Intrinsic subtypes and genomic signatures of primary breast cancer and prognosis after systemic relapse

The breast cancer tumor microenvironment and precision medicine: immunogenicity and conditions favoring response to immunotherapy

Some main recent researches that have dissected tumor microenvironment (TME) by imaging mass cytometry (IMC) in different subtypes of primary breast cancer samples were considered. The many phenotypic variants, clusters of epithelial tumor and immune cells, their structural features as well as the main genetic aberrations, sub-clonal heterogeneity and their systematic classification also have been examined. Mutational evolution has been assessed in primary and metastatic breast cancer samples. Overall, based on these findings the current concept of precision medicine is questioned and challenged by alternative therapeutic strategies. In the last two decades, immunotherapy as a powerful and harmless tool to fight cancer has received huge attention. Thus, the tumor immune microenvironment (TIME) composition, its prognostic role for clinical course as well as a novel definition of immunogenicity in breast cancer are proposed. Investigational clinical trials carried out by us and other findings suggest that G0-G1 state induced in endocrine-dependent metastatic breast cancer is more suitable for successful immune manipulation. Residual micro-metastatic disease seems to be another specific condition that can significantly favor the immune response in breast and other solid tumors.

Prognostic value of intrinsic subtypes in hormone-receptor-positive metastatic breast cancer: systematic review and meta-analysis

BACKGROUND

In hormone receptor-positive (HoR+) breast cancer (BC), gene expression analysis identifies luminal A (LumA), luminal B (LumB), human epidermal growth factor receptor 2 (HER2)-enriched (HER2-E), basal-like (BL) intrinsic subtypes and a normal-like group. This classification has an established prognostic value in early-stage HoR+ BC. Here, we carried out a trial-level meta-analysis to determine the prognostic ability of subtypes in metastatic BC (MBC).

MATERIALS AND METHODS

We systematically reviewed all the available prospective phase II/III trials in HoR+ MBC where subtype was assessed. The primary endpoint was progression-free survival (PFS)/time to progression (TTP) of the LumA subtype compared to non-LumA. Secondary endpoints were PFS/TTP of each individual subtype, according to treatment, menopausal and HER2 status and overall survival (OS). The random-effect model was applied, and heterogeneity assessed through Cochran's Q and I². Threshold for significance was set at P < 0.05. The study was registered in PROSPERO (ID: CRD42021255769).

RESULTS

Seven studies were included (2536 patients). Non-LumA represented 55.2% and was associated with worse PFS/TTP than LumA [hazard ratio (HR) 1.77, P < 0.001, I² = 61%], independently of clinical HER2 status [P_{subgroup difference} (P_sub) = 0.16], systemic treatment (P_sub = 0.96) and menopausal status (P_sub = 0.12). Non-LumA tumors also showed worse OS (HR 2.00, P < 0.001, I² = 65%), with significantly different outcomes for LumB (PFS/TTP HR 1.46; OS HR 1.41), HER2-E (PFS/TTP HR 2.39; OS HR 2.08) and BL (PFS/TTP HR 2.67; OS HR 3.26), separately (PFS/TTP P_sub = 0.01; OS P_sub = 0.005). Sensitivity analyses supported the main result. No publication bias was observed.

CONCLUSIONS

In HoR+ MBC, non-LumA disease is associated with poorer PFS/TTP and OS than LumA, independently of HER2, treatment and menopausal status. Future trials in HoR+ MBC should consider this clinically relevant biological classification.

Clinical implications of the intrinsic molecular subtypes in hormone receptor-positive and HER2-negative metastatic breast cancer

Traditionally, the classification of breast cancer relies on the expression of immunohistochemical (IHC) biomarkers readily available in clinical practice. Using highly standardized and reproducible assays across patient cohorts, intrinsic molecular subtypes of breast cancer - also called "intrinsic subtypes" (IS) - have been identified based on the expression of 50 genes. Although IHC-based subgroups and IS moderately correlate to each other, they are not superimposable. In fact, non-luminal biology has been detected in a substantial proportion (5-20%) of hormone receptor-positive (HoR+) tumors, has prognostic value, and identifies reduced and increased sensitivity to endocrine therapy and chemotherapy, respectively. During tumor progression, a shift toward a non-luminal estrogen-independent and more aggressive phenotype has been demonstrated. Intrinsic genomic instability and cell plasticity, alone or combined with external constraints deriving from treatment selective pressure or interplay with the tumor microenvironment, may represent the determinants of such biological diversity between primary and metastatic disease, and during metastatic tumor evolution. In this review, we describe the distribution and the clinical behavior of IS as the disease progresses, focusing on HoR+/HER2-negative advanced breast cancer. In addition, we provide an overview of the ongoing clinical trials aiming to validate the predictive and prognostic value of IS towards their incorporation into routine care.

Future of biomarker evaluation in the realm of artificial intelligence algorithms: application in improved therapeutic stratification of patients with breast and prostate cancer

Clinical workflows in oncology depend on predictive and prognostic biomarkers. However, the growing number of complex biomarkers contributes to costly and delayed decision-making in routine oncology care and treatment. As cancer is expected to rank as the leading cause of death and the single most important barrier to increasing life expectancy in the 21st century, there is a major emphasis on precision medicine, particularly individualisation of treatment through better prediction of patient outcome. Over the past few years, both surgical and pathology specialties have suffered cutbacks and a low uptake of pathology specialists means a solution is required to enable high-throughput screening and personalised treatment in this area to alleviate bottlenecks. Digital imaging in pathology has undergone an exponential period of growth. Deep-learning (DL) platforms for hematoxylin and eosin (H&E) image analysis, with preliminary artificial intelligence (AI)-based grading capabilities of specimens, can evaluate image characteristics which may not be visually apparent to a pathologist and offer new possibilities for better modelling of disease appearance and possibly improve the prediction of disease stage and patient outcome. Although digital pathology and AI are still emerging areas, they are the critical components for advancing personalised medicine. Integration of transcriptomic analysis, clinical information and AI-based image analysis is yet an uncultivated field by which healthcare professionals can make improved treatment decisions in cancer. This short review describes the potential application of integrative AI in offering better detection, quantification, classification, prognosis and prediction of breast and prostate cancer and also highlights the utilisation of machine learning systems in biomarker evaluation.

Pan-cancer application of a lung-adenocarcinoma-derived gene-expression-based prognostic predictor

Gene-expression profiling can be used to classify human tumors into molecular subtypes or risk groups, representing potential future clinical tools for treatment prediction and prognostication. However, it is less well-known how prognostic gene signatures derived in one malignancy perform in a pan-cancer context. In this study, a gene-rule-based single sample predictor (SSP) called classifier for lung adenocarcinoma molecular subtypes (CLAMS) associated with proliferation was tested in almost 15 000 samples from 32 cancer types to classify samples into better or worse prognosis. Of the 14 malignancies that presented both CLAMS classes in sufficient numbers, survival outcomes were significantly different for breast, brain, kidney and liver cancer. Patients with samples classified as better prognosis by CLAMS were generally of lower tumor grade and disease stage, and had improved prognosis according to other type-specific classifications (e.g. PAM50 for breast cancer). In all, 99.1% of non-lung cancer cases classified as better outcome by CLAMS were comprised within the range of proliferation scores of lung adenocarcinoma cases with a predicted better prognosis by CLAMS. This finding demonstrates the potential of tuning SSPs to identify specific levels of for instance tumor proliferation or other transcriptional programs through predictor training. Together, pan-cancer studies such as this may take us one step closer to understanding how gene-expression-based SSPs act, which gene-expression programs might be important in different malignancies, and how to derive tools useful for prognostication that are efficient across organs.

Comprehensive description of the current breast cancer microenvironment advancements via single-cell analysis

Breast cancer is a heterogeneous disease with a complex microenvironment consisting of tumor cells, immune cells, fibroblasts and vascular cells. These cancer-associated cells shape the tumor microenvironment (TME) and influence the progression of breast cancer and the therapeutic responses in patients. The exact composition of the intra-tumoral cells is mixed as the highly heterogeneous and dynamic nature of the TME. Recent advances in single-cell technologies such as single-cell DNA sequencing (scDNA-seq), single-cell RNA sequencing (scRNA-seq) and mass cytometry have provided new insights into the phenotypic and functional diversity of tumor-infiltrating cells in breast cancer. In this review, we have outlined the recent progress in single-cell characterization of breast tumor ecosystems, and summarized the phenotypic diversity of intra-tumoral cells and their potential prognostic relevance.

PAM50 Intrinsic Subtype Profiles in Primary and Metastatic Breast Cancer Show a Significant Shift toward More Aggressive Subtypes with Prognostic Implications

Simple Summary

The majority of breast cancer deaths are caused by the spread of the disease to distant locations. The biological processes and molecular characteristics that eventually transform breast cancer into a life-threatening metastatic disease are not fully understood. The molecular subtyping of breast cancer into four tumor subtypes—namely luminal A, luminal B, human epidermal growth factor receptor 2-enriched, and basal-like subtypes—has been implemented for therapeutic guidance in patients with early breast cancer. It is not settled whether molecular subtypes in metastatic tissue can guide the choice of systemic therapy and how these subtypes may change throughout tumor progression. In this study, breast cancer subtypes at different stages of the disease were investigated, and we found changes to more unfavorable subtypes to be common throughout the progression of the disease. These findings suggests that molecular subtyping in metastatic disease could add important prognostic and predictive information to complement information from the primary tumor.

Abstract

Background: PAM50 breast cancer intrinsic subtyping adds prognostic information in early breast cancer; however, the role in metastatic disease is unclear. We aimed to identify PAM50 subtypes in primary tumors (PTs) and metastases to outline subtype changes and their prognostic role. Methods: RNA was isolated from PTs, lymph node metastases (LNMs), and distant metastases (DMs) in metastatic breast cancer patients (n = 140) included in a prospective study (NCT01322893). Gene expression analyses were performed using the Breast Cancer 360 (BC360) assay from Nano-String. The subtype shifts were evaluated using McNemar and symmetry tests, and clinical outcomes were evaluated with log-rank tests and Cox regression. Results: The PAM50 subtype changed in 25/59 of paired samples between PTs and LNMs (P_symmetry = 0.002), in 31/61 between PTs and DMs (P_symmetry < 0.001), and in 16/38 between LNMs and DMs (P_symmetry = 0.004). Shifts toward subtypes with worse outcomes were the most common. Patients with shifts from the luminal PT to non-luminal DM subtypes had worse progression-free survival compared to patients with a stable subtype (hazard ratio (HR): 2.3; 95% confidence interval (CI): 1.14–4.68, p = 0.02). Conclusion: Strong evidence of PAM50 subtype shifts toward unfavorable subtypes were seen between PTs and metastatic samples. For patients with a shift in subtype from luminal PT to non-luminal DM, a worse prognosis was noted.

Genomic Signatures in Luminal Breast Cancer

Background

Breast cancer is a very heterogeneous disease and luminal breast carcinomas represent the hormone receptor-positive tumors among all breast cancer subtypes. In this context, multigene signatures were developed to gain further prognostic and predictive information beyond clinical parameters and traditional immunohistochemical markers.

Summary

For early breast cancer patients these molecular tools can guide clinicians to decide on the extension of endocrine therapy to avoid over- and undertreatment by adjuvant chemotherapy. Beside the predictive and prognostic value, a few genomic tests are also able to provide intrinsic subtype classification. In this review, we compare the most frequently used and commercially available molecular tests (OncotypeDX®, MammaPrint®, Prosigna®, EndoPredict®, and Breast Cancer Index^SM). Moreover, we discuss the clinical utility of molecular profiling for advanced breast cancer of the luminal subtype.

Key Messages

Multigene assays can help to de-escalate systemic therapy in early-stage breast cancer. Only the Oncotype DX® and MammaPrint®test are validated by entirely prospective and randomized phase 3 trials. More clinical evidence is needed to support the use of genomic tests in node-positive disease. Recent developments in high-throughput sequencing technology will provide further insights to understand the heterogeneity of luminal breast cancers in early-stage and metastatic disease.

Primary 21-Gene Recurrence Score and Disease Outcome in Loco-Regional and Distant Recurrent Breast Cancer Patients

Background: The 21-gene recurrence score (RS) assay has been proven prognostic and predictive for hormone receptor-positive/HER2-negative, node-negative early breast cancer patients. However, whether primary 21-gene RS can predict prognosis in recurrent breast cancer patients remained unknown.

Patients and Methods: Consecutive breast cancer patients operated in Comprehensive Breast Health Center, Shanghai Ruijin Hospital between January 2009 and December 2018 were retrospectively analyzed. Patients with available 21-gene RS result for the primary tumor and reporting disease recurrence during follow-up were included. Association of 21-gene RS and overall survival (OS), post-recurrence overall survival (PR-OS), post-recurrence progression-free survival (PR-PFS), and first-line systemic treatment after recurrence were compared among different groups.

Results: A total of 74 recurrent patients were included, with 10, 27, 37 patients in the RS <18, 18–30, and ≥ 31 groups, respectively. Recurrent patients with RS ≥ 31 were more likely to receive chemotherapy as their first-line treatment compared to those with RS <31 (P = 0.025). Compared to those with RS <31, patients with RS ≥ 31 had significantly worse OS (P = 0.025), worse PR-OS (P = 0.026), and a trend of inferior PR-PFS (P = 0.106). Multivariate analysis demonstrated that primary ER expression level (OS: P = 0.009; PR-OS: P = 0.017) and histological grade (OS: P = 0.003; PR-OS: P = 0.009), but not primary 21-gene RS (OS: P = 0.706; PR-OS: P = 0.120), were independently associated with worse OS and PR-OS.

Conclusions: High primary 21-gene RS tended to be associated with worse disease outcome in loco-regional and distant recurrent breast cancer patients, which could influence the first-line systemic treatment after relapse, warranting further clinical evaluation.

Comparing blood versus tissue-based biomarkers expression in breast cancer patients

Molecular markers have been used as a tool for diagnostic approaches, staging, and evaluation of therapeutic responses in patients with cancer. Cancer molecular markers can also help clinicians to make decision on therapy and prognosis evaluation at the time of diagnosis. In the early diagnosis of breast cancer (BC), estrogen and progesterone receptors (ER/PR) expression levels should be determined through immunohistochemistry (IHC). In molecular genetics, there are some important tissue-based markers that can also be found in blood, such as Mammaglobin (MAM), Cytokeratin 19 (CK19), Mucin (MUC), Proto-oncogene (c-Myc), antigen Ki-67 (Ki67), and Carcinoembryonic antigen (CEA). In this study, the positive level of the marker genes in both blood and tissue of the BC patients were compared using reverse transcription polymerase chain reaction (RT-PCR) method. In addition, the importance of blood vs. tissue-based markers in BC diagnosis also demonstrated and is discussed. CEA (O), ERβ, CK19 and, c-Myc molecular markers were significantly different between blood of normal and patients while there was no significant difference of these markers in tissue samples. Blood-based biomarkers can be used for the early diagnosis of BC. Comparing blood versus tissue-based biomarkers indicated that there are correlations between markers in blood and tissue, since blood markers can be substitute to tissue markers in BC patients in the future.

Review of concepts in therapeutic decision-making in HER2-negative luminal metastatic breast cancer

Purpose

Hormone receptor (HR)-positive, Human epidermal growth factor receptor 2 (HER2)-negative metastatic breast cancer (MBC) requires a therapeutic approach that takes into account multiple factors, with treatment being based on anti-estrogen hormone therapy (HT). As consensus documents are valuable tools that assist in the decision-making process for establishing clinical strategies and optimize the delivery of health services, this consensus document has been created with the aim of developing recommendations on cretiera for hormone sensitivity and resistance in HER2-negative luminal MBC and facilitating clinical decision-making.

Methods

This consensus document was generated using a modification of the RAND/UCLA methodology, which included the definition of the project and identification of issues of interest, a non-exhaustive systematic review of the literature, an analysis and synthesis of the scientific evidence, preparation of recommendations, and external evaluation with a panel of 64 medical oncologists specializing in breast cancer.

Results

A Spanish panel of experts reached consensus on 32 of the 32 recommendations/conclusions presented in the first round and were accepted with an approval rate of 100% about definition of metastatic disease not susceptible to local curative treatment, definition of hormone sensitivity and hormone resistance in metastatic luminal disease and therapeutic decision-making.

Conclusion

We have developed a consensus document with recommendations on the treatment of patients with HER2-negative luminal MBC that will help to improve therapeutic benefits.

Association between MRI background parenchymal enhancement and lymphovascular invasion and estrogen receptor status in invasive breast cancer

Objectives:

In magnetic resonance imaging (MRI), background parenchymal enhancement (BPE) is associated with breast cancer risk, but the associations between BPE and clinical characteristics and histological features are unknown. This study aimed to investigate the association between BPE and clinical characteristics (including age, menopausal status, and tumor histological characteristics) in patients with invasive breast cancer.

Methods:

This was a retrospective study of 163 patients with invasive breast cancer (164 lesions, 1 patient had bilateral cancer) confirmed by surgery and pathological examination, treated between January 2014 and December 2016 at our university (Kunming Medical University). The patients were divided into two groups: extremely minimal and mild enhancement (low BPE group, n = 78) vs moderate and marked enhancement (high BPE group, n = 86).

Results:

Compared with the low BPE group, the high BPE group showed higher frequencies of patients < 50 years of age (88% vs 38%, p < 0.0001), premenopausal (87% vs 29%, p < 0.0001), T1 staging (35% vs 15%, p = 0.027), Grade II (57% vs 37%, p = 0.03), lymphovascular invasion (83% vs 13%, p < 0.0001), and positive estrogen receptor (ER) (79% vs 42%, p < 0.0001). The Spearman correlation coefficients (r) between BPE and age, menopausal status, lymphovascular invasion, and ER status were −0.521 (p < 0.0001), –0.588 (p < 0.0001), 0.697 (p < 0.0001), and 0.377 (p < 0.0001), respectively.

Conclusion:

BPE is negatively associated with age and menopausal status, and is positively associated with lymphovascular invasion and positive ER status.

Advances in knowledge:

BPE is not correlated with T staging and histological classification in patients with invasive breast cancer.

Metformin Targets Glucose Metabolism in Triple Negative Breast Cancer

Metformin is the most widely administered anti-diabetic agent worldwide. In patients receiving metformin for metabolic syndrome or diabetes, it reduces the incidence and improves the survival of breast cancer (BC) patients. We have previously shown that metformin is particularly potent against triple negative breast cancer (TNBC), with a reduction of proliferation, oncogenicity and motility, inhibition of pro-oncogenic signaling pathways and induction of apoptosis. These BCs are well recognized to be highly dependent on glucose/glucosamine (metabolized through anaerobic glycolysis) and lipids, which are metabolized for the production of energy and cellular building blocks to sustain a high rate of proliferation. We have previously demonstrated that metformin inhibits lipid metabolism, specifically targeting fatty acid synthase (FASN), cholesterol biosynthesis and GM1 lipid rafts in TNBC. We also reported that glucose promotes phenotypic aggression and reduces metformin efficacy. We now show that metformin inhibits several key enzymes requisite to glucose metabolism in TNBC, providing additional insight into why metformin is especially toxic to this subtype of BC. Our data suggests that the use of metformin to target key metabolic defects in lipid and carbohydrate metabolism in cancer may be broadly applicable, especially against highly aggressive malignant cells.

Intrinsic subtypes and genomic signatures of primary breast cancer and prognosis after systemic relapse

Molecular subtypes and gene expression signatures are widely used in early breast cancer but their role in metastatic disease is less explored. Two hundred-twenty patients diagnosed with primary breast cancer and subsequent relapse in Stockholm, Sweden between 1997 and 2006 were identified and their primary tumor was assessed for immunohistochemistry (IHC)- and PAM50-based subtypes, risk of recurrence (ROR-S) score, 21-gene and 70-gene signatures using research-based microarray expression profiles. Clinical and pathological data were retrospectively collected. Post-relapse survival within intrinsic subtypes and genomic signatures was investigated by Kaplan-Meier and Cox regression methods. ROR weighted for proliferation index (ROR-P) was explored and the prognostic contribution provided when combined to a clinical model estimated as change in LR- χ(2). IHC classified 27%, 24%, 36% and 13% of the tumors as luminal A, luminal B, HER2+ and triple negative, respectively. PAM50 categorized 22%, 24%, 26%, 22%, 6% of the tumors as luminal A, luminal B, HER2-enriched, basal-like and normal-like. Triple negative and basal tumors had a significantly shorter median post-relapse survival in comparison with luminal. Overall, neither IHC nor PAM50 subtypes, 21- and 70- gene profiles were prognostic in multivariable models. Low and medium ROR-S had a longer survival compared with the high-risk group (23 vs 10 months; p = 0.04). ROR-P independently correlated with post-relapse survival (p = 0.002) and provided the most significant prognostic information when added to a clinical model. ROR score from primary tumor represents an independent prognostic factor of post-relapse survival beyond classical clinical and pathological variables.