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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. Ageing is a known risk factor in the development of cancers, but its association with molecular alterations is not fully explored. Here, the authors analyse pan-cancer age-associated molecular alterations in datasets from the TCGA, PCAWG and AACR-GENIE projects and identify prognostic biomarkers.

Profiling tumors at single-cell resolution provides an opportunity to understand complexities underpinning lymph-node metastases in head and neck squamous-cell carcinoma. Single-cell RNAseq (scRNAseq) analysis of cancer-cell trajectories identifies a subpopulation of pre-metastatic cells, driven by actionable pathways including AXL and AURK. Blocking these two proteins blunts tumor invasion in patient-derived cultures. Furthermore, scRNAseq analyses of tumor-infiltrating CD8 + T-lymphocytes show two distinct trajectories to T-cell dysfunction, corroborated by their clonal architecture based on single-cell T-cell receptor sequencing. By determining key modulators of these trajectories, followed by validation using external datasets and functional experiments, we uncover a role for SOX4 in mediating T-cell exhaustion. Finally, interactome analyses between pre-metastatic tumor cells and CD8 + T-lymphocytes uncover a putative role for the Midkine pathway in immune-modulation and this is confirmed by scRNAseq of tumors from humanized mice. Aside from specific findings, this study demonstrates the importance of tumor heterogeneity analyses in identifying key vulnerabilities during early metastasis. The molecular mechanisms underlying lymph-node metastasis in head and neck squamous-cell carcinoma remain to be investigated. Here, the authors perform single-cell RNA sequencing of cancer cells and CD8 + T cells and suggest potential mechanisms of immune evasion during early metastasis.

Spontaneous preterm birth (sPTB) remains a significant global health challenge and a leading cause of neonatal mortality and morbidity. Despite advancements in neonatal care, the prediction of sPTB remains elusive, in part due to complex etiologies and heterogeneous patient populations. This study aimed to validate and extend information on gene expression biomarkers previously described for predicting sPTB using maternal whole blood from the All Our Families pregnancy cohort study based in Calgary, Canada. The results of this study are two-fold: first, using additional replicates of maternal blood samples from the All Our Families cohort, we were unable to repeat the findings of a 2016 study which identified top maternal gene expression predictors for sPTB. Second, we conducted a secondary analysis of the original gene expression dataset from the 2016 study using five modelling approaches (random forest, elastic net regression, unregularized logistic regression, L2-regularized logistic regression, and multilayer perceptron neural network) followed by external validation using a pregnancy cohort based in Detroit, USA. The top performing model (random forest classification) suggested promising performance (area under the receiver operating curve, AUROC 0.99 in the training set), but performance was significantly degraded on the test set (AUROC 0.54) and further degraded in external validation (AUROC 0.50), suggesting poor generalizability, likely due to overfitting exacerbated by a low feature-to-noise ratio. Similar performance was observed in the other four learning models. Prediction was not improved when using higher complexity machine learning (e.g., neural network) approaches over traditional statistical learning (e.g., logistic regression). These findings underscore the challenges in translating biomarker discovery into clinically useful predictive models for sPTB. This study highlights the critical need for rigorous methodological safeguards and external validation in biomarker research. It also emphasizes the impact of data noise and overfitting on model performance, particularly in high-dimensional omics datasets. Future research should prioritize robust validation strategies and explore mechanistic insights to improve our understanding and prediction of sPTB.

Single-agent checkpoint inhibitor (CPI) activity in Epstein-Barr Virus (EBV) related nasopharyngeal carcinoma (NPC) is limited. Dual CPI shows increased activity in solid cancers. In this single-arm phase II trial (NCT03097939), 40 patients with recurrent/metastatic EBV-positive NPC who failed prior chemotherapy receive nivolumab 3 mg/kg every 2 weeks and ipilimumab 1 mg/kg every 6 weeks. Primary outcome of best overall response rate (BOR) and secondary outcomes (progression-free survival [PFS], clinical benefit rate, adverse events, duration of response, time to progression, overall survival [OS]) are reported. The BOR is 38% with median PFS and OS of 5.3 and 19.5 months, respectively. This regimen is well-tolerated and treatment-related adverse events requiring discontinuation are low. Biomarker analysis shows no correlation of outcomes to PD-L1 expression or tumor mutation burden. While the BOR does not meet pre-planned estimates, patients with low plasma EBV-DNA titre (<7800 IU/ml) trend to better response and PFS. Deep immunophenotyping of pre- and on-treatment tumor biopsies demonstrate early activation of the adaptive immune response, with T-cell cytotoxicity seen in responders prior to any clinically evident response. Immune-subpopulation profiling also identifies specific PD-1 and CTLA-4 expressing CD8 subpopulations that predict for response to combined immune checkpoint blockade in NPC. Dual PD-1 and CTLA-4 checkpoint blockade has proven effective in several cancer types. Here the authors report the results of a clinical trial of anti-PD1 (nivolumab) and anti-CTLA4 (ipilimumab) in patients with recurrent/metastatic EBV-positive nasopharyngeal carcinoma.

Recently mapped transcriptomic landscapes reveal the extent of heterogeneity in cancer‐associated fibroblasts (CAFs) beyond previously established single‐gene markers. Functional analyses of individual CAF subsets within the tumor microenvironment are critical to develop more accurate CAF‐targeting therapeutic strategies. However, there is a lack of robust preclinical models that reflect this heterogeneity in vitro. In this study, single‐cell RNA sequencing datasets acquired from head and neck squamous cell carcinoma tissues to predict microenvironmental and cellular features governing individual CAF subsets are leveraged. Some of these features are then incorporated into a tunable hyaluronan‐based hydrogel system to culture patient‐derived CAFs. Control over hydrogel degradability and integrin adhesiveness enabled derivation of the predominant myofibroblastic and inflammatory CAF subsets, as shown through changes in cell morphology and transcriptomic profiles. Last, using these hydrogel‐cultured CAFs, microtubule dynamics are identified, but not actomyosin contractility, as a key mediator of CAF plasticity. The recapitulation of CAF heterogeneity in vitro using defined hydrogels presents unique opportunities for advancing the understanding of CAF biology and evaluation of CAF‐targeting therapeutics. In this study, Ho and co‐workers leveraged the use of bioengineered hyaluronan hydrogels to recapitulate cancer‐associated fibroblast (CAF) heterogeneity in vitro in head and neck squamous cell carcinoma. Tuning hydrogel degradation alters microtubule dynamics in CAFs, which plays a key role in the plasticity between myofibroblastic and inflammatory CAF subsets.

Abstract Living organisms are continuously challenged by changes in their environment that can propagate to stresses at the cellular level, such as rapid changes in osmolarity or oxygen tension. To survive these sudden changes, cells have developed stress-responsive mechanisms that tune cellular processes. The response of Saccharomyces cerevisiae to osmostress includes a massive reprogramming of gene expression. Identifying the inherent features of stress-responsive genes is of significant interest for understanding the basic principles underlying the rewiring of gene expression upon stress. Here, we generated a comprehensive catalog of osmostress-responsive genes from 5 independent RNA-seq experiments. We explored 30 features of yeast genes and found that 25 (83%) were distinct in osmostress-responsive genes. We then identified 13 non-redundant minimal osmostress gene traits and used statistical modeling to rank the most stress-predictive features. Intriguingly, the most relevant features of osmostress-responsive genes are the number of transcription factors targeting them and gene conservation. Using data on HeLa samples, we showed that the same features that define yeast osmostress-responsive genes can predict osmostress-responsive genes in humans, but with changes in the rank-ordering of feature-importance. Our study provides a holistic understanding of the basic principles of the regulation of stress-responsive gene expression across eukaryotes.

Proteins play a central role in cancer biology: they are the most common drug targets and biomarkers. Sex influences the proteome in many diseases, ranging from neurological to cardiovascular. In cancer, sex is associated with incidence, progression and therapeutic response, as well as characteristics of the tumour genome and transcriptome. The extent to which sex differences impact the cancer proteome remains largely unknown. To fill this gap, we quantified sex differences across 1,590 proteomes from eight cancer types, identifying 901 genes with sex-differential proteins abundance in adenocarcinomas of the lung, and 20 genes across five other tumour types: squamous cell carcinoma of the lung, hepatocellular carcinomas, clear cell cancers of the kidney, adenocarcinomas of the pancreas and glioblastoma. A subset of these protein differences could be rationalized by sex-differential copy number aberrations. Pathway analysis showed that male-biased proteins in lung adenocarcinoma were enriched in MYC and E2F target pathways. These findings highlight the modest impact of sex on the cancer proteome, but the very limited power of existing proteomics cohorts for these analyses.

Patient sex influences a wide range of cancer phenotypes, including prevalence, response to therapy and survival endpoints. Molecular sex differences have been identified at all levels of the central dogma. It is hypothesized that these molecular differences may drive the observed clinical sex differences. Yet despite a growing catalog of molecular sex differences in a range of cancer types, their specific functional consequences remain unclear. To directly assess how patient sex impacts cancer cell function, we evaluated 1,209 cell lines subjected to CRISPR knockout, RNAi knockdown or drug exposures. Despite limited statistical power, we identified pan- and per-cancer sex differences in gene essentiality in six sex-linked and fourteen autosomal genes, and in drug sensitivity for two compounds. These data fill a gap in our understanding of the link between sex-differential molecular effects and patient phenotypes. They call for much more careful and systematic consideration of sex-specific effects in mechanistic and functional studies.

ObjectivesProteins play a central role in cancer biology. They are the most common drug targets and biomarkers. Sex influences the proteome in many diseases ranging from neurological to cardiovascular. In cancer, sex is associated with incidence, progression and therapeutic response, as well as characteristics of the tumour genome and transcriptome. This study aimed to characterise the extent to which sex differences impact the cancer proteome.DesignRetrospective analysis of publicly available proteogenomic datasets.SettingClinical Proteomic Tumor Analysis Consortium cancer cohorts.ParticipantsA total of 1590 proteomes from eight cancer types.InterventionsNot applicable.Main outcome measuresSex-differential protein abundance and its association with copy number aberrations, biological pathways and gene dependency.ResultsWe identified 901 genes with sex-differential protein abundance in adenocarcinomas of the lung and 20 genes across five other tumour types: squamous cell carcinoma of the lung, hepatocellular carcinomas, clear cell cancers of the kidney, adenocarcinomas of the pancreas and glioblastoma. A subset of these protein differences could be rationalised by sex-differential copy number aberrations. Pathway analysis showed that male-biased proteins in lung adenocarcinoma were enriched in MYC and E2F target pathways, and female-biased proteins were enriched in metabolic and stress-response pathways. These genes also exhibited stronger CRISPR gene dependency in cell lines derived from lung adenocarcinoma.ConclusionsThese findings highlight the modest impact of sex on the cancer proteome, but the very limited power of existing proteomics cohorts for these analyses.

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 characterize them, we analyzed 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 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 more activity of DNA damage repair signatures in those of older patients. We find that known cancer driver genes 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 clear clinical implications. Competing Interest Statement The authors have declared no competing interest. Footnotes * -Q-Q plots for the p-value distributions for our CNA results to Supplementary Figures -Bonferroni-adjusted p-values in supplementary materials * https://gdac.broadinstitute.org/ * https://dcc.icgc.org * https://portal.gdc.cancer.gov/projects/GENIE-MSK