Virtual Library

Start Your Search

Jinhong Kim



Author of

  • +

    P3.03 - Biology (Not CME Accredited Session) (ID 969)

    • Event: WCLC 2018
    • Type: Poster Viewing in the Exhibit Hall
    • Track:
    • Presentations: 1
    • Moderators:
    • Coordinates: 9/26/2018, 12:00 - 13:30, Exhibit Hall
    • +

      P3.03-02 - Single-Cell RNA-Seq in Human Lung Cancer (ID 13405)

      12:00 - 13:30  |  Presenting Author(s): Jinhong Kim

      • Abstract

      Background

      Lung cancer has the highest mortality rate amongst cancers primarily due to delay of diagnosis. Until recently, the focus on genomic and transcriptomic characterization of lung cancers has guided to the diagnosis and treatments of the cancers. Although RNA sequencing (RNA-seq) has been used for transcriptome profiling in cancer research, few studies have employed single cell (sc) RNA-seq approaches to investigation of the heterogeneity among individual cells directly isolated from resected surgical human lung cancer tissues. The aim of our work is to identify new biomarkers indicating early stage versus late stage of lung cancers. As an initiative step, the current study presents a profile of differentially expressed genes (DEG) of single cells of lung cancer tissues collected from diverse lung cancer patients.

      a9ded1e5ce5d75814730bb4caaf49419 Method

      Tumours resected from lung cancer patients were prepared for scRNA-seq. Microfluidic system, C1 (Fluidigm), was used to capture individual cells, and full-length cDNAs (FL-cDNA) were synthesized in the system. Next-generation sequencing (Illumina) of FL-cDNA libraries generated sequence reads from transcriptomes of single and bulk cells, respectively. Following read mapping, DEGs were selected by >2x fold-change difference in normalized expression values. qPCR was employed to validate transcriptional changes in selected DEGs comparing expression values resulted from scRNA-seq and bulk RNA-seq. Both gene set enrichment and signaling pathway analyses were used to identify mechanism of action for validated DEG-encoding molecules.

      4c3880bb027f159e801041b1021e88e8 Result

      Raw scRNA-seq datasets were processed for read mapping. More than 1 million processed reads per single-cell FL-cDNA library were used for DEG selection. We examined standard deviation of normalized expression value per selected DEG to characterize transcriptomic features of individual tumour cells. We identified a minimum of a dozen DEGs showing certain fold-change differences in each stage of lung cancers. Selected DEGs were validated by qPCR whereby unique patterns of gene expression at specific stages were confirmed.

      8eea62084ca7e541d918e823422bd82e Conclusion

      The heterogeneity of lung cancers coupled with late stage diagnosis contributes to poor outcomes. The advent of scRNA-seq allows for the identification of differential gene expression and stage-specific markers that can be used to improve our understanding of highly complex intratumour heterogeneity in lung cancers. In the future, our discoveries will lead to the development of targeted therapies based on validated DEGs, ultimately allowing for early diagnosis, treatment and improved lung cancer survivorship.

      PAM is supported by CCSRI-i2I and DMRF.

      6f8b794f3246b0c1e1780bb4d4d5dc53

      Information from this presentation has been removed upon request of the author.

  • +

    P3.13 - Targeted Therapy (Not CME Accredited Session) (ID 979)

    • Event: WCLC 2018
    • Type: Poster Viewing in the Exhibit Hall
    • Track:
    • Presentations: 1
    • Moderators:
    • Coordinates: 9/26/2018, 12:00 - 13:30, Exhibit Hall
    • +

      P3.13-31 - Creating a Precision Medicine Pipeline for Lung Cancers. (ID 14006)

      12:00 - 13:30  |  Author(s): Jinhong Kim

      • Abstract

      Background

      The vision of High Mortality Cancer Precision Medicine Pipeline (HMCPMP) is to improve high mortality cancer outcomes. Our mission of HMCPMP is to establish precision medicine pipelines for high mortality cancers that start with the patient and ends with precise treatments for the patient based on their lung cancer’s unique molecular profile. HMCPMP leverages expertise areas of i) single-cell gene-discovery that allows for the identification of new barcodes and therefore new targets, ii) animal models that recapitulate human cancers, and patient-derived xenographs that allow for pre-clinical trials in mouse models to test novel drugs and drug development, iii) immunology and cancer stem cell biology to explore the role the immune system and stem cell niche impact tumourigenesis, iv) single-cell fluidics/3D organ systems that allow for understanding the heterogeneity of cancers and vi) data mining that allows for better treatment choices and the discovery of new treatment options based on their lung cancer’s barcodes.

      a9ded1e5ce5d75814730bb4caaf49419 Method

      We have created a patient-centered precision medicine pipeline that moves resected lung cancers from the surgical suite to banking, through to single cell molecular diagnostics to validation (Figure 1). pipeline_lung_cancers.jpg

      4c3880bb027f159e801041b1021e88e8 Result

      Single-cell genomic profiling of resected lung cancers were prepared using a microfluidics platform Fluidigm C1 followed by transcriptome sequencing per single cell. We identified differentially expressed genes (DEGs) for stage, sex, and smoking status, followed by validation by quantitative PCR. We are currently pursuing new barcodes that differentially distinguish between the stratified subgroups of patients.

      8eea62084ca7e541d918e823422bd82e Conclusion

      The significance of HMCPMP research is improved health, survivorship, and quality of life for people living with lung cancer. Although the economics of lung cancer are important and likely drivers of federal, provincial and regional research initiative, HMCPMP considers the human when faced with the diagnosis of lung cancer.

      PAM is funded by the Dalhousie Medical Research Foundation and the Canadian Cancer Society

      6f8b794f3246b0c1e1780bb4d4d5dc53

      Information from this presentation has been removed upon request of the author.