28 research outputs found
The growth pattern of transplanted normal and nodular hepatocytes
Overt neoplasia is often the end result of a long biological process beginning with the appearance of focal lesions of altered tissue morphology. While the putative clonal nature of focal lesions has often been emphasized, increasing attention is being devoted to the possible role of an altered growth pattern in the evolution of carcinogenesis. Here we compare the growth patterns of normal and nodular hepatocytes in a transplantation system that allows their selective clonal proliferation in vivo. Rats were pre-treated with retrorsine, which blocks the growth of resident hepatocytes, and were then transplanted with hepatocytes isolated from either normal liver or hepatocyte nodules. Both cell types were able to proliferate extensively in the recipient liver, as expected. However, their growth pattern was remarkably different. Clusters of normal hepatocytes integrated in the host liver, displaying a normal histology; however, transplanted nodular hepatocytes formed new hepatocyte nodules, with altered morphology and sharp demarcation from surrounding host liver. Both the expression and distribution of proteins involved in cell polarity, cell communication, and cell adhesion, including connexin 32, E-cadherin, and matrix metalloproteinase-2, were altered in clusters of nodular hepatocytes. Furthermore, we were able to show that down-regulation of connexin 32 and E-cadherin in nodular hepatocyte clusters was independent of growth rate. These results support the concept that a dominant pathway towards neoplastic disease in several organs involves defect(s) in tissue pattern formation
Cancer Biomarker Discovery: The Entropic Hallmark
Background: It 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 Findings: Using 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/Significance: We 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-throughput 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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Prognostic value of Ki67 in localized prostate carcinoma: a multi-institutional study of >1000 prostatectomies.
BackgroundExpanding interest in and use of active surveillance for early state prostate cancer (PC) has increased need for prognostic biomarkers. Using a multi-institutional tissue microarray resource including over 1000 radical prostatectomy samples, we sought to correlate Ki67 expression captured by an automated image analysis system with clinicopathological features and validate its utility as a clinical grade test in predicting cancer-specific outcomes.MethodsAfter immunostaining, the Ki67 proliferation index (PI) of tumor areas of each core (three cancer cores/case) was analyzed using a nuclear quantification algorithm (Aperio). We assessed whether Ki67 PI was associated with clinicopathological factors and recurrence-free survival (RFS) including biochemical recurrence, metastasis or PC death (7-year median follow-up).ResultsIn 1004 PCs (∼4000 tissue cores) Ki67 PI showed significantly higher inter-tumor (0.68) than intra-tumor variation (0.39). Ki67 PI was associated with stage (P<0.0001), seminal vesicle invasion (SVI, P=0.02), extracapsular extension (ECE, P<0.0001) and Gleason score (GS, P<0.0001). Ki67 PI as a continuous variable significantly correlated with recurrence-free, overall and disease-specific survival by multivariable Cox proportional hazard model (hazards ratio (HR)=1.04-1.1, P=0.02-0.0008). High Ki67 score (defined as ⩾5%) was significantly associated with worse RFS (HR=1.47, P=0.0007) and worse overall survival (HR=2.03, P=0.03).ConclusionsIn localized PC treated by radical prostatectomy, higher Ki67 PI assessed using a clinical grade automated algorithm is strongly associated with a higher GS, stage, SVI and ECE and greater probability of recurrence
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Outcomes of Active Surveillance for Clinically Localized Prostate Cancer in the Prospective, Multi-Institutional Canary PASS Cohort
Purpose Active surveillance represents a strategy to address the overtreatment of prostate cancer, yet uncertainty regarding individual patient outcomes remains a concern. We evaluated outcomes in a prospective multicenter study of active surveillance. Materials and Methods We studied 905 men in the prospective Canary PASS enrolled between 2008 and 2013. We collected clinical data at study entry and at prespecified intervals, and determined associations with adverse reclassification, defined as increased Gleason grade or greater cancer volume on followup biopsy. We also evaluated the relationships of clinical parameters with pathology findings in participants who underwent surgery after a period of active surveillance. Results At a median followup of 28 months 24% of participants experienced adverse reclassification, of whom 53% underwent treatment while 31% continued on active surveillance. Overall 19% of participants received treatment, 68% with adverse reclassification, while 32% opted for treatment without disease reclassification. In multivariate Cox proportional hazards modeling the percent of biopsy cores with cancer, body mass index and prostate specific antigen density were associated with adverse reclassification (p=0.01, 0.04, 0.04, respectively). Of 103 participants subsequently treated with radical prostatectomy 34% had adverse pathology, defined as primary pattern 4-5 or nonorgan confined disease, including 2 with positive lymph nodes, with no significant relationship between risk category at diagnosis and findings at surgery (p=0.76). Conclusions Most men remain on active surveillance at 5 years without adverse reclassification or adverse pathology at surgery. However, clinical factors had only a modest association with disease reclassification, supporting the need for approaches that improve the prediction of this outcome