Background: Biomarker gene expression is becoming more commonly utilized for clinical decision-making in oncology clinical practice. However, complex tumor tissue comprises a population of cancer cells (CC) and the tumor microenvironment (TME), causing expression signals belonging to the CC and TME calculated from bulk RNA-seq of the tumor tissue to be indistinguishable. To circumvent this, Helenus, a gene expression deconvolution tool, was developed to estimate TME-specific gene expression, consequently, providing precise CC-specific gene expression. Methods: Helenus performs the “subtraction” of TME gene expression from the total expression calculated from bulk RNA-seq of the tumor tissue. To accurately reconstruct the CC expression profile, LightGBM gene models were trained on artificial transcriptomes created from > 1,000 different solid tumor cancer cell lines and > 3,000 samples of various TME cellular proportions. The LighGBM gene models included genes expressed predominantly in the TME (e.g., CD3E), both the TME and the CC (e.g., BCL6), or in the CC (e.g., HER2). The input features included: 1) RNA percentages of TME cell types predicted by the cell deconvolution tool Kassandra (Zaitsev et al., 2022); 2) evaluation of TME target gene expression via the estimation of its weighted average expression profile in TME cell populations; and 3) a set of TME- and CC-specific genes. The resulting predictions were adjusted based on the CC cell fraction. To evaluate Helenus’ performance, CC and TME RNA were mixed at different ratios using various cancer cell lines and peripheral blood-derived TME cell populations and suspensions of tumor cells prepared from cancer tissue across multiple tumor purity dilutions. Results: Helenus deconvolution resulted in an increased concordance correlation value from 0.73 to 0.98 between the real gene expression profile of pure CC and the reconstructed CC expression from bulk RNA-seq. Helenus showed high concordance between the gene expression profile of sorted cancer cell lines and the deconvolved gene expression across a wide range of CC RNA concentrations (20-90%) mixed with imitated TME RNA at varying concentrations. Helenus demonstrated high performance calculating gene expression of multiple clinically relevant biomarkers in the TME:cancer cell line mixes: CD274 (PD-L1) (mean absolute error [MAE] ~3.5-fold reduction); HLA-A (~2.8-fold MAE reduction); MKI67 (Ki-67, ~2.2-fold MAE reduction), ERBB2 (HER2, ~1.7-fold MAE reduction). Helenus deconvolved CC expression and found significant correlations with CC gene amplifications and deletions (e.g., BCL-2, VNN3) independent of tumor purity (p < 0.003). Conclusion: Helenus, the CC gene expression deconvolution tool, was developed with high accuracy to contribute to tumor diagnosis, disease monitoring, treatment decisions, and clinically relevant biomarker identification. Citation Format: Valentina Beliaeva, Ekaterina Ivleva, Boris Shpak, Daniil Litvinov, Anastasia Zotova, Krystle Nomie, Daniiar Dyikanov, Alexander Kuznetsov, Maria Savchenko, Aleksandr Zaitsev, Nathan Fowler, Alexander Bagaev. Computational cancer cell gene expression deconvolution from tumor bulk RNA-seq via the machine learning algorithm Helenus. [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 5401.
e15050 Background: Complex tumor tissue is composed of malignant cells and diverse tumor microenvironment (TME) cellular populations. Depending on the cancer type, the percentage of malignant cells present in tumor tissue varies, with percentages sometimes below 10%. TME cellular transcripts may comprise the majority of the total transcripts in a tumor, potentially resulting in biases during biomarker development and for clinical decision-making. However, TME gene expression can be subtracted from total tumor gene expression, resulting in only malignant cell expression. Methods: A computational tool was developed for the “subtraction” of TME-specific gene expression from total gene expression in an array of solid tumors, producing in silico “purified'' malignant cell gene expression. To extract the malignant cell RNA expression values, a machine learning model was trained on an artificial transcriptome dataset created from different solid tumor cancer cell lines with the addition of various TME cellular proportions. The artificial transcriptomes, both training and test, were composed of 7,114 samples of purified TME cell types and 3,143 cancer cell lines. The final model relied on the following three major parameters: 1) RNA percentages of deconvolved TME cell types; 2) the weighted sum of the average expression of a malignant cell target gene produced by different TME cell populations where the RNA percentages (weights in the weighted sum) of the TME cell populations were predicted by deconvolution; and 3) a set of genes expressed predominantly in the TME. To experimentally validate the computational model, different proportions of COLO829 (cutaneous melanoma), MCF-7 (invasive ductal carcinoma), and K562 (chronic myeloid leukemia) cell lines were mixed in vitro with PBMCs, creating controlled representations of tumor tissue, and RNA was extracted and sequenced. Gene expression was quantified and analyzed with the computational model, with comparisons to pure cancer cell line expression. Results: The expression of at least 120 clinically relevant biomarkers was reconstructed by applying the model to artificial transcriptomes in which the percentage of malignant cells varied from 10% to 90%. The concordance correlation coefficient between pure cancer cell lines and the extracted malignant cell expression increased on average from 0.75 to 0.9 compared to unprocessed data (e.g., PTEN from 0.2 to 0.88, RB1 from 0.57 to 0.89, ERBB2 from 0.83 to 0.99). In vitro validation showed that the tool improved the concordance correlation coefficient and mean absolute error (MAE) for many tumor biomarkers. For example, the PTEN correlation coefficient increased by 0.33 and its MAE was reduced 3-fold. Conclusions: This novel computational tool can aid in treatment decision-making based on malignant cell expression, promoting the use of gene expression for personalized therapeutics.
У статті представлені результати типолого-статистичного аналізу крем’яної колекції роз копу VII верхньопалеолітичної стоянки Пушкарі І (Новгород-Сіверський р-н Чернігівська обл.). Всього крем’яна колекція нараховує 36777 одиниць, з яких 4% складають знаряддя праці. Окрім ретушованих пластин і пластинок, найчисельніші групи становлять мікролітичні вироби (28%) та вістря великих форм (11%). Різців 4%, скребачки 2,4%, інші категорії знарядь представлені нечисельними серіями. Крем’яний комплекс є характерним для своєрідного пушкарівського типу пізньограветських пам’яток Півночі України.
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