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August 4, 2017

Image Analysis Tool Predicts Long-Term Recurrence of Breast Cancer

  • Scientists at the Institute for Cancer Research (ICR) in the U.K. have developed a computer tool that can predict the likelihood of breast cancer recurrence within 10 years of treatment in women with the most common estrogen receptor-positive (ER+) form of the disease. The image analysis system is used with routinely collected tumor samples to look specifically for clusters, or "hotspots," of tumor infiltrating lymphocytes (TILs), which can’t be detected readily by using a microscope.

    The results from tests with the new system are reported today in the Journal of the National Cancer Institute, in a paper entitled “Relevance of Spatial Heterogeneity of Immune Infiltration for Predicting Risk of Recurrence After Endocrine Therapy of ER+ Breast Cancer.” The researchers, led by Yinyin Yuan, Ph.D., say the results indicate that different immunosuppressive mechanisms are at work in ER+ and ER– forms of breast cancer. The findings may also help to explain why newer forms of immunotherapy have demonstrated mixed levels of success against different types of breast cancer in clinical trials.

    “What this study also tells us is that the immune system probably has a key role to play in how breast cancer responds to hormone treatment,” commented Paul Workman, FMedSci, FRS, chief executive at the ICR. “Measuring the immune response to cancer could be important in future to help identify patients who could benefit from immunotherapy.”

    The ICR team tested the predictive power of their image analysis system on samples from 1178 women with ER+ breast cancer who had received treatment with either anastrozole or tamoxifen. The findings showed that while the overall abundance of TILs had no predictive value for disease recurrence, the presence of immune cell clusters, or hotspots, in and around the tumors was associated with a 25% higher chance of relapse within 10 years of treatment, compared with when TILs were more evenly dispersed. The results indicated that these spatial scores were as good as current tests, including the immunohistochemistry-based IHC4 test and the Oncotype DX 21-gene recurrence score (RS), at predicting late relapse.

    Prior studies by the ICR researchers had found that immune cell hotspots in ER– breast cancer were associated with an opposite, lower chance of relapse, and this suggests that different immune system responses impact on the two types of breast cancer.

    “The difference may be due to immune composition and functionality in the two subtypes and mechanisms by which immune response contributes to hormonal therapy resistance,” the authors write. “Our findings support different immunosuppressive mechanisms in the ER+ and ER– subtypes, and in light of these results call for the development of novel cancer therapeutics targeting the pathways that reverse these mechanisms specifically for ER+ disease.”

    The team, in addition, questions whether the different immunosuppressive mechanisms might also impact on the effectiveness of treatment with anti-programmed cell death-1 (PD-1) immunotherapy. “This may also help explain why anti-PD1 checkpoint inhibition, despite demonstrating activity as monotherapy in early-phase trials in ER+ breast cancer, had low response rates compared with triple-negative breast cancer and was highly variable among trials,” they conclude. “Further, we speculate that immune scores may be useful as predictive biomarkers for immunotherapy, given the limited clinical utility of PDL1 expression in guiding patient selection.”

    Larger studies will be needed to validate the image analysis test before it can be used widely to pick out patients who are at a relatively higher risk of cancer recurrence, admits Dr. Yuan, who is team leader in computational pathology at the ICR. However, she suggests, given that the samples used in the study were collected as part of routine clinical practice, “implementing an immune hotspot test would be relatively easy and cost effective…it might also be possible to predict which patients would respond to immunotherapy.”
     

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