WEE1 inhibitor adavosertib in combination with carboplatin in advanced TP53 mutated ovarian cancer: A biomarker-enriched phase II study.

OBJECTIVE: In the first part of this phase II study (NCT01164995), the combination of carboplatin and adavosertib (AZD1775) was shown to be safe and effective in patients with TP53 mutated platinum-resistant ovarian cancer (PROC). Here, we present the results of an additional safety and efficacy cohort and explore predictive biomarkers for resistance and response to this combination treatment. METHODS: This is a phase II, open-label, non-randomized study. Patients with TP53 mutated PROC received carboplatin AUC 5 mg/ ml·min intravenously and adavosertib 225 mg BID orally for 2.5 days in a 21-day cycle. The primary objective is to determine the efficacy and safety of carboplatin and adavosertib. Secondary objectives include progression-free survival (PFS), changes in circulating tumor cells (CTC) and exploration of genomic alterations. RESULTS: Thirty-two patients with a median age of 63 years (39-77 years) were enrolled and received treatment. Twenty-nine patients were evaluable for efficacy. Bone marrow toxicity, nausea and vomiting were the most common adverse events. Twelve patients showed partial response (PR) as best response, resulting in an objective ORR of 41% in the evaluable patients (95% CI: 23%-61%). The median PFS was 5.6 months (95% CI: 3.8-10.3). In patients with tumors harboring CCNE1 amplification, treatment efficacy was slightly but not significantly better. CONCLUSIONS: Adavosertib 225 mg BID for 2.5 days and carboplatin AUC 5 could be safely combined and showed anti-tumor efficacy in patients with PROC. However, bone marrow toxicity remains a point of concern, since this is the most common reason for dose reductions and dose delays.

An Anatomically Resolved Mouse Brain Proteome Reveals Parkinson Disease-relevant Pathways.

Here, we present a mouse brain protein atlas that covers 17 surgically distinct neuroanatomical regions of the adult mouse brain, each less than 1 mm3 in size. The protein expression levels are determined for 6,500 to 7,500 gene protein products from each region and over 12,000 gene protein products for the entire brain, documenting the physiological repertoire of mouse brain proteins in an anatomically resolved and comprehensive manner. We explored the utility of our spatially defined protein profiling methods in a mouse model of Parkinson's disease. We compared the proteome from a vulnerable region (substantia nigra pars compacta) of wild type and parkinsonian mice with that of an adjacent, less vulnerable, region (ventral tegmental area) and identified several proteins that exhibited both spatiotemporal- and genotype-restricted changes. We validated the most robustly altered proteins using an alternative profiling method and found that these modifications may highlight potential new pathways for future studies. This proteomic atlas is a valuable resource that offers a practical framework for investigating the molecular intricacies of normal brain function as well as regional vulnerability in neurological diseases. All of the mouse regional proteome profiling data are published on line at http://mbpa.bprc.ac.cn/.

Distinct Genomic Profiles Are Associated with Treatment Response and Survival in Ovarian Cancer.

The majority of patients with ovarian cancer ultimately develop recurrent chemotherapy-resistant disease. Treatment stratification is mainly based on histological subtype and stage, prior response to platinum-based chemotherapy, and time to recurrent disease. Here, we integrated clinical treatment, treatment response, and survival data with whole-genome sequencing profiles of 132 solid tumor biopsies of metastatic epithelial ovarian cancer to explore genome-informed stratification opportunities. Samples from primary and recurrent disease harbored comparable numbers of single nucleotide variants and structural variants. Mutational signatures represented platinum exposure, homologous recombination deficiency, and aging. Unsupervised hierarchical clustering based on genomic input data identified specific ovarian cancer subgroups, characterized by homologous recombination deficiency, genome stability, and duplications. The clusters exhibited distinct response rates and survival probabilities which could thus potentially be used for genome-informed therapy stratification for more personalized ovarian cancer treatment.

A multi-platform reference for somatic structural variation detection.

Accurate detection of somatic structural variation (SV) in cancer genomes remains a challenging problem. This is in part due to the lack of high-quality, gold-standard datasets that enable the benchmarking of experimental approaches and bioinformatic analysis pipelines. Here, we performed somatic SV analysis of the paired melanoma and normal lymphoblastoid COLO829 cell lines using four different sequencing technologies. Based on the evidence from multiple technologies combined with extensive experimental validation, we compiled a comprehensive set of carefully curated and validated somatic SVs, comprising all SV types. We demonstrate the utility of this resource by determining the SV detection performance as a function of tumor purity and sequence depth, highlighting the importance of assessing these parameters in cancer genomics projects. The truth somatic SV dataset as well as the underlying raw multi-platform sequencing data are freely available and are an important resource for community somatic benchmarking efforts.

Familial Occurrence of Adult Granulosa Cell Tumors: Analysis of Whole-Genome Germline Variants.

Adult granulosa cell tumor (AGCT) is a rare ovarian cancer subtype, with a peak incidence around 50-55 years. Although AGCT can occur in specific syndromes, a genetic predisposition for AGCT has not been identified. The aim of this study is to identify a genetic variant in families with AGCT patients, potentially contributing to tumor evolution. We identified four families, each including two women diagnosed with AGCT. Whole-genome sequencing was performed to identify overlapping germline variants or affected genes. Familial relationship was evaluated using genealogy and genomic analyses. Patient characteristics, medical (family) history, and pedigrees were collected. Findings were compared to a reference group of 33 unrelated AGCT patients. Mean age at diagnosis was 38 years (range from 17 to 60) versus 51 years in the reference group, and seven of eight patients were premenopausal. In two families, three first degree relatives were diagnosed with breast cancer. Furthermore, polycystic ovary syndrome (PCOS) and subfertility was reported in three families. Predicted deleterious variants in PIK3C2G, BMP5, and LRP2 were identified. In conclusion, AGCTs occur in families and could potentially be hereditary. In these families, the age of AGCT diagnosis is lower and cases of breast cancer, PCOS, and subfertility are present. We could not identify an overlapping genetic variant or affected locus that may explain a genetic predisposition for AGCT.

Whole Genome Analysis of Ovarian Granulosa Cell Tumors Reveals Tumor Heterogeneity and a High-Grade TP53-Specific Subgroup.

Adult granulosa cell tumors (AGCTs) harbor a somatic FOXL2 c.402C>G mutation in ~95% of cases and are mainly surgically removed due to limited systemic treatment effect. In this study, potentially targetable genomic alterations in AGCTs were investigated by whole genome sequencing on 46 tumor samples and matched normal DNA. Copy number variant (CNV) analysis confirmed gain of chromosome 12 and 14, and loss of 22. Pathogenic TP53 mutations were identified in three patients with highest tumor mutational burden and mitotic activity, defining a high-grade AGCT subgroup. Within-patient tumor comparisons showed 29-80% unique somatic mutations per sample, suggesting tumor heterogeneity. A higher mutational burden was found in recurrent tumors, as compared to primary AGCTs. FOXL2-wildtype AGCTs harbored DICER1, TERT(C228T) and TP53 mutations and similar CNV profiles as FOXL2-mutant tumors. Our study confirms that absence of the FOXL2 c.402C>G mutation does not exclude AGCT diagnosis. The lack of overlapping variants in targetable cancer genes indicates the need for personalized treatment for AGCT patients.

Towards a Hierarchical Strategy to Explore Multi-Scale IP/MS Data for Protein Complexes.

Protein interaction in cells can be described at different levels. At a low interaction level, proteins function together in small, stable complexes and at a higher level, in sets of interacting complexes. All interaction levels are crucial for the living organism, and one of the challenges in proteomics is to measure the proteins at their different interaction levels. One common method for such measurements is immunoprecipitation followed by mass spectrometry (IP/MS), which has the potential to probe the different protein interaction forms. However, IP/MS data are complex because proteins, in their diverse interaction forms, manifest themselves in different ways in the data. Numerous bioinformatic tools for finding protein complexes in IP/MS data are currently available, but most tools do not provide information about the interaction level of the discovered complexes, and no tool is geared specifically to unraveling and visualizing these different levels. We present a new bioinformatic tool to explore IP/MS datasets for protein complexes at different interaction levels and show its performance on several real-life datasets. Our tool creates clusters that represent protein complexes, but unlike previous methods, it arranges them in a tree-shaped structure, reporting why specific proteins are predicted to build a complex and where it can be divided into smaller complexes. In every data analysis method, parameters have to be chosen. Our method can suggest values for its parameters and comes with adapted visualization tools that display the effect of the parameters on the result. The tools provide fast graphical feedback and allow the user to interact with the data by changing the parameters and examining the result. The tools also allow for exploring the different organizational levels of the protein complexes in a given dataset. Our method is available as GNU-R source code and includes examples at www.bdagroup.nl.

Inferring protein-protein interaction complexes from immunoprecipitation data.

BACKGROUND: Protein-protein interactions in cells are widely explored using small-scale experiments. However, the search for protein complexes and their interactions in data from high throughput experiments such as immunoprecipitation is still a challenge. We present "4N", a novel method for detecting protein complexes in such data. Our method is a heuristic algorithm based on Near Neighbor Network (3N) clustering. It is written in R, it is faster than model-based methods, and has only a small number of tuning parameters. We explain the application of our new method to real immunoprecipitation results and two artificial datasets. We show that the method can infer protein complexes from protein immunoprecipitation datasets of different densities and sizes. FINDINGS: 4N was applied on the immunoprecipitation dataset that was presented by the authors of the original 3N in Cell 145:787-799, 2011. The test with our method shows that it can reproduce the original clustering results with fewer manually adapted parameters and, in addition, gives direct insight into the complex-complex interactions. We also tested 4N on the human "Tip49a/b" dataset. We conclude that 4N can handle the contaminants and can correctly infer complexes from this very dense dataset. Further tests were performed on two artificial datasets of different sizes. We proved that the method predicts the reference complexes in the two artificial datasets with high accuracy, even when the number of samples is reduced. CONCLUSIONS: 4N has been implemented in R. We provide the sourcecode of 4N and a user-friendly toolbox including two example calculations. Biologists can use this 4N-toolbox even if they have a limited knowledge of R. There are only a few tuning parameters to set, and each of these parameters has a biological interpretation. The run times for medium scale datasets are in the order of minutes on a standard desktop PC. Large datasets can typically be analyzed within a few hours.