Expression patterns of small numbers of transcripts from functionally-related pathways predict survival in multiple cancers
Abstract Background Genetic profiling of cancers for variations in copy number, structure or expression of certain genes has improved diagnosis, risk-stratification and therapeutic decision-making. However the tumor-restricted nature of these changes limits their application to certain cancer types...
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| Format: | Article |
| Language: | English |
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BMC
2019-07-01
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| Series: | BMC Cancer |
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| Online Access: | http://link.springer.com/article/10.1186/s12885-019-5851-6 |
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| author | Jordan Mandel Huabo Wang Daniel P. Normolle Wei Chen Qi Yan Peter C. Lucas Panayiotis V. Benos Edward V. Prochownik |
| author_facet | Jordan Mandel Huabo Wang Daniel P. Normolle Wei Chen Qi Yan Peter C. Lucas Panayiotis V. Benos Edward V. Prochownik |
| author_sort | Jordan Mandel |
| collection | DOAJ |
| description | Abstract Background Genetic profiling of cancers for variations in copy number, structure or expression of certain genes has improved diagnosis, risk-stratification and therapeutic decision-making. However the tumor-restricted nature of these changes limits their application to certain cancer types or sub-types. Tests with broader prognostic capabilities are lacking. Methods Using RNAseq data from 10,227 tumors in The Cancer Genome Atlas (TCGA), we evaluated 212 protein-coding transcripts from 12 cancer-related pathways. We employed t-distributed stochastic neighbor embedding (t-SNE) to identify expression pattern difference among each pathway’s transcripts. We have previously used t-SNE to show that survival in some cancers correlates with expression patterns of transcripts encoding ribosomal proteins and enzymes for cholesterol biosynthesis and fatty acid oxidation. Results Using the above 212 transcripts, t-SNE-assisted transcript pattern profiling identified patient cohorts with significant survival differences in 30 of 34 different cancer types comprising 9350 tumors (91.4% of all TCGA cases). Small subsets of each pathway’s transcripts, comprising no more than 50–60 from the original group, played particularly prominent roles in determining overall t-SNE patterns. In several cases, further refinements in long-term survival could be achieved by sequential t-SNE profiling with two pathways’ transcripts, by a combination of t-SNE plus whole transcriptome profiling or by employing t-SNE on immuno-histochemically defined breast cancer subtypes. In two cancer types, individuals with Stage IV disease at presentation could be readily subdivided into groups with highly significant survival differences based on t-SNE-based tumor sub-classification. Conclusions t-SNE-assisted profiling of a small number of transcripts allows the prediction of long-term survival across multiple cancer types. |
| first_indexed | 2024-12-13T12:07:16Z |
| format | Article |
| id | doaj.art-96d1780f6dc24126b213171d3927b91a |
| institution | Directory Open Access Journal |
| issn | 1471-2407 |
| language | English |
| last_indexed | 2024-12-13T12:07:16Z |
| publishDate | 2019-07-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Cancer |
| spelling | doaj.art-96d1780f6dc24126b213171d3927b91a2022-12-21T23:46:55ZengBMCBMC Cancer1471-24072019-07-0119111510.1186/s12885-019-5851-6Expression patterns of small numbers of transcripts from functionally-related pathways predict survival in multiple cancersJordan Mandel0Huabo Wang1Daniel P. Normolle2Wei Chen3Qi Yan4Peter C. Lucas5Panayiotis V. Benos6Edward V. Prochownik7The Division of Hematology/Oncology, Children’s Hospital of Pittsburgh of UPMC, Children’s Hospital of Pittsburgh of UPMC, Rangos Research CenterThe Division of Hematology/Oncology, Children’s Hospital of Pittsburgh of UPMC, Children’s Hospital of Pittsburgh of UPMC, Rangos Research CenterThe Department of Biostatistics and The University of Pittsburgh Graduate School of Public HealthThe Division of Pulmonary Medicine, Allergy and Immunology, Children’s Hospital of Pittsburgh of UPMCThe Division of Pulmonary Medicine, Allergy and Immunology, Children’s Hospital of Pittsburgh of UPMCThe Division of Hematology/Oncology, Children’s Hospital of Pittsburgh of UPMC, Children’s Hospital of Pittsburgh of UPMC, Rangos Research CenterThe Department of Computational and Systems Biology, The University of Pittsburgh Medical CenterThe Division of Hematology/Oncology, Children’s Hospital of Pittsburgh of UPMC, Children’s Hospital of Pittsburgh of UPMC, Rangos Research CenterAbstract Background Genetic profiling of cancers for variations in copy number, structure or expression of certain genes has improved diagnosis, risk-stratification and therapeutic decision-making. However the tumor-restricted nature of these changes limits their application to certain cancer types or sub-types. Tests with broader prognostic capabilities are lacking. Methods Using RNAseq data from 10,227 tumors in The Cancer Genome Atlas (TCGA), we evaluated 212 protein-coding transcripts from 12 cancer-related pathways. We employed t-distributed stochastic neighbor embedding (t-SNE) to identify expression pattern difference among each pathway’s transcripts. We have previously used t-SNE to show that survival in some cancers correlates with expression patterns of transcripts encoding ribosomal proteins and enzymes for cholesterol biosynthesis and fatty acid oxidation. Results Using the above 212 transcripts, t-SNE-assisted transcript pattern profiling identified patient cohorts with significant survival differences in 30 of 34 different cancer types comprising 9350 tumors (91.4% of all TCGA cases). Small subsets of each pathway’s transcripts, comprising no more than 50–60 from the original group, played particularly prominent roles in determining overall t-SNE patterns. In several cases, further refinements in long-term survival could be achieved by sequential t-SNE profiling with two pathways’ transcripts, by a combination of t-SNE plus whole transcriptome profiling or by employing t-SNE on immuno-histochemically defined breast cancer subtypes. In two cancer types, individuals with Stage IV disease at presentation could be readily subdivided into groups with highly significant survival differences based on t-SNE-based tumor sub-classification. Conclusions t-SNE-assisted profiling of a small number of transcripts allows the prediction of long-term survival across multiple cancer types.http://link.springer.com/article/10.1186/s12885-019-5851-6Cancer metabolismMycNotchPI3 kinaseTP53Transcriptional profiling |
| spellingShingle | Jordan Mandel Huabo Wang Daniel P. Normolle Wei Chen Qi Yan Peter C. Lucas Panayiotis V. Benos Edward V. Prochownik Expression patterns of small numbers of transcripts from functionally-related pathways predict survival in multiple cancers BMC Cancer Cancer metabolism Myc Notch PI3 kinase TP53 Transcriptional profiling |
| title | Expression patterns of small numbers of transcripts from functionally-related pathways predict survival in multiple cancers |
| title_full | Expression patterns of small numbers of transcripts from functionally-related pathways predict survival in multiple cancers |
| title_fullStr | Expression patterns of small numbers of transcripts from functionally-related pathways predict survival in multiple cancers |
| title_full_unstemmed | Expression patterns of small numbers of transcripts from functionally-related pathways predict survival in multiple cancers |
| title_short | Expression patterns of small numbers of transcripts from functionally-related pathways predict survival in multiple cancers |
| title_sort | expression patterns of small numbers of transcripts from functionally related pathways predict survival in multiple cancers |
| topic | Cancer metabolism Myc Notch PI3 kinase TP53 Transcriptional profiling |
| url | http://link.springer.com/article/10.1186/s12885-019-5851-6 |
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