High PD-L2 Predicts Early Recurrence of ER-Positive Breast Cancer

Functional protein biomarkers based on distributions of expression levels in single-cell imaging data

MOTIVATION

The intra-tumor heterogeneity of protein expression is well recognized and may provide important information for cancer prognosis and predicting treatment responses. Analytic methods that account for spatial heterogeneity remain methodologically complex and computationally demanding for single-cell protein expression. For many functional proteins, single-cell expressions vary independently of spatial localization in a substantial proportion of the tumor tissues, and incorporation of spatial information may not affect the prognostic value of such protein biomarkers.

RESULTS

We developed a new framework for using the distributions of functional single-cell protein expression levels as cancer biomarkers. The quantile functions of single-cell expressions are used to fully capture the heterogeneity of protein expression across all cancer cells. The quantile index (QI) biomarker is defined as an integral of an unspecified function which may depend linearly or nonlinearly on a tissue-specific quantile function. Linear and nonlinear versions of QI biomarkers based on single-cell expressions of ER, Ki67, TS, and CyclinD3 were derived and evaluated as predictors of progression-free survival or high mitotic index in a large breast cancer dataset. We evaluated performance and demonstrated the advantages of nonlinear QI biomarkers through simulation studies.

AVAILABILITY AND IMPLEMENTATION

The associated R package Qindex is available at https://CRAN.R-project.org/package=Qindex and R package hyper.gam is available at https://github.com/tingtingzhan/hyper.gam. Examples of R code and detailed instructions could be found in vignette quantile-index-predictor (https://CRAN.R-project.org/package=hyper.gam/vignettes/applications.html#quantile-index-predictor).

Divergent Cellular Expression Patterns of PD-L1 and PD-L2 Proteins in Breast Cancer

PD-L1 immunohistochemistry (IHC) has become an established method for predicting cancer response to targeted anti-PD1 immunotherapies, including breast cancer (BC). The alternative PD-1 ligand, PD-L2, remains understudied but may be a complementary predictive marker. Prospective analysis of 32 breast cancers revealed divergent expression patterns of PD-L1 and PD-L2. PD-L1-positivity was higher in immune cells than in cancer cells (median = 5.0% vs. 0.0%; p = 0.001), whereas PD-L2-positivity was higher in cancer cells than immune cells (median = 30% vs. 5.0%; p = 0.001). Percent positivity of PD-L1 and PD-L2 were not correlated, neither in cancer cells nor immune cells. Based on a cut-point of ≥1% positivity, ER+ tumors (n = 23) were frequently PD-L2-positive (73.9%), whereas only 40.9% were PD-L1-positive. These data suggest differential control of cellular PD-L1 and PD-L2 expression in BC and a potential role for PD-L2 IHC as a complementary marker to PD-L1 to improve selection of aggressive ER+ BC that may benefit from anti-PD-1 therapy.

Programmed cell death-ligand 2: new insights in cancer

Immunotherapy has revolutionized cancer treatment, with the anti-PD-1/PD-L1 axis therapy demonstrating significant clinical efficacy across various tumor types. However, it should be noted that this therapy is not universally effective for all PD-L1-positive patients, highlighting the need to expedite research on the second ligand of PD-1, known as Programmed Cell Death Receptor Ligand 2 (PD-L2). As an immune checkpoint molecule, PD-L2 was reported to be associated with patient's prognosis and plays a pivotal role in cancer cell immune escape. An in-depth understanding of the regulatory process of PD-L2 expression may stratify patients to benefit from anti-PD-1 immunotherapy. Our review focuses on exploring PD-L2 expression in different tumors, its correlation with prognosis, regulatory factors, and the interplay between PD-L2 and tumor treatment, which may provide a notable avenue in developing immune combination therapy and improving the clinical efficacy of anti-PD-1 therapies.

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.

Comparison of Clinical Trial Results of the Recently Approved Immunotherapeutic Drugs for Advanced Biliary Tract Cancers

The recently approved immunotherapeutic drugs are Keytruda (pembrolizumab) and Imfinzi (durvalumab) for advanced biliary tract cancers that inhibit PD-1 receptor and PD-L1 ligand, respectively. In this perspective, the results of the two clinical trials, i.e., TOPAZ-1 (NCT03875235) and KEYNOTE-966 (NCT04003636), are critically appraised, compared, and discussed to assess the benefits of these two drugs in the context of the treatment of advanced biliary tract cancers with a focus on PD-L1 status and MIS (microsatellite instability) status and therapy responsiveness in the subgroups. Analyzing the PD-L2 status in biliary tract cancer patients can aid in assessing the prognostic value of PD-L2 expression in determining the clinical response and this may aid in appropriate patient stratification.

An alternatively spliced PD-L1 isoform PD-L1∆3, and PD-L2 expression in breast cancers: implications for eligibility scoring and immunotherapy response

Targeting PD-1/PD-L1 has shown substantial therapeutic response and unprecedented long-term durable responses in the clinic. However, several challenges persist, encompassing the prediction of treatment effectiveness and patient responses, the emergence of treatment resistance, and the necessity for additional biomarkers. Consequently, we comprehensively explored the often-overlooked isoforms of crucial immunotherapy players, leveraging transcriptomic analysis, structural modeling, and immunohistochemistry (IHC) data. Our investigation has led to the identification of an alternatively spliced isoform of PD-L1 that lacks exon 3 (PD-L1∆3) and the IgV domain required to interact with PD-1. PD-L1∆3 is expressed more than the canonical isoform in a subset of breast cancers and other TCGA tumors. Using the deep learning-based protein modeling tool AlphaFold2, we show the lack of a possible interaction between PD-L1∆3 and PD-1. In addition, we present data on the expression of an additional ligand for PD-1, PD-L2. PD-L2 expression is widespread and positively correlates with PD-L1 levels in breast and other tumors. We report enriched epithelial-mesenchymal transition (EMT) signature in high PD-L2 transcript expressing (PD-L2 > PD-L1) tumors in all breast cancer subtypes, highlighting potential crosstalk between EMT and immune evasion. Notably, the estrogen gene signature is downregulated in ER + breast tumors with high PD-L2. The data on PD-L2 IHC positivity but PD-L1 negativity in breast tumors, together with our results on PD-L1∆3, highlight the need to utilize PD-L2 and PD-L1 isoform-specific antibodies for staining patient tissue sections to offer a more precise prediction of the outcomes of PD-1/PD-L1 immunotherapy.

Quantile Index Biomarkers Based on Single-Cell Expression Data

Current histocytometry methods enable single-cell quantification of biomolecules in tumor tissue sections by multiple detection technologies, including multiplex fluorescence-based immunohistochemistry or in situ hybridization. Quantitative pathology platforms can provide distributions of cellular signal intensity (CSI) levels of biomolecules across the entire cell populations of interest within the sampled tumor tissue. However, the heterogeneity of CSI levels is usually ignored, and the simple mean signal intensity value is considered a cancer biomarker. Here we consider the entire distribution of CSI expression levels of a given biomolecule in the cancer cell population as a predictor of clinical outcome. The proposed quantile index (QI) biomarker is defined as the weighted average of CSI distribution quantiles in individual tumors. The weight for each quantile is determined by fitting a functional regression model for a clinical outcome. That is, the weights are optimized so that the resulting QI has the highest power to predict a relevant clinical outcome. The proposed QI biomarkers were derived for proteins expressed in cancer cells of malignant breast tumors and demonstrated improved prognostic value compared with the standard mean signal intensity predictors. The R package Qindex implementing QI biomarkers has been developed. The proposed approach is not limited to immunohistochemistry data and can be based on any cell-level expressions of proteins or nucleic acids.

Selection of optimal quantile protein biomarkers based on cell-level immunohistochemistry data

BACKGROUND

Protein biomarkers of cancer progression and response to therapy are increasingly important for improving personalized medicine. Advanced quantitative pathology platforms enable measurement of protein expression in tissues at the single-cell level. However, this rich quantitative cell-by-cell biomarker information is most often not exploited. Instead, it is reduced to a single mean across the cells of interest or converted into a simple proportion of binary biomarker-positive or -negative cells.

RESULTS

We investigated the utility of retaining all quantitative information at the single-cell level by considering the values of the quantile function (inverse of the cumulative distribution function) estimated from a sample of cell signal intensity levels in a tumor tissue. An algorithm was developed for selecting optimal cutoffs for dichotomizing cell signal intensity distribution quantiles as predictors of continuous, categorical or survival outcomes. The proposed algorithm was used to select optimal quantile biomarkers of breast cancer progression based on cancer cells' cell signal intensity levels of nuclear protein Ki-67, Proliferating cell nuclear antigen, Programmed cell death 1 ligand 2, and Progesterone receptor. The performance of the resulting optimal quantile biomarkers was validated and compared to the standard cancer compartment mean signal intensity markers using an independent external validation cohort. For Ki-67, the optimal quantile biomarker was also compared to established biomarkers based on percentages of Ki67-positive cells. For proteins significantly associated with PFS in the external validation cohort, the optimal quantile biomarkers yielded either larger or similar effect size (hazard ratio for progression-free survival) as compared to cancer compartment mean signal intensity biomarkers.

CONCLUSION

The optimal quantile protein biomarkers yield generally improved prognostic value as compared to the standard protein expression markers. The proposed methodology has a broad application to single-cell data from genomics, transcriptomics, proteomics, or metabolomics studies at the single cell level.

Immune Checkpoint Profiling in Humanized Breast Cancer Mice Revealed Cell-Specific LAG-3/PD-1/TIM-3 Co-Expression and Elevated PD-1/TIM-3 Secretion

Checkpoint blockade is particularly based on PD-1/PD-L1-inhibiting antibodies. However, an efficient immunological tumor defense can be blocked not only by PD-(L)1 but also by the presence of additional immune checkpoint molecules. Here, we investigated the co-expression of several immune checkpoint proteins and the soluble forms thereof (e.g., PD-1, TIM-3, LAG-3, PD-L1, PD-L2 and others) in humanized tumor mice (HTM) simultaneously harboring cell line-derived (JIMT-1, MDA-MB-231, MCF-7) or patient-derived breast cancer and a functional human immune system. We identified tumor-infiltrating T cells with a triple-positive PD-1, LAG-3 and TIM-3 phenotype. While PD-1 expression was increased in both the CD4 and CD8 T cells, TIM-3 was found to be upregulated particularly in the cytotoxic T cells in the MDA-MB-231-based HTM model. High levels of soluble TIM-3 and galectin-9 (a TIM-3 ligand) were detected in the serum. Surprisingly, soluble PD-L2, but only low levels of sPD-L1, were found in mice harboring PD-L1-positive tumors. Analysis of a dataset containing 3039 primary breast cancer samples on the R2 Genomics Analysis Platform revealed increased TIM-3, galectin-9 and LAG-3 expression, not only in triple-negative breast cancer but also in the HER2⁺ and hormone receptor-positive breast cancer subtypes. These data indicate that LAG-3 and TIM-3 represent additional key molecules within the breast cancer anti-immunity landscape.