Personalization of cancer treatment using predictive simulation.

BACKGROUND: The personalization of cancer treatments implies the reconsideration of a one-size-fits-all paradigm. This move has spawned increased use of next generation sequencing to understand mutations and copy number aberrations in cancer cells. Initial personalization successes have been primarily driven by drugs targeting one patient-specific oncogene (e.g., Gleevec, Xalkori, Herceptin). Unfortunately, most cancers include a multitude of aberrations, and the overall impact on cancer signaling and metabolic networks cannot be easily nullified by a single drug. METHODS: We used a novel predictive simulation approach to create an avatar of patient cancer cells using point mutations and copy number aberration data. Simulation avatars of myeloma patients were functionally screened using various molecularly targeted drugs both individually and in combination to identify drugs that are efficacious and synergistic. Repurposing of drugs that are FDA-approved or under clinical study with validated clinical safety and pharmacokinetic data can provide a rapid translational path to the clinic. High-risk multiple myeloma patients were modeled, and the simulation predictions were assessed ex vivo using patient cells. RESULTS: Here, we present an approach to address the key challenge of interpreting patient profiling genomic signatures into actionable clinical insights to make the personalization of cancer therapy a practical reality. Through the rational design of personalized treatments, our approach also targets multiple patient-relevant pathways to address the emergence of single therapy resistance. Our predictive platform identified drug regimens for four high-risk multiple myeloma patients. The predicted regimes were found to be effective in ex vivo analyses using patient cells. CONCLUSIONS: These multiple validations confirm this approach and methodology for the use of big data to create personalized therapeutics using predictive simulation approaches.

Epithelioid gastrointestinal stromal tumor of the stomach with liver metastases in a 12-year-old girl: aspiration cytology and molecular study.

Gastrointestinal stromal tumor (GIST), a stromal tumor of the gastrointestinal tract defined as CD117 (c-kit)-positive neoplasm, occurs primarily in adults. GIST with CD117 (c-kit) mutation and certain cytogenetic abnormalities is associated with malignancy, though a definite relationship between prognosis and molecular alterations remains to be elucidated. We report the cytologic features of an epithelioid GIST arising in the stomach of a child and metastatic to the liver, and the molecular mutational analysis of both the primary gastric tumor and the liver metastasis. Literature of pediatric GISTs was also reviewed. Fine needle aspiration of the liver metastasis, processed by Ultrafast Papanicolaou stain, showed fragments of cohesive small epithelioid cells with bland oval nuclei and unipolar cytoplasm transected by capillaries. Immunohistochemically, all nodules in the stomach and liver expressed CD117 (c-kit). Interestingly, some of the gastric tumor clusters were uniformly CD34 positive, whereas others were uniformly CD34 negative, suggesting heterogeneity of tumor clones. The presence of neurosecretory granules further subtyped the tumor into gastric autonomic nerve tumor (GANT). Molecular mutational analysis, performed in both the gastric tumor and the liver metastasis, showed no sequence abnormality in exons 9, 11, and 13 of CD117 (c-kit). Cytogenetic study revealed normal karyotype. These features might suggest a different molecular mechanism leading to malignancy in certain GISTs arising in children.