title: Proof concept for clinical justification of network mapping for personalized cancer therapeutics.
abstract: To identify signature targets associated with patient-specific cancer lesions based on tumor versus normal tissue differential protein and mRNA coexpression patterns for the purpose of synthesizing cancer-specific customized RNA interference knockdown therapeutics. Analysis of biopsied tissue involved two-dimensional difference in-gel electrophoresis (2D-DIGE) analysis coupled with MALDI-TOF/TOF mass spectrometry for proteomic assessment. Standard microarray techniques were utilized for mRNA analysis. Priority was assigned to overexpressed protein targets with co-overexpressed genes with a high likelihood of functional nodal centrality in the cancer network as defined by the interactive databases BIND, HPRD and ResNet. HPLC-grade small interfering RNA (siRNA) duplexes were utilized to assess knockdown of target proteins in expressive cell lines as measured by western blot. Seven patients with metastatic cancer underwent biopsy. One patient (RW001) had biopsies from two disease sites 10 months apart. Seven priority proteins were identified, one for each patient (RACK 1, Ras related nuclear protein, heat-shock 27 kDa protein 1, superoxide dismutase, enolase1, stathmin1 and cofilin1). Prioritized proteins in RW001 from the two disease sites over time were the same. We demonstrated >80% siRNA inhibition of RACK 1 and stathmin1 of inexpressive malignant cell lines with correlated cell kill. Identification of functionally relevant target gene fingerprints, unique to an individual's cancer, is feasible 'at the bedside' and can be utilized to synthesize siRNA knockdown therapeutics. Further animal safety testing followed by clinical study is recommended.
authors.affiliation: Mary Crowley Medical Research Center, Dallas, TX, USA. jnemunaitis@mcmrc.com
authors.forename: J
authors.initials: J
authors.lastname: Nemunaitis
authors.forename: N
authors.initials: N
authors.lastname: Senzer
authors.forename: I
authors.initials: I
authors.lastname: Khalil
authors.forename: Y
authors.initials: Y
authors.lastname: Shen
authors.forename: P
authors.initials: P
authors.lastname: Kumar
authors.forename: A
authors.initials: A
authors.lastname: Tong
authors.forename: J
authors.initials: J
authors.lastname: Kuhn
authors.forename: J
authors.initials: J
authors.lastname: Lamont
authors.forename: M
authors.initials: M
authors.lastname: Nemunaitis
authors.forename: D
authors.initials: D
authors.lastname: Rao
authors.forename: Y-A
authors.initials: YA
authors.lastname: Zhang
authors.forename: Y
authors.initials: Y
authors.lastname: Zhou
authors.forename: J
authors.initials: J
authors.lastname: Vorhies
authors.forename: P
authors.initials: P
authors.lastname: Maples
authors.forename: C
authors.initials: C
authors.lastname: Hill
authors.forename: D
authors.initials: D
authors.lastname: Shanahan
mesh_terms: D000328:Adult; D000368:Aged; D005260:Female; D023281:Genomics; D006801:Humans; D008297:Male; D008875:Middle Aged; D009369:Neoplasms / Q000188:drug therapy* / Q000235:genetics / Q000378:metabolism; D040901:Proteomics; D034622:RNA Interference / Q000502:physiology*