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  • 1
    In: JAMA Network Open, American Medical Association (AMA), Vol. 2, No. 10 ( 2019-10-25), p. e1913968-
    Type of Medium: Online Resource
    ISSN: 2574-3805
    Language: English
    Publisher: American Medical Association (AMA)
    Publication Date: 2019
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  • 2
    In: Cancer Research, American Association for Cancer Research (AACR), Vol. 82, No. 12_Supplement ( 2022-06-15), p. 54-54
    Abstract: The use of sequencing-based assays for clinical management of pediatric cancer patients has become increasingly common. However, for many pediatric patients, gene panel based sequencing tests yield few actionable results. Given the complex genomic alterations present in many pediatric cancers, especially high-risk solid tumors, we hypothesized that an unbiased approach might reveal more actionable findings and lead to a more comprehensive understanding of these diseases. To accomplish this, we integrated whole-genome sequencing (WGS) with RNAseq in the analysis of a pediatric oncology cohort, with a focus on longitudinal cases to capture potential tumor evolution in metastatic or treated cases. Our cohort consists of 269 high-risk pediatric oncology patients, including patients with relapsed/refractory disease, metastatic disease at diagnosis, prior cancer history, a rare diagnosis, or an estimated overall survival & lt;50%. Solid tumors, CNS tumors, and leukemia/lymphomas are all represented. In total, 391 samples were characterized using WGS (tumor ~60X; germline ~30X) and/or RNAseq (tumor, polyA selected, ≥20 million reads). For 85 of these patients, multiple samples were collected at different time points (diagnosis, resection, relapse, etc.) to identify changes in the cancer over time. If panel testing was performed as part of their clinical care, a comparison to the integrated WGS/RNA analysis was made. WGS was used to identify variants (SNVs), structural rearrangements (SVs), mutational signatures, and copy-number alterations (CNAs). RNAseq was used to identify gene expression outliers, gene fusions, and confirm the expression of variants identified using WGS. The combination of WGS and RNAseq was then used to identify and prioritize potentially actionable variants for each patient. Our results show that the integration of WGS and RNAseq can provide more and higher-quality actionable information than either modality alone, whilst also capturing the majority of actionable variants detected by panel sequencing. RNAseq identified not only druggable fusions and expression outliers, but also many rare and novel fusions. WGS provided fusion validation but highlighted the limitations of WGS alone in identifying fusions resulting from complex SVs. Conversely, WGS was adept at capturing genome-wide patterns of CNAs and loss of heterozygosity that are missed by gene-centric panels. Further RNAseq integration enabled prioritization of expressed SNVs as well as CNAs and SVs that significantly alter gene expression. We also used WGS to extract mutational signatures and tracked their evolution across longitudinal samples. We found potentially biologically significant differences in therapy-induced mutations caused by platinum and alkylating agents. Our unbiased approach has enabled further discovery that advances our understanding of these rare and highly aggressive malignancies. Citation Format: Henry J. Martell, Avanthi Tayi Shah, Alex G. Lee, Bogdan Tanasa, Stanley G. Leung, Aviv Spillinger, Heng-Yi Liu, Inge Behroozfard, Phuong Dinh, Maria V. Pons Ventura, Florette K. Hazard, Arun Rangaswami, Sheri L. Spunt, Norman J. Lacayo, Tabitha Cooney, Jennifer G. Michlitsch, Anurag K. Agrawal, Marcus R. Breese, E. Alejandro Sweet-Cordero. Integrative analysis of whole-genome and RNA sequencing in high-risk pediatric malignancies [abstract] . In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 54.
    Type of Medium: Online Resource
    ISSN: 1538-7445
    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2022
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  • 3
    Online Resource
    Online Resource
    SAGE Publications ; 2021
    In:  Ear, Nose & Throat Journal
    In: Ear, Nose & Throat Journal, SAGE Publications
    Type of Medium: Online Resource
    ISSN: 0145-5613 , 1942-7522
    Language: English
    Publisher: SAGE Publications
    Publication Date: 2021
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  • 4
  • 5
    Online Resource
    Online Resource
    American Association for Cancer Research (AACR) ; 2017
    In:  Clinical Cancer Research Vol. 23, No. 18 ( 2017-09-15), p. 5329-5338
    In: Clinical Cancer Research, American Association for Cancer Research (AACR), Vol. 23, No. 18 ( 2017-09-15), p. 5329-5338
    Abstract: Survival of children with cancers has dramatically improved over the past several decades. This success has been achieved through improvement of combined modalities in treatment approaches, intensification of cytotoxic chemotherapy for those with high-risk disease, and refinement of risk stratification incorporating novel biologic markers in addition to traditional clinical and histologic features. Advances in cancer genomics have shed important mechanistic insights on disease biology and have identified “driver” genomic alterations, aberrant activation of signaling pathways, and epigenetic modifiers that can be targeted by novel agents. Thus, the recently described genomic and epigenetic landscapes of many childhood cancers have expanded the paradigm of precision medicine in the hopes of improving outcomes while minimizing toxicities. In this review, we will discuss the biologic rationale for molecularly targeted therapies in genomically defined subsets of pediatric leukemias, solid tumors, and brain tumors. Clin Cancer Res; 23(18); 5329–38. ©2017 AACR.
    Type of Medium: Online Resource
    ISSN: 1078-0432 , 1557-3265
    RVK:
    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2017
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  • 6
    In: Molecular Cancer Therapeutics, American Association for Cancer Research (AACR), Vol. 20, No. 10 ( 2021-10-01), p. 2016-2025
    Abstract: Most circulating tumor DNA (ctDNA) assays are designed to detect recurrent mutations. Pediatric sarcomas share few recurrent mutations but rather are characterized by translocations and copy-number changes. We applied Cancer Personalized Profiling by deep Sequencing (CAPP-Seq) for detection of translocations found in the most common pediatric sarcomas. We also applied ichorCNA to the combined off-target reads from our hybrid capture to simultaneously detect copy-number alterations (CNA). We analyzed 64 prospectively collected plasma samples from 17 patients with pediatric sarcoma. Translocations were detected in the pretreatment plasma of 13 patients and were confirmed by tumor sequencing in 12 patients. Two of these patients had evidence of complex chromosomal rearrangements in their ctDNA. We also detected copy-number changes in the pretreatment plasma of 7 patients. We found that ctDNA levels correlated with metastatic status and clinical response. Furthermore, we detected rising ctDNA levels before relapse was clinically apparent, demonstrating the high sensitivity of our assay. This assay can be utilized for simultaneous detection of translocations and CNAs in the plasma of patients with pediatric sarcoma. While we describe our experience in pediatric sarcomas, this approach can be applied to other tumors that are driven by structural variants.
    Type of Medium: Online Resource
    ISSN: 1535-7163 , 1538-8514
    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2021
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    SSG: 12
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  • 7
    In: Clinical Cancer Research, American Association for Cancer Research (AACR), ( 2023-09-13), p. OF1-OF16
    Abstract: Models to study metastatic disease in rare cancers are needed to advance preclinical therapeutics and to gain insight into disease biology. Osteosarcoma is a rare cancer with a complex genomic landscape in which outcomes for patients with metastatic disease are poor. As osteosarcoma genomes are highly heterogeneous, multiple models are needed to fully elucidate key aspects of disease biology and to recapitulate clinically relevant phenotypes. Experimental Design: Matched patient samples, patient-derived xenografts (PDX), and PDX-derived cell lines were comprehensively evaluated using whole-genome sequencing and RNA sequencing. The in vivo metastatic phenotype of the PDX-derived cell lines was characterized in both an intravenous and an orthotopic murine model. As a proof-of-concept study, we tested the preclinical effectiveness of a cyclin-dependent kinase inhibitor on the growth of metastatic tumors in an orthotopic amputation model. Results: PDXs and PDX-derived cell lines largely maintained the expression profiles of the patient from which they were derived despite the emergence of whole-genome duplication in a subset of cell lines. The cell lines were heterogeneous in their metastatic capacity, and heterogeneous tissue tropism was observed in both intravenous and orthotopic models. Single-agent dinaciclib was effective at dramatically reducing the metastatic burden. Conclusions: The variation in metastasis predilection sites between osteosarcoma PDX-derived cell lines demonstrates their ability to recapitulate the spectrum of the disease observed in patients. We describe here a panel of new osteosarcoma PDX-derived cell lines that we believe will be of wide use to the osteosarcoma research community.
    Type of Medium: Online Resource
    ISSN: 1078-0432 , 1557-3265
    RVK:
    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2023
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  • 8
    In: Cancer Research, American Association for Cancer Research (AACR), Vol. 78, No. 19_Supplement ( 2018-10-01), p. B49-B49
    Abstract: A promising tool for noninvasive disease monitoring is analysis of circulating tumor DNA (ctDNA). Healthy individuals carry 1-10 ng/ml of cell-free DNA (cfDNA) in the blood; in oncology patients, ctDNA, which is released from tumor cells, comprises a fraction of the cfDNA and carries tumor-specific alterations, such as mutations, translocations, and copy number alterations. Most ctDNA assays lack robust translocation detection capabilities, since they are designed for adult cancers, which are commonly characterized by mutations and copy number alterations. Even assays that are designed specifically for translocation detection have inherent limitations. They are often PCR-based with laborious methods. First, the patient’s primary tumor sample is sequenced to determine the unique sequence across the translocation breakpoint. This is followed by design of patient-specific primers that can only be used for that individual patient. We sought to design an off-the-shelf, broadly applicable ctDNA assay for translocation detection across pediatric Ewing sarcoma (ES), osteosarcoma (OS), rhabdomyosarcoma (RMS), and synovial sarcoma (SS). Recent work by our collaborators at Stanford University led to the development of CAncer Personalized Profiling by deep Sequencing (CAPP-Seq), a method capable of ultraspecific and ultrasensitive detection of ctDNA. Utilizing COSMIC and TCGA data, recent sequencing publications defining the landscape of pediatric sarcomas, and our own in-house sequencing data, we designed a pediatric sarcoma CAPP-Seq selector. This selector is comprised of biotinylated oligonucleotides that tile across the introns where translocation breakpoints occur in these pediatric sarcomas. The selector is applied to a sequencing library prepared from patient cfDNA to enrich for the genomic regions of interest via hybrid capture. The resulting enriched library undergoes next-generation sequencing to allow for detection and quantification of circulating tumor DNA. We have isolated cfDNA from pediatric sarcoma patients and found that their cfDNA levels are higher than levels found in adult oncology patients and healthy individuals, likely due to a large fraction of contributing ctDNA. We have applied our selector to pretreatment plasma samples from 5 EWS patients, 2 OS patients, 4 RMS patients, and 1 SS patient. We detected translocations in 10/12 of these samples. Tumor was available for 7/12 of these patients, and we were able to confirm our plasma results by whole-genome sequencing of the tumor, as a validation of our findings. Additionally, we have applied our selector to serial plasma samples collected over the course of treatment and found that ctDNA levels correlate with clinical status. We have detected translocations at allelic frequencies & lt;0.01%, demonstrating that our method is ultrasensitive and could be used to detect minimal residual disease. Our work demonstrates that CAPP-Seq can serve as an ultrasensitive, broadly applicable tool for circulating tumor translocation detection and offers promise as a method for noninvasive diagnosis and disease monitoring. Citation Format: Avanthi Tayi Shah, Tej D. Azad, Jake J. Chabon, Marcus Breese, Bogdan Tanasa, Aviv Spillinger, Stanley G. Leung, Maximilian Diehn, Ash A. Alizadeh, E. Alejandro Sweet-Cordero. Quantitating circulating tumor DNA in translocation-positive sarcoma patients using CAPP-Seq [abstract]. In: Proceedings of the AACR Special Conference: Pediatric Cancer Research: From Basic Science to the Clinic; 2017 Dec 3-6; Atlanta, Georgia. Philadelphia (PA): AACR; Cancer Res 2018;78(19 Suppl):Abstract nr B49.
    Type of Medium: Online Resource
    ISSN: 0008-5472 , 1538-7445
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    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2018
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  • 9
    In: Cancer Research, American Association for Cancer Research (AACR), Vol. 77, No. 13_Supplement ( 2017-07-01), p. 2466-2466
    Abstract: In the UC Santa Cruz Treehouse Childhood Cancer Initiative (treehousegenomics.soe.ucsc.edu), we are exploring the utility of using RNA-Seq analysis of tumor samples from children to identify potential novel therapeutic options for each individual. Within a single RNA-Seq data set, the gene expression measurements are not equally accurate. The identification of activated, druggable pathways requires accurate gene-level expression measurements. We receive samples from a variety of clinical and research settings, and the quantity and complexity of the available input material and the depth of sequencing differ. These factors inspired us to develop a tool that will allow us to identify accurate measurements in most RNA-Seq samples we receive. First, we characterized the relationship between depth of sequencing and the accuracy of the gene expression measurement. We analyzed subsets of reads in samples with more than 50 million Uniquely Mapped, Exonic, Non-duplicate (UMEND) reads. UMEND reads typically constitute over 80% of the reads in a high quality experiment with sufficient starting material. We compared gene expression across the subsets of reads to calculate how many UMEND reads are required to produce consistent measurements. We found that, on average, genes expressed at 1-5 TPM in our data require 30 million reads to be accurately measured. For this calculation, we define accuracy as the condition in which 75% of genes are measured to within 25% of the true value. Secondly, we use these known relationships to identify genes that have been accurately measured in our tumor RNA-Seq samples. For a sample with 15 million UMEND reads, we find that genes expressed above 5 TPM can be accurately measured and are retained. In the first twelve samples analyzed, samples with more than 10 million UMEND reads retained at least 46% of the genes expressed above zero. We exclude as references those samples with fewer than 10 million UMEND reads due to the marked gene loss after thresholding for this group. Using accurately measured genes allows us to more confidently assess similarity to other samples, identify enriched pathways, and confirm the expression of drug targets and related molecules under consideration. For example, we reconsidered the CDK4 inhibitor Palbociclib in one patient because the expression of RB1, downstream effector required for Palbociclib-mediated tumor cell death, was under our accuracy threshold. Accuracy thresholds can also be used in experiment planning. Accuracy thresholding allows us to better assess the value of an RNA-Seq data set and, if necessary, identify the subset of genes whose expression can be confidently considered in a clinical setting. Our experience points to the importance of careful quality control in this process. Citation Format: Holly Beale, Du Linh Lam, John Vivian, Yulia Newton, Avanthi Tayi Shah, Isabel Bjork, Ted Goldstein, Angela N. Brooks, Josh Stuart, Sofie Salama, E. Alejandro Sweet-Cordero, David Haussler1, Olena Morozova. Identifying confidently measured genes in single pediatric cancer patient samples using RNA sequencing [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 2466. doi:10.1158/1538-7445.AM2017-2466
    Type of Medium: Online Resource
    ISSN: 0008-5472 , 1538-7445
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    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2017
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  • 10
    In: Cancer Research, American Association for Cancer Research (AACR), Vol. 77, No. 13_Supplement ( 2017-07-01), p. 4890-4890
    Abstract: Genomic characterization used in pediatric cancer clinical trials is limited to the detection of somatic mutations and gene fusions in well-characterized cancer genes. However, these approaches do not reveal actionable therapeutic targets for the majority of pediatric cancer patients. Incorporation of gene expression information into clinical genomic analysis is hindered by the lack of appropriate computational methods, designed for single patients rather than patient cohorts. UC Santa Cruz Treehouse Childhood Cancer Initiative (treehousegenomics.soe.ucsc.edu) enables the incorporation of gene expression information into the genomic evaluation of pediatric cancer patients. We leverage large cancer RNA sequencing datasets, including The Cancer Genome Atlas, Therapeutically Applicable Research to Generate Effective Treatments, Medulloblastoma Advanced Genomics International Consortium, International Cancer Genome Consortium, and published research and clinical studies. Through our “pan-cancer analysis”, we compare each prospective tumor’s RNA sequencing and/or mutational profile to over 11,000 uniformly analyzed tumor profiles using our Tumor Map method. Tumor Map visualizes single tumors together with the reference compendium and identifies samples that are most similar to the given tumor based on the gene expression profiles. We also developed a gene expression outlier analysis to identify transcripts that are over expressed in the given tumor. These pan-cancer gene expression analyses are used in conjunction with mutation data to nominate molecular pathways that may be driving the disease in each child, providing useful information to the medical teams. We aim to evaluate this approach in partnership with pediatric cancer clinical genomic trials at Stanford University, UC San Francisco, Children’s Hospital of Orange County, University of Michigan, Children’s Mercy Hospital, and British Columbia Children’s Hospital. The analysis of the first 27 patients at Stanford, most with refractory solid tumors, provided evidence of the potential clinical utility of incorporating gene expression information into the genomic evaluation of pediatric cancer patients. In all cases, we identified candidate driver molecular pathways that could be targeted by existing FDA-approved therapies or therapies available through a clinical trial. The most frequently identified molecular targets were receptor tyrosine kinases and cyclin-dependent kinases. For 3 patients with no treatment options prior to our work, the analysis contributed to treatment decisions. This study provides a framework for incorporating gene expression information into the clinical interpretation of pediatric cancer genomic data. We underscore the importance of releasing the data to the community immediately following generation, so that they may benefit new patients. Citation Format: Olena Morozova, Yulia Newton, Avanthi Tayi Shah, Holly Beale, Du Linh Lam, John Vivian, Isabel Bjork, Theodore Goldstein, Josh Stuart, Sofie Salama, E. Alejandro Sweet-Cordero, David Haussler. A pan-cancer analysis framework for incorporating gene expression information into clinical interpretation of pediatric cancer genomic data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 4890. doi:10.1158/1538-7445.AM2017-4890
    Type of Medium: Online Resource
    ISSN: 0008-5472 , 1538-7445
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    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2017
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