Are you developing personalized medicine for cancer? Do you want to know the activity of your compound on tumour cells containing a defined variety of cancer genotypes? At NTRC we have set up Oncolines™, a unique panel of cancer cell lines derived from a diverse set of human tumors.

Oncolines™ characteristics

For a list of cell lines in Oncolines™, click here

For a list of cancer gene mutations that are represented in Oncolines™, click here

What kind of results do I get from Oncolines™?

What distinguishes Oncolines™ from other cell panels?

For more information about NTRC Oncolines™ please contact us at services@ntrc.nl

Compound activity parameters from dose response curves

In an Oncolines™ experiment, dose-response growth curves are measured on 102 cell lines in parallel [1] or a selection of cell lines. From each curve we derive IC50, LD50 and GI50 parameters, as well as maximum effects and hill parameters (curve steepness).[2] We also check the cell doubling times every time, for every assay. All these parameters can be used to correlate drug response to cellular genetic background. The differences between IC50, LD50 and GI50 are subtle but important and in the figure they are outlined for your convenience:

[1] J.C.M. Uitdehaag et al. (2014) Comparison of the cancer gene targeting and biochemical selectivities of all targeted kinase inhibitors approved for clinical use. PLoS ONE 9: e92146
[2]. R.H. Shoemaker (2006) The NCI60 human tumor cell line anticancer drug screen. Nature Reviews Cancer, 6: 814-823.

Identification of novel drug sensitivity markers

Because all Oncolines™ cell lines have been genetically characterized, the activity parameters of small molecule compounds and antibodies can be statistically coupled to the presence of changes in cancer genes, such as mutations, translocations, amplifications or deletions.[3] At NTRC, we report bioinfomatics analyses as free service in our report: a series of waterfall plots and a volcano plot.

A waterfall plot (shown above) is a sorted bar graph that allows a quick visual scan to see if cell lines with certain mutations are preferentially inhibited. In the example, cell lines with mutations in the gene BRAF are coloured red. It is clear that this compound (dabrafenib) preferentially inhibits cell lines with mutations in BRAF [1] (red).

A volcano plot shows the average IC50 shift between mutant and non-mutant cell lines and the statistical significance of that difference. The power of the volcano analysis is that it allows simultaneous testing of changes in many validated cancer genes. For the response to dabrafenib (Tafinlar®), it is clear that BRAF mutation confers sensitivity for this compound (left). As another example, the BTK-inhibitor ibrutinib (Imbruvica®) also inhibits EGFR and ERBB2 therefore inhibits cell lines containing activating mutations in these genes (right). Volcano analysis can reveal unexpected drug sensitivity markers and we already have found several by applying our own kinase reference inhibitor set [1].

[3]. F. Iorio et al. (2016) A landscape of pharmacogenomic interactions in cancer. Cell 166: 740-754

Genetic makeup

Sequencing of the human genome has led to an increased understanding of the mechanisms that underlie the onset and progression of cancer. This knowledge is applied in the development of novel cancer therapies. The Oncolines™ panel of cancer cell lines is derived from a diverse set of human tumors (top right). Oncolines™ contains at least three representative cell lines for 98 oncogenic mutations [3, 4]. NTRC has developed its own proprietary workflow to filter cancer-causing genes from other mutations, which takes into account evidence from mutational hotspots and occurrence in patients.

[4] M.S. Lawrence et al. (2014) Discovery and saturation analysis of cancer genes across 21 tumour types. Nature 505: 495-501.

Test range and data quality

Often compounds need to be prioritized on basis of small differences and also drug sensitivity is often based on subtle shifts, so reproducibility is a crucial factor in cell proliferation studies. Therefore we have a workflow ensuring that stable cell batches are used for the Oncolines™ panel. As a test we regularly profile doxorubicin in all 102 cell lines. Historically, outliers in IC50s were never more than a factor of 2.1 away from each other. The highest and lowest IC50 in a cell line generally differed only a factor of 1.4. This is why we believe that we deliver the best possible data.
Oncolines™ dose reponse curves are calculated on basis of 9 duplicate points, spanning four log decades in concentrations (31.6 uM – 3.16 nM). The high starting concentration and large range means that most IC50s will fall inside the measured range and there is no need to extrapolate as some panels do. Furthermore, if your compound is too potent to give an interpretable curve within the concentration range tested, we’ll dilute it and retest it for free.

Low passage and culturing in original media

The culturing conditions of cells can often make a big difference in cell line response. Yet often in cell line panels, cell lines are cultured in one-size-fits-all media. At NTRC we believe in culturing every cell line in the medium originally recommended by ATCC, from whom we licensed our cell lines. In this way, we can measure drug responses under optimal growth conditions of cells.

We also realize that cell lines can undergo changes when cultured for extended cell line generations. Therefore, we maintain our cell banks with an absolute minimum of cell passages. Our master cell banks are within 3 passages of the original ATCC vial, and our working cell banks within 6 passages.

Human tumor cell lines in the Oncolines™ cell panel

Solid tumors in grey; hematologic tumors in blue

Cell line Cancer type Tissue origin
5637 Bladder carcinoma Urinary bladder
769-P Renal cell adenocarcinoma Kidney
786-O Renal cell adenocarcinoma Kidney
A-172 Glioblastoma Central nervous system
A-204 Rhabdomyosarcoma Soft tissue, muscle
A375 Malignant melanoma Skin
A388 Epidermoid carcinoma Derived from metastatic site
A-427 Lung carcinoma Lung
A-498 Renal carcinoma Kidney
A-549 Lung carcinoma Lung
A-704 Kidney adenocarcinoma Kidney
ACHN Renal cell adenocarcinoma Kidney
AN3 CA Endometrium adenocarcinoma Uterus
AsPC-1 Pancreas adenocarcinoma Pancreas
AU-565 Breast adenocarcinoma Breast
BT-20 Breast carcinoma Breast
BT-549 Breast ductal carcinoma Breast
BxPC-3 Pancreas adenocarcinoma Pancreas
C-33 A Cervix carcinoma Uterus
CAL 27 Squamous cell carcinoma, tongue Aero-digestive tract
CCF-STTG1 Astrocytoma, brain Central nervous system
CCRF-CEM Acute lymphoblastic leukemia (ALL) Blood
COLO 205 Colorectal adenocarcinoma Gastrointestinal tract
COLO 829 Malignant melanoma Skin
Daoy Medulloblastoma, brain Central nervous system
DB Large cell lymphoma, B lymphoblast Blood
DLD-1 Colorectal adenocarcinoma Gastrointestinal tract
DoTc2 4510 Cervix carcinoma Uterus
DU 145 Prostate carcinoma Prostate
DU4475 Breast carcinoma Breast
ES-2 Clear cell carcinoma, ovary Ovary
FaDu Squamous cell carcinoma, pharynx Aero-digestive tract
G-361 Malignant melanoma Skin
HCT116 Colorectal carcinoma Gastrointestinal tract
HCT-15 Colorectal adenocarcinoma Gastrointestinal tract
HL-60 Acute promyelocytic leukemia Blood
Hs 578T Breast carcinoma Breast
Hs 746T Gastric carcinoma Gastrointestinal tract
Hs 766T Pancreas carcinoma Pancreas
HT Diffuse mixed lymphoma, B lymphoblast Blood
HT-1080 Fibrosarcoma Soft tissue
HuTu 80 Duodenum adenocarcinoma Gastrointestinal tract
J82 Transitional cell carcinoma, urinary bladder Bladder
JAR Choriocarcinoma, placenta Placenta
Jurkat E6.1 Acute T cell leukemia Blood
K-562 Chronic myelogenous leukemia (CML) Blood
KATO III Gastric carcinoma Gastrointestinal tract
KG-1 Acute myelogenous leukemia (AML) Blood
KLE Endometrium adenocarcinoma Uterus
KU812 Chronic myelogenous leukemia (CML) Blood
LNCaP FGC Prostate carcinoma Prostate
LoVo Colorectal adenocarcinoma Gastrointestinal tract
LS 174T Colorectal adenocarcinoma Gastrointestinal tract
LS411N Dukes’ type B, colorectal carcinoma Gastrointestinal tract
MCF7 Breast adenocarcinoma Breast
MeWo Malignant melanoma Skin
MG-63 Osteosarcoma Bone
MIA PaCa-2 Pancreas carcinoma Pancreas
MOLT-4 Acute lymphoblastic leukemia (ALL) Blood
NCCIT Teratocarcinoma, testis Embryo, testis
NCI-H460 Large cell lung carcinoma Lung
NCI-H661 Large cell lung carcinoma Lung
NCI-H82 Small cell lung carcinoma Lung
OVCAR-3 Ovary adenocarcinoma Ovary
PA-1 Ovary teratocarcinoma Ovary
PC-3 Prostate adenocarcinoma Prostate
PFSK-1 Malignant primitive neuroectodermal tumor, brain Central nervous system
RD Rhabdomyosarcoma, muscle Soft tissue
RKO Colon carcinoma Gastrointestinal tract
RL Non-Hodgkin’s lymphoma, B lymphoblast Blood
RL95-2 Endometrium carcinoma Uterus
RPMI-7951 Malignant melanoma Skin
RS4-11 Acute lymphoblastic leukemia (ALL) Blood
RT4 Bladder papilloma Bladder
SHP-77 Small cell lung carcinoma Lung
SJCRH30 Rhabdomyosarcoma Soft tissue
SK-N-AS Neuroblastoma Central nervous system
SK-N-FI Neuroblastoma Central nervous system
SNU-5 Gastric carcinoma Gastrointestinal tract
SNU-C2B Colorectal carcinoma Gastrointestinal tract
SR Large cell immunoblastic lymphoma Blood
SU-DHL-1 Large cell lymphoma; diffuse histiocytic lymphoma Blood
SU-DHL-6 Large cell lymphoma; follicular B cell lymphoma Blood
SUP-T1 T-cell lymphoblastic lymphoma Blood
SW48 Colorectal adenocarcinoma Gastrointestinal tract
SW480 Colorectal adenocarcinoma Gastrointestinal tract
SW620 Colorectal adenocarcinoma Gastrointestinal tract
SW626 Ovary adenocarcinoma Ovary
SW837 Rectum adenocarcinoma Gastrointestinal tract
SW872 Liposarcoma Soft tissue
SW900 Squamous cell carcinoma, lung Lung
SW948 Colorectal adenocarcinoma Gastrointestinal tract
SW982 Synovial sarcoma Soft tissue
T24 Bladder transitional cell carcinoma Bladder
T98G Glioblastoma multiformea, brain Central nervous system
TCCSUP Transitional cell carcinoma, bladder Bladder
THP-1 Acute monocytic leukemia (AML) Blood
TT Thyroid carcinoma Thyroid
U-118 MG Glioblastoma; astrocytoma, brain Central nervous system
U-2 OS Osteosarcoma Bone
U-87 MG Glioblastoma, brain Central nervous system
VA-ES-BJ Epithelioid sarcoma Soft tissue

List of mutations in the Oncolines™ panel

Mutant Gene* Cell line
ABL-driven A-204, K-562, KU812
APC COLO 205, DLD-1, DU4475, HCT-15, Jurkat E6.1, LoVo, LS411N, SW480, SW620, SW626, SW837, SW948
ARID1A C-33 A, Hs 766T, Jurkat E6.1, LoVo, LS 174T, LS411N, NCI-H460, RKO, RL95-2, SNU-5, TCCSUP, THP-1
ATM LNCaP FGC, RL95-2, SW948
BRAF A375, BxPC-3, COLO 205, COLO 829, DU4475, ES-2, G-361, LS411N, RKO, RPMI-7951, SW872, SW982
BRCA2 DoTc2 4510, HCT 116, HCT-15, LNCaP FGC, RKO, RL95-2, SW48
CDKN2A 786-O, A-172, A-427, A-498, A-549, A375, A375, ACHN, AsPC-1, BT-20, BxPC-3, CAL 27, CCRF-CEM, COLO 829, Daoy, DU 145, G-361, HCT 116, HL-60, HT-1080, Jurkat E6.1, K-562, MCF-7,  MeWo, MG-63, MIA PaCa-2, MOLT-4, NCI-H460, RS4-11, RT4, SNU-5, SR, SUP-T1, SW872, SW900, SW982,T98G, THP-1, U-118 MG, U-87 MG, VA-ES-BJ
CHD4 HCT 116, MOLT-4, SUP-T1
CREBBP DU 145, Jurkat E6.1, SU-DHL-1, SU-DHL-6
CTNNB1 A-427, HCT 116, HuTu 80, LS 174T, SW48
EGFR A388, BT-20, SW48
EP300 BxPC-3, DLD-1, HCT 116, HCT-15, HT, LS411N, MCF7, MOLT-4, RKO, RL, RL95-2, SU-DHL-6, SW48, SW620, T24
ERBB2 AU-565, J82, MOLT-4, SNU-C2B
FAT1 DoTc2 4510, DU 145, FaDu, Jurkat E6.1, RKO, SW900, T24
FBXW7 AN3 CA, AsPC-1, C-33 A, CCRF-CEM, Jurkat E6.1, KLE, LoVo, LS411N, SW48, SW837
FLG A-498, A-704, A388, HCT-15, MeWo, MOLT-4, NCI-H460, NCI-H82, SHP-77, SNU-C2B, TT
KRAS A427, A-549, AsPC-1, CCRF-CEM, DLD-1, DU 145, HCT-15, HCT 116, Hs 766T, KLE, LoVo, LS 174T, MIA PaCa-2, NCI-H460, SHP-77, SNU-C2B, SW480, SW620, SW620, SW626, SW837, SW900, SW900, SW948
LRP1B Hs 766T, NCI-H460, OVCAR-3, U-2 OS
MYC AU-565, Hs 578T, MG-63, NCI-H460, NCI-H82, SW620
NCOR1 DLD-1, HCT-15, HCT 116, RKO, SW48
NF1 Daoy, HCT 116, MeWo, RD, RKO, SK-N-FI, SW900, U-87 MG
NRAS CCRF-CEM, HL-60, HT-1080, MOLT-4, PA-1, RD, SK-N-AS, THP-1
NSD1 AN3 CA, CCRF-CEM, DoTc2 4510, RKO, SW900
PBRM1 A-704, A388, ACHN, LS 174T, SUP-T1
PIK3CA A388, BT-20, C-33 A, DLD-1, HCT-15, HCT 116, LS 174T, MCF7, NCI-H460, RKO, SUP-T1, SW948, TCCSUP
PIK3R1 AN3 CA, Daoy, Hs 578T, LNCaP FGC, RL95-2
PTEN 786-O, A-172, AN3 CA, BT-549, C-33 A, CCF-STTG1, CCRF-CEM, COLO 829, J82, Jurkat E6.1, LNCaP FGC, LS411N, MOLT-4, NCCIT, PC-3, RL95-2, RPMI-7951, SW872, THP-1
RB1 5637, BT-20, BT-549, DU 145, DU4475, TCCSUP
SETD2 A-498, AN3 CA, LNCaP FGC, SW48
SMAD4 AU-565, BxPC-3, CAL 27, COLO 205, FaDu, Hs 766T, RT4, SW626, SW948
SMARCA4 A-549, C-33 A, DLD-1, HCT 116, HuTu 80, Jurkat E6.1, MOLT-4, RL95-2, RS4-11, SNU-C2B
SPEN BT-20, HT, LoVo, RL95-2
STK11 A-549, DU 145, G-361, NCI-H460
TP53 5637, 786-O, A388, A-704, AN3 CA, AsPC-1, AU-565, BT-20, BxPC-3, C-33 A, CAL 27, CCRF-CEM, Daoy, DB, DLD-1, ES-2, FaDu, HCT-15, Hs 578T, Hs 746T, HT, Jurkat E6.1, K-562, KLE, KU812, LS411N, MeWo, MIA PaCa-2, MOLT-4, NCCIT, NCI-H661, OVCAR-3, PC-3, PFSK-1, RD, RL95-2, RPMI-7951, SHP-77, SJCRH30, SK-N-AS, SK-N-FI, SNU-C2B, SU-DHL-1, SU-DHL-6, SUP-T1, SW480, SW620, SW837, SW900, SW948, T24, T98G, TCCSUP, THP-1, TT, U-118 MG
XIRP2 DU 145, HCT 116, LNCaP FGC, LS411N, MeWo, SK-N-AS
ZFHX3 AN3 CA, C-33 A, HCT-15, LS 174T, RKO, RL95-2, SR, SW48

*The table shows the most commonly occurring and best known cancer genes (38 in total). Oncolines™ includes in total 98 genes in the genomic marker analysis. Genes are either mutated, part of a translocation or have altered copy numbers (italic).