A novel 20-gene prognostic score in pancreatic adenocarcinoma

Pharmacogenomics-based subtype decoded implications for risk stratification and immunotherapy in pancreatic adenocarcinoma

BACKGROUND

With fatal malignant peculiarities and poor survival rate, outcomes of pancreatic adenocarcinoma (PAAD) were frustrated by non-response and even resistance to therapy due to heterogeneity across clinical patients. Nevertheless, pharmacogenomics has been developed for individualized-treatment and still maintains obscure in PAAD.

METHODS

A total of 964 samples from 10 independent multi-center cohorts were enrolled in our study. With drug response data from the profiling of relative inhibition simultaneously in mixtures (PRISM) and genomics of drug sensitivity in cancer (GDSC) databases, we established and validated multidimensionally three pharmacogenomics-classified subtypes using non-negative matrix factorization (NMF) and nearest template prediction (NTP) algorithms, separately. The heterogenous biological characteristics and precision medicine strategies among subtypes were further investigated.

RESULTS

Three pharmacogenomics-classified subtypes after stable and reproducible validation, distinguished in six aspects of prognosis, biological peculiarities, immune landscapes, genomic variations, immunotherapy and individualized management strategies. Subtype 2 was close to immunocompetent phenotype and projected to immunotherapy; Subtype 3 held most favorable outcomes and metabolic pathways distinctively, promising to be treated with first-line agents. Subtype 1 with worst prognosis, was anticipated to chromosome instability (CIN) phenotype and resistant to chemotherapeutic agents. In addition, ITGB6 contributed to subtype 1 resistance to 5-fluorouracil, and knockdown of ITGB6 enhanced sensitivity to 5-fluorouracil in in vitro experiments. Ultimately, appropriate clinical stratified treatments were assigned to corresponding subtypes according to pharmacogenomic transcripts. Some limitations were not taken into account, thus needs to be supported by more research.

CONCLUSION

A span-new molecular subtype exploited for PAAD uncovered an insight into precise medication on ground of pharmacogenomics, and highly refined multiple clinical management strategies for specific patients.

Targeting Cancer: Microenvironment and Immunotherapy Innovations

In 2024, the United States was projected to experience 2 million new cancer diagnoses and approximately 611,720 cancer-related deaths, reflecting a broader global trend in which cancer cases are anticipated to exceed 35 million by 2050. This increasing burden highlights ongoing challenges in cancer treatment despite significant advances that have reduced cancer mortality by 31% since 1991. Key obstacles include the disease's inherent heterogeneity and complexity, such as treatment resistance, cancer stem cells, and the multifaceted tumor microenvironment (TME). The TME-comprising various tumor and immune cells, blood vessels, and biochemical factors-plays a crucial role in tumor growth and resistance to therapies. Recent innovations in cancer treatment, particularly in the field of immuno-oncology, have leveraged insights into TME interactions. An emerging example is the FDA-approved therapy using tumor-infiltrating lymphocytes (TILs), demonstrating the potential of cell-based approaches in solid tumors. However, TIL therapy is just one of many strategies being explored. This review provides a comprehensive overview of the emerging field of immuno-oncology, focusing on how novel therapies targeting or harnessing components of the TME could enhance treatment efficacy and address persistent challenges in cancer care.

Co‑expression of SLC20A1 and ALDH1A3 is associated with poor prognosis, and SLC20A1 is required for the survival of ALDH1‑positive pancreatic cancer stem cells

Solute carrier family 20 member 1 (SLC20A1) is a sodium/inorganic phosphate symporter, which has been identified as a prognostic marker in several types of cancer, including pancreatic cancer. However, to the best of our knowledge, the association between SLC20A1 expression and cancer stem cell (CSC) markers, such as aldehyde dehydrogenase 1 (ALDH1), in pancreatic ductal adenocarcinoma (PDAC), and the role of SLC20A1 in PDAC CSCs remains unclear. In the present study, a genomic dataset of primary pancreatic cancer (The Cancer Genome Atlas, Pan-Cancer Atlas) was downloaded and analyzed. Kaplan-Meier analysis and multivariate Cox regression analysis were performed to evaluate the overall survival, disease-specific survival (DSS), disease-free interval (DFI) and progression-free interval (PFI). Subsequently, SLC20A1 small interfering RNA (siRNA) knockdown (KD) was induced in the PANC-1 and MIA-PaCa-2 PDAC cell lines, and in sorted high ALDH1 activity (ALDH1high) cells, after which, cell viability, in vitro tumor sphere formation, cell death and caspase-3 activity were examined. The results revealed that patients with high expression of SLC20A1 (SLC20A1 high) at tumor stage I had a poor prognosis compared with patients with low expression of SLC20A1 (SLC20A1 low) in terms of DSS, DFI and PFI. In addition, patients with high expression of SLC20A1 and ALDH1A3 (SLC20A1 high ALDH1A3 high) exhibited poorer clinical outcomes than patients with high expression of SLC20A1 and low expression of ALDH1A3 (SLC20A1 high ALDH1A3 low), low expression of SLC20A1 and high expression of ALDH1A3 (SLC20A1 low ALDH1A3 high) and SLC20A1 low ALDH1A3 low. SLC20A1 siRNA KD in ALDH1high cells isolated from PANC-1 and MIA-PaCa-2 cell lines resulted in suppression of in vitro tumorsphere formation, and enhancement of cell death and caspase-3 activity. These results suggested that SLC20A1 was involved in cell survival via the suppression of caspase-3-dependent apoptosis, and contributed to cancer progression and poor clinical outcomes in PDAC. In conclusion, SLC20A1 may be used as a prognostic marker and novel therapeutic target of ALDH1-positive pancreatic CSCs.

Breast Cancer Plasticity after Chemotherapy Highlights the Need for Re-Evaluation of Subtyping in Residual Cancer and Metastatic Tissues

This research paper presents a novel approach to identifying biomarkers that can be used to prognosticate patients with triple-negative breast cancer (TNBC) eligible for neoadjuvant therapy. The study utilized survival and RNA sequencing data from a cohort of TNBC patients and identified 276 genes whose expression was related to survival in such patients. The gene expression data were then used to classify patients into two major groups based on the presence or absence of Wingless/Integrated-pathway (Wnt-pathway) and mesenchymal (Mes) markers (Wnt/Mes). Patients with a low expression of Wnt/Mes-related genes had a favorable outcome, with no deaths observed during follow-up, while patients with a high expression of Wnt/Mes genes had a higher mortality rate of 50% within 19 months. The identified gene list could be validated and potentially used to shape treatment options for TNBC patients eligible for neoadjuvant therapy providing valuable insights into the development of more effective treatments for TNBC. Our data also showed significant variation in gene expression profiles before and after chemotherapy, with most tumors switching to a more mesenchymal/stem cell-like profile. To verify this observation, we performed an in silico analysis to classify breast cancer tumors in Prediction Analysis of Microarray 50 (PAM50) molecular classes before treatment and after treatment using gene expression data. Our findings demonstrate that following drug intervention and metastasis, certain tumors undergo a transition to alternative subtypes, resulting in diminished therapeutic efficacy. This underscores the necessity for reevaluation of patients who have experienced relapse or metastasis post-chemotherapy, with a focus on molecular subtyping. Tailoring treatment strategies based on these refined subtypes is imperative to optimize therapeutic outcomes for affected individuals.

The immune escape signature predicts the prognosis and immunotherapy sensitivity for pancreatic ductal adenocarcinoma

Background

Pancreatic ductal adenocarcinoma (PDAC) is one of the deadliest malignancies worldwide. Immune escape is considered to be a reason for immunotherapy failure in PDAC. In this study, we explored the correlation between immune escape-related genes and the prognosis of PDAC patients.

Methods

1163 PDAC patients from four public databases, including The Cancer Genome Atlas (TCGA), International Cancer Genome Consortium (ICGC), Array-express, and Gene Expression Omnibus (GEO), were included in our study. Cox regression analysis was used to identify the 182 immune genes which were significantly associated with overall survival (OS). And then we established an immune escape-related gene prognosis index (IEGPI) score using several datasets as the training cohort and validated it using the validation cohort. Kaplan-Meier (KM) and Cox regression analysis were used to detect the relationship of IEGPI score with OS. We further explored the relationship between the IEGPI and immune indexes. And the prediction value of response for immunotherapy in Tumor Immune Dysfunction and Exclusion (TIDE) dataset.

Results

We establish an IEGPI score based on 27 immune escape genes which were significantly related to the prognosis of OS in PDAC patients. Patients in the high-IEGPI group had a significantly worse overall survival rate compared with that in the low-IEGPI groups by KM curves and cox-regression. 5 of the 32 cancer types in TCGA could be significantly distinguished in survival rates through the low- and high-IEGPI groups. Moreover, the correlation between the IEGPI score was negatively correlated with an immune score in several datasets. And higher IEGPI better recurrence-free survival (RFS) and OS in the patients after patients were treated with both PD-1 and CTLA4 in the public datasets (P<0.05). Intriguingly, by using RT-PCR, we verified that the gene of PTPN2, CEP55, and JAK2 were all higher in the BxPC-3 and PANC-1 than HPDE5 cells. Lastly, we found that the IEGPI score was higher in K-rasLSL.G12D/+, p53LSL.R172H/+, Pdx1Cre (KPC) mice model with anti-PD-L1 than that without anti-PD-L1.

Conclusion

Using the immune escape-related genes, our study established and validated an IEGPI score in PDAC patients from the public dataset. IEGPI score has the potential to serve as a prognostic marker and as a tool for selecting tumor patients suitable for immunotherapy in clinical practice.

Development and validation of a novel 3-gene prognostic model for pancreatic adenocarcinoma based on ferroptosis-related genes

Background

Molecular markers play an important role in predicting clinical outcomes in pancreatic adenocarcinoma (PAAD) patients. Analysis of the ferroptosis-related genes may provide novel potential targets for the prognosis and treatment of PAAD.

Methods

RNA-sequence and clinical data of PAAD was downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) public databases. The PAAD samples were clustered by a non-negative matrix factorization (NMF) algorithm. The differentially expressed genes (DEGs) between different subtypes were used by “limma_3.42.2” package. The R software package clusterProfiler was used for functional enrichment analysis. Then, a multivariate Cox proportional and LASSO regression were used to develop a ferroptosis-related gene signature for pancreatic adenocarcinoma. A nomogram and corrected curves were constructed. Finally, the expression and function of these signature genes were explored by qRT-PCR, immunohistochemistry (IHC) and proliferation, migration and invasion assays.

Results

The 173 samples were divided into 3 categories (C1, C2, and C3) and a 3-gene signature model (ALOX5, ALOX12, and CISD1) was constructed. The prognostic model showed good independent prognostic ability in PAAD. In the GSE62452 external validation set, the molecular model also showed good risk prediction. KM-curve analysis showed that there were significant differences between the high and low-risk groups, samples with a high-risk score had a worse prognosis. The predictive efficiency of the 3-gene signature-based nomogram was significantly better than that of traditional clinical features. For comparison with other models, that our model, with a reasonable number of genes, yields a more effective result. The results obtained with qPCR and IHC assays showed that ALOX5 was highly expressed, whether ALOX12 and CISD1 were expressed at low levels in tissue samples. Finally, function assays results suggested that ALOX5 may be an oncogene and ALOX12 and CISD1 may be tumor suppressor genes.

Conclusions

We present a novel prognostic molecular model for PAAD based on ferroptosis-related genes, which serves as a potentially effective tool for prognostic differentiation in pancreatic cancer patients.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-021-02431-8.

Development and validation of a four-lipid metabolism gene signature for diagnosis of pancreatic cancer

Abnormal lipid metabolism is closely related to the malignant biological behavior of tumor cells. Such abnormal lipid metabolism provides energy for rapid proliferation, and certain genes related to lipid metabolism encode important components of tumor signaling pathways. In this study, we analyzed pancreatic cancer datasets from The Cancer Genome Atlas and searched for prognostic genes related to lipid metabolism in the Molecular Signature Database. A risk score model was built and verified using the GSE57495 dataset and International Cancer Genome Consortium dataset. Four molecular subtypes and 4249 differentially expressed genes (DEGs) were identified. The DEGs obtained by Weighted Gene Coexpression Network Construction analysis were intersected with 4249 DEGs to obtain a total of 1340 DEGs. The final prognosis model included CA8, CEP55, GNB3 and SGSM2, and these had a significant effect on overall survival. The area under the curve at 1, 3 and 5 years was 0.72, 0.79 and 0.87, respectively. These same results were obtained using the validation cohort. Survival analysis data showed that the model could stratify the prognosis of patients with different clinical characteristics, and the model has clinical independence. Functional analysis indicated that the model is associated with multiple cancer-related pathways. Compared with published models, our model has a higher C-index and greater risk value. In summary, this four-gene signature is an independent risk factor for pancreatic cancer survival and may be an effective prognostic indicator.

Genomic Heterogeneity of Pancreatic Ductal Adenocarcinoma and Its Clinical Impact

Pancreatic ductal adenocarcinoma (PDAC) is one of the leading causes of cancer death due to limited advances in recent years in early diagnosis and personalized therapy capable of overcoming tumor resistance to chemotherapy. In the last decades, significant advances have been achieved in the identification of recurrent genetic and molecular alterations of PDAC including those involving the KRAS, CDKN2A, SMAD4, and TP53 driver genes. Despite these common genetic traits, PDAC are highly heterogeneous tumors at both the inter- and intra-tumoral genomic level, which might contribute to distinct tumor behavior and response to therapy, with variable patient outcomes. Despite this, genetic and genomic data on PDAC has had a limited impact on the clinical management of patients. Integration of genomic data for classification of PDAC into clinically defined entities-i.e., classical vs. squamous subtypes of PDAC-leading to different treatment approaches has the potential for significantly improving patient outcomes. In this review, we summarize current knowledge about the most relevant genomic subtypes of PDAC including the impact of distinct patterns of intra-tumoral genomic heterogeneity on the classification and clinical and therapeutic management of PDAC.

Development and validation of glycolysis-related prognostic score for prediction of prognosis and chemosensitivity of pancreatic ductal adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is a lethal malignancy with aggressive biological behaviour. Its rapid proliferation and tumour growth require reprogramming of glucose metabolism or the Warburg effect. However, the association between glycolysis-related genes with clinical features and prognosis of PDAC is still unknown. Here, we used the meta-analysis to correlate the hazard ratios (HR) of 106 glycolysis genes from MSigDB by the cox proportional hazards regression analysis in 6 clinical data sets of PDAC patients to form a training cohort, and a single group of PDAC patients from the TCGA, ICGC, Arrayexpress and GEO databases to form the validation cohort. Then, a glycolysis-related prognosis (GRP) score based on 29 glycolysis prognostic genes was established in 757 PDAC patients from the training composite cohort and validated in 267 ICGC-CA validation cohort (all P < .05). In addition, including PADC, the prognostic value was also confirmed in other 7 out of 30 pan-cancer cohorts. The GRP score was significantly related to specific metabolism pathways, immune genes and immune cells in the patients with PADC (all P < .05). Finally, by combining with immune cells, the GRP score also well-predicted the chemosensitivity of patients with PADC in the TCGA cohort (AUC = 0.709). In conclusion, this study developed a GRP score for patients with PDAC in predicting prognosis and chemosensitivity for PDAC.

Epigenetic Regulatory Enzymes: mutation Prevalence and Coexistence in Cancers

Epigenetic regulation is an important layer of transcriptional control with the particularity to affect the broad spectrum of genome. Over the years, largely due to the substantial number of recurrent mutations, there have been hundreds of novel driver genes characterized in various cancers. Additionally, the relative contribution of two dysregulated epigenomic entities (DNA methylation and histone modifications) that gradually drive the cancer phenotype remains in the research focus. However, a complex scenario arises when the disease phenotype does not harbor any relevant mutation or an abnormal transcription level. Although the cancer landscape involves the contribution of multiple genetic and non-genetic factors, herein, we discuss specifically the mutation spectrum of epigenetically-related enzymes in cancer. In addition, we address the coexistence of these two epigenetic entities in malignant human diseases, especially cancer. We suggest that the study of epigenetically-related somatic mutations in the early cellular differentiation stage of embryonic development might help to understand their later-staged footprints in the cancer genome. Furthermore, understanding the co-occurrence and/or inverse association of different disease types and redefining the general definition of "healthy" controls could provide insights into the genome reorganization.

Progress in Natural Compounds/siRNA Co-delivery Employing Nanovehicles for Cancer Therapy

Chemotherapy using natural compounds, such as resveratrol, curcumin, paclitaxel, docetaxel, etoposide, doxorubicin, and camptothecin, is of importance in cancer therapy because of the outstanding therapeutic activity and multitargeting capability of these compounds. However, poor solubility and bioavailability of natural compounds have limited their efficacy in cancer therapy. To circumvent this hurdle, nanocarriers have been designed to improve the antitumor activity of the aforementioned compounds. Nevertheless, cancer treatment is still a challenge, demanding novel strategies. It is well-known that a combination of natural products and gene therapy is advantageous over monotherapy. Delivery of multiple therapeutic agents/small interfering RNA (siRNA) as a potent gene-editing tool in cancer therapy can maximize the synergistic effects against tumor cells. In the present review, co-delivery of natural compounds/siRNA using nanovehicles are highlighted to provide a backdrop for future research.