Discordance of the PAM50 Intrinsic Subtypes Compared with Immunohistochemistry-Based Surrogate in Breast Cancer Patients: Potential Implication of Genomic Alterations of Discordance

Clinicopathological characteristics, treatment patterns and outcomes in patients with HER2-positive breast cancer based on hormone receptor status: a retrospective study

BACKGROUND

Different hormone receptor (HR) expression patterns have significant biological and therapeutic implications in patients with human epidermal growth factor receptor 2 (HER2)-positive breast cancer. However, the distinction between HR-positive /HER2-positive (HR+/HER2+) and HR-negative/HER2-positive (HR-/HER2+) subtypes remains unclear.

METHODS

This retrospective study analyzed 828 patients with HER2-positive breast cancer at the First Affiliated Hospital of Xi'an Jiaotong University from 2012 to 2022. Baseline characteristics were compared by chi-square test. Survival outcomes were estimated by Kaplan-Meier method.

RESULTS

In total, 56.3% (n = 466) had HR-positive and 43.7% (n = 362) had HR-negative disease. Comparatively, HR+/HER2 + breast cancers presented favorable clinicopathological features. At a median follow-up of 49 months, 199 disease-free survival (DFS) events and 99 deaths were observed. HR+/HER2 + patients had significantly better survival outcomes than HR-/HER2 + patients. HR-positive status was an independent protective factor for overall survival (OS) [P = 0.032; hazard ratio, 0.61; 95% confidence interval (CI), 0.39-0.96] and DFS (P = 0.001; hazard ratio, 0.61; 95% CI, 0.46-0.81). HR+/HER2 + patients were significantly less sensitive to neoadjuvant therapy than HR-/HER2 + patients. In the first-line treatment for HR+/HER2 + advanced breast cancer, receiving endocrine therapy significantly improved advanced-OS (P < 0.001; hazard ratio, 0.33; 95% CI, 0.18-0.59) and progression-free survival (PFS) (P < 0.001; hazard ratio, 0.38; 95% CI, 0.25-0.58) compared with not receiving endocrine therapy. Moreover, maintenance endocrine therapy after HER2-targeted therapy and chemotherapy is associated with significant advanced-OS and PFS benefits compared with no maintenance endocrine therapy (advanced-OS: P < 0.001; hazard ratio, 0.05; 95% CI, 0.03-0.12; PFS: P < 0.001; hazard ratio, 0.35; 95% CI, 0.21-0.57).

CONCLUSIONS

This study reveals the high heterogeneity of HER2-positive breast cancer related to HR status in clinicopathological features, metastasis patterns, and outcomes. Large randomized controlled trials are warranted to optimize treatment strategies for the HER2-positive breast cancer population.

PAM50 breast cancer subtypes and survival of patients in rural Ethiopia without adjuvant treatment: a prospective observational study

PURPOSE

Survival rates of breast cancer (BC) patients are particularly low in rural regions in sub-Saharan Africa (SSA) which is due to limited access to therapy. In recent years, gene expression profiling (GEP) of BC showed a strong prognostic value in patients with local tumour surgery and (neo)adjuvant treatment. The aim of this study was to evaluate the impact of intrinsic subtypes on survival of patients in rural Ethiopia without any (neo)adjuvant therapy.

METHODS

In total, 113 female patients from Aira Hospital with histologically proven BC and treated only with surgery were included in this study. All samples were analysed by immunohistochemistry (IHC) for estrogen receptor, progesterone receptor, HER2 and Ki67, as well as RNA-expression analysis for PAM50 subtyping.

RESULTS

A positive hormone receptor status was found in 69.0% of the tumours and intrinsic subtyping demonstrated Luminal B to be the most common subtype (34.5%). Follow-up data was available for 79 of 113 patients. Two-year overall survival (OS) was 57.3% and a considerably worse OS was observed in patients with Basal-like BC compared to Luminal A BC. Moreover, advanced tumours showed an increased risk of mortality.

CONCLUSION

The OS was very low in the patient cohort that received no (neo)adjuvant treatment. Immunohistochemistry and GEP confirmed endocrine-sensitive tumours in more than half of the patients, with a large proportion of Luminal B, HER2-enriched and Basal-like tumours so that adjuvant chemotherapy should be recommended.

Defining the Regulatory Logic of Breast Cancer Using Single-Cell Epigenetic and Transcriptome Profiling

Annotation of the cis-regulatory elements that drive transcriptional dysregulation in cancer cells is critical to improving our understanding of tumor biology. Herein, we present a compendium of matched chromatin accessibility (scATAC-seq) and transcriptome (scRNA-seq) profiles at single-cell resolution from human breast tumors and healthy mammary tissues processed immediately following surgical resection. We identify the most likely cell-of-origin for luminal breast tumors and basal breast tumors and then introduce a novel methodology that implements linear mixed-effects models to systematically quantify associations between regions of chromatin accessibility (i.e. regulatory elements) and gene expression in malignant cells versus normal mammary epithelial cells. These data unveil regulatory elements with that switch from silencers of gene expression in normal cells to enhancers of gene expression in cancer cells, leading to the upregulation of clinically relevant oncogenes. To translate the utility of this dataset into tractable models, we generated matched scATAC-seq and scRNA-seq profiles for breast cancer cell lines, revealing, for each subtype, a conserved oncogenic gene expression program between in vitro and in vivo cells. Together, this work highlights the importance of non-coding regulatory mechanisms that underlie oncogenic processes and the ability of single-cell multi-omics to define the regulatory logic of BC cells at single-cell resolution.

Migratory Tumor Cells Cooperate with Cancer Associated Fibroblasts in Hormone Receptor-Positive and HER2-Negative Breast Cancer

Hormone receptor-positive and HER2-negative breast cancer (HR+/HER2-BC) is the most common type with a favorable prognosis under endocrine therapy. However, it still demonstrates unpredictable progression and recurrences influenced by high tumoral diversity and microenvironmental status. To address these heterogeneous molecular characteristics of HR+/HER2-BC, we aimed to simultaneously characterize its transcriptomic landscape and genetic architecture at the same resolution. Using advanced single-cell RNA and DNA sequencing techniques together, we defined four distinct tumor subtypes. Notably, the migratory tumor subtype was closely linked to genomic alterations of EGFR, related to the tumor-promoting behavior of IL6-positive inflammatory tumor-associated fibroblast, and contributing to poor prognosis. Our study comprehensively utilizes integrated analysis to uncover the complex dynamics of this breast cancer subtype, highlighting the pivotal role of the migratory tumor subtype in influencing surrounding cells. This sheds light on potential therapeutic targets by offering enhanced insights for HR+/HER2-BC treatment.

Discovery of a proliferation essential gene signature and actin-like 6A as potential biomarkers for predicting prognosis and neoadjuvant chemotherapy response in triple-positive breast cancer

BACKGROUND

Patients with triple-positive breast cancer (TPBC) have a higher risk of recurrence and lower survival rates than patients with other luminal breast cancers. However, there are few studies on the predictive biomarkers of prognosis and treatment responses in TPBC.

METHODS

Proliferation essential genes (PEGs) were acquired from clustered regularly interspaced short palindromic repeats-associated protein 9 (CRISPR-Cas9) technology, and cohorts of patients with TPBC were obtained from public databases and our cohort. To develop a TPBC-PEG signature, Cox regression and least absolute shrinkage and selection operator regression analyses were applied. Functional analyses were performed with gene set enrichment analysis. The relationship between candidate genes and neoadjuvant chemotherapy (NACT) sensitivity was explored via real-time quantitative polymerase chain reaction (RT-qPCR) and immunohistochemistry (IHC) on the basis of clinical samples.

RESULTS

Among 900 TPBC-PEGs, 437 showed significant differential expression between TPBC and normal tissues. Three prognostic PEGs (actin-like 6A [ACTL6A], chaperonin containing TCP1 subunit 2 [CCT2], and threonyl-TRNA synthetase [TARS]) were identified and used to construct the PEG signature. Patients with high PEG signature scores exhibited a worse overall survival and lower sensitivity to NACT than patients with low PEG signature scores. RT-qPCR results indicated that ACTL6A and CCT2 expression were significantly upregulated in patients who lacked sensitivity to NACT. IHC results showed that the ACTL6A protein was highly expressed in patients with NACT resistance and nonpathological complete responses.

CONCLUSIONS

This efficient PEG signature prognostic model can predict the outcomes of TPBC. Furthermore, ACTL6A expression level was associated with the response to NACT, and could serve as an important factor in predicting prognosis and drug sensitivity of patients with TPBC.

Molecular subtyping improves breast cancer diagnosis in the Copenhagen Breast Cancer Genomics Study

BACKGROUNDIntrinsic molecular subtypes define distinct biological breast cancers and can be used to further improve diagnosis and risk allocation.METHODSThe Copenhagen Breast Cancer Genomics Study (CBCGS) prospectively included women diagnosed with breast cancer at Rigshospitalet from 2014 to 2021. Eligible patients were females with a primary invasive breast cancer (T1c, if N0M0; otherwise, any T, any N, or any M stage) and no prior malignancy. All patients underwent molecular profiling with the CIT256 and PAM50 molecular profile.RESULTSIn the study period, 2,816 patients were included in the CBCGS. Molecular subtyping showed an increase in nonluminal (molecular-apocrine, luminal C, and Basal-like) as compared with luminal (luminal A, luminal B, and Normal-like) subtypes with increasing stage from I to IV. Across all stages, we found a significant difference in survival among subtypes; 91% of patients with LumA were alive at 5 years compared with 91% for LumB, 84% for LumC, 82% for mApo, and 80% for Basal-like. We identified 442 tumors (16%) that were discordant in subtype between CIT256 and IHC. Discordant subtype proved to be a risk factor of death among patients with IHC luminal breast cancer (hazard ratio [HR], 2.08; 95% CI, 1.51-2.86) in a multivariable Cox regression analysis. Discordance occurred more often among patients with N3, stage IV, or grade III disease.CONCLUSIONOur findings indicate that molecular subtypes are a predominant classification for survival. Assessment is particularly crucial for patients with IHC luminal breast cancer with known high-risk factors, since they are at an increased risk of harboring an aggressive molecular subtype.

Breast Cancer Molecular Subtyping in Practice: A Real-World Study of the APIS Breast Cancer Subtyping Assay in a Consecutive Series of Breast Core Biopsies

The APIS Breast Cancer Subtyping Kit is an mRNA-based assessment of the seven parameters including three biomarkers routinely assessed in all the newly diagnosed breast cancers (BC), oestrogen receptor (ER), progesterone receptor (PR) and HER-2 and an additional four genes that create a novel proliferation signature, MKI67, PCNA, CCNA2 and KIF23. Taken together, the data are used to produce a molecular subtype for every sample. The kit was evaluated against the current standard protocol of immunohistochemistry (IHC) and/or in situ hybridisation (ISH) in breast cancer patients. The data were presented at the weekly breast multidisciplinary team (MDT) meeting. A total of 98 consecutive cases of pre-operative breast cancer core biopsies and two core biopsies of nodal metastases yielding 100 cases were assessed. IHC and APIS results were available for 100 and 99 cases. ER was concordant in 97% cases, PR was concordant in 89% and HER-2 results were concordant with IHC/ISH in 100% of the cases. Ki-67 IHC was discordant in 3% of cases when compared with MK167 alone but discordant in 24% when compared with the four-gene proliferation signature. In conclusion, our study indicates that the APIS Breast Cancer Subtyping Kit is highly concordant when compared to the results produced for ER/PR/HER-2 by IHC and/or ISH. The assay could play a role in the routine assessment of newly diagnosed breast cancer (BC) specimens.

iCluF: an unsupervised iterative cluster-fusion method for patient stratification using multiomics data

Motivation

Patient stratification is crucial for the effective treatment or management of heterogeneous diseases, including cancers. Multiomic technologies facilitate molecular characterization of human diseases; however, the complexity of data warrants the need for the development of robust data integration tools for patient stratification using machine-learning approaches.

Results

iCluF iteratively integrates three types of multiomic data (mRNA, miRNA, and DNA methylation) using pairwise patient similarity matrices built from each omic data. The intermediate omic-specific neighborhood matrices implement iterative matrix fusion and message passing among the similarity matrices to derive a final integrated matrix representing all the omics profiles of a patient, which is used to further cluster patients into subtypes. iCluF outperforms other methods with significant differences in the survival profiles of 8581 patients belonging to 30 different cancers in TCGA. iCluF also predicted the four intrinsic subtypes of Breast Invasive Carcinomas with adjusted rand index and Fowlkes-Mallows scores of 0.72 and 0.83, respectively. The Gini importance score showed that methylation features were the primary decisive players, followed by mRNA and miRNA to identify disease subtypes. iCluF can be applied to stratify patients with any disease containing multiomic datasets.

Availability and implementation

Source code and datasets are available at https://github.com/GudaLab/iCluF_core.

The role of tumor microenvironment in drug resistance: emerging technologies to unravel breast cancer heterogeneity

Breast cancer is a highly heterogeneous disease, at both inter- and intra-tumor levels, and this heterogeneity is a crucial determinant of malignant progression and response to treatments. In addition to genetic diversity and plasticity of cancer cells, the tumor microenvironment contributes to tumor heterogeneity shaping the physical and biological surroundings of the tumor. The activity of certain types of immune, endothelial or mesenchymal cells in the microenvironment can change the effectiveness of cancer therapies via a plethora of different mechanisms. Therefore, deciphering the interactions between the distinct cell types, their spatial organization and their specific contribution to tumor growth and drug sensitivity is still a major challenge. Dissecting intra-tumor heterogeneity is currently an urgent need to better define breast cancer biology and to develop therapeutic strategies targeting the microenvironment as helpful tools for combined and personalized treatment. In this review, we analyze the mechanisms by which the tumor microenvironment affects the characteristics of tumor heterogeneity that ultimately result in drug resistance, and we outline state of the art preclinical models and emerging technologies that will be instrumental in unraveling the impact of the tumor microenvironment on resistance to therapies.

Detecting the expression of HRs and BCL2 via IHC can help identify luminal A-like subtypes of triple-positive breast cancers

BACKGROUND

Triple-positive breast cancer (TPBC) is a tumor that simultaneously expresses estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor-2 (HER2). Luminal A-like TPBC is a special subtype with a favorable prognosis but benefits less from HER2-targeted therapy. However, little is known about how to identify luminal A-like TPBCs. Therefore, our study aims to explore a clinically feasible method to identify luminal A-like TPBCs using immunohistochemical (IHC) markers.

METHODS

Our cohort enrolled consecutive 190 patients with early-stage TPBCs diagnosed, treated and followed up in our hospital between 2013 and 2019. Patients whose IHC staining displayed ≥ 50% in both ER and PR scores and B-cell lymphoma 2 (BCL2) positivity were classified as cohort A (n = 64), and the rest were enrolled in cohort B (n = 126). Kaplan-Meier plotter and log-rank test were used to compare the survival difference between cohort A and cohort B and the efficacy of trastuzumab therapy in the two cohorts.

RESULTS

The disease-free survival (DFS) of patients in cohort A was significantly better than in cohort B (p = 0.031). In cohort A, there was no statistically significant difference in DFS between patients treated with trastuzumab and those without trastuzumab (p = 0.663). While in cohort B, patients treated with trastuzumab had significantly better DFS than those without trastuzumab (p = 0.032). Multivariate survival analysis showed that cohort A was associated with better DFS(95%CI 1.046-11.776, p = 0.042).

CONCLUSION

TPBCs consist of heterogeneous subtypes. Detecting the expression of ER, PR and BCL2 via IHC can help identify luminal A-like TPBCs. This study will enable individualized treatment of TPBCs.

Discordance between PAM50 intrinsic subtyping and immunohistochemistry in South African women with breast cancer

PURPOSE

Breast cancer is a heterogeneous disease with different gene expression profiles, treatment options and outcomes. In South Africa, tumors are classified using immunohistochemistry. In high-income countries multiparameter genomic assays are being utilized with implications for tumor classification and treatment.

METHODS

In a cohort of 378 breast cancer patients from the SABCHO study, we investigated the concordance between tumor samples classified by IHC and the PAM50 gene assay.

RESULTS

IHC classified patients as ER-positive (77.5%), PR-positive (70.6%), and HER2-positive (32.3%). These results, together with Ki67, were used as surrogates for intrinsic subtyping, and showed 6.9% IHC-A-clinical, 72.7% IHC-B-clinical, 5.3% IHC-HER2-clinical and 15.1% triple negative cancer (TNC). Typing using the PAM50 gave 19.3% luminal-A, 32.5% luminal-B, 23.5% HER2-enriched and 24.6% basal-like. The basal-like and TNC had the highest concordance, while the luminal-A and IHC-A group had the lowest concordance. By altering the cutoff for Ki67, and realigning the HER2/ER/PR-positive patients to IHC-HER2, we improved concordance with the intrinsic subtypes.

CONCLUSION

We suggest that the Ki67 be changed to a cutoff of 20-25% in our population to better reflect the luminal subtype classifications. This change would inform treatment options for breast cancer patients in settings where genomic assays are unaffordable.

Systemically Identifying Triple-Negative Breast Cancer Subtype-Specific Prognosis Signatures, Based on Single-Cell RNA-Seq Data

Triple-negative breast cancer (TNBC) is a highly heterogeneous disease with different molecular subtypes. Although progress has been made, the identification of TNBC subtype-associated biomarkers is still hindered by traditional RNA-seq or array technologies, since bulk data detected by them usually have some non-disease tissue samples, or they are confined to measure the averaged properties of whole tissues. To overcome these constraints and discover TNBC subtype-specific prognosis signatures (TSPSigs), we proposed a single-cell RNA-seq-based bioinformatics approach for identifying TSPSigs. Notably, the TSPSigs we developed mostly were found to be disease-related and involved in cancer development through investigating their enrichment analysis results. In addition, the prognostic power of TSPSigs was successfully confirmed in four independent validation datasets. The multivariate analysis results showed that TSPSigs in two TNBC subtypes-BL1 and LAR, were two independent prognostic factors. Further, analysis results of the TNBC cell lines revealed that the TSPSigs expressions and drug sensitivities had significant associations. Based on the preceding data, we concluded that TSPSigs could be exploited as novel candidate prognostic markers for TNBC patients and applied to individualized treatment in the future.

A Simple Method for Robust and Accurate Intrinsic Subtyping of Breast Cancer

Motivation

The PAM50 signature/method is widely used for intrinsic subtyping of breast cancer samples. However, depending on the number and composition of the samples included in a cohort, the method may assign different subtypes to the same sample. This lack of robustness is mainly due to the fact that PAM50 subtracts a reference profile, which is computed using all samples in the cohort, from each sample before classification. In this paper we propose modifications to PAM50 to develop a simple and robust single-sample classifier, called MPAM50, for intrinsic subtyping of breast cancer. Like PAM50, the modified method uses a nearest centroid approach for classification, but the centroids are computed differently, and the distances to the centroids are determined using an alternative method. Additionally, MPAM50 uses unnormalized expression values for classification and does not subtract a reference profile from the samples. In other words, MPAM50 classifies each sample independently, and so avoids the previously mentioned robustness issue.

Results

A training set was employed to find the new MPAM50 centroids. MPAM50 was then tested on 19 independent datasets (obtained using various expression profiling technologies) containing 9637 samples. Overall good agreement was observed between the PAM50- and MPAM50-assigned subtypes with a median accuracy of 0.792, which (we show) is comparable with the median concordance between various implementations of PAM50. Additionally, MPAM50- and PAM50-assigned intrinsic subtypes were found to agree comparably with the reported clinical subtypes. Also, survival analyses indicated that MPAM50 preserves the prognostic value of the intrinsic subtypes. These observations demonstrate that MPAM50 can replace PAM50 without loss of performance. On the other hand, MPAM50 was compared with 2 previously published single-sample classifiers, and with 3 alternative modified PAM50 approaches. The results indicated a superior performance by MPAM50.

Conclusions

MPAM50 is a robust, simple, and accurate single-sample classifier of intrinsic subtypes of breast cancer.

Treatment strategies for hormone receptor-positive, human epidermal growth factor receptor 2-positive (HR+/HER2+) metastatic breast cancer: A review

Hormone receptor-positive HER2-positive (HR+/HER2+) metastatic breast cancer (MBC) is a unique subtype of breast cancer. Most current guidelines recommend that combination regimens based on anti-HER2 therapy should be used as first-line treatment for HER2+ MBC, irrespective of HR status. Endocrine therapy can be applied as maintenance therapy for patients who are intolerant to chemotherapy or post-chemotherapy. Increasing evidence suggests that complex molecular crosstalk between HR and HER2 pathways may affect the sensitivity to both HER2-targeted and endocrine therapy in patients with HR+/HER2+ breast cancer. Recent research and clinical trials have revealed that a combination of endocrine therapy and anti-HER2 approaches without chemotherapy provides along-term disease control for some patients, but the challenge lies in how to accurately identify the subsets of patients who can benefit from such a de-chemotherapy treatment strategy. In this review, we aim to summarize the results of preclinical and clinical studies in HR+/HER2+ MBC and discuss the possibility of sparing chemotherapy in this subgroup of patients.

Association of Endocrine Therapy for HR+/ERBB2+ Metastatic Breast Cancer With Survival Outcomes

IMPORTANCE

Evidence suggests that patients with human epidermal growth factor receptor 2-positive (ERBB2+ [formerly HER2+]) metastatic breast cancer (MBC) have different clinical characteristics and outcomes according to their hormone receptor (HR) status. The place of endocrine therapy (ET) for patients with HR+/ERBB2+ is still not clearly defined in this setting.

OBJECTIVE

To evaluate the association of HR status and first-line inclusion of ET with outcomes among patients with ERBB2+ MBC.

DESIGN, SETTING, AND PARTICIPANTS

This cohort study was an analysis of clinical data from the French clinical Epidemiological Strategy and Medical Economics (ESME) cohort, including patients with MBC who started treatment between 2008 and 2017. The last date of follow-up was June 18, 2020. Data were analyzed from May 2021 to May 2022.

EXPOSURES

Patients were treated with first-line ERBB2-targeted therapy and either chemotherapy (CT) with or without ET or ET alone. For the study of the association of maintenance ET with outcomes, we included patients treated with first-line ERBB2-targeted therapy with CT and with or without maintenance ET.

MAIN OUTCOMES AND MEASURES

Median overall survival (OS) and median first-line progression-free survival (PFS) were reported using the Kaplan-Meier method. Cox proportional hazards models and a propensity score were constructed to report and adjust for prognostic factors. Multivariable analysis included age at MBC, time to MBC, number of metastatic sites, type of metastases, and Eastern Cooperative Oncology Group performance status.

RESULTS

Among 4145 women with ERBB2+ MBC, 2696 patients had HR+ (median [IQR] age, 58.0 [47.0-67.0] years) and 1449 patients had HR- (56.0 [47.0-64.0] years) tumors. The median OS for patients with HR+ vs HR- tumors was 55.9 months (95% CI, 53.7-59.4 months) vs 42.0 months (95% CI, 38.8-45.2 months), confirmed in multivariable analysis (hazard ratio, 1.40; 95% CI, 1.26-1.56; P < .001). The median PFS for patients with HR+ vs HR- tumors was 12.2 months (95% CI, 11.5-12.9 months) vs 9.8 months (95% CI, 9.2-11.0 months; P = .01), and the HR was 1.15 (95% CI, 1.06-1.26; P < .001). In multivariable analysis, no significant difference was found in OS or PFS for 1520 patients treated with ERBB2-targeted therapy with CT and with or without ET vs 203 patients receiving ERBB2-targeted therapy with ET, regardless of type of ERBB2-targeted therapy (trastuzumab or trastuzumab with pertuzumab). This result was confirmed by matching patients using a propensity score. Using the time-dependent ET variable among patients with ERBB2-targeted therapy with CT, those with maintenance ET had significantly better PFS (hazard ratio, 0.70; 95% CI, 0.60-0.82; P < .001) and OS (hazard ratio, 0.47; 95% CI, 0.39-0.57; P < .001).

CONCLUSIONS AND RELEVANCE

These results suggest that ET-containing first-line regimens may be associated with benefits among a subgroup of patients with HR+/ERBB2+ MBC.

Risk Stratification for Breast Cancer Patient by Simultaneous Learning of Molecular Subtype and Survival Outcome Using Genetic Algorithm-Based Gene Set Selection

Simple Summary

Patient stratification is clinically important because it allows us to understand the characteristics and establish treatment strategies for a group. Transcriptomic data play an important role in determining molecular subtypes and predicting survival. In the case of breast cancer, although the order of prognosis according to molecular subtypes is well known, there is heterogeneity even within a subtype. Therefore, patient stratification considering both molecular subtypes and survival outcomes is required. In this study, a methodology to handle this problem is presented. A genetic algorithm is used to select a set of genes, and a risk score is assigned to each patient using their expression level. According to the risk score, patients are ordered and stratified considering molecular subtypes and survival outcomes. Consequently, informative genes for patient stratification with respect to both aspects could be nominated, and the usefulness of the risk score was shown through comparison with other indicators.

Abstract

Patient stratification is a clinically important task because it allows us to establish and develop efficient treatment strategies for particular groups of patients. Molecular subtypes have been successfully defined using transcriptomic profiles, and they are used effectively in clinical practice, e.g., PAM50 subtypes of breast cancer. Survival prediction contributed to understanding diseases and also identifying genes related to prognosis. It is desirable to stratify patients considering these two aspects simultaneously. However, there are no methods for patient stratification that consider molecular subtypes and survival outcomes at once. Here, we propose a methodology to deal with the problem. A genetic algorithm is used to select a gene set from transcriptome data, and their expression quantities are utilized to assign a risk score to each patient. The patients are ordered and stratified according to the score. A gene set was selected by our method on a breast cancer cohort (TCGA-BRCA), and we examined its clinical utility using an independent cohort (SCAN-B). In this experiment, our method was successful in stratifying patients with respect to both molecular subtype and survival outcome. We demonstrated that the orders of patients were consistent across repeated experiments, and prognostic genes were successfully nominated. Additionally, it was observed that the risk score can be used to evaluate the molecular aggressiveness of individual patients.

Understanding Breast Cancers through Spatial and High-Resolution Visualization Using Imaging Technologies

Breast cancer is the most common cancer affecting women worldwide. Although many analyses and treatments have traditionally targeted the breast cancer cells themselves, recent studies have focused on investigating entire cancer tissues, including breast cancer cells. To understand the structure of breast cancer tissues, including breast cancer cells, it is necessary to investigate the three-dimensional location of the cells and/or proteins comprising the tissues and to clarify the relationship between the three-dimensional structure and malignant transformation or metastasis of breast cancers. In this review, we aim to summarize the methods for analyzing the three-dimensional structure of breast cancer tissue, paying particular attention to the recent technological advances in the combination of the tissue-clearing method and optical three-dimensional imaging. We also aimed to identify the latest methods for exploring the relationship between the three-dimensional cell arrangement in breast cancer tissues and the gene expression of each cell. Finally, we aimed to describe the three-dimensional imaging features of breast cancer tissues using noninvasive photoacoustic imaging methods.

Molecular Subtyping of Invasive Breast Cancer Using a PAM50-Based Multigene Expression Test-Comparison with Molecular-Like Subtyping by Tumor Grade/Immunohistochemistry and Influence on Oncologist's Decision on Systemic Therapy in a Real-World Setting

In intermediate risk hormone receptor (HR) positive, HER2 negative breast cancer (BC), the decision regarding adjuvant chemotherapy might be facilitated by multigene expression tests. In all, 142 intermediate risk BCs were investigated using the PAM50-based multigene expression test Prosigna® in a prospective multicentric study. In 119/142 cases, Prosigna® molecular subtyping was compared with local and two central (C1 and C6) molecular-like subtypes relying on both immunohistochemistry (IHC; HRs, HER2, Ki-67) and IHC + tumor grade (IHC+G) subtyping. According to local IHC, 35.4% were Luminal A-like and 64.6% Luminal B-like subtypes (local IHC+G subtype: 31.9% Luminal A-like; 68.1% Luminal B-like). In contrast to local and C1 subtyping, C6 classified >2/3 of cases as Luminal A-like. Pairwise agreement between Prosigna® subtyping and molecular-like subtypes was fair to moderate depending on molecular-like subtyping method and center. The best agreement was observed between Prosigna® (53.8% Luminal A; 44.5% Luminal B) and C1 surrogate subtyping (Cohen’s kappa = 0.455). Adjuvant chemotherapy was suggested to 44.2% and 88.6% of Prosigna® Luminal A and Luminal B cases, respectively. Out of all Luminal A-like cases (locally IHC/IHC+G subtyping), adjuvant chemotherapy was recommended if Prosigna® testing classified as Prosigna® Luminal A at high / intermediate risk or upgraded to Prosigna® Luminal B.

Prognostic significance of different molecular typing methods and immune status based on RNA sequencing in HR-positive and HER2-negative early-stage breast cancer

BACKGROUND

This study was conducted to evaluate the prognostic significance of different molecular typing methods and immune status based on RNA sequencing (RNA-seq) in hormone receptor (HR)-positive and human epidermal growth factor receptor 2 (HER2)-negative (HR + /HER2-) early-stage breast cancer and develop a modified immunohistochemistry (IHC)-based surrogate for intrinsic subtype analysis.

METHODS

The gene expression profiles of samples from 87 HR + /HER2- early-stage breast cancer patients were evaluated using the RNA-seq of Oncotype Dx recurrence score (RS), PAM50 risk of recurrence (ROR), and immune score. Intrinsic tumor subtypes were determined using both PAM50- and IHC-based detection of estrogen receptor, progesterone receptor, Ki-67, epidermal growth factor receptor, and cytokeratins 14 and 5/6. Prognostic variables were analyzed through Cox regression analysis of disease-free survival (DFS) and distant metastasis-free survival (DMFS).

RESULTS

Survival analysis showed that ROR better predicted recurrence and distant metastasis compared to RS (for DFS: ROR, P = 0.000; RS, P = 0.027; for DMFS, ROR, P = 0.047; RS, P = 0.621). Patients with HR + /HER2- early-stage breast cancer was classified into the luminal A, luminal B, HER2-enriched, and basal-like subtypes by PAM50. Basal-like subgroups showed the shortest DFS and DMFS. A modified IHC-based surrogate for intrinsic subtype analysis improved the concordance with PAM50 from 66.7% to 73.6%, particularly for basal-like subtype identification. High level of TILs and high expression of immune genes predicted poor prognosis. Multi-factor Cox analysis showed that IHC-based basal-like markers were the only independent factors affecting DMFS.

CONCLUSIONS

Prognosis is better evaluated by PAM50 ROR in early-stage HR + /HER2- breast cancer and significantly differs among intrinsic subtypes. The modified IHC-based subtype can improve the basal-like subtype identification of PAM50. High immunity status and IHC-based basal-like markers are negative prognostic factors.

The Transcriptomic Portrait of Locally Advanced Breast Cancer and Its Prognostic Value in a Multi-Country Cohort of Latin American Patients

Purposes

Most molecular-based published studies on breast cancer do not adequately represent the unique and diverse genetic admixture of the Latin American population. Searching for similarities and differences in molecular pathways associated with these tumors and evaluating its impact on prognosis may help to select better therapeutic approaches.

Patients and Methods

We collected clinical, pathological, and transcriptomic data of a multi-country Latin American cohort of 1,071 stage II-III breast cancer patients of the Molecular Profile of Breast Cancer Study (MPBCS) cohort. The 5-year prognostic ability of intrinsic (transcriptomic-based) PAM50 and immunohistochemical classifications, both at the cancer-specific (OSC) and disease-free survival (DFS) stages, was compared. Pathway analyses (GSEA, GSVA and MetaCore) were performed to explore differences among intrinsic subtypes.

Results

PAM50 classification of the MPBCS cohort defined 42·6% of tumors as LumA, 21·3% as LumB, 13·3% as HER2E and 16·6% as Basal. Both OSC and DFS for LumA tumors were significantly better than for other subtypes, while Basal tumors had the worst prognosis. While the prognostic power of traditional subtypes calculated with hormone receptors (HR), HER2 and Ki67 determinations showed an acceptable performance, PAM50-derived risk of recurrence best discriminated low, intermediate and high-risk groups. Transcriptomic pathway analysis showed high proliferation (i.e. cell cycle control and DNA damage repair) associated with LumB, HER2E and Basal tumors, and a strong dependency on the estrogen pathway for LumA. Terms related to both innate and adaptive immune responses were seen predominantly upregulated in Basal tumors, and, to a lesser extent, in HER2E, with respect to LumA and B tumors.

Conclusions

This is the first study that assesses molecular features at the transcriptomic level in a multicountry Latin American breast cancer patient cohort. Hormone-related and proliferation pathways that predominate in PAM50 and other breast cancer molecular classifications are also the main tumor-driving mechanisms in this cohort and have prognostic power. The immune-related features seen in the most aggressive subtypes may pave the way for therapeutic approaches not yet disseminated in Latin America.

Clinical Trial Registration

ClinicalTrials.gov (Identifier: NCT02326857).

Association of HER-2/CEP17 Ratio and HER-2 Copy Number With pCR Rate in HER-2-Positive Breast Cancer After Dual-Target Neoadjuvant Therapy With Trastuzumab and Pertuzumab

Objective

To explore the correlation between HER-2 status and pathological complete response (pCR) in HER-2-positive breast cancer after dual anti-HER-2 neoadjuvant therapy with trastuzumab and pertuzumab.

Methods

A total of 57 HER-2-positive breast cancer patients admitted to the Second Affiliated Hospital of Anhui Medical University and Xijing Hospital Affiliated to Air Force Military Medical University, between January 1, 2019 and September 30, 2020, were enrolled in this multicenter retrospective study. HER-2 status, including HER-2/CEP17 ratio and HER-2/cell number ratio, was detected by FISH. The correlation between HER-2 status/clinicopathological data and pCR was analyzed. The ROC curve was drawn to determine the cutoff value.

Results

IHC assessment revealed 40 (70.18%) patients with IHC 3+ and 17 (29.82%) with IHC 2+. 41/57 (71.93%) patients achieved pCR. FISH revealed that the ratio of HER-2/chromosome 17 centromere (HER-2/CEP17) (p<0.001) and the ratio of HER-2/cell number (p<0.001) was significantly correlated with the pCR rate. ROC analysis showed that patients with an HER-2/CEP17 ratio ≥4.495 or HER-2/cell number ≥11.650 have a high pCR rate after dual anti-HER-2 neoadjuvant therapy, suggesting its predictive significance.

Conclusion

The response to dual-targeted neoadjuvant therapy with trastuzumab and pertuzumab was adequate in hormone receptor-negative, HER-2-positive breast cancer patients. HER-2/CEP17 ratio and HER-2/cell number ratio were crucial for predicting efficacy.

HER2+ Breast Cancer Escalation and De-Escalation Trial Design: Potential Role of Intrinsic Subtyping

Long-term outcomes in breast cancer patients differ based on the molecular subtype, with HER2-E being the most aggressive one. Advances in clinical practice have dramatically shifted HER2+ breast cancer prognosis. Risk adapted strategies to individualize therapies are necessary. De-escalation approaches have been encouraged based on the risks of clinical-pathological factors. Molecular gene subtyping could further accurately define HER2 addicted tumours that are sensitive to anti-HER2 therapies, thus sparing unnecessary treatments. The transition from immunochemistry to molecular profiling in HER2+ breast cancer is discussed.

Poor histologic tumor response after adjuvant therapy in basal-like HER2-positive breast carcinoma

AIMS

HER2-positive breast carcinomas are all treated with first-line anti-HER2 therapy. However, immunohistochemical and molecular profiling demonstrates significant heterogeneity among HER2-positive carcinomas. Basal-like HER2-positive breast carcinomas are poorly differentiated from pure HER2-positive breast carcinomas.

MATERIALS AND METHODS

Seventy-five patients with HER2-positive, ER- and PR-negative breast carcinomas who received anti-HER2 based neoadjuvant therapy were retrospectively analyzed. Thirty-seven cases were classified as basal-like HER2-positive breast carcinoma with any positivity for CK5/6, and thirty-eight cases were classified as pure HER2-positive breast carcinoma with completely negativity for CK5/6. The clinicopathological features and tumor responses after neoadjuvant therapy and outcomes were analyzed.

RESULTS

Compared to non-basal HER2-positive breast carcinoma, basal-like HER2-positive breast carcinoma showed distinctive histologic features including poor differentiation and syncytial tumor cells with pushing, invasive borders and a significantly higher proportion of apocrine metaplasia. They also demonstrated significantly higher histologic grade; 18/37 (48.6%) of basal-like carcinomas were grade 3, whereas only 5/38 (13.2%) of non-basal carcinomas were grade 3 (p = 0.001), Furthermore, basal-like HER2-positive breast carcinomas were more likely to be positive or completely negative for p53 (p = 0.009), and demonstrated a higher percentage of TP53 mutation (p = 0.17). These tumors were less responsive to anti-HER2 based neoadjuvant therapy, with Miller-Payne grades 1-3 higher than pure HER2-positive breast carcinoma (25/37 [67.6%] vs 16/38 [42.1%]), and the percentage of grade 4-5 was lower (12/37 [32.4%] vs 22/38 [57.9%]; p = 0.027).

CONCLUSIONS

Basal-like HER2-positive breast carcinoma has distinctive clinicopathological features and less histologic tumor response after neoadjuvant therapy. There is urgent need to recognize basal-like HER2-positive breast carcinoma to be treated precisely.

Improving Prognosis of Surrogate Assay for Breast Cancer Patients by Absolute Quantitation of Ki67 Protein Levels Using Quantitative Dot Blot (QDB) Method

Background

Immunohistochemistry (IHC)-based surrogate assay is the prevailing method in daily clinical practice to determine the necessity of chemotherapy for Luminal-like breast cancer patients worldwide. It relies on Ki67 scores to separate Luminal A-like from Luminal B-like breast cancer subtypes. Yet, IHC-based Ki67 assessment is known to be plagued with subjectivity and inconsistency to undermine the performance of the surrogate assay. A novel method needs to be explored to improve the clinical utility of Ki67 in daily clinical practice.

Materials and Methods

The Ki67 protein levels in a cohort of 253 specimens were assessed with IHC and quantitative dot blot (QDB) methods, respectively, and used to assign these specimens into Luminal A-like and Luminal B-like subtypes accordingly. Their performances were compared with the Kaplan–Meier, univariate, and multivariate survival analyses of the overall survival (OS) of Luminal-like patients.

Results

The surrogate assay based on absolutely quantitated Ki67 levels (cutoff at 2.31 nmol/g) subtyped the Luminal-like patients more effectively than that based on Ki67 scores (cutoff at 14%) (Log rank test, p = 0.00052 vs. p = 0.031). It is also correlated better with OS in multivariate survival analysis [hazard ratio (HR) at 6.89 (95% CI: 2.66–17.84, p = 0.0001) vs. 2.14 (95% CI: 0.89–5.11, p = 0.087)].

Conclusions

Our study showed that the performance of the surrogate assay may be improved significantly by measuring Ki67 levels absolutely, quantitatively, and objectively using the QDB method.

A single-cell and spatially resolved atlas of human breast cancers

Breast cancers are complex cellular ecosystems where heterotypic interactions play central roles in disease progression and response to therapy. However, our knowledge of their cellular composition and organization is limited. Here we present a single-cell and spatially resolved transcriptomics analysis of human breast cancers. We developed a single-cell method of intrinsic subtype classification (SCSubtype) to reveal recurrent neoplastic cell heterogeneity. Immunophenotyping using cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) provides high-resolution immune profiles, including new PD-L1/PD-L2⁺ macrophage populations associated with clinical outcome. Mesenchymal cells displayed diverse functions and cell-surface protein expression through differentiation within three major lineages. Stromal-immune niches were spatially organized in tumors, offering insights into antitumor immune regulation. Using single-cell signatures, we deconvoluted large breast cancer cohorts to stratify them into nine clusters, termed 'ecotypes', with unique cellular compositions and clinical outcomes. This study provides a comprehensive transcriptional atlas of the cellular architecture of breast cancer.

Precision treatment exploration of breast cancer based on heterogeneity analysis of lncRNAs at the single-cell level

BACKGROUND

Breast cancer (BC) is a complex disease with high heterogeneity, which often leads to great differences in treatment results. Current common molecular typing method is PAM50, which shows positive results for precision medicine; however, room for improvement still remains because of the different prognoses of subtypes. Therefore, in this article, we used lncRNAs, which are more tissue-specific and developmental stage-specific than other RNAs, as typing markers and combined single-cell expression profiles to retype BC, to provide a new method for BC classification and explore new precise therapeutic strategies based on this method.

METHODS

Based on lncRNA expression profiles of 317 single cells from 11 BC patients, SC3 was used to retype BC, and differential expression analysis and enrichment analysis were performed to identify biological characteristics of new subtypes. The results were validated for survival analysis using data from TCGA. Then, the downstream regulatory genes of lncRNA markers of each subtype were searched by expression correlation analysis, and these genes were used as targets to screen therapeutic drugs, thus proposing new precision treatment strategies according to the different subtype compositions of patients.

RESULTS

Seven lncRNA subtypes and their specific biological characteristics are obtained. Then, 57 targets and 210 drugs of 7 subtypes were acquired. New precision medicine strategies were proposed according to the different compositions of patient subtypes.

CONCLUSIONS

For patients with different subtype compositions, we propose a strategy to select different drugs for different patients, which means using drugs targeting multi subtype or combinations of drugs targeting a single subtype to simultaneously kill different cancer cells by personalized treatment, thus reducing the possibility of drug resistance and even recurrence.

PAM50- and immunohistochemistry-based subtypes of breast cancer and their relationship with breast cancer mortality in a population-based study

PURPOSE

We evaluated the prognostic ability of immunohistochemistry (IHC)-based vs. PAM50-based subtypes for breast cancer mortality in a population-based study of breast cancer.

METHODS

We included a total of 463 breast cancer cases from the population-based Long Island Breast Cancer Study Project (LIBCSP). IHC-based markers were abstracted from the medical records, while the PAM50-based intrinsic subtypes were assessed from tumor tissues using NanoString nCounter^® Analysis System. Cox proportional hazards models were used to estimate hazards ratios (HRs) for breast cancer-specific mortality associated with subtypes.

RESULTS

For IHC-based hormone receptor-positive (HR+) tumors (n = 361), 68.7% were classified as luminal subtypes by PAM50; for HR- tumors (n = 102), 95.1% were classified as non-luminal subtypes. Compared to HR+/HER2- subtype, HR- patients had significantly higher breast cancer mortality (HR-/HER2+: HR = 2.84, 95% CI = 1.58-5.11; triple-negative breast cancer: HR = 2.42, 95% CI = 1.44-4.06). Compared to luminal A, a higher mortality rate was observed for all other PAM50-based subtypes: luminal B (HR = 4.03, 95% CI = 1.97-8.22), HER2-enriched (HR = 6.82, 95% CI = 3.29-14.14) and basal-like (HR = 4.71, 95% CI = 2.24-9.93). Additional subtyping of HR+ patients by PAM50 provided future risk stratification where luminal B patients in this group had significant higher mortality than luminal A patients (HR = 3.93, 95% CI = 1.92-8.03). Similar results were also observed among 291 HR+/HER2- patients, but not among the HR- patients.

CONCLUSIONS

Our study suggests that for HR+ patients, especially HR+/HER2- patients, additional PAM50-based subtyping would provide better prognostic stratification and improve disease management.

A Topic Modeling Analysis of TCGA Breast and Lung Cancer Transcriptomic Data

Topic modeling is a widely used technique to extract relevant information from large arrays of data. The problem of finding a topic structure in a dataset was recently recognized to be analogous to the community detection problem in network theory. Leveraging on this analogy, a new class of topic modeling strategies has been introduced to overcome some of the limitations of classical methods. This paper applies these recent ideas to TCGA transcriptomic data on breast and lung cancer. The established cancer subtype organization is well reconstructed in the inferred latent topic structure. Moreover, we identify specific topics that are enriched in genes known to play a role in the corresponding disease and are strongly related to the survival probability of patients. Finally, we show that a simple neural network classifier operating in the low dimensional topic space is able to predict with high accuracy the cancer subtype of a test expression sample.

Clinical implications of HER2 mRNA expression and intrinsic subtype in refractory HER2-positive metastatic breast cancer treated with pan-HER inhibitor, poziotinib

INTRODUCTION

We explored clinical implication of intrinsic molecular subtype in human epidermal growth factor receptor 2 (HER2) + metastatic breast cancer (BC) with pan-HER inhibitor from a phase II clinical trial of poziotinib in refractory HER2+BC patients.

METHODS

For this translational research correlated with phase II clinical trial, we performed an nCounter expression assay, using gene panel including 50 genes for PAM50 prediction and targeted deep sequencing.

RESULTS

From 106 participants, we obtained 97 tumor tissues and analyzed gene expression in 91 of these samples. Of 91 HER2+BCs, 40 (44.0%) were HER2-enriched (E) intrinsic molecular subtype, 17 (18.7%) of Luminal A, 16 (17.6%) of Basal-like, 14 (15.4%) of Luminal B and 4 (4.4%) of Normal-like. HER2-E subtype was associated with hormone receptor negativity (odds ratio [OR] 2.93; p = 0.019), 3 + of HER2 immunohistochemistry(IHC) (OR 5.64; p = 0.001), high mRNA expression of HER2 (OR 14.43; p = 0.001) and copy number(CN) amplification of HER2 (OR 12.80; p = 0.005). In genetic alterations, alteration was more frequently observed in HER2-E subtype (OR 3.84; p = 0.022) but there was no association between PIK3CA alteration and HER2-E subtype (p = 0.655). In terms of drug efficacy, high mRNA expression of HER2 was the most powerful predictor of poziotinib response (median progression-free survival [PFS): 4.63 months [high] vs. 2.56 [low]; p < .001). In a combination prediction model, median PFS of intrinsic subtypes except Her2-E with high HER2 mRNA expression without PIK3CA genetic alteration was 6.83 months and that of the remaining group was 1.74 months (p < .001).

CONCLUSION

HER2-E subtype was associated with hormone receptor status, HER2 IHC, CN and mRNA expression and TP53 mutation. In survival analysis, the information of level of HER2 mRNA expression, intrinsic molecular subtype and PI3K pathway alteration would be independent predictors to poziotinib treatment. ClinicalTrials.gov identifier: NCT02418689.

Subtype-specific risk models for accurately predicting the prognosis of breast cancer using differentially expressed autophagy-related genes

Emerging evidence suggests that the dysregulation of autophagy-related genes (ARGs) is coupled with the carcinogenesis and progression of breast cancer (BRCA). We constructed three subtype-specific risk models using differentially expressed ARGs. In Luminal, Her-2, and Basal-like BRCA, four- (BIRC5, PARP1, ATG9B, and TP63), three- (ITPR1, CCL2, and GAPDH), and five-gene (PRKN, FOS, BAX, IFNG, and EIF4EBP1) risk models were identified, which all have a receiver operating characteristic > 0.65 in the training and testing dataset. Multivariable Cox analysis showed that those risk models can accurately and independently predict the overall survival of BRCA patients. Comprehensive analysis showed that the 12 identified ARGs were correlated with the overall survival of BRCA patients; six of the ARGs (PARP1, TP63, CCL2, GAPDH, FOS, and EIF4EBP1) were differentially expressed between BRCA and normal breast tissue at the protein level. In addition, the 12 identified ARGs were highly interconnected and displayed high frequency of copy number variation in BRCA samples. Gene set enrichment analysis suggested that the deactivation of the immune system was the important driving force for the progression of Basal-like BRCA. This study demonstrated that the 12 ARG signatures were potential multi-dimensional biomarkers for the diagnosis, prognosis, and treatment of BRCA.

Do 21-Gene Recurrence Score Influence Chemotherapy Decisions in T1bN0 Breast Cancer Patients?

Purpose: Hormone receptor (HR)-positive breast cancer patients with tumor size ≤1.0 cm and negative node have favorable outcomes. The 21-gene Recurrence Score (RS) could predict response to chemotherapy for HR+ breast cancer, but its role in T1bN0 disease is challenging.

Methods: T1bN0 breast cancer patients diagnosed between January 2014 and June 2019 with RS results were included and categorized as Low- (RS < 18), Intermediate- (RS 18–30), or High-risk (RS > 30) groups. Univariate and multivariate analysis were used to assess factors associated with RS distribution and chemotherapy recommendation. Chemotherapy decisions change and patient adherence after 21-gene RS testing were also evaluated.

Results: Among 237 patients with T1bN0 tumors, proportions of Low-, Intermediate-, and High-risk RS were 19.8, 63.3, and 16.9%, respectively. Multivariate analysis found that ER expression (P = 0.011), PR expression (P < 0.001), and Ki-67 index (P = 0.001) were independently associated with RS distribution. Adjuvant chemotherapy was recommended for 31.6% of patients, which was more frequently given to patients with higher tumor grade [Odds ratio (OR) = 2.99 for grade II, OR = 59.19 for grade III, P = 0.006], lymph vascular invasion (OR = 8.22, P = 0.032), Luminal-B subtype (OR = 5.68, P < 0.001), and Intermediate-to High-risk RS (OR = 10.01 for Intermediate-risk, OR = 192.42 for High-risk, P < 0.001). Chemotherapy decision change was found in 18.6% of patients, mainly in those with Intermediate- to High-risk RS tumor with the majority from no-chemotherapy to chemotherapy. The treatment compliance rate after the 21-gene RS testing with MDT was 95.4%.

Conclusion: RS category was related to ER, PR, and Ki-67 expression, which was recognized as an independent factor of chemotherapy recommendation in T1bN0 breast cancer. The 21-gene RS testing would lead to a chemotherapy decision change rate of 18.6% as well as a high treatment adherence, which can be applied in T1bN0 patients.

DGM-CM6: A New Model to Predict Distant Recurrence Risk in Operable Endocrine-Responsive Breast Cancer

To investigate the prognostic value of DGM-CM6 (Distant Genetic Model-Clinical variable Model 6) for endocrine-responsive breast cancer (ERBC) patients, we analyzed 752 operable breast cancer patients treated in a Taiwan cancer center from 2005 to 2014. Among them, 490 ERBC patients (identified by the PAM50 or immunohistochemistry method) were classified by DGM-CM6 into low- and high-risk groups (cutoff <33 and ≥33, respectively). Significant differences were observed between the DGM-CM6 low- and high-risk groups for 10-year distant recurrence-free survival (DRFS) in both lymph node (LN)- (P < 0.05) and LN+ patients (P < 0.05). Multivariate analysis confirmed the independent strength of DGM-CM6 for the prediction of high- vs. low- risk groups for DRFS (P < 0.0001, HR: 6.76, 95% CI, 1.8–25.42) and overall survival (P = 0.01, HR: 6.06, 95% CI:1.55–23.47), respectively. In summary, DGM-CM6 may be used to classify low- and high-risk groups for 10-year distant recurrence in both LN- and LN+ ERBC patients in the Asian population. A large scale clinical trial is warranted.

Treatment selection for patients with equivocal HER2 status and in luminal versus HER2-enriched disease

Equivocal HER2 status has been variably defined in the past, and its clinical implications have long been debated. In the 2018 focused update, ASCO/CAP guidelines recommended that tumours with double-equivocal (by immunohistochemistry and in situ hybridization assays) HER2 status should be considered HER2-negative due to the lack of evidence for any benefit of HER2-targeted therapy. The biology and the response to systemic therapies of tumours co-expressing HR and HER2 is quite complex. There is an extensive bi-directional cross-talk between these 2 pathways, that may result in both intrinsic and acquired resistance to endocrine agents, as well as in lower sensitivity to HER2-targeted therapies. In fact, neoadjuvant studies indicate that pCR rates are significantly lower in HER2-positive/ER-positive than ER-negative tumours, regardless the type of HER2 targeted treatment. The recent identification of different subtypes of HER2-positive breast cancer, according to the co-expression of HR and/or the molecular (intrinsic) subtyping, has prompted a renewed interest for clinical studies aimed at better tailoring the systemic therapy for these patients. A subgroup of them might not need chemotherapy if treated with dual HER2 blockade, and this option has been tested in a number of neo-adjuvant trials. In addition, triple targeting of HR, HER2, and CDK4/6 pathways simultaneously may be an effective treatment and overcome the drug resistance mechanisms that are typical of the disease. Finally, HER2-positive breast cancer may well benefit from immunotherapeutic interventions with anti-programmed cell death protein 1 (PD-1) and programmed cell death ligand 1 (PD-L1) agents.

Hormone Receptor-Status Prediction in Breast Cancer Using Gene Expression Profiles and Their Macroscopic Landscape

The cost of next-generation sequencing technologies is rapidly declining, making RNA-seq-based gene expression profiling (GEP) an affordable technique for predicting receptor expression status and intrinsic subtypes in breast cancer patients. Based on the expression levels of co-expressed genes, GEP-based receptor-status prediction can classify clinical subtypes more accurately than can immunohistochemistry (IHC). Using data from The Cancer Genome Atlas Breast Invasive Carcinoma (TCGA BRCA) and Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) datasets, we identified common predictor genes found in both datasets and performed receptor-status prediction based on these genes. By assessing the survival outcomes of patients classified using GEP- or IHC-based receptor status, we compared the prognostic value of the two methods. We found that GEP-based HR prediction provided higher concordance with the intrinsic subtypes and a stronger association with treatment outcomes than did IHC-based hormone receptor (HR) status. GEP-based prediction improved the identification of patients who could benefit from hormone therapy, even in patients with non-luminal breast cancer. We also confirmed that non-matching subgroup classification affected the survival of breast cancer patients and that this could be largely overcome by GEP-based receptor-status prediction. In conclusion, GEP-based prediction provides more reliable classification of HR status, improving therapeutic decision making for breast cancer patients.

Dual HER2 Blockade versus a Single Agent in Trastuzumab-Containing Regimens for HER2-Positive Early Breast Cancer: A Systematic Review and Meta-Analysis of Randomized Controlled Trials

Purpose

Although trastuzumab is the standard of care for patients with human epidermal growth factor receptor 2 (HER2)- positive early breast cancer (EBC), drug resistance and disease relapse occur. Therefore, we performed a meta-analysis to assess the efficacy and safety of trastuzumab-containing dual anti-HER2 therapy compared to trastuzumab alone.

Methods

A systematic search was performed to identify eligible randomized controlled trials (RCTs). Main outcomes including event-free survival/invasive disease-free survival (EFS/iDFS), overall survival (OS), and safety were considered.

Results

Ten RCTs were included (15,284 patients). Significant improvements were observed in both EFS/iDFS (HR 0.86, p=0.0003) and OS (HR 0.86, p=0.02) with trastuzumab-based dual anti-HER2 therapy, especially in adjuvant treatment, while in the neoadjuvant setting, dual-targeted therapy also achieved a substantial pathological complete response (pCR) benefit (HR 1.34, p=0.0002). Subgroup analysis revealed that the EFS/iDFS benefit was slightly higher with trastuzumab plus pertuzumab or plus neratinib than trastuzumab plus lapatinib, while OS benefit was significant with trastuzumab plus lapatinib, but there were no subgroup differences (interaction test, p=0.80 and 0.24, resp.). In addition, EFS/iDFS benefit was unrelated to hormone receptor status but pronounced in the lymph node-positive (LN+) subgroup, which should be interpreted cautiously for lacking interaction (p=0.18). Besides, patients receiving dual therapy, especially with the lapatinib-containing regimen, experienced more toxicity, but no increase in cardiotoxicity.

Conclusions

Despite being associated with more toxicity, trastuzumab-containing dual anti-HER2 therapy is superior to trastuzumab single agent for HER2-positive EBC independent of hormone receptor status. The correlation between survival and LN status needs further verification. Trastuzumab plus pertuzumab or plus neratinib is the preferred regimen with substantial efficacy and lower toxicity.

MRPS23 amplification and gene expression in breast cancer; association with proliferation and the non-basal subtypes

PURPOSE

MRPS23 is recognized as a driver of proliferation in luminal breast cancer. The aims of the present study were to describe MRPS23 copy number change in breast cancer, and to assess associations between MRPS23 copy number change and molecular subtype, proliferation and prognosis, and between MRPS23 gene expression and molecular subtype and prognosis.

METHODS

Using fluorescence in situ hybridization (FISH), we examined MRPS23 and centromere 17 copy number in 590 formalin-fixed, paraffin-embedded primary tumours and 144 corresponding lymph node metastases from a cohort of Norwegian breast cancer patients. Furthermore, we analysed MRPS23 gene expression data in 1971 primary breast cancer tumours from the METABRIC dataset. We used Pearson's χ² test to assess associations between MRPS23 copy number and molecular subtype and proliferation, and between MRPS23 expression and molecular subtype. We studied prognosis by estimating hazard ratios and cumulative incidence of death from breast cancer according to MRPS23 copy number and MRPS23 expression status.

RESULTS

We found MRPS23 amplification (mean MRPS23 copy number ≥ 6 and/or MRPS23/chromosome 17 ratio ≥ 2) in 8% of primary tumours. Copy number increase associated with non-basal subtypes and higher tumour cell proliferation (Ki67). Higher MRPS23 expression associated with the Luminal B subtype. We found no significant association between MRPS23 amplification or MRSP23 gene expression, and prognosis.

CONCLUSION

Amplification of MRPS23 is associated with higher proliferation and non-basal subtypes in breast cancer. High MRPS23 expression is associated with the Luminal B subtype.

Molecular portraits and trastuzumab responsiveness of estrogen receptor-positive, progesterone receptor-positive, and HER2-positive breast cancer

Background: Estrogen receptor-positive, progesterone receptor-positive, and HER2-positive breast cancers (triple-positive breast cancers, TPBCs) account for 5% to 10% of all breast cancers. The clinical and molecular features of TPBCs remain elusive. In this study, we aim to analyze the multiomics landscape and responsiveness of TPBCs to trastuzumab. Methods: We employed five cohorts. The first cohort was from the Surveillance, Epidemiology, and End Results database (n=32,056) and was used to determine the clinical characteristics of TPBC. The second, third and fourth cohorts were from The Cancer Genome Atlas (n=162), GSE2603 (n=37) and GSE2109 (n=30) datasets, respectively, and were used to examine the genomic features and molecular classification of TPBC. The fifth cohort comprised TPBC patients treated at Fudan University Shanghai Cancer Center (FUSCC, n=171) and was used to investigate an immunohistochemistry-defined luminal A-like subgroup of TPBC. Results: Patients with TPBC had a significantly better prognosis than those with ER-PR-HER2+ breast cancer. Genomic analysis revealed that TPBCs showed a lower TP53 mutation rate (30% vs. 69%, P < 0.001) and lower levels of HER2 mRNA and protein expression than ER-PR-HER2+ breast cancers. More than 40% of TPBCs were classified as the luminal A intrinsic subtype, with an even lower HER2 expression level. Based on the immunohistochemical detection of CDCA8, BCL2 and STC2, we identified a luminal A-like subgroup of TPBCs in the FUSCC cohort (CDCA8-negative, BCL2- and/or STC2-positive). Patients with luminal A-like TPBC had a better prognosis and benefited less from trastuzumab than those with TPBC of other subtypes. Conclusions: TPBCs consist of clinically and genomically heterogeneous subgroups that may require different therapeutic strategies. The luminal A-like subgroup of TPBCs is associated with a better prognosis and reduced benefit from trastuzumab.