/CCL-cGPS

A clinical genomics-guided prioritizing strategy enables accurately selecting proper cancer cell lines for biomedical research

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CCL-cGPS

A clinical genomics-guided prioritizing strategy enables accurately selecting proper cancer cell lines for biomedical research

Selecting appropriate cell lines to represent a disease is crucial for the success of biomedical research, since the usage of less relevant cell lines could bring misleading results. However, no systematic guidance is available yet for the cell lines selection. Here we developed a clinical Genomics-guided Prioritizing Strategy for Cancer Cell Lines (CCL-cGPS) to sort cell lines. Statistical analyses revealed CCL-cGPS selected cell lines were among the most appropriate models. Moreover, we observed a linear correlation between the drug response and CCL-cGPS score of cell lines for breast and thyroid cancers. Using CCL-cGPS selected RT4 cells, we identified mebendazole and digitoxin as candidate drugs against bladder cancer, and validate their promising anticancer effect through in vitro and in vivo experiments. Additionally, a web tool was developed. CCL-cGPS bridges the gap between tumors and cell lines, presenting a helpful guide to select the most suitable cell line models.

Cite

DOI: 10.1016/j.isci.2020.101748

Shao et al., A clinical genomics-guided prioritizing strategy enables selecting proper cancer cell lines for biomedical research, iScience (2020), doi:10.1016/j.isci.2020.101748.

Detail

For each tumor subtype, CCL-cGPS suggests several cancer cell lines that mimic clinical tumor patients.

  • Bladder cancer (BLCA)

    • Non-papillary: SCaBER, HT-1197, UM-UC-3, SW-1710, VM-CUB1
    • Papillary: RT4 (commonly-used), VM-CUB1, RT112_84, SW_780

  • Breast cancer (BRCA)

    • Infiltrating ductal: HCC2218, HCC1428, HCC2157, HCC1599, ZR-75-30
    • Infiltrating lobular: UACC-812, CAL-148, YMB-1, ZR-75-1, ZR-75-30
    • ER+: HCC2218, HCC1428, ZR-75-30, MDA-MB-134-VI, CAL-148
    • HER2+: SK-BR-3 (commonly-used), HCC202
    • Triple negative: HCC1599, HCC2157, HCC1569, CAL-85-1, HCC1143

  • Bile duct cancer (CHOL)

    • Intrahepatic: SNU-245

  • Colorectal cancer (COADREAD)

    • Colon adenocarcinoma (COAD): NCI-H747, LS123, SW1417, RCM-1, C2BBe1
    • Colon mucinous adenocarcinoma: CL-11, Hs_675_T, MDST8
    • Rectal adenocarcinoma (READ): NCI-H747, SW1417, COLO-678, SW1463, RCM-1 (commonly-used)
    • KRAS mutated: HCC-56, LS123, SW1463, SW948, COLO-678

  • Esophagus cancer (ESCA)

    • Esophagus adenocarcinoma: OE19, KYSE-270, OE33 (commonly-used), TE-1
    • Esophagus squamous cell carcinoma: COLO-680N, KYSE-140, TE-9, T_T

  • Glioma

    • Glioblastoma multiforme (GBM): KNS-81, GOS-3, SNU-626, LN-229, SNU-738
    • Astrocytoma (LGG): KNS-81, DK-MG, SNU-626, SNU-738, SNU-466
    • Oligoastrocytoma (LGG): GOS-3, SNU-466, A172, DK-MG, KNS-81
    • Oligodendroglioma (LGG): GOS-3, SNU-626, DK-MG, A172, SNU-466

  • Head and neck squamous cell carcinoma (HNSC)

    • HNSC: BICR_31, SCC-4, PE_CA-PJ34_clone_C12, BICR_18, PE_CA-PJ41_clone_D2

  • Kidney cancer

    • Kidney chromophobe (KICH): KMRC-3, SNU-349, VMRC-RCW, ACHN
    • Kidney renal clear cell carcinoma (KIRC): A-704, KMRC-3, SNU-349, OSRC2, TUHR4TKB
    • Kidney renal papillary cell carcinoma (KIRP): A-704, Caki-1, VMRC-RCW, ACHN (commonly-used), OSRC2

  • Liver cancer

    • Liver hepatocellular carcinoma (LIHC): SNU-449, SNU-878, Alexander_cells, SK-HEP-1, JHH-1

  • Lung cancer

    • Lung adenocarcinoma (LUAD): NCI-H1869, Sq-1, NCI-H2444, KNS-62, NCI-H1944
    • LUAD KRAS mutated: LCLC-103H, NCI-H1781, NCI-H20
    • LUAD ALK translocated: NCI-H2110
    • Lung squamous cell carcinoma (LUSC): HCC-95, LC-1_sq-SF, LC1F, RERF-LC-Sq1, NCI-H2196

  • Mesothelioma (MESO)

    • Epithelioid: JL-1, IST-MES1
    • Biphasic: NCI-H2052

  • Ovarian cancer (OV)

    • OV: OVSAHO, JHOS-2, COV362, KURAMOCHI, OAW28

  • Pancreatic cancer (PAAD)

  • Prostatic cancer (PRAD)

  • Melanoma (SKCM)

    • SKCM: COLO_792, RVH-421, MEL-HO, Hs_839_T, Malme-3M

  • Stomach cancer (STAD)

    • Diffuse: GCIY, NUGC-3, NUGC-4
    • Mucinous: ECC10, HuG1-N, KE-39, MKN1, RERF-GC-1B
    • Tubular: NCI-N87, NUGC-4, SH-10-TC, ECC10

  • Thyroid cancer (THCA)

  • Endometrium cancer

    • Endometrioid (UCEC): MFE-280, EFE-184, KLE, MFE-319
    • Serous (UCEC): MFE-280, EFE-184, SNU-1077, KLE
    • Mixed (UCEC): HEC-265
    • Uterine carcinosarcoma (UCS): EFE-184, KLE, MFE-280

Download

  • Basic information of cancer cell lines in (CCLE) and tumor patients in (TCGA) can be downloaded indata/
  • Gene expression profiles of cancer cell lines and tumor patients can be downloaded in the releases page.
  • Similarity ranking of cell lines for each tumor subtype based on Kolmogorov-Smirnov statistic can be downloaded in inR/
  • RNA-seq data of RT4 cell line treated with control, mebendazole and digitoxin with three replicates can be download in the releases page.

About

CCL-cGPS repository was developed by Xin Shao. Should you have any questions, please contact Xin Shao at xin_shao@zju.edu.cn. For more information, please refer to our published paper 10.1016/j.isci.2020.101748 or visit our website tcm.zju.edu.cn/cgps.