Meta-analysis of gene expression profiles in breast cancer: toward a unified understanding of breast cancer subtyping and prognosis signatures

Integrated single-cell analysis reveals distinct epigenetic-regulated cancer cell states and a heterogeneity-guided core signature in tamoxifen-resistant breast cancer

BACKGROUND

Inter- and intra-tumor heterogeneity is considered a significant factor contributing to the development of endocrine resistance in breast cancer. Recent advances in single-cell RNA sequencing (scRNA-seq) and single-cell ATAC sequencing (scATAC-seq) allow us to explore inter- and intra-tumor heterogeneity at single-cell resolution. However, such integrated single-cell analysis has not yet been demonstrated to characterize the transcriptome and chromatin accessibility in breast cancer endocrine resistance.

METHODS

In this study, we conducted an integrated analysis combining scRNA-seq and scATAC-seq on more than 80,000 breast tissue cells from two normal tissues (NTs), three primary tumors (PTs), and three tamoxifen-treated recurrent tumors (RTs). A variety of cell types among breast tumor tissues were identified, PT- and RT-specific cancer cell states (CSs) were defined, and a heterogeneity-guided core signature (HCS) was derived through such integrated analysis. Functional experiments were performed to validate the oncogenic role of BMP7, a key gene within the core signature.

RESULTS

We observed a striking level of cell-to-cell heterogeneity among six tumor tissues and delineated the primary to recurrent tumor progression, underscoring the significance of these single-cell level tumor cell clusters classified from scRNA-seq data. We defined nine CSs, including five PT-specific, three RT-specific, and one PT-RT-shared CSs, and identified distinct open chromatin regions of CSs, as well as a HCS of 137 genes. In addition, we predicted specific transcription factors (TFs) associated with the core signature and novel biological/metabolism pathways that mediate the communications between CSs and the tumor microenvironment (TME). We finally demonstrated that BMP7 plays an oncogenic role in tamoxifen-resistant breast cancer cells through modulating MAPK signaling pathways.

CONCLUSIONS

Our integrated single-cell analysis provides a comprehensive understanding of the tumor heterogeneity in tamoxifen resistance. We envision this integrated single-cell epigenomic and transcriptomic measure will become a powerful approach to unravel how epigenetic factors and the tumor microenvironment govern the development of tumor heterogeneity and to uncover potential therapeutic targets that circumvent heterogeneity-related failures.

Nomograms for predicting recurrence of HER2-positive breast cancer with different HR status based on ultrasound and clinicopathological characteristics

PURPOSE

This study aimed to identify ultrasound and clinicopathological characteristics related to recurrence in HER2-positive (HER2+) breast cancer, and to develop nomograms for predicting recurrence.

METHODS

In this dual-center study, we retrospectively enrolled 570 patients with HER2+ breast cancer. The ultrasound and clinicopathological characteristics of hormone receptor (HR)-/HER2+ patients and HR+/HER2+ patients were analyzed separately according to HR status. Eighty percent of the original samples from HR-/HER2+ and HR+/HER2+ patients were extracted by bootstrap sampling as the training cohorts, while the remaining 20% were used as the external validation cohorts. Informative characteristics were screened through univariate and multivariable Cox regression in the training cohorts and used to develop nomograms for predicting recurrence. The predictive accuracy was calculated using Harrell's C-index and calibration curves.

RESULTS

Three informative characteristics (axillary nodal status, calcification, and Adler degree) were identified in HR-/HER2+ patients, and another three (histological grade, axillary nodal status, and echogenic halo) in HR+/HER2+ patients. Based on these, two separate nomograms were constructed to assess recurrence risk. In the training cohorts, the C-index was 0.740 (95% CI: 0.667-0.811) for HR-/HER2+ nomogram, and 0.749 (95% CI: 0.679-0.820) for HR+/HER2+ nomogram. In the validation cohorts, the C-index was 0.708 (95% CI: 0.540-0.877) for HR-/HER2+ group, and 0.705 (95% CI: 0.557-0.853) for HR+/HER2+ group. The calibration curves also indicated the excellent accuracy of the nomograms.

CONCLUSIONS

Ultrasound performance of HER2+ breast cancers with different HR status was significantly different. Nomograms integrating ultrasound and clinicopathological characteristics exhibited favorable performance and have the potential to serve as a reliable method for predicting recurrence in heterogeneous breast cancer.

The role of the ALKBH5 RNA demethylase in invasive breast cancer

BACKGROUND

N6-methyladenosine (m6A) is the most common internal RNA modification and is involved in regulation of RNA and protein expression. AlkB family member 5 (ALKBH5) is a m6A demethylase. Given the important role of m6A in biological mechanisms, m6A and its regulators, have been implicated in many disease processes, including cancer. However, the contribution of ALKBH5 to invasive breast cancer (BC) remains poorly understood. The aim of this study was to evaluate the clinicopathological value of ALKBH5 in BC.

METHODS

Publicly available data were used to investigate ALKBH5 mRNA alterations, prognostic significance, and association with clinical parameters at the genomic and transcriptomic level. Differentially expressed genes (DEGs) and enriched pathways with low or high ALKBH5 expression were investigated. Immunohistochemistry (IHC) was used to assess ALKBH5 protein expression in a large well-characterised BC series (n = 1327) to determine the clinical significance and association of ALKBH5 expression.

RESULTS

Reduced ALKBH5 mRNA expression was significantly associated with poor prognosis and unfavourable clinical parameters. ALKBH5 gene harboured few mutations and/or copy number alternations, but low ALKBH5 mRNA expression was seen. Patients with low ALKBH5 mRNA expression had a number of differentially expressed genes and enriched pathways, including the cytokine-cytokine receptor interaction pathway. Low ALKBH5 protein expression was significantly associated with unfavourable clinical parameters associated with tumour progression including larger tumour size and worse Nottingham Prognostic Index group.

CONCLUSION

This study implicates ALKBH5 in BC and highlights the need for further functional studies to decipher the role of ALKBH5 and RNA m6A methylation in BC progression.

Advancements in Understanding the Hide-and-Seek Strategy of Hibernating Breast Cancer Cells and Their Implications in Oncology from a Broader Perspective: A Comprehensive Overview

Despite recent advancements in technology, breast cancer still poses a significant threat, often resulting in fatal consequences. While early detection and treatments have shown some promise, many breast cancer patients continue to struggle with the persistent fear of the disease returning. This fear is valid, as breast cancer cells can lay dormant for years before remerging, evading traditional treatments like a game of hide and seek. The biology of these dormant breast cancer cells presents a crucial yet poorly understood challenge in clinical settings. In this review, we aim to explore the mysterious world of dormant breast cancer cells and their significant impact on patient outcomes and prognosis. We shed light on the elusive role of the G9a enzyme and many other epigenetic factors in breast cancer recurrence, highlighting its potential as a target for eliminating dormant cancer cells and preventing disease relapse. Through this comprehensive review, we not only emphasise the urgency of unravelling the dynamics of dormant breast cancer cells to improve patient outcomes and advance personalised oncology but also provide a guide for fellow researchers. By clearly outlining the clinical and research gaps surrounding dormant breast cancer cells from a molecular perspective, we aim to inspire further exploration of this critical area, ultimately leading to improved patient care and treatment strategies.

Establishing a model composed of immune-related gene-modules to predict tumor immunotherapy response

At present, tumor immunotherapy has been widely applied to treat various cancers. However, the accuracy of predicting treatment efficacy has not yet achieved a significant breakthrough. This study aimed to construct a prediction model based on the modified WGCNA algorithm to precisely judge the anti-tumor immune response. First, we used a murine colon cancer model to screen corresponding DEGs according to different groups. GSEA was used to analyze the potential mechanisms of the immune-related DEGs (irDEGs) in each group. Subsequently, the intersection of the irDEGs in every group was acquired, and 7 gene-modules were mapped. Finally, 4 gene-modules including cogenes, antiPD-1 immu-genes, chemo immu-genes and comb immu-genes, were selected for subsequent study. Furthermore, a clinical dataset of gastric cancer patients receiving immunotherapy was enrolled, and the irDEGs were identified. A total of 34 vital irDEGs were obtained from the intersections of the vital irDEGs and the four gene-modules. Next, the vital irDEGs were analyzed by the modified WGCNA algorithm, and the correlation coefficients between the 4 gene-modules and the response status to immunotherapy were calculated. Thus, a prediction model based on correlation coefficients was built, and the corresponding model scores were acquired. The AUC calculated according to the model score was 0.727, which was non-inferior to that of the ESTIMATE score and the TIDE score. Meanwhile, the AUC calculated according to the classification of the model scores was 0.705, which was non-inferior to that of the ESTIMATE classification and the TIDE classification. The prediction accuracy of the model was validated in clinical datasets of other cancers.

Association of residual ductal carcinoma in situ with breast cancer treatment outcomes after neoadjuvant chemotherapy according to hormone receptor status

PURPOSE

This research aimed to clarify the impact of residual ductal carcinoma in situ(DCIS) in surgical specimens obtained after neoadjuvant chemotherapy(NAC) for breast cancer on the associated prognosis outcomes.

METHODS

This retrospective study was performed on a cohort of 1,009 patients who achieved pCR following NAC for breast cancer and underwent subsequent breast surgery at a single institution between January 2008 and December 2019. Overall survival, local recurrence-free survival, distant metastasis-free survival, and disease-free survival of the residual and non-residual DCIS groups were the outcomes compared, with further subgroup analysis performed according to hormone receptor status.

RESULTS

260 individuals (25.8%) presented with residual DCIS. Based on a median follow-up of 54.0 months, no significant differences in outcomes were observed between the two groups. Patients with residual DCIS and hormone receptor-negative (HR-) breast cancer demonstrated a significant decrease in distant metastasis-free survival (p = 0.030) compared to those without residual DCIS. In the HR + cohort, no significant difference was observed between the two groups. Multivariate analysis of the HR- cohort demonstrated a significant association between residual DCIS and an elevated risk for distant recurrence (hazard ratio = 2.3, 95% confidence interval = 1.01-5.20, p = 0.047).

CONCLUSIONS

Residual DCIS following NAC may impact breast cancer outcomes, particularly with respect to the occurrence of distant metastasis in HR- patients. Therefore, clinicians must vigilantly monitor patients with residual DCIS after NAC, and further research is needed to expand our understanding of the clinical implications of residual DCIS.

The VEGF-Hypoxia Signature Is Upregulated in Basal-like Breast Tumors from Women of African Ancestry and Associated with Poor Outcomes in Breast Cancer

PURPOSE

Black women experience the highest breast cancer mortality rate compared with women of other racial/ethnic groups. To gain a deeper understanding of breast cancer heterogeneity across diverse populations, we examined a VEGF-hypoxia gene expression signature in breast tumors from women of diverse ancestry.

EXPERIMENTAL DESIGN

We developed a NanoString nCounter gene expression panel and applied it to breast tumors from Nigeria (n = 182) and the University of Chicago (Chicago, IL; n = 161). We also analyzed RNA sequencing data from Nigeria (n = 84) and The Cancer Genome Atlas (TCGA) datasets (n = 863). Patient prognosis was analyzed using multiple datasets.

RESULTS

The VEGF-hypoxia signature was highest in the basal-like subtype compared with other subtypes, with greater expression in Black women compared with White women. In TCGA dataset, necrotic breast tumors had higher scores for the VEGF-hypoxia signature compared with non-necrosis tumors (P < 0.001), with the highest proportion in the basal-like subtype. Furthermore, necrotic breast tumors have higher scores for the proliferation signature, suggesting an interaction between the VEGF-hypoxia signature, proliferation, and necrosis. T-cell gene expression signatures also correlated with the VEGF-hypoxia signature when testing all tumors in TCGA dataset. Finally, we found a significant association of the VEGF-hypoxia profile with poor outcomes when using all patients in the METABRIC (P < 0.0001) and SCAN-B datasets (P = 0.002).

CONCLUSIONS

These data provide further evidence for breast cancer heterogeneity across diverse populations and molecular subtypes. Interventions selectively targeting VEGF-hypoxia and the immune microenvironment have the potential to improve overall survival in aggressive breast cancers that disproportionately impact Black women in the African Diaspora.

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

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

Exploration of the relationship between tumor-infiltrating lymphocyte score and histological grade in breast cancer

BACKGROUND

The histological grade is an important factor in the prognosis of invasive breast cancer and is vital to accurately identify the histological grade and reclassify of Grade2 status in breast cancer patients.

METHODS

In this study, data were collected from 556 invasive breast cancer patients, and then randomly divided into training cohort (n = 335) and validation cohort (n = 221). All patients were divided into actual low risk group (Grade1) and high risk group (Grade2/3) based on traditional histological grade, and tumor-infiltrating lymphocyte score (TILs-score) obtained from multiphoton images, and the TILs assessment method proposed by International Immuno-Oncology Biomarker Working Group (TILs-WG) were also used to differentiate between high risk group and low risk group of histological grade in patients with invasive breast cancer. Furthermore, TILs-score was used to reclassify Grade2 (G2) into G2 /Low risk and G2/High risk. The coefficients for each TILs in the training cohort were retrieved using ridge regression and TILs-score was created based on the coefficients of the three kinds of TILs.

RESULTS

Statistical analysis shows that TILs-score is significantly correlated with histological grade, and is an independent predictor of histological grade (odds ratio [OR], 2.548; 95%CI, 1.648-3.941; P < 0.0001), but TILs-WG is not an independent predictive factor for grade (P > 0.05 in the univariate analysis). Moreover, the risk of G2/High risk group is higher than that of G2/Low risk group, and the survival rate of patients with G2/Low risk is similar to that of Grade1, while the survival rate of patients with G2/High risk is even worse than that of patients with G3.

CONCLUSION

Our results suggest that TILs-score can be used to predict the histological grade of breast cancer and potentially to guide the therapeutic management of breast cancer patients.

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

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

Analysis of Interleukin-6 Gene Variants (rs1800795, rs1800796, rs1554606, rs1800797, rs2069840, rs12700386, and rs2069861) as Prognostic Markers in Breast Cancer: A Systematic Review, Meta-Analysis, and Network Analysis

Interleukin-6 (IL-6) has obviously tumor-promoting and tumor-inhibitory effects and can induce an epithelial-mesenchymal transition phenotype in human breast cancer (BC) cells and implicate its potential to promote BC metastasis. Herein, we aimed to evaluate the association of IL-6 variants (rs1800795, rs1800796, rs1554606, rs1800797, rs2069840, rs12700386, and rs2069861) with the susceptibility to BC. The databases of PubMed/Medline, Web of Science, Scopus, and Cochrane Library were searched until December 19, 2022, without any restrictions. The quality assessment of each study was performed based on the Newcastle-Ottawa Scale tool. The Review Manager 5.3 software presented the effect sizes including odds ratio (OR) along with a 95% confidence interval (CI). Both publication bias and sensitivity analyses were carried out by the Comprehensive Meta-Analysis version 2.0 software. A total of 2,508 records were identified among databases and at last, 27 articles were entered into the meta-analysis. Seven polymorphisms of IL-6 were entered into the analyses. Just rs1800797 polymorphism in the dominant model (OR = 1.51; 95% CI = 1.15-2.00; P = 0.003) and rs2069840 polymorphism in heterozygous (OR = 0.89; 95% CI = 0.81-0.97; P = 0.008) and dominant (OR = 0.91; 95% CI = 0.84-0.99; P = 0.02) models had a significant association with the BC risk. In conclusion, among 7 polymorphisms and despite a few included cases, the present meta-analysis recommended that the AA+GA genotype of rs1800797 polymorphism had a significantly elevated risk and the GC and the CC+GC genotypes of rs2069840 polymorphism had a protective role in the BC patients.

Immunohistochemical-Based Molecular Subtypes of Female Breast Cancer: A Retrospective Cross-Sectional Study at Cheikh Khalifa Hospital in Casablanca, Morocco

INTRODUCTION

Breast cancer is a major public health concern worldwide and the most prevalent form of cancer in Morocco. This study aimed to describe the histological and immunohistochemical profiles of breast cancer in women admitted to Cheikh Khalifa Hospital in Casablanca, Morocco.

METHODS

This is a retrospective cross-sectional study. All histologically confirmed female breast cancer cases diagnosed between January 2017 and May 2021 at the Cheikh Khalifa University Hospital were included in the study. Data were collected from patient electronic medical records. Using an electronic sheet, information was collected about the socio-demographic characteristics of the patients, clinical features, histopathology, molecular characteristics, treatment received, and progression.

RESULTS

Invasive carcinoma of no special type was the most common type of cancer accounting for 90.7% of all cases. The majority of the tumors (56.1%) were grade II tumors. About 42.1% of tumors were lymph node-positive and only 13.4% developed distant metastasis. Immunohistochemical data revealed that 57.9% of the tumors in this study were hormone receptor-positive (ER+ and PR+), 74.4% were estrogen receptor-positive (ER+), 58.5% were progesterone receptor positive (PR+), and 18.9% were HER2 positive (HER2+). The most common molecular subtype was Luminal A-like (43.9%). A statistically significant difference was found in histological grades across the four molecular subtypes (P < 0.001).

CONCLUSIONS

Our findings should be used to guide breast cancer management policies in Morocco. Larger cohort studies are needed to determine the specificity of the breast cancer profile in Morocco as well as the epidemiological risk factors specific to every subtype.

Ferroptosis Inducers Upregulate PD-L1 in Recurrent Triple-Negative Breast Cancer

(1) Background: Triple-negative breast cancer (TNBC) is a distinct subgroup of breast cancer presenting a high level of recurrence, and neo-adjuvant chemotherapy is beneficial in its therapy management. Anti-PD-L1 immunotherapy improves the effect of neo-adjuvant therapy in TNBC. (2) Methods: Immune-modulation and ferroptosis-related R-packages were developed for integrative omics analyses under ferroptosis-inducer treatments: TNBC cells stimulated with ferroptosis inducers (GSE173905, GSE154425), single cell data (GSE191246) and mass spectrometry on breast cancer stem cells. Clinical association analyses were carried out with breast tumors (TCGA and METABRIC cohorts). Protein-level validation was investigated through protein atlas proteome experiments. (3) Results: Erastin/RSL3 ferroptosis inducers upregulate CD274 in TNBC cells (MDA-MB-231 and HCC38). In breast cancer, CD274 expression is associated with overall survival. Breast tumors presenting high expression of CD274 upregulated some ferroptosis drivers associated with prognosis: IDO1, IFNG and TNFAIP3. At the protein level, the induction of Cd274 and Tnfaip3 was confirmed in breast cancer stem cells under salinomycin treatment. In a 4T1 tumor treated with cyclophosphamide, the single cell expression of Cd274 was found to increase both in myeloid- and lymphoid-infiltrated cells, independently of its receptor Pdcd1. The CD274 ferroptosis-driver score computed on a breast tumor transcriptome stratified patients on their prognosis: low score was observed in the basal subgroup, with a higher level of recurrent risk scores (oncotypeDx, ggi and gene70 scores). In the METABRIC cohort, CD274, IDO1, IFNG and TNFAIP3 were found to be overexpressed in the TNBC subgroup. The CD274 ferroptosis-driver score was found to be associated with overall survival, independently of TNM classification and age diagnosis. The tumor expression of CD274, TNFAIP3, IFNG and IDO1, in a biopsy of breast ductal carcinoma, was confirmed at the protein level (4) Conclusions: Ferroptosis inducers upregulate PD-L1 in TNBC cells, known to be an effective target of immunotherapy in high-risk early TNBC patients who received neo-adjuvant therapy. Basal and TNBC tumors highly expressed CD274 and ferroptosis drivers: IFNG, TNFAIP3 and IDO1. The CD274 ferroptosis-driver score is associated with prognosis and to the risk of recurrence in breast cancer. A potential synergy of ferroptosis inducers with anti-PD-L1 immunotherapy is suggested for recurrent TNBC.

Tamoxifen Response at Single-Cell Resolution in Estrogen Receptor-Positive Primary Human Breast Tumors

PURPOSE

In estrogen receptor-positive (ER+)/HER2- breast cancer, multiple measures of intratumor heterogeneity are associated with a worse response to endocrine therapy. We sought to develop a novel experimental model to measure heterogeneity in response to tamoxifen treatment in primary breast tumors.

EXPERIMENTAL DESIGN

To investigate heterogeneity in response to treatment, we developed an operating room-to-laboratory pipeline for the collection of live normal breast specimens and human tumors immediately after surgical resection for processing into single-cell workflows for experimentation and genomic analyses. Live primary cell suspensions were treated ex vivo with tamoxifen (10 μmol/L) or control media for 12 hours, and single-cell RNA libraries were generated using the 10X Genomics droplet-based kit.

RESULTS

In total, we obtained and processed normal breast tissue from two women undergoing reduction mammoplasty and tumor tissue from 10 women with ER+/HER2- invasive breast carcinoma. We demonstrate differences in tamoxifen response by cell type and identify distinctly responsive and resistant subpopulations within the malignant cell compartment of human tumors. Tamoxifen resistance signatures from resistant subpopulations predict poor outcomes in two large cohorts of ER+ breast cancer patients and are enriched in endocrine therapy-resistant tumors.

CONCLUSIONS

This novel ex vivo model system now provides the foundation to define responsive and resistant subpopulations within heterogeneous human tumors, which can be used to develop precise single cell-based predictors of response to therapy and to identify genes and pathways driving therapeutic resistance.

Leveraging hypoxia in triple-negative breast cancer as a promising treatment strategy

Current treatment strategies for triple-negative breast cancer (TNBC) are based upon conventional chemotherapy, immunotherapy, or a combination of both. The treatment regimen for chemotherapy is often a combination of two or more drugs, either dose dense or low dose for synergy. Anthracyclines, alkylating agents, antimicrotubule agents, and antimetabolites for early-stage TNBC; and antimetabolites, non-taxane microtubule inhibitors, and cross-linker platinums for late-stage TNBC are usually administered in the clinical setting. Newer options for patients with advanced TNBC, such as poly (ADP-ribose) polymerase (PARP) inhibitors and immune checkpoint inhibitors, have recently emerged for cases where surgery is not a viable option and the disease has metastasized. This review outlines the current trends in hypoxia-inspired treatment strategies for TNBC with a focus on clinical trials.

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

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

SOX4-SMARCA4 complex promotes glycolysis-dependent TNBC cell growth through transcriptional regulation of Hexokinase 2

Tumor cells rely on increased glycolytic capacity to promote cell growth and progression. While glycolysis is known to be upregulated in the majority of triple negative (TNBC) or basal-like subtype breast cancers, the mechanism remains unclear. Here, we used integrative genomic analyses to identify a subset of basal-like tumors characterized by increased expression of the oncogenic transcription factor SOX4 and its co-factor the SWI/SNF ATPase SMARCA4. These tumors are defined by unique gene expression programs that correspond with increased tumor proliferation and activation of key metabolic pathways, including glycolysis. Mechanistically, we demonstrate that the SOX4-SMARCA4 complex mediates glycolysis through direct transcriptional regulation of Hexokinase 2 (HK2) and that aberrant HK2 expression and altered glycolytic capacity are required to mediate SOX4-SMARCA4-dependent cell growth. Collectively, we have defined the SOX4-SMARCA4-HK2 signaling axis in basal-like breast tumors and established that this axis promotes metabolic reprogramming which is required to maintain tumor cell growth.

Differences in clinical outcomes between HER2-negative and HER2-positive luminal B breast cancer

The clinical features and prognosis of breast cancer can vary widely, depending on the molecular subtype. Luminal B breast cancers are usually either estrogen receptor-positive and/or progesterone receptor-positive with high proliferation of Ki67 index, or HER2 positive (HER2+). The authors compared the clinicopathologic factors and survival rates of different subtypes of luminal B breast cancer according to HER2 status. Between 2009 and 2013, 1131 cases of breast cancer were reviewed and characterized as 1 of 4 different molecular subtypes based on their immunohistochemical results: luminal A, luminal B, HER2+, or triple-negative breast cancer. From these, luminal B breast cancers were extracted and the clinical features and prognosis of the HER2- and the HER2 + subtypes were compared. Survival differed significantly based on the molecular subtype regardless of whether or not the patient received treatment with neoadjuvant chemotherapy. While patients with HER2- luminal B breast cancer who received neoadjuvant chemotherapy had better prognoses, patients with HER2 + luminal B breast cancer who did not receive neoadjuvant chemotherapy had better prognoses. Luminal B breast cancers showed different clinical outcomes and survival rates according to HER2 gene overexpression type. Physicians should consider these results when they establish a treatment strategy.

A three-gene signature marks the time to locoregional recurrence in luminal-like breast cancer

BACKGROUND

Gene expression profiling (GEP)-based prognostic signatures are being rapidly integrated into clinical decision making for systemic management of breast cancer patients. However, GEP remains relatively underdeveloped for locoregional risk assessment. Yet, locoregional recurrence (LRR), especially early after surgery, is associated with poor survival.

PATIENTS AND METHODS

GEP was carried out on two independent luminal-like breast cancer cohorts of patients developing early (≤5 years after surgery) or late (>5 years) LRR and used, by a training and testing approach, to build a gene signature able to intercept women at risk of developing early LRR. The GEP data of two in silico datasets and of a third independent cohort were used to explore its prognostic value.

RESULTS

Analysis of the first two cohorts led to the identification of three genes, CSTB, CCDC91 and ITGB1, whose expression, derived by principal component analysis, generated a three-gene signature significantly associated with early LRR in both cohorts (P value <0.001 and 0.005, respectively), overcoming the discriminatory capability of age, hormone receptor status and therapy. Remarkably, the integration of the signature with these clinical variables led to an area under the curve of 0.878 [95% confidence interval (CI) 0.810-0.945]. In in silico datasets we found that the three-gene signature retained its association, showing higher values in the early relapsed patients. Moreover, in the third additional cohort, the signature significantly associated with relapse-free survival (hazard ratio 1.56, 95% CI 1.04-2.35).

CONCLUSIONS

Our three-gene signature represents a new exploitable tool to aid treatment choice in patients with luminal-like breast cancer at risk of developing early recurrence.

PAK1 copy number in breast cancer-Associations with proliferation and molecular subtypes

INTRODUCTION

P21-activated kinase 1 (PAK1) is known to be overexpressed in several human tumour types, including breast cancer (BC). It is located on chromosome 11 (11q13.5-q14.1) and plays a significant role in proliferation in BC. In this study we aimed to assess PAK1 gene copy number (CN) in primary breast tumours and their corresponding lymph node metastases, and associations between PAK1 CN and proliferation status, molecular subtype, and prognosis. In addition, we aimed to study associations between CNs of PAK1 and CCND1. Both genes are located on the long arm of chromosome 11 (11q13).

METHODS

Fluorescence in situ hybridization for PAK1 and Chromosome enumeration probe (CEP)11 were used on tissue microarray sections from a series of 512 BC cases. Copy numbers were estimated by counting the number of fluorescent signals for PAK1 and CEP11 in 20 tumour cell nuclei. Pearson's x2 test was performed to assess associations between PAK1 CN and tumour features, and between PAK1 and CCND1 CNs. Cumulative risk of death from BC and hazard ratios were estimated in analysis of prognosis.

RESULTS

We found mean PAK1 CN ≥4<6 in 26 (5.1%) tumours, and CN ≥ 6 in 22 (4.3%) tumours. The proportion of cases with copy number increase (mean CN ≥4) was highest among HER2 type and Luminal B (HER2-) tumours. We found an association between PAK1 CN increase, and high proliferation, and high histological grade, but not prognosis. Of cases with PAK1 CN ≥ 6, 30% also had CCND1 CN ≥ 6.

CONCLUSIONS

PAK1 copy number increase is associated with high proliferation and high histological grade, but not with prognosis. PAK1 CN increase was most frequent in the HER2 type and Luminal B (HER2-) subtype. PAK1 CN increase is associated with CN increase of CCND1.

A new prognostic model including immune biomarkers, genomic proliferation tumor markers (AURKA and MYBL2) and clinical-pathological features optimizes prognosis in neoadjuvant breast cancer patients

Background

Up to 30% of breast cancer (BC) patients treated with neoadjuvant chemotherapy (NCT) will relapse. Our objective was to analyze the predictive capacity of several markers associated with immune response and cell proliferation combined with clinical parameters.

Methods

This was a single-center, retrospective cohort study of BC patients treated with NCT (2001-2010), in whom pretreatment biomarkers were analyzed: neutrophil-to-lymphocyte ratio (NLR) in peripheral blood, CD3+ tumor-infiltrating lymphocytes (TILs), and gene expression of AURKA, MYBL2 and MKI67 using qRT-PCR.

Results

A total of 121 patients were included. Median followup was 12 years. In a univariate analysis, NLR, TILs, AURKA, and MYBL2 showed prognostic value for overall survival. In multivariate analyses, including hormone receptor, HER2 status, and response to NCT, NLR (HR 1.23, 95% CI 1.01-1.75), TILs (HR 0.84, 95% CI 0.73-0.93), AURKA (HR 1.05, 95% CI 1.00-1.11) and MYBL2 (HR 1.19, 95% CI 1.05-1.35) remained as independent predictor variables.

Conclusion

Consecutive addition of these biomarkers to a regression model progressively increased its discriminatory capacity for survival. Should independent cohort studies validate these findings, management of early BC patients may well be changed.

Combining histological grade, TILs, and the PD-1/PD-L1 pathway to identify immunogenic tumors and de-escalate radiotherapy in early breast cancer: a secondary analysis of a randomized clinical trial

BACKGROUND

The implementation of immunological biomarkers for radiotherapy (RT) individualization in breast cancer requires consideration of tumor-intrinsic factors. This study aimed to investigate whether the integration of histological grade, tumor-infiltrating lymphocytes (TILs), programmed cell death protein-1 (PD-1), and programmed death ligand-1 (PD-L1) can identify tumors with aggressive characteristics that can be downgraded regarding the need for RT.

METHODS

The SweBCG91RT trial included 1178 patients with stage I-IIA breast cancer, randomized to breast-conserving surgery with or without adjuvant RT, and followed for a median time of 15.2 years. Immunohistochemical analyses of TILs, PD-1, and PD-L1 were performed. An activated immune response was defined as stromal TILs ≥10% and PD-1 and/or PD-L1 expression in ≥1% of lymphocytes. Tumors were categorized as high-risk or low-risk using assessments of histological grade and proliferation as measured by gene expression. The risk of ipsilateral breast tumor recurrence (IBTR) and benefit of RT were then analyzed with 10 years follow-up based on the integration of immune activation and tumor-intrinsic risk group.

RESULTS

Among high-risk tumors, an activated immune infiltrate was associated with a reduced risk of IBTR (HR 0.34, 95% CI 0.16 to 0.73, p=0.006). The incidence of IBTR in this group was 12.1% (5.6-25.0) without RT and 4.4% (1.1-16.3) with RT. In contrast, the incidence of IBTR in the high-risk group without an activated immune infiltrate was 29.6% (21.4-40.2) without RT and 12.8% (6.6-23.9) with RT. Among low-risk tumors, no evidence of a favorable prognostic effect of an activated immune infiltrate was seen (HR 2.0, 95% CI 0.87 to 4.6, p=0.100).

CONCLUSIONS

Integrating histological grade and immunological biomarkers can identify tumors with aggressive characteristics but a low risk of IBTR despite a lack of RT boost and systemic therapy. Among high-risk tumors, the risk reduction of IBTR conferred by an activated immune infiltrate is comparable to treatment with RT. These findings may apply to cohorts dominated by estrogen receptor-positive tumors.

Early assessment of circulating exosomal lncRNA-GC1 for monitoring neoadjuvant chemotherapy response in gastric cancer

BACKGROUND

The timing of surgery for patients with gastric cancer (GC) who undergo neoadjuvant chemotherapy (neoCT) was mainly guided by serial radiologic imaging. However, an earlier assessment was indispensable to avoid delayed treatment for nonresponders and excessive toxicity for responders. Our previous study has identified circulating extracellular vesicles-derived lncRNA-GC1 as a biomarker for early detection and monitoring progression of GC. However, the potential role of neoCT remains poorly understood.

METHODS

In this explorative biomarker analysis, we conducted a multi-cohort study to examine longitudinal levels of circulating extracellular vesicles-derived lncRNA-GC1 in 798 patients enrolled in the RESONANCE study (NCT01583361). Both circulating extracellular vesicles-derived lncRNA-GC1 and traditional gastrointestinal biomarkers were assessed at defined time nodes. Computed tomography (CT) scans were performed before treatment and 8-10 weeks and assessed based on the RECIST criteria.

RESULTS

Circulating extracellular vesicles-derived lncRNA-GC1 could be detected in 96.3% of patients at baseline, and significant reductions were observed before cycle 2 (P<0.0001). Levels of circulating extracellular vesicles-derived lncRNA-GC1 showed a stronger correlation with tumor burden and exhibited earlier dynamic changes than the traditional gastrointestinal biomarkers during the first cycle of neoCT. Strong agreement was observed between circulating extracellular vesicles-derived lncRNA-GC1 response (reduction >50%) and radiographic response (Cohen's κ, 0.704). Importantly, circulating extracellular vesicles-derived lncRNA-GC1 maintained predictive value in two external cohorts. Patients with circulating extracellular vesicles-derived lncRNA-GC1 response showed superior disease-free survival [hazard ratio (HR), 0.6238; 95% CI, 0.4095-0.9501; P=0.0118] and overall survival (HR, 0.6131; 95% CI, 0.4016-0.9358; P=0.0090).

CONCLUSION

Circulating extracellular vesicles-derived lncRNA-GC1 is an early marker of neoCT efficacy and predicts superior survival in GC patients treated with neoCT.

Tamoxifen Response at Single Cell Resolution in Estrogen Receptor-Positive Primary Human Breast Tumors

In ER+/HER2- breast cancer, multiple measures of intra-tumor heterogeneity are associated with worse response to endocrine therapy. To investigate heterogeneity in response to treatment, we developed an operating room-to-laboratory pipeline for the collection of live human tumors and normal breast specimens immediately after surgical resection for processing into single-cell workflows for experimentation and genomic analyses. We demonstrate differences in tamoxifen response by cell type and identify distinctly responsive and resistant subpopulations within the malignant cell compartment of human tumors. Tamoxifen resistance signatures from 3 distinct resistant subpopulations are prognostic in large cohorts of ER+ breast cancer patients and enriched in endocrine therapy resistant tumors. This novel ex vivo model system now provides a foundation to define responsive and resistant sub-populations within heterogeneous tumors, to develop precise single cell-based predictors of response to therapy, and to identify genes and pathways driving resistance to therapy.

Molecular breast cancer subtype identification using photoacoustic spectral analysis and machine learning at the biomacromolecular level

Breast cancer threatens the health of women worldwide, and its molecular subtypes largely determine the therapy and prognosis of patients. However, an uncomplicated and accurate method to identify subtypes is currently lacking. This study utilized photoacoustic spectral analysis (PASA) based on the partial least squares discriminant algorithm (PLS-DA) to identify molecular breast cancer subtypes at the biomacromolecular level in vivo. The area of power spectrum density (APSD) was extracted to semi-quantify the biomacromolecule content. The feature wavelengths were obtained via the variable importance in projection (VIP) score and the selectivity ratio (Sratio), to identify the biomarkers. The PASA achieved an accuracy of 84%. Most of the feature wavelengths fell into the collagen-dominated absorption waveband, which was consistent with the histopathological results. This paper proposes a successful method for identifying molecular breast cancer subtypes and proves that collagen can be treated as a biomarker for molecular breast cancer subtyping.

Prognostic significance of pretreatment 18F-fluorodeoxyglucose positron emission tomography/computed tomography in patients with T2N1 hormone receptor-positive, ERBB2-negative breast cancer who underwent adjuvant chemotherapy

PURPOSE

To determine whether tumor uptake of ¹⁸F-fluorodeoxyglucose (¹⁸F-FDG) is associated with invasive disease-free survival (IDFS) in patients with hormone receptor (HR)-positive ERBB2-negative early-stage breast cancer treated with adjuvant chemotherapy.

METHODS

This is a single-center cohort study of women with breast cancer who underwent surgery between 2008 and 2015 at Asan Medical Center, Seoul, Korea. Patients were enrolled if they were diagnosed with HR-positive ERBB2-negative breast cancer with histology of invasive ductal carcinoma, had an American Joint Committee on Cancer pathologic tumor stage of T2N1 with 1-3 positive axillary nodes, underwent preoperative ¹⁸F-FDG positron emission tomography/computed tomography (PET/CT), and underwent breast cancer surgery followed by anthracycline- or taxane-based adjuvant chemotherapy. The primary outcome measure was IDFS. The maximum standardized uptake value (SUVmax) was dichotomized using a predefined cut-off of 4.14.

RESULTS

A total of 129 patients were included. The median follow-up period for IDFS in those without recurrence was 82 months (interquartile range, 65-106). Multivariable Cox analysis showed that SUVmax was independently associated with IDFS [adjusted hazard ratio 2.49; 95% confidence interval (CI), 1.06-5.84]. Ten-year IDFS estimates via the Kaplan-Meier method were 0.60 (95% CI, 0.42-0.74) and 0.82 (95% CI, 0.65-0.91) for high and low SUVmax groups, respectively. The overall association between SUVmax and IDFS appeared to be consistent across subgroups divided according to age, progesterone receptor status, histologic grade, or presence of lymphovascular invasion.

CONCLUSION

High SUVmax on preoperative ¹⁸F-FDG PET/CT was independently associated with reduced long-term IDFS in T2N1 HR-positive ERBB2-negative breast cancer patients who underwent adjuvant chemotherapy.

Circulating tumor DNA reveals complex biological features with clinical relevance in metastatic breast cancer

Liquid biopsy has proven valuable in identifying individual genetic alterations; however, the ability of plasma ctDNA to capture complex tumor phenotypes with clinical value is unknown. To address this question, we have performed 0.5X shallow whole-genome sequencing in plasma from 459 patients with metastatic breast cancer, including 245 patients treated with endocrine therapy and a CDK4/6 inhibitor (ET + CDK4/6i) from 2 independent cohorts. We demonstrate that machine learning multi-gene signatures, obtained from ctDNA, identify complex biological features, including measures of tumor proliferation and estrogen receptor signaling, similar to what is accomplished using direct tumor tissue DNA or RNA profiling. More importantly, 4 DNA-based subtypes, and a ctDNA-based genomic signature tracking retinoblastoma loss-of-heterozygosity, are significantly associated with poor response and survival outcome following ET + CDK4/6i, independently of plasma tumor fraction. Our approach opens opportunities for the discovery of additional multi-feature genomic predictors coming from ctDNA in breast cancer and other cancer-types.

Pharmacovigilance Analysis of Heart Failure Associated With Anti-HER2 Monotherapies and Combination Regimens for Cancer

Background

Trastuzumab improves outcomes in patients with HER2-overexpressing malignancies but is associated with decreases in left ventricular ejection fraction. Heart failure (HF) risks from other anti-HER2 therapies are less clear.

Objectives

Using World Health Organization pharmacovigilance data, the authors compared HF odds across anti-HER2 regimens.

Methods

In VigiBase, 41,976 patients had adverse drug reactions (ADRs) with anti-HER2 monoclonal antibodies (trastuzumab, n = 16,900; pertuzumab, n = 1,856), antibody-drug conjugates (trastuzumab emtansine [T-DM1], n = 3,983; trastuzumab deruxtecan, n = 947), and tyrosine kinase inhibitors (afatinib, n = 10,424; lapatinib, n = 5,704; neratinib, n = 1,507; tucatinib, n = 655); additionally, 36,052 patients had ADRs with anti-HER2-based combination regimens. Most patients had breast cancer (monotherapies, n = 17,281; combinations, n = 24,095). Outcomes included comparison of HF odds with each monotherapy relative to trastuzumab, within each therapeutic class, and among combination regimens.

Results

Of 16,900 patients with trastuzumab-associated ADRs, 2,034 (12.04%) had HF reports (median time to onset 5.67 months; IQR: 2.85-9.32 months) compared with 1% to 2% with antibody-drug conjugates. Trastuzumab had higher odds of HF reporting relative to other anti-HER2 therapies collectively in the overall cohort (reporting OR [ROR]: 17.37; 99% CI: 14.30-21.10) and breast cancer subgroup (ROR: 17.10; 99% CI: 13.12-22.27). Pertuzumab/T-DM1 had 3.4 times higher odds of HF reporting than T-DM1 monotherapy; tucatinib/trastuzumab/capecitabine had similar odds as tucatinib. Among metastatic breast cancer regimens, HF odds were highest with trastuzumab/pertuzumab/docetaxel (ROR: 1.42; 99% CI: 1.17-1.72) and lowest with lapatinib/capecitabine (ROR: 0.09; 99% CI: 0.04-0.23).

Conclusions

Trastuzumab and pertuzumab/T-DM1 had higher odds of HF reporting than other anti-HER2 therapies. These data provide large-scale, real-world insight into which HER2-targeted regimens would benefit from left ventricular ejection fraction monitoring.

The Evolution of Ki-67 and Breast Carcinoma: Past Observations, Present Directions, and Future Considerations

The 1983 discovery of a mouse monoclonal antibody-the Ki-67 antibody-that recognized a nuclear antigen present only in proliferating cells represented a seminal discovery for the pathologic assessment of cellular proliferation in breast cancer and other solid tumors. Cellular proliferation is a central determinant of prognosis and response to cytotoxic chemotherapy in patients with breast cancer, and since the discovery of the Ki-67 antibody, Ki-67 has evolved as an important biomarker with both prognostic and predictive potential in breast cancer. Although there is universal recognition among the international guideline recommendations of the value of Ki-67 in breast cancer, recommendations for the actual use of Ki-67 assays in the prognostic and predictive evaluation of breast cancer remain mixed, primarily due to the lack of assay standardization and inconsistent inter-observer and inter-laboratory reproducibility. The treatment of high-risk ER-positive/human epidermal growth factor receptor-2 (HER2) negative breast cancer with the recently FDA-approved drug abemaciclib relies on a quantitative assessment of Ki-67 expression in the treatment decision algorithm. This further reinforces the urgent need for standardization of Ki-67 antibody selection and staining interpretation, which will hopefully lead to multidisciplinary consensus on the use of Ki-67 as a prognostic and predictive marker in breast cancer. The goals of this review are to highlight the historical evolution of Ki-67 in breast cancer, summarize the present literature on Ki-67 in breast cancer, and discuss the evolving literature on the use of Ki-67 as a companion diagnostic biomarker in breast cancer, with consideration for the necessary changes required across pathology practices to help increase the reliability and widespread adoption of Ki-67 as a prognostic and predictive marker for breast cancer in clinical practice.

Long term trends of breast cancer incidence according to proliferation status

BACKGROUND

Long-term breast cancer incidence trends according to proliferation status are poorly described. We studied time-trends in breast cancer incidence, using mitotic count and Ki-67 as markers of proliferation.

METHODS

Among 83,298 Norwegian women followed for breast cancer occurrence 1961-2012, 2995 incident breast cancers were diagnosed. Ki-67 was assessed using immunohistochemistry on tissue microarrays and mitoses were counted on whole sections. We compared incidence rates according to proliferation status among women born 1886-1928 and 1929-1977, estimating age-specific incidence rate ratios. We performed multiple imputations to account for unknown proliferation status. Mean values of Ki-67 and mitotic counts were calculated, according to age and birth year. We performed separate incidence analyses for HER2⁺ and triple negative breast cancers.

RESULTS

Among women aged 40-69 years, incidence rates of tumours with low-proliferative activity were higher among those born in 1929 or later, compared to before 1929, according to Ki-67 and mitotic count. Incidence rates of tumours with high-proliferative activity were also higher in women born in 1929 or later compared to before 1929 according to Ki-67, but not according to mitotic count. Mean values of Ki-67 and mitotic count varied according to age and birth year. In subtype-specific analyses we found an increase of high-proliferative HER2⁺ tumours according to Ki-67 in women born in 1929 or later, compared to before 1929.

CONCLUSIONS

There has been a temporal increase in both low- and high-proliferative breast cancers.

Signatures of Breast Cancer Progression in the Blood: What Could Be Learned from Circulating Tumor Cell Transcriptomes

Gene expression profiling has revolutionized our understanding of cancer biology, showing an unprecedented ability to impact patient management especially in breast cancer. The vast majority of breast cancer gene expression signatures derive from the analysis of the tumor bulk, an experimental approach that limits the possibility to dissect breast cancer heterogeneity thoroughly and might miss the message hidden in biologically and clinically relevant cell populations. During disease progression or upon selective pressures, cancer cells undergo continuous transcriptional changes, which inevitably affect tumor heterogeneity, response to therapy and tendency to disseminate. Therefore, metastasis-associated signatures and transcriptome-wide gene expression measurement at single-cell resolution hold great promise for the future of breast cancer clinical care. Seen from this perspective, transcriptomics of circulating tumor cells (CTCs) represent an attractive opportunity to bridge the knowledge gap and develop novel biomarkers. This review summarizes the current state-of-the-science on CTC gene expression analysis in breast cancer, addresses technical and clinical issues related to the application of CTC-derived signatures, and discusses potential research directions.

Comprehensive Characterization of the Regulatory Landscape of Adrenocortical Carcinoma: Novel Transcription Factors and Targets Associated with Prognosis

We reconstructed a transcriptional regulatory network for adrenocortical carcinoma (ACC) using transcriptomic and clinical data from The Cancer Genome Atlas (TCGA)-ACC cohort. We investigated the association of transcriptional regulatory units (regulons) with overall survival, molecular phenotypes, and immune signatures. We annotated the ACC regulons with cancer hallmarks and assessed single sample regulon activities in the European Network for the Study of Adrenal Tumors (ENSAT) cohort. We found 369 regulons associated with overall survival and subdivided them into four clusters: RC1 and RC2, associated with good prognosis, and RC3 and RC4, associated with worse outcomes. The RC1 and RC3 regulons were highly correlated with the 'Steroid Phenotype,' while the RC2 and RC4 regulons were highly correlated with a molecular proliferation signature. We selected two regulons, NR5A1 (steroidogenic factor 1, SF-1) and CENPA (Centromeric Protein A), that were consistently associated with overall survival for further downstream analyses. The CENPA regulon was the primary regulator of MKI-67 (a marker of proliferation KI-67), while the NR5A1 regulon is a well-described transcription factor (TF) in ACC tumorigenesis. We also found that the ZBTB4 (Zinc finger and BTB domain-containing protein 4) regulon, which is negatively associated with CENPA in our transcriptional regulatory network, is also a druggable anti-tumorigenic TF. We anticipate that the ACC regulons may be used as a reference for further investigations concerning the complex molecular interactions in ACC tumors.

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

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

A novel twelve-gene signature to predict neoadjuvant chemotherapy response and prognosis in breast cancer

Background

Accurate evaluation of the response to neoadjuvant chemotherapy (NAC) provides important information about systemic therapies for breast cancer, which implies pharmacological response, prognosis, and guide further therapy. Gene profiles overcome the shortcomings of the relatively limited detection indicators of the classical pathological evaluation criteria and the subjectivity of observation, but are complicated and expensive. Therefore, it is essential to develop a more accurate, repeatable, and economical evaluation approach for neoadjuvant chemotherapy responses.

Methods

We analyzed the transcriptional profiles of chemo-resistant breast cancer cell lines and tumors of chemo-resistant breast cancer patients in the GSE25066 dataset. We preliminarily screened out common significantly differentially expressed genes and constructed a NAC response risk model using LASSO regression and univariate and multivariate analyses. The differences in bioinformatic features of tumor cells, immune characteristics, and prognosis were compared between high and low-risk group. The potential drugs that could reverse chemotherapy resistance in breast cancer were screened by the CMap database.

Results

Thirty-six genes were commonly up/down-regulated in both NAC chemo-resistant tumors and cells compared to the sensitive tumors and wild-type cells. Through LASSO regression, we obtained a risk model composed of 12 genes. The risk model divided patients into high and low-risk groups. Univariate and multivariate Cox regression analyses suggested that the risk score is an independent prognostic factor for evaluating NAC response in breast cancer. Tumors in risk groups exhibited significant differences in molecular biological characteristics, tumor-infiltrating lymphocytes, and immunosuppressive molecule expression. Our results suggested that the risk score was also a good prognostic factor for breast cancer. Finally, we screened potential drugs that could reverse chemotherapy resistance in breast cancer.

Conclusion

A novel 12 gene-signature could be used to predict NAC response and predict prognosis in breast cancer.

A convolutional neural network model for survival prediction based on prognosis-related cascaded Wx feature selection

Great advances in deep learning have provided effective solutions for prediction tasks in the biomedical field. However, accurate prognosis prediction using cancer genomics data remains challenging due to the severe overfitting problem caused by curse of dimensionality inherent to high-throughput sequencing data. Moreover, there are unique challenges to perform survival analysis, arising from the difficulty in utilizing censored samples whose events of interest are not observed. Convolutional neural network (CNN) models provide us the opportunity to extract meaningful hierarchical features to characterize cancer subtype and prognosis outcomes. On the other hand, feature selection can mitigate overfitting and reduce subsequent model training computation burden by screening out significant genes from redundant genes. To accomplish model simplification, we developed a concise and efficient survival analysis model, named CNN-Cox model, which combines a special CNN framework with prognosis-related feature selection cascaded Wx, with the advantage of less computation demand utilizing light training parameters. Experiment results show that CNN-Cox model achieved consistent higher C-index values and better survival prediction performance across seven cancer type datasets in The Cancer Genome Atlas cohort, including bladder carcinoma, head and neck squamous cell carcinoma, kidney renal cell carcinoma, brain low-grade glioma, lung adenocarcinoma (LUAD), lung squamous cell carcinoma, and skin cutaneous melanoma, compared with the existing state-of-the-art survival analysis methods. As an illustration of model interpretation, we examined potential prognostic gene signatures of LUAD dataset using the proposed CNN-Cox model. We conducted protein-protein interaction network analysis to identify potential prognostic genes and further analyzed the biological function of 13 hub genes, including ANLN, RACGAP1, KIF4A, KIF20A, KIF14, ASPM, CDK1, SPC25, NCAPG, MKI67, HJURP, EXO1, HMMR, whose high expression is significantly associated with poor survival of LUAD patients. These findings confirmed that CNN-Cox model is effective in extracting not only prognosis factors but also biologically meaningful gene features. The codes are available at the GitHub website: https://github.com/wangwangCCChen/CNN-Cox .

The role of HER2 alterations in clinicopathological and molecular characteristics of breast cancer and HER2-targeted therapies: a comprehensive review

Breast cancer (BC) is the most common malignancy in women and one of the leading causes of cancer mortality, despite significant treatment advancements over the last decades. Human epidermal growth factor receptor-2 (HER2) is a member of the ERBB family of receptor tyrosine kinases which have long been known to mediate cancer cell growth and invasion through constitutive activation of oncogenic downstream signaling, such as PI3K/Akt/mTOR and MAPK. Overexpression/amplification of HER2 in various tumors, especially BC, offers the possible therapeutic potential for target therapies. HER2-targeted therapies, either with a combination of chemotherapy or through multi-anti-HER2 therapies without chemotherapy, have significantly improved the prognosis of HER2-positive tumors. In recent years, novel anti-HER2 agents and combination therapies have garnered much attention, especially for heavily treated advanced or metastatic BCs. HER2-positive BC is biologically a heterogeneous group depending on HER2 activation mechanisms, hormone receptor status, genome variations, tumor heterogeneity, and treatment resistance, which affect the treatment benefit and patients' outcomes. This review will discuss HER2 alternations (gene amplification or receptor overexpression) in BC, their correlation with clinicopathological characteristics and molecular characteristics, and HER2-based therapies in tumors with HER2 overexpression/amplification.

Replicability in cancer omics data analysis: measures and empirical explorations

In biomedical research, the replicability of findings across studies is highly desired. In this study, we focus on cancer omics data, for which the examination of replicability has been mostly focused on important omics variables identified in different studies. In published literature, although there have been extensive attention and ad hoc discussions, there is insufficient quantitative research looking into replicability measures and their properties. The goal of this study is to fill this important knowledge gap. In particular, we consider three sensible replicability measures, for which we examine distributional properties and develop a way of making inference. Applying them to three The Cancer Genome Atlas (TCGA) datasets reveals in general low replicability and significant across-data variations. To further comprehend such findings, we resort to simulation, which confirms the validity of the findings with the TCGA data and further informs the dependence of replicability on signal level (or equivalently sample size). Overall, this study can advance our understanding of replicability for cancer omics and other studies that have identification as a key goal.

Clinical validity and clinical utility of Ki67 in early breast cancer

Ki67 represents an immunohistochemical nuclear localized marker that is widely used in surgical pathology. Nuclear immunoreactivity for Ki67 indicates that cells are cycling and are in G1- to S-phase. The percentage of Ki67-positive tumor cells (Ki67 index) therefore provides an estimate of the growth fraction in tumor specimens. In breast cancer (BC), tumor cell proliferation rate is one of the most relevant prognostic markers and Ki67 is consequently helpful in prognostication similar to histological grading and mRNA profiling-based BC risk stratification. In BCs treated with short-term preoperative endocrine therapy, Ki67 dynamics enable distinguishing between endocrine sensitive and resistant tumors. Despite its nearly universal use in pathology laboratories worldwide, no internationally accepted consensus has yet been achieved for some methodological details related to Ki67 immunohistochemistry (IHC). Controversial issues refer to choice of IHC antibody clones, scoring methods, inter-laboratory reproducibility, and the potential value of computer-assisted imaging analysis and/or artificial intelligence for Ki67 assessment. Prospective clinical trials focusing on BC treatment have proven that Ki67, as determined by standardized central pathology assessment, is of clinical validity. Clinical utility has been demonstrated in huge observational studies.

Connexins orchestrate progression of breast cancer metastasis to the brain by promoting FAK activation

Brain metastasis is a complication of increasing incidence in patients with breast cancer at advanced disease stage. It is a severe condition characterized by a rapid decline in quality of life and poor prognosis. There is a critical clinical need to develop effective therapies to prevent and treat brain metastases. Here, we describe a unique and robust spontaneous preclinical model of breast cancer metastasis to the brain (4T1-BM₂) in mice that has been instrumental in uncovering molecular mechanisms guiding metastatic dissemination and colonization of the brain. Key experimental findings were validated in the additional murine D2A1-BM₂ model and in human MDA231-BrM₂ model. Gene expression analyses and functional studies, coupled with clinical transcriptomic and histopathological investigations, identified connexins (Cxs) and focal adhesion kinase (FAK) as master molecules orchestrating breast cancer colonization of the brain. Cx31 promoted homotypic tumor cell adhesion, heterotypic tumor-astrocyte interaction, and FAK phosphorylation. FAK signaling prompted NF-κB activation inducing Lamc2 expression and laminin 332 (laminin 5) deposition, α6 integrin-mediated adhesion, and sustained survival and growth within brain parenchyma. In the MDA231-BrM₂ model, the human homologous molecules CX43, LAMA4, and α3 integrin were involved. Systemic treatment with FAK inhibitors reduced brain metastasis progression. In conclusion, we report a spontaneous model of breast cancer metastasis to the brain and identified Cx-mediated FAK-NF-κB signaling as a mechanism promoting cell-autonomous and microenvironmentally controlled cell survival for brain colonization. Considering the limited therapeutic options for brain metastatic disease in cancer patients, we propose FAK as a therapeutic candidate to further pursue in the clinic.

Assessment of MRI to estimate metastatic dissemination risk and prometastatic effects of chemotherapy

Metastatic dissemination in breast cancer is regulated by specialized intravasation sites called “tumor microenvironment of metastasis” (TMEM) doorways, composed of a tumor cell expressing the actin-regulatory protein Mena, a perivascular macrophage, and an endothelial cell, all in stable physical contact. High TMEM doorway number is associated with an increased risk of distant metastasis in human breast cancer and mouse models of breast carcinoma. Here, we developed a novel magnetic resonance imaging (MRI) methodology, called TMEM Activity-MRI, to detect TMEM-associated vascular openings that serve as the portal of entry for cancer cell intravasation and metastatic dissemination. We demonstrate that TMEM Activity-MRI correlates with primary tumor TMEM doorway counts in both breast cancer patients and mouse models, including MMTV-PyMT and patient-derived xenograft models. In addition, TMEM Activity-MRI is reduced in mouse models upon treatment with rebastinib, a specific and potent TMEM doorway inhibitor. TMEM Activity-MRI is an assay that specifically measures TMEM-associated vascular opening (TAVO) events in the tumor microenvironment, and as such, can be utilized in mechanistic studies investigating molecular pathways of cancer cell dissemination and metastasis. Finally, we demonstrate that TMEM Activity-MRI increases upon treatment with paclitaxel in mouse models, consistent with prior observations that chemotherapy enhances TMEM doorway assembly and activity in human breast cancer. Our findings suggest that TMEM Activity-MRI is a promising precision medicine tool for localized breast cancer that could be used as a non-invasive test to determine metastatic risk and serve as an intermediate pharmacodynamic biomarker to monitor therapeutic response to agents that block TMEM doorway-mediated dissemination.

Breast cancer stage prediction: a computational approach guided by transcriptome analysis

Breast cancer is the second leading cancer among women in terms of mortality rate. In recent years, its incidence frequency has been continuously rising across the globe. In this context, the new therapeutic strategies to manage the deadly disease attracts tremendous research focus. However, finding new prognostic predictors to refine the selection of therapy for the various stages of breast cancer is an unattempted issue. Aberrant expression of genes at various stages of cancer progression can be studied to identify specific genes that play a critical role in cancer staging. Moreover, while many schemes for subtype prediction in breast cancer have been explored in the literature, stage-wise classification remains a challenge. These observations motivated the proposed two-phased method: stage-specific gene signature selection and stage classification. In the first phase, meta-analysis of gene expression data is conducted to identify stage-wise biomarkers that were then used in the second phase of cancer classification. From the analysis, 118, 12 and 4 genes respectively in stage I, stage II and stage III are determined as potential biomarkers. Pathway enrichment, gene network and literature analysis validate the significance of the identified genes in breast cancer. In this study, machine learning methods were combined with principal component and posterior probability analysis. Such a scheme offers a unique opportunity to build a meaningful model for predicting breast cancer staging. Among the machine learning models compared, Support Vector Machine (SVM) is found to perform the best for the selected datasets with an accuracy of 92.21% during test data evaluation. Perhaps, biomarker identification performed here for stage-specific cancer treatment would be a meaningful step towards predictive medicine. Significantly, the determination of correct cancer stage using the proposed 134 gene signature set can possibly act as potential target for breast cancer therapeutics.

Deeper insights into long-term survival heterogeneity of pancreatic ductal adenocarcinoma (PDAC) patients using integrative individual- and group-level transcriptome network analyses

Pancreatic ductal adenocarcinoma (PDAC) is categorized as the leading cause of cancer mortality worldwide. However, its predictive markers for long-term survival are not well known. It is interesting to delineate individual-specific perturbed genes when comparing long-term (LT) and short-term (ST) PDAC survivors and integrate individual- and group-based transcriptome profiling. Using a discovery cohort of 19 PDAC patients from CHU-Liège (Belgium), we first performed differential gene expression analysis comparing LT to ST survivor. Second, we adopted systems biology approaches to obtain clinically relevant gene modules. Third, we created individual-specific perturbation profiles. Furthermore, we used Degree-Aware disease gene prioritizing (DADA) method to develop PDAC disease modules; Network-based Integration of Multi-omics Data (NetICS) to integrate group-based and individual-specific perturbed genes in relation to PDAC LT survival. We identified 173 differentially expressed genes (DEGs) in ST and LT survivors and five modules (including 38 DEGs) showing associations to clinical traits. Validation of DEGs in the molecular lab suggested a role of REG4 and TSPAN8 in PDAC survival. Via NetICS and DADA, we identified various known oncogenes such as CUL1 and TGFB1. Our proposed analytic workflow shows the advantages of combining clinical and omics data as well as individual- and group-level transcriptome profiling.

Breast cancer in the era of precision medicine

Breast cancer is a heterogeneous disorder with different molecular subtypes and biological characteristics for which there are diverse therapeutic approaches and clinical outcomes specific to any molecular subtype. It is a global health concern due to a lack of efficient therapy regimens that might be used for all disease subtypes. Therefore, treatment customization for each patient depending on molecular characteristics should be considered. Precision medicine for breast cancer is an approach to diagnosis, treatment, and prevention of the disease that takes into consideration the patient's genetic makeup. Precision medicine provides the promise of highly individualized treatment, in which each individual breast cancer patient receives the most appropriate diagnostics and targeted therapies based on the genetic profile of cancer. The knowledge about the molecular features and development of breast cancer treatment approaches has increased, which led to the development of new targeted therapeutics. Tumor genomic profiling is the standard of care for breast cancer that could contribute to taking steps to better management of malignancies. It holds great promise for accurate prognostication, prediction of response to common systemic therapies, and individualized monitoring of the disease. The emergence of targeted treatment has significantly enhanced the survival of patients with breast cancer and contributed to reducing the economic costs of the health system. In this review, we summarized the therapeutic approaches associated with the molecular classification of breast cancer to help the best treatment selection specific to the target patient.

Luminal androgen receptor breast cancer subtype and investigation of the microenvironment and neoadjuvant chemotherapy response

Triple-negative breast cancer (TNBC) is the most aggressive breast cancer subtype with low overall survival rates and high molecular heterogeneity; therefore, few targeted therapies are available. The luminal androgen receptor (LAR) is the most consistently identified TNBC subtype, but the clinical utility has yet to be established. Here, we constructed a novel genomic classifier, LAR-Sig, that distinguishes the LAR subtype from other TNBC subtypes and provide evidence that it is a clinically distinct disease. A meta-analysis of seven TNBC datasets (n = 1086 samples) from neoadjuvant clinical trials demonstrated that LAR patients have significantly reduced response (pCR) rates than non-LAR TNBC patients (odds ratio = 2.11, 95% CI: 1.33, 2.89). Moreover, deconvolution of the tumor microenvironment confirmed an enrichment of luminal epithelium corresponding with a decrease in basal and myoepithelium in LAR TNBC tumors. Increased immunosuppression in LAR patients may lead to a decreased presence of cycling T-cells and plasma cells. While, an increased presence of myofibroblast-like cancer-associated cells may impede drug delivery and treatment. In summary, the lower levels of tumor infiltrating lymphocytes (TILs), reduced immune activity in the micro-environment, and lower pCR rates after NAC, suggest that new therapeutic strategies for the LAR TNBC subtype need to be developed.

Comparative analysis of the molecular subtype landscape in canine and human mammary gland tumors

Breast cancers in humans belong to one of several intrinsic molecular subtypes each with different tumor biology and different clinical impact. Mammary gland tumors in dogs are proposed as a relevant comparative model for human breast cancer; however, it is still unclear whether the intrinsic molecular subtypes have the same significance in dogs and humans. Using publicly available data, we analyzed gene expression and whole-exome sequencing data from 158 canine mammary gland tumors. We performed molecular subtyping using the PAM50 method followed by subtype-specific comparisons of gene expression characteristics, mutation patterns and copy number profiles between canine tumors and human breast tumors from The Cancer Genome Atlas (TCGA) breast cancer cohort (n = 1097). We found that luminal A canine tumors greatly resemble luminal A human tumors both in gene expression characteristics, mutations and copy number profiles. Also, the basal-like canine and human tumors were relatively similar, with low expression of luminal epithelial markers and high expression of genes involved in cell proliferation. There were, however, distinct differences in immune-related gene expression patterns in basal-like tumors between the two species. Characteristic HER2-enriched and luminal B subtypes were not present in the canine cohort, and we found no tumors with high-level ERBB2 amplifications. Benign and malignant canine tumors displayed similar PAM50 subtype characteristics. Our findings indicate that deeper understanding of the different molecular subtypes in canine mammary gland tumors will further improve the value of canines as comparative models for human breast cancer.

Protein expression of the gp78 E3-ligase predicts poor breast cancer outcome based on race

Women of African ancestry suffer higher rates of breast cancer mortality compared to all other groups in the United States. Though the precise reasons for these disparities remain unclear, many recent studies have implicated a role for differences in tumor biology. Using an epitope-validated antibody against the endoplasmic reticulum-associated degradation (ERAD) E3 ubiquitin ligase, gp78, we show that elevated levels of gp78 in patient breast cancer cells predict poor survival. Moreover, high levels of gp78 are associated with poor outcomes in both ER-positive and ER-negative tumors, and breast cancers expressing elevated amounts of gp78 protein are enriched in gene expression pathways that influence cell cycle, metabolism, receptor-mediated signaling, and cell stress response pathways. In multivariate analysis adjusted for subtype and grade, gp78 protein is an independent predictor of poor outcomes in women of African ancestry. Furthermore, gene expression signatures, derived from patients stratified by gp78 protein expression, are strong predictors of recurrence and pathological complete response in retrospective clinical trial data and share many common features with gene sets previously identified to be overrepresented in breast cancers based on race. These findings implicate a prominent role for gp78 in tumor progression and offer new insights into our understanding of racial differences in breast cancer outcomes.

Transcriptome-wide prediction of prostate cancer gene expression from histopathology images using co-expression based convolutional neural networks

Motivation

Molecular phenotyping by gene expression profiling is central in contemporary cancer research and in molecular diagnostics but remains resource intense to implement. Changes in gene expression occurring in tumours cause morphological changes in tissue, which can be observed on the microscopic level. The relationship between morphological patterns and some of the molecular phenotypes can be exploited to predict molecular phenotypes from routine haematoxylin and eosin-stained whole slide images (WSIs) using convolutional neural networks (CNNs). In this study, we propose a new, computationally efficient approach to model relationships between morphology and gene expression.

Results

We conducted the first transcriptome-wide analysis in prostate cancer, using CNNs to predict bulk RNA-sequencing estimates from WSIs for 370 patients from the TCGA PRAD study. Out of 15 586 protein coding transcripts, 6618 had predicted expression significantly associated with RNA-seq estimates (FDR-adjusted P-value <1×10⁻⁴) in a cross-validation and 5419 (81.9%) of these associations were subsequently validated in a held-out test set. We furthermore predicted the prognostic cell-cycle progression score directly from WSIs. These findings suggest that contemporary computer vision models offer an inexpensive and scalable solution for prediction of gene expression phenotypes directly from WSIs, providing opportunity for cost-effective large-scale research studies and molecular diagnostics.

Availability and implementation

A self-contained example is available from http://github.com/phiwei/prostate_coexpression. Model predictions and metrics are available from doi.org/10.5281/zenodo.4739097.

Supplementary information

Supplementary data are available at Bioinformatics online.

Association between tumor 18F-fluorodeoxyglucose metabolism and survival in women with estrogen receptor-positive, HER2-negative breast cancer

We examined whether ¹⁸F-fluorodeoxyglucose metabolism is associated with distant relapse-free survival (DRFS) and overall survival (OS) in women with estrogen receptor (ER)-positive, HER2-negative breast cancer. This was a cohort study examining the risk factors for survival that had occurred at the start of the study. A cohort from Asan Medical Center, Korea, recruited between November 2007 and December 2014, was included. Patients received anthracycline-based neoadjuvant chemotherapy. The maximum standardized uptake value (SUV) of ¹⁸F-fluorodeoxyglucose positron emission tomography/computed tomography (PET/CT) was measured. The analysis included 466 women. The median (interquartile range) follow-up period without distant metastasis or death was 6.2 (5.3-7.6) years. Multivariable analysis of hazard ratio (95% confidence interval [CI]) showed that the middle and high tertiles of SUV were prognostic for DRFS (2.93, 95% CI 1.62-5.30; P < 0.001) and OS (4.87, 95% CI 1.94-12.26; P < 0.001). The 8-year DRFS rates were 90.7% (95% CI 85.5-96.1%) for those in the low tertile of maximum SUV vs. 73.7% (95% CI 68.0-79.8%) for those in the middle and high tertiles of maximum SUV. ¹⁸F-fluorodeoxyglucose PET/CT may assess the risk of distant metastasis and death in ER-positive, HER2-negative patients.

Investigation of genes and pathways involved in breast cancer subtypes through gene expression meta-analysis

BACKGROUND

Molecular-based studies have revealed heterogeneity in Breast cancer BC while also improving classification and treatment. However, efforts are underway to distinguish between distinct subtypes of breast cancer. In this study, the results of several microarray studies were combined to identify genes and pathways specific to each BC subtype.

METHODS

Meta-analysis of multiple gene expression profile datasets was screened to find differentially expressed genes (DEGs) across subtypes of BC and normal breast tissue samples. Protein-protein interaction network and gene set enrichment analysis were used to identify critical genes and pathways associated with BC subtypes. The differentially expressed genes from meta-analysis was validated using an independent comprehensive breast cancer RNA-sequencing dataset obtained from the Cancer Genome Atlas (TCGA).

RESULTS

We identified 110 DEGs (13 DEGs in all and 97 DEGs in each subtype) across subtypes of BC. All subtypes had a small set of shared DEGs enriched in the Chemokine receptor bind chemokine pathway. Luminal A specific were enriched in the translational elongation process in mitochondria, and the enhanced process in luminal B subtypes was interferon-alpha/beta signaling. Cell cycle and mitotic DEGs were enriched in the basal-like group. All subtype-specific DEG genes (100%) were successfully validated for Luminal A, Luminal B, ERBB2, and Normal-like. However, the validation percentage for Basal-like group was 77.8%.

CONCLUSION

Integrating researches such as a meta-analysis of gene expression might be more effective in uncovering subtype-specific DEGs and pathways than a single-study analysis. It would be more beneficial to increase the number of studies that use matched BC subtypes along with GEO profiling approaches to reach a better result regarding DEGs and reduce probable biases. However, achieving 77.8% overlap in basal-specific genes and complete concordance in specific genes related to other subtypes can implicate the strength of our analysis for discovering the subtype-specific genes.

Decision Theory versus Conventional Statistics for Personalized Therapy of Breast Cancer

Estrogen and progesterone receptors being present or not represents one of the most important biomarkers for therapy selection in breast cancer patients. Conventional measurement by immunohistochemistry (IHC) involves errors, and numerous attempts have been made to increase precision by additional information from gene expression. This raises the question of how to fuse information, in particular, if there is disagreement. It is the primary domain of Dempster–Shafer decision theory (DST) to deal with contradicting evidence on the same item (here: receptor status), obtained through different techniques. DST is widely used in technical settings, such as self-driving cars and aviation, and is also promising to deliver significant advantages in medicine. Using data from breast cancer patients already presented in previous work, we focus on comparing DST with classical statistics in this work, to pave the way for its application in medicine. First, we explain how DST not only considers probabilities (a single number per sample), but also incorporates uncertainty in a concept of ‘evidence’ (two numbers per sample). This allows for very powerful displays of patient data in so-called ternary plots, a novel and crucial advantage for medical interpretation. Results are obtained according to conventional statistics (ODDS) and, in parallel, according to DST. Agreement and differences are evaluated, and the particular merits of DST discussed. The presented application demonstrates how decision theory introduces new levels of confidence in diagnoses derived from medical data.