Protein-based immune profiles of basal-like vs. luminal breast cancers

Gene expression in metastatic breast cancer-patterns in primary tumors and metastatic tissue with prognostic potential

Background: Metastatic breast cancer (MBC) is the main cause of breast cancer-related death. The outcome of MBC varies, and there is a lack of biomarkers to aid in prognostication. The primary aim of this study was to evaluate the prognostic value of gene expression (GEX) signatures in the primary tumor (PT) and distant metastasis (DM) for progression-free survival (PFS) and overall survival (OS). The secondary aim was to describe GEX changes through MBC evolution and to identify MBC subtypes. Methods: RNA was extracted from the PT, lymph node metastasis (LNM), and DM from MBC patients in a prospective observational study (n = 142; CTC-MBC NCT01322893) and was subjected to GEX analysis retrospectively using the NanoString Breast Cancer 360™ panel. 31 continuous GEX variables in DMs and PTs were analyzed for PFS and OS by Cox regression analysis and Kaplan-Meier estimates. Multivariable Cox regressions were adjusted for number of DM sites and CTCs, visceral metastasis, ECOG status, age at MBC diagnosis and, in additional analyses, PAM50 subtype. Differential GEX analyses and Euclidean distances were used to describe subgroup differences and visualize within-patient heterogeneity. Results: Compared to DM GEX, GEX of the PT was at least equally useful for predicting MBC outcome. The strongest marker for a favorable PFS, both when expressed in the PT and the DM was AR, even after adjustment for prognostic markers including PAM50. GEX signatures related to hormone responsiveness, including ESR1, FOXA1, PGR, and AR were favorable prognostic markers, and the p53 signature was unfavorable for PFS when expressed in PT or DM. The previously published PAM50MET signature was prognostic for both PFS and OS. We established five distinct DM GEX profiles where two associated with liver and bone metastases, respectively. Finally, we identified four DM GEX profiles able to identify MBCs with poor OS in this cohort. Conclusion: GEX of both DM and PT are useful in MBC prognostication. GEX of AR adds prognostic information for MBC. Our descriptive analyses illuminate the biological differences between MBCs in relation to outcome and metastatic site.

Progress in research on tumor microenvironment-based spatial omics technologies

Spatial omics technology integrates the concept of space into omics research and retains the spatial information of tissues or organs while obtaining molecular information. It is characterized by the ability to visualize changes in molecular information and yields intuitive and vivid visual results. Spatial omics technologies include spatial transcriptomics, spatial proteomics, spatial metabolomics, and other technologies, the most widely used of which are spatial transcriptomics and spatial proteomics. The tumor microenvironment refers to the surrounding microenvironment in which tumor cells exist, including the surrounding blood vessels, immune cells, fibroblasts, bone marrow-derived inflammatory cells, various signaling molecules, and extracellular matrix. A key issue in modern tumor biology is the application of spatial omics to the study of the tumor microenvironment, which can reveal problems that conventional research techniques cannot, potentially leading to the development of novel therapeutic agents for cancer. This paper summarizes the progress of research on spatial transcriptomics and spatial proteomics technologies for characterizing the tumor immune microenvironment.

Distinct spatial immune microlandscapes are independently associated with outcomes in triple-negative breast cancer

The utility of spatial immunobiomarker quantitation in prognostication and therapeutic prediction is actively being investigated in triple-negative breast cancer (TNBC). Here, with high-plex quantitative digital spatial profiling, we map and quantitate intraepithelial and adjacent stromal tumor immune protein microenvironments in systemic treatment-naïve (female only) TNBC to assess the spatial context in immunobiomarker-based prediction of outcome. Immune protein profiles of CD45-rich and CD68-rich stromal microenvironments differ significantly. While they typically mirror adjacent, intraepithelial microenvironments, this is not uniformly true. In two TNBC cohorts, intraepithelial CD40 or HLA-DR enrichment associates with better outcomes, independently of stromal immune protein profiles or stromal TILs and other established prognostic variables. In contrast, intraepithelial or stromal microenvironment enrichment with IDO1 associates with improved survival irrespective of its spatial location. Antigen-presenting and T-cell activation states are inferred from eigenprotein scores. Such scores within the intraepithelial compartment interact with PD-L1 and IDO1 in ways that suggest prognostic and/or therapeutic potential. This characterization of the intrinsic spatial immunobiology of treatment-naïve TNBC highlights the importance of spatial microenvironments for biomarker quantitation to resolve intrinsic prognostic and predictive immune features and ultimately inform therapeutic strategies for clinically actionable immune biomarkers.

The Landscape of Immune Microenvironments in Racially Diverse Breast Cancer Patients

BACKGROUND

Immunotherapy is a rapidly evolving treatment option in breast cancer; However, the breast cancer immune microenvironment is understudied in Black and younger (<50 years) patients.

METHODS

We used histologic and RNA-based immunoprofiling methods to characterize the breast cancer immune landscape in 1,952 tumors from the Carolina Breast Cancer Study (CBCS), a population-based study that oversampled Black (n = 1,030) and young women (n = 1,039). We evaluated immune response leveraging markers for 10 immune cell populations, compared profiles to those in The Cancer Genome Atlas (TCGA) Project [n = 1,095 tumors, Black (n = 183), and young women (n = 295)], and evaluated in association with clinical and demographic variables, including recurrence.

RESULTS

Consensus clustering identified three immune clusters in CBCS (adaptive-enriched, innate-enriched, or immune-quiet) that varied in frequency by race, age, tumor grade and subtype; however, only two clusters were identified in TCGA, which were predominantly comprised of adaptive-enriched and innate-enriched tumors. In CBCS, the strongest adaptive immune response was observed for basal-like, HER2-positive (HER2+), triple-negative breast cancer (TNBC), and high-grade tumors. Younger patients had higher proportions of adaptive-enriched tumors, particularly among estrogen receptor (ER)-negative (ER-) cases. Black patients had higher frequencies of both adaptive-enriched and innate-enriched tumors. Immune clusters were associated with recurrence among ER- tumors, with adaptive-enriched showing the best and innate-enriched showing the poorest 5-year recurrence-free survival.

CONCLUSIONS

These data suggest that immune microenvironments are intricately related to race, age, tumor subtype, and grade.

IMPACT

Given higher mortality among Black and young women, more defined immune classification using cell-type-specific panels could help explain higher recurrence and ultimately lead to targetable interventions.

Multiplex Tissue Imaging: Spatial Revelations in the Tumor Microenvironment

Simple Summary

Cancer is the leading cause of death worldwide, and the overall aging of the population results in an increased risk of a cancer diagnosis during a person’s lifetime. Diagnosis and treatment at an early stage will typically increase the chances of survival. Tumors can develop therapy resistance, and it is difficult to predict how individual patients will respond to therapy. Most studies that aim to resolve this problem have focused on studying the composition and characteristics of dissociated tumors, while ignoring the role of cell localization and interactions within the tumor microenvironment. In the past decade, technological innovations have enabled multiplex imaging analyses of intact tumors to study localization and interaction parameters, which can be used as biomarkers, or can be correlated with treatment responses and clinical outcomes.

Abstract

The tumor microenvironment is a complex ecosystem containing various cell types, such as immune cells, fibroblasts, and endothelial cells, which interact with the tumor cells. In recent decades, the cancer research field has gained insight into the cellular subtypes that are involved in tumor microenvironment heterogeneity. Moreover, it has become evident that cellular interactions in the tumor microenvironment can either promote or inhibit tumor development, progression, and drug resistance, depending on the context. Multiplex spatial analysis methods have recently been developed; these have offered insight into how cellular crosstalk dynamics and heterogeneity affect cancer prognoses and responses to treatment. Multiplex (imaging) technologies and computational analysis methods allow for the spatial visualization and quantification of cell–cell interactions and properties. These technological advances allow for the discovery of cellular interactions within the tumor microenvironment and provide detailed single-cell information on properties that define cellular behavior. Such analyses give insights into the prognosis and mechanisms of therapy resistance, which is still an urgent problem in the treatment of multiple types of cancer. Here, we provide an overview of multiplex imaging technologies and concepts of downstream analysis methods to investigate cell–cell interactions, how these studies have advanced cancer research, and their potential clinical implications.

Effector memory cytotoxic CD3+/CD8+/CD45RO+ T cells are predictive of good survival and a lower risk of recurrence in triple-negative breast cancer

Triple-negative breast cancer (TNBC) with high tumour-infiltrating lymphocytes (TILs) has been associated with a promising prognosis. To better understand the prognostic value of immune cell subtypes in TNBC, we characterised TILs and the interaction between tumour cells and immune cell subtypes. A total of 145 breast cancer tissues were stained by multiplex immunofluorescence (mIF), including panel 1 (PD-L1, PD-1, CD3, CD8, CD68 and CK) and panel 2 (Foxp3, Granzyme B, CD45RO, CD3, CD8 and CK). Phenotypes were analysed and quantified by pathologists using InForm software. We found that in the ER-negative (ER <1% and HER2-negative) group and the ER/PR-low positive (ER 1-9% and HER2-negative) group, 11.2% and 7.1% of patients were PD-L1⁺ by the tumour cell score, 29.0% and 28.6% were PD-L1⁺ by the modified immune cell score and 30.8% and 32.1% were PD-L1⁺ by the combined positive score. We combined ER-negative and ER/PR-low positive cases for the survival analysis since a 10% cut-off is often used in clinical practice for therapeutic purposes. The densities of PD-L1⁺ tumour cells (HR: 0.366, 95% CI: 0.138-0.970; p = 0.043) within the tumour compartment and CD3⁺ immune cells in the total area (tumour and stromal compartments combined) (HR: 0.213, 95% CI: 0.070-0.642; p = 0.006) were favourable prognostic biomarkers for overall survival (OS) in TNBC. The density of effector/memory cytotoxic T cells (CD3⁺CD8⁺CD45RO⁺) in the tumour compartment was an independent prognostic biomarker for OS (HR: 0.232, 95% CI: 0.086-0.628; p = 0.004) and DFS (HR: 0.183, 95% CI: 0.1301-0.744; p = 0.009) in TNBC. Interestingly, spatial data suggested that patients with a higher density of PD-L1⁺ tumour cells had shorter cell-cell distances from tumour cells to cytotoxic T cells (p < 0.01). In conclusion, we found that phenotyping tumour immune cells by mIF is highly informative in understanding the immune microenvironment in TNBC. PD-L1⁺ tumour cells, total T cells and effector/memory cytotoxic T cells are promising prognostic biomarkers in TNBC.

Quantitative analysis of breast cancer tissue composition and associations with tumor subtype

The tumor microenvironment is an important determinant of breast cancer progression, but standard methods for describing the tumor microenvironment are lacking. Measures of microenvironment composition such as stromal area and immune infiltrate are labor-intensive and few large studies have systematically collected this data. However, digital histologic approaches are becoming more widely available, allowing high-throughput, quantitative estimation. We applied such methods to tissue microarrays of tumors from 1687 women (mean 4 cores per case) in the Carolina Breast Cancer Study Phase 3. Tumor composition was quantified as percentage of epithelium, stroma, adipose, and lymphocytic infiltrate (with the latter as presence/absence using a ≥1% cutoff). Composition proportions and presence/absence were evaluated in association with clinical and molecular features of breast cancer (intrinsic subtype and RNA-based risk of recurrence [ROR] scores) using multivariable linear and logistic regression. Lower stromal content was associated with aggressive tumor phenotypes, including triple-negative (31.1% vs. 41.6% in HR+/HER2-; RFD [95% CI]: -10.5%, [-13.1, -7.9]), Basal-like subtypes (29.0% vs. 44.0% in Luminal A; RFD [95% CI]: -14.9%, [-17.8, -12.0]), and high RNA-based PAM50 ROR scores (27.6% vs. 48.1% in ROR low; RFD [95% CI]: -20.5%, [24.3, 16.7]), after adjusting for age and race. HER2+ tumors also had lower stromal content, particularly among RNA-based HER2-enriched (35.2% vs. 44.0% in Luminal A; RFD [95% CI]: -8.8%, [-13.8, -3.8]). Similar associations were observed between immune infiltrate and tumor phenotypes. Quantitative digital image analysis of the breast cancer microenvironment showed significant associations with demographic characteristics and biological indicators of aggressive behavior.

Best Practices for Spatial Profiling for Breast Cancer Research with the GeoMx® Digital Spatial Profiler

Breast cancer is a heterogenous disease with variability in tumor cells and in the surrounding tumor microenvironment (TME). Understanding the molecular diversity in breast cancer is critical for improving prediction of therapeutic response and prognostication. High-plex spatial profiling of tumors enables characterization of heterogeneity in the breast TME, which can holistically illuminate the biology of tumor growth, dissemination and, ultimately, response to therapy. The GeoMx Digital Spatial Profiler (DSP) enables researchers to spatially resolve and quantify proteins and RNA transcripts from tissue sections. The platform is compatible with both formalin-fixed paraffin-embedded and frozen tissues. RNA profiling was developed at the whole transcriptome level for human and mouse samples and protein profiling of 100-plex for human samples. Tissue can be optically segmented for analysis of regions of interest or cell populations to study biology-directed tissue characterization. The GeoMx Breast Cancer Consortium (GBCC) is composed of breast cancer researchers who are developing innovative approaches for spatial profiling to accelerate biomarker discovery. Here, the GBCC presents best practices for GeoMx profiling to promote the collection of high-quality data, optimization of data analysis and integration of datasets to advance collaboration and meta-analyses. Although the capabilities of the platform are presented in the context of breast cancer research, they can be generalized to a variety of other tumor types that are characterized by high heterogeneity.