Characteristics and Outcome of AKT1 E17K-Mutant Breast Cancer Defined through AACR Project GENIE, a Clinicogenomic Registry

AKT inhibitors have promising activity in AKT1 ^E17K-mutant estrogen receptor (ER)-positive metastatic breast cancer, but the natural history of this rare genomic subtype remains unknown. Utilizing AACR Project GENIE, an international clinicogenomic data-sharing consortium, we conducted a comparative analysis of clinical outcomes of patients with matched AKT1 ^E17K-mutant (n = 153) and AKT1-wild-type (n = 302) metastatic breast cancer. AKT1-mutant cases had similar adjusted overall survival (OS) compared with AKT1-wild-type controls (median OS, 24.1 vs. 29.9, respectively; P = 0.98). AKT1-mutant cases enjoyed longer durations on mTOR inhibitor therapy, an observation previously unrecognized in pivotal clinical trials due to the rarity of this alteration. Other baseline clinicopathologic features, as well as durations on other classes of therapy, were broadly similar. In summary, we demonstrate the feasibility of using a novel and publicly accessible clincogenomic registry to define outcomes in a rare genomically defined cancer subtype, an approach with broad applicability to precision oncology. SIGNIFICANCE: We delineate the natural history of a rare genomically distinct cancer, AKT1 ^E17K-mutant ER-positive breast cancer, using a publicly accessible registry of real-world patient data, thereby illustrating the potential to inform drug registration through synthetic control data.See related commentary by Castellanos and Baxi, p. 490.

Unifying cancer and normal RNA sequencing data from different sources

Driven by the recent advances of next generation sequencing (NGS) technologies and an urgent need to decode complex human diseases, a multitude of large-scale studies were conducted recently that have resulted in an unprecedented volume of whole transcriptome sequencing (RNA-seq) data, such as the Genotype Tissue Expression project (GTEx) and The Cancer Genome Atlas (TCGA). While these data offer new opportunities to identify the mechanisms underlying disease, the comparison of data from different sources remains challenging, due to differences in sample and data processing. Here, we developed a pipeline that processes and unifies RNA-seq data from different studies, which includes uniform realignment, gene expression quantification, and batch effect removal. We find that uniform alignment and quantification is not sufficient when combining RNA-seq data from different sources and that the removal of other batch effects is essential to facilitate data comparison. We have processed data from GTEx and TCGA and successfully corrected for study-specific biases, enabling comparative analysis between TCGA and GTEx. The normalized datasets are available for download on figshare.

Genetic Predictors of Response to Systemic Therapy in Esophagogastric Cancer

The incidence of esophagogastric cancer is rapidly rising, but only a minority of patients derive durable benefit from current therapies. Chemotherapy as well as anti-HER2 and PD-1 antibodies are standard treatments. To identify predictive biomarkers of drug sensitivity and mechanisms of resistance, we implemented prospective tumor sequencing of patients with metastatic esophagogastric cancer. There was no association between homologous recombination deficiency defects and response to platinum-based chemotherapy. Patients with microsatellite instability-high tumors were intrinsically resistant to chemotherapy but more likely to achieve durable responses to immunotherapy. The single Epstein-Barr virus-positive patient achieved a durable, complete response to immunotherapy. The level of ERBB2 amplification as determined by sequencing was predictive of trastuzumab benefit. Selection for a tumor subclone lacking ERBB2 amplification, deletion of ERBB2 exon 16, and comutations in the receptor tyrosine kinase, RAS, and PI3K pathways were associated with intrinsic and/or acquired trastuzumab resistance. Prospective genomic profiling can identify patients most likely to derive durable benefit to immunotherapy and trastuzumab and guide strategies to overcome drug resistance.Significance: Clinical application of multiplex sequencing can identify biomarkers of treatment response to contemporary systemic therapies in metastatic esophagogastric cancer. This large prospective analysis sheds light on the biological complexity and the dynamic nature of therapeutic resistance in metastatic esophagogastric cancers. Cancer Discov; 8(1); 49-58. ©2017 AACR.See related commentary by Sundar and Tan, p. 14See related article by Pectasides et al., p. 37This article is highlighted in the In This Issue feature, p. 1.

Mutational landscape of metastatic cancer revealed from prospective clinical sequencing of 10,000 patients

Tumor molecular profiling is a fundamental component of precision oncology, enabling the identification of genomic alterations in genes and pathways that can be targeted therapeutically. The existence of recurrent targetable alterations across distinct histologically defined tumor types, coupled with an expanding portfolio of molecularly targeted therapies, demands flexible and comprehensive approaches to profile clinically relevant genes across the full spectrum of cancers. We established a large-scale, prospective clinical sequencing initiative using a comprehensive assay, MSK-IMPACT, through which we have compiled tumor and matched normal sequence data from a unique cohort of more than 10,000 patients with advanced cancer and available pathological and clinical annotations. Using these data, we identified clinically relevant somatic mutations, novel noncoding alterations, and mutational signatures that were shared by common and rare tumor types. Patients were enrolled on genomically matched clinical trials at a rate of 11%. To enable discovery of novel biomarkers and deeper investigation into rare alterations and tumor types, all results are publicly accessible.

The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data

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.

The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data

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.

Pathway Commons, a web resource for biological pathway data

Pathway Commons (http://www.pathwaycommons.org) is a collection of publicly available pathway data from multiple organisms. Pathway Commons provides a web-based interface that enables biologists to browse and search a comprehensive collection of pathways from multiple sources represented in a common language, a download site that provides integrated bulk sets of pathway information in standard or convenient formats and a web service that software developers can use to conveniently query and access all data. Database providers can share their pathway data via a common repository. Pathways include biochemical reactions, complex assembly, transport and catalysis events and physical interactions involving proteins, DNA, RNA, small molecules and complexes. Pathway Commons aims to collect and integrate all public pathway data available in standard formats. Pathway Commons currently contains data from nine databases with over 1400 pathways and 687 000 interactions and will be continually expanded and updated.

cPath: open source software for collecting, storing, and querying biological pathways

Background

Biological pathways, including metabolic pathways, protein interaction networks, signal transduction pathways, and gene regulatory networks, are currently represented in over 220 diverse databases. These data are crucial for the study of specific biological processes, including human diseases. Standard exchange formats for pathway information, such as BioPAX, CellML, SBML and PSI-MI, enable convenient collection of this data for biological research, but mechanisms for common storage and communication are required.

Results

We have developed cPath, an open source database and web application for collecting, storing, and querying biological pathway data. cPath makes it easy to aggregate custom pathway data sets available in standard exchange formats from multiple databases, present pathway data to biologists via a customizable web interface, and export pathway data via a web service to third-party software, such as Cytoscape, for visualization and analysis. cPath is software only, and does not include new pathway information. Key features include: a built-in identifier mapping service for linking identical interactors and linking to external resources; built-in support for PSI-MI and BioPAX standard pathway exchange formats; a web service interface for searching and retrieving pathway data sets; and thorough documentation. The cPath software is freely available under the LGPL open source license for academic and commercial use.

Conclusion

cPath is a robust, scalable, modular, professional-grade software platform for collecting, storing, and querying biological pathways. It can serve as the core data handling component in information systems for pathway visualization, analysis and modeling.