Comprehensive testing for breast cancer evaluating DNA and RNA

Genomic Testing cooperative (GTC) provides a comprehensive genomic profiling of breast cancer including evaluating:

– Actional mutations
– Gene amplification and deletion
– Hormonal expression levels
– Microsatellite stability (MSI)
– Tumor mutation burden (TMB)
– Homologous recombination deficiency (HRD) status and homologous recombination repair (HRR) gene testing

The GTC tests for breast cancer (Liquid Trace Solid Tumor and Solid Tumor Profile Plus) includes comprehensive DNA and RNA testing with artificial intelligence (AI) evaluation of expression profile. Furthermore, GTC offers liquid biopsy testing for breast cancer monitoring and minimal residual (MRD) evaluation. The liquid biopsy is unique by evaluating cfDNA and cfRNA to provide higher sensitivity and more comprehensive information on the disease and the host, especially the immune system of the host. This liquid biopsy approach is used extensively by neuro-oncologists to evaluate cerebral spinal fluid (CSF) for brain metastases.

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References:

1. Homologous Recombination Abnormalities Associated With BRCA1/2 Mutations as Predicted by Machine Learning of Targeted Next-Generation Sequencing Data. Albitar M, Zhang H, Pecora A, Waintraub S, Graham D, Hellmann M, McNamara D, Charifa A, De Dios I, Ma W, Goy A. Breast Cancer (Auckl). 2023 Sep 30;17:11782234231198979. doi: 10.1177/11782234231198979. eCollection 2023.
2. The use of transcriptomic data in developing biomarkers in breast cancer
3. Maher Albitar MD, Andre Goy MD, Andrew Pecora MD, Deena Graham MD, Donna McNamara MD, Ahmad Charifa MD, Andrew IP MD, Wanlong Ma MS, Stanley Waintraub MD. ImmunoMedicine, 17 December 2023. https://doi.org/10.1002/imed.1051.
4. Differential Diagnosis of Hematologic and Solid Tumors Using Targeted Transcriptome and Artificial Intelligence.
5. Zhang H, Qureshi MA, Wahid M, Charifa A, Ehsan A, Ip A, De Dios I, Ma W, Sharma I, McCloskey J, Donato M, Siegel D, Gutierrez M, Pecora A, Goy A, Albitar M. Am J Pathol. 2023 Jan;193(1):51-59. doi: 10.1016/j.ajpath.2022.09.006. Epub 2022 Oct 13. PMID: 36243045
6. Charifa A, Lam A, Zhang H, Ip A, Pecora A, Waintraub S, Graham D, McNamara D, Gutierrez M, Jennis A, Sharma I, Estella J, Ma W, Goy A, Albitar M. Predicting PD-L1 Status in Solid Tumors Using Transcriptomic Data and Artificial Intelligence Algorithms. J Immunother. 2024 Jan;47(1):10–5. doi: 10.1097/

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