ImPlatelet classifier: Image-converted RNA biomarker profiles enable blood-based cancer diagnostics
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA665489 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR12705200.fastq.gz | Fastqsanger.gz | |||
| SRR12705201.fastq.gz | Fastqsanger.gz | |||
| SRR12705202.fastq.gz | Fastqsanger.gz | |||
| SRR12705203.fastq.gz | Fastqsanger.gz | |||
| SRR12705204.fastq.gz | Fastqsanger.gz |
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Project description:Abstract: Sequencing technologies have enabled in-depth analysis of liquid biopsies in cancer, offering a minimally invasive sample collection. The most widely used material is blood which, next to circulating tumor cells and circulating tumor DNA, is the source of tumor-educated platelets (TEPs). Methods: We developed imPlatelet method which converts RNA-sequenced platelet data to images, additionally implementing biological knowledge from the Kyoto Encyclopedia of Genes and Genomes Pathway. First, we tested imPlatelet method on a cohort of 401 non-small cell lung cancer patients and 62 sarcoma patients. Next, we applied the developed tool to platelets collected from a new, independent cohort of 28 ovarian cancer patients and 30 non-cancer benign gynaecological conditions. Results: imPlatelet provided excellent discrimination between cancer cases and healthy controls, with accuracy equal to 1 in training, validation and independent datasets. When discriminating between ovarian cancer cases and benign conditions, imPlatelet reached 0.91 balanced accuracy, with sensitivity and specificity equal to 0.95 and 0.88, respectively, in an independent test set. ImPlatelet outperformed current state-of-the-art method thromboSeq in the aspects of balanced classification accuracy, the computational power needed, user experience, and execution time. Conclusions: According to our knowledge, this is the first study implementing an image-based deep learning approach combined with biological knowledge to classify human samples. Our results on classification of ovarian cancer considerably outperform previously published methods and our own alternative attempts of discrimination. We show that a deep learning image-based classifier accurately identifies cancer, despite the limited number of samples and even among non-cancer conditions which affect platelet transcriptome making the diagnosis difficult.
2021-10-06 | GSE158508 | GEO
Project description:Liquid biopsies offer a minimally invasive sample collection, outperforming traditional biopsies employed for cancer evaluation. The widely used material is blood, which is the source of tumor-educated platelets. Here, we developed the imPlatelet classifier, which converts RNA-sequenced platelet data into images in which each pixel corresponds to the expression level of a certain gene. Biological knowledge from the Kyoto Encyclopedia of Genes and Genomes was also implemented to improve accuracy. Images obtained from samples can then be compared against standard images for specific cancers to determine a diagnosis. We tested imPlatelet on a cohort of 401 non-small cell lung cancer patients, 62 sarcoma patients, and 28 ovarian cancer patients. imPlatelet provided excellent discrimination between lung cancer cases and healthy controls, with accuracy equal to 1 in the independent dataset. When discriminating between noncancer cases and sarcoma or ovarian cancer patients, accuracy equaled 0.91 or 0.95, respectively, in the independent datasets. According to our knowledge, this is the first study implementing an image-based deep-learning approach combined with biological knowledge to classify human samples. The performance of imPlatelet considerably exceeds previously published methods and our own alternative attempts of sample discrimination. We show that the deep-learning image-based classifier accurately identifies cancer, even when a limited number of samples are available.
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Project description:The development of mirror-image biology systems and related applications is hindered by the lack of effective methods to sequence mirror-image (D-) proteins. Although natural-chirality (L-) proteins can be sequenced by bottom–up liquid chromatography–tandem mass spectrometry (LC–MS/MS), the sequencing of long D-peptides and D-proteins with the same strategy requires digestion by a site-specific D-protease before mass analysis. Here we apply solid-phase peptide synthesis and native chemical ligation to chemically synthesize a mirror-image version of trypsin, a widely used protease for site-specific protein digestion. Using mirror-image trypsin digestion and LC–MS/MS, we sequence a mirror-image large subunit ribosomal protein (L25) and a mirror-image Sulfolobus solfataricus P2 DNA polymerase IV (Dpo4), and distinguish between different mutants of D-Dpo4. We also perform writing and reading of digital information in a long D-peptide of 50 amino acids. Thus, mirror-image trypsin digestion in conjunction with LC–MS/MS may facilitate practical applications of D-peptides and D-proteins as potential therapeutic and informational tools.
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Project description:The inherent diagnostic limitations of thyroid fine needle aspiration (FNA), especially in the “indeterminate” category, can be partially overcome by molecular analyses. We aimed at the identification of miRNAs that could be used to improve the discrimination of indeterminate FNAs. miRNA expression profiling was performed for 17 follicular carcinomas (FTCs) and 8 follicular adenomas (FAs). The microarray results underwent cross-comparison using three additional microarray data sets. Candidate miRNAs were validated by qPCR in an independent set of 32 FTCs and 46 FAs. Sixty-eight differentially expressed miRNAs were identified. Thirteen miRNAs could be confirmed by cross comparison. A two-miRNA-classifier was established improving the diagnostic applicability and resulted in a sensitivity of 82% and a specificity of 49%. We present a classifier that has the potential to be successfully evaluated in cytology material for its capability to discriminate (mutation negative) indeterminate cytologies and thereby improving the pre-surgical diagnostics of thyroid nodules.
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Project description:Background: With the incidence of papillary thyroid carcinoma rising worldwide, the decision about lobectomy versus total thyroidectomy will become relevant for an increasing number of patients. There exists no reliable biomarker for metastatic potential or tendency of recurrence that could assist in the risk stratification of patients. Objective: To develop a gene expression classifier for metastatic potential by measuring RNA expression in the primary tumor at the time of cancer surgery. Further, to investigate the ability of the gene expression classifier to identify metastatic and recurrent cases. Method: Genome-wide expression analyses. The development cohort consisted of freshly frozen tissues from 38 patients collected between the years 1986 and 2009. Validation was performed on a consecutive cohort of formalin fixated paraffin embedded surgical tissue specimens from 183 patients treated at Odense and Aarhus University Hospitals. Results: A 17 gene classifier (ADAMTS1, ANTXR1, C7, CXCL12, EBF1, FBLN2, FOSL2, GGT5, GPR124, JAM3, LRIG1, NDRG1, PRRX1, ROBO1, SORL1, TCF4, and ZEB1) was identified based on the expression values of these genes in the groups with and without metastasis in the development cohort. The 17 gene classifier for regional and/or distant metastasis identified was tested against the clinical status in the validation cohort. Sensitivity was 51.6% (95% CI 41.2%-61.8) and specificity 61.6 % (95% CI 50.5%-71.9%). Further, the Kaplan-Meyer method was used to estimate whether the classifier was useful as a prognostic marker for recurrences. Log-rank testing failed to identify any significance (p=0.32). Conclusion: A 17 gene classifier for metastatic potential was developed, and the results showed a clear biological difference between groups. However, through validation, the prognostic significance of this classifier could not be shown in identifying metastatic cases or in the ability of dichotomizing patients according to risk of recurrence after primary treatment. 38 patients with papillary thyroid carcinoma from a consecutive cohort of patients, no replicates
2015-01-20 | E-GEOD-65074 | biostudies-arrayexpress
Project description:Background: With the incidence of papillary thyroid carcinoma rising worldwide, the decision about lobectomy versus total thyroidectomy will become relevant for an increasing number of patients. There exists no reliable biomarker for metastatic potential or tendency of recurrence that could assist in the risk stratification of patients. Objective: To develop a gene expression classifier for metastatic potential by measuring RNA expression in the primary tumor at the time of cancer surgery. Further, to investigate the ability of the gene expression classifier to identify metastatic and recurrent cases. Method: Genome-wide expression analyses. The development cohort consisted of freshly frozen tissues from 38 patients collected between the years 1986 and 2009. Validation was performed on a consecutive cohort of formalin fixated paraffin embedded surgical tissue specimens from 183 patients treated at Odense and Aarhus University Hospitals. Results: A 17 gene classifier (ADAMTS1, ANTXR1, C7, CXCL12, EBF1, FBLN2, FOSL2, GGT5, GPR124, JAM3, LRIG1, NDRG1, PRRX1, ROBO1, SORL1, TCF4, and ZEB1) was identified based on the expression values of these genes in the groups with and without metastasis in the development cohort. The 17 gene classifier for regional and/or distant metastasis identified was tested against the clinical status in the validation cohort. Sensitivity was 51.6% (95% CI 41.2%-61.8) and specificity 61.6 % (95% CI 50.5%-71.9%). Further, the Kaplan-Meyer method was used to estimate whether the classifier was useful as a prognostic marker for recurrences. Log-rank testing failed to identify any significance (p=0.32). Conclusion: A 17 gene classifier for metastatic potential was developed, and the results showed a clear biological difference between groups. However, through validation, the prognostic significance of this classifier could not be shown in identifying metastatic cases or in the ability of dichotomizing patients according to risk of recurrence after primary treatment.
2015-01-20 | GSE65074 | GEO