Second-generation Prognostic Signatures

The second-generation signatures, also microarray- or qRT PCR-based, improved upon the first-generation signatures by boosting accuracy of determine risk for long-term recurrence.

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These scores include PAM 50 (Prosigna), the Breast Cancer Index (BCI), and EndoPredict. Of these, Prosigna is FDA-approved to assign patients into 1 of 3 risk groups for recurrence between 5 to 15 years after the primary diagnosis.13

BCI is based on the expression of the HOXB13 and IL17BR genes and is for use for patients with estrogen receptor–positive disease and classifies patients into 1 of 3 risk groups for early and late distant recurrence.

EndoPredict is the newest prognostic signature and has been validated in patients with estrogen receptor positive or negative disease, as well as HER2-negative tumors. The EndoPredict score can be combined with tumor size and nodal status, called the EndoPredictClin, to provide an even better long-term prognosis of distant recurrence.

Concordance Among Prognostic Signatures

With so many first- and second-generation prognostic signatures available, it can be difficult to decide which tools to incorporate into practice. “They are not recommended as part of the standard of care in expert guidelines, including National Comprehensive Cancer Network and ASCO,” Joseph A. Sparano, MD, of the Albert Einstein College of Medicine in New York, New York, who is not affiliated with the ASCO Educational Book article, told Cancer Therapy Advisor.

Dr Sparano uses prognostic signatures “routinely in ER-positive, HER2-negative breast cancer with 0 to 3 positive nodes, where the information may be useful in making a treatment decision.” He told Cancer Therapy Advisor that the “level of evidence supporting their use,” is an important feature when selecting a specific prognostic signature.

Various first- and second-generation prognostic signatures provide a similar prediction, despite the use of different genes and algorithms. The exception to this is the 2-gene BCI signature. In a study of 295 tumor samples, MammaPrint, Oncotype DX, intrinsic subtypes, and the wound response provided a similar prediction for risk, though the BCI signature did not.14

The ability of the BCI signature to determine outcomes was positive in another study, but negative in another.15,16 Drs Ribnikar and Cardoso suggested that “a model based on the analysis of only 2 genes is much more likely to be sensitive to technical differences in analysis platforms than 1 based on many genes.”

RELATED: Some Patients With Endocrine-responsive Early-stage Breast Cancer May Avoid Chemotherapy

Gene expression prognostic signatures continue to evolve to provide greater accuracy for both short- and long-term risk of recurrence, and currently provide value in terms of aiding treatment decisions.

They should, however, be used together with clinicopathologic factors. “Adjuvant treatment decision making in breast cancer involves an integration of the available clinicopathologic factors and new genomic tools, whenever appropriate, as well as a detailed and comprehensible discussion with each individual patient,” wrote Drs Ribnikar and Cardoso.

References

  1. Ribnikar D, Cardoso F. Tailoring chemotherapy in early-stage breast cancer: based on tumor biology or tumor burden? Am Soc Clin Oncol Educ Book. 2016;35:e31-e38.
  2. National Comprehensive Cancer Network. NCCN Clinical Practice Guidelines in Oncology. Breast Cancer, V.2. 2016. https://www.nccn.org/professionals/physician_gls/pdf/breast.pdf. Accessed June 14, 2016.
  3. Senkus E, Kyriakides S, Ohno S, et al; ESMO Guidelines Committee. Primary breast cancer: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up. Ann Oncol. 2015;26(Suppl 5):v8-v30. 
  4. Olivotto IA, Bajdik CD, Ravdin PM, et al. Population-based validation of the prognostic model ADJUVANT! for early breast cancer. J Clin Oncol. 2005;23:2716-2725. 
  5. Perou CM, Sørlie T, Eisen MB, et al. Molecular portraits of human breast tumours. Nature. 2000;406:747-752. 
  6. Hu Z, Fan C, Oh DS, et al. The molecular portraits of breast tumors are conserved across microarray platforms.BMC Genomics. 2006;7:96. 
  7. Pusztai L, Mazouni C, Anderson K, et al. Molecular classification of breast cancer: limitations and potential.Oncologist. 2006;11:868-877. 
  8. Sapino A, Roepman P, Linn SC, et al. MammaPrint molecular diagnostics on formalin-fixed, paraffin-embedded tissue. J Mol Diagn. 2014;16:190-197. 
  9. Mook S, Schmidt MK, Viale G, et al; TRANSBIG Consortium. The 70-gene prognosis-signature predicts disease outcome in breast cancer patients with 1-3 positive lymph nodes in an independent validation study. Breast Cancer Res Treat. 2009;116:295-302. 
  10. Piccart M, Rutgers E, van’t Veer L, et al. Primary analysis of the EORTC 10041/ BIG 3-04 MINDACT study: a prospective, randomized study evaluating the clinical utility of the 70-gene signature (MammaPrint) combined with common clinical-pathological criteria for selection of patients for adjuvant chemotherapy in breast cancer with 0 to 3 positive nodes. Presented at the 2016 AACR Annual Meeting; New Orleans, LA; April 16-20, 2016.
  11. Sparano JA, Gray RJ, Makower DF, et al. Prospective validation of a 21-gene expression assay in breast cancer. N Engl J Med. 2015;373:2005-2014.
  12. NCT00310180. Hormone Therapy With or Without Combination Chemotherapy in Treating Women Who Have Undergone Surgery for  Node-Negative Breast Cancer (The TAILORx Trial) (TAILORx). National Cancer Institute. https://clinicaltrials.gov/ct2/show/NCT00310180. Accessed June 14, 2016.
  13. Gnant M, Filipits M, Greil R, et al; Austrian Breast and Colorectal Cancer Study Group. Predicting distant recurrence in receptor-positive breast cancer patients with limited clinicopathological risk: using the PAM50 Risk of Recurrence score in 1478 postmenopausal patients of the ABCSG-8 trial treated with adjuvant endocrine therapy alone. Ann Oncol. 2014;25:339-345. 
  14. Fan C, Oh DS, Wessels L, et al. Concordance among gene-expression-based predictors for breast cancer. N Engl J Med. 2006;355:560-569.
  15. Reid JF, Lusa L, De Cecco L, et al. Limits of predictive models using microarray data for breast cancer clinical treatment outcome. J Natl Cancer Inst. 2005;97:927-930.

Topics:

Breast Cancer