An integrative analysis of the age-associated multi-omic landscape across cancers

Prognostic impact and causality of age on oncological outcomes in women with endometrial cancer: a multimethod analysis of the randomised PORTEC-1, PORTEC-2, and PORTEC-3 trials

BACKGROUND

Numerous studies have shown that older women with endometrial cancer have a higher risk of recurrence and cancer-related death. However, it remains unclear whether older age is a causal prognostic factor, or whether other risk factors become increasingly common with age. We aimed to address this question with a unique multimethod study design using state-of-the-art statistical and causal inference techniques on datasets of three large, randomised trials.

METHODS

In this multimethod analysis, data from 1801 women participating in the randomised PORTEC-1, PORTEC-2, and PORTEC-3 trials were used for statistical analyses and causal inference. The cohort included 714 patients with intermediate-risk endometrial cancer, 427 patients with high-intermediate risk endometrial cancer, and 660 patients with high-risk endometrial cancer. Associations of age with clinicopathological and molecular features were analysed using non-parametric tests. Multivariable competing risk analyses were performed to determine the independent prognostic value of age. To analyse age as a causal prognostic variable, a deep learning causal inference model called AutoCI was used.

FINDINGS

Median follow-up as estimated using the reversed Kaplan-Meier method was 12·3 years (95% CI 11·9-12·6) for PORTEC-1, 10·5 years (10·2-10·7) for PORTEC-2, and 6·1 years (5·9-6·3) for PORTEC-3. Both overall recurrence and endometrial cancer-specific death significantly increased with age. Moreover, older women had a higher frequency of deep myometrial invasion, serous tumour histology, and p53-abnormal tumours. Age was an independent risk factor for both overall recurrence (hazard ratio [HR] 1·02 per year, 95% CI 1·01-1·04; p=0·0012) and endometrial cancer-specific death (HR 1·03 per year, 1·01-1·05; p=0·0012) and was identified as a significant causal variable.

INTERPRETATION

This study showed that advanced age was associated with more aggressive tumour features in women with endometrial cancer, and was independently and causally related to worse oncological outcomes. Therefore, our findings suggest that older women with endometrial cancer should not be excluded from diagnostic assessments, molecular testing, and adjuvant therapy based on their age alone.

FUNDING

None.

Age-dependent molecular variations in osteosarcoma: implications for precision oncology across pediatric, adolescent, and adult patients

Background

Osteosarcoma is a leading subtype of bone tumor affecting adolescents and adults. Comparative molecular characterization among different age groups, especially in pediatric, adolescents and adults, is scarce.

Methods

We collected samples from 194 osteosarcoma patients, encompassing pediatric, adolescent, and adult cohorts. Genomic analyses were conducted to reveal prevalent mutations and compare molecular features in pediatric, adolescent, and adult patients.

Results

Samples from 194 osteosarcoma patients across pediatric to adult ages were analyzed, revealing key mutations such as TP53, FLCN, NCOR1, and others. Children and adolescents showed more gene amplifications and HRD mutations, while adults had a greater Tumor Mutational Burden (TMB). Mutations in those over 15 were mainly in cell cycle and PI3K/mTOR pathways, while under 15s had more in cell cycle and angiogenesis with higher VEGFA, CCND3, TFEB mutations. CNV patterns varied with age: VEGFA and XPO5 amplifications more in under 25s, and CDKN2A/B deletions in over 25s. Genetic alterations in genes like MCL1 and MYC were associated with poor prognosis, with VEGFA mutations also indicating worse outcomes. 58% of patients had actionable mutations, suggesting opportunities for targeted therapies. Age-specific patterns were observed, with Multi-TKI mutations more common in younger patients and CDK4/6 inhibitor mutations in adults, highlighting the need for personalized treatment approaches in osteosarcoma. In a small group of patients with VEGFR amplification, postoperative treatment with multi-kinase inhibitors resulted in a PR in 3 of 13 cases, especially in patients under 15. A significant case involved a 13-year-old with a notable tumor size reduction achieving PR, even with other genetic alterations present in some patients with PD.

Conclusion

This study delineates the molecular differences among pediatric, adolescent, and adult osteosarcoma patients at the genomic level, emphasizing the necessity for precision diagnostics and treatment strategies, and may offer novel prognostic biomarkers for patients with osteosarcoma. These findings provide a significant scientific foundation for the development of individualized treatment approaches tailored to patients of different age groups.

Proteomic features of soft tissue tumours in adolescents and young adults

BACKGROUND

Adolescents and young adult (AYA) patients with soft tissue tumours including sarcomas are an underserved group with disparities in treatment outcomes.

METHODS

To define the molecular features between AYA and older adult (OA) patients, we analysed the proteomic profiles of a large cohort of soft tissue tumours across 10 histological subtypes (AYA n = 66, OA n = 243), and also analysed publicly available functional genomic data from soft tissue tumour cell lines (AYA n = 5, OA n = 8).

RESULTS

Biological hallmarks analysis demonstrates that OA tumours are significantly enriched in MYC targets compared to AYA tumours. By comparing the patient-level proteomic data with functional genomic profiles from sarcoma cell lines, we show that the mRNA splicing pathway is an intrinsic vulnerability in cell lines from OA patients and that components of the spliceosome complex are independent prognostic factors for metastasis free survival in AYA patients.

CONCLUSIONS

Our study highlights the importance of performing age-specific molecular profiling studies to identify risk stratification tools and targeted agents tailored for the clinical management of AYA patients.

Nomogram built based on machine learning to predict recurrence in early-stage hepatocellular carcinoma patients treated with ablation

Introduction

To analyze the risk factors affecting recurrence in early-stage hepatocellular carcinoma (HCC) patients treated with ablation and then establish a nomogram to provide a clear and accessible representation of the patients' recurrence risk.

Methods

Collect demographic and clinical data of 898 early-stage HCC patients who underwent ablation treatment at Beijing You'an Hospital, affiliated with Capital Medical University from January 2014 to December 2022. Patients admitted from 2014 to 2018 were included in the training cohort, while 2019 to 2022 were in the validation cohort. Lasso and Cox regression was used to screen independent risk factors for HCC patients recurrence, and a nomogram was then constructed based on the screened factors.

Results

Age, gender, Barcelona Clinic Liver Cancer (BCLC) stage, tumor size, globulin (Glob) and γ-glutamyl transpeptidase (γ-GT) were finally incorporated in the nomogram for predicting the recurrence-free survival (RFS) of patients. We further confirmed that the nomogram has optimal discrimination, consistency and clinical utility by the C-index, Receiver Operating Characteristic Curve (ROC), calibration curve and Decision Curve Analysis (DCA). Moreover, we divided the patients into different risk groups and found that the nomogram can effectively identify the high recurrence risk patients by the Kaplan-Meier curves.

Conclusion

This study developed a nomogram using Lasso-Cox regression to predict RFS in early-stage HCC patients following ablation, aiding clinicians in identifying high-risk groups for personalized follow-up treatments.

Spatiotemporal distribution of mediastinal neoplasms: A comprehensive multi-center study

OBJECTIVES

Mediastinal neoplasms are typical but uncommon thoracic diseases with increasing incidence and unfavorable prognoses. A comprehensive understanding of their spatiotemporal distribution is essential for accurate diagnosis and timely treatment. However, previous studies are limited in scale and data coverage. Therefore, this study aims to elucidate the distribution of mediastinal lesions, offering valuable insights into this disease.

MATERIALS AND METHODS

This multi-center, hospital-based observational study included 20 nationwide institutions. A retrospective search of electronic medical records from January 1st, 2009, to December 31st, 2020, was conducted, collecting sociodemographic data, computed tomography images, and pathologic diagnoses. Analysis focused on age, sex, time, location, and geographical region. Comparative assessments were made with global data from a multi-center database.

RESULTS

Among 7,765 cases, thymomas (30.7%), benign mediastinal cysts (23.4%), and neurogenic tumors (10.0%) were predominant. Distribution varied across mediastinal compartments, with thymomas (39.6%), benign cysts (28.1%), and neurogenic tumors (51.9%) most prevalent in the prevascular, visceral, and paravertebral mediastinum, respectively. Age-specific variations were notable, with germ cell tumors prominent in patients under 18 and aged 18-29, while thymomas were more common in patients over 30. The composition of mediastinal lesions across different regions of China remained relatively consistent, but it differs from that of the global population.

CONCLUSION

This study revealed significant heterogeneity in the spatiotemporal distribution of mediastinal neoplasms. These findings provide useful demographic data when considering the differential diagnosis of mediastinal lesions, and would be beneficial for tailoring disease prevention and control strategies.

Multi-Omics Integration for Liver Cancer Using Regression Analysis

Genetic biomarkers have played a pivotal role in the classification, prognostication, and guidance of clinical cancer therapies. Large-scale and multi-dimensional analyses of entire cancer genomes, as exemplified by projects like The Cancer Genome Atlas (TCGA), have yielded an extensive repository of data that holds the potential to unveil the underlying biology of these malignancies. Mutations stand out as the principal catalysts of cellular transformation. Nonetheless, other global genomic processes, such as alterations in gene expression and chromosomal re-arrangements, also play crucial roles in conferring cellular immortality. The incorporation of multi-omics data specific to cancer has demonstrated the capacity to enhance our comprehension of the molecular mechanisms underpinning carcinogenesis. This report elucidates how the integration of comprehensive data on methylation, gene expression, and copy number variations can effectively facilitate the unsupervised clustering of cancer samples. We have identified regressors that can effectively classify tumor and normal samples with an optimal integration of RNA sequencing, DNA methylation, and copy number variation while also achieving significant p-values. Further, these regressors were trained using linear and logistic regression with k-means clustering. For comparison, we employed autoencoder- and stacking-based omics integration and computed silhouette scores to evaluate the clusters. The proof of concept is illustrated using liver cancer data. Our analysis serves to underscore the feasibility of unsupervised cancer classification by considering genetic markers beyond mutations, thereby emphasizing the clinical relevance of additional global cellular parameters that contribute to the transformative process in cells. This work is clinically relevant because changes in gene expression and genomic re-arrangements have been shown to be signatures of cellular transformation across cancers, as well as in liver cancers.

Integrative analysis of senescence-related genes identifies robust prognostic clusters with distinct features in hepatocellular carcinoma

INTRODUCTION

Senescence refers to a state of permanent cell growth arrest and is regarded as a tumor suppressive mechanism, whereas accumulative evidence demonstrate that senescent cells play an adverse role during cancer progression. The scarcity of specific and reliable markers reflecting senescence level in cancer impede our understanding of this biological basis.

OBJECTIVES

Senescence-related genes (SRGs) were collected for integrative analysis to reveal the role of senescence in hepatocellular carcinoma (HCC).

METHODS

Consensus clustering was used to subtype HCC based on SRGs. Several computational methods, including single sample gene set enrichment analysis (ssGSEA), fuzzy c-means algorithm, were performed. Data of drug sensitivities were utilized to screen potential therapeutic agents for different senescence patients. Additionally, we developed a method called signature-related gene analysis (SRGA) for identification of markers relevant to phenotype of interest. Experimental strategies consisting quantitative real-time PCR (qRT-PCR), β-galactosidase assay, western blot, and tumor-T cell co-culture system were used to validate the findings in vitro.

RESULTS

We identified three robust prognostic clusters of HCC patients with distinct survival outcome, mutational landscape, and immune features. We further extracted signature genes of senescence clusters to construct the senescence scoring system and profile senescence level in HCC at bulk and single-cell resolution. Senescence-induced stemness reprogramming was confirmed both in silico and in vitro. HCC patients with high senescence were immune suppressed and sensitive to Tozasertib and other drugs. We suggested that MAFG, PLIN3, and 4 other genes were pertinent to HCC senescence, and MAFG potentially mediated immune suppression, senescence, and stemness.

CONCLUSION

Our findings provide insights into the role of SRGs in patients stratification and precision medicine.

Deciphering age-specific molecular features in cervical cancer and constructing an angio-immune prognostic model

Cancer incidence is increasingly seen in younger individuals. Molecular distinctions between young and elderly patients at onset are understudied. This study used public databases to explore genomic, transcriptomic, and immune-related features across age groups in cervical cancer. Additionally, it aims to create a prognostic model applicable across diverse age cohorts, enabling precise patient stratification, and personalized therapies. Gene mutations, expression data, and clinicopathological information were obtained from 317 cervical cancer patients. These patients were divided into a young group and an old group based on the median age of onset. The characteristics of differential gene mutation, gene expression, and immune cells analysis were analyzed by R software. Finally, the prognostic model was constructed by univariate Cox, least absolute shrinkage and selection operator, and multivariate Cox regression analyses of angiogenic and immune gene sets. Its validity was further confirmed using an additional 300 cervical squamous cell carcinoma and endocervical adenocarcinoma tissues. Cervical cancer patients at elderly onset age exhibit a significantly higher frequency of NOTCH1 and TP53 driver mutations compared to young patients, along with a notably higher tumor mutational burden. However, there were no significant differences between the 2 groups in terms of genomic instability and age-related mutational signatures. Differential gene expression analysis revealed that the young group significantly upregulated interferon-alpha and gamma responses and exhibited significantly higher activity in multiple metabolic pathways. Immune microenvironment analysis indicated enrichment of dendritic cells and natural killer cells in the young group, while transforming growth factor-β signature was enriched in the elderly group, indicating a higher degree of immune exclusion. A multigene prognostic model based on angiogenesis and T cell immune gene sets showed excellent prognostic performance independent of clinical factors such as age. High-risk groups identified by the model exhibit significant activation of tumor-promoting processes, such as metastasis and angiogenesis. Our study reveals distinct patterns in cancer-driving mechanisms, biological processes, and immune system status between young and elderly patients at onset with cervical cancer. These findings shed light on the age-specific underlying mechanisms of carcinogenesis. Furthermore, an independent molecular prognostic model is constructed to provide valuable references for patient stratification and the development of potential drug targets.

Somatic mutations in human ageing: New insights from DNA sequencing and inherited mutations

The accumulation of somatic mutations is a driver of cancer and has long been associated with ageing. Due to limitations in quantifying mutation burden with age in non-cancerous tissues, the impact of somatic mutations in other ageing phenotypes is unclear. Recent advances in DNA sequencing technologies have allowed the large-scale quantification of somatic mutations in ageing tissues. These studies have revealed a gradual accumulation of mutations in normal tissues with age as well as a substantial clonal expansion driven mostly by cancer-related mutations. Nevertheless, it is difficult to envision how the burden and stochastic nature of age-related somatic mutations identified so far can explain most ageing phenotypes that develop gradually. Studies across species have also found that longer-lived species have lower somatic mutation rates, though these could be due to selective pressures acting on other phenotypes such as perhaps cancer. Recent studies in patients with higher somatic mutation burden and no signs of accelerated ageing further question the role of somatic mutations in ageing. Overall, with a few exceptions like cancer, recent DNA sequencing studies and inherited mutations do not support the idea that somatic mutations accumulating with age drive ageing phenotypes, and the phenotypic role, if any, of somatic mutations in ageing remains unclear.

Nomogram combining pre-operative clinical characteristics and spectral CT parameters for predicting the WHO/ISUP pathological grading in clear cell renal cell carcinoma

PURPOSE

To develop a novel clinical-spectral-computed tomography (CT) nomogram incorporating clinical characteristics and spectral CT parameters for the preoperative prediction of the WHO/ISUP pathological grade in clear cell renal cell carcinoma (ccRCC).

METHODS

Seventy-three ccRCC patients who underwent spectral CT were included in this retrospective analysis from December 2020 to June 2023. The subjects were pathologically divided into low- and high-grade groups (WHO/ISUP 1/2, n = 52 and WHO/ISUP 3/4, n = 21, respectively). Information on clinical characteristics, conventional CT imaging features, and spectral CT parameters was collected. Multivariate logistic regression analyses were conducted to create a nomogram combing clinical data and image data for preoperatively predicting the pathological grade of ccRCC, and the area under the curve (AUC) was utilized to assess the predictive performance of the model.

RESULTS

Multivariate logistic regression analyses revealed that age, systemic immune-inflammation index (SII), and the slope of the spectrum curve in the cortex phase (CP-K) were independent predictors for predicting high-grade ccRCC. The clinical-spectral-CT model exhibited high evaluation efficacy, with an AUC of 0.933 (95% confidence interval [CI]: 0.878-0.998; sensitivity: 0.810; specificity: 0.923). The calibration curve revealed that the predicted probability of the clinical-spectral-CT nomogram could better fit the actual probability, with high calibration. The Hosmer-Lemeshow test showed that the model had a good fitness (χ2 = 5.574, p = 0.695).

CONCLUSION

The clinical-spectral-CT nomogram has the potential to predict WHO/ISUP grading of ccRCC preoperatively.

Aging-related biomarker discovery in the era of immune checkpoint inhibitors for cancer patients

Older patients with cancer, particularly those over 75 years of age, often experience poorer clinical outcomes compared to younger patients. This can be attributed to age-related comorbidities, weakened immune function, and reduced tolerance to treatment-related adverse effects. In the immune checkpoint inhibitors (ICI) era, age has emerged as an influential factor impacting the discovery of predictive biomarkers for ICI treatment. These age-linked changes in the immune system can influence the composition and functionality of tumor-infiltrating immune cells (TIICs) that play a crucial role in the cancer response. Older patients may have lower levels of TIICs infiltration due to age-related immune senescence particularly T cell function, which can limit the effectivity of cancer immunotherapies. Furthermore, age-related immune dysregulation increases the exhaustion of immune cells, characterized by the dysregulation of ICI-related biomarkers and a dampened response to ICI. Our review aims to provide a comprehensive understanding of the mechanisms that contribute to the impact of age on ICI-related biomarkers and ICI response. Understanding these mechanisms will facilitate the development of treatment approaches tailored to elderly individuals with cancer.

Comprehensive analysis of cellular senescence and immune microenvironment in papillary thyroid carcinoma

Senescence-induced therapy was previously considered as an effective treatment for tumors, and cellular senescence was initially regarded as an effective mechanism against cancer. However, whether cell senescence-related genes can be used to predict the prognosis of papillary thyroid carcinoma (PTC) and immunotherapy remains unclear. We developed and validated a cell senescence-related signature (CSRS) by analyzing the gene expression of 278 genes related to cellular senescence in 738 patients with PTC. Additionally, further analysis showed that CSRS was a reliable predictor of patient outcomes in combination with immune checkpoint expression and drug susceptibility, and patients with high risk scores may benefit from immunotherapy. The findings of this study demonstrate that CSRS serves as an immunotherapeutic response and prognosis biomarker affecting the tumor immune microenvironment of PTC.

A Competing Risk Nomogram for Prediction of Prognosis in Patients With Primary Squamous Cell Thyroid Carcinoma

Objective: Primary squamous cell thyroid carcinoma (PSCTC) is an extremely rare carcinoma, accounting for less than 1% of all thyroid carcinomas. However, the factors contributing to PSCTC outcomes remain unclear. This study aimed to identify the prognostic factors and develop a prognostic predictive model for patients with PSCTC. Methods: The analysis included patients diagnosed with thyroid carcinoma between 1975 and 2016 from the Surveillance, Epidemiology, and End Results database. Prognostic differences among the 5 pathological types of thyroid carcinomas were analyzed. To determine prognostic factors in PSCTC patients, the Cox regression model and Fine-Gray competing risk model were utilized. Based on the Fine-Gray competing risk model, a nomogram was established for predicting the prognosis of patients with PSCTC. Results: A total of 198,757 thyroid carcinoma patients, including 218 PSCTC patients, were identified. We found that PSCTC and anaplastic thyroid cancer had the worst prognosis among the 5 pathological types of thyroid carcinoma (P < .001). According to univariate and multivariate Cox regression analyses, age (71-95 years) was an independent risk factor for poorer overall survival and disease-specific survival in PSCTC patients. Using Fine-Gray regression analysis, the total number of in situ/malignant tumors for patient (Number 1) (≥2) was identified as an independent protective factor for prognosis of PSCTC. The area under the curve, the concordance index (C-index), calibration curves and decision curve analysis revealed that the nomogram was capable of predicting the prognosis of PSCTC patients accurately. Conclusion: The competing risk nomogram is highly accurate in predicting prognosis for patients with PSCTC, which may help clinicians to optimize individualized treatment decisions.

Risk Factors and Comorbidities Associated With Hepatocellular Carcinoma in Patients With Chronic Hepatitis B Virus Infection

INTRODUCTION/OBJECTIVES

Chronic hepatitis B virus infection (CHBVI) is a major public health problem affecting about 296 million people worldwide. HBV infects the liver, and when it becomes chronic, may cause cirrhosis and hepatocellular carcinoma (HCC). The aim of our study was to identify the risk factors and comorbid medical conditions that were associated with HCC in patients who had CHBVI.

METHODS

We performed a retrospective electronic medical record review of adult patients diagnosed with CHBVI, who presented to our primary care office between October 1, 2017 and October 21, 2022. Selected variables in patients with CHBVI with HCC (HCC group) were compared to those without HCC (NoHCC group).

RESULTS

Among 125 patients with CHBVI, 24% had HCC and 76% did not have HCC. There were higher frequencies of association of certain comorbidities in the HCC group compared to NoHCC group, such as anemia (63.3% vs 26.3%; P < .001), ascites (53.3% vs 1.1%; P < .001), portal hypertension (43.3% vs 0.0%; P < .001), chronic kidney disease (40.0% vs 13.7%; P = .002), and HCV coinfection (13.3% vs 7.4%; P < .001). The logistic regression model showed increased odds of HCC for each year of increase in age (OR = 1.06, 95% CI = 1.01-1.11; P = .014), and increased odds in men (OR = 5.96, 95% CI = 1.71-20.73; P = .005). Although Asians represented the racial majority in both the groups, there was no significant difference in the race distribution between the two groups.

CONCLUSION

In patients with CHBVI, increasing age and male sex are factors associated with increased odds of having HCC. Patients with CHBVI and HCC have higher frequencies of association of tobacco use, recreational drug use, anemia, ascites, portal hypertension, chronic kidney disease, and co-infection with HCV.

Causal genetic regulation of DNA replication on immune microenvironment in colorectal tumorigenesis: Evidenced by an integrated approach of trans-omics and GWAS

The interplay between DNA replication stress and immune microenvironment alterations is known to play a crucial role in colorectal tumorigenesis, but a comprehensive understanding of their association with and relevant biomarkers involved in colorectal tumorigenesis is lacking. To address this gap, we conducted a study aiming to investigate this association and identify relevant biomarkers. We analyzed transcriptomic and proteomic profiles of 904 colorectal tumor tissues and 342 normal tissues to examine pathway enrichment, biological activity, and the immune microenvironment. Additionally, we evaluated genetic effects of single variants and genes on colorectal cancer susceptibility using data from genome-wide association studies (GWASs) involving both East Asian (7062 cases and 195745 controls) and European (24476 cases and 23073 controls) populations. We employed mediation analysis to infer the causal pathway, and applied multiplex immunofluorescence to visualize colocalized biomarkers in colorectal tumors and immune cells. Our findings revealed that both DNA replication activity and the flap structure-specific endonuclease 1 ( FEN1) gene were significantly enriched in colorectal tumor tissues, compared with normal tissues. Moreover, a genetic variant rs4246215 G>T in FEN1 was associated with a decreased risk of colorectal cancer (odds ratio = 0.94, 95% confidence interval: 0.90-0.97, P meta = 4.70 × 10 -9). Importantly, we identified basophils and eosinophils that both exhibited a significantly decreased infiltration in colorectal tumors, and were regulated by rs4246215 through causal pathways involving both FEN1 and DNA replication. In conclusion, this trans-omics incorporating GWAS data provides insights into a plausible pathway connecting DNA replication and immunity, expanding biological knowledge of colorectal tumorigenesis and therapeutic targets.

How is Big Data reshaping preclinical aging research?

The exponential scientific and technological progress during the past 30 years has favored the comprehensive characterization of aging processes with their multivariate nature, leading to the advent of Big Data in preclinical aging research. Spanning from molecular omics to organism-level deep phenotyping, Big Data demands large computational resources for storage and analysis, as well as new analytical tools and conceptual frameworks to gain novel insights leading to discovery. Systems biology has emerged as a paradigm that utilizes Big Data to gain insightful information enabling a better understanding of living organisms, visualized as multilayered networks of interacting molecules, cells, tissues and organs at different spatiotemporal scales. In this framework, where aging, health and disease represent emergent states from an evolving dynamic complex system, context given by, for example, strain, sex and feeding times, becomes paramount for defining the biological trajectory of an organism. Using bioinformatics and artificial intelligence, the systems biology approach is leading to remarkable advances in our understanding of the underlying mechanism of aging biology and assisting in creative experimental study designs in animal models. Future in-depth knowledge acquisition will depend on the ability to fully integrate information from different spatiotemporal scales in organisms, which will probably require the adoption of theories and methods from the field of complex systems. Here we review state-of-the-art approaches in preclinical research, with a focus on rodent models, that are leading to conceptual and/or technical advances in leveraging Big Data to understand basic aging biology and its full translational potential.

iGenSig-Rx: an integral genomic signature based white-box tool for modeling cancer therapeutic responses using multi-omics data

Multi-omics sequencing is expected to become clinically routine within the next decade and transform clinical care. However, there is a paucity of viable and interpretable genome-wide modeling methods that can facilitate rational selection of patients for tailored intervention. Here we develop an integral genomic signature-based method called iGenSig-Rx as a white-box tool for modeling therapeutic response based on clinical trial datasets with improved cross-dataset applicability and tolerance to sequencing bias. This method leverages high-dimensional redundant genomic features to address the challenges of cross-dataset modeling, a concept similar to the use of redundant steel rods to reinforce the pillars of a building. Using genomic datasets for HER2 targeted therapies, the iGenSig-Rx model demonstrates stable predictive power across four independent clinical trials. More importantly, the iGenSig-Rx model offers the level of transparency much needed for clinical application, allowing for clear explanations as to how the predictions are produced, how the features contribute to the prediction, and what are the key underlying pathways. We expect that iGenSig-Rx as a class of biologically interpretable multi-omics modeling methods will have broad applications in big-data based precision oncology. The R package is available: https://github.com/wangxlab/iGenSig-Rx. NOTE: the Github website will be released upon publication and the R package is available for review through google drive: https://drive.google.com/drive/folders/1KgecmUoon9-h2Dg1rPCyEGFPOp28Ols3?usp=sharing.

Multi-omics reveals aging-related pathway in natural aging mouse liver

Aging is associated with gradual changes in liver structure, altered metabolites and other physiological/pathological functions in hepatic cells. However, its characterized phenotypes based on altered metabolites and the underlying biological mechanism are unclear. Advancements in high-throughput omics technology provide new opportunities to understand the pathological process of aging. Here, in our present study, both metabolomics and phosphoproteomics were applied to identify the altered metabolites and phosphorylated proteins in liver of young (the WTY group) and naturally aged (the WTA group) mice, to find novel biomarkers and pathways, and uncover the biological mechanism. Analysis showed that the body weights, alanine aminotransferase (ALT) and aspartate aminotransferase (AST) increased in the WTA group. The grips decreased with age, while the triglyceride (TG) and cholesterol (TC) did not change significantly. The increase of fibrosis, accumulation of inflammatory cells, hepatocytes degeneration, the deposition of lipid droplets and glycogen, the damaged mitochondria, and deduction of endoplasmic reticulum were observed in the aging liver under optical and electron microscopes. In addition, a network of metabolites and phosphorylated proteomes of the aging liver was established. Metabolomics detected 970 metabolites in the positive ion mode and 778 metabolites in the negative ion mode. A total of 150 pathways were pooled. Phosphoproteomics identified 2618 proteins which contained 16621 phosphosites. A total of 164 pathways were detected. 65 common pathways were detected in two omics. Phosphorylated protein heat shock protein HSP 90-alpha (HSP90A) and v-raf murine viral oncogene homolog B1(BRAF), related to cancer pathway, were significantly upregulated in aged mice liver. Western blot verified that protein expression of MEK and ERK, downstream of BRAF pathway were elevated in the liver of aging mice. However, the protein expression of BRAF was not a significant difference. Overall, these findings revealed a close link between aging and cancer and contributed to our understanding of the multi-omics changes in natural aging.

Considerations and Approaches for Cancer Immunotherapy in the Aging Host

Advances in cancer immunotherapy are improving treatment successes in many distinct cancer types. Nonetheless, most tumors fail to respond. Age is the biggest risk for most cancers, and the median population age is rising worldwide. Advancing age is associated with manifold alterations in immune cell types, abundance, and functions, rather than simple declines in these metrics, the consequences of which remain incompletely defined. Our understanding of the effects of host age on immunotherapy mechanisms, efficacy, and adverse events remains incomplete. A deeper understanding of age effects in all these areas is required. Most cancer immunotherapy preclinical studies examine young subjects and fail to assess age contributions, a remarkable deficit given the known importance of age effects on immune cells and factors mediating cancer immune surveillance and immunotherapy efficacy. Notably, some cancer immunotherapies are more effective in aged versus young hosts, while others fail despite efficacy in the young. Here, we review our current understanding of age effects on immunity and associated nonimmune cells, the tumor microenvironment, cancer immunotherapy, and related adverse effects. We highlight important knowledge gaps and suggest areas for deeper enquiries, including in cancer immune surveillance, treatment response, adverse event outcomes, and their mitigation.

Transcriptomic analysis reveals a tissue-specific loss of identity during ageing and cancer

INTRODUCTION

Understanding changes in cell identity in cancer and ageing is of great importance. In this work, we analyzed how gene expression changes in human tissues are associated with tissue specificity during cancer and ageing using transcriptome data from TCGA and GTEx.

RESULTS

We found significant downregulation of tissue-specific genes during ageing in 40% of the tissues analyzed, which suggests loss of tissue identity with age. For most cancer types, we have noted a consistent pattern of downregulation in genes that are specific to the tissue from which the tumor originated. Moreover, we observed in cancer an activation of genes not usually expressed in the tissue of origin as well as an upregulation of genes specific to other tissues. These patterns in cancer were associated with patient survival. The age of the patient, however, did not influence these patterns.

CONCLUSION

We identified loss of cellular identity in 40% of the tissues analysed during human ageing, and a clear pattern in cancer, where during tumorigenesis cells express genes specific to other organs while suppressing the expression of genes from their original tissue. The loss of cellular identity observed in cancer is associated with prognosis and is not influenced by age, suggesting that it is a crucial stage in carcinogenesis.

Disclosing transcriptomics network-based signatures of glioma heterogeneity using sparse methods

Gliomas are primary malignant brain tumors with poor survival and high resistance to available treatments. Improving the molecular understanding of glioma and disclosing novel biomarkers of tumor development and progression could help to find novel targeted therapies for this type of cancer. Public databases such as The Cancer Genome Atlas (TCGA) provide an invaluable source of molecular information on cancer tissues. Machine learning tools show promise in dealing with the high dimension of omics data and extracting relevant information from it. In this work, network inference and clustering methods, namely Joint Graphical lasso and Robust Sparse K-means Clustering, were applied to RNA-sequencing data from TCGA glioma patients to identify shared and distinct gene networks among different types of glioma (glioblastoma, astrocytoma, and oligodendroglioma) and disclose new patient groups and the relevant genes behind groups' separation. The results obtained suggest that astrocytoma and oligodendroglioma have more similarities compared with glioblastoma, highlighting the molecular differences between glioblastoma and the others glioma subtypes. After a comprehensive literature search on the relevant genes pointed our from our analysis, we identified potential candidates for biomarkers of glioma. Further molecular validation of these genes is encouraged to understand their potential role in diagnosis and in the design of novel therapies.

Demographic confounders distort inference of gene regulatory and gene co-expression networks in cancer

Gene regulatory networks (GRNs) and gene co-expression networks (GCNs) allow genome-wide exploration of molecular regulation patterns in health and disease. The standard approach for obtaining GRNs and GCNs is to infer them from gene expression data, using computational network inference methods. However, since network inference methods are usually applied on aggregate data, distortion of the networks by demographic confounders might remain undetected, especially because gene expression patterns are known to vary between different demographic groups. In this paper, we present a computational framework to systematically evaluate the influence of demographic confounders on network inference from gene expression data. Our framework compares similarities between networks inferred for different demographic groups with similarity distributions obtained for random splits of the expression data. Moreover, it allows to quantify to which extent demographic groups are represented by networks inferred from the aggregate data in a confounder-agnostic way. We apply our framework to test four widely used GRN and GCN inference methods as to their robustness w. r. t. confounding by age, ethnicity and sex in cancer. Our findings based on more than $ {44000}$ inferred networks indicate that age and sex confounders play an important role in network inference for certain cancer types, emphasizing the importance of incorporating an assessment of the effect of demographic confounders into network inference workflows. Our framework is available as a Python package on GitHub: https://github.com/bionetslab/grn-confounders.

The metastasis patterns and their prognostic features in patients with de novo metastatic breast cancer of different ages

PURPOSE

The prognostic outcomes of metastasis patterns in patients with de novo metastatic breast cancer (dnMBC) of different ages are unknown. Our study used a large-scale data to investigate the metastasis patterns and prognostic features in dnMBC of different ages.

METHODS

Total 24,698 women with dnMBC in the Surveillance, Epidemiology and End Results database (2010-2018) were divided into three groups by age. Chi-squared test was used to compare metastasis patterns and logistic regression was performed to investigate the risk of age and specific organ metastases. Kaplan-Meier survival curves were used to compare the overall survival.

RESULTS

In three groups, young group had the largest proportion of liver metastases (35.2% vs. 28.2% vs. 21.1%, p < 0.001), and elderly group had the largest proportion of lung metastases (22.6% vs. 30.0% vs. 35.0%, p < 0.001) and the lowest proportion of bone metastases (65.7% vs. 67.6% vs. 64.4%, p < 0.001). In young group, patients with liver metastases had better prognosis than patients with lung metastases (MST: 34 months vs. 29 months, p = 0.041), but in middle-aged and elderly groups, the prognosis of lung metastases was better than that of liver metastases (MST in middle-aged group: 24 months vs. 20 months, p = 0.002; MST in elderly group: 12 months vs. 6 months, p < 0.001).

CONCLUSION

DnMBC patients at different age have distinct metastasis patterns and prognostic features. The findings lend support to consideration of tailored management and surveillance strategies for different age patients.

m6A-TSHub: Unveiling the Context-specific m6A Methylation and m6A-affecting Mutations in 23 Human Tissues

As the most pervasive epigenetic marker present on mRNAs and long non-coding RNAs (lncRNAs), N6-methyladenosine (m6A) RNA methylation has been shown to participate in essential biological processes. Recent studies have revealed the distinct patterns of m6A methylome across human tissues, and a major challenge remains in elucidating the tissue-specific presence and circuitry of m6A methylation. We present here a comprehensive online platform, m6A-TSHub, for unveiling the context-specific m6A methylation and genetic mutations that potentially regulate m6A epigenetic mark. m6A-TSHub consists of four core components, including (1) m6A-TSDB, a comprehensive database of 184,554 functionally annotated m6A sites derived from 23 human tissues and 499,369 m6A sites from 25 tumor conditions, respectively; (2) m6A-TSFinder, a web server for high-accuracy prediction of m6A methylation sites within a specific tissue from RNA sequences, which was constructed using multi-instance deep neural networks with gated attention; (3) m6A-TSVar, a web server for assessing the impact of genetic variants on tissue-specific m6A RNA modifications; and (4) m6A-CAVar, a database of 587,983 The Cancer Genome Atlas (TCGA) cancer mutations (derived from 27 cancer types) that were predicted to affect m6A modifications in the primary tissue of cancers. The database should make a useful resource for studying the m6A methylome and the genetic factors of epitranscriptome disturbance in a specific tissue (or cancer type). m6A-TSHub is accessible at www.xjtlu.edu.cn/biologicalsciences/m6ats.

Integration of single-cell regulon atlas and multi-omics data for prognostic stratification and personalized treatment prediction in human lung adenocarcinoma

Transcriptional programs are often dysregulated in cancers. A comprehensive investigation of potential regulons is critical to the understanding of tumorigeneses. We first constructed the regulatory networks from single-cell RNA sequencing data in human lung adenocarcinoma (LUAD). We next introduce LPRI (Lung Cancer Prognostic Regulon Index), a precision oncology framework to identify new biomarkers associated with prognosis by leveraging the single cell regulon atlas and bulk RNA sequencing or microarray datasets. We confirmed that LPRI could be a robust biomarker to guide prognosis stratification across lung adenocarcinoma cohorts. Finally, a multi-omics data analysis to characterize molecular alterations associated with LPRI was performed from The Cancer Genome Atlas (TCGA) dataset. Our study provides a comprehensive chart of regulons in LUAD. Additionally, LPRI will be used to help prognostic prediction and developing personalized treatment for future studies.

The Clinical Significance of Genetic Variation in Ovarian Cancer

Genetic variation is a well-known contributor to the onset and progression of cancer. The goal of this study is to provide a comprehensive examination of the nucleotide and chromosomal variation associated with the onset and progression of serous ovarian cancer. Using a variety of computational and statistical methods, we examine the exome sequence profiles of genetic variants present in the primary tumors of 432 ovarian cancer patient samples to compute: (1) the tumor mutational burden for all genes and (2) the chromosomal copy number alterations associated with the onset/progression of ovarian cancer. Tumor mutational burden is reduced in the late vs. early stages, with the highest levels being associated with loss-of-function mutations in DNA-repair genes. Nucleotide variation and copy number alterations associated with known cancer driver genes are selectively favored over ovarian cancer development. The results indicate that genetic variation is a significant contributor to the onset and progression of ovarian cancer. The measurement of the relative levels of genetic variation associated with individual ovarian cancer patient tumors may be a clinically valuable predictor of potential tumor aggressiveness and resistance to chemotherapy. Tumors found to be associated with high levels of genetic variation may help in the clinical identification of high-risk ovarian cancer patients who could benefit from more frequent monitoring.

Decreased SPTBN2 expression regulated by the ceRNA network is associated with poor prognosis and immune infiltration in low‑grade glioma

The majority of low-grade gliomas (LGGs) in adults invariably progress to glioblastoma over time. Spectrin β non-erythrocytic 2 (SPTBN2) is detected in numerous tumors and is involved in tumor occurrence and metastasis. However, the specific roles and detailed mechanisms of SPTBN2 in LGG are largely unknown. The present study performed pan-cancer analysis for the expression and prognosis of SPTBN2 in LGG using The Cancer Genome Atlas and The Genotype-Tissue Expression. Western blotting was used to detect the amount of SPTBN2 between glioma tissues and normal brain tissues. Subsequently, based on expression, prognosis, correlation and immune infiltration, non-coding RNAs (ncRNAs) were identified that regulated SPTBN2 expression. Finally, tumor immune infiltrates associated with SPTBN2 and prognosis were performed. Lower expression of SPTBN2 was correlated with an unfavorable outcome in LGG. A significant correlation between the low SPTBN2 mRNA expression and poor clinicopathological features was observed, including wild-type isocitrate dehydrogenase status (P<0.001), 1p/19q non-codeletion (P<0.001) and elders (P=0.019). The western blotting results revealed that, compared with normal brain tissues, the amount of SPTBN2 was significantly lower in LGG tissues (P=0.0266). Higher expression of five microRNAs (miRs/miRNAs), including hsa-miR-15a-5p, hsa-miR-15b-5p, hsa-miR-16-5p, hsa-miR-34c-5p and hsa-miR-424-5p, correlated with poor prognosis by targeting SPTBN2 in LGG. Subsequently, four long ncRNAs (lncRNAs) [ARMCX5-GPRASP2, BASP1-antisense RNA 1 (AS1), EPB41L4A-AS1 and LINC00641] were observed in the regulation of SPTBN2 via five miRNAs. Moreover, the expression of SPTBN2 was significantly correlated with tumor immune infiltration, immune checkpoint expression and biomarkers of immune cells. In conclusion, SPTBN2 was lowly expressed and correlated with an unfavorable prognosis in LGG. A total of six miRNAs and four lncRNAs were identified as being able to modulate SPTBN2 in a lncRNA-miRNA-mRNA network of LGG. Furthermore, the current findings also indicated that SPTBN2 possessed anti-tumor roles by regulating tumor immune infiltration and immune checkpoint expression.

A Pan-Cancer Analysis of Prognostic and Immunological Roles for Cell Death Genes

The dysregulation of cell death is closely associated with the development, progression, tumor microenvironment (TME), and prognosis of cancer. However, there is no study that comprehensively explores the prognostic and immunological role of cell death in human pan-cancer. We used published human pan-cancer RNA-sequencing and clinical data to explore the prognostic and immunological roles of programmed cell death, which included apoptosis, autophagy, ferroptosis, necroptosis, and pyroptosis. A total of 9925 patients were included for bioinformatic analysis, with 6949 and 2976 patients in the training cohort and validation cohort, respectively. Five-hundred and ninety-nine genes were defined as programmed-cell-death-related genes. In the training cohort, 75 genes were identified to define PAGscore by survival analysis. According to the median value of PAGscore, patients were divided into high- and low-risk groups, and subsequent analyses demonstrated that the high-risk group had a higher level of genomic mutation frequency, hypoxia score, immuneScore, expression of immune genes, activity of malignant signaling pathways, and cancer immunity cycle. Most anti-tumor and pro-tumor components of the TME showed greater activity in high-risk patients. Scores of malignant cell properties were also higher in high-risk patients. These findings were confirmed in the validation cohort and external cohort. Our study constructed a reliable gene signature to distinguish prognosis-favorable and prognosis-unfavorable patients and demonstrated that cell death was significantly associated with cancer prognosis and the TME.

Identification of immunotherapy and chemotherapy-related molecular subtypes in colon cancer by integrated multi-omics data analysis

Background

Colon cancer is a highly heterogeneous disease, and identifying molecular subtypes can provide insights into deregulated pathways within tumor subsets, which may lead to personalized treatment options. However, most prognostic models are based on single-pathway genes.

Methods

In this study, we aimed to identify three clinically relevant subtypes of colon cancer based on multiple signaling pathways-related genes. Integrative multi-omics analysis was used to explain the biological processes contributing to colon cancer aggressiveness, recurrence, and progression. Machine learning methods were employed to identify the subtypes and provide medication guidance for distinct subtypes using the L1000 platform. We developed a robust prognostic model (MKPC score) based on gene pairs and validated it in one internal test set and three external test sets. Risk-related genes were extracted and verified by qPCR.

Results

Three clinically relevant subtypes of colon cancer were identified based on multiple signaling pathways-related genes, which had significantly different survival state (Log-Rank test, p&lt;0.05). Integrative multi-omics analysis revealed biological processes contributing to colon cancer aggressiveness, recurrence, and progression. The developed MKPC score, based on gene pairs, was robust in predicting prognosis state (Log-Rank test, p&lt;0.05), and risk-related genes were successfully verified by qPCR (t test, p&lt;0.05). An easy-to-use web tool was created for risk scoring and therapy stratification in colon cancer patients, and the practical nomogram can be extended to other cancer types.

Conclusion

In conclusion, our study identified three clinically relevant subtypes of colon cancer and developed a robust prognostic model based on gene pairs. The developed web tool is a valuable resource for researchers and clinicians in risk scoring and therapy stratification in colon cancer patients, and the practical nomogram can be extended to other cancer types.

Pan-cancer transcriptomic analysis identified six classes of immunosenescence genes revealed molecular links between aging, immune system and cancer

Aging is a complex process that significantly impacts the immune system. The aging-related decline of the immune system, termed immunosenescence, can lead to disease development, including cancer. The perturbation of immunosenescence genes may characterize the associations between cancer and aging. However, the systematical characterization of immunosenescence genes in pan-cancer remains largely unexplored. In this study, we comprehensively investigated the expression of immunosenescence genes and their roles in 26 types of cancer. We developed an integrated computational pipeline to identify and characterize immunosenescence genes in cancer based on the expression profiles of immune genes and clinical information of patients. We identified 2218 immunosenescence genes that were significantly dysregulated in a wide variety of cancers. These immunosenescence genes were divided into six categories based on their relationships with aging. Besides, we assessed the importance of immunosenescence genes in clinical prognosis and identified 1327 genes serving as prognostic markers in cancers. BTN3A1, BTN3A2, CTSD, CYTIP, HIF1AN, and RASGRP1 were associated with ICB immunotherapy response and served as prognostic factors after ICB immunotherapy in melanoma. Collectively, our results furthered the understanding of the relationship between immunosenescence and cancer and provided insights into immunotherapy for patients.

Immune Pathways with Aging Characteristics Improve Immunotherapy Benefits and Drug Prediction in Human Cancer

(1) Background: Perturbation of immune-related pathways can make substantial contributions to cancer. However, whether and how the aging process affects immune-related pathways during tumorigenesis remains largely unexplored. (2) Methods: Here, we comprehensively investigated the immune-related genes and pathways among 25 cancer types using genomic and transcriptomic data. (3) Results: We identified several pathways that showed aging-related characteristics in various cancers, further validated by conventional aging-related gene sets. Genomic analysis revealed high mutation burdens in cytokines and cytokines receptors pathways, which were strongly correlated with aging in diverse cancers. Moreover, immune-related pathways were found to be favorable prognostic factors in melanoma. Furthermore, the expression level of these pathways had close associations with patient response to immune checkpoint blockade therapy in melanoma and non-small cell lung cancer. Applying a net-work-based method, we predicted immune- and aging-related genes in pan-cancer and utilized these genes for potential immunotherapy drug discovery. Mapping drug target data to our top-ranked genes identified potential drug targets, FYN, JUN, and SRC. (4) Conclusions: Taken together, our systematic study helped interpret the associations among immune-related pathways, aging, and cancer and could serve as a resource for promoting clinical treatment.

Pathological perspectives in pilocytic astrocytomas: Extent of resection as the sole critical factor for recurrence-free survival, and the challenge of evaluating conclusions derived from limited data

Introduction: Pilocytic astrocytoma (PA) is one of the most common primary intracranial neoplasms in childhood with an overall favorable prognosis. Despite decades of experience, there are still diagnostic and treatment challenges and unresolved issues regarding risk factors associated with recurrence, most often due to conclusions of publications with limited data. We analyzed 499 patients with PA diagnosed in a single institution over 30 years in order to provide answers to some of the unresolved issues. Materials and Methods: We identified pilocytic astrocytomas diagnosed at the University of California, San Francisco, between 1989 and 2019, confirmed the diagnoses using the WHO 2021 essential and desirable criteria, and performed a retrospective review of the demographic and clinical features of the patients and the radiological, pathologic and molecular features of the tumors. Results: Among the patients identified from pathology archives, 499 cases fulfilled the inclusion criteria. Median age at presentation was 12 years (range 3.5 months - 73 years) and the median follow-up was 78.5 months. Tumors were predominantly located in the posterior fossa (52.6%). There were six deaths, but there were confounding factors that prevented a clear association of death to tumor progression. Extent of resection was the only significant factor for recurrence-free survival. Recurrence-free survival time was 321.0 months for gross total resection, compared to 160.9 months for subtotal resection (log rank, p <0.001). Conclusion: Multivariate analysis was able to identify extent of resection as the only significant variable to influence recurrence-free survival. We did not find a statistically significant association between age, NF1 status, tumor location, molecular alterations, and outcome. Smaller series with apparently significant results may have suffered from limited sample size, limited variables, acceptance of univariate analysis findings as well as a larger p value for biological significance. PA still remains a predominantly surgical disease and every attempt should be made to achieve gross total resection since this appears to be the most reliable predictor of recurrence-free survival.

High MutS homolog 2 expression predicts poor prognosis and is related to immune infiltration in endometrial carcinoma

Several studies have shown that MutS homolog 2 (MSH2) is highly expressed in many cancer tissues. Transcriptome expression data were collected from the Cancer Genome Atlas (TCGA) database. We analyzed the expression of MSH2 in normal and tumor tissues, the relationship between MSH2 expression and various prognostic factors, and the relationship between MSH2 expression and overall survival, disease specific survival, and progression free interval. We also examined MSH2 promoter methylation between endometrial cancer and normal endometrial tissues, and identified the prognostic value of MSH2 methylation in endometrial cancer. MSH2 was highly expressed in endometrial cancer tumor tissues compared with normal tissues. High MSH2 expression might be an independent prognostic factor for OS, DSS, and PFI. Further, high MSH2 expression was correlated with age and histological type, but not with BMI, clinical stage, tumor invasion, or other clinical features. MSH2 promoter methylation in endometrial cancer was significantly lower than in normal tissues. Additionally, MSH2 levels, OS, DSS, and PFI were associated with BMI, age, tumor invasion, and histological type. ssGSEA showed that MSH2 expression was positively correlated with the infiltration of Th2 cells, Tcm cells, T helper cells, and Tgd cells, whereas it was negatively correlated with NK CD56 bright cells, pDC cells, iDC cells, cytotoxic cells, and neutrophils. Increased MSH2 expression and reduced MSH2 methylation in endometrial cancer predicts poor prognosis. MSH2 may be used as a biomarker for the diagnosis and prognosis of endometrial cancer and as an immunotherapy target.

Cellular senescence in cancer: clinical detection and prognostic implications

Cellular senescence is a state of stable cell-cycle arrest with secretory features in response to cellular stress. Historically, it has been considered as an endogenous evolutionary homeostatic mechanism to eliminate damaged cells, including damaged cells which are at risk of malignant transformation, thereby protecting against cancer. However, accumulation of senescent cells can cause long-term detrimental effects, mainly through the senescence-associated secretory phenotype, and paradoxically contribute to age-related diseases including cancer. Besides its role as tumor suppressor, cellular senescence is increasingly being recognized as an in vivo response in cancer patients to various anticancer therapies. Its role in cancer is ambiguous and even controversial, and senescence has recently been promoted as an emerging hallmark of cancer because of its hallmark-promoting capabilities. In addition, the prognostic implications of cellular senescence have been underappreciated due to the challenging detection and sparse in and ex vivo evidence of cellular senescence in cancer patients, which is only now catching up. In this review, we highlight the approaches and current challenges of in and ex vivo detection of cellular senescence in cancer patients, and we discuss the prognostic implications of cellular senescence based on in and ex vivo evidence in cancer patients.

Differences in Prostate Cancer Transcriptomes by Age at Diagnosis: Are Primary Tumors from Older Men Inherently Different?

Older age at diagnosis is consistently associated with worse clinical outcomes in prostate cancer. We sought to characterize gene expression profiles of prostate tumor tissue by age at diagnosis. We conducted a discovery analysis in The Cancer Genome Atlas prostate cancer dataset (n = 320; 29% of men >65 years at diagnosis), using linear regressions of age at diagnosis and mRNA expression and adjusting for TMPRSS2:ERG fusion status and race. This analysis identified 13 age-related candidate genes at FDR < 0.1, six of which were also found in an analysis additionally adjusted for Gleason score. We then validated the 13 age-related genes in a transcriptome study nested in the Health Professionals Follow-up Study and Physicians' Health Study (n = 374; 53% of men >65 years). Gene expression differences by age in the 13 candidate genes were directionally consistent, and age at diagnosis was weakly associated with the 13-gene score. However, the age-related genes were not consistently associated with risk of metastases and prostate cancer-specific death. Collectively, these findings argue against tumor genomic differences as a main explanation for age-related differences in prostate cancer prognosis.

PREVENTION RELEVANCE

Older age at diagnosis is consistently associated with worse clinical outcomes in prostate cancer. This study with independent discovery and validation sets and long-term follow-up suggests that prevention of lethal prostate cancer should focus on implementing appropriate screening, staging, and treatment among older men without expecting fundamentally different tumor biology.

Genomic disparities between cancers in adolescent and young adults and in older adults

Cancers cause significant mortality and morbidity in adolescents and young adults (AYAs), but their biological underpinnings are incompletely understood. Here, we analyze clinical and genomic disparities between AYAs and older adults (OAs) in more than 100,000 cancer patients. We find significant differences in clinical presentation between AYAs and OAs, including sex, metastasis rates, race and ethnicity, and cancer histology. In most cancer types, AYA tumors show lower mutation burden and less genome instability. Accordingly, most cancer genes show less mutations and copy number changes in AYAs, including the noncoding TERT promoter mutations. However, CTNNB1 and BRAF mutations are consistently overrepresented in AYAs across multiple cancer types. AYA tumors also exhibit more driver gene fusions that are frequently observed in pediatric cancers. We find that histology is an important contributor to genetic disparities between AYAs and OAs. Mutational signature analysis of hypermutators shows stronger endogenous mutational processes such as MMR-deficiency but weaker exogenous processes such as tobacco exposure in AYAs. Finally, we demonstrate a panoramic view of clinically actionable genetic events in AYA tumors.

Arrhythmic events associated with immune checkpoint inhibitors therapy: A real‐world study based on the Food and Drug Administration Adverse Event Reporting System database

BACKGROUND

Although arrhythmias have been reported in patients treated with immune checkpoint inhibitors (ICIs), the association between arrhythmias and ICIs has not been thoroughly evaluated in real-world studies. We aimed to describe the major features of ICI-related arrhythmic events and identify the factors that contributed to death.

METHODS

A disproportionality analysis was performed using data from the Food and Drug Administration Adverse Event Reporting System (FAERS) database from January 2011 to December 2021. Reporting odds ratios (RORs), proportional reporting ratio and information component were used to assess whether adverse arrhythmic events were associated with ICIs. The clinical characteristics of patients with ICI-associated arrhythmias were compared with fatal and non-fatal arrhythmias. The time to onset (TTO), fatality rates of arrhythmic events were also investigated.

RESULTS

We identified a total of 1945 cases of ICI-related arrhythmic events. Men (64.78%) were identified significantly more frequently than women (28.84%). The median age was 68 years ([interquartile range, IQR] 60-75 years). Anti-programmed cell death-1 (PD-1) and anti-programmed cell death ligand-1 (PD-L1) were associated with adverse arrhythmic events, corresponding to ROR 1.11 (95% confidence interval [CI] 1.05-1.17) and ROR 1.34 (95% CI 1.20-1.49), respectively. However, anti-cytotoxic T-lymphocyte associated protein 4 or combination immunotherapy did not appear to be associated with arrhythmic events. Atrial fibrillation (N  = 576, 0.62%), cardiac arrest (N  = 284, 0.31%), tachycardia (N  = 175, 0.19%) were the most common adverse arrhythmic events. Sudden death and complete atrioventricular block are adverse events that are significantly associated with ICI-related arrhythmic events and have strong signal intensity. The TTO of cases that resulted in death (30 days [IQR] 11-73.75) was significantly earlier than that of cases that did not result in death (33 days [IQR 10.5-88.5], p  = 0.003). ICI-related arrhythmic events were severe with death occurring in 507 (26.07%) of 1945 arrhythmias cases.

CONCLUSIONS

Treatment with PD-1/PD-L1 may cause arrhythmic events, which are severe and tend to occur early on during treatment. It is important to identify ICI-related arrhythmias as early as possible, and to manage them appropriately.

An integrative machine-learning meta-analysis of high-throughput omics data identifies age-specific hallmarks of Alzheimer's disease

Alzheimer's disease (AD) is an incredibly complex and presently incurable age-related brain disorder. To better understand this debilitating disease, we collated and performed a meta-analysis on publicly available RNA-Seq, microarray, proteomics, and microRNA samples derived from AD patients and non-AD controls. 4089 samples originating from brain tissues and blood remained after applying quality filters. Since disease progression in AD correlates with age, we stratified this large dataset into three different age groups: < 75 years, 75-84 years, and ≥ 85 years. The RNA-Seq, microarray, and proteomics datasets were then combined into different integrated datasets. Ensemble machine learning was employed to identify genes and proteins that can accurately classify samples as either AD or control. These predictive inputs were then subjected to network-based enrichment analyses. The ability of genes/proteins associated with different pathways in the Molecular Signatures Database to diagnose AD was also tested. We separately identified microRNAs that can be used to make an AD diagnosis and subjected the predicted gene targets of the most predictive microRNAs to an enrichment analysis. The following key themes emerged from our machine learning and bioinformatics analyses: cell death, cellular senescence, energy metabolism, genomic integrity, glia, immune system, metal ion homeostasis, oxidative stress, proteostasis, and synaptic function. Many of the results demonstrated unique age-specificity. For example, terms highlighting cellular senescence only emerged in the earliest and intermediate age ranges while the majority of results relevant to cell death appeared in the youngest patients. Existing literature corroborates the importance of these hallmarks in AD.

Age-associated differences in the cancer molecular landscape

Cancer is an age-related disease, as incidence and mortality for most types of cancer increase with age. However, how molecular alterations in tumors differ among patients of different ages remains poorly understood. Recent studies have shed light on the age-associated molecular landscapes in cancer. Here, we summarize the main findings of these current studies, highlighting major differences in the genomic, transcriptomic, epigenetic, and immunological landscapes between cancer in younger and older patients. Importantly, some cancer driver genes are mutated more frequently in younger or older patients. We discuss the potential roles of aging-related processes in shaping these age-related differences in cancer. We further emphasize the remaining unsolved questions that could provide important insights that will have implications in personalized medicine.

A set of common buccal CpGs that predict epigenetic age and associate with lifespan-regulating genes

Epigenetic aging clocks are computational models that use DNA methylation sites to predict age. Since cheek swabs are non-invasive and painless, collecting DNA from buccal tissue is highly desirable. Here, we review 11 existing clocks that have been applied to buccal tissue. Two of these were exclusively trained on adults and, while moderately accurate, have not been used to capture health-relevant differences in epigenetic age. Using 130 common CpGs utilized by two or more existing buccal clocks, we generate a proof-of-concept predictor in an adult methylomic dataset. In addition to accurately estimating age (r = 0.95 and mean absolute error = 3.88 years), this clock predicted that Down syndrome subjects were significantly older relative to controls. A literature and database review of CpG-associated genes identified numerous genes (e.g., CLOCK, ELOVL2, and VGF) and molecules (e.g., alpha-linolenic acid, glycine, and spermidine) reported to influence lifespan and/or age-related disease in model organisms.

•

130 CpGs have been used by two or more aging clocks applied to human buccal tissue

•

Common CpG genes are linked to the adaptive immune system and telomere maintenance

•

Common CpGs can be used to build a novel, proof-of-concept epigenetic aging clock

•

Several compounds associated with common CpG genes regulate lifespan in animals

How Genetics and Genomics Advances Are Rewriting Pediatric Cancer Research and Clinical Care

In the last two decades, thanks to the data that have been obtained from the Human Genome Project and the development of next-generation sequencing (NGS) technologies, research in oncology has produced extremely important results in understanding the genomic landscape of pediatric cancers, which are the main cause of death during childhood. NGS has provided significant advances in medicine by detecting germline and somatic driver variants that determine the development and progression of many types of cancers, allowing a distinction between hereditary and non-hereditary cancers, characterizing resistance mechanisms that are also related to alterations of the epigenetic apparatus, and quantifying the mutational burden of tumor cells. A combined approach of next-generation technologies allows us to investigate the numerous molecular features of the cancer cell and the effects of the environment on it, discovering and following the path of personalized therapy to defeat an "ancient" disease that has had victories and defeats. In this paper, we provide an overview of the results that have been obtained in the last decade from genomic studies that were carried out on pediatric cancer and their contribution to the more accurate and faster diagnosis in the stratification of patients and the development of new precision therapies.

Transcriptional Heterogeneity of Cellular Senescence in Cancer

Cellular senescence plays a paradoxical role in tumorigenesis through the expression of diverse senescence-associated (SA) secretory phenotypes (SASPs). The heterogeneity of SA gene expression in cancer cells not only promotes cancer stemness but also protects these cells from chemotherapy. Despite the potential correlation between cancer and SA biomarkers, many transcriptional changes across distinct cell populations remain largely unknown. During the past decade, single-cell RNA sequencing (scRNA-seq) technologies have emerged as powerful experimental and analytical tools to dissect such diverse senescence-derived transcriptional changes. Here, we review the recent sequencing efforts that successfully characterized scRNA-seq data obtained from diverse cancer cells and elucidated the role of senescent cells in tumor malignancy. We further highlight the functional implications of SA genes expressed specifically in cancer and stromal cell populations in the tumor microenvironment. Translational research leveraging scRNA-seq profiling of SA genes will facilitate the identification of novel expression patterns underlying cancer susceptibility, providing new therapeutic opportunities in the era of precision medicine.

Geographic encoding of transcripts enabled high-accuracy and isoform-aware deep learning of RNA methylation

As the most pervasive epigenetic mark present on mRNA and lncRNA, N⁶-methyladenosine (m⁶A) RNA methylation regulates all stages of RNA life in various biological processes and disease mechanisms. Computational methods for deciphering RNA modification have achieved great success in recent years; nevertheless, their potential remains underexploited. One reason for this is that existing models usually consider only the sequence of transcripts, ignoring the various regions (or geography) of transcripts such as 3′UTR and intron, where the epigenetic mark forms and functions. Here, we developed three simple yet powerful encoding schemes for transcripts to capture the submolecular geographic information of RNA, which is largely independent from sequences. We show that m⁶A prediction models based on geographic information alone can achieve comparable performances to classic sequence-based methods. Importantly, geographic information substantially enhances the accuracy of sequence-based models, enables isoform- and tissue-specific prediction of m⁶A sites, and improves m⁶A signal detection from direct RNA sequencing data. The geographic encoding schemes we developed have exhibited strong interpretability, and are applicable to not only m⁶A but also N¹-methyladenosine (m¹A), and can serve as a general and effective complement to the widely used sequence encoding schemes in deep learning applications concerning RNA transcripts.

Making sense of the ageing methylome

Over time, the human DNA methylation landscape accrues substantial damage, which has been associated with a broad range of age-related diseases, including cardiovascular disease and cancer. Various age-related DNA methylation changes have been described, including at the level of individual CpGs, such as differential and variable methylation, and at the level of the whole methylome, including entropy and correlation networks. Here, we review these changes in the ageing methylome as well as the statistical tools that can be used to quantify them. We detail the evidence linking DNA methylation to ageing phenotypes and the longevity strategies aimed at altering both DNA methylation patterns and machinery to extend healthspan and lifespan. Lastly, we discuss theories on the mechanistic causes of epigenetic ageing.

Age influences on the molecular presentation of tumours

Cancer is often called a disease of aging. There are numerous ways in which cancer epidemiology and behaviour change with the age of the patient. The molecular bases for these relationships remain largely underexplored. To characterise them, we analyse age-associations in the nuclear and mitochondrial somatic mutational landscape of 20,033 tumours across 35 tumour-types. Age influences both the number of mutations in a tumour (0.077 mutations per megabase per year) and their evolutionary timing. Specific mutational signatures are associated with age, reflecting differences in exogenous and endogenous oncogenic processes such as a greater influence of tobacco use in the tumours of younger patients, but higher activity of DNA damage repair signatures in those of older patients. We find that known cancer driver genes such as CDKN2A and CREBBP are mutated in age-associated frequencies, and these alter the transcriptome and predict for clinical outcomes. These effects are most striking in brain cancers where alterations like SUFU loss and ATRX mutation are age-dependent prognostic biomarkers. Using three cancer datasets, we show that age shapes the somatic mutational landscape of cancer, with clinical implications.

An Integrative Multi-Omics Analysis of The Molecular Links between Aging and Aggressiveness in Thyroid Cancers

Aging modifies risk in all cancers, but age is used as a clinical staging criterion uniquely in thyroid cancer (TC). The molecular drivers of age-dependent TC onset and aggressiveness remain poorly understood. We applied an integrative, multi-omics data analysis approach to characterize these signatures. Our analysis reveals that aging, independent of BRAF^V600E mutational status, drives a significant accumulation of aggressiveness-related markers and poorer survival outcomes, most noticeably at age 55 and over. We identified that chromosomal alterations in loci 1p/1q as aging-associated drivers of aggressiveness, and that depleted infiltration with tumor surveillant CD8+T and follicular helper T cells, dysregulation of proteostasis- and senescence-related processes, and ERK1/2 signaling cascade are key features of the aging thyroid and TC onset/progression and aggressiveness in aging patients but not in young individuals. A panel of 23 genes, including those related to cell division such as CENPF, ERCC6L, and the kinases MELK and NEK2, were identified and rigorously characterized as aging-dependent and aggressiveness-specific markers. These genes effectively stratified patients into aggressive clusters with distinct phenotypic enrichment and genomic/transcriptomic profiles. This panel also showed excellent performance in predicting metastasis stage, BRAF^V600E, TERT promoter mutation, and survival outcomes and was superior to the American Thyroid Association (ATA) methodology in predicting aggressiveness risk. Our analysis established clinically relevant biomarkers for TC aggressiveness factoring in aging as an important component.

Positive selection and enhancer evolution shaped lifespan and body mass in great apes

Within primates, the great apes are outliers both in terms of body size and lifespan, since they include the largest and longest-lived species in the order. Yet, the molecular bases underlying such features are poorly understood. Here, we leveraged an integrated approach to investigate multiple sources of molecular variation across primates, focusing on over ten thousand genes, including ∼1,500 previously associated with lifespan, and additional ∼9,000 for which an association with longevity has never been suggested. We analyzed dN/dS rates, positive selection, gene expression (RNA-seq) and gene regulation (ChIP-seq). By analyzing the correlation between dN/dS, maximum lifespan and body mass we identified 276 genes whose rate of evolution positively correlates with maximum lifespan in primates. Further, we identified 5 genes, important for tumor suppression, adaptive immunity, metastasis and inflammation, under positive selection exclusively in the great ape lineage. RNA-seq data, generated from the liver of six species representing all the primate lineages, revealed that 8% of ∼1,500 genes previously associated with longevity are differentially expressed in apes relative to other primates. Importantly, by integrating RNA-seq with ChIP-seq for H3K27ac (which marks active enhancers), we show that the differentially expressed longevity genes are significantly more likely than expected to be located near a novel "ape-specific" enhancer. Moreover, these particular ape-specific enhancers are enriched for young transposable elements, and specifically SINE-Vntr-Alus (SVAs). In summary, we demonstrate that multiple evolutionary forces have contributed to the evolution of lifespan and body size in primates.

Pan-cancer analysis reveals molecular patterns associated with age

Older age is a strong risk factor for several diseases, including cancer. The etiology and biology of age-associated differences among cancers are poorly understood. To address this knowledge gap, we aim to delineate differences in tumor molecular characteristics between younger and older patients across a variety of tumor types from The Cancer Genome Atlas. We show that these groups exhibit widespread molecular differences in select tumor types. Our work shows that tumors in younger individuals exhibit a dysregulated molecular aging phenotype and are associated with hallmarks of premature senescence. Additionally, we find that these tumors are enriched for driver gene mutations, resulting in homologous recombination defects. Lastly, we observe a trend toward decreased immune infiltration and function in older patients and find that, immunologically, young tumor tissue resembles aged healthy tissue. Taken together, we find that tumors from young individuals possess unique characteristics that may be leveraged for therapy.