Breast cancer is the most common malignancy in United States women, accounting for >40,000 deaths each year. These breast tumors are comprised of phenotypically diverse populations of breast cancer cells. Using a model in which human breast cancer cells were grown in immunocompromised mice, we found that only a minority of breast cancer cells had the ability to form new tumors. We were able to distinguish the tumorigenic (tumor initiating) from the nontumorigenic cancer cells based on cell surface marker expression. We prospectively identified and isolated the tumorigenic cells as CD44+CD24−/lowLineage− in eight of nine patients. As few as 100 cells with this phenotype were able to form tumors in mice, whereas tens of thousands of cells with alternate phenotypes failed to form tumors. The tumorigenic subpopulation could be serially passaged: each time cells within this population generated new tumors containing additional CD44+CD24−/lowLineage− tumorigenic cells as well as the phenotypically diverse mixed populations of nontumorigenic cells present in the initial tumor. The ability to prospectively identify tumorigenic cancer cells will facilitate the elucidation of pathways that regulate their growth and survival. Furthermore, because these cells drive tumor development, strategies designed to target this population may lead to more effective therapies.

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Prospective identification of tumorigenic breast cancer cells

Recent advances in sequencing technology make it possible to comprehensively catalogue genetic variation in population samples, creating a foundation for understanding human disease, ancestry and evolution. The amounts of raw data produced are prodigious and many computational steps are required to translate this output into high-quality variant calls. We present a unified analytic framework to discover and genotype variation among multiple samples simultaneously that achieves sensitive and specific results across five sequencing technologies and three distinct, canonical experimental designs. Our process includes (1) initial read mapping; (2) local realignment around indels; (3) base quality score recalibration; (4) SNP discovery and genotyping to find all potential variants; and (5) machine learning to separate true segregating variation from machine artifacts common to next-generation sequencing technologies. We discuss the application of these tools, instantiated in the Genome Analysis Toolkit (GATK), to deep whole-genome, whole-exome capture, and multi-sample low-pass (~4×) 1000 Genomes Project datasets.

/pdf/a-framework-for-variation-discovery-and-genotyping-using-1mhes6klgp.pdf

A framework for variation discovery and genotyping using next-generation DNA sequencing data

The mediators and cellular effectors of inflammation are important constituents of the local environment of tumours. In some types of cancer, inflammatory conditions are present before a malignant change occurs. Conversely, in other types of cancer, an oncogenic change induces an inflammatory microenvironment that promotes the development of tumours. Regardless of its origin, 'smouldering' inflammation in the tumour microenvironment has many tumour-promoting effects. It aids in the proliferation and survival of malignant cells, promotes angiogenesis and metastasis, subverts adaptive immune responses, and alters responses to hormones and chemotherapeutic agents. The molecular pathways of this cancer-related inflammation are now being unravelled, resulting in the identification of new target molecules that could lead to improved diagnosis and treatment.

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Cancer-related inflammation.

Data visualization is an essential component of genomic data analysis. However, the size and diversity of the data sets produced by today’s sequencing and array-based profiling methods present major challenges to visualization tools. The Integrative Genomics Viewer (IGV) is a high-performance viewer that efficiently handles large heterogeneous data sets, while providing a smooth and intuitive user experience at all levels of genome resolution. A key characteristic of IGV is its focus on the integrative nature of genomic studies, with support for both array-based and next-generation sequencing data, and the integration of clinical and phenotypic data. Although IGV is often used to view genomic data from public sources, its primary emphasis is to support researchers who wish to visualize and explore their own data sets or those from colleagues. To that end, IGV supports flexible loading of local and remote data sets, and is optimized to provide high-performance data visualization and exploration on standard desktop systems. IGV is freely available for download from http://www.broadinstitute.org/igv, under a GNU LGPL open-source license.

/pdf/integrative-genomics-viewer-igv-high-performance-genomics-3wp9qtayn5.pdf

Integrative Genomics Viewer (IGV): high-performance genomics data visualization and exploration

The systematic translation of cancer genomic data into knowledge of tumour biology and therapeutic possibilities remains challenging. Such efforts should be greatly aided by robust preclinical model systems that reflect the genomic diversity of human cancers and for which detailed genetic and pharmacological annotation is available. Here we describe the Cancer Cell Line Encyclopedia (CCLE): a compilation of gene expression, chromosomal copy number and massively parallel sequencing data from 947 human cancer cell lines. When coupled with pharmacological profiles for 24 anticancer drugs across 479 of the cell lines, this collection allowed identification of genetic, lineage, and gene-expression-based predictors of drug sensitivity. In addition to known predictors, we found that plasma cell lineage correlated with sensitivity to IGF1 receptor inhibitors; AHR expression was associated with MEK inhibitor efficacy in NRAS-mutant lines; and SLFN11 expression predicted sensitivity to topoisomerase inhibitors. Together, our results indicate that large, annotated cell-line collections may help to enable preclinical stratification schemata for anticancer agents. The generation of genetic predictions of drug response in the preclinical setting and their incorporation into cancer clinical trial design could speed the emergence of 'personalized' therapeutic regimens.

The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity

Paralleling the growth of ever more cost efficient methods to sequence the whole genome in minute fragments of tissue has been the identification of increasingly numerous molecular abnormalities in cancers—mutations, amplifications, insertions and deletions of genes, and patterns of differential gene expression, i.e., overexpression of growth factors and underexpression of tumor suppressor genes. These abnormalities can be translated into assays to be used in clinical decision making. In general terms, the result of such an assay is subject to a large number of variables regarding the characteristics of the available sample, particularities of the used assay, and the interpretation of the results. This review discusses the effects of these variables on assays of tissue-based biomarkers, classified by macromolecule—DNA, RNA (including micro RNA, messenger RNA, long noncoding RNA, protein, and phosphoprotein). Since the majority of clinically applicable biomarkers are immunohistochemically detectable proteins this review focuses on protein biomarkers. However, the principles outlined are mostly applicable to any other analyte. A variety of preanalytical variables impacts on the results obtained, including analyte stability (which is different for different analytes, i.e., DNA, RNA, or protein), period of warm and of cold ischemia, fixation time, tissue processing, sample storage time, and storage conditions. In addition, assay variables play an important role, including reagent specificity (notably but not uniquely an issue concerning antibodies used in immunohistochemistry), technical components of the assay, quantitation, and assay interpretation. Finally, appropriateness of an assay for clinical application is an important issue. Reference is made to publicly available guidelines to improve on biomarker development in general and requirements for clinical use in particular. Strategic goals are formulated in order to improve on the quality of biomarker reporting, including issues of analyte quality, experimental detail, assay efficiency and precision, and assay appropriateness.

Methodological requirements for valid tissue-based biomarker studies that can be used in clinical practice

Hemangiopericytoma of breast: report of a case with ultrastructural and immunohistochemical findings.

Factors predictive of rectal bleeding after 103Pd and supplemental beam radiation for prostate cancer.

Surgical pathology and the patient: a systematic review evaluating the primary audience of pathology reports

A definitive diagnosis of cancer may be rendered by microscopic assessment of only a few cells in an appropriate clinical setting due to the distinctive nuclear structure of most cancer cells in comparison to nuclei of normal human cells. The molecular architecture of non-neoplastic human nuclei--of the nuclear matrix and of matrix-associated proteins and nucleic acids--is being characterized in exquisite molecular detail. What is missing is the application of the findings and tools of molecular biology to understanding the cytological structure of cancer nuclei. This article delves into the basis of nuclear structure at different levels of resolution--light microscopic, electron microscopic, and molecular.

Lawrence D. True

Papers

Methodological requirements for valid tissue-based biomarker studies that can be used in clinical practice

Hemangiopericytoma of breast: report of a case with ultrastructural and immunohistochemical findings.

Factors predictive of rectal bleeding after 103Pd and supplemental beam radiation for prostate cancer.

Surgical pathology and the patient: a systematic review evaluating the primary audience of pathology reports

The cancer nuclear microenvironment: Interface between light microscopic cytology and molecular phenotype