Trends in cancer最新文献

While the initial transformation of cancer cells is driven by genetic alterations, tumor cell behaviors and functional states are dynamically regulated by cell-intrinsic factors including proteins, metabolites and lipids, and extrinsic microenvironmental factors. Emerging multi-omics technologies highlighted that cancer cells exhibit distinct lipidome compositions and employ specific lipid metabolic circuits for chemical conversions - collectively defined as 'lipotypes'. We review the interplay between cancer lipotypes and cellular states, focusing on interpreting how being at different positions along the spectra of representative lipid metabolic axes influences cancerous traits. We aim to instill a system biology perspective to integrate 'lipotypes' into the established 'genotype-phenotype' framework in cancer.

Whole-genome doubling (WGD) has recently emerged as one of the most common genomic alterations in cancer and is associated with genomic instability, drug resistance, and metastasis. However, WGD also generates unique vulnerabilities that create a therapeutic window between cancer cells and healthy cells. Over the past few years, there has been a rapid growth in our understanding of WGD at a molecular level. In this review, we discuss the causes and immediate cellular effects of, and therapeutic considerations for, WGD in cancer.

The serotonin transporter (SERT) and histone H3 serotonylation (H3Q5ser) integrate extracellular neurotransmitter signals to chromatin regulation in cancer. Here, we discuss how the SERT-H3Q5ser axis shapes tumor immunity and epigenetic plasticity, and highlight emerging strategies to therapeutically target this neuro-epigenetic interface.

Treatment resistance remains a formidable barrier to curing lymphomas, driven in part by their ability to alter their phenotypic and molecular profiles under therapeutic pressure. A growing body of evidence suggests that a clinically minute population of drug-tolerant persister (DTP) cells, which undergo non-genetic adaptations to survive therapy, are responsible for seeding relapse. We highlight the substantial progress being made to characterize DTP populations, and postulate that they confer novel vulnerabilities for therapeutic targeting. We propose the addition of therapies that proactively address these therapy-induced adaptations to delay or prevent the emergence of resistance in aggressive B cell lymphomas. We contend that this approach can deepen clinical responses, spare patients unnecessary toxicity, and advance progress towards achieving a true cure.

Global cancer incidence is rising. In most high-income countries, it is projected that one in two people will be diagnosed with cancer in their lifetime, with incidence rates among middle- and low-income countries also set to follow these projections. Cancer incidence is increasing due to a multitude of factors, including an ageing population, lifestyle factors, and improved detection. Earlier cancer detection and improved treatments mean that people are now living longer with and beyond cancer. Given these occurrences and projections, planning for 'when' and not 'if' we get cancer is critical. Supportive care in cancer provides high-value return on investment and significantly improves outcomes, yet is clearly underfunded and inconsistently prioritised. Supportive care must be prioritised globally to ensure functioning and wellbeing at all levels of society.

Cancer cells often retain lineage- and tissue of origin-specific programs established prior to malignant transformation. This observation has been elaborated by advances in single-cell and lineage-tracing technologies, which provide high-resolution mapping of these features. Here, we provide an overview of these recent technological developments and examine how the tissue of origin shapes tumor behavior and vulnerabilities. We discuss how the preferential selection of oncogenic drivers by specific tissues leads to distinct genetic alterations across cancers. We then explore the continued dependence of cancer cells on lineage-specific physiological functions and signaling pathways, thereby revealing lineage-dependent therapeutic targets. Finally, we highlight how lineage-specific cell surface marker expression informs precision immunotherapies. Together, these insights are driving a shift toward therapies tailored to the developmental and functional identities of cancer cells.

Cancer cells require sufficient nutrients to support biomass generation, rapid proliferation, and survival. Thus, extensive reprogramming of amino acid metabolism is necessary for tumor initiation and progression under strenuous conditions. One metabolic pathway that has garnered attention is branched chain amino acid (BCAA) catabolism, a pathway that is highly altered across malignancies. This review examines current insights into how circulating BCAAs and their aberrant catabolic enzymes impact both cancer cells and the surrounding tumor microenvironment.

Cancer vaccines have emerged as a promising strategy in cancer immunotherapy, capable of eliciting robust antitumor immune responses by targeting tumor-associated antigens or tumor-specific antigens. Among the various vaccine platforms, RNA-based vaccines have garnered substantial attention, especially in light of the success of mRNA vaccines during the COVID-19 pandemic. This review outlines the fundamental characteristics of different RNA vaccine modalities, summarizes recent clinical applications in cancer treatment, and highlights strategies aimed at improving their efficacy and safety. Furthermore, we discuss the current challenges facing RNA vaccine development and offer perspectives on future directions in this rapidly advancing field.

Intratumoral heterogeneity in pancreatic cancer poses a significant challenge, contributing to disease aggressiveness and complicating treatment. A recent study by Li et al. reveals that this heterogeneity is maintained by tumor-intrinsic reciprocal signaling between SPP1 and GREM1 in the epithelial and mesenchymal cell populations of pancreatic cancer.

Fairness in artificial intelligence (AI) is often assessed with flawed metrics, particularly in oncology where patient diversity and structural inequities shape outcomes. Ground truth labels, predictions, and demographic attributes all carry biases that distort fairness evaluations. We argue for rethinking fairness frameworks to better capture equity in cancer care.