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. 2017 Oct 24;4:170157. doi: 10.1038/sdata.2017.157

Small non-coding RNA transcriptome of the NCI-60 cell line panel

Erin A Marshall 1,a, Adam P Sage 1, Kevin W Ng 1, Victor D Martinez 1, Natalie S Firmino 1, Kevin L Bennewith 1, Wan L Lam 1
PMCID: PMC5654365  PMID: 29064465

Abstract

Only 3% of the transcribed human genome is translated into protein, and small non-coding RNAs from these untranslated regions have demonstrated critical roles in transcriptional and translational regulation of proteins. Here, we provide a resource that will facilitate cell line selection for gene expression studies involving sncRNAs in cancer research. As the most accessible and tractable models of tumours, cancer cell lines are widely used to study cancer development and progression. The NCI-60 panel of 59 cancer cell lines was curated to provide common models for drug screening in 9 tissue types; however, its prominence has extended to use in gene regulation, xenograft models, and beyond. Here, we present the complete small non-coding RNA (sncRNA) transcriptomes of these 59 cancer cell lines. Additionally, we examine the abundance and unique sequences of annotated microRNAs (miRNAs), PIWI-interacting RNAs (piRNAs), small nuclear RNAs (snRNAs), and small nucleolar RNAs (snoRNAs), and reveal novel unannotated microRNA sequences.

Subject terms: Cancer genomics, RNA sequencing, Non-coding RNAs

Background & Summary

The NCI-60 Human Tumour Cell Lines Screen is an initiative started by the National Institutes of Health (NIH) in the late 1980s, focusing on the development of 59 human tumour cell lines for use as an in vitro drug screen model1–3 (Table 1 (available online only)). These cell lines, derived from nine solid and blood malignancies, have shown great utility both in its original purpose for therapeutic screening as well as in basic cancer research (reviewed by Shoemaker et al.4). They have since been extensively characterized for various molecular features, including karyotypic complexity1, DNA fingerprinting2, gene expression microarray profiling5,6, and human leukocyte antigen typing3. However, the small non-coding RNA (sncRNA) transcriptomes of the NCI-60 cell lines have yet to have been reported at the sequencing level.

Table 1. NCI-60 cell line characteristics.

Tissue Cell line Subtype Cell type of origin Doubling time (hrs) Year of origin Culture conditions
BREAST BT-549 Ductal Carcinoma Epithelial 53.9 1978 RPMI-1640+10% FBS
  HS-578T Carcinoma Epithelial 53.8 1977 DMEM+10% FBS
  MCF-7 Adenocarcinoma Epithelial 25.4 1973 MEM+10% FBS
  MDA-MB-231 Adenocarcinoma Epithelial 41.9 1974 L-15 Medium+10% FBS
  T-47D Ductal Carcinoma Epithelial 45.5 1981 DMEM+10% FBS
CNS SF-539 Gliblastoma multiforme Right Temporal Lobe 35.4 1986 MEM+10% FBS
  SF-295 Glioblastoma Left Temporal Lobe 29.5 1986 RPMI-1640+10% FBS
  SF-268 Highly Anaplastic Astrocytoma Right Parietal Lobe 33.1 1987 RPMI-1640+10% FBS
  U251 Glioblastoma Non-Epithelial (CNS) 23.8 1978 EMEM+10% FBS
  SNB-75 Glioblastoma Non-Epithelial (CNS) 62.8 1988 RPMI-1640+10% FBS
  SNB-19 Astrocytoma Left parieto-occipital 34.6 1980 Ham's F10+10% FBS
COLON COLO 205 Dukes' Type D, colorectal adenocarcinoma Epithelial 23.8 1975 RPMI-1640+10% FBS
  HCT-15 Colon adenocarcinoma Epithelial 20.6 1979 RPMI-1640+10% FBS
  HCC2998 Colon adenocarcinoma Epithelial 31.5 1988 RPMI-1640+10% FBS
  HCT-116 Colon adenocarcinoma Epithelial 17.4 1981 McCoy's 5a+10% FBS
  HT-29 Colon adenocarcinoma Epithelial 19.5 1964 RPMI-1640+10% FBS
  KM12 Colon adenocarcinoma Epithelial 23.7 1988 DMEM+10% FBS
  SW-620 Dukes' Type D, colorectal adenocarcinoma Epithelial 20.4 1977 L15+10% FBS
LEUKEMIA CCRF-CEM Acute lymphoblastic leukemia T lymphoblast 26.7 1964 RPMI-1640+10% FBS
  HL-60(TB) Acutre promyelocytic leukemia Promyeloblast 28.6 1977 IMDM+20% FBS
  K-562 Chronic myelogenous leukemia Lymphoblast 19.6 1975 IMDM+20% FBS
  MOLT-4 Acute lymphoblastic leukemia Lymphoblast 27.9 1980 RPMI-1640+10% FBS
  RPMI 8226 Plasmacytoma; myeloma B Lymphocyte 33.5 1966 RPMI-1640+10% FBS
  SR Large cell immunoblastic lymphoma Lymphoblast 28.7 1983 RPMI-1640+10% FBS
MELANOMA MALME-3M Malignant melanoma Fibroblast 46.2 1975 IMDM+20% FBS
  LOX-IMVI Amelanotic melanoma Skin 20.5 1988 RPMI-1640+10% FBS
  M14 Malignant melanoma Skin 26.3 1976 RPMI-1640+10% FBS
  MDA-MB-435 Previously described as ductal carcinoma Melanocyte 25.8 1976 L15+10% FBS
  SK-MEL-28 Cutaneous melanoma Skin 35.1 1976 DMEM+10% FBS
  SK-MEL-5 Malignant melanoma Stellate 25.2 1977 MEM+10% FBS
  SK-MEL-2 Malignant melanoma Skin 45.5 1975 MEM+10% FBS
  UACC-257 Malignant melanoma Skin 38.5 1991 RPMI-1640+10% FBS
  UACC-62 Melanotic melanoma Skin 31.3 1991 RPMI-1640+10% FBS
NSCLC A549 Adenocarcinoma Epithelial 22.9 1972 RPMI-1640+10% FBS
  EKVX Adenocarcinoma Epithelial 43.6 1988 RPMI-1640+10% FBS
  HOP 92 Large cell carcinoma Epithelial 79.5 1991 RPMI-1640+10% FBS
  HOP 62 Adenocarcinoma Epithelial 39 1989 RPMI-1640+10% FBS
  NCI-H23 Adenocarcinoma Epithelial 33.4 1980 RPMI-1640+10% FBS
  NCI-H322M Small cell bronchioloalveolar carcinoma Epithelial 35.3 1991 RPMI-1640+10% FBS
  NCI-H226 Squamous cell carcinoma; mesothelioma Epithelial 61 1980 RPMI-1640+10% FBS
  NCI-H460 Carcinoma; large cell lung cancer Epithelial 17.8 1982 RPMI-1640+10% FBS
  NCI-H522 Adenocarcinoma Epithelial 38.2 1985 RPMI-1640+10% FBS
OVARIAN IGR-OV1 Ovarian endometrioid adenocarcinoma Endometreoid 31 1985 RPMI-1640+10% FBS
  NCI/ADR-RES High Grade Ovarian Serous Adenocarcinoma Epithelial 34 1986 RPMI-1640+20% FBS
  OVCAR-3 Adenocarcinoma Epithelial 34.7 1982 RPMI-1640+10% FBS
  OVCAR-8 High Grade Ovarian Serous Adenocarcinoma Epithelial 26.4 1984 RPMI-1640+10% FBS
  OVCAR-4 High Grade Ovarian Serous Adenocarcinoma Epithelial 41.4 1984 RPMI-1640+10% FBS
  OVCAR-5 High Grade Ovarian Serous Adenocarcinoma Epithelial 48.8 1984 RPMI-1640+10% FBS
  SK-OV-3 Adenocarcinoma Epithelial 48.7 1973 RPMI-1640+10% FBS
PROSTATE DU-145 Prostate carcinoma Epithelial 32.3 1978 MEM+10% FBS
  PC-3 Adenocarcinoma Epithelial 27.1 1980 F-12K+10% FBS
RENAL A498 Carcinoma Epithelial 66.8 1977 MEM+10% FBS
  CAKI-1 Clear cell renal cell carcinoma Epithelial 39 1975 McCoy's 5a+10% FBS
  786-0 Renal cell adenocarcinoma Epithelial 22.4 1976 RPMI-1640+10% FBS
  ACHN Renal cell adenocarcinoma Epithelial 27.5 1979 MEM+10% FBS
  RXF393 Renal cell carcinoma Epithelial 62.9 1991 RPMI-1640+10% FBS
  SN12C Renal cell carcinoma Epithelial 29.5 1986 DMEM+10% FBS
  TK-10 Clear cell renal cell carcinoma Epithelial 51.3 1987 RPMI-1640+10% FBS
  UO-31 Renal cell carcinoma Epithelial 41.7 1991 DMEM+10% FBS
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The advent of next-generation sequencing has revealed the large proportion of non-coding genes in the human genome, and the relevance of these non-coding species in regulating the expression of both neighbouring and distant protein-coding genes. In the context of cancer, microRNAs (miRNAs) remain the best-studied non-coding RNA species, and have been implicated in all stages of cancer: initiation, progression, and response to therapy (reviewed by Hayes et al.7). Recent advances in the bioinformatic tools used for the discovery of small non-coding RNA have considerably expanded the number of known miRNA sequences8. Other types of sncRNA, including PIWI-interacting RNAs (piRNAs), small nuclear RNAs (snRNAs), and small nucleolar RNAs (snoRNAs) are emerging topics in cancer biology (reviewed by Ng et al. and Mannoor et al.9,10). Beyond their functions in gene regulation, sncRNAs are attractive prognostic biomarkers due to their abundance and stability in various biofluids11.

We sequenced the sncRNA transcriptomes of the 59 cell lines in the panel (Fig. 1). SncRNA profiles were generated using the OASIS analysis platform v2.0 (ref. 12). For known sncRNA species (miRNAs, piRNAs, snoRNA, snRNA, and rRNA), high quality reads were mapped to the hg38 build of the human genome and quantified based on annotations containing their specific chromosomal locations. Detection of novel miRNAs was performed using well-established prediction algorithms that assess reads for miRNA folding characteristics, among other factors that indicate the probability that the tested sequence belongs to the miRNA family of sncRNAs13. In total, the genomic loci of 49,961 sncRNAs were examined. Using a detection threshold of greater than or equal to 5 reads across all tissues, we detected a total of 24,621 unique sncRNAs [Data Citation 1].

Figure 1. Experimental workflow.

Figure 1

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Graphical representation of experimental procedure used to extract, process, and analyze RNA from cell lines.

We then examined the genomic distribution of the detected sncRNAs across all tissue types (Table 2,Fig. 2). Notably, sncRNAs are expressed across all chromosomes in every tissue type assessed. SncRNA loci commonly expressed among all tissues may indicate their involvement in preserved biological or cancer-relevant processes, whereas differences in expression may denote tissue specificity.

Table 2. Average number of sncRNA species detected and sequencing coverage per tissue type.

  Number of sncRNA
 Sequencing details
  Total miRNA novel miRNA piRNA snoRNA snRNA Other Average number of reads per sample Average contig length Avg. quality %GC Average coverage
Tissue type 24,794 2,509 288 19,018 259 1,602 412 23,457,233 22.34 33.25 46.26% 28.94
Breast 11,079 1,905 183 6,909 239 1,248 403 25,310,156 22.32 33.23 45.81% 25.36
CNS 10,120 1,793 180 6,150 236 1,175 397 25,633,608 22.35 33.34 44.57% 45.08
Colon 13,985 1,977 211 8,050 232 1,276 392 25,850,349 22.22 33.2 46.15% 22.97
Leukemia 10,728 1,841 185 5,604 228 1,112 387 18,921,562 22.41 33.25 48.77% 20.23
Melanoma 9,536 1,830 179 4,694 224 1,020 383 16,116,671 22.36 32.64 46.56% 28.45
NSCLC 15,707 2,051 232 9,385 236 1,293 398 23,407,812 22.34 33.3 46.18% 22.8
Ovarian 10,422 1,916 188 6,312 238 1,167 398 27,221,870 22.4 34.12 46.56% 41.49
Prostate 6,167 1,393 121 2,589 216 771 344 35,637,053 22.3 33.54 44.44% 30.11
Renal 14,532 1,943 209 8,549 234 1,288 396 23,949,373 22.3 33 45.99% 27.05
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Figure 2. Genome-wide distribution of expressed small non-coding RNA by tissue type.

Figure 2

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Genomic position of sncRNAs detected (reads≥5) in each tissue type in reference to the hg38 chromosome build karyotype. From inner-most ring to outer: breast (red), CNS (magenta), colon (purple), leukemia (blue), melanoma (teal), lung (green), ovarian (yellow), prostate (orange), and renal (red).

We also examined the relative frequency of detection for each sncRNA species, both in the entire NCI-60 cell line panel and in lines grouped by organ type (Fig. 3a). Beyond those annotated in miRBase (v.21), novel unannotated miRNAs were determined by integrating secondary structure formation potential with free energy scoring14. These novel miRNAs represent an increase of approximately 10% of total miRNAs expressed across all tissue types (Fig. 3b), highlighting the constant expansion of the known non-coding transcriptome as sequencing technologies and bioinformatic tools advance. Consistent with the number of annotated loci in the human genome, piRNAs represent the largest proportion of sncRNA species expressed, followed by miRNA and snRNA (Fig. 3a). Of note, an appreciable number of tissue-specific piRNA sequences across all tissues analyzed increased the relative fraction of piRNAs for all tissues expressed (Fig. 3c,d). Thus, as parts of the small non-coding RNA transcriptome are significantly understudied, we provide this resource to the research community for studying sncRNA-related genetic and epigenetic regulation in cancer using the NCI-60 cell models.

Figure 3. sncRNA distribution by tissue type.

Figure 3

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(a) Relative fraction of sncRNA species detected per tissue type. (b) Average fraction of currently annotated (blue) and novel unannotated (red) miRNA per tissue type. (c) Relative fraction of tissue-specific unique sncRNA sequences detected per tissue type. (d) Fraction of tissue-specific unique sncRNA species.

Methods

Cell line and sequencing information

Cell line doubling times were obtained directly from the National Institutes of Health NCI (https://dtp.cancer.gov/discovery_development/nci-60/cell_list.htm), and year-of-origin information refers to data of first publication containing the cell line (Table 1 (available online only)). Cell lines were obtained directly from the National Cancer Institute (NCI), were thawed and passaged twice precisely before total RNA was manually extracted using phenol-chloroform protocols from all cell lines using Trizol reagent (Invitrogen, CA, USA). 5,000 ng of extracted RNA per sample was used for sequencing input. Sequencing was performed in accordance with The Cancer Genome Atlas miRNA sequencing protocol (described by Chu et al.15). Briefly, after ligation to adaptors, 15 cycles of PCR was performed for amplification (98 °C-15 s, 62 °C-30 s and 72 °C-15 s), followed by 5 min at 72 °C. Small RNA exclusion was performed using gel extraction on a 3% MetaPhor Agarose gel (Lonza Inc., Basel, Switzerland), selecting species shorter that 200 nucleotides in order to enrich for targets optimized at 22 nucleotides in length, and was subsequently ethanol-precipitated. Library quality was confirmed by analysis on the Agilent Bioanalyzer DNA1000 chip (Agilent Technologies). Small non-coding RNA sequencing was performed on the Illumina HiSeq2500 platform at the Michael Smith Genome Sciences Centre at the BC Cancer Research Centre, with 8 multiplexed libraries per sequencing lane (Table 3 (available online only), Fig. 1)15,16. Data resulting from small non-coding RNA sequencing can be found on the Sequence Read Archive [Data Citation 2].

Table 3. Sequencing quality metrics for sequenced cell line.

Tissue External ID Sample name (.fastq) Total reads Total alignments Aligned Total unaligned Unaligned Total unique Unique Total non-unique Non-unique Coverage Avg. coverage depth Avg. length Avg. quality %GC
Breast BT-549 MX1381-C9C07ANXX-1-CACTGT 26,779,757 20,087,593 74.84% 6,738,932 25.16% 19,994,122 74.66% 46,703 0.17% 0.62% 23.36 22.33 33.02 47.70%
  HS-578T MX1383-C9C07ANXX-3-GGGGTT 24,961,809 20,875,168 83.42% 4,137,590 16.58% 20,773,358 83.22% 50,861 0.20% 0.35% 41.95 22.13 34.19 45.13%
  MCF-7 MX1384-C9C07ANXX-4-CTGGGT 43,741,142 32,170,069 73.27% 11,690,617 26.73% 31,931,263 73.00% 119,262 0.27% 0.81% 28.46 22.27 33.55 42.37%
  MDA-MB-231 MX1384-C9C07ANXX-4-GCCGGT 16,798,227 11,552,251 68.68% 5,261,551 31.32% 11,521,123 68.59% 15,553 0.09% 0.43% 19.43 22.45 33.25 46.01%
  T-47D MX1387-C9C07ANXX-7-CTAAGG 14,269,847 9,004,918 63.04% 5,274,372 36.96% 8,986,041 62.97% 9,434 0.07% 0.48% 13.58 22.4 32.16 47.83%
CNS SF-268 MX1386-C9C07ANXX-6-TAGTTG 35,537,775 32,273,096 90.47% 3,385,641 9.53% 32,031,463 90.13% 120,671 0.34% 0.28% 84.29 22.35 34.68 46.13%
  SF-295 MX1386-C9C07ANXX-6-CCGGTG 26,519,319 23,694,543 89.10% 2,890,215 10.90% 23,563,806 88.86% 65,298 0.25% 0.24% 70.66 22.39 34.43 42.31%
  SF-539 MX1386-C9C07ANXX-6-ATCGTG 25,500,558 23,012,418 90.00% 2,549,670 10.00% 22,889,448 89.76% 61,440 0.24% 0.25% 65.79 22.25 34.49 46.43%
  SNB-19 MX1387-C9C07ANXX-7-GCGTGG 32,717,872 21,567,743 65.75% 11,204,285 34.25% 21,459,531 65.59% 54,056 0.17% 0.85% 18.3 22.44 32.24 43.60%
  SNB-75 MX1387-C9C07ANXX-7-CATGGG 12,680,815 7,882,308 62.10% 4,805,816 37.90% 7,867,693 62.04% 7,306 0.06% 0.37% 15.13 22.2 32.05 45.20%
  U251 MX1387-C9C07ANXX-7-ATTCCG 20,845,308 13,006,365 62.30% 7,858,915 37.70% 12,966,435 62.20% 19,958 0.10% 0.58% 16.29 22.47 32.17 43.76%
Colon COLO 205 MX1381-C9C07ANXX-1-TCAAGT 18,351,752 13,907,298 75.66% 4,466,918 24.34% 13,862,401 75.54% 22,433 0.12% 0.44% 22.77 22.23 33.19 45.58%
  HCC2998 MX1382-C9C07ANXX-2-GTAGCC 22,418,943 16,741,250 74.53% 5,710,380 25.47% 16,675,921 74.38% 32,642 0.15% 0.88% 13.4 21.83 32.83 47.12%
  HCT-116 MX1382-C9C07ANXX-2-TACAAG 19,321,624 13,822,479 71.42% 5,521,561 28.58% 13,777,662 71.31% 22,401 0.12% 0.54% 18.43 22.23 33.06 46.02%
  HCT-15 MX1382-C9C07ANXX-2-ATGTTT 22,372,577 15,816,346 70.57% 6,585,278 29.43% 15,758,281 70.44% 29,018 0.13% 0.62% 18.15 22.24 32.92 46.00%
  HT-29 MX1383-C9C07ANXX-3-CAAGTT 43,295,013 36,704,351 84.42% 6,746,834 15.58% 36,392,459 84.06% 155,720 0.36% 0.64% 40.79 22.23 34.19 45.58%
  KM12 MX1383-C9C07ANXX-3-GTCCTT 21,097,776 17,022,531 80.52% 4,108,873 19.48% 16,955,321 80.37% 33,582 0.16% 0.41% 29.68 22.35 34.03 46.33%
  SW-620 MX1387-C9C07ANXX-7-TTGCGG 34,094,755 22,349,352 65.38% 11,803,916 34.62% 22,232,420 65.21% 58,419 0.17% 0.92% 17.57 22.46 32.21 46.44%
Leukemia CCRF-CEM MX1381-C9C07ANXX-1-GATCTG 16,675,728 12,545,480 75.12% 4,148,470 24.88% 12,509,054 75.01% 18,204 0.11% 0.40% 22.55 22.48 33.4 49.05%
  HL-60(TB) MX1382-C9C07ANXX-2-TGCTTT 14,448,374 9,588,877 66.29% 4,870,207 33.71% 9,567,467 66.22% 10,700 0.07% 0.51% 13.5 22.12 32.72 49.74%
  K-562 MX1383-C9C07ANXX-3-TCGCTT 35,495,593 28,500,403 80.03% 7,089,711 19.97% 28,311,603 79.76% 94,279 0.27% 0.60% 34.46 22.43 34.08 48.92%
  MOLT-4 MX1384-C9C07ANXX-4-GAGAGT 17,384,261 12,413,016 71.30% 4,989,141 28.70% 12,377,237 71.20% 17,883 0.10% 0.49% 18.25 22.49 33.61 48.27%
  RPMI 8226 MX1385-C9C07ANXX-5-AGGAAT 17,460,565 14,241,842 81.43% 3,242,340 18.57% 14,194,637 81.30% 23,588 0.14% 0.46% 22.37 22.47 34.06 48.91%
  SR MX1387-C9C07ANXX-7-CCACTC 12,064,850 6,751,739 55.92% 5,318,416 44.08% 6,741,131 55.87% 5,303 0.04% 0.48% 10.23 22.48 31.62 47.70%
Melanoma LOX-IMVI MX1383-C9C07ANXX-3-CCTATT 31,657,831 25,924,121 81.64% 5,812,011 18.36% 25,767,653 81.39% 78,167 0.25% 0.47% 39.9 22.38 34.1 45.79%
  M14 MX1383-C9C07ANXX-3-GTTTGT 16,456,810 13,115,289 79.57% 3,361,434 20.43% 13,075,479 79.45% 19,897 0.12% 0.32% 29.6 22.45 34.08 47.29%
  MALME-3M MX1383-C9C07ANXX-3-AGATGT 97,640 42,454 43.48% 55,187 56.52% 42,452 43.48% 1 0% 0.01% 4.85 22.33 29.76 46.02%
  MDA-MB-435 MX1384-C9C07ANXX-4-TATCGT 16,462,112 11,741,084 71.22% 4,736,983 28.78% 11,709,183 71.13% 15,946 0.10% 0.50% 16.9 22.36 33.4 47.73%
  SK-MEL-2 MX1386-C9C07ANXX-6-TGAGTG 30,951,870 27,402,647 88.25% 3,636,213 11.75% 27,228,840 87.97% 86,817 0.28% 0.32% 61.56 22.51 34.52 46.50%
  SK-MEL-28 MX1386-C9C07ANXX-6-CGCCTG 14,712,878 12,743,244 86.48% 1,988,450 13.52% 12,705,638 86.36% 18,790 0.13% 0.22% 41.57 22.26 34.24 46.57%
  SK-MEL-5 MX1386-C9C07ANXX-6-GCCATG 12,394,333 10,855,860 87.48% 1,552,173 12.52% 10,828,474 87.37% 13,686 0.11% 0.19% 41.26 22.5 34.4 46.38%
  UACC-257 MX1388-C9C07ANXX-8-AGCTAG 46,062 9,213 20.00% 36,849 80.00% 9,213 20.00% 0 0% 0% 2.13 22.17 26.55 46.63%
  UACC-62 MX1388-C9C07ANXX-8-GTATAG 22,270,501 15,834,593 70.97% 6,465,114 29.03% 15,776,225 70.84% 29,162 0.13% 0.62% 18.26 22.24 32.7 46.13%
NSCLC A549 MX1381-C9C07ANXX-1-GCCTAA 24,542,337 18,533,593 75.35% 6,048,721 24.65% 18,453,696 75.19% 39,920 0.16% 0.53% 25.18 22.39 33.18 45.04%
  EKVX MX1382-C9C07ANXX-2-AAGCTA 18,358,000 13,056,340 71.01% 5,321,501 28.99% 13,016,669 70.90% 19,830 0.11% 0.46% 20.08 22.13 32.99 45.71%
  HOP 62 MX1382-C9C07ANXX-2-GCATTT 22,874,592 16,227,434 70.81% 6,678,209 29.19% 16,165,375 70.67% 31,008 0.14% 0.58% 19.83 22.18 32.86 45.15%
  HOP 92 MX1382-C9C07ANXX-2-CGTACG 16,671,005 11,307,213 67.74% 5,378,661 32.26% 11,277,492 67.65% 14,852 0.09% 0.49% 16.63 22.16 32.7 45.80%
  NCI-H226 MX1384-C9C07ANXX-4-TCTTCT 18,745,702 12,999,215 69.24% 5,766,399 30.76% 12,959,420 69.13% 19,883 0.11% 0.52% 17.92 22.38 33.28 47.25%
  NCI-H23 MX1384-C9C07ANXX-4-CTATCT 19,234,429 13,677,426 71.00% 5,578,694 29.00% 13,634,066 70.88% 21,669 0.11% 0.51% 19.47 22.44 33.36 48.52%
  NCI-H322M MX1384-C9C07ANXX-4-GATGCT 40,017,514 30,184,128 75.16% 9,938,560 24.84% 29,974,031 74.90% 104,923 0.26% 0.95% 22.98 22.47 33.58 45.76%
  NCI-H460 MX1385-C9C07ANXX-5-AGCGCT 12,712,247 9,871,928 77.57% 2,851,889 22.43% 9,848,794 77.47% 11,564 0.09% 0.32% 22.39 22.38 33.72 47.50%
  NCI-H522 MX1385-C9C07ANXX-5-CGGCCT 37,514,479 30,982,049 82.29% 6,643,642 17.71% 30,759,900 81.99% 110,937 0.30% 0.55% 40.76 22.53 34.02 44.85%
Ovarian IGR-OV1 MX1383-C9C07ANXX-3-AGTCTT 28,718,340 23,886,771 82.95% 4,897,725 17.05% 23,754,573 82.72% 66,042 0.23% 0.42% 41.25 22.41 34.16 46.10%
  NCI/ADR-RES MX1384-C9C07ANXX-4-ATCAGT 22,291,994 16,348,364 73.20% 5,974,630 26.80% 16,286,408 73.06% 30,956 0.14% 0.54% 21.99 22.37 33.49 47.02%
  OVCAR-3 MX1385-C9C07ANXX-5-AATTAT 29,981,152 25,036,208 83.26% 5,017,926 16.74% 24,890,371 83.02% 72,855 0.24% 0.46% 38.94 22.41 34.16 47.68%
  OVCAR-4 MX1385-C9C07ANXX-5-CCGTAT 28,168,179 22,957,608 81.29% 5,271,643 18.71% 22,835,575 81.07% 60,961 0.22% 0.43% 38.31 22.39 34.02 47.21%
  OVCAR-5 MX1385-C9C07ANXX-5-TAGGAT 24,989,662 20,898,933 83.43% 4,141,256 16.57% 20,797,961 83.23% 50,445 0.20% 0.40% 37.13 22.3 34.22 45.64%
  OVCAR-8 MX1385-C9C07ANXX-5-ATAGAT 21,340,411 17,484,019 81.76% 3,891,733 18.24% 17,413,389 81.60% 35,289 0.17% 0.35% 36.62 22.44 34.22 46.25%
  SK-OV-3 MX1386-C9C07ANXX-6-AAAATG 35,063,352 31,836,638 90.46% 3,344,843 9.54% 31,600,666 90.12% 117,843 0.34% 0.30% 76.21 22.49 34.54 46.04%
Prostate DU-145 MX1382-C9C07ANXX-2-CTGATC 42,719,250 30,543,575 71.24% 12,284,018 28.76% 30,327,141 70.99% 108,091 0.25% 1.01% 21.68 22.17 32.93 42.80%
  PC-3 MX1385-C9C07ANXX-5-GCTCAT 28,554,855 23,674,991 82.68% 4,944,659 17.32% 23,545,544 82.46% 64,652 0.23% 0.44% 38.54 22.42 34.15 46.08%
Renal 786-0 MX1381-C9C07ANXX-1-CGTGAT 28,239,458 21,417,620 75.65% 6,875,229 24.35% 21,310,920 75.47% 53,309 0.19% 0.53% 28.79 22.32 33.21 45.18%
  A498 MX1381-C9C07ANXX-1-ACATCG 23,983,341 18,160,244 75.56% 5,861,378 24.44% 18,083,724 75.40% 38,239 0.16% 0.49% 26.57 22.26 33.14 44.19%
  ACHN MX1381-C9C07ANXX-1-TGGTCA 26,253,335 20,447,816 77.70% 5,854,225 22.30% 20,350,495 77.52% 48,615 0.19% 0.50% 29.38 22.42 33.4 44.66%
  CAKI-1 MX1381-C9C07ANXX-1-ATTGGC 20,365,416 15,568,695 76.31% 4,825,051 23.69% 15,512,066 76.17% 28,299 0.14% 0.38% 29.39 22.37 33.42 44.53%
  RXF393 MX1386-C9C07ANXX-6-CTTTTG 29,863,988 26,677,423 89.05% 3,269,280 10.95% 26,512,156 88.78% 82,552 0.28% 0.34% 57.33 22.42 34.42 48.85%
  SN12C MX1387-C9C07ANXX-7-TGTTGG 23,377,833 15,428,844 65.88% 7,976,649 34.12% 15,373,563 65.76% 27,621 0.12% 0.67% 16.45 22.28 32.21 45.57%
  TK-10 MX1387-C9C07ANXX-7-TTCTCG 26,774,556 17,534,744 65.36% 9,275,498 34.64% 17,463,407 65.22% 35,651 0.13% 1.16% 10.66 21.92 31.69 49.55%
  UO-31 MX1388-C9C07ANXX-8-TCTGAG 12,737,055 8,368,476 65.64% 4,376,740 34.36% 8,352,163 65.57% 8,152 0.06% 0.34% 17.82 22.44 32.53 45.42%
MEAN
 23,457,233 18,073,055 74.34% 5,429,370 25.66% 17,982,756 74.18% 45,107 0.16% 0.49% 29 22.34 33.25 46.26%
Standard Deviation 9,294,225 7,957,399 11.69% 2,519,478 11.69% 7,888,112 11.63% 35,871 0.08% 0.22% 17 0.14 1.28 1.64%
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Pre-processing and small non-coding RNA species detection

Small-RNA sequencing data was analyzed according to published protocols17. In order to extract information for the sncRNA species of interest, unaligned reads (in FASTQ format) were trimmed for adaptors (Cutadapt v1.7.1) and based on sequencing quality (‘trim bases’ from Partek Flow v6.0.17.0614) to reach a Phred quality score ≥20 (Fig. 4a–d). FASTQ files were then aligned using the Spliced Transcripts Alignment to a Reference (STAR v2.4.1d) aligner to the human genome (hg38)18. Quantification algorithms (featureCounts v1.4.6 (ref. 19) were applied using chromosomal location annotations for known miRNA (Mirbase v.21 (ref. 20), piRNA (piRNAbank v.2 (ref. 21), snoRNA (Ensembl v.84 (ref. 22), and snRNA (Ensembl v.84 (ref. 22) locations12. Detection of novel miRNA is performed using the miRDeep2 algorithm (v2.0.0.5), which considers the relative free energy of miRNAs and their random folding P-values13. Chromosomal position of expressed small RNAs was plotted against and hg38 karyotype obtained from UCSC Genome Browser (Fig. 2). According to OASIS sncRNA software recommendation (v2.0), sncRNA species were considered expressed if the total reads across all samples considered summed to ≥5 reads12. Data resulting from species quantification can be found in Data Citation 1.

Figure 4. Sequencing and mapping quality.

Figure 4

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(a) Phred quality score per sncRNA base position. (b) Genome-wide read depth (column) and genome coverage (line) per sample. (c) Fraction of sequencing reads per Phred score. (d) Percentage of total reads aligned (unique: green, unaligned: red).

Normalization and quantification

Raw reads were scaled/normalized using reads per kilobase exon per million mapped reads (RPKM) method23, and expression correlation matrices were created using Pearson scores with unsupervised hierarchal clustering performed using one-minus-Pearson correlation scores (Fig. 5). For validation of sncRNA expression, we then correlated miRNA species present in two published microarray cohorts of the NCI-60 cell lines. For the 50 (of the 59) cell lines also present in the Sanger Cell Line Database24 (http://www.cancerrxgene.org/translation/CellLine), raw reads from each unique sequence were correlated with expression of the sequence previously detected by microarray by rank-normalized Spearman`s correlations (Table 4 (available online only)), and performed a similar analysis against all cell lines present in the cohort described by Sokilde et al.5.

Figure 5.

Figure 5

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Data Records

Raw unaligned sequencing reads (in FASTQ file format) are available through the Sequence Read Archive (Data Citation 2). Raw sequencing file names (in FASTQ format) are listed in Table 3 (available online only). A summary of raw sequencing reads for each detected small RNA species are available at through Figshare (Data Citation 1).

Technical Validation

High-throughput sequencing allows for direct in-depth analyses of the human genome, recently revealing a critical role for the expression of the non-coding transcriptome in both genetic and epigenetic regulatory processes.

Sequencing quality control

We examined only high-confidence reads from miRNA sequencing. Samples were sequenced to an average depth of 22.34±0.14 (mean±s.d.; Table 3 (available online only), Fig. 4b). In order to assure only the calling of high-quality sequencing reads, we filtered detected reads to only to include Phred scores ≥20. On average, samples had a Phred score of 33.24±1.28 (Table 3 (available online only), Fig. 4c). Additionally, reads for each sample had an average percent GC content of 46.26±1.6% (Table 3) (available online only). Unsupervised hierarchical clustering and similarity (one-minus-Spearman correlation) of normalized reads revealed relative similarity of sncRNA expression profiles across all cell lines and tissue types analyzed (Fig. 5).

miRNA detection validation

In order to validate the detection of the sncRNA species in these cell lines, we correlated the raw reads per miRNA detected with corresponding miRNA detected by microarray24,25. This analysis was performed for the 50 NCI-60 cell lines present in the Sanger Cell Line miRNA Normalized Data from the Broad Institute (http://www.broadinstitute.org/cgi-bin/cancer/datasets.cgi; File name: Sanger_miR_data1.pn.cn.matlab2.res). Using Spearman’s Rank-Order correlation, we analyzed the correlation of this RMA-normalized miRNA expression to reads obtained from sequencing this cell line panel. Expression of miRNAs in all lines analyzed correlated significantly between sequencing and microarray analysis (Table 4 (available online only); P-values <0.0001, rmean=0.67). Similarly, we correlated sequencing-detected miRNA expression against a complete NCI-60 microarray cohort described by Sokilde et al.5. In this study, profiling was performed on the LNA-enhanced mercury Dx 9.2 microarray platform, and data was log2-normalized after pre-processing (Table 4; P-value range <0.0001–0.0647, rmean=0.28). Microarray data from multiple platforms was compared to sequencing data presented here in order to de-emphasize platform bias and illustrate the need for comprehensive profiling when considering small RNA expression26.

Table 4. Spearman correlation of sequenced NCI-60 cell lines to published miRNA microarray expression levels.

  Broad
 Sokilde et al.
Cell line r 95% confidence interval p (two-tailed) r 95% confidence interval p (two-tailed)
786-0 0.7064 0.651–0.7543 <0.0001 0.3252 0.1976–0.4421 <0.0001
A598 n/a n/a n/a 0.2644 0.1327–0.3869 <0.0001
A549 0.6809 0.6218–0.7324 <0.0001 0.1815 0.04619–0.3103 0.0071
ACHN 0.6959 0.6389–0.7453 <0.0001 0.27 0.1387–0.392 <0.0001
BT-549 n/a n/a n/a 0.3202 0.1922–0.4376 <0.0001
CAKI-1 0.7004 0.6441–0.7492 <0.0001 0.1604 0.02453–0.2905 0.0175
CCRF-CEM n/a n/a n/a 0.1251 -0.01157–0.2571 0.0647
COLO-205 0.6766 0.6169–0.7287 <0.0001 0.2974 0.1677–0.4169 <0.0001
DU-145 0.726 0.6735–0.7711 <0.0001 0.3293 0.2019–0.4457 <0.0001
EKVX 0.6248 0.558–0.6836 <0.0001 0.2634 0.1317–0.386 <0.0001
HCC2998 0.6512 0.5879–0.7066 <0.0001 0.3391 0.2125–0.4545 <0.0001
HCT-116 0.7019 0.6458–0.7505 <0.0001 0.3393 0.2127–0.4547 <0.0001
HCT-15 0.7193 0.6658–0.7654 <0.0001 0.2704 0.1391–0.3924 <0.0001
HL-60 0.6358 0.5704–0.6932 <0.0001 0.2533 0.1211–0.3767 0.0002
HOP-62 0.6899 0.632–0.7401 <0.0001 0.2566 0.1245–0.3798 0.0001
HOP-92 0.6576 0.5951–0.7121 <0.0001 0.2288 0.09537–0.3542 0.0006
HS-578-T 0.6283 0.5619–0.6867 <0.0001 0.2857 0.1554–0.4063 <0.0001
HT-29 0.6956 0.6386–0.7451 <0.0001 0.2875 0.1572–0.4079 <0.0001
IGR-OV1 0.6986 0.642–0.7476 <0.0001 0.3065 0.1774–0.4251 <0.0001
K-562 n/a n/a n/a 0.2657 0.1341–0.3881 <0.0001
KM12 n/a n/a n/a 0.3265 0.1989–0.4432 <0.0001
LOX-IMVI 0.6699 0.6091–0.7228 <0.0001 0.2637 0.132–0.3862 <0.0001
M14 0.6938 0.6365–0.7435 <0.0001 0.2585 0.1265–0.3815 0.0001
MALME-3M 0.5848 0.513–0.6485 <0.0001 0.3069 0.1779–0.4256 <0.0001
MCF-7 0.7235 0.6707–0.769 <0.0001 0.3271 0.1996–0.4438 <0.0001
MDA-MB-231 0.7125 0.658–0.7596 <0.0001 0.1837 0.04847–0.3123 0.0064
MDA-MB-435 0.7081 0.653–0.7558 <0.0001 0.2907 0.1607–0.4109 <0.0001
MOLT-4 0.7034 0.6476–0.7518 <0.0001 0.2809 0.1502–0.402 <0.0001
NCI/ADR-RES n/a n/a n/a 0.3046 0.1754–0.4234 <0.0001
NCI-H226 0.6901 0.6323–0.7403 <0.0001 0.3613 0.2365–0.4743 <0.0001
NCI-H23 0.631 0.565–0.689 <0.0001 0.2649 0.1332–0.3873 <0.0001
NCI-H322M n/a n/a n/a 0.3624 0.2378–0.4754 <0.0001
NCI-H460 0.6395 0.5746–0.6964 <0.0001 0.2794 0.1486–0.4006 <0.0001
NCI-H522 0.7047 0.649–0.7529 <0.0001 0.2856 0.1552–0.4062 <0.0001
OVCAR-3 0.6951 0.638–0.7446 <0.0001 0.3629 0.2383–0.4758 <0.0001
OVCAR-4 0.6731 0.6128–0.7256 <0.0001 0.3422 0.2158–0.4573 <0.0001
OVCAR-5 0.7101 0.6552–0.7575 <0.0001 0.2952 0.1655–0.415 <0.0001
OVCAR-8 0.6613 0.5994–0.7154 <0.0001 0.3407 0.2142–0.456 <0.0001
PC-3 0.6373 0.5721–0.6945 <0.0001 0.2893 0.1592–0.4096 <0.0001
RPMI-8226 0.6704 0.6098–0.7233 <0.0001 0.2641 0.1325–0.3866 <0.0001
RXF393 0.5656 0.4915–0.6316 <0.0001 0.3085 0.1797–0.427 <0.0001
SF-268 0.6788 0.6193–0.7305 <0.0001 0.2812 0.1505–0.4022 <0.0001
SF-295 0.6831 0.6243–0.7343 <0.0001 0.2986 0.1691–0.418 <0.0001
SF-539 0.6623 0.6005–0.7162 <0.0001 0.3037 0.1745–0.4227 <0.0001
SK-MEL-2 0.6294 0.5632–0.6876 <0.0001 0.2851 0.1546–0.4057 <0.0001
SK-MEL-28 0.6765 0.6167–0.7286 <0.0001 0.2867 0.1564–0.4072 <0.0001
SK-MEL-5 0.6095 0.5408–0.6702 <0.0001 0.3681 0.2439–0.4804 <0.0001
SK-OV-3 0.658 0.5956–0.7125 <0.0001 0.2613 0.1295–0.384 <0.0001
SN12C 0.7182 0.6645–0.7644 <0.0001 0.321 0.193–0.4382 <0.0001
SNB-19 0.6126 0.5441–0.6729 <0.0001 0.2332 0.09998–0.3583 0.0005
SNB-75 n/a n/a n/a 0.294 0.1642–0.4139 <0.0001
SR n/a n/a n/a 0.1599 0.02397–0.29 0.0179
SW-620 0.7124 0.6578–0.7595 <0.0001 0.3805 0.2575–0.4915 <0.0001
T-47D 0.6827 0.6238–0.7339 <0.0001 0.3928 0.2709–0.5023 <0.0001
TK-10 0.7137 0.6593–0.7606 <0.0001 0.1981 0.06341–0.3258 0.0032
U-251 0.6242 0.5573–0.6831 <0.0001 0.2862 0.1558–0.4067 <0.0001
UACC-257 0.4873 0.4049–0.5618 <0.0001 0.2332 0.09994–0.3583 0.0005
UACC-62 0.676 0.6161–0.7281 <0.0001 0.3074 0.1785–0.426 <0.0001
UO-31 n/a n/a n/a 0.2975 0.1672–0.4175 <0.0001
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Additional information

How to cite this article: Marshall, E. A. et al. Small non-coding RNA transcriptome of the NCI-60 cell line panel. Sci. Data 4:170157 doi: 10.1038/sdata.2017.157 (2017).

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Supplementary Material

sdata2017157-isa1.zip (4KB, zip)

Acknowledgments

This work was supported by grants from the Canadian Institutes for Health Research (CIHR FRN-143345), the National Institute of Health (NIH-1R01HD089713-01) and scholarships from CIHR and the University of British Columbia. The authors would like to thank May Zhang, Miwa Suzuki and Emma Conway for assistance with cell culture and RNA harvesting. The authors would also like to thank Monica Fuss for assistance in data analysis.

Footnotes

The authors declare no competing financial interests.

Data Citations

  1. Marshall E. A. 2017. figshare. https://doi.org/10.6084/m9.figshare.c.3811156
  2. 2017. NCBI Sequence Read Archive. SRP109305

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Citations

  1. Marshall E. A. 2017. figshare. https://doi.org/10.6084/m9.figshare.c.3811156
  2. 2017. NCBI Sequence Read Archive. SRP109305

Supplementary Materials

sdata2017157-isa1.zip (4KB, zip)

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