumors lacked molecular subtype information when tissue was collected.
+Since CBTN specimen collection began in 2011, most tumors lacked molecular subtype information when tissue was collected.
 Moreover, PBTA does not yet feature methylation arrays which are increasingly used to inform molecular subtyping and cancer diagnosis.
 Therefore, we created analysis modules to systematically consider key genomic features of tumors described by the WHO in 2016 or Ryall and colleagues [@doi:10.1016/j.ccell.2020.03.011].
-Coupled with clinician and pathologist review, we generated research-grade high confidence integrated diagnoses for 60% (644/1074) of tumors  (**Table S1**) without methylation data, a major innovation of this project. <!--SAMPLECOUNT-->
-We could then align OpenPBTA specimen diagnoses with WHO classifications (e.g., tumors formerly ascribed primitive neuro-ectodermal tumor [PNET] diagnoses), discover rarer tumor entities (e.g., H3-mutant ependymoma, meningioma with _YAP1::FAM118B_ fusion), as well as identify and correct data entry errors (e.g., an embryonal tumor with multilayer rosettes (ETMR) incorrectly entered as a medulloblastoma) and histologically mis-identified specimens (e.g., Ewing sarcoma sample labeled as a craniopharyngioma).
+Coupled with clinician and pathologist review, we generated high-confidence research-grade integrated diagnoses for 60% (644/1074) of tumors  (**Table S1**) without methylation data, a major innovation of this project. <!--SAMPLECOUNT-->
+We then aligned OpenPBTA specimen diagnoses with WHO classifications (e.g., tumors formerly ascribed primitive neuro-ectodermal tumor [PNET] diagnoses), discovered rarer tumor entities (e.g., H3-mutant ependymoma, meningioma with _YAP1::FAM118B_ fusion), as well as identified and corrected data entry errors (e.g., an embryonal tumor with multilayer rosettes (ETMR) incorrectly entered as a medulloblastoma) and histologically mis-identified specimens (e.g., Ewing sarcoma sample labeled as a craniopharyngioma).
 Uniquely, we used transcriptomic classification to subtype 122 medulloblastomas into SHH, WNT, Group 3, or Group 4 with `MedulloClassifier` [@doi:10.1371/journal.pcbi.1008263] and `MM2S` [@doi:10.1186/s13029-016-0053-y], with 95% (41/43) and 91% (39/43) accuracy, respectively.
 
 In total, we subtyped low-grade gliomas (LGGs) (N = 290), HGGs (N = 141), embryonal tumors (N = 126), ependymomas (N = 33), tumors of sellar region (N = 27), mesenchymal non-meningothelial tumors (N = 11), glialneuronal tumors (N = 10), and chordomas (N = 6), where Ns represent unique tumors (**Table {@tbl:Table1}**).<!--SAMPLECOUNT-->
@@ -132,7 +132,7 @@ GSVA showed upregulation (ANOVA Bonferroni-corrected p < 0.01) of DNA repair, G2
 The two ultra-hypermutated tumors (> 100 Mutations/Mb) were from patients with mismatch repair deficiency syndrome [@doi:10.1093/neuonc/noz192].
 
 <!--SAMPLECOUNT-->
-Considering embryonal tumors, 25% (15/60) of ependymomas were _C11orf95::RELA_ (now, _ZFTA::RELA_) fusion-positive [@doi:10.1038/nature13109], and 68% (21/31) of craniopharyngiomas contained _CTNNB1_ mutations (**Figure {@fig:Fig2}D**).
+We observed that 25% (15/60) of ependymomas were _C11orf95::RELA_ (now, _ZFTA::RELA_) fusion-positive [@doi:10.1038/nature13109] and 68% (21/31) of craniopharyngiomas contained _CTNNB1_ mutations (**Figure {@fig:Fig2}D**).
 We observed somatic mutations or fusions in _NF2_ in 41% (7/17) of meningiomas, 5% (3/60) of ependymomas, and 25% (3/12) of schwannomas, as well as rare fusions in _ERBB4_, _YAP1_, and/or _QKI_ in 10% (6/60) of ependymomas.
 DNETs harbored alterations in MAPK/PI3K pathway genes, as was previously reported [@doi:10.1093/jnen/nlz101], including _FGFR1_ (21%, 4/19), _PDGFRA_ (10%, 2/19), and _BRAF_ (5%, 1/19).
 
@@ -150,10 +150,10 @@ In embryonal tumors, _CTNNB1_ mutations significantly co-occurred with _TP53_ mu
 _FGFR1_ and _PIK3CA_ mutations significantly co-occurred in LGGs (OR = 77.25, 95% CI: 10.0 - 596.8, q = 3.12e-3), consistent with previous findings [@doi:10.1200/JCO.2010.31.1670; @doi:10.1186/s40478-020-01027-z].
 Of HGG tumors with _TP53_ or _PPM1D_ mutations, 53/55 (96.3%) had mutations in only one of these genes (OR = 0.17, 95% CI: 0.04 - 0.89, q = 0.056), recapitulating previous observations that these mutations are usually mutually exclusive in HGGs [@https://doi.org/10.1038/ng.2938].
 
-CNV and SV analyses revealed that HGG, DMG, and medulloblastoma tumors has the most unstable genomes, while craniopharyngiomas and schwannomas generally lacked somatic CNV (**Figure {@fig:S3}C**).
+CNV and SV analyses revealed that HGG, DMG, and medulloblastoma tumors had the most unstable genomes, while craniopharyngiomas and schwannomas generally lacked somatic CNV (**Figure {@fig:S3}C**).
 These CNV patterns largely aligned with our TMB estimates (**Figure {@fig:S2}H**).
 SV and CNV breakpoint densities were significantly correlated (linear regression p = 1.05e-38; **Figure {@fig:Fig3}C**), and as expected, the number of chromothripsis regions called increased with breakpoint density (**Figure {@fig:S3}D-E**).
-We identified chromothripsis events in 31% (N = 12/39) of DMGs and in 44% (N = 21/48) of other HGGs (**Figure {@fig:Fig3}D**), and we found evidence of chromothripsis in over 15% of sarcomas, PXAs, metastatic secondary tumors, chordomas, glial-neuronal tumors, germinomas, meningiomas, ependymomas, medulloblastomas, ATRTs, and other embryonal tumors.
+We identified chromothripsis events in 31% (N = 12/39) of DMGs and in 44% (N = 21/48) of other HGGs (**Figure {@fig:Fig3}D**), and found evidence of chromothripsis in over 15% of sarcomas, PXAs, metastatic secondary tumors, chordomas, glial-neuronal tumors, germinomas, meningiomas, ependymomas, medulloblastomas, ATRTs, and other embryonal tumors.
 
 We assessed the contributions of eight adult CNS-specific mutational signatures from the RefSig database [@doi:10.1038/s43018-020-0027-5] across tumors (**Figure {@fig:Fig3}E** and **Figure {@fig:S4}A**).
 Signature 1, which reflects normal spontaneous deamination of 5-methylcytosine, predominated in stage 0 and/or 1 tumors characterized by low TMBs (**Figure {@fig:S2}H**) such as pilocytic astrocytomas, gangliogliomas, other LGGs, and craniopharyngiomas (**Figure {@fig:S4}A**).
@@ -175,21 +175,21 @@ Since batch correction was not feasible (see **Limitations of the Study** and **
 
 #### Prediction of _TP53_ oncogenicity and telomerase activity
 
-We applied TCGA-trained classifier [@doi:10.1016/j.celrep.2018.03.076] to calculate a _TP53_ score, a proxy for _TP53_ gene or pathway dysregulation, and subsequently infer tumor _TP53_ inactivation status.
-We identified "true positive" _TP53_ alterations from high-confidence SNVs, CNVs, SVs, and fusions in _TP53_, annotating tumors as "activated" if they harbored one of p.R273C or p.R248W gain-of-function mutations [@doi:10.1038/ng0593-42], or "lost" if 1) the given patient had a Li Fraumeni Syndrome (LFS) predisposition diagnosis, 2) the tumor harbored a known hotspot mutation, or 3) the tumor contained two hits (e.g. both SNV and CNV), suggesting both alleles were affected.
+We applied a TCGA-trained classifier [@doi:10.1016/j.celrep.2018.03.076] to calculate a _TP53_ score, a proxy for _TP53_ gene or pathway dysregulation, and subsequently infer tumor _TP53_ inactivation status.
+We identified "true positive" _TP53_ alterations from high-confidence SNVs, CNVs, SVs, and fusions in _TP53_, annotating tumors as "activated" if they harbored one of p.R273C or p.R248W gain-of-function mutations [@doi:10.1038/ng0593-42], or "lost" if 1) the patient had a Li Fraumeni Syndrome (LFS) predisposition diagnosis, 2) the tumor harbored a known hotspot mutation, or 3) the tumor contained two hits (e.g. both SNV and CNV), suggesting both alleles were affected.
 If the _TP53_ mutation did not reside within the DNA-binding domain or no alterations in _TP53_ were detected, we annotated the tumor as "other," indicating an unknown _TP53_ alteration status.
 The classifier achieved a high accuracy (AUROC = 0.86) for rRNA-depleted, stranded tumors, but it did not perform as well on the poly-A tumors in this cohort (AUROC = 0.62; **Figure {@fig:S5}A**).
 
-We observed that "activated" and "lost" tumors had similar _TP53_ scores (**Figure {@fig:Fig4}B**, Wilcoxon p = 0.92), contrasting our expectation that "activated" tumors would have higher _TP53_ scores.
+We observed that "activated" and "lost" tumors had similar _TP53_ scores (**Figure {@fig:Fig4}B**, Wilcoxon p = 0.92), contrasting our expectation that "lost" tumors would have higher _TP53_ scores.
 This difference suggests that classifier scores > 0.5 may actually represent an oncogenic, or altered, _TP53_ phenotype rather than solely _TP53_ inactivation, as interpreted previously [@doi:10.1016/j.celrep.2018.03.076].
 However, "activated" tumors showed higher _TP53_ expression compared to those with _TP53_ "loss" mutations (Wilcoxon p = 0.006, **Figure {@fig:Fig4}C**).
 DMGs, medulloblastomas, HGGs, DNETs, ependymomas, and craniopharyngiomas, all known to harbor _TP53_ mutations, had the highest median _TP53_ scores (**Figure {@fig:Fig4}D**).
 By contrast, gangliogliomas, LGGs, meningiomas, and schwannomas had the lowest median scores.
 
-We hypothesized that tumors from patients with LFS (N = 8) would have higher _TP53_ scores, which we indeed observed for 8/10 tumors from LFS patients (**Table S3**).
-Although two tumors from LFS patients had low _TP53_ scores (`BS_DEHJF4C7` at 0.09 and `BS_ZD5HN296` at 0.28), pathology reports confirmed that both patients were diagnosed with LFS with a _TP53_ pathogenic germline variant.
+We hypothesized that tumors (N = 10) from patients with LFS (N = 8) would have higher _TP53_ scores, which we indeed observed for 8/10 tumors (**Table S3**).
+Although two tumors had low _TP53_ scores (`BS_DEHJF4C7` at 0.09 and `BS_ZD5HN296` at 0.28), pathology reports confirmed that both patients were diagnosed with LFS and harbored a _TP53_ pathogenic germline variant.
 These two LFS tumors also had low tumor purity (16% and 37%, respectively), suggesting that accurate classification may require a certain level of tumor content.
-We suggest that this classifier could be generally applied to infer _TP53_ function in the absence of a predicted oncogenic _TP53_ alteration or DNA sequencing.
+We suggest this classifier could be generally applied to infer _TP53_ function in the absence of a predicted oncogenic _TP53_ alteration or DNA sequencing.
 
 We used gene expression data to predict telomerase activity using EXpression-based Telomerase ENzymatic activity Detection (`EXTEND`) [@doi:10.1038/s41467-020-20474-9] as a surrogate measure of malignant potential [@doi:10.1038/s41467-020-20474-9; @doi:10.1093/carcin/bgp268], where higher `EXTEND` scores indicate higher telomerase activity.
 Aggressive tumors such as DMGs, other HGGs, and MB had high `EXTEND` scores (**Figure {@fig:Fig4}D**), and low-grade lesions such as schwannomas, GNGs, DNETs, and other LGGs had among the lowest scores (**Table S3**), supporting previous reports that aggressive tumor phenotypes have higher telomerase activity [@doi:10.1007/s13277-016-5045-7; @doi:10.1038/labinvest.3700710; @doi:10.1007/s12032-016-0736-x; @doi:10.1111/j.1750-3639.2010.00372.x].
@@ -206,12 +206,12 @@ Three of these patients harbored pathogenic germline variants in one of the afor
 While we did not detect a _known_ pathogenic variant in the germline of PT_VTM2STE3, this patient's pathology report contained a self-reported _PMS2_ variant, and we indeed found 19 intronic variants of unknown significance (VUS) in their _PMS2_.
 This is not surprising since an estimated 49% of germline _PMS2_ variants in patients with CMMRD and/or Lynch syndrome are VUS [@doi:10.1136/jmedgenet-2020-107627].
 Interestingly, while the cell line derived from patient PT_VTM2STE3's tumor at progression was not hypermutated (TMB = 5.7 Mut/Mb), it only contained the MMR2 signature, suggesting selective pressure to maintain a mismatch repair (MMR) phenotype _in vitro_.
-Only one of the two cell lines derived from patient PT_JNEV57VK's progressive tumor was hypermutated (TMB = 35.9 Mut/Mb).
-Their hypermutated cell line was strongly weighted towards signature 11, while their non-hypermutated cell line showed several lesser signature weights (1, 11, 18, 19, MMR2; **Table S2**).
+Only one of the two cell lines derived from patient `PT_JNEV57VK`'s progressive tumor was hypermutated (TMB = 35.9 Mut/Mb).
+The hypermutated cell line was strongly weighted towards signature 11, while the non-hypermutated cell line showed several lesser signature weights (1, 11, 18, 19, MMR2; **Table S2**).
 This mutational process plasticity highlights the importance of careful genomic characterization and model selection for preclinical studies.
 
 Signature 18, which has been associated with high genomic instability and can induce a hypermutator phenotype [@doi:10.1038/s43018-020-0027-5], was uniformly represented among hypermutant solid tumors.
-Additionally, all hypermutant HGG tumors or cell lines had dysfunctional _TP53_ (**Table {@tbl:Table2}**), consistent with previous findings that tumors with high genomic instability depend on _TP53_ dysregulation [@doi:10.1038/s43018-020-0027-5].
+Additionally, all hypermutant HGG tumors or cell lines had dysfunctional _TP53_ (**Table {@tbl:Table2}**), consistent with previous findings that tumors with high genomic instability signatures require _TP53_ dysregulation [@doi:10.1038/s43018-020-0027-5].
 With one exception, hypermutant and ultra-hypermutant tumors had high _TP53_ scores (> 0.5) and telomerase activity.
 Interestingly, none of the hypermutant tumors showed evidence of signature 3 (present in homologous recombination deficient tumors), signature 8 (arises from double nucleotide substitutions/unknown etiology), or signature N6 (a universal CNS tumor signature).
 The mutual exclusivity of signatures 3 and MMR2 corroborates previous suggestions that tumors do not generally feature both deficient homologous repair and mismatch repair [@doi:10.1016/j.celrep.2018.03.076].
@@ -235,11 +235,11 @@ The mutual exclusivity of signatures 3 and MMR2 corroborates previous suggestion
 Table: **Patients with hypermutant tumors.** Listed are patients with at least one hypermutant or ultra-hypermutant tumor or cell line. Pathogenic (P) or likely pathogenic (LP) germline variants, coding region TMB, phase of therapy, therapeutic interventions, cancer predisposition (CMMRD = Constitutional mismatch repair deficiency), and molecular subtypes are included. {#tbl:Table2}
 
 Next, we asked whether transcriptomic classification of _TP53_ dysregulation and/or telomerase activity recapitulate these oncogenic biomarkers' known prognostic influence.
-We identified several expected trends, including a significant overall survival benefit following full tumor resection (HR = 0.35, 95% CI = 0.2 - 0.62, p < 0.001) or if the tumor was an LGG (HR = 0.046, 95% CI = 0.0062 - 0.34, p = 0.003), and a significant risk if the was an HGG (HR = 6.2, 95% CI = 4.0 - 9.5, p < 0.001) (**Figure {@fig:Fig4}F**; **STAR Methods**).
+We identified several expected trends, including a significant overall survival benefit following full tumor resection (HR = 0.35, 95% CI = 0.2 - 0.62, p < 0.001) or if the tumor was an LGG (HR = 0.046, 95% CI = 0.0062 - 0.34, p = 0.003), and a significant risk if the tumor was an HGG (HR = 6.2, 95% CI = 4.0 - 9.5, p < 0.001) (**Figure {@fig:Fig4}F**; **STAR Methods**).
 High telomerase scores were associated with poor prognosis across brain tumor histologies (HR = 20, 95% CI = 6.4 - 62, p < 0.001), demonstrating that `EXTEND` scores calculated from RNA-Seq are an effective rapid surrogate measure for telomerase activity.
 Higher _TP53_ scores were associated with significant survival risks (**Table S4**) within DMGs (HR = 6436, 95% CI = 2.67 - 1.55e7, p = 0.03) and ependymomas (HR = 2003, 95% CI = 9.9 - 4.05e5, p = 0.005).
-Given this result, we next assessed whether different HGG molecular subtypes carry different survival risks.
-We found that DMG H3 K28 tumors with _TP53_ loss had significantly worse prognosis (HR = 2.8, CI = 1.4-5.6, p = 0.003) than did DMG H3 K28 tumors with wildtype _TP53_ (**Figure {@fig:Fig4}G** and **Figure {@fig:Fig4}H**), reflecting results from two recent restrospective analyses of DIPG tumors [@doi:10.1158/1078-0432.CCR-22-0803; @doi:10.1007/s11060-021-03890-9].
+Given this result, we next assessed whether different HGG molecular subtypes carry different survival risks if stratified by _TP53_ status.
+We found that DMG H3 K28 tumors with _TP53_ loss had significantly worse prognosis (HR = 2.8, CI = 1.4-5.6, p = 0.003) than those with wildtype _TP53_ (**Figure {@fig:Fig4}G** and **Figure {@fig:Fig4}H**), recapitulating results from two recent restrospective analyses of DIPG tumors [@doi:10.1158/1078-0432.CCR-22-0803; @doi:10.1007/s11060-021-03890-9].
 
 ![**_TP53_ and telomerase activity** A, Receiver Operating Characteristic for _TP53_ classifier run on stranded FPKM RNA-Seq. B, Violin and strip plots of _TP53_ scores plotted by _TP53_ alteration type (N<sub>activated</sub> = 11, N<sub>lost</sub> = 100, N<sub>other</sub> = 866). C, Violin and strip plots of _TP53_ RNA expression plotted by _TP53_ activation status (N<sub>activated</sub> = 11, N<sub>lost</sub> = 100, N<sub>other</sub> = 866). D, Box plots of _TP53_ and telomerase (EXTEND) scores across cancer groups. TMB status is highlighted in orange (hypermutant) or red (ultra-hypermutant). E, Heatmap of RefSig mutational signatures for patients with at least one hypermutant tumor or cell line. F, Forest plot depicting prognostic effects of _TP53_ and telomerase scores on overall survival (OS), controlling for extent of tumor resection, LGG group, and HGG group. G, Forest plot depicting the effect of molecular subtype on HGG OS. For F and G, hazard ratios (HR) with 95% confidence intervals and p-values (multivariate Cox) are listed. Significant p-values are denoted with black diamonds. Reference groups are denoted by grey diamonds. H, Kaplan-Meier curve of HGGs by molecular subtype. Box plot represents 5% (lower whisker), 25% (lower box), 50% (median), 75% (upper box), and 95% (upper whisker) quantiles.](https://raw.githubusercontent.com/AlexsLemonade/OpenPBTA-analysis/37ec62fdc2fd9ff157f2f2c10b69e9bb36673363/figures/pngs/figure4.png?sanitize=true){#fig:Fig4 width="7in"}