Development and verification of the PAM50-based Prosigna breast cancer gene signature assay

Sample Preparation Approach Influences PAM50 Risk of Recurrence Score in Early Breast Cancer

The PAM50 gene expression subtypes and the associated risk of recurrence (ROR) score are used to predict the risk of recurrence and the benefits of adjuvant therapy in early-stage breast cancer. The Prosigna assay includes the PAM50 subtypes along with their clinicopathological features, and is approved for treatment recommendations for adjuvant hormonal therapy and chemotherapy in hormone-receptor-positive early breast cancer. The Prosigna test utilizes RNA extracted from macrodissected tumor cells obtained from formalin-fixed, paraffin-embedded (FFPE) tissue sections. However, RNA extracted from fresh-frozen (FF) bulk tissue without macrodissection is widely used for research purposes, and yields high-quality RNA for downstream analyses. To investigate the impact of the sample preparation approach on ROR scores, we analyzed 94 breast carcinomas included in an observational study that had available gene expression data from macrodissected FFPE tissue and FF bulk tumor tissue, along with the clinically approved Prosigna scores for the node-negative, hormone-receptor-positive, HER2-negative cases (n = 54). ROR scores were calculated in R; the resulting two sets of scores from FFPE and FF samples were compared, and treatment recommendations were evaluated. Overall, ROR scores calculated based on the macrodissected FFPE tissue were consistent with the Prosigna scores. However, analyses from bulk tissue yielded a higher proportion of cases classified as normal-like; these were samples with relatively low tumor cellularity, leading to lower ROR scores. When comparing ROR scores (low, intermediate, and high), discordant cases between the two preparation approaches were revealed among the luminal tumors; the recommended treatment would have changed in a minority of cases.

A Novel Prognostic Model Based on the Serum Iron Level for Patients With Early-Stage Triple-Negative Breast Cancer

The dysregulation of iron homeostasis has been explored in malignancies. However, studies focusing on the association between the serum iron level and prognosis of patients with early-stage triple-negative breast cancer (TNBC) are scarce. Accordingly, in current study, 272 patients with early-stage TNBC treated at Sun Yat-sen University Cancer Center (SYSUCC) between September 2005 and October 2016 were included as a training cohort, another 86 patients from a previous randomized trial, SYSUCC-001, were analyzed as a validation cohort (SYSUCC-001 cohort). We retrospectively collected their clinicopathological data and tested the serum iron level using blood samples at the diagnosis. In the training cohort, patients were divided into low-iron and high-iron groups according to the serum iron level cut-off of 17.84 μmol/L determined by maximally selected rank statistics. After a median follow-up of 87.10 months, patients with a low iron had a significantly longer median disease-free survival (DFS) of 89.13 [interquartile range (IQR): 66.88-117.38] months and median overall survival (OS) of 92.85 (IQR: 68.83-117.38) months than those in the high-iron group (median DFS: 75.25, IQR: 39.76-105.70 months, P = 0.015; median OS: 77.17, IQR: 59.38-110.28 months, P = 0.015). Univariate and multivariate Cox analysis demonstrated the serum iron level to be an independent predictor for DFS and OS. Then, a prognostic nomogram incorporating the serum iron level, T stage and N stage was developed for individualized prognosis predictions. It had good discriminative ability with a C-index of DFS (0.729; 95% CI 0.666-0.792) and OS (0.739; 95% CI 0.666-0.812), respectively. Furtherly, we validated the predictive model in the SYSUCC-001 cohort, which also showed excellent predictive performance with a C-index of DFS (0.735; 95% CI 0.614-0.855) and OS (0.722; 95% CI 0.577-0.867), respectively. All these suggested that the serum iron level might be a potential prognostic biomarker for patients with early-stage TNBC, the predictive model based on it might be served as a practical tool for individualized survival predictions.

Molecular Classification of Bladder Urothelial Carcinoma Using NanoString-Based Gene Expression Analysis

Simple Summary

Our study aimed to apply a quantitative method based on mRNA counting as nCounter (NanoString Technologies, Inc). This method can obtain precise and accurate measures of RNA expression compared to RT-PCR, and which might represent an alternative to the NGS-genomic/transcriptomic profiling frequently used to generate molecular data in bladder cancer and provide clinically meaningful datasets for the molecular classification of bladder cancer. The current study generated a four-gene classifier, incorporating GATA3 and KRT20 (typically related to luminal molecular subtype) and KRT5 and KRT14 (typically related to basal molecular subtype). This methodology allowed us to explore differences in clinicopathologic parameters and potential sensitivities to ICI immunotherapy in a cohort series of 91 urothelial carcinomas of the bladder.

Abstract

Molecular classification of bladder carcinoma is a relevant topic in modern bladder cancer oncology due to its potential to improve oncological outcomes. The available molecular classifications are generally based on transcriptomic profiles, generating highly diverse categories with limited correlation. Implementation of molecular classification in practice is typically limited due to the high complexity of the required technology, the elevated costs, and the limited availability of this technology worldwide. We have conducted a gene expression analysis using a four-gene panel related to luminal and basal subtypes in a series of 91 bladder cancer cases. NanoString-based gene expression analysis using typically luminal (GATA3+/KRT20+) and basal markers (KRT14+/KRT5+/GATA3low/-/KRT20low/-) classified urothelial bladder carcinoma samples as luminal, basal, and a third category (KRT14-/KRT5-/GATA3-/KRT20-), null/double negative (non-luminal/non-basal). These three categories were meaningful in terms of overall cancer-specific survival (p < 0.0001) or when classified as conventional urothelial carcinoma and variant histology urothelial carcinoma (p < 0.0001), NMIBC vs. MIBC (p < 0.001), or by AJCC stage category Ta (p = 0.0012) and T1 (p < 0.0001) but did not reach significance in T2-T4 (p = 0.563). PD-L1 expression (low vs. high) was also different according to molecular subtype, with high PD-L1 expression mostly seen in basal and null subtypes and carcinomas with variant histology (p = 0.002). Additionally, the luminal subtype was enriched in NMIBC with favorable cancer-specific survival (p < 0.0001). In contrast, basal and null subtypes resulted in aggressive MIBC tumors with shorter cancer-specific survival (p < 0.0001), some of which presented variant histology. In conclusion, a comprehensive evaluation of a gene classifier related to molecular taxonomy using NanoString technology is feasible. Therefore, it might represent an accessible and affordable tool in this rapidly expanding area of precision genomics.

Impact of the menstrual cycle on commercial prognostic gene signatures in oestrogen receptor-positive primary breast cancer

PURPOSE

Changes occur in the expression of oestrogen-regulated and proliferation-associated genes in oestrogen receptor (ER)-positive breast tumours during the menstrual cycle. We investigated if Oncotype® DX recurrence score (RS), Prosigna® (ROR) and EndoPredict® (EP/EPclin) prognostic tests, which include some of these genes, vary according to the time in the menstrual cycle when they are measured.

METHODS

Pairs of test scores were derived from 30 ER-positive/human epidermal growth factor receptor-2-negative tumours sampled at two different points of the menstrual cycle. Menstrual cycle windows were prospectively defined as either W1 (days 1-6 and 27-35; low oestrogen and low progesterone) or W2 (days 7-26; high oestrogen and high or low progesterone).

RESULTS

The invasion module score of RS was lower (- 10.9%; p = 0.098), whereas the ER (+ 16.6%; p = 0.046) and proliferation (+ 7.3%; p = 0.13) module scores were higher in W2. PGR expression was significantly increased in W2 (+ 81.4%; p = 0.0029). Despite this, mean scores were not significantly different between W1 and W2 for any of the tests and the two measurements showed high correlation (r = 0.72-0.93). However, variability between the two measurements led to tumours being assigned to different risk categories in the following proportion of cases: RS 22.7%, ROR 27.3%, EP 13.6% and EPclin 13.6%.

CONCLUSION

There are significant changes during the menstrual cycle in the expression of some of the genes and gene module scores comprising the RS, ROR and EP/EPclin scores. These did not affect any of the prognostic scores in a systematic fashion, but there was substantial variability in paired measurements.

Comprehensive Analysis of Splicing Factor and Alternative Splicing Event to Construct Subtype-Specific Prognosis-Predicting Models for Breast Cancer

Recent evidence suggests that splicing factors (SFs) and alternative splicing (AS) play important roles in cancer progression. We constructed four SF-risk-models using 12 survival-related SFs. In Luminal-A, Luminal-B, Her-2, and Basal-Like BRCA, SF-risk-models for three genes (PAXBP1, NKAP, and NCBP2), four genes (RBM15B, PNN, ACIN1, and SRSF8), three genes (LSM3, SNRNP200, and SNU13), and three genes (SRPK3, PUF60, and PNN) were constructed. These models have a promising prognosis-predicting power. The co-expression and protein-protein interaction analysis suggest that the 12 SFs are highly functional-connected. Pathway analysis and gene set enrichment analysis suggests that the functional role of the selected 12 SFs is highly context-dependent among different BRCA subtypes. We further constructed four AS-risk-models with good prognosis predicting ability in four BRCA subtypes by integrating the four SF-risk-models and 21 survival-related AS-events. This study proposed that SFs and ASs were potential multidimensional biomarkers for the diagnosis, prognosis, and treatment of BRCA.

Predicting Molecular Phenotypes from Histopathology Images: A Transcriptome-Wide Expression-Morphology Analysis in Breast Cancer

Molecular profiling is central in cancer precision medicine but remains costly and is based on tumor average profiles. Morphologic patterns observable in histopathology sections from tumors are determined by the underlying molecular phenotype and therefore have the potential to be exploited for prediction of molecular phenotypes. We report here the first transcriptome-wide expression-morphology (EMO) analysis in breast cancer, where individual deep convolutional neural networks were optimized and validated for prediction of mRNA expression in 17,695 genes from hematoxylin and eosin-stained whole slide images. Predicted expressions in 9,334 (52.75%) genes were significantly associated with RNA sequencing estimates. We also demonstrated successful prediction of an mRNA-based proliferation score with established clinical value. The results were validated in independent internal and external test datasets. Predicted spatial intratumor variabilities in expression were validated through spatial transcriptomics profiling. These results suggest that EMO provides a cost-efficient and scalable approach to predict both tumor average and intratumor spatial expression from histopathology images. SIGNIFICANCE: Transcriptome-wide expression morphology deep learning analysis enables prediction of mRNA expression and proliferation markers from routine histopathology whole slide images in breast cancer.

A Clinically Applicable Gene Expression based Score predicts Resistance to Induction Treatment in Acute Myeloid Leukemia

Prediction of induction failure in AML is possible using cytogenetic data and a gene expression–based classifier.

Integration of PS29MRC in the clinical routine or trials may be facilitated by gene expression analysis with the NanoString platform.

Prediction of resistant disease at initial diagnosis of acute myeloid leukemia (AML) can be achieved with high accuracy using cytogenetic data and 29 gene expression markers (Predictive Score 29 Medical Research Council; PS29MRC). Our aim was to establish PS29MRC as a clinically usable assay by using the widely implemented NanoString platform and further validate the classifier in a more recently treated patient cohort. Analyses were performed on 351 patients with newly diagnosed AML intensively treated within the German AML Cooperative Group registry. As a continuous variable, PS29MRC performed best in predicting induction failure in comparison with previously published risk models. The classifier was strongly associated with overall survival. We were able to establish a previously defined cutoff that allows classifier dichotomization (PS29MRCdic). PS29MRCdic significantly identified induction failure with 59% sensitivity, 77% specificity, and 72% overall accuracy (odds ratio, 4.81; P = 4.15 × 10⁻¹⁰). PS29MRCdic was able to improve the European Leukemia Network 2017 (ELN-2017) risk classification within every category. The median overall survival with high PS29MRCdic was 1.8 years compared with 4.3 years for low-risk patients. In multivariate analysis including ELN-2017 and clinical and genetic markers, only age and PS29MRCdic were independent predictors of refractory disease. In patients aged ≥60 years, only PS29MRCdic remained as a significant variable. In summary, we confirmed PS29MRC as a valuable classifier to identify high-risk patients with AML. Risk classification can still be refined beyond ELN-2017, and predictive classifiers might facilitate clinical trials focusing on these high-risk patients with AML.

YAP and TAZ are transcriptional co-activators of AP-1 proteins and STAT3 during breast cellular transformation

The YAP and TAZ paralogs are transcriptional co-activators recruited to target sites by TEAD proteins. Here, we show that YAP and TAZ are also recruited by JUNB (a member of the AP-1 family) and STAT3, key transcription factors that mediate an epigenetic switch linking inflammation to cellular transformation. YAP and TAZ directly interact with JUNB and STAT3 via a WW domain important for transformation, and they stimulate transcriptional activation by AP-1 proteins. JUNB, STAT3, and TEAD co-localize at virtually all YAP/TAZ target sites, yet many target sites only contain individual AP-1, TEAD, or STAT3 motifs. This observation and differences in relative crosslinking efficiencies of JUNB, TEAD, and STAT3 at YAP/TAZ target sites suggest that YAP/TAZ is recruited by different forms of an AP-1/STAT3/TEAD complex depending on the recruiting motif. The different classes of YAP/TAZ target sites are associated with largely non-overlapping genes with distinct functions. A small minority of target sites are YAP- or TAZ-specific, and they are associated with different sequence motifs and gene classes from shared YAP/TAZ target sites. Genes containing either the AP-1 or TEAD class of YAP/TAZ sites are associated with poor survival of breast cancer patients with the triple-negative form of the disease.

Molecular Testing in Breast Cancer: Current Status and Future Directions

Molecular testing in breast cancer is a rapidly developing field that is becoming increasingly integral to patient care. This article provides an overview of currently available molecular assays and testing modalities that have prognostic, predictive, and therapeutic value. These include multigene assays for invasive breast cancer (Oncotype DX, MammaPrint, Prosigna, and Breast Cancer Index) and ductal carcinoma in situ (Oncotype DX DCIS and DCISionRT) and companion tests to detect PIK3CA mutations and NTRK fusions. The various assays related to immune checkpoint inhibitors, consisting of immunohistochemistry with anti-programmed death-ligand 1 antibodies SP142 and 22C3 and detection of microsatellite instability, mismatch repair deficiency, and tumor mutational burden are also discussed. Finally, the practical utility and hopeful promise of next-generation sequencing panels and circulating tumor (cell-free) DNA assays are evaluated. This review should serve as a useful and practical reference for practicing pathologists, molecular pathologists, clinicians, and researchers.

Molecular Prognostic Factors for Distant Metastases in Premenopausal Patients with HR+/HER2- Early Breast Cancer

Molecular factors that drive metastasis in premenopausal patients with hormone receptor positive (HR+), human epidermal growth factor receptor 2 negative (HER2−), early breast cancer (EBC) are largely unknown. To identify markers/signatures contributing to metastasis, we analyzed molecular changes in tumors from premenopausal patients who developed metastasis (M1) and who did not (M0). Ninety-seven premenopausal patients with HR+/HER2− EBC were included (M1, n = 48, median distant metastasis-free survival (DMFS): 54 (7–184) months; M0, n = 49, median follow-up: 149 (121–191) months). Gene expression profiling on tumor RNA (Breast Cancer 360^TM panel, Nanostring) was performed, followed by comprehensive bioinformatic and statistical analyses. Significantly enhanced ROR (risk of recurrence) scores and reduced signature scores of PGR (progesterone receptor), claudin-low, and mammary stemness were determined in M1. These differences were significantly associated with shorter DMFS in univariate survival analyses. Gene set enrichment analysis showed an enriched mTORC1 pathway in M1. Moreover, a metastasis signature of 19 differentially expressed genes (DEGs) that were DMFS-related was defined. Multivariate analysis including the four signatures, 19 DEGs, pN, and pT status, identified LRP2, IBSP, and SCUBE2 as independent prognostic factors. We identified prognostic gene signatures and single-gene markers for distant metastasis in premenopausal HR+/HER2− EBC potentially applicable in future clinical practice.

Molecular subtyping of breast cancer intrinsic taxonomy with oligonucleotide microarray and NanoString nCounter

Breast cancer intrinsic subtypes have been identified based on the transcription of a predefined gene expression (GE) profiles and algorithm (PAM50). This study compared molecular subtyping with oligonucleotide microarray and NanoString nCounter assay. A total of 109 Taiwanese breast cancers (24 with adjacent normal breast tissues) were assayed with Affymetrix Human Genome U133 plus 2.0 microarrays and 144 were assayed with the NanoString nCounter while 64 patients were assayed for both platforms. Subtyping with the nearest centroid (single sample prediction) was performed, and 16 out of 24 (67%) matched normal breasts were categorized as the normal breast-like subtype. For 64 breast cancers assayed for both platforms, 41 (65%, one unclassified by microarray) were predicted with an identical subtype, resulting in a fair Kappa statistic of 0.60. Taking nCounter subtyping as the gold standard, prediction accuracy was 43% (3/7), 81% (13/16), 25% (5/20), and 100% (20/20) for basal-like, HER2-enriched, luminal A and luminal B subtype predicted from microarray GE profiles. Microarray identified more luminal B cases from luminal A subtype predicted by nCounter. It's not uncommon to use microarray for breast cancer molecular subtyping for research. Our study showed that fundamental discrepancy existed between distinct GE assays, and cross platform equivalence should be carefully appraised when molecular subtyping was conducted with oligonucleotide microarray.

Prediction of Late Recurrence and Distant Metastasis in Early-stage Breast Cancer: Overview of Current and Emerging Biomarkers

Recently, a significant number of breast cancer (BC) patients have been diagnosed at an early stage. It is therefore critical to accurately predict the risk of recurrence and distant metastasis for better management of BC in this setting. Clinicopathologic patterns, particularly lymph node status, tumor size, and hormonal receptor status are routinely used to identify women at increased risk of recurrence. However, these factors have limitations regarding their predictive ability for late metastasis risk in patients with early BC. Emerging molecular signatures using gene expression-based approaches have improved the prognostic and predictive accuracy for this indication. However, the use of their based-scores for risk assessment has provided contradictory findings. Therefore, developing and using newly emerged alternative predictive and prognostic biomarkers for identifying patients at high- and low-risk is of great importance. The present review discusses some serum biomarkers and multigene profiling scores for predicting late recurrence and distant metastasis in early-stage BC based on recently published studies and clinical trials.

DysPIA: A Novel Dysregulated Pathway Identification Analysis Method

Differential co-expression-based pathway analysis is still limited and not widely used. In most current methods, the pathways were considered as gene sets, but the gene regulation relationships were not considered, and the computational speed was slow. In this article, we proposed a novel Dysregulated Pathway Identification Analysis (DysPIA) method to overcome these shortcomings. We adopted the idea of Correlation by Individual Level Product into analysis and performed a fast enrichment analysis. We constructed a combined gene-pair background which was much more sufficient than the background used in Edge Set Enrichment Analysis. In simulation study, DysPIA was able to identify the causal pathways with high AUC (0.9584 to 0.9896). In p53 mutation data, DysPIA obtained better performance than other methods. It obtained more potential dysregulated pathways that could be literature verified, and it ran much faster (∼1,700-8,000 times faster than other methods when 10,000 permutations). DysPIA was also applied to breast cancer relapse dataset and breast cancer subtype dataset. The results show that DysPIA is effective and has a great biological significance. R packages "DysPIA" and "DysPIAData" are constructed and freely available on R CRAN (https://cran.r-project.org/web/packages/DysPIA/index.html and https://cran.r-project.org/web/packages/DysPIAData/index.html), and on GitHub (https://github.com/lemonwang2020).

Kinectin 1 promotes the growth of triple-negative breast cancer via directly co-activating NF-kappaB/p65 and enhancing its transcriptional activity

Triple-negative breast cancer (TNBC) is the most challenging subtype of breast cancer. Various endeavor has been made to explore the molecular biology basis of TNBC. Herein, we reported a novel function of factor Kinectin 1 (KTN1) as a carcinogenic promoter in TNBC. KTN1 expression in TNBC was increased compared with adjacent tissues or luminal or Her2 subtypes of breast cancer, and TNBC patients with high KTN1 expression have poor prognosis. In functional studies, knockdown of KTN1 inhibited the proliferation and invasiveness of TNBC both in vitro and in vivo, while overexpression of KTN1 promoted cancer cell proliferation and invasiveness. RNA-seq analysis revealed that the interaction of cytokine-cytokine receptor, particularly CXCL8 gene, was upregulated by KTN1, which was supported by the further experiments. CXCL8 depletion inhibited the tumorigenesis and progression of TNBC. Additionally, rescue experiments validated that KTN1-mediated cell growth acceleration in TNBC was dependent on CXCL8 both in vitro and in vivo. Furthermore, it was found that KTN1 enhanced the phosphorylation of NF-κB/p65 protein at Ser536 site, and specifically bound to NF-κB/p65 protein in the nucleus and cytoplasm of cells. Moreover, the transcription of CXCL8 gene was directly upregulated by the complex of KTN1 and NF-κB/p65 protein. Taken together, our results elucidated a novel mechanism of KTN1 gene in TNBC tumorigenesis and progression. KTN1 may be a potential molecular target for the development of TNBC treatment.

Luminal Breast Cancer: Risk of Recurrence and Tumor-Associated Immune Suppression

Hormone-receptor positive (HR+) breast cancer (BC) (including the luminal A and the luminal B subtypes) is the most common type of tumor in women diagnosed with early-stage BC (EBC). It represents a highly heterogeneous subgroup that is characterized by different risks of relapse. The aim of this review is to discuss the possible role played by the immune response in predicting this risk, along with the most common clinical and pathological factors and molecular tools that have been developed and are already in use. As opposed to what has previously been observed in the most aggressive human epidermal growth factor receptor 2 (HER2)-positive and triple-negative breast cancer (TNBC) subtypes, a high proportion of tumor-infiltrating lymphocytes (TILs)-reflecting a spontaneous and pre-existing immune response to the tumor-has been linked to a worse prognosis in HR+ EBC. This work provides some immune biological rationale explaining these findings and provides the basics to understand the principal clinical trials that are testing immunotherapy in HR+ (luminal) BC.

Concordance between results of inexpensive statistical models and multigene signatures in patients with ER+/HER2- early breast cancer

Multigene signatures (MGS) are used to guide adjuvant chemotherapy (aCT) decisions in patients diagnosed with estrogen receptor (ER)-positive HER2-negative early breast cancer. We used results from three MGS (Oncotype DX® (ODX), MammaPrint® (MP) or Prosigna®) and assessed the concordance between high or low risk of recurrence and the predicted risk of recurrence based on statistical models. In addition, we looked at the impact of MGS results on final aCT administration during the multidisciplinary meeting (MDM). We retrospectively included 129 patients with ER-positive HER2-negative early breast cancer for which MGS testing was performed after MDM at University Hospitals Leuven between May 2013 and April 2019 in case there was doubt about aCT recommendation. Tumor tissue was analyzed either by ODX (N = 44), MP (N = 28), or Prosigna® (N = 57). Eight statistical models were computed: Magee equations (ME), Memorial Sloan Kettering simplified risk score (MSK-SRS), Breast Cancer Recurrence Score Estimator (BCRSE), OncotypeDXCalculator (ODXC), new Adjuvant! Online (nAOL), Mymammaprint.com (MyMP), PREDICT, and SiNK. Concordance, negative percent agreement, and positive percent agreement were calculated. Of 129 cases, 53% were MGS low and 47% MGS high risk. Concordances of 100.0% were observed between risk results obtained by ODX and ME. For MP, BCRSE demonstrated the best concordance, and for Prosigna® the average of ME. Concordances of <50.0% were observed between risk results obtained by ODX and nAOL, ODX and MyMP, ODX and SiNK, MP and MSK-SRS, MP and nAOL, MP and MyMP, MP and SiNK, and Prosigna® and ODXC. Integration of MGS results during MDM resulted in change of aCT recommendation in 47% of patients and a 15% relative and 9% absolute reduction. In conclusion, statistical models, especially ME and BCRSE, can be useful in selecting ER-positive HER2-negative early breast cancer patients who may need MGS testing resulting in enhanced cost-effectiveness and reduced delay in therapeutic decision-making.

Uncovering the Subtype-Specific Molecular Characteristics of Breast Cancer by Multiomics Analysis of Prognosis-Associated Genes, Driver Genes, Signaling Pathways, and Immune Activity

Breast cancer is a heterogeneous malignant disease with different prognoses and has been divided into four molecular subtypes. It is believed that molecular events occurring in breast stem/progenitor cells contribute to the carcinogenesis and development of different breast cancer subtypes. However, these subtype-specific molecular characteristics are largely unknown. In this study, we employed 1217 breast cancer samples from The Cancer Genome Atlas (TCGA) database for a multiomics analysis of the molecular characteristics of different breast cancer subtypes based on PAM50 algorithms. We detected the expression changes of subtype-specific genes and revealed that the expression of particular subtype-specific genes significantly affected prognosis. We also investigated the mutations and copy number variations (CNVs) of breast cancer driver genes and the representative genes of ten signaling pathways in different subtypes and revealed several subtype-specifically altered genes. Moreover, we detected the infiltration of various immune cells in different subtypes of breast cancer and showed that the infiltration levels of major immune cell types are different among these subtypes. Additionally, we investigated the factors affecting the immune infiltration level and the immune cytolytic activity in different breast cancer subtypes, namely, the mutation burden, genome instability and cancer-associated fibroblast (CAF) infiltration. This study may shed light on the molecular events contributing to carcinogenesis and development and provide potential markers and targets for the clinical diagnosis and treatment of different breast cancer subtypes.

INSTIGO Trial: Evaluation of a Plasma Protein Profile as a Predictive Biomarker for Metastatic Relapse of Triple Negative Breast Cancer

Background

Triple negative breast cancer (TNBC) accounts for 10-20% of breast cancers but has no specific therapy. While TNBC may be more sensitive to chemotherapy than other types of breast cancer, it has a poor prognosis. Most TNBC relapses occur during the five years following treatment, however predictive biomarkers of metastatic relapse are still lacking. High tumour-infiltrating lymphocytes (TILs) levels before and after neo-adjuvant chemotherapy (NAC) are associated with lower relapse risk and longer survival but TILs assessment is highly error-prone and still not introduced into the clinic. Therefore, having reliable biomarker of relapse, but easier to assess, remains essential for TNBC management. Searching for such biomarkers among serum/plasma proteins, circulating tumoral DNA (ctDNA) and blood cells appear relevant.

Methods

This single-centre and prospective study aims to discover predictive biomarkers of TNBC relapse and particularly focuses on plasma proteins. Blood samples will be taken at diagnosis, on the day of first-line or post-NAC surgery, on the day of radiotherapy start, then 6 months and one year after radiotherapy. A blood sample will be taken at the time of metastatic relapse diagnosis. Blood samples will be used for circulating protein quantification, blood cell counts and circulating tumour DNA quantification. A tumour RNA signature, based on the analysis of the RNA expression of 6 genes, will also be tested from the initial biopsy taken routinely. In NAC patients, TILs quantity will be assessed on TNBC pre-treatment biopsy and surgical specimen.

Ethics and Dissemination

INSTIGO belongs to category 2 interventional research on humans. This study has been approved by the SUD-EST IV ethics committee and is conducted in accordance with the Declaration of Helsinki and General Data Protection Regulation (GDPR). Study findings will be published in peer-reviewed medical journals.

Clinical Trial Registration

ClinicalTrials.gov, identifier NCT04438681.

Anti-cancer therapy with cyclin-dependent kinase inhibitors: impact and challenges

The introduction of cyclin-dependent kinase 4/6 inhibitors (CKIs) has marked a major development in the standard treatment of advanced breast cancer. Extensive preclinical, translational and clinical research efforts into CKI agents are ongoing, and clinical application of this class of systemic anti-cancer therapy is anticipated to expand beyond metastatic breast cancer treatment. Emerging evidence indicates that mechanisms by which CKI agents exert their therapeutic effect transcend their initially expected impacts on cell cycle control into the realms of cancer immunology and metabolism. The recent expansion in our understanding of the multifaceted impact of CKIs on tumour biology has the potential to improve clinical study design, therapeutic strategies and ultimately patient outcomes. This review contextualises the current status of CKI therapy by providing an overview of the original and emerging insights into mechanisms of action and the evidence behind their current routine use in breast cancer management. Recent preclinical and clinical studies into CKIs across tumour types are discussed, including a synthesis of the more than 300 clinical trials of CKI-combination treatments registered as of November 2020. Key challenges and opportunities anticipated in the 2020s are explored, including treatment resistance, combination therapy strategies and potential biomarker development.

The temporal mutational and immune tumour microenvironment remodelling of HER2-negative primary breast cancers

The biology of breast cancer response to neoadjuvant therapy is underrepresented in the literature and provides a window-of-opportunity to explore the genomic and microenvironment modulation of tumours exposed to therapy. Here, we characterised the mutational, gene expression, pathway enrichment and tumour-infiltrating lymphocytes (TILs) dynamics across different timepoints of 35 HER2-negative primary breast cancer patients receiving neoadjuvant eribulin therapy (SOLTI-1007 NEOERIBULIN-NCT01669252). Whole-exome data (N = 88 samples) generated mutational profiles and candidate neoantigens and were analysed along with RNA-Nanostring 545-gene expression (N = 96 samples) and stromal TILs (N = 105 samples). Tumour mutation burden varied across patients at baseline but not across the sampling timepoints for each patient. Mutational signatures were not always conserved across tumours. There was a trend towards higher odds of response and less hazard to relapse when the percentage of subclonal mutations was low, suggesting that more homogenous tumours might have better responses to neoadjuvant therapy. Few driver mutations (5.1%) generated putative neoantigens. Mutation and neoantigen load were positively correlated (R2 = 0.94, p = <0.001); neoantigen load was weakly correlated with stromal TILs (R2 = 0.16, p = 0.02). An enrichment in pathways linked to immune infiltration and reduced programmed cell death expression were seen after 12 weeks of eribulin in good responders. VEGF was downregulated over time in the good responder group and FABP5, an inductor of epithelial mesenchymal transition (EMT), was upregulated in cases that recurred (p < 0.05). Mutational heterogeneity, subclonal architecture and the improvement of immune microenvironment along with remodelling of hypoxia and EMT may influence the response to neoadjuvant treatment.

Prognostic value of the 6-gene OncoMasTR test in hormone receptor-positive HER2-negative early-stage breast cancer: Comparative analysis with standard clinicopathological factors

AIM

The aim of the study was to assess the prognostic performance of a 6-gene molecular score (OncoMasTR Molecular Score [OMm]) and a composite risk score (OncoMasTR Risk Score [OM]) and to conduct a within-patient comparison against four routinely used molecular and clinicopathological risk assessment tools: Oncotype DX Recurrence Score, Ki67, Nottingham Prognostic Index and Clinical Risk Category, based on the modified Adjuvant! Online definition and three risk factors: patient age, tumour size and grade.

METHODS

Biospecimens and clinicopathological information for 404 Irish women also previously enrolled in the Trial Assigning Individualized Options for Treatment [Rx] were provided by 11 participating hospitals, as the primary objective of an independent translational study. Gene expression measured via RT-qPCR was used to calculate OMm and OM. The prognostic value for distant recurrence-free survival (DRFS) and invasive disease-free survival (IDFS) was assessed using Cox proportional hazards models and Kaplan-Meier analysis. All statistical tests were two-sided ones.

RESULTS

OMm and OM (both with likelihood ratio statistic [LRS] P < 0.001; C indexes = 0.84 and 0.85, respectively) were more prognostic for DRFS and provided significant additional prognostic information to all other assessment tools/factors assessed (all LRS P ≤ 0.002). In addition, the OM correctly classified more patients with distant recurrences (DRs) into the high-risk category than other risk classification tools. Similar results were observed for IDFS.

DISCUSSION

Both OncoMasTR scores were significantly prognostic for DRFS and IDFS and provided additional prognostic information to the molecular and clinicopathological risk factors/tools assessed. OM was also the most accurate risk classification tool for identifying DR. A concise 6-gene signature with superior risk stratification was shown to increase prognosis reliability, which may help clinicians optimise treatment decisions.

An approach for normalization and quality control for NanoString RNA expression data

The NanoString RNA counting assay for formalin-fixed paraffin embedded samples is unique in its sensitivity, technical reproducibility and robustness for analysis of clinical and archival samples. While commercial normalization methods are provided by NanoString, they are not optimal for all settings, particularly when samples exhibit strong technical or biological variation or where housekeeping genes have variable performance across the cohort. Here, we develop and evaluate a more comprehensive normalization procedure for NanoString data with steps for quality control, selection of housekeeping targets, normalization and iterative data visualization and biological validation. The approach was evaluated using a large cohort ($N=\kern0.5em 1649$) from the Carolina Breast Cancer Study, two cohorts of moderate sample size ($N=359$ and$130$) and a small published dataset ($N=12$). The iterative process developed here eliminates technical variation (e.g. from different study phases or sites) more reliably than the three other methods, including NanoString's commercial package, without diminishing biological variation, especially in long-term longitudinal multiphase or multisite cohorts. We also find that probe sets validated for nCounter, such as the PAM50 gene signature, are impervious to batch issues. This work emphasizes that systematic quality control, normalization and visualization of NanoString nCounter data are an imperative component of study design that influences results in downstream analyses.

A Systemic Inflammation Response Score for Prognostic Prediction of Breast Cancer Patients Undergoing Surgery

Background: Systemic inflammatory response is related to the occurrence, progression, and prognosis of cancers. In this research, a novel systemic inflammation response score (SIRS) was calculated, and its prognostic value for postoperative stage I-III breast cancer (BC) patients was analyzed. Methods: 1583 BC patients were included in this research. Patients were randomly divided into a training cohort (n = 1187) and validation cohort (n = 396). SIRS was established in the training cohort based on independent prognostic hematological indicator, its relationship between prognosis and clinical features was analyzed. Then, a nomogram consisted of SIRS and clinical features was established, its performance was examined by calibration plots and receiver operating characteristic curve analysis. Results: The SIRS was an independent prognostic indicator for BC patients, and a high-SIRS was related to multifocality, advanced N stage, and worse prognosis. Incorporating SIRS into a nomogram could accurately predict the prognosis of BC patients, the results of receiver operating characteristic (ROC) curve analysis showed that the area under the curve (AUC) of nomogram was up to 0.806 in training cohort and 0.905 in the validation cohort. Conclusion: SIRS was associated with the prognosis of patients with breast cancer. Nomogram based on SIRS can accurately predict the prognosis of breast cancer patients.

DeCompress: tissue compartment deconvolution of targeted mRNA expression panels using compressed sensing

Targeted mRNA expression panels, measuring up to 800 genes, are used in academic and clinical settings due to low cost and high sensitivity for archived samples. Most samples assayed on targeted panels originate from bulk tissue comprised of many cell types, and cell-type heterogeneity confounds biological signals. Reference-free methods are used when cell-type-specific expression references are unavailable, but limited feature spaces render implementation challenging in targeted panels. Here, we present DeCompress, a semi-reference-free deconvolution method for targeted panels. DeCompress leverages a reference RNA-seq or microarray dataset from similar tissue to expand the feature space of targeted panels using compressed sensing. Ensemble reference-free deconvolution is performed on this artificially expanded dataset to estimate cell-type proportions and gene signatures. In simulated mixtures, four public cell line mixtures, and a targeted panel (1199 samples; 406 genes) from the Carolina Breast Cancer Study, DeCompress recapitulates cell-type proportions with less error than reference-free methods and finds biologically relevant compartments. We integrate compartment estimates into cis-eQTL mapping in breast cancer, identifying a tumor-specific cis-eQTL for CCR3 (C-C Motif Chemokine Receptor 3) at a risk locus. DeCompress improves upon reference-free methods without requiring expression profiles from pure cell populations, with applications in genomic analyses and clinical settings.

High-Throughput NanoString Analysis of Oncogenic Human Papillomavirus and Tumor Microenvironment Transcription in Head and Neck Squamous Cell Carcinoma

Human papillomaviruses (HPVs), specifically high-risk HPVs, are responsible for up to 3% of all cancers in women and up to 2% of all cancers in men. They have been identified as the etiological agent of cervical cancer and have been increasingly found to be the driver behind head and neck cancers of the oropharynx. A system in which we can simultaneously observe transcriptional changes to both a host's tumor microenvironment and its associated oncogenic driver (e.g., HPV) would be highly valuable for understanding HPV's role in tumorigenesis. This article describes a detailed methodology for utilizing high-throughput RNA analysis to study viral transcription in formalin-fixed, paraffin-embedded clinical tumor samples. Although our lab utilizes these methods for the study of head and neck cancer, the principles contained within are widely applicable to all fields of HPV study. © 2021 Wiley Periodicals LLC. Basic Protocol: HPV16 transcript analysis using NanoString Support Protocol 1: Preparation of RNA from formalin-fixed, paraffin-embedded slides Support Protocol 2: Preparation of RNA from cell lysates Support Protocol 3: Fluorometric RNA concentration and RNA integrity analysis Support Protocol 4: Determination of input RNA based on DV₃₀₀ calculation.

Precision Oncology via NMR-Based Metabolomics: A Review on Breast Cancer

Precision oncology is an emerging approach in cancer care. It aims at selecting the optimal therapy for the right patient by considering each patient’s unique disease and individual health status. In the last years, it has become evident that breast cancer is an extremely heterogeneous disease, and therefore, patients need to be appropriately stratified to maximize survival and quality of life. Gene-expression tools have already positively assisted clinical decision making by estimating the risk of recurrence and the potential benefit from adjuvant chemotherapy. However, these approaches need refinement to further reduce the proportion of patients potentially exposed to unnecessary chemotherapy. Nuclear magnetic resonance (NMR) metabolomics has demonstrated to be an optimal approach for cancer research and has provided significant results in BC, in particular for prognostic and stratification purposes. In this review, we give an update on the status of NMR-based metabolomic studies for the biochemical characterization and stratification of breast cancer patients using different biospecimens (breast tissue, blood serum/plasma, and urine).

Integration of Urinary EN2 Protein & Cell-Free RNA Data in the Development of a Multivariable Risk Model for the Detection of Prostate Cancer Prior to Biopsy

Simple Summary

Prostate cancer is a disease responsible for a large proportion of all male cancer deaths but there is a high chance that a patient will die with the disease rather than from. Therefore, there is a desperate need for improvements in diagnosing and predicting outcomes for prostate cancer patients to minimise overdiagnosis and overtreatment whilst appropriately treating men with aggressive disease, especially if this can be done without taking an invasive biopsy. In this work we develop a test that predicts whether a patient has prostate cancer and how aggressive the disease is from a urine sample. This model combines the measurement of a protein-marker called EN2 and the levels of 10 genes measured in urine and proves that integration of information from multiple, non-invasive biomarker sources has the potential to greatly improve how patients with a clinical suspicion of prostate cancer are risk-assessed prior to an invasive biopsy.

Abstract

The objective is to develop a multivariable risk model for the non-invasive detection of prostate cancer prior to biopsy by integrating information from clinically available parameters, Engrailed-2 (EN2) whole-urine protein levels and data from urinary cell-free RNA. Post-digital-rectal examination urine samples collected as part of the Movember Global Action Plan 1 study which has been analysed for both cell-free-RNA and EN2 protein levels were chosen to be integrated with clinical parameters (n = 207). A previously described robust feature selection framework incorporating bootstrap resampling and permutation was applied to the data to generate an optimal feature set for use in Random Forest models for prediction. The fully integrated model was named ExoGrail, and the out-of-bag predictions were used to evaluate the diagnostic potential of the risk model. ExoGrail risk (range 0–1) was able to determine the outcome of an initial trans-rectal ultrasound guided (TRUS) biopsy more accurately than clinical standards of care, predicting the presence of any cancer with an area under the receiver operator curve (AUC) = 0.89 (95% confidence interval(CI): 0.85–0.94), and discriminating more aggressive Gleason ≥ 3 + 4 disease returning an AUC = 0.84 (95% CI: 0.78–0.89). The likelihood of more aggressive disease being detected significantly increased as ExoGrail risk score increased (Odds Ratio (OR) = 2.21 per 0.1 ExoGrail increase, 95% CI: 1.91–2.59). Decision curve analysis of the net benefit of ExoGrail showed the potential to reduce the numbers of unnecessary biopsies by 35% when compared to current standards of care. Integration of information from multiple, non-invasive biomarker sources has the potential to greatly improve how patients with a clinical suspicion of prostate cancer are risk-assessed prior to an invasive biopsy.

Predicting tumor response to drugs based on gene-expression biomarkers of sensitivity learned from cancer cell lines

Background

Human cancer cell line profiling and drug sensitivity studies provide valuable information about the therapeutic potential of drugs and their possible mechanisms of action. The goal of those studies is to translate the findings from in vitro studies of cancer cell lines into in vivo therapeutic relevance and, eventually, patients’ care. Tremendous progress has been made.

Results

In this work, we built predictive models for 453 drugs using data on gene expression and drug sensitivity (IC₅₀) from cancer cell lines. We identified many known drug-gene interactions and uncovered several potentially novel drug-gene associations. Importantly, we further applied these predictive models to ~ 17,000 bulk RNA-seq samples from The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) database to predict drug sensitivity for both normal and tumor tissues. We created a web site for users to visualize and download our predicted data (https://manticore.niehs.nih.gov/cancerRxTissue). Using trametinib as an example, we showed that our approach can faithfully recapitulate the known tumor specificity of the drug.

Conclusions

We demonstrated that our approach can predict drugs that 1) are tumor-type specific; 2) elicit higher sensitivity from tumor compared to corresponding normal tissue; 3) elicit differential sensitivity across breast cancer subtypes. If validated, our prediction could have relevance for preclinical drug testing and in phase I clinical design.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-021-07581-7.

Prosigna test in breast cancer: real-life experience

PURPOSE

Genomic tests can guide the decision to administer adjuvant chemotherapy in women with hormone receptor (HR)-positive, Human Epidermal growth Factor 2 (HER2)-negative breast cancer (BC) at intermediate risk of recurrence. We assessed the decision-making and economic impact of the Prosigna test in a real-life setting.

METHODS

Retrospective cohort study of HR + , HER2- BC patients managed from 2016 to 2020, potential candidates for adjuvant chemotherapy, at intermediate risk of recurrence, in whom a Prosigna test was performed according to contemporary guidelines. The additional cost of chemotherapy over one year in terms of direct medical and non-medical costs was estimated in this study to be €9,737 (derived from a previous study, NCT02813317). The cost of the Prosigna test, as defined by the reimbursement system, was €1,849.

RESULTS

Among the 809 patients included in this study, 2.3 Prosigna tests had to be performed to avoid adjuvant chemotherapy for one patient. The number of tests that had to be performed to avoid chemotherapy for one patient was higher for patients with grade 3 tumors and pN1mic axillary node involvement and lower for grade 1 tumors or in the absence of axillary node involvement (pN0), but did not vary according to the 10-year overall survival gain predicted by the Predict online test. The cost saving related to withholding of adjuvant chemotherapy for one patient on the basis of the Prosigna test results was €5,485.

CONCLUSION

We present one of the largest cohorts of HR + , HER2- BC patients at intermediate risk of recurrence, in whom a Prosigna test was used to guide the adjuvant therapy decision in a real-life setting, resulting in a 44% decrease in the indication for chemotherapy.

Role of immune regulatory cells in breast cancer: Foe or friend?

Breast cancer (BC) is the most common cancer among women between the ages of 20 and 50, affecting more than 2.1 million people and causing the annual death of more than 627,000 women worldwide. Based on the available knowledge, the immune system and its components are involved in the pathogenesis of several malignancies, including BC. Cancer immunobiology suggests that immune cells can play a dual role and induce anti-tumor or immunosuppressive responses, depending on the tumor microenvironment (TME) signals. The most important effector immune cells with anti-tumor properties are natural killer (NK) cells, B, and T lymphocytes. On the other hand, immune and non-immune cells with regulatory/inhibitory phenotype, including regulatory T cells (Tregs), regulatory B cells (Bregs), tolerogenic dendritic cells (tDCs), tumor-associated macrophages (TAMs), tumor-associated neutrophils (TANs), myeloid-derived suppressor cells (MDSCs), mesenchymal stem cells (MSCs), and regulatory natural killer cells (NKregs), can promote the growth and development of tumor cells by inhibiting anti-tumor responses, inducing angiogenesis and metastasis, as well as the expression of inhibitory molecules and suppressor mediators of the immune system. However, due to the complexity of the interaction and the modification in the immune cells' phenotype and the networking of the immune responses, the exact mechanism of action of the immunosuppressive and regulatory cells is not yet fully understood. This review article reviews the immune responses involved in BC as well as the role of regulatory and inhibitory cells in the pathogenesis of the disease. Finally, therapeutic approaches based on inhibition of immunosuppressive responses derived from regulatory cells are discussed.

Extensive analysis of the molecular biomarkers excision repair cross complementing 1, ribonucleotide reductase M1, β-tubulin III, thymidylate synthetase, and topoisomerase IIα in breast cancer: Association with clinicopathological characteristics

Excision repair cross complementing 1 (ERCC1), ribonucleotide reductase M1 (RRM1), β-tubulin III (TUBB3), thymidylate synthetase (TYMS), and topoisomerase IIα (TOP2A) genes have been shown to be associated with the pathogenesis and prognosis of various types of carcinomas; however, their roles in breast cancer have not been fully validated. In this study, we evaluated the correlations among these biomarkers and the associations between their expression intensity and the clinicopathological characteristics to investigate whether the above genes are underlying biomarkers for patients with breast cancer.Ninety-seven tissue specimens collected from breast cancer patients. The expression levels of these biomarkers were measured by the multiplex branched DNA liquidchip (MBL) technology and clinicopathological characteristics were collected simultaneously.The expression levels of ERCC1, TUBB3, TYMS, and TOP2A were significantly associated with the characteristics of menopausal status, tumor size, lymph node metastasis, hormone receptor status, triple-negative status, Ki-67 index, and epidermal growth factor receptor. The expression intensity of ERCC1 negatively associated with that of TUBB3 and TYMS, and positively associated with that of RRM1. The expression intensity of TOP2A positively associated with that of TYMS. Hierarchical clustering analysis and difference test indicated that breast cancer with higher levels of TUBB3, TYMS, and TOP2A, as well as lower levels of ERCC1 and RRM1 tended to have higher histological grade and Ki-67 index.Our studies showed that ERCC1, TYMS, TUBB3, and TOP2A may be potential biomarkers for prognosis and individualized chemotherapy guidance, while there may be interactions between ERCC1 and RRM1, or TUBB3, or TYMS, as well as between TOP2A and TYMS in pathogenesis and development of breast cancer.

PAM-50 predicts local recurrence after breast cancer surgery in postmenopausal patients with ER+/HER2- disease: results from 1204 patients in the randomized ABCSG-8 trial

BACKGROUND

The aim of this study was to investigate whether the PAM-50-based 46-gene assay carries prognostic value for risk of local recurrence of breast cancer.

METHODS

The Austrian Breast and Colorectal Cancer Study Group (ABCSG) 8 RCT compared 5 years of tamoxifen with tamoxifen for 2 years followed by anastrozole for 3 years in postmenopausal women with endocrine receptor-positive breast cancer. This study included patients from the trial who had breast-conserving surgery for whom tumour blocks were available for PAM-50 analysis.

RESULTS

Tumour blocks from 1204 patients who had breast-conserving surgery were available for the PAM-50 analysis, and 1034 of these received radiotherapy. After a median follow-up of 10.8 years, 23 local events had been observed, corresponding to an overall local recurrence risk of 2.2 per cent. Univariable competing-risk analysis demonstrated that patients at low risk according to PAM-50 analysis (risk-of-recurrence (ROR) score less than 57) had a significantly lower incidence of local recurrence than those in the high-risk group at 5 years (0.1 (95 per cent c.i. 0 to 0.7) versus 2.2 (0.9 to 4.6) per cent respectively; subhazard ratio (SHR) 17.18, 95 per cent c.i. 2.06 to 142.88; P = 0.009) and 10 years (0.9 (0.4 to 2.0) versus 3.8 (1.9 to 6.6) per cent; SHR 4.76, 1.72 to 13.17; P = 0.003). Multivariable analyses that included ROR score, age, tumour size, nodal status, type of surgery, tumor grade, and trial-specific endocrine therapy confirmed that ROR score was an independent prognostic factor for risk of local recurrence. Analysis of the women randomized to radiotherapy or control after breast conservation showed that PAM-50 was not predictive of radiotherapy effect.

CONCLUSION

PAM-50 can be used as a prognostic tool for local recurrence risk in postmenopausal women with hormone receptor-positive breast cancer treated with endocrine therapy. The test was not predictive for the benefit of radiotherapy.

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.

Characterizing advanced breast cancer heterogeneity and treatment resistance through serial biopsies and comprehensive analytics

Molecular heterogeneity in metastatic breast cancer presents multiple clinical challenges in accurately characterizing and treating the disease. Current diagnostic approaches offer limited ability to assess heterogeneity that exists among multiple metastatic lesions throughout the treatment course. We developed a precision oncology platform that combines serial biopsies, multi-omic analysis, longitudinal patient monitoring, and molecular tumor boards, with the goal of improving cancer management through enhanced understanding of the entire cancer ecosystem within each patient. We describe this integrative approach using comprehensive analytics generated from serial-biopsied lesions in a metastatic breast cancer patient. The serial biopsies identified remarkable heterogeneity among metastatic lesions that presented clinically as discordance in receptor status and genomic alterations with mixed treatment response. Based on our study, we highlight clinical scenarios, such as rapid progression or mixed response, that indicate consideration for repeat biopsies to evaluate intermetastatic heterogeneity (IMH), with the objective of refining targeted therapy. We present a framework for understanding the clinical significance of heterogeneity in breast cancer between metastatic lesions utilizing multi-omic analyses of serial biopsies and its implication for effective personalized treatment.

Challenges in Adjuvant Therapy for Premenopausal Women Diagnosed With Luminal Breast Cancers

More than 90% of women with newly diagnosed breast cancer present with stage I to III disease and, with optimal multidisciplinary therapy, are likely to survive their disease. Of these patients, 70% are hormone receptor-positive and candidates for adjuvant endocrine therapy. The adoption of cumulatively better adjuvant treatments contributed to improved outcomes in patients with hormone receptor-positive, early-stage breast cancer. Premenopausal women with hormone receptor-positive breast cancer often present with complex disease and have inferior survival outcomes compared with their postmenopausal counterparts. Risk stratification strategies, including classic clinicopathologic features and newer gene expression assays, can assist in treatment decisions, including adjuvant chemotherapy use and type or duration of endocrine therapy. Gene expression assays may help identify patients who can safely forgo chemotherapy, although to a lesser extent among premenopausal patients, in whom they may play a role only in node-negative disease. Patients at lower risk of recurrence can be adequately treated with tamoxifen alone, whereas higher-risk patients benefit from ovarian function suppression with tamoxifen or an aromatase inhibitor. The role of adding newer therapies such as CDK4/6 inhibitors to adjuvant endocrine therapy is not yet clear. Breast cancer treatments are associated with several side effects, with major impact on patients' quality of life and treatment adherence, particularly in premenopausal women for whom these side effects may be more prominent as the result of the abrupt decrease in estrogen concentrations. Personalized management of treatment side effects, addressing patients' concerns, and health promotion should be an integral part of the care of premenopausal women diagnosed with luminal breast cancers.

Correlation of immune infiltration with clinical outcomes in breast cancer patients: The 25-gene prognostic signatures model

Purpose

Breast cancer is the most common cancer in women. The aim of this study was to build a prognostic signatures model based on the immune score of the ESTIMATE algorithm to predict survival of breast cancer patients.

Methods

The RNA‐seq expression data and clinical characteristics of patients were derived from TCGA and GSE88770 of GEO. The ESTIMATE algorithm was used to calculate the patients' immune scores and to obtain DEGs. The LASSO Cox regression model was applied to select prognostic genes. Survival analysis and the ROC curve were used to evaluate the predictive efficacy of the prognostic signatures model. Independent prognostic factors of breast cancer were assessed using the Cox regression analyses, and a nomogram was constructed to enhance the clinical value.

Results

Based on the immune score, we found that the high‐score group showed better clinical outcomes than the low‐score group. Twenty‐five (25) genes of 616 DEGs were confirmed as prognostic signatures through the LASSO Cox regression. The risk score for each patient was calculated according to the prognostic signatures. Survival analysis showed that the low‐risk group had longer overall survival than the high‐risk group. We also found that the risk score was an independent prognostic factor. To improve the clinical application value, a nomogram combing the risk score according to the 25‐gene prognostic signatures and several clinicopathological prognostic factors was constructed.

Conclusions

This study revealed the significance of immune infiltration and constructed a 25‐gene prognostic signatures model, that has a strong prognostic value for patients with breast cancer.

Recent Advances in Integrative Multi-Omics Research in Breast and Ovarian Cancer

The underlying molecular heterogeneity of cancer is responsible for the dynamic clinical landscape of this disease. The combination of genomic and proteomic alterations, including both inherited and acquired mutations, promotes tumor diversity and accounts for variable disease progression, therapeutic response, and clinical outcome. Recent advances in high-throughput proteogenomic profiling of tumor samples have resulted in the identification of novel oncogenic drivers, tumor suppressors, and signaling networks; biomarkers for the prediction of drug sensitivity and disease progression; and have contributed to the development of novel and more effective treatment strategies. In this review, we will focus on the impact of historical and recent advances in single platform and integrative proteogenomic studies in breast and ovarian cancer, which constitute two of the most lethal forms of cancer for women, and discuss the molecular similarities of these diseases, the impact of these findings on our understanding of tumor biology as well as the clinical applicability of these discoveries.

bc-GenExMiner 4.5: new mining module computes breast cancer differential gene expression analyses

‘Breast cancer gene-expression miner’ (bc-GenExMiner) is a breast cancer–associated web portal (http://bcgenex.ico.unicancer.fr). Here, we describe the development of a new statistical mining module, which permits several differential gene expression analyses, i.e. ‘Expression’ module. Sixty-two breast cancer cohorts and one healthy breast cohort with their corresponding clinicopathological information are included in bc-GenExMiner v4.5 version. Analyses are based on microarray or RNAseq transcriptomic data. Thirty-nine differential gene expression analyses, grouped into 13 categories, according to clinicopathological and molecular characteristics (‘Targeted’ and ‘Exhaustive’) and gene expression (‘Customized’), have been developed. Output results are visualized in four forms of plots. This new statistical mining module offers, among other things, the possibility to compare gene expression in healthy (cancer-free), tumour-adjacent and tumour tissues at once and in three triple-negative breast cancer subtypes (i.e. C1: molecular apocrine tumours; C2: basal-like tumours infiltrated by immune suppressive cells and C3: basal-like tumours triggering an ineffective immune response). Several validation tests showed that bioinformatics process did not alter the pathobiological information contained in the source data. In this work, we developed and demonstrated that bc-GenExMiner ‘Expression’ module can be used for exploratory and validation purposes.

Database URL: http://bcgenex.ico.unicancer.fr

FGFR4 regulates tumor subtype differentiation in luminal breast cancer and metastatic disease

Mechanisms driving tumor progression from less aggressive subtypes to more aggressive states represent key targets for therapy. We identified a subset of luminal A primary breast tumors that give rise to HER2-enriched (HER2E) subtype metastases, but remain clinically HER2 negative (cHER2-). By testing the unique genetic and transcriptomic features of these cases, we developed the hypothesis that FGFR4 likely participates in this subtype switching. To evaluate this, we developed 2 FGFR4 genomic signatures using a patient-derived xenograft (PDX) model treated with an FGFR4 inhibitor, which inhibited PDX growth in vivo. Bulk tumor gene expression analysis and single-cell RNA sequencing demonstrated that the inhibition of FGFR4 signaling caused molecular switching. In the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) breast cancer cohort, FGFR4-induced and FGFR4-repressed signatures each predicted overall survival. Additionally, the FGFR4-induced signature was an independent prognostic factor beyond subtype and stage. Supervised analysis of 77 primary tumors with paired metastases revealed that the FGFR4-induced signature was significantly higher in luminal/ER+ tumor metastases compared with their primaries. Finally, multivariate analysis demonstrated that the FGFR4-induced signature also predicted site-specific metastasis for lung, liver, and brain, but not for bone or lymph nodes. These data identify a link between FGFR4-regulated genes and metastasis, suggesting treatment options for FGFR4-positive patients, whose high expression is not caused by mutation or amplification.

Hormonal modulation of ESR1 mutant metastasis

Estrogen receptor alpha gene (ESR1) mutations occur frequently in ER-positive metastatic breast cancer, and confer clinical resistance to aromatase inhibitors. Expression of the ESR1 Y537S mutation induced an epithelial-mesenchymal transition (EMT) with cells exhibiting enhanced migration and invasion potential in vitro. When small subpopulations of Y537S ESR1 mutant cells were injected along with WT parental cells, tumor growth was enhanced with mutant cells becoming the predominant population in distant metastases. Y537S mutant primary xenograft tumors were resistant to the antiestrogen tamoxifen (Tam) as well as to estradiol (E2) withdrawal. Y537S ESR1 mutant primary tumors metastasized efficiently in the absence of E2; however, Tam treatment significantly inhibited metastasis to distant sites. We identified a nine-gene expression signature, which predicted clinical outcomes of ER-positive breast cancer patients, as well as breast cancer metastasis to the lung. Androgen receptor (AR) protein levels were increased in mutant models, and the AR agonist dihydrotestosterone significantly inhibited estrogen-regulated gene expression, EMT, and distant metastasis in vivo, suggesting that AR may play a role in distant metastatic progression of ESR1 mutant tumors.

A Phenomic Perspective on Factors Influencing Breast Cancer Treatment: Integrating Aging and Lifestyle in Blood and Tissue Biomarker Profiling

Breast cancer is the most common malignancy among women worldwide. Over the last four decades, diagnostic and therapeutic procedures have improved substantially, giving patients with localized disease a better chance of cure, and those with more advanced cancer, longer periods of disease control and survival. However, understanding and managing heterogeneity in the clinical response exhibited by patients remains a challenge. For some treatments, biomarkers are available to inform therapeutic options, assess pathological response and predict clinical outcomes. Nevertheless, some measurements are not employed universally and lack sensitivity and specificity, which might be influenced by tissue-specific alterations associated with aging and lifestyle. The first part of this article summarizes available and emerging biomarkers for clinical use, such as measurements that can be made in tumor biopsies or blood samples, including so-called liquid biopsies. The second part of this article outlines underappreciated factors that could influence the interpretation of these clinical measurements and affect treatment outcomes. For example, it has been shown that both adiposity and physical activity can modify the characteristics of tumors and surrounding tissues. In addition, evidence shows that inflammaging and immunosenescence interact with treatment and clinical outcomes and could be considered prognostic and predictive factors independently. In summary, changes to blood and tissues that reflect aging and patient characteristics, including lifestyle, are not commonly considered clinically or in research, either for practical reasons or because the supporting evidence base is developing. Thus, an aim of this article is to encourage an integrative phenomic approach in oncology research and clinical management.

Current Achievements and Applications of Transcriptomics in Personalized Cancer Medicine

Over the last decades, transcriptome profiling emerged as one of the most powerful approaches in oncology, providing prognostic and predictive utility for cancer management. The development of novel technologies, such as revolutionary next-generation sequencing, enables the identification of cancer biomarkers, gene signatures, and their aberrant expression affecting oncogenesis, as well as the discovery of molecular targets for anticancer therapies. Transcriptomics contribute to a change in the holistic understanding of cancer, from histopathological and organic to molecular classifications, opening a more personalized perspective for tumor diagnostics and therapy. The further advancement on transcriptome profiling may allow standardization and cost reduction of its analysis, which will be the next step for transcriptomics to become a canon of contemporary cancer medicine.

The genomic landscape of breast cancer brain metastases: a systematic review

Breast cancer brain metastases are an increasing clinical problem. Studies have shown that brain metastases from breast cancer have a distinct genomic landscape to that of the primary tumour, including the presence of mutations that are absent in the primary breast tumour. In this Review, we aim to review and evaluate genomic sequencing data for breast cancer brain metastases by searching PubMed, Embase, and Scopus for relevant articles published in English between database inception and May 30, 2020. Extracted information includes data for mutations, receptor status (eg, immunohistochemistry and Prediction Analysis of Microarray 50 [PAM50]), and copy number alterations from published manuscripts and supplementary materials. Of the 431 articles returned by the database search, 13 (3%) breast cancer brain metastases sequencing studies, comprising 164 patients with sequenced brain metastases, met all our inclusion criteria. We identified 268 mutated genes that were present in two or more breast cancer brain metastases samples. Of these 268 genes, 22 (8%) were mutated in five or more patients and pathway enrichment analysis showed their involvement in breast cancer-related signalling pathways, regulation of gene transcription, cell cycle, and DNA repair. Actionability analysis using the Drug Gene Interaction Database revealed that 15 (68%) of these 22 genes are actionable drug targets. In addition, immunohistochemistry and PAM50 data showed receptor discordancy between primary breast cancers and their paired brain metastases. This systematic review provides a detailed overview of the most commonly mutated genes identified in samples of breast cancer brain metastases and their clinical relevance. These data highlight the differences between primary breast cancers and brain metastases and the importance of acquiring and analysing brain metastasis samples for further study.

Retrospective Validation of a 168-Gene Expression Signature for Glioma Classification on a Single Molecule Counting Platform

Gene expression profiling has been shown to be comparable to other molecular methods for glioma classification. We sought to validate a gene-expression based glioma classification method. Formalin-fixed paraffin embedded tissue and flash frozen tissue collected at the Augusta University (AU) Pathology Department between 2000-2019 were identified and 2 mm cores were taken. The RNA was extracted from these cores after deparaffinization and bead homogenization. One hundred sixty-eight genes were evaluated in the RNA samples on the nCounter instrument. Forty-eight gliomas were classified using a supervised learning algorithm trained by using data from The Cancer Genome Atlas. An ensemble of 1000 linear support vector models classified 30 glioma samples into TP1 with classification confidence of 0.99. Glioma patients in TP1 group have a poorer survival (HR (95% CI) = 4.5 (1.3-15.4), p = 0.005) with median survival time of 12.1 months, compared to non-TP1 groups. Network analysis revealed that cell cycle genes play an important role in distinguishing TP1 from non-TP1 cases and that these genes may play an important role in glioma survival. This could be a good clinical pipeline for molecular classification of gliomas.

Significance of Oncotype DX 21-Gene Test and Expression of Long Non-Coding RNA MALAT1 in Early and Estrogen Receptor-Positive Breast Cancer Patients

Objective

To investigate the association between the recurrence score (RS) obtained by Oncotype DX 21-gene test and long non-coding RNA (lncRNA) MALAT1 expression in early and estrogen receptor-positive (ER⁺) breast cancer.

Materials and Methods

The Oncotype DX 21-gene test and MALAT1 expression detection were performed in tumor samples from 76 ER⁺ and early breast cancer patients with the Surplex liquid chip. The RS value was calculated based on the expression of total 21 genes. The level of MALAT1 was measured in both tumor tissue and para-tumor tissue, and relatively quantified with an internal control gene. Mann–Whitney U-test or Kruskal–Wallis test were used to analyze the association between MALAT1 level and different clinical pathological characteristics, including age, tumor stage, disease grade, lymph node status, Ki-67 expression, and progesterone receptor (PR) status. The association between the RS and different characteristics was analyzed by Wilcoxon rank-sum test. Correlation between two parameters was analyzed by Spearman’s rank correlation analysis.

Results

The expression of MALAT1 was more abundant in tumor tissue (2.992 ± 2.256) than that in adjacent normal tissue (1.641±1.438, Z=−2.594, p=0.009), and it was not correlated with any clinical pathological characteristics. According to the old criteria for RS stratification, 52.7% of patients were in low risk (RS<18), 36.8% of patients were in medium risk (18≤RS≤30), and 10.5% of patients were in high risk (RS>30). While under the new criteria, 18.4% were in low risk group (RS<11), 63.2% were in a medium risk group (11≤RS≤26), and 18.4% were in a high risk group (RS>26). The Oncotype DX 21-gene results only correlated with Ki-67 expression under both new and old criteria, and it was not related with other cancer characteristics. The expression of lncRNA MALAT1 was significantly correlated with the Oncotype DX 21-gene results under the old criteria.

Conclusion

MALAT1 is a novel breast cancer biomarker independent of tumor stage, disease grade and lymph node status. MALAT1 level is associated with the Oncotype DX 21-gene RS value. Therefore, combination of MALAT1 and the Oncotype DX 21-gene test may be used to predict prognosis in ER⁺ and early stage breast cancer.

Triple Negative Breast Cancer: A Review of Present and Future Diagnostic Modalities

Triple-negative breast cancer (TNBC) is an aggressive breast type of cancer with no expression of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor-2 (HER2). It is a highly metastasized, heterogeneous disease that accounts for 10-15% of total breast cancer cases with a poor prognosis and high relapse rate within five years after treatment compared to non-TNBC cases. The diagnostic and subtyping of TNBC tumors are essential to determine the treatment alternatives and establish personalized, targeted medications for every TNBC individual. Currently, TNBC is diagnosed via a two-step procedure of imaging and immunohistochemistry (IHC), which are operator-dependent and potentially time-consuming. Therefore, there is a crucial need for the development of rapid and advanced technologies to enhance the diagnostic efficiency of TNBC. This review discusses the overview of breast cancer with emphasis on TNBC subtypes and the current diagnostic approaches of TNBC along with its challenges. Most importantly, we have presented several promising strategies that can be utilized as future TNBC diagnostic modalities and simultaneously enhance the efficacy of TNBC diagnostic.

Molecular Drivers of Oncotype DX, Prosigna, EndoPredict, and the Breast Cancer Index: A TransATAC Study

PURPOSE

The Oncotype DX Recurrence Score (RS), Prosigna Prediction Analysis of Microarray 50 (PAM50) Risk of Recurrence (ROR), EndoPredict (EP), and Breast Cancer Index (BCI) are used clinically for estimating risk of distant recurrence for patients receiving endocrine therapy. Discordances in estimates occur between them. We aimed to identify the molecular features that drive the tests and lead to these differences.

PATIENTS AND METHODS

Analyses for RS, ROR, EP, and BCI were conducted by the manufacturers in the TransATAC sample collection that consisted of the tamoxifen or anastrozole arms of the ATAC trial. Estrogen receptor-positive/human epidermal growth factor receptor 2 (HER2)-negative cases without chemotherapy treatment were included in which all four tests were available (n = 785). Clinicopathologic features included in some tests were excluded from the comparisons. Estrogen, proliferation, invasion, and HER2 module scores from RS were used to characterize the respective molecular features. Spearman correlation and analysis of variance tests were applied.

RESULTS

There were moderate to strong correlations among the four molecular scores (ρ = 0.63-0.74) except for RS versus ROR (ρ = 0.32) and RS versus BCI (ρ = 0.35). RS had strong negative correlation with its estrogen module (ρ = -0.79) and moderate positive correlation with its proliferation module (ρ = 0.36). RS's proliferation module explained 72.5% of ROR's variance, while the estrogen module explained only 0.6%. Most of EP's and BCI's variation was accounted for by the proliferation module (50.0% and 54.3%, respectively) and much less by the estrogen module (20.2% and 2.7%, respectively).

CONCLUSION

In contrast to common understanding, RSs are determined more strongly by estrogen-related features and only weakly by proliferation markers. However, the EP, BCI, and particularly ROR scores are determined largely by proliferative features. These relationships help to explain the differences in the prognostic performance of the tests.

Invasive lobular carcinoma of the breast: the increasing importance of this special subtype

Invasive lobular carcinoma (ILC) is the most common of the breast cancer special types, accounting for up to 15% of all breast cancer cases. ILCs are noted for their lack of E-cadherin function, which underpins their characteristic discohesive growth pattern, with cells arranged in single file and dispersed throughout the stroma. Typically, tumours are luminal in molecular subtype, being oestrogen and progesterone receptor positive, and HER2 negative. Since last reviewing the lobular literature (McCart Reed et al., Breast Cancer Res 17:12, 2015), there has been a considerable increase in research output focused on this tumour type, including studies into the pathology and management of disease, a high-resolution definition of the genomic landscape of tumours as well as the evolution of several potential therapeutic avenues. There abounds a huge amount of new data, which we will review herein.