Clinical utility of gene-expression signatures in early stage breast cancer

Galgo: a bi-objective evolutionary meta-heuristic identifies robust transcriptomic classifiers associated with patient outcome across multiple cancer types

MOTIVATION

Statistical and machine-learning analyses of tumor transcriptomic profiles offer a powerful resource to gain deeper understanding of tumor subtypes and disease prognosis. Currently, prognostic gene-expression signatures do not exist for all cancer types, and most developed to date have been optimized for individual tumor types. In Galgo, we implement a bi-objective optimization approach that prioritizes gene signature cohesiveness and patient survival in parallel, which provides greater power to identify tumor transcriptomic phenotypes strongly associated with patient survival.

RESULTS

To compare the predictive power of the signatures obtained by Galgo with previously studied subtyping methods, we used a meta-analytic approach testing a total of 35 large population-based transcriptomic biobanks of four different cancer types. Galgo-generated colorectal and lung adenocarcinoma signatures were stronger predictors of patient survival compared to published molecular classification schemes. One Galgo-generated breast cancer signature outperformed PAM50, AIMS, SCMGENE and IntClust subtyping predictors. In high-grade serous ovarian cancer, Galgo signatures obtained similar predictive power to a consensus classification method. In all cases, Galgo subtypes reflected enrichment of gene sets related to the hallmarks of the disease, which highlights the biological relevance of the partitions found.

AVAILABILITY AND IMPLEMENTATION

The open-source R package is available on www.github.com/harpomaxx/galgo.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

CDK 4/6 inhibitors mired in uncertainty in HR positive and HER2 negative early breast cancer

Cell-cycle abnormalities are common in estrogen receptor- and/or progesterone receptor-positive, and HER2-non-overexpressing (HR+/HER2-) breast cancer, and have long been considered potential therapeutic targets. Cyclin-dependent kinase (CDK) 4/6 inhibitors have dramatically changed the therapeutic management of HR+/HER2-advanced breast cancer by prolonging progression-free and overall survival when given in combination with endocrine therapy. In this article, available data from PALLAS and monarchE trials regarding the efficacy and toxicity of adjuvant combined therapy with CDK 4/6 inhibitors and endocine therapy in HR+/HER2-early breast cancer are reviewed, and relevant issues including study hypothesis, patient selection, and duration of follow-up are discussed.

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HR+/HER2-early BC patients have continuous risk of relapse and need new therapies

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Current short follow-up precludes any final conclusion re. adjuvant CDK4/6 inhibitors

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The proportional hazard assumption was hampered by the low number of events

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Wide point estimate 95%CI translated into imprecise number needed to treat (NNT)

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Besides efficacy, toxicity, compliance and cost are issues to consider in decision-making

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Research efforts need to continue to establish CDK4/6 inhibitor predictive biomarkers

Impact of Commercialized Genomic Tests on Adjuvant Treatment Decisions in Early Stage Breast Cancer Patients

Introduction

Advances in genomic techniques have been valuable in guiding decisions regarding the treatment of early breast cancer (EBC) patients. These multigene assays include Oncotype DX, Prosigna, and Endopredict. There has generally been a tendency to overtreat or undertreat patients, and having reliable prognostic factors could significantly improve rates of appropriate treatment administration. In this study, we showcase the impact of genomic tests on adjuvant treatment decisions in EBC patients.

Materials and Methods

This is a retrospective study that includes EBC patients treated between December 2016 and February 2018. The physician's choice of treatment was recorded before and after obtaining the results of the genomics tests. Baseline demographics and pathological data were collected from medical records.

Results

A total of 75 patients were included. Fifty patients underwent Oncotype DX genomic analysis, 11 patients underwent Prosigna analysis, and 14 patients underwent Endopredict analysis. A total of 21 physicians' plans (28%) were initially undecided and then carried out after obtaining genomic test results. 13 patients were planned to undergo endocrine therapy alone, while 8 were planned to undergo both endocrine therapy and chemotherapy. Treatment was changed in 26 patients (34.67%). The decision to deescalate therapy was taken in 19 patients (25.33%). The decision to escalate treatment was made in 7 patients (9.33%).

Conclusion

Our study demonstrates the importance of genomics testing, as it assisted physicians in avoiding unnecessary adjuvant chemotherapy in 25.33% of patients, thus reducing side effects of chemotherapy and the financial burden on patients.

HER2 splice variants in breast cancer: investigating their impact on diagnosis and treatment outcomes

Overexpression of the HER2 receptor occurs in approximately 20% of breast cancer patients. HER2 positivity is associated with poor prognosis and aggressive tumour phenotypes, which led to rapid progress in HER2 targeted therapeutics and diagnostic testing. Whilst these advances have greatly increased patients' chances of survival, resistance to HER2 targeted therapies, be that intrinsic or acquired, remains a problem. Different forms of the HER2 protein exist within tumours in tandem and can display altered biological activities. Interest in HER2 variants in breast cancer increased when links between resistance to anti-HER2 therapies and a particular variant, Δ16-HER2, were identified. Moreover, the P100 variant potentially reduces the efficacy of the anti-HER2 therapy trastuzumab. Another variant, Herstatin, exhibits 'auto-inhibitory' behaviour. More recently, new HER2 variants have been identified and are currently being assessed for their pro- and anti-cancer properties. It is important when directing the care of patients to consider HER2 variants collectively. This review considers HER2 variants in the context of the tumour environment where multiple variants are co-expressed at altered ratios. This study also provides an up to date account of the landscape of HER2 variants and links this to patterns of resistance against HER2 therapies and treatment plans.

Prognostic and predictive value of Pleckstrin homology-like domain, family A family members in breast cancer

Aim: The PHLDA (pleckstrin homology like domain, family A) gene family encodes proteins capable of inhibiting AKT (serine/threonine kinase) signaling through phosphoinositol binding competition. Results & methodology: Using in silico analysis, we found that Luminal A and B patients' short relapse-free survival was associated with low PHLDA1 or PHLDA3 and high PHLDA2 expression. In a cohort of 393 patients with luminal breast cancer evaluated by immunohistochemistry on tissue microarrays, we found a direct association of PHLDA3 expression with hormonal therapy response (p = 0.013). Conclusion: Our findings provide new information on the role played by the PHLDA family members as prognostic markers in breast cancer, and more importantly, we provide evidence that they might also predict a response to endocrine therapy.

MPA/DMBA-driven mammary carcinomas

The polycyclic aromatic hydrocarbon 7,12-dimethylbenz[a]anthracene (DMBA, D) administered per os to wild-type female mice bearing slow-release medroxyprogesterone (MPA, M) pellets s.c. drives the formation of mammary carcinomas that recapitulate numerous immunobiological features of human luminal B breast cancer. In particular, M/D-driven mammary carcinomas established in immunocompetent C57BL/6 female mice (1) express hormone receptors, (2) emerge by evading natural immunosurveillance and hence display a scarce immune infiltrate largely polarized toward immunosuppression, (3) exhibit exquisite sensitivity to CDK4/CDK6 inhibitors, and (4) are largely resistant to immunotherapy with immune checkpoint blockers targeting PD-1. Thus, M/D-driven mammary carcinomas evolving in immunocompetent female mice stand out as a privileged preclinical platform for the study of luminal B breast cancer. Here, we provide a detailed protocol for the establishment of M/D-driven mammary carcinomas in wild-type C57BL/6 female mice. This protocol can be easily adapted to generate M/D-driven mammary carcinomas in female mice with most genetic backgrounds (including genetically-engineered mice).

Genomic Signatures in Luminal Breast Cancer

Background

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

Summary

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

Key Messages

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

Radiogenomic signatures reveal multiscale intratumour heterogeneity associated with biological functions and survival in breast cancer

Advanced tumours are often heterogeneous, consisting of subclones with various genetic alterations and functional roles. The precise molecular features that characterize the contributions of multiscale intratumour heterogeneity to malignant progression, metastasis, and poor survival are largely unknown. Here, we address these challenges in breast cancer by defining the landscape of heterogeneous tumour subclones and their biological functions using radiogenomic signatures. Molecular heterogeneity is identified by a fully unsupervised deconvolution of gene expression data. Relative prevalence of two subclones associated with cell cycle and primary immunodeficiency pathways identifies patients with significantly different survival outcomes. Radiogenomic signatures of imaging scale heterogeneity are extracted and used to classify patients into groups with distinct subclone compositions. Prognostic value is confirmed by survival analysis accounting for clinical variables. These findings provide insight into how a radiogenomic analysis can identify the biological activities of specific subclones that predict prognosis in a noninvasive and clinically relevant manner.

Tumours are made up of heterogeneous subclones. Here, the authors show using breast cancer imaging and gene expression datasets that these subclones can be inferred by the deconvolution of gene expression data, mapped to MRI derived radiogenomic signatures and used to estimate prognosis.

Construction of an immune-related genes nomogram for the preoperative prediction of axillary lymph node metastasis in triple-negative breast cancer

Immune system disorder is associated with metastasis of triple-negative breast cancers (TNBCs). A robust, individualized immune-related genes (IRGs)-based classifier was aimed to develop and validate in our study to precisely estimate the axillary lymph node (ALN) status preoperatively in patients with early-stage TNBC. We first analyzed RNA sequencing profiles in TNBC patients from The Cancer Genome Atlas database by using bioinformatics approaches, and screened 23 differentially expressed IRGs. A 9-gene panel was generated with an area under the curve (AUC) of 0.77 [95% confidence interval (95% CI), 0.68-0.87]. We detected the 9 ALN-status-related IRGs in the training set (n = 133) and developed a reduced and optimized five-IRGs signature, which effectively distinguished TNBC patients with ALN metastasis (AUC, 0.80; 95% CI, 0.65-0.86), and was superior to preoperative ultrasound-based ALN status (AUC, 0.73; 95% CI, 0.53-0.93). Predictive efficiency (AUC, 0.77; 95% CI 0.61-0.93) of this five-IRGs signature was validated in the validation set (n = 81). Furthermore, IRGs nomogram incorporated IRGs signature with US-based ALN status showed higher ALN status prediction efficacy than US-based ALN status and five-IRGs signature alone in both training and validation sets. IRGs nomogram may aid in identifying patients who can be exempted from axillary surgery.Novelty and impact: An immune-related genes (IRGs) nomogram was first developed and externally validated in our study, which incorporated the IRGs signature with ultrasound (US)-based axillary lymph nodes (ALN) status. IRGs nomogram is superior to IRGs signature alone for preoperative estimation of ALN metastasis in patients with triple-negative breast cancer (TNBC). It is a favourable biomarker for preoperatively predicting ALN metastasis risk and may aid in clinical decision-making in early-stage TNBCs.

High-dimensional single-index models with censored responses

In this article, we study the estimation of high-dimensional single index models when the response variable is censored. We hybrid the estimation methods for high-dimensional single-index models (but without censorship) and univariate nonparametric models with randomly censored responses to estimate the index parameters and the link function and apply the proposed methods to analyze a genomic dataset from a study of diffuse large B-cell lymphoma. We evaluate the finite sample performance of the proposed procedures via simulation studies and establish large sample theories for the proposed estimators of the index parameter and the nonparametric link function under certain regularity conditions.

Racial and ethnic disparities in 21-gene recurrence scores, chemotherapy, and survival among women with hormone receptor-positive, node-negative breast cancer

PURPOSE

Cutoffs of the 21-gene recurrence score (RS), a commonly used genomic assay for hormone receptor-positive breast cancer, have been updated. Little is known about racial/ethnic differences in RS results, RS-guided chemotherapy use, and outcomes on updated cutoff (RS ≥ 31 defined as high-risk) in the real-world setting.

METHODS

A total of 81,937 women [75.0% whites, 7.7% blacks, 8.3% Asian American/Pacific Islanders (AAPIs), and 9.0% Hispanics] diagnosed with hormone receptor-positive breast cancer between 2004 and 2015, who received the 21-gene assay, were identified from the Surveillance, Epidemiology, and End Results. Logistic regressions estimated the race-associated odds ratios (ORs) of RS and chemotherapy use. Cox regressions estimated the race-associated hazard ratios (HRs) of breast cancer-specific and all-cause mortality.

RESULTS

Compared with white women, black women were more likely to have RS-defined high-risk tumors (adjusted OR [aOR] 1.29; 95% CI 1.16-1.42). In high RS, blacks had lower odds of chemotherapy use (aOR 0.76; 95% CI 0.62-0.94) than whites, particularly among women ≥ 65 years (aOR 0.51; 95% CI 0.35-0.76), while AAPI and Hispanic women had no variation in chemotherapy use compared with whites in high RS. Black women had a higher risk of breast cancer-specific mortality (HR 1.37; 95% CI 1.12-1.67) and all-cause mortality compared with white women after adjusting for demographic and pathological factors, county-level socioeconomic deprivation, treatments and RS; AAPIs had lower mortality and Hispanics had similar mortality.

CONCLUSIONS

Black women were more likely to have a high-risk RS tumor and less likely to receive chemotherapy in the group of high RS, especially those ≥ 65 years. Further studies are needed to identify barriers to chemotherapy in black patients with high RS scores.

Correlation of circulating or disseminated tumor cells with the Oncotype DX Recurrence Score

PURPOSE

New biomarkers are emerging to predict recurrence risk in women with early-stage breast cancer. High Oncotype DX Recurrence Score® (RS) is associated with worse disease-free and overall survival. Similarly, circulating tumor cells (CTCs, blood) and disseminated tumor cells (DTCs, bone marrow) have prognostic value in breast cancer. We investigated the association between high RS and CTCs or DTCs.

METHODS

Using a prospective database, we evaluated patients with hormone receptor-positive/HER2-negative, node-negative invasive breast cancer from 1/2005 to 1/2017. RS was classified using TAILORx study cutoff points: low (< 11), intermediate (11-25), and high (> 25). CTCs were assessed using CellSearch® and DTCs using cytospin specimens of bone marrow aspirates. Positive result was defined as one or more CTCs or DTCs identified. Chi-square analyses were utilized to evaluate the relationship between RS and CTCs or DTCs.

RESULTS

233 patients were identified from a prospective database, of which 96 had RS results. Of these patients, 88 had CTC results and 58 had DTC results. CTCs were detected in 17/88 (19%) patients, while DTCs were detected in 20/58 (34%). Patients with high RS were not more likely to have CTCs (18%) compared to patients with low/intermediate RS (20%; p = 0.919). Similarly, high RS was not associated with DTC detection, with DTCs present in 40% of patients with high RS versus 33% with low/intermediate RS (p = 0.687). In the subgroup of patients ≤ 50 years, no associations were found between high RS and CTCs (p = 0.383) or DTCs (p = 0.234).

CONCLUSIONS

High Oncotype DX RS did not correlate with CTCs in blood or DTCs in bone marrow in our study.

MKL1 Mediates TGF-β Induced RhoJ Transcription to Promote Breast Cancer Cell Migration and Invasion

Differential regulation of gene transcription contributes to cancer metastasis. We investigated the involvement of a Rho GTPase (RhoJ) in breast cancer metastasis focusing on the mechanism underlying RhoJ trans-activation by pro-metastatic cues. We report that expression of RhoJ was up-regulated in malignant breast cancer cells compared to more benign ones. Higher RhoJ expression was also detected in human breast cancer biopsy specimens of advanced stages. RhoJ depletion attenuated breast cancer cell migration and invasion in vitro and metastasis in vivo. The pro-metastatic stimulus TGF-β activated RhoJ via megakaryocytic leukemia 1 (MKL1). MKL1 interacted with and was recruited by ETS-related gene 1 (ERG1) to the RhoJ promoter to activate transcription. In conclusion, our data delineate a novel transcriptional pathway that contributes to breast cancer metastasis. Targeting the ERG1-MKL1-RhoJ axis may be considered as a reasonable approach to treat malignant breast cancer.

Current advances in the diagnosis and personalized treatment of breast cancer: lessons from tumor biology

Breast cancer treatment has advanced enormously in the last decade. Most of this is due to advances reached in the knowledge regarding tumor biology, mainly in the field of diagnosis and treatment. This review brings information about how the genomics-based information contributed to advances in breast cancer diagnosis and prognosis perspective, as well as presents how tumor biology discoveries fostered the main therapeutic approaches available to treat such patients, based on a personalized point of view.

Development and validation of an oxidative phosphorylation-related gene signature in lung adenocarcinoma

Aim: To develop an oxidative phosphorylation (OXPHOS)-related gene signature of lung adenocarcinoma (LUAD). Materials & methods: We split The Cancer Genome Atlas LUAD cohort into a training set and a test set; we used the least absolute shrinkage and selection operator Cox method to structure the OXPHOS-related prognostic signature in the training set and verified in the test set and GSE30219 dataset. Meanwhile, the diagnostic model was constructed using the logistic Cox method. Results: The signature consisted of seven genes (LDHA, CFTR, HSPD1, SNHG3, MAP1LC3C, COX6B2, and TWIST1). LUAD patients were divided into high- and low-risk groups, demonstrating good diagnostic and prognostic capabilities. Conclusion: We developed the first-ever OXPHOS-related signature with both prognostic predictive power and diagnostic efficacy.

Pattern-recognition-based Sensor Arrays for Cell Characterization: From Materials and Data Analyses to Biomedical Applications

To capture a broader scope of complex biological phenomena, alternatives to conventional sensing based on specificity for cell detection and characterization are needed. Pattern-recognition-based sensing is an analytical method designed to mimic mammalian sensory systems for analyte identification based on the pattern recognition of multivariate data, which are generated using an array of multiple probes that cross-reactively interact with analytes. This sensing approach is significantly different from conventional specific cell sensing based on highly specific probes, including antibodies against biomarkers. Encouraged by the advantages of this technique, such as the simplicity, rapidity, and tunability of the systems without requiring a priori knowledge of biomarkers, numerous sensor arrays have been developed over the past decade and used in a variety of cell sensing applications; these include disease diagnosis, drug discovery, and fundamental research. This review summarizes recent progress in pattern-recognition-based cell sensing, with a particular focus on guidelines for designing materials and arrays, techniques for analyzing response patterns, and applications of sensor systems that are focused primarily for the biomedical field.

The 21-Gene Recurrence Score Assay and Prediction of Chemotherapy Benefit: A Propensity Score-Matched Analysis of the SEER Database

Background: To evaluate the performance of the 21-gene recurrence score (RS) assay in predicting chemotherapy benefit in the Surveillance, Epidemiology, and End Results population, we aimed to assess breast cancer-specific mortality (BCSM) by chemotherapy use within each of the RS categories. Methods: Data on breast cancer (BC) cases diagnosed between 2004 and 2015 with available RS results were released. Our analysis included patients with hormone receptor-positive, node-negative early-stage BC (n = 89,402), and three RS groups were defined; RS < 11, low; RS 11–25, intermediate; RS > 25, high. A propensity score matched-analysis was performed to assess and compare BCSM. Results: Chemotherapy was significantly associated with a reduced risk of BC death among patients in the high RS group (hazard ratio = 0.782; 95% CI, 0.618–0.990; p = 0.041). However, in the low and intermediate RS groups, there were no significant differences in BCSM between patients who received chemotherapy and those who did not. Among those with RS 11–25, chemotherapy benefit varied with tumor size (p = 0.001). Conclusions: Our findings provide real-world evidence that the 21-gene RS assay is predictive of chemotherapy benefit among patients in clinical practice. More refined risk estimates would be needed for patients with an intermediate RS.

Expression Patterns of Immune Genes Reveal Heterogeneous Subtypes of High-Risk Neuroblastoma

High risk neuroblastoma (HR-NB) remains difficult to treat, and its overall survival (OS) is still below 50%. Although HR-NB is a heterogeneous disease, HR-NB patients are currently treated in a similar fashion. Through unsupervised biclustering, we further stratified HR-NB patients into two reproducible and clinically distinct subtypes, including an ultra-high risk neuroblastoma (UHR-NB) and high risk neuroblastoma (HR-NB). The UHR-NB subtype consistently had the worst OS in multiple independent cohorts ( P < 0 . 008 ). Out of 283 neuroblastoma-specific immune genes that were used for stratification, 39 of them were differentiated in UHR-NB, including four upregulated and 35 downregulated, as compared to HR-NB. The four UHR-NB upregulated genes (ADAM22, GAL, KLHL13 and TWIST1) were all upregulated in MYCN amplified neuroblastoma in 5 additional cohorts. TWIST1 and ADAM22 were also positively correlated with cancer stage, while GAL was an independent OS predictor in addition to MYCN and age. Furthermore, we identified 26 commonly upregulated and 311 downregulated genes in UHR-NB from all 4723 immune-related genes. While 43 KEGG pathways with molecular functions were enriched in the downregulated immune-related genes, only the P53 signaling pathway was enriched in the upregulated ones, which suggested that UHR-NB was a TP53 related subtype with reduced immune activities.

Analysis of Immune Checkpoint Drug Targets and Tumor Proteotypes in Non-Small Cell Lung Cancer

New therapeutics targeting immune checkpoint proteins have significantly advanced treatment of non-small cell lung cancer (NSCLC), but protein level quantitation of drug targets presents a critical problem. We used multiplexed, targeted mass spectrometry (MS) to quantify immunotherapy target proteins PD-1, PD-L1, PD-L2, IDO1, LAG3, TIM3, ICOSLG, VISTA, GITR, and CD40 in formalin-fixed, paraffin-embedded (FFPE) NSCLC specimens. Immunohistochemistry (IHC) and MS measurements for PD-L1 were weakly correlated, but IHC did not distinguish protein abundance differences detected by MS. PD-L2 abundance exceeded PD-L1 in over half the specimens and the drug target proteins all displayed different abundance patterns. mRNA correlated with protein abundance only for PD-1, PD-L1, and IDO1 and tumor mutation burden did not predict abundance of any protein targets. Global proteome analyses identified distinct proteotypes associated with high PD-L1-expressing and high IDO1-expressing NSCLC. MS quantification of multiple drug targets and tissue proteotypes can improve clinical evaluation of immunotherapies for NSCLC.

Clinicopathological characteristics, adjuvant chemotherapy decision and disease outcome in patients with breast cancer with a 21-gene recurrence score of 26-30

Recurrence score (RS) could be used to predict clinical outcomes and chemotherapy efficacy in patients with hormone receptor (HR)-positive, human epidermal growth factor receptor 2 (HER2)-negative and lymph node-negative breast cancer. However, the clinical features and management of patients with an RS of 26–30 are not completely understood. In the present study, 783 patients with HR⁺/HER2⁻, lymph node-negative early breast cancer and RS ≥18 were included and categorized into RS=18−25 (47.8%), 26–30 (25.5%) or ≥31 (26.7%) groups. Clinicopathological characteristics, adjuvant chemotherapy usage and disease outcomes were compared. Alterations in the adjuvant chemotherapy recommendation after 21-gene RS testing were also analyzed. The results indicated that patients with RS=26−30 had higher progesterone receptor (PR) expression [odds ratio (OR)=2.84; P<0.001] and lower Ki-67 index (OR, 1.88; P=0.032) compared with patients with RS ≥31. Multivariate analysis demonstrated that age ≤50 years (OR, 5.75; P=0.001) and luminal-B subtype (OR, 7.75; P<0.001) were factors that were independently associated with chemotherapy usage in the RS=26−30 group. Among 104 patients who were not recommended chemotherapy before 21-gene RS testing, the treatment decision for 52 patients was changed to recommend chemotherapy once an RS of 26–30 was identified. The patient adherence rate to the treatment recommendation was 95.0% (190/200). After a median follow-up of 21.5 months, 6 patients displayed disease recurrence in the RS=26−30 group, and there was no significant difference between patients receiving chemotherapy and patients not receiving chemotherapy. In conclusion, patients with RS=26−30 had tumors with higher PR expression and lower Ki-67 index compared with those of patients with RS ≥31. Age, luminal subtype and RS testing influenced chemotherapy usage in patients with RS=26−30; however, no significant benefit from adjuvant chemotherapy was observed in a short term of 2 years.

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

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

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

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

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

Cancer genomics predicts disease relapse and therapeutic response to neoadjuvant chemotherapy of hormone sensitive breast cancers

Several studies provide insight into the landscape of breast cancer genomics with the genomic characterization of tumors offering exceptional opportunities in defining therapies tailored to the patient's specific need. However, translating genomic data into personalized treatment regimens has been hampered partly due to uncertainties in deviating from guideline based clinical protocols. Here we report a genomic approach to predict favorable outcome to treatment responses thus enabling personalized medicine in the selection of specific treatment regimens. The genomic data were divided into a training set of N = 835 cases and a validation set consisting of 1315 hormone sensitive, 634 triple negative breast cancer (TNBC) and 1365 breast cancer patients with information on neoadjuvant chemotherapy responses. Patients were selected by the following criteria: estrogen receptor (ER) status, lymph node invasion, recurrence free survival. The k-means classification algorithm delineated clusters with low- and high- expression of genes related to recurrence of disease; a multivariate Cox's proportional hazard model defined recurrence risk for disease. Classifier genes were validated by Immunohistochemistry (IHC) using tissue microarray sections containing both normal and cancerous tissues and by evaluating findings deposited in the human protein atlas repository. Based on the leave-on-out cross validation procedure of 4 independent data sets we identified 51-genes associated with disease relapse and selected 10, i.e. TOP2A, AURKA, CKS2, CCNB2, CDK1 SLC19A1, E2F8, E2F1, PRC1, KIF11 for in depth validation. Expression of the mechanistically linked disease regulated genes significantly correlated with recurrence free survival among ER-positive and triple negative breast cancer patients and was independent of age, tumor size, histological grade and node status. Importantly, the classifier genes predicted pathological complete responses to neoadjuvant chemotherapy (P < 0.001) with high expression of these genes being associated with an improved therapeutic response toward two different anthracycline-taxane regimens; thus, highlighting the prospective for precision medicine. Our study demonstrates the potential of classifier genes to predict risk for disease relapse and treatment response to chemotherapies. The classifier genes enable rational selection of patients who benefit best from a given chemotherapy thus providing the best possible care. The findings encourage independent clinical validation.

p53 and BLC2 Immunohistochemical Expression Across Molecular Subtypes in 1099 Early Breast Cancer Patients With Long-Term Follow-up: An Observational Study

INTRODUCTION

p53 and antiapoptotic B-cell leukemia/lymphoma 2 (BLC2) have been proposed as prognostic markers for early breast cancer (BC), although their relationship with conventional parameters and patient prognosis, as well as their distribution within the molecular BC subtypes remains uncertain.

PATIENTS AND METHODS

In this observational study, we analyzed the immunohistochemical expression of p53 and BLC2 in 1099 early BC patients surgically treated between 2000 and 2006 and followed for at least 5 years, also considering their association with pathologic factors and molecular subtypes, as well as their influence on disease-free survival.

RESULTS

p53 and BLC2 are distributed differently across molecular subtypes (P < .0001); in particular, p53 positivity and BLC2 negativity seems to be associated with more aggressive conventional tumor phenotypes. Moreover, BLC2 negativity seems to be a significant discriminating factor for disease-free survival (P = .003) according to Kaplan-Meier analysis, while p53 seems to have no discriminating effect. Among patients with discordant p53/BLC2 phenotype, the combination p53⁺BLC2^- seems to be associated with the worst outcomes (P = .007) and significantly influenced the clinical course of node-negative patients treated only with hormone therapy (P = .004).

CONCLUSION

These two biomarkers, in addition to conventional pathologic factors and molecular subtype, could help define the risk and outcome of BC.

The prognostic and predictive role of 21-gene recurrence scores in hormone receptor-positive early-stage breast cancer

Over the past two decades, gene expression profiling of breast cancer has emerged as an important tool in early-stage breast cancer management. The approach provides important information on underlying biological mechanisms, breast cancer classification, future risk potential of developing recurrent metastatic disease, and provides beneficial clues for adjuvant chemotherapy in hormone receptor (HR) positive breast cancer. Of the commercially available genomic tests for breast cancer, the prognostic and predictive value of 21-gene recurrence score tests have been validated using both retrospective data and prospective clinical trials. In this paper, we reviewed the current evidence on 21-gene expression profiles for HR-positive HER2-negative early-stage breast cancer management. We show that current evidence supports endocrine therapy alone as an appropriate adjuvant systemic therapy for approximately 70% of women with HR-positive, HER2-negative, node-negative breast cancer. Evolving evidence also suggests that 21-gene recurrence scores have predictive values for node-positive breast cancer and that chemotherapy can be avoided in more than half of women with nodes 1 to 3 positive HR-positive breast cancer. Furthermore, retrospective data also supports the predictive role of 21-gene recurrence scores for adjuvant radiation therapy. A prospective trial in this area is ongoing.

Harmonizing gene signatures to predict benefit from adjuvant chemotherapy in early breast cancer

PURPOSE OF REVIEW

Breast cancer is a heterogeneous disease, including different subtypes with their own biology, prognosis, clinical characteristics and treatment. To date, traditional clinical and pathological determinants remain the main factors guiding treatment decision-making; however, the development of multigene assays improved the ability to predict the risk of recurrence in patients with early-stage breast cancer. These tools underwent an extensive independent validation and have already been partly incorporated into clinical practice.

RECENT FINDINGS

The current article summarizes current evidence for the use of the different genomic assays in clinical practice, their characteristics and validation studies. A few studies comparing available genomic assays revealed that they provide different information with a modest correlation and that they are not interchangeable; other trials are currently ongoing in this setting.

SUMMARY

Variability across different gene signatures may be a challenge for the optimal management of the individual patient, hence each assay should be used for the clinical setting in which it has been validated.

Breast Cancer: A Molecularly Heterogenous Disease Needing Subtype-Specific Treatments

Breast cancer is the most commonly occurring cancer in women. There were over two-million new cases in world in 2018. It is the second leading cause of death from cancer in western countries. At the molecular level, breast cancer is a heterogeneous disease, which is characterized by high genomic instability evidenced by somatic gene mutations, copy number alterations, and chromosome structural rearrangements. The genomic instability is caused by defects in DNA damage repair, transcription, DNA replication, telomere maintenance and mitotic chromosome segregation. According to molecular features, breast cancers are subdivided in subtypes, according to activation of hormone receptors (estrogen receptor and progesterone receptor), of human epidermal growth factors receptor 2 (HER2), and or BRCA mutations. In-depth analyses of the molecular features of primary and metastatic breast cancer have shown the great heterogeneity of genetic alterations and their clonal evolution during disease development. These studies have contributed to identify a repertoire of numerous disease-causing genes that are altered through different mutational processes. While early-stage breast cancer is a curable disease in about 70% of patients, advanced breast cancer is largely incurable. However, molecular studies have contributed to develop new therapeutic approaches targeting HER2, CDK4/6, PI3K, or involving poly(ADP-ribose) polymerase inhibitors for BRCA mutation carriers and immunotherapy.

Programmed death-ligand 1 gene expression is a prognostic marker in early breast cancer and provides additional prognostic value to 21-gene and 70-gene signatures in estrogen receptor-positive disease

Gene and protein expression of programmed death‐ligand 1 (PD‐L1) are prognostic in early breast cancer (BC), but their prognostic information is inconsistent at least in some biological subgroups. The validated prognostic gene signatures (GS) in BC are mainly based on proliferation and estrogen receptor (ER)‐related genes. Here, we aimed to explore the prognostic capacity of PD‐L1 expression at the protein vs mRNA levels and to investigate the prognostic information that PD‐L1 can potentially add to routinely used GS. Gene expression data were derived from two early BC cohorts (cohort 1: 562 patients; cohort 2: 1081 patients). Tissue microarrays from cohort 1 were immunohistochemically (IHC) stained for PD‐L1 using the SP263 clone. GS scores (21‐gene, 70‐gene) were calculated, and likelihood‐ratio (LR) tests and concordance indices were used to evaluate the additional prognostic information for each signature. The immune cell composition was also evaluated using the CIBERSORT in silico tool. PD‐L1 gene and protein expressions were independently associated with better prognosis. In ER+/HER2− patients, PD‐L1 gene expression provided significant additional prognostic information beyond that of both 21‐GS [LR‐Δχ² = 15.289 and LR‐Δχ² = 8.812, P < 0.01 for distant metastasis‐free interval (DMFI) in cohorts 1 and 2, respectively] and 70‐GS score alone (LR‐Δχ² = 18.198 and LR‐Δχ² = 8.467, P < 0.01 for DMFI in cohorts 1 and 2, respectively). PD‐L1 expression was correlated with IHC‐determined CD3+ cells (r = 0.41, P < 0.001) and with CD8+ (r = 0.62, P < 0.001) and CD4+ memory activated (r = 0.66, P < 0.001) but not with memory resting (r = −0.063, P = 0.14) or regulatory (r = −0.12, P < 0.01) T cells in silico. PD‐L1 gene expression represents a promising favorable prognostic marker and can provide additional prognostic value to 21‐ and 70‐gene scores in ER+/HER2− BC.

Adjuvant chemotherapy could not bring survival benefit to HR-positive, HER2-negative, pT1b-c/N0-1/M0 invasive lobular carcinoma of the breast: a propensity score matching study based on SEER database

BACKGROUND

The benefit of adjuvant chemotherapy in invasive lobular carcinoma (ILC) is still unclear. The objective of the current study was to elucidate the effectiveness of adjuvant chemotherapy in hormone receptor (HR)-positive, human epidermal growth factor receptor 2 (HER2)-negative, pT1b-c/N0-1/M0 ILC.

METHODS

Based on Surveillance, Epidemiology, and End-Results (SEER) database, we identified original 12,334 HR-positive, HER2-negative, pT1b-c/N0-1/M0 ILC patients, who were then divided into adjuvant chemotherapy group and control group. End-points were overall survival (OS) and breast cancer-specific mortality (BCSM). Aiming to minimize the selection bias of baseline characteristics, Propensity Score Matching (PSM) method was used.

RESULTS

In a total of 12,334 patients with HR-positive, HER2-negative, pT1b-c/N0-1/M0 ILC, 1785 patients (14.5%) were allocated into adjuvant chemotherapy group and 10,549 (85.5%) into control group. Used PSM, the 1785 patients in adjuvant chemotherapy group matched to the 1785 patients in control group. By Kaplan-Meier survival analyses, we observed no beneficial effect of adjuvant chemotherapy on OS in both original samples (P = 0.639) and matched samples (P = 0.962), however, ineffective or even contrary results of adjuvant chemotherapy on BCSM both in original samples (P = 0.001) and in matched samples (P = 0.002). In both original and matched multivariate Cox models, we observed ineffectiveness of adjuvant chemotherapy on OS (hazard ratio (HR) for overall survival = 0.82, 95% confidence interval (CI) [0.62-1.09]; P = 0.172 and HR = 0.90, 95%CI [0.65-1.26]; P = 0.553, respectively), unexpectedly promoting effect of adjuvant chemotherapy on BCSM (HR = 2.33, 95%CI [1.47-3.67]; P = 0.001 and HR = 2.41, 95%CI [1.32-4.39]; P = 0.004, respectively). Standard surgery was beneficial to the survival of patients. Lymph node metastasis was detrimental to survival and radiotherapy brought survival benefit in original samples, but two issues had unobvious effect in matched samples.

CONCLUSION

In this study, adjuvant chemotherapy did not improve survival for patients with HR-positive, HER2-negative pT1b-c/N0-1/M0 ILC.

A Novel Prognostic Signature of mRNA-lncRNA in Breast Cancer

Comprehensive genomic testing will be required to identify appropriate targets for the precision therapy of breast cancer. Although RNA sequencing (RNA-seq) is an unparalleled platform for this purpose, existing molecular-based prognostic signatures are not optimal for RNA-seq data. In this study, we analyzed RNA-seq datasets to generate a novel prognostic gene signature for breast cancer patients. RNA-seq and clinical datasets from breast cancer patients were obtained from The Cancer Genome Atlas and randomly assigned to training (n = 379) and test (n = 378) cohorts. Using the training cohort, sequential univariate Cox analysis, robust likelihood-based survival analysis, and stepwise multivariable Cox analysis identified a five-gene signature composed of one long noncoding RNA gene and four protein-coding genes. The five-gene signature was then used to dichotomize patients into risk groups and validated using Kaplan-Meier and multivariable Cox analyses. In the full test cohort, the high-risk group had worse overall survival (hazard ratio [HR] = 4.74, 95% confidence interval [CI] = 2.33-9.64, p < 0.0001) and worse relapse-free survival (HR = 2.26, 95% CI = 1.11-4.61, p = 0.024) than the low-risk group. Similarly, overall survival was worse in the high-risk group within nearly all clinically important subsets, including early stage disease (I/II) (HR = 7.87, 95% CI = 3.69-16.77, p < 0.0001), and luminal A (HR = 4.23, 95% CI = 1.11-16.12, p = 0.034), luminal B (HR = 12.79, 95% CI = 2.74-59.69, p = 0.001), and basal (HR = 18.11, 95% CI = 3.21-102.05, p = 0.001) subtypes. Notably, the five-gene signature exhibited superior prognostic performance compared with the Oncotype DX 21-gene signature. This novel five-gene signature may therefore be a powerful prognostic tool for personalized treatment of breast cancer patients as part of an integrated RNA-seq clinical sequencing program.

Metastatic Conditioning of Myeloid Cells at a Subcutaneous Synthetic Niche Reflects Disease Progression and Predicts Therapeutic Outcomes

Monitoring metastatic events in distal tissues is challenged by their sporadic occurrence in obscure and inaccessible locations within these vital organs. A synthetic biomaterial scaffold can function as a synthetic metastatic niche to reveal the nature of these distal sites. These implanted scaffolds promote tissue ingrowth, which upon cancer initiation is transformed into a metastatic niche that captures aggressive circulating tumor cells. We hypothesized that immune cell phenotypes at synthetic niches reflect the immunosuppressive conditioning within a host that contributes to metastatic cell recruitment and can identify disease progression and response to therapy. We analyzed the expression of 632 immune-centric genes in tissue biopsied from implants at weekly intervals following inoculation. Specific immune populations within implants were then analyzed by single-cell RNA-seq. Dynamic gene expression profiles in innate cells, such as myeloid-derived suppressor cells, macrophages, and dendritic cells, suggest the development of an immunosuppressive microenvironment. These dynamics in immune phenotypes at implants was analogous to that in the diseased lung and had distinct dynamics compared with blood leukocytes. Following a therapeutic excision of the primary tumor, longitudinal tracking of immune phenotypes at the implant in individual mice showed an initial response to therapy, which over time differentiated recurrence versus survival. Collectively, the microenvironment at the synthetic niche acts as a sentinel by reflecting both progression and regression of disease. SIGNIFICANCE: Immune dynamics at biomaterial implants, functioning as a synthetic metastatic niche, provides unique information that correlates with disease progression. GRAPHICAL ABSTRACT: http://cancerres.aacrjournals.org/content/canres/80/3/602/F1.large.jpg.See related commentary by Wolf and Elisseeff, p. 377.

Development of an Immune-Related Prognostic Signature in Breast Cancer

BACKGROUND

Although increased early detection, diagnosis and treatment have improved the outcome of breast cancer patients, prognosis estimation still poses challenges due to the disease heterogeneity. Accumulating data indicated an evident correlation between tumor immune microenvironment and clinical outcomes.

OBJECTIVE

To construct an immune-related signature that can estimate disease prognosis and patient survival in breast cancer.

METHODS

Gene expression profiles and clinical data of breast cancer patients were collected from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, which were further divided into a training set (n = 499), a testing set (n = 234) and a Meta-validation set (n = 519). In the training set, immune-related genes were recognized using combination of gene expression data and ESTIMATE algorithm-derived immune scores. An immune-related prognostic signature was generated with LASSO Cox regression analysis. The prognostic value of the signature was validated in the testing set and the Meta-validation set.

RESULTS

A total of 991 immune-related genes were identified. Twelve genes with non-zero coefficients in LASSO analysis were used to construct an immune-related prognostic signature. The 12-gene signature significantly stratified patients into high and low immune risk groups in terms of overall survival independent of clinical and pathologic factors. The signature also significantly stratified overall survival in clinical defined groups, including stage I/II disease. Several biological processes, such as immune response, were enriched among genes in the immune-related signature. The percentage of M2 macrophage infiltration was significantly different between low and high immune risk groups. Time-dependent ROC curves indicated good performance of our signature in predicting the 1-, 3- and 5-year overall survival for patients from the full TCGA cohort. Furthermore, the composite signature derived by integrating immune-related signature with clinical factors, provided a more accurate estimation of survival relative to molecular signature alone.

CONCLUSION

We developed a 12-gene prognostic signature, providing novel insights into the identification of breast cancer with a high risk of death and assessment of the possibility of immunotherapy incorporation in personalized breast cancer management.

Is there an association between body mass index and 21-gene recurrence score?

PURPOSE

The 21-gene recurrence score (RS) is an established predictor of recurrence for early stage, hormone receptor positive breast cancer. The association between RS and other risk factors such as obesity has not been fully explored. We hypothesized that patients with obesity may present with primary breast cancers with higher recurrence scores.

METHODS

We identified 1546 patients who have body mass index (BMI) recorded around the time of RS assay. Obesity was classified as per CDC definitions of overweight (BMI 25-30 kg/m²) and obesity (BMI >30 kg/m²). RS was assessed as a continuous variable and according to pre- and post-TAILORx classifications. Kaplan Meier survival analysis was employed to assess the interaction between RS and BMI on overall survival (OS) and disease-free survival (DFS).

RESULTS

In univariate analyses, the median RS in patients with overweight was 15, which was significantly lower than the median RS (16) of patients with normal weight (p = 0.03). The overall recurrence rate of patients with obesity was 4.1%, which was significantly worse than the overall recurrence rate of patients with normal and overweight of 2.6% and 1.5%, respectively (p = 0.05). In multivariate analyses using the inverse probability weighted regression adjustment (IPWRA) method to adjust for imbalances between subgroups, patients with overweight or obesity had significantly lower RS than patients with normal weight, correlating to an average decrease in RS value of 2.37 and 1.71, respectively (both p < 0.01). A similar relationship was seen between BMI categories and RS as a categorical variable stratified according to pre- or post-TAILORx categories. This inverse effect was predominantly seen in post-menopausal patients. Despite the generally lower RS in patients with obesity, a high RS in these patients is associated with diminished DFS (p = 0.04).

CONCLUSION

Tumors in post-menopausal women with higher BMI generally have lower RS. DFS is significantly worse in women with obesity whose RS ≥ 30. The reasons for poor outcomes for postmenopausal patients with obesity despite lower presenting RS merits further study.

RNA-seq analysis identified hormone-related genes associated with prognosis of triple negative breast cancer

Triple negative breast cancer (TNBC) is an aggressive subtype of breast cancer that currently lacks effective biomarkers and therapeutic targets required to investigate the diagnosis and treatment of TNBC. Here we performed a comprehensive differential analysis of 165 TNBC samples by integrating RNA-seq data of breast tumor tissues and adjacent normal tissues from both our cohort and The Cancer Genome Atlas (TCGA). Pathway enrichment analysis was conducted to evaluate the biological function of TNBC-specific expressed genes. Further multivariate Cox proportional hazard regression was performed to evaluate the effect of these genes on TNBC prognosis. In this report, we identified a total of 148 TNBC-specific expressed genes that were primarily enriched in mammary gland morphogenesis and hormone levels related pathways, suggesting that mammary gland morphogenesis might play a unique role in TNBC patients differing from other breast cancer types. Further survival analysis revealed that nine genes ( FSIP1, ADCY5, FSD1, HMSD, CMTM5, AFF3, CYP2A7, ATP1A2, and C11orf86) were significantly associated with the prognosis of TNBC patients, while three of them ( ADCY5, CYP2A7, and ATP1A2) were involved in the hormone-related pathways. These findings indicated the vital role of the hormone-related genes in TNBC tumorigenesis and may provide some independent prognostic markers as well as novel therapeutic targets for TNBC.

Tumour-Infiltrating Immune Cell-Based Subtyping and Signature Gene Analysis in Breast Cancer Based on Gene Expression Profiles

Tumour-infiltrating immune cells have been indicated to play an important role in prognosis prediction and therapy sensitivity for breast cancer. In recent years, estimating the abundance of immune cells based on tumour transcriptome data has provided a novel way to analyse the clinical significance of various immune cell subsets. This study integrated breast cancer tissue transcriptome datasets from the Gene Expression Omnibus (GEO), the Cancer Genome Atlas-Breast Cancer (TCGA-BRCA) and the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) cohorts. A novel breast cancer immunotyping and a new prognostic model based on tumour-infiltrating immune cell subsets have been established, aiming to provide new clues regarding prognostic prediction and precision therapy for breast cancer. The key differentially expressed gene between different breast cancer immunotypes has also been identified. We performed unsupervised clustering analysis and construct a novel immunotyping which could classify breast cancer cases into immunotype A (B_cell^high NK^high CD8⁺_T^high CD4⁺_memory_T_activated^high γδT^low Mast_cell_activated^low Neutrophil^low) and immunotype B (B_cell^low NK^low CD8⁺_T^low CD4⁺_memory_T_activated^low γδT^high Mast_cell_activated^high Neutrophil^high) in luminal B, HER2-enriched and basal-like subtypes. The 5-year (85.7% vs. 73.4%) and 10-year OS (75.60% vs. 61.73%) of immunotype A population were significantly higher than those of immunotype B. A novel tumour-infiltrating immune cell-based prognostic model had also been established and the result immunorisk score (IRS) could serve as a new prognostic factor for luminal B, HER2-enriched and basal-like breast cancer. The higher IRS was, the worse prognosis was. We further screened the differentially expressed genes between immunotype A and B and identified a novel breast cancer immune-related gene, prostaglandin D2 synthase (PTGDS) and higher PTGDS mRNA expression level was positively correlated with earlier TNM stage. Immune-related signaling pathways analysis and immune cell subsets correlation analysis revealed that PTGDS expression was related with abundance of B cells, CD4⁺ T cells and CD8⁺ T cells, which was finally validated by immunohistochemical and immunofluorescence staining. We established a novel immunotyping and a tumour-infiltrating immune cell-based prognostic prediction model in luminal B, HER2-enriched and basal-like breast cancer by analyzing the prognostic significance of multiple immune cell subsets. A novel breast cancer immune signature gene PTDGS was discovered, which might serve as a protective prognostic factor and play an important role in breast cancer development and lymphocyte-related immune response.

Genetic determinants of the molecular portraits of epithelial cancers

The ability to characterize and predict tumor phenotypes is crucial to precision medicine. In this study, we present an integrative computational approach using a genome-wide association analysis and an Elastic Net prediction method to analyze the relationship between DNA copy number alterations and an archive of gene expression signatures. Across breast cancers, we are able to quantitatively predict many gene signatures levels within individual tumors with high accuracy based upon DNA copy number features alone, including proliferation status and Estrogen-signaling pathway activity. We can also predict many other key phenotypes, including intrinsic molecular subtypes, estrogen receptor status, and TP53 mutation. This approach is also applied to TCGA Pan-Cancer, which identify repeatedly predictable signatures across tumor types including immune features in lung squamous and basal-like breast cancers. These Elastic Net DNA predictors could also be called from DNA-based gene panels, thus facilitating their use as biomarkers to guide therapeutic decision making.

Effective precision medicine strategies rely on the ability to predict tumour behaviour based on molecular characteristics. Here, the authors build models to predict multiple distinct gene expression patterns using DNA copy number alterations

Cluster analysis on high dimensional RNA-seq data with applications to cancer research - An evaluation study

BACKGROUND

Clustering of gene expression data is widely used to identify novel subtypes of cancer. Plenty of clustering approaches have been proposed, but there is a lack of knowledge regarding their relative merits and how data characteristics influence the performance. We evaluate how cluster analysis choices affect the performance by studying four publicly available human cancer data sets: breast, brain, kidney and stomach cancer. In particular, we focus on how the sample size, distribution of subtypes and sample heterogeneity affect the performance.

RESULTS

In general, increasing the sample size had limited effect on the clustering performance, e.g. for the breast cancer data similar performance was obtained for n = 40 as for n = 330. The relative distribution of the subtypes had a noticeable effect on the ability to identify the disease subtypes and data with disproportionate cluster sizes turned out to be difficult to cluster. Both the choice of clustering method and selection method affected the ability to identify the subtypes, but the relative performance varied between data sets, making it difficult to rank the approaches. For some data sets, the performance was substantially higher when the clustering was based on data from only one sex compared to data from a mixed population. This suggests that homogeneous data are easier to cluster than heterogeneous data and that clustering males and females individually may be beneficial and increase the chance to detect novel subtypes. It was also observed that the performance often differed substantially between females and males.

CONCLUSIONS

The number of samples seems to have a limited effect on the performance while the heterogeneity, at least with respect to sex, is important for the performance. Hence, by analyzing the genders separately, the possible loss caused by having fewer samples could be outweighed by the benefit of a more homogeneous data.

Imaging Phenotypes of Breast Cancer Heterogeneity in Preoperative Breast Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI) Scans Predict 10-Year Recurrence

PURPOSE

Identifying imaging phenotypes and understanding their relationship with prognostic markers and patient outcomes can allow for a noninvasive assessment of cancer. The purpose of this study was to identify and validate intrinsic imaging phenotypes of breast cancer heterogeneity in preoperative breast dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) scans and evaluate their prognostic performance in predicting 10 years recurrence.

EXPERIMENTAL DESIGN

Pretreatment DCE-MRI scans of 95 women with primary invasive breast cancer with at least 10 years of follow-up from a clinical trial at our institution (2002-2006) were retrospectively analyzed. For each woman, a signal enhancement ratio (SER) map was generated for the entire segmented primary lesion volume from which 60 radiomic features of texture and morphology were extracted. Intrinsic phenotypes of tumor heterogeneity were identified via unsupervised hierarchical clustering of the extracted features. An independent sample of 163 women diagnosed with primary invasive breast cancer (2002-2006), publicly available via The Cancer Imaging Archive, was used to validate phenotype reproducibility.

RESULTS

Three significant phenotypes of low, medium, and high heterogeneity were identified in the discovery cohort and reproduced in the validation cohort (P < 0.01). Kaplan-Meier curves showed statistically significant differences (P < 0.05) in recurrence-free survival (RFS) across phenotypes. Radiomic phenotypes demonstrated added prognostic value (c = 0.73) predicting RFS.

CONCLUSIONS

Intrinsic imaging phenotypes of breast cancer tumor heterogeneity at primary diagnosis can predict 10-year recurrence. The independent and additional prognostic value of imaging heterogeneity phenotypes suggests that radiomic phenotypes can provide a noninvasive characterization of tumor heterogeneity to augment personalized prognosis and treatment.

A novel gene panel for prediction of lymph-node metastasis and recurrence in patients with thyroid cancer

BACKGROUND

Although well-differentiated papillary thyroid cancer may remain indolent, lymph node metastases and the recurrence rates are approximately 50% and 20%, respectively. No current biomarkers are able to predict metastatic lymphadenopathy and recurrence in early stage papillary thyroid cancer. Hence, identifying prognostic biomarkers predicting cervical lymph-node metastases would prove very helpful in determining treatment.

METHODS

The database of the Cancer Genome Atlas included 495 papillary thyroid cancer samples. Using this database, we developed a machine learning model to define a gene signature that could predict lymph-node metastasis (N0 or N1). Kruskal-Wallis tests, univariate and multivariate logistic and Cox regression models, and Kaplan-Meier analyses were performed to correlate the gene signature with clinical outcomes.

RESULTS

We identified a panel of 25 genes and constructed a risk score that can differentiate N0 and N1 papillary thyroid cancer samples (P < .001) with a sensitivity of 86%, a specificity of 62%, a positive predictive value of 93%, and a negative predictive value of 42%. This panel represents an independent biomarker to predict metastatic lymphadenopathy (OR = 8.06, P < .001) specifically in patients with T1 lesions (OR = 7.65, P = .002) and disease-free survival (HR = 2.64, P = .043).

CONCLUSION

This novel 25-gene panel may be used as a potential prognostic marker for accurately predicting lymph-node metastasis and disease-free survival in patients with early-stage papillary thyroid cancer.

Region-specific glucocorticoid receptor promoter methylation has both positive and negative prognostic value in patients with estrogen receptor-positive breast cancer

BACKGROUND

The glucocorticoid receptor (NR3C1, GR) is frequently downregulated in breast tumors, and evidence suggests it acts as a tumor suppressor in estrogen receptor-positive (ER+) breast cancer. We previously found that methylation of the GR promoter CpG island represses gene expression and occurs in ER+ breast tumors. In this study, the prognostic and predictive value of GR methylation was examined in ER+ patients from the CCTG MA.12 clinical trial of tamoxifen versus placebo in women with early breast cancer.

METHODS

We developed a targeted multiplex bisulfite next-generation sequencing assay to detect methylation at multiple GR promoter regions in DNA from formalin-fixed paraffin-embedded (FFPE) samples. Following validation in a small cohort of breast tumors, ER+ FFPE tumor samples from MA.12 (n = 208) were tested. Survival analyses evaluated the impact of GR promoter methylation on patient overall survival (OS) and disease-free survival (DFS).

RESULTS

An analysis of TCGA data found that GR methylation is prevalent in ER+ tumors and is associated with decreased gene expression and analysis of public microarray data (KM Plotter) linked decreased GR expression to a poor outcome. In MA.12, two GR promoter regions (U and C) each had prognostic value, but with opposite effects on the outcome. U methylation was associated with poor OS (HR = 1.79, P = 0.041) whereas C methylation was associated with better OS (HR = 0.40, P = 0.040) and DFS (HR = 0.49, P = 0.037). The classification of patients based on the methylation status of the two regions was prognostic for OS (P = 0.006) and DFS (P = 0.041) and revealed a group of patients (U methylated, C unmethylated) with very poor outcomes. Placebo-treated patients in this high-risk group had worse OS (HR = 2.86, P = 0.002) and DFS (HR = 2.09, P = 0.014) compared to the rest of the cohort.

CONCLUSION

Region-specific GR promoter methylation was an independent prognostic marker for patient survival and identified a subset of patients with poor prognosis, particularly without tamoxifen treatment. These findings provide a foundation for future studies into GR methylation as a promising prognostic biomarker in ER+ breast cancer.

GSIAR: gene-subcategory interaction-based improved deep representation learning for breast cancer subcategorical analysis using gene expression, applicable for precision medicine

Tumor subclass detection and diagnosis is inevitable requirement for personalized medical treatment and refinement of the effects that the somatic cells show towards other clinical conditions. The genome of these somatic cells exhibits mutations and genetic variations of the breast cancer cells and helps in understanding the characteristic behavior of the cancer cells. But their analysis is limited to clustering and there is requirement to analyze what else can be done with the data for identifying the tumor subcategory and the stages of subclasses. In this work, we have extended the work with similar data (consisting of 105 breast tumor cell lines) to solve other detection and characterization problems through computation and intelligent representation learning. Most of our work comprises of systematic data cleaning, analysis, and building prediction models with deep computational architectures and establish that the transformed data can help in better distinction of the respective categories. Our main contribution is the novel gene-subcategory interaction-based regularization (GSIAR) based data selection and analysis concept, alongside the prediction, proven to enhance the performance of the classification techniques. Graphical Abstract A graphical abstract of our model - Gene-subcategory interaction affinity-based regularization (GSIAR).

Value of Molecular Classification for Prognostic Assessment of Adrenocortical Carcinoma

IMPORTANCE

The risk stratification of adrenocortical carcinoma (ACC) based on tumor proliferation index and stage is limited. Adjuvant therapy after surgery is recommended for most patients. Pan-genomic studies have identified distinct molecular groups closely associated with outcome.

OBJECTIVE

To compare the molecular classification for prognostic assessment of ACC with other known prognostic factors.

DESIGN, SETTING, AND PARTICIPANTS

In this retrospective biomarker analysis, ACC tumor samples from 368 patients who had undergone surgical tumor removal were collected from March 1, 2005, to September 30, 2015 (144 in the training cohort and 224 in the validation cohort) at 21 referral centers with a median follow-up of 35 months (interquartile range, 18-74 months). Data were analyzed from March 2016 to March 2018.

EXPOSURES

Meta-analysis of pan-genomic studies (transcriptome, methylome, chromosome alteration, and mutational profiles) was performed on the training cohort. Targeted biomarker analysis, including targeted gene expression (BUB1B and PINK1), targeted methylation (PAX5, GSTP1, PYCARD, and PAX6), and targeted next-generation sequencing, was performed on the training and validation cohorts.

MAIN OUTCOMES AND MEASURES

Disease-free survival. Cox proportional hazards regression and C indexes were used to assess the prognostic value of each model.

RESULTS

Of the 368 patients (mean [SD] age, 49 [16] years), 144 were in the training cohort (100 [69.4%] female) and 224 were in the validation cohort (142 [63.4%] female). In the training cohort, pan-genomic measures classified ACC into 3 molecular groups (A1, A2, and A3-B), with 5-year survival of 9% for group A1, 45% for group A2, and 82% for group A3-B (log-rank P < .001). Molecular class was an independent prognostic factor of recurrence in stage I to III ACC after complete surgery (hazard ratio, 55.91; 95% CI, 8.55-365.40; P < .001). The combination of European Network for the Study of Adrenal Tumors (ENSAT) stage, tumor proliferation index, and molecular class provided the most discriminant prognostic model (C index, 0.88). In the validation cohort, the molecular classification, determined by targeted biomarker measures, was confirmed as an independent prognostic factor of recurrence (hazard ratio, 5.96 [95% CI, 1.81-19.58], P = .003 for the targeted classifier combining expression, methylation, and chromosome alterations; and 2.61 [95% CI, 1.31-5.19], P = .006 for the targeted classifier combining methylation, chromosome alterations, and mutational profile). The prognostic value of the molecular markers was limited for patients with stage IV ACC.

CONCLUSIONS AND RELEVANCE

The findings suggest that in localized ACC, targeted classifiers may be used as independent markers of recurrence. The determination of molecular class may improve individual prognostic assessment and thus may spare unnecessary adjuvant treatment.

TRAIL Mediated Signaling in Breast Cancer: Awakening Guardian Angel to Induce Apoptosis and Overcome Drug Resistance

Sequencing technologies have allowed us to characterize highly heterogeneous molecular landscape of breast cancer with unprecedented details. Tremendous breakthroughs have been made in unraveling contributory role of signaling pathways in breast cancer development and progression. It is becoming progressively more understandable that deregulation of spatio-temporally controlled pathways underlie development of resistance against different drugs. TRAIL mediated signaling has attracted considerable appreciation because of its characteristically unique ability to target cancer cells while leaving normal cells intact. Discovery of TRAIL was considered as a paradigm shift in molecular oncology because of its conspicuous ability to selectively target cancer cells. There was an exponential growth in the number of high-quality reports which highlighted cancer targeting ability of TRAIL and scientists worked on the development of TRAIL-based therapeutics and death receptor targeting agonistic antibodies to treat cancer. However, later studies challenged simplistic view related to tumor targeting ability of TRAIL. Detailed mechanistic insights revealed that overexpression of anti-apoptotic proteins, inactivation of pro-apoptotic proteins and downregulation of death receptors were instrumental in impairing apoptosis in cancer cells. Therefore researchers started to give attention to identification of methodologies and strategies to overcome the stumbling blocks associated with TRAIL-based therapeutics. Subsequent studies gave us a clear picture of signaling cascade of TRAIL and how deregulation of different proteins abrogated apoptosis. In this chapter we have attempted to provide an overview of the TRAIL induced signaling, list of proteins frequently deregulated and modern approaches to strategically restore apoptosis in TRAIL-resistant breast cancers.

Cross comparison and prognostic assessment of breast cancer multigene signatures in a large population-based contemporary clinical series

Multigene expression signatures provide a molecular subdivision of early breast cancer associated with patient outcome. A gap remains in the validation of such signatures in clinical treatment groups of patients within population-based cohorts of unselected primary breast cancer representing contemporary disease stages and current treatments. A cohort of 3520 resectable breast cancers with RNA sequencing data included in the population-based SCAN-B initiative (ClinicalTrials.gov ID NCT02306096) were selected from a healthcare background population of 8587 patients diagnosed within the years 2010-2015. RNA profiles were classified according to 19 reported gene signatures including both gene expression subtypes (e.g. PAM50, IC10, CIT) and risk predictors (e.g. Oncotype DX, 70-gene, ROR). Classifications were analyzed in nine adjuvant clinical assessment groups: TNBC-ACT (adjuvant chemotherapy, n = 239), TNBC-untreated (n = 82), HER2+/ER- with anti-HER2+ ACT treatment (n = 110), HER2+/ER+ with anti-HER2 + ACT + endocrine treatment (n = 239), ER+/HER2-/LN- with endocrine treatment (n = 1113), ER+/HER2-/LN- with endocrine + ACT treatment (n = 243), ER+/HER2-/LN+ with endocrine treatment (n = 423), ER+/HER2-/LN+ with endocrine + ACT treatment (n = 433), and ER+/HER2-/LN- untreated (n = 200). Gene signature classification (e.g., proportion low-, high-risk) was generally well aligned with stratification based on current immunohistochemistry-based clinical practice. Most signatures did not provide any further risk stratification in TNBC and HER2+/ER- disease. Risk classifier agreement (low-, medium/intermediate-, high-risk groups) in ER+ assessment groups was on average 50-60% with occasional pair-wise comparisons having <30% agreement. Disregarding the intermediate-risk groups, the exact agreement between low- and high-risk groups was on average ~80-95%, for risk prediction signatures across all assessment groups. Outcome analyses were restricted to assessment groups of TNBC-ACT and endocrine treated ER+/HER2-/LN- and ER+/HER2-/LN+ cases. For ER+/HER2- disease, gene signatures appear to contribute additional prognostic value even at a relatively short follow-up time. Less apparent prognostic value was observed in the other groups for the tested signatures. The current study supports the usage of gene expression signatures in specific clinical treatment groups within population-based breast cancer. It also stresses the need of further development to reach higher consensus in individual patient classifications, especially for intermediate-risk patients, and the targeting of patients where current gene signatures and prognostic variables provide little support in clinical decision-making.

A 23 gene-based molecular prognostic score precisely predicts overall survival of breast cancer patients

BACKGROUND

Although many prognosis-predicting molecular scores for breast cancer have been developed, they are applicable to only limited disease subtypes. We aimed to develop a novel prognostic score that is applicable to a wider range of breast cancer patients.

METHODS

We initially examined The Cancer Genome Atlas breast cancer cohort to identify potential prognosis-related genes. We then performed a meta-analysis of 36 international breast cancer cohorts to validate such genes. We trained artificial intelligence models (random forest and neural network) to predict prognosis precisely, and we finally validated our prediction with the log-rank test.

FINDINGS

We identified a comprehensive list of 184 prognosis-related genes, most of which have been not extensively studied to date. We then established a universal molecular prognostic score (mPS) that relies on the expression status of only 23 of these genes. The mPS system is almost universally applicable to breast cancer patients (log-rank P < 0.05) in a manner independent of platform (microarray or RNA sequencing).

INTERPRETATION

The mPS system is simple and cost-effective to apply and yet is able to reveal previously unrecognized heterogeneity among patient subpopulations in a platform-independent manner. The combination of mPS and clinical stage stratifies prognosis even more precisely and should prove of value for avoidance of overtreatment. In addition, the prognosis-related genes uncovered in this study are potential drug targets. FUND: This work was supported by KAKENHI grants from the Ministry of Education, Culture, Sports, Science, and Technology of Japan to H.S. (19K20403) and to K.I·N (18H05215).

PLC-gamma-1 phosphorylation status is prognostic of metastatic risk in patients with early-stage Luminal-A and -B breast cancer subtypes

Background

Phospholipase Cγ1 (PLCγ1) is highly expressed in human tumours. Our previous studies reported that both stable and inducible PLCγ1 down-regulation can inhibit formation of breast-cancer-derived experimental lung metastasis. Further, high expression of PLCγ1 and its constitutively activated forms (i.e., PLCγ1-pY1253, PLCγ1-pY783) is associated with worse clinical outcome in terms of incidence of distant metastases, but not of local relapse in T1-T2, N0 breast cancer patients.

Methods

In the present retrospective study, we analysed the prognostic role of PLCγ1 in early breast cancer patients stratified according to the St. Gallen criteria and to their menopausal status. PLCγ1-pY1253 and PLCγ1-pY783 protein expression levels were determined by immunohistochemistry on tissue microarrays, and were correlated with patients’ clinical data, using univariate and multivariate statistical analyses.

Results

In our series, the prognostic value of PLCγ1 overexpression was restricted to Luminal type tumours. From multivariate analyses, pY1253-PLCγ1^High was an independent prognostic factor only in postmenopausal patients with Luminal-B tumours (hazard ratio [HR], 2.4; 95% confidence interval [CI], 1.1–5.3; P = 0.034). Conversely, PLCγ1-pY783^High was a remarkably strong risk factor (HR, 20.1; 95% CI, 2.2–178.4; P = 0.003) for pre/perimenopausal patients with Luminal-A tumours.

Conclusions

PLCγ1 overexpression is a strong predictive surrogate marker of development of metastases in early Luminal-A and -B breast cancer patients, being able to discriminate patients with high and low risk of metastases. Therefore, targeting the PLCγ1 pathway can be considered of potential benefit for prevention of metastatic disease.

Electronic supplementary material

The online version of this article (10.1186/s12885-019-5949-x) contains supplementary material, which is available to authorized users.

A novel hydrogen sulfide-releasing donor, HA-ADT, suppresses the growth of human breast cancer cells through inhibiting the PI3K/AKT/mTOR and Ras/Raf/MEK/ERK signaling pathways

Breast cancer is one of the most frequent cancers among women worldwide. Hyaluronic acid (HA) is one of the best biopolymers in terms of safety issues and has been widely used in drug delivery and tissue engineering. 5-(4-hydroxyphenyl)-3H-1,2-dithiol-3-thione (ADT-OH) is a commonly used H2S donor. In this study, we designed and synthesized a conjugate, HA-ADT, by connecting HA with ADT-OH through chemical reactions. Our results indicated that HA-ADT could produce more H2S than NaHS and GYY4137. HA-ADT exerted more potent inhibitory effects than NaHS and GYY4137 in the proliferation, viability, migration, and invasion of human breast cancer cells. Similar trends were observed in the apoptosis and the protein levels of phospho (p)-PI3K, p-AKT, p-mTOR, H-RAS, p-RAF, p-MEK, and p-ERK in human breast cancer cells. Furthermore, HA-ADT exhibited more powerful inhibitory effects on the growth of human breast cancer xenograft tumors in nude mice. In conclusion, HA-ADT could suppress the growth of human breast cancer cells through the inhibition of the PI3K/AKT/mTOR and RAS/RAF/MEK/ERK signaling pathways. HA-ADT and its derivatives might be of great potential in the treatment of different types of cancer.

LncRNA LINC00668 promotes the progression of breast cancer by inhibiting apoptosis and accelerating cell cycle

Objective: To elucidate how lncRNA 00668 (LINC00668) influences the development of breast cancer (BC).

Materials and methods: Genome-wide expression profile of BC and paracancerous tissues were downloaded from The Cancer Genome Atlas (TCGA) and BC tissues and paracancerous tissues enrolled from our hospital for analyzing the expression level of LINC00668 and its correlation with prognosis. GSEA was conducted to analyze the potential functions of LINC00668. By transfection of sh-LINC00668 in BC cells, proliferation, apoptosis, cell cycle and colony formation of BC cells were accessed. Western blot was conducted to detect protein expressions of Ki-67, CDK4, Bcl-2, p21 and genes in AKT/mTOR pathways after LINC00668 knockdown in BC cells. Finally, tumor-bearing nude mice were administrated with BC cells. We compared the proliferative rate in mice with different administrations. Immunohistochemistry was carried out to access expression levels of Ki-67, CDK4, Bcl-2 and P21 in mice.

Results: Both TCGA data and BC tissues harvested from our hospital indicated the higher expression of LINC00668 in BC tissues. LINC00668 expression was negatively correlated to prognosis of BC patients. GSEA pointed out that LINC00668 is enriched in regulations of cell cycle and apoptosis. By transfection of sh-LINC00668 in MDA-MB-231 and MDA-MB-436 cells, the proliferative and colony formation abilities of BC cells decreased. Besides, LINC00668 knockdown in BC cells induced apoptosis and arrested cell cycle. LINC00668 knockdown downregulated Ki-67, CDK4 and Bcl-2, but upregulated p21. The AKT/mTOR pathway was inhibited after LINC00668 silenced. In vivo experiments demonstrated the decreased proliferative rate in tumor-bearing mice administrated with sh-LINC00668 transfected BC cells. Consistently, immunohistochemical results showed lower positive expressions of Ki-67, CDK4 and Bcl-2, but higher positive expression of p21 in sh-LINC00668 group.

Conclusion: LINC00668 is highly expressed in BC tissues and can promote the progression of BC by inhibiting apoptosis and accelerating cell cycle progression.