抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1（PfPMP1）与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用，在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员，通过启动转录不同的var基因变异体为抗原变异提供了分子基础。

疟原虫var基因转换速率变化导致抗原变异[英]／Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A

The cBio Cancer Genomics Portal (http://cbioportal.org) is an open-access resource for interactive exploration of multidimensional cancer genomics data sets, currently providing access to data from more than 5,000 tumor samples from 20 cancer studies. The cBio Cancer Genomics Portal significantly lowers the barriers between complex genomic data and cancer researchers who want rapid, intuitive, and high-quality access to molecular profiles and clinical attributes from large-scale cancer genomics projects and empowers researchers to translate these rich data sets into biologic insights and clinical applications.

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The cBio Cancer Genomics Portal: An Open Platform for Exploring Multidimensional Cancer Genomics Data

The cBioPortal for Cancer Genomics (http://cbioportal.org) provides a Web resource for exploring, visualizing, and analyzing multidimensional cancer genomics data. The portal reduces molecular profiling data from cancer tissues and cell lines into readily understandable genetic, epigenetic, gene expression, and proteomic events. The query interface combined with customized data storage enables researchers to interactively explore genetic alterations across samples, genes, and pathways and, when available in the underlying data, to link these to clinical outcomes. The portal provides graphical summaries of gene-level data from multiple platforms, network visualization and analysis, survival analysis, patient-centric queries, and software programmatic access. The intuitive Web interface of the portal makes complex cancer genomics profiles accessible to researchers and clinicians without requiring bioinformatics expertise, thus facilitating biological discoveries. Here, we provide a practical guide to the analysis and visualization features of the cBioPortal for Cancer Genomics.

Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal

Rapid improvements in sequencing and array-based platforms are resulting in a flood of diverse genome-wide data, including data from exome and whole-genome sequencing, epigenetic surveys, expression profiling of coding and noncoding RNAs, single nucleotide polymorphism (SNP) and copy number profiling, and functional assays. Analysis of these large, diverse data sets holds the promise of a more comprehensive understanding of the genome and its relation to human disease. Experienced and knowledgeable human review is an essential component of this process, complementing computational approaches. This calls for efficient and intuitive visualization tools able to scale to very large data sets and to flexibly integrate multiple data types, including clinical data. However, the sheer volume and scope of data pose a significant challenge to the development of such tools.

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Integrative genomics viewer

We analysed primary breast cancers by genomic DNA copy number arrays, DNA methylation, exome sequencing, messenger RNA arrays, microRNA sequencing and reverse-phase protein arrays. Our ability to integrate information across platforms provided key insights into previously defined gene expression subtypes and demonstrated the existence of four main breast cancer classes when combining data from five platforms, each of which shows significant molecular heterogeneity. Somatic mutations in only three genes (TP53, PIK3CA and GATA3) occurred at >10% incidence across all breast cancers; however, there were numerous subtype-associated and novel gene mutations including the enrichment of specific mutations in GATA3, PIK3CA and MAP3K1 with the luminal A subtype. We identified two novel protein-expression-defined subgroups, possibly produced by stromal/microenvironmental elements, and integrated analyses identified specific signalling pathways dominant in each molecular subtype including a HER2/phosphorylated HER2/EGFR/phosphorylated EGFR signature within the HER2-enriched expression subtype. Comparison of basal-like breast tumours with high-grade serous ovarian tumours showed many molecular commonalities, indicating a related aetiology and similar therapeutic opportunities. The biological finding of the four main breast cancer subtypes caused by different subsets of genetic and epigenetic abnormalities raises the hypothesis that much of the clinically observable plasticity and heterogeneity occurs within, and not across, these major biological subtypes of breast cancer.

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Comprehensive molecular portraits of human breast tumours

Adulteration and mislabeling of honey to mask its true origin have become a global concern. Pollen microscopy, the current gold standard for identifying honey's geographical and plant origins, is laborious, requires extensive training, and fails to identify filtered honey and honey spiked with pollen from a more favorable plant to disguise its origins. We successfully isolated pollen-free DNA from filtered honey using three types of adsorbents: (i) anti-dsDNA antibodies coupled to magnetic microspheres; (ii) anion-exchange adsorbent; and (iii) ceramic hydroxyapatite. The internal transcribed spacer 2 region of the captured pollen-free DNA was polymerase chain reaction-amplified and subjected to next-generation sequencing. Using an in-house bioinformatics pipeline, initial experiments showed that anion exchange had the greatest capacity to capture trace pollen-free DNA, and it was successfully applied to isolate DNA from five honey samples. Enrichment of trace pollen-free DNA from filtered honey samples opens a new approach for identifying the true origins of honey.

Isolation and Barcoding of Trace Pollen-free DNA for Authentication of Honey.


 Background: Rhabdomyosarcoma (RMS) is the most common soft tissue sarcoma in children and has one of the poorest survival rates among pediatric cancers, underscoring the need to identify factors which may be leveraged to improve therapeutic options for these individuals.
 Methods: We carried out a genome-wide association study of overall survival (OS) and event-free survival (EFS) in 920 RMS patients from COG protocols and randomly divided them into discovery (n=642) and replication (n=278) cohorts. Genotyping was conducted using the Illumina OmniExpress or Global Screening Array and imputed using the Haplotype Reference Consortium. We used Cox proportional hazards regression to calculate an adjusted hazard ratio (aHR) and P value for each common variant (minor allele frequency [MAF]>5%) for OS and EFS while adjusting for age at diagnosis, tumor stage, histological subtype, and the top five principal components. Analyses were also conducted by histological subtype: embryonal RMS (ERMS, n=544) and alveolar RMS (ARMS, n=268). Finally, we performed a meta-analysis of the results from the discovery and replication cohorts to generate a summary aHR and P value for each single nucleotide polymorphism (SNP).
 Results: We identified an intergenic SNP at chr8q21.13 associated with worse RMS EFS across subtypes (aHR=2.08, P=2.80x10-9), which had consistent effects across the discovery (aHR=1.91, P=5.05x10-6) and replication (aHR=2.62, P=7.16x10-5) cohorts. This SNP lies in a region which spans the genomic binding site for GATA2 and GATA3, transcription factors that are recognized to contribute to cancer development. We also identified a significant association between a SNP at chr12q21.1 and worse EFS (aHR=2.04, P=3.35x10-8) with consistent effects across the discovery and replication cohorts. Based on data from the Genotype-Tissue Expression project (GTEx), this SNP is associated with expression of SLCO1B1, a gene which encodes a liver anion transporter linked to RMS treatment-related toxicities. In subtype-specific analyses, we identified a SNP at chr17q21.32 that was significantly associated with worse ARMS OS (129 events; aHR=3.18, P=3.12x10-8; discovery: aHR=3.19, P=6.23x10-4; replication: aHR=3.16, P=1.43x10-3). In GTEx, this SNP is associated with expression and splicing of genes including PITPNM3, KIAA0753, and MED31 across various tissues. No SNPs were significantly associated with ERMS OS or EFS.
 Conclusion: In the first GWAS of RMS survival outcomes, we identified two SNPs that were significantly associated with worse EFS across RMS subtypes. Further, we identified a SNP that was associated with OS in ARMS patients, a subtype that is associated with worse outcomes. Additional investigation of the impact of these SNPs may further support their consideration for novel risk stratification protocols.
 Citation Format: Bailey A. Martin-Giacalone, Michael E. Scheurer, Javed Khan, Stephen J. Chanock, Shengchao Alfred Li, Meredith Yeager, Deborah A. Marquez-Do, Donald A. Barkauskas, David Hall, Matthew T. McEvoy, Melissa A. Richard, Pagna Sok, Austin L. Brown, Aniko Sabo, Stephen X. Skapek, Douglas S. Hawkins, Rajkumar Venkatramani, Lisa Mirabello, Philip J. Lupo. Identification of common germline variants associated with pediatric rhabdomyosarcoma survival: A report from the Children's Oncology Group (COG) [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 683.

Abstract 683: Identification of common germline variants associated with pediatric rhabdomyosarcoma survival: A report from the Children's Oncology Group (COG)

Aniko Sabo

Papers

Isolation and Barcoding of Trace Pollen-free DNA for Authentication of Honey.

Abstract 683: Identification of common germline variants associated with pediatric rhabdomyosarcoma survival: A report from the Children's Oncology Group (COG)