Diagnostic utility of one-stop fusion gene panel to detect TFE3/TFEB gene rearrangement and amplification in renal cell carcinomas.

MiT family translocation renal cell carcinoma (MiT-RCC) harbors translocations involving the TFE3 or TFEB genes. RCC with TFEB amplification is also identified and is associated with a more aggressive clinical course. Accurate diagnosis of MiT-RCC is crucial for patient management. In this study, we evaluated the performance of the Archer FusionPlex assay for detection of MiT-RCC with TFE3 or TFEB translocations and TFEB amplifications. RNA was extracted from 49 RCC FFPE tissue samples with known TFE3/TFEB status (26 TFE3 FISH positive, 12 TFEB FISH positive, 4 TFEB amplified (1 case both split and amplified), and 8 FISH negative) using the Covaris extraction kit. Target enriched cDNA libraries were prepared using the Archer FusionPlex kit and sequenced on the Illumina NextSeq 550. We demonstrate that the age of the specimen, quality of RNA, and sequencing metrics are important for fusion detection. Fusions were identified in 20 of 21 cases less than 2 years old, and TFE3/TFEB rearrangements were detected in all cases with Fusion QC ≥ 100. The assay identified intrachromosomal inversions in two cases (TFE3-RBM10 and NONO-TFE3), usually difficult to identify by FISH assays. TFEB mRNA expression and the TFEB/TFE3 mRNA expression ratio were significantly higher in RCCs with TFEB fusion and TFEB gene amplification compared to tumors without TFEB fusion or amplification. A cutoff TFEB/TFE3 ratio of 0.5 resulted in 97.3% concordance to FISH results with no false negatives. Our study demonstrates that the FusionPlex assay successfully identifies TFE3 and TFEB fusions including intrachromosomal inversions. Age of the specimen and certain sequencing metrics are important for successful fusion detection. Furthermore, mRNA expression levels may be used for predicting cases harboring TFEB amplification, thereby streamlining testing. This assay enables accurate molecular detection of multiple subtypes of MiT-RCCs in a convenient workflow.

PD-L1 (22C3) Expression Correlates with Clinical and Molecular Features of Lung Adenocarcinomas in Cytological Samples.

INTRODUCTION: PD-L1 expression is the most widely used predictive marker for immune checkpoint inhibitor (ICI) therapy in patients with lung adenocarcinoma. However, the current understanding of the association between PD-L1 expression and treatment response is suboptimal. A significant percentage of patients have only a cytological specimen available for clinical management. Therefore, it is relevant to examine the impact of molecular features on PD-L1 expression in cytological samples and how it might correlate with a therapeutic response. METHODS: We evaluated patients diagnosed with adenocarcinoma of the lung who had both in-house targeted next-generation sequencing analysis and paired PD-L1 (22C3) immunohistochemical staining performed on the same cell blocks. We explored the association between molecular features and PD-L1 expression. In patients who underwent ICIs therapy, we assessed how a specific gene mutation impacted a therapeutic response. RESULTS: 145 patients with lung adenocarcinoma were included in this study. PD-L1-high expression was found to be more common in pleural fluid than in other sample sites. Regional lymph node samples showed a higher proportion of PD-L1-high expression (29%) compared with lung samples (6%). The predictive value of PD-L1 expression was retained in cytological samples. Mutations in KRAS were also associated with a PD-L1-high expression. However, tumors with TP53 or KRAS mutations showed a lower therapy response rate regardless of the PD-L1 expression. CONCLUSION: Cytological samples maintain a predictive value for PD-L1 expression in patients with lung adenocarcinoma as regards the benefit of ICI treatment. Specific molecular alterations additionally impact PD-L1 expression and its predictive value.

The Interpretation of Sequence Variants in Myeloid Neoplasms.

OBJECTIVES: To provide an overview of the challenges encountered during the interpretation of sequence variants detected by next-generation sequencing (NGS) in myeloid neoplasms, as well as the limitations of the technology with the goal of preventing the over- or undercalling of alterations that may have a significant effect on patient management. METHODS: Review of the peer-reviewed literature on the interpretation, reporting, and technical challenges of NGS assays for myeloid neoplasms. RESULTS: NGS has been integrated widely and rapidly into the standard evaluating of myeloid neoplasms. Review of the literature reveals that myeloid sequence variants are challenging to detect and interpret. Large insertions and guanine-cytosine-heavy areas prove technically challenging while frameshift and truncating alterations may be classified as variants of uncertain significance by tertiary analysis informatics pipelines due to their absence in the literature and databases. CONCLUSIONS: The analysis and interpretation of NGS results in myeloid neoplasia are challenging due to the varied number of detectable gene alterations. Familiarity with the genomic landscape of myeloid malignancies and knowledge of the tools available for the interpretation of sequence variants are essential to facilitate translation into clinical and therapy decisions.

Integration of Technical, Bioinformatic, and Variant Assessment Approaches in the Validation of a Targeted Next-Generation Sequencing Panel for Myeloid Malignancies.

CONTEXT: - Detection of variants in hematologic malignancies is increasingly important because of a growing number of variants impacting diagnosis, prognosis, and treatment response, and as potential therapeutic targets. The use of next-generation sequencing technologies to detect variants in hematologic malignancies in a clinical diagnostic laboratory setting allows for efficient identification of routinely tested markers in multiple genes simultaneously, as well as the identification of novel and rare variants in other clinically relevant genes. OBJECTIVE: - To apply a systematic approach to evaluate and validate a commercially available next-generation sequencing panel (TruSight Myeloid Sequencing Panel, Illumina, San Diego, California) targeting 54 genes. In this manuscript, we focused on the parameters that were used to evaluate assay performance characteristics. DATA SOURCES: - Analytical validation was performed using samples containing known variants that had been identified previously. Cases were selected from different disease types, with variants in a range of genes. Panel performance characteristics were assessed and genomic regions requiring additional analysis or wet-bench approaches identified. CONCLUSIONS: - We validated the performance characteristics of a myeloid next-generation sequencing panel for detection of variants. The TruSight Myeloid Sequencing Panel covers more than 95% of target regions with depth greater than 500×. However, because of unique variant types such as large insertions or deletions or genomic regions of high GC content, variants in CEBPA, FLT3, and CALR required supplementation with non-next-generation sequencing assays or with informatics approaches to address deficiencies in performance. The use of multiple bioinformatics approaches (2 variant callers and informatics scripts) allows for maximizing calling of true positives, while identifying limitations in using either method alone.

Classifying prion and prion-like phenomena.

The universe of prion and prion-like phenomena has expanded significantly in the past several years. Here, we overview the challenges in classifying this data informatically, given that terms such as "prion-like", "prion-related" or "prion-forming" do not have a stable meaning in the scientific literature. We examine the spectrum of proteins that have been described in the literature as forming prions, and discuss how "prion" can have a range of meaning, with a strict definition being for demonstration of infection with in vitro-derived recombinant prions. We suggest that although prion/prion-like phenomena can largely be apportioned into a small number of broad groups dependent on the type of transmissibility evidence for them, as new phenomena are discovered in the coming years, a detailed ontological approach might be necessary that allows for subtle definition of different "flavors" of prion / prion-like phenomena.

Interaction networks of prion, prionogenic and prion-like proteins in budding yeast, and their role in gene regulation.

Prions are transmissible, propagating alternative states of proteins. Prions in budding yeast propagate heritable phenotypes and can function in large-scale gene regulation, or in some cases occur as diseases of yeast. Other 'prionogenic' proteins are likely prions that have been determined experimentally to form amyloid in vivo, and to have prion-like domains that are able to propagate heritable states. Furthermore, there are over 300 additional 'prion-like' yeast proteins that have similar amino-acid composition to prions (primarily a bias for asparagines and glutamines). Here, we examine the protein functional and interaction networks that involve prion, prionogenic and prion-like proteins. Set against a marked overall preference for N/Q-rich prion-like proteins not to interact with each other, we observe a significant tendency of prion/prionogenic proteins to interact with other, N/Q-rich prion-like proteins. This tendency is mostly due to a small number of networks involving the proteins NUP100p, LSM4p and PUB1p. In general, different data analyses of functional and interaction networks converge to indicate a strong linkage of prionogenic and prion-like proteins, to stress-granule assembly and related biological processes. These results further elucidate how prions may impact gene regulation, and reveal a broader horizon for the functional relevance of N/Q-rich prion-like domains.

PrionHome: a database of prions and other sequences relevant to prion phenomena.

Prions are units of propagation of an altered state of a protein or proteins; prions can propagate from organism to organism, through cooption of other protein copies. Prions contain no necessary nucleic acids, and are important both as both pathogenic agents, and as a potential force in epigenetic phenomena. The original prions were derived from a misfolded form of the mammalian Prion Protein PrP. Infection by these prions causes neurodegenerative diseases. Other prions cause non-Mendelian inheritance in budding yeast, and sometimes act as diseases of yeast. We report the bioinformatic construction of the PrionHome, a database of >2000 prion-related sequences. The data was collated from various public and private resources and filtered for redundancy. The data was then processed according to a transparent classification system of prionogenic sequences (i.e., sequences that can make prions), prionoids (i.e., proteins that propagate like prions between individual cells), and other prion-related phenomena. There are eight PrionHome classifications for sequences. The first four classifications are derived from experimental observations: prionogenic sequences, prionoids, other prion-related phenomena, and prion interactors. The second four classifications are derived from sequence analysis: orthologs, paralogs, pseudogenes, and candidate-prionogenic sequences. Database entries list: supporting information for PrionHome classifications, prion-determinant areas (where relevant), and disordered and compositionally-biased regions. Also included are literature references for the PrionHome classifications, transcripts and genomic coordinates, and structural data (including comparative models made for the PrionHome from manually curated alignments). We provide database usage examples for both vertebrate and fungal prion contexts. Using the database data, we have performed a detailed analysis of the compositional biases in known budding-yeast prionogenic sequences, showing that the only abundant bias pattern is for asparagine bias with subsidiary serine bias. We anticipate that this database will be a useful experimental aid and reference resource. It is freely available at: http://libaio.biol.mcgill.ca/prion.

LPS-annotate: complete annotation of compositionally biased regions in the protein knowledgebase.

Compositional bias (i.e. a skew in the composition of a biological sequence towards a subset of residue types) can occur at a wide variety of scales, from compositional biases of whole genomes, down to short regions in individual protein and gene-DNA sequences that are compositionally biased (CB regions). Such CB regions are made from a subset of residue types that are strewn along the length of the region in an irregular way. Here, we have developed the database server LPS-annotate, for the analysis of such CB regions, and protein disorder in protein sequences. The algorithm defines compositional bias through a thorough search for lowest-probability subsequences (LPSs) (i.e., the least likely sequence regions in terms of composition). Users can (i) initially annotate CB regions in input protein or nucleotide sequences of interest, and then (ii) query a database of greater than 1,500,000 pre-calculated protein-CB regions, for investigation of further functional hypotheses and inferences, about the specific CB regions that were discovered, and their protein disorder propensities. We demonstrate how a user can search for CB regions of similar compositional bias and protein disorder, with a worked example. We show that our annotations substantially augment the CB-region annotations that already exist in the UniProt database, with more comprehensive annotation of more complex CB regions. Our analysis indicates tens of thousands of CB regions that do not comprise globular domains or transmembrane domains, and that do not have a propensity to protein disorder, indicating a large cohort of protein-CB regions of biophysically uncharacterized types. This server and database is a conceptually novel addition to the workbench of tools now available to molecular biologists to generate hypotheses and inferences about the proteins that they are investigating. It can be accessed at http://libaio.biol.mcgill.ca/lps-annotate.html. Database URL: http://libaio.biol.mcgill.ca/lps-annotate.html.