Integrating proteomic and phosphoproteomic data for pathway analysis in breast cancer

Recent Contributions of Mass Spectrometry-Based "Omics" in the Studies of Breast Cancer

Breast cancer (BC) is one of the most heterogeneous groups of cancer. As every biotype of BC is unique and presents a particular "omic" signature, they are increasingly characterized nowadays with novel mass spectrometry (MS) strategies. BC therapeutic approaches are primarily based on the two features of human epidermal growth factor receptor 2 (HER2) and estrogen receptor (ER) positivity. Various strategic MS implementations are reported in studies of BC also involving data independent acquisitions (DIAs) of MS which report novel differential proteomic, lipidomic, proteogenomic, phosphoproteomic, and metabolomic characterizations associated with the disease and its therapeutics. Recently many "omic" studies have aimed to identify distinct subsidiary biotypes for diagnosis, prognosis, and targets of treatment. Along with these, drug-induced-resistance phenotypes are characterized by "omic" changes. These identifying aspects of the disease may influence treatment outcomes in the near future. Drug quantifications and characterizations are also done regularly and have implications in therapeutic monitoring and in drug efficacy assessments. We report these studies, mentioning their implications toward the understanding of BC. We briefly provide the MS instrumentation principles that are adopted in such studies as an overview with a brief outlook on DIA-MS strategies. In all of these, we have chosen a model cancer for its revelations through MS-based "omics".

Detection of genetic mutations in patients with breast cancer from Saudi Arabia using Ion AmpliSeq™ Cancer Hotspot Panel v.2.0

Next-Generation Sequencing allows for quick and precise sequencing of multiple genes concurrently. Recently, this technology has been employed for the identification of novel gene mutations responsible for disease manifestation among breast cancer (BC) patients, the most common type of cancer amongst Arabian women, and the major cause of disease-associated death in women worldwide. Genomic DNA was extracted from the peripheral blood of 32 Saudi Arabian BC patients with histologically confirmed invasive BC stages I-III and IV, as well from 32 healthy Saudi Arabian women using a QIAamp^® DNA Mini Kit. The isolated DNA was quantified using a Qubit™ dsDNA BR Assay Kit with a Qubit 2.0 Fluorometer. Ion semiconductor sequencing technology with an Ion S5 System and AmpliSeq™ Cancer Hotspot Panel v2 were utilized to analyze ~2,800 mutations described in the Catalogue of Somatic Mutations in Cancer from 50 oncogenes and tumor suppressor genes. Ion Reporter Software v.5.6 was used to evaluate the genomic alterations in all the samples after alignment to the hg19 human reference genome. The results showed that out of the 50 genes, 26 mutations, including 17 (65%) missense point mutations (single nucleotide variants), and 9 (35%) frameshift (insertion/deletion) mutations, were identified in 11 genes across the cohort in 61 samples (95%). Mutations were predominantly focused on two genes, PIK3CA and TP53, in the BC genomes of the sample set. PIK3CA mutation, c.1173A>G located in exon 9, was identified in 15 patients (46.9%). The TP53 mutations detected were a missense mutation (c.215C>G) in 26 patients (86.70%) and 1 frameshift mutation (c.215_216insG) in 1 patient (3.33%), located within exon 3 and 5, respectively. This study revealed specific mutation profiles for every BC patient, Thus, the results showed that Ion Torrent DNA Sequencing technology may be a possible diagnostic and prognostic method for developing personalized therapy based on the patient's individual BC genome.

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.

Prognostic Value of the TP53 Mutation Location in Metastatic Breast Cancer as Detected by Next-Generation Sequencing

Purpose

The status of TP53 mutations was measured in cell-free DNA from patients with metastatic breast cancer (MBC) to investigate disease characteristics and the prognostic role of different locations of the TP53 mutation site.

Patients and Methods

Blood samples were taken from a total of 187 patients diagnosed with MBC who were treated at the Department of Breast Oncology, Peking University Cancer Hospital between January 2013 and March 2020. Next-generation sequencing was used to investigate the TP53 mutation spectra of circulating free DNA in these blood samples.

Results

Among the 187 MBC patients, TP53-mutated patients had a significantly shorter median disease-free survival (DFS) and overall survival (OS) compared with TP53 wild-type patients (P=0.001 and P=0.006, respectively). Additionally, in hormone receptor positive/HER2 negative (HR+/HER2-) and triple negative (TNBC) cohorts, TP53-mutated patients had a significantly shorter median DFS than TP53 wild-type patients (P=0.038 and P=0.023, respectively). The 79 patients with TP53 mutations carried 87 somatic TP53 mutations, of which most (77.0%) mapped to the DNA-binding domain (DBD) of the protein encoded by TP53 exons 5–8. In patients with TP53 mutations, those occurring in the non-DBD had a significantly shorter median DFS and OS than TP53 wild type (P<0.001 and P=0.001, respectively). Additionally, patients with non-missense mutations in the DBD had a significantly shorter median DFS and OS than TP53 wild-type patients (P=0.001 and P<0.001, respectively). TP53-mutated patients had a significantly shorter DFS than TP53 wild-type patients in the adjuvant endocrine therapy sensitive group (P=0.008), but differences in the endocrine therapy resistant group were not significant.

Conclusion

TP53-mutated MBC patients had a significantly worse outcome than TP53 wild-type patients especially those in HR+/HER2– and TNBC cohorts. Of TP53-mutated patients, those with non-missense mutations in the DBD had worse breast cancer-related survival. TP53 mutations were also associated with endocrine resistance.

Topologically inferring active miRNA-mediated subpathways toward precise cancer classification by directed random walk

Accurate predictions of classification biomarkers and disease status are indispensable for clinical cancer diagnosis and research. However, the robustness of conventional gene biomarkers is limited by issues with reproducibility across different measurement platforms and cohorts of patients. In this study, we collected 4775 samples from 12 different cancer datasets, which contained 4636 TCGA samples and 139 GEO samples. A new method was developed to detect miRNA‐mediated subpathway activities by using directed random walk (miDRW). To calculate the activity of each miRNA‐mediated subpathway, we constructed a global directed pathway network (GDPN) with genes as nodes. We then identified miRNAs with expression levels which were strongly inversely correlated with differentially expressed target genes in the GDPN. Finally, each miRNA‐mediated subpathway activity was integrated with the topological information, differential levels of miRNAs and genes, expression levels of genes, and target relationships between miRNAs and genes. The results showed that the proposed method yielded a more robust and accurate overall performance compared with other existing pathway‐based, miRNA‐based, and gene‐based classification methods. The high‐frequency miRNA‐mediated subpathways are more reliable in classifying samples and for selecting therapeutic strategies.

The International Conference on Intelligent Biology and Medicine (ICIBM) 2018: systems biology on diverse data types

Between June 10–12, 2018, the International Conference on Intelligent Biology and Medicine (ICIBM 2018) was held in Los Angeles, California, USA. The conference included 11 scientific sessions, four tutorials, one poster session, four keynote talks and four eminent scholar talks that covered a wide range of topics in 3D genome structure analysis and visualization, next generation sequencing analysis, computational drug discovery, medical informatics, cancer genomics and systems biology. Systems biology has been a main theme in ICIBM 2018, with exciting advances presented in many areas of systems biology, covering various different data types such as gene regulation, circular RNAs expression, single-cell RNA-Seq, inter-chromosomal interactions, metabolomics, proteomics and phosphoproteomics. Here, we describe ten high quality papers to be published in BMC Systems Biology.