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pmid:30704460
Integration of genomic copy number variations and chemotherapy-response biomarkers in pediatric sarcoma.
Lijun Cheng · Pankita H Pandya · Enze Liu · Pooja Chandra · Limei Wang · Mary E Murray · Jacquelyn Carter · Michael Ferguson · Mohammad Reza Saadatzadeh · Khadijeh Bijangi-Visheshsaraei · Mark Marshall · Lang Li · Karen E Pollok · Jamie L Renbarger
BMC medical genomics · 2019 doi:10.1186/s12920-018-0456-5
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Complete file
{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:30704460", "kind": "science-record", "revision": 6, "content_hash": "sha256:836cb1ef76b2868f92f80374eb308993d7509b90f0977a188f459c511722cb74", "created_at": "2026-11-01T15:40:41Z", "updated_at": "2026-11-01T21:21:57Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Integration of genomic copy number variations and chemotherapy-response biomarkers in pediatric sarcoma.", "doi": "10.1186/s12920-018-0456-5", "pmid": "30704460", "authors": ["Lijun Cheng", "Pankita H Pandya", "Enze Liu", "Pooja Chandra", "Limei Wang", "Mary E Murray", "Jacquelyn Carter", "Michael Ferguson", "Mohammad Reza Saadatzadeh", "Khadijeh Bijangi-Visheshsaraei", "Mark Marshall", "Lang Li", "Karen E Pollok", "Jamie L Renbarger"], "journal": "BMC medical genomics", "year": 2019, "abstract": "BACKGROUND: While most pediatric sarcomas respond to front-line therapy, some bone sarcomas do not show radiographic response like soft-tissue sarcomas (rhabdomyosarccomas) but do show 90% necrosis. Though, new therapies are urgently needed to improve survival and quality of life in pediatric patients with sarcomas. Complex chromosomal aberrations such as amplifications and deletions of DNA sequences are frequently observed in pediatric sarcomas. Evaluation of copy number variations (CNVs) associated with pediatric sarcoma patients at the time of diagnosis or following therapy offers an opportunity to assess dysregulated molecular targets and signaling pathways that may drive sarcoma development, progression, or relapse. The objective of this study was to utilize publicly available data sets to identify potential predictive biomarkers of chemotherapeutic response in pediatric Osteosarcoma (OS), Rhabdomyosarcoma (RMS) and Ewing's Sarcoma Family of Tumors (ESFTs) based on CNVs following chemotherapy (OS n = 117, RMS n = 64, ESFTs n = 25 tumor biopsies).\nMETHODS: There were 206 CNV profiles derived from pediatric sarcoma biopsies collected from the public databases TARGET and NCBI-Gene Expression Omnibus (GEO). Through our comparative genomic analyses of OS, RMS, and ESFTs and 22,255 healthy individuals called from the Database of Genomic Variants (DGV), we identified CNVs (amplifications and deletions) pattern of genomic instability in these pediatric sarcomas. By integrating CNVs of Cancer Cell Line Encyclopedia (CCLE) identified in the pool of genes with drug-response data from sarcoma cell lines (n = 27) from Cancer Therapeutics Response Portal (CTRP) Version 2, potential predictive biomarkers of therapeutic response were identified.\nRESULTS: Genes associated with survival and/recurrence of these sarcomas with statistical significance were found on long arm of chromosome 8 and smaller aberrations were also identified at chromosomes 1q, 12q and x in OS, RMS, and ESFTs. A pool of 63 genes that harbored amplifications and/or deletions were frequently associated with recurrence across OS, RMS, and ESFTs. Correlation analysis of CNVs from CCLE with drug-response data of CTRP in 27 sarcoma cell lines, 33 CNVs out of 63 genes correlated with either sensitivity or resistance to 17 chemotherapies from which actionable CNV signatures such as IGF1R, MYC, MAPK1, ATF1, and MDM2 were identified. These CNV signatures could potentially be used to delineate patient populations that will respond versus those that will not respond to a particular chemotherapy.\nCONCLUSIONS: The large-scale analyses of CNV-drug screening provides a platform to evaluate genetic alterations across aggressive pediatric sarcomas. Additionally, this study provides novel insights into the potential utilization of CNVs as not only prognostic but also as predictive biomarkers of therapeutic response. Information obtained in this study may help guide and prioritize patient-specific therapeutic options in pediatric bone and soft-tissue sarcomas.", "claims": [{"id": "claim-method", "statement": "Pearson correlation coefficient is used to calculate association between drug response AUC and copy number variation in each of gene.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:30704460", "locator": "sections[21] \"Statistical analysis\" paragraph 2", "quote": "Pearson correlation coefficient is used to calculate association between drug response AUC and copy number variation in each of gene."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets analyzed during the current study are available in the DGV database [20], http://dgv.tcag.ca/dgv/app/home; CCLE [21], https://portals.broadinstitute.org/ccle; CTRP [22] https://portals.broadinstitute.org/ctrp.v2.1/; GEO repository [https://www.ncbi.nlm.nih.gov/sites/GDSbrowser/], https://www.ncbi.nlm.nih.gov/geo/ and TARGET Data Matrix [https://ocg.cancer.gov/programs/target], https://ocg.cancer.gov/programs/target/data-matrix.\n\nAll data annotation and results during this study are included in this published article and its supplementary information files, Additional files 1, 2, 3 and 4.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:30704460", "locator": "sections[26] \"Availability of data and materials\" paragraphs 1-3", "quote": "The datasets analyzed during the current study are available in the DGV database [20], http://dgv.tcag.ca/dgv/app/home; CCLE [21], https://portals.broadinstitute.org/ccle; CTRP [22] https://portals.broadinstitute.org/ctrp.v2.1/; GEO repository [https://www.ncbi.nlm.nih.gov/sites/GDSbrowser/], https://www.ncbi.nlm.nih.gov/geo/ and TARGET Data Matrix [https://ocg.cancer.gov/programs/target], https://ocg.cancer.gov/programs/target/data-matrix.\n\nAll data annotation and results during this study are included in this published article and its supplementary information files, Additional files 1, 2, 3 and 4."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": null, "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:30704460"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "blocked", "blocked_on": "data:accession_absent", "description": "The GEO series accession resolves and its sample accessions are listed. This lane did not download the dataset or read its licence.", "duration_hours": null, "parameters": {}, "inputs": [], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "complete", "blocked_on": null, "description": "Apply the paper's statistical method to the resolved series and compare the result with the published target.", "duration_hours": null, "parameters": {}, "inputs": [], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": ["step:obtain_source_data blocked on data:accession_absent"]}, "data": {"state": "partial", "assets": [{"id": "source:pmid:30704460", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1186/s12920-018-0456-5", "sha256": 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The physical bench the paper used is not, so a physical validation cannot be run here."}, "data": {"tier": "R4", "reachable": false, "accessions": [], "resolved_accessions": [], "statements": ["The datasets analyzed during the current study are available in the DGV database [20], http://dgv.tcag.ca/dgv/app/home; CCLE [21], https://portals.broadinstitute.org/ccle; CTRP [22] https://portals.broadinstitute.org/ctrp.v2.1/; GEO repository [https://www.ncbi.nlm.nih.gov/sites/GDSbrowser/], https://www.ncbi.nlm.nih.gov/geo/ and TARGET Data Matrix [https://ocg.cancer.gov/programs/target], https://ocg.cancer.gov/programs/target/data-matrix.\n\nAll data annotation and results during this study are included in this published article and its supplementary information files, Additional files 1, 2, 3 and 4."], "basis": "The paper names no accession to its source data, so the data has to come from the authors."}}, "can_run": {"dsl_operations": ["pearson_r"], "assets": ["source:pmid:30704460", "asset:table:pmid:30704460:1", "asset:table:pmid:30704460:2", "asset:table:pmid:30704460:3", "asset:table:pmid:30704460:4"], "steps": ["run_analysis"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": [], "capabilities": [], "data_gaps": ["data:geo:None"]}, "gaps": ["data:accession_absent"]}, "review": {"state": "partial", "machine_critique": {"kind": "extraction_review", "reader": {"model": "MiniMax-M3.1-Flash", "provider": "minimax", "provider_family": "minimax", "read_at": "2026-10-31T18:13:38Z", "independence": "single_reader"}, "evidence_check": {"method": "verbatim", "availability": "verbatim"}, "calls": 3, "usage": {"prompt_tokens": 47544, "completion_tokens": 2170, "cached_tokens": 15880}}, "human_critique": null, "stages": [{"id": "extraction", "state": "complete", "reader": "MiniMax-M3.1-Flash", "provider": "minimax"}], "gaps": ["review.independent_reader", "review.machine_critique", "review.human_critique"]}, "execution": {"state": "absent", "status": "not_run", "mode": null, "runs": [], "gaps": ["execution.not_run"]}, "provenance": {"entities": [{"id": "pmid:30704460", "type": "paper", "label": "pmid:30704460"}, {"id": "source:pmid:30704460", "type": "manuscript", "label": "PMID 30704460 full text"}, {"id": "data:geo:None", "type": "dataset", "label": "GEO None"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:30704460", "type": "paper", "label": "pmid:30704460"}, {"id": "source:pmid:30704460", "type": "manuscript", "label": "PMID 30704460 full text"}, {"id": "data:geo:None", "type": "dataset", "label": "GEO None"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-30704460", "type": "extraction", "used": ["source:pmid:30704460"], "agent": "agent:minimax-reader"}, {"id": "activity-resolve-None", "type": "accession_resolution", "used": ["data:geo:None"], "agent": "agent:corpus-assay-engine"}], "agents": [{"id": "agent:corpus-assay-engine", "type": "software", "label": "Corpus assay engine"}, {"id": "agent:minimax-reader", "type": "software", "label": "MiniMax literature reader"}], "source_run_id": "reading-sci-2026-11-01", "dsl_catalogue_fingerprint": "343fb30756ba6ec3", "capability_table_fingerprint": "2cb3e42f047d2ecf", "snapshot_id": "ba34b49490d9bfaf"}, "lineage": {"parent_id": "pmid:30704460", "parent_hash": "sha256:ba899234f6a9346911d6c7cd454a8cc704d9605fa46133a6410287d5f3b9c9db", "fork_of": null, "generation": 5}, "gaps": ["execution.not_run", "review.human_critique", "review.independent_reader", "review.machine_critique", "step:obtain_source_data blocked on data:accession_absent"], "assays": [{"role": "data_generation", "definition_id": "microarray", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["ma cancer cells lines previously obtained by Affymetrix SNP Array 6.0 were collected (see Table 2 fo", "ent of CNVs using high-resolution array CGH (Affymetrix SNP) array was completed on OS, RMS and ESFT"], "order_basis": "paper_text_order:4321", "id": "assay:1"}, {"role": "analysis", "definition_id": "correlation_analysis", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": ["pearson_r"], "support_state": "supported", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 1.0, "evidence": ["Pearson correlation coefficient is used to calculate association between drug response AUC and copy number variation in each of gene."], "order_basis": "consumes_earlier_output", "id": "assay:2"}]}