Heterogeneity of colorectal carcinoma (CRC) represents a major hurdle towards personalized medicine. Efforts based on whole tumor profiling demonstrated that the CRC molecular subtypes were associated with specific tumor morphological patterns representing tumor subregions. We hypothesize that whole-tumor molecular descriptors depend on the morphological heterogeneity with significant impact on current molecular predictors. We investigated intra-tumor heterogeneity by morphology-guided transcriptomics to better understand the links between gene expression and tumor morphology represented by six morphological patterns (morphotypes): complex tubular, desmoplastic, mucinous, papillary, serrated, and solid/trabecular. Whole-transcriptome profiling by microarrays of 202 tumor regions (morphotypes, tumor-adjacent normal tissue, supportive stroma, and matched whole tumors) from 111 stage II-IV CRCs identified morphotype-specific gene expression profiles and molecular programs and differences in their cellular buildup. The proportion of cell types (fibroblasts, epithelial and immune cells) and differentiation of epithelial cells were the main drivers of the observed disparities with activation of EMT and TNF-α signaling in contrast to MYC and E2F targets signaling, defining major gradients of changes at molecular level. Several gene expression-based (including single-cell) classifiers, prognostic and predictive signatures were examined to study their behavior across morphotypes. Most exhibited important morphotype-dependent variability within same tumor sections, with regional predictions often contradicting the whole-tumor classification. The results show that morphotype-based tumor sampling allows the detection of molecular features that would otherwise be distilled in whole tumor profile, while maintaining histopathology context for their interpretation. This represents a practical approach at improving the reproducibility of expression profiling and, by consequence, of gene-based classifiers.

Purpose Renal cell carcinoma belongs among the deadliest malignancies despite great progress in therapy and accessibility of primary care. One of the main unmet medical needs remains the possibility of early diagnosis before the tumor dissemination and prediction of early relapse and disease progression after a successful nephrectomy. In our study, we aimed to identify novel diagnostic and prognostic biomarkers using next-generation sequencing on a novel cohort of RCC patients. Methods Global expression profiles have been obtained using next-generation sequencing of paired tumor and non-tumor tissue of 48 RCC patients. Twenty candidate lncRNA have been selected for further validation on an independent cohort of paired tumor and non-tumor tissue of 198 RCC patients. Results Sequencing data analysis showed significant dysregulation of more than 2800 lncRNAs. Out of 20 candidate lncRNAs selected for validation, we confirmed that 14 of them are statistically significantly dysregulated. In order to yield better discriminatory results, we combined several best performing lncRNAs into diagnostic and prognostic models. A diagnostic model consisting of AZGP1P1, CDKN2B-AS1, COL18A1, and RMST achieved AUC 0.9808, sensitivity 95.96%, and specificity 90.4%. The model for prediction of early relapse after nephrectomy consists of COLCA1, RMST, SNHG3, and ZNF667-AS1 and achieved AUC 0.9241 with sensitivity 93.75% and specificity 71.07%. Notably, no combination has outperformed COLCA1 alone. Lastly, a model for stage consists of ZNF667-AS1, PVT1, RMST, LINC00955, and TCL6 and achieves AUC 0.812, sensitivity 85.71%, and specificity 69.41%. Conclusion In our work, we identified several lncRNAs as potential biomarkers and developed models for diagnosis and prognostication in relation to stage and early relapse after nephrectomy.

Introduction: Currently used molecular diagnostic tests for colorectal cancer (CRC) are underperforming and more sensitive, non-invasive biomarkers are needed. Long non-coding RNAs (lncRNA) and microRNA (miRNA) have shown potential as diagnostic biomarkers. Unfortunately, the identification of non-coding RNA circulating biomarkers in blood serum is significantly burdened by abundant RNA specimens from disrupted blood cells. Recently, small extracellular vesicles (sEVs) emerged as potential reservoirs of clinically relevant biomarkers, including lncRNAs and miRNAs. In theory, sEVs protect RNAs from degradation and might serve as a source of intact RNA for further analyses. However, there is a lack of evidence supporting the superior quality of RNA extracted from sEVs over RNA from whole blood serum. This study aimed to analyze the RNA content of blood serum and the sEVs derived from the blood serum of CRC patients and healthy controls using RNA sequencing. Moreover, small RNA sequencing was used to evaluate the difference in miRNA profiles of sEVs and corresponding blood sera of CRC patients and healthy controls. Methods: Spin-column chromatography (Exiqon), precipitation-based method (Norgen), and size-exclusion chromatography (iZON) were used to extract sEVs from blood sera. The concentration of sEVs was measured by dynamic light scattering (DLS), the size was evaluated by electron microscopy (EM), and sEV-specific content was analyzed by western blot and qRT-PCR. RNA was extracted using the column-based method. Next-generation sequencing (NGS) analyses of blood serum and sEVs extracted from blood serum included samples from 10 CRC patients and 10 healthy controls for RNAseq, and 5 CRC patients and 5 healthy controls for small RNAseq. Differential expression analysis was carried out in R using DESeq2 package. Results: DLS and EM showed that size-exclusion chromatography yielded the purest population of sEVs characterized according to ISEV recommendations. Extraction of sEVs and subsequent RNA extraction and sequencing library preparation from ultra-low input samples were optimized. Over 30k different RNAs were identified in the sEVs derived from blood sera of CRC patients and healthy controls, including lncRNAs, miRNAs, and protein-coding RNAs. A detailed comparison of the transcriptome of blood sera and corresponding sEVs is a part of the poster. Conclusion: sEVs could serve as a source of RNA biomarkers; however, proper characterization and optimal methodology are necessary. This work was supported by the Ministry of Health of the Czech Republic grant No. NU20-03-00127, by The project National Institute for Cancer Research (Programme EXCELES, ID Project No. LX22NPO5102) - Funded by the European Union - Next Generation EU, by the project BBMRI-CZ, nr. LM2018125, and in co-operation with CEMCOF, CEITEC MU (CIISB) supported by MEYS CR, LM2018127. Citation Format: Tana Machackova, Petra Vychytilova-Faltejskova, Marie Madrzyk, Karolina Trachtova, Marketa Pavlikova, Jan Kotoucek, Jana Halamkova, Dagmar Al Tukmachi, Jiri Sana, Petra Pokorna, Milana Sachlova, Ondrej Slaby. Utility of RNA sequencing for transcriptome analysis of small extracellular vesicles derived from blood sera of colorectal cancer patients [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 6709.

Background:Acute myeloid leukemia (AML) is a malignancy with a high prevalence of NRAS mutations. Transforming point mutations, located dominantly at hotspot codons 12, 13 and 61 of NRAS gene, occur in 11 to 22% of patients. Mutations in NRAS gene stimulate signalling activity of RAS pathway, resulting in an increased cellular proliferation and reduced apoptosis. Prognostic significance of NRAS gene mutations in AML patients remains controversial.Aims:The aim of our work was to analyze the association of NRAS gene mutations in AML with patient characteristics and outcomes.Methods:A total of 297 consecutive AML (non‐acute promyelocyte leukemia) patients (median age 53 years; range 19–70) with curative therapy were analyzed by next generation sequencing (NGS) using ClearSeq AML panel (Agilent Technologies) on MiSeq and NextSeq (Illumina). The NRAS mutations detected by NGS with variant allele frequency (VAF) ≥1.0% were verified by NRAS StripAssay (ViennaLab). Only mutations scored as positive by both methods were included in further analyses. Overall survival (OS) was analyzed by log‐rank (Mantel‐Cox) test. The comparative statistical analyses were evaluated by t‐test and Fisher's exact test.Results:In total, 84 mutations were identified in 66/297 (22.2%) analyzed AML patients. Fifteen patients (22.7%) were carriers of multiple gene mutations (range 1–3 mutations per patient). The mutations were most frequently found in the codon G12 (44/84 mutations, 52.4%), the most frequent gene mutations were: G12D (26/84; 31.0% mutations), G13D (15/84; 17.9%) and G12S (14/84; 16.7%). Furthermore, rare mutation G60E was identified in one patient. The median VAF of NRAS mutations was 7.0% (range 1.0–53.4%). Frequency of co‐mutation with FLT3‐TKD reached statistical significance (p = 0.025) and a trend for co‐mutation with NPM1 (p = 0.082) and DNMT3A (p = 0.091). A trend for mutual exclusivity was observed for FLT3‐ITD mutations with allelic ratio above 0.5 (p = 0.063). Occurrence of NRAS mutations was associated with younger age (median 48 vs. 54 years, respectively, p = 0.012), and a trend towards higher white blood cell count (median 41.0 vs. 14.2, respectively, p = 0.069). There was no association with OS, blast percentage in BM or sex. The presence of multiple NRAS mutations was not associated with analyzed characteristics nor patient outcomes.Summary/Conclusion:Our study showed NRAS gene mutations detected by two independent methods in more than one fifth of AML patients with curative therapy. Most of them have low VAF, therefore suggesting their later occurrence during leukemogenesis and limited proliferative potential. Although approximately 5% of AML patients harbor multiple NRAS mutations, we were not able to identify the difference between patients with one or more NRAS mutations.Supported by Ministry of Health of the Czech Republic, grant nr. 15–25809A, project FNBr. 65269705 and project MUNI/A/1105/2018. All rights reserved.