Literature DB >> 9111646

Intratumoral nuclear morphologic heterogeneity in prostate cancer.

H G van der Poel1, G O Oosterhof, H E Schaafsma, F M Debruyne, J A Schalken.   

Abstract

OBJECTIVES: Tumor heterogeneity can be measured by quantifying variance of nuclear characteristics by image analysis. Heterogeneity of cell nuclear features correlated with increased local progression in prostate cancer. In the present study, the influence of tumor heterogeneity on prostate-specific antigen (PSA) recurrence after radical retropubic prostatectomy was analyzed and tumor heterogeneity was compared in patients with and without neoadjuvant hormonal therapy.
METHODS: Retrospectively, radical prostatectomy material of 44 patients without and 12 patients with neoadjuvant hormonal treatment with a postoperative follow-up of at least 4 years was studied. Each prostatectomy specimen was systematically embedded in paraffin, and each tumor area within the prostate was marked and analyzed by an image analysis system for 32 nuclear features comprising nuclear shape, size, DNA content, and chromatin pattern. Several clinical features were available: preoperative serum PSA, hemoglobin concentration, Karnofsky score, tumor stage, and Gleason score.
RESULTS: Increased tumor heterogeneity, as expressed by differences in karyometric values between tumor areas in nuclear shape and chromatin pattern within the tumor, was significantly correlated with earlier PSA recurrence rate. As compared with nonpretreated patients, hormonally pretreated specimens showed smaller and less heterogeneous tumors. In particular, chromatin pattern heterogeneity was decreased in patients who underwent preoperative hormonal treatment compared with patients who were not pretreated. However, decreased heterogeneity was accompanied by a higher percentage of aneuploid areas per tumor in the pretreated patients. Cox regression analysis showed that karyometric determination of nuclear shape heterogeneity in combination with preoperative PSA level could predict time to PSA recurrence after radical prostatectomy in patients without hormonal pretreatment.
CONCLUSIONS: Increase in karyometric tumor heterogeneity in nuclear shape and chromatin pattern was correlated with a shorter PSA recurrence-free interval after radical prostatectomy. Preoperative PSA and karyometric tumor heterogeneity were the best predictors of PSA recurrence in a multivariate analysis. Intratumoral heterogeneity was decreased in patients with prostate cancer who underwent neoadjuvant hormonal therapy.

Entities:  

Mesh:

Year:  1997        PMID: 9111646     DOI: 10.1016/s0090-4295(96)00557-2

Source DB:  PubMed          Journal:  Urology        ISSN: 0090-4295            Impact factor:   2.649


  7 in total

1.  Tracing the tumor lineage.

Authors:  Nicholas E Navin; James Hicks
Journal:  Mol Oncol       Date:  2010-05-05       Impact factor: 6.603

2.  The influence of PSA flare in mCRPC patients treated with alpha-emitting radiopharmaceuticals.

Authors:  Francesco Ceci; Giulia Polverari; Jeremie Calais; Paolo Castellucci
Journal:  Eur J Nucl Med Mol Imaging       Date:  2018-12       Impact factor: 9.236

Review 3.  Insight into the heterogeneity of breast cancer through next-generation sequencing.

Authors:  Hege G Russnes; Nicholas Navin; James Hicks; Anne-Lise Borresen-Dale
Journal:  J Clin Invest       Date:  2011-10-03       Impact factor: 14.808

4.  Impact of PSA flare-up in patients with hormone-refractory prostate cancer undergoing chemotherapy.

Authors:  Thomas Nelius; Tobias Klatte; Werner de Riese; Stephanie Filleur
Journal:  Int Urol Nephrol       Date:  2007-06-30       Impact factor: 2.370

5.  Elite model for the generation of induced pluripotent cancer cells (iPCs).

Authors:  Jason Lai; Chiou Mee Kong; Dashayini Mahalingam; Xiaojin Xie; Xueying Wang
Journal:  PLoS One       Date:  2013-02-13       Impact factor: 3.240

6.  Future medical applications of single-cell sequencing in cancer.

Authors:  Nicholas Navin; James Hicks
Journal:  Genome Med       Date:  2011-05-31       Impact factor: 11.117

7.  A Hidden Markov Model to estimate population mixture and allelic copy-numbers in cancers using Affymetrix SNP arrays.

Authors:  Philippe Lamy; Claus L Andersen; Lars Dyrskjot; Niels Torring; Carsten Wiuf
Journal:  BMC Bioinformatics       Date:  2007-11-09       Impact factor: 3.169

  7 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.