Somatic Mutations Research Articles

Tracking Rare Single Donor and Recipient Immune and Leukemia Cells after Allogeneic Hematopoietic Cell Transplantation Using Mitochondrial DNA Mutations.

Combined tracking of clonal evolution and chimeric cell phenotypes could enable detection of the key cellular populations associated with response following therapy, including after allogeneic hematopoietic stem cell transplantation (HSCT). We demonstrate that mitochondrial DNA (mtDNA) mutations coevolve with somatic nuclear DNA mutations at relapse post-HSCT and provide a sensitive means to monitor these cellular populations. Furthermore, detection of mtDNA mutations via single-cell assay for transposase-accessible chromatin with select antigen profiling by sequencing (ASAP-seq) simultaneously determines not only donor and recipient cells but also their phenotype at frequencies of 0.1% to 1%. Finally, integration of mtDNA mutations, surface markers, and chromatin accessibility profiles enables the phenotypic resolution of leukemic populations from normal immune cells, thereby providing fresh insights into residual donor-derived engraftment and short-term clonal evolution following therapy for post-transplant leukemia relapse. As throughput evolves, we envision future development of single-cell sequencing-based post-transplant monitoring as a powerful approach for guiding clinical decision-making. Significance: mtDNA mutations enable single-cell tracking of leukemic clonal evolution and donor-recipient origin following allogeneic HSCT. This provides unprecedented insight into chimeric cellular phenotypes of early immune reconstitution, incipient relapse, and quality of donor engraftment with immediate translational potential for future clinical post-transplant monitoring and decision-making.

Improving Individualized Rhabdomyosarcoma Prognosis Predictions Using Somatic Molecular Biomarkers.

Molecular markers, such as FOXO1 fusion genes and TP53 and MYOD1 mutations, increasingly influence risk-stratified treatment selection for pediatric rhabdomyosarcoma (RMS). This study aims to integrate molecular and clinical data to produce individualized prognosis predictions that can further improve treatment selection. Clinical variables and somatic mutation data for 20 genes from 641 RMS patients in the United Kingdom and the United States were used to develop three Cox proportional hazard models for predicting event-free survival (EFS). The 'Baseline Clinical' (BC) model included treatment location, age, fusion status, and risk group. The 'Gene Enhanced 2' (GE2) model added TP53 and MYOD1 mutations to the BC predictors. The 'Gene Enhanced 6' (GE6) model further included NF1, MET, CDKN2A, and MYCN mutations, selected through LASSO regression. Model performance was assessed using likelihood ratio (LR) tests and optimism-adjusted, bootstrapped validation and calibration metrics. The GE6 model demonstrated superior predictive performance, offering 39% more predictive information than the BC model (LR p<0.001) and 15% more than the GE2 model (LR p<0.001). The GE6 model achieved the highest discrimination with a C-index of 0.7087, a Nagalkerke R2 of 0.205, and appropriate calibration. Mutations in TP53, MYOD1, CDKN2A, MET, and MYCN were associated with higher hazards, while NF1 mutation correlated with lower hazard. Individual prognosis predictions varied between models in ways that may suggest different treatments for the same patient. For example, the 5-year EFS for a 10-year-old patient with high-risk, fusion-negative, NF1-positive disease was 50.0% (95% confidence interval: 39-64%) from BC but 76% (64-90%) from GE6. Incorporating molecular markers into RMS prognosis models improves prognosis predictions. Individualized prognosis predictions may suggest alternative treatment regimens compared to traditional risk-classification schemas. Improved clinical variables and external validation are required prior to implementing these models into clinical practice.

ARID1A-Deficient Tumors Acquire Immunogenic Neoantigens during the Development of Resistance to Targeted Therapy.

Neoantigen-based immunotherapy is an attractive potential treatment for previously intractable tumors. To effectively broaden the application of this approach, stringent biomarkers are crucial to identify responsive patients. ARID1A, a frequently mutated subunit of SWI/SNF chromatin remodeling complex, has been reported to determine tumor immunogenicity in some cohorts; however, mutations and deletions of ARID1A are not always linked to clinical responses to immunotherapy. In this study, we investigated immunotherapeutic responses based on ARID1A status in targeted therapy-resistant cancers. Mouse and human BRAFV600E melanomas with or without ARID1A expression were transformed into resistant to vemurafenib, an FDA-approved specific BRAFV600E inhibitor. Anti-PD-1 antibody treatment enhanced antitumor immune responses in vemurafenib-resistant ARID1A-deficient tumors but not in ARID1A-intact tumors or vemurafenib-sensitive ARID1A-deficient tumors. Neoantigens derived from accumulated somatic mutations during vemurafenib resistance were highly expressed in ARID1A-deficient tumors and promoted tumor immunogenicity. Furthermore, the newly generated neoantigens could be utilized as immunotherapeutic targets by vaccines. Finally, targeted therapy resistance-specific neoantigen in experimental human melanoma cells lacking ARID1A were validated to elicit T-cell receptor responses. Collectively, the classification of ARID1A-mutated tumors based on vemurafenib resistance as an additional indicator of immunotherapy response will enable a more accurate prediction to guide cancer treatment. Furthermore, the neoantigens that emerge with therapy resistance can be promising therapeutic targets for refractory tumors. Significance: Chemotherapy resistance promotes the acquisition of immunogenic neoantigens in ARID1A-deficient tumors that confer sensitivity to immune checkpoint blockade and can be utilized for developing antitumor vaccines, providing strategies to improve immunotherapy efficacy.

Mechanisms that clear mutations drive field cancerization in mammary tissue

Oncogenic mutations are abundant in the tissues of healthy individuals, but rarely form tumours1–3. Yet, the underlying protection mechanisms are largely unknown. To resolve these mechanisms in mouse mammary tissue, we use lineage tracing to map the fate of wild-type and Brca1−/−;Trp53−/− cells, and find that both follow a similar pattern of loss and spread within ducts. Clonal analysis reveals that ducts consist of small repetitive units of self-renewing cells that give rise to short-lived descendants. This offers a first layer of protection as any descendants, including oncogenic mutant cells, are constantly lost, thereby limiting the spread of mutations to a single stem cell-descendant unit. Local tissue remodelling during consecutive oestrous cycles leads to the cooperative and stochastic loss and replacement of self-renewing cells. This process provides a second layer of protection, leading to the elimination of most mutant clones while enabling the minority that by chance survive to expand beyond the stem cell-descendant unit. This leads to fields of mutant cells spanning large parts of the epithelial network, predisposing it for transformation. Eventually, clone expansion becomes restrained by the geometry of the ducts, providing a third layer of protection. Together, these mechanisms act to eliminate most cells that acquire somatic mutations at the expense of driving the accelerated expansion of a minority of cells, which can colonize large areas, leading to field cancerization.

Aberrant SWI/SNF Complex Members Are Predominant in Rare Ovarian Malignancies-Therapeutic Vulnerabilities in Treatment-Resistant Subtypes.

SWI/SNF (SWItch/Sucrose Non-Fermentable) is the most frequently mutated chromatin-remodelling complex in human malignancy, with over 20% of tumours having a mutation in a SWI/SNF complex member. Mutations in specific SWI/SNF complex members are characteristic of rare chemoresistant ovarian cancer histopathological subtypes. Somatic mutations in ARID1A, encoding one of the mutually exclusive DNA-binding subunits of SWI/SNF, occur in 42-67% of ovarian clear cell carcinomas (OCCC). The concomitant somatic or germline mutation and epigenetic silencing of the mutually exclusive ATPase subunits SMARCA4 and SMARCA2, respectively, occurs in Small cell carcinoma of the ovary, hypercalcaemic type (SCCOHT), with SMARCA4 mutation reported in 69-100% of SCCOHT cases and SMARCA2 silencing seen 86-100% of the time. Somatic ARID1A mutations also occur in endometrioid ovarian cancer (EnOC), as well as in the chronic benign condition endometriosis, possibly as precursors to the development of the endometriosis-associated cancers OCCC and EnOC. Mutation of the ARID1A paralogue ARID1B can also occur in both OCCC and SCCOHT. Mutations in other SWI/SNF complex members, including SMARCA2, SMARCB1 and SMARCC1, occur rarely in either OCCC or SCCOHT. Abrogated SWI/SNF raises opportunities for pharmacological inhibition, including the use of DNA damage repair inhibitors, kinase and epigenetic inhibitors, as well as immune checkpoint blockade.

Integrating machine learning and multi-omics analysis to develop an immune-derived multiple programmed cell death signature for predicting clinical outcomes in gastric cancer

Abstract Objectives Metastasis of tumor cells is the leading reason for mortality among patients diagnosed with gastric cancer (GC). Emerging evidence indicated a strong correlation between programmed cell death (PCD) and the invasion and metastasis of tumor cells. Therefore, we aimed to develop a programmed cell death signature to assess the prognosis and therapeutic efficacy in GC patients. Methods Here, we collected 1911 PCD-related genes from 19 different PCD patterns, and developed an immune-derived multiple programmed cell death index (MPCDI) using the integrating machine learning and multi-omics analysis, and systematically dissected heterogeneity in GC patients. Subsequently, we divided GC patients into two categories, namely high-MPCDI group and low-MPCDI group, using the median MPCDI as the threshold. We performed a comprehensive analysis of the clinical characteristics, somatic mutations, immune infiltration, drug sensitivity, and immunotherapeutic efficacy of the two groups. Results Survival and immunotherapy response analyses indicated that the high-MPCDI patients experienced a poorer overall survival (p=0.018) and were more resistant to commonly used chemotherapeutic drugs but benefited from immunotherapy compared to the low-MPCDI patients. In addition, MPCDI was confirmed as a standalone risk factor for overall survival, and nomograms can provide a precise tool for the clinical diagnosis of GC patients. Conclusions Taken together, the MPCDI can serve as a robust clinical diagnostic classifier to guide medication administration and improve outcomes in GC patients.

TESMIN is a Prognostic Biomarker Associated with Immunosuppression and Activated Cell Cycle in Lung Adenocarcinoma.

The Testis Expressed Metallothionein Like Protein (TESMIN) gene encodes highly conserved, cysteine-rich, low-molecular proteins that are activated by and have an affinity for heavy metal ions. Previous literature has shown its association with cancer. Nevertheless, no thorough bioinformatics analysis of TESMIN has been done yet in lung adenocarcinoma (LUAD). Differential expression of TESMIN between cancer and normal tissues was confirmed by analyzing databases and immunohistochemistry staining. Enrichment analysis was adopted to explore biological functions. The relationship of TESMIN with immune infiltration was evaluated by ssGSVA, with immunotherapy response predicted by TCIA and TIDE tools, with mutational traits analyzed by R software. Drug sensitivity analysis was implemented via GSCA tool, pRRophetic algorithms, and CellMiner database. The results demonstrated that TESMIN expression was upregulated in tumor tissue and related to Ki67. TESMIN was associated with poor survival and significantly related to age, gender, N stage, M stage, pathological stage, and survival status. TESMIN- related genes (TRGs) were primarily involved in cell division and cancer-related enrichment pathways. TESMIN was associated with high frequencies of somatic mutations and an immunosuppressive tumor microenvironment. Interestingly, patients with elevated levels of TESMIN expression benefited more from commonly used chemotherapy drugs such as cisplatin, paclitaxel, vinorelbine, and docetaxel, whereas those with low levels of TESMIN expression showed favorable clinical responses to immunotherapy. As a prognostic biomarker associated with the cell cycle and immune infiltration, TESMIN may serve as an effective target for predicting the sensitivity to immunotherapy and chemotherapy.

Advances in pathogenesis research and challenges in treatment development for acute myeloid leukemia.

Acute myeloid leukemia (AML) develops when hematopoietic stem cells acquire chromosomal and genetic abnormalities, transforming into leukemia stem cells (LSCs) and further gaining driver mutations. Advances in genomic analysis have identified numerous new gene mutations involved in AML development. Recent research has shown that individuals with germline mutations in genes like DDX41 and CEBPA develop AML upon acquiring additional somatic mutations, and the latest WHO classification separates AML with such mutations into distinct disease groups. LSCs are regulated by different metabolic processes than normal stem cells, contributing to drug resistance and relapse. LSCs rely on oxidative phosphorylation (OXPHOS) metabolism for energy production, and venetoclax inhibits this process, affecting LSCs. Resistant LSCs show enhanced glycolysis, which suggests that targeting both OXPHOS and glycolysis is crucial. While targeted therapies like FLT3, BCL-2, and IDH inhibitors have shown efficacy, resistance remains an issue, highlighting the need for new treatment strategies. CAR-T cell therapy is an emerging immunotherapy that shows particular promise for targeting CD123 and CLL-1, with acceptable toxicity. Future developments in CAR-T cell therapy and other immunotherapies are anticipated to improve AML treatment outcomes.

Somatic Mosaicism in Brain Disorders.

Research efforts over the past decade have defined the genetic landscape of somatic variation in the brain. Neurons accumulate somatic mutations from development through aging with potentially profound functional consequences. Recent studies have revealed the contribution of somatic mosaicism to various brain disorders including focal epilepsy, neuropsychiatric disease, and neurodegeneration. One notable finding is that the effect of somatic mosaicism on clinical outcomes can vary depending on contextual factors, such as the developmental origin of a variant or the number and type of cells affected. In this review, we highlight current knowledge regarding the role of somatic mosaicism in brain disorders and how biological context can mediate phenotypes. First, we identify the origins of brain somatic variation throughout the lifespan of an individual. Second, we explore recent discoveries that suggest somatic mosaicism contributes to various brain disorders. Finally, we discuss neuropathological associations of brain mosaicism in different biological contexts and potential clinical utility.

Prognostic prediction signature and molecular subtype for liver cancer: A CTC/CTM‑related gene prediction model and independent external validation.

Liver cancer is the second leading cause of tumor-related death worldwide, and a serious threat to lives and health. Circulating tumor cells (CTCs) facilitate the progression of various cancers, including liver cancer. The relationship between CTC/circulating tumor microemboli-related genes (CRGs) and the prognosis of liver cancer is unclear. The aim of the present study was to identify CTC/circulating tumour microemboli-related genes (CRGs) in hepatocellular carcinoma and to investigate their clinical significance. Transcriptomic data from The Cancer Genome Atlas (International Cancer Genome Consortium (ICGC) and GSE117623 databases were combined, and differentially expressed CRGs were identified. These were subsequently analyzed via least absolute shrinkage and selection operator and multivariate Cox analyses, and a five-gene risk signature was constructed. The signature was validated in the ICGC and GSE14520 dataset with survival analysis and receiver operating characteristic curve analysis. Immunocyte infiltration, tumor mutation burden (TMB), tumor immune dysfunction and exclusion (TIDE), and the somatic mutation rate were also compared between high- and low-risk groups, based on the median predictive index, to further evaluate the immunotherapeutic value of the model. Molecular subtypes of liver cancer were characterized by the non-negative matrix factorization method and potential therapeutic compounds were evaluated for different subtypes. Nomograms were utilized to predict the prognosis of patients, and the signature was compared with previous literature models. Additionally, the biological function of one of the CRGs, tumor protein p53 inducible protein 3 (TP53I3), in liver cancer was further explored through in vitro experiments. Analysis of the prognostic characteristics of the five CRGs led to the identification of two liver cancer subtypes. Patients in the low-risk group had a longer survival compared with those in the high-risk group, and patients in the latter group were associated with a higher TMB, immunocyte infiltration and somatic mutation rate, and lower TIDE scores. The prognostic profile was validated in the ICGC and GSE14520 datasets and exhibited a good predictive performance. In vitro analysis showed that the knockdown of TP53I3 suppressed liver cancer cell proliferation. In summary, CRGs were used to develop a new prognostic signature to predict the prognosis of patients with liver cancer. This signature may be used to assess the prognosis of patients and may provide new insights for clinical management strategies. In addition, TP53I3 is potentially a therapeutic target for liver cancer.

Gene-Specific Machine Learning Models to Classify Driver Mutations in Clonal Hematopoiesis.

There is no general consensus on the set of mutations capable of driving the age-related clonal expansions in hematopoietic stem cells known as clonal hematopoiesis, and current variant classifications typically rely on rules derived from expert knowledge. In this issue of Cancer Discovery, Damajo and colleagues trained and validated machine learning models without prior knowledge of clonal hematopoiesis driver mutations to classify somatic mutations in blood for 12 genes in a purely data-driven way. See related article by Demajo et al., p. 1717 (9).

Identification of an Amino Acid Metabolism Reprogramming Signature for Predicting Prognosis, Immunotherapy Efficacy, and Drug Candidates in Colon Cancer.

Colon cancer ranked third among the most frequently diagnosed cancers worldwide. Amino acid metabolic reprogramming was related to the occurrence and development of colon cancer. We looked for the amino acid metabolism genes (AMGs) associated with amino acid metabolism from molecular signatures database as prognostic markers and constructed amino acid metabolism scoring model (AMS). According to AMS, the patients were divided into high AMS and low AMS groups, and the prognostic characteristics, molecular phenotypes, somatic cell mutation characteristics, immune cell infiltration characteristics, and immunotherapy effect of the two groups were systematically analyzed. Finally, the compounds targeting AMGs were also screened. We screen out 6 prognostic AMGs (P < 0.05) and construct an AMS model based on them. K-M curve indicated that OS in low AMS group was significantly higher than that in high group (P < 0.05), which were validated in multiple datasets. And different AMS groups had different molecular phenotypes, somatic cell mutation characteristics and immune cell infiltration characteristics. Low AMS group had a better effect for immunotherapy. In addition, we predicted potential therapeutic compounds that could bind to AMGs target proteins. AMS model can be used as a hierarchical tool to evaluate the prognosis, immune infiltration characteristics and immunotherapy response ability of colon cancer. And the compounds screened based on AMGs may become new anti-tumor drugs.

Characteristics of DNMT3a mutation in acute myeloid leukemia and its prognostic implication

BackgroundAcute myeloid leukemia (AML) is a clonal disorder arising from the differentiation arrest of myeloid precursor and malignant proliferation of a bone marrow derived, self-renewing stem or progenitor cells inside the bone marrow (BM) and blood due to numerous genetic mutations. Some mutations can also adjust DNA methylation and may play a critical function in pathogenesis in Cytogenetically Normal Acute Myeloid Leukemia (CN-AML). Somatic mutations in DNMT3a were pronounced in approximately 20% and ∼30–35% of overall AML and CN-AML, respectively. Most DNMT3a mutations in AML have been observed to be heterozygous, A missense mutation, R882, located inside Hot spot exon 23, has been found to be the maximum common mutation. This is a preliminary study conducted on 20 adult Egyptian patients newly diagnosed as AML where Sanger sequencing of Hotspot Exon 23 of DNMT3a gene was performed on their initial bone marrow samples and were followed up to 3 months post-induction therapy. Only De Novo AML patients were included in our study.ResultsOur results revealed that overall DNMT3a mutations were present in 25% of our patients, 10% having the R882 (rs147001633) mutation being 5% R882C and 5% R882H. Immunophenotyping analysis among Mutated DNMT3a (R882 and Non R882) and Wild DNMT3a revealed that AML markers exhibited no significant differences except for myeloperoxidase positivity which was significant among the groups (0.050). Regarding cytogenetics, only one case of the mutated DNMT3a had positive FISH inv (16), where the rest were FISH negative. After 28 days of induction, 75% of all our patients achieved complete response (CR), 20% achieve partial response (PR) out of which 75% are DNMT3a mutated. After 3 months follow-up, 10% of all patients faced mortality where 5% was DNMT3a wild type (died due to treatment-related mortality) and 5% was R882 mutated DNMT3a.ConclusionDNMT3a mutations are present in 25% (5/20) of our AML patients, with 10% (2/20) having the R882 mutation being 5% (1/20) R882C and 5% (1/20) R882H. R882 mutation is associated with resistance to chemotherapy, and poorer outcomes, highlighting its poorer prognostic significance in AML.

Prognostic cellular senescence-related lncRNAs patterns to predict clinical outcome and immune response in colon cancer.

Cellular senescence (CS) is believed to be a major factor in the evolution of cancer. However, CS-related lncRNAs (CSRLs) involved in colon cancer regulation are not fully understood. Our goal was to create a novel CSRLs prognostic model for predicting prognosis and immunotherapy and exploring its potential molecular function in colon cancer. The mRNA sequencing data and relevant clinical information of GDC TCGA Colon Cancer (TCGA-COAD) were obtained from UCSC Xena platform, and CS-associated genes was acquired from the CellAge website. Pearson correlation analysis was used to identify CSRLs. Then we used Kaplan-Meier survival curve analysis and univariate Cox analysis to acquire prognostic CSRL. Next, we created a CSRLs prognostic model using LASSO and multivariate Cox analysis, and evaluated its prognostic power by Kaplan-Meier and ROC curve analysis. Besides, we explored the difference in tumor microenvironment, somatic mutation, immunotherapy, and drug sensitivity between high-risk and low-risk groups. Finally, we verified the functions of MYOSLID in cell experiments. Three CSRLs (AC025165.1, LINC02257 and MYOSLID) were identified as prognostic CSRLs. The prognostic model exhibited a powerful predictive ability for overall survival and clinicopathological features in colon cancer. Moreover, there was a significant difference in the proportion of immune cells and the expression of immunosuppressive point biomarkers between the different groups. The high-risk group benefited from the chemotherapy drugs, such as Teniposide and Mitoxantrone. Finally, cell proliferation and CS were suppressed after MYOSLID knockdown. CSRLs are promising biomarkers to forecast survival and therapeutic responses in colon cancer patients. Furthermore, MYOSLID, one of 3-CSRLs in the prognostic model, could dramatically regulate the proliferation and CS of colon cancer.

Investigation of the Relationship Between DNA Mismatch Repair Genes and Microsatellite Instability in Solid Tumors

Aim: In this study, we aimed to detect the MSI status and somatic mutations in MMR genes (MSH2, MSH6, MLH1, PMS2) of a total of 55 solid tumors diagnosed with colorectal, endometrium and ovarian cancer by NGS method and to reveal the relationship between them. Material and Method: DNA isolation was performed by taking 10-micron sections from paraffin-embedded tissue samples of 55 patients diagnosed with kolorektal, endometrium ve ovarian solid tümörlerinin and Kapa NGS DNA extraction kit was used for sequence analysis. The purity and concentration of the DNA obtained was measured by Qubit fluoremeter, and NadPrep DNA Universal Library Preparation Kit was used for high quality library preparation. Bioinformatics analyses were performed on the Genomize Seq platform. The MSI value was analysed by Roche Navify Mutation Caller software and percentage MSI values were determined using the MSIsensor2 pipeline for secondary analysis of NGS. Results: All ovarian tumors were in the MSI-Stable category. The average MSI value was 2.01. One sample had an MSI of zero. In addition, no mutations in the MSH2 and MLH1 genes were detected in any of the ovarian tumors. 3 of 4 endometrial tumors were in the MSI-Stable category, and 1 tumor was in the MSI-low (MSI value: 13.2) category. No variants were detected in the MSH2 and PMS2 genes in endometrial tumors. 2 of the 41 colorectal tumors (Case4, Case16) were in the MSI-High category. No variants were detected in the MMR genes in 19 tumors. Conclusion: Although frame-shift and stop-gain mutations were detected in 23 tumors that would cause protein deficiency in MMR genes, MSI-H was not detected, as expected, except for two colorectal tumors. Therefore, the results of our study emphasise the need to define new predictive biomarkers clinically within the framework of algorithms to predict response to immunotherapy and determine prognosis.

Identifying Genetic Mutations in Vascular Anomalies Using a Sequencing Panel for Childhood Cancers: A Pilot Study

Introduction: Genetic mutations have been identified in the pathogenesis of vascular anomalies. Due to overlaps in genetic variants causing vascular anomalies and cancer, we used a next-generation sequencing panel for genomic profiling of childhood cancers to detect somatic mutations in children with vascular anomalies. We aim to review the utility of an oncology panel for the molecular diagnosis of vascular anomalies. Methods: Nine patients with histologically confirmed vascular anomalies were included. DNA was extracted from formalin-fixed paraffin-embedded tissue specimens obtained from affected tissue following diagnostic punch biopsies of the skin and core biopsies of the vascular malformation or tumor during sclerotherapy or surgical excision. Molecular analysis of the tissue samples was performed using AmpliSeq for Childhood Cancer DNA Assay Panel. Results: Two patients had antenatally detected vascular anomalies. The median age at diagnosis for the remaining patients was 7.0 years (IQR, 0.6–10.0 years). Seven were diagnosed with vascular malformations, while 2 had vascular tumors. Pathological somatic mutations were identified in 4 patients, leading to a diagnostic yield of 44.4%. Two different PIK3CA mutations were identified in 3 cases: 1 in a case of macrocystic lymphatic malformation, the other in a case of Congenital Lipomatous Overgrowth, Vascular malformations, Epidermal nevus, Spinal/Skeletal anomalies syndrome and Klippel–Trenaunay syndrome. BRAF mutation was identified in a patient with a veno-lymphatic malformation. Conclusion: An oncology next-generation sequencing panel can be used for genetic profiling of vascular anomalies. However, a more customized and sensitive panel may be of better diagnostic yield, as detection of somatic mutations in vascular anomalies is challenging due to tissue mosaicism, low-abundant genetic variants, and specimen limitations.

Machine learning-based cell death marker for predicting prognosis and identifying tumor immune microenvironment in prostate cancer

BackgroundProstate cancer (PCa) incidence and mortality rates are rising, necessitating precise prognostic tools to guide personalized treatment. Dysregulation of programmed cell death pathways in tumor suppression and cancer development has garnered increasing attention, providing a new research direction for identifying biomarkers and potential therapeutic targets. MethodsIntegrating multiple database resources, we constructed and optimized a prognostic signature based on the expression of programmed cell death-related genes (PCDRG) using ten machine learning algorithms. Model performance and prognostic effects were further evaluated. We analyzed the relationships between signature and clinicopathological features, somatic mutations, drug sensitivity, and the tumor immune microenvironment, and constructed a nomogram. The expression level of PCDRGs were evaluated and compared. ResultsOf 1560 PCDRGs, 149 were differentially expressed in PCa, with 34 associated with biochemical recurrence. The PCDRG-derived index (PCDI), constructed using the random forest algorithm, exhibited optimal prognostic performance, successfully stratifying PCa patients into two groups with significant prognostic differences. Patients with high PCDI scores exhibited poorer survival and lower immunotherapy benefit. PCDI was closely associated with the infiltration of specific immune cells, particularly positive correlations with macrophages and T helper cells, and negative correlations with neutrophils, suggesting that PCDI may influence the tumor immune microenvironment, thereby affecting patient prognosis and treatment response. PCDI was associated with age, pathological stage, somatic mutations, and drug sensitivity. The PCDI-based nomogram demonstrated excellent performance in predicting biochemical recurrence in PCa patients. Finally, the differential expression of these PCDRGs was verified based on cell lines and PCa patient expression profile data. ConclusionThis study developed an effective prognostic indicator for prostate cancer, PCDI, using machine learning approaches. PCDI reflects the link between aberrant programmed cell death pathways and disease progression and treatment response.

Comprehensive Analyses of Somatic Copy Number Alterations and Mutations Based on the Adenoma-Carcinoma Sequence.

Identifying molecular alterations in the adenoma and carcinoma components within the same tumor would greatly contribute to understanding the neoplastic progression of early colorectal cancer. We examined somatic copy number alterations (SCNAs) and mutations involved in the adenoma and carcinoma components obtained from the same tumor in 46 cases of microsatellite-stable carcinoma in adenoma, using a genome-wide SNP array and gene mutation panel. In addition, we also performed hierarchical clustering to determine the SCNA frequencies in the tumors, resulting in stratification of the samples into two subgroups according to SCNA frequency. Subgroup 1 was characterized by multiple SCNAs and carcinoma components exclusively, while Subgroup 2 was characterized by a low frequency of SCNAs and both the adenoma and carcinoma components. The numbers of total genes and genes with gains were higher in the carcinoma than adenoma components. The three most frequent gains in both components were located at 1p36.33-1q44, 2p25.3-2q37.3, and 3p26.3-3q29. However, no candidate genes mapped to these regions. APC and KRAS mutations were common in both components, whereas the frequency of TP53 mutations was statistically higher in the carcinoma than adenoma component. However, TP53 mutations were not correlated with SCNA frequency. We suggest that considerable SCNAs and TP53 mutations are required for progression from adenoma to carcinoma within the same intramucosal neoplastic lesion.

Inflammation in myelodysplastic syndrome pathogenesis

Inflammation is a key driver of the progression of preleukemic myeloid conditions, such as clonal hematopoiesis of indeterminate potential (CHIP) and clonal cytopenia of undetermined significance (CCUS), to myelodysplastic syndromes (MDS). Inflammation is a critical mediator in the complex interplay of the genetic, epigenetic, and microenvironmental factors contributing to clonal evolution. Under inflammatory conditions, somatic mutations in TET2, DNMT3A, and ASXL1, the most frequently mutated genes in CHIP and CCUS, induce a competitive advantage to hematopoietic stem and progenitor cells, which leads to their clonal expansion in the bone marrow. Chronic inflammation also drives metabolic reprogramming and immune system deregulation, further promoting the expansion of malignant clones. This review underscores the urgent need to fully elucidate the role of inflammation in MDS initiation and highlights the potential of the therapeutical targeting of inflammatory pathways as an early intervention in MDS.

281. PROTEOMIC, TRANSCRIPTOMIC AND GENOMIC ANALYSIS OF ESOPHAGEAL ADENOCARCINOMA UNCOVERS CANDIDATE THERAPEUTIC TARGETS AND CANCER-SELECTIVE POST-TRANSCRIPTIONAL REGULATION

Abstract Background Addressing the poor prognosis associated with esophageal adenocarcinoma (EAC) has been hindered by the absence of biomarkers for early disease detection and therapeutic targets. Despite extensive research on the somatic mutations associated with EAC, the impact of genomic aberrations on protein expression and cancer phenotype remains unclear. Methods We conducted a quantitative proteomic analysis using tumour tissue and patient-matched adjacent normal esophageal and gastric tissues from 23 patients undergoing resection for EAC. We further examined the relationship between transcript and protein levels using patient-matched whole transcriptome RNA sequencing and proteomic data from 7 patients and integrated these findings with data from a cohort of 264 EAC patients with RNA sequencing and 454 patients with whole-genome sequencing, as well as external published datasets. Results We quantified protein expression from 5897 genes in EAC and normal tissues. Several potential biomarkers showing selective expression in EAC, such as the transmembrane protein GPA33, were identified. We validated the EAC-specific expression of GPA33 in an external cohort of 115 patients, suggesting its utility as a diagnostic and therapeutic target. Additionally, integrated analysis of protein and RNA expression in EAC and normal tissues revealed genes with discordant protein and RNA levels, indicating post-transcriptional regulation of protein expression. These outlier genes, including SLC25A30, TAOK2, and AGMAT, exhibited rare somatic mutations, suggesting post-transcriptional mechanisms driving this EAC-specific phenotype. AGMAT, as an example, was found to be overexpressed at the protein level in EAC compared to adjacent normal tissues, with a proposed EAC-selective, post-transcriptional mechanism regulating protein abundance. Conclusion Through quantitative proteomic analysis, we identified GPA33 as a candidate EAC biomarker. Integrated analysis of the proteome, transcriptome, and genome in EAC uncovered genes with tumor-selective post-transcriptional regulation of protein expression, offering potential exploitable vulnerabilities for therapeutic intervention.