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@ARTICLE{ElHarouni:307373,
      author       = {D. ElHarouni$^*$ and R. Hernansaiz-Ballesteros and H.
                      Peterziel$^*$ and G. Balasubramanian$^*$ and C. Previti$^*$
                      and K. Schramm$^*$ and M. Blattner-Johnson$^*$ and R.
                      Kabbe$^*$ and B. Jones$^*$ and S. Oppermann$^*$ and D.
                      Jones$^*$ and S. Pfister$^*$ and O. Witt$^*$ and J.
                      Saez-Rodriguez and I. Oehme$^*$ and N. Jäger$^*$ and M.
                      Schlesner},
      title        = {{I}ntegrative {M}ultiomics and {D}rug {S}ensitivity
                      {P}rofiling {R}eveal {P}otential {B}iomarkers and
                      {T}herapeutic {S}trategies in {P}ediatric {S}olid {T}umors.},
      journal      = {Cancer research},
      volume       = {nn},
      issn         = {0099-7013},
      address      = {Philadelphia, Pa.},
      publisher    = {AACR},
      reportid     = {DKFZ-2025-03017},
      pages        = {nn},
      year         = {2025},
      note         = {#EA:B062#LA:B062# / epub},
      abstract     = {Cure rates for childhood malignancies using established
                      therapy protocols have increased to an average of $80\%$ but
                      have reached a plateau. Moreover, survival rates are
                      particularly low for some pediatric tumors-such as high-risk
                      group 3 medulloblastomas, osteosarcomas, Ewing sarcomas,
                      high-risk neuroblastomas, and high-grade gliomas-and dismal
                      for patients with relapsed malignancies. A functional drug
                      response profiling platform for pediatric solid and brain
                      tumors has been established within the INFORM program to
                      identify patient-specific vulnerabilities and biomarkers and
                      to unravel molecular mechanisms associated with drug
                      response profiles for clinical translation. In this study,
                      we performed a multiomics analysis using drug sensitivity
                      profiles, as well as genomic and transcriptomic data, of 81
                      pediatric solid tumor samples. The integrative analysis
                      suggested two multiomics signatures associated with drug
                      sensitivity. One signature distinguished neuroblastoma
                      samples with sensitivity to navitoclax, a BCL2 family
                      inhibitor. A second signature was specific to a subset of
                      Wilms tumors harboring the SIX1 (Q177R) hotspot mutation
                      that displayed high expression of MGAM, PTPN14, STAT4, and
                      KDM2B and high sensitivity to MEK inhibitors. A
                      patient-specific causal interaction network analysis
                      suggested possible molecular interactions between MEK
                      inhibitors and the SIX1 mutation in Wilms tumor samples. In
                      conclusion, the integration of drug sensitivity profiling
                      and multiomics data revealed potential biomarkers that may
                      be associated with drug sensitivity in pediatric solid
                      tumors. Patient-specific causal interaction network analysis
                      further elucidated the interaction between inhibitors and
                      signature biomarkers, providing insights that may inform
                      clinical translation.The combination of multiomics analysis
                      and drug sensitivity profiling identified two signatures
                      related to drug sensitivity in pediatric solid tumors,
                      contributing to the advancement of functional precision
                      medicine and personalized treatment strategies. This article
                      is part of a special series: Driving Cancer Discoveries with
                      Computational Research, Data Science, and Machine
                      Learning/AI .},
      cin          = {B062 / HD01 / B310 / W610 / B360},
      ddc          = {610},
      cid          = {I:(DE-He78)B062-20160331 / I:(DE-He78)HD01-20160331 /
                      I:(DE-He78)B310-20160331 / I:(DE-He78)W610-20160331 /
                      I:(DE-He78)B360-20160331},
      pnm          = {312 - Funktionelle und strukturelle Genomforschung
                      (POF4-312)},
      pid          = {G:(DE-HGF)POF4-312},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:41417259},
      doi          = {10.1158/0008-5472.CAN-24-1938},
      url          = {https://inrepo02.dkfz.de/record/307373},
}