Genomic Architecture Characterizes Tumor Progression Paths and Fate in Breast Cancer Patients

Abstract

Distinct molecular subtypes of breast carcinomas have been identified, but translation into clinical use has been limited. We have developed two platform independent algorithms to explore genomic architectural distortion using array comparative genomic hybridization (aCGH) data to measure 1) whole arm gains and losses (WAAI) and 2) complex rearrangements (CAAI). By applying CAAI and WAAI to data from 595 breast cancer patients we were able to separate the cases into eight subgroups with different distributions of genomic distortion. Within each subgroup data from expression analyses, sequencing and ploidy indicated that progression occurs along separate paths into more complex genotypes. Histological grade had prognostic impact only in the Luminal related groups while the complexity identified by CAAI had an overall independent prognostic power. This study emphasizes the relationship between structural genomic alterations, molecular subtype and clinical behavior, and show that objective score of genomic complexity (CAAI) is an independent prognostic marker in breast cancer.

Approximate entropy of network parameters

“…While we have previously shown that more aggressive breast cancers are characterized by an increased molecular entropy at the gene expression level [40], the results obtained in this manuscript demonstrate that more aggressive breast cancers (ER negative or high grade) are also characterized by a higher entropy at the genomic copy-number level. In this regard, an appealing feature of the entropy measures considered here is that they are sample specific, and thus may provide valuable insights into tumor taxonomy as shown recently using a different nonentropic set of measures in [41]. It will therefore be very interesting to explore and compare the entropy measures considered here to other proposed measures in the context of tumor classification and prediction of clinical outcome.…”

Breast Cancer Prognostication and Risk Prediction in the Post-Genomic Era

“…As previously pointed out (Sørlie et al 2010), an important issue for the molecular subtyping of breast cancers is the need for a clear definition of the molecular subtypes of breast cancer and standardized analytical methods to identify them. Until a consistent taxonomy is established, it is expected for inconsistent results when comparing assignments by various approaches that do not comprise the same entities.…”

“…In additional to mRNA expression profiling, other genetic information such as genomic complexity inferred from aCGH data (Russnes et al 2010) also has possibilities to be translated into clinical applications for breast cancer. More recently, next generation DNA sequencing has been used to support the goals of personalized medicine.…”

“…With the traditional diagnostic tools, patients with the same clinico-pathological parameters can have markedly different clinical courses. Individual tumors can frequently exhibit heterogeneous patterns of somatic mutations (Bamford et al 2004, Stephens et al 2009, Russnes et al 2010) gene amplifications and deletions (Russnes et al 2010), epigenetic profiles (Rønneberg et al 2010), and gene expression portraits (Perou et al 2000). Efforts to significantly impact cancer patient outcomes will require the development of robust strategies to subdivide such heterogeneous panels of cancers into biologically and clinically homogenous subgroups, for the purposes of personalizing treatment protocols and identifying optimal drug targets.…”

A transfer function approach for predicting rare cell capture microdevice performance

“…Circulating epithelial cells (CECs) of pancreatic origin have been found in the blood of pancreatic cyst patients, and may enable the early detection of pancreatic cancer . Finally, circulating tumor cells (CTCs), which are shed from a primary tumor into the circulatory system and are thought to contribute to metastasis (Maheswaran and Haber 2010), can inform patient prognosis (Cristofanilli et al 2004), therapeutic efficacy Stott et al 2010), and the genetics of disseminating cancer cells (Russnes et al 2010;Navin et al 2011;Pratt et al 2014;Lohr et al 2014). A challenge in studying these cells is that they are exceedingly rare-as few as 1 CTC per 100 million blood cells, for example (Racila et al 1998;Krivacic et al 2004)-requiring engineered solutions to isolate as many of the target cells as possible, while capturing few blood cells.…”