Interplay between DNA damage repair and apoptosis shapes cancer evolution through aneuploidy and microsatellite instability

Targeting vulnerabilities of aneuploid cells for cancer therapy

Aneuploidy is a common feature of cancer that drives tumor evolution, but it also creates cellular vulnerabilities that might be exploited therapeutically. Recent advances in genomic technologies and experimental models have uncovered diverse cellular consequences of aneuploidy, revealing dependencies on mitotic regulation, DNA replication and repair, proteostasis, metabolism, and immune interactions. Harnessing aneuploidy for precision oncology requires the combination of genomic, functional, and clinical studies that will enable translation of our improved understanding of aneuploidy to targeted therapies. In this review we discuss approaches to targeting both highly aneuploid cells and cells with specific common aneuploidies, summarize the biological underpinning of these aneuploidy-induced vulnerabilities, and explore their therapeutic implications.

iModEst: disentangling -omic impacts on gene expression variation across genes and tissues

Many regulatory factors impact the expression of individual genes including, but not limited, to microRNA, long non-coding RNA (lncRNA), transcription factors (TFs), cis-methylation, copy number variation (CNV), and single-nucleotide polymorphisms (SNPs). While each mechanism can influence gene expression substantially, the relative importance of each mechanism at the level of individual genes and tissues is poorly understood. Here, we present the integrative Models of Estimated gene expression (iModEst), which details the relative contribution of different regulators to the gene expression of 16,000 genes and 21 tissues within The Cancer Genome Atlas (TCGA). Specifically, we derive predictive models of gene expression using tumour data and test their predictive accuracy in cancerous and tumour-adjacent tissues. Our models can explain up to 70% of the variance in gene expression across 43% of the genes within both tumour and tumour-adjacent tissues. We confirm that TF expression best predicts gene expression in both tumour and tumour-adjacent tissue whereas methylation predictive models in tumour tissues does not transfer well to tumour adjacent tissues. We find new patterns and recapitulate previously reported relationships between regulator and gene-expression, such as CNV-predicted FGFR2 expression and SNP-predicted TP63 expression. Together, iModEst offers an interactive, comprehensive atlas of individual regulator-gene-tissue expression relationships as well as relationships between regulators.

CasGen: A Regularized Generative Model for CRISPR Cas Protein Design with Classification and Margin-Based Optimization

Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)-associated proteins (Cas) systems have revolutionized genome editing by providing high precision and versatility. However, most genome editing applications rely on a limited number of well-characterized Cas9 and Cas12 variants, constraining the potential for broader genome engineering applications. In this study, we extensively explored Cas9 and Cas12 proteins and developed CasGen, a novel transformer-based deep generative model with margin-based latent space regularization to enhance the quality of newly generative Cas9 and Cas12 proteins. Specifically, CasGen employs a strategies that combine classification to filter out non-Cas sequences, Bayesian optimization of the latent space to guide functionally relevant designs, and thorough structural validation using AlphaFold-based analyses to ensure robust protein generation. We collected a comprehensive dataset with 3,021 Cas9, 597 Cas12, and 597 Non-Cas protein sequences from reputable biological databases such as InterPro and PDB. To validate the generated proteins, we performed sequence alignment using the BLAST tool to ensure novelty and filter out highly similar sequences to existing Cas proteins. Structural prediction using AlphaFold2 and AlphaFold3 confirmed that the generated proteins exhibit high structural similarity to known Cas9 and Cas12 variants, with TM-scores between 0.70 and 0.85 and root-mean-square deviation (RMSD) values below 2.00 Å. Sequence identity analysis further demonstrated that the generated Cas9 orthologs exhibited 28% to 55% identity with known variants, while Cas12a variants show up to 48% identity. Our results demonstrate that the proposed Cas generative model has significant potential to expand the genome editing toolkit by designing diverse Cas proteins that retain functional integrity. The developed deep generative approach offers a promising avenue for synthetic biology and therapeutic applications, enableling the development of more precise and versatile Cas-based genome editing tools.

Expression quantitative trait loci influence DNA damage-induced apoptosis in cancer

BACKGROUND

Genomic instability and evading apoptosis are two fundamental hallmarks of cancer and closely linked to DNA damage response (DDR). By analyzing expression quantitative trait loci (eQTL) upon cell stimulation (called exposure eQTL (e2QTL)) it is possible to identify context specific gene regulatory variants and connect them to oncological diseases based on genome-wide association studies (GWAS).

RESULTS

We isolate CD8+ T cells from 461 healthy donors and stimulate them with high doses of 5 different carcinogens to identify regulatory mechanisms of DNA damage-induced apoptosis. Across all stimuli, we find 5,373 genes to be differentially expressed, with 85% to 99% of these genes being suppressed. While upregulated genes are specific to distinct stimuli, downregulated genes are shared across conditions but exhibit enrichment in biological processes depending on the DNA damage type. Analysis of eQTL reveals 654 regulated genes across conditions. Among them, 47 genes are significant e2QTL, representing a fraction of 4% to 5% per stimulus. To unveil disease relevant genetic variants, we compare eQTL and e2QTL with GWAS risk variants. We identify gene regulatory variants for KLF2, PIP4K2A, GPR160, RPS18, ARL17B and XBP1 that represent risk variants for oncological diseases.

CONCLUSION

Our study highlights the relevance of gene regulatory variants influencing DNA damage-induced apoptosis in cancer. The results provide new insights in cellular mechanisms and corresponding genes contributing to inter-individual effects in cancer development.

Machine Learning Reveals Aneuploidy Characteristics in Cancers: The Impact of BEX4

BACKGROUND

Aneuploidy is crucial yet under-explored in cancer pathogenesis. Specifically, the involvement of brain expressed X-linked gene 4 (BEX4) in microtubule formation has been identified as a potential aneuploidy mechanism. Nevertheless, BEX4's comprehensive impact on aneuploidy incidence across different cancer types remains unexplored.

METHODS

Patients from The Cancer Genome Atlas (TCGA) were stratified into high-score (training) and low-score (control) groups based on the aneuploidy score. Mfuzz expression pattern clustering and functional enrichment were applied to genes with BEX4 as the core to explore their regulatory mechanisms. Various machine learning techniques were employed to screen aneuploidy-associated genes, after which aneuploidy characteristic subtypes were established in cancers. Moreover, the aneuploidy characteristics across multiple cancer types were investigated by integrating the extent of tumor cell stemness acquisition and a series of immune traits. Immunohistochemistry and proliferation assay mainly verified the anti-tumor effect of different BEX4 level.

RESULTS

Functional clustering results showed that aneuploidy and stemness were significantly associated in kidney chromophobe (KICH) and thyroid carcinoma (THCA). And cell metabolism and cell cycle had key effects. Residual analysis indicates superior screening performance by random forest (RF). An aneuploid feature gene set with BEX4 as the core was screened to construct a Nomogram model. BEX4, calmodulin regulated spectrin associated protein 2 (CAMSAP2), and myristoylated alanine rich protein kinase C substrate (MARCKS) were identified as aneuploidy characteristic hub genes. Molecular subtypes in thymoma (THYM), thyroid carcinoma (THCA), and kidney chromophobe (KICH) showed significant differences in tumor cell stemness among different subtypes. The competitive endogenous RNA (ceRNA)-Genes network revealed that hub genes, co-regulated by hsa-miR-425-5p, hsa-miR-200c-3p, and others, regulate microtubules, centrosomes, and microtubule cytoskeleton. Furthermore, elevated BEX4 emerged as a significant protective factor in Pancreatic adenocarcinoma (PAAD), KICH, kidney renal papillary cell carcinoma (KIRP), and kidney renal clear cell carcinoma (KIRC).

CONCLUSIONS

BEX4, CAMSAP2, and MARCKS specifically express in microtubules, centrioles, and cytoskeletons, influencing tumor chromosome division and inducing aneuploidy. Additionally, the relationship between the acquisition of tumor cell stemness and the severity of aneuploidy varies significantly across tumor types, displaying positive and negative correlations.

Centromeres in cancer: Unraveling the link between chromosomal instability and tumorigenesis

Centromeres are critical structures involved in chromosome segregation, maintaining genomic stability, and facilitating the accurate transmission of genetic information. They are key in coordinating the assembly and help keep the correct structure, location, and function of the kinetochore, a proteinaceous structure vital for ensuring proper chromosome segregation during cell division. Abnormalities in centromere structure can lead to aneuploidy or chromosomal instability, which have been implicated in various diseases, including cancer. Accordingly, abnormalities in centromeres, such as structural rearrangements and dysregulation of centromere-associated proteins, disrupt gene function, leading to uncontrolled cell growth and tumor progression. For instance, altered expression of CENP-A, CENP-E, and others such as BUB1, BUBR1, MAD1, and INCENP, have been shown to ascribe to centromere over-amplification, chromosome missegregation, aneuploidy, and chromosomal instability; this, in turn, can culminate in tumor progression. These centromere abnormalities also promoted tumor heterogeneity by generating genetically diverse cell populations within tumors. Advanced techniques like fluorescence in situ hybridization (FISH) and chromosomal microarray analysis are crucial for detecting centromere abnormalities, enabling accurate cancer classification and tailored treatment strategies. Researchers are exploring strategies to disrupt centromere-associated proteins for targeted cancer therapies. Thus, this review explores centromere abnormalities in cancer, their molecular mechanisms, diagnostic implications, and therapeutic targeting. It aims to advance our understanding of centromeres' role in cancer and develop advanced diagnostic tools and targeted therapies for improved cancer management and treatment.

Copy number variations in endometrial cancer: from biological significance to clinical utility

The molecular basis of endometrial cancer, which is the most common malignancy of the female reproductive organs, relies not only on onset of mutations but also on copy number variations, the latter consisting of gene gains or losses. In this review, we introduce copy number variations and discuss their involvement in endometrial cancer to determine the perspectives of clinical applicability. We performed a literature analysis on PubMed of publications over the past 30 years and annotated clinical information, including histological and molecular subtypes, adopted molecular techniques for identification of copy number variations, their locations, and the genes involved. We highlight correlations between the presence of some specific copy number variations and myometrial invasion, lymph node metastasis, advanced International Federation of Gynecology and Obstetrics (FIGO) stage, high grade, drug response, and cancer progression. In particular, type I endometrial cancer cells have few copy number variations and are mainly located in 8q and 1q, while type II, high grade, and advanced FIGO stage endometrial cancer cells are aneuploid and have a greater number of copy number variations. As expected, the higher the number of copy number variations the worse the prognosis, especially if they amplify CCNE1, ERBB2, KRAS, MYC, and PIK3CA oncogenes. Great variability in copy number and location among patients with the same endometrial cancer histological or molecular subtype emerged, making them interesting candidates to be explored for the improvement of patient stratification. Copy number variations have a role in endometrial cancer progression, and therefore their detection may be useful for more accurate prediction of prognosis. Unfortunately, only a few studies have been carried out on the role of copy number variations according to the molecular classification of endometrial cancer, and even fewer have explored the correlation with drugs. For these reasons, further studies, also using single cell RNA sequencing, are needed before reaching a clinical application.

DNA-methylome-derived epigenetic fingerprint as an immunophenotype indicator of durable clinical immunotherapeutic benefits in head and neck squamous cell carcinoma

BACKGROUND

Cancer immunotherapy provides durable response and improves survival in a subset of head and neck squamous cell carcinoma (HNSC) patients, which may due to discriminative tumor microenvironment (TME). Epigenetic regulations play critical roles in HNSC tumorigenesis, progression, and activation of functional immune cells. This study aims to identify an epigenetic signature as an immunophenotype indicator of durable clinical immunotherapeutic benefits in HNSC patients.

METHODS

Unsupervised consensus clustering approach was applied to distinguish immunophenotypes based on five immune signatures in The Cancer Genome Atlas (TCGA) HNSC cohort. Two immunophenotypes (immune 'Hot' and immune 'Cold') that had different TME features, diverse prognosis, and distinct DNA methylation patterns were recognized. Immunophenotype-related methylated signatures (IPMS) were identified by the least absolute shrinkage and selector operation algorithm. Additionally, the IPMS score by deconvolution algorithm was constructed as an immunophenotype classifier to predict clinical outcomes and immunotherapeutic response.

RESULTS

The 'Hot' HNSC immunophenotype had higher immunoactivity and better overall survival (p = 0.00055) compared to the 'Cold' tumors. The immunophenotypes had distinct DNA methylation patterns, which was closely associated with HNSC tumorigenesis and functional immune cell infiltration. 311 immunophenotype-related methylated CpG sites (IRMCs) was identified from TCGA-HNSC dataset. IPMS score model achieved a strong clinical predictive performance for classifying immunophenotypes. The area under the curve value (AUC) of the IPMS score model reached 85.9% and 89.8% in TCGA train and test datasets, respectively, and robustness was verified in five HNSC validation datasets. It was also validated as an immunophenotype classifier for predicting durable clinical benefits (DCB) in lung cancer patients who received anti-PD-1/PD-L1 immunotherapy (p = 0.017) and TCGA-SKCM patients who received distinct immunotherapy (p = 0.033).

CONCLUSIONS

This study systematically analyzed DNA methylation patterns in distinct immunophenotypes to identify IPMS with clinical prognostic potential for personalized epigenetic anticancer approaches in HNSC patients. The IPMS score model may serve as a reliable epigenome prognostic tool for clinical immunophenotyping to guide immunotherapeutic strategies in HNSC.

Whole exome sequencing identifies common mutational landscape of cervix and endometrium small cell neuroendocrine carcinoma

Background

Primary small cell neuroendocrine carcinomas of the cervix and endometrium are rare gynecological malignancies with limited treatment options. This study aimed to improve the understanding of the carcinogenesis process and identify potential therapeutic targets for these two tumor types by constructing the mutational landscape at the whole exome level.

Methods

Primary tumor tissues and their matched blood samples were obtained from 10 patients with small cell cervical neuroendocrine carcinoma (NECC) and five patients with small cell endometrial neuroendocrine carcinoma (NECE). Whole exome sequencing was performed to construct the somatic mutation profiles. Mutational signature and recurrent mutated gene analysis were used to identify tumor subtypes and common carcinogenesis processes.

Results

Based on the burden of different mutational signatures, the NECCs in this work can be divided into two subtypes, including the mismatch repair deficiency like (dMMR-like) type (4/10) and the high spontaneous deamination type (6/10). Components of the PI3K/AKT signaling and RAS signaling were exclusively mutated in these two subtypes, respectively. The integration of human papillomavirus made a limited contribution to tumorigenesis in NECC (20%). The dysfunction of the mismatch repair system and microsatellite instability are the major features of NECE. PI3K/AKT, JAK/STAT signaling, and chromatin remodeling activity were the common mutated pathways in NECE. PIK3CA, WNK2, and KMT2B underwent mutations in both the dMMR-like subtype of NECC (50% - 75%) and in NECE (60% - 80%) specimens, while exhibiting infrequent mutational occurrences in publicly available data pertaining to neuroendocrine carcinomas of the lung or bladder (< 10%).

Conclusion

We identified the two subtypes of NECC with distinct mutated pathways and potential therapy targets. The dMMR-like type NECC and NECE may share a similar carcinogenesis process that include dysfunction of PI3K/AKT signaling, cell cycle, antiapoptotic processes, and chromatin remodeling activity.

MSI-XGNN: an explainable GNN computational framework integrating transcription- and methylation-level biomarkers for microsatellite instability detection

Microsatellite instability (MSI) is a hypermutator phenotype caused by DNA mismatch repair deficiency. MSI has been reported in various human cancers, particularly colorectal, gastric and endometrial cancers. MSI is a promising biomarker for cancer prognosis and immune checkpoint blockade immunotherapy. Several computational methods have been developed for MSI detection using DNA- or RNA-based approaches based on next-generation sequencing. Epigenetic mechanisms, such as DNA methylation, regulate gene expression and play critical roles in the development and progression of cancer. We here developed MSI-XGNN, a new computational framework for predicting MSI status using bulk RNA-sequencing and DNA methylation data. MSI-XGNN is an explainable deep learning model that combines a graph neural network (GNN) model to extract features from the gene-methylation probe network with a CatBoost model to classify MSI status. MSI-XGNN, which requires tumor-only samples, exhibited comparable performance with two well-known methods that require tumor-normal paired sequencing data, MSIsensor and MANTIS and better performance than several other tools. MSI-XGNN also showed good generalizability on independent validation datasets. MSI-XGNN identified six MSI markers consisting of four methylation probes (EPM2AIP1|MLH1:cg14598950, EPM2AIP1|MLH1:cg27331401, LNP1:cg05428436 and TSC22D2:cg15048832) and two genes (RPL22L1 and MSH4) constituting the optimal feature subset. All six markers were significantly associated with beneficial tumor microenvironment characteristics for immunotherapy, such as tumor mutation burden, neoantigens and immune checkpoint molecules such as programmed cell death-1 and cytotoxic T-lymphocyte antigen-4. Overall, our study provides a powerful and explainable deep learning model for predicting MSI status and identifying MSI markers that can potentially be used for clinical MSI evaluation.

Computational Methods Summarizing Mutational Patterns in Cancer: Promise and Limitations for Clinical Applications

Since the rise of next-generation sequencing technologies, the catalogue of mutations in cancer has been continuously expanding. To address the complexity of the cancer-genomic landscape and extract meaningful insights, numerous computational approaches have been developed over the last two decades. In this review, we survey the current leading computational methods to derive intricate mutational patterns in the context of clinical relevance. We begin with mutation signatures, explaining first how mutation signatures were developed and then examining the utility of studies using mutation signatures to correlate environmental effects on the cancer genome. Next, we examine current clinical research that employs mutation signatures and discuss the potential use cases and challenges of mutation signatures in clinical decision-making. We then examine computational studies developing tools to investigate complex patterns of mutations beyond the context of mutational signatures. We survey methods to identify cancer-driver genes, from single-driver studies to pathway and network analyses. In addition, we review methods inferring complex combinations of mutations for clinical tasks and using mutations integrated with multi-omics data to better predict cancer phenotypes. We examine the use of these tools for either discovery or prediction, including prediction of tumor origin, treatment outcomes, prognosis, and cancer typing. We further discuss the main limitations preventing widespread clinical integration of computational tools for the diagnosis and treatment of cancer. We end by proposing solutions to address these challenges using recent advances in machine learning.

MSINGB: A Novel Computational Method Based on NGBoost for Identifying Microsatellite Instability Status from Tumor Mutation Annotation Data

Microsatellite instability (MSI), a vital mutator phenotype caused by DNA mismatch repair deficiency, is frequently observed in several tumors. MSI is recognized as a critical molecular biomarker for diagnosis, prognosis, and therapeutic selection in several cancers. Identifying MSI status for current gold standard methods based on experimental analysis is laborious, time-consuming, and costly. Although several computational methods based on machine learning have been proposed to identify MSI status, we need to further understand which machine learning model would favor identification for MSI and which feature subset is strongly related to MSI. On this basis, more effective machine learning-based methods can be developed to improve the performance of MSI status identification. In this work, we present MSINGB, an NGBoost-based method for identifying MSI status from tumor somatic mutation annotation data. MSINGB first evaluates the prediction performance of 11 popular machine learning algorithms and 9 deep learning models to identify MSI. Among 20 models, NGBoost, a novel natural gradient boosting method, achieves the overall best performance. MSINGB then introduces two feature selection strategies to find the compact feature subset, which is strongly related to MSI, and employs the SHAP approach to interpreting how selected features impact the model prediction. MSINGB achieves a better prediction performance on both the tenfold cross-validation test and independent test compared with state-of-the-art methods.

Follicular Thyroid Adenoma and Follicular Thyroid Carcinoma-A Common or Distinct Background? Loss of Heterozygosity in Comprehensive Microarray Study

Pre- and postsurgical differentiation between follicular thyroid adenoma (FTA) and follicular thyroid cancer (FTC) represents a significant diagnostic challenge. Furthermore, it remains unclear whether they share a common or distinct background and what the mechanisms underlying follicular thyroid lesions malignancy are. The study aimed to compare FTA and FTC by the comprehensive microarray and to identify recurrent regions of loss of heterozygosity (LOH). We analyzed formalin-fixed paraffin-embedded (FFPE) samples acquired from 32 Caucasian patients diagnosed with FTA (16) and FTC (16). We used the OncoScan™ microarray assay (Affymetrix, USA), using highly multiplexed molecular inversion probes for single nucleotide polymorphism (SNP). The total number of LOH was higher in FTC compared with FTA (18 vs. 15). The most common LOH present in 21 cases, in both FTA (10 cases) and FTC (11 cases), was 16p12.1, which encompasses many cancer-related genes, such as TP53, and was followed by 3p21.31. The only LOH present exclusively in FTA patients (56% vs. 0%) was 11p11.2-p11.12. The alteration which tended to be detected more often in FTC (6 vs. 1 in FTA) was 12q24.11-q24.13 overlapping FOXN4, MYL2, PTPN11 genes. FTA and FTC may share a common genetic background, even though differentiating rearrangements may also be detected.

Ploidy Status of Ovarian Cancer Cell Lines and Their Association with Gene Expression Profiles

As a cancer type potentially dominated by copy number variations, ovarian cancer shows hyperploid karyotypes and large-scale chromosome alterations, which might be promising biomarkers correlated with tumor metastasis and chemoresistance. Experimental studies have provided more information about the roles of aneuploids and polyploids in ovarian cancer. However, ploidy evaluation of ovarian cancer cell lines is still limited, even in some ploidy-related research. Herein, the ploidy landscape of 51 ovarian cancer cell lines from the Cancer Cell Line Encyclopedia (CCLE) were analyzed, and the ploidy statuses of 13 human ovarian cancer cell lines and 2 murine cell lines were evaluated using G-banding and flow cytometry. Most human ovarian cancer cell lines were aneuploid, with modal numbers of 52-86 and numerical complexity ranging from 5 to 12. A2780, COV434 and TOV21G were screened as diploid cell lines, with a modal number of 46, a low aneuploid score and a near-diploid ploidy value. Two murine cell lines, both OV2944-HM1 and ID-8, were near-tetraploid. Integrated information on karyotypes, aneuploid score and ploidy value supplied references for a nondiploid model construction and a parallel analysis of diploid versus aneuploid. Moreover, the gene expression profiles were compared between diploid and aneuploid cell lines. The functions of differentially expressed genes were mainly enriched in terms of protein function regulation, TGF-β signaling and cell adhesion molecules. Genes downregulated in the aneuploid group were mainly related to metabolism and protein function regulation, and genes upregulated in the aneuploid group were mainly involved in immune regulation. Differentially expressed genes were randomly distributed on all chromosomes, while chromosome 1 alteration might contribute to immune-related alterations in aneuploid cell lines. Chromosome 19 alteration might be potentially significant for aneuploid ovarian cancer cell lines and patients, which needs further verification in ploidy research.

Impact of somatic mutations and transcriptomic alterations on cancer aneuploidy

Aneuploidy has been recognized as one of hallmark of tumorigenesis since the early 20th century. Recent developments in structural variation analysis in the human genome have revealed the diversity of aneuploidy in cancer. However, the effects of gene mutation and expression in tumors on aneuploidy remain poorly understood. Here, we performed whole exome analysis of over 5,000 Japanese cancer cases and investigated the impact of somatic mutations and gene expression alterations on aneuploidy. First, we evaluated tumor content and genomic alterations that could influence aneuploidy. Next, we compared the aneuploidy frequency in 18 cancer types and observed that TP53 mutations were associated with the aneuploidy on specific chromosomes in colorectal and gastric cancers. Finally, we used expression analysis to isolate pathways involved in aneuploidy accumulation from tumors without TP53 mutations. Chromosomal instability and cell cycle aberration were associated with aneuploidy in TP53 wild-type tumors, and 26 commonly upregulated genes were identified in aneuploidy-high solid tumors without TP53 mutations. Among them, two cancer-related genes (CENPA and PBK) were involved in aneuploidy. Our integrated analysis revealed that both TP53 mutations and transcriptomic alterations independent of somatic mutations affect aneuploidy accumulation. Our findings will facilitate further understanding of diverse aneuploidies in the tumorigenesis.

DNA ploidy and stroma predicted the risk of recurrence in low-risk stage III colorectal cancer

BACKGROUND

For clinically low-risk stage III colorectal cancer, the decision on cycles of adjuvant chemotherapy after surgery is disputed. The present study investigates the use of additional biomarkers of ploidy and stroma-ratio(PS) to stratify patients with low-risk stage III colorectal cancer, providing a basis for individualized treatment in the future.

METHODS

This study retrospectively enrolled 198 patients with clinical-low-risk stage III colorectal cancer (T1-3N1M0) and analyzed the DNA ploidy and stroma ratio of FFPE tumor tissues. The patients were divided into PS-low-risk group (Diploidy or Low-stroma) and PS-high-risk group (Non-diploid and High-stroma). For survival analyses, Kaplan-Meier and Cox regression models were used.

RESULTS

The results showed that the 5-year DFS of the PS-high-risk group was significantly lower than that in the PS-low-risk group (78.6 vs. 91.2%, HR = 2.606 [95% CI: 1.011-6.717], P = 0.039). Besides, in the PS-low-risk group, the 5 year OS (98.2 vs. 86.7%, P = 0.022; HR = 5.762 [95% CI: 1.281-25.920]) and DFS (95.6, vs 79.9%, P = 0.019; HR = 3.7 [95% CI: 1.24-11.04]) of patients received adjuvant chemotherapy for > 3 months were significantly higher than those received adjuvant chemotherapy for < 3 months. We also found that the PS could stratify the prognosis of patients with dMMR tumors. The 5-year OS (96.3 vs 71.4%, P = 0.037) and DFS (92.6 vs 57.1%, P = 0.015) were higher in the PS-low-risk dMMR patients than those in the PS-high-risk dMMR patients.

CONCLUSION

In this study, we found that PS can predict the prognosis of patients with stage III low-risk CRC. Besides, it may guide the decision on postoperative adjuvant chemotherapy.

Nondiploid cancer cells: Stress, tolerance and therapeutic inspirations

Aberrant ploidy status is a prominent characteristic in malignant neoplasms. Approximately 90% of solid tumors and 75% of haematopoietic malignancies contain aneuploidy cells, and 30%-60% of tumors undergo whole-genome doubling, indicating that nondiploidy might be a prevalent genomic aberration in cancer. Although the role of aneuploid and polyploid cells in cancer remains to be elucidated, recent studies have suggested that nondiploid cells might be a dangerous minority that severely challenges cancer management. Ploidy shifts cause multiple fitness coasts for cancer cells, mainly including genomic, proteotoxic, metabolic and immune stresses. However, nondiploid comprises a well-adopted subpopulation, with many tolerance mechanisms evident in cells along with ploidy shifts. Aneuploid and polyploid cells elegantly maintain an autonomous balance between the stress and tolerance during adaptive evolution in cancer. Breaking the balance might provide some inspiration for ploidy-selective cancer therapy and alleviation of ploidy-related chemoresistance. To understand of the complex role and therapeutic potential of nondiploid cells better, we reviewed the survival stresses and adaptive tolerances within nondiploid cancer cells and summarized therapeutic ploidy-selective alterations for potential use in developing future cancer therapy.

Role of Senescence in Tumorigenesis and Anticancer Therapy

Although the role of senescence in many physiological and pathological processes is becoming more identifiable, many aspects of senescence are still enigmatic. A special attention is paid to the role of this phenomenon in tumor development and therapy. This review mainly deals with a large spectrum of oncological issues, beginning with therapy-induced senescence and ending with oncogene-induced senescence. Moreover, the role of senescence in experimental approaches, such as primary cancer cell culture or reprogramming into stem cells, is also beginning to receive further consideration. Additional focus is made on senescence resulting from mitotic catastrophe processes triggered by events occurring during mitosis and jeopardizing chromosomal stability. It has to be also realized that based on recent findings, the basics of senescent cell property interpretation, such as irreversibility of proliferation blockade, can be undermined. It shows that the definition of senescence probably requires updating. Finally, the role of senescence is lately more understandable in the immune system, especially since senescence can diminish the effectiveness of the chimeric antigen receptor T-cell (CAR-T) therapy. In this review, we summarize the current knowledge regarding all these issues.

Printer center nanoparticles alter the DNA repair capacity of human bronchial airway epithelial cells

Nano-enabled, toner-based printing equipment emit nanoparticles during operation. The bioactivity of these nanoparticles as documented in a plethora of published toxicological studies raises concerns about their potential health effects. These include pro-inflammatory effects that can lead to adverse epigenetic alterations and cardiovascular disorders in rats. At the same time, their potential to alter DNA repair pathways at realistic doses remains unclear. In this study, size-fractionated, airborne particles from a printer center in Singapore were sampled and characterized. The PM_0.1 size fraction (particles with an aerodynamic diameter less than 100 nm) of printer center particles (PCP) were then administered to human lung adenocarcinoma (Calu-3) or lymphoblastoid (TK6) cells. We evaluated plasma membrane integrity, mitochondrial activity, and intracellular reactive oxygen species (ROS) generation. Moreover, we quantified DNA damage and alterations in the cells' capacity to repair 6 distinct types of DNA lesions. Results show that PCP altered the ability of Calu-3 cells to repair 8oxoG:C lesions and perform nucleotide excision repair, in the absence of acute cytotoxicity or DNA damage. Alterations in DNA repair capacity have been correlated with the risk of various diseases, including cancer, therefore further genotoxicity studies are needed to assess the potential risks of PCP exposure, at both occupational settings and at the end-consumer level.

Laser activatable nanographene colloids for chemo-photothermal combined gene therapy of triple-negative breast cancer

This investigation reports the green approach for developing laser activatable nanoscale-graphene colloids (nGC-CO-FA) for chemo-photothermal combined gene therapy of triple-negative breast cancer (TNBC). The nano colloid was found to be nanometric as characterized by SEM, AFM, and zeta sizer (68.2 ± 2.1 nm; 13.8 ± 1.2 mV). The doxorubicin (Dox) loaded employing hydrophobic interaction/π-π stacking showed >80% entrapment efficiency with a sustained pH-dependent drug release profile. It can efficiently incorporate siRNA and Dox and successfully co-localize them inside TNBC cells to obtain significant anticancer activity as evaluated using CCK-8 assay, apoptosis assay, cell cycle analysis, cellular uptake, fluorescence assay, endosomal escape study, DNA content analysis, and gene silencing efficacy studies. nGC-CO-FA/Dox/siRNA released the Dox in temperature- and a pH-responsive manner following NIR-808 laser irradiation. The synergistic photo-chemo-gene therapy using near infrared-808 nm laser (NIR-808) irradiation was found to be more effective as compared to without NIR-808 laser-treated counterparts (∆T: 37 ± 1.1 °C → to 49.2 ± 3.1 °C; 10 min; 0.5 W/cm²), suggesting the pivotal role of photothermal combined gene-therapy in the treatment of TNBC.

Epigenome signature as an immunophenotype indicator prompts durable clinical immunotherapy benefits in lung adenocarcinoma

Intertumoral immune heterogeneity is a critical reason for distinct clinical benefits of immunotherapy in lung adenocarcinoma (LUAD). Tumor immunophenotype (immune 'Hot' or 'Cold') suggests immunological individual differences and potential clinical treatment guidelines. However, employing epigenome signatures to determine tumor immunophenotypes and responsive treatment is not well understood. To delineate the tumor immunophenotype and immune heterogeneity, we first distinguished the immune 'Hot' and 'Cold' tumors of LUAD based on five immune expression signatures. In terms of clinical presentation, the immune 'Hot' tumors usually had higher immunoactivity, lower disease stages and better survival outcomes than 'Cold' tumors. At the epigenome levels, we observed that distinct DNA methylation patterns between immunophenotypes were closely associated with LUAD development. Hence, we identified a set of five CpG sites as the immunophenotype-related methylation signature (iPMS) for tumor immunophenotyping and further confirmed its efficiency based on a machine learning framework. Furthermore, we found iPMS and immunophenotype-related immune checkpoints (IPCPs) could contribute to the risk of tumor progression, implying IPCP has the potential to be a novel immunotherapy blockade target. After further parsing of the role of iPMS-predicted immunophenotypes, we found immune 'Hot' was a protective factor leading to better survival outcomes when patients received the anti-PD-1/PD-L1 immunotherapy. And iPMS was also a well-performed signature (AUC = 0.752) for predicting the durable/nondurable clinical benefits. In summary, our study explored the role of epigenome signature in clinical tumor immunophenotyping. Utilizing iPMS to characterize tumor immunophenotypes will facilitate developing personalized epigenetic anticancer approaches.

Targeting the DNA damage response in immuno-oncology: developments and opportunities

Immunotherapy has revolutionized cancer treatment and substantially improved patient outcome with regard to multiple tumour types. However, most patients still do not benefit from such therapies, notably because of the absence of pre-existing T cell infiltration. DNA damage response (DDR) deficiency has recently emerged as an important determinant of tumour immunogenicity. A growing body of evidence now supports the concept that DDR-targeted therapies can increase the antitumour immune response by (1) promoting antigenicity through increased mutability and genomic instability, (2) enhancing adjuvanticity through the activation of cytosolic immunity and immunogenic cell death and (3) favouring reactogenicity through the modulation of factors that control the tumour-immune cell synapse. In this Review, we discuss the interplay between the DDR and anticancer immunity and highlight how this dynamic interaction contributes to shaping tumour immunogenicity. We also review the most innovative preclinical approaches that could be used to investigate such effects, including recently developed ex vivo systems. Finally, we highlight the therapeutic opportunities presented by the exploitation of the DDR-anticancer immunity interplay, with a focus on those in early-phase clinical development.

Consequences of mitotic failure - The penalties and the rewards

Eukaryotic cells are usually diploid, meaning they contain two copies of each chromosome. However, aberrant chromosome numbers due to both, chromosome gains and losses, are often observed in nature. They can occur as a planned developmental step, but are more often an uninvited result of mitotic failure. Recent discoveries have improved our understanding of the cellular effects of aneuploidy - uneven chromosome numbers, and polyploidy - multiplication of entire sets of chromosomes - in eukaryotic cells. The results show that mitotic errors lead to rapid and extensive modifications of many cellular processes and affect proliferation, proteome balance, genome stability and more. The findings picture the cellular response to aneuploidy and polyploidy as a complex, tissue and context dependent network of events. Here I review the latest discoveries, with an emphasis on pathological aspects of aneuploidy and polyploidy in human cells.

Incorporating Machine Learning into Established Bioinformatics Frameworks

The exponential growth of biomedical data in recent years has urged the application of numerous machine learning techniques to address emerging problems in biology and clinical research. By enabling the automatic feature extraction, selection, and generation of predictive models, these methods can be used to efficiently study complex biological systems. Machine learning techniques are frequently integrated with bioinformatic methods, as well as curated databases and biological networks, to enhance training and validation, identify the best interpretable features, and enable feature and model investigation. Here, we review recently developed methods that incorporate machine learning within the same framework with techniques from molecular evolution, protein structure analysis, systems biology, and disease genomics. We outline the challenges posed for machine learning, and, in particular, deep learning in biomedicine, and suggest unique opportunities for machine learning techniques integrated with established bioinformatics approaches to overcome some of these challenges.

Evolving insights: how DNA repair pathways impact cancer evolution

Viewing cancer as a large, evolving population of heterogeneous cells is a common perspective. Because genomic instability is one of the fundamental features of cancer, this intrinsic tendency of genomic variation leads to striking intratumor heterogeneity and functions during the process of cancer formation, development, metastasis, and relapse. With the increased mutation rate and abundant diversity of the gene pool, this heterogeneity leads to cancer evolution, which is the major obstacle in the clinical treatment of cancer. Cells rely on the integrity of DNA repair machineries to maintain genomic stability, but these machineries often do not function properly in cancer cells. The deficiency of DNA repair could contribute to the generation of cancer genomic instability, and ultimately promote cancer evolution. With the rapid advance of new technologies, such as single-cell sequencing in recent years, we have the opportunity to better understand the specific processes and mechanisms of cancer evolution, and its relationship with DNA repair. Here, we review recent findings on how DNA repair affects cancer evolution, and discuss how these mechanisms provide the basis for critical clinical challenges and therapeutic applications.

Mutation-selection balance and compensatory mechanisms in tumour evolution

Intratumour heterogeneity and phenotypic plasticity, sustained by a range of somatic aberrations, as well as epigenetic and metabolic adaptations, are the principal mechanisms that enable cancers to resist treatment and survive under environmental stress. A comprehensive picture of the interplay between different somatic aberrations, from point mutations to whole-genome duplications, in tumour initiation and progression is lacking. We posit that different genomic aberrations generally exhibit a temporal order, shaped by a balance between the levels of mutations and selective pressures. Repeat instability emerges first, followed by larger aberrations, with compensatory effects leading to robust tumour fitness maintained throughout the tumour progression. A better understanding of the interplay between genetic aberrations, the microenvironment, and epigenetic and metabolic cellular states is essential for early detection and prevention of cancer as well as development of efficient therapeutic strategies.