Identification of patients with low‐risk for aneuploidy: Comparative discriminatory models using linear and machine‐learning classifiers in prostate cancer

Venkataraman, Girish; Heinze, Georg; Holmes, Earle W.; Ananthanarayanan, Vijayalakshmi; Bostwick, David G.; Paner, Gladell P.; garza, Carrie M. Bradford-De La; Brown, Herbert R.; Flanigan, Robert C.; Wojcik, Eva M.

doi:10.1002/pros.20629

Cited by 3 publications

(2 citation statements)

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“…“ In sum, cancer is caused by chromosomal disorganization, which increases karyotypic entropy ” [601]. Regarding the cancer types studied in this paper, one particular “measure of disorder of a system”, aneuploidy , has been observed in poorly-differentiated prostate cancer cells and it is often associated with a more agreessive phenotype [602], [603], increased PSA levels [604], [605], and correlate with Gleason score [606], [607], [608]. Gene fusions and chromosomal rearrangements are other source of increase in the “disorder” of the genome organization and they are increasingly being recognized as a major player in prostate cancer progression [609].…”

Section: Discussionmentioning

confidence: 99%

Cancer Biomarker Discovery: The Entropic Hallmark

Berretta

Moscato

2010

PLoS ONE

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BackgroundIt is a commonly accepted belief that cancer cells modify their transcriptional state during the progression of the disease. We propose that the progression of cancer cells towards malignant phenotypes can be efficiently tracked using high-throughput technologies that follow the gradual changes observed in the gene expression profiles by employing Shannon's mathematical theory of communication. Methods based on Information Theory can then quantify the divergence of cancer cells' transcriptional profiles from those of normally appearing cells of the originating tissues. The relevance of the proposed methods can be evaluated using microarray datasets available in the public domain but the method is in principle applicable to other high-throughput methods.Methodology/Principal FindingsUsing melanoma and prostate cancer datasets we illustrate how it is possible to employ Shannon Entropy and the Jensen-Shannon divergence to trace the transcriptional changes progression of the disease. We establish how the variations of these two measures correlate with established biomarkers of cancer progression. The Information Theory measures allow us to identify novel biomarkers for both progressive and relatively more sudden transcriptional changes leading to malignant phenotypes. At the same time, the methodology was able to validate a large number of genes and processes that seem to be implicated in the progression of melanoma and prostate cancer.Conclusions/SignificanceWe thus present a quantitative guiding rule, a new unifying hallmark of cancer: the cancer cell's transcriptome changes lead to measurable observed transitions of Normalized Shannon Entropy values (as measured by high-througput technologies). At the same time, tumor cells increment their divergence from the normal tissue profile increasing their disorder via creation of states that we might not directly measure. This unifying hallmark allows, via the the Jensen-Shannon divergence, to identify the arrow of time of the processes from the gene expression profiles, and helps to map the phenotypical and molecular hallmarks of specific cancer subtypes. The deep mathematical basis of the approach allows us to suggest that this principle is, hopefully, of general applicability for other diseases.

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Section: Discussionmentioning

confidence: 99%

Cancer Biomarker Discovery: The Entropic Hallmark

Berretta

Moscato

2010

PLoS ONE

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“…There is an extended application of CI techniques in medical problems as shown from the very recent literature. Actually, there are CI applications in neuropathology 7-9 and brainrelated diseases, [10][11][12][13] in pulmonary embolism diagnosis, 14 lung cancer detection, 15 diabetes and diabetes-related malfunctions, [16][17] diagnosis and classification of prostate cancer, [18][19] diagnosis of aphasia, 20 acute appendicitis, 21 cardiovascular disorders, [22][23][24][25][26] in computer aided diagnosis for breast cancer, [27][28][29][30][31][32][33][34][35][36] in generation of diagnostic hypotheses for toxoplasma infections in pregnancy, 37 in hypertention detection 38 and in assessment of osteoarthritis. 39 …”

Section: Diagnosismentioning

confidence: 99%

Computational Intelligence in Medicine and Biology: A Survey

Vasilakos¹,

Spyrou²

2008

Jnl of Comp & Theo Nano

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Modern Medicine and Biology are dependent on systems and methods for high throughput data acquisition and management, detailed analysis of huge amount of data, knowledge extraction and predictive ability. In other words, the life sciences do not simply need sufficient computers, they require computational intelligence. This paper describes the state of the art applications of computational intelligence in Medicine and Biology comprising a large part of the current research and applications in the fields of Medical Informatics and Bioinformatics as well.

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Nuclear morphometry, nucleomics and prostate cancer progression

Veltri¹,

Christudass²,

Isharwal³

2012

Asian J Androl

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Prostate cancer (PCa) results from a multistep process. This process includes initiation, which occurs through various aging events and multiple insults (such as chronic infection, inflammation and genetic instability through reactive oxygen species causing DNA double-strand breaks), followed by a multistep process of progression. These steps include several genetic and epigenetic alterations, as well as alterations to the chromatin structure, which occur in response to the carcinogenic stress-related events that sustain proliferative signaling. Events such as evading growth suppressors, resisting cell death, enabling replicative immortality, inducing angiogenesis, and activating invasion and metastasis are readily observed. In addition, in conjunction with these critical drivers of carcinogenesis, other factors related to the etiopathogenesis of PCa, involving energy metabolism and evasion of the immune surveillance system, appear to be involved. In addition, when cancer spread and metastasis occur, the 'tumor microenvironment' in the bone of PCa patients may provide a way to sustain dormancy or senescence and eventually establish a 'seed and soil' site where PCa proliferation and growth may occur over time. When PCa is initiated and progression ensues, significant alterations in nuclear size, shape and heterochromatin (DNA transcription) organization are found, and key nuclear transcriptional and structural proteins, as well as multiple nuclear bodies can lead to precancerous and malignant changes. These series of cellular and tissue-related malignancy-associated events can be quantified to assess disease progression and management.

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Identification of patients with low‐risk for aneuploidy: Comparative discriminatory models using linear and machine‐learning classifiers in prostate cancer

Cited by 3 publications

References 72 publications

Cancer Biomarker Discovery: The Entropic Hallmark

Cancer Biomarker Discovery: The Entropic Hallmark

Computational Intelligence in Medicine and Biology: A Survey

Nuclear morphometry, nucleomics and prostate cancer progression

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