A Phase Ib Study of Preoperative, Locoregional IRX-2 Cytokine Immunotherapy to Prime Immune Responses in Patients with Early-Stage Breast Cancer

Trial watch: chemotherapy-induced immunogenic cell death in oncology

Immunogenic cell death (ICD) refers to an immunologically distinct process of regulated cell death that activates, rather than suppresses, innate and adaptive immune responses. Such responses culminate into T cell-driven immunity against antigens derived from dying cancer cells. The potency of ICD is dependent on the immunogenicity of dying cells as defined by the antigenicity of these cells and their ability to expose immunostimulatory molecules like damage-associated molecular patterns (DAMPs) and cytokines like type I interferons (IFNs). Moreover, it is crucial that the host's immune system can adequately detect the antigenicity and adjuvanticity of these dying cells. Over the years, several well-known chemotherapies have been validated as potent ICD inducers, including (but not limited to) anthracyclines, paclitaxels, and oxaliplatin. Such ICD-inducing chemotherapeutic drugs can serve as important combinatorial partners for anti-cancer immunotherapies against highly immuno-resistant tumors. In this Trial Watch, we describe current trends in the preclinical and clinical integration of ICD-inducing chemotherapy in the existing immuno-oncological paradigms.

Immunotherapy for Metastatic Triple Negative Breast Cancer: Current Paradigm and Future Approaches

OPINION STATEMENT

In approximately 15-20% of the patients diagnosed with breast cancer, it comprises the triple negative (TN) subtype, which until recently lacked targets for specific treatments and is known for its aggressive clinical behavior in patients with metastatic disease. TNBC is considered the most immunogenic breast cancer subtype due to higher levels of tumor infiltrating lymphocytes (TILs), tumor mutational burden and PD-L1 expression, providing a rationale for immunotherapy. The addition of pembrolizumab to chemotherapy as first-line treatment resulted in significantly improved PFS and OS for PD-L1 positive mTNBC, leading to FDA approval. However, response rate of ICB in unselected patients is low. Ongoing (pre)clinical trials aim to further optimize ICB efficacy and widen its application beyond PD-L1 positive breast tumors. Novel immunomodulatory approaches to induce a more inflamed tumor microenvironment include dual checkpoint blockade, bispecific antibodies, immunocytokines, adoptive cell therapies, oncolytic viruses, and cancer vaccines. Preclinical data for these novel strategies seems promising, but solid clinical data to further support its application for mTNBC is awaited. Biomarkers capturing the degree of immunogenicity such as but not limited to TILs, CD8 T cell levels, and IFNg signatures could support deciding which therapeutic strategy is most appropriate for which patient. Given 1) the accumulating therapy options for patients with metastatic disease and 2) the heterogeneity of mTNBC from inflamed to immune-desert tumors, the challenge is to work towards immunomodulatory strategies for specific subgroups of patients with TNBC to enable personalized (immuno)therapy for patients with metastatic disease.

Research progress on immunotherapy in triple‑negative breast cancer (Review)

Triple-negative breast cancer (TNBC) is a highly heterogeneous and aggressive malignancy. Due to the absence of estrogen receptors and progesterone receptors and the lack of overexpression of human epidermal growth factor receptor 2, TNBC responds poorly to endocrine and targeted therapies. As a neoadjuvant therapy, chemotherapy is usually the only option for TNBC; however, chemotherapy may induce tumor resistance. The emergence of immunotherapy as an adjuvant therapy is expected to make up for the deficiency of chemotherapy. Most of the research on immunotherapies has been performed on advanced metastatic TNBC, which has provided significant clinical benefits. In the present review, possible immunotherapy targets and ongoing immunotherapy strategies were discussed. In addition, progress in research on immune checkpoint inhibitors in early TNBC was outlined.

Dissecting Tumor-Immune Microenvironment in Breast Cancer at a Spatial and Multiplex Resolution

The tumor immune microenvironment (TIME) is an important player in breast cancer pathophysiology. Surrogates for antitumor immune response have been explored as predictive biomarkers to immunotherapy, though with several limitations. Immunohistochemistry for programmed death ligand 1 suffers from analytical problems, immune signatures are devoid of spatial information and histopathological evaluation of tumor infiltrating lymphocytes exhibits interobserver variability. Towards improved understanding of the complex interactions in TIME, several emerging multiplex in situ methods are being developed and gaining much attention for protein detection. They enable the simultaneous evaluation of multiple targets in situ, detection of cell densities/subpopulations as well as estimations of functional states of immune infiltrate. Furthermore, they can characterize spatial organization of TIME—by cell-to-cell interaction analyses and the evaluation of distribution within different regions of interest and tissue compartments—while digital imaging and image analysis software allow for reproducibility of the various assays. In this review, we aim to provide an overview of the different multiplex in situ methods used in cancer research with special focus on breast cancer TIME at the neoadjuvant, adjuvant and metastatic setting. Spatial heterogeneity of TIME and importance of longitudinal evaluation of TIME changes under the pressure of therapy and metastatic progression are also addressed.

Novel classes of immunotherapy for breast cancer

Immune-checkpoint inhibitors have profoundly changed the treatment landscape for many tumor types. Despite marked improvements in disease control for highly immunogenic cancers, the clinical impact of checkpoint inhibitors in breast cancers to date is limited. Breast cancer is a heterogeneous disease with different levels of PD-L1 expression and variable tumor microenvironment (TME) composition according to molecular subtype. With emerging evidence of the role of different factors involved in immune evasion, there are promising new immunotherapy targets that will reshape early drug development for metastatic breast cancer. This review examines the available evidence for existing and emerging immuno-oncology (IO) approaches including small molecules targeting different regulators of the cancer-immunity cycle.

GIANA allows computationally-efficient TCR clustering and multi-disease repertoire classification by isometric transformation

Similarity in T-cell receptor (TCR) sequences implies shared antigen specificity between receptors, and could be used to discover novel therapeutic targets. However, existing methods that cluster T-cell receptor sequences by similarity are computationally inefficient, making them impractical to use on the ever-expanding datasets of the immune repertoire. Here, we developed GIANA (Geometric Isometry-based TCR AligNment Algorithm) a computationally efficient tool for this task that provides the same level of clustering specificity as TCRdist at 600 times its speed, and without sacrificing accuracy. GIANA also allows the rapid query of large reference cohorts within minutes. Using GIANA to cluster large-scale TCR datasets provides candidate disease-specific receptors, and provides a new solution to repertoire classification. Querying unseen TCR-seq samples against an existing reference differentiates samples from patients across various cohorts associated with cancer, infectious and autoimmune disease. Our results demonstrate how GIANA could be used as the basis for a TCR-based non-invasive multi-disease diagnostic platform.

Grouping T-cell receptors (TCRs) by sequence similarity could lead to new immunological insights. Here, the authors propose a tool that allows the rapid clustering of millions of TCR sequences, identifying TCRs potentially associated with the response to cancer, infectious and autoimmune diseases.

Multiplex immunofluorescence to measure dynamic changes in tumor-infiltrating lymphocytes and PD-L1 in early-stage breast cancer

BACKGROUND

The H&E stromal tumor-infiltrating lymphocyte (sTIL) score and programmed death ligand 1 (PD-L1) SP142 immunohistochemistry assay are prognostic and predictive in early-stage breast cancer, but are operator-dependent and may have insufficient precision to characterize dynamic changes in sTILs/PD-L1 in the context of clinical research. We illustrate how multiplex immunofluorescence (mIF) combined with statistical modeling can be used to precisely estimate dynamic changes in sTIL score, PD-L1 expression, and other immune variables from a single paraffin-embedded slide, thus enabling comprehensive characterization of activity of novel immunotherapy agents.

METHODS

Serial tissue was obtained from a recent clinical trial evaluating loco-regional cytokine delivery as a strategy to promote immune cell infiltration and activation in breast tumors. Pre-treatment biopsies and post-treatment tumor resections were analyzed by mIF (PerkinElmer Vectra) using an antibody panel that characterized tumor cells (cytokeratin-positive), immune cells (CD3, CD8, CD163, FoxP3), and PD-L1 expression. mIF estimates of sTIL score and PD-L1 expression were compared to the H&E/SP142 clinical assays. Hierarchical linear modeling was utilized to compare pre- and post-treatment immune cell expression, account for correlation of time-dependent measurement, variation across high-powered magnification views within each subject, and variation between subjects. Simulation methods (Monte Carlo, bootstrapping) were used to evaluate the impact of model and tissue sample size on statistical power.

RESULTS

mIF estimates of sTIL and PD-L1 expression were strongly correlated with their respective clinical assays (p < .001). Hierarchical linear modeling resulted in more precise estimates of treatment-related increases in sTIL, PD-L1, and other metrics such as CD8+ tumor nest infiltration. Statistical precision was dependent on adequate tissue sampling, with at least 15 high-powered fields recommended per specimen. Compared to conventional t-testing of means, hierarchical linear modeling was associated with substantial reductions in enrollment size required (n = 25➔n = 13) to detect the observed increases in sTIL/PD-L1.

CONCLUSION

mIF is useful for quantifying treatment-related dynamic changes in sTILs/PD-L1 and is concordant with clinical assays, but with greater precision. Hierarchical linear modeling can mitigate the effects of intratumoral heterogeneity on immune cell count estimations, allowing for more efficient detection of treatment-related pharmocodynamic effects in the context of clinical trials.

TRIAL REGISTRATION

NCT02950259 .