Questions Raised About Prognostic, Predictive Role of Genomic Signatures in Early Breast Cancer

Targeted Therapies in Oncology, June 2018, Volume 7, Issue 6

In a presentation during a controversy session at the 11th European Breast Cancer Conference, Angelo Di Leo, MD, PhD, explained where genomic signatures have shown the most benefit and potential ways forward to make recurrence scores a truly beneficial marker for clinicians to use.

Angelo Di Leo, MD, PhD

Genomic signatures, such as MammaPrint and the Oncotype DX Recurrence Score, have been used for many years to denote patients who have a lower or higher risk of disease recurrence and to predict patients who would benefit from particular treatments. However, the true predictive and prognostic value of these genomic signatures in early breast cancer has been called into question on occasion.

In a presentation during a controversy session at the 11th European Breast Cancer Conference, Angelo Di Leo, MD, PhD, explained where genomic signatures have shown the most benefit and potential ways forward to make recurrence scores a truly beneficial marker for clinicians to use.


“In my opinion, prognosis by genomic signatures tend to overestimate the risk in the bad prognosis cohort,” said Di Leo, head of the Sandro Pitigliani Medical Oncology Unit and chair of the Department of Oncology, Hospital of Prato, Istituto Toscano Tumori in Prato, Italy.

He pointed to the results of the phase III MINDACT trial, in which patients’ genomic risk was determined using the 70-gene signature test, MammaPrint. In the randomized study, women with a high clinical and/or genomic risk received adjuvant chemotherapy and those with low risk did not.1

At 5 years, 90.6% of the women who were classified as high risk both clinically and by MammaPrint score, were found to be free of distant metastases. Di Leo, however, doubted that 90.6% of high-risk patients could truly be saved with adjuvant chemotherapy alone. “The current genomic tools may overestimate the risk of disease relapse,” he said.

Di Leo explained that the genomic signatures look at the biology of the primary tumor, and based on that information, make assumptions about each patient’s tumor on the presence of micrometastatic disease. “What we may need is a tool that can detect the presence of micrometastatic disease and combine this information with the biology information that you can get from the primary tumor.”

He pointed to other ways to prognosticate outcomes for women with breast cancer, such as mutation tracking with circulating tumor DNA (ctDNA) testing and metabolomic studies.

Preliminary results from a pilot study of tracking mutations with ctDNA showed that this method could be used to predict outcomes in patients with early breast cancer. Sequential collections of serum samples were performed in women with early breast cancer who had undergone surgery looking for the presence of minimal residual disease in ctDNA. Di Leo explained that patients in which ctDNA was detected had a poor prognosis for disease-free survival, whereas those who were ctDNA negative had a good prognosis (P<.0001).2

The mutation tracking showed greater sensitivity for predicting metastatic relapse, and had a median lead time of 7.9 months for identifying disease relapse over clinical relapse.

Metabolomic studies looked instead at the pattern of metabolites that are typically associated with the presence of metastasis to distinguish between early and metastatic disease and identify patients likely to relapse.

Di Leo referenced 2 metabolomic studies of patients with early breast cancer, one focusing on estrogen receptor (ER)—positive disease and the other on ER-negative disease. In each study the investigators took serum samples from the participants and compared the pattern of metabolites of the patients with early breast cancer to the pattern typical of metastatic breast cancer to create a prognostic model for patients likely to relapse. Those who had a similar metabolic pattern to the model were classified as high risk.3,4

In the ER-positive study, the prognostic model was able to differentiate between early and metastatic breast cancer, with an accuracy of 84.9%. In addition, the model achieved an accuracy of 71.3% for predicting relapse, with a sensitivity of 70.8% and a specificity of 71.4%.3

A metabolomic risk model accurately predicted relapse in patients with ER-negative breast cancer with 90% sensitivity, 67% specificity, and 73% accuracy. The model could also distinguish between early and metastatic disease in 83.7% of cases.4

Across the 2 studies, patients classified as high risk using the metabolomic risk model had a worse prognosis than those with a low risk for relapse.

Di Leo recommended combining the information from each of these approaches for a better prognostic tool: “The next steps should be integrating standard pathology and genomic tools with other types of tools that can detect the presence of micrometastatic disease. The best prognosis in my opinion can be evaluated if we have a tool that can estimate the presence of micrometastatic disease and, even better, that can measure the presence of micrometastatic disease.”


“The clinician is interested in understanding if a patient with ER-positive disease has to receive adjuvant therapy or not. That’s the key question for a clinician,” Di Leo said.

Investigators have sought to find the answer to fill this clinical need in several studies. A retrospective analysis of the NSABP B20 trial, for example, investigated the relationship between the Oncotype DX Recurrence Score and adjuvant chemotherapy benefit in 651 postmenopausal women with ER-positive, node-negative breast cancer; 227 were randomly assigned to tamoxifen treatment and 424 to tamoxifen plus chemotherapy. In the study, patients who had a high recurrence score (≥31) received a large benefit from added chemotherapy (relative risk [RR], 0.26; 95% CI, 0.13-0.53), and those with a low recurrence score (<18) had little-to-no benefit from chemotherapy (RR, 1.31; 95% CI, 0.46-3.78) (TABLE 1).5

“The results are very consistent with the recurrence score. When you have the so-called low-risk patients, it seems that there is no benefit from adjuvant chemotherapy, it seems that the benefit is confined to the population with high-risk, in that case you see the benefit of adjuvant chemotherapy,” Di Leo commented.

These results were recently confirmed with the randomized phase III TAILORx trial presented at the 2018 ASCO Annual Meeting, which showed again that women with hormone receptor (HR)—positive, HER2-negative breast cancer and high recurrence scores (range, 26-100), as per the Oncotype DX Recurrence Score, receive a benefit from added adjuvant chemotherapy, whereas women with a low recurrence score (range, 0-10) do not. The study results also demonstrated that a majority of women with intermediate risk scores did not need added chemotherapy.6

At 9 years, women with a low recurrence score in the TAILORx trial had a distant recurrence rate of 3.2% with endocrine therapy alone, and women with a high recurrence score had a 13.2% rate of distant recurrence with endocrine therapy and added chemotherapy. In the intermediate group, for women treated with either endocrine therapy alone or in combination with chemotherapy, the overall rate of distant recurrence was 5.3% (TABLE 27).

“I think the [Oncotype DX] Recurrence Score is the only signature that has been tested in the ideal context,” said Di Leo, relating to a randomized study versus retrospective data in this setting. Other genomic signatures have not yet demonstrated predictive value in this setting.

Di Leo also suggested that the PAM50 gene expression signature looked promising in identifying the patients with ER-positive breast cancer who would benefit the most from treatment with endocrine therapy.

In an unplanned retrospective analysis of the phase III EGF30008 trial, which looked at postmenopausal women with HR-positive invasive breast cancer treated with letrozole (Femara) with or without lapatinib (Tykerb), researchers found that benefit from treatment differed by breast cancer subtype. Using PAM50, women were classified into subtypes: luminal A, luminal B, HER2-enriched, basal-like, and normal-like. Responses to treatment differed across the subtypes. Women with luminal A, HER2-negative disease benefited the most from letrozole alone, with a median progression-free survival (PFS) of 16.9 months from letrozole monotherapy. Alternatively, women with HER2-enriched or basal-like disease did not benefit as much from letrozole monotherapy, with a median PFS of 4.7 and 4.1 months, respectively. This group derived a greater benefit from added lapatinib therapy.8

Activity for anti-HER2 therapy could also be predicted using the PAM50 test in patients with HER2-positive breast cancer, according to the results of the phase II PAMELA trial. The patients, who were again classified into one of the 5 subtypes, were treated with lapatinib and trastuzumab (Herceptin). More patients with the HER2-enriched subtype (n = 101) achieved a pathological complete response compared with those with non—HER2-enriched subtypes (n = 50; 41% vs 10%; odds ratio, 6.2; 95% CI, 2.3-16.8;P= .0004).9

Di Leo suggested that further validation of the predictive value of the PAM50 gene signature, or other signatures, are needed in both of these settings.


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