A cell proliferation signature is a marker of extremely poor outcome in a subpopulation of breast cancer patients

Hormone Receptor Positive Breast Cancer in Young Women: A Review

The global incidence of hormone-positive breast cancer (HR+ BC) in young women is rising, though the underlying reasons remain unclear. HR+ disease in younger women appears to represent a distinct clinical entity compared to that in older women, exhibiting distinct clinicopathological characteristics, outcomes and responses to treatment. Despite these differences, there is a paucity of large-volume data focusing on young women with HR+ in contemporary literature. Hormone receptor positive breast cancer in young women is associated with poorer prognoses compared to older women. Additionally, early age onset breast cancer presents unique challenges, including concerns related to fertility, the toxic effects of therapeutic agents, and specific surgical considerations. The purpose of this review is to report the existing literature on HR+ disease in young women.

Distinct genomic, transcriptomic, and immune profiles for tumor and non-tumor mucosal regions in early gastric cancer

In early gastric cancer, local recurrence develops after endoscopic resection by field cancerization. Understanding the nature of cancer-prone environments is important to establish effective strategies to prevent recurrence. We hypothesized that the molecular/immune profiles in non-tumor (cancer-prone) tissue differ according to the relative distance from the gastric tumor. For this purpose, we performed whole-exome and transcriptome sequencing of 16 early gastric cancer samples with paired non-tumor mucosa 1 cm (N1) and 3 cm (N3) away from the tumor. The whole exome sequencing revealed mutations in both the tumor and non-tumor mucosa. TTN was the most frequently altered gene in tumors (31 %) and was the second most frequently altered gene in N1 (25 %) samples; however, the mutation rate was significantly lower in N3 (12 %) samples (P = 0.0046). Moreover, the expression levels of TTN mRNA were higher in tumors than in the N1 and N3 samples and were significantly associated with TTN mutations (P = 0.04). TP53 mutations were mainly observed in tumors (50 %) and in 6.3 % of N1, with no mutation detected in N3 samples. Transcriptome sequencing revealed that the expression of the epithelial-mesenchymal transition signature, mesenchymal signature, and proliferation signature was increased in tumors, whereas programmed death-ligand 1 expression was decreased in the non-tumor mucosa. In the tumor, although the numbers of M0/M1 macrophages, neutrophils, and eosinophils increased, plasma cell numbers were markedly decreased compared to non-tumor mucosa. In conclusion, non-tumor mucosa at 1 cm and 3 cm from the tumor harbored different genomic, transcriptomic, and immune cell profiles. The non-tumor mucosa closer to the tumor (1 cm) exhibited similar genomic and transcriptomic features. These findings can offer clinical guidance for acquiring a safe horizontal margin in endoscopic resection for early gastric cancer.

Network-based transfer of pan-cancer immunotherapy responses to guide breast cancer prognosis

Breast cancer prognosis is complicated by tumor heterogeneity. Traditional methods focus on cancer-specific gene signatures, but cross-cancer strategies that provide deeper insights into tumor homogeneity are rarely used. Immunotherapy, particularly immune checkpoint inhibitors, results from variable responses across cancers, offering valuable prognostic insights. We introduced a network-based transfer (NBT) of pan-cancer immunotherapy responses to enhance breast cancer prognosis using node embedding and heat diffusion algorithms, identifying gene signatures netNE and netHD. Our results showed that netHD and netNE outperformed seven established breast cancer signatures in prognostic metrics, with netHD excelling. All nine gene signatures were grouped into three clusters, with netHD and netNE enriching the immune-related interferon-gamma pathway. Stratifying TCGA patients into two groups based on netHD revealed significant immunological differences and variations in 20 of 50 cancer hallmarks, emphasizing immune-related markers. This approach leverages pan-cancer insights to enhance breast cancer prognosis, facilitating insight transfer and improving tumor homogeneity understanding.Abstract graph of network-based insights translating pan-cancer immunotherapy responses to breast cancer prognosis. This abstract graph illustrates the conceptual framework for transferring immunotherapy response insights from pan-cancer studies to breast cancer prognosis. It highlights the integration of PPI networks to bridge genetic data and clinical phenotypes. The network-based method facilitates the identification of prognostic gene signatures in breast cancer by leveraging immunotherapy response information, providing a novel perspective on tumor homogeneity and its implications for clinical outcomes.

A transcriptomic biomarker predictive of cell proliferation for use in adverse outcome pathway-informed testing and assessment

High-throughput transcriptomics (HTTr) is increasingly being used to identify molecular targets of chemicals that can be linked to adverse outcomes. Cell proliferation (CP) is an important key event in chemical carcinogenesis. Here, we describe the construction and characterization of a gene expression biomarker that is predictive of the CP status in human and rodent tissues. The biomarker was constructed from 30 genes known to be increased in expression in prostate cancers relative to surrounding tissues and in cycling human MCF-7 cells after estrogen receptor (ER) agonist exposure. Using a large compendium of gene expression profiles to test utility, the biomarker could identify increases in CP in (i) 308 out of 367 tumor vs. normal surrounding tissue comparisons from 6 human organs, (ii) MCF-7 cells after activation of ER, (iii) after partial hepatectomy in mice and rats, and (iv) the livers of mice and rats after exposure to nongenotoxic hepatocarcinogens. The biomarker identified suppression of CP (i) under conditions of p53 activation by DNA damaging agents in human cells, (ii) in human A549 lung cells exposed to therapeutic anticancer kinase inhibitors (dasatinib, nilotnib), and (iii) in the mouse liver when comparing high levels of CP at birth to the low background levels in the adult. The responses using the biomarker were similar to those observed using conventional markers of CP including PCNA, Ki67, and BrdU labeling. The CP biomarker will be a useful tool for interpretation of HTTr data streams to identify CP status after exposure to chemicals in human cells or in rodent tissues.

BUB1 regulates non-homologous end joining pathway to mediate radioresistance in triple-negative breast cancer

BACKGROUND

Triple-negative breast cancer (TNBC) is a highly aggressive form of breast cancer subtype often treated with radiotherapy (RT). Due to its intrinsic heterogeneity and lack of effective targets, it is crucial to identify novel molecular targets that would increase RT efficacy. Here we demonstrate the role of BUB1 (cell cycle Ser/Thr kinase) in TNBC radioresistance and offer a novel strategy to improve TNBC treatment.

METHODS

Gene expression analysis was performed to look at genes upregulated in TNBC patient samples compared to other subtypes. Cell proliferation and clonogenic survivals assays determined the IC50 of BUB1 inhibitor (BAY1816032) and radiation enhancement ratio (rER) with pharmacologic and genomic BUB1 inhibition. Mammary fat pad xenografts experiments were performed in CB17/SCID. The mechanism through which BUB1 inhibitor sensitizes TNBC cells to radiotherapy was delineated by γ-H2AX foci assays, BLRR, Immunoblotting, qPCR, CHX chase, and cell fractionation assays.

RESULTS

BUB1 is overexpressed in BC and its expression is considerably elevated in TNBC with poor survival outcomes. Pharmacological or genomic ablation of BUB1 sensitized multiple TNBC cell lines to cell killing by radiation, although breast epithelial cells showed no radiosensitization with BUB1 inhibition. Kinase function of BUB1 is mainly accountable for this radiosensitization phenotype. BUB1 ablation also led to radiosensitization in TNBC tumor xenografts with significantly increased tumor growth delay and overall survival. Mechanistically, BUB1 ablation inhibited the repair of radiation-induced DNA double strand breaks (DSBs). BUB1 ablation stabilized phospho-DNAPKcs (S2056) following RT such that half-lives could not be estimated. In contrast, RT alone caused BUB1 stabilization, but pre-treatment with BUB1 inhibitor prevented stabilization (t1/2, ~8 h). Nuclear and chromatin-enriched fractionations illustrated an increase in recruitment of phospho- and total-DNAPK, and KAP1 to chromatin indicating that BUB1 is indispensable in the activation and recruitment of non-homologous end joining (NHEJ) proteins to DSBs. Additionally, BUB1 staining of TNBC tissue microarrays demonstrated significant correlation of BUB1 protein expression with tumor grade.

CONCLUSIONS

BUB1 ablation sensitizes TNBC cell lines and xenografts to RT and BUB1 mediated radiosensitization may occur through NHEJ. Together, these results highlight BUB1 as a novel molecular target for radiosensitization in women with TNBC.

Molecular classification and intratumoral heterogeneity of gastric adenocarcinoma

Gastric cancers frequently harbor striking histological complexity and diversity between lesions as well as within single lesions, known as inter- and intratumoral heterogeneity, respectively. The latest World Health Organization Classification of Tumors designated more than 30 histological subtypes for gastric epithelial tumors, assigning 12 subtypes for gastric adenocarcinoma (GAD). Meanwhile, recent advances in genome-wide analyses have provided molecular aspects to the histological classification of GAD, and consequently revealed different molecular traits underlying these histological subtypes. Moreover, accumulating knowledge of comprehensive molecular profiles has led to establishing molecular classifications of GAD, which are often associated with clinical biomarkers for therapeutics and prognosis. However, most of our knowledge of GAD molecular profiles is based on inter-tumoral heterogeneity, and the molecular profiles underlying intratumoral heterogeneity are yet to be determined. In this review, recently established molecular classifications of GAD are introduced in the aspect of pathological diagnosis and are discussed in the context of intratumoral heterogeneity.

BUB1 Inhibition Sensitizes TNBC Cell Lines to Chemotherapy and Radiotherapy

BUB1 is overexpressed in most human solid cancers, including breast cancer. Higher BUB1 levels are associated with a poor prognosis, especially in patients with triple-negative breast cancer (TNBC). Women with TNBC often develop resistance to chemotherapy and radiotherapy, which are still the mainstay of treatment for TNBC. Our previous studies demonstrated that a BUB1 kinase inhibitor (BAY1816032) reduced tumor cell proliferation and significantly enhanced radiotherapy efficacy in TNBC. In this study, we evaluated the effectiveness of BAY1816032 with a PARP inhibitor (olaparib), platinum agent (cisplatin), and microtubule poison (paclitaxel) alone or in combination with radiotherapy using cytotoxicity and clonogenic survival assays. BUB1 inhibitors sensitized BRCA1/2 wild-type SUM159 and MDA-MB-231 cells to olaparib, cisplatin, and paclitaxel synergistically (combination index; CI < 1). BAY1816032 significantly increased the radiation sensitization of SUM159 and MDA-MB-231 by olaparib, cisplatin, or paclitaxel at non-toxic concentrations (doses well below the IC50 concentrations). Importantly, the small molecular inhibitor of BUB1 synergistically (CI < 1) sensitized the BRCA mutant TNBC cell line HCC1937 to olaparib. Furthermore, the BUB1 inhibitor significantly increased the radiation enhancement ratio (rER) in HCC1937 cells (rER 1.34) compared to either agent alone (BUB1i rER 1.19; PARPi rER 1.04). The data presented here are significant as they provide proof that inhibition of BUB1 kinase activity sensitizes TNBC cell lines to a PARP inhibitor and radiation, irrespective of BRCA1/2 mutation status. Due to the ability of the BUB1 inhibitor to sensitize TNBC to different classes of drugs (platinum, PARPi, microtubule depolarization inhibitors), this work strongly supports the role of BUB1 as a novel molecular target to improve chemoradiation efficacy in TNBC and provides a rationale for the clinical evaluation of BAY1816032 as a chemosensitizer and chemoradiosensitizer in TNBC.

Prognostic significance and immune landscape of a cell cycle progression-related risk model in bladder cancer

BACKGROUND

A greater emphasis has been placed on the part of cell cycle progression (CCP) in cancer in recent years. Nevertheless, the precise connection between CCP-related genes and bladder cancer (BCa) has remained elusive. This study endeavors to establish and validate a reliable risk model incorporating CCP-related factors, aiming to predict both the prognosis and immune landscape of BCa.

METHODS

Clinical information and RNA sequencing data were collected from the GEO and TCGA databases. Univariate and multivariate Cox regression analyses were conducted to construct a risk model associated with CCP. The performance of the model was assessed using ROC and Kaplan-Meier survival analyses. Functional enrichment analysis was employed to investigate potential cellular functions and signaling pathways. The immune landscape was characterized using CIBERSORT algorithms. Integration of the risk model with various clinical variables led to the development of a nomogram.

RESULTS

To build the risk model, three CCP-related genes (RAD54B, KPNA2, and TPM1) were carefully chosen. ROC and Kaplan-Meier survival analysis confirm that our model has good performance. About immunological infiltration, the high-risk group showed decreased levels of regulatory T cells and dendritic cells coupled with increased levels of activated CD4 + memory T cells, M2 macrophages, and neutrophils. Furthermore, the nomogram showed impressive predictive power for OS at 1, 3, and 5 years.

CONCLUSION

This study provides new insights into the association between the CCP-related risk model and the prognosis of BCa, as well as its impact on the immune landscape.

BUB1 regulates non-homologous end joining pathway to mediate radioresistance in triple-negative breast cancer

Background

Triple-negative breast cancer (TNBC) is a highly aggressive form of breast cancer subtype often treated with radiotherapy (RT). Due to its intrinsic heterogeneity and lack of effective targets, it is crucial to identify novel molecular targets that would increase RT efficacy. Here we demonstrate the role of BUB1 (cell cycle Ser/Thr kinase) in TNBC radioresistance and offer a novel strategy to improve TNBC treatment.

Methods

Gene expression analysis was performed to look at genes upregulated in TNBC patient samples compared to other subtypes. Cell proliferation and clonogenic survivals assays determined the IC 50 of BUB1 inhibitor (BAY1816032) and radiation enhancement ratio (rER) with pharmacologic and genomic BUB1 inhibition. Mammary fat pad xenografts experiments were performed in CB17/SCID. The mechanism through which BUB1 inhibitor sensitizes TNBC cells to radiotherapy was delineated by γ-H2AX foci assays, BLRR, Immunoblotting, qPCR, CHX chase, and cell fractionation assays.

Results

BUB1 is overexpressed in BC and its expression is considerably elevated in TNBC with poor survival outcomes. Pharmacological or genomic ablation of BUB1 sensitized multiple TNBC cell lines to cell killing by radiation, although breast epithelial cells showed no radiosensitization with BUB1 inhibition. Kinase function of BUB1 is mainly accountable for this radiosensitization phenotype. BUB1 ablation also led to radiosensitization in TNBC tumor xenografts with significantly increased tumor growth delay and overall survival. Mechanistically, BUB1 ablation inhibited the repair of radiation-induced DNA double strand breaks (DSBs). BUB1 ablation stabilized phospho-DNAPKcs (S2056) following RT such that half-lives could not be estimated. In contrast, RT alone caused BUB1 stabilization, but pre-treatment with BUB1 inhibitor prevented stabilization (t 1/2 , ∼8 h). Nuclear and chromatin-enriched fractionations illustrated an increase in recruitment of phospho- and total-DNAPK, and KAP1 to chromatin indicating that BUB1 is indispensable in the activation and recruitment of non-homologous end joining (NHEJ) proteins to DSBs. Additionally, BUB1 staining of TNBC tissue microarrays demonstrated significant correlation of BUB1 protein expression with tumor grade.

Conclusions

BUB1 ablation sensitizes TNBC cell lines and xenografts to RT and BUB1 mediated radiosensitization may occur through NHEJ. Together, these results highlight BUB1 as a novel molecular target for radiosensitization in women with TNBC.

Stromal Signals Dominate Gene Expression Signature Scores That Aim to Describe Cancer Cell-intrinsic Stemness or Mesenchymality Characteristics

Epithelial-to-mesenchymal transition (EMT) in cancer cells confers migratory abilities, a crucial aspect in the metastasis of tumors that frequently leads to death. In multiple studies, authors proposed gene expression signatures for EMT, stemness, or mesenchymality of tumors based on bulk tumor expression profiling. However, recent studies suggested that noncancerous cells from the microenvironment or macroenvironment heavily influence such signature profiles. Here, we strengthen these findings by investigating 11 published and frequently referenced gene expression signatures that were proposed to describe EMT-related (EMT, mesenchymal, or stemness) characteristics in various cancer types. By analyses of bulk, single-cell, and pseudobulk expression data, we show that the cell type composition of a tumor sample frequently dominates scores of these EMT-related signatures. A comprehensive, integrated analysis of bulk RNA sequencing (RNA-seq) and single-cell RNA-seq data shows that stromal cells, most often fibroblasts, are the main drivers of EMT-related signature scores. We call attention to the risk of false conclusions about tumor properties when interpreting EMT-related signatures, especially in a clinical setting: high patient scores of EMT-related signatures or calls of "stemness subtypes" often result from low cancer cell content in tumor biopsies rather than cancer cell-specific stemness or mesenchymal/EMT characteristics.

SIGNIFICANCE

Cancer self-renewal and migratory abilities are often characterized via gene module expression profiles, also called EMT or stemness gene expression signatures. Using published clinical tumor samples, cancer cell lines, and single cancer cells, we highlight the dominating influence of noncancer cells in low cancer cell content biopsies on their scores. We caution on their application for low cancer cell content clinical cancer samples with the intent to assign such characteristics or subtypes.

Surface roughness modulates EGFR signaling and stemness of triple-negative breast cancer cells

Introduction: Cancer stem cells (CSC), a major culprit of drug-resistant phenotypes and tumor relapse, represent less than 2 % of the bulk of TNBC cells, making them difficult to isolate, study, and thus, limiting our understanding of the pathogenesis of the disease. Current methods for CSC enrichment, such as 3D spheroid culture, genetic modification, and stem cell conditioning, are time consuming, expensive, and unsuitable for high-throughput assays. One way to address these limitations is to use topographical stimuli to enhance CSC populations in planar culture. Physical cues in the breast tumor microenvironment can influence cell behavior through changes in the mechanical properties of the extracellular matrix (ECM). In this study, we used topographical cues on polystyrene films to investigate their effect on the proteome and stemness of standard TNBC cell lines. Methods: The topographical polystyrene-based array was generated using razor printing and polishing methods. Proteome data were analyzed and enriched bioprocesses were identified using R software. Stemness was assessed measuring CD44, CD24 and ALDH markers using flow cytometry, immunofluorescence, detection assays, and further validated with mammosphere assay. EGF/EGFR expression and activity was evaluated using enzyme-linked immunosorbent assay (ELISA), immunofluorescence and antibody membrane array. A dose-response assay was performed to further investigate the effect of surface topography on the sensitivity of cells to the EGFR inhibitor. Results: Surface roughness enriched the CSC population and modulated epidermal growth factor receptor (EGFR) signaling activity in TNBC cells. Enhanced proliferation of MDA-MB-468 cells in roughness correlated with upregulation of the epidermal growth factor (EGF) ligand, which in turn corresponded with a 3-fold increase in the expression of EGFR and a 42% increase in its phosphorylation compared to standard smooth culture surfaces. The results also demonstrated that phenotypic changes associated with topographical (roughness) stimuli significantly decreased the drug sensitivity to the EGFR inhibitor gefitinib. In addition, the proportion of CD44+/CD24-/ALDH+ was enhanced on surface roughness in both MDA-MB-231 and MDA-MB-468 cell lines. We also demonstrated that YAP/TAZ activation decreased in a roughness-dependent manner, confirming the mechanosensing effect of the topographies on the oncogenic activity of the cells. Discussion: Overall, this study demonstrates the potential of surface roughness as a culture strategy to influence oncogenic activity in TNBC cells and enrich CSC populations in planar cultures. Such a culture strategy may benefit high-throughput screening studies seeking to identify compounds with broader tumor efficacy.

Molecular phenotyping of malignant canine mammary tumours: Detection of high-risk group and its relationship with clinicomolecular characteristics

Canine mammary gland tumours (CMTs) constitute the most common cancer in female dogs and comprise approximately 50% of all canine cancers. With the advent of high-throughput technologies such as microarray and next-generation sequencing, the molecular phenotyping (classification) of various cancers has been extensively developed. The present study used a canine RNA-sequencing dataset, namely GSE119810, to classify 113 malignant CMTs and 64 matched normal samples via an unsupervised hierarchical algorithm with a view to evaluating the association between the resulting subtypes (clusters) (n = 4) and clinical and molecular characteristics. Finally, a molecular classifier was developed, and it detected 1 high-risk molecular subtype in the training dataset (GSE119810) and 2 independent validation datasets (GSE20718 and GSE22516). Our results revealed four molecular subtypes (C2-C5) in malignant CMTs. Furthermore, the normal samples constituted a distinct group in the clustering analysis. Marked significant associations were observed between the molecular subtypes (especially C5) and clinical/molecular features, including positive lymphatic invasion, high tumour grades, histopathology diagnoses, short survival and high TP53 mutation rates (ps <.05). The high-risk subtype (C5) was further characterized through the development of a cell cycle-based gene signature, which comprised 37 proliferation-related genes according to the support vector machine algorithm. This signature identified the high-risk group in both training and validation datasets (ps <.001). In the validation analysis, our potential classifier robustly predicted patients with positive lymphatic invasion, metastases and short survival.

MicroRNA-205-5p targets E2F1 to promote autophagy and inhibit pulmonary fibrosis in silicosis through impairing SKP2-mediated Beclin1 ubiquitination

Silicosis is an occupational disease characterized by extensive pulmonary fibrosis, and the underlying pathological process remains uncertain. Herein, we explored the molecular mechanism by which microRNA‐205‐5p (miR‐205‐5p) affects the autophagy of alveolar macrophages (AMs) and pulmonary fibrosis in mice with silicosis through the E2F transcription factor 1 (E2F1)/S‐phase kinase‐associated protein 2 (SKP2)/Beclin1 axis. Alveolar macrophages (MH‐S cells) were exposed to crystalline silica (CS) to develop an in vitro model, and mice were treated with CS to establish an in vivo model. Decreased Beclin1 and increased SKP2 and E2F1 were identified in mice with silicosis. We silenced or overexpressed miR‐205‐5p, E2F1, SKP2 and Beclin1 to investigate their potential roles in pulmonary fibrosis in vivo and autophagy in vitro. Recombinant adenovirus mRFP‐GFP‐LC3 was transduced into the MH‐S cells to assay autophagic flow. Knocking down Beclin1 promoted pulmonary fibrosis and suppressed the autophagy. Co‐immunoprecipitation and ubiquitination assays suggested that SKP2 induced K48‐linked ubiquitination of Beclin1. Furthermore, chromatin immunoprecipitation‐PCR revealed the site where E2F1 bound to the SKP2 promoter between 1638 bp and 1645 bp. As shown by dual‐luciferase reporter gene assay, the transfection with miR‐205‐5p mimic inhibited the luciferase activity of the wild‐type E2F1 3′untranslated region, suggesting that miR‐205‐5p targeted E2F1. Additionally, miR‐205‐5p overexpression increased autophagy and reduced the pulmonary fibrosis, while overexpression of E2F1 or SKP2 or inhibition of Beclin1 could annul this effect. The current study elucidated that miR‐205‐5p targeted E2F1, thereby inhibiting SKP2‐mediated Beclin1 ubiquitination to promote macrophage autophagy and inhibit pulmonary fibrosis in mice with silicosis.

Pregnancy Associated Breast Cancer Gene Expressions : New Insights on Their Regulation Based on Rare Correlated Patterns

Breast-cancer (BC) is the most common invasive cancer in women, with considerable death. Given that, BC is classified as a hormone-dependent cancer, when it collides with pregnancy, different questions may arise for which there are still no convincing answers. To deal with this issue, two new frameworks are proposed within this paper: CoRaM and Dist-CoRaM. The former is the first unified framework dedicated to the extraction of a generic basis of Correlated-Rare Association rules from gene expression data. The proposed approach has been successfully applied on a breast-cancer Gene Expression Matrix (GSE1379) with very promising results. The latter, the Dist-CoRaM approach, is a big-data processing based on Apache spark framework, dealing with correlation mining from micro-array pregnancy associated breast-cancer assays (PABC) data. It is successfully applied on the (GSE31192) gene expression matrix (GEM). The correlated patterns of gene-sets shed light on the fact that PABC exhibits heightened aggressiveness compared to cancers for Non-PABC women. Our findings suggest that higher levels of estrogen and progesterone hormones, unfortunately, are very keen to the increase of the tumor aggressiveness and the proliferation of the cancer.

Prediction of Tumor Cellularity in Resectable PDAC from Preoperative Computed Tomography Imaging

Simple Summary

Pancreatic ductal adenocarcinoma (PDAC) remains a devastating disease. However, variations in tumor biology influence individual patient outcomes greatly. We previously showed a strong association between magnetic resonance imaging-based tumor cell estimates and patient survival. In this study we aimed to transfer this finding to more broadly applied computed tomography (CT) imaging for non-invasive risk stratification. We correlated in vivo CT imaging with histopathological analyses and could show a strong association between regional Hounsfield Units (HU) and tumor cellularity. In conclusion, our study suggests CT-based tumor cell estimates as a widely applicable way of non-invasive tumor cellularity characterization in PDAC.

Abstract

Background: PDAC remains a tumor entity with poor prognosis and a 5-year survival rate below 10%. Recent research has revealed invasive biomarkers, such as distinct molecular subtypes, predictive for therapy response and patient survival. Non-invasive prediction of individual patient outcome however remains an unresolved task. Methods: Discrete cellularity regions of PDAC resection specimen (n = 43) were analyzed by routine histopathological work up. Regional tumor cellularity and CT-derived Hounsfield Units (HU, n = 66) as well as iodine concentrations were regionally matched. One-way ANOVA and pairwise t-tests were performed to assess the relationship between different cellularity level in conventional, virtual monoenergetic 40 keV (monoE 40 keV) and iodine map reconstructions. Results: A statistically significant negative correlation between regional tumor cellularity in histopathology and CT-derived HU from corresponding image regions was identified. Radiological differentiation was best possible in monoE 40 keV CT images. However, HU values differed significantly in conventional reconstructions as well, indicating the possibility of a broad clinical application of this finding. Conclusion: In this study we establish a novel method for CT-based prediction of tumor cellularity for in-vivo tumor characterization in PDAC patients.

Cyclin E2 Promotes Whole Genome Doubling in Breast Cancer

Genome doubling is an underlying cause of cancer cell aneuploidy and genomic instability, but few drivers have been identified for this process. Due to their physiological roles in the genome reduplication of normal cells, we hypothesised that the oncogenes cyclins E1 and E2 may be drivers of genome doubling in cancer. We show that both cyclin E1 (CCNE1) and cyclin E2 (CCNE2) mRNA are significantly associated with high genome ploidy in breast cancers. By live cell imaging and flow cytometry, we show that cyclin E2 overexpression promotes aberrant mitosis without causing mitotic slippage, and it increases ploidy with negative feedback on the replication licensing protein, Cdt1. We demonstrate that cyclin E2 localises with core preRC (pre-replication complex) proteins (MCM2, MCM7) on the chromatin of cancer cells. Low CCNE2 is associated with improved overall survival in breast cancers, and we demonstrate that low cyclin E2 protects from excess genome rereplication. This occurs regardless of p53 status, consistent with the association of high cyclin E2 with genome doubling in both p53 null/mutant and p53 wildtype cancers. In contrast, while cyclin E1 can localise to the preRC, its downregulation does not prevent rereplication, and overexpression promotes polyploidy via mitotic slippage. Thus, in breast cancer, cyclin E2 has a strong association with genome doubling, and likely contributes to highly proliferative and genomically unstable breast cancers.

Cancer prognosis with shallow tumor RNA sequencing

Disrupted molecular pathways are often robustly associated with disease outcome in cancer^1-3. Although biologically informative transcriptional pathways can be revealed by RNA sequencing (RNA-seq) at up to hundreds of folds reduction in conventionally used coverage^4-6, it remains unknown how low-depth sequencing datasets perform in the challenging context of developing transcriptional signatures to predict clinical outcomes. Here we assessed the possibility of cancer prognosis with shallow tumor RNA-seq, which would potentially enable cost-effective assessment of much larger numbers of samples for deeper biological and predictive insights. By statistically modeling the relative risk of an adverse outcome for thousands of subjects in The Cancer Genome Atlas^7-13, we present evidence that subsampled tumor RNA-seq data with a few hundred thousand reads per sample provide sufficient information for outcome prediction in several types of cancer. Analysis of predictive models revealed robust contributions from pathways known to be associated with outcomes. Our findings indicate that predictive models of outcomes in cancer may be developed with dramatically increases in sample numbers at low cost, thus potentially enabling the development of more realistic predictive models that incorporate diverse variables and their interactions. This strategy could also be used, for example, in longitudinal analysis of multiple regions of a tumor alongside treatment for quantitative modeling and prediction of outcome in personalized oncology.

Socioeconomic inequalities in metastasis, recurrence, stage and grade of breast cancer: a hospital-based retrospective cohort study

Introduction

This study aims to estimate the Socio-Economic Status (SES) inequality on the metastasis, recurrence, stage and grade in Breast Cancer (BC).

Methods

This retrospective cohort study conducted on 411 BC patients in Arak, Iran. Asset-based questionnaire used to estimate the household SES. For calculate of SES inequality was used from Concentration Index (C). Moreover for investigate the association between recurrence and metastasis with other variables were used from multilevel logistic regression and analysis of variance were used to investigate the relationship between SES and other variables. The data were analyzed with Stata (v.13) software.

Results

Results of analysis of variance showed statistical significant relationship between SES with, insurance, surgery, grade, stage, recurrence and metastasis (p-value < 0.05). Moreover the Odds Ratio (OR) were significant of recurrence with age, academic level of education, supplementary insurance history of BC in first-degree relatives, stage and grade, also, metastasis with age of > 80 years, insurance, supplementary insurance, history of BC in first-degree relatives, chemotherapy, radiotherapy, stage and grade four. The total C index obtained 0.015 (0.002, 0.026), 0.011 (0.003, 0.031), – 0.014 (– 0.034, – 0.001) and – 0.042 (– 0.061, – 0.002) for metastasis, recurrence, stage and grade of BC respectively.

Conclusions

Our results showed evidence of inequality in the metastasis, recurrence, stage and grade in BC patients.

Prediction of Late Breast Cancer-Specific Mortality in Recurrence-Free Breast Cancer Survivors Treated for Five Years with Tamoxifen

Purpose

The extension of endocrine therapy beyond 5 years for recurrence-free survivors of breast cancer improves survival; however, the issue on how to clinically identify appropriate candidates remains controversial. This study aimed to identify prognostic factors for breast-cancer-specific mortality in patients who have had 5 years of tamoxifen treatment and categorize subgroups based on the risk of death using combinations of these prognostic factors to assist in the clinical decision to perform further endocrine therapy.

Methods

In total, 3,158 patients with breast cancer were enrolled. Breast cancer-specific survival rates after 5 years of tamoxifen treatment were calculated, and associated prognostic factors were analyzed using a Cox proportional-hazards model.

Results

An age extreme at diagnosis (i.e., < 40 or ≥ 60 years), tumor size > 2 cm, and positive lymphovascular invasion were robust independent prognostic factors for late breast cancer-specific death in tamoxifen-treated patients (hazard ratio [HR] = 2.162, 1.739, and 1.993; p = 0.001, 0.047, and 0.011, respectively). Lymph node metastasis and progesterone receptor negativity had borderline significance in this regard (HR = 1.741 and 1.638, p = 0.099 and 0.061). The study patients were classified into four groups according to the number of prognostic indicators, i.e., low, intermediate, high, and extremely high risk. The additional 5- and 10-year cumulative risks of breast cancer-specific death were 0.8% and 1.5% in the low-risk group, 0.9% and 3.9% in the intermediate-risk group, 1.3% and 7.3% in the high-risk group, and 4.8% and 13.8% in the extremely high-risk group, respectively.

Conclusion

This new risk stratification system for late mortality in breast cancer can be used to identify the right candidates for extended endocrine therapy after 5 years of tamoxifen treatment.

Reproduction of molecular subtypes of gastric adenocarcinoma by transcriptome sequencing of archival tissue

Gastric cancer (GC) is a heterogeneous disease, so molecular classification is important for selecting the most appropriate treatment strategies for GC patients. To be applicable in the clinic, there is an urgent need for a platform that will allow screening real-life archival tissue specimens. For this purpose, we performed RNA sequencing of 50 samples from our Asian Cancer Research Group (ACRG) GC cohort to reproduce the molecular subtypes of GC using archival tissues with different platforms. We filtered out genes from the epithelial-to-mesenchymal transition (EMT) and microsatellite instability-high (MSI) signatures (coefficient ≤ 0.4) followed by the ACRG molecular subtype strategy. Overall accuracy of reproduction of ACRG subtype was 66% (33/50). Given the importance of EMT subtype in future clinical trials, we further developed the minimum number of genes (10 genes) for EMT signatures correlating highly with the original EMT signatures (correlation ≥ 0.65). Using our 10-gene model, we could classify EMT subtypes with high sensitivity (0.9576) and specificity (0.811). In conclusion, we reproduced ACRG GC subtypes using different platforms and could predict EMT subtypes with 10 genes and are now planning to use them in our prospective clinical study of precision oncology in GC.

Oncoprotein HBXIP induces PKM2 via transcription factor E2F1 to promote cell proliferation in ER-positive breast cancer

We have reported that hepatitis B X-interacting protein (HBXIP, also termed LAMTOR5) can act as an oncogenic transcriptional co-activator to modulate gene expression, promoting breast cancer development. Pyruvate kinase muscle isozyme M2 (PKM2), encoded by PKM gene, has emerged as a key oncoprotein in breast cancer. Yet, the regulatory mechanism of PKM2 is still unexplored. Here, we report that HBXIP can upregulate PKM2 to accelerate proliferation of estrogen receptor positive (ER+) breast cancer. Immunohistochemistry analysis using breast cancer tissue microarray uncovered a positive association between the expression of HBXIP and PKM2. We also discovered that PKM2 expression was positively related with HBXIP expression in clinical breast cancer patients by real-time PCR assay. Interestingly, in ER+ breast cancer cells, HBXIP was capable of upregulating PKM2 expression at mRNA and protein levels in a dose-dependent manner, as well as increasing the activity of PKM promoter. Mechanistically, HBXIP could stimulate PKM promoter through binding to the -779/-579 promoter region involving co-activation of E2F transcription factor 1 (E2F1). In function, cell viability, EdU, colony formation, and xenograft tumor growth assays showed that HBXIP contributed to accelerating cell proliferation through PKM2 in ER+ breast cancer. Collectively, we conclude that HBXIP induces PKM2 through transcription factor E2F1 to facilitate ER+ breast cancer cell proliferation. We provide new evidence for the mechanism of transcription regulation of PKM2 in promotion of breast cancer progression.

Pan-tumor genomic biomarkers for PD-1 checkpoint blockade-based immunotherapy

Programmed cell death protein-1 (PD-1) and programmed cell death ligand-1 (PD-L1) checkpoint blockade immunotherapy elicits durable antitumor effects in multiple cancers, yet not all patients respond. We report the evaluation of >300 patient samples across 22 tumor types from four KEYNOTE clinical trials. Tumor mutational burden (TMB) and a T cell-inflamed gene expression profile (GEP) exhibited joint predictive utility in identifying responders and nonresponders to the PD-1 antibody pembrolizumab. TMB and GEP were independently predictive of response and demonstrated low correlation, suggesting that they capture distinct features of neoantigenicity and T cell activation. Analysis of The Cancer Genome Atlas database showed TMB and GEP to have a low correlation, and analysis by joint stratification revealed biomarker-defined patterns of targetable-resistance biology. These biomarkers may have utility in clinical trial design by guiding rational selection of anti-PD-1 monotherapy and combination immunotherapy regimens.

PRL-3 Promotes Ubiquitination and Degradation of AURKA and Colorectal Cancer Progression via Dephosphorylation of FZR1

The oncogenic phosphatase PRL-3 is highly expressed in metastatic colorectal cancer but not in nonmetastatic colorectal cancer or noncolorectal cancer metastatic cancers. Although the proinvasive capacity of PRL-3 has been validated in multiple types of cancer, its impact on colorectal cancer progression and the underlying mechanisms remain poorly understood. Here, we report that overexpressed PRL-3 stimulates G₂-M arrest, chromosomal instability (CIN), self-renewal, and growth of colorectal cancer cells in xenograft models, while colorectal cancer cell proliferation is decreased. PRL-3-induced G₂-M arrest was associated with decreased expression of Aurora kinase A (AURKA). PRL-3-promoted slow proliferation, CIN, self-renewal, and growth in xenografts were counteracted by ectopic expression of AURKA. Conversely, knockdown of PRL-3 resulted in low proliferation, S-phase arrest, impaired self-renewal, increased apoptosis, and diminished xenograft growth independently of AURKA. Analysis of colorectal cancer specimens showed that expression of PRL-3 was associated with high status of CIN and poor prognosis, which were antagonized by expression of AURKA. PRL-3 enhanced AURKA ubiquitination and degradation in a phosphatase-dependent fashion. PRL-3 interacted with AURKA and FZR1, a regulatory component of the APC/C^FZR1 complex. Destabilization of AURKA by PRL-3 required PRL-3-mediated dephosphorylation of FZR1 and assembly of the APC/C^FZR1 complex. Our study suggests that PRL-3-regulated colorectal cancer progression is collectively determined by distinct malignant phenotypes and further reveals PRL-3 as an essential regulator of APC/C^FZR1 in controlling the stability of AURKA. SIGNIFICANCE: Dephosphorylation of FZR1 by PRL-3 facilitates the activity of APC/C^FZR1 by destabilizing AURKA, thus influencing aggressive characteristics and overall progression of colorectal cancer.

Identifying biomarkers of papillary renal cell carcinoma associated with pathological stage by weighted gene co-expression network analysis

Although papillary renal cell carcinoma (PRCC) accounts for 10%–15% of renal cell carcinoma (RCC), no predictive molecular biomarker is currently applicable to guiding disease stage of PRCC patients. The mRNASeq data of PRCC and adjacent normal tissue in The Cancer Genome Atlas was analyzed to identify 1148 differentially expressed genes, on which weighted gene co-expression network analysis was performed. Then 11 co-expressed gene modules were identified. The highest association was found between blue module and pathological stage (r = 0.45) by Pearson's correlation analysis. Functional enrichment analysis revealed that biological processes of blue module focused on nuclear division, cell cycle phase, and spindle (all P < 1e-10). All 40 hub genes in blue module can distinguish localized (pathological stage I, II) from non-localized (pathological stage III, IV) PRCC (P < 0.01). A good molecular biomarker for pathological stage of RCC must be a prognostic gene in clinical practice. Survival analysis was performed to reversely validate if hub genes were associated with pathological stage. Survival analysis unveiled that all hub genes were associated with patient prognosis (P < 0.01). The validation cohort GSE2748 verified that 30 hub genes can differentiate localized from non-localized PRCC (P < 0.01), and 18 hub genes are prognosis-associated (P < 0.01).

ROC curve indicated that the 17 hub genes exhibited excellent diagnostic efficiency for localized and non-localized PRCC (AUC > 0.7). These hub genes may serve as a biomarker and help to distinguish different pathological stages for PRCC patients.

Molecular Characterization of Gastric Carcinoma: Therapeutic Implications for Biomarkers and Targets

Palliative chemotherapy is the mainstay of treatment of advanced gastric carcinoma (GC). Monoclonal antibodies including trastuzumab, ramucirumab, and pembrolizumab have been shown to provide additional benefits. However, the clinical outcomes are often unpredictable and they can vary widely among patients. Currently, no biomarker is available for predicting treatment response in the individual patient except human epidermal growth factor receptor 2 (HER2) amplification and programmed death-ligand 1 (PD-L1) expression for effectiveness of trastuzumab and pembrolizumab, respectively. Multi-platform molecular analysis of cancer, including GC, may help identify predictive biomarkers to guide selection of therapeutic agents. Molecular classification of GC by The Cancer Genome Atlas Research Network and the Asian Cancer Research Group is expected to identify therapeutic targets and predictive biomarkers. Complementary to molecular characterization of GC is molecular profiling by expression analysis and genomic sequencing of tumor DNA. Initial analysis of patients with gastroesophageal carcinoma demonstrates that the ratio of progression-free survival (PFS) on molecular profile (MP)-based treatment to PFS on treatment prior to molecular profiling exceeds 1.3, suggesting the potential value of MP in guiding selection of individualized therapy. Future strategies aiming to integrate molecular classification and profiling of tumors with therapeutic agents for achieving the goal of personalized treatment of GC are indicated.

Proliferation genes in lung development associated with the prognosis of lung adenocarcinoma but not squamous cell carcinoma

There are many similarities between embryonic development and tumorigenesis, and gene expression profiles show that certain correlations exist between the gene signature during development and the clinical phenotypes of different cancers. Our group previously reported the gene expression profiles of human lung development, and the expression of one group of proliferation-related genes (PTN1 genes) steadily decreased during lung development. Here, we examined the prognostic value of PTN1 genes in 5 independent lung adenocarcinoma (ADC) and 5 lung independent squamous cell carcinoma (SCC) microarray datasets and found that the expression levels of PTN1 genes were associated with survival in lung ADC but not lung SCC. All of the lung ADC datasets contained a set of highly correlated genes from PTN1 genes, but the lung SCC datasets had no similar set of genes. We identified 63 unique core genes from the PTN1 genes in the 5 lung ADC datasets: 17 of these core genes appeared in at least 4 of the lung ADC datasets, and the 17 corresponding proteins clearly interacted more strongly with each other in lung ADC than in lung SCC. Moreover, 16 of the 17 core genes play major roles in the G₂ /M phase of the cell cycle. These data indicate that proliferation-related genes in lung development have a significant prognostic value for lung ADC; the synergistic effects of the 17 core genes play an important role in lung ADC prognosis. These genes may have significant clinical implications for the treatment and prognosis of lung ADC.

Applications of Machine Learning in Cancer Prediction and Prognosis

Machine learning is a branch of artificial intelligence that employs a variety of statistical, probabilistic and optimization techniques that allows computers to “learn” from past examples and to detect hard-to-discern patterns from large, noisy or complex data sets. This capability is particularly well-suited to medical applications, especially those that depend on complex proteomic and genomic measurements. As a result, machine learning is frequently used in cancer diagnosis and detection. More recently machine learning has been applied to cancer prognosis and prediction. This latter approach is particularly interesting as it is part of a growing trend towards personalized, predictive medicine. In assembling this review we conducted a broad survey of the different types of machine learning methods being used, the types of data being integrated and the performance of these methods in cancer prediction and prognosis. A number of trends are noted, including a growing dependence on protein biomarkers and microarray data, a strong bias towards applications in prostate and breast cancer, and a heavy reliance on “older” technologies such artificial neural networks (ANNs) instead of more recently developed or more easily interpretable machine learning methods. A number of published studies also appear to lack an appropriate level of validation or testing. Among the better designed and validated studies it is clear that machine learning methods can be used to substantially (15–25%) improve the accuracy of predicting cancer susceptibility, recurrence and mortality. At a more fundamental level, it is also evident that machine learning is also helping to improve our basic understanding of cancer development and progression.

Identification of Interacting Stromal Axes in Triple-Negative Breast Cancer

Triple-negative breast cancer (TNBC) is a molecularly heterogeneous cancer that is difficult to treat. Despite the role it may play in tumor progression and response to therapy, microenvironmental (stromal) heterogeneity in TNBC has not been well characterized. To address this challenge, we investigated the transcriptome of tumor-associated stroma isolated from TNBC (n = 57). We identified four stromal axes enriched for T cells (T), B cells (B), epithelial markers (E), or desmoplasia (D). Our analysis method (STROMA4) assigns a score along each stromal axis for each patient and then combined the axis scores to subtype patients. Analysis of these subtypes revealed that prognostic capacity of the B, T, and E scores was governed by the D score. When compared with a previously published TNBC subtyping scheme, the STROMA4 method better captured tumor heterogeneity and predicted patient benefit from therapy with increased sensitivity. This approach produces a simple ontology that captures TNBC heterogeneity and informs how tumor-associated properties interact to affect prognosis. Cancer Res; 77(17); 4673-83. ©2017 AACR.

RNA sequencing-based cell proliferation analysis across 19 cancers identifies a subset of proliferation-informative cancers with a common survival signature

Despite advances in cancer diagnosis and treatment strategies, robust prognostic signatures remain elusive in most cancers. Cell proliferation has long been recognized as a prognostic marker in cancer, but the generation of comprehensive, publicly available datasets allows examination of the links between cell proliferation and cancer characteristics such as mutation rate, stage, and patient outcomes. Here we explore the role of cell proliferation across 19 cancers (n = 6,581 patients) by using tissue-based RNA sequencing data from The Cancer Genome Atlas Project and calculating a 'proliferative index' derived from gene expression associated with Proliferating Cell Nuclear Antigen (PCNA) levels. This proliferative index is significantly associated with patient survival (Cox, p-value < 0.05) in 7 of 19 cancers, which we have defined as "proliferation-informative cancers" (PICs). In PICs, the proliferative index is strongly correlated with tumor stage and nodal invasion. PICs demonstrate reduced baseline expression of proliferation machinery relative to non-PICs. Additionally, we find the proliferative index is significantly associated with gross somatic mutation burden (Spearman, p = 1.76 x 10-23) as well as with mutations in individual driver genes. This analysis provides a comprehensive characterization of tumor proliferation indices and their association with disease progression and prognosis in multiple cancer types and highlights specific cancers that may be particularly susceptible to improved targeting of this classic cancer hallmark.

Molecular genetics complexity impeding research progress in breast and ovarian cancers

Breast and ovarian cancer are heterogeneous diseases. While breast cancer accounts for 25% of cancers worldwide, ovarian cancer accounts for 3.5% of all cancers and it is considered to be the most lethal type of cancer among women. In Oman, breast cancer accounts for 25% and ovarian cancer for 4.5% of all cancer cases. Various risk factors, including variable biological and clinical traits, are involved in the onset of breast and ovarian cancer. Although highly developed diagnostic and therapeutic methods have paved the way for better management, targeted therapy against specific biomarkers has not yet shown any significant improvement, particularly in triple-negative breast cancer and epithelial ovarian cancer, which are associated with high mortality rates. Thus, elucidating the mechanisms underlying the pathology of these diseases is expected to improve their prevention, prognosis and management. The aim of the present study was to provide a comprehensive review and updated information on genomics and proteomics alterations associated with cancer pathogenesis, as reported by several research groups worldwide. Furthermore, molecular research in our laboratory, aimed at identifying new pathways involved in the pathogenesis of breast and ovarian cancer using microarray and chromatin immunoprecipitation (ChIP), is discussed. Relevant candidate genes were found to be either up- or downregulated in a cohort of breast cancer cases. Similarly, ChIP analysis revealed that relevant candidate genes were regulated by the E2F5 transcription factor in ovarian cancer tissue. An ongoing study aims to validate these genes with a putative role as biological markers that may contribute to the development of targeted therapies for breast and ovarian cancer.

MCM2: An alternative to Ki-67 for measuring breast cancer cell proliferation

Breast cancer is a heterogeneous disease comprising a diversity of tumor subtypes that manifest themselves in a wide variety of clinical, pathological, and molecular features. One important subset, luminal breast cancers, comprises two clinically distinct subtypes luminal A and B each of them endowed with its own genetic program of differentiation and proliferation. Luminal breast cancers were operationally defined as follows: Luminal A: ER+, PR+, HER2-, Ki-67<14% and Luminal B: ER+ and/or PR+, HER2-,Ki-67≥14% or, alternatively ER+ and/or PR+, HER2+, any Ki-67. There is currently a need for a clinically robust and validated immunohistochemical assay that can help distinguish between luminal A and B breast cancer. MCM2 is a family member of the minichromosome maintenance protein complex whose role in DNA replication and cell proliferation is firmly established. As MCM2 appears to be an attractive alternative to Ki-67, we sought to study the expression of MCM2 and Ki-67 in different histological grades and molecular subtypes of breast cancer focusing primarily on ER-positive tumors. MCM2 and Ki-67 mRNA expression were studied using in silico analysis of available DNA microarray and RNA-sequencing data of human breast cancer. We next used immunohistochemistry to evaluate protein expression of MCM2 and Ki-67 on tissue microarrays of invasive breast carcinoma. We found that MCM2 and Ki-67 are highly expressed in breast tumors of high histological grades, comprising clinically aggressive tumors such as triple-negative, HER2-positive and luminal B subtypes. MCM2 expression was detected at higher levels than that of Ki-67 in normal breast tissues and in breast cancers. The bimodal distribution of MCM2 scores in ER+/HER2- breast tumors led to the identification of two distinct subgroups with different relapse-free survival rates. In conclusion, MCM2 expression can help sorting out two clinically important subsets of luminal breast cancer whose treatment and clinical outcomes are likely to diverge.

Prognostic values of negative estrogen or progesterone receptor expression in patients with luminal B HER2-negative breast cancer

BACKGROUND

The luminal subtype of breast cancer is sensitive to anti-estrogen therapy and shows a better prognosis than that of human epidermal growth factor receptor2 (HER2)-enriched or triple-negative breast cancer. However, the luminal type of breast cancer is heterogeneous and can have aggressive clinical features. We investigated the clinical implications of single hormone receptor negativity in a luminal B HER2-negative group.

METHODS

We collected luminal B HER2-negative breast cancer data that were estrogen receptor (ER) and/or progesterone receptor (PR) positive, Ki 67 high (>14 %), and HER2 negative and divided them into the ER- and PR-positive group and the ER- or PR-negative group. We analyzed the clinical and pathological data and survival according to ER or PR loss.

RESULTS

There were no statistical differences in TNM stage, breast and axillary operative methods, or number of tumors between the ER- and PR-positive group and ER- or PR-negative group. However, the ER- or PR-negative group was associated with older age (≥45 years), higher histological grade, lower Bcl-2 expression, and far higher Ki 67 (>50 %). Disease-free survival (DFS) and overall survival (OS) were shorter in the ER- or PR-negative group than that in the ER- and PR-positive group (p = 0.0038, p = 0.0071).

CONCLUSIONS

ER- or PR-negative subgroup showed worse prognosis than ER- and PR-positive subgroup in the luminal B HER2-negative group. We could consider the negativity of ER or PR as prognostic marker in luminal B HER2-negative subtype of breast cancer.

Clinical and microarray analysis of breast cancers of all subtypes from two prospective preoperative chemotherapy studies

Background:

We aimed to analyse clinical and gene expression profiles to predict pathologic complete response and disease-free survival using two consecutive, prospective, preoperative chemotherapy trial cohorts.

Methods:

Clinicopathological and gene expression data were evaluated in a cohort from two consecutive phase II preoperative studies that included patients with stage IIA–IIIC breast cancer of all subtypes. Analysed specimens were obtained before preoperative chemotherapy, and cDNA microarray analyses were performed using the Affymetrix Gene Chip U133 plus 2.0.

Results:

Between December 2005 and December 2010, 122 patients were analysed. The pathologic complete response rate was significantly higher in HER2+ and HR−/HER2− cancers. Age, pathologic complete response, HR−/HER2− status, and lymph node positivity (⩾4) were significant poor prognostic factors for disease-free survival. For the cDNA microarray analyses, sufficient tumour samples were available from 78 of the 107 patients (73%). An 8-gene signature predictive of pathologic complete response and a 17-gene signature predictive of prognosis were identified. Patients were categorised into low-risk (n=45) and high-risk groups (n=33) (HR 70.0, P=0.004).

Conclusions:

This study yielded preliminary data on the expression of specific genes predicting pathologic complete response and disease-free survival in a cohort of chemonaïve breast cancer patients. Further validation may distinguish those who would benefit most from perioperative chemotherapy as well as those needing further intervention.

Dane M, Esch A, Feiler HS, Wang NJ, Hardwicke MA, Laquerre S, Jackson J, W. Wood K, Weber B, Spellman PT, Aparicio S, Wooster R, Caldas C, Gray JW. Genome co-amplification upregulates a mitotic gene network activity that predicts outcome and response to mitotic protein inhibitors in breast cancer

BACKGROUND

High mitotic activity is associated with the genesis and progression of many cancers. Small molecule inhibitors of mitotic apparatus proteins are now being developed and evaluated clinically as anticancer agents. With clinical trials of several of these experimental compounds underway, it is important to understand the molecular mechanisms that determine high mitotic activity, identify tumor subtypes that carry molecular aberrations that confer high mitotic activity, and to develop molecular markers that distinguish which tumors will be most responsive to mitotic apparatus inhibitors.

METHODS

We identified a coordinately regulated mitotic apparatus network by analyzing gene expression profiles for 53 malignant and non-malignant human breast cancer cell lines and two separate primary breast tumor datasets. We defined the mitotic network activity index (MNAI) as the sum of the transcriptional levels of the 54 coordinately regulated mitotic apparatus genes. The effect of those genes on cell growth was evaluated by small interfering RNA (siRNA).

RESULTS

High MNAI was enriched in basal-like breast tumors and was associated with reduced survival duration and preferential sensitivity to inhibitors of the mitotic apparatus proteins, polo-like kinase, centromere associated protein E and aurora kinase designated GSK462364, GSK923295 and GSK1070916, respectively. Co-amplification of regions of chromosomes 8q24, 10p15-p12, 12p13, and 17q24-q25 was associated with the transcriptional upregulation of this network of 54 mitotic apparatus genes, and we identify transcription factors that localize to these regions and putatively regulate mitotic activity. Knockdown of the mitotic network by siRNA identified 22 genes that might be considered as additional therapeutic targets for this clinically relevant patient subgroup.

CONCLUSIONS

We define a molecular signature which may guide therapeutic approaches for tumors with high mitotic network activity.

An Integrated Genome-Wide Systems Genetics Screen for Breast Cancer Metastasis Susceptibility Genes

Metastasis remains the primary cause of patient morbidity and mortality in solid tumors and is due to the action of a large number of tumor-autonomous and non-autonomous factors. Here we report the results of a genome-wide integrated strategy to identify novel metastasis susceptibility candidate genes and molecular pathways in breast cancer metastasis. This analysis implicates a number of transcriptional regulators and suggests cell-mediated immunity is an important determinant. Moreover, the analysis identified novel or FDA-approved drugs as potentially useful for anti-metastatic therapy. Further explorations implementing this strategy may therefore provide a variety of information for clinical applications in the control and treatment of advanced neoplastic disease.

Metastasis, the spread and growth of tumor cells from the original tumor to secondary sites throughout the body, is the primary cause of cancer-related death for most solid tumor types. The process of metastasis is very complex, requiring multiple individual steps and the cooperation of different cell types during the dissemination and proliferation steps. Many genes are involved in this process, but at present few have been identified and characterized. In this study, we have integrated multiple genome-wide analysis methods to try to identify large numbers of candidate metastasis-associated genes and pathways based on a highly metastatic mouse model. Using this strategy, we have identified a number of genes that predict outcome of human breast cancer. These genes implicate specific molecular and cellular pathways in the metastatic process that might be used to intervene in the process. Furthermore, this integrated analysis implicates pre-existing drugs that might be re-purposed to help prevent or reduce metastatic burden in patients. The combined results obtained from this analytical strategy therefore provide an important platform for further genome-wide analysis into the etiology of metastatic disease.

CD10-Equipped Melanoma Cells Acquire Highly Potent Tumorigenic Activity: A Plausible Explanation of Their Significance for a Poor Prognosis

CD10 has been widely used in cancer diagnosis. We previously demonstrated that its expression in melanoma increased with tumor progression and predicted poor patient survival. However, the mechanism by which CD10 promotes melanoma progression remains unclear. In order to elucidate the role of CD10 in melanoma, we established CD10-overexpressing A375 melanoma cells and performed DNA microarray and qRT–PCR analyses to identify changes in the gene expression profile. The microarray analysis revealed that up-regulated genes in CD10-A375 were mostly involved in cell proliferation, angiogenesis, and resistance to apoptosis; down-regulated genes mostly belonged to the categories associated with cell adhesion and migration. Accordingly, in functional experiments, CD10-A375 showed significantly greater cell proliferation in vitro and higher tumorigenicity in vivo; CD10 enzymatic inhibitors, thiorphan and phosphoramidon, significantly blocked the tumor growth of CD10-A375 in mice. In migration and invasion assays, CD10-A375 displayed lower migratory and invasive capacity than mock-A375. CD10 augmented melanoma cell resistance to apoptosis mediated by etoposide and gemcitabine. These findings indicate that CD10 may promote tumor progression by regulating the expression profiles of genes related to cell proliferation, angiogenesis, and resistance to apoptosis.

Biological and Clinical Significance of MAD2L1 and BUB1, Genes Frequently Appearing in Expression Signatures for Breast Cancer Prognosis

To investigate the biologic relevance and clinical implication of genes involved in multiple gene expression signatures for breast cancer prognosis, we identified 16 published gene expression signatures, and selected two genes, MAD2L1 and BUB1. These genes appeared in 5 signatures and were involved in cell-cycle regulation. We analyzed the expression of these genes in relation to tumor features and disease outcomes. In vitro experiments were also performed in two breast cancer cell lines, MDA-MB-231 and MDA-MB-468, to assess cell proliferation, migration and invasion after knocking down the expression of these genes. High expression of these genes was found to be associated with aggressive tumors and poor disease-free survival of 203 breast cancer patients in our study, and the association with survival was confirmed in an online database consisting of 914 patients. In vitro experiments demonstrated that lowering the expression of these genes by siRNAs reduced tumor cell growth and inhibited cell migration and invasion. Our investigation suggests that MAD2L1 and BUB1 may play important roles in breast cancer progression, and measuring the expression of these genes may assist the prediction of breast cancer prognosis.

The prognostic impact of age in different molecular subtypes of breast cancer

Breast cancer is a heterogeneous entity composed of distinct molecular subgroups with different molecular and clinical features. We analyzed the association between molecular breast cancer subgroups, age at diagnosis, and prognosis in a compilation of publicly available gene expression datasets. Affymetrix gene expression data (U133A or U133Plus2.0 arrays) of 4467 breast cancers from 40 datasets were compiled and homogenized. Breast cancer subgroups were defined based on expression of ESR1, PR, HER2, and Ki67. Event-free survival was calculated as recurrence-free survival or distant metastasis-free survival if recurrence-free survival was not available. Young age at diagnosis is associated with higher frequency of triple negative and HER2 subtypes and lower frequency of luminal A breast cancers. The 5-year event-free survival rates of patients aged less than 40, between 40 and 50, and >50 years were 54.3 ± 3.5, 68.5 ± 1.9, and 70.4 ± 1.3 %, respectively. When controlling for breast cancer subtype, we found that age <40 years remained significantly associated with poor prognosis in triple negative breast cancer. The effect was modest in luminal tumors and not found in HER2 subtype. Both subtypes and age retained their significances in multivariate analysis. Association of age at diagnosis with molecular breast cancer subtype contributes to its important role as prognostic factor among patients with breast cancer. Still, within the group of triple negative breast cancer, young age <40 years has a significant prognostic value which was retained in multivariate analysis.

Evaluation of an Optimal Cut-Off Point for the Ki-67 Index as a Prognostic Factor in Primary Breast Cancer: A Retrospective Study

The Ki-67 index is an important biomarker for indicating the proliferation of cancer cells and is considered to be an effective prognostic factor for breast cancer. However, a standard cut-off point for the Ki-67 index has not yet been established. Therefore, the aim of this retrospective study was to determine an optimal cut-off point in order to establish it as a more accurate prognostic factor. Immunohistochemical analysis of the Ki-67 index was performed on 4329 patients with primary breast cancer from August 1987 to March 2012. Out of this sample, there were 3186 consecutive cases from September 1997 with simultaneous evaluations of ER, PgR and HER2 status. Cox's proportional hazard model was used to perform univariate and multivariate analyses of the factors related to OS. The hazard ratios (HR) and the p values were then compared to determine the optimal cut-off point for the Ki-67 index. The median Ki-67 index value was 20.5% (mean value 26.2%). The univariate analysis revealed that there was a statistically significant negative correlation with DFS and OS and the multivariate analysis revealed that the Ki-67 index value was a significant factor for DFS and OS. The top seven cut-off points were then carefully chosen based on the results of the univariate analysis using the lowest p-values and the highest HR as the main selection criteria. The multivariate analysis of the factors for OS showed that the cut-off point of 20% had the highest HR in all of the cases. However, the cutoff point of 20% was only a significant factor for OS in the Luminal/HER2- subtype. There was no correlation between the Ki-67 index value and OS in any of the other subtypes. These data indicate that the optimal cut-off point of 20% is the most effective prognostic factor for Luminal/HER2- breast cancer.

KPNA2 is a nuclear export protein that contributes to aberrant localisation of key proteins and poor prognosis of breast cancer

Background:

It is recognised that modulations of the nuclear import of macromolecules have a role in changing cellular phenotypes and carcinogenesis. We and others have noticed that aberrant subcellular localisation of DNA damage response (DDR) proteins in breast cancer (BC) is associated with loss-of-function phenotype. This study aims to investigate the biological and clinical significance of the nucleocytoplasmic transport protein karyopherin α-2 (KPNA2), and its role in controlling DDR proteins subcellular localisation in BC.

Methods:

A large (n=1494) and well-characterised series of early-stage invasive BC with a long-term follow-up was assessed for KPNA2 protein by using immunohistochemistry.

Results:

KPNA2 expression was associated with the subcellular localisation of key DDR proteins that showed cytoplasmic expression including BRCA1, RAD51, SMC6L1, γH2AX, BARD1, UBC9, PIAS1 and CHK1. High level of KPNA2 was associated not only with cytoplasmic localisation of these proteins but also with their low/negative nuclear expression. Positive KPNA2 expression was associated with negative oestrogen receptor and triple-negative phenotype. Survival analysis showed that KPNA2 was associated with poor outcome (P<0.0001), but this effect was not independent of other prognostic variables.

Conclusions:

This study provides further evidence for the complexity of DDR mechanism in BC, and that KNPA2 has a role in the aberrant subcellular localisation of DDR proteins with subsequent impaired function.

Molecular analysis of gastric cancer identifies subtypes associated with distinct clinical outcomes

Gastric cancer, a leading cause of cancer-related deaths, is a heterogeneous disease. We aim to establish clinically relevant molecular subtypes that would encompass this heterogeneity and provide useful clinical information. We use gene expression data to describe four molecular subtypes linked to distinct patterns of molecular alterations, disease progression and prognosis. The mesenchymal-like type includes diffuse-subtype tumors with the worst prognosis, the tendency to occur at an earlier age and the highest recurrence frequency (63%) of the four subtypes. Microsatellite-unstable tumors are hyper-mutated intestinal-subtype tumors occurring in the antrum; these have the best overall prognosis and the lowest frequency of recurrence (22%) of the four subtypes. The tumor protein 53 (TP53)-active and TP53-inactive types include patients with intermediate prognosis and recurrence rates (with respect to the other two subtypes), with the TP53-active group showing better prognosis. We describe key molecular alterations in each of the four subtypes using targeted sequencing and genome-wide copy number microarrays. We validate these subtypes in independent cohorts in order to provide a consistent and unified framework for further clinical and preclinical translational research.

KLF15 in breast cancer: a novel tumor suppressor?

PURPOSE

Krüppel-like factor 15 (KLF15) is a transcription factor that is involved in various biological processes, including cellular proliferation, differentiation and death. In addition, KLF15 has recently been implicated in the development of several human malignancies, including breast cancer. In vitro breast cancer studies have pointed at a putative role in the regulation of cell proliferation. As yet, however, KLF15 expression analyses in primary human breast cancers have not been reported. Here, we set out to investigate the clinical and biological significance of KLF15 expression in human breast cancers.

METHODS

KLF15 expression was evaluated by immunohistochemistry in 54 primary invasive ductal breast carcinomas, and its status was correlated with various clinicopathological parameters. We also assessed KLF15 expression in vitro in 4 breast cancer-derived cell lines using Western blotting, and examined the effects of exogenous KLF15 expression on cell cycle progression using flow cytometry. Concomitant (changes in) p21, p27 and TOPO2A expression levels were examined using real-time RT-PCR and immunocytochemistry, respectively.

RESULTS

In ~90% of the primary breast carcinoma tissues tested, KLF15 was found to be expressed and localized in either the cytoplasm, the nucleus or both. Predominant nuclear immunoreactivity was found to be associated with clinicopathological factors predicting a better clinical outcome (i.e., ER positive, HER2 negative, low grade, low Ki-67 expression). The breast cancer-derived cell lines tested showed a low KLF15 expression with a predominant cytoplasmic localization. Subsequent exogenous KLF15 over-expression resulted in a predominant nuclear localization and a concomitant decreased cellular proliferation and an arrest at the G0/G1 phase of the cell cycle. In addition, we found that nuclear KLF15 expression results in up-regulation of p21, a pivotal suppressor of the G1 to S phase transition of the cell cycle.

CONCLUSIONS

Our results indicate that nuclear KLF15 expression suppresses breast cancer cell proliferation at least partially through p21 up-regulation and subsequent cell cycle arrest. This is a first study addressing the role of KLF15 in breast cancer development.

A Novel Subset of Human Tumors That Simultaneously Overexpress Multiple E2F-responsive Genes Found in Breast, Ovarian, and Prostate Cancers

Reasoning that overexpression of multiple E2F-responsive genes might be a useful marker for RB1 dysfunction, we compiled a list of E2F-responsive genes from the literature and evaluated their expression in publicly available gene expression microarray data of patients with breast cancer, serous ovarian cancer, and prostate cancer. In breast cancer, a group of tumors was identified, each of which simultaneously overexpressed multiple E2F-responsive genes. Seventy percent of these genes were concerned with cell cycle progression, DNA repair, or mitosis. These E2F-responsive gene overexpressing (ERGO) tumors frequently exhibited additional evidence of Rb/E2F axis dysfunction, were mostly triple negative, and preferentially overexpressed multiple basal cytokeratins, suggesting that they overlapped substantially with the basal-like tumor subset. ERGO tumors were also identified in serous ovarian cancer and prostate cancer. In these cancer types, there was no evidence for a tumor subset comparable to the breast cancer basal-like subset. A core group of about 30 E2F-responsive genes were overexpressed in all three cancer types. Thus, it appears that disorders of the Rb/E2F axis can arise at multiple organ sites and produce tumors that simultaneously overexpress multiple E2F-responsive genes.

Grading of breast cancer on needle core biopsy: does a reduction in mitotic count threshold improve agreement with grade on excised specimens?

Background

Accurate assessment of invasive breast cancer grade on needle core biopsy (NCB) is important as it guides treatment.

Aim

To assess the agreement between grade of invasive cancer on NCB and excision and determine whether altering the mitotic count score threshold on NCB improves it.

Methods

Pathology slides from patients with an NCB diagnosis of invasive breast cancer who underwent subsequent breast conservation surgery in 2012 were reviewed. Mitotic counts were assessed and tumour grade agreement between NCB and excision evaluated. The mitotic count thresholds were altered and grade agreement was reassessed.

Results

In 283/359 (79%) cases, there was concurrence on histological grade between NCB and excision. Reduction in mitotic count thresholds did not improve either overall tumour grade agreement or agreement within any subgroup of patients.

Conclusions

In our experience, the current mitotic count score thresholds are appropriate and should be maintained.

Chromatin CKAP2, a new proliferation marker, as independent prognostic indicator in breast cancer

Background

The level of proliferation activity is a strong prognostic or predictive indicator in breast cancer, but its optimal measurement is still in debate, necessitating new proliferation markers. In the present study, the prognostic significance of the CKAP2-positive cell count (CPCC), a new proliferation marker, was evaluated, and the results were compared with those for the mitotic activity index (MAI).

Methods

This study included 375 early-stage breast cancer samples collected from two institutions between 2000 and 2006. Immunohistochemical staining was performed using a CKAP2 monoclonal antibody. Cox proportional hazard regression models were fitted to determine the association between the CPCC and relapse-free survival (RFS) amongst three groups formed on the basis of the CPCC or MAI value: groups 2 and 3 showing the middle and highest values, respectively, and group 1 the lowest.

Results

After adjustment for age, T stage, N stage, HER2 status, estrogen receptor status, progesterone receptor status, institution, and year of surgical resection, the CPCC was associated with a significantly worse RFS {hazard ratio [HR]  = 4.10 (95% CI: 1.64–10.29) for group 2; HR  = 4.35 (95% CI: 2.04–10.35) for group 3}. Moreover, its prognostic significance was similar to or higher than that based on the MAI {HR  = 2.05 (95% CI: 0.94–4.65) for group 2; HR  = 2.35 (95% CI: 1.09–5.10) for group 3}. In subgroup analyses, the CPCC showed a prognostic significance in the luminal A and triple-negative subgroups, but not in the HER2-positive subgroup.

Conclusions

Chromatin CKAP2 is an independent prognostic marker for RFS in early-stage breast cancer, and could potentially replace the MAI in clinical evaluation of proliferation activity. Additionally, our study results suggest that the prognostic significance of proliferation activity differs among the various subgroups of breast cancer.

The oncoprotein HBXIP up-regulates SCG3 through modulating E2F1 and miR-509-3p in hepatoma cells

Hepatitis B X-interacting protein (HBXIP) is an important oncoprotein in hepatocarcinogenesis. Here, we found that the expression levels of HBXIP were positively associated with those of Secretogranin III (SCG3) in clinical hepatocellular carcinoma tissues. We identified that HBXIP up-regulated the expression of SCG3 through modulating both E2F transcription factor 1 (E2F1) and miR-509-3p. HBXIP suppressed miR-509-3p through activating NF-κB. In function, we showed that SCG3 increased the proliferation of hepatoma cells and HBXIP enhanced the proliferation of the cells via SCG3 in vitro and in vivo. Thus, we conclude that HBXIP facilitates the proliferation of hepatoma cells through up-regulating SCG3.

Poly-omic prediction of complex traits: OmicKriging

High-confidence prediction of complex traits such as disease risk or drug response is an ultimate goal of personalized medicine. Although genome-wide association studies have discovered thousands of well-replicated polymorphisms associated with a broad spectrum of complex traits, the combined predictive power of these associations for any given trait is generally too low to be of clinical relevance. We propose a novel systems approach to complex trait prediction, which leverages and integrates similarity in genetic, transcriptomic, or other omics-level data. We translate the omic similarity into phenotypic similarity using a method called Kriging, commonly used in geostatistics and machine learning. Our method called OmicKriging emphasizes the use of a wide variety of systems-level data, such as those increasingly made available by comprehensive surveys of the genome, transcriptome, and epigenome, for complex trait prediction. Furthermore, our OmicKriging framework allows easy integration of prior information on the function of subsets of omics-level data from heterogeneous sources without the sometimes heavy computational burden of Bayesian approaches. Using seven disease datasets from the Wellcome Trust Case Control Consortium (WTCCC), we show that OmicKriging allows simple integration of sparse and highly polygenic components yielding comparable performance at a fraction of the computing time of a recently published Bayesian sparse linear mixed model method. Using a cellular growth phenotype, we show that integrating mRNA and microRNA expression data substantially increases performance over either dataset alone. Using clinical statin response, we show improved prediction over existing methods. We provide an R package to implement OmicKriging (http://www.scandb.org/newinterface/tools/OmicKriging.html).

Visual gene-network analysis reveals the cancer gene co-expression in human endometrial cancer

Background

Endometrial cancers (ECs) are the most common form of gynecologic malignancy. Recent studies have reported that ECs reveal distinct markers for molecular pathogenesis, which in turn is linked to the various histological types of ECs. To understand further the molecular events contributing to ECs and endometrial tumorigenesis in general, a more precise identification of cancer-associated molecules and signaling networks would be useful for the detection and monitoring of malignancy, improving clinical cancer therapy, and personalization of treatments.

Results

ECs-specific gene co-expression networks were constructed by differential expression analysis and weighted gene co-expression network analysis (WGCNA). Important pathways and putative cancer hub genes contribution to tumorigenesis of ECs were identified. An elastic-net regularized classification model was built using the cancer hub gene signatures to predict the phenotypic characteristics of ECs. The 19 cancer hub gene signatures had high predictive power to distinguish among three key principal features of ECs: grade, type, and stage. Intriguingly, these hub gene networks seem to contribute to ECs progression and malignancy via cell-cycle regulation, antigen processing and the citric acid (TCA) cycle.

Conclusions

The results of this study provide a powerful biomarker discovery platform to better understand the progression of ECs and to uncover potential therapeutic targets in the treatment of ECs. This information might lead to improved monitoring of ECs and resulting improvement of treatment of ECs, the 4^th most common of cancer in women.