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Supplementary Data 1. This supplementary data comprises a step-by-step description on how our gene-expression based classification models were generated and validated

Supplementary Figure 1. Highlights the EFS and OS for the cohort of neuroblastoma patients with MYCN-amplified disease (n=114, Fig 1a) and for th subcohort of patients >18 months of age with stage 4, MYCN non-amplified disease (n=102, fig. 1b). F, favorable; UF, unfavorable.

Purpose: To optimize neuroblastoma treatment stratification, we aimed at developing a novel risk estimation system by integrating gene expression–based classification and established prognostic markers.Experimental Design: Gene expression profiles were generated from 709 neuroblastoma specimens using customized 4 × 44 K microarrays. Classification models were built using 75 tumors with contrasting courses of disease. Validation was performed in an independent test set (n = 634) by Kaplan–Meier estimates and Cox regression analyses.Results: The best-performing classifier predicted patient outcome with an accuracy of 0.95 (sensitivity, 0.93; specificity, 0.97) in the validation cohort. The highest potential clinical value of this predictor was observed for current low-risk patients [5-year event-free survival (EFS), 0.84 ± 0.02 vs. 0.29 ± 0.10; 5-year overall survival (OS), 0.99 ± 0.01 vs. 0.76 ± 0.11; both P < 0.001] and intermediate-risk patients (5-year EFS, 0.88 ± 0.06 vs. 0.41 ± 0.10; 5-year OS, 1.0 vs. 0.70 ± 0.09; both P < 0.001). In multivariate Cox regression models for low-risk/intermediate-risk patients, the classifier outperformed risk assessment of the current German trial NB2004 [EFS: hazard ratio (HR), 5.07; 95% confidence interval (CI), 3.20–8.02; OS: HR, 25.54; 95% CI, 8.40–77.66; both P < 0.001]. On the basis of these findings, we propose to integrate the classifier into a revised risk stratification system for low-risk/intermediate-risk patients. According to this system, we identified novel subgroups with poor outcome (5-year EFS, 0.19 ± 0.08; 5-year OS, 0.59 ± 0.1), for whom we propose intensified treatment, and with beneficial outcome (5-year EFS, 0.87 ± 0.05; 5-year OS, 1.0), who may benefit from treatment de-escalation.Conclusions: Combination of gene expression–based classification and established prognostic markers improves risk estimation of patients with low-risk/intermediate-risk neuroblastoma. We propose to implement our revised treatment stratification system in a prospective clinical trial. Clin Cancer Res; 21(8); 1904–15. ©2014 AACR.See related commentary by Attiyeh and Maris, p. 1782

Supplementary Table 1. Clinical co-variates for the 709 patients who participated in the study. This supplementary table comprises detailed information on clinical co-variates for all 709 neuroblastoma patients who participated in the study.

Supplementary Table 2. External performance validation of the top five classifiers. This supplementary table comprises the external performance metrics of the top five classifiers. Indicated are the values for classification accuracy, sensitivity , specificity and Matthew's correlation coefficient (MCC) in the prediction of 325 patients of the test set who fulfilled the criteria for classifier training (Favorable, n=187; Unfavorable, n=138).

Supplementary Table 4. Transcripts that contribute to the SVM_th10 classifier. This supplementary table highlights the transcripts that constitute the SVM_th10 classifier. Indicated for each feature are the Oligo-ID, the Gene symbols, the Entrez gene IDs, the RefSeq IDs, the Gene IDs and the Transcript IDs.

Legends to Supplementaries. This documents comprises the legends to the supplementary figures and tables

Supplementary Data 2. This supplementary data comprises the r-algorithm scripts required to perform the both model selection and validation as described in the step-by-step protocol

Supplementary Table S1. Microarray analysis.

Legends for Supplementary Figures S1-S7.

Supplementary Figure S7. TCGA colorectal cancer database analysis on selected targets.

Supplementary Methods. Description of additional methods and procedures used in the study.

The microRNA (miRNA) landscape changes during the progression of cancer. We defined a metastasis-associated miRNA landscape using a systematic approach. We profiled and validated miRNA and mRNA expression in a unique series of human colorectal metastasis tissues together with their matched primary tumors and corresponding normal tissues. We identified an exclusive miRNA signature that is differentially expressed in metastases. Three of these miRNAs were identified as key drivers of an EMT-regulating network acting though a number of novel targets. These targets include SIAH1, SETD2, ZEB2, and especially FOXN3, which we demonstrated for the first time as a direct transcriptional suppressor of N-cadherin. The modulation of N-cadherin expression had significant impact on migration, invasion, and metastasis in two different in vivo models. The significant deregulation of the miRNAs defining the network was confirmed in an independent patient set as well as in a database of diverse malignancies derived from more than 6,000 patients. Our data define a novel metastasis-orchestrating network based on systematic hypothesis generation from metastasis tissues. Cancer Res; 75(15); 3010–9. ©2015 AACR.

Drug sensitivity prediction constitutes one of the main challenges in personalized medicine. The major difficulty of this problem stems from the fact that the sensitivity of cancer cells to treatment depends on an unknown subset of a large number of biological features. Although feature selection is the key to interpretable results and identification of potential biomarkers, a comprehensive assessment of feature selection methods for drug sensitivity prediction has so far not been performed. We propose feature selection approaches driven by prior knowledge of drug targets, target pathways, and gene expression signatures. We asses these methodologies on Genomics of Drug Sensitivity in Cancer (GDSC) dataset, a panel of around 1000 cell lines screened against multiple anticancer compounds. We compare our results with a baseline model utilizing genome-wide gene expression features and common data-driven feature selection techniques. Together, 2484 unique models were evaluated, providing a comprehensive study of feature selection strategies for the drug response prediction problem. For 23 drugs, the models achieve better predictive performance when the features are selected according to prior knowledge of drug targets and pathways. The best correlation of observed and predicted response using the test set is achieved for Linifanib (r=0.75). Extending the drug-dependent features with gene expression signatures yields models that are most predictive of drug response for 60 drugs, with the best performing example of Dabrafenib. Examples of how pre-selection of features benefits the model interpretability are given for Dabrafenib, Linifanib and Quizartinib. Based on GDSC drug data, we find that feature selection driven by prior knowledge tends to yield better results for drugs targeting specific genes and pathways, while models with the genome-wide features perform better for drugs affecting general mechanisms such as metabolism and DNA replication. For a significant group of the compounds, even a very small number of features based on simple drug properties is often highly predictive of drug sensitivity, can explain the mechanism of drug action and be used as guidelines for their prescription. In general, choosing appropriate feature selection strategies has the potential to develop interpretable models that are indicative for therapy design.

PURPOSE MET exon 14 skipping alterations ( METex14) comprise a diverse set of actionable oncogene drivers in non–small-cell lung cancer (NSCLC). Recent studies have established the efficacy of tyrosine kinase inhibitors for this patient population. The landscape of co-occurring genetic alterations in METex14 NSCLC and their potential impact to therapeutic sensitivities has not yet been fully described. MATERIALS AND METHODS METex14 NSCLC cases were collected from three cohorts: the VISION trial, and data sets from Guardant360 and GenePlus. Clinicopathologic characteristics and METex14 mutation sites were analyzed and compared across data sets. Co-occurring genetic alterations and the clonality relationships to METex14 were evaluated. RESULTS Of 40,824 NSCLCs, 692 METex14 cases (1.7%) were identified, including 332 in Guardant360, 188 in VISION, and 172 in GenePlus. The demographics and mutation type and/or sites were similar in the Asian versus Western cohorts. MET amplification, which were found to be associated with sensitivity to MET kinase inhibitors, co-occurs in 7.6%-13.8% of cases, whereas kinase domain secondary mutation of MET co-occurs in 5%-6%. When co-occurring with METex14, EGFR mutations were often identified as the dominant clone (78%, 7 of 9), whereas when co-occurring, METex14 (39%, 7 of 18) and KRAS (44%, 8 of 18) had similar rates of clonal dominance. PIK3CA and PTEN mutations were almost always subclones (89%, 16 of 18) to METex14. Moreover, RET-CCDC6 fusion and EGFR mutation were detected following crizotinib treatment in two patients, suggesting novel mechanisms of resistance. CONCLUSION METex14 mutations frequently co-occur with other potential driver oncogenes with differing patterns of clonal dominance observed among the drivers. This cellular context can provide insights into whether METex14 is acting as a primary oncogenic driver or resistance mechanism and help guide treatment choices.

Primary lymphomas of the central nervous system (PCNSL) are mainly diffuse large B-cell lymphomas (DLBCLs) confined to the central nervous system (CNS). Despite extensive research, the molecular alterations leading to PCNSL have not been fully elucidated. In order to provide a comprehensive description of the genomic and transcriptional landscape of PCNSL, we here performed whole-genome and transcriptome sequencing and integrative analysis of 51 lymphomas presenting in the CNS, including 42 EBV-negative PCNSL, 6 secondary CNS lymphomas (SCNSL) and 3 EBV+ CNSL and matched controls. The results were compared to an independent validation cohort of 31 FFPE CNSL specimens (PCNSL, n = 19; SCNSL, n = 9; EBV+ CNSL, n = 3) as well as 39 FL and 36 systemic DLBCL cases outside the CNS. Somatic genomic alterations in PCNSL mainly affect the JAK-STAT, NFkB, and B-cell receptor signaling pathways, with hallmark recurrent mutations including MYD88 L265P (67%) and CD79B (63%), CDKN2A deletions (83%) and also non-coding RNA genes such as MALAT1 (70%), NEAT (60%), and MIR142 (80%). Kataegis events, which affected 15 of 50 identified driver genes and 21 of the top 50 mutated ncRNAs, played a decisive role in shaping the mutational repertoire of PCNSL. Compared to systemic DLBCL, PCNSLs exhibited significantly more focal deletions in 6p21 targeting the HLA-D locus that encodes for MHC class II molecules as a potential mechanism of immune evasion. Mutational signatures correlating with DNA replication and mitosis (SBS1, ID1 and ID2) were significantly enriched in PCNSL (SBS1: p = 0.0027, ID1/ID2: p < 1×10−4). Furthermore, TERT gene expression was significantly higher in PCNSL compared to ABC-DLBCL (p = 0.027). Although PCNSL share many genetic alterations with systemic ABC-DLBCL in the same signaling pathways, transcriptome analysis clearly distinguished both into distinct molecular subtypes. EBV+ CNSL cases may be distinguished by lack of recurrent mutational hotspots apart from IG and HLA-DRB loci.

Copy number alterations constitute important phenomena in tumor evolution. Whole genome single cell sequencing gives insight into copy number profiles of individual cells, but is highly noisy. Here, we propose CONET, a probabilistic model for joint inference of the evolutionary tree on copy number events and copy number calling. CONET employs an efficient MCMC procedure to search the space of possible model structures and parameters and utilizes both per-bin and per-breakpoint data. We introduce a range of model priors and penalties for efficient regularization. CONET achieves excellent performance on simulated data and for 260 cells from xenograft breast cancer sample.

Computational models for drug sensitivity prediction have the potential to revolutionise personalized cancer medicine. Drug sensitivity assays, as well as profiling of cancer cell lines and drugs becomes increasingly available for training such models. Machine learning methods for drug sensitivity prediction must be optimized for: (i) leveraging the wealth of information about both cancer cell lines and drugs, (ii) predictive performance and (iii) interpretability. Multiple methods were proposed for predicting drug sensitivity from cancer cell line features, some in a multi-task fashion. So far, no such model leveraged drug inhibition profiles. Recent neural network-based recommender systems arise as models capable of predicting cancer cell line response to drugs from their biological features with high prediction accuracy. These models, however, require a tailored approach to model interpretability. In this work, we develop a neural network recommender system for kinase inhibitor sensitivity prediction called DEERS. The model utilizes molecular features of the cancer cell lines and kinase inhibition profiles of the drugs. DEERS incorporates two autoencoders to project cell line and drug features into 10-dimensional hidden representations and a feed-forward neural network to combine them into response prediction. We propose a novel model interpretability approach offering the widest possible assessment of the specific genes and biological processes that underlie the action of the drugs on the cell lines. The approach considers also such genes and processes that were not included in the set of modeled features. Our approach outperforms simpler matrix factorization models, achieving R=0.82 correlation between true and predicted response for the unseen cell lines. Using the interpretability analysis, we evaluate correlation of all human genes with each of the hidden cell line dimensions. Subsequently, we identify 67 biological processes associated with these dimensions. Combined with drug response data, these associations point at the processes that drive the cell line sensitivity to particular compounds. Detailed case studies are shown for PHA-793887, XMD14-99 and Dabrafenib. Our framework provides an expressive, multitask neural network model with a custom interpretability approach for inferring underlying biological factors and explaining cancer cell response to drugs.

9121 Background: Tepotinib, a potent, highly selective, oral, MET inhibitor, showed meaningful activity in patients (pts) with NSCLC with high-level METamp by LBx in VISION. Exploratory biomarker analyses are presented herein. Methods: Pts had 0–2 prior therapy lines, high-level METamp by LBx (Guardant360; MET copy number ≥2.5), and no MET exon 14 skipping or EGFR/ ALK alterations. Pts received tepotinib 500 mg once daily (450 mg active moiety). Primary endpoint was objective response by independent review; data cut-off: Aug 20, 2021. Exploratory biomarker analysis included LBx at baseline (BL), on treatment, and end of treatment (EOT). Early molecular response (eMR) was defined as undetectable METamp 6–8 weeks on treatment. Results: 24 pts were enrolled (median age: 63.4 years [yrs]; smokers: 88%; ECOG PS 1: 88%; adenocarcinoma: 67%). Treatment duration was ≥1 yr in five pts and ≥2 yrs in two pts (both ongoing). Overall, objective response rate (ORR) was 41.7% (95% CI: 22.1, 63.4). Treatment-naïve pts (n=7) had an ORR of 71.4% (29.0, 96.3), median (m) DOR was 14.3 months (2.8, not estimable [ne]), and mPFS was 15.6 months (1.4, ne). BL biomarker analyses according to clinical benefit (CR/PR/SD [n=11] vs PD/NE [n=13]) showed association with better outcomes in pts with focal METamp, or without MYCamp or RB1 mutation (Table). MYCamp/ RB1 mutation was detected in 4/7 pts with neuroendocrine/not otherwise specified histology; MYCamp in 2/3 pts with neuroendocrine histology. Low BL ctDNA mutant allele frequency (MAF) was associated with better outcomes. 14 pts had eMRs (ORR 71.4%); persistent METamp (n=4) was associated with lack of clinical response. 2/9 pts with EOT biomarker profiles had emerging resistance mechanisms (MET kinase domain mutations Y1230 and D1228); both had METamp re-emergence. Treatment-related adverse events included edema (composite term; any grade: 46%; Grade 3: 13%) and constipation (any grade: 17%; Grade ≥3: 0%). Conclusions: Tepotinib showed meaningful activity, especially in first line, in the first trial of a MET inhibitor in EGFR WT NSCLC with high-level METamp to enroll based on a convenient LBx assay. BL biomarker analyses indicated focal METamp, MYC/RB1 WT, and low ctDNA MAF were associated with improved outcomes. Serial LBx could monitor molecular response and evaluate resistance. Clinical trial information: NCT02864992. [Table: see text]

BACKGROUND: Current risk stratification systems for neuroblastoma patients consider clinical, histopathological, and genetic variables, and additional prognostic markers have been proposed in recent years. We here sought to select highly informative covariates in a multistep strategy based on consecutive Cox regression models, resulting in a risk score that integrates hazard ratios of prognostic variables. METHODS: A cohort of 695 neuroblastoma patients was divided into a discovery set (n = 75) for multigene predictor generation, a training set (n = 411) for risk score development, and a validation set (n = 209). Relevant prognostic variables were identified by stepwise multivariable L1-penalized least absolute shrinkage and selection operator (LASSO) Cox regression, followed by backward selection in multivariable Cox regression, and then integrated into a novel risk score. RESULTS: The variables stage, age, MYCN status, and two multigene predictors, NB-th24 and NB-th44, were selected as independent prognostic markers by LASSO Cox regression analysis. Following backward selection, only the multigene predictors were retained in the final model. Integration of these classifiers in a risk scoring system distinguished three patient subgroups that differed substantially in their outcome. The scoring system discriminated patients with diverging outcome in the validation cohort (5-year event-free survival, 84.9 ± 3.4 vs 63.6 ± 14.5 vs 31.0 ± 5.4; P

9012 Background: In the VISION study, tepotinib in METex14 skipping NSCLC pts (Cohort A) had robust and durable clinical activity. Serial LBx samples were collected for biomarker analyses, presented herein. Methods: LBx samples taken at baseline (BL), Week 6, 12, & end of treatment (EOT) were analyzed using Guardant360® CDx (73 genes). Investigator (INV)-assessed clinical outcome was evaluated per BL biomarker profiles and by molecular response (MR; defined as > 75% depletion from BL in METex14 variant allele frequency [VAF] ctDNA confirmed in 2 consecutive samples) or molecular progression (MP; defined as VAF increase > 0 from BL). Acquired resistance was investigated in EOT samples, following progression per INV. Results: LBx pts (n = 99) had a median age of 72 yrs (range 49–88), 53% were male, 44% never smokers, 85% had adenocarcinoma. INV ORR was 53% (95% CI 42, 63); ORR in 1L (n = 44) was 59% (43, 74) & ≥2L (n = 55) was 47% (33, 61). 94 pts had BL biomarker profiles; these were similar in 1L and ≥2L pts, except EGFR amp: 1/43 1L [2%] vs 8/51 ≥2L [16%]. Outcomes were not impacted by location/type of METex14 alteration. 1 pt with concomitant MET M1250T mutation had a PFS of 17.3 months. A trend towards better efficacy was seen in pts with concomitant MET amp (6 responses in 8 pts). Response to tepotinib occurred both in pts with wt or mutant TP53; however, there was a trend for longer mDOR in pts with wt TP53 (18.3 [95% CI 9.7, ne] vs 7.1 [4.7, 10.9] months) and mPFS (9.5 [6.7, 19.7] vs 5.1 [2.8, 6.9] months). Concomitant oncogenic mutations were rare, with no responses in 3 pts with KRAS, NRAS alterations and 3 responses in 5 pts with PI3K/AKT alterations. 65 pts had 2 consecutive on-treatment samples: 46 (71%) had confirmed MR, 5 (8%) had confirmed MP, 14 (22%) had no change in VAF or lacked confirmation. MR was associated with clinical response and MP was associated with no response/short PFS (Table). 52 pts with progression had EOT LBx samples. Emerging MET resistance mutations (Y1230H/C & D1228H/N) occurred in 7 (13%) pts, all responders and 5/7 had PFS > 10 months. Analyses on non-MET-driven resistance mechanisms will be presented. Conclusions: LBx biomarker analysis from the largest on-treatment data set for a MET inhibitor in METex14 skipping NSCLC, showed that ctDNA depletion in METex14 VAF is associated with improved clinical response in pts treated with tepotinib. This suggests serial LBx could help us to monitor response/non-response, understand resistance, and guide trials that test escalation/de-escalation strategies to improve outcomes and maximize QOL. Clinical trial information: NCT02864992. [Table: see text]

Purpose: In the ongoing, single-arm, Phase 2 VISION study (NCT02864992), tepotinib (a highly selective MET inhibitor) has shown durable clinical activity in NSCLC patients (pts) with METex14 skipping. Here, we report the biomarker profile of pts screened with liquid biopsy (LBx) for inclusion in the study. Methods: Pts with advanced NSCLC and previously confirmed EGFR/ALK wild-type status were prospectively screened for MET alterations using plasma samples collected during pre-screening/screening. Plasma ctDNA sequencing was performed at a central laboratory using a 73-gene next-generation sequencing panel (Guardant360®). Results: Of 6,034 pts screened, 813 pts had results pending at data cut and of 5,221 pts tested, sequencing failed in 41 pts. The success rate of LBx testing was consistent across centers. Of 5,180 patients analyzed, 694 pts (13.4%) had no mutation detected. Despite the intent of screening EGFR/ALK wild-type pts, 327 pts (6.3%) had EGFR mutations and 49 (0.9%) had ALK/ROS fusions. 188 pts (3.6%) had METex14 skipping (Table) and 256 pts (4.9%) had MET amplification (amp) without METex14 skipping. The median age of METex14 pts was 72 years; 47% were male; 46% were never smokers and 65% had adenocarcinoma histology. In METex14 pts, the most frequently occurring driver co-alterations were amp in MET (13.3%), EGFR (7.4%), CDK4 (6.4%), BRAF (5.3%) and CDK6 (4.8%), and mutations in GNAS (5.3%). In METamp pts, the most frequently occurring driver co-alterations were CDK6 (60.5%), BRAF (43.4%), EGFR (28.9%), MYC (21.9%) and CCNE1 (19.9%) amps. Overall, TP53 mutations were detected in 55.9% of METex14 pts and 79.7% of METamp pts. Table:Characterization of METex14 skipping (n=188)n (%)Insertions/deletions (indels)93 (49.5)Indel at acceptor site54 (28.7)Indel at donor site36 (19.1)Whole exon 14 deletion3 (1.6)Single nucleotide variation (SNV)95 (50.5)SNV at donor site93 (49.5)SNV at acceptor site2 (1.1)SNVs and indelsSNV/indel at donor site129 (68.6)SNV/indel at acceptor site56 (29.8)SNV/indel whole exon 14 deletion3 (1.6) Conclusions: In this population, METex14 skipping can be successfully detected through non-invasive LBx analysis using a next-generation sequencing panel. The rate of METex14 skipping and the genomic profile and demographics of pts were similar to previously reported data. Citation Format: Xiuning Le, Dariusz Kowalski, Byoung Chul Cho, Pierfranco Conte, Enriqueta Felip, Marina Chiara Garassino, Santiago Viteri, Gee-Chen Chang, John Richart, Luiz Paz-Ares, Dilafruz Juraeva, Josef Straub, Christopher Stroh, Paul Paik. Liquid biopsy to detect MET exon 14 skipping (METex14) and MET amplification in patients with advanced NSCLC: Biomarker analysis from VISION study [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 3385.

9556 Background: Preliminary tepotinib data showed durable activity in pts with NSCLC with METex14 skipping prospectively identified by liquid (L+) or tissue (T+) biopsy. Having met target enrollment of ≥60 L+ pts & ≥60 T+ pts, we report primary data. Methods: VISION Cohort A enrolled pts with advanced EGFR/ALK wt, METex14 skipping NSCLC (asymptomatic brain metastases [BM] allowed), who received oral tepotinib 500 mg QD. On-study treatment decisions were based on investigator assessment (INV) of response. Primary endpoint was objective response rate (ORR) by independent review committee (IRC) analyzed in 3 primary ITT sets: L+ and/or T+, L+, T+. 2ary endpoints included ORR by INV, duration of response (DOR), disease control rate (DCR), PFS, OS, & safety. Preplanned analyses were performed in pts with BM at baseline (BL). BL/on-treatment ctDNA plasma samples (L+) were analyzed using a 73-gene NGS panel (Guardant360). Deep molecular responses (MR), defined as > 75% depletion of METex14, were compared with objective responses (OR). Results: By data cut-off (1 Oct 19) 151 pts received tepotinib (safety set); 99 L+/T+, 66 L+, 60 T+ pts comprised the 3 ITT sets with ≥6-month [m] follow-up. Across treatment lines (n = 44 1L, n = 55 ≥2L), primary ORR & mPFS [95% CI] in 99 L+/T+ pts were 43% [34–54] & 8.6 m [6.9–11.0] by IRC and 56% [45–66] & 9.5 m [6.7–13.5] by INV. ORR was similar in L+ or T+ pts (table) or in T+L− pts (n = 25): 40% [21–61] by IRC and 48% [28–69] by INV. Only 2 pts were T−L+. Outcomes were also comparable in pts with BM (n = 11): IRC ORR 55% [23–83] & mPFS 10.9 m [8.0–ne]. 34/51 pts (67%) with matched BL/on-treatment L+ samples had deep MR strongly associated with clinical response: 32/34 pts (94%) with MR had disease control (INV), including 29/34 pts (85%) with OR; 2/34 pts had progressive disease. Further biomarker data will be presented. Grade ≥3 treatment-related adverse events (TRAEs) were reported by 37/151 pts (25%). 13 pts (9%) discontinued due to TRAEs. Conclusions: Tepotinib is a promising targeted therapy with durable clinical activity and manageable toxicity in pts with METex14 skipping NSCLC L+ or T+, including pts with BM. High ORR & DCR in pts with ctDNA molecular responses support that MET inhibition in METex14+ tumor cells can lead to clinical benefit. Clinical trial information: NCT02864992 . [Table: see text]

CD8+ T cell recognition of tumor cells is typically based on the detection of specific MHC–peptide complexes, while natural killer (NK) cell recognition relies on the detection of NK ligands by an array of NK receptors. In this study we uncovered a distinct small population of CD8+ T cells expressing NKp30, a potent activating NK receptor, on peripheral blood from healthy donors. Those innate-like CD8+ T cells, coexpressing FcεRIγ and PZLF, could be generated and differentiated from a population of peripheral blood CD8+ T cells as result of IL-15–driven acquisition of broad innate features. This unique effector population could potently control the growth of tumors in an NK-like manner, making it promising for cancer immunotherapy by its dual target-recognition potential.

CD8+ T cells are considered prototypical cells of adaptive immunity. Here, we uncovered a distinct CD8+ T cell population expressing the activating natural killer (NK) receptor NKp30 in the peripheral blood of healthy individuals. We revealed that IL-15 could de novo induce NKp30 expression in a population of CD8+ T cells and drive their differentiation toward a broad innate transcriptional landscape. The adaptor FceRIγ was concomitantly induced and was shown to be crucial to enable NKp30 cell-surface expression and function in CD8+ T cells. FceRIγ de novo expression required promoter demethylation and was accompanied by acquisition of the signaling molecule Syk and the “innate” transcription factor PLZF. IL-15–induced NKp30+CD8+ T cells exhibited high NK-like antitumor activity in vitro and were able to synergize with T cell receptor signaling. Importantly, this population potently controlled tumor growth in a preclinical xenograft mouse model. Our study, while blurring the borders between innate and adaptive immunity, reveals a unique NKp30+FceRIγ+CD8+ T cell population with high antitumor therapeutic potential.

The microRNA (miRNA) landscape changes during the progression of cancer. We defined a metastasis-associated miRNA landscape using a systematic approach. We profiled and validated miRNA and mRNA expression in a unique series of human colorectal metastasis tissues together with their matched primary tumors and corresponding normal tissues. We identified an exclusive miRNA signature that is differentially expressed in metastases. Three of these miRNAs were identified as key drivers of an EMT-regulating network acting though a number of novel targets. These targets include SIAH1, SETD2, ZEB2, and especially FOXN3, which we demonstrated for the first time as a direct transcriptional suppressor of N-cadherin. The modulation of N-cadherin expression had significant impact on migration, invasion, and metastasis in two different in vivo models. The significant deregulation of the miRNAs defining the network was confirmed in an independent patient set as well as in a database of diverse malignancies derived from more than 6,000 patients. Our data define a novel metastasis-orchestrating network based on systematic hypothesis generation from metastasis tissues. Cancer Res; 75(15); 3010–9. ©2015 AACR.

The present study investigated the genetic contribution to alcohol dependence (AD) using genome-wide association data from three German samples. These comprised patients with: (i) AD; (ii) chronic alcoholic pancreatitis (ACP); and (iii) alcohol-related liver cirrhosis (ALC). Single marker, gene-based, and pathway analyses were conducted. A significant association was detected for the ADH1B locus in a gene-based approach (puncorrected = 1.2 × 10-6; pcorrected = 0.020). This was driven by the AD subsample. No association with ADH1B was found in the combined ACP + ALC sample. On first inspection, this seems surprising, since ADH1B is a robustly replicated risk gene for AD and may therefore be expected to be associated also with subgroups of AD patients. The negative finding in the ACP + ALC sample, however, may reflect genetic stratification as well as random fluctuation of allele frequencies in the cases and controls, demonstrating the importance of large samples in which the phenotype is well assessed. OA gold

Gene expression data from microarrays are being applied to predict preclinical and clinical endpoints, but the reliability of these predictions has not been established. In the MAQC-II project, 36 independent teams analyzed six microarray data sets to generate predictive models for classifying a sample with respect to one of 13 endpoints indicative of lung or liver toxicity in rodents, or of breast cancer, multiple myeloma or neuroblastoma in humans. In total, >30,000 models were built using many combinations of analytical methods. The teams generated predictive models without knowing the biological meaning of some of the endpoints and, to mimic clinical reality, tested the models on data that had not been used for training. We found that model performance depended largely on the endpoint and team proficiency and that different approaches generated models of similar performance. The conclusions and recommendations from MAQC-II should be useful for regulatory agencies, study committees and independent investigators that evaluate methods for global gene expression analysis.

Purpose To evaluate the impact of a predefined gene expression–based classifier for clinical risk estimation and cytotoxic treatment decision making in neuroblastoma patients. Patients and Methods Gene expression profiles of 440 internationally collected neuroblastoma specimens were investigated by microarray analysis, 125 of which were examined prospectively. Patients were classified as either favorable or unfavorable by a 144-gene prediction analysis for microarrays (PAM) classifier established previously on a separate set of 77 patients. PAM classification results were compared with those of current prognostic markers and risk estimation strategies. Results The PAM classifier reliably distinguished patients with contrasting clinical courses (favorable [n = 249] and unfavorable [n = 191]; 5-year event free survival [EFS] 0.84 ± 0.03 v 0.38 ± 0.04; 5-year overall survival [OS] 0.98 ± 0.01 v 0.56 ± 0.05, respectively; both P < .001). Moreover, patients with divergent outcome were robustly discriminated in both German and international cohorts and in prospectively analyzed samples (P ≤ .001 for both EFS and OS for each). In subgroups with clinical low-, intermediate-, and high-risk of death from disease, the PAM predictor significantly separated patients with divergent outcome (low-risk 5-year OS: 1.0 v 0.75 ± 0.10, P < .001; intermediate-risk: 1.0 v 0.82 ± 0.08, P = .042; and high-risk: 0.81 ± 0.08 v 0.43 ± 0.05, P = .001). In multivariate Cox regression models based on both EFS and OS, PAM was a significant independent prognostic marker (EFS: hazard ratio [HR], 3.375; 95% CI, 2.075 to 5.492; P < .001; OS: HR, 11.119, 95% CI, 2.487 to 49.701; P < .001). The highest potential clinical impact of the classifier was observed in patients currently considered as non–high-risk (n = 289; 5-year EFS: 0.87 ± 0.02 v 0.44 ± 0.07; 5-year OS: 1.0 v 0.80 ± 0.06; both P < .001). Conclusion Gene expression–based classification using the 144-gene PAM predictor can contribute to improved treatment stratification of neuroblastoma patients.

Microarray-based prediction of clinical endpoints may be performed using either a one-color approach reflecting mRNA abundance in absolute intensity values or a two-color approach yielding ratios of fluorescent intensities. In this study, as part of the MAQC-II project, we systematically compared the classification performance resulting from one- and two-color gene-expression profiles of 478 neuroblastoma samples. In total, 196 classification models were applied to these measurements to predict four clinical endpoints, and classification performances were compared in terms of accuracy, area under the curve, Matthews correlation coefficient and root mean-squared error. Whereas prediction performance varied with distinct clinical endpoints and classification models, equivalent performance metrics were observed for one- and two-color measurements in both internal and external validation. Furthermore, overlap of selected signature genes correlated inversely with endpoint prediction difficulty. In summary, our data strongly substantiate that the choice of platform is not a primary factor for successful gene expression based-prediction of clinical endpoints.

CD8+ T cells are considered prototypical cells of adaptive immunity. Here, we uncovered a distinct CD8+ T cell population, expressing the activating NK receptor, NKp30, in peripheral blood of healthy individuals. We revealed that IL-15 could de novo induce NKp30 expression in a population of CD8+ T cells, and further drive their differentiation towards a broad innate transcriptional landscape. The adapter FcɛRIγ was concomitantly induced, and shown to be crucial to enable NKp30 cell surface expression and function in CD8+ T cells. FcɛRIγ de novo expression required promoter demethylation and was accompanied by the acquisition of the signaling molecule Syk and the “innate” transcription factor PLZF. IL-15-induced NKp30+CD8+ T cells exhibited high NK-like anti-tumor activity in vitro, able to synergize with TCR-signaling. Importantly, this population potently controlled tumor growth in a preclinical xenograft mouse model. Our study, while blurring borders between innate and adaptive, reveals a unique NKp30+FcɛRIγ+CD8+ T cell population with high anti-tumor therapeutic potential.

BackgroundPathological gambling is a behavioural addiction with negative economic, social, and psychological consequences. Identification of contributing genes and pathways may improve understanding of aetiology and facilitate therapy and prevention. Here, we report the first genome-wide association study of pathological gambling. Our aims were to identify pathways involved in pathological gambling, and examine whether there is a genetic overlap between pathological gambling and alcohol dependence.MethodsFour hundred and forty-five individuals with a diagnosis of pathological gambling according to the Diagnostic and Statistical Manual of Mental Disorders were recruited in Germany, and 986 controls were drawn from a German general population sample. A genome-wide association study of pathological gambling comprising single marker, gene-based, and pathway analyses, was performed. Polygenic risk scores were generated using data from a German genome-wide association study of alcohol dependence.ResultsNo genome-wide significant association with pathological gambling was found for single markers or genes. Pathways for Huntington's disease (P-value = 6.63 × 10−3); 5′-adenosine monophosphate-activated protein kinase signalling (P-value = 9.57 × 10−3); and apoptosis (P-value = 1.75 × 10−2) were significant. Polygenic risk score analysis of the alcohol dependence dataset yielded a one-sided nominal significant P-value in subjects with pathological gambling, irrespective of comorbid alcohol dependence status.ConclusionsThe present results accord with previous quantitative formal genetic studies which showed genetic overlap between non-substance- and substance-related addictions. Furthermore, pathway analysis suggests shared pathology between Huntington's disease and pathological gambling. This finding is consistent with previous imaging studies.

Genetic factors play as large a role as environmental factors in the etiology of alcohol dependence. Although genome-wide association studies (GWAS) enable systematic searches for loci not hitherto implicated in the etiology of alcohol dependence, many true findings may be missed due to correction for multiple testing. The aim of the present study was to circumvent this limitation by searching for biological system-level differences, and then following up these findings in humans and animals. Gene-set based analysis of GWAS data from 1333 cases and 2168 controls identified 19 significantly associated gene-sets of which five could be replicated in an independent sample. Clustered in these gene-sets were novel and previously identified susceptibility genes. The most frequently present gene, ie in 6 out of 19 gene-sets, was X-ray repair complementing defective repair in Chinese hamster cells 5 (XRCC5). Previous human and animal studies have implicated XRCC5 in alcohol sensitivity. This phenotype is inversely correlated with the development of alcohol dependence, presumably since more alcohol is required to achieve the desired effects. In the present study, the functional role of XRCC5 in alcohol dependence was further validated in animals and humans. Drosophila mutants with reduced function of Ku80-the homolog of mammalian XRCC5-due to RNAi silencing showed reduced sensitivity to ethanol. In humans with free access to intravenous ethanol self-administration in the laboratory, the maximum achieved blood alcohol concentration was influenced in an allele-dose dependent manner by genetic variation in XRCC5. In conclusion, our convergent approach identified new candidates and generated independent evidence for the involvement of XRCC5 in alcohol dependence.Neuropsychopharmacology accepted article preview online, 18 July 2014; doi:10.1038/npp.2014.178.

A real-time polymerase chain reaction (PCR) method was applied to quantify the nifH gene pool in cucumber shoot and root and to evaluate how nitrogen (N) supply and plant age affect the nifH gene pool. In shoots, the relative abundance of the nifH gene was affected neither by different stages of plant growth nor by N supply. In roots, higher numbers of diazotrophic bacteria were found compared with that in the shoot. The nifH gene pool in roots significantly increased with plant age, and unexpectedly, the pool size was positively correlated with N supply. The relative abundance of nifH gene copy numbers in roots was also positively correlated (r = 0.96) with total N uptake of the plant. The data suggest that real-time PCR-based nifH gene quantification in combination with N-content analysis can be used as an efficient way to perform further studies to evaluate the direct contribution of the N2-fixing plant-colonizing plant growth promoting bacteria to plant N nutrition.Key words: real-time PCR, biological nitrogen fixation, cucumber, N nutrition, plant growth promoting bacteria.

Background: Neuroblastoma (NB), a neural crest-derived tumor of the sympathetic nervous system, is the most common extracranial solid tumor in children. We have previously shown that genomic rearrangements activate proto-oncogenic telomerase by juxtaposing active enhancer elements to the TERT gene in a large fraction of high-risk NBs. In the present study, we applied a global approach integrating whole genome sequencing (WGS), Chromatin immunoprecipitation sequencing (ChIP-seq) and RNA sequencing (RNA-seq) data of NB cells and tumors to identify further key oncogenes activated by enhancer-hijacking in NB. Methods: WGS was applied to search for structural rearrangements in 120 NB tumors and five NB cell lines. Corresponding RNA-seq data were used to discover mono-allelic and/or outlier expression of candidate genes potentially involved in enhancer hijacking events. ChIP-seq of 34 NB tumors and 17 NB cell lines was applied to identify active enhancer elements in NB. Circular chromatin conformation capture sequencing (4C-seq) was used to confirm physical promoter-enhancer interactions in NB cell lines. Results: WGS analyses revealed that chromosomal rearrangements are common events in NB tumors and cell lines and frequently affect regions harboring proto-oncogenes and lineage specific enhancers. ChIP-seq analyses of the chromatin mark histone 3 lysine 27 acetylation (H3K27ac), surrogate for enhancer activity, confirmed that these rearrangements recurrently juxtapose active enhancer elements to oncogenes including MYCN and MYC in NB. Intriguingly, quantification of H3K27ac ChIP-seq profiles uncovered that the enhancer elements translocated to MYC were among the most active ones within the respective epigenomes. 4C-seq analyses proofed physical interactions between translocated enhancer elements and promoters of the respective oncogenes, which is in line with their elevated expression in rearranged cases. Conclusions: Our study reveals that structural rearrangements in high-risk neuroblastoma frequently juxtapose strong enhancers to key oncogenes, including MYCN and MYC, leading to physical promoter-enhancer interactions which likely drive overexpression of the oncogenes observed in rearranged cases. The common mechanism of oncogene activation by enhancer-hijacking may open a therapeutic window for epigenetic drugs including BET or CDK7 inhibitors in high-risk NBs. Citation Format: Daniel Dreidax, Moritz Gartlgruber, Sebastian Steinhauser, Larisa Savelyeva, Ron Schwessinger, Umut Toprak, Nati Ha, Dilafruz Juraeva, Martin Peifer, Matthias Fischer, Stefan Gröschel, Kai-Oliver Henrich, Young-Gyu Park, Benedikt Brors, Matthias Schlesner, Carl Herrmann, Frank Westermann. Activation of proto-oncogenes by enhancer-hijacking in high-risk neuroblastoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 1023. doi:10.1158/1538-7445.AM2017-1023

Purpose: To optimize neuroblastoma treatment stratification, we aimed at developing a novel risk estimation system by integrating gene expression–based classification and established prognostic markers. Experimental Design: Gene expression profiles were generated from 709 neuroblastoma specimens using customized 4 × 44 K microarrays. Classification models were built using 75 tumors with contrasting courses of disease. Validation was performed in an independent test set (n = 634) by Kaplan–Meier estimates and Cox regression analyses. Results: The best-performing classifier predicted patient outcome with an accuracy of 0.95 (sensitivity, 0.93; specificity, 0.97) in the validation cohort. The highest potential clinical value of this predictor was observed for current low-risk patients [5-year event-free survival (EFS), 0.84 ± 0.02 vs. 0.29 ± 0.10; 5-year overall survival (OS), 0.99 ± 0.01 vs. 0.76 ± 0.11; both P < 0.001] and intermediate-risk patients (5-year EFS, 0.88 ± 0.06 vs. 0.41 ± 0.10; 5-year OS, 1.0 vs. 0.70 ± 0.09; both P < 0.001). In multivariate Cox regression models for low-risk/intermediate-risk patients, the classifier outperformed risk assessment of the current German trial NB2004 [EFS: hazard ratio (HR), 5.07; 95% confidence interval (CI), 3.20–8.02; OS: HR, 25.54; 95% CI, 8.40–77.66; both P < 0.001]. On the basis of these findings, we propose to integrate the classifier into a revised risk stratification system for low-risk/intermediate-risk patients. According to this system, we identified novel subgroups with poor outcome (5-year EFS, 0.19 ± 0.08; 5-year OS, 0.59 ± 0.1), for whom we propose intensified treatment, and with beneficial outcome (5-year EFS, 0.87 ± 0.05; 5-year OS, 1.0), who may benefit from treatment de-escalation. Conclusions: Combination of gene expression–based classification and established prognostic markers improves risk estimation of patients with low-risk/intermediate-risk neuroblastoma. We propose to implement our revised treatment stratification system in a prospective clinical trial. Clin Cancer Res; 21(8); 1904–15. ©2014 AACR. See related commentary by Attiyeh and Maris, p. 1782

Bipolar disorder (BD) is a highly heritable psychiatric disease characterized by recurrent episodes of mania and depression. To identify new BD genes and pathways, the present study employed a three-step approach. First, gene-expression profiles of BD patients were assessed during both a manic and an euthymic phase. These profiles were compared intra-individually and with the gene-expression profiles of controls. Second, those differentially expressed genes that were considered potential trait markers of BD were validated using data from the Psychiatric Genomics Consortiums' genome-wide association study (GWAS) of BD. Third, the implicated molecular mechanisms were investigated using pathway analytical methods. In the present patients, this novel approach identified: (i) sets of differentially expressed genes specific to mania and euthymia; and (ii) a set of differentially expressed genes that were common to both mood states. In the GWAS data integration analysis, one gene (STAB1) remained significant (P=1.9 × 10(-4)) after adjustment for multiple testing. STAB1 is located in close proximity to PBMR1 and the NEK4-ITIH1-ITIH3-ITIH4 region, which are the top findings from GWAS meta-analyses of mood disorder, and a combined BD and schizophrenia data set. Pathway analyses in the mania versus control comparison revealed three distinct clusters of pathways tagging molecular mechanisms implicated in BD, for example, energy metabolism, inflammation and the ubiquitin proteasome system. The present findings suggest that STAB1 is a new and highly promising candidate gene in this region. The combining of gene expression and GWAS data may provide valuable insights into the biological mechanisms of BD. peerReviewed

Cell competition is an emerging principle underlying selection for cellular fitness during development and disease. Competition may be relevant for cancer, but an experimental link between defects in competition and tumorigenesis is elusive. In the thymus, T lymphocytes develop from precursors that are constantly replaced by bone-marrow-derived progenitors. Here we show that in mice this turnover is regulated by natural cell competition between 'young' bone-marrow-derived and 'old' thymus-resident progenitors that, although genetically identical, execute differential gene expression programs. Disruption of cell competition leads to progenitor self-renewal, upregulation of Hmga1, transformation, and T-cell acute lymphoblastic leukaemia (T-ALL) resembling the human disease in pathology, genomic lesions, leukaemia-associated transcripts, and activating mutations in Notch1. Hence, cell competition is a tumour suppressor mechanism in the thymus. Failure to select fit progenitors through cell competition may explain leukaemia in X-linked severe combined immune deficiency patients who showed thymus-autonomous T-cell development after therapy with gene-corrected autologous progenitors.

Background Genome-wide association studies are a powerful tool for unravelling the genetic background of complex disorders such as major depression. Methods We conducted a genome-wide association study of 604 patients with major depression and 1364 population based control subjects. The top hundred findings were followed up in a replication sample of 409 patients and 541 control subjects. Results Two SNPs showed nominally significant association in both the genome-wide association study and the replication samples: 1) rs9943849 ( p combined = 3.24E-6) located upstream of the carboxypeptidase M ( CPM ) gene and 2) rs7713917 ( p combined = 1.48E-6), located in a putative regulatory region of HOMER1 . Further evidence for HOMER1 was obtained through gene-wide analysis while conditioning on the genotypes of rs7713917 ( p combined = 4.12E-3). Homer1 knockout mice display behavioral traits that are paradigmatic of depression, and transcriptional variants of Homer1 result in the dysregulation of cortical-limbic circuitry. This is consistent with the findings of our subsequent human imaging genetics study, which revealed that variation in single nucleotide polymorphism rs7713917 had a significant influence on prefrontal activity during executive cognition and anticipation of reward. Conclusion Our findings, combined with evidence from preclinical and animal studies, suggest that HOMER1 plays a role in the etiology of major depression and that the genetic variation affects depression via the dysregulation of cognitive and motivational processes.

Neuroblastoma is an embryonal malignancy of the sympathetic nervous system. Spontaneous regression and differentiation of neuroblastoma is observed in a subset of patients, and has been suggested to represent delayed activation of physiologic molecular programs of fetal neuroblasts. Homeobox genes constitute an important family of transcription factors, which play a fundamental role in morphogenesis and cell differentiation during embryogenesis. In this study, we demonstrate that expression of the majority of the human HOX class I homeobox genes is significantly associated with clinical covariates in neuroblastoma using microarray expression data of 649 primary tumors. Moreover, a HOX gene expression-based classifier predicted neuroblastoma patient outcome independently of age, stage and MYCN amplification status. Among all HOX genes, HOXC9 expression was most prominently associated with favorable prognostic markers. Most notably, elevated HOXC9 expression was significantly associated with spontaneous regression in infant neuroblastoma. Re-expression of HOXC9 in three neuroblastoma cell lines led to a significant reduction in cell viability, and abrogated tumor growth almost completely in neuroblastoma xenografts. Neuroblastoma growth arrest was related to the induction of programmed cell death, as indicated by an increase in the sub-G1 fraction and translocation of phosphatidylserine to the outer membrane. Programmed cell death was associated with the release of cytochrome c from the mitochondria into the cytosol and activation of the intrinsic cascade of caspases, indicating that HOXC9 re-expression triggers the intrinsic apoptotic pathway. Collectively, our results show a strong prognostic impact of HOX gene expression in neuroblastoma, and may point towards a role of Hox-C9 in neuroblastoma spontaneous regression.

// Diana T. Lau 1 , Claudia L. Flemming 1 , Samuele Gherardi 2 , Giovanni Perini 2 , Andre Oberthuer 3 , Matthias Fischer 3 , Dilafruz Juraeva 4 , Benedikt Brors 4 , Chengyuan Xue 1 , Murray D. Norris 1 , Glenn M. Marshall 1,5 , Michelle Haber 1 , Jamie I. Fletcher 1,* and Lesley J. Ashton 6,* 1 Children’s Cancer Institute Australia, Lowy Cancer Research Centre, Randwick, NSW, Australia 2 Department of Biology, University of Bologna, Bologna, Italy 3 Children’s Hospital, Department of Pediatric Oncology and Hematology, University of Cologne and Centre for Molecular Medicine Cologne, Cologne, Germany 4 Division of Theoretical Bioinformatics, German Cancer Research Center, Heidelberg, Germany 5 Kids Cancer Centre, Sydney Children’s Hospital, Randwick, NSW, Australia 6 Faculty of Medicine, School of Women’s and Children’s Health, University of New South Wales, Sydney, NSW, Australia and Research Portfolio, University of Sydney, Sydney, NSW, Australia * These authors have contributed equally to this work Correspondence to: Jamie I. Fletcher, email: // Lesley J. Ashton, email: // Keywords : MYCN, MYC, SLC19A1, methotrexate, neuroblastoma Received : February 10, 2015 Accepted : March 10, 2015 Published : March 30, 2015 Abstract MYCN amplification occurs in 20% of neuroblastomas and is strongly related to poor clinical outcome. We have identified folate-mediated one-carbon metabolism as highly upregulated in neuroblastoma tumors with MYCN amplification and have validated this finding experimentally by showing that MYCN amplified neuroblastoma cell lines have a higher requirement for folate and are significantly more sensitive to the antifolate methotrexate than cell lines without MYCN amplification. We have demonstrated that methotrexate uptake in neuroblastoma cells is mediated principally by the reduced folate carrier (RFC; SLC19A1 ), that SLC19A1 and MYCN expression are highly correlated in both patient tumors and cell lines, and that SLC19A1 is a direct transcriptional target of N-Myc. Finally, we assessed the relationship between SLC19A1 expression and patient survival in two independent primary tumor cohorts and found that SLC19A1 expression was associated with increased risk of relapse or death, and that SLC19A1 expression retained prognostic significance independent of age, disease stage and MYCN amplification. This study adds upregulation of folate-mediated one-carbon metabolism to the known consequences of MYCN amplification, and suggests that this pathway might be targeted in poor outcome tumors with MYCN amplification and high SLC19A1 expression.