Molecular biology of breast cancer metastasis: Clinical implications of experimental studies on metastatic inefficiency

Recent technological advances have led to an increasing ability to detect isolated tumour cells and groups of tumour cells in patients' blood, lymph nodes or bone marrow. However, the clinical significance of these cells is unclear. Should they be considered as evidence of metastasis, necessitating aggressive treatment, or are they in some cases unrelated to clinical outcome? Quantitative experimental studies on the basic biology of metastatic inefficiency are providing clues that may help in understanding the significance of these cells. This understanding will be of use in guiding clinical studies to assess the significance of isolated tumour cells and micrometastases in cancer patients

Molecular biology of breast cancer metastasis: Genetic regulation of human breast carcinoma metastasis

The present is an overview of recent data that describes the genetic underpinnings of the suppression of cancer metastasis. Despite the explosion of new information about the genetics of cancer, only six human genes have thus far been shown to suppress metastasis functionally. Not all have been shown to be functional in breast carcinoma. Several additional genes inhibit various steps of the metastatic cascade, but do not necessarily block metastasis when tested using in vivo assays. The implications of this are discussed. Two recently discovered metastasis suppressor genes block proliferation of tumor cells at a secondary site, offering a new target for therapeutic intervention

Metastasis Suppressors and the Tumor Microenvironment

The most dangerous attribute of cancer cells is their ability to metastasize. Throughout the process of metastasis, tumor cells interact with other tumor cells, host cells and extracellular molecules. This brief review explores how a new class of molecules – metastasis suppressors – regulate tumor cell–microenvironmental interactions. Data are presented which demonstrate that metastasis suppressors act at multiple steps of the metastatic cascade. A brief discussion for how metastasis suppressor regulation of cellular interactions might be exploited is presented

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