Dataset Information

A novel strategy for precise prognosis management and treatment option in colon adenocarcinoma with TP53 mutations.

ABSTRACT:

Background

TP53 is one of the most frequent mutated genes in colon cancer. Although colon cancer with TP53 mutations has a high risk of metastasis and worse prognosis generally, it showed high heterogeneity clinically.

Methods

A total of 1,412 colon adenocarcinoma (COAD) samples were obtained from two RNA-seq cohorts and three microarray cohorts, including the TCGA-COAD (N = 408), the CPTAC-COAD (N = 106), GSE39582 (N = 541), GSE17536 (N = 171) and GSE41258 (N = 186). The LASSO-Cox method was used to establish the prognostic signature based on the expression data. The patients were divided into high-risk and low-risk groups based on the median risk score. The efficiency of the prognostic signature was validated in various cohorts, including TP53-mutant and TP53 wild-type. The exploration of potential therapeutic targets and agents was performed by using the expression data of TP53-mutant COAD cell lines obtained from the CCLE database and the corresponding drug sensitivity data obtained from the GDSC database.

Results

A 16-gene prognostic signature was established in TP53-mutant COAD. The high-risk group had significantly inferior survival time compared to the low-risk group in all TP53-mutant datasets, while the prognostic signature failed to classify the prognosis of COAD with TP53 wild-type properly. Besides, the risk score was the independent poor factor for the prognosis in TP53-mutant COAD and the nomogram based on the risk score was also shown good predictive efficiency in TP53-mutant COAD. Moreover, we identified SGPP1, RHOQ, and PDGFRB as potential targets for TP53-mutant COAD, and illuminated that the high-risk patients might benefit from IGFR-3801, Staurosporine, and Sabutoclax.

Conclusion

A novel prognostic signature with great efficiency was established especially for COAD patients with TP53 mutations. Besides, we identified novel therapeutic targets and potential sensitive agents for TP53-mutant COAD with high risk. Our findings provided not only a new strategy for prognosis management but also new clues for drug application and precision treatment in COAD with TP53 mutations.

SUBMITTER: Niu L

PROVIDER: S-EPMC9947352 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A novel strategy for precise prognosis management and treatment option in colon adenocarcinoma with TP53 mutations.

Niu Lei L Liu Langbiao L Cai Jun J

Frontiers in surgery 20230209

<h4>Background</h4>TP53 is one of the most frequent mutated genes in colon cancer. Although colon cancer with TP53 mutations has a high risk of metastasis and worse prognosis generally, it showed high heterogeneity clinically.<h4>Methods</h4>A total of 1,412 colon adenocarcinoma (COAD) samples were obtained from two RNA-seq cohorts and three microarray cohorts, including the TCGA-COAD (<i>N</i> = 408), the CPTAC-COAD (<i>N</i> = 106), GSE39582 (<i>N</i> = 541), GSE17536 (<i>N</i> = 171) and GSE4 ...[more]

PMID: 36843983

Similar Datasets

Project description:BackgroundThe TP53 tumor suppressor gene is one of the most mutated genes in lung adenocarcinoma (LUAD) and plays a vital role in regulating the occurrence and progression of cancer. We aimed to elucidate the association between TP53 mutations, response to immunotherapies and the prognosis of LUAD.MethodsGenomic, transcriptomic, and clinical data of LUAD were downloaded from The Cancer Genome Atlas (TCGA) dataset. Gene ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, gene set enrichment analysis (GSEA). Gene set variation analysis (GSVA) were performed to determine the differences in biological pathways. A merged protein-protein interaction (PPI) network was constructed and analyzed. MSIpred was used to analyze the correlation between the expression of the TP53 gene, tumor mutation burden (TMB) and tumor microsatellite instability (MSI). CIBERSORT was used to calculate the abundance of immune cells. Univariate and multivariate Cox regression analyses were used to determine the prognostic value of TP53 mutations in LUAD.ResultsTP53 was the most frequently mutated in LUAD, with a mutational frequency of 48%. GO and KEGG enrichment analysis, GSEA, and GSVA results showed a significant upregulation of several signaling pathways, including PI3K-AKT mTOR (P < 0.05), Notch (P < 0.05), E2F target (NES = 1.8, P < 0.05), and G2M checkpoint (NES = 1.7, P < 0.05). Moreover, we found a significant correlation between T cells, plasma cells, and TP53 mutations (R2 < 0.01, P = 0.040). Univariate and multivariate Cox regression analyses revealed that the survival prognosis of LUAD patients was related to TP53 mutations (Hazard Ratio (HR) = 0.72 [95% CI, 0.53 to 0.98], P < 0.05), cancer status (P < 0.05), and treatment outcomes (P < 0.05). Lastly, the Cox regression models showed that TP53 exhibited good power in predicting three- and five-year survival rates.ConclusionsTP53 may be an independent predictor of response to immunotherapy in LUAD, and patients with TP53 mutations have higher immunogenicity and immune cell infiltration.

Project description:In recent years, tumor immunotherapy has become an important treatment program and popular research focus. However, the use of immune checkpoint inhibitors (ICI) in the treatment of colorectal cancer still has limitations due to the current markers only being able to predict the prognosis of a small number of patients. As the chemokine signaling pathway can promote the anti-tumor response of the immune system by recruiting immune cells, we explored the relationship between mutations in the chemokine signaling pathway and the prognosis of colon adenocarcinoma (COAD) patients receiving ICI treatment. To analyze the relationship between chemokine mutation status and the prognosis of patients receiving ICI treatment, clinical and mutation data, with immunotherapy, for a COAD cohort was obtained from "cbioportal." Then, combining this with COAD cohort data from The Cancer Genome Atlas (TCGA) database, the panorama of gene mutation, immunogenicity, and difference in tumor microenvironment (TME) of chemokine pathways with different mutation statuses were analyzed. High-mut status has been proved to be a prognostic indicator of COAD patients receiving ICI treatment by Univariate and Multivariate Cox regression analysis. CIBERSORT analysis showed that the infiltration degree of M1 macrophages, neutrophils, and activated natural killer (NK) cells was higher in those with high-mut status. Immunogenicity of the high-mut group was also significantly increased, with the mutation number of tumor mutation burden (TMB), neoantigen load (NAL), DNA damage repair (DDR) pathway and microsatellite instability biomarker (MSI-H) being significantly higher. In this study, we found that the mutation state of chemokine pathways is closely associated with the prognosis of COAD patients undergoing ICI treatment. The higher number of TMB, NAL, and DDR mutations and inflammatory TME, may be the mechanism of behind a better prognosis. This discovery provides a possible idea for ICI therapy of COAD.

Project description:ObjectiveThis study endeavored to explore the optimal treatment strategy and conduct a prognostic analysis for patients diagnosed with pT4M0 (pathologic stage T4) colon adenocarcinoma (COAD).Methods and materialsA total of 8,843 patients diagnosed with pT4M0 COAD between January 2010 and December 2015 were included in this study from the Surveillance, Epidemiology, and End Results (SEER) database. These patients were randomly divided into a training set and an internal validation set using a 7:3 ratio. Variables that demonstrated statistical significance (P<0.05) in univariate COX regression analysis or held clinical significance were incorporated into the multivariate COX regression model. Subsequently, this model was utilized to formulate a nomogram. The predictive accuracy and discriminability of the nomogram were assessed using the C-index, area under the curve (AUC), and calibration curves. Decision curve analysis (DCA) was conducted to confirm the clinical validity of the model.ResultsIn the entire SEER cohort, the 3-year overall survival (OS) rate (74.22% vs. 63.20%, P<0.001) and the 3-year cancer-specific survival (CSS) rate (76.25% vs. 66.98%, P<0.001) in the surgery combined with postoperative adjuvant therapy (S+ADT) group surpassed those in the surgery (S) group. Multivariate COX regression analysis of the training set unveiled correlations between age, race, N stage, serum CEA (carcinoembryonic antigen), differentiation, number of resected lymph nodes, and treatment modalities with OS and CSS. Nomograms for OS and CSS were meticulously crafted based on these variables, achieving C-indexes of 0.692 and 0.690 in the training set, respectively. The robust predictive ability of the nomogram was further affirmed through receiver operating characteristic (ROC) and calibration curves in both the training and validation sets.ConclusionIn individuals diagnosed with pT4M0 COAD, the integration of surgery with adjuvant chemoradiotherapy demonstrated a substantial extension of long-term survival. The nomogram, which incorporated key factors such as age, race, differentiation, N stage, serum CEA level, tumor size, and the number of resected lymph nodes, stood as a dependable tool for predicting OS and CSS rates. This predictive model held promise in aiding clinicians by identifying high-risk patients and facilitating the development of personalized treatment plans.

Project description:BackgroundTP53 mutations have a prognostic significance in relapsed and refractory diffuse large B-cell lymphoma (rrDLBCL) patients, and their treatment still faces a great challenge. This study aimed to evaluate the prognosis of patients with TP53 mutations (TP53mut) in the context of CAR-T therapy (Chimeric antigen receptor T-cell therapy) as well as explore the heterogeneity in their cohort and identify the possible risk factors.MethodsA retrospective study was conducted to investigate the clinical characteristics of rrDLBCL patients with TP53 mutations and their prognostic factors, receiving CAR-T therapy. And the expression level of TP53 and DDX3X, which was an important co-mutation of TP53 revealed in the cohort, were explored in public databases and cell lines.ResultsThe median overall survival time of 40 patients with TP53 mutations was 24.5 months, while their median progression-free survival time after CAR-T was 6.8 months. There were no significant differences in the ORR (objective remission rate, X2 = 3.0498, p > 0.05) and PFS (after CAR-T therapy) between the patients with wild-type and mutated TP53 genes after CAR-T therapy, while the OS of patients with TP53 mutations was significantly worse (p < 0.01). In patients with TP53 mutations, the performance status (ECOG score) was identified as the most important prognostic factor, while the efficacies of induction and salvage treatments were also correlated with the prognosis. Among molecular indicators, the co-mutations of Chr-17 and those located on the exon 5 of the TP53 gene showed a tendency for a worse prognosis. Moreover, the patients with TP53-DDX3X co-mutations were identified as a subgroup with an extremely bad prognosis. The expression levels of DDX3X and TP53 were explored in a public database and the cell lines with their co-mutations, which indicated that inhibiting the DDX3X gene could affect the proliferation of rrDLBCL cells and the expression of TP53.ConclusionsThis study indicated rrDLBCL patients with TP53 mutations was still the group of poor prognosis in the CAR-T therapy era. CAR-T therapy can benefit some TP53mut patients, and the performance status (ECOG) might help predict their prognosis. The study also revealed a subgroup of TP53-DDX3X co-mutations in rrDLBCL, which showed a strong clinical significance.

Dataset Information

A novel strategy for precise prognosis management and treatment option in colon adenocarcinoma with TP53 mutations.

Background

Methods

Results

Conclusion

Publications

A novel strategy for precise prognosis management and treatment option in colon adenocarcinoma with TP53 mutations.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets