Association Between Biomarkers and Clinical Outcomes of Pembrolizumab Monotherapy in Patients With Metastatic Triple-Negative Breast Cancer: KEYNOTE-086 Exploratory Analysis

Artificial intelligence in digital histopathology for predicting patient prognosis and treatment efficacy in breast cancer

INTRODUCTION

Histological images contain phenotypic information predictive of patient outcomes. Due to the heavy workload of pathologists, the time-consuming nature of quantitatively assessing histological features, and human eye limitations to recognize spatial patterns, manually extracting prognostic information in routine pathological workflows remains challenging. Digital pathology has facilitated the mining and quantification of these features utilizing whole-slide image (WSI) scanners and artificial intelligence (AI) algorithms. AI algorithms to identify image-based biomarkers from the tumor microenvironment (TME) have the potential to revolutionize the field of oncology, reducing delays between diagnosis and prognosis determination, allowing for rapid stratification of patients and prescription of optimal treatment regimes, thereby improving patient outcomes.

AREAS COVERED

In this review, the authors discuss how AI algorithms and digital pathology can predict breast cancer patient prognosis and treatment outcomes using image-based biomarkers, along with the challenges of adopting this technology in clinical settings.

EXPERT OPINION

The integration of AI and digital pathology presents significant potential for analyzing the TME and its diagnostic, prognostic, and predictive value in breast cancer patients. Widespread clinical adoption of AI faces ethical, regulatory, and technical challenges, although prospective trials may offer reassurance and promote uptake, ultimately improving patient outcomes by reducing diagnosis-to-prognosis delivery delays.

Tumor-Infiltrating Lymphocytes (TILs) in Breast Cancer: Prognostic and Predictive Significance across Molecular Subtypes

Tumor-infiltrating lymphocytes (TILs) are pivotal in the immune response against breast cancer (BC), with their prognostic and predictive significance varying across BC subtypes. In triple-negative BC (TNBC), higher TIL levels correlate with improved prognosis and treatment response, guiding therapeutic strategies and potentially offering avenues for treatment de-escalation. In metastatic TNBC, TILs identify patients with enhanced immunotherapy response. HER2+ BC, similar to TNBC, exhibits positive correlations between TILs and treatment response, especially in neoadjuvant settings. Luminal BC generally has low TILs, with limited prognostic impact. Single hormone receptor-positive BCs show distinct TIL associations, emphasizing subtype-specific considerations. TILs in ductal carcinoma in situ (DCIS) display ambiguous prognostic significance, necessitating further investigation. Standardizing TIL assessment methods is crucial for unlocking their full potential as biomarkers, guiding treatment decisions, and enhancing patient care in BC.

Novel immunotherapies for breast cancer: Focus on 2023 findings

Immunotherapy has emerged as a revolutionary approach in cancer therapy, and recent advancements hold significant promise for breast cancer (BCa) management. Employing the patient's immune system to combat BCa has become a focal point in immunotherapeutic investigations. Strategies such as immune checkpoint inhibitors (ICIs), adoptive cell transfer (ACT), and targeting the tumor microenvironment (TME) have disclosed encouraging clinical outcomes. ICIs, particularly programmed cell death protein 1 (PD-1)/PD-L1 inhibitors, exhibit efficacy in specific BCa subtypes, including triple-negative BCa (TNBC) and human epidermal growth factor receptor 2 (HER2)-positive cancers. ACT approaches, including tumor-infiltrating lymphocytes (TILs) and chimeric antigen receptor (CAR) T-cell therapy, showed promising clinical outcomes in enhancing tumor recognition and elimination. Targeting the TME through immune agonists and oncolytic viruses signifies a burgeoning field of research. While challenges persist in patient selection, resistance mechanisms, and combination therapy optimization, these novel immunotherapies hold transformative potential for BCa treatment. Continued research and clinical trials are imperative to refine and implement these innovative approaches, paving the way for improved outcomes and revolutionizing the management of BCa. This review provides a concise overview of the latest immunotherapies (2023 studies) in BCa, highlighting their potential and current status.

Immune characteristics and clinical significance of peripheral blood lymphocytes in breast cancer

BACKGROUND

In the context of breast cancer (BC), the correlation between lymphocytes and clinical outcomes, along with treatment response, has garnered attention. Despite this, few investigations have delved into the interplay among distinct peripheral blood lymphocyte (PBL) types, immune attributes, and their clinical implications within the BC landscape.

METHODS

The primary objective of this study was to scrutinize the baseline status of PBL subsets in patients with primary BC, track their dynamic changes throughout treatment, and ascertain their interrelation with prognosis. Flow cytometry was employed to analyse PBLs from a cohort of 74 BC patients.

RESULTS

Our analysis revealed that baseline levels of Treg and PD-L1 + T cells were lower in BC patients compared to the reference values. Notably, a disparity in baseline PD-L1 + T cell levels surfaced between patients who underwent adjuvant therapy and those subjected to neoadjuvant therapy (NAT). Furthermore, a meticulous evaluation of PBL subsets before and after treatment underscored discernible alterations in 324 + T cells and CD19 + CD32 + B cells over the course of therapy. Strikingly, heightened CD4 + T cell levels at baseline were linked to enhanced event-free survival (EFS) (p = 0.02) and a robust response to chemotherapy.

CONCLUSIONS

These results indicate that PBLs may serve as a significant marker to assess the immune status of BC patients, and therapy has the potential to modify patient immune profiles. In addition, peripheral blood CD4 + T cell levels may serve as promising biomarkers for diagnosis and prognosis in future studies of BC.

Discordance in PD-L1 expression using 22C3 and SP142 assays between primary and metastatic triple-negative breast cancer

AIMS

SP142 and 22C3 assays are approved companion diagnostic assays for anti-PD-1/PD-L1 therapy selection in metastatic triple-negative breast cancer (TNBC). The discordance in PD-L1 status between primary and metastatic tumors in the same patient has been poorly characterized. Here, we examined the concordance of PD-L1 status between the two assays and between primary tumors and metastases for each assay.

METHODS

We retrospectively evaluated tumor samples from 160 patients with TNBC, including 45 patients with paired primary and metastatic tumors. PD-L1 status was assessed using SP142 and 22C3 assays, to determine the immune cell (IC) score, tumor cell (TC) score (SP142 and 22C3), and combined proportion score (CPS: 22C3).

RESULTS

The concordance of PD-L1 positivity at diagnostic cutoffs for SP142 (IC ≥ 1) and 22C3 (CPS ≥ 10) was substantial (κ = 0.80) in primary tumors and moderate (κ = 0.60) in metastatic tumors. In comparison, between primary and metastatic tumors, the concordance with 22C3 was moderate (κ = 0.50), whereas that with SP142 was poor (κ = -0.03). Among patients who were PD-L1 negative for both assays in primary tumors, 7/30 (23.3%) were PD-L1 positive for both or either 22C3 or SP142 in the metastatic tumors.

CONCLUSIONS

The inter-assay concordance of PD-L1 positivity at diagnostic cutoffs was substantial in primary tumors and moderate in metastatic tumors. Discordance between PD-L1 status in primary and metastatic tumors was frequently observed, especially with SP142. Some patients with a PD-L1-negative status in primary tumors may still be candidates for immunotherapy, depending on the PD-L1 status in their metastatic tumors.

Clinical utility of genomic signatures for the management of early and metastatic triple-negative breast cancer

PURPOSE OF REVIEW

This comprehensive review aims to provide timely and relevant insights into the current therapeutic landscape for triple-negative breast cancer (TNBC) and the molecular features underlying this subtype. It emphasizes the need for more reliable biomarkers to refine prognostication and optimize therapy, considering the aggressive nature of TNBC and its limited targeted treatment options.

RECENT FINDINGS

The review explores the multidisciplinary management of early TNBC, which typically involves systemic chemotherapy, surgery, and radiotherapy. It highlights the emergence of immune checkpoint inhibitors (ICIs), poly(ADP-ribose) polymerase (PARP) inhibitors, and antibody-drug conjugates (ADCs) as promising therapeutic strategies for TNBC. Recent clinical trials investigating the use of ICIs in combination with chemotherapy and the approval of pembrolizumab and atezolizumab for PD-L1-positive metastatic TNBC are discussed. The efficacy of PARP inhibitors and ADCs in treating TNBC patients with specific genetic alterations is also highlighted.

SUMMARY

The findings discussed in this review have significant implications for clinical practice and research in TNBC. The identification of distinct molecular subtypes through gene expression profiling has enabled a better understanding of TNBC heterogeneity and its clinical implications. This knowledge has the potential to guide treatment decisions, as different subtypes display varying responses to neoadjuvant chemotherapy. Furthermore, the review emphasizes the importance of developing reliable genomic and transcriptomic signatures as biomarkers to refine patient prognostication and optimize therapy selection in TNBC. Integrating these signatures into clinical practice may lead to more personalized treatment approaches, improving outcomes for TNBC patients.

Tumor Mutational Burden in Breast Cancer: Current Evidence, Challenges, and Opportunities

Tumor mutational burden (TMB) correlates with tumor neoantigen burden, T cell infiltration, and response to immune checkpoint inhibitors in many solid tumor types. Based on data from the phase II KEYNOTE-158 study, the anti-PD-1 antibody pembrolizumab was granted approval for treating patients with advanced solid tumors and TMB ≥ 10 mutations per megabase. However, this trial did not include any patients with metastatic breast cancer; thus, several questions remain unanswered about the true role of TMB as a predictive biomarker of benefit to immune checkpoint inhibitor therapy in breast cancer. In this review, we will discuss the challenges and opportunities in establishing TMB as a predictive biomarker of benefit to immunotherapy in metastatic breast cancer.

Incorporating lesion-to-lesion heterogeneity into early oncology decision making

RECISTv1.1 (Response Evaluation Criteria In Solid Tumors) is the most commonly used response grading criteria in early oncology trials. In this perspective, we argue that RECISTv1.1 is ambiguous regarding lesion-to-lesion variation that can introduce bias in decision making. We show theoretical examples of how lesion-to-lesion variability causes bias in RECISTv1.1, leading to misclassification of patient response. Next, we review immune checkpoint inhibitor (ICI) clinical trial data and find that lesion-to-lesion heterogeneity is widespread in ICI-treated patients. We illustrate the implications of ignoring lesion-to-lesion heterogeneity in interpreting biomarker data, selecting treatments for patients with progressive disease, and go/no-go decisions in drug development. Further, we propose that Quantitative Systems Pharmacology (QSP) models can aid in developing better metrics of patient response and treatment efficacy by capturing patient responses robustly by considering lesion-to-lesion heterogeneity. Overall, we believe patient response evaluation with an appreciation of lesion-to-lesion heterogeneity can potentially improve decision-making at the early stage of oncology drug development and benefit patient care.