An evolution-based machine learning to identify cancer type-specific driver mutations.

Identifying cancer type-specific driver mutations is crucial for illuminating distinct pathologic mechanisms across various tumors and providing opportunities of patient-specific treatment. However, although many computational methods were developed to predict driver mutations in a type-specific manner, the methods still have room to improve. Here, we devise a novel feature based on sequence co-evolution analysis to identify cancer type-specific driver mutations and construct a machine learning (ML) model with state-of-the-art performance. Specifically, relying on 28 000 tumor samples across 66 cancer types, our ML framework outperformed current leading methods of detecting cancer driver mutations. Interestingly, the cancer mutations identified by sequence co-evolution feature are frequently observed in interfaces mediating tissue-specific protein-protein interactions that are known to associate with shaping tissue-specific oncogenesis. Moreover, we provide pre-calculated potential oncogenicity on available human proteins with prediction scores of all possible residue alterations through user-friendly website (http://sbi.postech.ac.kr/w/cancerCE). This work will facilitate the identification of cancer type-specific driver mutations in newly sequenced tumor samples.

expHRD: an individualized, transcriptome-based prediction model for homologous recombination deficiency assessment in cancer.

BACKGROUND: Homologous recombination deficiency (HRD) stands as a clinical indicator for discerning responsive outcomes to platinum-based chemotherapy and poly ADP-ribose polymerase (PARP) inhibitors. One of the conventional approaches to HRD prognostication has generally centered on identifying deleterious mutations within the BRCA1/2 genes, along with quantifying the genomic scars, such as Genomic Instability Score (GIS) estimation with scarHRD. However, the scarHRD method has limitations in scenarios involving tumors bereft of corresponding germline data. Although several RNA-seq-based HRD prediction algorithms have been developed, they mainly support cohort-wise classification, thereby yielding HRD status without furnishing an analogous quantitative metric akin to scarHRD. This study introduces the expHRD method, which operates as a novel transcriptome-based framework tailored to n-of-1-style HRD scoring. RESULTS: The prediction model has been established using the elastic net regression method in the Cancer Genome Atlas (TCGA) pan-cancer training set. The bootstrap technique derived the HRD geneset for applying the expHRD calculation. The expHRD demonstrated a notable correlation with scarHRD and superior performance in predicting HRD-high samples. We also performed intra- and extra-cohort evaluations for clinical feasibility in the TCGA-OV and the Genomic Data Commons (GDC) ovarian cancer cohort, respectively. The innovative web service designed for ease of use is poised to extend the realms of HRD prediction across diverse malignancies, with ovarian cancer standing as an emblematic example. CONCLUSIONS: Our novel approach leverages the transcriptome data, enabling the prediction of HRD status with remarkable precision. This innovative method addresses the challenges associated with limited available data, opening new avenues for utilizing transcriptomics to inform clinical decisions.

Evolutionary rewiring of regulatory networks contributes to phenotypic differences between human and mouse orthologous genes.

Mouse models have been engineered to reveal the biological mechanisms of human diseases based on an assumption. The assumption is that orthologous genes underlie conserved phenotypes across species. However, genetically modified mouse orthologs of human genes do not often recapitulate human disease phenotypes which might be due to the molecular evolution of phenotypic differences across species from the time of the last common ancestor. Here, we systematically investigated the evolutionary divergence of regulatory relationships between transcription factors (TFs) and target genes in functional modules, and found that the rewiring of gene regulatory networks (GRNs) contributes to the phenotypic discrepancies that occur between humans and mice. We confirmed that the rewired regulatory networks of orthologous genes contain a higher proportion of species-specific regulatory elements. Additionally, we verified that the divergence of target gene expression levels, which was triggered by network rewiring, could lead to phenotypic differences. Taken together, a careful consideration of evolutionary divergence in regulatory networks could be a novel strategy to understand the failure or success of mouse models to mimic human diseases. To help interpret mouse phenotypes in human disease studies, we provide quantitative comparisons of gene expression profiles on our website (http://sbi.postech.ac.kr/w/RN).

Enzyme activity engineering based on sequence co-evolution analysis.

The utility of engineering enzyme activity is expanding with the development of biotechnology. Conventional methods have limited applicability as they require high-throughput screening or three-dimensional structures to direct target residues of activity control. An alternative method uses sequence evolution of natural selection. A repertoire of mutations was selected for fine-tuning enzyme activities to adapt to varying environments during the evolution. Here, we devised a strategy called sequence co-evolutionary analysis to control the efficiency of enzyme reactions (SCANEER), which scans the evolution of protein sequences and direct mutation strategy to improve enzyme activity. We hypothesized that amino acid pairs for various enzyme activity were encoded in the evolutionary history of protein sequences, whereas loss-of-function mutations were avoided since those are depleted during the evolution. SCANEER successfully predicted the enzyme activities of beta-lactamase and aminoglycoside 3'-phosphotransferase. SCANEER was further experimentally validated to control the activities of three different enzymes of great interest in chemical production: cis-aconitate decarboxylase, α-ketoglutaric semialdehyde dehydrogenase, and inositol oxygenase. Activity-enhancing mutations that improve substrate-binding affinity or turnover rate were found at sites distal from known active sites or ligand-binding pockets. We provide SCANEER to control desired enzyme activity through a user-friendly webserver.

The implication of holocytochrome c synthase mutation in Korean familial hypoplastic amelogenesis imperfecta.

OBJECTIVES: This study aimed to comprehensively characterise genetic variants of amelogenesis imperfecta in a single Korean family through whole-exome sequencing and bioinformatics analysis. MATERIAL AND METHODS: Thirty-one individuals of a Korean family, 9 of whom were affected and 22 unaffected by amelogenesis imperfecta, were enrolled. Whole-exome sequencing was performed on 12 saliva samples, including samples from 8 affected and 4 unaffected individuals. The possible candidate genes associated with the disease were screened by segregation analysis and variant filtering. In silico mutation impact analysis was then performed on the filtered variants based on sequence conservation and protein structure. RESULTS: Whole-exome sequencing data revealed an X-linked dominant, heterozygous genomic missense mutation in the mitochondrial gene holocytochrome c synthase (HCCS). We also found that HCCS is potentially related to the role of mitochondria in amelogenesis. The HCCS variant was expected to be deleterious in both evolution-based and large population-based analyses. Further, the variant was predicted to have a negative effect on catalytic function of HCCS by in silico analysis of protein structure. In addition, HCCS had significant association with amelogenesis in literature mining analysis. CONCLUSIONS: These findings suggest new evidence for the relationship between amelogenesis and mitochondria function, which could be implicated in the pathogenesis of amelogenesis imperfecta. CLINICAL RELEVANCE: The discovery of HCCS mutations and a deeper understanding of the pathogenesis of amelogenesis imperfecta could lead to finding solutions for the fundamental treatment of this disease. Furthermore, it enables dental practitioners to establish predictable prosthetic treatment plans at an early stage by early detection of amelogenesis imperfecta through personalised medicine.

Evolutionary coupling analysis identifies the impact of disease-associated variants at less-conserved sites.

Genome-wide association studies have discovered a large number of genetic variants in human patients with the disease. Thus, predicting the impact of these variants is important for sorting disease-associated variants (DVs) from neutral variants. Current methods to predict the mutational impacts depend on evolutionary conservation at the mutation site, which is determined using homologous sequences and based on the assumption that variants at well-conserved sites have high impacts. However, many DVs at less-conserved but functionally important sites cannot be predicted by the current methods. Here, we present a method to find DVs at less-conserved sites by predicting the mutational impacts using evolutionary coupling analysis. Functionally important and evolutionarily coupled sites often have compensatory variants on cooperative sites to avoid loss of function. We found that our method identified known intolerant variants in a diverse group of proteins. Furthermore, at less-conserved sites, we identified DVs that were not identified using conservation-based methods. These newly identified DVs were frequently found at protein interaction interfaces, where species-specific mutations often alter interaction specificity. This work presents a means to identify less-conserved DVs and provides insight into the relationship between evolutionarily coupled sites and human DVs.

Two-Level Blockchain System for Digital Crime Evidence Management.

Digital evidence, such as evidence from CCTV and event data recorders, is highly valuable in criminal investigations, and is used as definitive evidence in trials. However, there are risks when digital evidence obtained during the investigation of a case is managed through a physical hard disk drive until it is submitted to the court. Previous studies have focused on the integrated management of digital evidence in a centralized system, but if a centralized system server is attacked, major operations and investigation information may be leaked. Therefore, there is a need to reliably manage digital evidence and investigation information using blockchain technology in a distributed system environment. However, when large amounts of data-such as evidence videos-are stored in a blockchain, the data that must be processed only within one block before being created increase, causing performance degradation. Therefore, we propose a two-level blockchain system that separates digital evidence into hot and cold blockchains. In the criminal investigation process, information that frequently changes is stored in the hot blockchain, and unchanging data such as videos are stored in the cold blockchain. To evaluate the system, we measured the storage and inquiry processing performance of digital crime evidence videos according to the different capacities in the two-level blockchain system.

Capicua suppresses colorectal cancer progression via repression of ETV4 expression.

BACKGROUND: Although major driver gene mutations have been identified, the complex molecular heterogeneity of colorectal cancer (CRC) remains unclear. Capicua (CIC) functions as a tumor suppressor in various types of cancers; however, its role in CRC progression has not been examined. METHODS: Databases for gene expression profile in CRC patient samples were used to evaluate the association of the levels of CIC and Polyoma enhancer activator 3 (PEA3) group genes (ETS translocation variant 1 (ETV1), ETV4, and ETV5), the best-characterized CIC targets in terms of CIC functions, with clinicopathological features of CRC. CIC and ETV4 protein levels were also examined in CRC patient tissue samples. Gain- and loss-of function experiments in cell lines and mouse xenograft models were performed to investigate regulatory functions of CIC and ETV4 in CRC cell growth and invasion. qRT-PCR and western blot analyses were performed to verify the CIC regulation of ETV4 expression in CRC cells. Rescue experiments were conducted using siRNA against ETV4 and CIC-deficient CRC cell lines. RESULTS: CIC expression was decreased in the tissue samples of CRC patients. Cell invasion, migration, and proliferation were enhanced in CIC-deficient CRC cells and suppressed in CIC-overexpressing cells. Among PEA3 group genes, ETV4 levels were most dramatically upregulated and inversely correlated with the CIC levels in CRC patient samples. Furthermore, derepression of ETV4 was more prominent in CIC-deficient CRC cells, when compared with that observed for ETV1 and ETV5. The enhanced cell proliferative and invasive capabilities in CIC-deficient CRC cells were completely recovered by knockdown of ETV4. CONCLUSION: Collectively, the CIC-ETV4 axis is not only a key module that controls CRC progression but also a novel therapeutic and/or diagnostic target for CRC.

Divergence of Noncoding Regulatory Elements Explains Gene-Phenotype Differences between Human and Mouse Orthologous Genes.

Mice have been widely used as a model organism to investigate human gene-phenotype relationships based on a conjecture that orthologous genes generally perform similar functions and are associated with similar phenotypes. However, phenotypes associated with orthologous genes often turn out to be quite different between human and mouse. Herein, we devised a method to quantitatively compare phenotypes annotations associated with mouse models and human. Using semantic similarity comparisons, we identified orthologous genes with different phenotype annotations, of which the similarity score is on a par with that of random gene pairs. Analysis of sequence evolution and transcriptomic changes revealed that orthologous genes with phenotypic differences are correlated with changes in noncoding regulatory elements and tissue-specific expression profiles rather than changes in protein-coding sequences. To map accurate gene-phenotype relationships using model organisms, we propose that careful consideration of the evolutionary divergence of noncoding regulatory elements and transcriptomic profiles is essential.

Capicua suppresses hepatocellular carcinoma progression by controlling the ETV4-MMP1 axis.

Hepatocellular carcinoma (HCC) is developed by multiple steps accompanying progressive alterations of gene expression, which leads to increased cell proliferation and malignancy. Although environmental factors and intracellular signaling pathways that are critical for HCC progression have been identified, gene expression changes and the related genetic factors contributing to HCC pathogenesis are still insufficiently understood. In this study, we identify a transcriptional repressor, Capicua (CIC), as a suppressor of HCC progression and a potential therapeutic target. Expression of CIC is posttranscriptionally reduced in HCC cells. CIC levels are correlated with survival rates in patients with HCC. CIC overexpression suppresses HCC cell proliferation and invasion, whereas loss of CIC exerts opposite effects in vivo as well as in vitro. Levels of polyoma enhancer activator 3 (PEA3) group genes, the best-known CIC target genes, are correlated with lethality in patients with HCC. Among the PEA3 group genes, ETS translocation variant 4 (ETV4) is the most significantly up-regulated in CIC-deficient HCC cells, consequently promoting HCC progression. Furthermore, it induces expression of matrix metalloproteinase 1 (MMP1), the MMP gene highly relevant to HCC progression, in HCC cells; and knockdown of MMP1 completely blocks the CIC deficiency-induced HCC cell proliferation and invasion. CONCLUSION: Our study demonstrates that the CIC-ETV4-MMP1 axis is a regulatory module controlling HCC progression. (Hepatology 2018;67:2287-2301).

Sequential Process Combination of Photocatalytic Oxidation and Dark Reduction for the Removal of Organic Pollutants and Cr(VI) using Ag/TiO2.

We investigated a sequential photocatalysis-dark reaction, wherein organic pollutants were degraded on Ag/TiO2 under UV irradiation and the dark reduction of hexavalent chromium (Cr(VI)) was subsequently followed. The photocatalytic oxidation of 4-chlorophenol (4-CP), a test organic substrate, induced the generation of degradation intermediates and the storage of electrons in Ag/TiO2 which were then utilized for reducing Cr(VI) in the postirradiation period. The dark reduction efficiency of Cr(VI) was much higher with Ag/TiO2 (87%), compared with bare TiO2 (27%) and Pt/TiO2 (22%). The Cr(VI) removal by Ag/TiO2 (87%) was contributed by adsorption (31%), chemical reduction by intermediates of 4-CP degradation (26%), and reduction by electrons stored in Ag (30%). When formic acid, humic acid or ethanol was used as an alternative organic substrate, the electron storage effect was also observed. The postirradiation removal of Cr(VI) on Ag/TiO2 continued for hours, which is consistent with the observation that a residual potential persisted on the Ag/TiO2 electrode in the dark whereas little residual potential was observed on bare TiO2 and Pt/TiO2 electrodes. The stored electrons in Ag/TiO2 and their transfer to Cr(VI) were also indicated by the UV-visible absorption spectral change. Moreover, the electrons stored in the preirradiated Ag/TiO2 reacted with O2 with showing a sign of low-level OH radical generation in the dark period.

Heterogeneous catalytic oxidation of As(III) on nonferrous metal oxides in the presence of H2O2.

The oxidation of As(III) (arsenite) to As(V) (arsenate), a critical pretreatment process for total arsenic removal, is easily achieved using chemical oxidation methods. Hydrogen peroxide (H2O2) is widely used as an environmentally benign oxidant but its practical use for the arsenite oxidation is limited by the strong pH dependence and slow oxidation kinetics. This study demonstrated that H2O2-induced oxidation of As(III) can be markedly enhanced in the presence of nonferrous metal oxides (e.g., WO3, TiO2, ZrO2) as a heterogeneous catalyst working over a wide pH range in ambient reaction conditions. In particular, TiO2 is an ideal catalyst because it is not only active and stable but also easily available and inexpensive. Although the photocatalytic oxidation of As(III) on TiO2 was intensively studied, the thermal catalytic activities of TiO2 and other nonferrous metal oxides for the arsenic oxidation have been little investigated. The heterogeneous oxidation rate increased with increasing the TiO2 surface area and [H2O2] and weakly depended on pH whereas the homogeneous oxidation by H2O2 alone was favored only at alkaline condition. The oxidation rate in the TiO2/H2O2 system was not reduced at all in the absence of dioxygen. It was not retarded at all by OH radical scavengers but markedly inhibited by hydroperoxyl radical scavengers. It is proposed that the surface complexation of H2O2 on TiO2 induces the generation of the surface hydroperoxyl radical through an inner-sphere electron transfer, which subsequently reacts with As(III). The catalytic activity of TiO2 was maintained without showing any sign of deactivation. The heterogeneous catalytic oxidation is proposed as a viable method for the preoxidation treatment of As(III)-contaminated water under ambient conditions.

Arsenite oxidation initiated by the UV photolysis of nitrite and nitrate.

This study demonstrates that the production of reactive oxidizing species (e.g., hydroxyl radical (•OH)) during the photolysis of nitrite (NO2(-)) or nitrate (NO3(-)) leads to the oxidative conversion of arsenite (As(III)) to arsenate (As(V)). While the direct UV photolytic oxidation of As(III) was absent, nitrite (20 or 200 µM) addition markedly accelerated the oxidation of As(III) under UV irradiation (λ > 295 nm), which implies a role of NO2(-) as a photosensitizer for As(III) oxidation. Nitrate-mediated photooxidation of As(III) revealed an initial lag phase during which NO3(-) is converted into NO2(-). UV-Photosensitized oxidation of As(III) was kinetically enhanced under acidic pH condition where nitrous acid (HNO2) with a high quantum yield for •OH production is a predominant form of nitrite. On the other hand, alkaline pH that favors the photoinduced transformation of NO3(-) to NO2(-) significantly facilitated the catalytic reduction/oxidation cycling, which enabled the complete oxidation of As(III) at the condition of [As(III)]/[NO2(-)] ≫ 1 and markedly accelerated NO3(-)-sensitized oxidation of As(III). The presence of O2 and N2O as electron scavengers enhanced the photochemical dissociation of NO2(-) via intermolecular electron transfer, initiating the oxidative As(III) conversion route probably involving NO2• and superoxide radical anion (O2•(-)) as alternative oxidants. The outdoor experiment demonstrated the capability of NO2(-) for the photosensitized production of oxidizing species and the subsequent oxidation of As(III) into As(V) under solar irradiation.

Investigation of the effect of adapalene 0.3%/benzoyl peroxide 2.5% gel in Korean patients with acne: a randomized, double-blind clinical trial, with a histopathological and immunohistochemical study.

BACKGROUND: Acne vulgaris poses significant physical and psychological challenges worldwide. Data of adapalene 0.3%/benzoyl peroxide 2.5% gel (A0.3/BPO2.5) for acne treatment in Asian patients is limited. METHODS: In this randomized double-blind clinical trial, 49 Korean patients with moderate-to-severe acne and scars were assigned to the A0.3/BPO2.5 (N.=37) or vehicle (N.=12) group. Acne and acne scar severity scores were assessed at baseline and 4, 8, 12, and 24 weeks. The primary outcomes were treatment success rate (reduction of ≥2 Investigator's Global Assessment grade and reaching a grade of 0 or 1) and proportional acne lesion and scar count reduction against the baseline. To assess histological changes, 2-mm punch biopsies were performed at baseline and week 24 on the respective inflammatory lesions or scars. RESULTS: At week 24, the A0.3/BPO2.5 group had a significantly higher treatment success rate than the vehicle group. The total acne count, inflammatory lesion count, and non-inflammatory lesion count percentages (against baselines) with A0.3/BPO2.5 and the vehicle were 12.1% vs. 96.7%, 8.0% vs. 101.2%, and 13.3% vs. 98.9%, respectively (all P<0.001). Scar count percentages (against baselines) with A0.3/BPO2.5 and the vehicle were 27.3% and 96.5%, respectively (P<0.001). Significant elevations in collagen 1 and 3, elastin, CK15, and p63 levels, with increases of 172.7%, 230.6%, 176.5%, 286.2%, and 105.9%, respectively, in comparison to baseline (all P<0.05). No major adverse events leading to discontinuation were observed. CONCLUSIONS: A0.3/BPO2.5 was an effective and safe treatment for acne and acne scars in Asian patients supported by robust histopathological and immunohistochemical evidence.

Cell-cell communication network-based interpretable machine learning predicts cancer patient response to immune checkpoint inhibitors.

Immune checkpoint inhibitors (ICIs) have revolutionized cancer treatment. However, only some patients respond to ICIs, and current biomarkers for ICI efficacy have limited performance. Here, we devised an interpretable machine learning (ML) model trained using patient-specific cell-cell communication networks (CCNs) decoded from the patient's bulk tumor transcriptome. The model could (i) predict ICI efficacy for patients across four cancer types (median AUROC: 0.79) and (ii) identify key communication pathways with crucial players responsible for patient response or resistance to ICIs by analyzing more than 700 ICI-treated patient samples from 11 cohorts. The model prioritized chemotaxis communication of immune-related cells and growth factor communication of structural cells as the key biological processes underlying response and resistance to ICIs, respectively. We confirmed the key communication pathways and players at the single-cell level in patients with melanoma. Our network-based ML approach can be used to expand ICIs' clinical benefits in cancer patients.

Photooxidation of arsenite under 254 nm irradiation with a quantum yield higher than unity.

Arsenite (As(III)) in water was demonstrated to be efficiently oxidized to arsenate (As(V)) under 254 nm UV irradiation without needing any chemical reagents. Although the molar absorption coefficient of As(III) at 254 nm is very low (2.49 ± 0.1 M(-1)cm(-1)), the photooxidation proceeded with a quantum yield over 1.0, which implies a chain of propagating oxidation cycles. The rate of As(III) photooxidation was highly enhanced in the presence of dissolved oxygen, which can be ascribed to its dual role as an electron acceptor of photoexcited As(III) and a precursor of oxidizing radicals. The in situ production of H2O2 was observed during the photooxidation of As(III) and its subsequent photolysis under UV irradiation produced OH radicals. The addition of tert-butyl alcohol as OH radical scavenger significantly reduced (but not completely inhibited) the oxidation rate, which indicates that OH radicals as well as superoxide serve as an oxidant of As(III). Superoxide, H2O2, and OH radicals were all in situ generated from the irradiated solution of As(III) in the presence of dissolved O2 and their subsequent reactions with As(III) induce the regeneration of some oxidants, which makes the overall quantum yield higher than 1. The homogeneous photolysis of arsenite under 254 nm irradiation can be also proposed as a new method of generating OH radicals.

Drug approval prediction based on the discrepancy in gene perturbation effects between cells and humans.

BACKGROUND: Poor translation between in vitro and clinical studies due to the cells/humans discrepancy in drug target perturbation effects leads to safety failures in clinical trials, thus increasing drug development costs and reducing patients' life quality. Therefore, developing a predictive model for drug approval considering the cells/humans discrepancy is needed to reduce drug attrition rates in clinical trials. METHODS: Our machine learning framework predicts drug approval in clinical trials based on the cells/humans discrepancy in drug target perturbation effects. To evaluate the discrepancy to predict drug approval (1404 approved and 1070 unapproved drugs), we analysed CRISPR-Cas9 knockout and loss-of-function mutation rate-based gene perturbation effects on cells and humans, respectively. To validate the risk of drug targets with the cells/humans discrepancy, we examined the targets of failed and withdrawn drugs due to safety problems. FINDINGS: Drug approvals in clinical trials were correlated with the cells/humans discrepancy in gene perturbation effects. Genes tolerant to perturbation effects on cells but intolerant to those on humans were associated with failed drug targets. Furthermore, genes with the cells/humans discrepancy were related to drugs withdrawn due to severe side effects. Motivated by previous studies assessing drug safety through chemical properties, we improved drug approval prediction by integrating chemical information with the cells/humans discrepancy. INTERPRETATION: The cells/humans discrepancy in gene perturbation effects facilitates drug approval prediction and explains drug safety failures in clinical trials. FUNDING: S.K. received grants from the Korean National Research Foundation (2021R1A2B5B01001903 and 2020R1A6A1A03047902) and IITP (2019-0-01906, Artificial Intelligence Graduate School Program, POSTECH).

Development of bioinformatics and multi-omics analyses in organoids.

Pre-clinical models are critical in gaining mechanistic and biological insights into disease progression. Recently, patient-derived organoid models have been developed to facilitate our understanding of disease development and to improve the discovery of therapeutic options by faithfully recapitulating in vivo tissues or organs. As technological developments of organoid models are rapidly growing, computational methods are gaining attention in organoid researchers to improve the ability to systematically analyze experimental results. In this review, we summarize the recent advances in organoid models to recapitulate human diseases and computational advancements to analyze experimental results from organoids. [BMB Reports 2023; 56(1): 43-48].

Information about immune cell proportions and tumor stage improves the prediction of recurrence in patients with colorectal cancer.

Predicting cancer recurrence is essential to improving the clinical outcomes of patients with colorectal cancer (CRC). Although tumor stage information has been used as a guideline to predict CRC recurrence, patients with the same stage show different clinical outcomes. Therefore, there is a need to develop a method to identify additional features for CRC recurrence prediction. Here, we developed a network-integrated multiomics (NIMO) approach to select appropriate transcriptome signatures for better CRC recurrence prediction by comparing the methylation signatures of immune cells. We validated the performance of the CRC recurrence prediction based on two independent retrospective cohorts of 114 and 110 patients. Moreover, to confirm that the prediction was improved, we used both NIMO-based immune cell proportions and TNM (tumor, node, metastasis) stage data. This work demonstrates the importance of (1) using both immune cell composition and TNM stage data and (2) identifying robust immune cell marker genes to improve CRC recurrence prediction.

Deconvolution of bulk tumors into distinct immune cell states predicts colorectal cancer recurrence.

Predicting colorectal cancer recurrence after tumor resection is crucial because it promotes the administration of proper subsequent treatment or management to improve the clinical outcomes of patients. Several clinical or molecular factors, including tumor stage, metastasis, and microsatellite instability status, have been used to assess the risk of recurrence, although their predictive ability is limited. Here, we predicted colorectal cancer recurrence based on cellular deconvolution of bulk tumors into two distinct immune cell states: cancer-associated (tumor-infiltrating immune cell-like) and noncancer-associated (peripheral blood mononuclear cell-like). Prediction model performed significantly better when immune cells were deconvoluted into two states rather than a single state, suggesting that the difference in cancer recurrence was better explained by distinct states of immune cells. It indicates the importance of distinguishing immune cell states using cellular deconvolution to improve the prediction of colorectal cancer recurrence.