Monitoring of breast cancer progression via aptamer-based detection of circulating tumor cells in clinical blood samples

Introduction: Breast cancer (BC) diagnostics lack noninvasive methods and procedures for screening and monitoring disease dynamics. Admitted CellSearch® is used for fluid biopsy and capture of circulating tumor cells of only epithelial origin. Here we describe an RNA aptamer (MDA231) for detecting BC cells in clinical samples, including blood. The MDA231 aptamer was originally selected against triple-negative breast cancer cell line MDA-MB-231 using cell-SELEX.Methods: The aptamer structure in solution was predicted using mFold program and molecular dynamic simulations. The affinity and specificity of the evolved aptamers were evaluated by flow cytometry and laser scanning microscopy on clinical tissues from breast cancer patients. CTCs were isolated form the patients’ blood using the developed method of aptamer-based magnetic separation. Breast cancer origin of CTCs was confirmed by cytological, RT-qPCR and Immunocytochemical analyses.Results: MDA231 can specifically recognize breast cancer cells in surgically resected tissues from patients with different molecular subtypes: triple-negative, Luminal A, and Luminal B, but not in benign tumors, lung cancer, glial tumor and healthy epithelial from lungs and breast. This RNA aptamer can identify cancer cells in complex cellular environments, including tumor biopsies (e.g., tumor tissues vs. margins) and clinical blood samples (e.g., circulating tumor cells). Breast cancer origin of the aptamer-based magnetically separated CTCs has been proved by immunocytochemistry and mammaglobin mRNA expression.Discussion: We suggest a simple, minimally-invasive breast cancer diagnostic method based on non-epithelial MDA231 aptamer-specific magnetic isolation of circulating tumor cells. Isolated cells are intact and can be utilized for molecular diagnostics purposes

Analysis of molecular phenotypes in normal mucosa and colorectal cancer in embryonic anatomical parts of the colon

Background: Differences in the embryonic development of the colonic mucosa determine the physiological embryonic-anatomical asymmetry of its structure and can manifest themselves via different molecular phenotypes (expression profiles) of the colon segments. These molecular characteristics are hypothesized to determine differences in the carcinogenesis mechanisms and influence the prognosis of right- or left-sided colorectal cancer (CRC). Studies of the tumors molecular phenotypes depending on their localization may be of interest for assessment of the prognosis and choice of treatment for CRC. Aim: To perform comparative analysis of molecular phenotypes of the normal colonic mucosa and adenocarcinoma CRC tissues depending on the natural embryonic anatomic asymmetry of the colon. Materials and methods: We performed a retrospective study of molecular phenotypes (mRNA expression of 61 genes) from different embryonic-anatomical parts of healthy colon and CRC. The normal group included 254 samples of mucosa from three different parts of the colon from 74 healthy donors who had no cancer and no organic abnormalities of the colon, including 90 samples from the right colon, 116 from the left colon, and 48 from the rectum. The CRC group consisted of 154 samples of localized stage T1–4N0–2M0 adenocarcinoma from 154 patients who had not received neoadjuvant radio- and chemotherapy, including 40 samples from the right colon, 54 from the left colon, and 60 from the rectum. The relative mRNA abundance of 61 genes was assessed by reverse-transcriptase polymerase chain reaction. In both groups, the resulting expression phenotypes were compared between the anatomical parts of the colon. Statistical management of the data included the discriminant analysis with stepwise inclusion of variables. Results: Based on the assessment of the mRNA level of the studied genes, a discriminant model was built that allows for classification of the normal group samples according to their anatomic origin in the colon with an accuracy of 95.8%. The most significant (p 0.05) for classification are the following 19 genes: CCND1, SCUBE2, TERT, BAG1, NDRG, IL1b, IL2Ra, IL7, ESR1, TGFb, IGF1, MMP9, MMP11, PAPPA, CD45, CD69, TLR2, TLR4, LIFR. The discriminant model built for the CRC group included 27 genes and made it possible to differentiate samples from three parts of colon with an accuracy of 75.2%. A statistically significant (p 0.05) contribution to the samples differentiation by the discriminant model was made by the COX-2, BIRC5, LIFR, TPA, IL1b, MMP11, MMP7, and P16INK4A genes. When combining samples from the two groups into one model in accordance with their embryonic-anatomical origin, there was a clear separation of tumor tissue samples and healthy colonic mucosa in the discriminant function space. Conclusion: The analysis of CRC gene expression profiles using the discriminant model showed that genetic changes in the colonic mucosa in CRC flatten the molecular phenotypic boundaries of the embryonic-anatomical parts. These changes are specific to CRC, forming a particular “pathological” molecular phenotype

Pancreatic cancer: statistics and treatment in the Russian Federation

Pancreatic cancer (PC) is one of the most fatal types of oncological disease in the world and is an extremely aggressive cancer with a poor prognosis. The objective of this review was to analyze the domestic data of the incidence of PC in the Russian Federation and to analyze the protocols that are used for the management of this group of patients in Russian clinical centers. For the analysis of the literature sources, the data in the elibrary.ru database published in the period from 2015 to 2019 were used. The methodology that was used in each study was examined in order to ensure its reliability, and these data were selected as potential sources of evidence for the preparation of national recommendations. The study results influence the level of evidence assigned to the publication. Updates to the national recommendations are conducted at least once every three years, and these updates depend on new information about the diagnosis and management of patients with PC

State of the Art of Chromosome 18-Centric HPP in 2016: Transcriptome and Proteome Profiling of Liver Tissue and HepG2 Cells

A gene-centric approach was applied for a large-scale study of expression products of a single chromosome. Transcriptome profiling of liver tissue and HepG2 cell line was independently performed using two RNA-Seq platforms (SOLiD and Illumina) and also by Droplet Digital PCR (ddPCR) and quantitative RT-PCR. Proteome profiling was performed using shotgun LC–MS/MS as well as selected reaction monitoring with stable isotope-labeled standards (SRM/SIS) for liver tissue and HepG2 cells. On the basis of SRM/SIS measurements, protein copy numbers were estimated for the Chromosome 18 (Chr 18) encoded proteins in the selected types of biological material. These values were compared with expression levels of corresponding mRNA. As a result, we obtained information about 158 and 142 transcripts for HepG2 cell line and liver tissue, respectively. SRM/SIS measurements and shotgun LC–MS/MS allowed us to detect 91 Chr 18-encoded proteins in total, while an intersection between the HepG2 cell line and liver tissue proteomes was ∼66%. In total, there were 16 proteins specifically observed in HepG2 cell line, while 15 proteins were found solely in the liver tissue. Comparison between proteome and transcriptome revealed a poor correlation (<i>R</i>² ≈ 0.1) between corresponding mRNA and protein expression levels. The SRM and shotgun data sets (obtained during 2015–2016) are available in PASSEL (PASS00697) and ProteomeExchange/PRIDE (PXD004407). All measurements were also uploaded into the in-house Chr 18 Knowledgebase at http://kb18.ru/protein/matrix/416126

Chromosome 18 Transcriptoproteome of Liver Tissue and HepG2 Cells and Targeted Proteome Mapping in Depleted Plasma: Update 2013

We report the results obtained in 2012–2013 by the Russian Consortium for the Chromosome-centric Human Proteome Project (C-HPP). The main scope of this work was the transcriptome profiling of genes on human chromosome 18 (Chr 18), as well as their encoded proteome, from three types of biomaterials: liver tissue, the hepatocellular carcinoma-derived cell line HepG2, and blood plasma. The transcriptome profiling for liver tissue was independently performed using two RNaseq platforms (SOLiD and Illumina) and also by droplet digital PCR (ddPCR) and quantitative RT-PCR. The proteome profiling of Chr 18 was accomplished by quantitatively measuring protein copy numbers in the three types of biomaterial (the lowest protein concentration measured was 10^–13 M) using selected reaction monitoring (SRM). In total, protein copy numbers were estimated for 228 master proteins, including quantitative data on 164 proteins in plasma, 171 in the HepG2 cell line, and 186 in liver tissue. Most proteins were present in plasma at 10⁸ copies/μL, while the median abundance was 10⁴ and 10⁵ protein copies per cell in HepG2 cells and liver tissue, respectively. In summary, for liver tissue and HepG2 cells a “transcriptoproteome” was produced that reflects the relationship between transcript and protein copy numbers of the genes on Chr 18. The quantitative data acquired by RNaseq, PCR, and SRM were uploaded into the “Update_2013” data set of our knowledgebase (www.kb18.ru) and investigated for linear correlations

Chromosome 18 Transcriptoproteome of Liver Tissue and HepG2 Cells and Targeted Proteome Mapping in Depleted Plasma: Update 2013

We report the results obtained in 2012–2013 by the Russian Consortium for the Chromosome-centric Human Proteome Project (C-HPP). The main scope of this work was the transcriptome profiling of genes on human chromosome 18 (Chr 18), as well as their encoded proteome, from three types of biomaterials: liver tissue, the hepatocellular carcinoma-derived cell line HepG2, and blood plasma. The transcriptome profiling for liver tissue was independently performed using two RNaseq platforms (SOLiD and Illumina) and also by droplet digital PCR (ddPCR) and quantitative RT-PCR. The proteome profiling of Chr 18 was accomplished by quantitatively measuring protein copy numbers in the three types of biomaterial (the lowest protein concentration measured was 10^–13 M) using selected reaction monitoring (SRM). In total, protein copy numbers were estimated for 228 master proteins, including quantitative data on 164 proteins in plasma, 171 in the HepG2 cell line, and 186 in liver tissue. Most proteins were present in plasma at 10⁸ copies/μL, while the median abundance was 10⁴ and 10⁵ protein copies per cell in HepG2 cells and liver tissue, respectively. In summary, for liver tissue and HepG2 cells a “transcriptoproteome” was produced that reflects the relationship between transcript and protein copy numbers of the genes on Chr 18. The quantitative data acquired by RNaseq, PCR, and SRM were uploaded into the “Update_2013” data set of our knowledgebase (www.kb18.ru) and investigated for linear correlations

Chromosome 18 Transcriptoproteome of Liver Tissue and HepG2 Cells and Targeted Proteome Mapping in Depleted Plasma: Update 2013

We report the results obtained in 2012–2013 by the Russian Consortium for the Chromosome-centric Human Proteome Project (C-HPP). The main scope of this work was the transcriptome profiling of genes on human chromosome 18 (Chr 18), as well as their encoded proteome, from three types of biomaterials: liver tissue, the hepatocellular carcinoma-derived cell line HepG2, and blood plasma. The transcriptome profiling for liver tissue was independently performed using two RNaseq platforms (SOLiD and Illumina) and also by droplet digital PCR (ddPCR) and quantitative RT-PCR. The proteome profiling of Chr 18 was accomplished by quantitatively measuring protein copy numbers in the three types of biomaterial (the lowest protein concentration measured was 10^–13 M) using selected reaction monitoring (SRM). In total, protein copy numbers were estimated for 228 master proteins, including quantitative data on 164 proteins in plasma, 171 in the HepG2 cell line, and 186 in liver tissue. Most proteins were present in plasma at 10⁸ copies/μL, while the median abundance was 10⁴ and 10⁵ protein copies per cell in HepG2 cells and liver tissue, respectively. In summary, for liver tissue and HepG2 cells a “transcriptoproteome” was produced that reflects the relationship between transcript and protein copy numbers of the genes on Chr 18. The quantitative data acquired by RNaseq, PCR, and SRM were uploaded into the “Update_2013” data set of our knowledgebase (www.kb18.ru) and investigated for linear correlations