Melanoma Molecular Profile

Decisive answers for treating metastatic melanoma

Problem Statement

Over 76,000 People Will Develop Melanoma in 20161.
Melanoma is Rising

In 2016, it’s estimated that melanoma will kill over 10,000 patients in the U.S. With 21.8 new cases and 2.7 deaths per 100,000 annually, melanoma is the 6th most common cancer. Rising at an alarming rate, incidence of melanoma has risen on an average of 1.4% each year over the last 10 years. 84% of melanoma are diagnosed at the local stage, while 4% are diagnosed at the distant stage, which is metastatic melanoma.1

Traditional Test Methods are Insufficient

Key mutations in BRAF, NRAS, and c-KIT often go undetected by traditional PCR-based tests. Missing these indicators leaves oncologists with incomplete information, leading to treatment plans that are less targeted and personalized.

Clinical Need

Traditional metastatic melanoma test methodologies do not always detect all the key, clinically relevant mutations in BRAF, NRAS, and c-KIT
Identifying Relevant Mutations

Identification of mutations helps physicians decide treatments for patients with metastatic melanoma. Among BRAF mutations, V600E and V600K mutations are associated with response to BRAF inhibitors.2, 3

Potential Treatment

Today, there are several treatments available for patients with a documented V600E or K mutation of the BRAF gene. Identifying these mutations early opens up an effective pathway for treatment with BRAF inhibitors.

Melanoma Molecular Profile

Molecular Profiling for Metastatic Melanoma means more treatment options for your patients

How it works

The Melanoma Molecular Profile utilizes capture-based NGS to detect and identify major class of genomic alterations that may not be detected by other test methodologies.

  • Base Substititions
  • Insertions and Deletions
  • Copy Number Variations (CNVs)
  • Identifies all clinically relevant BRAF V600 mutations as recommended by NCCN 4
Tissue Collection

We start with formalin fixed, paraffin-embedded (FFPE) tissue to test for 15 actionable metastatic melanoma genes in a single profile.

Identifying the Tumor-Rich Area

We perform a pathologist review on all slides to identify the tumor-rich region for testing.

The Melanoma Molecular Profile

The Melanoma Molecular Profile uses capture-based NGS to detect the following genomic alterations:

Gene List - AKT3, BRAF, CCND1, CDK4, ERBB4, GNA11, GNAQ, KIT, MAP2K1, MAP3K9, MITF, NF1, NRAS, PIK3CA, PTEN

The Melanoma Molecular Profile Advantage

A comprehensive test for a confident course of treatment
Make informed clinical decisions using the Melanoma Molecular Profile
  • Includes all 7 melanoma driver genes (BRAF, NRAS, KIT, GNAQ, GNA11, MITF, and NF1) 6
  • Uses capture-based NGS to detect copy number variation (8 genes) with therapeutic relevancy 6
  • Reports targeted therapeutic options 7, 8
  • Identifies clinical trials or therapeutic options for refractory patients 9, 10
  • Expands potential options for patients who previously tested negative by traditional detection technologies 11

References

  1. NIH National Cancer Institute. SEER Stat Fact Sheets: Melanoma of the Skin. Web site: http://seer.cancer.gov/statfacts/html/melan.html. Accessed April 29, 2016.
  2. Chapman PB, et al. Improved Survival with Vemurafenib in Melanoma with BRAF V600E Mutation. N Engl J Med 2011; 364:2507-16.
  3. Rubinstein JC et al. Incidence of the V600K mutation among melanoma patients with BRAF mutations, and potential therapeutic response to the specific BRAF inhibitor PLX4032. J Transl Med 2010;8:67.
  4. NCCN Guidelines Version 2.2016 – Melanoma.
  5. Data on file at Genoptix. Product Performance Specifications.
  6. Shtivelman et al. Pathways and therapeutic targets in melanoma; Oncotarget, Vol. 5, No. 7; 1701-1752.
  7. Frampton GM, Fichtenholtz A, Otto GA, et al. Development and validation of a clinical cancer genomic profiling test based on massively parallel DNA sequencing. Nat Biotechnology 2013; 31:1023-31. doi:10.1038/nbt.2696.
  8. Shendure J, Ji H. Next-generation DNA sequencing. Nat Biotechnology 2008;26:1135-45
  9. Roychowdhury S et al. Personalized Oncology through Integrative High-Throughput Sequencing: A Pilot Study. Science Translational Medicine. 2001;3(111);111ra121. doi:10.1126/scitranslmed.3003161.
  10. Drilon et al. Clin Cancer Res-2015-Drilon-1078-0432.CCR-14-2683.
  11. Dearing KR et al. Translating next generation sequencing from clinical trials to clinical practice for the treatment of advanced cancers. Personalized Medicine. 2015;12(2):155-62.

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