Dr Wolf on Biomarkers Predicting Responses to Immunotherapy in Breast Cancer


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Denise M. Wolf, PhD, discusses research into biomarkers predicting responses to immunotherapy in the phase 2 I-SPY2 trial in patients with early-stage breast cancer.

Denise M. Wolf, PhD, Department of Laboratory Medicine, University of California, San Francisco (UCSF), UCSF Health, discusses research into biomarkers predicting responses to immunotherapy in the phase 2 I-SPY2 trial (NCT01042379) in patients with early-stage breast cancer.

At the 2023 ASCO Annual Meeting, investigators presented data on the evaluation of immune subtyping in response predictive subtypes of patients with early-stage breast cancer. In this study, investigators firstinvestigated a set of continuous immune-related biomarkers in this population, Wolf begins. These biomarkers represented different genes that are part of the checkpoints that prevent a patient's immune system from recognizing and destroying cancer, she says. These biomarkers included immune cell subpopulation signatures and tumor-immune signaling signatures, Wolf adds. The investigators aimed to determine which biomarkers were associated with response as a continuous variable, which patient groups responded, and to which arm of therapy they responded, Wolf explains.

One of investigators’ main observations was a group of biomarkers that can be classified as immune tumor signaling, which are enriched for chemokines and cytokines but not for T-cell receptors and other cell population signatures, Wolf expands. Those biomarkers are highly associated with response to immunotherapy in both hormone receptor–positive and triple-negative breast cancers, she emphasizes.

Furthermore, this study included a multiplex immunofluorescence analysis conducted by an immunologist, Wolf continues. Multiplex immunofluorescence involves staining tumor sections from patients with different antibodiesto reveal the kinds of immune cells that are present in a given patient, how many are present, and what their relative spatial orientation is compared with the tumor cells, Wolf says. For example, in tumors, this analysis can determine the presence of immune cells, which ones are present, and whether they are mixed in with the tumor cells or segregated, she explains.

The study investigators used these data to understand the biology of predictive signature captures, Wolf says, explaining that the strongest correlations were found between spatial proximity measures that indicate a high level of colocalization between PD-1–positive T-cells and PD-L1–positive tumor cells. This correlation was strongest in patients with triple-negative breast cancer, Wolf concludes.

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