Dataset Information

Molecular signature comprising 11 platelet-genes enables accurate blood-based diagnosis of NSCLC.

ABSTRACT:

Background

Early diagnosis is crucial for effective medical management of cancer patients. Tissue biopsy has been widely used for cancer diagnosis, but its invasive nature limits its application, especially when repeated biopsies are needed. Over the past few years, genomic explorations have led to the discovery of various blood-based biomarkers. Tumor Educated Platelets (TEPs) have, of late, generated considerable interest due to their ability to infer tumor existence and subtype accurately. So far, a majority of the studies involving TEPs have offered marker-panels consisting of several hundreds of genes. Profiling large numbers of genes incur a significant cost, impeding its diagnostic adoption. As such, it is important to construct minimalistic molecular signatures comprising a small number of genes.

Results

To address the aforesaid challenges, we analyzed publicly available TEP expression profiles and identified a panel of 11 platelet-genes that reliably discriminates between cancer and healthy samples. To validate its efficacy, we chose non-small cell lung cancer (NSCLC), the most prevalent type of lung malignancy. When applied to platelet-gene expression data from a published study, our machine learning model could accurately discriminate between non-metastatic NSCLC cases and healthy samples. We further experimentally validated the panel on an in-house cohort of metastatic NSCLC patients and healthy controls via real-time quantitative Polymerase Chain Reaction (RT-qPCR) (AUC = 0.97). Model performance was boosted significantly after artificial data-augmentation using the EigenSample method (AUC = 0.99). Lastly, we demonstrated the cancer-specificity of the proposed gene-panel by benchmarking it on platelet transcriptomes from patients with Myocardial Infarction (MI).

Conclusion

We demonstrated an end-to-end bioinformatic plus experimental workflow for identifying a minimal set of TEP associated marker-genes that are predictive of the existence of cancers. We also discussed a strategy for boosting the predictive model performance by artificial augmentation of gene expression data.

SUBMITTER: Goswami C

PROVIDER: S-EPMC7590669 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature

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Publications

Molecular signature comprising 11 platelet-genes enables accurate blood-based diagnosis of NSCLC.

Goswami Chitrita C Chawla Smriti S Thakral Deepshi D Pant Himanshu H Verma Pramod P Malik Prabhat Singh PS Jayadeva Gupta Ritu R Ahuja Gaurav G Sengupta Debarka D

BMC genomics 20201027 1

<h4>Background</h4>Early diagnosis is crucial for effective medical management of cancer patients. Tissue biopsy has been widely used for cancer diagnosis, but its invasive nature limits its application, especially when repeated biopsies are needed. Over the past few years, genomic explorations have led to the discovery of various blood-based biomarkers. Tumor Educated Platelets (TEPs) have, of late, generated considerable interest due to their ability to infer tumor existence and subtype accura ...[more]

PMID: 33287695

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Project description:Background:Well- and dedifferentiated liposarcoma (WD/DDLPS) are rare mesenchymal malignant tumors that account for 20% of all sarcomas in adults. The WD form is a low-grade malignancy with a favourable prognosis which may progress to DDLPS, a high-grade aggressive counterpart. WDLPS is referred to as atypical lipomatous tumour (ALT) when localised in extremities, due to its better prognosis. Currently the final differential diagnosis to distinguish between more aggressive and less aggressive form is based on post-surgical histological examination and no molecular biomarkers for early detection are available. Methods:Quantitative polymerase chain reaction (qPCR) analysis of 11 metabolic genes involved in general and adipose tissue-specific metabolism, was performed on ALT (=?8), WDLPS (=?9) and DDLPS (=?20) samples. Subsequent statistical analysis was carried out to determine genes that most accurately can predict DDLPS differential diagnosis. Selected genes were further validated in a separate cohort by qPCR and the data statistically analysed. Deep sequencing was performed on DDLPS specimen from the metastatic patient and on five random WDLPS specimens. Results:We established a three-gene signature based on PNPLA2, LIPE and PLIN1, which identified DDLPS with 100% sensitivity and 90% specificity, even in specimens from the WD component of DDLPS tumors. Interestingly, the PNPLA2 gene is deleted in 45% of DDLPS samples analyzed under TCGA project, and the deletion is associated with significantly lower PNPLA2 expression level. However, other mechanisms causing loss or downregulation of the expression of these three genes may be involved. Moreover, the significantly lower level of PNPLA2 is associated with R1 surgical margins, compare to R0 margins, which suggests the more invasive tumor phenotype in the absence of PNPLA2. Conclusions:The identified metabolic signature allows highly accurate differential diagnosis between WD- and DDLPS even in samples containing lipid droplets, a marker of differentiation, which makes it very suitable for the use on biopsies. In respect to the pathogenesis of the disease, our results give a new insight into possible molecular mechanisms involved and support the recent observation that deletion of PNPLA2 is a novel factor in liposarcoma progression.

Project description:BackgroundTissue biopsy-based cancer diagnosis has limitations because of the fact that tumor tissues are in constant evolution and extremely heterogeneous. The current study was aimed to examine whether tumor-educated blood platelets (TEPs) might be a potential all-in-one source for blood-based cancer diagnostics to overcome the limitations of conventional cancer biopsy.MethodsIn the present study, we evaluated the expression pattern of MAGI2 antisense RNA 3 (MAGI2-AS3) and ZNFX1 antisense RNA 1 (ZFAS1) in both plasma and platelets of 101 non-small-cell lung cancer (NSCLC) patients. Receiver operating characteristic (ROC) curve was generated to evaluate their diagnostic potential. In addition, epidermal growth factor receptor (EGFR) mutations were detected in DNA and RNA samples of platelets for companion diagnostics.ResultsOur results showed that the levels of MAGI2-AS3 and ZFAS1 in both plasma and platelets of NSCLC patients were significantly downregulated than those in healthy controls. A positive correlation of long noncoding RNA expression was observed between platelets and plasma (r=0.738 for MAGI2-AS3, r=0.751 for ZFAS1, respectively). By ROC analysis, we found that molecular interrogation of MAGI2-AS3 and ZFAS1 in TEPs and plasma can offer valuable diagnostic performance for NSCLC patients (area under the ROC curve [AUC] MAGI2-AS3 = 0.853/0.892, and AUC ZFAS1 =0.780/0.744 for diagnosing adenocarcinoma and squamous cell carcinoma cases from controls, respectively). Clinicopathologic characteristic analysis further revealed that MAGI2-AS3 level significantly correlated with tumor-node-metastasis (TNM) stage (p=0.001 in TEPs, p=0.003 in plasma), lymph-node metastasis (p=0.016 in TEPs, p=0.023 in plasma), and distant metastasis (p=0.045 in TEPs, p=0.045 in plasma), while ZFAS1 level was only correlated with TNM stage (p=0.005 in TEPs, p=0.044 in plasma). Furthermore, EGFRvIII RNA existed in both TEPs and plasma, but EGFR intracellular mutations cannot be detected in DNA of TEPs isolated from NSCLC.ConclusionOur data suggested that TEP is a promising source for NSCLC diagnosis and companion diagnostics.

Dataset Information

Molecular signature comprising 11 platelet-genes enables accurate blood-based diagnosis of NSCLC.

Background

Results

Conclusion

Publications

Molecular signature comprising 11 platelet-genes enables accurate blood-based diagnosis of NSCLC.

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