Predicting bladder cancer behavior by molecular expression profiling

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@PhdThesis{Mitra:thesis,
  author =       "Anirban Pradip Mitra",
  title =        "Predicting bladder cancer behavior by molecular
                 expression profiling",
  school =       "Keck School of Medicine, University of Southern
                 California",
  year =         "2009",
  type =         "Pathobiology",
  address =      "Los Angeles, California, USA",
  month =        aug,
  keywords =     "genetic algorithms, genetic programming, urothelial
                 carcinoma, prognosis, quantitative expression
                 profiling, microarray, immunohistochemistry",
  URL =          "http://phdtree.org/pdf/25533566-predicting-bladder-cancer-behavior-by-molecular-expression-profiling/",
  URL =          "http://digitallibrary.usc.edu/cdm/ref/collection/p15799coll127/id/247267",
  size =         "223 pages",
  abstract =     "Urothelial carcinoma of the urinary bladder is the
                 seventh most common type of cancer worldwide. In the
                 western world, cigarette smoke is the most commonly
                 implicated carcinogen for this disease. Bladder cancer
                 presents itself as two prognostic variants -- the more
                 common noninvasive Ta tumours that frequently recur but
                 rarely invade the basement membrane, and the less
                 common invasive tumors that tend to progress and
                 metastasize. Traditional prognostic metrics, including
                 tumor and nodal stage, are currently the best clinical
                 predictors of subsequent behavior. While lymph node
                 metastasis forebodes a poor prognosis, early detection
                 can allow for radical lymphadenectomy with a curative
                 intent. This manuscript begins by describing a study
                 that used gene expression profiles generated from
                 primary bladder tumours to construct signatures that
                 could identify nodal metastasis. Genetic programming
                 was used to identify classifiers that showed a strong
                 predilection for ICAM1, MAP2K6 and KDR, and could
                 detect nodal metastasis with reasonable sensitivity and
                 specificity. Using similar pathway-based profiling
                 approaches, this manuscript further describes studies
                 that sought to determine if such molecular alterations
                 could supplement traditional pathologic staging to
                 better predict clinical outcome. The manuscript
                 documents the identification and validation of a
                 concise, biologically relevant gene panel comprising of
                 JUN, MAP2K6, STAT3, and ICAM1 that could predict
                 recurrence and survival in bladder cancer. Another
                 study highlights attempts to identify genes profiled
                 from primary noninvasive Ta tumours at first
                 presentation that could predict local recurrence and
                 tumour progression. The final study describes efforts
                 to semi-quantitatively profile expressions of select
                 proteins from primary bladder cancer tissues to analyse
                 associations of their alterations with cigarette
                 smoking, nonsteroidal anti-inflammatory drug use, and
                 clinical outcome across all disease stages in a
                 population-based cohort. These studies underscore the
                 concept that a pathway-specific approach to profiling
                 relevant biomolecules in bladder cancer can identify
                 markers of prognostic significance, and patients who
                 will recur and/or progress despite definitive surgery
                 alone. Such identification of specific molecular
                 alterations in individual tumours will allow for a more
                 accurate and personalized prediction of prognosis, and
                 also identify potential therapeutic targets.",
  notes =        "Unrestricted",
}

Genetic Programming entries for Anirban P Mitra

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