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@ARTICLE{Gobits:307660,
      author       = {R. Gobits and N. Schleußner$^*$ and G. R. Oliver and M.
                      Rutenberg Schoenberg and A. M. de Jesus Domingues and P.
                      Ramkumar and S. W. F. Suen and M. P. M. Koomen and F.
                      Paolucci and K. Martens and A. Guiseris Martinez$^*$ and J.
                      Volk$^*$ and C. Artmann$^*$ and M. Mastel$^*$ and K.
                      Ouyang$^*$ and M. Kloor and E. D. Bankaitis and H. E. Stoub
                      and J. Puschhof$^*$ and K. Brown and S. Pretzer and D. A.
                      Nelson and E. Struminger and A. Zessin and A. Brown and C.
                      Evans and D. Yetsko and M. Harrington and G. Salg and M.
                      Schneider and T. Schmidt and E. Helman and D. Plenker and C.
                      Barnett and R. T. Jones and B. C. Köhler$^*$ and E.
                      Driehuis and R.-F. Jackstadt$^*$},
      title        = {{F}unctional {P}recision {M}edicine {U}sing
                      {M}icro{O}rgano{S}pheres for {T}reatment {R}esponse
                      {P}rediction in {A}dvanced {C}olorectal {C}ancer.},
      journal      = {JCO precision oncology},
      volume       = {10},
      number       = {10},
      issn         = {2473-4284},
      address      = {Alexandria, VA},
      publisher    = {American Society of Clinical Oncology},
      reportid     = {DKFZ-2026-00117},
      pages        = {e2500501},
      year         = {2026},
      note         = {DKFZ-ZMBH Alliance / #NCTZFB26# / #DKTKZFB26# /
                      #EA:A013#LA:A013#},
      abstract     = {Neoadjuvant chemotherapy is a key component of curative
                      treatment in advanced colorectal cancer (CRC). However,
                      $30\%-40\%$ of patients show progression on treatment,
                      underscoring the need for predictive tools to guide up-front
                      treatment selection. Scalable and reproducible methods for
                      patient stratification remain limited. MicroOrganoSpheres
                      (MOS) are droplet-encapsulated 3D tumor models that allow
                      for high-throughput functional drug testing. Here, we
                      evaluate the potential of tumor-derived MOS to predict
                      response to chemotherapy in patients with CRC.MOS droplets
                      were generated from 37 primary and/or metastatic tumor
                      samples collected from 21 patients. MOS response to
                      chemotherapy was quantified using AI-based imaging analysis
                      and compared with clinical response (RECIST/disease-free
                      survival [DFS]) and lesion-specific outcomes (pathologic
                      response/percent tumor volume change).MOS chemoprediction
                      assay showed high reproducibility (coefficients of variation
                      ≤ $2.5\%).$ MOS drug sensitivity recapitulated patient
                      response with $83\%$ accuracy in the full sample cohort and
                      $100\%$ accuracy when derived from primary tumors. Patients
                      with sensitive MOS showed longer DFS. Individual MOS
                      analysis revealed preservation of intratumor heterogeneity
                      in vitro and enabled identification of drug-resistant
                      clones.MOS technology offers a scalable and robust
                      functional precision medicine platform with potential to
                      guide clinical decision making in CRC. The platform
                      accurately predicts patient response to chemotherapy and
                      provides insights into intrapatient and intratumor
                      heterogeneity.},
      keywords     = {Humans / Colorectal Neoplasms: drug therapy / Colorectal
                      Neoplasms: pathology / Precision Medicine: methods / Male /
                      Female / Middle Aged / Aged / Neoadjuvant Therapy /
                      Treatment Outcome},
      cin          = {A013 / D300 / HD01 / HD02},
      ddc          = {610},
      cid          = {I:(DE-He78)A013-20160331 / I:(DE-He78)D300-20160331 /
                      I:(DE-He78)HD01-20160331 / I:(DE-He78)HD02-20160331},
      pnm          = {311 - Zellbiologie und Tumorbiologie (POF4-311)},
      pid          = {G:(DE-HGF)POF4-311},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:41538757},
      doi          = {10.1200/PO-25-00501},
      url          = {https://inrepo02.dkfz.de/record/307660},
}