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

Prognostic significance of the ratio of absolute neutrophil to lymphocyte counts for breast cancer patients with ER/PR-positivity and HER2-negativity in neoadjuvant setting

The aim of this study was to determine the predictive or prognostic impact of absolute neutrophil count/absolute lymphocyte count ratio (NLR) in breast cancer patients with estrogen receptor/progesterone receptor (ER/PR)-positive and human epidermal growth factor receptor 2 (HER2)-negative subtype who have received neoadjuvant chemotherapy (NAC). We performed retrospective analysis of 157 patients with primary breast cancer with ER/PR-positive and HER2-negative subtype who were treated with NAC, followed by definitive surgical resection. The median follow-up after surgery was 21 months (range, 1-108 months). On univariate analysis, high NLR (>2.25) correlated with poorer recurrence-free survival (RFS) and overall survival (OS) (P = 0.001 and P < 0.001). Subgroup analysis of non-pathologic complete response (pCR) subgroup showed that high NLR was significant for RFS and OS (P = 0.001 and P < 0.001). Particularly, high NLR patients had inferior clinical outcomes in the high clinical stage. Uni- and multivariate Cox analysis showed NLR to be an only predictor of RFS and OS. The NLR is an independent prognostic factor for RFS and OS in breast cancer patients with ER/PR-positive and HER2-negative subtype receiving NAC. The NLR provides additional prognostic information to choose suitable patients who might profit from further therapy