Sam Q. Sun

Management of atypical cranial meningiomas, part 1: predictors of recurrence and the role of adjuvant radiation after gross total resection.

BACKGROUND Indications for external beam radiation therapy (EBRT) for atypical meningiomas (AMs) remain unclear. OBJECTIVE To analyze features associated with recurrence in AM patients after gross total resection (GTR) and to assess the relative benefit of EBRT in a retrospective cohort study. METHODS One hundred fifty-one primary AMs after GTR (88 female patients; median follow-up, 45.0 months) were examined for possible predictors of recurrence (age, sex, location, volume, bone involvement, brain invasion). The Fisher exact and Wilcoxon rank-sum tests were used to analyze the association between these predictors and use of EBRT. The impact on recurrence for these predictors and EBRT was analyzed with Kaplan-Meier and Cox regression. RESULTS Of 151 patients, 13 (8.6%) experienced recurrence after GTR (median, 47.0 months). Multivariate analysis identified elevated mitotic index (P = .007) and brain invasion (P = .002) as predictors of recurrence. Larger volume (P = .96) was not associated with recurrence but was more likely to prompt EBRT (P = .001). Recurrences occurred in 11 of 112 with GTR (9.8%; median, 44 months) and 2 of 39 with GTR/EBRT (5.1%; median, 133 months). The 2-, 5-, and 10-year progression-free survival rates after GTR vs GTR/EBRT were 97%, 86%, and 68% vs 100%, 100%, and 78%. Kaplan-Meier analysis demonstrated no difference in progression-free survival or overall survival after GTR vs GTR/EBRT (P = .8, P > .99). CONCLUSION Brain invasion and high mitotic rates may predict recurrence. After GTR of AMs, EBRT appears not to affect progression-free survival and overall survival, suggesting that observation rather than EBRT may be indicated after GTR.

Protein-structure-guided discovery of functional mutations across 19 cancer types

Local concentrations of mutations are well known in human cancers. However, their three-dimensional spatial relationships in the encoded protein have yet to be systematically explored. We developed a computational tool, HotSpot3D, to identify such spatial hotspots (clusters) and to interpret the potential function of variants within them. We applied HotSpot3D to >4,400 TCGA tumors across 19 cancer types, discovering >6,000 intra- and intermolecular clusters, some of which showed tumor and/or tissue specificity. In addition, we identified 369 rare mutations in genes including TP53, PTEN, VHL, EGFR, and FBXW7 and 99 medium-recurrence mutations in genes such as RUNX1, MTOR, CA3, PI3, and PTPN11, all mapping within clusters having potential functional implications. As a proof of concept, we validated our predictions in EGFR using high-throughput phosphorylation data and cell-line-based experimental evaluation. Finally, mutation–drug cluster and network analysis predicted over 800 promising candidates for druggable mutations, raising new possibilities for designing personalized treatments for patients carrying specific mutations.

Management of Atypical Cranial Meningiomas, Part 2: Predictors of Progression and the Role of Adjuvant Radiation After Subtotal Resection.

BACKGROUND The efficacies of adjuvant stereotactic radiosurgery (SRS) and external beam radiation therapy (EBRT) for atypical meningiomas (AMs) after subtotal resection (STR) remain unclear. OBJECTIVE To analyze the clinical, histopathological, and radiographic features associated with progression in AM patients after STR. METHODS Fifty-nine primary AMs after STR were examined for predictors of progression, including the impact of SRS and EBRT, in a retrospective cohort study. RESULTS Twenty-seven patients (46%) progressed after STR (median, 30 months). On univariate analysis, spontaneous necrosis positively (hazard ratio = 5.2; P = .006) and adjuvant radiation negatively (hazard ratio = 0.3; P = .009) correlated with progression; on multivariate analysis, only adjuvant radiation remained independently significant (hazard ratio = 0.3; P = .006). SRS and EBRT were associated with greater local control (LC; P = .02) and progression-free survival (P = .007). The 2-, 5-, and 10-year actuarial LC rates after STR vs STR/EBRT were 60%, 34%, and 34% vs 96%, 65%, and 45%. The 2-, 5-, and 10-year actuarial progression-free survival rates after STR vs STR/EBRT were 60%, 30%, and 26% vs 96%, 65%, and 45%. Compared with STR alone, adjuvant radiation therapy significantly improved LC in AMs that lack spontaneous necrosis (P = .003) but did not improve LC in AMs with spontaneous necrosis (P = .6). CONCLUSION Adjuvant SRS or EBRT improved LC of AMs after STR but only for tumors without spontaneous necrosis. Spontaneous necrosis may aid in decisions to administer adjuvant SRS or EBRT after STR of AMs.

Proteogenomic integration reveals therapeutic targets in breast cancer xenografts.

Recent advances in mass spectrometry (MS) have enabled extensive analysis of cancer proteomes. Here, we employed quantitative proteomics to profile protein expression across 24 breast cancer patient-derived xenograft (PDX) models. Integrated proteogenomic analysis shows positive correlation between expression measurements from transcriptomic and proteomic analyses; further, gene expression-based intrinsic subtypes are largely re-capitulated using non-stromal protein markers. Proteogenomic analysis also validates a number of predicted genomic targets in multiple receptor tyrosine kinases. However, several protein/phosphoprotein events such as overexpression of AKT proteins and ARAF, BRAF, HSP90AB1 phosphosites are not readily explainable by genomic analysis, suggesting that druggable translational and/or post-translational regulatory events may be uniquely diagnosed by MS. Drug treatment experiments targeting HER2 and components of the PI3K pathway supported proteogenomic response predictions in seven xenograft models. Our study demonstrates that MS-based proteomics can identify therapeutic targets and highlights the potential of PDX drug response evaluation to annotate MS-based pathway activities.

Simpson Grade I-III Resection of Spinal Atypical (World Health Organization Grade II) Meningiomas is Associated With Symptom Resolution and Low Recurrence

BACKGROUND Because of their rarity, outcomes regarding spinal atypical meningiomas (AMs) remain unclear. OBJECTIVE To describe the recurrence rate and postoperative outcomes after resection of spinal AMs, and to discuss an appropriate resection strategy and adjuvant therapy for spinal AMs. METHODS Data from all patients who presented with spinal AMs to 2 tertiary referral centers between 1998 and 2013 were obtained by chart review. RESULTS From 102 patients with spinal meningioma, 20 AM tumors (7 cervical, 11 thoracic, 2 thoracolumbar) were identified in 18 patients (median age, 50 years [range, 19-75] at time of resection; 11% male; median follow-up, 32 months [range, 1-179] after resection). Before resection, patients had sensory deficits (70%), pain (70%), weakness (60%), ataxia (50%), spasticity (65%), and incontinence (35%). One tumor presented asymptomatically. Simpson grade I, II, III, and IV resection were achieved in 3 (15%), 13 (65%), 2 (10%), and 2 (10%) tumors, respectively. One patient that underwent Simpson grade III resection received adjuvant radiation therapy. After Simpson grade I-III or gross total resection, no tumors recurred (0%; confidence interval, 0%-17.6%). After Simpson grade IV resection, 1 tumor recurred (50%; confidence interval, 1.3%-98.7%). With the exception of 1 patient who had bilateral paraplegia perioperatively, all other patients experienced improvement of preoperative symptoms after surgery (median time, 3.6 months [range, 1-13] after resection). CONCLUSION Despite published cases suggesting an aggressive clinical course for spinal AMs, this series of spinal AMs reports that gross total resection without adjuvant radiation therapy resulted in symptom resolution and low recurrence.

Radiation Therapy for Residual or Recurrent Atypical Meningioma: The Effects of Modality, Timing, and Tumor Pathology on Long-Term Outcomes.

BACKGROUND Optimal use of stereotactic radiosurgery (SRS) vs external beam radiation therapy (EBRT) for treatment of residual/recurrent atypical meningioma is unclear. OBJECTIVE To analyze features associated with progression after radiation therapy. METHODS Fifty radiation-naive patients who received SRS or EBRT for residual and/or recurrent atypical meningioma were examined for predictors of progression using Cox regression and Kaplan-Meier analyses. RESULTS Thirty-two patients (64%) received adjuvant radiation after subtotal resection, 12 patients (24%) received salvage radiation after progression following subtotal resection, and 6 patients (12%) received salvage radiation after recurrence following gross total resection. Twenty-one patients (42%) received SRS (median 18 Gy), and 7 (33%) had tumor progression. Twenty-nine patients (58%) received EBRT (median 54 Gy), and 13 (45%) had tumor progression. Whereas tumor volume (P = .53), SRS vs EBRT (P = .45), and adjuvant vs salvage (P = .34) were not associated with progression after radiation therapy, spontaneous necrosis (hazard ratio [HR] = 82.3, P < .001), embolization necrosis (HR = 15.6, P = .03), and brain invasion (HR = 3.8, P = .008) predicted progression in univariate and multivariate analyses. Tumors treated with SRS/EBRT had 2- and 5-year actuarial locoregional control rates of 91%/88% and 71%/69%, respectively. Tumors with spontaneous necrosis, embolization necrosis, and no necrosis had 2- and 5-year locoregional control rates of 76%, 92%, and 100% and 36%, 73%, and 100%, respectively (P < .001). CONCLUSION This study suggests that necrosis may be a negative predictor of radiation response regardless of radiation timing or modality. ABBREVIATIONS AM, atypical meningiomaEBRT, external beam radiation therapyGTR, gross total resectionLC, locoregional controlOS, overall survivalPOE, preoperative embolizationRT, radiation therapySRS, stereotactic radiosurgerySTR, subtotal resection.

Database of evidence for precision oncology portal

Summary: A database of curated genomic variants with clinically supported drug therapies and other oncological annotations is described. The accompanying web portal provides a search engine with two modes: one that allows users to query gene, cancer type, variant type or position for druggable mutations, and another to search for and to visualize, on three‐dimensional protein structures, putative druggable sites that cluster with known druggable mutations. Availability and implementation: http://dinglab.wustl.edu/depo

Corrigendum: Protein-structure-guided discovery of functional mutations across 19 cancer types

Unnur Styrkarsdottir, Hannes Helgason, Asgeir Sigurdsson, Gudmundur L Norddahl, Arna B Agustsdottir, Louise N Reynard, Amanda Villalvilla, Gisli H Halldorsson, Aslaug Jonasdottir, Audur Magnusdottir, Asmundur Oddson, Gerald Sulem, Florian Zink, Gardar Sveinbjornsson, Agnar Helgason, Hrefna S Johannsdottir, Anna Helgadottir, Hreinn Stefansson, Solveig Gretarsdottir, Thorunn Rafnar, Ina S Almdahl, Anne Brækhus, Tormod Fladby, Geir Selbæk, Farhad Hosseinpanah, Fereidoun Azizi, Jung Min Koh, Nelson L S Tang, Maryams Danesphour, Jose I Mayordomo, Corrine Welt, Peter S Braund, Nilesh J Samani, Lambertus A Kiemeney, L Stefan Lohmander, Claus Christiansen, Ole A Andreassen, arcOGEN consortium, Olafur Magnusson, Gisli Masson, Augustine Kong, Ingileif Jonsdottir, Daniel Gudbjartsson, Patrick Sulem, Helgi Jonsson, John Loughlin, Thorvaldur Ingvarsson, Unnur Thorsteinsdottir & Kari Stefansson Nat. Genet.; doi:10.1038/ng.3816; corrected online 17 April 2017

Integrative omics analyses broaden treatment targets in human cancer

BackgroundAlthough large-scale, next-generation sequencing (NGS) studies of cancers hold promise for enabling precision oncology, challenges remain in integrating NGS with clinically validated biomarkers.MethodsTo overcome such challenges, we utilized the Database of Evidence for Precision Oncology (DEPO) to link druggability to genomic, transcriptomic, and proteomic biomarkers. Using a pan-cancer cohort of 6570 tumors, we identified tumors with potentially druggable biomarkers consisting of drug-associated mutations, mRNA expression outliers, and protein/phosphoprotein expression outliers identified by DEPO.ResultsWithin the pan-cancer cohort of 6570 tumors, we found that 3% are druggable based on FDA-approved drug-mutation interactions in specific cancer types. However, mRNA/phosphoprotein/protein expression outliers and drug repurposing across cancer types suggest potential druggability in up to 16% of tumors. The percentage of potential drug-associated tumors can increase to 48% if we consider preclinical evidence. Further, our analyses showed co-occurring potentially druggable multi-omics alterations in 32% of tumors, indicating a role for individualized combinational therapy, with evidence supporting mTOR/PI3K/ESR1 co-inhibition and BRAF/AKT co-inhibition in 1.6 and 0.8% of tumors, respectively. We experimentally validated a subset of putative druggable mutations in BRAF identified by a protein structure-based computational tool. Finally, analysis of a large-scale drug screening dataset lent further evidence supporting repurposing of drugs across cancer types and the use of expression outliers for inferring druggability.ConclusionsOur results suggest that an integrated analysis platform can nominate multi-omics alterations as biomarkers of druggability and aid ongoing efforts to bring precision oncology to patients.

Pan-cancer analysis of somatic mutations across 21 neuroendocrine tumor types

Dear Editor, Neuroendocrine tumors (NETs) comprise a heterogeneous spectrum of neoplasms originating from neuroendocrine cells in various organs — most commonly in the endocrine glands and the gastrointestinal tract. The molecular and etiological features of NETs arising from different organs are still far from clarified. Therefore, systematic analysis of genomic alternations and their contribution to core pathways in NETs is urgently needed for the development of novel diagnostic, therapeutic strategies and personalized management of patients. Here, we investigated somatic mutations across 21 NET types through pan-cancer analysis and identified 86 candidate driver genes. Further analysis of druggability and panel sequencing of these genes provide potential diagnostic and therapeutic targets for NETs. To investigate the landscape of common and specific somatic mutations in NETs, we collected mutation data of 1,103 tumors (1,034 published and 69 new whole-exome sequencing data) from 38 research projects (Supplementary information, Table S1). We performed whole-exome sequencing on tumor-normal pairs of 38 insulinoma (INS), 20 Cushing’s disease (CD) induced by corticotroph pituitary adenoma and 11 pheochromocytoma (PCC) (Supplementary information, Tables S2–5, Figure S1). Using these data, we compiled somatic mutation data across 21 NET types. The data set consisted of five types of adrenocortical tumors: aldosterone-producing adenomas (APA), cortisol-producing adrenocortical adenomas (ACA), ACTH-independent macronodular adrenocortical hyperplasia (AIMAH), adrenocortical carcinomas (ACC) and adrenocortical oncocytoma; seven types of pituitary tumors: growth hormone-secreting pituitary adenomas, gonadotropins including follicle-stimulating hormone and luteinizing hormone pituitary adenomas, prolactin pituitary adenomas, thyrotropin-stimulating hormone pituitary adenomas, CD induced by corticotroph pituitary adenoma, plurihormonal pituitary adenoma and nonfunctioning pituitary adenomas; two types of pancreatic tumors: non-functional NETs (PNETs) and INS; medullary thyroid cancer (MTC), parathyroid adenomas and parathyroid carcinomas (PTC); pulmonary carcinoids (PC); PCC and paraganglioma (PCC/PGL); small intestine NETs (SINET) and neuroblastoma (NB) (Supplementary information, Table S6). The data from 21 NET types were re-analyzed and annotated to obtain a uniform set of somatic mutations (Supplementary information, Tables S7 and 8). Malignant NETs have a larger number of non-silent mutations and a higher mutation frequency than benign NETs (P= 7.33 × 10; Supplementary information, Figures S2 and 3). Mutation spectrum across 21 NET types reveals that the C−>T transversion is the predominant substitution, consistent with findings in other cancer types (Supplementary information, Figure S4 and Table S9). To comprehensively identify the significantly mutated genes (SMGs) with a statistically higher mutation rate in NETs, we performed systematic and stepwise analysis using the MuSiC suite. The results of SMG analysis are associated with the background mutation rates (BMRs) of tumor types and BMRs between benign and malignant tumors are significantly different (Supplementary information, Figure S5). Therefore, MuSiC analyses of 21 NETs were separately conducted in the combined benign set, combined malignant set, combined organ set (adrenal, gastrointestinal, pituitary and thyroid) and individual tumor types. The resulting SMGs were further filtered by gene mutation frequency, deleterious mutation rate and gene expression (Supplementary information, Figure S6). We reliably identified a total of 86 candidate driver genes in NETs, including 80 SMGs and 6 known cancer genes (Supplementary information, Figure S7 and Table S10). Of the 86 candidate driver genes, 34 are novel SMGs in NETs and 52 have been reported in previous studies of specific NET types. The mutations of 86 genes showed common and specific distribution in NETs. Novel type-specific SMGs were identified in less frequently mutated genes, such as DNMT3A in benign NETs and AHNAK, COL1A1, SF3B1 and ZNF292 in malignant NETs (Fig. 1a and Supplementary information, Figure S8). Our data reveal that MEN1 is the most common SMGs in NETs (8 out of 21 types). Notably, mutations of three novel SMGs and six known candidate driver genes are identified in as least five NET types, indicating that more common driver genes emerge across both benign and malignant NETs (Supplementary information, Figure S9). To comprehensively understand the mechanistic classification and further illustrate the cellular processes involved in NETs, we performed gene ontology and hierarchical clustering analysis. The 86 genes were classified into 20 categories of cellular processes (Supplementary information, Figure S10). Clustering analysis showed that in addition to MEN1, RET and GNAS, mutations in transcription factor genes (YY1, CTNNB1, NF1) and mutations in genome integrity genes (ATM, ATRX, TP53) are critical for clustering of multiple NET types, suggesting the importance of these SMGs in molecular classification of NETs (Supplementary information, Figure S11). Clustering of the 21 types of NET and 13 other cancers with the 86 candidate driver genes and previously described cancer genes showed that the majority of NETs, except ACC, are distinctive from common malignant tumors (Supplementary information, Figure S12). Notably, chromatin modification and remodeling genes (22 genes in the categories of histone modifiers, genome integrity and DNA methylation) are the most significant set in NETs. In silico prediction analysis showed that all the 22 genes have pathogenic or truncating mutations (131/171, 76.6% in total), supporting the functional roles of the chromatin modification and remodeling genes in NETs (Supplementary information, Figures S13 and 14, Tables S11 and 12). MEN1 is the representative mutated driver gene in diverse inherited and sporadic NET types. The variant