Comprehensive molecular profiling of multiple myeloma identifies refined copy number and expression subtypes.

Multiple myeloma is a treatable, but currently incurable, hematological malignancy of plasma cells characterized by diverse and complex tumor genetics for which precision medicine approaches to treatment are lacking. The Multiple Myeloma Research Foundation's Relating Clinical Outcomes in Multiple Myeloma to Personal Assessment of Genetic Profile study ( NCT01454297 ) is a longitudinal, observational clinical study of newly diagnosed patients with multiple myeloma (n = 1,143) where tumor samples are characterized using whole-genome sequencing, whole-exome sequencing and RNA sequencing at diagnosis and progression, and clinical data are collected every 3 months. Analyses of the baseline cohort identified genes that are the target of recurrent gain-of-function and loss-of-function events. Consensus clustering identified 8 and 12 unique copy number and expression subtypes of myeloma, respectively, identifying high-risk genetic subtypes and elucidating many of the molecular underpinnings of these unique biological groups. Analysis of serial samples showed that 25.5% of patients transition to a high-risk expression subtype at progression. We observed robust expression of immunotherapy targets in this subtype, suggesting a potential therapeutic option.

Identifying treatment options for BRAFV600 wild-type metastatic melanoma: A SU2C/MRA genomics-enabled clinical trial.

Although combination BRAF and MEK inhibitors are highly effective for the 40-50% of cutaneous metastatic melanomas harboring BRAFV600 mutations, targeted agents have been ineffective for BRAFV600wild-type (wt) metastatic melanomas. The SU2C Genomics-Enabled Medicine for Melanoma Trial utilized a Simon two-stage optimal design to assess whether comprehensive genomic profiling improves selection of molecular-based therapies for BRAFV600wt metastatic melanoma patients who had progressed on standard-of-care therapy, which may include immunotherapy. Of the response-evaluable patients, binimetinib was selected for 20 patients randomized to the genomics-enabled arm, and nine were treated on the alternate treatment arm. Response rates for 27 patients treated with targeted recommendations included one (4%) partial response, 18 (67%) with stable disease, and eight (30%) with progressive disease. Post-trial genomic and protein pathway activation mapping identified additional drug classes that may be considered for future studies. Our results highlight the complexity and heterogeneity of metastatic melanomas, as well as how the lack of response in this trial may be associated with limitations including monotherapy drug selection and the dearth of available single and combination molecularly-driven therapies to treat BRAFV600wt metastatic melanomas.

Leveraging Spatial Variation in Tumor Purity for Improved Somatic Variant Calling of Archival Tumor Only Samples.

Archival tumor samples represent a rich resource of annotated specimens for translational genomics research. However, standard variant calling approaches require a matched normal sample from the same individual, which is often not available in the retrospective setting, making it difficult to distinguish between true somatic variants and individual-specific germline variants. Archival sections often contain adjacent normal tissue, but this tissue can include infiltrating tumor cells. As existing comparative somatic variant callers are designed to exclude variants present in the normal sample, a novel approach is required to leverage adjacent normal tissue with infiltrating tumor cells for somatic variant calling. Here we present lumosVar 2.0, a software package designed to jointly analyze multiple samples from the same patient, built upon our previous single sample tumor only variant caller lumosVar 1.0. The approach assumes that the allelic fraction of somatic variants and germline variants follow different patterns as tumor content and copy number state change. lumosVar 2.0 estimates allele specific copy number and tumor sample fractions from the data, and uses a to model to determine expected allelic fractions for somatic and germline variants and to classify variants accordingly. To evaluate the utility of lumosVar 2.0 to jointly call somatic variants with tumor and adjacent normal samples, we used a glioblastoma dataset with matched high and low tumor content and germline whole exome sequencing data (for true somatic variants) available for each patient. Both sensitivity and positive predictive value were improved when analyzing the high tumor and low tumor samples jointly compared to analyzing the samples individually or in-silico pooling of the two samples. Finally, we applied this approach to a set of breast and prostate archival tumor samples for which tumor blocks containing adjacent normal tissue were available for sequencing. Joint analysis using lumosVar 2.0 detected several variants, including known cancer hotspot mutations that were not detected by standard somatic variant calling tools using the adjacent tissue as presumed normal reference. Together, these results demonstrate the utility of leveraging paired tissue samples to improve somatic variant calling when a constitutional sample is not available.

De novo construction of an expanded transcriptome assembly for the western tarnished plant bug, Lygus hesperus.

BACKGROUND: The plant bug Lygus hesperus Knight is a polyphagous pest of many economically important crops. Despite its pest status, little is known about the molecular mechanisms responsible for much of the biology of this species. Earlier Lygus transcriptome assemblies were limited by low read depth, or because they focused on specific conditions. To generate a more comprehensive transcriptome, we supplemented previous datasets with new reads corresponding to specific tissues (heads, antennae, and male reproductive tissues). This transcriptome augments current Lygus molecular resources and provides the foundational knowledge critical for future comparative studies. FINDINGS: An expanded, Trinity-based de novo transcriptome assembly for L. hesperus was generated using previously published whole body Illumina data, supplemented with 293 million bp of new raw sequencing data corresponding to five tissue-specific cDNA libraries and 11 Illumina sequencing runs. The updated transcriptome consists of 22,022 transcripts (average length of 2075 nt), 62 % of which contain complete open reading frames. Significant coverage of the BUSCO (benchmarking universal single-copy orthologs) dataset and robust metrics indicate that the transcriptome is a quality assembly with a high degree of completeness. Initial assessment of the new assembly's utility revealed that the length and abundance of transcripts predicted to regulate insect physiology and chemosensation have improved, compared with previous L. hesperus assemblies. CONCLUSIONS: This transcriptome represents a significant expansion of Lygus transcriptome data, and improves foundational knowledge about the molecular mechanisms underlying L. hesperus biology. The dataset is publically available in NCBI and GigaDB as a resource for researchers.