Guidelines for using sigQC for systematic evaluation of gene signatures

Molecular analysis of archival diagnostic prostate cancer biopsies identifies genomic similarities in cases with progression post-radiotherapy, and those with de novo metastatic disease

BACKGROUND

It is important to identify molecular features that improve prostate cancer (PCa) risk stratification before radical treatment with curative intent. Molecular analysis of historical diagnostic formalin-fixed paraffin-embedded (FFPE) prostate biopsies from cohorts with post-radiotherapy (RT) long-term clinical follow-up has been limited. Utilizing parallel sequencing modalities, we performed a proof-of-principle sequencing analysis of historical diagnostic FFPE prostate biopsies. We compared patients with (i) stable PCa (sPCa) postprimary or salvage RT, (ii) progressing PCa (pPCa) post-RT, and (iii) de novo metastatic PCa (mPCa).

METHODS

A cohort of 19 patients with diagnostic prostate biopsies (n = 6 sPCa, n = 5 pPCa, n = 8 mPCa) and mean 4 years 10 months follow-up (diagnosed 2009-2016) underwent nucleic acid extraction from demarcated malignancy. Samples underwent 3'RNA sequencing (3'RNAseq) (n = 19), nanoString analysis (n = 12), and Illumina 850k methylation (n = 8) sequencing. Bioinformatic analysis was performed to coherently identify differentially expressed genes and methylated genomic regions (MGRs).

RESULTS

Eighteen of 19 samples provided useable 3'RNAseq data. Principal component analysis (PCA) demonstrated similar expression profiles between pPCa and mPCa cases, versus sPCa. Coherently differentially methylated probes between these groups identified ~600 differentially MGRs. The top 50 genes with increased expression in pPCa patients were associated with reduced progression-free survival post-RT (p < 0.0001) in an external cohort.

CONCLUSIONS

3'RNAseq, nanoString and 850k-methylation analyses are each achievable from historical FFPE diagnostic pretreatment prostate biopsies, unlocking the potential to utilize large cohorts of historic clinical samples. Profiling similarities between individuals with pPCa and mPCa suggests biological similarities and historical radiological staging limitations, which warrant further investigation.

A novel network-based method identifies a cuproplasia-related pan-cancer gene signature to predict patient outcome

Copper is a vital micronutrient involved in many biological processes and is an essential component of tumour cell growth and migration. Copper influences tumour growth through a process called cuproplasia, defined as abnormal copper-dependent cell-growth and proliferation. Copper-chelation therapy targeting this process has demonstrated efficacy in several clinical trials against cancer. While the molecular pathways associated with cuproplasia are partially known, genetic heterogeneity across different cancer types has limited the understanding of how cuproplasia impacts patient survival. Utilising RNA-sequencing data from The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) datasets, we generated gene regulatory networks to identify the critical cuproplasia-related genes across 23 different cancer types. From this, we identified a novel 8-gene cuproplasia-related gene signature associated with pan-cancer survival, and a 6-gene prognostic risk score model in low grade glioma. These findings highlight the use of gene regulatory networks to identify cuproplasia-related gene signatures that could be used to generate risk score models. This can potentially identify patients who could benefit from copper-chelation therapy and identifies novel targeted therapeutic strategies.

Metabolic symbiosis between oxygenated and hypoxic tumour cells: An agent-based modelling study

Deregulated metabolism is one of the hallmarks of cancer. It is well-known that tumour cells tend to metabolize glucose via glycolysis even when oxygen is available and mitochondrial respiration is functional. However, the lower energy efficiency of aerobic glycolysis with respect to mitochondrial respiration makes this behaviour, namely the Warburg effect, counter-intuitive, although it has now been recognized as source of anabolic precursors. On the other hand, there is evidence that oxygenated tumour cells could be fuelled by exogenous lactate produced from glycolysis. We employed a multi-scale approach that integrates multi-agent modelling, diffusion-reaction, stoichiometric equations, and Boolean networks to study metabolic cooperation between hypoxic and oxygenated cells exposed to varying oxygen, nutrient, and inhibitor concentrations. The results show that the cooperation reduces the depletion of environmental glucose, resulting in an overall advantage of using aerobic glycolysis. In addition, the oxygen level was found to be decreased by symbiosis, promoting a further shift towards anaerobic glycolysis. However, the oxygenated and hypoxic populations may gradually reach quasi-equilibrium. A sensitivity analysis using Latin hypercube sampling and partial rank correlation shows that the symbiotic dynamics depends on properties of the specific cell such as the minimum glucose level needed for glycolysis. Our results suggest that strategies that block glucose transporters may be more effective to reduce tumour growth than those blocking lactate intake transporters.

m5C regulator-mediated methylation modification phenotypes characterized by distinct tumor microenvironment immune heterogenicity in colorectal cancer

The RNA 5-methylcytosine (m5C) modification has been demonstrated to be an important epigenetic regulator and to impact colorectal cancer (CRC) progression. However, the potential roles of m5C modification in immune cell infiltration in the CRC tumor microenvironment (TME) remain unknown. The m5C modification phenotypes were comprehensively evaluated based on 14 m5C regulators in a meta-CRC cohort of 1792 patients and systematically correlated with the m5C modification phenotypes, immune cell infiltration characteristics and known biological processes. The m5Cscore model was constructed by principal component analysis (PCA) algorithms to quantify the m5C modification phenotypes of individual CRC samples and was used to predict the immunotherapy response. We identified three m5C modification phenotypes associated with distinct clinical outcomes and biological processes among the 1792 meta-CRC patients. Three phenotypes with a highly consistent TME landscape and characteristics were revealed: immune excluded, immune desert and immune inflammation. The meta-CRC patients were divided into high and low m5Cscore subgroups based on the m5Cscore. The m5Cscore was confirmed to have a negative correlation with infiltrating immune cells and PD-L1 expression and a positive correlation with tumor mutation burden (TMB), mutation rate and microsatellite instability (MSI) score. Moreover, patients in the low m5Cscore group had better immunotherapy responses and significant durable survival benefits in independent anti-PD-1/L1 immunotherapy cohorts for the immune checkpoint inhibitor (ICI) therapeutic strategy. This study revealed that m5C modification plays a crucial role in TME composition and complexity. Comprehensive evaluation of the m5C modification phenotypes of individual patients will enhance our understanding of TME characteristics and promote the application of more appropriate and personalized treatment strategies.

Exploiting convergent phenotypes to derive a pan-cancer cisplatin response gene expression signature

Precision medicine offers remarkable potential for the treatment of cancer, but is largely focused on tumors that harbor actionable mutations. Gene expression signatures can expand the scope of precision medicine by predicting response to traditional (cytotoxic) chemotherapy agents without relying on changes in mutational status. We present a new signature extraction method, inspired by the principle of convergent phenotypes, which states that tumors with disparate genetic backgrounds may evolve similar phenotypes independently. This evolutionary-informed method can be utilized to produce consensus signatures predictive of response to over 200 chemotherapeutic drugs found in the Genomics of Drug Sensitivity in Cancer (GDSC) Database. Here, we demonstrate its use by extracting the Cisplatin Response Signature (CisSig). We show that this signature can predict cisplatin response within carcinoma-based cell lines from the GDSC database, and expression of the signatures aligns with clinical trends seen in independent datasets of tumor samples from The Cancer Genome Atlas (TCGA) and Total Cancer Care (TCC) database. Finally, we demonstrate preliminary validation of CisSig for use in muscle-invasive bladder cancer, predicting overall survival in a small cohort of patients who undergo cisplatin-containing chemotherapy. This methodology can be used to produce robust signatures that, with further clinical validation, may be used for the prediction of traditional chemotherapeutic response, dramatically increasing the reach of personalized medicine in cancer.

NF-κB over-activation portends improved outcomes in HPV-associated head and neck cancer

Evolving understanding of head and neck squamous cell carcinoma (HNSCC) is leading to more specific diagnostic disease classifications. Among HNSCC caused by the human papilloma virus (HPV), tumors harboring defects in TRAF3 or CYLD are associated with improved clinical outcomes and maintenance of episomal HPV. TRAF3 and CYLD are negative regulators of NF-κB and inactivating mutations of either leads to NF-κB overactivity. Here, we developed and validated a gene expression classifier separating HPV+ HNSCCs based on NF-κB activity. As expected, the novel classifier is strongly enriched in NF-κB targets leading us to name it the NF-κB Activity Classifier (NAC). High NF-κB activity correlated with improved survival in two independent cohorts. Using NAC, tumors with high NF-κB activity but lacking defects in TRAF3 or CYLD were identified; thus, while TRAF3 or CYLD gene defects identify the majority of tumors with NF-κB activation, unknown mechanisms leading to NF-kB activity also exist. The NAC correctly classified the functional consequences of two novel CYLD missense mutations. Using a reporter assay, we tested these CYLD mutations revealing that their activity to inhibit NF-kB was equivalent to the wild-type protein. Future applications of the NF-κB Activity Classifier may be to identify HPV+ HNSCC patients with better or worse survival with implications for treatment strategies.

Integrated multi-omics analysis reveals miR-20a as a regulator for metabolic colorectal cancer

Single-driver molecular events specific to the metabolic colorectal cancer (CRC) have not been clearly elucidated. Herein, we identified 12 functional miRNAs linked to activated metabolism by integrating multi-omics features in metabolic CRC. These miRNAs exhibited significantly enriched CRC driver miRNAs, significant impacts on CRC cell growth and significantly correlated metabolites. Importantly, miR-20a is minimally expressed in normal colorectal tissues but highly expressed in metabolic CRC, suggesting the potential therapeutic target. Bioinformatics analyses further revealed miR-20a as the most powerful determinant that regulates a cascade of dysregulated events, including Wnt signaling pathway, core enzymes involved in FA metabolism program and triacylglycerol abundances. In vitro assays demonstrated that elevated miR-20a up-regulated FA synthesis enzymes via Wnt/β-catenin signaling, and finally promoted proliferative and migration of metabolic CRC cells. Overall, our study revealed that miR-20a promoted progression of metabolic CRC by regulating FA metabolism and served as a potential target for preventing tumor metastasis.

RosettaSX: Reliable gene expression signature scoring of cancer models and patients

Gene expression signatures have proven their potential to characterize important cancer phenomena like oncogenic signaling pathway activities, cellular origins of tumors, or immune cell infiltration into tumor tissues. Large collections of expression signatures provide the basis for their application to data sets, but the applicability of each signature in a new experimental context must be reassessed. We apply a methodology that utilizes the previously developed concept of coherent expression of genes in signatures to identify translatable signatures before scoring their activity in single tumors. We present a web interface (www.rosettasx.com) that applies our methodology to expression data from the Cancer Cell Line Encyclopaedia and The Cancer Genome Atlas. Configurable heat maps visualize per-cancer signature scores for 293 hand-curated literature-derived gene sets representing a wide range of cancer-relevant transcriptional modules and phenomena. The platform allows users to complement heatmaps of signature scores with molecular information on SNVs, CNVs, gene expression, gene dependency, and protein abundance or to analyze own signatures. Clustered heatmaps and further plots to drill-down results support users in studying oncological processes in cancer subtypes, thereby providing a rich resource to explore how mechanisms of cancer interact with each other as demonstrated by exemplary analyses of 2 cancer types.

Replication catastrophe induced by cyclic hypoxia leads to increased APOBEC3B activity

Tumor heterogeneity includes variable and fluctuating oxygen concentrations, which result in the accumulation of hypoxic regions in most solid tumors. Tumor hypoxia leads to increased therapy resistance and has been linked to genomic instability. Here, we tested the hypothesis that exposure to levels of hypoxia that cause replication stress could increase APOBEC activity and the accumulation of APOBEC-mediated mutations. APOBEC-dependent mutational signatures have been well-characterized, although the physiological conditions which underpin them have not been described. We demonstrate that fluctuating/cyclic hypoxic conditions which lead to replication catastrophe induce the expression and activity of APOBEC3B. In contrast, stable/chronic hypoxic conditions which induce replication stress in the absence of DNA damage are not sufficient to induce APOBEC3B. Most importantly, the number of APOBEC-mediated mutations in patient tumors correlated with a hypoxia signature. Together, our data support the conclusion that hypoxia-induced replication catastrophe drives genomic instability in tumors, specifically through increasing the activity of APOBEC3B.

Radiosensitivity index emerges as a potential biomarker for combined radiotherapy and immunotherapy

In the era of immunotherapy, there lacks of a reliable genomic predictor to identify optimal patient populations in combined radiotherapy and immunotherapy (CRI). The purpose of this study is to investigate whether genomic scores defining radiosensitivity are associated with immune response. Genomic data from Merged Microarray-Acquired dataset (MMD) were established and the Cancer Genome Atlas (TCGA) were obtained. Based on rank-based regression model including 10 genes, radiosensitivity index (RSI) was calculated. A total of 12832 primary tumours across 11 major cancer types were analysed for the association with DNA repair, cellular stemness, macrophage polarisation, and immune subtypes. Additional 585 metastatic tissues were extracted from MET500. RSI was stratified into RSI-Low and RSI-High by a cutpoint of 0.46. Proteomic differential analysis was used to identify significant proteins according to RSI categories. Gene Set Variance Analysis (GSVA) was applied to measure the genomic pathway activity (18 genes for T-cell inflamed activity). Kaplan-Meier analysis was performed for survival analysis. RSI was significantly associated with homologous DNA repair, cancer stemness and immune-related molecular features. Lower RSI was associated with higher fraction of M1 macrophage. Differential proteomic analysis identified significantly higher TAP2 expression in RSI-Low colorectal tumours. In the TCGA cohort, dominant interferon-γ (IFN-γ) response was characterised by low RSI and predicted better response to programmed cell death 1 (PD-1) blockade. In conclusion, in addition to radiation response, our study identified RSI to be associated with various immune-related features and predicted response to PD-1 blockade, thus, highlighting its potential as a candidate biomarker for CRI.

Epigenetic dysregulation of immune-related pathways in cancer: bioinformatics tools and visualization

Cancer immune evasion is one of the hallmarks of carcinogenesis. Cancer cells employ multiple mechanisms to avoid immune recognition and suppress antitumor immune responses. Recently, accumulating evidence has indicated that immune-related pathways are epigenetically dysregulated in cancer. Most importantly, the epigenetic footprint of immune-related pathways is associated with the patient outcome, underscoring the crucial need to understand this process. In this review, we summarize the current evidence for epigenetic regulation of immune-related pathways in cancer and describe bioinformatics tools, informative visualization techniques, and resources to help decipher the cancer epigenome.

Abnormal patterns of genomic chemical modification help tumors elude immunological destruction, but sophisticated computational tools could help identify and overcome these survival mechanisms. Immunotherapy can be a potent weapon against cancer, but many tumors evolve the ability to protect themselves by subduing the immune response. Sungjune Kim and colleagues at the Moffitt Cancer Center, Tampa, USA, have reviewed efforts to study how chemical alterations to DNA that affect gene expression contribute to this process. Considerable evidence indicates a role for a modification called methylation in this immune evasion, and researchers now have access to vast repositories of tumor-specific gene methylation profiles. The authors describe these data resources, and highlight some of the software tools that are helping oncologists to identify patterns in the data that might lead to better therapies.

Protein structure-based gene expression signatures

Gene expression signatures (GES) connect phenotypes to differential messenger RNA (mRNA) expression of genes, providing a powerful approach to define cellular identity, function, and the effects of perturbations. The use of GES has suffered from vague assessment criteria and limited reproducibility. Because the structure of proteins defines the functional capability of genes, we hypothesized that enrichment of structural features could be a generalizable representation of gene sets. We derive structural gene expression signatures (sGES) using features from multiple levels of protein structure (e.g., domain and fold) encoded by the mRNAs in GES. Comprehensive analyses of data from the Genotype-Tissue Expression Project (GTEx), the all RNA-seq and ChIP-seq sample and signature search (ARCHS4) database, and mRNA expression of drug effects on cardiomyocytes show that sGES are useful for characterizing biological phenomena. sGES enable phenotypic characterization across experimental platforms, facilitates interoperability of expression datasets, and describe drug action on cells.

Mitochondrial Inhibitor Atovaquone Increases Tumor Oxygenation and Inhibits Hypoxic Gene Expression in Patients with Non-Small Cell Lung Cancer

PURPOSE

Tumor hypoxia fuels an aggressive tumor phenotype and confers resistance to anticancer treatments. We conducted a clinical trial to determine whether the antimalarial drug atovaquone, a known mitochondrial inhibitor, reduces hypoxia in non-small cell lung cancer (NSCLC).

PATIENTS AND METHODS

Patients with NSCLC scheduled for surgery were recruited sequentially into two cohorts: cohort 1 received oral atovaquone at the standard clinical dose of 750 mg twice daily, while cohort 2 did not. Primary imaging endpoint was change in tumor hypoxic volume (HV) measured by hypoxia PET-CT. Intercohort comparison of hypoxia gene expression signatures using RNA sequencing from resected tumors was performed.

RESULTS

Thirty patients were evaluable for hypoxia PET-CT analysis, 15 per cohort. Median treatment duration was 12 days. Eleven (73.3%) atovaquone-treated patients had meaningful HV reduction, with median change -28% [95% confidence interval (CI), -58.2 to -4.4]. In contrast, median change in untreated patients was +15.5% (95% CI, -6.5 to 35.5). Linear regression estimated the expected mean HV was 55% (95% CI, 24%-74%) lower in cohort 1 compared with cohort 2 (P = 0.004), adjusting for cohort, tumor volume, and baseline HV. A key pharmacodynamics endpoint was reduction in hypoxia-regulated genes, which were significantly downregulated in atovaquone-treated tumors. Data from multiple additional measures of tumor hypoxia and perfusion are presented. No atovaquone-related adverse events were reported.

CONCLUSIONS

This is the first clinical evidence that targeting tumor mitochondrial metabolism can reduce hypoxia and produce relevant antitumor effects at the mRNA level. Repurposing atovaquone for this purpose may improve treatment outcomes for NSCLC.

Derivation and Application of Molecular Signatures to Prostate Cancer: Opportunities and Challenges

Prostate cancer is a high-incidence cancer that requires improved patient stratification to ensure accurate predictions of risk and treatment response. Due to the significant contributions of transcription factors and epigenetic regulators to prostate cancer progression, there has been considerable progress made in developing gene signatures that may achieve this. Some of these are aligned to activities of key drivers such as the androgen receptor, whilst others are more agnostic. In this review, we present an overview of these signatures, the strategies for their derivation, and future perspectives on their continued development and evolution.

Disruption of hypoxia-inducible fatty acid binding protein 7 induces beige fat-like differentiation and thermogenesis in breast cancer cells

BACKGROUND

Humans produce heat through non-shivering thermogenesis, a metabolic process that occurs in inducible beige adipocytes expressing uncoupling protein 1 (UCP1). UCP1 dissipates the proton gradient of the mitochondrial inner membrane and converts that energy into heat. It is unclear whether cancer cells can exhibit autonomous thermogenesis. Previously, we found that the knockdown of hypoxia-inducible fatty acid binding protein 7 (FABP7) increased reactive oxygen species (ROS) in breast cancer cells. ROS are known to induce beige adipocyte differentiation.

METHODS

We investigated the association of tumor hypoxia, FABP7, and UCP1 across breast cancer patients using METABRIC and TCGA data sets. Furthermore, using a breast cancer cell line, HCC1806, we tested the effect of FABP7 knockdown on cellular physiology including thermogenesis.

RESULTS

We found a strong mutual exclusivity of FABP7 and UCP1 expression both in METABRIC and in TCGA, indicating major metabolic phenotypic differences. FABP7 was preferentially distributed in poorly differentiated-, estrogen receptor (ER) negative tumors. In contrast, UCP1 was highly expressed in normal ducts and well-differentiated-, ER positive-, less hypoxic tumors. In the cell line-based experiments, UCP1 and its transcriptional regulators were upregulated upon FABP7 knockdown. UCP1 was induced in about 20% of cancer cells, and the effect was increased further in hypoxia. UCP1 depolarized mitochondrial membranes at the site of expression. UCP1 induction was associated with the increase in proton leak, glycolysis, and maximal respiration, mimicking the typical energy profile of beige adipocytes. Most importantly, UCP1 induction elevated cancer cell temperature associated with increased vulnerability to hypoxia and γ-irradiation.

CONCLUSIONS

We demonstrated that breast cancer cells can undergo thermogenesis through UCP1 induction. Disrupting FABP7-mediated fatty acid metabolism can unlock UCP1-mediated thermogenesis, potentially making it possible to develop therapies to target thermogenesis. Further study would be warranted to investigate the effect of rise in temperature of cancer cells on patients' outcomes and the relationship to other metabolic pathways.

Role of gene signatures combined with pathology in classification of oropharynx head and neck cancer

Treatment personalisation remains an unmet need in oropharynx cancer (OPC). We aimed to determine whether gene expression signatures improved upon clinico-pathological predictors of outcome in OPC. The clinico-pathological predictors, AJCC version 7 (AJCC 7), AJCC 8, and a clinical algorithm, were assessed in 4 public series of OPC (n = 235). Literature review identified 16 mRNA gene expression signatures of radiosensitivity, HPV status, tumour hypoxia, and microsatellite instability. We quality tested signatures using a novel sigQC methodology, and added signatures to clinico-pathological variables as predictors of survival, in univariate and multivariate analyses. AJCC 7 Stage was not predictive of recurrence-free survival (RFS) or overall survival (OS). AJCC 8 significantly predicted RFS and OS. Gene signature quality was highly variable. Among HPV-positive cases, signatures for radiosensitivity, hypoxia, and microsatellite instability revealed significant underlying inter-tumour biological heterogeneity, but did not show prognostic significance when adjusted for clinical covariates. Surprisingly, among HPV-negative cases, a gene signature for HPV status was predictive of survival, even after adjustment for clinical covariates. Across the whole series, several gene signatures representing HPV and microsatellite instability remained significant in multivariate analysis. However, quality control and independent validation remain to be performed to add prognostic information above recently improved clinico-pathological variables.

Clonal selection confers distinct evolutionary trajectories in BRAF-driven cancers

Molecular determinants governing the evolution of tumor subclones toward phylogenetic branches or fixation remain unknown. Using sequencing data, we model the propagation and selection of clones expressing distinct categories of BRAF mutations to estimate their evolutionary trajectories. We show that strongly activating BRAF mutations demonstrate hard sweep dynamics, whereas mutations with less pronounced activation of the BRAF signaling pathway confer soft sweeps or are subclonal. We use clonal reconstructions to estimate the strength of "driver" selection in individual tumors. Using tumors cells and human-derived murine xenografts, we show that tumor sweep dynamics can significantly affect responses to targeted inhibitors of BRAF/MEK or DNA damaging agents. Our study uncovers patterns of distinct BRAF clonal evolutionary dynamics and nominates therapeutic strategies based on the identity of the BRAF mutation and its clonal composition.

RadiationGeneSigDB: a database of oxic and hypoxic radiation response gene signatures and their utility in pre-clinical research

OBJECTIVE

Radiation therapy is among the most effective and widely used modalities of cancer therapy in current clinical practice. In this era of personalized radiation medicine, high-throughput data now provide the means to investigate novel biomarkers of radiation response. Large-scale efforts have identified several radiation response signatures, which poses two challenges, namely, their analytical validity and redundancy of gene signatures.

METHODS

To address these fundamental radiogenomics questions, we curated a database of gene expression signatures predictive of radiation response under oxic and hypoxic conditions. RadiationGeneSigDB has a collection of 11 oxic and 24 hypoxic signatures with the standardized gene list as a gene symbol, Entrez gene ID, and its function. We present the utility of this database by gaining an understanding of hypoxia-associated miRNA by applying a penalized multivariate model; by comparing breast cancer oxic signatures in cell line data vs patient data; and by comparing the similarity of head and neck cancer hypoxia signatures at the pathway level in clinical tumour data.

RESULTS

We obtained a set of miRNA highly associated both positively and negatively to the hypoxia gene signatures, across pan-cancer. In addition, we identified moderate correlations between breast cancer oxic signatures in patient data, and significant differences across molecular subtypes. Moreover, we also found that different set of pathways to be enriched using the head and neck hypoxia signatures, although, they are found to be concordant when applied on the patient data.

CONCLUSION

This valuable, curated repertoire of published gene expression signatures provides motivating case studies for how to search for similarities in radiation response for tumours arising from different tissues across model systems under oxic and hypoxic conditions, and how a well-curated set of gene signatures can be used to generate novel biological hypotheses about the functions of non-coding RNA.

ADVANCES IN KNOWLEDGE

We envision that RadiationSigDB database will help accelerate preclinical radiotherapeutic discovery pipelines in terms of analytical validity of novel biomarkers of radiation response and the need for ensemble approaches to clinical genomic biomarkers.

An integrative view on breast cancer signature panels

Introduction: Breast cancer is heterogeneous with distinct clinical outcomes. Diverse types of markers are available on the market for breast cancer prognosis, diagnosis, and therapeutics, with distinct assay approaches. These, though they enlarge our selection pool for characterizing breast cancer patients and help improve the precision on the therapeutics, they can complicate our understanding and choice of marker panels. Areas covered: This review aims at classifying the commonly used marker panels according to their functionalities and detection approaches, comparing their advantages and disadvantages, and identifying their shared features to gain a comprehensive understanding of the diversified molecular profiles that drive breast cancer heterogeneity. Expert opinion: Our effort will contribute as a guidebook for clinicians on the use of breast cancer signature panels for disease management, and for researchers on the establishment of novel marker panels with improved precision and reduced complexity. We propose that collectively analyzing all available marker panels is equally important as investigating on entirely novel marker panels. Advances in technologies capturing signals from multiple levels are of practical importance in breaking through limitations on translating markers into clinical use.