Realize the Potential of Precision Oncology

Effective and personalized cancer diagnosis and management relies on a complete genomic map of the malignancy, including the full range of chromosomal abnormalities which are difficult to detect with traditional cytogenetics and molecular methods. The CytoTerra^® Cytogenetics Platform leverages the unique strengths of Genomic Proximity Mapping™ (GPM) to provide a high-resolution view of translocation and inversion breakpoints, as well as copy number variants (CNVs), using a single NGS-based assay. CytoTerra is compatible with a wide range of sample types, thereby unlocking the complexities of both solid tumors and blood cancers. From one sample to a plate of 96, the CytoTerra Platform meets modern cytogenetic needs without specialized instrumentation.

		Simultaneously detect all major types of genomic rearrangements that cause and characterize cancer, on a genome-wide scale.
		Unlock the wealth of potential diagnostic and prognostic information contained within fresh, frozen, or non-viable, archival FFPE samples. GPM does not require actively dividing cells or high molecular weight DNA extraction.
		Reduce turnaround time and cost by characterizing the breadth of chromosome abnormalities in a single, scalable NGS-based assay.

 

 

 

Detect all major types of re-arrangements

Circos plot provides a genome-wide view	Coverage and minor allele frequency support deletion calls	Detection of t(6;11) creating a KMT2A::AFDN fusion	Identification of inv(16) creating a CBFB::MYH11 fusion

Automated analytics yield actionable results The CytoTerra® Platform provides a sample-to-report NGS-based cytogenetics assay that starts with a solid tumor (e.g., FFPE) or hematological sample, and ends with a comprehensive and actionable report in standard and sequence-based nomenclature. Fully automated, cloud-based analysis of paired-end Illumina® sequencing data is performed with Phase Genomics’ proprietary computational tools. The entire process can be completed in less than a week, and is scalable to large numbers of samples.