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  • 1
    In: Nature Genetics, Springer Science and Business Media LLC, Vol. 50, No. 5 ( 2018-5), p. 727-736
    Type of Medium: Online Resource
    ISSN: 1061-4036 , 1546-1718
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    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2018
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  • 2
    Online Resource
    Online Resource
    Springer Science and Business Media LLC ; 1999
    In:  Nature Genetics Vol. 23, No. 4 ( 1999-12), p. 452-456
    In: Nature Genetics, Springer Science and Business Media LLC, Vol. 23, No. 4 ( 1999-12), p. 452-456
    Type of Medium: Online Resource
    ISSN: 1061-4036 , 1546-1718
    RVK:
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 1999
    detail.hit.zdb_id: 1494946-5
    SSG: 12
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  • 3
    Online Resource
    Online Resource
    American Society of Hematology ; 2022
    In:  Blood Vol. 140, No. Supplement 1 ( 2022-11-15), p. 6737-6738
    In: Blood, American Society of Hematology, Vol. 140, No. Supplement 1 ( 2022-11-15), p. 6737-6738
    Type of Medium: Online Resource
    ISSN: 0006-4971 , 1528-0020
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    Language: English
    Publisher: American Society of Hematology
    Publication Date: 2022
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  • 4
    In: Blood, American Society of Hematology, Vol. 140, No. 4 ( 2022-07-28), p. 401-405
    Type of Medium: Online Resource
    ISSN: 0006-4971 , 1528-0020
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    Language: English
    Publisher: American Society of Hematology
    Publication Date: 2022
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  • 5
    In: Genome Research, Cold Spring Harbor Laboratory, Vol. 18, No. 10 ( 2008-10), p. 1638-1642
    Abstract: Forward genetic mutational studies, adaptive evolution, and phenotypic screening are powerful tools for creating new variant organisms with desirable traits. However, mutations generated in the process cannot be easily identified with traditional genetic tools. We show that new high-throughput, massively parallel sequencing technologies can completely and accurately characterize a mutant genome relative to a previously sequenced parental (reference) strain. We studied a mutant strain of Pichia stipitis , a yeast capable of converting xylose to ethanol. This unusually efficient mutant strain was developed through repeated rounds of chemical mutagenesis, strain selection, transformation, and genetic manipulation over a period of seven years. We resequenced this strain on three different sequencing platforms. Surprisingly, we found fewer than a dozen mutations in open reading frames. All three sequencing technologies were able to identify each single nucleotide mutation given at least 10–15-fold nominal sequence coverage. Our results show that detecting mutations in evolved and engineered organisms is rapid and cost-effective at the whole-genome level using new sequencing technologies. Identification of specific mutations in strains with altered phenotypes will add insight into specific gene functions and guide further metabolic engineering efforts.
    Type of Medium: Online Resource
    ISSN: 1088-9051
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    Language: English
    Publisher: Cold Spring Harbor Laboratory
    Publication Date: 2008
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  • 6
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    Online Resource
    American Association for Cancer Research (AACR) ; 2019
    In:  Cancer Research Vol. 79, No. 13_Supplement ( 2019-07-01), p. 441-441
    In: Cancer Research, American Association for Cancer Research (AACR), Vol. 79, No. 13_Supplement ( 2019-07-01), p. 441-441
    Abstract: Purpose: Challenges with distinguishing circulating tumor DNA from next-generation sequencing (NGS) artifacts limits variant searches to established solid tumor mutations. Identifying the source(s) of errors associated with the NGS analytics of circulating cell-free DNA (ccfDNA) would enable the determination of an optimal strategy for eliminating noise and broaden ccfDNA clinical applications. Methods: Buffy coat DNA and ccfDNA were isolated from seven healthy adults. For each participant, a single buffy coat DNA library was generated using duplex adapters (dual unique molecular identifiers [UMIs], dual index), while two ccfDNA libraries were separately produced - one library with singleton adapters (single UMI, single index) and one library with duplex adapters. The assignment of a UMI to each template DNA molecule prior to library formation reduces false positives. A family is a set of DNA amplicons (PCR duplicates) with the same UMI. Representing a family with a single consensus sequence reduces PCR errors and sequencing artifacts. Duplex adapters have been developed to abrogate the early PCR errors that beset singleton adapters. Results: The error rate using duplex adapters was significantly lower by 26.4±5.9% (P & lt; 0.001) compared to singleton adapters at family size ≥2, where family size is defined as the number of PCR duplicates that yield a single consensus sequence. Due to the persistence of noise in both singleton and duplex adapters even at large family sizes (i.e., ≥10), we explored potential sources of the residual error. Noise in ccfDNA due to effects from clonal hematopoiesis of indeterminate potential (CHIP) accounted for & lt;4% of error. Removing locations with errors present in all seven samples (i.e., highly patterned error likely due to regions difficult to sequence, align, or both) reduced noise in the duplex and singleton adapters by 18.7±5.1% (P & lt; 0.001) and 14.5±3.1% (P & lt; 0.001), respectively. Finally, we explored the effects of stochastic noise as a source of error. A complete replicate with duplex adapters was generated beginning from the source ccfDNA and library preparation. Using replicate data, the error rate was reduced by an additional 59.9±4.3% (P & lt; 0.001) for the duplex adapters at family size ≥2. Using duplex adapters, accounting for CHIP artifacts, removing locations with highly patterned errors, and including replicate data reduced error by 85.4±4.6% (P & lt; 0.001) compared to the error rate for singleton adapters at family size ≥2. Error continued to decline with each family size increment. Conclusion: Early stochastic PCR errors are a principal source of NGS noise that persist despite duplex molecular barcoding and after removal of patterned errors. Replicates are necessary to eliminate noise and their use in NGS analytics may broaden ccfDNA applications particularly in pre-metastatic and recurrent solid-tumor malignancies by enabling untargeted variant investigations. Citation Format: Hunter R. Underhill, Preetida J. Bhetariya, Sabine Hellwig, David A. Nix, Carrie L. Fuertes, Gabor T. Marth, Mary P. Bronner. The stochastic nature of errors in next-generation sequencing of circulating cell-free DNA [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 441.
    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: 2019
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  • 7
    Online Resource
    Online Resource
    American Association for Cancer Research (AACR) ; 2019
    In:  Cancer Research Vol. 79, No. 13_Supplement ( 2019-07-01), p. 2910-2910
    In: Cancer Research, American Association for Cancer Research (AACR), Vol. 79, No. 13_Supplement ( 2019-07-01), p. 2910-2910
    Abstract: Metastatic breast cancer is a deadly disease with low 5-year survival rate (22%). Because it is extremely difficult to track metastatic spread in a living patient, the patterns of new organ colonization are currently poorly understood. In this study, we have collected tumor biopsies at initial diagnosis and at mastectomy necessitated by the patient’s relapse, and autopsies from twenty-six metastatic tumors across seven organs and two skin tissues as normal controls via a rapid autopsy procedure within hours after the patient’s death. All samples were subjected to 60X Illumina whole genome sequencing and bulk RNA sequencing. We used copy number variation (CNV) and short variant (SNP, INDEL) data to reconstruct the phylogenetic relationships among the tumor samples, and established how the tumor evolved at the subclone level across different metastatic sites, using our SubcloneSeeker algorithm. We selected tumor samples with the lowest normal tissue contamination for transcriptomic analysis. Differentially expressed gene and pathways were identified from RNA-seq data. Our analysis revealed that the cancer first escaped to the lung, an event that occurred before the time of initial diagnosis. This was followed by four distinct ‘waves’ (G1, G2, G3 and G4) of massive metastatic expansion, beginning with the abdominal organs (involving samples in G1), then the lymph nodes (G2 and G4), brain and bones (G3). We see clear evidence for metastatic “recolonization” where further evolved subclones invaded already established, earlier metastatic sites. Subclone analysis also revealed a bifurcating pattern of evolution in the primary tumor, in which two lineages gave rise to the G1 and G2-G4 metastases respectively. Consistent with the genomic findings, unsupervised clustering based on the gene expression data showed that samples in G1, and samples in G2-G4 formed distinct clusters. Comparing gene expression profiles between G1 (n=4) and G3 (n=9) samples showed that samples in G1 exhibited enrichment in Integrin activation pathways and samples in G3 presented a signature for chromosome 17 amplification. These distinct metastatic phenotypes indicate different survival mechanisms for G1 and G3 metastatic tumors. The high number of sampled sites in this study, 30 in all, allowed us to reconstruct the evolution of the disease in this patient with unprecedented resolution, identify novel patterns of metastatic colonization, and identify distinct metastatic phenotypes. This study suggests the need for more comprehensive collection and evaluation of metastatic sites to ensure that therapies chosen to target specific genomic aberrations are able to combat the tumor at all metastatic sites. Citation Format: Xiaomeng Huang, Yi Qiao, Samuel W. Brady, Adam Cohen, Andrea H. Bild, Gabor T. Marth. Multi-sites rapid-autopsy data reveals aggressive metastatic colonization and metastasis phenotypes [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 2910.
    Type of Medium: Online Resource
    ISSN: 0008-5472 , 1538-7445
    RVK:
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    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2019
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  • 8
    Online Resource
    Online Resource
    American Association for Cancer Research (AACR) ; 2019
    In:  Cancer Research Vol. 79, No. 13_Supplement ( 2019-07-01), p. 2220-2220
    In: Cancer Research, American Association for Cancer Research (AACR), Vol. 79, No. 13_Supplement ( 2019-07-01), p. 2220-2220
    Abstract: Purpose: Establishing a reliable bioinformatics variant calling workflow is crucial for untargeted searches of low-frequency variants in plasma circulating cell-free DNA (ccfDNA). We sought to benchmark the performance of nine variant calling algorithms on simulated data and cancer patient ccfDNA sequencing samples. Methods: A simulated tumor dataset was constructed by in-silico introduction of pathogenic variants (303 SNVs and 91 INDELs; VAF of 0.1-12%) into a healthy control sequencing sample (128 gene capture panel; 128kb; 4200X depth). An unspiked replicate served as a control. Tumor and control datasets were analyzed by nine somatic variant calling algorithms available in the public domain (Table 1). Sensitivity and specificity were calculated for each caller. Variant calling algorithms that proved most successful in synthetic data (Lofreq, Mutect) were applied to 24 EGFR-mutant lung cancer patient ccfDNA library samples captured and sequenced under conditions identical to the benchmarking data set. Droplet digital PCR (ddPCR) was used to independently determine the VAFs of two EGFR SNVs (T790M and L858R) and exon 19 deletions in all lung cancer libraries (detected range: 0.1-48.5%). Results: Table 1:Sensitivity (Sens.) and false discovery rate (FDR) as determined for 9 variant callers. For synthetic data, results are shown for 1.6% VAF (NC = no call)CallerSynthetic data (1.6% VAF)Lung cancer ccfDNASNVINDELSNVINDEL% Sens.% FDR% Sens.% FDR% Sens. & lt;1% VAF% Sens. & gt;1% VAF% Sens. & lt;1% VAF% Sens. & gt;1% VAFLofreq87.15.792.35.675.094.466.7100Mutect47.52.291.26.762.588.916.787.5Mutect235.63.296.71.1Strelka17.83.57.69.2FreebayesNCNCNCNCVarscanNCNCNCNCVardictNCNCNCNCLumpy13.986.17.792.1DellyNCNCNCNC Conclusion: Performance of variant callers differed greatly, with Lofreq providing the best sensitivity at low VAF. In cancer ccfDNA libraries Lofreq detected most but not all low-frequency variants, indicating that ccfDNA diagnostics require optimized variant calling workflows to improve detection of variants with a VAF & lt;1%. Citation Format: Preetida J. Bhetariya, Sabine Hellwig, David A. Nix, Gabor T. Marth, Mary P. Bronner, Hunter R. Underhill. Benchmarking of somatic variant calling algorithms for detection of circulating tumor DNA [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Phil adelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 2220.
    Type of Medium: Online Resource
    ISSN: 0008-5472 , 1538-7445
    RVK:
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    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2019
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  • 9
    Online Resource
    Online Resource
    American Association for Cancer Research (AACR) ; 2018
    In:  Cancer Research Vol. 78, No. 13_Supplement ( 2018-07-01), p. 2195-2195
    In: Cancer Research, American Association for Cancer Research (AACR), Vol. 78, No. 13_Supplement ( 2018-07-01), p. 2195-2195
    Abstract: Metastatic breast cancer is an advanced-stage disease in which the cancer cells have spread to distant organs. To understand the patterns of metastatic colonization in a patient who presented with aggressive disease, we have collected tumor biopsies at initial diagnosis, at mastectomy necessitated by the patient's relapse, twenty-six metastatic tumors across seven organs and two normal tissue control skin biopsies via a rapid autopsy procedure within hours after the patient's death. All biopsy samples were subjected to 60X Illumina whole genome sequencing. Our analysis revealed extensive chromosomal changes including amplifications, deletions, LOH and translocations, as well as known driver mutations in RB1, TP53, and PTEN in all tumor samples. We used the CNV and LOH data to reconstruct the phylogenetic relationships among the tumor samples. We used the somatic SNV allele frequencies in copy number-normal regions to refine these phylogenetic relationships, and to establish the evolution of the tumor at the subclonal level across different metastatic sites, with an extension of our SubcloneSeeker algorithm. Our analysis reveals the lung as the first metastatic site. In fact, this invasion occurred before relapse at the primary site in the breast, indicating early metastatic escape. This initial invasion was followed by distinct waves of massive metastatic expansion, beginning with the abdominal organs, then the lymph nodes, brain and bones. As the initial invasion, each metastatic wave involved other distinct sites in the lungs, these sites serving as incubators where subclones further evolved prior to seeding new metastatic sites. Finally, we see clear evidence for metastatic “recolonization” where such further evolved subclones invaded already established, earlier metastatic sites. The high number of biopsied sites in this study, 30 in all, allowed us to reconstruct the evolution of the aggressive disease in our patient with unprecedented resolution, and to identify characteristic patterns of metastatic colonization. If confirmed in additional, similarly high resolution datasets, these patterns will lead to better understanding of the metastatic process, and guide effective clinical intervention. Citation Format: Xiaomeng Huang, Yi Qiao, Samuel W. Brady, Adam Cohen, Andrea H. Bild, Gabor T. Marth. Temporal and spatial dynamics of metastatic colonization revealed by 26 rapid-autopsy tumor biopsies from a TNBC patient [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 2195.
    Type of Medium: Online Resource
    ISSN: 0008-5472 , 1538-7445
    RVK:
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    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2018
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  • 10
    In: Cancer Research, American Association for Cancer Research (AACR), Vol. 78, No. 13_Supplement ( 2018-07-01), p. LB-038-LB-038
    Abstract: Patient-derived xenografts (PDX) are valuable, clinically-relevant models of cancer. Their close genomic, phenotypic, and temporal association with patient tumors makes them well-suited for pre-clinical and co-clinical studies that assess the potential of new therapeutics. However, PDX models are not amenable to large-scale drug sensitivity studies due to their high cost and low throughput capacity. To address this, we established and characterized long-term PDX organoid (PDxO) cultures from breast cancer (BC) PDXs and evaluated their utility in therapeutic studies. To establish PDxO culture conditions, we extensively tested medium supplements, including growth factors, kinase inhibitors, conditioned medium, and antioxidants, along with extracellular matrix composition in 3D gels. Using optimized culture conditions for each BC subtype, we established PDxOs from 16 PDX lines in the HCI series, with a PDX to PDxO success rate of 85%. Around 20% of PDxOs contained aggressive mouse stroma, which had to be eliminated by FACS for long-term culture success. PDxOs were maintained for over 1 year, during which we tracked viability, doubling time, organoid size, genomics, epithelial character, and tumorigenicity. Doubling times stabilized after 60 days of initial culture, with a mean of 6.4±1.7 days for triple negative (TN) lines and 7.2±1 days for ER+ lines. Mean organoid size remained stable, with ER+ PDxOs significantly smaller than TN PDxOs, at 91 and 262 cells/organoid respectively (p=0.0012). PDxOs histologically resembled their PDX counterparts when stained for H & E, Vimentin, and CAM 5.2. RNA-Seq and copy-number variation analyses showed that PDxOs clustered with their PDXs and patient tumors. To assess if culturing affected tumorigenicity, we re-implanted PDxOs into mice following early and late passaging. 5/5 early passage and 5/6 late passage PDxOs engrafted and formed tumors. Resulting tumors showed similar gene expression profiles as their original PDXs by RNA-Seq. These data suggest that PDxOs generally recapitulate the molecular and genomic features of their originating PDXs and patient tumors. Having established PDxO cultures, we evaluated their utility as a therapeutic screening platform. We developed a screen to differentiate compounds with cytotoxic and cytostatic effects. NCI CTEP and clinically-approved BC therapies (n=40) were screened in quadruplicate 8-point dose response curves on all 16 PDxOs across three biological replicates. Drug response profiles were stable across biological replicates, spanning up to 1 year in culture. PDxOs exhibited diverse responses to therapies targeting cIAP1, PI3K, and CHK1/2. Initial work to evaluate concordance between PDxO and in vivo PDX responses returned trending linear correlations between PDxO dose response data and change in PDX growth rate following treatment (R2 = 0.52-0.77; p = 0.045-0.12, across 3 PDXs and 9 compounds). Ongoing work aims to confirm concordance between PDX and PDxO models. Our work demonstrates that PDxO models are cost-efficient, easy to maintain, and grow indefinitely - making them renewable and accessible cancer models. PDxOs are a powerful parallel resource to PDX models, especially useful for efficient determination of PDX drug response. We are currently expanding our PDxO bank to include 100 models which will be deposited with the NCI as part of the PDXNet effort. Citation Format: Katrin P. Guillen, Sandra D. Scherer, Yi Qiao, Satya S. Pathi, James M. Graham, Maihi Fujita, Yoko S. DeRose, Jason Gertz, Gabor T. Marth, Katherine E. Varley, Alana L. Welm, Bryan E. Welm. Predicting breast cancer therapy response using a patient-derived xenograft organoid screening platform [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr LB-038.
    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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    detail.hit.zdb_id: 1432-1
    detail.hit.zdb_id: 410466-3
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