The clinical and prognostic use of circulating tumour cells in breast cancer

Adjuvant therapies such as endocrine or cytotoxic chemotherapy have been demonstrated to improve overall survival in early breast cancer patients. A blood test to monitor patients at risk of relapse is needed to identify those patients who would benefit from these treatments and those for whom it is not necessary. This is in favour of detecting disseminated tumour cells (DTCs) from painful bone marrow aspirates, currently the gold‐standard method for detecting minimal residual disease (MRD). The use of circulating tumour cells (CTCs) enriched from the blood was investigated for this purpose along with their characterisation in the metastatic setting to enable individualised therapy. Sixty‐four primary breast cancer patients were followed up for up to 12 years post surgery for any MRD present. This analysis looked at measurements of DTCs in the bone marrow, CTCs in the blood and circulating‐free DNA (cfDNA) in the plasma over the follow up period. Patients who had involved lymph nodes at surgery, were significantly more likely to have CTCs present than low risk patients with no nodes positive, (70% compared to 39% respectively, p = 0.042). Our analysis also looked at the relationship of cfDNA to DTCs and CTCs. An inverse relationship of cell death in the blood (manifesting as blood cfDNA) to bone marrow DTCs by qRT‐PCR was apparent. This may be due to tumour dormancy mechanisms ‐ cycles of tumour cell proliferation and cell death occurring in the bone marrow, evidence not shown before in patient samples. Combined use of these markers could therefore be used as a monitoring system for impending metastatic disease and a rationale for further treatment. We also participated in a multi–centre study to assess the effects of lapatinib; a targeted therapy against two members of the human epidermal growth factor receptor family (EGFR and HER2). This was in advanced breast cancer patients and used CTCs as a surrogate marker. Our study selected patients on the basis of EGFR positivity in CTCs that were present in the blood. Four out of 12 patients (33%) demonstrated an initial decrease in the number of EGFR positive CTCs in response to Lapatinib, however this was limited and all patients were taken off study with progressive disease. We also explored a novel method in development to detect viable CTCs. This used an in situ hybridisation method to amplify signals from mRNA transcripts of tumour markers in CTCs. The use of CTCs is a very useful and promising tool for studying both the biology of breast cancer, and also as a non‐invasive analytical tool in the clinical setting to gain predictive and prognostic information

The potential for liquid biopsies in the precision medical treatment of breast cancer.

Currently the clinical management of breast cancer relies on relatively few prognostic/predictive clinical markers (estrogen receptor, progesterone receptor, HER2), based on primary tumor biology. Circulating biomarkers, such as circulating tumor DNA (ctDNA) or circulating tumor cells (CTCs) may enhance our treatment options by focusing on the very cells that are the direct precursors of distant metastatic disease, and probably inherently different than the primary tumor's biology. To shift the current clinical paradigm, assessing tumor biology in real time by molecularly profiling CTCs or ctDNA may serve to discover therapeutic targets, detect minimal residual disease and predict response to treatment. This review serves to elucidate the detection, characterization, and clinical application of CTCs and ctDNA with the goal of precision treatment of breast cancer

CancerLinker: Explorations of Cancer Study Network

Interactive visualization tools are highly desirable to biologist and cancer researchers to explore the complex structures, detect patterns and find out the relationships among bio-molecules responsible for a cancer type. A pathway contains various bio-molecules in different layers of the cell which is responsible for specific cancer type. Researchers are highly interested in understanding the relationships among the proteins of different pathways and furthermore want to know how those proteins are interacting in different pathways for various cancer types. Biologists find it useful to merge the data of different cancer studies in a single network and see the relationships among the different proteins which can help them detect the common proteins in cancer studies and hence reveal the pattern of interactions of those proteins. We introduce the CancerLinker, a visual analytic tool that helps researchers explore cancer study interaction network. Twenty-six cancer studies are merged to explore pathway data and bio-molecules relationships that can provide the answers to some significant questions which are helpful in cancer research. The CancerLinker also helps biologists explore the critical mutated proteins in multiple cancer studies. A bubble graph is constructed to visualize common protein based on its frequency and biological assemblies. Parallel coordinates highlight patterns of patient profiles (obtained from cBioportal by WebAPI services) on different attributes for a specified cancer studyComment: 7 pages, 9 figure

Machine Learning and Integrative Analysis of Biomedical Big Data.

Recent developments in high-throughput technologies have accelerated the accumulation of massive amounts of omics data from multiple sources: genome, epigenome, transcriptome, proteome, metabolome, etc. Traditionally, data from each source (e.g., genome) is analyzed in isolation using statistical and machine learning (ML) methods. Integrative analysis of multi-omics and clinical data is key to new biomedical discoveries and advancements in precision medicine. However, data integration poses new computational challenges as well as exacerbates the ones associated with single-omics studies. Specialized computational approaches are required to effectively and efficiently perform integrative analysis of biomedical data acquired from diverse modalities. In this review, we discuss state-of-the-art ML-based approaches for tackling five specific computational challenges associated with integrative analysis: curse of dimensionality, data heterogeneity, missing data, class imbalance and scalability issues

Perspective: Melanoma diagnosis and monitoring: Sunrise for melanoma therapy but early detection remains in the shade

Last revised 25 Jul 2016.Melanoma is one of the most dangerous forms of cancer. The five-year survival rate is 98% if it is detected early. However, this rate plummets to 63% for regional disease and 17% when tumors have metastasized, that is, spread to distant sites. Furthermore, the incidence of melanoma has been rising by about 3% per year, whereas the incidence of cancers that are more common is decreasing. A handful of targeted therapies have recently become available that have finally shown real promise for treatment, but for reasons that remain unclear only a fraction of patients respond long term. These drugs often increase survival by only a few months in metastatic patient groups before relapse occurs. More effective treatment may be possible if a diagnosis can be made when the tumor burden is still low. Here, an overview of the current state-of-the-art is provided along with an argument for newer technologies towards early point-of-care diagnosis of melanoma

Histopathological image analysis : a review

Over the past decade, dramatic increases in computational power and improvement in image analysis algorithms have allowed the development of powerful computer-assisted analytical approaches to radiological data. With the recent advent of whole slide digital scanners, tissue histopathology slides can now be digitized and stored in digital image form. Consequently, digitized tissue histopathology has now become amenable to the application of computerized image analysis and machine learning techniques. Analogous to the role of computer-assisted diagnosis (CAD) algorithms in medical imaging to complement the opinion of a radiologist, CAD algorithms have begun to be developed for disease detection, diagnosis, and prognosis prediction to complement the opinion of the pathologist. In this paper, we review the recent state of the art CAD technology for digitized histopathology. This paper also briefly describes the development and application of novel image analysis technology for a few specific histopathology related problems being pursued in the United States and Europe