The value of comprehensive genomic sequencing to maximize the identification of clinically actionable alterations in advanced cancer patients: a case series.

PURPOSE: We present seven cases of advanced cancer patients who initially underwent tumor testing utilizing smaller, panel-based tests, followed by a variety of therapeutic treatments which ultimately resulted in progression of their disease. These cases demonstrate the value of utilizing WES/RNA seq and characterization following disease progression in these patients and the determination of clinically targetable alterations as well as acquired resistance mutations. MATERIALS AND METHODS: All patients are part of an IRB approved observational study. WES and RNA sequencing were performed, using GEM ExTra® on tumor and blood samples obtained during routine clinical care. To accurately determine somatic versus germline alterations the test was performed with paired normal testing from peripheral blood. RESULTS: The presented cases demonstrate the clinical impact of actionable findings uncovered using GEM ExTra® in patients with advanced disease who failed many rounds of treatment. Unique alterations were identified resulting in newly identified potential targeted therapies, mechanisms of resistance, and variation in the genomic characterization of the primary versus the metastatic tumor. CONCLUSIONS: Taken together our results demonstrate that GEM ExTra® maximizes detection of actionable mutations, thus allowing for appropriate treatment selection for patients harboring both common and rare genomic alterations.

Analytic validation and clinical utilization of the comprehensive genomic profiling test, GEM ExTra®.

We developed and analytically validated a comprehensive genomic profiling (CGP) assay, GEM ExTra, for patients with advanced solid tumors that uses Next Generation Sequencing (NGS) to characterize whole exomes employing a paired tumor-normal subtraction methodology. The assay detects single nucleotide variants (SNV), indels, focal copy number alterations (CNA), TERT promoter region, as well as tumor mutation burden (TMB) and microsatellite instability (MSI) status. Additionally, the assay incorporates whole transcriptome sequencing of the tumor sample that allows for the detection of gene fusions and select special transcripts, including AR-V7, EGFR vIII, EGFRvIV, and MET exon 14 skipping events. The assay has a mean target coverage of 180X for the normal (germline) and 400X for tumor DNA including enhanced probe design to facilitate the sequencing of difficult regions. Proprietary bioinformatics, paired with comprehensive clinical curation results in reporting that defines clinically actionable, FDA-approved, and clinical trial drug options for the management of the patient's cancer. GEM ExTra demonstrated analytic specificity (PPV) of > 99.9% and analytic sensitivity of 98.8%. Application of GEM ExTra to 1,435 patient samples revealed clinically actionable alterations in 83.9% of reports, including 31 (2.5%) where therapeutic recommendations were based on RNA fusion findings only.

A novel CLTC-FOSB gene fusion in pseudomyogenic hemangioendothelioma of bone.

Pseudomyogenic hemangioendothelioma, an uncommon mesenchymal neoplasm composed of plump spindled and/or epithelioid endothelial cells, may present multicentrically and tends to locally recur but rarely metastasizes. Morphologic resemblance to epithelioid sarcoma and other spindle cell neoplasms may result in diagnostic confusion. Molecular characterization of pseudomyogenic hemangioendothelioma has revealed these neoplasms often harbor a rearrangement of the FOSB gene with SERPINE1 or ACTB as recurrent fusion gene partners. Herein, a case of a fibular pseudomyogenic hemangioendothelioma with minimal extension into the adjacent soft tissue arising in a 17 year-old male is presented. The neoplasm exhibited sheets of epithelioid cells with abundant eosinophilic cytoplasm and variably eccentric nuclei. RNA sequencing revealed a novel CLTC-FOSB fusion transcript that was subsequently confirmed by direct sequencing of reverse transcription-polymerase chain reaction products demonstrating an in-frame fusion between exon 17 of the clathrin heavy chain (CLTC) gene and exon 2 of the FOSB (FosB proto-oncogene, AP-1 transcription factor subunit) gene. CLTC-FOSB fusion has not been described in a neoplasm before.

Epithelioid Fibrous Histiocytoma With Dot-Like Perinuclear ALK Expression and PRKAR2A-ALK Fusion.

Epithelioid fibrous histiocytoma (EFH) is a rare, benign, cutaneous neoplasm. This fibrohistiocytic tumor was once believed to be a variant of fibrous histiocytoma, but EFH is now known to be a distinct entity based on the presence of ALK gene rearrangements in most cases. The pattern of immunohistochemical expression of ALK in EFH in the literature thus far describes both granular cytoplasmic staining and nuclear staining. We present a case of EFH with dot-like Golgi pattern perinuclear ALK expression, a previously undescribed staining pattern. We surmised this unique staining pattern could be due to a novel fusion partner, and using FISH, we confirmed a rearrangement of the ALK (2p23) locus. Further investigation with whole transcriptome sequencing led to the discovery of PRKAR2A-ALK fusion, and the function of this fusion partner reflects a Golgi-predominant localization of the protein. Attention to the distinct immunohistochemical pattern of ALK expression may provide clues to the function of the fusion partner.

A novel case of an aggressive superficial spindle cell sarcoma in an adult resembling fibrosarcomatous dermatofibrosarcoma protuberans and harboring an EML4-NTRK3 fusion.

A subset of soft tissue sarcomas often harbors recurrent fusions involving protein kinases. While some of these fusion events have shown utility in arriving at a precise diagnosis, novel fusions in otherwise difficult to classify sarcomas continue to be identified. We present a case of a 40-year-old female who noted a lower back nodule in 2010 that was initially labeled as a dermatofibrosarcoma protuberans with fibrosarcomatous transformation. The lesion recurred the following year and metastasized to the groin 6 years later. Because of some morphologic peculiarities, molecular characterization was pursued in the metastatic focus, which revealed the neoplasm was negative for the COL1A1-PDGFB fusion. However, anchored multiplex polymerase chain reaction for targeted next-generation sequencing (Archer Dx) detected an EML4-NTRK3 fusion, which was confirmed by reverse transcription-PCR, Sanger sequencing and RNA sequencing analysis of the recurrent and metastatic specimens. Although various soft tissue neoplasms involving fusions with NTRK genes are well-reported, the current case could not be easily classified in any of the established entities. Nevertheless, it raises interesting questions regarding the importance of classification, prognosis, and treatment for some of these tyrosine kinase fusion-driven sarcomas.

Sequences of 95 human MHC haplotypes reveal extreme coding variation in genes other than highly polymorphic HLA class I and II.

The most polymorphic part of the human genome, the MHC, encodes over 160 proteins of diverse function. Half of them, including the HLA class I and II genes, are directly involved in immune responses. Consequently, the MHC region strongly associates with numerous diseases and clinical therapies. Notoriously, the MHC region has been intractable to high-throughput analysis at complete sequence resolution, and current reference haplotypes are inadequate for large-scale studies. To address these challenges, we developed a method that specifically captures and sequences the 4.8-Mbp MHC region from genomic DNA. For 95 MHC homozygous cell lines we assembled, de novo, a set of high-fidelity contigs and a sequence scaffold, representing a mean 98% of the target region. Included are six alternative MHC reference sequences of the human genome that we completed and refined. Characterization of the sequence and structural diversity of the MHC region shows the approach accurately determines the sequences of the highly polymorphic HLA class I and HLA class II genes and the complex structural diversity of complement factor C4A/C4B It has also uncovered extensive and unexpected diversity in other MHC genes; an example is MUC22, which encodes a lung mucin and exhibits more coding sequence alleles than any HLA class I or II gene studied here. More than 60% of the coding sequence alleles analyzed were previously uncharacterized. We have created a substantial database of robust reference MHC haplotype sequences that will enable future population scale studies of this complicated and clinically important region of the human genome.

Canvas: versatile and scalable detection of copy number variants.

MOTIVATION: Versatile and efficient variant calling tools are needed to analyze large scale sequencing datasets. In particular, identification of copy number changes remains a challenging task due to their complexity, susceptibility to sequencing biases, variation in coverage data and dependence on genome-wide sample properties, such as tumor polyploidy or polyclonality in cancer samples. RESULTS: We have developed a new tool, Canvas, for identification of copy number changes from diverse sequencing experiments including whole-genome matched tumor-normal and single-sample normal re-sequencing, as well as whole-exome matched and unmatched tumor-normal studies. In addition to variant calling, Canvas infers genome-wide parameters such as cancer ploidy, purity and heterogeneity. It provides fast and easy-to-run workflows that can scale to thousands of samples and can be easily incorporated into variant calling pipelines. AVAILABILITY AND IMPLEMENTATION: Canvas is distributed under an open source license and can be downloaded from https://github.com/Illumina/canvas CONTACT: eroller@illumina.com SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Very long haplotype tracts characterized at high resolution from HLA homozygous cell lines.

The HLA region of chromosome 6 contains the most polymorphic genes in humans. Spanning ~5 Mbp the densely packed region encompasses approximately 175 expressed genes including the highly polymorphic HLA class I and II loci. Most of the other genes and functional elements are also polymorphic, and many of them are directly implicated in immune function or immune-related disease. For these reasons, this complex genomic region is subject to intense scrutiny by researchers with the common goal of aiding further understanding and diagnoses of multiple immune-related diseases and syndromes. To aid assay development and characterization of the classical loci, a panel of cell lines partially or fully homozygous for HLA class I and II was assembled over time by the International Histocompatibility Working Group (IHWG). Containing a minimum of 88 unique HLA haplotypes, we show that this panel represents a significant proportion of European HLA allelic and haplotype diversity (60-95 %). Using a high-density whole genome array that includes 13,331 HLA region SNPs, we analyzed 99 IHWG cells to map the coordinates of the homozygous tracts at a fine scale. The mean homozygous tract length within chromosome 6 from these individuals is 21 Mbp. Within HLA, the mean haplotype length is 4.3 Mbp, and 65 % of the cell lines were shown to be homozygous throughout the entire region. In addition, four cell lines are homozygous throughout the complex KIR region of chromosome 19 (~250 kbp). The data we describe will provide a valuable resource for characterizing haplotypes, designing and refining imputation algorithms and developing assay controls.

Whole-genome gene expression profiling of formalin-fixed, paraffin-embedded tissue samples.

BACKGROUND: We have developed a gene expression assay (Whole-Genome DASL), capable of generating whole-genome gene expression profiles from degraded samples such as formalin-fixed, paraffin-embedded (FFPE) specimens. METHODOLOGY/PRINCIPAL FINDINGS: We demonstrated a similar level of sensitivity in gene detection between matched fresh-frozen (FF) and FFPE samples, with the number and overlap of probes detected in the FFPE samples being approximately 88% and 95% of that in the corresponding FF samples, respectively; 74% of the differentially expressed probes overlapped between the FF and FFPE pairs. The WG-DASL assay is also able to detect 1.3-1.5 and 1.5-2 -fold changes in intact and FFPE samples, respectively. The dynamic range for the assay is approximately 3 logs. Comparing the WG-DASL assay with an in vitro transcription-based labeling method yielded fold-change correlations of R(2) approximately 0.83, while fold-change comparisons with quantitative RT-PCR assays yielded R(2) approximately 0.86 and R(2) approximately 0.55 for intact and FFPE samples, respectively. Additionally, the WG-DASL assay yielded high self-correlations (R(2)>0.98) with low intact RNA inputs ranging from 1 ng to 100 ng; reproducible expression profiles were also obtained with 250 pg total RNA (R(2) approximately 0.92), with approximately 71% of the probes detected in 100 ng total RNA also detected at the 250 pg level. When FFPE samples were assayed, 1 ng total RNA yielded self-correlations of R(2) approximately 0.80, while still maintaining a correlation of R(2) approximately 0.75 with standard FFPE inputs (200 ng). CONCLUSIONS/SIGNIFICANCE: Taken together, these results show that WG-DASL assay provides a reliable platform for genome-wide expression profiling in archived materials. It also possesses utility within clinical settings where only limited quantities of samples may be available (e.g. microdissected material) or when minimally invasive procedures are performed (e.g. biopsied specimens).

Mismatch oligonucleotides in human and yeast: guidelines for probe design on tiling microarrays.

BACKGROUND: Mismatched oligonucleotides are widely used on microarrays to differentiate specific from nonspecific hybridization. While many experiments rely on such oligos, the hybridization behavior of various degrees of mismatch (MM) structure has not been extensively studied. Here, we present the results of two large-scale microarray experiments on S. cerevisiae and H. sapiens genomic DNA, to explore MM oligonucleotide behavior with real sample mixtures under tiling-array conditions. RESULTS: We examined all possible nucleotide substitutions at the central position of 36-nucleotide probes, and found that nonspecific binding by MM oligos depends upon the individual nucleotide substitutions they incorporate: C-->A, C-->G and T-->A (yielding purine-purine mispairs) are most disruptive, whereas A-->X were least disruptive. We also quantify a marked GC skew effect: substitutions raising probe GC content exhibit higher intensity (and vice versa). This skew is small in highly-expressed regions (+/- 0.5% of total intensity range) and large (+/- 2% or more) elsewhere. Multiple mismatches per oligo are largely additive in effect: each MM added in a distributed fashion causes an additional 21% intensity drop relative to PM, three-fold more disruptive than adding adjacent mispairs (7% drop per MM). CONCLUSION: We investigate several parameters for oligonucleotide design, including the effects of each central nucleotide substitution on array signal intensity and of multiple MM per oligo. To avoid GC skew, individual substitutions should not alter probe GC content. RNA sample mixture complexity may increase the amount of nonspecific hybridization, magnify GC skew and boost the intensity of MM oligos at all levels.

Integrative microarray analysis of pathways dysregulated in metastatic prostate cancer.

Microarrays have been used to identify genes involved in cancer progression. We have now developed an algorithm that identifies dysregulated pathways from multiple expression array data sets without a priori definition of gene expression thresholds. Integrative microarray analysis of pathways (IMAP) was done using existing expression array data from localized and metastatic prostate cancer. Comparison of metastatic cancer and localized disease in multiple expression array profiling studies using the IMAP approach yielded a list of about 100 pathways that were significantly dysregulated (P < 0.05) in prostate cancer metastasis. The pathway that showed the most significant dysregulation, HIV-I NEF, was validated at both the transcript level and the protein level by quantitative PCR and immunohistochemical analysis, respectively. Validation by unsupervised analysis on an independent data set using the gene expression signature from the HIV-I NEF pathway verified the accuracy of our method. Our results indicate that this pathway is especially dysregulated in hormone-refractory prostate cancer.

Toward a universal microarray: prediction of gene expression through nearest-neighbor probe sequence identification.

A generic DNA microarray design applicable to any species would greatly benefit comparative genomics. We have addressed the feasibility of such a design by leveraging the great feature densities and relatively unbiased nature of genomic tiling microarrays. Specifically, we first divided each Homo sapiens Refseq-derived gene's spliced nucleotide sequence into all of its possible contiguous 25 nt subsequences. For each of these 25 nt subsequences, we searched a recent human transcript mapping experiment's probe design for the 25 nt probe sequence having the fewest mismatches with the subsequence, but that did not match the subsequence exactly. Signal intensities measured with each gene's nearest-neighbor features were subsequently averaged to predict their gene expression levels in each of the experiment's thirty-three hybridizations. We examined the fidelity of this approach in terms of both sensitivity and specificity for detecting actively transcribed genes, for transcriptional consistency between exons of the same gene, and for reproducibility between tiling array designs. Taken together, our results provide proof-of-principle for probing nucleic acid targets with off-target, nearest-neighbor features.

Systematic prediction and validation of breakpoints associated with copy-number variants in the human genome.

Copy-number variants (CNVs) are an abundant form of genetic variation in humans. However, approaches for determining exact CNV breakpoint sequences (physical deletion or duplication boundaries) across individuals, crucial for associating genotype to phenotype, have been lacking so far, and the vast majority of CNVs have been reported with approximate genomic coordinates only. Here, we report an approach, called BreakPtr, for fine-mapping CNVs (available from http://breakptr.gersteinlab.org). We statistically integrate both sequence characteristics and data from high-resolution comparative genome hybridization experiments in a discrete-valued, bivariate hidden Markov model. Incorporation of nucleotide-sequence information allows us to take into account the fact that recently duplicated sequences (e.g., segmental duplications) often coincide with breakpoints. In anticipation of an upcoming increase in CNV data, we developed an iterative, "active" approach to initially scoring with a preliminary model, performing targeted validations, retraining the model, and then rescoring, and a flexible parameterization system that intuitively collapses from a full model of 2,503 parameters to a core one of only 10. Using our approach, we accurately mapped >400 breakpoints on chromosome 22 and a region of chromosome 11, refining the boundaries of many previously approximately mapped CNVs. Four predicted breakpoints flanked known disease-associated deletions. We validated an additional four predicted CNV breakpoints by sequencing. Overall, our results suggest a predictive resolution of approximately 300 bp. This level of resolution enables more precise correlations between CNVs and across individuals than previously possible, allowing the study of CNV population frequencies. Further, it enabled us to demonstrate a clear Mendelian pattern of inheritance for one of the CNVs.

An efficient pseudomedian filter for tiling microrrays.

BACKGROUND: Tiling microarrays are becoming an essential technology in the functional genomics toolbox. They have been applied to the tasks of novel transcript identification, elucidation of transcription factor binding sites, detection of methylated DNA and several other applications in several model organisms. These experiments are being conducted at increasingly finer resolutions as the microarray technology enjoys increasingly greater feature densities. The increased densities naturally lead to increased data analysis requirements. Specifically, the most widely employed algorithm for tiling array analysis involves smoothing observed signals by computing pseudomedians within sliding windows, a O(n2logn) calculation in each window. This poor time complexity is an issue for tiling array analysis and could prove to be a real bottleneck as tiling microarray experiments become grander in scope and finer in resolution. RESULTS: We therefore implemented Monahan's HLQEST algorithm that reduces the runtime complexity for computing the pseudomedian of n numbers to O(nlogn) from O(n2logn). For a representative tiling microarray dataset, this modification reduced the smoothing procedure's runtime by nearly 90%. We then leveraged the fact that elements within sliding windows remain largely unchanged in overlapping windows (as one slides across genomic space) to further reduce computation by an additional 43%. This was achieved by the application of skip lists to maintaining a sorted list of values from window to window. This sorted list could be maintained with simple O(log n) inserts and deletes. We illustrate the favorable scaling properties of our algorithms with both time complexity analysis and benchmarking on synthetic datasets. CONCLUSION: Tiling microarray analyses that rely upon a sliding window pseudomedian calculation can require many hours of computation. We have eased this requirement significantly by implementing efficient algorithms that scale well with genomic feature density. This result not only speeds the current standard analyses, but also makes possible ones where many iterations of the filter may be required, such as might be required in a bootstrap or parameter estimation setting. Source code and executables are available at http://tiling.gersteinlab.org/pseudomedian/.

Immunity against Ixodes scapularis salivary proteins expressed within 24 hours of attachment thwarts tick feeding and impairs Borrelia transmission.

In North America, the black-legged tick, Ixodes scapularis, an obligate haematophagus arthropod, is a vector of several human pathogens including Borrelia burgdorferi, the Lyme disease agent. In this report, we show that the tick salivary gland transcriptome and proteome is dynamic and changes during the process of engorgement. We demonstrate, using a guinea pig model of I. scapularis feeding and B. burgdorferi transmission, that immunity directed against salivary proteins expressed in the first 24 h of tick attachment - and not later - is sufficient to evoke all the hallmarks of acquired tick-immunity, to thwart tick feeding and also to impair Borrelia transmission. Defining this subset of proteins will promote a mechanistic understanding of novel I. scapularis proteins critical for the initiation of tick feeding and for Borrelia transmission.

Assessing the need for sequence-based normalization in tiling microarray experiments.

MOTIVATION: Increases in microarray feature density allow the construction of so-called tiling microarrays. These arrays, or sets of arrays, contain probes targeting regions of sequenced genomes at regular genomic intervals. The unbiased nature of this approach allows for the identification of novel transcribed sequences, the localization of transcription factor binding sites (ChIP-chip), and high resolution comparative genomic hybridization, among other uses. These applications are quickly growing in popularity as tiling microarrays become more affordable. To reach maximum utility, the tiling microarray platform needs be developed to the point that 1 nt resolutions are achieved and that we have confidence in individual measurements taken at this fine of resolution. Any biases in tiling array signals must be systematically removed to achieve this goal. RESULTS: Towards this end, we investigated the importance of probe sequence composition on the efficacy of tiling microarrays for identifying novel transcription and transcription factor binding sites. We found that intensities are highly sequence dependent and can greatly influence results. We developed three metrics for assessing this sequence dependence and use them in evaluating existing sequence-based normalizations from the tiling microarray literature. In addition, we applied three new techniques for addressing this problem; one method, adapted from similar work on GeneChip brand microarrays, is based on modeling array signal as a linear function of probe sequence, the second method extends this approach by iterative weighting and re-fitting of the model, and the third technique extrapolates the popular quantile normalization algorithm for between-array normalization to probe sequence space. These three methods perform favorably to existing strategies, based on the metrics defined here. AVAILABILITY: http://tiling.gersteinlab.org/sequence_effects/

A supervised hidden markov model framework for efficiently segmenting tiling array data in transcriptional and chIP-chip experiments: systematically incorporating validated biological knowledge.

MOTIVATION: Large-scale tiling array experiments are becoming increasingly common in genomics. In particular, the ENCODE project requires the consistent segmentation of many different tiling array datasets into 'active regions' (e.g. finding transfrags from transcriptional data and putative binding sites from ChIP-chip experiments). Previously, such segmentation was done in an unsupervised fashion mainly based on characteristics of the signal distribution in the tiling array data itself. Here we propose a supervised framework for doing this. It has the advantage of explicitly incorporating validated biological knowledge into the model and allowing for formal training and testing. METHODOLOGY: In particular, we use a hidden Markov model (HMM) framework, which is capable of explicitly modeling the dependency between neighboring probes and whose extended version (the generalized HMM) also allows explicit description of state duration density. We introduce a formal definition of the tiling-array analysis problem, and explain how we can use this to describe sampling small genomic regions for experimental validation to build up a gold-standard set for training and testing. We then describe various ideal and practical sampling strategies (e.g. maximizing signal entropy within a selected region versus using gene annotation or known promoters as positives for transcription or ChIP-chip data, respectively). RESULTS: For the practical sampling and training strategies, we show how the size and noise in the validated training data affects the performance of an HMM applied to the ENCODE transcriptional and ChIP-chip experiments. In particular, we show that the HMM framework is able to efficiently process tiling array data as well as or better than previous approaches. For the idealized sampling strategies, we show how we can assess their performance in a simulation framework and how a maximum entropy approach, which samples sub-regions with very different signal intensities, gives the maximally performing gold-standard. This latter result has strong implications for the optimum way medium-scale validation experiments should be carried out to verify the results of the genome-scale tiling array experiments.

Extrapolating traditional DNA microarray statistics to tiling and protein microarray technologies.

A credit to microarray technology is its broad application. Two experiments--the tiling microarray experiment and the protein microarray experiment--are exemplars of the versatility of the microarrays. With the technology's expanding list of uses, the corresponding bioinformatics must evolve in step. There currently exists a rich literature developing statistical techniques for analyzing traditional gene-centric DNA microarrays, so the first challenge in analyzing the advanced technologies is to identify which of the existing statistical protocols are relevant and where and when revised methods are needed. A second challenge is making these often very technical ideas accessible to the broader microarray community. The aim of this chapter is to present some of the most widely used statistical techniques for normalizing and scoring traditional microarray data and indicate their potential utility for analyzing the newer protein and tiling microarray experiments. In so doing, we will assume little or no prior training in statistics of the reader. Areas covered include background correction, intensity normalization, spatial normalization, and the testing of statistical significance.