GPS Cancer

Molecular Insights You Can Rely On

Molecular Insights You Can Rely On – Tumor-Normal Sequencing of DNA + RNA Expression

When faced with a difficult treatment decision, GPS Cancer® offers a precise and comprehensive molecular profile, providing oncologists with unprecedented insights into the molecular signature of each patient’s cancer to inform personalized treatment strategies. This unique profile:

Includes whole genome/exome sequencing of 20,000 genes and 3 billion base pairs

Incorporates whole transcriptome sequencing of over 200,000 RNA transcripts

Compares a patient’s tumor genome to their normal genome and provides pharmacogenomic analysis for potential drug toxicity and/or interactions

Based on the tumor’s “molecular fingerprint”, GPS Cancer offers insight into therapies that may have potential benefit, including FDA approved therapies and active clinical trials, and therapies to which the cancer may be resistant. GPS Cancer is performed in CAP-accredited, CLIA-certified labs.

How Precise Is Your Precision Medicine?

When striving to deliver precision cancer care, it’s essential to have precise information. When a test identifies a target for a drug, you need to be confident that the target is there.

At NantHealth, we believe that looking at tumor DNA is not enough. Increasingly, research is showing that tumor-only sequencing can allow false positive mutation calls by not adequately filtering out germline mutations. Likewise, alterations in DNA are sometimes not transcribed into altered RNA or expressed as protein, and as such, may not be viable targets for drug treatment.

Why Tumor-Normal Sequencing

Tumor-normal sequencing may help avoid inappropriate therapies due to misinterpretation of inherited mutations as somatic and confirms provenance — i.e., that the tumor being tested comes from that patient.


John Hopkins – “a tumor-only sequencing approach could not definitively identify germline changes in cancer-predisposing genes and led to additional false-positive findings comprising 31% and 65% of alterations identified in targeted and exome analyses, respectively, including in potentially actionable genes.” ¹


Moffitt Cancer Center – “Matched tumor/normal mutation detection is more appropriate for applications requiring high precision such as novel mutation detection and mutation signature analysis and remains the optimal approach.” ² 


NantOmics and NantHealth – With tumor-only sequencing, 29% of lung cancer patients included in the study had at least one false-positive variant in a druggable gene.³ Download below.

Why RNA?

Whole transcriptome (RNA) sequencing may help avoid inappropriate therapies by confirming genomic alterations that may result in expression of abnormal protein. RNA sequencing provides a quantitative measure of gene expression, and identifies gene fusions resulting from genomic translocations.

Advancements in genome (DNA) sequencing have been instrumental in understanding genomic alterations that may drive a patient’s cancer, but genomic sequencing alone is only part of the story.

DNA is the blueprint for RNA. Emerging research4 shows that alterations in DNA are sometimes not transcribed into altered RNA, or expressed as protein. Likewise, alterations are sometimes introduced at the RNA or protein level that are not detectable at the DNA level.

RNA is the blueprint for protein. To make the most informed treatment decisions, it is essential to understand the impact of genomic alterations on RNA expression and protein.

References: 1 Jones et al. Personalized genomic analyses for cancer mutation discovery and interpretation. Sci Transl Med. 2015 April 15; 7(283): 283ra53. 2 Teer JK et al. Evaluating somatic tumor mutation detection without matched normal samples. Human Genomics. 2017;11:22. 3 Rabizadeh et al. Comprehensive genomic transcriptomic tumor-normal gene panel analysis for enhanced precision in patients with lung cancer. Oncotarget. 2018; 9:19223-19232. This is an open-access article distributed under the terms of the Creative Commons Attribution License 3.0 (CC BY 3.0). 4 Wang Q, Xia J, Jia P, Pao W, Zhao Z. Application of next generation sequencing to human gene fusion detection: computational tools, features and perspectives. Briefings in Bioinformatics. 2013;14(4):506-519.May;7(5):201-4.

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Research Oncotarget (April 2018)