The inactivation of the retinoblastoma (Rb) tumor suppressor gene in mice results in ectopic proliferation, apoptosis, and impaired differentiation in extraembryonic, neural, and erythroid lineages, culminating in fetal death by embryonic day 15.5 (E15.5). Here we show that the specific loss of Rb in trophoblast stem (TS) cells, but not in trophoblast derivatives, leads to an overexpansion of trophoblasts, a disruption of placental architecture, and fetal death by E15.5. Despite profound placental abnormalities, fetal tissues appeared remarkably normal, suggesting that the full manifestation of fetal phenotypes requires the loss of Rb in both extraembryonic and fetal tissues. Loss of Rb resulted in an increase of E2f3 expression, and the combined ablation of Rb and E2f3 significantly suppressed Rb mutant phenotypes. This rescue appears to be cell autonomous since the inactivation of Rb and E2f3 in TS cells restored placental development and extended the life of embryos to E17.5. Taken together, these results demonstrate that loss of Rb in TS cells is the defining event causing lethality of Rb −/− embryos and reveal the convergence of extraembryonic and fetal functions of Rb in neural and erythroid development. We conclude that the Rb pathway plays a critical role in the maintenance of a mammalian stem cell population.[Keywords: Rb; development; placenta; stem cells] Supplemental material is available at http://www.genesdev.org. The retinoblastoma (Rb) tumor suppressor gene was identified more than two decades ago as the gene responsible for retinoblastoma, but has since been implicated in most human cancers. In contrast to retinoblastoma patients, inheritance of one deleted copy of Rb in mice did not induce retinoblastoma but did increase risk of pituitary and thyroid cancers (Jacks et al. 1992;Hu et al. 1994;Maandag et al. 1994;Williams et al. 1994;Robanus-Maandag et al. 1998;Yamasaki et al. 1998). Deletion of both copies of Rb in mice resulted in a broad range of severe abnormalities that lead to lethality by embryonic day 15.5 (E15.5) (Clarke et al. 1992;Jacks et al. 1992;Lee et al. 1992;Wu et al. 2003). Because Rb is normally expressed in all tissues of the mouse embryo, it was assumed that these developmental abnormalities were due to the absence of Rb protein in the tissues affected. Subsequent analysis of chimeric embryos suggested that Rb function is likely to be much more complex than initially suspected (Maandag et al. 1994;Lipinski et al. 2001). Indeed, recent findings showed that Rb-deficient embryos supplied with a wild-type placenta could develop to term and suggested a critical function of Rb in the placenta that might underlie many of the fetal developmental abnormalities observed in Rb −/− embryos Wu et al. 2003).Because Rb is involved in so many important pro-

The integration of imaging and genomic data is critical to forming a better understanding of disease. Large public datasets, such as The Cancer Genome Atlas, present a unique opportunity to integrate these complementary data types for in silico scientific research. In this letter, we focus on the aspect of pathology image analysis and illustrate the challenges associated with analyzing and integrating large-scale image datasets with molecular characterizations. We present an example study of diffuse glioma brain tumors, where the morphometric analysis of 81 million nuclei is integrated with clinically relevant transcriptomic and genomic characterizations of glioblastoma tumors. The preliminary results demonstrate the potential of combining morphometric and molecular characterizations for in silico research.

Background and objectiveMorphologic variations of disease are often linked to underlying molecular events and patient outcome, suggesting that quantitative morphometric analysis may provide further insight into disease mechanisms. In this paper a methodology for the subclassification of disease is developed using image analysis techniques. Morphologic signatures that represent patient-specific tumor morphology are derived from the analysis of hundreds of millions of cells in digitized whole slide images. Clustering these signatures aggregates tumors into groups with cohesive morphologic characteristics. This methodology is demonstrated with an analysis of glioblastoma, using data from The Cancer Genome Atlas to identify a prognostically significant morphology-driven subclassification, in which clusters are correlated with transcriptional, genetic, and epigenetic events.Materials and methodsMethodology was applied to 162 glioblastomas from The Cancer Genome Atlas to identify morphology-driven clusters and their clinical and molecular correlates. Signatures of patient-specific tumor morphology were generated from analysis of 200 million cells in 462 whole slide images. Morphology-driven clusters were interrogated for associations with patient outcome, response to therapy, molecular classifications, and genetic alterations. An additional layer of deep, genome-wide analysis identified characteristic transcriptional, epigenetic, and copy number variation events.Results and discussionAnalysis of glioblastoma identified three prognostically significant patient clusters (median survival 15.3, 10.7, and 13.0 months, log rank p=1.4e-3). Clustering results were validated in a separate dataset. Clusters were characterized by molecular events in nuclear compartment signaling including developmental and cell cycle checkpoint pathways. This analysis demonstrates the potential of high-throughput morphometrics for the subclassification of disease, establishing an approach that complements genomics.

We address the problem of efficient execution of a computation pattern, referred to here as the irregular wavefront propagation pattern (IWPP), on hybrid systems with multiple CPUs and GPUs. The IWPP is common in several image processing operations. In the IWPP, data elements in the wavefront propagate waves to their neighboring elements on a grid if a propagation condition is satisfied. Elements receiving the propagated waves become part of the wavefront. This pattern results in irregular data accesses and computations. We develop and evaluate strategies for efficient computation and propagation of wavefronts using a multi-level queue structure. This queue structure improves the utilization of fast memories in a GPU and reduces synchronization overheads. We also develop a tile-based parallelization strategy to support execution on multiple CPUs and GPUs. We evaluate our approaches on a state-of-the-art GPU accelerated machine (equipped with 3 GPUs and 2 multicore CPUs) using the IWPP implementations of two widely used image processing operations: morphological reconstruction and euclidean distance transform. Our results show significant performance improvements on GPUs. The use of multiple CPUs and GPUs cooperatively attains speedups of 50× and 85× with respect to single core CPU executions for morphological reconstruction and euclidean distance transform, respectively.

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