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Biomedical subjects

David E Axelrod

Publications and source records attributed to David E Axelrod.

5 recordsLinked to original sources
PubMed2006

Evolution of cooperation among tumor cells.

The evolution of cooperation has a well established theoretical framework based on game theory. This approach has made valuable contributions to a wide variety of disciplines, including political science, economics, and evolutionary biology. Existing cancer theory suggests that individual clones of cancer cells evolve independently from one another, acquiring all of the genetic traits or hallmarks necessary to form a malignant tumor. It is also now recognized that tumors are heterotypic, with cancer cells interacting with normal stromal cells within the tissue microenvironment, including endothelial, stromal, and nerve cells. This tumor cell-stromal cell interaction in itself is a form of commensalism, because it has been demonstrated that these nonmalignant cells support and even enable tumor growth. Here, we add to this theory by regarding tumor cells as game players whose interactions help to determine their Darwinian fitness. We marshal evidence that tumor cells overcome certain host defenses by means of diffusible products. Our original contribution is to raise the possibility that two nearby cells can protect each other from a set of host defenses that neither could survive alone. Cooperation can evolve as by-product mutualism among genetically diverse tumor cells. Our hypothesis supplements, but does not supplant, the traditional view of carcinogenesis in which one clonal population of cells develops all of the necessary genetic traits independently to form a tumor. Cooperation through the sharing of diffusible products raises new questions about tumorigenesis and has implications for understanding observed phenomena, designing new experiments, and developing new therapeutic approaches.

Biological Evolution↗
PubMed2006

Breast cancer prognosis by combinatorial analysis of gene expression data.

INTRODUCTION: The potential of applying data analysis tools to microarray data for diagnosis and prognosis is illustrated on the recent breast cancer dataset of van 't Veer and coworkers. We re-examine that dataset using the novel technique of logical analysis of data (LAD), with the double objective of discovering patterns characteristic for cases with good or poor outcome, using them for accurate and justifiable predictions; and deriving novel information about the role of genes, the existence of special classes of cases, and other factors. METHOD: Data were analyzed using the combinatorics and optimization-based method of LAD, recently shown to provide highly accurate diagnostic and prognostic systems in cardiology, cancer proteomics, hematology, pulmonology, and other disciplines. RESULTS: LAD identified a subset of 17 of the 25,000 genes, capable of fully distinguishing between patients with poor, respectively good prognoses. An extensive list of 'patterns' or 'combinatorial biomarkers' (that is, combinations of genes and limitations on their expression levels) was generated, and 40 patterns were used to create a prognostic system, shown to have 100% and 92.9% weighted accuracy on the training and test sets, respectively. The prognostic system uses fewer genes than other methods, and has similar or better accuracy than those reported in other studies. Out of the 17 genes identified by LAD, three (respectively, five) were shown to play a significant role in determining poor (respectively, good) prognosis. Two new classes of patients (described by similar sets of covering patterns, gene expression ranges, and clinical features) were discovered. As a by-product of the study, it is shown that the training and the test sets of van 't Veer have differing characteristics. CONCLUSION: The study shows that LAD provides an accurate and fully explanatory prognostic system for breast cancer using genomic data (that is, a system that, in addition to predicting good or poor prognosis, provides an individualized explanation of the reasons for that prognosis for each patient). Moreover, the LAD model provides valuable insights into the roles of individual and combinatorial biomarkers, allows the discovery of new classes of patients, and generates a vast library of biomedical research hypotheses.

Breast Neoplasms↗
PubMed2005

Evaluation of pathways for progression of heterogeneous breast tumors.

To better understand the progression of heterogeneous breast cancers, four models of progession pathways have been evaluated. The models describe the progression through the grades of ductal carcinoma in situ (DCIS) 1, 2, and 3, and through the grades of invasive ductal carcinoma (IDC) 1, 2, and 3. The first three pathways, termed linear, nonlinear, and branched, describe DCIS as a progenitor of IDC, and grades of DCIS progressing into grades of IDC. The fourth pathway, termed parallel, describes DCIS and IDC as diverging from a common progenitor and progressing through grades in parallel. The best transition rates for the linear, nonlinear, and branched pathways were sought using a random search in combination with a directed search based on the Nelder-Mead simplex method. Parameter values for the parallel pathway were determined with heuristic graphs. Results of computer simulation were compared with clinically observed frequencies of grades of DCIS and grades of IDC that were reported to occur together in heterogeneous tumors. Each of the four pathways could simulate frequencies that resembled, to varying degrees, the clinical observations. The parallel pathway produced the best correspondence with clinical observations. These results quantify the traditional descriptions in which grades of DCIS are the progenitors of grades of IDC. The results also raise the alternative possibility that, in some tumors with both components, DCIS and IDC may have diverged from a common progenitor.

Algorithms↗
PubMed2004

About-weekly variations in nocturia.

The aim of this study was to assess components of variation in nocturia and to determine any putative geomagnetic influence. A 54-year old man with benign prostatic hyperplasia had recorded for about 4 years the number of times he awoke each night to urinate. The data have been reanalyzed for chronomics, the mapping of time structures (chronomes), involving the computation of least squares spectra of the urinary record and of environmental variables recorded during the same 4-year span. In addition to the previously reported monthly variation, other periodicities have been documented, including two separate components with periods of one week and of a near-week. The precise 7-day period may be a mainly exogenous resonance with external influences such as a weekly social schedule, whereas the near-week may be a partial resonance with natural changes in geomagnetics, reflecting in part changes in other non-photic natural environmental factors.

Biological Clocks↗