Germline and sporadic mTOR pathway mutations in low-grade oncocytic tumor of the kidney.
TL;DR: In this article, the authors describe 22 low-grade oncocytic tumor (LOT) cases corresponding to 7 patients presenting with a median age of 75 years (range 63-86 years) and male to female ratio 2:5.
About: This article is published in Modern Pathology.The article was published on 2021-09-20. It has received 23 citations till now. The article focuses on the topics: Eosinophilic & TSC1.
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TL;DR: A detailed understanding of the pathologic features of these distinctive tumors, which include chromophobe-like features and eosinophilia, with some of the tumors unclassified, is provided in this paper.
Abstract: Tuberous sclerosis complex (TSC) is an autosomal dominant disorder in which renal manifestations are prominent. There are three major renal lesions in TSC: angiomyolipomas, cysts, and renal cell carcinoma (RCC). Major recent advances have revolutionized our understanding of TSC-associated RCC, including two series that together include more than 100 TSC-RCC cases, demonstrating a mean age at onset of about 36 years, tumors in children as young as 7, and a striking 2:1 female predominance. These series also provide the first detailed understanding of the pathologic features of these distinctive tumors, which include chromophobe-like features and eosinophilia, with some of the tumors unclassified. This pathologic heterogeneity is distinctive and reminiscent of the pathologic heterogeneity in Birt–Hogg–Dube-associated RCC, which also includes chromophobe-like tumors. Additional advances include the identification of sporadic counterpart tumors that carry somatic TSC1/TSC2/mTOR mutations. These include unclassified eosinophilic tumors, eosinophilic solid cystic RCC (ESC-RCC), and RCC with leiomyomatous stroma (RCCLMS). A variety of epithelial renal neoplasms have been identified both in patients with tuberous sclerosis complex (TSC) and in the nonsyndromic setting associated with somatic mutations in the TSC1 and TSC2 genes. Interestingly, whether tumors are related to a germline or somatic TSC1/2 mutation, these tumors often display similar morphologic and immunophenotypic features. Finally, recent work has identified molecular links between TSC and BHD-associated tumors, involving the TFEB/TFE3 transcription factors.
19 citations
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13 citations
TL;DR: Recurrent tuberous sclerosis / MTOR pathway gene alterations in LOT supports its consideration as a distinct morphologic, immunohistochemical, and genetic entity, and PIK3CA is another pathway member that may be altered in these tumors.
Abstract: Low‐grade oncocytic tumour (LOT) of the kidney has recently emerged as a potential novel tumour type. Despite similarity to oncocytoma or eosinophilic chromophobe renal cell carcinoma, it shows diffuse keratin 7 immunohistochemistry (IHC) and negative KIT (CD117), which differs from both. We aimed to identify the molecular characteristics of these tumours. Seventeen tumours (one male, 16 female, nine previously published) fitting the original description of this entity (solid eosinophilic cell morphology, often with areas of tumour cells loosely stretched in oedematous stroma, and the above IHC features) were analysed with a next‐generation sequencing panel of 324 cancer‐associated genes from formalin‐fixed, paraffin‐embedded tissue. All tumours harboured at least one alteration in either TSC1 (n = 7, 41%), TSC2 (n = 2, 12%), MTOR (n = 5, 29%) or PIK3CA (n = 4, 24%). Four tumours harboured a second alteration, including two NF2, one each in conjunction with MTOR and TSC2 alterations, one PTEN with TSC1 alteration and one tumour with both MTOR and TSC1 alterations. No other renal cancer‐related or recurring gene alterations were identified. In addition to the previously described IHC findings, 16 of 16 were positive for GATA3. Eleven patients with follow‐up had no metastases or recurrent tumours. Recurrent tuberous sclerosis/MTOR pathway gene alterations in LOT support its consideration as a distinct morphological, immunohistochemical and genetic entity. PIK3CA is another pathway member that may be altered in these tumours. Further study will be necessary to determine whether tumour behaviour or syndromic associations differ from those of oncocytoma and chromophobe carcinoma, warranting different clinical consideration.
11 citations
TL;DR: In this paper , the authors evaluated the clinicopathologic and IHC profiles of 39 eosinophilic renal tumors with targeted DNA sequencing-confirmed TSC/MTOR mutations.
Abstract: Background: Several TSC1/2- or MTOR-mutated eosinophilic renal tumor subsets are emerging, including eosinophilic solid and cystic renal cell carcinoma (ESC RCC), eosinophilic vacuolated tumors (EVTs) and low-grade oncocytic tumors (LOTs). “Unclassified renal tumors with TSC/MTOR mutations” (TSC-mt RCC-NOS) do not meet the criteria for other histomolecular subtypes. Whether these tumors represent a continuum of 1 TSC/MTOR-mutation-associated disease is unknown. Design: We evaluated the clinicopathologic and IHC profiles of 39 eosinophilic renal tumors with targeted DNA sequencing-confirmed TSC/MTOR mutations. Twenty-eight of these, plus 6 ChRCC, 5 RO, 5 ccRCC, 7 MiT RCC and 6 normal renal tissues, were profiled transcriptionally by RNA-seq. Results: The 39 cases were reclassified based on morphological and IHC features as ESC RCC (12), EVT (9), LOT, (8) and TSC-mt RCC-NOS (10). The mutation profiles demonstrated consistency; ESC RCCs (12/12) had TSC mutations, and most LOTs (7/8) had MTOR mutations. Ten TSC-mt RCC-NOSs exhibited heterogeneous morphology, arising a differential diagnosis with other renal tumors, including MiT RCC, PRCC and epithelioid PEComa. RNA sequencing-based clustering segregated ESC RCC, EVT and LOT from each other and other renal tumors, indicating expression profile-level differences. Most TSC-mt RCC-NOSs (6/7) formed a mixed cluster with ESC RCC, indicating similar expression signatures; one TSC-mt RCC-NOS with unusual biphasic morphology clustered with EVT. Conclusions: We expanded the TSC/MTOR-associated eosinophilic renal tumor morphologic spectrum, identified gene mutation characteristics, and highlighted differential diagnosis challenges, especially with MiT RCC. ESC RCC, EVT, and LOT having distinct expression profiles. TSC-mt RCC-NOS may cluster with recognized TSC/MTOR-associated entities.
11 citations
TL;DR: Despite the common genetic background, it appears that the tumors with TSC/mTOR mutations represent a diverse group of distinct renal neoplasms.
Abstract: A number of recently described renal tumor entities share an eosinophilic/oncocytic morphology, somewhat solid architectural growth pattern, and tendency to present as low-stage tumors. The vast majority of such tumors follow a non-aggressive clinical behavior. In this review, we discuss the morphological, immunohistochemical, and molecular genetic profiles of the three most recent novel/emerging renal entities associated with TSC/mTOR pathway mutations. These are eosinophilic solid and cystic renal cell carcinoma, eosinophilic vacuolated tumors, and low-grade oncocytic tumors, which belong to a heterogeneous group of renal tumors, demonstrating mostly solid architecture, eosinophilic/oncocytic cytoplasm, and overlapping morphological and immunohistochemical features between renal oncocytoma and chromophobe renal cell carcinoma. All three tumors also share a molecular genetic background with mutations in the mTORC1 pathway (TSC1/TSC2/mTOR/RHEB). Despite the common genetic background, it appears that the tumors with TSC/mTOR mutations represent a diverse group of distinct renal neoplasms.
9 citations
References
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TL;DR: Burrows-Wheeler Alignment tool (BWA) is implemented, a new read alignment package that is based on backward search with Burrows–Wheeler Transform (BWT), to efficiently align short sequencing reads against a large reference sequence such as the human genome, allowing mismatches and gaps.
Abstract: Motivation: The enormous amount of short reads generated by the new DNA sequencing technologies call for the development of fast and accurate read alignment programs. A first generation of hash table-based methods has been developed, including MAQ, which is accurate, feature rich and fast enough to align short reads from a single individual. However, MAQ does not support gapped alignment for single-end reads, which makes it unsuitable for alignment of longer reads where indels may occur frequently. The speed of MAQ is also a concern when the alignment is scaled up to the resequencing of hundreds of individuals.
Results: We implemented Burrows-Wheeler Alignment tool (BWA), a new read alignment package that is based on backward search with Burrows–Wheeler Transform (BWT), to efficiently align short sequencing reads against a large reference sequence such as the human genome, allowing mismatches and gaps. BWA supports both base space reads, e.g. from Illumina sequencing machines, and color space reads from AB SOLiD machines. Evaluations on both simulated and real data suggest that BWA is ~10–20× faster than MAQ, while achieving similar accuracy. In addition, BWA outputs alignment in the new standard SAM (Sequence Alignment/Map) format. Variant calling and other downstream analyses after the alignment can be achieved with the open source SAMtools software package.
Availability: http://maq.sourceforge.net
Contact: [email protected]
43,862 citations
TL;DR: The Spliced Transcripts Alignment to a Reference (STAR) software based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure outperforms other aligners by a factor of >50 in mapping speed.
Abstract: Motivation Accurate alignment of high-throughput RNA-seq data is a challenging and yet unsolved problem because of the non-contiguous transcript structure, relatively short read lengths and constantly increasing throughput of the sequencing technologies. Currently available RNA-seq aligners suffer from high mapping error rates, low mapping speed, read length limitation and mapping biases. Results To align our large (>80 billon reads) ENCODE Transcriptome RNA-seq dataset, we developed the Spliced Transcripts Alignment to a Reference (STAR) software based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure. STAR outperforms other aligners by a factor of >50 in mapping speed, aligning to the human genome 550 million 2 × 76 bp paired-end reads per hour on a modest 12-core server, while at the same time improving alignment sensitivity and precision. In addition to unbiased de novo detection of canonical junctions, STAR can discover non-canonical splices and chimeric (fusion) transcripts, and is also capable of mapping full-length RNA sequences. Using Roche 454 sequencing of reverse transcription polymerase chain reaction amplicons, we experimentally validated 1960 novel intergenic splice junctions with an 80-90% success rate, corroborating the high precision of the STAR mapping strategy. Availability and implementation STAR is implemented as a standalone C++ code. STAR is free open source software distributed under GPLv3 license and can be downloaded from http://code.google.com/p/rna-star/.
30,684 citations
TL;DR: EdgeR as mentioned in this paper is a Bioconductor software package for examining differential expression of replicated count data, which uses an overdispersed Poisson model to account for both biological and technical variability and empirical Bayes methods are used to moderate the degree of overdispersion across transcripts, improving the reliability of inference.
Abstract: Summary: It is expected that emerging digital gene expression (DGE) technologies will overtake microarray technologies in the near future for many functional genomics applications. One of the fundamental data analysis tasks, especially for gene expression studies, involves determining whether there is evidence that counts for a transcript or exon are significantly different across experimental conditions. edgeR is a Bioconductor software package for examining differential expression of replicated count data. An overdispersed Poisson model is used to account for both biological and technical variability. Empirical Bayes methods are used to moderate the degree of overdispersion across transcripts, improving the reliability of inference. The methodology can be used even with the most minimal levels of replication, provided at least one phenotype or experimental condition is replicated. The software may have other applications beyond sequencing data, such as proteome peptide count data. Availability: The package is freely available under the LGPL licence from the Bioconductor web site (http://bioconductor.org).
29,413 citations
TL;DR: The GATK programming framework enables developers and analysts to quickly and easily write efficient and robust NGS tools, many of which have already been incorporated into large-scale sequencing projects like the 1000 Genomes Project and The Cancer Genome Atlas.
Abstract: Next-generation DNA sequencing (NGS) projects, such as the 1000 Genomes Project, are already revolutionizing our understanding of genetic variation among individuals. However, the massive data sets generated by NGS—the 1000 Genome pilot alone includes nearly five terabases—make writing feature-rich, efficient, and robust analysis tools difficult for even computationally sophisticated individuals. Indeed, many professionals are limited in the scope and the ease with which they can answer scientific questions by the complexity of accessing and manipulating the data produced by these machines. Here, we discuss our Genome Analysis Toolkit (GATK), a structured programming framework designed to ease the development of efficient and robust analysis tools for next-generation DNA sequencers using the functional programming philosophy of MapReduce. The GATK provides a small but rich set of data access patterns that encompass the majority of analysis tool needs. Separating specific analysis calculations from common data management infrastructure enables us to optimize the GATK framework for correctness, stability, and CPU and memory efficiency and to enable distributed and shared memory parallelization. We highlight the capabilities of the GATK by describing the implementation and application of robust, scale-tolerant tools like coverage calculators and single nucleotide polymorphism (SNP) calling. We conclude that the GATK programming framework enables developers and analysts to quickly and easily write efficient and robust NGS tools, many of which have already been incorporated into large-scale sequencing projects like the 1000 Genomes Project and The Cancer Genome Atlas.
20,557 citations
TL;DR: FeatureCounts as discussed by the authors is a read summarization program suitable for counting reads generated from either RNA or genomic DNA sequencing experiments, which implements highly efficient chromosome hashing and feature blocking techniques.
Abstract: MOTIVATION: Next-generation sequencing technologies generate millions of short sequence reads, which are usually aligned to a reference genome. In many applications, the key information required for downstream analysis is the number of reads mapping to each genomic feature, for example to each exon or each gene. The process of counting reads is called read summarization. Read summarization is required for a great variety of genomic analyses but has so far received relatively little attention in the literature. RESULTS: We present featureCounts, a read summarization program suitable for counting reads generated from either RNA or genomic DNA sequencing experiments. featureCounts implements highly efficient chromosome hashing and feature blocking techniques. It is considerably faster than existing methods (by an order of magnitude for gene-level summarization) and requires far less computer memory. It works with either single or paired-end reads and provides a wide range of options appropriate for different sequencing applications. AVAILABILITY AND IMPLEMENTATION: featureCounts is available under GNU General Public License as part of the Subread (http://subread.sourceforge.net) or Rsubread (http://www.bioconductor.org) software packages.
14,103 citations