Mutational Landscape Research Articles

The landscape of targetable gene mutations in neuroendocrine cervical cancer

The landscape of targetable gene mutations in neuroendocrine cervical cancer

Overall survival and mutational landscape comparison between Appalachian and non-Appalachian patients with ovarian cancer: An Oncology Research Information Exchange Network (ORIEN) analysis

Overall survival and mutational landscape comparison between Appalachian and non-Appalachian patients with ovarian cancer: An Oncology Research Information Exchange Network (ORIEN) analysis

PD-L1 Expression Varies in Thyroid Cancer Types and Is Associated with Decreased Progression Free Survival (PFS) in Patients with Anaplastic Thyroid Cancer

Background: Thyroid cancer (TC) remains a significant clinical challenge worldwide, with a subset of patients facing aggressive disease progression and therapeutic resistance. Immune checkpoint inhibitors targeting programmed death-ligand 1 (PD-L1) have emerged as promising therapeutic approaches for various malignancies, yet their efficacy in TC remains uncertain. The objective of this study was to investigate PD-L1 expression in aggressive TC and its association with histological subtypes, molecular mutation, and progression-free survival. Methods: This is a retrospective study of patients with advanced TC seen in two tertiary health care centers. Included in this study were patients with advanced TC with recurrence or progression on therapy for whom tumor molecular profiling and PD-L1 status were available. Kaplan–Meier estimators were utilized to analyze the progression-free survival (PFS) between patients with PD-L1 positive and negative status in Anaplastic TC (ATC) subgroup. Results: A total of 176 patients with advanced thyroid cancer were included (48.9% female). Of the patients, 13 had ATC, 11 Medullary TC (MTC), 81 Papillary TC Classic Variant (PTCCV), 20 Follicular TC (FTC), 8 Oncocytic TC (OTC), 10 Poorly Differentiated TC (PDTC), and 30 had the Papillary TC Follicular Variant (PTCFV). BRAF mutation was present in 41%, TERT in 30%, RAS in 19%, TP53 in 10%, and RET in 8.6% of patients. PD-L1 positivity was significantly different across different TC types and histological subtypes (p < 0.01): Patients with OTC had the highest frequency of PD-L1 positivity (71%), followed by ATC (69%), PTCCV (28.5%), and FTC (11%). Patients with MTC and PTCFV did not exhibit any PD-L1 positivity. TP53 mutation was positively associated with PD-L1 expression (21.6% vs. 7.5%, p = 0.03), and RAS mutation was negatively associated with PD-L1 expression (8.1% vs. 24.2% p = 0.04). Among patients with ATC, positive PD-L1 expression was associated with lower PFS (p = 0.002). Conclusions: PD-L1 expression varies across different TC types and histological subtypes and may be modulated by the mutational landscape. PD-L1 expression in ATC is associated with shorter PFS. Follow up studies are warranted to elucidate the molecular mechanism driving the observed differences in immune pathways, potentially paving the way for the development of more effective and personalized immune therapies for patients with aggressive TC.

Identification of genetic variants by using a whole exome sequencing approach in Taiwanese patients with Brugada Syndrome

Abstract Background Brugada Syndrome (BrS) is a life-threatening arrhythmia disease primarily affecting young men. Many genetic studies have been conducted mainly in Caucasian BrS patients to identify potential genetic drivers. SCN5A mutations are less common in Asian BrS patients (around 10%) compared to Caucasian patients (20-30%). This means the pathogenic process of more than 70% of BrS patients remains unclear. With the advancement in NGS technologies, we can conduct genome-wide screening of DNA variants in BrS patients efficiently. Purpose Since less than 30% of the BrS patients have known genetic drivers for the disease, we aimed to identify more DNA variants in BrS patients by using the whole exome sequencing (WES) approach. Furthermore, we evaluated the landscape of DNA variants in the two groups of BrS patients according to whether they have SCN5A mutations or not. Methods A total of 201 BrS patients were recruited for the WES experiments. After obtaining the raw sequencing data, we performed the quality check procedures using the multiQC and Trimmomatic softwares (Figure 1). For reads passing the quality check, the BWA algorithm was used for the alignment. Next, the samtools, bamtools, and Picard methods were used to remove duplicates and perform the sorting. Subsequently, those sorted sequencing files were used to call the DNA variants by using the GATK approach and annotate them using the ANNOVAR method. The 201 BrS patients were classified into two different groups based on their mutation status of SCN5A, presentation of sudden cardiac death (SCD), and SCD along with syncope. The gene-based algorithm, SKAT, was used to analyze the summarized effects of DNA variants located in a single gene. For each DNA variant, Fisher's exact test was used to compare the allele frequency in the BrS patients under their groupings. Results The WES approach in the 201 BrS patients revealed a total of 205,454 variants, which were further categorized into two groups, common variants (51,867) and rare variants (153,587), based on their allele frequencies (5%). The SKAT algorithm was performed in the 30 genes that have been previously reported to be associated with BrS. No significant differences were observed in any gene in BrS patients for the common variants. Intriguingly, KCNJ8, SCN1B, ZFPM2, MAPRE2, and KCNE5 showed significant differences in allele frequencies between the SCN5A+ and SCN5A- patients (Figure 2). Fisher's exact test further revealed that three DNA variants (chr19:35524980, chr12:21926440, chr6:126210346) were significantly enriched in SCN5A+ patients. The 5 DNA variants located in ZFPM2 showed a linkage disequilibrium block and were enriched in SCN5A+ patients. Conclusions The WES approach in BrS patients revealed DNA variants that were reported in previous studies. Several DNA variants were significantly enriched in SCN5A+ patients, suggesting that the genome-wide mutation landscape was different in SCN5A+ and SCN5A- BrS patients.Figure 1Figure 2

Tumor organoids improve mutation detection of pancreatic ductal adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) presents challenges in detecting somatic mutations due to its complex cellular composition. This study investigated the utility of patient-derived organoids (PDOs) to overcome these obstacles and enhance somatic mutation identification. Surgically resected PDAC tumors and their paired PDOs from 21 patients were examined. Whole-exome sequencing (WES) of tumor tissue, organoids, and peripheral blood mononuclear cells was performed to identify somatic mutations. Our findings demonstrate that PDOs retained about 80% of the somatic mutations from the original tumors, showing high concordance in mutation types. PDOs exhibited increased tumor purity and uncovered key driver mutations, aiding in identifying clinically relevant genomic alterations. Moreover, eight cycles of FOLFIRINOX treatment did not significantly alter the mutational landscape at the DNA level, indicating the stability of the mutational profile after therapeutic pressure in patients. In conclusion, PDOs are potentially important tools for exploring the somatic mutational landscape of PDAC. While they can reveal mutations that may be challenging to detect through traditional biopsy sequencing due to the inherently low tumor purity of PDAC, it is important to note that PDOs may not always fully recapitulate all mutations found in primary tumors. Despite this limitation, PDOs can still offer critical insights into the genomic complexities of PDAC, which is crucial for the development of personalized vaccines and therapies for this disease.

Integration of transcriptomics and machine learning for insights into breast cancer: exploring lipid metabolism and immune interactions

BackgroundBreast cancer (BRCA) represents a substantial global health challenge marked by inadequate early detection rates. The complex interplay between the tumor immune microenvironment and fatty acid metabolism in BRCA requires further investigation to elucidate the specific role of lipid metabolism in this disease.MethodsWe systematically integrated nine machine learning algorithms into 184 unique combinations to develop a consensus model for lipid metabolism-related prognostic genes (LMPGS). Additionally, transcriptomics analysis provided a comprehensive understanding of this prognostic signature. Using the ESTIMATE method, we evaluated immune infiltration among different risk subgroups and assessed their responsiveness to immunotherapy. Tailored treatments were screened for specific risk subgroups. Finally, we verified the expression of key genes through in vitro experiments.ResultsWe identified 259 differentially expressed genes (DEGs) related to lipid metabolism through analysis of the cancer genome atlas program (TCGA) database. Subsequently, via univariate Cox regression analysis and C-index analysis, we developed an optimal machine learning algorithm to construct a 21-gene LMPGS model. We used optimal cutoff values to divide the lipid metabolism prognostic gene scores into two groups according to high and low scores. Our study revealed distinct biological functions and mutation landscapes between high-scoring and low-scoring patients. The low-scoring group presented a greater immune score, whereas the high-scoring group presented enhanced responses to both immunotherapy and chemotherapy drugs. Single-cell analysis highlighted significant upregulation of CPNE3 in epithelial cells. Moreover, by employing molecular docking, we identified niclosamide as a potential targeted therapeutic drug. Finally, our experiments demonstrated high expression of MTMR9 and CPNE3 in BRCA and their significant correlation with prognosis.ConclusionBy employing bioinformatics and diverse machine learning algorithms, we successfully identified genes associated with lipid metabolism in BRCA and uncovered potential therapeutic agents, thereby offering novel insights into the mechanisms and treatment strategies for BRCA.

Disulfidptosis-related subtype and prognostic signature in prostate cancer

BackgroundDisulfidptosis refers to cell death caused by the accumulation and bonding of disulfide in the cytoskeleton protein of SLC7A11-high level cells under glucose deprivation. However, the role of disulfidptosis-related genes (DRGs) in prostate cancer (PCa) classification and regulation of the tumor microenvironment remains unclear.MethodsFirstly, we analyzed the expression and mutation landscape of DRGs in PCa. We observed the expression levels of SLC7A11 in PCa cells through in vitro experiments and assessed the inhibitory effect of the glucose transporter inhibitor BAY-876 on SLC7A11-high cells using CCK-8 assay. Subsequently, we performed unsupervised clustering of the PCa population and analyzed the differentially expressed genes (DEGs) between clusters. Using machine learning techniques to select a minimal gene set and developed disulfidoptosis-related risk signatures for PCa. We analyzed the tumor immune microenvironment and the sensitivity to immunotherapy in different risk groups. Finally, we validated the accuracy of the prognostic signatures genes using single-cell sequencing, qPCR, and western blot.ResultsAlthough SLC7A11 can increase the migration ability of tumor cells, BAY-876 effectively suppressed the viability of prostate cancer cells, particularly those with high SLC7A11 expression. Based on the DRGs, PCa patients were categorized into two clusters (A and B). The risk label, consisting of a minimal gene set derived from DEGs, included four genes. The expression levels of these genes in PCa were initially validated through in vitro experiments, and the accuracy of the risk label was confirmed in an external dataset. Cluster-B exhibited higher expression levels of DRG, representing lower risk, better prognosis, higher immune cell infiltration, and greater sensitivity to immune checkpoint blockade, whereas Cluster A showed the opposite results. These findings suggest that DRGs may serve as targets for PCa classification and treatment. Additionally, we constructed a nomogram that incorporates DRGs and clinical pathological features, providing clinicians with a quantitative method to assess the prognosis of PCa patients.ConclusionThis study analyzed the potential connection between disulfidptosis and PCa, and established a prognostic model related to disulfidptosis, which holds promise as a valuable tool for the management and treatment of PCa patients.

Somatic mutation rates scale with time not growth rate in long-lived tropical trees

The rates of appearance of new mutations play a central role in evolution. However, mutational processes in natural environments and their relationship with growth rates are largely unknown, particular in tropical ecosystems with high biodiversity. Here, we examined the somatic mutation landscapes of two tropical trees, Shorea laevis (slow-growing) and S. leprosula (fast-growing), in central Borneo, Indonesia. Using newly constructed genomes, we identified a greater number of somatic mutations in tropical trees than in temperate trees. In both species, we observed a linear increase in the number of somatic mutations with physical distance between branches. However, we found that the rate of somatic mutation accumulation per meter of growth was 3.7-fold higher in S. laevis than in S. leprosula. This difference in the somatic mutation rate was scaled with the slower growth rate of S. laevis compared to S. leprosula, resulting in a constant somatic mutation rate per year between the two species. We also found that somatic mutations are neutral within an individual, but those mutations transmitted to the next generation are subject to purifying selection. These findings suggest that somatic mutations accumulate with absolute time and older trees have a greater contribution towards generating genetic variation.

Somatic mutation rates scale with time not growth rate in long-lived tropical trees.

The rates of appearance of new mutations play a central role in evolution. However, mutational processes in natural environments and their relationship with growth rates are largely unknown, particular in tropical ecosystems with high biodiversity. Here, we examined the somatic mutation landscapes of two tropical trees, Shorea laevis (slow-growing) and S. leprosula (fast-growing), in central Borneo, Indonesia. Using newly constructed genomes, we identified a greater number of somatic mutations in tropical trees than in temperate trees. In both species, we observed a linear increase in the number of somatic mutations with physical distance between branches. However, we found that the rate of somatic mutation accumulation per meter of growth was 3.7-fold higher in S. laevis than in S. leprosula. This difference in the somatic mutation rate was scaled with the slower growth rate of S. laevis compared to S. leprosula, resulting in a constant somatic mutation rate per year between the two species. We also found that somatic mutations are neutral within an individual, but those mutations transmitted to the next generation are subject to purifying selection. These findings suggest that somatic mutations accumulate with absolute time and older trees have a greater contribution towards generating genetic variation.

ScTML: a pan-cancer single-cell landscape of multiple mutation types.

Investigating mutations, including single nucleotide variations (SNVs), gene fusions, alternative splicing and copy number variations (CNVs), is fundamental to cancer study. Recent computational methods and biological research have demonstrated the reliability and biological significance of detecting mutations from single-cell transcriptomic data. However, there is a lack of a single-cell-level database containing comprehensive mutation information in all types of cancer. Establishing a single-cell mutation landscape from the huge emerging single-cell transcriptomic data can provide a critical resource for elucidating the mechanisms of tumorigenesis and evolution. Here, we developed scTML (http://sctml.xglab.tech/), the first database offering a pan-cancer single-cell landscape of multiple mutation types. It includes SNVs, insertions/deletions, gene fusions, alternative splicing and CNVs, along with gene expression, cell states and other phenotype information. The data are from 74 datasets with 2 582 633 cells, including 35 full-length (Smart-seq2) transcriptomic single-cell datasets (all publicly available data with raw sequencing files), 23 datasets from 10X technology and 16 spatial transcriptomic datasets. scTML enables users to interactively explore multiple mutation landscapes across tumors or cell types, analyze single-cell-level mutation-phenotype associations and detect cell subclusters of interest. scTML is an important resource that will significantly advance deciphering intra-tumor and inter-tumor heterogeneity, and how mutations shape cell phenotypes.

Single-cell sequencing technology to characterize stem T-cell subpopulations in acute T-lymphoblastic leukemia and the role of stem T-cells in the disease process.

Precursor T-cell acute lymphoblastic leukemia (Pre-T ALL) is a malignant neoplastic disease in which T-cells proliferate in the bone marrow. Single-cell sequencing technology could identify characteristic cell types, facilitating the study of the therapeutic mechanisms in Pre-T ALL. The single-cell sequencing data (scRNA-seq) of Pre-T ALL were obtained from public databases. Key immune cell subpopulations involved in the progression of Pre-T ALL were identified by clustering and annotating the cellular data using AUCell. Next, pseudo-temporal analysis was performed to identify the differentiation trajectories of immune cell subpopulations using Monocle. Copy number mutation landscape of cell subpopulations was characterized by inferCNV. Finally, cellphoneDB was used to analyze intercellular communication relationships. A total of 10 cellular subpopulations were classified, with Pre-T ALL showing a higher proportion of NK/T cells. NK/T cells were further clustered into two subpopulations. Stem T cells showed a high expression of marker genes related to hematopoietic stem cells, Naive T cells had a high expression of CCR7, CCR7, RCAN3, and NK cells high-expressed KLRD1, TRDC. The cell proliferation was reduced and the activation of T cell was increased during the differentiation of stem T cells to Naive T cells. We observed interaction between stem T cells with dendritic cells such as CD74-COPA, CD74-MIF as well as co-inhibition-related interactions such as LGALS9-HAVCR2, TGFB1-TGFBR3. Stem T cells were involved in the development of Pre-T-ALL through the regulatory effects of transcription factors (TFs) KLF2 and FOS and multiple ligand-receptor pairs.

Comprehensive mutational profiling identifies new driver events in cutaneous leiomyosarcoma.

Cutaneous leiomyosarcoma (cLMS) is a rare soft tissue neoplasm, showing smooth muscle differentiation, that arises from the mesenchymal cells of the dermis. To-date, genetic investigation of these tumours has involved studies with small sample sizes and limited analyses that identified recurrent somatic mutations in RB1 and TP53, copy number gain of MYCOD and IGF1R, and copy number loss of PTEN. To better understand the molecular pathogenesis of cLMS, we comprehensively explored the mutational landscape of these rare tumours to identify candidate driver events. In this retrospective, multi-institutional study, we performed whole-exome sequencing and RNA sequencing on 38 cases of cLMS. TP53 and RB1 were identified as significantly mutated, thus, represent validated driver genes of cLMS. COSMIC mutational signatures SBS7a/b and DBS1 were recurrent, thus, ultraviolet light exposure may be an aetiological factor driving cLMS. Analysis of significantly recurrent somatic copy number alterations, which represent candidate driver events, found focal (<10Mb) deletions encompassing TP53 and KDM6B, and amplifications encompassing ZMYM2, MYOCD, MAP2K4 and NCOR1. A larger (24 Mb) recurrent deletion encompassing CYLD was also identified as significant. Significantly recurrent broad copy number alterations, involving at least half of a chromosome arm, included deletions of 6p/q, 10p/q, 11q, 12q, 13q and 16p/q, and amplification of 15q. Notably PTEN is located on 10q, RB1 on 13q and IGFR1 on 15q. Fusion gene analysis identified recurrent CRTC1/3::MAML2 fusions, as well as many novel fusions in individual samples. Our analysis of the largest number of cLMS cases to-date highlights the importance of large cohort sizes and the exploration beyond small targeted gene panels when performing molecular analyses, as it allowed a comprehensive exploration of the mutational landscape of these tumours and identification of novel candidate driver events. It also uniquely afforded the opportunity to compare the molecular phenotype of cLMS with LMS of other tissue types, such as uterine and soft tissue LMS. Given that molecular profiling has resulted in the development of novel targeted treatment approaches for uterine and soft tissue LMS, our study now allows the same opportunities to become available for patients with cLMS.

Analyzing the Mutational Landscape of Prostate Cancer Susceptibility Genes through Next-Generation Sequencing (NGS) Analysis

Prostate cancer, a significant public health concern, exhibits a complex genetic landscape influenced by a variety of susceptibility genes. This study deciphers the intricate genetic makeup of prostate cancer by examining mutations in 14 prostate cancer susceptibility genes. The Next-Generation Sequencing (NGS) approach was employed on a cohort of 45 prostate cancer patients to detect mutations. The gnomAD database was used to analyze mutational frequencies in the Asian population suffering from prostate cancer. The cBioPortal database was employed to check the presence of observed mutations in The Cancer Genome Atlas (TCGA) dataset. In addition, this study employed RT-qPCR and Immunohistochemistry (IHC) techniques were utilized to check the functional consequences of the observed pathogenic mutations. Finally, Metascape and DrugBank resources were used for gene enrichment and drug prediction analyses. Results of the NGS analysis revealed a total of 29 mutations (pathogenic and benign) within the examined prostate cancer cohort, distributed across four key genes (BRCA1/2, TP53, and PMS2) out of the total analyzed 14 genes. Pathogenic mutations in BRCA1/2 and TP53 genes are of particular interest due to their fundamental roles in DNA repair, cell cycle regulation, and tumor suppression. Functional consequence analyses (RT-qPCR and IHC) demonstrated the down-regulation of BRCA1/2 and TP53 genes in prostate cancer samples with pathogenic mutations, reinforcing the disruption of their tumor suppressor roles. Lastly, drug prediction analysis uncovered promising therapeutic options by identifying drugs capable of enhancing the mRNA expression of these genes. This opens new avenues for tailored treatment strategies aimed at restoring normal cellular functions of the BRCA1/2 and TP53 genes. In conclusion, this study provides a comprehensive view of genetic mutations in prostate cancer susceptibility genes, ranging from benign to pathogenic. It emphasizes the genetic complexity of prostate cancer and offers insights into potential mechanisms driving this malignancy. These findings lay the groundwork for further research, personalized treatment approaches, and enhanced clinical management of prostate cancer.

GRNAde: Geometric Deep Learning for 3D RNA inverse design.

Computational RNA design tasks are often posed as inverse problems, where sequences are designed based on adopting a single desired secondary structure without considering 3D geometry and conformational diversity. We introduce gRNAde, a geometric RNA design pipeline operating on 3D RNA backbones to design sequences that explicitly account for structure and dynamics. gRNAde uses a multi-state Graph Neural Network and autoregressive decoding to generates candidate RNA sequences conditioned on one or more 3D backbone structures where the identities of the bases are unknown. On a single-state fixed backbone re-design benchmark of 14 RNA structures from the PDB identified by Das et al. (2010), gRNAde obtains higher native sequence recovery rates (56% on average) compared to Rosetta (45% on average), taking under a second to produce designs compared to the reported hours for Rosetta. We further demonstrate the utility of gRNAde on a new benchmark of multi-state design for structurally flexible RNAs, as well as zero-shot ranking of mutational fitness landscapes in a retrospective analysis of a recent ribozyme. Open source code: github.com/chaitjo/geometric-rna-design.

Genomic landscape and preclinical models of angiosarcoma.

Angiosarcoma is a cancer that develops in blood or lymphatic vessels that presents a significant clinical challenge due to its rarity and aggressive features. Clinical outcomes have not improved in decades, highlighting a need for innovative therapeutic strategies to treat the disease. Genetically, angiosarcomas exhibit high heterogeneity and complexity with many recurrent mutations. However, recent studies have identified some common features within anatomic and molecular subgroups. To identify potential therapeutic vulnerabilities, it is essential to understand and integrate the mutational landscape of angiosarcoma with the models that exist to study the disease. In this review, we will summarize the insights gained from reported genomic alterations in molecular and anatomic subtypes of angiosarcoma, discuss several potential actionable targets, and highlight the preclinical disease models available in the field.

DeepNeoAG: Neoantigen epitope prediction from melanoma antigens using a synergistic deep learning model combining protein language models and multi-window scanning convolutional neural networks

Neoantigens, derived from tumor-specific mutations, play a crucial role in eliciting anti-tumor immune responses and have emerged as promising targets for personalized cancer immunotherapy. Accurately identifying neoantigens from a vast pool of potential candidates is crucial for developing effective therapeutic strategies. This study presents a novel deep learning model that leverages the power of protein language models (PLMs) and multi-window scanning convolutional neural networks (CNNs) to predict neoantigens from normal tumor antigens with high accuracy. In this study, we present DeepNeoAG, a novel framework combines the global sequence-level information captured by a pre-trained PLM with the local sequence-based information features extracted by a multi-window scanning CNN, enabling a comprehensive representation of the protein's mutational landscape. We demonstrate the superior performance of DeepNeoAG compared to existing methods and highlight its potential to accelerate the development of personalized cancer immunotherapies.

B-214 Mutational Landscapes of Acute Myeloid Leukemia and Myelodysplastic Syndromes: A Database-Driven Comprehensive Myeloid Panel Analysis of DNA Variants and Gene Fusions

Abstract Background Myeloid malignancies, including Acute Myeloid Leukemia (AML) and Myelodysplastic Syndromes (MDS), are characterized by a complex spectrum of genetic mutations. These mutations play a critical role in the pathogenesis of the diseases and are essential for accurate diagnosis, prognosis, and the development of personalized treatment strategies. This study aimed to compare the molecular profiles of AML and MDS by analyzing a database of comprehensive NGS myeloid panel results, which concurrently evaluates DNA variants and gene fusions, to identify distinct mutational patterns and differences between these conditions. Methods A retrospective analysis was conducted on a de-identified dataset from myeloid panel tests applied at the time of diagnosis for myeloid malignancies during 2023. Cases indicated for AML and MDS testing were identified, and relevant demographic, specimen, and test outcome data were retrieved from the database. The dataset was compiled from data obtained using the AmpliSeq for Illumina Myeloid Panel, performed on the MiSeq system (both from Illumina). This panel targets 40 DNA genes (17 full-length, 23 hotspots) and 29 RNA fusion genes, covering 629 potential fusions, with a limit of detection at a 5% allele frequency. Variants were classified according to the CAP/ASCO/AMP guidelines on the Frankling (Genoox) platform. Demographic, specimen, and test outcome data—including gene fusions, mutated genes grouped by biological function (epigenetic regulators, signaling and kinase pathway genes, tumor suppressors, transcription factors, and RNA spliceosome), and DNA variants (limited to tiers 1 and 2)—were analyzed and compared using the Mann-Whitney U test or chi-square test, as appropriate. Results A total of 106 cases were evaluated, comprising 53 AML (31 females) and 53 MDS (25 females). The average age was 56.15±21.62 years for AML and 64.79±18.18 years for MDS (p=0.026). Blood sample distribution was 26 for AML and 33 for MDS (p=0.24). Fusion genes, found exclusively in AML, appeared in 34% (18/53) of cases (p&amp;lt;0.0001), with the most common being CBFB::MYH11 (50%, 9/18) and RUNX1::RUNX1T1 (22%, 4/18). Somatic DNA variants were more prevalent in AML cases at 87% (46/53) compared to 64% (34/53) in MDS cases (p=0.012). There were 87 Tier 1/2 DNA variants in AML and 53 in MDS, with a median (min-max) of 2 (1-7) variants per case for AML and 1 (1-5) for MDS (p=0.02). For AML versus MDS, the distribution of DNA variants by mutated gene biological function was as follows: epigenetic regulators [29.88% (26/87) versus 33.96% (18/53), p=0.75], signaling and kinase pathway genes [31.03% (27/87) versus 5.66% (3/53), p=0.0008], tumor suppressors [16.09% (14/87) versus 11.32% (6/53), p=0.59], transcription factors [17.24% (15/87) versus 20.75% (11/53), p=0.76], and RNA spliceosome [5.75% (5/87) versus 28.30% (15/53), p=0.0005]. Conclusions Our study underscored distinct molecular profiles for AML and MDS when both DNA variants and gene fusions are analyzed. AML cases were younger, displayed a higher frequency of gene fusions, and a greater diversity of DNA variants compared to MDS. AML cases also showed a higher prevalence of mutations in signaling and kinase pathway genes. MDS was characterized by a higher prevalence of mutations in RNA spliceosome genes.

Molecular characterization and biomarker identification in paediatric B-cell acute lymphoblastic leukaemia.

B-cell acute lymphoblastic leukaemia (B-ALL) is the most prevalent hematologic malignancy in children and a leading cause of mortality. Managing B-ALL remains challenging due to its heterogeneity and relapse risk. This study aimed to delineate the molecular features of paediatric B-ALL and explore the clinical utility of circulating tumour DNA (ctDNA). We analysed 146 patients with paediatric B-ALL who received systemic chemotherapy. The mutational landscape was profiled in bone marrow (BM) and plasma samples using next-generation sequencing. Minimal residual disease (MRD) testing on day 19 of induction therapy evaluated treatment efficacy. RNA sequencing identified gene fusions in 61% of patients, including 37 novel fusions. Specifically, the KMT2A-TRIM29 novel fusion was validated in a boy who responded well to initial therapy but relapsed after 1 year. Elevated mutation counts and maximum variant allele frequency in baseline BM were associated with significantly poorer chemotherapy response (p = 0.0012 and 0.028, respectively). MRD-negative patients exhibited upregulation of immune-related pathways (p < 0.01) and increased CD8+ T cell infiltration (p = 0.047). Baseline plasma ctDNA exhibited high mutational concordance with the paired BM samples and was significantly associated with chemotherapy efficacy. These findings suggest that ctDNA and BM profiling offer promising prognostic insights for paediatric B-ALL management.

Prediction of Bladder Cancer Prognosis and Immune Microenvironment Assessment Using Machine Learning and Deep Learning Models

Bladder cancer (BCa) is a heterogeneous malignancy characterized by distinct immune subtypes, primarily due to differences in tumor-infiltrating immune cells and their functional characteristics. Therefore, understanding the tumor immune microenvironment (TIME) landscape in BCa is crucial for prognostic prediction and guiding precision therapy. In this study, we integrated 10 machine learning algorithms to develop an immune-related machine learning signature (IRMLS) and subsequently created a deep learning model to detect the IRMLS subtype based on pathological images. The IRMLS proved to be an independent prognostic factor for overall survival (OS) and demonstrated robust and stable performance(p<0.01). The high-risk group exhibited an immune-inflamed phenotype, associated with poorer prognosis and higher levels of immune cell infiltration. We further investigated the cancer immune cycle and mutation landscape within the IRMLS model, confirming that the high-risk group is more sensitive to immune checkpoint immunotherapy (ICI) and adjuvant chemotherapy with cisplatin(p=2.8e-10), docetaxel(p=8.8e-13), etoposide(p=1.8e-07), and paclitaxel(p=6.2e-13). In conclusion, we identified and validated a machine learning-based molecular characteristic, IRMLS, which reflects various aspects of the BCa biological process and offers new insights into personalized precision therapy for BCa patients.

Distinct landscape and clinical implications of therapy-related clonal hematopoiesis.

Therapy-related clonal hematopoiesis (t-CH) is defined as clonal hematopoiesis detected in individuals previously treated with chemotherapy and/or radiation therapy. With the increased use of genetic analysis in oncological care, the detection of t-CH among cancer patients is becoming increasingly common. t-CH arises through the selective bottleneck imposed by chemotherapies and potentially through direct mutagenesis from chemotherapies, resulting in a distinct mutational landscape enriched with mutations in DNA damage-response pathway genes such as TP53, PPM1D, and CHEK2. Emerging evidence sheds light on the mechanisms of t-CH development and potential strategies to mitigate its emergence. Due to its unique characteristics that predominantly affect cancer patients, t-CH has clinical implications distinct from those of CH in the general population. This Review discusses the potential mechanisms of t-CH development, its mutational landscape, mutant-drug relationships, and its clinical significance. We highlight the distinct nature of t-CH and call for intensified research in this field.