Discovery of clinically relevant fusions in pediatric cancer

Background Pediatric cancers typically have a distinct genomic landscape when compared to adult cancers and frequently carry somatic gene fusion events that alter gene expression and drive tumorigenesis. Sensitive and specific detection of gene fusions through the analysis of next-generation-based RNA sequencing (RNA-Seq) data is computationally challenging and may be confounded by low tumor cellularity or underlying genomic complexity. Furthermore, numerous computational tools are available to identify fusions from supporting RNA-Seq reads, yet each algorithm demonstrates unique variability in sensitivity and precision, and no clearly superior approach currently exists. To overcome these challenges, we have developed an ensemble fusion calling approach to increase the accuracy of identifying fusions. Results Our Ensemble Fusion (EnFusion) approach utilizes seven fusion calling algorithms: Arriba, CICERO, FusionMap, FusionCatcher, JAFFA, MapSplice, and STAR-Fusion, which are packaged as a fully automated pipeline using Docker and Amazon Web Services (AWS) serverless technology. This method uses paired end RNA-Seq sequence reads as input, and the output from each algorithm is examined to identify fusions detected by a consensus of at least three algorithms. These consensus fusion results are filtered by comparison to an internal database to remove likely artifactual fusions occurring at high frequencies in our internal cohort, while a “known fusion list” prevents failure to report known pathogenic events. We have employed the EnFusion pipeline on RNA-Seq data from 229 patients with pediatric cancer or blood disorders studied under an IRB-approved protocol. The samples consist of 138 central nervous system tumors, 73 solid tumors, and 18 hematologic malignancies or disorders. The combination of an ensemble fusion-calling pipeline and a knowledge-based filtering strategy identified 67 clinically relevant fusions among our cohort (diagnostic yield of 29.3%), including RBPMS-MET, BCAN-NTRK1, and TRIM22-BRAF fusions. Following clinical confirmation and reporting in the patient’s medical record, both known and novel fusions provided medically meaningful information. Conclusions The EnFusion pipeline offers a streamlined approach to discover fusions in cancer, at higher levels of sensitivity and accuracy than single algorithm methods. Furthermore, this method accurately identifies driver fusions in pediatric cancer, providing clinical impact by contributing evidence to diagnosis and, when appropriate, indicating targeted therapies.

Current Knowledge in Genetics, Molecular Diagnostic Tools, and Treatments for Mantle Cell Lymphomas

Mantle Cell lymphoma (MCL) is a mature B-cell lymphoma with a well-known hallmark genetic alteration in most cases, t (11,14)(q13q32)/CCND1-IGH. However, our understanding of the genetic and epigenetic alterations in MCL has evolved over the years, and it is now known that translocations involving CCND2, or cryptic insertion of enhancer elements of IGK or IGL gene, can also lead to MCL. On a molecular level, MCL can be broadly classified into two subtypes, conventional MCL (cMCL) and non-nodal MCL (nnMCL), each with different postulated tumor cell origin, clinical presentation and behavior, mutational pattern as well as genomic complexity. This article reviews both the common and rare alterations in MCL on a gene mutational, chromosomal arm, and epigenetic level, in the context of their contribution to the lymphomagenesis and disease evolution in MCL. This article also summarizes the important prognostic factors, molecular diagnostic tools, and treatment options based on the most recent MCL literature.

Colistin Resistance Onset Strategies and Genomic Mosaicism in Clinical Acinetobacter baumannii Lineages

The treatment of multidrug-resistant Gram-negative infections is based on colistin. As result, COL-resistance (COL-R) can develop and spread. In Acinetobacter baumannii, a crucial step is to understand COL-R onset and stability, still far to be elucidated. COL-R phenotypic stability, onset modalities, and phylogenomics were investigated in a clinical A. baumannii sample showing a COL resistant (COLR) phenotype at first isolation. COL-R was confirmed by Minimum-Inhibitory-Concentrations as well as investigated by Resistance-Induction assays and Population-Analysis-Profiles (PAPs) to determine: (i) stability; (ii) inducibility; (iii) heteroresistance. Genomics was performed by Mi-Seq Whole-Genome-Sequencing, Phylogenesis, and Genomic Epidemiology by bioinformatics. COLRA. baumannii were subdivided as follows: (i) 3 A. baumannii with stable and high COL MICs defining the “homogeneous-resistant” onset phenotype; (ii) 6 A. baumannii with variable and lower COL MICs displaying a “COL-inducible” onset phenotype responsible for adaptive-resistance or a “subpopulation” onset phenotype responsible for COL-heteroresistance. COL-R stability and onset strategies were not uniquely linked to the amount of LPS and cell envelope charge. Phylogenomics categorized 3 lineages clustering stable and/or unstable COL-R phenotypes with increasing genomic complexity. Likewise, different nsSNP profiling in genes already associated with COL-R marked the stable and/or unstable COL-R phenotypes. Our investigation finds out that A. baumannii can range through unstable or stable COLR phenotypes emerging via different “onset strategies” within phylogenetic lineages displaying increasing genomic mosaicism.

Whole Genome Sequencing of B-Cell Neoplasms with t(14;19)(q32;q13) Reveals Different Entities

Introduction: The t(14;19)(q32;q13) is a rare cytogenetic abnormality found in <0.1% of all B-cell neoplasms. The molecular features of this translocation are not well characterized. IGH-BCL3 rearrangement has been found in some tumors identified as "atypical" chronic lymphocytic leukemia (CLL) with aggressive clinical evolution. This translocation has also been observed in other B-cell neoplasms without clear evidence of the target gene. The mechanisms generating this translocation, the genomic profile of alterations of these cases, and whether different molecular features may be associated with specific entities are not known. Aim: To elucidate the genomic features of B-cell neoplasms carrying the t(14;19) and their relationship to pathological characteristics of the tumors. Materials and methods: We sequenced the whole-genome (WGS) of 13 cases in which the t(14;19) had been identified by conventional cytogenetics and/or FISH using a BCL3 break-apart probe. In six of these cases we performed RNA-seq. Pathological and clinical revision was conducted in all cases, 8 of them with tissue biopsies. Results: The breakpoints of the t(14;19) were characterized at base-pair resolution using WGS. All breakpoints in chr14 were found within any of the class switch recombination (CSR) regions suggesting an aberrant CSR as the mechanism causing this alteration. The breakpoints on chr19 were found upstream (13 kb) the 5' untranslated region (UTR) of BCL3 in 8/13 (61.5%) cases. One additional case had the breakpoint further upstream (49 kb) of BCL3 truncating CEACAM16. The four remaining cases had breakpoints downstream of BCL3; two cases within CBLC, one in BCAM, and one after NECTIN2. Of note, the further upstream BCL3 case and the downstream BCL3 cases had mutated IGHV, while all upstream BCL3 cases had unmutated IGHV. Based on RNA-seq data, all upstream BCL3 cases (n=5) showed an upregulation of BCL3, while one downstream case with RNA-seq available showed upregulation of NECTIN2 and low levels of BCL3. The pathology review identified the four downstream BCL3 cases as marginal zone lymphomas whereas the cases with breakpoints upstream BCL3 (n=3 with tissue available) and the case further upstream BCL3 were classified as "atypical" CLL. We next characterized the genomic landscape of these tumors based on the breakpoint on chr19 (upstream and downstream BCL3). The analysis of the WGS showed a lower number of mutations, copy number alterations (CNA), and structural variants (SV) in the upstream BCL3 group compared to the downstream BCL3 cases (mean of 2429.5 vs 6271.7 somatic mutations, 3.1 vs 11.7 CNA, and 4.4 vs 18 SV, respectively). In terms of specific driver mutations, the downstream BCL3 group carried mutations in genes previously described in MZL, such as KMT2D, NOTCH2, or KLF2 found in two cases. All but one case with the breakpoint upstream BCL3 carried trisomy 12 (tri12), which was absent in all cases with a downstream breakpoint. Finally, we performed a differential expression analysis between 5 atypical CLL cases with BCL3 rearrangements vs 4 CLL without t(14;19) [all unmutated IGHV]. This analysis showed 578 genes upregulated and 720 genes downregulated in the BCL3-rearranged cases (q <0.05), including remarkable differences in the expression of previously described CLL hallmark genes, such as upregulation of EBF1 and downregulation of LEF1, FMOD, ADTRP, CLNK, IGSF3, TCF4. An analysis of the RNA-seq data of 294 CLL cases lacking the t(14;19) (Puente et al., Nature 2015) indicated that this transcriptional program was not related to IGHV mutational status nor to the presence of tri12. Nonetheless, we identified a small set of tri12 mutated IGHV CLL lacking the t(14;19) with a similar modulation of the expression of the above hallmark genes. Conclusions: We have characterized the breakpoints of the t(14;19) at base-pair resolution and evidenced marked molecular and pathological differences of the tumors according to the location of the breakpoint. Tumors carrying the breakpoint downstream BCL3 exhibit a higher genomic complexity, driver alterations, and pathological features corresponding to MZL. Contrarily, tumors with the breakpoint upstream of BCL3 upregulate BCL3 and display lower genomic complexity as well as CLL-like features. Nonetheless, these cases have a different gene expression profile compared to conventional CLL characterized by LEF1 downregulation and EBF1 overexpression. Disclosures Navarro: Nocartis: Honoraria; Roche: Honoraria; EUSA: Consultancy, Research Funding; Pharma: Consultancy; GILEAD: Research Funding; Pharma: Research Funding.

675 Genomic drivers of large B-cell lymphoma resistance to CD19 CAR-T therapy

BackgroundCD19-directed chimeric antigen receptor-reprogrammed autologous T cells are breakthrough immunotherapies for heavily pretreated patients with diffuse large B-cell lymphoma (DLBCL), but across CAR-19 products, ~60% of patients fail to respond or relapse. Inflammatory markers and clinical factors associate with impaired responses, but tumor-intrinsic resistance drivers are largely undefined.MethodsTo characterize the genomic mechanisms involved resistance to CAR-19, we interrogated whole genome sequencing (WGS) from 28 relapsed/refractory (r/r) aggressive lymphoma patients uniformly treated with axicabtagene ciloleucel (axi-cel).ResultsBecause prognostic factors defined in the frontline treatment setting are largely inapplicable to CAR-19, we leveraged the WGS data, including comparative analyses with untreated DLBCL cases in the Pan-Cancer Analysis of Whole Genomes (PCAWG) (figure 1). In analyses of individual mutated genes, TP53 was significantly enriched (p=0.002) in CAR-19 patients, but did not predict outcome. However, mutations in either NFKBIA or MYC associated with worse PFS after CAR-19 (p=0.04, p=0.025 respectively). We next identified 12 single base substitution (SBS) mutational signatures in our cohort and found presence of APOBEC (SBS2 and SBS13) signatures associated with worse PFS, with 4/5 patients progressing (p=0.03). Copy number analysis by GISTIC2.0 revealed focal deletions of RHOA and RB1 to be significantly enriched in our cohort and independently predicted poor outcome (p=0.0007, p=0.05 respectively). WGS identifies structural variants and complex events. We found chromothripsis, a catastrophic shattering and reassembly of chromosomes, in 39.3% of r/r DLBCL, which was strongly associated with poor CAR-19 outcome, with 9/11 affected cases progressing (p=0.041). Finally, reduced expression (n=3) or genomic alteration (n=3) of CD19 did not associate with poor outcome. One case with durable response contained a sub-clonal CD19 mutation (L174V) previously reported as associated with CAR-19 resistance. These findings demonstrate predominance of CD19-independent resistance and indicate antigen-mediated tumor killing is not the only mechanism of tumor eradication. Genomic complexity appears to promote an immunosuppressive tumor microenvironment (TME), limiting CAR-19 efficacy.ConclusionsLeveraging the resolution of WGS, we observed that markers of genomic complexity (chromothripsis and APOBEC) and specific genomic alterations (RHOA and RB1 deletions) associate with resistance to CAR-19 immunotherapy for aggressive B-cell lymphomas (figure 1). 93.8% of CAR-19 relapsed patients contained at least one or these genomic alterations. Recent patient data demonstrate that an immunosuppressed TME leads to CAR-19 failure. Combining these findings with our genomics findings, successful CAR-19 therapy must overcome the immune-exhausted TME to mobilize the host immune system and eliminate the tumor.Abstract 675 Figure 1Genomic alterations associated with disease progression. (a) The heatmap shows the significant genomic alteration present in at least 4 patients associated with progression after CD19 CAR-T cell therapy. (b) Kaplan-Meier curve of progression free survival with the combination of statistically significant genomic anomalies

Genome Sequence Resource of Burkholderia glumae UAPB13

Burkholderia glumae causes Bacterial Panicle Blight of rice. Here, we report the genomic sequence of B. glumae strain UAPB13 isolated from fields in Arkansas. The assembled genome consists of 123 scaffolds totaling 6,504,483 bp representing two chromosomes and two plasmids. The genomic complexity of B. glumae warrants the sequencing of additional strains. This additional genomic sequence will enable us to further understand this pathogen and the disease it causes.

Genomic and Transcriptomic Characteristics of Esophageal Adenocarcinoma

Esophageal adenocarcinoma (EAC) is a deadly disease with limited options for targeted therapy. With the help of next-generation sequencing studies over the last decade, we gained an understanding of the genomic architecture of EAC. The tumor suppressor gene TP53 is mutated in 70 to 80% of tumors followed by genomic alterations in CDKN2A, KRAS, ERBB2, ARID1A, SMAD4 and a long tail of less frequently mutated genes. EAC is characterized by a high burden of point mutations and genomic rearrangements, resulting in amplifications and deletions of genomic regions. The genomic complexity is likely hampering the efficacy of targeted therapies. Barrett’s esophagus (BE), a metaplastic response of the esophagus to gastro-esophageal reflux disease, is the main risk factor for the development of EAC. Almost all EACs are derived from BE. The sequence from BE to EAC provides an opportunity to study the genomic evolution towards EAC. While the overlap of point mutations between BE and EAC within the same patient is, at times, surprisingly low, there is a correlation between the complexity of the genomic copy number profile and the development of EAC. Transcriptomic analyses separated EAC into a basal and a classical subtype, with the basal subtype showing a higher level of resistance to chemotherapy. In this review, we provide an overview of the current knowledge of the genomic and transcriptomic characteristics of EAC and their relevance for the development of the disease and patient care.

Linking Immunity with Genomics in Sarcomas: Is Genomic Complexity an Immunogenic Trigger?

Sarcomas comprise a collection of highly heterogeneous malignancies that can be grossly grouped in the categories of sarcomas with simple or complex genomes. Since the outcome for most sarcoma patients has barely improved in the last decades, there is an urgent need for improved therapies. Immunotherapy, and especially T cell checkpoint blockade, has recently been a game-changer in cancer therapy as it produced significant and durable treatment responses in several cancer types. Currently, only a small fraction of sarcoma patients benefit from immunotherapy, supposedly due to a general lack of somatically mutated antigens (neoantigens) and spontaneous T cell immunity in most cancers. However, genomic events resulting from chromosomal instability are frequent in sarcomas with complex genomes and could drive immunity in those tumors. Improving our understanding of the mechanisms that shape the immune landscape of sarcomas will be crucial to overcoming the current challenges of sarcoma immunotherapy. This review focuses on what is currently known about the tumor microenvironment in sarcomas and how this relates to their genomic features. Moreover, we discuss novel therapeutic strategies that leverage the tumor microenvironment to increase the clinical efficacy of immunotherapy, and which could provide new avenues for the treatment of sarcomas.

Epistasis and Evolution

The concept of epistasis was introduced into evolutionary theory more than a hundred years ago. Its history is marked by controversies regarding its importance for the evolutionary process, as exemplified by the debate between Ronald Fisher and Sewall Wright in the wake of the modern synthesis. In this case the disagreement was about the shape of the adaptive landscape, which is determined by epistasis. Wright believed that epistasis causes the adaptive landscape to be rugged with many local peaks, whereas Fisher viewed evolution as a smooth, steady progression toward a unique optimum. Even today, the different meanings attributed to epistasis continue to spawn confusion. Nevertheless, a consensus is emerging, according to which the term should be used to designate interactions between genetic effects on phenotypes in the broadest sense. Stated differently, in the presence of epistasis the phenotypic effects of a gene depend on its genetic context. In evolutionary theory the phenotype of primary interest is organismal fitness, but principally the concept applies to any genotype-phenotype map. Reflecting the Fisherian view, throughout the 20th century epistasis was often considered to be a residual perturbation on the main effects of individual genes. Following the advent of sequencing techniques providing insights into the molecular basis of genotype-phenotype maps, over the past two decades it has become clear, however, that epistasis is the rule rather than an exception. This has motivated a large number of empirical studies exploring the patterns and evolutionary consequences of epistasis across a wide range of scales of organismal and genomic complexity. Correspondingly, mathematical and computational tools have been developed for the analysis of experimental data, and models have been constructed to elucidate the mechanistic and statistical origins of genetic interactions. Despite a certain inherent vagueness, the concept takes center stage in modern evolutionary thought as a framework for organizing the accumulating understanding of the relationship among genotype, phenotype, and organism.