Evolutionary connectionism: algorithmic principles underlying the evolution of biological organisation in evo-devo, evo-eco and evolutionary transitions

The mechanisms of variation, selection and inheritance, on which evolution by natural selection depends, are not fixed over evolutionary time. Current evolutionary biology is increasingly focussed on understanding how the evolution of developmental organisations modifies the distribution of phenotypic variation, the evolution of ecological relationships modifies the selective environment, and the evolution of reproductive relationships modifies the heritability of the evolutionary unit. The major transitions in evolution, in particular, involve radical changes in developmental, ecological and reproductive organisations that instantiate variation, selection and inheritance at a higher level of biological organisation. However, current evolutionary theory is poorly equipped to describe how these organisations change over evolutionary time and especially how that results in adaptive complexes at successive scales of organisation (the key problem is that evolution is self-referential, i.e. the products of evolution change the parameters of the evolutionary process). Here we first reinterpret the central open questions in these domains from a perspective that emphasises the common underlying themes. We then synthesise the findings from a developing body of work that is building a new theoretical approach to these questions by converting well-understood theory and results from models of cognitive learning. Specifically, connectionist models of memory and learning demonstrate how simple incremental mechanisms, adjusting the relationships between individually-simple components, can produce organisations that exhibit complex system-level behaviours and improve the adaptive capabilities of the system. We use the term “evolutionary connectionism” to recognise that, by functionally equivalent processes, natural selection acting on the relationships within and between evolutionary entities can result in organisations that produce complex system-level behaviours in evolutionary systems and modify the adaptive capabilities of natural selection over time. We review the evidence supporting the functional equivalences between the domains of learning and of evolution, and discuss the potential for this to resolve conceptual problems in our understanding of the evolution of developmental, ecological and reproductive organisations and, in particular, the major evolutionary transitions

Prognostic factors in prostate cancer

Prognostic factors in organ confined prostate cancer will reflect survival after surgical radical prostatectomy. Gleason score, tumour volume, surgical margins and Ki-67 index have the most significant prognosticators. Also the origins from the transitional zone, p53 status in cancer tissue, stage, and aneuploidy have shown prognostic significance. Progression-associated features include Gleason score, stage, and capsular invasion, but PSA is also highly significant. Progression can also be predicted with biological markers (E-cadherin, microvessel density, and aneuploidy) with high level of significance. Other prognostic features of clinical or PSA-associated progression include age, IGF-1, p27, and Ki-67. In patients who were treated with radiotherapy the survival was potentially predictable with age, race and p53, but available research on other markers is limited. The most significant published survival-associated prognosticators of prostate cancer with extension outside prostate are microvessel density and total blood PSA. However, survival can potentially be predicted by other markers like androgen receptor, and Ki-67-positive cell fraction. In advanced prostate cancer nuclear morphometry and Gleason score are the most highly significant progression-associated prognosticators. In conclusion, Gleason score, capsular invasion, blood PSA, stage, and aneuploidy are the best markers of progression in organ confined disease. Other biological markers are less important. In advanced disease Gleason score and nuclear morphometry can be used as predictors of progression. Compound prognostic factors based on combinations of single prognosticators, or on gene expression profiles (tested by DNA arrays) are promising, but clinically relevant data is still lacking

Two Positive Nodes Represent a Significant Cut-off Value for Cancer Specific Survival in Patients with Node Positive Prostate Cancer. A New Proposal Based on a Two-Institution Experience on 703 Consecutive N plus Patients Treated with Radical Prostatectomy, Extended Pelvic Lymph Node Dissection and Adjuvant Therapy

Background: Currently, the 2002 American Joint Committee on Cancer (AJCC) staging system of prostate cancer does not include any stratification of patients according to the number of positive nodes. However, node positive (N+) patients share heterogeneous outcomes according to the extent of lymph node invasion (LNI). Objective: To test whether the accuracy of cancer specific survival (CSS) predictions may be improved if node positive patients are stratified according to the number of positive nodes. Design, Setting, and Participants: The study cohort included 703 N+ MO patients treated with radical prostatectomy (RP) and extended pelvic lymph node dissection (ePLND) between September 1988 and January 2003 at two large Academic Institutions. Number of positive nodes was dichotomized according to the most informative cut-off predicting CSS. Kaplan-Meier curves assessed cancer specific survival rates. Predictive accuracy of the current N stage and of the new N classification in predicting CSS was quantified with Harrell's concordance index after adjusting for pathological (T) stage and internally validated with 200 boostraps resamples. Differences in predictive accuracy were compared with the Mantel-Haentzel test. Results and Limitations: Mean follow-up was 113.7 months (median: 112.5, range 3.5-243). The mean number of nodes removed was 13.9 (range: 2-52). The mean number of positive nodes was 2.3 (range: 1-31). The most informative cut-off of positive nodes in predicting CSS was 2. Of all, 532 (75.7%) patients had 2 or less positive nodes, while 171 (24.3%) had more than 2 positive nodes. Patients with 2 or less positive nodes had significantly better CSS outcome at 15 year follow-up compared to patients with more than 2 positive nodes (84% vs 62%; p 2 positive nodes) was 65.0% vs 60.1% when the number of positive nodes was not considered (4.9% gain; p < 0.001). Conclusions: We demonstrated that patients with up to 2 positive nodes experienced excellent CSS, which was significantly higher compared to patients with more than 2 positive nodes. Moreover, a significant improvement in CSS prediction was reached when the number of positive nodes was considered. Thus, our results reinforce the need for a stratification of node positive patients according to the number of positive nodes and may warrant consideration in the next revision of the pathologic TNM classification. (c) 2008 European Association of Urology. Published by Elsevier B.V. All rights reserved