Molecular Oncology最新文献

Acute lymphoblastic leukemia (ALL), the most common cancer in children, is overall divided into two subtypes, B-cell precursor ALL (B-ALL) and T-cell ALL (T-ALL), which have different molecular characteristics. Despite massive progress in understanding the disease trajectories of ALL, ALL remains a major cause of death in children. Thus, further research exploring the biological foundations of ALL is essential. Here, we examined the diagnostic, prognostic, and therapeutic potential of gene expression data in pediatric patients with ALL. We discovered a subset of expression markers differentiating B- and T-ALL: CCN2, VPREB3, NDST3, EBF1, RN7SKP185, RN7SKP291, SNORA73B, RN7SKP255, SNORA74A, RN7SKP48, RN7SKP80, LINC00114, a novel gene (ENSG00000227706), and 7SK. The expression level of these markers all demonstrated significant effects on patient survival, comparing the two subtypes. We also discovered four expression subgroups in the expression data with eight genes driving separation between two of these predicted subgroups. A subset of the 14 markers could distinguish B- and T-ALL in an independent cohort of patients with ALL. This study can enhance our knowledge of the transcriptomic profile of different ALL subtypes.

While the incidence of endometrial cancer is increasing among all US women, Black women face higher mortality rates. The reasons for this remain unclear. In this study, whole genome differential methylation analysis, along with state-of-the-art computational methods such as the recursive feature elimination technique and supervised/unsupervised machine learning models, was used to identify 38 epigenetic signature genes (ESGs) and four core-ESGs (cg19933311: TRPC5; cg09651654: APOBEC1; cg27299712: PLEKHG5; cg03150409: WHSC1) in endometrial tumors from Black and White women, incorporating genetic ancestry estimation. Methylation at two Core-ESGs, namely APOBEC1 and PLEKHG5, showed statistically significant overall survival differences between the two ancestral groups (Likelihood ratio test; P value = 0.006). Moreover, our comprehensive ancestry-based analysis revealed that tumors from women with high African ancestry exhibited increased hypomethylation compared to those with low African ancestry. These hypomethylated genes were enriched in drug metabolism pathways, indicating a potential link between genetic ancestry, epigenetic modifications, and pharmacogenomic responses. Combining ancestry, race, and disease type may help identify which patient groups will benefit most from these biomarkers for targeted treatments.

Monitoring levels of circulating tumour-derived DNA (ctDNA) provides both a noninvasive snapshot of tumour burden and also potentially clonal evolution. Here, we describe how applying a novel statistical model to serial ctDNA measurements from shallow whole genome sequencing (sWGS) in metastatic breast cancer patients produces a rapid and inexpensive predictive assessment of treatment response and progression-free survival. A cohort of 149 patients had DNA extracted from serial plasma samples (total 1013, mean samples per patient = 6.80). Plasma DNA was assessed using sWGS and the tumour fraction in total cell-free DNA estimated using ichorCNA. This approach was compared with ctDNA targeted sequencing and serial CA15-3 measurements. We identified a transition point of 7% estimated tumour fraction to stratify patients into different categories of progression risk using ichorCNA estimates and a time-dependent Cox Proportional Hazards model and validated it across different breast cancer subtypes and treatments, outperforming the alternative methods. We used the longitudinal ichorCNA values to develop a Bayesian learning model to predict subsequent treatment response with a sensitivity of 0.75 and a specificity of 0.66. In patients with metastatic breast cancer, a strategy of sWGS of ctDNA with longitudinal tracking of tumour fraction provides real-time information on treatment response. These results encourage a prospective large-scale clinical trial to evaluate the clinical benefit of early treatment changes based on ctDNA levels.

Squamous cell lung carcinoma (SqCC) is the second most common histological subtype of lung cancer. Besides tumor-initiating and promoting DNA, RNA, and epigenetic alterations, aberrant cell metabolism is a hallmark of carcinogenesis. This study aimed to identify SqCC-specific key regulators that could eventually be used as new anticancer targets. Transcriptional and metabolomic data were gathered for a cohort of resected lung cancers. SqCC-specific differentially expressed genes were integrated with metabolic data. Findings were validated in cohorts of tumors, normal specimens, and cell lines. In situ protein expression of SLC6A8 was investigated. Differential gene expression analysis identified a subset of SqCC-specific genes with metabolic functions through the Reactome database, and/or correlated to specific metabolites through GEMs models. Metabolic profiling identified seven SqCC-specific metabolites, of which increased creatine levels, in particular, matched to SqCC-specific expression of SLC6A8. Expression of the gene appeared tumor cell-associated. Elevated creatine levels and overexpression of its transporter SLC6A8 appear a distinct metabolic feature of SqCC. Considering ongoing clinical trials in other malignancies, exploring SLC6A8 inhibition in SqCC appears motivated based on a metabolic addiction hypothesis.

Colorectal cancer (CRC) is a heterogenous disease with distinct biological and clinical subgroups, each with different prognoses and responses to therapy. In this case report, taking inspiration from a case of locally advanced CRC with serine/threonine-protein kinase B-raf (BRAF) V600E mutation, we highlight an atypical consensus molecular subtype 1 (CMS1). Deep multi-omic analyses showed a limited expression of programmed cell death protein 1 (PD-1) and reduced T-cell infiltration, including CD8⁺ and natural killer (NK) cells, in the analyzed CMS1 tumor. In parallel, a reduced activation of the JAK/STAT pathway was detected, suggesting a lack of clinical response to immunotherapy with checkpoint inhibitors. Furthermore, the finding of up-regulated expression of WEE1 G2 checkpoint kinase (WEE1), checkpoint kinase 1 (CHK1), and checkpoint kinase 2 (CHK2), poly(ADP-ribose) polymerase (PARP), and heat shock protein 90 (HSP90) suggests a potential alternative therapeutic approach using inhibitors of the cell cycle, HSP90, or PARP in combination with conventional chemotherapy, targeted agents, or immunotherapy. This paradigmatic case should stimulate a regular deep omics analysis to improve precision medicine. We therefore suggest that full mutational and expression profiling analyses of CRC subtypes should be undertaken to improve therapeutic strategies in CRC treatment.

Epigenetic regulators, such as the SWI/SNF complex, with important roles in tissue development and homeostasis, are frequently mutated in cancer. ARID1A, a subunit of the SWI/SNF complex, is mutated in approximately 20% of all bladder tumors; however, the consequences of this remain poorly understood. Finding truncations to be the most common mutation, we generated loss- and gain-of-function models to conduct RNA-Seq, interactome analyses, Omni-ATAC-Seq, and functional studies to characterize ARID1A-affected pathways potentially suitable for the treatment of ARID1A-deficient bladder cancers. We observed decreased cell proliferation and deregulation of stress-regulated pathways, including DNA repair, in ARID1A-deficient cells. Furthermore, ARID1A was linked to alternative splicing and translational regulation on RNA and interactome levels. ARID1A deficiency drastically reduced the accessibility of chromatin, especially around introns and distal enhancers, in a functional enrichment analysis. Less accessible chromatin areas were mapped to pathways such as cell proliferation and DNA damage response. Indeed, the G2/M checkpoint appeared impaired after DNA damage in ARID1A-deficient cells. Together, our data highlight the broad impact of ARID1A loss and the possibility of targeting proliferative and DNA repair pathways for treatment.

Proteasomes are involved in the maintenance of cellular protein homeostasis and the control of numerous cellular pathways. Single proteasome genes or subunits have been identified as important players in cancer development and progression without considering the proteasome as a multisubunit protease. We here conducted a comprehensive pan-cancer analysis encompassing transcriptional, epigenetic, mutational landscapes, pathway enrichments, and survival outcomes linked to the 20S proteasome core complex. The impact of proteasome gene expression on patient survival exhibited a cancer type-dependent pattern. Increased proteasome expression correlated with elevated activation of oncogenic pathways, such as DNA repair, MYC-controlled gene networks, MTORC1 signalling, oxidative phosphorylation, as well as metabolic pathways including glycolysis and fatty acid metabolism. Accordingly, potential loss of function variants of proteasome subunit genes are associated with improved patient survival. The TCGA-derived outcomes were further supported by gene expression analysis of THP-1 cells. Our study highlighted the importance of studying the proteasome as an enzymatic functional unit rather than separated subunits.

Colorectal cancer (CRC) remains a leading cause of cancer-related mortality, with metastatic CRC (mCRC) posing significant challenges due to tumor heterogeneity and resistance to therapy. Circulating tumor cells (CTC) and circulating hybrid cells (CHC) detected via liquid biopsies have emerged as promising biomarkers for monitoring disease progression. This study aimed to evaluate the prognostic utility of automated CTC enumeration using the ACCEPT software compared to a manual method and assess the potential clinical relevance of CHC in mCRC. A retrospective analysis of CellSearch^® images from 67 mCRC patients was conducted, correlating CTC and CHC counts with progression-free survival and overall survival (OS). Automated enumeration demonstrated improved accuracy and reduced variability, confirming the prognostic significance of CTC counts for OS. However, CHC enumeration showed no significant association with clinical outcomes, suggesting sporadic detection rather than consistent prognostic value. These findings underscore the reliability of automated CTC enumeration in mCRC prognosis while highlighting the need for further research into the biological and clinical roles of CHC.

B-cell chronic lymphocytic leukemia (B-CLL) is characterized by highly heterogeneous genomic alterations and altered signaling pathways, with limited studies on its proteome. Our study presents a comprehensive analysis of the proteome and phosphoproteome in B-CLL and CLL-like monoclonal B-cell lymphocytosis (MBL) primary cells. Using high-resolution mass spectrometry, we identified 2970 proteins and 316 phosphoproteins across five tumor samples, including 55 newly identified phosphopeptides (ProteomeXchange-PXD005997). Our multifaceted approach also integrated protein microarrays and western blotting for further data validation in a new patient cohort of 14 patients. Despite sharing 73% of their proteomes, the phosphoproteomes varied significantly among samples, independent of cytogenetic alterations and immunoglobulin heavy variable cluster (IGHV) mutational status. We identified common functional hallmarks in B-CLL and MBL phosphoproteomes, notably tonic signaling (low-level, constitutive signaling) of the B-cell antigen-specific receptor (BCR) and nuclear factor NF-kappa-B (NF-kβ)/signal transducer and activator of transcription 3 (STAT3) pathways. Nine phosphoproteins involved in BCR signaling were further validated, showing a high correlation with early disease stages. Our study advances the field by providing a detailed perspective on the proteome and phosphoproteome of B-CLL cells, revealing signaling pathways crucial for disease development and progression. Integrating diverse proteomics techniques and identifying novel phosphopeptides offers new insights into CLL biology, potentially informing future therapeutic strategies and biomarker development for early diagnosis and personalized treatment.

Spatial transcriptomics (ST) has emerged as a powerful tool to map gene expression patterns to the local tissue structure in cancer, enabling unprecedented insights into cellular heterogeneity and tumour microenvironments. As the technology matures, developing new, spatially informed analytical frameworks will be essential to fully leverage its potential to elucidate the complex organisation and emerging properties of cancer tissues. Here, we highlight key challenges in cancer spatial transcriptomics, focusing on three emerging topics: (a) defining cell states, (b) delineating cellular niches and (c) integrating spatial data with other modalities that can pave the way towards clinical translation. We discuss multiple analytical approaches that are currently implemented or could be adapted in the future in order to tackle these challenges, including classical biostatistics methods as well as methods inherited from geospatial analytics or artificial intelligence. In the rapidly expanding landscape of ST, such methodologies lay the foundation for biological discoveries that conceptualise cancer as an evolving system of interconnected niches.