Dataset Information

Impact of between-tissue differences on pan-cancer predictions of drug sensitivity.

ABSTRACT: Increased availability of drug response and genomics data for many tumor cell lines has accelerated the development of pan-cancer prediction models of drug response. However, it is unclear how much between-tissue differences in drug response and molecular characteristics may contribute to pan-cancer predictions. Also unknown is whether the performance of pan-cancer models could vary by cancer type. Here, we built a series of pan-cancer models using two datasets containing 346 and 504 cell lines, each with MEK inhibitor (MEKi) response and mRNA expression, point mutation, and copy number variation data, and found that, while the tissue-level drug responses are accurately predicted (between-tissue ? = 0.88-0.98), only 5 of 10 cancer types showed successful within-tissue prediction performance (within-tissue ? = 0.11-0.64). Between-tissue differences make substantial contributions to the performance of pan-cancer MEKi response predictions, as exclusion of between-tissue signals leads to a decrease in Spearman's ? from a range of 0.43-0.62 to 0.30-0.51. In practice, joint analysis of multiple cancer types usually has a larger sample size, hence greater power, than for one cancer type; and we observe that higher accuracy of pan-cancer prediction of MEKi response is almost entirely due to the sample size advantage. Success of pan-cancer prediction reveals how drug response in different cancers may invoke shared regulatory mechanisms despite tissue-specific routes of oncogenesis, yet predictions in different cancer types require flexible incorporation of between-cancer and within-cancer signals. As most datasets in genome sciences contain multiple levels of heterogeneity, careful parsing of group characteristics and within-group, individual variation is essential when making robust inference.

SUBMITTER: Lloyd JP

PROVIDER: S-EPMC7906305 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Impact of between-tissue differences on pan-cancer predictions of drug sensitivity.

Lloyd John P JP Soellner Matthew B MB Merajver Sofia D SD Li Jun Z JZ

PLoS computational biology 20210225 2

Increased availability of drug response and genomics data for many tumor cell lines has accelerated the development of pan-cancer prediction models of drug response. However, it is unclear how much between-tissue differences in drug response and molecular characteristics may contribute to pan-cancer predictions. Also unknown is whether the performance of pan-cancer models could vary by cancer type. Here, we built a series of pan-cancer models using two datasets containing 346 and 504 cell lines, ...[more]

PMID: 33630864

Similar Datasets

Project description:Phosphofructokinase, platelet (PFKP) is a rate-limiting enzyme of glycolysis that plays a decisive role in various human physio-pathological processes. PFKP has been reported to have multiple functions in different cancer types, including lung cancer and breast cancer. However, no systematic pancancer analysis of PFKP has been performed; this type of analysis could elucidate the clinical value of PFKP in terms of diagnosis, prognosis, drug sensitivity, and immunological correlation. Systematic bioinformation analysis of PFKP was performed based on several public datasets, including The Cancer Genome Atlas (TCGA), Cancer Cell Line Encyclopedia (CCLE), Genotype-Tissue Expression Project (GTEx), and Human Protein Atlas (HPA). Prospective carcinogenesis of PFKP across cancers was estimated by expression analysis, effect on patient prognosis, diagnosis significance evaluation, and immunity regulation estimation. Then, pancancer functional enrichment of PFKP was also assessed through its effect on the signaling score and gene expression profile. Finally, upstream expression regulation of PFKP was explored by promoter DNA methylation and transcription factor (TF) prediction. Our analysis revealed that high expression of PFKP was found in most cancer types. Additionally, a high level of PFKP displayed a significant correlation with poor prognosis in patients across cancers. The diagnostic value of PFKP was performed based on its positive correlation with programmed cell death-ligand 1 (PD-L1). We also found an obvious immune-regulating effect of PFKP in most cancer types. PFKP also had a strong negative correlation with several cancer drugs. Finally, ectopic expression of PFKP may depend on DNA methylation and several predicated transcription factors, including the KLF (KLF transcription factor) and Sp (Sp transcription factor) families. This pancancer analysis revealed that a high expression level of PFKP might be a useful biomarker and predictor in most cancer types. Additionally, the performance of PFKP across cancers also suggested its meaningful role in cancer immunity regulation, even in immunotherapy and drug resistance. Overall, PFKP might be explored as an auxiliary monitor for pancancer early prognosis and diagnosis.

Project description:Understanding the heterogeneous drug response of cancer patients is essential to precision oncology. Pioneering genomic analyses of individual cancer subtypes have begun to identify key determinants of resistance, including up-regulation of multi-drug resistance (MDR) genes and mutational alterations of drug targets. However, these alterations are sufficient to explain only a minority of the population, and additional mechanisms of drug resistance or sensitivity are required to explain the remaining spectrum of patient responses to ultimately achieve the goal of precision oncology. We hypothesized that a pan-cancer analysis of in vitro drug sensitivities across numerous cancer lineages will improve the detection of statistical associations and yield more robust and, importantly, recurrent determinants of response. In this study, we developed a statistical framework based on the meta-analysis of expression profiles to identify pan-cancer markers and mechanisms of drug response. Using the Cancer Cell Line Encyclopaedia (CCLE), a large panel of several hundred cancer cell lines from numerous distinct lineages, we characterized both known and novel mechanisms of response to cytotoxic drugs including inhibitors of Topoisomerase 1 (TOP1; Topotecan, Irinotecan) and targeted therapies including inhibitors of histone deacetylases (HDAC; Panobinostat) and MAP/ERK kinases (MEK; PD-0325901, AZD6244). Notably, our analysis implicated reduced replication and transcriptional rates, as well as deficiency in DNA damage repair genes in resistance to TOP1 inhibitors. The constitutive activation of several signaling pathways including the interferon/STAT-1 pathway was implicated in resistance to the pan-HDAC inhibitor. Finally, a number of dysregulations upstream of MEK were identified as compensatory mechanisms of resistance to the MEK inhibitors. In comparison to alternative pan-cancer analysis strategies, our approach can better elucidate relevant drug response mechanisms. Moreover, the compendium of putative markers and mechanisms identified through our analysis can serve as a foundation for future studies into these drugs.

Project description:Motivation:Proteomics profiling is increasingly being used for molecular stratification of cancer patients and cell-line panels. However, systematic assessment of the predictive power of large-scale proteomic technologies across various drug classes and cancer types is currently lacking. To that end, we carried out the first pan-cancer, multi-omics comparative analysis of the relative performance of two proteomic technologies, targeted reverse phase protein array (RPPA) and global mass spectrometry (MS), in terms of their accuracy for predicting the sensitivity of cancer cells to both cytotoxic chemotherapeutics and molecularly targeted anticancer compounds. Results:Our results in two cell-line panels demonstrate how MS profiling improves drug response predictions beyond that of the RPPA or the other omics profiles when used alone. However, frequent missing MS data values complicate its use in predictive modeling and required additional filtering, such as focusing on completely measured or known oncoproteins, to obtain maximal predictive performance. Rather strikingly, the two proteomics profiles provided complementary predictive signal both for the cytotoxic and targeted compounds. Further, information about the cellular-abundance of primary target proteins was found critical for predicting the response of targeted compounds, although the non-target features also contributed significantly to the predictive power. The clinical relevance of the selected protein markers was confirmed in cancer patient data. These results provide novel insights into the relative performance and optimal use of the widely applied proteomic technologies, MS and RPPA, which should prove useful in translational applications, such as defining the best combination of omics technologies and marker panels for understanding and predicting drug sensitivities in cancer patients. Availability and implementation:Processed datasets, R as well as Matlab implementations of the methods are available at https://github.com/mehr-een/bemkl-rbps. Contact:mehreen.ali@helsinki.fi or tero.aittokallio@fimm.fi. Supplementary information:Supplementary data are available at Bioinformatics online.

Project description:BackgroundProtein tyrosine phosphatase non-receptor type 1 (PTPN1), a member of the protein tyrosine phosphatase superfamily, has been identified as an oncogene and therapeutic target in various cancers. However, its precise role in determining the prognosis of human cancer and immunological responses remains elusive. This study investigated the relationship between PTPN1 expression and clinical outcomes, immune infiltration, and drug sensitivity in human cancers, which will improve understanding regarding its prognostic value and immunological role in pan-cancer.MethodsThe PTPN1 expression profile was obtained from The Cancer Genome Atlas and Cancer Cell Line Encyclopedia databases. Kaplan-Meier, univariate Cox regression, and time-dependent receiver operating characteristic curve analyses were utilized to clarify the relationship between PTPN1 expression and the prognosis of pan-cancer patients. The relationships between PTPN1 expression and the presence of tumor-infiltrated immune cells were analyzed using Estimation of Stromal and Immune cells in Malignant Tumor tissues using Expression data and Tumor Immune Estimation Resource. The cell counting kit-8 (CCK-8) assay was performed to examine the effects of PTPN1 level on the sensitivity of breast cancer cells to paclitaxel. Immunohistochemistry and immunoblotting were used to investigate the relationship between PTPN1 expression, immune cell infiltration, and immune checkpoint gene expression in human breast cancer tissues and a mouse xenograft model.ResultsThe pan-cancer analysis revealed that PTPN1 was frequently up-regulated in various cancers. High PTPN1 expression was associated with poor prognosis in most cancers. Furthermore, PTPN1 expression correlated highly with the presence of tumor-infiltrating immune cells and the expression of immune checkpoint pathway marker genes in different cancers. Furthermore, PTPN1 significantly predicted the prognosis for patients undergoing immunotherapy. The results of the CCK-8 viability assay revealed that PTPN1 knockdown increased the sensitivity of MDA-MB-231 and MCF-7 cells to paclitaxel. Finally, our results demonstrated that PTPN1 was associated with immune infiltration and immune checkpoint gene expression in breast cancer.ConclusionPTPN1 was overexpressed in multiple cancer types and correlated with the clinical outcome and tumor immunity, suggesting it could be a valuable potential prognostic and immunological biomarker for pan-cancer.

Dataset Information

Impact of between-tissue differences on pan-cancer predictions of drug sensitivity.

Publications

Impact of between-tissue differences on pan-cancer predictions of drug sensitivity.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets