Integrated proteotranscriptomics of breast cancer reveals globally increased protein-mRNA concordance associated with subtypes and survival

A comprehensive map of alternative polyadenylation in African American and European American lung cancer patients

Deciphering the post-transcriptional mechanisms (PTM) regulating gene expression is critical to understand the dynamics underlying transcriptomic regulation in cancer. Alternative polyadenylation (APA)-regulation of mRNA 3'UTR length by alternating poly(A) site usage-is a key PTM mechanism whose comprehensive analysis in cancer remains an important open challenge. Here we use a method and analysis pipeline that sequences 3'end-enriched RNA directly to overcome the saturation limitation of traditional 5'-3' based sequencing. We comprehensively map the APA landscape in lung cancer in a cohort of 98 tumor/non-involved tissues derived from European American and African American patients. We identify a global shortening of 3'UTR transcripts in lung cancer, with notable functional implications on the expression of both coding and noncoding genes. We find that APA of non-coding RNA transcripts (long non-coding RNAs and microRNAs) is a recurrent event in lung cancer and discover that the selection of alternative polyA sites is a form of non-coding RNA expression control. Our results indicate that mRNA transcripts from EAs are two times more likely than AAs to undergo APA in lung cancer. Taken together, our findings comprehensively map and identify the important functional role of alternative polyadenylation in determining transcriptomic heterogeneity in lung cancer.

Argininosuccinate lyase is a metabolic vulnerability in breast development and cancer

Epithelial-to-mesenchymal transition (EMT) is fundamental to both normal tissue development and cancer progression. We hypothesized that EMT plasticity defines a range of metabolic phenotypes and that individual breast epithelial metabolic phenotypes are likely to fall within this phenotypic landscape. To determine EMT metabolic phenotypes, the metabolism of EMT was described within genome-scale metabolic models (GSMMs) using either transcriptomic or proteomic data from the breast epithelial EMT cell culture model D492. The ability of the different data types to describe breast epithelial metabolism was assessed using constraint-based modeling which was subsequently verified using ¹³C isotope tracer analysis. The application of proteomic data to GSMMs provided relatively higher accuracy in flux predictions compared to the transcriptomic data. Furthermore, the proteomic GSMMs predicted altered cholesterol metabolism and increased dependency on argininosuccinate lyase (ASL) following EMT which were confirmed in vitro using drug assays and siRNA knockdown experiments. The successful verification of the proteomic GSMMs afforded iBreast2886, a breast GSMM that encompasses the metabolic plasticity of EMT as defined by the D492 EMT cell culture model. Analysis of breast tumor proteomic data using iBreast2886 identified vulnerabilities within arginine metabolism that allowed prognostic discrimination of breast cancer patients on a subtype-specific level. Taken together, we demonstrate that the metabolic reconstruction iBreast2886 formalizes the metabolism of breast epithelial cell development and can be utilized as a tool for the functional interpretation of high throughput clinical data.

Abnormally elevated ubiquilin‑1 expression in breast cancer regulates metastasis and stemness via AKT signaling

Ubiquilin-1 (UBQLN1) is an essential factor for the maintenance of proteostasis in cells. It is important for the regulation of different protein degradation mechanisms, including the ubiquitin-proteasome system, autophagy and endoplasmic reticulum-associated protein degradation pathways. However, the role of UBQLN1 in cancer progression remains largely unknown. In the present study, the expression, functions and molecular mechanisms of UBQLN1 in breast cancer tissue samples and cell lines were explored. Immunohistochemical and bioinformatics analyses revealed that UBQLN1 expression was significantly upregulated in breast cancer tissues and cell lines. UBQLN1 expression in breast cancer was significantly associated with lymph node metastasis and TNM stage. Moreover, a high UBQLN1 expression was a predictor of an unfavorable survival in patients with breast cancer. In vitro, UBQLN1 silencing markedly inhibited cell migration and invasion, epithelial-to-mesenchymal transition (EMT) and MMP expression. UBQLN1 silencing attenuated the stem cell-like properties of breast cancer cells, including their mammosphere-forming abilities. UBQLN1 knockdown also enhanced breast cancer cell chemosensitivity to paclitaxel. The expression levels of the stem cell markers. Aldehyde dehydrogenase 1 (ALDH1), Oct-4 and Sox2 were significantly decreased in the cells in which UBQLN1 was silenced, whereas breast cancer stem cells exhibited an increased expression of UBQLN1. Mechanistically, UBQLN1 knockdown inhibited the activation of AKT signaling, as revealed by the increased PTEN expression and the decreased expression of phosphorylated AKT in cells in which UBQLN1 was silenced. On the whole, the present study demonstrates that UBQLN1 is aberrantly upregulated in breast cancer and predicts a poor prognosis. The silencing of UBQLN1 inhibited the invasion, EMT and stemness of breast cancer cells, possibly via AKT signaling.

Proteomics and its applications in breast cancer

Breast cancer is an individually unique, multi-faceted and chameleonic disease, an eternal challenge for the new era of high-integrated precision diagnostic and personalized oncomedicine. Besides traditional single-omics fields (such as genomics, epigenomics, transcriptomics and metabolomics) and multi-omics contributions (proteogenomics, proteotranscriptomics or reproductomics), several new "-omics" approaches and exciting proteomics subfields are contributing to basic and advanced understanding of these "multiple diseases termed breast cancer": phenomics/cellomics, connectomics and interactomics, secretomics, matrisomics, exosomics, angiomics, chaperomics and epichaperomics, phosphoproteomics, ubiquitinomics, metalloproteomics, terminomics, degradomics and metadegradomics, adhesomics, stressomics, microbiomics, immunomics, salivaomics, materiomics and other biomics. Throughout the extremely complex neoplastic process, a Breast Cancer Cell Continuum Concept (BCCCC) has been modeled in this review as a spatio-temporal and holistic approach, as long as the breast cancer represents a complex cascade comprising successively integrated populations of heterogeneous tumor and cancer-associated cells, that reflect the carcinoma's progression from a "driving mutation" and formation of the breast primary tumor, toward the distant secondary tumors in different tissues and organs, via circulating tumor cell populations. This BCCCC is widely sustained by a Breast Cancer Proteomic Continuum Concept (BCPCC), where each phenotype of neoplastic and tumor-associated cells is characterized by a changing and adaptive proteomic profile detected in solid and liquid minimal invasive biopsies by complex proteomics approaches. Such a profile is created, beginning with the proteomic landscape of different neoplastic cell populations and cancer-associated cells, followed by subsequent analysis of protein biomarkers involved in epithelial-mesenchymal transition and intravasation, circulating tumor cell proteomics, and, finally, by protein biomarkers that highlight the extravasation and distant metastatic invasion. Proteomics technologies are producing important data in breast cancer diagnostic, prognostic, and predictive biomarkers discovery and validation, are detecting genetic aberrations at the proteome level, describing functional and regulatory pathways and emphasizing specific protein and peptide profiles in human tissues, biological fluids, cell lines and animal models. Also, proteomics can identify different breast cancer subtypes and specific protein and proteoform expression, can assess the efficacy of cancer therapies at cellular and tissular level and can even identify new therapeutic target proteins in clinical studies.

Survival analysis in breast cancer using proteomic data from four independent datasets

Breast cancer clinical treatment selection is based on the immunohistochemical determination of four protein biomarkers: ESR1, PGR, HER2, and MKI67. Our aim was to correlate immunohistochemical results to proteome-level technologies in measuring the expression of these markers. We also aimed to integrate available proteome-level breast cancer datasets to identify and validate new prognostic biomarker candidates. We searched studies involving breast cancer patient cohorts with published survival and proteomic information. Immunohistochemistry and proteomic technologies were compared using the Mann-Whitney test. Receiver operating characteristics (ROC) curves were generated to validate discriminative power. Cox regression and Kaplan-Meier survival analysis were calculated to assess prognostic power. False Discovery Rate was computed to correct for multiple hypothesis testing. We established a database integrating protein expression data and survival information from four independent cohorts for 1229 breast cancer patients. In all four studies combined, a total of 7342 unique proteins were identified, and 1417 of these were identified in at least three datasets. ESR1, PGR, and HER2 protein expression levels determined by RPPA or LC-MS/MS methods showed a significant correlation with the levels determined by immunohistochemistry (p < 0.0001). PGR and ESR1 levels showed a moderate correlation (correlation coefficient = 0.17, p = 0.0399). An additional panel of candidate proteins, including apoptosis-related proteins (BCL2,), adhesion markers (CDH1, CLDN3, CLDN7) and basal markers (cytokeratins), were validated as prognostic biomarkers. Finally, we expanded our previously established web tool designed to validate survival-associated biomarkers by including the proteomic datasets analyzed in this study ( https://kmplot.com/ ). In summary, large proteomic studies now provide sufficient data enabling the validation and ranking of potential protein biomarkers.

High expression of DNAJA1 (HDJ2) predicts unfavorable survival outcomes in breast cancer

Aim: DNAJA1 is associated with several cancers, but its biomarker potential in breast cancer is not adequately known. Materials & methods: Q-RT-PCR, immunohistochemistry, COBRA methods and in silico tools (KM-Plotter, UALCAN) were used to analyze the expression level, methylation status and prognostic value of DNAJA1 in breast cancer. Results: DNAJA1 expression was significantly higher in clinical tumor samples compared with normal samples. High DNAJA1 mRNA expression is associated with poor survival values in breast cancer. DNAJA1 promoter region is hypomethylated in cell lines and clinical samples. Conclusion: High DNAJA1 expression predicts poor clinical survival outcomes for breast cancer. Other than promoter methylation, epigenetic factors also warrant investigation in future studies as a regulatory mechanism of DNAJA1 expression in breast cancer.

Common and mutation specific phenotypes of KRAS and BRAF mutations in colorectal cancer cells revealed by integrative -omics analysis

BACKGROUND

Genes in the Ras pathway have somatic mutations in at least 60 % of colorectal cancers. Despite activating the same pathway, the BRAF V600E mutation and the prevalent mutations in codon 12 and 13 of KRAS have all been linked to different clinical outcomes, but the molecular mechanisms behind these differences largely remain to be clarified.

METHODS

To characterize the similarities and differences between common activating KRAS mutations and between KRAS and BRAF mutations, we used genome editing to engineer KRAS G12C/D/V and G13D mutations in colorectal cancer cells that had their mutant BRAF V600E allele removed and subjected them to transcriptome sequencing, global proteomics and metabolomics analyses.

RESULTS

By intersecting differentially expressed genes, proteins and metabolites, we uncovered (i) two-fold more regulated genes and proteins when comparing KRAS to BRAF mutant cells to those lacking Ras pathway mutation, (ii) five differentially expressed proteins in KRAS mutants compared to cells lacking Ras pathway mutation (IFI16, S100A10, CD44, GLRX and AHNAK2) and 6 (CRABP2, FLNA, NXN, LCP1, S100A10 and S100A2) compared to BRAF mutant cells, (iii) 19 proteins expressed differentially in a KRAS mutation specific manner versus BRAF V600E cells, (iv) regulation of the Integrin Linked Kinase pathway by KRAS but not BRAF mutation, (v) regulation of amino acid metabolism, particularly of the tyrosine, histidine, arginine and proline pathways, the urea cycle and purine metabolism by Ras pathway mutations, (vi) increased free carnitine in KRAS and BRAF mutant RKO cells.

CONCLUSIONS

This comprehensive integrative -omics analysis confirms known and adds novel genes, proteins and metabolic pathways regulated by mutant KRAS and BRAF signaling in colorectal cancer. The results from the new model systems presented here can inform future development of diagnostic and therapeutic approaches targeting tumors with KRAS and BRAF mutations.

WBP2 promotes BTRC mRNA stability to drive migration and invasion in triple-negative breast cancer via NF-κB activation

WW‐domain‐binding protein 2 (WBP2) is an oncogene that drives breast carcinogenesis through regulating Wnt, estrogen receptor (ER), and Hippo signaling. Recent studies have identified neoteric modes of action of WBP2 other than its widely recognized function as a transcriptional coactivator. Here, we identified a previously unexplored role of WBP2 in inflammatory signaling in breast cancer via an integrated proteogenomic analysis of The Cancer Genome Atlas Breast Invasive Carcinoma (TCGA BRCA) dataset. WBP2 was shown to enhance the migration and invasion in triple‐negative breast cancer (TNBC) cells especially under tumor necrosis factor alpha (TNF‐α) stimulation. Molecularly, WBP2 potentiates TNF‐α‐induced nuclear factor kappa B (NF‐κB) transcriptional activity and nuclear localization through aggrandizing ubiquitin‐mediated proteasomal degradation of its upstream inhibitor, NF‐κB inhibitor alpha (NFKBIA; also known as IκBα). We further demonstrate that WBP2 induces mRNA stability of beta‐transducin repeat‐containing E3 ubiquitin protein ligase (BTRC), which targets IκBα for ubiquitination and degradation. Disruption of IκBα rescued the impaired migratory and invasive phenotypes in WBP2‐silenced cells, while loss of BTRC ameliorated WBP2‐driven migration and invasion. Clinically, the WBP2‐BTRC‐IκBα signaling axis correlates with poorer prognosis in breast cancer patients. Our findings reveal a pivotal mechanism of WBP2 in modulating BTRC‐IκBα‐NF‐κB pathway to promote TNBC aggressiveness.

Engineering Breast Cancer On-chip-Moving Toward Subtype Specific Models

Breast cancer is the second leading cause of death among women worldwide, and while hormone receptor positive subtypes have a clear and effective treatment strategy, other subtypes, such as triple negative breast cancers, do not. Development of new drugs, antibodies, or immune targets requires significant re-consideration of current preclinical models, which frequently fail to mimic the nuances of patient-specific breast cancer subtypes. Each subtype, together with the expression of different markers, genetic and epigenetic profiles, presents a unique tumor microenvironment, which promotes tumor development and progression. For this reason, personalized treatments targeting components of the tumor microenvironment have been proposed to mitigate breast cancer progression, particularly for aggressive triple negative subtypes. To-date, animal models remain the gold standard for examining new therapeutic targets; however, there is room for in vitro tools to bridge the biological gap with humans. Tumor-on-chip technologies allow for precise control and examination of the tumor microenvironment and may add to the toolbox of current preclinical models. These new models include key aspects of the tumor microenvironment (stroma, vasculature and immune cells) which have been employed to understand metastases, multi-organ interactions, and, importantly, to evaluate drug efficacy and toxicity in humanized physiologic systems. This review provides insight into advanced in vitro tumor models specific to breast cancer, and discusses their potential and limitations for use as future preclinical patient-specific tools.

Evaluation of Extracellular Matrix Composition to Improve Breast Cancer Modeling

The development of resistance to therapy is a significant obstacle to effective therapeutic regimens. Evaluating the effects of oncology drugs in the laboratory setting is limited by the lack of translational models that accurately recapitulate cell-microenvironment interactions present in tumors. Acquisition of resistance to therapy is facilitated, in part, by the composition of the tumor extracellular matrix (ECM), with the primary current in vitro model using collagen I (COL I). Here we seek to identify the prevalence of COL I-enhanced expression in the triple-negative breast cancer (TNBC) subtype. Furthermore, we identify if methods of response to therapy are altered depending on matrix composition. We demonstrated that collagen content varies in patient tumor samples across subtypes, with COL I expression dramatically increased in typically less aggressive estrogen receptor (ER)-positive(ER+)/progesterone receptor (PGR)-positive (PGR+) cancers irrespective of patient age or race. These findings are of significance considering how frequently COL I is implicated in tumor progression. In vitro analyses of ER+ and ER-negative (ER-) cell lines were used to determine the effects of ECM content (collagen I, collagen IV, fibronectin, and laminin) on proliferation, cellular phenotype, and survival. Neither ER+ nor ER- cells demonstrated significant increases in proliferation when cultured on these ECM substrates. ER- cells cultured on these substrates were sensitized to both chemotherapy and targeted therapy. In addition, MDA-MB-231 cells expressed different morphologies, binding affinities, and stiffness across these substrates. We also demonstrated that ECM composition significantly alters transcription of senescence-associated pathways across ER+ and ER- cell lines. Together, these results suggest that complex matrix composites should be incorporated into in vitro tumor models, especially for the drug-resistant TNBC subtype. Impact statement The importance of tumor extracellular matrix (ECM) in disease progression is often inadequately represented in models of breast cancer that rely heavily on collagen I and Matrigel. Through immunohistochemistry analysis of patient breast tumors, we show a wide variation in collagen content based on subtype, specifically a repression of fibril collagens in the receptor negative subtype, irrespective of age and race. We also demonstrated that tumor ECM composition alters cellular elasticity and oncogenic pathway activation demonstrating that physiologically relevant three-dimensional models of breast cancer should include an ECM that is subtype specific.

Identification of disease-promoting stromal components by comparative proteomic and transcriptomic profiling of canine mammary tumors using laser-capture microdissected FFPE tissue

Cancer-associated stroma (CAS) profoundly influences progression of tumors including mammary carcinoma (mCA). Canine simple mCA represent relevant models of human mCA, notably also with respect to CAS. While transcriptomic changes in CAS of mCA are well described, it remains unclear to what extent these translate to the protein level. Therefore, we sought to gain insight into the proteomic changes in CAS and compare them with transcriptomic changes in the same tissue. To this end, we analyzed CAS and matched normal stroma using laser-capture microdissection (LCM) and LC-MS/MS in a cohort of 14 formalin-fixed paraffin embedded (FFPE) canine mCAs that we had previously characterized using LCM-RNAseq. Our results reveal clear differences in protein abundance between CAS and normal stroma, which are characterized by changes in the extracellular matrix, the cytoskeleton, and cytokines such as TNF. The proteomics- and RNAseq-based analyses of LCM-FFPE show a substantial degree of correlation, especially for the most deregulated targets and a comparable activation of pathways. Finally, we validate transcriptomic upregulation of LTBP2, IGFBP2, COL6A5, POSTN, FN1, COL4A1, COL12A1, PLOD2, COL4A2, and IGFBP7 in CAS on the protein level and demonstrate their adverse prognostic value for human breast cancer. Given the relevance of canine mCA as a model for the human disease, our analysis substantiates these targets as disease-promoting stromal components with implications for breast cancer in both species.

HERC1 Regulates Breast Cancer Cells Migration and Invasion

Simple Summary

Breast cancer has the highest incidence and mortality in women worldwide, and, despite formidable advances in its prevention, detection, and treatment, the development of metastasis foci still represents a significant reduction in patients’ survival and life quality. The Ubiquitin-Proteasome System plays a fundamental role in the maintenance of protein balance, and its dysregulation has been associated with malignant transformation and tumor cells invasive potential. The objective of our work was focused on the identification of ubiquitination-related genes that could represent putative molecular targets for the treatment of breast cancer dissemination. For that purpose, we performed a genetic study and identified and validated HERC1 (HECT and RLD Domain Containing E3 Ubiquitin Protein Ligase Family Member 1) as a regulator of migration and invasion. We confirmed that its depletion reduces tumorigenicity and the appearance of metastasis foci and determined that HERC1 protein expression inversely correlates with breast cancer patients’ overall survival. Altogether, we demonstrate that HERC1 might represent a novel therapeutic target in breast cancer.

Abstract

Tumor cell migration and invasion into adjacent tissues is one of the hallmarks of cancer and the first step towards secondary tumors formation, which represents the leading cause of cancer-related deaths. This process is considered an unmet clinical need in the treatment of this disease, particularly in breast cancers characterized by high aggressiveness and metastatic potential. To identify and characterize genes with novel functions as regulators of tumor cell migration and invasion, we performed a genetic loss-of-function screen using a shRNA library directed against the Ubiquitin Proteasome System (UPS) in a highly invasive breast cancer derived cell line. Among the candidates, we validated HERC1 as a gene regulating cell migration and invasion. Furthermore, using animal models, our results indicate that HERC1 silencing affects primary tumor growth and lung colonization. Finally, we conducted an in silico analysis using publicly available protein expression data and observed an inverse correlation between HERC1 expression levels and breast cancer patients’ overall survival. Altogether, our findings demonstrate that HERC1 might represent a novel therapeutic target for the development or improvement of breast cancer treatment.

High nuclear TPX2 expression correlates with TP53 mutation and poor clinical behavior in a large breast cancer cohort, but is not an independent predictor of chromosomal instability

BACKGROUND

Targeting Protein for Xenopus Kinesin Like Protein 2 (TPX2) is a microtubule associated protein that functions in mitotic spindle assembly. TPX2 also localizes to the nucleus where it functions in DNA damage repair during S-phase. We and others have previously shown that TPX2 RNA levels are strongly associated with chromosomal instability (CIN) in breast and other cancers, and TPX2 RNA levels have been demonstrated to correlate with aggressive behavior and poor clinical outcome across a range of solid malignancies, including breast cancer.

METHODS

We perform TPX2 IHC on a cohort of 253 primary breast cancers and adopt a clinically amenable scoring system to separate tumors into low, intermediate, or high TPX2 expression. We then correlate TPX2 expression against diverse pathologic parameters and important measures of clinical outcome, including disease-specific and overall survival. We link TPX2 expression to TP53 mutation and evaluate whether TPX2 is an independent predictor of chromosomal instability (CIN).

RESULTS

We find that TPX2 nuclear expression strongly correlates with high grade morphology, elevated clinical stage, negative ER and PR status, and both disease-specific and overall survival. We also show that increased TPX2 nuclear expression correlates with elevated ploidy, supernumerary centrosomes, and TP53 mutation. TPX2 nuclear expression correlates with CIN via univariate analyses but is not independently predictive when compared to ploidy, Ki67, TP53 mutational status, centrosome number, and patient age.

CONCLUSIONS

Our findings demonstrate a strong correlation between TPX2 nuclear expression and aggressive tumor behavior, and show that TPX2 overexpression frequently occurs in the setting of TP53 mutation and elevated ploidy. However, TPX2 expression is not an independent predictor of CIN where it fails to outperform existing clinical and pathologic metrics.

Cancer-associated exportin-6 upregulation inhibits the transcriptionally repressive and anticancer effects of nuclear profilin-1

Aberrant expression of nuclear transporters and deregulated subcellular localization of their cargo proteins are emerging as drivers and therapeutic targets of cancer. Here, we present evidence that the nuclear exporter exportin-6 and its cargo profilin-1 constitute a functionally important and frequently deregulated axis in cancer. Exportin-6 upregulation occurs in numerous cancer types and is associated with poor patient survival. Reducing exportin-6 level in breast cancer cells triggers antitumor effects by accumulating nuclear profilin-1. Mechanistically, nuclear profilin-1 interacts with eleven-nineteen-leukemia protein (ENL) within the super elongation complex (SEC) and inhibits the ability of the SEC to drive transcription of numerous pro-cancer genes including MYC. XPO6 and MYC are positively correlated across diverse cancer types including breast cancer. Therapeutically, exportin-6 loss sensitizes breast cancer cells to the bromodomain and extra-terminal (BET) inhibitor JQ1. Thus, exportin-6 upregulation is a previously unrecognized cancer driver event by spatially inhibiting nuclear profilin-1 as a tumor suppressor.

RNA m6A Methylation Regulators Subclassify Luminal Subtype in Breast Cancer

RNA N⁶-methyladenosine (m⁶A) methylation is the most prevalent epitranscriptomic modification in mammals, with a complex and fine-tuning regulatory system. Recent studies have illuminated the potential of m⁶A regulators in clinical applications including diagnosis, therapeutics, and prognosis. Based on six datasets of breast cancer in The Cancer Genome Atlas (TCGA) database and two additional proteomic datasets, we provide a comprehensive view of all the known m⁶A regulators in their gene expression, copy number variations (CNVs), DNA methylation status, and protein levels in breast tumors and their association with prognosis. Among four breast cancer subtypes, basal-like subtype exhibits distinct expression and genomic alteration in m⁶A regulators from other subtypes. Accordingly, four representative regulators (IGF2BP2, IGF2BP3, YTHDC2, and RBM15) are identified as basal-like subtype-featured genes. Notably, luminal A/B samples are subclassified into two clusters based on the methylation status of those four genes. In line with its similarity to basal-like subtype, cluster1 shows upregulation in immune-related genes and cell adhesion molecules, as well as an increased number of tumor-infiltrating lymphocytes. Besides, cluster1 has worse disease-free and progression-free survival, especially among patients diagnosed with stage II and luminal B subtype. Together, this study highlights the potential functions of m⁶A regulators in the occurrence and malignancy progression of breast cancer. Given the heterogeneity within luminal subtype and high risk of recurrence and metastasis in a portion of patients, the prognostic stratification of luminal A/B subtypes utilizing basal-featured m⁶A regulators may help to improve the accuracy of diagnosis and therapeutics of breast cancer.

Development of an engineered peptide antagonist against periostin to overcome doxorubicin resistance in breast cancer

Background

Chemoresistance is one of the main problems in treatment of cancer. Periostin (PN) is a stromal protein which is mostly secreted from cancer associated fibroblasts in the tumor microenvironment and can promote cancer progression including cell survival, metastasis, and chemoresistance. The main objective of this study was to develop an anti-PN peptide from the bacteriophage library to overcome PN effects in breast cancer (BCA) cells.

Methods

A twelve amino acids bacteriophage display library was used for biopanning against the PN active site. A selected clone was sequenced and analyzed for peptide primary structure. A peptide was synthesized and tested for the binding affinity to PN. PN effects including a proliferation, migration and a drug sensitivity test were performed using PN overexpression BCA cells or PN treatment and inhibited by an anti-PN peptide. An intracellular signaling mechanism of inhibition was studied by western blot analysis. Lastly, PN expressions in BCA patients were analyzed along with clinical data.

Results

The results showed that a candidate anti-PN peptide was synthesized and showed affinity binding to PN. PN could increase proliferation and migration of BCA cells and these effects could be inhibited by an anti-PN peptide. There was significant resistance to doxorubicin in PN-overexpressed BCA cells and this effect could be reversed by an anti-PN peptide in associations with phosphorylation of AKT and expression of survivin. In BCA patients, serum PN showed a correlation with tissue PN expression but there was no significant correlation with clinical data.

Conclusions

This finding supports that anti-PN peptide is expected to be used in the development of peptide therapy to reduce PN-induced chemoresistance in BCA.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-020-07761-w.

Alteration of Proteotranscriptomic Landscape Reveals the Transcriptional Regulatory Circuits Controlling Key-Signaling Pathways and Metabolic Reprogramming During Tumor Evolution

The proteotranscriptomic landscape depends on the transcription, mRNA-turnover, translation, and regulated-destruction of proteins. Gene-specific mRNA-to-protein correlation is the consequence of the dynamic interplays of the different regulatory processes of proteotranscriptomic landscape. So far, the critical impact of mRNA and protein stability on their subsequent correlation on a global scale remained unresolved. Whether the mRNA-to-protein correlations are constrained by their stability and conserved across mammalian species including human is unknown. Moreover, whether the stability-dependent correlation pattern is altered in the tumor has not been explored. To establish the quantitative relationship between stability and correlation between mRNA and protein levels, we performed a multi-omics data integration study across mammalian systems including diverse types of human tissues and cell lines in a genome-wide manner. The current study illuminated an important aspect of the mammalian proteotranscriptomic landscape by providing evidence that stability-constrained mRNA-to-protein correlation follows a hierarchical pattern that remains conserved across different tissues and mammalian species. By analyzing the tumor and non-tumor tissues, we further illustrated that mRNA-to-protein correlations deviate in tumor tissues. By gene-centric analysis, we harnessed the hierarchical correlation patterns to identify altered mRNA-to-protein correlation in tumors and characterized the tumor correlation-enhancing and -repressing genes. We elucidated the transcriptional regulatory circuits controlling the correlation-enhancing and -repressing genes that are associated with metabolic reprogramming and cancer-associated pathways in tumor tissue. By tightly controlling the mRNA-to-protein correlation of specific genes, the transcriptional regulatory circuits may enable the tumor cells to evolve in varying tumor microenvironment. The mRNA-to-protein correlation analysis thus can serve as a unique approach to identify the pathways prioritized by the tumor cells at different clinical stages. The component of transcriptional regulatory circuits identified by the current study can serve as potential candidates for stage-dependent anticancer therapy.

Integration of Online Omics-Data Resources for Cancer Research

The manifestations of cancerous phenotypes necessitate alterations at different levels of information-flow from genome to proteome. The molecular alterations at different information processing levels serve as the basis for the cancer phenotype to emerge. To understand the underlying mechanisms that drive the acquisition of cancer hallmarks it is required to interrogate cancer cells using multiple levels of information flow represented by different omics - such as genomics, epigenomics, transcriptomics, and proteomics. The advantage of multi-omics data integration comes with a trade-off in the form of an added layer of complexity originating from inherently diverse types of omics-datasets that may pose a challenge to integrate the omics-data in a biologically meaningful manner. The plethora of cancer-specific online omics-data resources, if able to be integrated efficiently and systematically, may facilitate the generation of new biological insights for cancer research. In this review, we provide a comprehensive overview of the online single- and multi-omics resources that are dedicated to cancer. We catalog various online omics-data resources such as The Cancer Genome Atlas (TCGA) along with various TCGA-associated data portals and tools for multi-omics analysis and visualization, the International Cancer Genome Consortium (ICGC), Catalogue of Somatic Mutations in Cancer (COSMIC), The Pathology Atlas, Gene Expression Omnibus (GEO), and PRoteomics IDEntifications (PRIDE). By comparing the strengths and limitations of the respective online resources, we aim to highlight the current biological and technological challenges and possible strategies to overcome these challenges. We outline the available schemes for the integration of the multi-omics dimensions for stratifying cancer patients and biomarker prediction based on the integrated molecular-signatures of cancer. Finally, we propose the multi-omics driven systems-biology approaches to realize the potential of precision onco-medicine as the future of cancer research. We believe this systematic review will encourage scientists and clinicians worldwide to utilize the online resources to explore and integrate the available omics datasets that may provide a window of opportunity to generate new biological insights and contribute to the advancement of the field of cancer research.

A DNA Methylation-Based Panel for the Prognosis and Dagnosis of Patients With Breast Cancer and Its Mechanisms

Objective

To identify DNA methylation related biomarkers in patients with breast cancer (BC).

Materials and Methods

A total of seven BC methylation studies including 1,438 BC patients or breast tissues were included in this study. An elastic net regularized Cox proportional hazards regression (CPH) model was used to build a multi-5′-C-phosphate-G-3′ methylation panel. The diagnosis and prognosis power of the panel was evaluated and validated using a Kaplan–Meier curve, univariate and multivariable CPH, subgroup analysis. A nomogram containing the panel was developed. The relationships between the panel-based methylation risk and the immune landscape and genomic metrics were investigated.

Results

Sixty-eight CpG sites were significantly correlated with the overall survival (OS) of BC patients, and based on the result of penalized CPH, a 28-CpG site based multi CpG methylation panel was found. The prognosis and diagnosis role of the panel was validated in the discovery set, validation set, and six independent cohorts, which indicated that higher methylation risk was associated with poor OS, and the panel outperformed currently available biomarkers and remained an independent factor after adjusting for other clinical features. The methylation risk was negatively correlated with innated and adaptive immune cells, and positively correlated with total mutation load, SCNA, and MATH.

Conclusions

We validated a multi CpG methylation panel that could independently predict the OS of BC patients. The Th2-mediated tumor promotion effect—suppression of innate and adaptive immunity—participated in the progression of high-risk BC. Patients with high methylation risk were associated with tumor heterogeneity and poor survival.

Ramos E, Choudhari R, L. Harrison A, Subramani R, Lakshmanaswamy R, Zilaie M, Gadad SS. Hypoxanthine Phosphoribosyl Transferase 1 Is Upregulated, Predicts Clinical Outcome and Controls Gene Expression in Breast Cancer

Hypoxanthine phosphoribosyl transferase 1 (HPRT1) is traditionally believed to be a housekeeping gene; however, recent reports suggest that it is upregulated in several cancers and is associated with clinical outcomes. HPRT1 is located on chromosome X and encodes the HPRT enzyme, which functions in recycling nucleotides to supply for DNA and RNA synthesis in actively dividing cells. Here, we used transcriptomic analyses to interrogate its expression across all known cancer types and elucidated its role in regulating gene expression in breast cancer. We observed elevated HPRT1 RNA levels in malignant tissues when compared to normal controls, indicating its potential as a diagnostic and prognostic marker. Further, in breast cancer, the subtype-specific analysis showed that its expression was highest in basal and triple-negative breast cancer, and HPRT1 knockdown in breast cancer cells suggested that HPRT1 positively regulates genes related to cancer pathways. Collectively, our results essentially highlight the importance of and change the way in which HPRT1's function is studied in biology, warranting careful examination of its role in cancer.

G9a-mediated repression of CDH10 in hypoxia enhances breast tumour cell motility and associates with poor survival outcome

Rationale: Epigenetic mechanisms are fundamental processes that can modulate gene expression, allowing cellular adaptation to environmental conditions. Hypoxia is an important factor known to initiate the metastatic cascade in cancer, activating cell motility and invasion by silencing cell adhesion genes. G9a is a histone methyltransferase previously shown to accumulate in hypoxic conditions. While its oncogenic activity has been previously reported, not much is known about the role G9a plays in the hypoxia-mediated metastatic cascade.

Methods: The role of G9a in cell motility in hypoxic condition was determined by inhibiting G9a either by short-hairpin mediated knock down or pharmacologically using a small molecule inhibitor. Through gene expression profiling, we identified CDH10 to be an important G9a target that regulates breast cancer cell motility. Lung metastasis assay in mice was used to determine the physiological significance of G9a.

Results: We demonstrate that, while hypoxia enhances breast cancer migratory capacity, blocking G9a severely reduces cellular motility under both normoxic and hypoxic conditions and prevents the hypoxia-mediated induction of cellular movement. Moreover, inhibition of G9a histone methyltransferase activity in mice using a specific small molecule inhibitor significantly reduced growth and colonisation of breast cancer cells in the lung. We identify the type-II cadherin CDH10 as being a novel hypoxia-dependent gene, directly repressed by G9a through histone methylation. CDH10 overexpression significantly reduces cellular movements in breast cancer cell lines and prevents the hypoxia-mediated increase in cell motility. In addition, we show that CDH10 expression is prognostic in breast cancer and that it is inversely correlated to EHMT2 (G9a) transcript levels in many tumor-types, including breast cancer.

Conclusion: We propose that G9a promotes cellular motility during hypoxic stress through the silencing of the cell adhesion molecule CDH10 and we describe CDH10 as a novel prognostic biomarker for breast cancer.

OSbrca: A Web Server for Breast Cancer Prognostic Biomarker Investigation With Massive Data From Tens of Cohorts

Potential prognostic mRNA biomarkers are exploited to assist in the clinical management and treatment of breast cancer, which is the first life-threatening tumor in women worldwide. However, it is technically challenging for untrained researchers to process high dimensional profiling data to screen and validate the potential prognostic values of genes of interests in multiple cohorts. Our aim is to develop an easy-to-use web server to facilitate the screening, developing, and evaluating of prognostic biomarkers in breast cancers. Herein, we collected more than 7,400 cases of breast cancer with gene expression profiles and clinical follow-up information from The Cancer Genome Atlas and Gene Expression Omnibus data, and built an Online consensus Survival analysis web server for Breast Cancers, abbreviated OSbrca, to generate the Kaplan–Meier survival plot with a hazard ratio and log rank P-value for given genes in an interactive way. To examine the performance of OSbrca, the prognostic potency of 128 previously published biomarkers of breast cancer was reassessed in OSbrca. In conclusion, it is highly valuable for biologists and clinicians to perform the preliminary assessment and validation of novel or putative prognostic biomarkers for breast cancers. OSbrca could be accessed at http://bioinfo.henu.edu.cn/BRCA/BRCAList.jsp.

Integrated pan-cancer gene expression and drug sensitivity analysis reveals SLFN11 mRNA as a solid tumor biomarker predictive of sensitivity to DNA-damaging chemotherapy

Background

Precision oncology seeks to integrate multiple layers of data from a patient’s cancer to effectively tailor therapy. Conventional chemotherapies are sometimes effective but accompanied by adverse events, warranting the identification of a biomarker of chemosensitivity.

Objective

Identify an mRNA biomarker that predicts chemosensitivity across solid tumor subtypes.

Methods

We performed a pan-solid tumor analysis integrating gene expression and drug sensitivity profiles from 3 cancer cell line datasets to identify transcripts correlated with sensitivity to a panel of chemotherapeutics. We then tested the ability of an mRNA biomarker to predictive clinical outcomes in cohorts of patients with breast, lung, or ovarian cancer.

Results

Expression levels of several mRNA transcripts were significantly correlated with sensitivity or resistance chemotherapeutics in cancer cell line datasets. The only mRNA transcript significantly correlated with sensitization to multiple classes of DNA-damaging chemotherapeutics in all 3 cell line datasets was encoded by Schlafen Family Member 11 (SLFN11). Analyses of multiple breast, lung, and ovarian cancer patient cohorts treated with chemotherapy confirmed SLFN11 mRNA expression as a predictive biomarker of longer overall survival and improved tumor response.

Conclusions

Tumor SLFN11 mRNA expression is a biomarker of sensitivity to an array of DNA-damaging chemotherapeutics across solid tumor subtypes.

Computer-aided drug repurposing for cancer therapy: Approaches and opportunities to challenge anticancer targets

Despite huge efforts made in academic and pharmaceutical worldwide research, current anticancer therapies achieve effective treatment in a limited number of neoplasia cases only. Oncology terms such as big killers - to identify tumours with yet a high mortality rate - or undruggable cancer targets, and chemoresistance, represent the current therapeutic debacle of cancer treatments. In addition, metastases, tumour microenvironments, tumour heterogeneity, metabolic adaptations, and immunotherapy resistance are essential features controlling tumour response to therapies, but still, lack effective therapeutics or modulators. In this scenario, where the pharmaceutical productivity and drug efficacy in oncology seem to have reached a plateau, the so-called drug repurposing - i.e. the use of old drugs, already in clinical use, for a different therapeutic indication - is an appealing strategy to improve cancer therapy. Opportunities for drug repurposing are often based on occasional observations or on time-consuming pre-clinical drug screenings that are often not hypothesis-driven. In contrast, in-silico drug repurposing is an emerging, hypothesis-driven approach that takes advantage of the use of big-data. Indeed, the extensive use of -omics technologies, improved data storage, data meaning, machine learning algorithms, and computational modeling all offer unprecedented knowledge of the biological mechanisms of cancers and drugs' modes of action, providing extensive availability for both disease-related data and drugs-related data. This offers the opportunity to generate, with time and cost-effective approaches, computational drug networks to predict, in-silico, the efficacy of approved drugs against relevant cancer targets, as well as to select better responder patients or disease' biomarkers. Here, we will review selected disease-related data together with computational tools to be exploited for the in-silico repurposing of drugs against validated targets in cancer therapies, focusing on the oncogenic signaling pathways activation in cancer. We will discuss how in-silico drug repurposing has the promise to shortly improve our arsenal of anticancer drugs and, likely, overcome certain limitations of modern cancer therapies against old and new therapeutic targets in oncology.

Clinical Significance of SERPINA1 Gene and Its Encoded Alpha1-antitrypsin Protein in NSCLC

High expression of SERPINA1 gene encoding acute phase protein, alpha1-antitrypsin (AAT), is associated with various tumors. We sought to examine the significance of SERPINA1 and AAT protein in non-small-cell lung cancer (NSCLC) patients and NSCLC cell lines. Tumor and adjacent non-tumor lung tissues and serum samples from 351 NSCLC patients were analyzed for SERPINA1 expression and AAT protein levels. We also studied the impact of SERPINA1 expression and AAT protein on H1975 and H661 cell behavior, in vitro. Lower SERPINA1 expression in tumor but higher in adjacent non-tumor lung tissues (n = 351, p = 0.016) as well as higher serum levels of AAT protein (n = 170, p = 0.033) were associated with worse survival rates. Specifically, in NSCLC stage III patients, higher blood AAT levels (>2.66 mg/mL) correlated with a poor survival (p = 0.002). Intriguingly, levels of serum AAT do not correlate with levels of C-reactive protein, neutrophils-to-leukocyte ratio, and do not correlate with SERPINA1 expression or AAT staining in the tumor tissue. Additional experiments in vitro revealed that external AAT and/or overexpressed SERPINA1 gene significantly improve cancer cell migration, colony formation and resistance to apoptosis. SERPINA1 gene and AAT protein play an active role in the pathogenesis of lung cancer and not just reflect inflammatory reaction related to cancer development.

Liver- and Microbiome-derived Bile Acids Accumulate in Human Breast Tumors and Inhibit Growth and Improve Patient Survival

PURPOSE

Metabolomics is a discovery tool for novel associations of metabolites with disease. Here, we interrogated the metabolome of human breast tumors to describe metabolites whose accumulation affects tumor biology.

EXPERIMENTAL DESIGN

We applied large-scale metabolomics followed by absolute quantification and machine learning-based feature selection using LASSO to identify metabolites that show a robust association with tumor biology and disease outcome. Key observations were validated with the analysis of an independent dataset and cell culture experiments.

RESULTS

LASSO-based feature selection revealed an association of tumor glycochenodeoxycholate levels with improved breast cancer survival, which was confirmed using a Cox proportional hazards model. Absolute quantification of four bile acids, including glycochenodeoxycholate and microbiome-derived deoxycholate, corroborated the accumulation of bile acids in breast tumors. Levels of glycochenodeoxycholate and other bile acids showed an inverse association with the proliferation score in tumors and the expression of cell-cycle and G₂-M checkpoint genes, which was corroborated with cell culture experiments. Moreover, tumor levels of these bile acids markedly correlated with metabolites in the steroid metabolism pathway and increased expression of key genes in this pathway, suggesting that bile acids may interfere with hormonal pathways in the breast. Finally, a proteome analysis identified the complement and coagulation cascade as being upregulated in glycochenodeoxycholate-high tumors.

CONCLUSIONS

We describe the unexpected accumulation of liver- and microbiome-derived bile acids in breast tumors. Tumors with increased bile acids show decreased proliferation, thus fall into a good prognosis category, and exhibit significant changes in steroid metabolism.