Cancer genome landscapes

Machine learning optimized DriverDetect software for high precision prediction of deleterious mutations in human cancers

The detection of cancer-driving mutations is important for understanding cancer pathology and therapeutics development. Prediction tools have been created to streamline the computation process. However, most tools available have heterogeneous sensitivity or specificity. We built a machine learning-derived algorithm, DriverDetect that combines the outputs of seven pre-existing tools to improve the prediction of candidate driver cancer mutations. The algorithm was trained with cancer gene-specific mutation datasets of cancer patients to identify cancer drivers. DriverDetect performed better than the individual tools or their combinations in the validation test. It has the potential to incorporate future novel prediction algorithms and can be retrained with new datasets, offering an expanded application to pan-cancer analysis for cross-cancer study. (115 words).

Combination of bulk RNA and single-cell sequencing unveils PANoptosis-related immunological ecology hallmarks and classification for clinical decision-making in hepatocellular carcinoma

PANoptosis is engaged in the program of immune response and carcinogenicity. Nonetheless, the actual impacts of PANoptosis on clinical management and oncology immunity in hepatocellular carcinoma (HCC) are not fully grasped. RNA-seq-derived computations were conducted to sort out the molecular subtypes and elucidate the disparities based on PANoptosis molecules. Single-cell sequencing (scRNA-seq) tools including Cytotrace and Addmodulescore were extracted to characterize diversification potency and quantify the PANoptosis motion. Transcriptional factors were inferred by the pySCENIC package and Cellchat program scrutinized the intercellular exchange across cell compartments. The PANoptosis score system originated by incorporating 10 machine learning algorithms and 101 compositions to project clinical results and deteriorate tendencies. Circulatory PANoptosis-associated protein HSP90AA1 was determined by enzyme-linked immunosorbent assay (ELISA). HCC individuals could be categorized into low- and high-PANoptosis groups with diverse biogenic and pharmacotherapy heterogeneity. Individuals in the elevated PANoptosis subtype were characterized as "hot tumor" conveying the increased presence of immunogenicity while reiterating an explicit negative connection with tumor stemness. Compared to immune and stromal cells, cancerous cells showcased decreased PANoptosis and heightened PANoptosis malignant cell subgroups might be tied to a substantial level of genomic expression of SREBF2, JUND, GATAD1, ZBTB20, SMAD5 and implied a more aggressive potential. The PANoptosis index, derived from machine learning, has been established to provide succinct frameworks for predicting outcomes and clarified the noteworthy utility of conventional regimens, as the differentiated power of HCC occurred together with vascular invasion and hepatocellular adenoma (HCA). The experiment confirmed that the circulating HSP90AA1 was aberrantly augmented in HCC patients, thus demonstrating its potential as a discriminatory biomarker. We systematically deciphered the molecular and immune ecosystem traits of PANoptosis in bulk and scRNA-seq degrees, which may deliver advantageous insights for customized treatment, awareness of the pathological process and prognosis scrutiny for HCC patients.

Self-Tumor Antigens in Solid Tumors Turned into Vaccines by α-gal Micelle Immunotherapy

A major reason for the failure of the immune system to detect tumor antigens (TAs) is the insufficient uptake, processing, and presentation of TAs by antigen-presenting cells (APCs). The immunogenicity of TAs in the individual patient can be markedly increased by the in situ targeting of tumor cells for robust uptake by APCs, without the need to identify and characterize the TAs. This is feasible by the intra-tumoral injection of α-gal micelles comprised of glycolipids presenting the carbohydrate-antigen "α-gal epitope" (Galα1-3Galβ1-4GlcNAc-R). Humans produce a natural antibody called "anti-Gal" (constituting ~1% of immunoglobulins), which binds to α-gal epitopes. Tumor-injected α-gal micelles spontaneously insert into tumor cell membranes, so that multiple α-gal epitopes are presented on tumor cells. Anti-Gal binding to these epitopes activates the complement system, resulting in the killing of tumor cells, and the recruitment of multiple APCs (dendritic cells and macrophages) into treated tumors by the chemotactic complement cleavage peptides C5a and C3a. In this process of converting the treated tumor into a personalized TA vaccine, the recruited APC phagocytose anti-Gal opsonized tumor cells and cell membranes, process the internalized TAs and transport them to regional lymph-nodes. TA peptides presented on APCs activate TA-specific T cells to proliferate and destroy the metastatic tumor cells presenting the TAs. Studies in anti-Gal-producing mice demonstrated the induction of effective protection against distant metastases of the highly tumorigenic B16 melanoma following injection of natural and synthetic α-gal micelles into primary tumors. This treatment was further found to synergize with checkpoint inhibitor therapy by the anti-PD1 antibody. Phase-1 clinical trials indicated that α-gal micelle immunotherapy is safe and can induce the infiltration of CD4+ and CD8+ T cells into untreated distant metastases. It is suggested that, in addition to converting treated metastases into an autologous TA vaccine, this treatment should be considered as a neoadjuvant therapy, administering α-gal micelles into primary tumors immediately following their detection. Such an immunotherapy will convert tumors into a personalized anti-TA vaccine for the period prior to their resection.

SSCI: Self-Supervised Deep Learning Improves Network Structure for Cancer Driver Gene Identification

The pathogenesis of cancer is complex, involving abnormalities in some genes in organisms. Accurately identifying cancer genes is crucial for the early detection of cancer and personalized treatment, among other applications. Recent studies have used graph deep learning methods to identify cancer driver genes based on biological networks. However, incompleteness and the noise of the networks will weaken the performance of models. To address this, we propose a cancer driver gene identification method based on self-supervision for graph convolutional networks, which can efficiently enhance the structure of the network and further improve predictive accuracy. The reliability of SSCI is verified by the area under the receiver operating characteristic curves (AUROC), the area under the precision-recall curves (AUPRC), and the F1 score, with respective values of 0.966, 0.964, and 0.913. The results show that our method can identify cancer driver genes with strong discriminative power and biological interpretability.

Novel prognostic signature for hepatocellular carcinoma using a comprehensive machine learning framework to predict prognosis and guide treatment

Background

Hepatocellular carcinoma (HCC) is highly aggressive, with delayed diagnosis, poor prognosis, and a lack of comprehensive and accurate prognostic models to assist clinicians. This study aimed to construct an HCC prognosis-related gene signature (HPRGS) and explore its clinical application value.

Methods

TCGA-LIHC cohort was used for training, and the LIRI-JP cohort and HCC cDNA microarray were used for validation. Machine learning algorithms constructed a prognostic gene label for HCC. Kaplan-Meier (K-M), ROC curve, multiple analyses, algorithms, and online databases were used to analyze differences between high- and low-risk populations. A nomogram was constructed to facilitate clinical application.

Results

We identified 119 differential genes based on transcriptome sequencing data from five independent HCC cohorts, and 53 of these genes were associated with overall survival (OS). Using 101 machine learning algorithms, the 10 most prognostic genes were selected. We constructed an HCC HPRGS with four genes (SOCS2, LCAT, ECT2, and TMEM106C). Good predictive performance of the HPRGS was confirmed by ROC, C-index, and K-M curves. Mutation analysis showed significant differences between the low- and high-risk patients. The low-risk group had a higher response to transcatheter arterial chemoembolization (TACE) and immunotherapy. Treatment response of high- and low-risk groups to small-molecule drugs was predicted. Linifanib was a potential drug for high-risk populations. Multivariate analysis confirmed that HPRGS were independent prognostic factors in TCGA-LIHC. A nomogram provided a clinical practice reference.

Conclusion

We constructed an HPRGS for HCC, which can accurately predict OS and guide the treatment decisions for patients with HCC.

Divergent iron regulatory states contribute to heterogeneity in breast cancer aggressiveness

Contact with dense collagen I (Col1) can induce collective invasion of triple negative breast cancer (TNBC) cells and transcriptional signatures linked to poor patient prognosis. However, this response is heterogeneous and not well understood. Using phenotype-guided sequencing analysis of invasive vs. noninvasive subpopulations, we show that these two phenotypes represent opposite sides of the iron response protein 1 (IRP1)-mediated response to cytoplasmic labile iron pool (cLIP) levels. Invasive cells upregulate iron uptake and utilization machinery characteristic of a low cLIP response, which includes contractility regulating genes that drive migration. Non-invasive cells upregulate iron sequestration machinery characteristic of a high cLIP response, which is accompanied by upregulation of actin sequestration genes. These divergent IRP1 responses result from Col1-induced transient expression of heme oxygenase I (HO-1), which cleaves heme and releases iron. These findings lend insight into the emerging theory that heme and iron fluxes regulate TNBC aggressiveness.

Insights into the A-C Mismatch Conformational Ensemble in Duplex DNA and its Role in Genetic Processes through a Structure-based Review

Knowing the conformational ensembles formed by mismatches is crucial for understanding how they are generated and repaired and how they contribute to genomic instability. Here, we review structural and energetic studies of the A-C mismatch in duplex DNA and use the information to identify critical conformational states in its ensemble and their significance in genetic processes. In the 1970s, Topal and Fresco proposed the A-C wobble stabilized by two hydrogen bonds, one requiring protonation of adenine-N1. Subsequent NMR and X-ray crystallography studies showed that the protonated A-C wobble was in dynamic equilibrium with a neutral inverted wobble. The mismatch was shown to destabilize duplex DNA in a sequence- and pH-dependent manner by 2.4-3.8 kcal/mol and to have an apparent pKa ranging between 7.2 and 7.7. The A-C mismatch conformational repertoire expanded as structures were determined for damaged and protein-bound DNA. These structures included Watson-Crick-like conformations forming through tautomerization of the bases that drive replication errors, the reverse wobble forming through rotation of the entire nucleotide proposed to increase the fidelity of DNA replication, and the Hoogsteen base-pair forming through the flipping of the adenine base which explained the unusual specificity of DNA polymerases that bypass DNA damage. Thus, the A-C mismatch ensemble encompasses various conformational states that can be selectively stabilized in response to environmental changes such as pH shifts, intermolecular interactions, and chemical modifications, and these adaptations facilitate critical biological processes. This review also highlights the utility of existing 3D structures to build ensemble models for nucleic acid motifs.

Human-mouse comparison of the multistage nature of radiation carcinogenesis in a mathematical model

Mouse models are vital for assessing risk from environmental carcinogens, including ionizing radiation, yet the interspecies difference in the dose response precludes direct application of experimental evidence to humans. Herein, we take a mathematical approach to delineate the mechanism underlying the human-mouse difference in radiation-related cancer risk. We used a multistage carcinogenesis model assuming a mutational action of radiation to analyze previous data on cancer mortality in the Japanese atomic bomb survivors and in lifespan mouse experiments. Theoretically, the model predicted that exposure will chronologically shift the age-related increase in cancer risk forward by a period corresponding to the time in which the spontaneous mutational process generates the same mutational burden as that the exposure generates. This model appropriately fitted both human and mouse data and suggested a linear dose response for the time shift. The effect per dose decreased with increasing age at exposure similarly between humans and mice on a per-lifespan basis (0.72- and 0.71-fold, respectively, for every tenth lifetime). The time shift per dose was larger by two orders of magnitude in humans (7.8 and 0.046 years per Gy for humans and mice, respectively, when exposed at ~35% of their lifetime). The difference was mostly explained by the two orders of magnitude difference in spontaneous somatic mutation rates between the species plus the species-independent radiation-induced mutation rate. Thus, the findings delineate the mechanism underlying the interspecies difference in radiation-associated cancer mortality and may lead to the use of experimental evidence for risk prediction in humans.

Integrating multi-omics and machine learning survival frameworks to build a prognostic model based on immune function and cell death patterns in a lung adenocarcinoma cohort

Introduction

The programmed cell death (PCD) plays a key role in the development and progression of lung adenocarcinoma. In addition, immune-related genes also play a crucial role in cancer progression and patient prognosis. However, further studies are needed to investigate the prognostic significance of the interaction between immune-related genes and cell death in LUAD.

Methods

In this study, 10 clustering algorithms were applied to perform molecular typing based on cell death-related genes, immune-related genes, methylation data and somatic mutation data. And a powerful computational framework was used to investigate the relationship between immune genes and cell death patterns in LUAD patients. A total of 10 commonly used machine learning algorithms were collected and subsequently combined into 101 unique combinations, and we constructed an immune-associated programmed cell death model (PIGRS) using the machine learning model that exhibited the best performance. Finally, based on a series of in vitro experiments used to explore the role of PSME3 in LUAD.

Results

We used 10 clustering algorithms and multi-omics data to categorize TCGA-LUAD patients into three subtypes. patients with the CS3 subtype had the best prognosis, whereas patients with the CS1 and CS2 subtypes had a poorer prognosis. PIGRS, a combination of 15 high-impact genes, showed strong prognostic performance for LUAD patients. PIGRS has a very strong prognostic efficacy compared to our collection. In conclusion, we found that PSME3 has been little studied in lung adenocarcinoma and may be a novel prognostic factor in lung adenocarcinoma.

Discussion

Three LUAD subtypes with different molecular features and clinical significance were successfully identified by bioinformatic analysis, and PIGRS was constructed using a powerful machine learning framework. and investigated PSME3, which may affect apoptosis in lung adenocarcinoma cells through the PI3K/AKT/Bcl-2 signaling pathway.

CD4+ T cells with convergent TCR recombination reprogram stroma and halt tumor progression in adoptive therapy

Cancers eventually kill hosts even when infiltrated by cancer-specific T cells. We examined whether cancer-specific T cell receptors of CD4+ T cells (CD4TCRs) from tumor-bearing hosts can be exploited for adoptive TCR therapy. We focused on CD4TCRs targeting an autochthonous mutant neoantigen that is only presented by stroma surrounding the MHC class II-negative cancer cells. The 11 most common tetramer-sorted CD4TCRs were tested using TCR-engineered CD4+ T cells. Three TCRs were characterized by convergent recombination for which multiple T cell clonotypes differed in their nucleotide sequences but encoded identical TCR α and β chains. These preferentially selected TCRs destroyed tumors equally well and halted progression through reprogramming of the tumor stroma. TCRs represented by single T cell clonotypes were similarly effective only if they shared CDR elements with preferentially selected TCRs in both α and β chains. Selecting candidate TCRs on the basis of these characteristics can help identify TCRs that are potentially therapeutically effective.

SJPedPanel: A Pan-Cancer Gene Panel for Childhood Malignancies to Enhance Cancer Monitoring and Early Detection

PURPOSE

The purpose of the study was to design a pan-cancer gene panel for childhood malignancies and validate it using clinically characterized patient samples.

EXPERIMENTAL DESIGN

In addition to 5,275 coding exons, SJPedPanel also covers 297 introns for fusions/structural variations and 7,590 polymorphic sites for copy-number alterations. Capture uniformity and limit of detection are determined by targeted sequencing of cell lines using dilution experiment. We validate its coverage by in silico analysis of an established real-time clinical genomics (RTCG) cohort of 253 patients. We further validate its performance by targeted resequencing of 113 patient samples from the RTCG cohort. We demonstrate its power in analyzing low tumor burden specimens using morphologic remission and monitoring samples.

RESULTS

Among the 485 pathogenic variants reported in RTCG cohort, SJPedPanel covered 86% of variants, including 82% of 90 rearrangements responsible for fusion oncoproteins. In our targeted resequencing cohort, 91% of 389 pathogenic variants are detected. The gene panel enabled us to detect ∼95% of variants at allele fraction (AF) 0.5%, whereas the detection rate is ∼80% at AF 0.2%. The panel detected low-frequency driver alterations from morphologic leukemia remission samples and relapse-enriched alterations from monitoring samples, demonstrating its power for cancer monitoring and early detection.

CONCLUSIONS

SJPedPanel enables the cost-effective detection of clinically relevant genetic alterations including rearrangements responsible for subtype-defining fusions by targeted sequencing of ∼0.15% of human genome for childhood malignancies. It will enhance the analysis of specimens with low tumor burdens for cancer monitoring and early detection.

Tracing the evolution of single-cell cancer 3D genomes: an atlas for cancer gene discovery

Although three-dimensional (3D) genome structures are altered in cancer cells, little is known about how these changes evolve and diversify during cancer progression. Leveraging genome-wide chromatin tracing to visualize 3D genome folding directly in tissues, we generated 3D genome cancer atlases of murine lung and pancreatic adenocarcinoma. Our data reveal stereotypical, non-monotonic, and stage-specific alterations in 3D genome folding heterogeneity, compaction, and compartmentalization as cancers progress from normal to preinvasive and ultimately to invasive tumors, discovering a potential structural bottleneck in early tumor progression. Remarkably, 3D genome architectures distinguish histologic cancer states in single cells, despite considerable cell-to-cell heterogeneity. Gene-level analyses of evolutionary changes in 3D genome compartmentalization not only showed compartment-associated genes are more homogeneously regulated, but also elucidated prognostic and dependency genes in lung adenocarcinoma and a previously unappreciated role for polycomb-group protein Rnf2 in 3D genome regulation. Our results demonstrate the utility of mapping the single-cell cancer 3D genome in tissues and illuminate its potential to identify new diagnostic, prognostic, and therapeutic biomarkers in cancer.

CDMPred: a tool for predicting cancer driver missense mutations with high-quality passenger mutations

Most computational methods for predicting driver mutations have been trained using positive samples, while negative samples are typically derived from statistical methods or putative samples. The representativeness of these negative samples in capturing the diversity of passenger mutations remains to be determined. To tackle these issues, we curated a balanced dataset comprising driver mutations sourced from the COSMIC database and high-quality passenger mutations obtained from the Cancer Passenger Mutation database. Subsequently, we encoded the distinctive features of these mutations. Utilizing feature correlation analysis, we developed a cancer driver missense mutation predictor called CDMPred employing feature selection through the ensemble learning technique XGBoost. The proposed CDMPred method, utilizing the top 10 features and XGBoost, achieved an area under the receiver operating characteristic curve (AUC) value of 0.83 and 0.80 on the training and independent test sets, respectively. Furthermore, CDMPred demonstrated superior performance compared to existing state-of-the-art methods for cancer-specific and general diseases, as measured by AUC and area under the precision-recall curve. Including high-quality passenger mutations in the training data proves advantageous for CDMPred's prediction performance. We anticipate that CDMPred will be a valuable tool for predicting cancer driver mutations, furthering our understanding of personalized therapy.

The small MAF transcription factor MAFG co-opts MITF to promote melanoma progression

Transcription factor deregulation potently drives melanoma progression by dynamically and reversibly controlling gene expression programs. We previously identified the small MAF family transcription factor MAFG as a putative driver of melanoma progression, prompting an in-depth evaluation of its role in melanoma. MAFG expression increases with human melanoma stages and ectopic MAFG expression enhances the malignant behavior of human melanoma cells in vitro, xenograft models, and genetic mouse models of spontaneous melanoma. Moreover, MAFG induces a melanoma phenotype switch from a melanocytic state to a more dedifferentiated state. Mechanistically, MAFG interacts with the lineage transcription factor MITF which is required for the pro-tumorigenic effects of MAFG. MAFG and MITF co-occupy numerous genomic sites and MAFG overexpression influences the expression of genes harboring binding sites for the MAFG~MITF complex. These results establish MAFG as a potent driver of melanomagenesis through dimerization with MITF and uncover an unappreciated mechanism of MITF regulation.

Cyclin B1: A potential prognostic and immunological biomarker in pan-cancer

Cyclin B1 (CCNB1) encodes a regulatory protein essential for the regulation of cell mitosis, particularly in controlling the G2/M transition phase of the cell cycle. Current research has implicated CCNB1 in the progression of various types of cancer, including gastric cancer, breast cancer, and non-small cell lung cancer. In this study, we conducted a pan-cancer analysis of CCNB1 to investigate its prognostic significance and immunological aspects. Our comprehensive investigation covered a wide range of analyses, including gene expression, promoter methylation, genetic alterations, immune infiltration, immune regulators, and enrichment studies. We utilized multiple databases and tools for this purpose, such as The Cancer Genome Atlas (TCGA), the Genotype-Tissue Expression (GTEx) project, the Human Protein Atlas (HPA), the University of Alabama at Birmingham CANcer data analysis Portal (UALCAN), the Gene Expression Profiling Interactive Analysis (GEPIA), the DNA Methylation Interactive Visualization Database (DNMIVD), the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING), Sangerbox, and cBioPortal. Data analyses were executed using GraphPad Prism software, R software, and various online tools. Our findings demonstrated a significant increase in CCNB1 expression across 28 cancer types. Elevated CCNB1 expression correlated with decreased overall survival (OS) in 11 cancer types and disease-free survival (DFS) in 12 cancer types. Additionally, DNA promoter methylation levels were significantly decreased in 14 cancer types. Furthermore, the study verified a significant association between CCNB1 expression and immune infiltration, immune modulators, microsatellite instability (MSI), and tumor mutational burden (TMB). Enrichment analysis indicated that CCNB1 is involved in multiple cellular pathways. Collectively, our results suggested that CCNB1 has the potential to serve as a valuable prognostic biomarker and may be a promising target for immunotherapy in various cancer types.

Single-cell transcriptional atlas of tumor-associated macrophages in breast cancer

BACKGROUND

The internal heterogeneity of breast cancer, notably the tumor microenvironment (TME) consisting of malignant and non-malignant cells, has been extensively explored in recent years. The cells in this complex cellular ecosystem activate or suppress tumor immunity through phenotypic changes, secretion of metabolites and cell-cell communication networks. Macrophages, as the most abundant immune cells within the TME, are recruited by malignant cells and undergo phenotypic remodeling. Tumor-associated macrophages (TAMs) exhibit a variety of subtypes and functions, playing significant roles in impacting tumor immunity. However, their precise subtype delineation and specific function remain inadequately defined.

METHODS

The publicly available single-cell transcriptomes of 49,141 cells from eight breast cancer patients with different molecular subtypes and stages were incorporated into our study. Unsupervised clustering and manual cell annotation were employed to accurately classify TAM subtypes. We then conducted functional analysis and constructed a developmental trajectory for TAM subtypes. Subsequently, the roles of TAM subtypes in cell-cell communication networks within the TME were explored using endothelial cells (ECs) and T cells as key nodes. Finally, analyses were repeated in another independent publish scRNA datasets to validate our findings for TAM characterization.

RESULTS

TAMs are accurately classified into 7 subtypes, displaying anti-tumor or pro-tumor roles. For the first time, we identified a new TAM subtype capable of proliferation and expansion in breast cancer-TUBA1B+ TAMs playing a crucial role in TAMs diversity and tumor progression. The developmental trajectory illustrates how TAMs are remodeled within the TME and undergo phenotypic and functional changes, with TUBA1B+ TAMs at the initial point. Notably, the predominant TAM subtypes varied across different molecular subtypes and stages of breast cancer. Additionally, our research on cell-cell communication networks shows that TAMs exert effects by directly modulating intrinsic immunity, indirectly regulating adaptive immunity through T cells, as well as influencing tumor angiogenesis and lymphangiogenesis through ECs.

CONCLUSIONS

Our study establishes a precise single-cell atlas of breast cancer TAMs, shedding light on their multifaceted roles in tumor biology and providing resources for targeting TAMs in breast cancer immunotherapy.

Simultaneous Detection of Collagen I Alpha II and Cytokeratin 19 mRNA by Multiplex qPCR in Liquid Biopsy in Diagnosis of Patients with Resectable Solid Tumors

The early detection of tumors is one of the key factors in increasing overall survival in cancer patients. A wide range of cancers still do not have a system of early diagnosis; therefore, the development of new non-invasive tools in this line is essential. Accordingly, the objective of our work was to develop a non-invasive screening method for the early detection of various carcinomas in plasma using a panel that combines two markers using RT-qPCR. A retrospective case-control study was conducted to develop a cancer screening test based on the detection of stromal and epithelial biomarkers (COL1A2 and KRT19) in plasma. The expression of biomarkers was evaluated using multiplex quantitative PCR applied to 47 cases with non-metastatic tumors and 13 control participants. For both biomarkers, a cut-off value was stablished using Youden's J index through ROC curve analysis and areas under the curve (AUC) were calculated. The plasma mRNA expression level of both biomarkers was significantly higher in diseased versus healthy patients. Moreover, ROC curve analysis showed an AUC value of 0.897 for the combined model. This model also resulted in a cutoff value of 0.664, as well as a sensitivity of 83% and a specificity of 84.6%. These results suggest that the plasma expression levels of COL1A2 and KRT19 could a have potential role in detecting various types of cancer at the early stages. The combined analysis of both stromal and epithelial biomarkers would provide a non-invasive screening method that would allow us to differentiate patients with an active neoplastic process.

Multi-omics landscape of Interferon-stimulated gene OASL reveals a potential biomarker in pan-cancer: from prognosis to tumor microenvironment

Background

OASL (Oligoadenylate Synthetase-Like), an interferon-induced protein in the OAS family, plays a significant role in anti-viral response. Studies have demonstrated its association with prognosis of certain tumors. However, the mechanism through which OASL affects tumors is unclear. A systemic pan-cancer study of OASL needs to be illustrated.

Methods

Analysis of OASL expression across 33 tumors was conducted utilizing TCGA, GTEx and CPTAC databases. COX and Log-Rank regressions were employed to calculate the prognosis. We validated the impact of OASL on apoptosis, migration, and invasion in pancreatic cancer cell lines. Moreover, we employed seven algorithms in bulk data to investigate the association of OASL expression and immune cell infiltration within tumor immune microenvironment (TIME) and ultimately validated at single-cell transcriptome level.

Results

We discovered elevated expression of OASL and its genetic heterogeneity in certain tumors, which link closely to prognosis. Validation experiments were conducted in PAAD and confirmed these findings. Additionally, OASL regulates immune checkpoint ligand such as programmed death ligand 1 (PD-L1), through IFN-γ/STAT1 and IL-6/JAK/STAT3 pathways in tumor cells. Meanwhile, OASL affects macrophages infiltration in TIME. By these mechanisms OASL could cause dysfunction of cytotoxic T lymphocytes (CTLs) in tumors.

Discussion

Multi-omics analysis reveals OASL as a prognostic and immunological biomarker in pan-cancer.

Ribosome specialization in cancer: a spotlight on ribosomal proteins

In the past few decades, our view of ribosomes has changed substantially. Rather than passive machines without significant variability, it is now acknowledged that they are heterogeneous, and have direct regulatory capacity. This 'ribosome heterogeneity' comes in many flavors, including in both the RNA and protein components of ribosomes, so there are many paths through which ribosome specialization could arise. It is easy to imagine that specialized ribosomes could have wide physiological roles, through the translation of specific mRNA populations, and there is now evidence for this in several contexts. Translation is highly dysregulated in cancer, needed to support oncogenic phenotypes and to overcome cellular stress. However, the role of ribosome specialization in this is not clear. In this review we focus on specialized ribosomes in cancer. Specifically, we assess the impact that post-translational modifications and differential ribosome incorporation of ribosomal proteins (RPs) have in this disease. We focus on studies that have shown a ribosome-mediated change in translation of specific mRNA populations, and hypothesize how such a process could be driving other phenotypes. We review the impact of RP-mediated heterogeneity in both intrinsic and extrinsic oncogenic processes, and consider how this knowledge could be leveraged to benefit patients.

Comprehensive analysis of the functions, prognostic and diagnostic values of RNA binding proteins in head and neck squamous cell carcinoma

BACKGROUND

Accumulating evidence has suggested that RNA binding protein (RBP) dysregulation plays an essential role during tumorigenesis. Here, we sought to explore the potential biological functions and clinical significance of RBP and develop diagnostic and prognostic signatures based on RBP in patients with head and neck squamous cell carcinoma (HNSCC).

METHODS

The differently expressed RBPs between HNSCC samples and their normal counterparts were identified using the Limma package. The immunohistochemistry (IHC) images of several RBPs were collected from the Human Protein Atlas database. The diagnostic signature based on RBP was built by LASSO-logistic regression and random forest. The prognostic signature based on RBP was constructed by LASSO and stepwise Cox regression analysis in the training cohort and validated in the validation cohort.

RESULTS

Eighty-four aberrantly expressed RBPs were obtained, comprising 41 up-regulated and 43 down-regulated RBPs. Seven RBP genes (CPEB3, PDCD4, ENDOU, PARP12, DNMT3B, IGF2BP1, EXO1) were identified as diagnostic-related hub genes. They were used to establish a diagnostic RBP signature risk score (DRBPS) model by the coefficients in least absolute shrinkage and selection operator (LASSO)-logistic regression analysis and showed high specificity and sensitivity in the training (area under the receiver operating characteristic curve (AUC) = 0.998), and in all validation cohorts (AUC > 0.95 for all). Similarly, seven RBP genes (MKRN3, ZC3H12D, EIF5A2, AFF3, SIDT1, RBM24, and NR0B1) were identified as prognosis-associated hub genes by LASSO and stepwise multiple Cox regression analyses and were used to construct the prognostic model named as PRBPS. The AUC of the time-dependent receiver operator characteristic curve of the prognostic model was 0.664 at 3 years and 0.635 at 5 years in the training cohort and 0.720, 0.777 in the validation cohort, showing a favorable predictive efficacy for prognosis in HNSCC.

CONCLUSIONS

Our results demonstrate the value of consideration of RBP in the diagnosis and prognosis for HNSCC and provide a novel insight into understanding the potential role of dysregulated RBP in HNSCC.

Heterogeneous pathogenesis of melanoma: BRAF mutations and beyond

Melanoma pathogenesis, conventionally perceived as a linear accumulation of molecular changes, discloses substantial heterogeneity driven by non-linear biological processes, including the direct transformation of melanocyte stem cells. This heterogeneity manifests in diverse biological phenotypes and developmental states, influencing variable responses to treatments. Unveiling the aberrant mechanisms steering melanoma initiation, progression, and metastasis is imperative. Beyond mutations in oncogenic and tumor suppressor genes, the involvement of distinct molecular pathways assumes a pivotal role in melanoma pathogenesis. Ultraviolet (UV) radiations, a principal factor in melanoma etiology, categorizes melanomas based on cumulative sun damage (CSD). The genomic landscape of lesions correlates with UV exposure, impacting mutational load and spectrum of mutations. The World Health Organization's 2018 classification underscores the interplay between sun exposure and genomic characteristics, distinguishing melanomas associated with CSD from those unrelated to CSD. The classification elucidates molecular features such as tumor mutational burden and copy number alterations associated with different melanoma subtypes. The significance of the mutated BRAF gene and its pathway, notably BRAFV600 variants, in melanoma is paramount. BRAF mutations, prevalent across diverse cancer types, present therapeutic avenues, with clinical trials validating the efficacy of targeted therapies and immunotherapy. Additional driver mutations in oncogenes further characterize specific melanoma pathways, impacting tumor behavior. While histopathological examination remains pivotal, challenges persist in molecularly classifying melanocytic tumors. In this review, we went through all molecular characterization that aid in discriminating common and ambiguous lesions. Integration of highly sensitive molecular diagnostic tests into the diagnostic workflow becomes indispensable, particularly in instances where histology alone fails to achieve a conclusive diagnosis. A diagnostic algorithm based on different molecular features inferred by the various studies is here proposed.

Identifying cooperating cancer driver genes in individual patients through hypergraph random walk

OBJECTIVE

Identifying cancer driver genes, especially rare or patient-specific cancer driver genes, is a primary goal in cancer therapy. Although researchers have proposed some methods to tackle this problem, these methods mostly identify cancer driver genes at single gene level, overlooking the cooperative relationship among cancer driver genes. Identifying cooperating cancer driver genes in individual patients is pivotal for understanding cancer etiology and advancing the development of personalized therapies.

METHODS

Here, we propose a novel Personalized Cooperating cancer Driver Genes (PCoDG) method by using hypergraph random walk to identify the cancer driver genes that cooperatively drive individual patient cancer progression. By leveraging the powerful ability of hypergraph in representing multi-way relationships, PCoDG first employs the personalized hypergraph to depict the complex interactions among mutated genes and differentially expressed genes of an individual patient. Then, a hypergraph random walk algorithm based on hyperedge similarity is utilized to calculate the importance scores of mutated genes, integrating these scores with signaling pathway data to identify the cooperating cancer driver genes in individual patients.

RESULTS

The experimental results on three TCGA cancer datasets (i.e., BRCA, LUAD, and COADREAD) demonstrate the effectiveness of PCoDG in identifying personalized cooperating cancer driver genes. These genes identified by PCoDG not only offer valuable insights into patient stratification correlating with clinical outcomes, but also provide an useful reference resource for tailoring personalized treatments.

CONCLUSION

We propose a novel method that can effectively identify cooperating cancer driver genes for individual patients, thereby deepening our understanding of the cooperative relationship among personalized cancer driver genes and advancing the development of precision oncology.

Molecular insights into the role of mixed lineage kinase 3 in cancer hallmarks

Mixed-lineage kinase 3 (MLK3) is a serine/threonine kinase of the MAPK Kinase kinase (MAP3K) family that plays critical roles in various biological processes, including cancer. Upon activation, MLK3 differentially activates downstream MAPKs, such as JNK, p38, and ERK. In addition, it regulates various non-canonical signaling pathways, such as β-catenin, AMPK, Pin1, and PAK1, to regulate cell proliferation, apoptosis, invasion, and metastasis. Recent studies have also uncovered other potentially diverse roles of MLK3 in malignancy, which include metabolic reprogramming, cancer-associated inflammation, and evasion of cancer-related immune surveillance. The role of MLK3 in cancer is complex and cancer-specific, and an understanding of its function at the molecular level aligned specifically with the cancer hallmarks will have profound therapeutic implications for diagnosing and treating MLK3-dependent cancers. This review summarizes the current knowledge about the effect of MLK3 on the hallmarks of cancer, providing insights into its potential as a promising anticancer drug target.

Radiomic-based prediction of lesion-specific systemic treatment response in metastatic disease

Despite sharing the same histologic classification, individual tumors in multi metastatic patients may present with different characteristics and varying sensitivities to anticancer therapies. In this study, we investigate the utility of radiomic biomarkers for prediction of lesion-specific treatment resistance in multi metastatic leiomyosarcoma patients. Using a dataset of n=202 lung metastases (LM) from n=80 patients with 1648 pre-treatment computed tomography (CT) radiomics features and LM progression determined from follow-up CT, we developed a radiomic model to predict the progression of each lesion. Repeat experiments assessed the relative predictive performance across LM volume groups. Lesion-specific radiomic models indicate up to a 4.5-fold increase in predictive capacity compared with a no-skill classifier, with an area under the precision-recall curve of 0.70 for the most precise model (FDR = 0.05). Precision varied by administered drug and LM volume. The effect of LM volume was controlled by removing radiomic features at a volume-correlation coefficient threshold of 0.20. Predicting lesion-specific responses using radiomic features represents a novel strategy by which to assess treatment response that acknowledges biological diversity within metastatic subclones, which could facilitate management strategies involving selective ablation of resistant clones in the setting of systemic therapy.

Cell fate simulation reveals cancer cell features in the tumor microenvironment

To elucidate the dynamic evolution of cancer cell characteristics within the tumor microenvironment (TME), we developed an integrative approach combining single-cell tracking, cell fate simulation, and 3D TME modeling. We began our investigation by analyzing the spatiotemporal behavior of individual cancer cells in cultured pancreatic (MiaPaCa2) and cervical (HeLa) cancer cell lines, with a focus on the α2-6 sialic acid (α2-6Sia) modification on glycans, which is associated with cell stemness. Our findings revealed that MiaPaCa2 cells exhibited significantly higher levels of α2-6Sia modification, correlating with enhanced reproductive capabilities, whereas HeLa cells showed less prevalence of this modification. To accommodate the in vivo variability of α2-6Sia levels, we employed a cell fate simulation algorithm that digitally generates cell populations based on our observed data while varying the level of sialylation, thereby simulating cell growth patterns. Subsequently, we performed a 3D TME simulation with these deduced cell populations, considering the microenvironment that could impact cancer cell growth. Immune cell landscape information derived from 193 cervical and 172 pancreatic cancer cases was used to estimate the degree of the positive or negative impact. Our analysis suggests that the deduced cells generated based on the characteristics of MiaPaCa2 cells are less influenced by the immune cell landscape within the TME compared to those of HeLa cells, highlighting that the fate of cancer cells is shaped by both the surrounding immune landscape and the intrinsic characteristics of the cancer cells.

Prognostic genome and transcriptome signatures in colorectal cancers

Colorectal cancer is caused by a sequence of somatic genomic alterations affecting driver genes in core cancer pathways1. Here, to understand the functional and prognostic impact of cancer-causing somatic mutations, we analysed the whole genomes and transcriptomes of 1,063 primary colorectal cancers in a population-based cohort with long-term follow-up. From the 96 mutated driver genes, 9 were not previously implicated in colorectal cancer and 24 had not been linked to any cancer. Two distinct patterns of pathway co-mutations were observed, timing analyses identified nine early and three late driver gene mutations, and several signatures of colorectal-cancer-specific mutational processes were identified. Mutations in WNT, EGFR and TGFβ pathway genes, the mitochondrial CYB gene and 3 regulatory elements along with 21 copy-number variations and the COSMIC SBS44 signature correlated with survival. Gene expression classification yielded five prognostic subtypes with distinct molecular features, in part explained by underlying genomic alterations. Microsatellite-instable tumours divided into two classes with different levels of hypoxia and infiltration of immune and stromal cells. To our knowledge, this study constitutes the largest integrated genome and transcriptome analysis of colorectal cancer, and interlinks mutations, gene expression and patient outcomes. The identification of prognostic mutations and expression subtypes can guide future efforts to individualize colorectal cancer therapy.

Precision Medicine in Cytopathology

Over the last decade, cancer diagnostics has undergone a notable transformation with increasing complexity. Minimally invasive diagnostic tests, driven by advanced imaging and early detection protocols, are redefining patient care and reducing the need for more invasive procedures. Modern cytopathologists now safeguard patient samples for vital biomarker and molecular testing. In this article, we explore ancillary testing modalities and the role of biomarkers in organ-specific contexts, underscoring the transformative impact of precision medicine. Finally, the advent of more than 80 Food and Drug Administration-approved predictive biomarkers signals a new era, guiding cancer care toward personalized and targeted strategies.

mRNA vaccines in tumor targeted therapy: mechanism, clinical application, and development trends

Malignant tumors remain a primary cause of human mortality. Among the various treatment modalities for neoplasms, tumor vaccines have consistently shown efficacy and promising potential. These vaccines offer advantages such as specificity, safety, and tolerability, with mRNA vaccines representing promising platforms. By introducing exogenous mRNAs encoding antigens into somatic cells and subsequently synthesizing antigens through gene expression systems, mRNA vaccines can effectively induce immune responses. Katalin Karikó and Drew Weissman were awarded the 2023 Nobel Prize in Physiology or Medicine for their great contributions to mRNA vaccine research. Compared with traditional tumor vaccines, mRNA vaccines have several advantages, including rapid preparation, reduced contamination, nonintegrability, and high biodegradability. Tumor-targeted therapy is an innovative treatment modality that enables precise targeting of tumor cells, minimizes damage to normal tissues, is safe at high doses, and demonstrates great efficacy. Currently, targeted therapy has become an important treatment option for malignant tumors. The application of mRNA vaccines in tumor-targeted therapy is expanding, with numerous clinical trials underway. We systematically outline the targeted delivery mechanism of mRNA vaccines and the mechanism by which mRNA vaccines induce anti-tumor immune responses, describe the current research and clinical applications of mRNA vaccines in tumor-targeted therapy, and forecast the future development trends of mRNA vaccine application in tumor-targeted therapy.

LINC01605 promotes malignant phenotypes of cervical cancer via miR-149-3p/WNT7B axis

BACKGROUND

Long non-coding RNAs (LncRNA) play a pivotal role in the progression of various malignancies. Despite recent identification as an oncogene associated with tumorigenesis. The precise role of LINC01605 in cervical cancer (CC) remains unclear. Therefore, the objective of this study was to investigate the influence of LINC01605 on proliferation and invasion of CC cells, while also exploring its potential underlying mechanisms.

METHODS

The expression of LINC01605 in CC cell lines was analyzed using the TCGA database and qRT-PCR. Various assays, including CCK-8 and transwell analysis, were conducted on CC cells to assess the influence of LINC01605 on their proliferation, migration, and invasion capabilities. Bioinformatics and dual luciferase reporter gene assays were employed to analyze the target genes of LINC01605 and miR-149-3p. To further investigate the mechanism of action, transfection and investigation were performed using specific siRNA, miRNA mimics, or inhibitors.

RESULTS

The expression of LINC01605 exhibited a significant increase in CC cell lines, and this upregulation was associated with an unfavorable prognosis. Modulating the expression of LINC01605, either by down-regulating or up-regulating it, exerted suppressive or stimulatory effects on the growth and invasion of HeLa and Siha cells. LINC01605 functioned as a competitive endogenous RNA (ceRNA) for miR-149-3p, with WNT7B being identified as a target gene of miR-149-3p. The involvement of LINC01605 in CC development is facilitated by its ability to regulate the expression of WNT7B through sequestering miR-149-3p.

CONCLUSION

Our study demonstrates that LINC01605 acts as a competitive endogenous RNA in modulating the effects of WNT7B on the proliferation and invasion of CC cells by sequestering miR-149-3p. This research provides novel insights into the involvement of LINC01605 in the advancement of CC.

Comparative genomic landscape of lower-grade glioma and glioblastoma

Biomarkers for classifying and grading gliomas have been extensively explored, whereas populations in public databases were mostly Western/European. Based on public databases cannot accurately represent Chinese population. To identify molecular characteristics associated with clinical outcomes of lower-grade glioma (LGG) and glioblastoma (GBM) in the Chinese population, we performed whole-exome sequencing (WES) in 16 LGG and 35 GBM tumor tissues. TP53 (36/51), TERT (31/51), ATRX (16/51), EFGLAM (14/51), and IDH1 (13/51) were the most common genes harboring mutations. IDH1 mutation (c.G395A; p.R132H) was significantly enriched in LGG, whereas PCDHGA10 mutation (c.A265G; p.I89V) in GBM. IDH1-wildtype and PCDHGA10 mutation were significantly related to poor prognosis. IDH1 is an important biomarker in gliomas, whereas PCDHGA10 mutation has not been reported to correlate with gliomas. Different copy number variations (CNVs) and oncogenic signaling pathways were identified between LGG and GBM. Differential genomic landscapes between LGG and GBM were revealed in the Chinese population, and PCDHGA10, for the first time, was identified as the prognostic factor of gliomas. Our results might provide a basis for molecular classification and identification of diagnostic biomarkers and even potential therapeutic targets for gliomas.

DNA damage response-related ncRNAs as regulators of therapy resistance in cancer

The DNA damage repair (DDR) pathway is a complex signaling cascade that can sense DNA damage and trigger cellular responses to DNA damage to maintain genome stability and integrity. A typical hallmark of cancer is genomic instability or nonintegrity, which is closely related to the accumulation of DNA damage within cancer cells. The treatment principles of radiotherapy and chemotherapy for cancer are based on their cytotoxic effects on DNA damage, which are accompanied by severe and unnecessary side effects on normal tissues, including dysregulation of the DDR and induced therapeutic tolerance. As a driving factor for oncogenes or tumor suppressor genes, noncoding RNA (ncRNA) have been shown to play an important role in cancer cell resistance to radiotherapy and chemotherapy. Recently, it has been found that ncRNA can regulate tumor treatment tolerance by altering the DDR induced by radiotherapy or chemotherapy in cancer cells, indicating that ncRNA are potential regulatory factors targeting the DDR to reverse tumor treatment tolerance. This review provides an overview of the basic information and functions of the DDR and ncRNAs in the tolerance or sensitivity of tumors to chemotherapy and radiation therapy. We focused on the impact of ncRNA (mainly microRNA [miRNA], long noncoding RNA [lncRNA], and circular RNA [circRNA]) on cancer treatment by regulating the DDR and the underlying molecular mechanisms of their effects. These findings provide a theoretical basis and new insights for tumor-targeted therapy and the development of novel drugs targeting the DDR or ncRNAs.

Immunotherapy for colorectal cancer

Colorectal cancer is the third most common cancer and the second most lethal cancer in the world. The main cause of the disease is due to dietary and behavioral factors. The treatment of this complex disease is mainly based on traditional treatments, including surgery, radiotherapy, and chemotherapy. Due to its high prevalence and high morbidity, more effective treatments with fewer side effects are urgently needed. In recent years, immunotherapy has become a potential therapeutic alternative and one of the fastest-developing treatments. Immunotherapy inhibits tumor growth by activating or enhancing the immune system to recognize and attack cancer cells. This review presents the latest immunotherapies for immune checkpoint inhibitors, cell therapy, tumor-infiltrating lymphocytes, and oncolytic viruses. Some of these have shown promising results in clinical trials and are used in clinical treatment.

Colorectal cancer-specific microbiome in peripheral circulation and cancer tissues

Introduction

Accumulating evidence has supported that gut microbiota and metabolite profiles play indispensable roles in the pathogenesis of colorectal cancer (CRC), which ranks as the third most common cancer and the second leading cause of cancer-related deaths worldwide. However, alterations in tumoral or circulating microbiomes in CRC remain incompletely understood. It has been well-documented that tissue or serum microbiomes with low microbial biomass could be screened by use of 2bRAD sequencing for microbiome (2bRAD-M) at the species resolution.

Methods

In order to validate the microbial biomarkers distinguishing CRC and the variations in microorganisms present in serum and tumors, we performed 2bRAD-M to characterize the microbiomes in serum and cancer tissues of CRC patients with and without lymph node or liver metastasis.

Results

The composition of dominated microbiota in serum was different from that of tissue samples, while the microbial community composition of tumors was similar to that of the tumor-adjacent tissues. The analysis of α-diversity and β-diversity has revealed notable variations in serum microbiota diversities in CRC patients, particularly those with liver metastasis. Multiple CRC-specific microbial species, such as Moraxella A cinereus, Flavobacterium sp001800905, and Acinetobacter albensis, were identified in serum. Complicated functions and KEGG pathways were also confirmed in CRC according to the metastasis status.

Discussion

This study has found significant alterations in the microbial compositions and diversities in CRC and CRC-specific microbial species in both circulation and cancer tissues, which may serve as promising biomarkers for the screening, diagnosis and prognosis prediction of CRC. In particular, CRC-specific bacterial taxa are promising markers, holding transformative potentials in establishing personalized screening and risk stratification, refining much earlier non-invasive diagnostic approaches, and enhancing diagnostic sensitivity.

Circulating Bacterial DNA in Colorectal Cancer Patients: The Potential Role of Fusobacterium nucleatum

Intestinal dysbiosis is a major contributor to colorectal cancer (CRC) development, leading to bacterial translocation into the bloodstream. This study aimed to evaluate the presence of circulated bacterial DNA (cbDNA) in CRC patients (n = 75) and healthy individuals (n = 25). DNA extracted from peripheral blood was analyzed using PCR, with specific primers targeting 16S rRNA, Escherichia coli (E. coli), and Fusobacterium nucleatum (F. nucleatum). High 16S rRNA and E. coli detections were observed in all patients and controls. Only the detection of F. nucleatum was significantly higher in metastatic non-excised CRC, compared to controls (p < 0.001), non-metastatic excised CRC (p = 0.023), and metastatic excised CRC (p = 0.023). This effect was mainly attributed to the presence of the primary tumor (p = 0.006) but not the presence of distant metastases (p = 0.217). The association of cbDNA with other clinical parameters or co-morbidities was also evaluated, revealing a higher detection of E. coli in CRC patients with diabetes (p = 0.004). These results highlighted the importance of bacterial translocation in CRC patients and the potential role of F. nucleatum as an intratumoral oncomicrobe in CRC.

Early detection of highly transmissible viral variants using phylogenomics

As demonstrated by the SARS-CoV-2 pandemic, the emergence of novel viral strains with increased transmission rates poses a serious threat to global health. Statistical models of genome sequence evolution may provide a critical tool for early detection of these strains. Using a novel stochastic model that links transmission rates to the entire viral genome sequence, we study the utility of phylogenetic methods that use a phylogenetic tree relating viral samples versus count-based methods that use case counts of variants over time exclusively to detect increased transmission rates and identify candidate causative mutations. We find that phylogenies in particular can detect novel transmission-enhancing variants very soon after their origin and may facilitate the development of early detection systems for outbreak surveillance.

In silico RNA isoform screening to identify potential cancer driver exons with therapeutic applications

Alternative splicing is crucial for cancer progression and can be targeted pharmacologically, yet identifying driver exons genome-wide remains challenging. We propose identifying such exons by associating statistically gene-level cancer dependencies from knockdown viability screens with splicing profiles and gene expression. Our models predict the effects of splicing perturbations on cell proliferation from transcriptomic data, enabling in silico RNA screening and prioritizing targets for splicing-based therapies. We identified 1,073 exons impacting cell proliferation, many from genes not previously linked to cancer. Experimental validation confirms their influence on proliferation, especially in highly proliferative cancer cell lines. Integrating pharmacological screens with splicing dependencies highlights the potential driver exons affecting drug sensitivity. Our models also allow predicting treatment outcomes from tumor transcriptomes, suggesting applications in precision oncology. This study presents an approach to identifying cancer driver exon and their therapeutic potential, emphasizing alternative splicing as a cancer target.

Comparative analysis of the mutational landscape and evolutionary patterns of pancreatic ductal adenocarcinoma metastases in the liver or peritoneum

Background

Pancreatic ductal adenocarcinoma (PDAC) often presents with liver or peritoneal metastases at diagnosis. Despite similar treatment approaches, patient outcomes vary between these metastatic sites. To improve targeted therapies for metastatic PDAC, a comprehensive analysis of the genetic profiles and evolutionary patterns at these distinct metastatic locations is essential.

Methods

We performed whole exome sequencing on 44 tissue samples from 27 PDAC patients, including primary tumours and matched liver or peritoneal metastases. We analysed somatic mutation profiles, signatures, and affected pathways for each group, and examined clonal evolution using subclonal architectures and phylogenetic trees.

Results

KRAS mutations remained the predominant driver alteration, with a prevalence of 89 % across all tumours. Notably, we observed site-specific differences in mutation frequencies, with KRAS alterations detected in 77.8 % (7/9) of peritoneal metastases and 87.5 % (7/8) of liver metastases. TP53 mutations exhibited a similar pattern, occurring in 55.6 % (5/9) of peritoneal and 37.5 % (3/8) of liver metastases. Intriguingly, we identified site-specific alterations in DNA repair pathway genes, including ATM and BRCA1, with distinct mutational profiles in liver versus peritoneal metastases. Furthermore, liver metastases demonstrated a significantly higher tumor mutational burden (TMB) compared to peritoneal metastases (median [IQR]: 2.14 [1.77-2.71] vs. 1.29 [1.21-1.69] mutations/Mb; P = 0.048).

Conclusions

In conclusion, metastasis of pancreatic cancer may be influenced by variables other than KRAS mutations, such as TP53. PDAC peritoneal and liver metastases may differ in potential therapeutic biomarkers. Further inquiry is needed on the biological mechanisms underlying metastasis and the treatment of diverse metastases.

Targeting amino acid-metabolizing enzymes for cancer immunotherapy

Despite the immune system's role in the detection and eradication of abnormal cells, cancer cells often evade elimination by exploitation of various immune escape mechanisms. Among these mechanisms is the ability of cancer cells to upregulate amino acid-metabolizing enzymes, or to induce these enzymes in tumor-infiltrating immunosuppressive cells. Amino acids are fundamental cellular nutrients required for a variety of physiological processes, and their inadequacy can severely impact immune cell function. Amino acid-derived metabolites can additionally dampen the anti-tumor immune response by means of their immunosuppressive activities, whilst some can also promote tumor growth directly. Based on their evident role in tumor immune escape, the amino acid-metabolizing enzymes glutaminase 1 (GLS1), arginase 1 (ARG1), inducible nitric oxide synthase (iNOS), indoleamine 2,3-dioxygenase 1 (IDO1), tryptophan 2,3-dioxygenase (TDO) and interleukin 4 induced 1 (IL4I1) each serve as a promising target for immunotherapeutic intervention. This review summarizes and discusses the involvement of these enzymes in cancer, their effect on the anti-tumor immune response and the recent progress made in the preclinical and clinical evaluation of inhibitors targeting these enzymes.

Therapeutic advances of targeting receptor tyrosine kinases in cancer

Receptor tyrosine kinases (RTKs), a category of transmembrane receptors, have gained significant clinical attention in oncology due to their central role in cancer pathogenesis. Genetic alterations, including mutations, amplifications, and overexpression of certain RTKs, are critical in creating environments conducive to tumor development. Following their discovery, extensive research has revealed how RTK dysregulation contributes to oncogenesis, with many cancer subtypes showing dependency on aberrant RTK signaling for their proliferation, survival and progression. These findings paved the way for targeted therapies that aim to inhibit crucial biological pathways in cancer. As a result, RTKs have emerged as primary targets in anticancer therapeutic development. Over the past two decades, this has led to the synthesis and clinical validation of numerous small molecule tyrosine kinase inhibitors (TKIs), now effectively utilized in treating various cancer types. In this manuscript we aim to provide a comprehensive understanding of the RTKs in the context of cancer. We explored the various alterations and overexpression of specific receptors across different malignancies, with special attention dedicated to the examination of current RTK inhibitors, highlighting their role as potential targeted therapies. By integrating the latest research findings and clinical evidence, we seek to elucidate the pivotal role of RTKs in cancer biology and the therapeutic efficacy of RTK inhibition with promising treatment outcomes.

Radiomic-Based Prediction of Lesion-Specific Systemic Treatment Response in Metastatic Disease

Despite sharing the same histologic classification, individual tumors in multi metastatic patients may present with different characteristics and varying sensitivities to anticancer therapies. In this study, we investigate the utility of radiomic biomarkers for prediction of lesion-specific treatment resistance in multi metastatic leiomyosarcoma patients. Using a dataset of n=202 lung metastases (LM) from n=80 patients with 1648 pre-treatment computed tomography (CT) radiomics features and LM progression determined from follow-up CT, we developed a radiomic model to predict the progression of each lesion. Repeat experiments assessed the relative predictive performance across LM volume groups. Lesion-specific radiomic models indicate up to a 4.5-fold increase in predictive capacity compared with a no-skill classifier, with an area under the precision-recall curve of 0.70 for the most precise model (FDR = 0.05). Precision varied by administered drug and LM volume. The effect of LM volume was controlled by removing radiomic features at a volume-correlation coefficient threshold of 0.20. Predicting lesion-specific responses using radiomic features represents a novel strategy by which to assess treatment response that acknowledges biological diversity within metastatic subclones, which could facilitate management strategies involving selective ablation of resistant clones in the setting of systemic therapy.

Genomic landscape of hepatocellular carcinoma in Egyptian patients by whole exome sequencing

BACKGROUND

Hepatocellular carcinoma (HCC) is the most common primary liver cancer. Chronic hepatitis and liver cirrhosis lead to accumulation of genetic alterations driving HCC pathogenesis. This study is designed to explore genomic landscape of HCC in Egyptian patients by whole exome sequencing.

METHODS

Whole exome sequencing using Ion Torrent was done on 13 HCC patients, who underwent surgical intervention (7 patients underwent living donor liver transplantation (LDLT) and 6 patients had surgical resection}.

RESULTS

Mutational signature was mostly S1, S5, S6, and S12 in HCC. Analysis of highly mutated genes in both HCC and Non-HCC revealed the presence of highly mutated genes in HCC (AHNAK2, MUC6, MUC16, TTN, ZNF17, FLG, MUC12, OBSCN, PDE4DIP, MUC5b, and HYDIN). Among the 26 significantly mutated HCC genes-identified across 10 genome sequencing studies-in addition to TCGA, APOB and RP1L1 showed the highest number of mutations in both HCC and Non-HCC tissues. Tier 1, Tier 2 variants in TCGA SMGs in HCC and Non-HCC (TP53, PIK3CA, CDKN2A, and BAP1). Cancer Genome Landscape analysis revealed Tier 1 and Tier 2 variants in HCC (MSH2) and in Non-HCC (KMT2D and ATM). For KEGG analysis, the significantly annotated clusters in HCC were Notch signaling, Wnt signaling, PI3K-AKT pathway, Hippo signaling, Apelin signaling, Hedgehog (Hh) signaling, and MAPK signaling, in addition to ECM-receptor interaction, focal adhesion, and calcium signaling. Tier 1 and Tier 2 variants KIT, KMT2D, NOTCH1, KMT2C, PIK3CA, KIT, SMARCA4, ATM, PTEN, MSH2, and PTCH1 were low frequency variants in both HCC and Non-HCC.

CONCLUSION

Our results are in accordance with previous studies in HCC regarding highly mutated genes, TCGA and specifically enriched pathways in HCC. Analysis for clinical interpretation of variants revealed the presence of Tier 1 and Tier 2 variants that represent potential clinically actionable targets. The use of sequencing techniques to detect structural variants and novel techniques as single cell sequencing together with multiomics transcriptomics, metagenomics will integrate the molecular pathogenesis of HCC in Egyptian patients.

A multiscale functional map of somatic mutations in cancer integrating protein structure and network topology

A major goal of cancer biology is to understand the mechanisms underlying tumorigenesis driven by somatically acquired mutations. Two distinct types of computational methodologies have emerged: one focuses on analyzing clustering of mutations within protein sequences and 3D structures, while the other characterizes mutations by leveraging the topology of protein-protein interaction network. Their insights are largely non-overlapping, offering complementary strengths. Here, we established a unified, end-to-end 3D structurally-informed protein interaction network propagation framework, NetFlow3D, that systematically maps the multiscale mechanistic effects of somatic mutations in cancer. The establishment of NetFlow3D hinges upon the Human Protein Structurome, a comprehensive repository we compiled that incorporates the 3D structures of every single protein as well as the binding interfaces of all known protein interactions in humans. NetFlow3D leverages the Structurome to integrate information across atomic, residue, protein and network levels: It conducts 3D clustering of mutations across atomic and residue levels on protein structures to identify potential driver mutations. It then anisotropically propagates their impacts across the protein interaction network, with propagation guided by the specific 3D structural interfaces involved, to identify significantly interconnected network "modules", thereby uncovering key biological processes underlying disease etiology. Applied to 1,038,899 somatic protein-altering mutations in 9,946 TCGA tumors across 33 cancer types, NetFlow3D identified 1,4444 significant 3D clusters throughout the Human Protein Structurome, of which ~55% would not have been found if using only experimentally-determined structures. It then identified 26 significantly interconnected modules that encompass ~8-fold more proteins than applying standard network analyses. NetFlow3D and our pan-cancer results can be accessed from http://netflow3d.yulab.org.

Clinical application of whole-genome sequencing of solid tumors for precision oncology

Genomic alterations in tumors play a pivotal role in determining their clinical trajectory and responsiveness to treatment. Targeted panel sequencing (TPS) has served as a key clinical tool over the past decade, but advancements in sequencing costs and bioinformatics have now made whole-genome sequencing (WGS) a feasible single-assay approach for almost all cancer genomes in clinical settings. This paper reports on the findings of a prospective, single-center study exploring the real-world clinical utility of WGS (tumor and matched normal tissues) and has two primary objectives: (1) assessing actionability for therapeutic options and (2) providing clarity for clinical questions. Of the 120 patients with various solid cancers who were enrolled, 95 (79%) successfully received genomic reports within a median of 11 working days from sampling to reporting. Analysis of these 95 WGS reports revealed that 72% (68/95) yielded clinically relevant insights, with 69% (55/79) pertaining to therapeutic actionability and 81% (13/16) pertaining to clinical clarity. These benefits include the selection of informed therapeutics and/or active clinical trials based on the identification of driver mutations, tumor mutational burden (TMB) and mutational signatures, pathogenic germline variants that warrant genetic counseling, and information helpful for inferring cancer origin. Our findings highlight the potential of WGS as a comprehensive tool in precision oncology and suggests that it should be integrated into routine clinical practice to provide a complete image of the genomic landscape to enable tailored cancer management.

Gene expression profiling in porocarcinoma indicates heterogeneous tumor development and substantiates poromas as precursor lesions

BACKGROUND AND OBJECTIVES

Malignant sweat gland tumors are rare, with the most common being eccrine porocarcinoma (EP). Approximately 18% of benign eccrine poroma (EPO) transit to EP. Previous research has provided first insights into the mutational landscape of EP. However, only few studies have performed gene expression analyses. This leaves a gap in the understanding of EP biology and potential drivers of malignant transformation from EPO to EP.

METHODS

Transcriptome profiling of 23 samples of primary EP and normal skin (NS). Findings from the EP samples were then tested in 17 samples of EPO.

RESULTS

Transcriptome profiling revealed diversity in gene expression and indicated biologically heterogeneous sub-entities as well as widespread gene downregulation in EP. Downregulated genes included CD74, NDGR1, SRRM2, CDC42, ANXA2, KFL9 and NOP53. Expression levels of CD74, NDGR1, SRRM2, ANXA2, and NOP53 showed a stepwise-reduction in expression from NS via EPO to EP, thus supporting the hypothesis that EPO represents a transitional state in EP development.

CONCLUSIONS

We demonstrated that EP is molecularly complex and that evolutionary trajectories correspond to tumor initiation and progression. Our results provide further evidence implicating the p53 axis and the EGFR pathway. Larger samples are warranted to confirm our findings.

Significance of MRI-based radiomics in predicting pathological complete response to neoadjuvant chemoradiotherapy of locally advanced rectal cancer: A narrative review

Neoadjuvant chemoradiotherapy is the standard treatment for patients with locally advanced rectal cancers owing to its ability to downstage primary tumours. Some patients can achieve pathological complete response after neoadjuvant therapy, and can adopt a "watch and wait" treatment strategy to avoid overtreatment. Therefore, it is essential to develop strategies for predicting responses to neoadjuvant therapy. Radiomics has shown great potential in extracting tumour features from high-throughput medical images for the construction of mathematics models for predicting the effects of anticancerous therapies. Herein, we explored MRI-based radiomics and found that it can predict responses of locally advanced rectal cancers to chemoradiation. Efficient radiomics model allow early-stage prediction of the effect of neoadjuvant chemoradiotherapy on locally advanced rectal cancers. It helps clinicians to make informed therapeutic decisions. In this review, we discuss the workflow of radiomics, and summarize the clinical application of MRI-based radiomics in predicting pathological complete response to neoadjuvant chemoradiotherapy of locally advanced rectal cancer.

Integrative genomics identifies SHPRH as a tumor suppressor gene in lung adenocarcinoma that regulates DNA damage response

BACKGROUND

Identification of driver mutations and development of targeted therapies has considerably improved outcomes for lung cancer patients. However, significant limitations remain with the lack of identified drivers in a large subset of patients. Here, we aimed to assess the genomic landscape of lung adenocarcinomas (LUADs) from individuals without a history of tobacco use to reveal new genetic drivers of lung cancer.

METHODS

Integrative genomic analyses combining whole-exome sequencing, copy number, and mutational information for 83 LUAD tumors was performed and validated using external datasets to identify genetic variants with a predicted functional consequence and assess association with clinical outcomes. LUAD cell lines with alteration of identified candidates were used to functionally characterize tumor suppressive potential using a conditional expression system both in vitro and in vivo.

RESULTS

We identified 21 genes with evidence of positive selection, including 12 novel candidates that have yet to be characterized in LUAD. In particular, SNF2 Histone Linker PHD RING Helicase (SHPRH) was identified due to its frequency of biallelic disruption and location within the familial susceptibility locus on chromosome arm 6q. We found that low SHPRH mRNA expression is associated with poor survival outcomes in LUAD patients. Furthermore, we showed that re-expression of SHPRH in LUAD cell lines with inactivating alterations for SHPRH reduces their in vitro colony formation and tumor burden in vivo. Finally, we explored the biological pathways associated SHPRH inactivation and found an association with the tolerance of LUAD cells to DNA damage.

CONCLUSIONS

These data suggest that SHPRH is a tumor suppressor gene in LUAD, whereby its expression is associated with more favorable patient outcomes, reduced tumor and mutational burden, and may serve as a predictor of response to DNA damage. Thus, further exploration into the role of SHPRH in LUAD development may make it a valuable biomarker for predicting LUAD risk and prognosis.

ECD-CDGI: An efficient energy-constrained diffusion model for cancer driver gene identification

The identification of cancer driver genes (CDGs) poses challenges due to the intricate interdependencies among genes and the influence of measurement errors and noise. We propose a novel energy-constrained diffusion (ECD)-based model for identifying CDGs, termed ECD-CDGI. This model is the first to design an ECD-Attention encoder by combining the ECD technique with an attention mechanism. ECD-Attention encoder excels at generating robust gene representations that reveal the complex interdependencies among genes while reducing the impact of data noise. We concatenate topological embedding extracted from gene-gene networks through graph transformers to these gene representations. We conduct extensive experiments across three testing scenarios. Extensive experiments show that the ECD-CDGI model possesses the ability to not only be proficient in identifying known CDGs but also efficiently uncover unknown potential CDGs. Furthermore, compared to the GNN-based approach, the ECD-CDGI model exhibits fewer constraints by existing gene-gene networks, thereby enhancing its capability to identify CDGs. Additionally, ECD-CDGI is open-source and freely available. We have also launched the model as a complimentary online tool specifically crafted to expedite research efforts focused on CDGs identification.

IMI-driver: Integrating multi-level gene networks and multi-omics for cancer driver gene identification

The identification of cancer driver genes is crucial for early detection, effective therapy, and precision medicine of cancer. Cancer is caused by the dysregulation of several genes at various levels of regulation. However, current techniques only capture a limited amount of regulatory information, which may hinder their efficacy. In this study, we present IMI-driver, a model that integrates multi-omics data into eight biological networks and applies Multi-view Collaborative Network Embedding to embed the gene regulation information from the biological networks into a low-dimensional vector space to identify cancer drivers. We apply IMI-driver to 29 cancer types from The Cancer Genome Atlas (TCGA) and compare its performance with nine other methods on nine benchmark datasets. IMI-driver outperforms the other methods, demonstrating that multi-level network integration enhances prediction accuracy. We also perform a pan-cancer analysis using the genes identified by IMI-driver, which confirms almost all our selected candidate genes as known or potential drivers. Case studies of the new positive genes suggest their roles in cancer development and progression.

Prognostic factors of 87 ovarian yolk sac tumor (OYST) patients and molecular characteristics of persistent and recurrent OYST

OBJECTIVE

We aimed to explore the characteristics of OYST, particularly for persistent and recurrent OYST, in order to explore potential treatment options and thereby improve patient outcomes.

METHODS

We retrospectively reviewed the clinical records of all patients with OYST at Fudan university Shanghai Cancer Center from December 3, 2005 to November 27, 2020. Furthermore, and performed whole-exome sequencing on 17 paired OYST (including 8 paired persistent and recurrent OYST) tumor and blood samples to elucidate the aberrant molecular features.

RESULTS

Totally, 87 OYST patients were included between 2007/03/13 and 2020/11/17. With a median follow-up of 73 [3-189] months, 22 patients relapsed or disease persisted. Overall, 17 patients died with a median overall survival of 21 [3-54] months. Univariate and multivariate analysis revealed tumor histology and residual lesions were independently associated with event free survival and overall survival, cycles to AFP normalization were another independent risk factor for overall survival. For the 8 persistent and recurrent OYST: cancer driver genes including ANKRD36, ANKRD62, DNAH8, MUC5B, NUP205, RYR2, STARD9, MUC16, TTN, ARID1A and PIK3CA were frequently mutated; cell cycle, ABC transporters, HR, NHEJ and AMPK signal pathway demonstrated as the most significantly enriched pathways; TMB, DNA MMR gene mutation and MSI were significantly higher. Mutation signature 11, 19 and 30 were the dominant contributors in persistent and recurrent OYST mutation.

CONCLUSION

Persistent and recurrent OYST associated with poor prognosis, and probably susceptible to immune checkpoint blockade therapy. Molecular characteristics contributed to predict the persistence and recurrence of OYST.

Evaluation of Efficacy and Safety in First-Line Treatment Methods for Extensive-Stage Small Cell Lung Cancer: A Comprehensive Comparative Study of Chemotherapy, Targeted Therapy Combined With Chemotherapy, and Immunotherapy Combined With Chemotherapy

BACKGROUND

Small cell lung cancer (SCLC) is a highly aggressive tumor with limited effectiveness in its standard chemotherapy treatment. Targeted antiangiogenic therapy and immune checkpoint inhibitors (ICIs) have demonstrated potential as alternative treatments for extensive-stage SCLC (ES-SCLC). However, there is insufficient comparative evidence available to determine the optimal first-line treatment option between ICIs plus chemotherapy and targeted antiangiogenic therapy plus chemotherapy.

OBJECTIVE

This study is aimed at analyzing clinical data from ES-SCLC patients treated at the First Affiliated Hospital of Bengbu Medical College between June 2021 and June 2023. The study compared the efficacy and safety of three first-line treatment regimens: standard chemotherapy, antiangiogenic therapy combined with chemotherapy, and immune combination therapy.

METHODS

Patients who met the inclusion criteria were divided into three groups: chemotherapy, immune combination therapy, and antiangiogenic therapy combined with chemotherapy. The study collected data on clinical characteristics, treatment regimens, and adverse reactions. The analysis included objective response rate (ORR), duration of response (DoR), disease control rate (DCR), progression-free survival (PFS), and treatment safety.

RESULTS

A total of 101 patients were included in the study, with 49 receiving chemotherapy alone, 19 receiving antiangiogenic therapy, and 33 receiving immune combination therapy. The ORRs were 78.9% for antiangiogenic therapy, 72.7% for immune combination therapy, and 42.9% for chemotherapy alone. The median PFS was 8.0 months for antiangiogenic therapy, 7.8 months for immune combination therapy, and 5.2 months for chemotherapy alone. Both combination therapy groups demonstrated superior efficacy compared to chemotherapy alone.

CONCLUSION

Targeted combined chemotherapy and immune combination chemotherapy showed superior efficacy as first-line treatments for ES-SCLC compared to chemotherapy alone, with manageable adverse reactions.