A three-gene model to robustly identify breast cancer molecular subtypes

Prognostic and predictive implications of sterile alpha motif and HD domain-containing protein 1 (SAMHD1) expression in breast cancer

Sterile alpha motif and HD domain-containing protein 1 (SAMHD1) is a dNTP hydrolase important for intracellular dNTP homeostasis and serves as tumor suppressor and modulator of antimetabolite efficacy in cancer, though largely unexplored in breast cancer (BC). A cohort of patients with early BC (n = 564) with available gene expression data (GEP) was used. SAMHD1 protein expression was assessed by immunohistochemistry performed on tissue microarrays. A large pooled transcriptomic dataset was used for validation (n = 2402). GEP data from the metastatic TEX randomized phase III trial (NCT01433614) were used for SAMHD1 predictive evaluation in response to capecitabine. SAMHD1 protein and mRNA levels were higher in HER2-enriched/HER2+ and basal-like (BL)/ER-/HER2- BC. Both SAMHD1 gene and protein expression were independently associated with favorable outcomes in BL tumors. In the pooled dataset, SAMHD1 gene expression was independently associated with favorable disease-free survival in the entire population and within the BL and HER2-enriched molecular subtypes. In metastatic BC, SAMHD1 mRNA levels were higher in responders receiving capecitabine. In conclusion SAMHD1 gene and protein expression represent promising prognostic biomarkers in BL early BC.

HDAC inhibitors modulate Hippo pathway signaling in hormone positive breast cancer

Breast cancer has constantly been the leading causes of death in women, and hormone receptor (HR) positive, HER2 negative is the majority subtype. Histone deacetylase (HDAC) inhibitors (HDACi) have shown clinical benefit in HR ( +) breast cancer patients. The Hippo pathway is an important cellular pathway involving proliferation, cell contact, and cancer. Hippo pathway proteins YAP/TAZ are often viewed as pro-tumorigenic; however, recent studies support a role of YAP as a tumor suppressor in HR ( +) breast cancer. Few studies have investigated the link between HDACi and the Hippo pathway. In our study, we demonstrate that HDACi induces transcriptional downregulation of YAP expression, while conversely activating a TEAD-mediated transcriptional program with upregulation of canonical Hippo pathway genes. We subsequently identified four Hippo canonical genes (CCDC80, GADD45A, F3, and TGFB2) that were upregulated by HDACi and associated with significantly improved survival in a HR ( +) breast cancer cohort. We further validated experimentally that HR ( +) breast cancer cells treated with HDACi resulted in upregulation of CCDC80 and GADD45A. A pan-cancer analysis of TCGA database demonstrated lower CCDC80 and GADD45A expression in tumor tissue compared to non-tumor samples in BRCA (breast cancer), LAML (acute myeloid leukemia), and UCS (uterine carcinosarcoma). Further analysis of HR ( +) breast cancer patients in the METABRIC dataset revealed high CCDC80 and/or GADD45A expression associated with significantly better survival outcomes compared to patients with low expression. Our study provides evidence for a novel mechanism of HDACi clinical activity, as well as a potential role for CCDC80 and GADD45A in HR ( +) breast cancer.

The Breast Cancer Classifier refines molecular breast cancer classification to delineate the HER2-low subtype

Current breast cancer classification methods, particularly immunohistochemistry and PAM50, face challenges in accurately characterizing the HER2-low subtype, a therapeutically relevant entity with distinct biological features. This notable gap can lead to misclassification, resulting in inappropriate treatment decisions and suboptimal patient outcomes. Leveraging RNA-seq and machine-learning algorithms, we developed the Breast Cancer Classifier (BCC), a unique transcriptomic classifier for more precise breast cancer subtyping, specifically by delineating and incorporating HER2-low as a distinct subtype. BCC also redefined the PAM50 Normal subtype into other subtypes, disputing its classification as a unique molecular group. Our statistical analysis not only confirmed the reproducibility and accuracy of BCC, but also revealed similarities in prognostic characteristics between the HER2-low and Basal subtypes. Addressing this gap in breast cancer classification is clinically significant because it not only improves treatment stratification, but also uncovers novel molecular and immunohistochemical features associated with the HER2-low and HER2-high subtypes, thereby advancing our understanding of breast cancer heterogeneity and providing guidance in precision oncology.

The spatially informed mFISHseq assay resolves biomarker discordance and predicts treatment response in breast cancer

Current assays fail to address breast cancer's complex biology and accurately predict treatment response. On a retrospective cohort of 1082 female breast tissues, we develop and validate mFISHseq, which integrates multiplexed RNA fluorescent in situ hybridization with RNA-sequencing, guided by laser capture microdissection. This technique ensures tumor purity, unbiased whole transcriptome profiling, and explicitly quantifies intratumoral heterogeneity. Here we show mFISHseq has 93% accuracy compared to immunohistochemistry. Our consensus subtyping and risk groups mitigate single sample discordance, provide early and late prognostic information, and identify high risk patients with enriched immune signatures, which predict response to neoadjuvant immunotherapy in the multicenter, phase II, prospective I-SPY2 trial. We identify putative antibody-drug conjugate (ADC)-responsive patients, as evidenced by a 19-feature T-DM1 classifier, validated on I-SPY2. Deploying mFISHseq as a research-use only test on 48 patients demonstrates clinical feasibility, revealing insights into the efficacy of targeted therapies, like CDK4/6 inhibitors, immunotherapies, and ADCs.

Spatial transcriptomics reveals substantial heterogeneity in triple-negative breast cancer with potential clinical implications

While triple-negative breast cancer (TNBC) is known to be heterogeneous at the genomic and transcriptomic levels, spatial information on tumor organization and cell composition is still lacking. Here, we investigate TNBC tumor architecture including its microenvironment using spatial transcriptomics on a series of 92 patients. We perform an in-depth characterization of tumor and stroma organization and composition using an integrative approach combining histomorphological and spatial transcriptomics. Furthermore, a detailed molecular characterization of tertiary lymphoid structures leads to identify a gene signature strongly associated to disease outcome and response to immunotherapy in several tumor types beyond TNBC. A stepwise clustering analysis identifies nine TNBC spatial archetypes, further validated in external datasets. Several spatial archetypes are associated with disease outcome and characterized by potentially actionable features. In this work, we provide a comprehensive insight into the complexity of TNBC ecosystem with potential clinical relevance, opening avenues for treatment tailoring including immunotherapy.

Transcriptomics and epigenetic data integration learning module on Google Cloud

Multi-omics (genomics, transcriptomics, epigenomics, proteomics, metabolomics, etc.) research approaches are vital for understanding the hierarchical complexity of human biology and have proven to be extremely valuable in cancer research and precision medicine. Emerging scientific advances in recent years have made high-throughput genome-wide sequencing a central focus in molecular research by allowing for the collective analysis of various kinds of molecular biological data from different types of specimens in a single tissue or even at the level of a single cell. Additionally, with the help of improved computational resources and data mining, researchers are able to integrate data from different multi-omics regimes to identify new prognostic, diagnostic, or predictive biomarkers, uncover novel therapeutic targets, and develop more personalized treatment protocols for patients. For the research community to parse the scientifically and clinically meaningful information out of all the biological data being generated each day more efficiently with less wasted resources, being familiar with and comfortable using advanced analytical tools, such as Google Cloud Platform becomes imperative. This project is an interdisciplinary, cross-organizational effort to provide a guided learning module for integrating transcriptomics and epigenetics data analysis protocols into a comprehensive analysis pipeline for users to implement in their own work, utilizing the cloud computing infrastructure on Google Cloud. The learning module consists of three submodules that guide the user through tutorial examples that illustrate the analysis of RNA-sequence and Reduced-Representation Bisulfite Sequencing data. The examples are in the form of breast cancer case studies, and the data sets were procured from the public repository Gene Expression Omnibus. The first submodule is devoted to transcriptomics analysis with the RNA sequencing data, the second submodule focuses on epigenetics analysis using the DNA methylation data, and the third submodule integrates the two methods for a deeper biological understanding. The modules begin with data collection and preprocessing, with further downstream analysis performed in a Vertex AI Jupyter notebook instance with an R kernel. Analysis results are returned to Google Cloud buckets for storage and visualization, removing the computational strain from local resources. The final product is a start-to-finish tutorial for the researchers with limited experience in multi-omics to integrate transcriptomics and epigenetics data analysis into a comprehensive pipeline to perform their own biological research.This manuscript describes the development of a resource module that is part of a learning platform named ``NIGMS Sandbox for Cloud-based Learning'' https://github.com/NIGMS/NIGMS-Sandbox. The overall genesis of the Sandbox is described in the editorial NIGMS Sandbox [16] at the beginning of this Supplement. This module delivers learning materials on the analysis of bulk and single-cell ATAC-seq data in an interactive format that uses appropriate cloud resources for data access and analyses.

HIGHLIGHTS

BRCA1, BRCA2 and PALB2 mRNA Expression as Prognostic Markers in Patients with Early Breast Cancer

Breast cancer (BC) poses a challenge in establishing new treatment strategies and identifying new prognostic and predictive markers due to the extensive genetic heterogeneity of BC. Very few studies have investigated the impact of mRNA expression of these genes on the survival of BC patients.

METHODS

We examined the impact of the mRNA expression of breast cancer gene type 1 (BRCA1), breast cancer gene type 2 (BRCA2), and partner and localizer of BRCA2 (PALB2) on the metastasis-free survival (MFS) of patients with early BC using microarray gene expression analysis.

RESULTS

The study was performed in a cohort of 461 patients with a median age of 62 years at initial diagnosis. The median follow-up time was 147 months. We could show that the lower expression of BRCA1 and BRCA2 is significantly associated with longer MFS (p < 0.050). On the contrary, the lower expression of PALB2 was correlated with a shorter MFS (p = 0.049). Subgroup survival analysis identified the prognostic influence of mRNA expression for BRCA1 among patients with luminal-B-like BC and for BRCA2 and PALB2 in the subset of patients with luminal-A-like BC (p < 0.050).

CONCLUSIONS

According to our observations, BRCA1, BRCA2, and PALB2 expression might become valuable biomarkers of disease progression.

Patterns of Chromosomal Instability and Clonal Heterogeneity in Luminal B Breast Cancer: A Pilot Study

Chromosomal instability (CIN), defined by variations in the number or structure of chromosomes from cell to cell, is recognized as a distinctive characteristic of cancer associated with the ability of tumors to adapt to challenging environments. CIN has been recognized as a source of genetic variation that leads to clonal heterogeneity (CH). Recent findings suggest a potential association between CIN and CH with the prognosis of BC patients, particularly in tumors expressing the epidermal growth factor receptor 2 (HER2+). In fact, information on the role of CIN in other BC subtypes, including luminal B BC, is limited. Additionally, it remains unknown whether CIN in luminal B BC tumors, above a specific threshold, could have a detrimental effect on the growth of human tumors or whether low or intermediate CIN levels could be linked to a more favorable BC patient prognosis when contrasted with elevated levels. Clarifying these relationships could have a substantial impact on risk stratification and the development of future therapeutic strategies aimed at targeting CIN in BC. This study aimed to assess CIN and CH in tumor tissue samples from ten patients with luminal B BC and compare them with established clinicopathological parameters. The results of this study reveal that luminal B BC patients exhibit intermediate CIN and stable aneuploidy, both of which correlate with lymphovascular invasion. Our results also provide valuable preliminary data that could contribute to the understanding of the implications of CIN and CH in risk stratification and the development of future therapeutic strategies in BC.

ZNF217 Gene Copy Number as a Marker of Response to Standard Therapy Drugs According to ERα Status in Breast Cancer

Purpose

The therapeutic decision for the management of breast cancer (BC) patients is based on the evaluation of prognostic factors alongside clinical and pathological parameters. Despite the use of standard biomarkers, response and resistance to therapy represent a challenge for clinicians. Among the new potential biomarkers for BC the ZNF217 gene have gained importance in recent years. However, while associations between ZNF217 gene copy number and clinicopathological characteristics have been established, its correlation with treatment response remains unclear.

Patients and Methods

This study aimed to evaluate the ZNF217 gene copy number and establish its associations with treatment response in estrogen receptor positive (ERα+) and ERα negative (ERα-) BC cell lines. In addition, a validation of the relationship between ZNF217 gene copy number and its prognostic value was performed using datasets of BC patients retrieved from the cBioPortal public database.

Results

Our data show that in ERα+ cells, ZNF217 gene copy number increase (amplification), while cell proliferation decreases in response to standard drug treatments. In contrast, both ZNF217 gene copy number (gain) and cell proliferation increases in response to standard drug treatments in ERα- cells. The results obtained align with findings from the cBioPortal database analysis, demonstrating that ERα+/HER2- low proliferation patients, exhibiting ZNF217 gene amplification or gain, have a significantly higher survival probability after treatment, compared to ERα-/HER2- and HER2+ patients.

Conclusion

Our results suggest that in ERα+ BC cells, ZNF217 gene amplification could be indicative of a favorable response, while in ERα- BC cells, ZNF217 gene gain could be postulated as a potential predictor of treatment resistance. A broader understanding of the role of ZNF217 gene in treatment response, together with prospective studies in BC patients, could contribute to confirming our data, as well as optimizing existing treatments and exploring novel approaches to improve overall cancer treatment outcomes.

Cooperative pro-tumorigenic adaptation to oncogenic RAS through epithelial-to-mesenchymal plasticity

In breast cancers, aberrant activation of the RAS/MAPK pathway is strongly associated with mesenchymal features and stemness traits, suggesting an interplay between this mitogenic signaling pathway and epithelial-to-mesenchymal plasticity (EMP). By using inducible models of human mammary epithelial cells, we demonstrate herein that the oncogenic activation of RAS promotes ZEB1-dependent EMP, which is necessary for malignant transformation. Notably, EMP is triggered by the secretion of pro-inflammatory cytokines from neighboring RAS-activated senescent cells, with a prominent role for IL-6 and IL-1α. Our data contrast with the common view of cellular senescence as a tumor-suppressive mechanism and EMP as a process promoting late stages of tumor progression in response to signals from the tumor microenvironment. We highlighted here a pro-tumorigenic cooperation of RAS-activated mammary epithelial cells, which leverages on oncogene-induced senescence and EMP to trigger cellular reprogramming and malignant transformation.

Molecular profiling in the management of hepatocellular carcinoma

PURPOSE OF REVIEW

The purpose of this review is to both summarize the current knowledge of hepatocellular carcinoma molecular biology and to suggest a framework in which to prospectively translate this knowledge into patient care. This is timely as recent guidelines recommend increased use of these technologies to advance personalized liver cancer care.

RECENT FINDINGS

The main themes covered here address germline and somatic genetic alterations recently discovered in hepatocellular carcinoma, largely owing to next generation sequencing technologies, and nascent efforts to translate these into contemporary practice.

SUMMARY

Early efforts of translating molecular profiling to hepatocellular carcinoma care demonstrate a growing number of potentially actionable alterations. Still lacking are a consensus on what biomarkers and technologies to adopt, at what scale and cost, and how to integrate them most effectively into care.

Involvement of RFC3 in tamoxifen resistance in ER-positive breast cancer through the cell cycle

Since the establishment of the molecular subtyping system, ER positive breast cancer was considered to be the most prevalent type of breast cancer, and endocrine therapy was a very important solution. However, numerous studies have shown that the cell cycle plays a key role in the progression and metastasis of breast cancer. The present study showed that RFC3 was involved in the cell cycle through DNA replication. Furthermore, RFC3 expression was significantly higher in breast cancer-resistant cells than in parental cells, which correlated with the cell cycle. We confirmed these results by established drug-resistant cell lines for breast cancer, raw letter analysis and immunohistochemical analysis of primary and recurrent tissues from three ER+ breast cancers. In addition, analysis of the results through an online database revealed that RFC3 expression was significantly associated with poor prognosis in ER+ breast cancer. We also demonstrated that in ER positive breast cancer-resistant cells, knockdown of RFC3 blocked the S-phase of cells and significantly attenuated cell proliferation, migration and invasion. Furthermore, RFC3 overexpression in ER positive breast cancer cells enhanced cell proliferation, migration and invasion. Taking all these findings into account, we could conclude that RFC3 was involved in endocrine resistance in breast cancer through the cell cycle. Thus, RFC3 may be a target to address endocrine therapy resistance in ER positive breast cancer and may be an independent prognostic factor in ER positive breast cancer.

Multiplexed RNA-FISH-guided Laser Capture Microdissection RNA Sequencing Improves Breast Cancer Molecular Subtyping, Prognostic Classification, and Predicts Response to Antibody Drug Conjugates

On a retrospective cohort of 1,082 FFPE breast tumors, we demonstrated the analytical validity of a test using multiplexed RNA-FISH-guided laser capture microdissection (LCM) coupled with RNA-sequencing (mFISHseq), which showed 93% accuracy compared to immunohistochemistry. The combination of these technologies makes strides in i) precisely assessing tumor heterogeneity, ii) obtaining pure tumor samples using LCM to ensure accurate biomarker expression and multigene testing, and iii) providing thorough and granular data from whole transcriptome profiling. We also constructed a 293-gene intrinsic subtype classifier that performed equivalent to the research based PAM50 and AIMS classifiers. By combining three molecular classifiers for consensus subtyping, mFISHseq alleviated single sample discordance, provided near perfect concordance with other classifiers (κ > 0.85), and reclassified 30% of samples into different subtypes with prognostic implications. We also use a consensus approach to combine information from 4 multigene prognostic classifiers and clinical risk to characterize high, low, and ultra-low risk patients that relapse early (< 5 years), late (> 10 years), and rarely, respectively. Lastly, to identify potential patient subpopulations that may be responsive to treatments like antibody drug-conjugates (ADC), we curated a list of 92 genes and 110 gene signatures to interrogate their association with molecular subtype and overall survival. Many genes and gene signatures related to ADC processing (e.g., antigen/payload targets, endocytosis, and lysosome activity) were independent predictors of overall survival in multivariate Cox regression models, thus highlighting potential ADC treatment-responsive subgroups. To test this hypothesis, we constructed a unique 19-feature classifier using multivariate logistic regression with elastic net that predicted response to trastuzumab emtansine (T-DM1; AUC = 0.96) better than either ERBB2 mRNA or Her2 IHC alone in the T-DM1 arm of the I-SPY2 trial. This test was deployed in a research-use only format on 26 patients and revealed clinical insights into patient selection for novel therapies like ADCs and immunotherapies and de-escalation of adjuvant chemotherapy.

Use of random integration to test equality of high dimensional covariance matrices

Testing the equality of two covariance matrices is a fundamental problem in statistics, and especially challenging when the data are high-dimensional. Through a novel use of random integration, we can test the equality of high-dimensional covariance matrices without assuming parametric distributions for the two underlying populations, even if the dimension is much larger than the sample size. The asymptotic properties of our test for arbitrary number of covariates and sample size are studied in depth under a general multivariate model. The finite-sample performance of our test is evaluated through numerical studies. The empirical results demonstrate that our test is highly competitive with existing tests in a wide range of settings. In particular, our proposed test is distinctly powerful under different settings when there exist a few large or many small diagonal disturbances between the two covariance matrices.

Concordance of RT-qPCR with immunohistochemistry and its beneficial role in breast cancer subtyping

This study was to compare the concordance of transcription-quantitative polymerase chain reaction (RT-qPCR) with immunohistochemistry (IHC) in determining estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2) and tumor proliferation index (Ki67) status in breast cancer, and to assess the prognosis based on different subtypes. Totally 323 breast cancer patients were selected, including 216 in the training set and 107 in the validation set. Logistic regression models were constructed using 5-fold cross-validation with the mRNA expression of each biomarker as the predictor and the corresponding IHC expression level as the binary response variable. Receiver operating characteristic curve was used to determine the cutoff value. When the thresholds of ER, PR, HER2, and Ki67 were 0.764, 0.709, 0.161, and 0.554, there existed high concordance rates between IHC and RT-qPCR in ER (94.4%), PR (88.0%) and HER2 (89.4%) and a medium concordance rate in Ki67 (67.8%), which were further confirmed in the validation set (ER: 81.3%, PR: 78.3%, HER2: 80.4%, and Ki67: 69.1%). Based on the subtyping stratified by RT-qPCR, the 5-year recurrence-free interval rates of patients with luminal, HER2-enriched, and triple-negative subtypes were 88% (95% CI: 0.84-0.93), 82% (95% CI: 0.73-0.92) and 58% (95% CI: 0.42-0.80), respectively, which were similar to those assessed by IHC (88%, 78% and 47%). RT-qPCR may be a complementary method to IHC, which can not only provide additional useful information in clinic, but also show more advantages over IHC in determining certain subtypes of breast cancer.

Poor Outcome in Postpartum Breast Cancer Patients Is Associated with Distinct Molecular and Immunologic Features

PURPOSE

Patients with postpartum breast cancer diagnosed after cessation of breastfeeding (postweaning, PP-BCPW) have a particularly poor prognosis compared with patients diagnosed during lactation (PP-BCDL), or to pregnant (Pr-BC) and nulliparous (NP-BC) patients, regardless of standard prognostic characteristics. Animal studies point to a role of the involution process in stimulation of tumor growth in the mammary gland. However, in women, the molecular mechanisms that underlie this poor prognosis of patients with PP-BCPW remain vastly underexplored, due to of lack of adequate patient numbers and outcome data.

EXPERIMENTAL DESIGN

We explored whether distinct prognostic features, common to all breast cancer molecular subtypes, exist in postpartum tumor tissue. Using detailed breastfeeding data, we delineated the postweaning period in PP-BC as a surrogate for mammary gland involution and performed whole transcriptome sequencing, immunohistochemical, and (multiplex) immunofluorescent analyses on tumor tissue of patients with PP-BCPW, PP-BCDL, Pr-BC, and NP-BC.

RESULTS

We found that patients with PP-BCPW having a low expression level of an immunoglobulin gene signature, but high infiltration of plasma B cells, have an increased risk for metastasis and death. Although PP-BCPW tumor tissue was also characterized by an increase in CD8+ cytotoxic T cells and reduced distance among these cell types, these parameters were not associated with differential clinical outcomes among groups.

CONCLUSIONS

These data point to the importance of plasma B cells in the postweaning mammary tumor microenvironment regarding the poor prognosis of PP-BCPW patients. Future prospective and in-depth research needs to further explore the role of B-cell immunobiology in this specific group of young patients with breast cancer.

The Expression of Trace Amine-Associated Receptors (TAARs) in Breast Cancer Is Coincident with the Expression of Neuroactive Ligand-Receptor Systems and Depends on Tumor Intrinsic Subtype

Currently, the contribution of trace amine-associated receptors (TAARs) to breast cancer (BC) is recognized, but their associations with various pathological characteristics are not yet understood. There is accumulated transcriptomic data for BC tumors, which are represented in publicly accessible databases. We estimated TAARs' (including TAAR1, TAAR2, TAAR5, TAAR6, TAAR8, and TAAR9) associations with BC stage, grade, and molecular subtypes in these data and identified that the expression of all TAARs was associated with more unfavorable cancer subtypes, including basal-like and HER2-positive tumors. Also, the significant upregulation of all TAARs was demonstrated in circulating tumor cells compared to the metastatic lesions. Considering that co-expressed genes are more likely to be involved in the same biologic processes, we analyzed genes that are co-expressed with TAARs in BC. These gene sets were enriched with the genes of the olfactory transduction pathway and neuroactive ligand-receptor interaction participants. TAARs are co-expressed with G-protein-coupled receptors of monoamine neurotransmitters including dopamine, norepinephrine, and serotonin as well as with other neuroactive ligand-specific receptors. Since TAAR1 is able to modulate the activity of monoamine receptors that are involved in the regulation of BC growth, TAAR1 and potentially other TAARs may be regarded as prospective therapeutic targets for breast cancer.

Redefining normal breast cell populations using long noncoding RNAs

Single-cell RNAseq has allowed unprecedented insight into gene expression across different cell populations in normal tissue and disease states. However, almost all studies rely on annotated gene sets to capture gene expression levels and sequencing reads that do not align to known genes are discarded. Here, we discover thousands of long noncoding RNAs (lncRNAs) expressed in human mammary epithelial cells and analyze their expression in individual cells of the normal breast. We show that lncRNA expression alone can discriminate between luminal and basal cell types and define subpopulations of both compartments. Clustering cells based on lncRNA expression identified additional basal subpopulations, compared to clustering based on annotated gene expression, suggesting that lncRNAs can provide an additional layer of information to better distinguish breast cell subpopulations. In contrast, these breast-specific lncRNAs poorly distinguish brain cell populations, highlighting the need to annotate tissue-specific lncRNAs prior to expression analyses. We also identified a panel of 100 breast lncRNAs that could discern breast cancer subtypes better than protein-coding markers. Overall, our results suggest that lncRNAs are an unexplored resource for new biomarker and therapeutic target discovery in the normal breast and breast cancer subtypes.

Homologous recombination deficiency derived from whole-genome sequencing predicts platinum response in triple-negative breast cancers

The high frequency of homologous recombination deficiency (HRD) is the main rationale of testing platinum-based chemotherapy in triple-negative breast cancer (TNBC), however, the existing methods to identify HRD are controversial and there is a medical need for predictive biomarkers. We assess the in vivo response to platinum agents in 55 patient-derived xenografts (PDX) of TNBC to identify determinants of response. The HRD status, determined from whole genome sequencing, is highly predictive of platinum response. BRCA1 promoter methylation is not associated with response, in part due to residual BRCA1 gene expression and homologous recombination proficiency in different tumours showing mono-allelic methylation. Finally, in 2 cisplatin sensitive tumours we identify mutations in XRCC3 and ORC1 genes that are functionally validated in vitro. In conclusion, our results demonstrate that the genomic HRD is predictive of platinum response in a large cohort of TNBC PDX and identify alterations in XRCC3 and ORC1 genes driving cisplatin response.

Sensitivity Analysis for Survival Prognostic Prediction with Gene Selection: A Copula Method for Dependent Censoring

Prognostic analysis for patient survival often employs gene expressions obtained from high-throughput screening for tumor tissues from patients. When dealing with survival data, a dependent censoring phenomenon arises, and thus the traditional Cox model may not correctly identify the effect of each gene. A copula-based gene selection model can effectively adjust for dependent censoring, yielding a multi-gene predictor for survival prognosis. However, methods to assess the impact of various types of dependent censoring on the multi-gene predictor have not been developed. In this article, we propose a sensitivity analysis method using the copula-graphic estimator under dependent censoring, and implement relevant methods in the R package "compound.Cox". The purpose of the proposed method is to investigate the sensitivity of the multi-gene predictor to a variety of dependent censoring mechanisms. In order to make the proposed sensitivity analysis practical, we develop a web application. We apply the proposed method and the web application to a lung cancer dataset. We provide a template file so that developers can modify the template to establish their own web applications.

Pan-cancer antagonistic inhibition pattern of ATM-driven G2/M checkpoint pathway vs other DNA repair pathways

DNA repair mechanisms keep genome integrity and limit tumor-associated alterations and heterogeneity, but on the other hand they promote tumor survival after radiation and genotoxic chemotherapies. We screened pathway activation levels of 38 DNA repair pathways in nine human cancer types (gliomas, breast, colorectal, lung, thyroid, cervical, kidney, gastric, and pancreatic cancers). We took RNAseq profiles of the experimental 51 normal and 408 tumor samples, and from The Cancer Genome Atlas and Clinical Proteomic Tumor Analysis Consortium databases - of 500/407 normal and 5752/646 tumor samples, and also 573 normal and 984 tumor proteomic profiles from Proteomic Data Commons portal. For all the samplings we observed a congruent trend that all cancer types showed inhibition of G2/M arrest checkpoint pathway compared to the normal samples, and relatively low activities of p53-mediated pathways. In contrast, other DNA repair pathways were upregulated in most of the cancer types. The G2/M checkpoint pathway was statistically significantly downregulated compared to the other DNA repair pathways, and this inhibition was strongly impacted by antagonistic regulation of (i) promitotic genes CCNB and CDK1, and (ii) GADD45 genes promoting G2/M arrest. At the DNA level, we found that ATM, TP53, and CDKN1A genes accumulated loss of function mutations, and cyclin B complex genes - transforming mutations. These findings suggest importance of activation for most of DNA repair pathways in cancer progression, with remarkable exceptions of G2/M checkpoint and p53-related pathways which are downregulated and neutrally activated, respectively.

The g3mclass is a practical software for multiclass classification on biomarkers

The analytes qualified as biomarkers are potent tools to diagnose various diseases, monitor therapy responses, and design therapeutic interventions. The early assessment of the diverseness of human disease is essential for the speedy and cost-efficient implementation of personalized medicine. We developed g3mclass, the Gaussian mixture modeling software for molecular assay data classification. This software automates the validated multiclass classifier applicable to single analyte tests and multiplexing assays. The g3mclass achieves automation using the original semi-constrained expectation-maximization (EM) algorithm that allows inference from the test, control, and query data that human experts cannot interpret. In this study, we used real-world clinical data and gene expression datasets (ERBB2, ESR1, PGR) to provide examples of how g3mclass may help overcome the problems of over-/underdiagnosis and equivocal results in diagnostic tests for breast cancer. We showed the g3mclass output's accuracy, robustness, scalability, and interpretability. The user-friendly interface and free dissemination of this multi-platform software aim to ease its use by research laboratories, biomedical pharma, companion diagnostic developers, and healthcare regulators. Furthermore, the g3mclass automatic extracting information through probabilistic modeling is adaptable for blending with machine learning and artificial intelligence.

Computational pharmacogenomic screen identifies drugs that potentiate the anti-breast cancer activity of statins

Statins, a family of FDA-approved cholesterol-lowering drugs that inhibit the rate-limiting enzyme of the mevalonate metabolic pathway, have demonstrated anticancer activity. Evidence shows that dipyridamole potentiates statin-induced cancer cell death by blocking a restorative feedback loop triggered by statin treatment. Leveraging this knowledge, we develop an integrative pharmacogenomics pipeline to identify compounds similar to dipyridamole at the level of drug structure, cell sensitivity and molecular perturbation. To overcome the complex polypharmacology of dipyridamole, we focus our pharmacogenomics pipeline on mevalonate pathway genes, which we name mevalonate drug-network fusion (MVA-DNF). We validate top-ranked compounds, nelfinavir and honokiol, and identify that low expression of the canonical epithelial cell marker, E-cadherin, is associated with statin-compound synergy. Analysis of remaining prioritized hits led to the validation of additional compounds, clotrimazole and vemurafenib. Thus, our computational pharmacogenomic approach identifies actionable compounds with pathway-specific activities.

Prognostic Impact of LAG-3 mRNA Expression in Early Breast Cancer

Background: Monoclonal antibodies against PD-1 or PD-L1 have been established in clinical practice for the treatment of both early and advanced/metastatic triple-negative breast cancer. Beyond the established immune checkpoints (ICPs) (PD-1 and CTLA-4), additional ICPs, such as lymphocyte activation gene-3 (LAG-3), are subject of current research. In the present retrospective gene-expression analysis, we evaluated the prognostic significance of LAG-3 in 461 patients with early breast cancer. In addition, we examined whether there was a correlation between the different ICP and CD8 expressions. Methods: Using microarray-based gene-expression analysis, we examined the prognostic significance of LAG-3 mRNA expression for metastasis-free survival (MFS) in the whole cohort of 461 breast cancer patients and among different molecular subtypes. Correlations were analyzed using Spearman’s rho correlation coefficient. Results: In the whole cohort, LAG-3 expression had no significant impact on MFS (p = 0.712, log-rank). In the subgroup analyses, there was a trend that a higher LAG-3 expression was associated with a favorable outcome in the luminal B (p = 0.217), basal-like (p = 0.370) and HER2 (p = 0.089) subtypes, although significance was not reached. In contrast, in a multivariate Cox regression analysis, adjusted for age, tumor size, axillary nodal status, histological grade of differentiation and proliferation marker Ki-67, LAG-3 showed a significant influence on MFS (HR 0.574; 95% CI 0.369−0.894; p = 0.014). High LAG-3 significantly correlated with CD8 (ρ = 0.571; p < 0.001). Conclusions: LAG-3 expression had an independent impact on MFS. In addition to PD-1 and PD-L1, further immune checkpoints, such as LAG-3, could serve as therapeutic targets in breast cancer.

Evaluation of Sensitivity to Endocrine Therapy Index (SET2,3) for Response to Neoadjuvant Endocrine Therapy and Longer-Term Breast Cancer Patient Outcomes (Alliance Z1031)

PURPOSE

To evaluate prediction of response and event-free survival (EFS) following neoadjuvant endocrine therapy by SET2,3 index of nonproliferation gene expression related to estrogen and progesterone receptors adjusted for baseline prognosis.

EXPERIMENTAL DESIGN

A correlative study was conducted of SET2,3 measured from gene expression profiles of diagnostic tumor (Agilent microarrays) in 379 women with cStage II-III breast cancer from the American College of Surgeons Oncology Group Z1031 neoadjuvant aromatase inhibitor trial SET2,3 was dichotomized using the previously published cutoff. Fisher exact test was used to assess the association between SET2,3 and low proliferation at week 2-4 [Ki67 ≤ 10% or complete cell-cycle arrest (CCCA; Ki67 ≤ 2.7%)] and PEPI-0 rate in cohort B, and the association between SET2,3 and ypStage 0/I in all patients. Cox models were used to assess EFS with respect to SET2,3 excluding cohort B patients who switched to chemotherapy.

RESULTS

Patients with high SET2,3 had higher rate of pharmacodynamic response than patients with low SET2,3 (Ki67 ≤ 10% in 88.2% vs. 56.9%, P < 0.0001; CCCA in 50.0% vs. 26.2%, P = 0.0054), but rate of ypStage 0/I (24.0% vs. 20.4%, P = 0.4580) or PEPI = 0 (28.4% vs. 20.6%, P = 0.3419) was not different. Patients with high SET2,3 had longer EFS than patients with low SET2,3 (HR, 0.52, 95% confidence interval: 0.34-0.80; P = 0.0026).

CONCLUSIONS

This exploratory analysis of Z1031 data demonstrated a higher rate of pharmacodynamic suppression of proliferation and longer EFS in high SET2,3 disease relative to low SET2,3 disease. The ypStage 0/I rate and PEPI = 0 rate were similar with respect to SET2,3.

Beta-2 Adrenergic Receptor Gene Expression in HER2-Positive Early-Stage Breast Cancer Patients: A Post-hoc Analysis of the NCCTG-N9831 (Alliance) Trial

BACKGROUND

Beta-2 adrenergic receptor (ß2AR) modulates immune activation and may enhance trastuzumab activity. We assessed the impact of ß2AR gene (ADRB2) expression on the outcomes of patients with HER2-positive early-stage breast cancer enrolled on the NCCTG-N9831 trial.

PATIENTS AND METHODS

This is a post-hoc analysis of the NCCTG-N9831 trial, which compared chemotherapy (arm A) versus chemotherapy plus trastuzumab (arms B&C) as adjuvant treatment of patients with HER2-positive early-stage breast cancer, with disease-free survival (DFS) as primary endpoint. Gene expression levels retrieved by DASL assay were used to classify patients as ADRB2-high or ADRB2-low. Hazard ratios (HRs) were calculated by a Cox proportional model adjusted for prognostic variables and ADRB2 expression. Correlations between ADRB2 expression and stromal tumor-infiltrating lymphocyte (TIL) levels were assessed with Pearson coefficient. A multivariable Cox regression model with interaction term was performed to assess the interaction between ADRB2 expression and treatment arm; and ADRB2 expression and a 8-gene signature previously shown to predict trastuzumab benefit.

RESULTS

Overall, 1,282 patients were included (ADRB2-high [N = 944] / ADRB2-low [N = 338]). A high expression of ADRB2 was associated with a longer DFS (P = .01) in the overall population. The addition of trastuzumab to chemotherapy improved DFS only in patients with ADRB2-high tumors (P < .01). ADRB2 expression was correlated with TIL levels (r = 0.24, P < .001). No association between ADRB2 expression and the 8-gene trastuzumab benefit signature was observed (P = .32).

CONCLUSION

Our findings suggest that a high ADRB2 expression is a favorable prognostic factor and may identify patients with HER2-positive early-stage breast cancer who benefit from adjuvant trastuzumab.

TRIAL REGISTRATION

clinicaltrials.gov NCT00005970.

Dynamic Risk Prediction via a Joint Frailty-Copula Model and IPD Meta-Analysis: Building Web Applications

Clinical risk prediction formulas for cancer patients can be improved by dynamically updating the formulas by intermediate events, such as tumor progression. The increased accessibility of individual patient data (IPD) from multiple studies has motivated the development of dynamic prediction formulas accounting for between-study heterogeneity. A joint frailty-copula model for overall survival and time to tumor progression has the potential to develop a dynamic prediction formula of death from heterogenous studies. However, the process of developing, validating, and publishing the prediction formula is complex, which has not been sufficiently described in the literature. In this article, we provide a tutorial in order to build a web-based application for dynamic risk prediction for cancer patients on the basis of the R packages joint.Cox and Shiny. We demonstrate the proposed methods using a dataset of breast cancer patients from multiple clinical studies. Following this tutorial, we demonstrate how one can publish web applications available online, which can be manipulated by any user through a smartphone or personal computer. After learning this tutorial, developers acquire the ability to build an online web application using their own datasets.

Reduced Expression of Annexin A6 Induces Metabolic Reprogramming That Favors Rapid Fatty Acid Oxidation in Triple-Negative Breast Cancer Cells

The ability of cancer cells to alter their metabolism is one of the major mechanisms underlying rapid tumor progression and/or therapeutic resistance in solid tumors, including the hard-to-treat triple-negative breast cancer (TNBC) subtype. Here, we assessed the contribution of the tumor suppressor, Annexin A6 (AnxA6), in the metabolic adaptation of basal-like (AnxA6-low) versus mesenchymal-like (AnxA6-high), as well as in lapatinib-resistant TNBC cells. Using model basal-like and mesenchymal-like TNBC cell lines, we show that TNBC cells also exhibit metabolic heterogeneity. The downregulation of AnxA6 in TNBC cells generally attenuated mitochondrial respiration, glycolytic flux, and cellular ATP production capacity resulting in a quiescent metabolic phenotype. We also show that AnxA6 depletion in mesenchymal-like TNBC cells was associated with a rapid uptake and mitochondrial fatty acid oxidation and diminished lipid droplet accumulation and altered the lipogenic metabolic phenotype of these cells to a lypolytic metabolic phenotype. The overexpression or chronic lapatinib-induced upregulation of AnxA6 in AnxA6-low TNBC cells reversed the quiescent/lypolytic phenotype to a more lipogenic/glycolytic phenotype with gluconeogenic precursors as additional metabolites. Collectively, these data suggest that the expression status of AnxA6 in TNBC cells underlies distinct metabolic adaptations of basal-like and mesenchymal-like TNBC subsets in response to cellular stress and/or therapeutic intervention and suggest AnxA6 as a biomarker for metabolic subtyping of TNBC subsets.

Tumor-infiltrating exhausted CD8+ T cells dictate reduced survival in premenopausal estrogen receptor-positive breast cancer

CD8⁺ tumor-infiltrating lymphocytes (TILs) are associated with improved survival in triple-negative breast cancer (TNBC) yet have no association with survival in estrogen receptor–positive (ER⁺) BC. The basis for these contrasting findings remains elusive. We identified subsets of BC tumors infiltrated by CD8⁺ T cells with characteristic features of exhausted T cells (T_EX). Tumors with abundant CD8⁺ T_EX exhibited a distinct tumor microenvironment marked by amplified interferon-γ signaling–related pathways and higher programmed death ligand 1 expression. Paradoxically, higher levels of tumor-infiltrating CD8⁺ T_EX associated with decreased overall survival of patients with ER⁺ BC but not patients with TNBC. Moreover, high tumor expression of a CD8⁺ T_EX signature identified dramatically reduced survival in premenopausal, but not postmenopausal, patients with ER⁺ BC. Finally, we demonstrated the value of a tumor T_EX signature score in identifying high-risk premenopausal ER⁺ BC patients among those with intermediate Oncotype DX Breast Recurrence Scores. Our data highlight the complex relationship between CD8⁺ TILs, interferon-γ signaling, and ER status in BC patient survival. This work identifies tumor-infiltrating CD8⁺ T_EX as a key feature of reduced survival outcomes in premenopausal patients with early-stage ER⁺ BC.

Immune-related Gene-based Prognostic Signature for the Risk Stratification Analysis of Breast Cancer

Background:

Breast cancer has plagued women for many years and caused many deaths around the world.

Method:

In this study, based on the weighted correlation network analysis, univariate Cox regression analysis, and least absolute shrinkage and selection operator, 12 immune-related genes were selected to construct the risk score for breast cancer patients. The multivariable Cox regression analysis, gene set enrichment analysis, and nomogram were also conducted in this study.

Results:

Good results were obtained in the survival analysis, enrichment analysis, multivariable Cox regression analysis and immune-related feature analysis. When the risk score model was applied in 22 breast cancer cohorts, the univariate Cox regression analysis demonstrated that the risk score model was significantly associated with overall survival in most of the breast cancer cohorts.

Conclusion:

Based on these results, we could conclude that the proposed risk score model may be a promising method and may improve the treatment stratification of breast cancer patients in the future work.

GRAND: a database of gene regulatory network models across human conditions

Gene regulation plays a fundamental role in shaping tissue identity, function, and response to perturbation. Regulatory processes are controlled by complex networks of interacting elements, including transcription factors, miRNAs and their target genes. The structure of these networks helps to determine phenotypes and can ultimately influence the development of disease or response to therapy. We developed GRAND (https://grand.networkmedicine.org) as a database for computationally-inferred, context-specific gene regulatory network models that can be compared between biological states, or used to predict which drugs produce changes in regulatory network structure. The database includes 12 468 genome-scale networks covering 36 human tissues, 28 cancers, 1378 unperturbed cell lines, as well as 173 013 TF and gene targeting scores for 2858 small molecule-induced cell line perturbation paired with phenotypic information. GRAND allows the networks to be queried using phenotypic information and visualized using a variety of interactive tools. In addition, it includes a web application that matches disease states to potentially therapeutic small molecule drugs using regulatory network properties.

Tumor Microenvironment Characterization in Breast Cancer Identifies Prognostic and Neoadjuvant Chemotherapy Relevant Signatures

Immune response which involves distinct immune cells is associated with prognosis of breast cancer. Nonetheless, less study have determined the associations of different types of immune cells with patient survival and treatment response. In this study, A total of 1,502 estrogen receptor(ER)-negative breast cancers from public databases were used to infer the proportions of 22 subsets of immune cells. Another 320 ER-negative breast cancer patients from Guangdong Provincial People's Hospital were also included and divided into the testing and validation cohorts. CD8⁺ T cells, CD4⁺ T cells, B cells, and M1 macrophages were associated with favourable outcome (all p <0.01), whereas Treg cells were strongly associated with poor outcome (p = 0.005). Using the LASSO model, we classified patients into the stromal immunotype A and B subgroups according to immunoscores. The 10 years OS and DFS rates were significantly higher in the immunotype A subgroup than immunotype B subgroup. Stromal immunotype was identified as an independent prognostic indicator in multivariate analysis in all cohorts and was also related to pathological complete response(pCR) after neoadjuvant chemotherapy. The nomogram that integrated the immunotype and clinicopathologic features showed good predictive accuracy for pCR and discriminatory power. The stromal immunotype A subgroup had higher expression levels of immune checkpoint molecules (PD-L1, PD-1, and CTLA-4) and cytokines (IL-2, INF-γ, and TGF-β). In addition, patients with immunotype A and B diseases had distinct mutation signatures. Therefore, The stromal immunotypes could predict survival and responses of ER-negative breast cancer patients to neoadjuvant chemotherapy.

The potential application of organoids in breast cancer research and treatment

Tumor heterogeneity is a major challenge for breast cancer researchers who have struggled to find effective treatments despite recent advances in oncology. Although the use of 2D cell culture methods in breast cancer research has been effective, it cannot model the heterogeneity of breast cancer as found within the body. The development of 3D culture of tumor cells and breast cancer organoids has provided a new approach in breast cancer research, allowing the identification of biomarkers, study of the interaction of tumor cells with the microenvironment, and for drug screening and discovery. In addition, the possibility of gene editing in organoids, especially using the CRISPR/Cas9 system, is convenient, and has allowed a more detailed study of tumor behavior in models closer to the physiological condition. The present review covers the application of organoids in breast cancer research. The recent use of gene-editing systems to provide insights into therapeutic approaches for breast cancer, is highlighted. The study of organoids and the possibility of gene manipulation may be a step towards the personalized treatment of breast cancer, which has so far remained unattainable due to the high heterogeneity of breast cancer.

Reducing variability of breast cancer subtype predictors by grounding deep learning models in prior knowledge

Deep learning neural networks have improved performance in many cancer informatics problems, including breast cancer subtype classification. However, many networks experience underspecificationwheremultiplecombinationsofparametersachievesimilarperformance, bothin training and validation. Additionally, certain parameter combinations may perform poorly when the test distribution differs from the training distribution. Embedding prior knowledge from the literature may address this issue by boosting predictive models that provide crucial, in-depth information about a given disease. Breast cancer research provides a wealth of such knowledge, particularly in the form of subtype biomarkers and genetic signatures. In this study, we draw on past research on breast cancer subtype biomarkers, label propagation, and neural graph machines to present a novel methodology for embedding knowledge into machine learning systems. We embed prior knowledge into the loss function in the form of inter-subject distances derived from a well-known published breast cancer signature. Our results show that this methodology reduces predictor variability on state-of-the-art deep learning architectures and increases predictor consistency leading to improved interpretation. We find that pathway enrichment analysis is more consistent after embedding knowledge. This novel method applies to a broad range of existing studies and predictive models. Our method moves the traditional synthesis of predictive models from an arbitrary assignment of weights to genes toward a more biologically meaningful approach of incorporating knowledge.

Correlation-based joint feature screening for semi-competing risks outcomes with application to breast cancer data

Ultrahigh-dimensional gene features are often collected in modern cancer studies in which the number of gene features p is extremely larger than sample size n. While gene expression patterns have been shown to be related to patients' survival in microarray-based gene expression studies, one has to deal with the challenges of ultrahigh-dimensional genetic predictors for survival predicting and genetic understanding of the disease in precision medicine. The problem becomes more complicated when two types of survival endpoints, distant metastasis-free survival and overall survival, are of interest in the study and outcome data can be subject to semi-competing risks due to the fact that distant metastasis-free survival is possibly censored by overall survival but not vice versa. Our focus in this paper is to extract important features, which have great impacts on both distant metastasis-free survival and overall survival jointly, from massive gene expression data in the semi-competing risks setting. We propose a model-free screening method based on the ranking of the correlation between gene features and the joint survival function of two endpoints. The method accounts for the relationship between two endpoints in a simply defined utility measure that is easy to understand and calculate. We show its favorable theoretical properties such as the sure screening and ranking consistency, and evaluate its finite sample performance through extensive simulation studies. Finally, an application to classifying breast cancer data clearly demonstrates the utility of the proposed method in practice.

Intra- and Interlaboratory Reproducibility of the Sensitivity to Endocrine Therapy Assay for Stage II/III Breast Cancer

BACKGROUND

The sensitivity to endocrine therapy assay (SET2,3) predicts treatment outcomes in Stage II-III breast cancer. SET2,3 measures transcription related to estrogen and progesterone receptors (SETER/PR index) and the molecular subtype (RNA4: ESR1, PGR, ERBB2, AURKA) from formalin-fixed paraffin-embedded (FFPE) tissue sections.

METHODS

We designed a nested study across 3 pathology laboratories, each testing 60 breast cancers twice in controlled batches. Laboratories macrodissected and directly homogenized the unstained FFPE tumor sections, then performed the QuantiGene Plex bead-based hybridization assay. SET2,3 was calculated centrally using predefined statistical R-scripts and applying pre-defined cutpoints. Concordance correlation coefficient (CCC) was calculated from continuous measurements and Kappa statistic from categorical results. A mixed-effects model estimated contributions to bias (fixed effects) and variance (random effects) from the replicated design.

RESULTS

Intralaboratory (CCC 0.96-0.99) and interlaboratory (CCC 0.98-0.99) SET2,3 results were concordant, with rates of agreement for high/low categorization within (Kappa 0.83-0.93) and between laboratories (Kappa 0.87-0.88). The relative contributions to overall variance of SET2,3 measurements were 96.90% from biological differences between cancers, 0.67% from interlaboratory variability, and 2.44% from residual causes including intralaboratory replicates. Similar results were obtained with SETER/PR, the baseline prognostic index calculated using pathological or clinical tumor and nodal staging information, and the 4 individual genes (ESR1, PGR, ERBB2, and AURKA).

CONCLUSION

Intra- and interpathology laboratory measurements of SET2,3 and its components were highly reproducible when tested from FFPE tumor sections.

Collagen XVII inhibits breast cancer cell proliferation and growth through deactivation of the AKT/mTOR signaling pathway

Collagen XVII (COL17), a cell-matrix adhesion protein, has been found to be suppressed in breast cancer. Our previous data demonstrated a preventive role of COL17 in breast cancer invasiveness. The present study used the stable COL17-overexpressing MCF7 and MDA-MB-231 cells to reveal an anti-proliferative effect of COL17 on breast cancer cell through mTOR deactivation. Cell proliferation was negatively correlated with the expression level of COL17 in a concentration-dependent manner in both conventional and three-dimensional (3D) culture systems. The correlation was confirmed by decreased expression of the proliferative marker Ki67 in COL17-expressing cells. In addition, overexpression of COL17 reduced the clonogenicity and growth of the cells. We demonstrated that COL17 affects the AKT/mTOR signaling pathway by deactivation of AKT, mTOR and downstream effectors, particularly 4EBP1. Moreover, mice xenografted with high COL17-expressing cells exhibited delayed tumor progression and prolonged survival time. The high expression of COL17A1 gene encoding COL17 is associated with low-proliferation tumors, extended tumor-free period, and overall survival of breast cancer patients. In conclusion, our results revealed the novel function of COL17 using in vitro and in vivo models and elucidated the related pathway in breast cancer cell growth and proliferation.

Meta-Analysis of Microdissected Breast Tumors Reveals Genes Regulated in the Stroma but Hidden in Bulk Analysis

Transcriptome data provide a valuable resource for the study of cancer molecular mechanisms, but technical biases, sample heterogeneity, and small sample sizes result in poorly reproducible lists of regulated genes. Additionally, the presence of multiple cellular components contributing to cancer development complicates the interpretation of bulk transcriptomic profiles. To address these issues, we collected 48 microarray datasets derived from laser capture microdissected stroma or epithelium in breast tumors and performed a meta-analysis identifying robust lists of differentially expressed genes. This was used to create a database with carefully harmonized metadata that we make freely available to the research community. As predicted, combining the results of multiple datasets improved statistical power. Moreover, the separate analysis of stroma and epithelium allowed the identification of genes with different contributions in each compartment, which would not be detected by bulk analysis due to their distinct regulation in the two compartments. Our method can be profitably used to help in the discovery of biomarkers and the identification of functionally relevant genes in both the stroma and the epithelium. This database was made to be readily accessible through a user-friendly web interface.

Heparanase: a potential marker of worse prognosis in estrogen receptor-positive breast cancer

Heparanase promotes tumor growth in breast tumors. We now evaluated heparanase protein and gene-expression status and investigated its impact on disease-free survival in order to gain better insight into the role of heparanase in ER-positive (ER+) breast cancer prognosis and to clarify its role in cell survival following chemotherapy. Using pooled analysis of gene-expression data, we found that heparanase was associated with a worse prognosis in estrogen receptor-positive (ER+) tumors (log-rank p < 10-10) and predictive to chemotherapy resistance (interaction p = 0.0001) but not hormonal therapy (Interaction p = 0.62). These results were confirmed by analysis of data from a phase III, prospective randomized trial which showed that heparanase protein expression is associated with increased risk of recurrence in ER+ breast tumors (log-rank p = 0.004). In vitro experiments showed that heparanase promoted tumor progression and increased cell viability via epithelial-mesenchymal transition, stemness, and anti-apoptosis pathways in luminal breast cancer. Taken together, our results demonstrated that heparanase is associated with worse outcomes and increased cell viability in ER+ BC.

Prognostic Significance of BIRC5/Survivin in Breast Cancer: Results from Three Independent Cohorts

Simple Summary

Survivin, coded by the BIRC5 gene, is the cell death preventing protein, which is important for cell division in normal and cancer cells. It is intensively studied as a cancer biomarker and target for antitumor therapy. In this study we asked if we could get clinically helpful information on how active BIRC5 is in breast cancer patients? We studied the BIRC5 protein level in tumor samples for breast cancer patients from a West Swedish cohort and its mRNA level in two different public gene expression databases. Survival analysis demonstrated that a higher BIRC5 protein or mRNA level was associated with poor survival in all cohorts and for different cancer subtypes. We show that BIRC5 is a promising independent cancer survival marker.

Abstract

Breast cancer (BC) histological and molecular classifications significantly improved the treatment strategy and prognosis. Inhibitor of apoptosis BIRC5/survivin is often overexpressed in cancers, however, indications of its importance in BC are inconsistent. We integrate BIRC5 protein and mRNA measures with clinical associates and long-term outcome in three independent cohorts Protein levels of BIRC5 were measured in primary lysates of 845 patients of the West Swedish BC cohort (VGR-BC) and linked to 5- and 27-years survival. The results were externally validated in transcriptomic data from METABRIC and SCAN-B cohorts. Survival analysis showed that high levels of BIRC5 were consistently associated with a poor probability of 5-year overall survival. High BIRC5 in VGR-BC contributed negatively to the disease-specific survival at 5 and 27 years. Subsets with different status by ER (estrogen receptor) expression and presence of nodal metastasis supported independent association of high BIRC5 with poor prognosis in all cohorts. In METABRIC and SCAN-B cohorts, high levels of BIRC5 mRNA were associated with the basal-like and luminal B molecular BC subtypes and with increasing histologic grade. BIRC5 is a sensitive survival marker that acts independent of ER and nodal status, and its levels need to be considered when making treatment decisions.

Technical Validity of a Customized Assay of Sensitivity to Endocrine Therapy Using Sections from Fixed Breast Cancer Tissue

BACKGROUND

We translated a multigene expression index to predict sensitivity to endocrine therapy for Stage II-III breast cancer (SET2,3) to hybridization-based expression assays of formalin-fixed paraffin-embedded (FFPE) tissue sections. Here we report the technical validity with FFPE samples, including preanalytical and analytical performance.

METHODS

We calibrated SET2,3 from microarrays (Affymetrix U133A) of frozen samples to hybridization-based assays of FFPE tissue, using bead-based QuantiGene Plex (QGP) and slide-based NanoString (NS). The following preanalytical and analytical conditions were tested in controlled studies: replicates within and between frozen and fixed samples, age of paraffin blocks, homogenization of fixed sections versus extracted RNA, core biopsy versus surgically resected tumor, technical replicates, precision over 20 weeks, limiting dilution, linear range, and analytical sensitivity. Lin's concordance correlation coefficient (CCC) was used to measure concordance between measurements.

RESULTS

SET2,3 index was calibrated to use with QGP (CCC 0.94) and NS (CCC 0.93) technical platforms, and was validated in two cohorts of older fixed samples using QGP (CCC 0.72, 0.85) and NS (CCC 0.78, 0.78). QGP assay was concordant using direct homogenization of fixed sections versus purified RNA (CCC 0.97) and between core and surgical sample types (CCC 0.90), with 100% accuracy in technical replicates, 1-9% coefficient of variation over 20 weekly tests, linear range 3.0-11.5 (log2 counts), and analytical sensitivity ≥2.0 (log2 counts).

CONCLUSIONS

Measurement of the novel SET2,3 assay was technically valid from fixed tumor sections of biopsy or resection samples using simple, inexpensive, hybridization methods, without the need for RNA purification.

Predicted sensitivity to endocrine therapy for stage II-III hormone receptor-positive and HER2-negative (HR+/HER2-) breast cancer before chemo-endocrine therapy

BACKGROUND

We proposed that a test for sensitivity to the adjuvant endocrine therapy component of treatment for patients with stage II-III breast cancer (SET2,3) should measure transcription related to estrogen and progesterone receptors (SET_ER/PR index) adjusted for a baseline prognostic index (BPI) combining clinical tumor and nodal stage with molecular subtype by RNA4 (ESR1, PGR, ERBB2, and AURKA).

PATIENTS AND METHODS

Patients with clinically high-risk, hormone receptor-positive (HR+), human epidermal growth factor receptor 2 (HER2)-negative (HR+/HER2-) breast cancer received neoadjuvant taxane-anthracycline chemotherapy, surgery with measurement of residual cancer burden (RCB), and then adjuvant endocrine therapy. SET2,3 was measured from pre-treatment tumor biopsies, evaluated first in an MD Anderson Cancer Center (MDACC) cohort (n = 307, 11 years' follow-up, U133A microarrays), cut point was determined, and then independent, blinded evaluation was carried out in the I-SPY2 trial (n = 268, high-risk MammaPrint result, 3.8 years' follow-up, Agilent-44K microarrays, NCI Clinical Trials ID: NCT01042379). Primary outcome measure was distant relapse-free survival. Multivariate Cox regression models tested prognostic independence of SET2,3 relative to RCB and other molecular prognostic signatures, and whether other prognostic signatures could substitute for SET_ER/PR or RNA4 components of SET2,3.

RESULTS

SET2,3 added independent prognostic information to RCB in the MDACC cohort: SET2,3 [hazard ratio (HR) 0.23, P = 0.004] and RCB (HR 1.77, P < 0.001); and the I-SPY2 trial: SET2,3 (HR 0.27, P = 0.031) and RCB (HR 1.68, P = 0.008). SET2,3 provided similar prognostic information irrespective of whether RCB-II or RCB-III after chemotherapy, and in both luminal subtypes. Conversely, RCB was most strongly prognostic in cancers with low SET2,3 status (MDACC P < 0.001, I-SPY2 P < 0.001). Other molecular signatures were not independently prognostic; they could effectively substitute for RNA4 subtype within the BPI component of SET2,3, but they could not effectively substitute for SET_ER/PR index.

CONCLUSIONS

SET2,3 added independent prognostic information to chemotherapy response (RCB) and baseline prognostic score or subtype. Approximately 40% of patients with clinically high-risk HR+/HER2- disease had high SET2,3 and could be considered for clinical trials of neoadjuvant endocrine-based treatment.

Basal-like breast cancer with low TGFβ and high TNFα pathway activity is rich in activated memory CD4 T cells and has a good prognosis

Basal-like breast cancer (BLBC) is a type of high-grade invasive breast cancer with high risk of recurrence, metastases, and poor survival. Immune activation in BLBC is a key factor that influences both cancer progression and therapeutic response, although its molecular mechanisms are not well clarified. In this study, we examined five cancer immunity-related pathways (IFNα, IFNγ, STAT3, TGFβ and TNFα) in four large independent breast cancer cohorts (n = 6,381) and their associations with the prognosis of breast cancer subtypes. Activities of the 5 pathways were calculated based on corresponding pathway signatures and associations between pathways and clinical outcomes were examined by survival analysis. Among the five PAM50-based subtypes, BLBC had the highest IFNα, IFNγ, TNFα pathway activities, and the lowest TGFβ activity. The IFNα, IFNγ, TNFα pathway activities were negatively correlated with BLBC recurrence. In contrast, positive association and no association with BLBC recurrence were observed for TGFβ and STAT3 pathways, respectively. TNFα/TGFβ pathway combination improved the prediction of recurrence and chemotherapy response of BLBCs. Immune cell subset analysis in BLBC showed that M0, M1 and M2 macrophage levels were associated with either TNFα or TGFβ pathways, whereas the level of activated memory CD4 T cells were associated with both pathways. Moreover, this T cell subset was most abundant in BLBCs with low TGFβ and high TNFα pathway activities. These results suggested that cooperation of TNFα and TGFβ signaling may be involved in the regulation of memory T cells and anti-cancer immunity in BLBCs. Our data also demonstrate that TNFα/TGFβ pathway combination may represent a better biomarker for BLBC prognosis and clinical management.

Targeting DNA Damage Response and Replication Stress in Pancreatic Cancer

BACKGROUND & AIMS

Continuing recalcitrance to therapy cements pancreatic cancer (PC) as the most lethal malignancy, which is set to become the second leading cause of cancer death in our society. The study aim was to investigate the association between DNA damage response (DDR), replication stress, and novel therapeutic response in PC to develop a biomarker-driven therapeutic strategy targeting DDR and replication stress in PC.

METHODS

We interrogated the transcriptome, genome, proteome, and functional characteristics of 61 novel PC patient-derived cell lines to define novel therapeutic strategies targeting DDR and replication stress. Validation was done in patient-derived xenografts and human PC organoids.

RESULTS

Patient-derived cell lines faithfully recapitulate the epithelial component of pancreatic tumors, including previously described molecular subtypes. Biomarkers of DDR deficiency, including a novel signature of homologous recombination deficiency, cosegregates with response to platinum (P < .001) and PARP inhibitor therapy (P < .001) in vitro and in vivo. We generated a novel signature of replication stress that predicts response to ATR (P < .018) and WEE1 inhibitor (P < .029) treatment in both cell lines and human PC organoids. Replication stress was enriched in the squamous subtype of PC (P < .001) but was not associated with DDR deficiency.

CONCLUSIONS

Replication stress and DDR deficiency are independent of each other, creating opportunities for therapy in DDR-proficient PC and after platinum therapy.