Multi-omics analysis of intra-tumoural and inter-tumoural heterogeneity in pancreatic ductal adenocarcinoma

Machine learning-based identification of biomarkers and drugs in immunologically cold and hot pancreatic adenocarcinomas

BACKGROUND

Pancreatic adenocarcinomas (PAADs) often exhibit a "cold" or immunosuppressive tumor milieu, which is associated with resistance to immune checkpoint blockade therapy; however, the underlying mechanisms are incompletely understood. Here, we aimed to improve our understanding of the molecular mechanisms occurring in the tumor microenvironment and to identify biomarkers, therapeutic targets, and potential drugs to improve PAAD treatment.

METHODS

Patients were categorized according to immunologically hot or cold PAAD subtypes with distinct disease outcomes. Cox regression and weighted correlation network analysis were performed to construct a novel gene signature, referred to as 'Downregulated in hot tumors, Prognostic, and Immune-Related Genes' (DPIRGs), which was used to develop prognostic models for PAAD via machine learning (ML). The role of DPIRGs in PAAD was comprehensively analyzed, and biomarker genes able to distinguish PAAD immune subtypes and predict prognosis were identified by ML. The expression of biomarkers was verified using public single-cell transcriptomic and proteomic resources. Drug candidates for turning cold tumors hot and corresponding target proteins were identified via molecular docking studies.

RESULTS

Using the DPIRG signature as input data, a combination of survival random forest and partial least squares regression Cox was selected from 137 ML combinations to construct an optimized PAAD prognostic model. The effects and molecular mechanisms of DPIRGs were investigated by analysis of genetic/epigenetic alterations, immune infiltration, pathway enrichment, and miRNA regulation. Biomarkers and potential therapeutic targets, including PLEC, TRPV1, and ITGB4, among others, were identified, and the cell type-specific expression of the biomarkers was validated. Drug candidates, including thalidomide, SB-431542, and bleomycin A2, were identified based on their ability to modulate DPIRG expression favorably.

CONCLUSIONS

By combining multiple ML algorithms, we developed a novel prognostic model with excellent performance in PAAD cohorts. ML also proved to be powerful for identifying biomarkers and potential targets for improved PAAD patient stratification and immunotherapy.

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.

Resolving tumor evolution: a phylogenetic approach

The evolutionary dynamics of cancer, characterized by its profound heterogeneity, demand sophisticated tools for a holistic understanding. This review delves into tumor phylogenetics, an essential approach bridging evolutionary biology with oncology, offering unparalleled insights into cancer's evolutionary trajectory. We provide an overview of the workflow, encompassing study design, data acquisition, and phylogeny reconstruction. Notably, the integration of diverse data sets emerges as a transformative step, enhancing the depth and breadth of evolutionary insights. With this integrated perspective, tumor phylogenetics stands poised to redefine our understanding of cancer evolution and influence therapeutic strategies.

Identification and validation of neutrophils-related subtypes and prognosis model in triple negative breast cancer

BACKGROUND

Neutrophils are considered to be crucial players in the initiation and progression of cancer. However, the complex relationship between neutrophils and cancer prognosis remains elusive, mainly due to the significant plasticity and diversity exhibited by these immune cells.

METHODS

As part of our thorough investigation, we examined 38 Neutrophils-Related Genes (NRGs) and the associated copy number variations (CNV), somatic mutations, and gene expression patterns in relation to triple negative breast cancer (TNBC). The interactions between these genes, their biological roles, and their possible prognostic significance were then examined. With the NRGs as our basis, we applied Lasso and Cox regression analyses to create a predictive model for overall survival (OS). Furthermore, TNBC tissue and a public database were used to assess changes in MYO1D expression (MYO1D is characterized as a member of the myosin-I family, a group of motor proteins based on actin), its connection to neutrophil infiltration, and the clinical importance of MYO1D in TNBC.

RESULTS

Four neutrophil-related genes were included in the development of a prognostic model based on neutrophils. The model was further shown to be an independent predicted factor for overall survival by multivariate Cox regression analysis. According to this study, neutrophil subtype B as well as gene subtype B, were associated with activated cancer immunity and poor prognosis of TNBC patients. Furthermore, considering that poor OS was linked to increased MYO1D expression, MYO1D was increased in TNBC tissues and associated with neutrophil infiltration. In vitro experiments also confirmed that MYO1D facilitates breast cancer invasion and metastasis.

CONCLUSION

Based on the degree of gene expression linked to neutrophils, a unique prognostic model was created. MYO1D could be a potential prognostic biomarker in TNBC patients and also a prospective target for therapy.

S100P as a potential biomarker for immunosuppressive microenvironment in pancreatic cancer: a bioinformatics analysis and in vitro study

BACKGROUND

Immunosuppression is a significant factor contributing to the poor prognosis of cancer. S100P, a member of the S100 protein family, has been implicated in various cancers. However, its role in the tumor microenvironment (TME) of pancreatic cancer remains unclear. This study aimed to investigate the potential impact of S100P on TME characteristics in patients with pancreatic cancer.

METHODS

Multiple data (including microarray, RNA-Seq, and scRNA-Seq) were obtained from public databases. The expression pattern of S100P was comprehensively evaluated in RNA-Seq data and validated in four different microarray datasets. Prognostic value was assessed through Kaplan-Meier plotter and Cox regression analyses. Immune infiltration levels were determined using the ESTIMATE and ssGSEA algorithms and validated at the single-cell level. Spearman correlation test was used to examine the correlation between S100P expression and immune checkpoint genes, and tumor mutation burden (TMB). DNA methylation analysis was performed to investigate the change in mRNA expression. Reverse transcription PCR (RT-PCR) and immunohistochemical (IHC) were utilized to validate the expression using five cell lines and 60 pancreatic cancer tissues.

RESULTS

This study found that S100P was differentially expressed in pancreatic cancer and was associated with poor prognosis (P < 0.05). Notably, S100P exhibited a significant negative-correlation with immune cell infiltration, particularly CD8 + T cells. Furthermore, a close association between S100P and immunotherapy was observed, as it strongly correlated with TMB and the expression levels of TIGIT, HAVCR2, CTLA4, and BTLA (P < 0.05). Intriguingly, higher S100P expression demonstrated a negative correlation with methylation levels (cg14323984, cg27027375, cg14900031, cg14140379, cg25083732, cg07210669, cg26233331, and cg22266967), which were associated with CD8 + T cells. In vitro RT-PCR validated upregulated S100P expression across all five pancreatic cancer cell lines, and IHC confirmed high S100P levels in pancreatic cancer tissues (P < 0.05).

CONCLUSION

These findings suggest that S100P could serve as a promising biomarker for immunosuppressive microenvironment, which may provide a novel therapeutic way for pancreatic cancer.

Single-Cell Lipidomics Using Analytical Flow LC-MS Characterizes the Response to Chemotherapy in Cultured Pancreatic Cancer Cells

In this work, we demonstrate the development and first application of nanocapillary sampling followed by analytical flow liquid chromatography-mass spectrometry for single-cell lipidomics. Around 260 lipids were tentatively identified in a single cell, demonstrating remarkable sensitivity. Human pancreatic ductal adenocarcinoma cells (PANC-1) treated with the chemotherapeutic drug gemcitabine can be distinguished from controls solely on the basis of their single-cell lipid profiles. Notably, the relative abundance of LPC(0:0/16:0) was significantly affected in gemcitabine-treated cells, in agreement with previous work in bulk. This work serves as a proof of concept that live cells can be sampled selectively and then characterized using automated and widely available analytical workflows, providing biologically relevant outputs.

Intertumoral heterogeneity impacts oncolytic vesicular stomatitis virus efficacy in mouse pancreatic cancer cells

Oncolytic virus (OV) therapy is a promising virus-based approach against various malignancies, including pancreatic ductal adenocarcinoma (PDAC). Our previous studies demonstrated that human PDAC cell lines are highly variable in their permissiveness to OVs. Mouse PDAC cell lines, which are widely used for in vivo examination of the adaptive immune responses during OV and other cancer therapies, have never been examined systematically for the impact of intertumoral heterogeneity (the differences observed between tumors in different patients) on OV virus efficacy. Here, we examined phenotypically and genotypically three commonly used allograftable mouse PDAC cell lines (C57BL6 genetic background): Panc02 (derived from chemically induced PDAC; also known as Pan02), and two cell lines originated from PDACs developed in two different KPC (KrasG12D, Trp53R172H, and PDX-1-Cre) mouse models. Our study (i) characterized the ability of a widely used attenuated oncolytic vesicular stomatitis virus VSV-ΔM51-GFP to infect, replicate in, and kill mouse PDAC cells; (ii) examined their innate antiviral responses; (iii) compared their permissiveness to a non-attenuated VSV-Mwt-GFP and chemotherapeutic drugs; and (iv) analyzed their karyotype and exome. Mouse PDAC cell lines showed high divergence in their permissiveness to VSV-ΔM51-GFP, which negatively correlated with their abilities to mount innate antiviral responses, while all three cell lines were highly permissive to VSV-Mwt-GFP. No correlation was found between resistance to VSV-ΔM51-GFP and chemotherapy. Also, mouse PDAC cell lines showed high divergence in their karyotype and exome. The exome analysis demonstrated that more VSV-ΔM51-GFP-permissive mouse PDAC cell lines harbor mutations in multiple important antiviral genes, such as TYK2, JAK2, and JAK3. IMPORTANCE Oncolytic virus (OV) therapy is a promising virus-based approach against various malignancies, including pancreatic ductal adenocarcinoma (PDAC). Our previous studies using various human PDAC cell lines demonstrated that they are highly variable in their permissiveness to OVs. In this study, we examined phenotypically and genotypically three commonly used allograftable mouse PDAC cell lines, which are widely used for in vivo examination of the adaptive immune responses during cancer therapies. Mouse PDAC cell lines showed high divergence in their permissiveness to oncolytic vesicular stomatitis virus (VSV), which negatively correlated with their abilities to mount innate antiviral responses. Also, we discovered that more VSV-permissive mouse PDAC cell lines harbor mutations in multiple important antiviral genes, such as TYK2, JAK2, and JAK3. Our study provides essential information about three model mouse PDAC cell lines and proposes a novel platform to study OV-based therapies against different PDACs in immunocompetent mice.

KIF18A improves migration and invasion of colorectal cancer (CRC) cells through inhibiting PTEN signaling

BACKGROUND

Kinesin family member 18A (KIF18A) is involved in the development of a variety of human malignancies. However, we have never known the influences of KIF18A on colorectal cancer (CRC). The study is designed to investigate the effect and molecular mechanism of KIF18A on the progression of colorectal cancer.

METHODS

We have not only analyzed the database using GEO, but have examined the effect of KIF18A on the development of CRC by subcutaneous tumorigenesis in nude mice. HE staining was used to observe tumor size. Besides, we make use of Western blotting to monitor the expression of related proteins. In addition, the scratch wound assay and Transwell assay were conducted to detect the effect of KIF18A on the migration and invasion of CRC cells.

RESULTS

The results of GEO database analysis suggested that KIF18A had a positive correlation with the growth of CRC. The results of subcutaneous tumorigenesis and HE staining in nude mice explained that KIF18A promoted the progression of CRC. Both scratch wound assay and Transwell indicated that the migration and invasion of CRC could be promoted by KIF18A. The results of Western blot illustrated that KIF18A could forward the migration and invasion of CRC cells, and inhibit PTEN, which promoted the activation of PI3K/Akt signaling pathway, thus bringing about the expression of MMP2 and MMP9.

CONCLUSION

In conclusion, KIF18A can further the activation of PI3K/Akt signaling pathway by means of inhibiting PTEN transcription. Therefore, it is inferred that that KIF18A is a therapeutic target for CRC.

Clinical and genetic characteristics in pancreatic cancer from Chinese patients revealed by whole exome sequencing

Background

Pancreatic ductal adenocarcinoma (PDAC) is one of the most fatal malignancies worldwide, mostly as a result of the absence of early detection and specific treatment solutions. Consequently, identifying mutational profiles and molecular biomarkers is essential for increasing the viability of precision therapy for pancreatic cancer.

Methods

We collected blood and tumor tissue samples from 47 Chinese pancreatic cancer patients and used whole-exome sequencing (WES) to evaluate the genetic landscape.

Results

Our results showed the most frequently somatic alteration genes were KRAS (74.5%), TP53(51.1%), SMAD4 (17%), ARID1A (12.8%), CDKN2A (12.8%), TENM4 (10.6%), TTN (8.5%), RNF43(8.5%), FLG (8.5%) and GAS6 (6.4%) in Chinese PDAC patients. We also found that three deleterious germline mutations (ATM c.4852C>T/p. R1618*, WRN c.1105C>T/p. R369*, PALB2 c.2760dupA/p. Q921Tfs*7) and two novel fusions (BRCA1-RPRML, MIR943 (intergenic)-FGFR3). When compared to the Cancer Genome Atlas (TCGA) database, there is a greater mutation frequency of TENM4 (10.6% vs. 1.6%, p = 0.01), GAS6(6.4% vs. 0.5%, p = 0.035), MMP17(6.4% vs. 0.5%, p = 0.035), ITM2B (6.4% vs. 0.5%, p = 0.035) and USP7 (6.4% vs. 0.5%, p= 0.035) as well as a reduced mutation frequency of SMAD4 (17.0% vs. 31.5%, p = 0.075) and CDKN2A (12.8% vs. 47.3%, p < 0.001) were observed in the Chinese cohort. Among the 41 individuals examined for programmed cell death ligand 1(PD-L1) expression, 15 (36.6%) had positive PD-L1 expression. The median tumor mutational burden (TMB) was found to be 12muts (range, 0124). The TMB index was higher in patients with mutant-type KRAS MUT/TP53 MUT (p < 0.001), CDKN2A (p = 0.547), or SMAD4 (p = 0.064) compared to patients with wild-type KRAS/TP53, CDKN2A, or SMAD4.

Conclusions

We exhibited real-world genetic traits and new alterations in Chinese individuals with cancer of the pancreas, which might have interesting implications for future individualized therapy and medication development.

Molecular genetic characteristics of thymic epithelial tumors with distinct histological subtypes

BACKGROUND

Due to the low incidence and histological heterogeneity, the molecular features and underlying carcinogenic mechanisms of thymic epithelial tumors (TETs) are yet to be fully elucidated, especially for different subtypes of TETs.

METHODS

Tumor tissue samples of 43 TETs with distinct histological subtypes were collected. We analyzed the molecular characteristics in different subtypes based on whole exome sequencing data.

RESULTS

The mutational profiles of the different subtypes of TETs varied. Compared with thymomas, thymic carcinomas (TCs) had a higher mutation frequency of MYO16 (33% vs. 3%, p = 0.024) and a lower frequency of ZNF729 mutations (0% vs. 35%, p = 0.044). No significant difference was observed in the median tumor mutation burden across different subtypes. The value of copy number variation burden, weighted genome instability index, and the number of amplified segments were all higher in TCs than thymomas, and they also tended to be higher in B3 thymoma than in non-B3 thymomas, while they had no significant differences between B3 thymoma and TCs. Clustering analyses revealed that Wnt, MAPK, Hedgehog, AMPK, and cell junction assembly signaling pathways were exclusively enriched in non-B3 thymomas, lysine degradation pathway in B3 thymoma, and extracellular matrix-receptor (ECM-receptor) interaction, positive regulation of cell cycle process, and activation of innate immune response pathways in TCs.

CONCLUSIONS

This study revealed distinct molecular landscapes of different subtypes of TETs, suggesting diverse pathogenesis of non-B3 thymomas, B3 thymomas, and TCs. Our findings warrant further validation in future large-scale studies and may provide a theoretical basis for potential personalized therapeutic strategies.

Single-cell and bulk RNA sequencing identifies T cell marker genes score to predict the prognosis of pancreatic ductal adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is one of the lethal malignancies, with limited biomarkers identified to predict its prognosis and treatment response of immune checkpoint blockade (ICB). This study aimed to explore the predictive ability of T cell marker genes score (TMGS) to predict their overall survival (OS) and treatment response to ICB by integrating single-cell RNA sequencing (scRNA-seq) and bulk RNA-seq data. Multi-omics data of PDAC were applied in this study. The uniform manifold approximation and projection (UMAP) was utilized for dimensionality reduction and cluster identification. The non-negative matrix factorization (NMF) algorithm was applied to molecular subtypes clustering. The Least Absolute Shrinkage and Selection Operator (LASSO)-Cox regression was adopted for TMGS construction. The prognosis, biological characteristics, mutation profile, and immune function status between different groups were compared. Two molecular subtypes were identified via NMF: proliferative PDAC (C1) and immune PDAC (C2). Distinct prognoses and biological characteristics were observed between them. TMGS was developed based on 10 T cell marker genes (TMGs) through LASSO-Cox regression. TMGS is an independent prognostic factor of OS in PDAC. Enrichment analysis indicated that cell cycle and cell proliferation-related pathways are significantly enriched in the high-TMGS group. Besides, high-TMGS is related to more frequent KRAS, TP53, and CDKN2A germline mutations than the low-TMGS group. Furthermore, high-TMGS is significantly associated with attenuated antitumor immunity and reduced immune cell infiltration compared to the low-TMGS group. However, high TMGS is correlated to higher tumor mutation burden (TMB), a low expression level of inhibitory immune checkpoint molecules, and a low immune dysfunction score, thus having a higher ICB response rate. On the contrary, low TMGS is related to a favorable response rate to chemotherapeutic agents and targeted therapy. By combining scRNA-seq and bulk RNA-seq data, we identified a novel biomarker, TMGS, which has remarkable performance in predicting the prognosis and guiding the treatment pattern for patients with PDAC.

Comprehensive machine-learning survival framework develops a consensus model in large-scale multicenter cohorts for pancreatic cancer

As the most aggressive tumor, the outcome of pancreatic cancer (PACA) has not improved observably over the last decade. Anatomy-based TNM staging does not exactly identify treatment-sensitive patients, and an ideal biomarker is urgently needed for precision medicine. Based on expression files of 1280 patients from 10 multicenter cohorts, we screened 32 consensus prognostic genes. Ten machine-learning algorithms were transformed into 76 combinations, of which we selected the optimal algorithm to construct an artificial intelligence-derived prognostic signature (AIDPS) according to the average C-index in the nine testing cohorts. The results of the training cohort, nine testing cohorts, Meta-Cohort, and three external validation cohorts (290 patients) consistently indicated that AIDPS could accurately predict the prognosis of PACA. After incorporating several vital clinicopathological features and 86 published signatures, AIDPS exhibited robust and dramatically superior predictive capability. Moreover, in other prevalent digestive system tumors, the nine-gene AIDPS could still accurately stratify the prognosis. Of note, our AIDPS had important clinical implications for PACA, and patients with low AIDPS owned a dismal prognosis, higher genomic alterations, and denser immune cell infiltrates as well as were more sensitive to immunotherapy. Meanwhile, the high AIDPS group possessed observably prolonged survival, and panobinostat may be a potential agent for patients with high AIDPS. Overall, our study provides an attractive tool to further guide the clinical management and individualized treatment of PACA.