Citations

Abstract MYC expression is frequently dysregulated in multiple myeloma (MM). In one comprehensive study, MYC structural variations (SV) were found in nearly half of MM cases (Affer et al. Leukemia 2014). The prevalence was higher in hyperdiploid (HRD) tumors (65%) compared to non-hyperdiploid (NHRD) tumors (36%). The large amount of tumor DNA required for all of the genomic studies performed may have biased the samples analyzed (e.g., to those with higher tumor burdens). To validate the findings of recurrent MYC SV in another dataset, we analyzed the CoMMpass data. We analyzed long-insert whole genome sequencing (WGS) data from diagnostic samples in 420 patients from the IA7 release (dbGAP phs000748) for SV. The results of clinical data, processed WES (SNV), RNASeq (gene expression) and WGS (copy number) data from IA8 (http://research.themmrf.org) were used to calculate survival, RAS and NFKB pathway mutation (WES), TC class (RNA), NFKB index (RNA) and hyperdiploid index (WGS). MYC SVs were identified in 38% of tumors. They were present in 53% HRD and 28% NHRD, and by TC in 55% D1, 43% D2, 36% MMSET, 26% MAF, 13% CCND. Juxtaposition of an Ig enhancer (IgH, IgK, IgL) close to MYC was the most common MYC SV, representing ~40% of the MYC SV. Other enhancers identified have mostly been reported previously, with the most frequent being NSMCE2 (12% of SV) and TXNDC5 (5% of SV). Intrachromosomal SV (deletions, inversions, duplications) not associated with any known enhancer were also frequent (18% of SV). As expected, MYC expression was higher in tumors with MYC SV compared to those without (~2.4 fold, p-value<0.0001). We used Gene Set Enrichment Analysis (GSEA) to identify activated pathways that might substitute for MYC in HRD patients without MYC SV and observed significant activation of the NFkB pathway. Interestingly, examining patients with RAS/MAPK pathway (NRAS, KRAS, BRAF, FGFR3 - abbreviated RAS) mutations (identified in 50% of all patients) the same pattern was observed: in the absence of RAS mutation, there was a significantly higher NFkB index. In general, the HRD tumors seem to have either activation of RAS and/or MYC, or activation of NFkB (Figure). There was no difference in overall survival in patients with versus those without MYC SV. We developed a clinically applicable sequencing platform to identify MYC SV, which cannot be reliably identified by FISH. We sought to validate this targeted capture approach, where in addition to the coding exons of 81 interesting genes described previously (M3P), we also pulled down the region surrounding MYC (2 Mb), IgH (0.5 Mb), IgK (50 kb) and IgL (100 kb) allowing us to additionally identify SV in MYC and Ig loci. Using this approach we identified IgH translocations in 29/30 samples with translocations previously identified by FISH (97%). Moreover, we identified MYC SV in 19/22 patients with SV previously identified by mate-pair WGS (86%). Importantly, sequencing identified the precise translocation breakpoint, and identity of the enhancer dysregulating MYC, which may be important variables. In one informative patient two different MYC SV were present at diagnosis, only one of which was still present following a partial response to four cycles of lenalidomide and dexamethasone. This suggests that the two MYC SV are in different subclones, one of which was much more sensitive to the treatment. Interestingly, the enhancer dysregulating MYC in the sensitive subclone harbors 5 strong Ikaros binding sites identified by ChIPseq, suggesting one intriguing mechanism for sensitivity to lenalidomide. To summarize, we verified in a large dataset that MYC expression is frequently dysregulated by SV in MM (38%), and the RAS/MAPK (50%) and NFKB (23%) pathways are frequently activated by mutations. Surprisingly, given their generally good prognosis, nearly half of HRD tumors seem to be MYC driven, while this is true for only a quarter of NHRD tumors. HRD tumors not driven by MYC or RAS appear to be driven by NFKB. Remarkably, the pathways most commonly activated by mutation in MM: CCND, MYC, RAS, NFKB are common to many cancers and have been studied extensively individually. To understand their clinical impact in MM we have developed a comprehensive custom capture sequencing panel that identified 97% of IgH translocations, 86% of MYC SV, and as well as SNV and CNV of 81 recurrently mutated genes. It will be important to include such a comprehensive genetic analysis to complement clinical trials in the future. Figure. Figure. Disclosures Stewart: Bristol Myers Squibb: Consultancy; Celgene: Consultancy; Takeda Oncology: Consultancy; Janssen Pharmaceuticals: Consultancy.

Abstract The IGHV4-34 gene is very frequent (~10%) in the B cell receptor immunoglobulin (BcR IG) gene repertoire of chronic lymphocytic leukemia (CLL). Over 30% of IGHV4-34 CLL cases can be assigned to different subsets with stereotyped BcR IG. The largest is subset #4 which represents ~1% of all CLL and ~10% of IGHV4-34 CLL and is considered a prototype for indolent disease. The BcR IG of a great majority (~85%) of IGHV4-34 CLL cases carry a significant load of somatic hypermutation (SHM), often with distinctive SHM patterns. This holds especially true for stereotyped subsets and is suggestive of particular modes of interactions with the selecting antigen(s). In detail, subsets #4 and #16, both involving IgG-switched cases (IgG-CLL), exhibit the greatest sequence similarity in SHM profiles, whereas they differ in this respect from IgM/D subsets #29 and #201. Prompted by these observations, here we explored the extent that these subset-biased SHM profiles in different IGHV4-34 stereotyped subsets were reflected in distinct demographics, clinical presentation, genomic aberrations and outcomes. Within a multi-institutional series of 20,331 CLL patients, 1790 (8.8%) expressed IGHV4-34 BcR IG. Following established bioinformatics approaches for the identification of BcR IG stereotypy, 573/1790 IGHV4-34 CLL cases (32%) were assigned to stereotyped subsets; of these, 340 cases (19% of all IGHV4-34 CLL and 60% of stereotyped IGHV4-34 cases) belonged to subsets #4, #16, #29 and #201, all concerning IGHV-mutated CLL (M-CLL). Clinicobiological information was available for 275/340 patients: #4, n=150; #16, n=44; #29, n=39; and #201, n=42. Comparisons between subsets revealed no differences in gender and age distribution. Interestingly, however, 36-43% of each subset cases were young for CLL (defined as patients aged ≤55 years), which is higher compared to general CLL cohorts, where young patients generally account for ~25% of cases. In contrast, significant differences were identified between subsets regarding: (i) disease stage at diagnosis, with >90% of IgG subsets #4 and #16 diagnosed at Binet stage A versus 83% in subset #201 and 74% in subset #29 (p=0.029); (ii) CD38 expression, ranging from 1% in subset #4 to 10% in subset #201 (p=0.013); (iii) the distribution of del(13q), peaking at a remarkable 92% in subset #29 versus only 37% in subset #16 (p<0.0001). Regarding other genomic aberrations, they were either absent (NOTCH1 mutations) or rare (SF3B1 mutations, trisomy 12, del(11q), TP53 aberrations due to either del(17p) and/or TP53 mutations). The sole exception concerned a high frequency (14%) of TP53 aberrations in subset #29 (p<0.05 compared with the other subsets), which is notable for M-CLL cases in general. Time to first treatment (TTFT) could be analyzed in 228 cases. IgG subsets #4 and #16 had significantly (p=0.036) longer TTFT (median TTFT: not yet reached) compared to the IgM/D subsets #29 and #201 (median TTFT: 11 and 12 years, respectively). In conclusion, we have identified distinct clinicobiological profiles for different stereotyped IGHV4-34 M-CLL subsets, highlighting subsets #4 and #16 as particularly indolent, which is important for both medical and social reasons, especially considering that a significant proportion of patients in these subsets are diagnosed at younger ages. Our findings support the notion that BcR IG stereotypy refines prognostication in CLL, superseding the crude immunogenetic distinction based on SHM load only. Additionally, the observed heterogeneity suggests that not all M-CLL are equal, prompting further research into the underlying biological background with the ultimate aim of tailored patient management. Disclosures Tausch: Gilead: Other: Travel support. Shanafelt:Glaxo-Smith_Kline: Research Funding; Genentech: Research Funding; Celgene: Research Funding; Polyphenon E Int'l: Research Funding; Hospira: Research Funding; Janssen: Research Funding; Pharmactckucs: Research Funding; Cephalon: Research Funding. Niemann:Gilead: Consultancy; Janssen: Consultancy; Roche: Consultancy; Novartis: Other: Travel grant. Langerak:InVivoScribe: Patents & Royalties: Licensing of IP and Patent on BIOMED-2-based methods for PCR-based Clonality Diagnostics.; DAKO: Patents & Royalties: Licensing of IP and Patent on Split-Signal FISH. Royalties for Dept. of Immunology, Erasmus MC, Rotterdam, NL; Roche: Other: Lab services in the field of MRD diagnostics provided by Dept of Immunology, Erasmus MC (Rotterdam). Hallek:Celgene: Honoraria, Other: Speakers Bureau and/or Advisory Boards, Research Funding; AbbVie: Honoraria, Other: Speakers Bureau and/or Advisory Boards, Research Funding; Roche: Honoraria, Other: Speakers Bureau and/or Advisory Boards, Research Funding; Boehringher Ingelheim: Honoraria, Other: Speakers Bureau and/or Advisory Boards; Pharmacyclics: Honoraria, Other: Speakers Bureau and/or Advisory Boards, Research Funding; Mundipharma: Honoraria, Other: Speakers Bureau and/or Advisory Boards, Research Funding; Janssen: Honoraria, Other: Speakers Bureau and/or Advisory Boards, Research Funding; Gilead: Honoraria, Other: Speakers Bureau and/or Advisory Boards, Research Funding. Ghia:Janssen Pharmaceuticals: Research Funding.

Background Genome-wide association studies (GWAS) conducted in populations of European ancestry (EA) have identified and confirmed 23 germline susceptibility loci for multiple myeloma (MM). The effect sizes of single nucleotide polymorphisms (SNPs) at these loci are small, therefore combining them into a single summary measure, known as a polygenic risk score (PRS), may provide a more meaningful risk factor. We have previously shown a PRS comprised of the 23 SNPs for MM contributes to increased risk of MM, with a 2.7-fold increase for highest vs. lowest PRS quintiles. Whether the MM-PRS is also associated with overall survival (OS) in MM cases has not been evaluated. We examined the association between MM-PRS and OS in two EA studies. Methods The first study consisted of 2,179 EA MM cases from ten studies included in the Multiple Myeloma Working Group within the International Lymphoma Consortium (InterLymph). Cases were diagnosed between 1970 and 2015 and genotyped using multiple platforms (Oncoarray, Affymetrix, Human660W-quad Beadchip, and Illumina arrays); 885 cases also had stage [based on International Staging System (ISS)] available. Each of the GWAS was subjected to rigorous standard quality control independently (prior to imputation via the Michigan imputation server based on the Haplotype Reference Consortium (HRC). The second study consisted of 515 newly diagnosed EA MM cases from CoMMpass (Relating Clinical Outcomes in Multiple Myeloma to Personal Assessment of Genetic Profile), diagnosed from 2011-2013, who had whole genome sequencing (WGS) performed on germline DNA. The WGS data was used to call common germline genetic variants through the Mayo Clinic bioinformatics pipeline. Briefly, genetic variants were detected with GenomeGPS, aligned to the hg19 reference genome, called using the GATK (V3.6) Haplotype Caller, and merged for multiple-sample joint calling. To reduce the false positive variants, variant quality score recalibration (VQSR) was applied for both SNPs and INDELs. After quality control, 458 EA samples remained. Follow-up was available for both studies and consisted of time from MM diagnosis date until death or date of last known follow-up. The PRS was constructed from the 23 MM SNPs using the published per allele odds ratio associated with MM risk. The published log odds ratios for each SNP were multiplied by the number of risk alleles (0, 1, 2) for the corresponding SNP, and summed, resulting in a unique score per person. Kaplan-Meier curves and Cox proportional hazard models were used to assess the association between PRS with MM OS considering two models: 1) adjusted for age, sex, study and 2) additional adjustment by stage (ISS). Hazard ratios (OR) and 95% confidence intervals (CI) were estimated. The PRS was evaluated both as a continuous variable, per standard deviation (SD), and as a categorical variable (quintiles). Results MM cases (N=2,179) in the InterLymph study were 59% male and 41% female and the median age was 61.0 years (26-90 years). Median follow-up time was 57.2 months (1.0-509.0 months) with 868 reported deaths. MM cases with stage information available consisted of 20% stage I (n=178), 53% stage II (n=466), and 27% stage III (n=241). No association was observed between PRS and OS in MM patients regardless of adjustment for stage (continuous PRS (HR: 1.03, 95% CI: 0.83-1.28, P=0.80) or by quintile PRS (p>0.05)) (Table). The CoMMpass EA MM cases (n=458) had similar distributions for sex (61% male and 39% females) but were slightly older 65 years (27-93 years) and had shorter follow-up time (median=39.75 months (0.13-77.2)) with 117 deaths. Stage was available for 96% of CoMMpass cases including 36% stage I (n=159), 33% stage II (n=146), and 31% stage III (n=134). We also observed no association of PRS and OS in the CoMMpass study (HR=1.02, 95% CI: 0.72 -1.46, P= 0.89), adjusted for age, sex, and stage (Table). Discussion A PRS score for MM risk is not associated with OS for MM cases in two EA populations. Given that prior studies have shown association of genetic variation with MM survival, efforts to identify additional loci associated with OS or MM specific survival are warranted. Future studies should also consider germline variants impact on molecular subtypes, specific therapies, and outcomes. Disclosures Kumar: Celgene: Consultancy, Research Funding; Janssen: Consultancy, Research Funding; Takeda: Research Funding.

Introduction: New Diffuse Large B-cell (DLBCL) treatments remain a clinical need despite the success of rituximab combined with CHOP chemotherapy (RCHOP), which results in durable responses in 60-70% of patients. Those refractory to, or who relapse following, first-line therapy have a very poor outcome, with only 20% surviving beyond 5 years. Rationally-targeted frontline strategies are needed, especially for those with high-risk disease. Significant advances have been made in the genomic classification of DLBCL, but none have impacted the design of phase III trials for untreated DLBCL patients. Building on new genetic profiling studies to personalize clinical treatment, an NCI initiative, could allow clinicians to add targeted therapies to the RCHOP backbone based on individual tumor signatures. The phase II ECOG-ACRIN1412 trial, which compared RCHOP combined with lenalidomide (R2CHOP) versus RCHOP showed significantly superior event free survival (EFS) and overall survival benefits for those treated with R2CHOP. Herein, we report the profile of a high-risk ABC/non-GCB subset of DLBCL driven by genomic alterations in inflammatory genes that are susceptible to front-line R2CHOP, but continue to experience poor outcome with RCHOP alone. Methods: We studied a total of 196 DLBCL patients. 47 were treated with R2CHOP from an investigator-initiated, open-label, single-arm phase II study (NCT00670358). 149 were newly diagnosed DLBCL cases treated with RCHOP, or R-immunochemotherapy (herein called RCHOP), and followed prospectively through the Molecular Epidemiology Resource (MER) of the University of Iowa/Mayo Clinic SPORE that served as a contemporary cohort. Patients from each treatment group were divided based on their event free survival at 24 months (EFS24). DNA alterations within these populations were identified through whole exome sequencing (WES). Variants were analyzed for their presence in EFS24 achievement or failure groups in both RCHOP and R2CHOP. Both tumor and germline sequencing was performed for 47/47 R2CHOP cases and 49/149 RCHOP cases. Gene expression data from the PanCan panel of 730 B-cell-related genes was analyzed to determine gene expression profiles characteristic of the high-risk/R2CHOP-profile on 59 available non-GCB DLBCL cases (45 RCHOP; 14 R2CHOP). A two-sided comparative marker analysis T statistic test was applied to assess what genes displayed differential expression based on achieving EFS24 in both R2CHOP and RCHOP populations. Positive values represented associations with achieving EFS24 and negative values were associated with cases of EFS24 failure. Results: Three genes were enriched in the RCHOP cases that failed EFS24 but achieved EFS24 with R2CHOP among non-GCB patients: PIM1, SPEN, and MYD88 (L265P), herein referred to as Responder Alterations. In R2CHOP cases, patients with a Responder Alteration had a better overall EFS (P = 0.051) compared to wild type patients. In contrast, RCHOP treated patients with a Responder Alteration in their tumor had a significantly worse overall EFS (P = 0.0004) compared to patients without a mutation. Together, PIM1, SPEN, or MYD88 (L265P) mutations were present in 38.0% (30/79) of all non-GCB cases. Collective R2CHOP and RCHOP EFS24 differential gene expression T values were significantly different for 18 previously-defined signatures. The R2CHOP cases that achieved EFS24 were enriched for genes involved in cell cycle, JAK-STAT, cytokine signaling, and NF-κB pathways based on these signatures and ontology analyses. Lastly, cases with WES and PanCan data were analyzed together to observe differential gene expression patterns between cases with and without signature mutations. These data suggest that R2CHOP disrupts tumors reliant on IRF4, NF-κB, and STAT transcription factors, leading to a loss of proliferative feedback systems. Conclusions: Our combined analysis of DNA and RNA across R2CHOP and RCHOP treatment cohorts identifies a high-risk non-GCB phenotype that encompasses approximately 38% of non-GCB patients, is capable of sustaining JAK-STAT and NF-κB signaling, and is sensitive to R2CHOP. Our study lays the groundwork for a precision therapy approach in DLBCL in which DNA or RNA profiles can be used to identify patients early in treatment who may not benefit from the current standard of care, RCHOP, and who would benefit from the addition of lenalidomide or other targeted agents. Disclosures Gandhi: Celgene Corporation: Employment, Equity Ownership, Patents & Royalties. Ansell:LAM Therapeutics: Research Funding; Seattle Genetics: Research Funding; LAM Therapeutics: Research Funding; Seattle Genetics: Research Funding; Regeneron: Research Funding; Bristol-Myers Squibb: Research Funding; Affimed: Research Funding; Trillium: Research Funding; Trillium: Research Funding; Regeneron: Research Funding; Bristol-Myers Squibb: Research Funding; LAM Therapeutics: Research Funding; Trillium: Research Funding; Affimed: Research Funding; LAM Therapeutics: Research Funding; Bristol-Myers Squibb: Research Funding; Trillium: Research Funding; Affimed: Research Funding; Bristol-Myers Squibb: Research Funding; Trillium: Research Funding; LAM Therapeutics: Research Funding; Bristol-Myers Squibb: Research Funding; Regeneron: Research Funding; Affimed: Research Funding; Seattle Genetics: Research Funding; Mayo Clinic Rochester: Employment; Regeneron: Research Funding; Seattle Genetics: Research Funding; Mayo Clinic Rochester: Employment; Seattle Genetics: Research Funding; LAM Therapeutics: Research Funding; LAM Therapeutics: Research Funding; LAM Therapeutics: Research Funding; Regeneron: Research Funding; Regeneron: Research Funding; Seattle Genetics: Research Funding; Seattle Genetics: Research Funding; Trillium: Research Funding; Trillium: Research Funding; Affimed: Research Funding; Affimed: Research Funding; Mayo Clinic Rochester: Employment; Mayo Clinic Rochester: Employment; Mayo Clinic Rochester: Employment; Seattle Genetics: Research Funding; Mayo Clinic Rochester: Employment; Bristol-Myers Squibb: Research Funding; Bristol-Myers Squibb: Research Funding; Affimed: Research Funding; Regeneron: Research Funding; Regeneron: Research Funding; Bristol-Myers Squibb: Research Funding; Regeneron: Research Funding; Seattle Genetics: Research Funding; Trillium: Research Funding; Mayo Clinic Rochester: Employment; Mayo Clinic Rochester: Employment; Trillium: Research Funding; Affimed: Research Funding; Mayo Clinic Rochester: Employment; Bristol-Myers Squibb: Research Funding; Affimed: Research Funding; LAM Therapeutics: Research Funding. Cerhan:Celgene: Research Funding; NanoString: Research Funding; Janssen: Membership on an entity's Board of Directors or advisory committees. Nowakowski:Curis: Research Funding; Bayer: Consultancy, Research Funding; Celgene: Consultancy, Research Funding; F. Hoffmann-La Roche Ltd: Research Funding; Genentech, Inc.: Research Funding; MorphoSys: Consultancy, Research Funding; NanoString: Research Funding; Selvita: Membership on an entity's Board of Directors or advisory committees. Novak:Celgene Coorperation: Research Funding.

Purpose: Multiple myeloma (MM) is a plasma cell (PC) malignancy with an increasing incidence in the US. Epidemiological studies demonstrate a 2-3 fold higher incidence of the pre-malignant monoclonal gammopathy of undetermined significance (MGUS) and MM with a ~4-year younger age of onset among African Americans (AA) compared to European Americans (EAs) (Fonseca, Leukemia, 2017). With equal access to care, AAs have better overall survival compared to EAs (Waxman, Blood, 2010). This disparity may be explained by ancestral-associated genetic predisposition of AAs to development of monoclonal gammopathies and to specific acquired, cytogenetically-defined subtypes. Using calculated genetic ancestry data, we have previously identified a higher prevalence of IgH translocations t(11;14), t(14;16) and t(14;20) in individuals with >80% African ancestry (Baughn, BCJ, 2018). Since SNP rs9344 encoding the CCND1 870G>A polymorphism has been reported in association with increased risk of t(11;14) (Weinhold, Nature Genetics, 2013), we investigated whether rs9344 correlates with African ancestry and with t(11;14) in our cohort of patients with plasma cell dyscrasias. Methods: We studied 898 patients with monoclonal gammopathies who had undergone uniform testing to identify MM-specific cytogenetic abnormalities. DNA from bone marrow samples was genotyped on the Precision Medicine Research Array and biogeographical ancestry was assessed using the Geographic Population Structure Origins tool (Elhaik, Nat Commun, 2014). Plasma cell proliferative disorder FISH of immunoglobulin (cIg)-stained positive plasma cells was performed as described (Baughn, BCJ, 2018). Individuals were divided into three ancestral groups: 1. EAs (<0.1% African ancestry and <30% Asian ancestry); 2. AAs (> 80% African ancestry); and 3. Other. Chi-squared test was used to determine the overall comparison between the 3 ancestral groups and also between ancestral groups 1 vs 2, and 2 vs 3 using pairwise comparison. All tests were two-sided with alpha level set at 0.05 for overall statistical significance. Pairwise comparison was considered statistically significant when p<0.025 based on Bonferroni method for multiple comparisons. Results: We identify increased risk of development of either a t(11;14), t(14;16) or t(14;20) in AAs (48.8%) compared to EAs (33.6%) (p-value=0.0051). To explore the genetic basis of increased t(11;14) specifically (37.4% AAs vs. 27.3% p-value=0.049), we evaluated the frequency of the G risk allele of rs9344 in relation to African ancestry. The frequency of the G risk allele was higher in AAs (0.81) compared to EAs (0.59) (p-value <0.0001) and also higher in t(11;14) cases (0.73) compared to non-t(11;14) controls (0.58) (p-value <0.0001). A multivariate model identified only rs9344 as significantly associated with t(11;14) after adjusting for age, gender and race group suggesting that it plays a role in the development of t(11;14) (p-value <0.001 for GG, p-value=0.005 for AG). To test if these results are replicable, we studied the MMRF CoMMpass cohort. This cohort includes individuals with newly diagnosed MM along with self-report race information and translocation data from long insert whole genome sequencing. Although t(11;14) was not enriched in self-reported Black individuals from this cohort (17.5% Black vs. 20.4% White, p-value=0.47), there was association between the rs9344 and self-reported Black race (p-value <0.0001) and with the presence of t(11;14) when the t(11;14) was analyzed by long insert whole genome sequencing (p-value=0.0001). Conclusions: To our knowledge, this study includes the largest group of African individuals with an abnormal plasma cell clone along with uniformly collected FISH, genotyping and ancestry data. We have identified in this diverse population the association of the CCND1 870G>A polymorphism (rs9344) with African ancestry and with t(11;14) suggesting that it plays a role in the development of t(11;14) plasma cell dyscrasias. Disclosures Elhaik: DNA Diagnostics Center: Consultancy. Baird:DNA Diagnostics Center: Employment. Cerhan:Janssen: Membership on an entity's Board of Directors or advisory committees; Celgene: Research Funding; NanoString: Research Funding. Bergsagel:Celgene: Consultancy; Ionis Pharmaceuticals: Consultancy; Janssen Pharmaceuticals: Consultancy. Dispenzieri:Alnylam: Research Funding; Akcea: Consultancy; Intellia: Consultancy; Janssen: Consultancy; Pfizer: Research Funding; Takeda: Research Funding; Celgene: Research Funding. Kumar:Janssen: Consultancy, Research Funding; Celgene: Consultancy, Research Funding; Takeda: Research Funding.

Abstract Motivation Functions of cancer driver genes vary substantially across tissues and organs. Distinguishing passenger genes, oncogenes (OGs) and tumor-suppressor genes (TSGs) for each cancer type is critical for understanding tumor biology and identifying clinically actionable targets. Although many computational tools are available to predict putative cancer driver genes, resources for context-aware classifications of OGs and TSGs are limited. Results We show that the direction and magnitude of somatic selection of protein-coding mutations are significantly different for passenger genes, OGs and TSGs. Based on these patterns, we develop a new method (genes under selection in tumors) to discover OGs and TSGs in a cancer-type specific manner. Genes under selection in tumors shows a high accuracy (92%) when evaluated via strict cross-validations. Its application to 10 172 tumor exomes found known and novel cancer drivers with high tissue-specificities. In 11 out of 13 OGs shared among multiple cancer types, we found functional domains selectively engaged in different cancers, suggesting differences in disease mechanisms. Availability and implementation An R implementation of the GUST algorithm is available at https://github.com/liliulab/gust. A database with pre-computed results is available at https://liliulab.shinyapps.io/gust. Supplementary information Supplementary data are available at Bioinformatics online.

Abstract BACKGROUND Treatment of temporal lobe glioma (TLG) frequently includes partial or complete temporal lobectomy (TL) followed by radiotherapy (RT). However, there are two approaches for temporal resection cavity RT, 1) standard target volumes (STV) targeting the entire TL resection cavity, dura, and peri-tumoral brain parenchyma or 2) modified target volumes (MTV) targeting only the adjacent peri-tumoral brain parenchyma. We report patterns of failure and a dosimetric comparison of these approaches. MATERIALS AND METHODS This was a retrospective review of 49 patients with WHO grade II-IV TLG who underwent partial or complete TL and post-operative RT between 1998 and 2018. Progression-free survival (PFS) was estimated using the Kaplan-Meier method. RESULTS The median patient age was 56 years (range,21–76). Patients were diagnosed with glioblastoma (n=32,65%), anaplastic glioma (n=10,20%) and low-grade glioma (n=7,14%). Treatment included partial TL with STV (n=33,67%), partial TL with MTV (n=5,10%), complete TL with STV (n=8,16%) and complete TL with MTV (n=3,6%). Mean RT dose was 60 Gy (range,40–76) in 30 fractions (range,15–39). At median follow-up time of 18 months (range, 3–161), 44 patients (90%) experienced recurrence: 34 (77%) in-field, 5 (11%) out-of-field, and 5 (11%) both in- and out-of-field. Among the 39 in-field failures, the location of recurrence included brain parenchyma (n=38,97%), ventricle (n=6,15%), and dura (n=5,13%). No patient experienced isolated dural recurrence regardless of tumor grade, extent of TL, or radiation volume. Median PFS was 20 months (95% confidence interval [CI]: 15–24). RT volume (STV vs. MTV) was not associated with worsened PFS (hazard ratio: 1.1, 95% CI: 0.5–2.6). MTV was associated with significant reductions in mean or max doses to brain stem, optic chiasm, optic nerves, hippocampus, and pituitary compared to STV. CONCLUSION Omitting RT to the entire TL cavity may reduce dose to multiple normal tissues with no detriments in dural recurrence or PFS.

Abstract Glioblastoma (GBM) is the most common and lethal primary brain tumor, and novel therapeutic strategies that make a substantial impact on outcomes are sorely needed. Immunotherapies have shown great promise in the treatment of a number of cancers in recent year, and a concerted effort is being made to apply these treatment paradigms to GBM. However, many GBM patients exhibit profound immunosuppression, likely limiting the efficacy of such approaches. The mechanisms of this immunosuppression are poorly understood, but tumor-derived extracellular vesicles (EVs), may play a role. We demonstrate that GBM-derived EVs induce the development of myeloid-derived suppressor cells (MDSCs) and non-classical monocytes (NCMs). We further demonstrate that these EV-induced monocytic cells are immunosuppressive, resulting in impaired T cell proliferation upon co-culture. We found that the immunosuppressive effects of tumor-derived EVs appear to be driven by the induction of these immunosuppressive monocytes, as EV treatment of T cells did not significantly impact T cell proliferation. Further, we sought to characterize the important of programmed death ligand 1 (PD-L1) in the induction of these immunosuppressive monocyte populations. We found PD-L1 to be expressed in the EVs from GBM cell lines, and that modulation in PD-L1 expression via either constitutive overexpression or shRNA-mediated knockdown resulted in concordant changes in expression in tumor-derived EVs. We demonstrate that PD-L1 is important for the induction of NCM but not for MDSCs. Taken together, these findings point to a significant role for tumor-derived EVs in the induction of immunosuppressive monocytes in GBM, and that these cells may be a driving force of systemic immunosuppression. PD-L1 is one factor expressed in EVs that has immunomodulatory properties, but additional EV cargo likely plays a major role in the induction of immunosuppressive cells.

Carrying premature termination codons in 1 allele of the ABCA7 gene is associated with an increased risk for Alzheimer’s disease (AD). While the primary function of ABCA7 is to regulate the transport of phospholipids and cholesterol, ABCA7 is also involved in maintaining homeostasis of the immune system. Since inflammatory pathways causatively or consequently participate in AD pathogenesis, we studied the effects of Abca7 haplodeficiency in mice on brain immune responses under acute and chronic conditions. When acute inflammation was induced through peripheral lipopolysaccharide injection in control or heterozygous Abca7 knockout mice, partial ABCA7 deficiency diminished proinflammatory responses by impairing CD14 expression in the brain. On breeding to App NL-G-F knockin mice, we observed increased amyloid-β (Aβ) accumulation and abnormal endosomal morphology in microglia. Taken together, our results demonstrate that ABCA7 loss of function may contribute to AD pathogenesis by altering proper microglial responses to acute inflammatory challenges and during the development of amyloid pathology, providing insight into disease mechanisms and possible treatment strategies.