Characterizing genomic alterations in cancer by complementary functional associations.

Systematic efforts to sequence the cancer genome have identified large numbers of mutations and copy number alterations in human cancers. However, elucidating the functional consequences of these variants, and their interactions to drive or maintain oncogenic states, remains a challenge in cancer research. We developed REVEALER, a computational method that identifies combinations of mutually exclusive genomic alterations correlated with functional phenotypes, such as the activation or gene dependency of oncogenic pathways or sensitivity to a drug treatment. We used REVEALER to uncover complementary genomic alterations associated with the transcriptional activation of β-catenin and NRF2, MEK-inhibitor sensitivity, and KRAS dependency. REVEALER successfully identified both known and new associations, demonstrating the power of combining functional profiles with extensive characterization of genomic alterations in cancer genomes

ARID1B is a specific vulnerability in ARID1A-mutant cancers

Summary Recent studies have revealed that ARID1A is frequently mutated across a wide variety of human cancers and also has bona fide tumor suppressor properties. Consequently, identification of vulnerabilities conferred by ARID1A mutation would have major relevance for human cancer. Here, using a broad screening approach, we identify ARID1B, a related but mutually exclusive homolog of ARID1A in the SWI/SNF chromatin remodeling complex, as the number one gene preferentially required for the survival of ARID1A-mutant cancer cell lines. We show that loss of ARID1B in ARID1A-deficient backgrounds destabilizes SWI/SNF and impairs proliferation. Intriguingly, we also find that ARID1A and ARID1B are frequently co-mutated in cancer, but that ARID1A-deficient cancers retain at least one ARID1B allele. These results suggest that loss of ARID1A and ARID1B alleles cooperatively promotes cancer formation but also results in a unique functional dependence. The results further identify ARID1B as a potential therapeutic target for ARID1A-mutant cancers

Heterotopic pregnancy following induction of ovulation with clomiphene citrate

Background: Although heterotopic gestation is common in assisted reproductive techniques, it is very rare in natural conception and clomiphene induced pregnancy. Diagnosis and appropriate intervention of heterotopic pregnancy requires a high index of suspicious.Case: In this paper a case of heterotopic pregnancy in a 30-year old woman with hemoperitoneum from ruptured tubal pregnancy with live intrauterine gestation at 9 weeks of gestation is reported.Conclusion: This case suggests that a heterotopic pregnancy must always be considered particularly after the induction of ovulation by clomiphene citrate or assisted reproductive technology. Every clinician treating women of reproductive age should keep this diagnosis in mind. It also demonstrates that early diagnosis is essential in order to salvage the intrauterine pregnancy and avoid maternal morbidity and mortalit

Group Normalization for Genomic Data

<div><p>Data normalization is a crucial preliminary step in analyzing genomic datasets. The goal of normalization is to remove global variation to make readings across different experiments comparable. In addition, most genomic loci have non-uniform sensitivity to any given assay because of variation in local sequence properties. In microarray experiments, this non-uniform sensitivity is due to different DNA hybridization and cross-hybridization efficiencies, known as the probe effect. In this paper we introduce a new scheme, called Group Normalization (GN), to remove both global and local biases in one integrated step, whereby we determine the normalized probe signal by finding a set of reference probes with similar responses. Compared to conventional normalization methods such as Quantile normalization and physically motivated probe effect models, our proposed method is general in the sense that it does not require the assumption that the underlying signal distribution be identical for the treatment and control, and is flexible enough to correct for nonlinear and higher order probe effects. The Group Normalization algorithm is computationally efficient and easy to implement. We also describe a variant of the Group Normalization algorithm, called Cross Normalization, which efficiently amplifies biologically relevant differences between any two genomic datasets.</p> </div

Signal Quality measure.

<p>Two tiling array signals corresponding to nucleosome occupancy at two different experimental conditions are shown for the <i>HXT3</i> locus. We use two conditions and a replicate to determine signal and noise, as follows. In condition A (with glucose), the highlighted region is nucleosome free, and in condition B (no glucose), it is nucleosome bound. <i>S</i> is the difference of the tiling array signal at two different conditions and reflects the signal strength. <i>N</i> is a measure of noise and is estimated by comparing the signal of two replicate microarrays at similar experimental condition. We evaluate <i>S</i> over a set of significantly changed probes (indicated with open circles) and <i>N</i> over all the probes as described in the text. The ratio <i>S/N</i> is a genome wide measure of Signal Quality.</p

Flowchart of Group Normalization.

<p>Control arrays are used to generate reference probe sets for each probe. Then we use the reference probe sets to estimate the probe parameters in the treatment arrays and to generate the normalized signal. We propose two distinct methods to normalize the arrays: a Binary method which parameterizes high and low signal for each probe (μ_low, μ_high); or a Quantile-based method which uses the rank of each probe in the reference set.</p

Genomic assays are often highly reproducible, but have significant efficiency variation across the genome.

<p>(A) Two genomic hybridization signals (biological replicates) from (Lee et al., 2007) shown along a portion of Chr III are highly reproducible, but deviate significantly from the expected constant signal. (B) Across the whole genome, these variations are highly reproducible. Two genomic hybridizations for the entire yeast genome are highly correlated (Pearson C = 0.966).</p

Overview of Group Normalization.

<p>Probes are shown sorted by on their values in a genomic hybridization (reference condition, black). For each probe, <i>N</i> = 1000 probes with closest signal in the genomic hybridization are assigned as reference set (dashed boxes) for each probe. Then high (red) and low (green) signal levels in the experimental condition (grey) are estimated from high and low probe signal ranges for each set of reference probes.</p