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How do we realise the potential of precision medicine?

Precision medicine

The shift towards precision medicine

Clinical practice is shifting towards an era of precision medicine where molecular insights enable treatment to be personalised to the unique genomic profile of a patient’s tumour.1–3 Cancer care is increasingly complex as more targetable genes are identified and treatment choice grows;4–8 in 2017, there were over 700 molecules in late-stage development, 90% of which were targeted treatments.9 An evolving approach to clinical diagnostics and decision-making is required if we are to manage this increasing complexity and realise the potential of precision medicine.4,10

Organ Based Biomarker Stratied Precision Medicine Chemotherapy Cancer is treated primarily according to its location in the body Personalised Treatment Molecular insights enable treatment to be personalised to the unique genomic profile  of a patients tumour Targeted Medicines Cancer therapy is selected based on both organ and biomarker 1990 2000 2010 2020 2030 1980
Genomic insights

Capturing clinically relevant genomic insights

There are four main classes of genomic alterations: base substitutions, insertions or deletions, copy number alterations and gene rearrangements. But are current diagnostic approaches up to the task of identifying them all? Single biomarker tests, using common diagnostic techniques such as PCR/IHC/FISH, and multigene hotspot NGS tests risk missing genomic alterations that may be critical to patients’ treatment plans.4,11–13

Furthermore, complex pan-tumour biomarkers, such as tumour mutational burden (TMB) and microsatellite instability (MSI), may provide further valuable insights to help personalise treatment plans. MSI has been shown to predict response to immunotherapy and TMB is emerging as a potential biomarker for enriched clinical benefit with immunotherapy.14–19 However, TMB and MSI can only be measured together effectively with comprehensive profiling of the tumour genome.14,15

MSI TMB Base sub s titutions Insertions and deletions C o p y number al t e r a tions T umour mut a tional bu r den Mic r os a t elli t e in s tability R ear r angements C ompl e x pan-tumour biomar k ers Four main classes of genomic alterations
Evolving approach

An evolution in diagnostics and clinical decision-making

Ensuring that cancer patients can benefit from the latest treatment innovations requires an evolving approach to clinical diagnostics and decision-making, one that:

✓   Identifies clinically relevant genomic alterations and signatures

✓   Provides clinical decision-making support

✓   Personalises patients’ treatment plans

Comprehensive genomic profiling is important to ensure patients can benefit from the latest treatment innovations.1,10,20

“The NCCN Panel strongly advises broader molecular profiling
(also known as precision medicine)”

NCCN Guidelines for NSCLC Version 5, 201820

“Multiplexed genetic sequencing panels are preferred where available over multiple single gene tests to identify other treatment options beyond EGFR, ALK, BRAF and ROS1.”

ASCO endorsement of CAP/IASLC/AMP guidelines for lung cancer, 201821,22

AMP, Association for Molecular Pathology. ASCO, American Society of Clinical Oncology. CAP, College of American Pathologists. FISH, fluorescence in situ hybridisation. IASLC, International Association for the Study of Lung Cancer. IHC, immunohistochemistry. MSI, microsatellite instability. NCCN, National Comprehensive Cancer Network. NGS, next generation sequencing. NSCLC, non-small cell lung cancer. PCR, polymerase chain reaction. TMB, tumour mutational burden.
References
  1. Rozenblum AB et al. J Thorac Oncol 2017; 12: 258–268.
  2. Schwaederle M, Kurzrock R. Oncoscience 2015; 2: 779–780.
  3. Mansinho A et al. Expert Rev Anticancer Ther 2017; 17: 563–565.
  4. Frampton GM et al. Nat Biotechnol 2013; 31: 1023–1031.
  5. Drilon A et al. Clin Cancer Res 2015; 21: 3631–3639.
  6. Hirsch FR et al. Lancet 2016; 388: 1012–1024.
  7. Baumgart M. Am J Hematol Oncol 2015; 11: 10–13.
  8. Chakravarty D et al. JCO Precis Oncol 2017; doi: 10.1200/PO.17.00011. 
  9. Global Oncology Trends Report 2018. Report by IQVIA Institute for Human Data Science. Available at: https://www.iqvia.com/-/media/iqvia/pdfs/institute-reports/global-oncology-trends-2018.pdf (Accessed March 2019).
  10. Gagan J, Van Allen EM. Genome Med 2015; 7: 80.
  11. Schrock AB et al. Clin Cancer Res 2016; 22: 3281–3285.
  12. Rankin A et al. Oncologist 2016; 21: 1306–1314.
  13. Suh JH et al. Oncologist 2016; 21: 684–691.
  14. Chalmers ZR et al. Genome Med 2017; 9: 34.
  15. Goodman AM et al. Mol Cancer Ther 2017; 16: 2598–2608.
  16. Le DT et al. N Engl J Med 2015; 372: 2509–2520.
  17. Johnson DB et al. Cancer Immunol Res 2016; 4: 959–967.
  18. Rizvi H et al. J Clin Oncol 2018; 36: 633–641.
  19. Hellmann MD et al. N Engl J Med 2018; 378: 2093–2104.
  20. NCCN Clinical Practice Guidelines in Oncology (NCCN Guidelines). Non-Small Lung Cancer. V.5.2018, 2018. Available at: https://www.nccn.org/professionals/physician_gls/recently_updated.aspx (Accessed March 2019).
  21. Kalemkerian GP et al. J Clin Oncol 2018; 36: 911–919.
  22. Lindeman NI et al. J Mol Diagn 2018; 20: 129–159.