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

Iodine-125 vs palladium-103 for low-risk prostate cancer: Preliminary urinary functional outcomes from a prospective randomized multicenter trial

Better characterization of the clinical outcome and response to therapy of extrapulmonary neuroendocrine (NE) carcinomas is an ongoing effort. However, this endeavor is belabored with notable challenges: (1) These carcinomas are rare. With the exception of the histologically distinctive and clinically indolent carcinoid (also termed well-differentiated neuroendocrine carcinoma by some investigators), NE carcinomas typically represent far fewer than 1% of carcinomas of solid organs. Consequently, most single centers have difficulty assembling a large series in a sufficiently short time so that a clinical outcome analysis is meaningful.1,2 (2) These carcinomas are not as well defined histologically as one might assume. The histologic definition is a bit fuzzy and, accordingly, subject to observer variability. The following questions can be raised when this issue is considered: • Are tumor architecture (an organoid growth pattern) and cytologic appearance (a finely clumped pattern of chromatin and inconspicuous nucleoli) of sufficient sensitivity and specificity to unequivocally identify a small cell NE carcinoma that will express NE tissue biomarkers—chromogranin and/or synaptophysin— if immunostained? • Or, should we demand immunohistochemical verification of an NE phenotype before accepting a tumor as an NE carcinoma? • And how should we classify carcinomas that are not pure NE carcinomas but that have an NE component and a component with a phenotype common to the organ of origin, eg, urothelial carcinoma with NE differentiation? • Furthermore, should we accept as NE carcinomas tumors that are composed of cells that have the histologic and cytologic appearance of undifferentiated non–small cell carcinomas but that express chromogranin and/or synaptophysin, such as large cell NE carcinomas of the lung?3 In this issue of the Journal, Alijo Serrano et al4 motivate us to raise these questions in their report of a small series of NE carcinomas of the urinary bladder. Their article confirms already published data that small cell NE carcinomas of the urinary bladder are often of high stage at initial diagnosis, tend to have a more rapidly progressive course than do “conventional” urothelial carcinomas, and express the NE markers chromogranin and synaptophysin, in addition to other markers expressed by NE carcinomas and non-NE carcinomas, such as thyroid transcription factor (TTF)-1 and CD57. In exploring the questions raised by this article, we limit our focus to the topic of the study by Alijo Serrano et al4—NE carcinomas of the urinary bladder. Let us now address the issues we raised.

/pdf/how-wide-is-the-spectrum-of-neuroendocrine-carcinoma-of-the-42e9hcu6m6.pdf

How wide is the spectrum of neuroendocrine carcinoma of the urinary bladder

Integration of imaging data across different molecular target types can provide in-depth insight into cell physiology and pathology, but remains challenging owing to poor compatibility between target-type-specific labeling methods. We show that cross-platform imaging analysis can be readily achieved through DNA encoding of molecular targets, which translates the molecular identity of various target types into a uniform in situ array of ssDNA tags for subsequent labeling with complementary imaging probes. The concept was demonstrated through multiplexed imaging of mRNAs and their corresponding proteins with multicolor quantum dots. The results reveal heterogeneity of cell transfection with siRNA and outline disparity in RNA interference (RNAi) kinetics at the level of both the mRNA and the encoded protein.

/pdf/cross-platform-dna-encoding-for-single-cell-imaging-of-gene-zfc0azpg18.pdf

Cross-Platform DNA Encoding for Single-Cell Imaging of Gene Expression.

Multifaceted Genitourinary Lymphoma

BACKGROUND
Pathologic variables that characterize response of prostate carcinoma to current neoadjuvant therapy have not been characterized in detail. This study reports (i) the histological features of prostate cancer treated with abiraterone and enzalutamide and inter-pathologist variance in identifying these features, and (ii) the effect of the novel androgen deprivation agents on residual cancer volume.

METHODS
We reviewed sections of prostatectomies from 37 patients treated with neoadjuvant agents and 22 untreated patients, tabulated the frequency of nine features of cancer (intact cancer glands, isolated cancer cells, poorly formed glands, cribriform architecture, clear spaces, intraductal carcinoma, solid sheets of cancer cells, prominent nucleoli, and previously described ABC grouping) and two features of benign glands (prominent basal cells and coalescent corpora amylacea). We used several methods, including a novel metric (visual grid system), to estimate residual tumor volume.

RESULTS
The most highly reproducible features were ABC grouping (κ = 0.56–0.7), presence of intraductal carcinoma (κ = 0.34–0.72), cribriform architecture (κ = 0.42–0.68), solid sheets of tumor cells (κ = 0.44–0.56), and coalescent corpora amylacea (κ = 0.4–0.54). Among poorly reproducible features were prominent nucleoli (κ = 0.03–0.11), clear spaces (κ = 0.05–0.07), and poorly formed cancer glands (κ = 0.02–0.1). Determination of tumor mass was excellent regardless of the method used—maximum tumor size (κ = 0.9–0.94), tumor area (κ = 0.94–0.96), and grid-based tumor cellularity (κ = 0.9).

CONCLUSIONS
We propose using a set of parameters including maximum tumor size, tumor area/volume, cellularity, volume, and ABC grouping for evaluating radical prostatectomies post-neoadjuvant therapy. Prostate 76:1285–1292, 2016. © 2016 Wiley Periodicals, Inc.

/pdf/a-novel-system-for-estimating-residual-disease-and-39cpe0lk6i.pdf

Lawrence D. True

Papers

Iodine-125 vs palladium-103 for low-risk prostate cancer: Preliminary urinary functional outcomes from a prospective randomized multicenter trial

How wide is the spectrum of neuroendocrine carcinoma of the urinary bladder

Cross-Platform DNA Encoding for Single-Cell Imaging of Gene Expression.

Multifaceted Genitourinary Lymphoma

A Novel System for Estimating Residual Disease and Pathologic Response to Neoadjuvant Treatment of Prostate Cancer