Chromatin Blueprint of Glioblastoma Stem Cells Reveals Common Drug Candidates for Distinct Subtypes
bioRxiv preprint doi: https://doi.org/10.1101/370726; this version posted July 17, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-ND 4.0 International license.
CHROMATIN BLUEPRINT OF GLIOBLASTOMA STEM CELLS REVEALS COMMON DRUG CANDIDATES FOR DISTINCT SUBTYPES
Paul Guilhamon1, Michelle M Kushida2, Graham MacLeod3, Seyed AM Tonekaboni1,4, Florence MG Cavalli2, Fiona J Coutinho2, Nishani Rajakulendran3, Xinghui Che2, Naghmeh Rastegar2, Mona Meyer2, Xiaoyang Lan2, Nuno M Nunes5, Uri Tabori6, Michael D Taylor2, Benjamin Haibe-Kains1,4,7,8, Stephane Angers3, Peter B Dirks2,8,*, Mathieu Lupien1,4,9,*
1Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G 1L7, Canada 2Developmental and Stem Cell Biology Program and Arthur and Sonia Labatt Brain Tumour Research Centre, The Hospital for Sick Children, Toronto, ON M5G 0A4, Canada 3Leslie Dan Faculty of Pharmacy, University of Toronto, Toronto, ON M5S 3M2, Canada 4Department of Medical Biophysics, University of Toronto, Toronto, ON M5S 1A8, Canada 5Program in Genetics and Genome Biology, Arthur and Sonia Labatt Brain Tumour Research Centre, The Hospital for Sick Children, Toronto, ON M5G 0A4, Canada 6Division of Hematology and Oncology, The Hospital for Sick Children, Toronto, ON M5G 1X8, Canada 7Department of Computer Science, University of Toronto, Toronto, ON M5S 1A8, Canada 8Departments of Molecular Genetics and Surgery, University of Toronto, Toronto, ON M5S 1A8, Canada 9Ontario Institute for Cancer Research, Toronto, ON M5G 0A3, Canada
*Corresponding authors: Mathieu Lupien ([email protected]) and Peter B Dirks ([email protected])
Summary
1 bioRxiv preprint doi: https://doi.org/10.1101/370726; this version posted July 17, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-ND 4.0 International license.
Glioblastoma (GBM) is an aggressive form of brain cancer with a median
survival of 12.6 months1 and for which the standard treatment of surgery,
radiotherapy and temozolomide, provides only an additional 2.5 months in the small
subset of responsive patients2. Despite extensive characterization and stratification
of the bulk primary tumours, no targeted therapies have been successfully
developed1,3.
GBM tumours are rooted in glioblastoma stem cells (GSCs) that have self-
renewal and tumour-initiating capacities4. GSCs also drive disease progression in
vivo5,6. Although the mutational landscape of GSCs is well established7,8, and their
epigenetic profile, based on DNA methylation and histone modifications, has been
described for a few samples7–9, the variability in chromatin accessibility and resulting
functional heterogeneity across GSCs has not been previously investigated. Indeed,
GSCs derived from different patient tumours were shown to share numerous
common features in their chromatin accessibility landscape10. However, phenotypic
variability, such as differentiation capacity, was also reported between GSCs that
exhibit specific differences in their chromatin accessibility11, warranting a
comprehensive assessment of heterogeneity across GSCs.
Here we reveal three novel and distinct GSC subtypes based on the
integrative analysis of chromatin accessibility, DNA methylation and gene expression
on a cohort of 27 patient tumour-derived GSCs. Each GSC subtype is regulated by a
specific set of transcription factors, uniquely essential for growth in the respective
subtypes. Through a single-cell clonal analysis, we show that a GBM tumour can
harbour more than one GSC subtype. In addition, we not only identify subtype-
biased growth inhibitors through our drug response screening assay but also show
that all GSC subtypes commonly express the serotonin receptor 5-HT2 and are
2 bioRxiv preprint doi: https://doi.org/10.1101/370726; this version posted July 17, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-ND 4.0 International license.
sensitive to the dopamine/serotonin receptor ligand perphenazine. Overall, our
results suggest that patients could benefit from serotonin receptor inhibitors or a
combination therapy to address the GSC heterogeneity using drugs targeting the
different GSC subtypes populating each GBM tumour.
In a cohort of 27 patient tumour-derived GSCs assessed for chromatin
accessibility using ATAC-seq, over 16% of accessible regions were shared by over
half the samples (Figure 1a). This indicates that a large set of chromatin accessibility
features is shared within GSCs, as previously reported10. We also compared the full
set of accessible regions in GSCs to those identified in human fetal neural stem cells
(HFNS), the closest available normal tissue to the GSCs5. Over 38% of accessible
genomic regions in GSCs were shared with HFNS (97,578 out of 255,890)
(Supplementary Figure 1). This suggests that the apparent homogeneity in
chromatin accessibility observed across the GSCs may in fact correspond to core
features also required by their normal counterparts, and not necessarily linked to the
shared tumourigenicity of the GSCs. Indeed, of the accessible regions shared
between GSCs and HFNS, over 40% can be found in more than half the GSC lines
(Figure 1b). Conversely, just over 1% of the ATAC-seq peaks exclusive to GSCs
were shared by more than half of the GSCs (Figure 1c). However, over a quarter
(26%) of those ATAC-seq peaks were common to subsets of three or more GSCs
suggesting the presence of subtypes within the GSC cohort.
To identify GSC subtypes, we integrated chromatin accessibility (ATAC-seq)
with data types previously used to subset GBMs, namely DNA methylation (Illumina
EPIC array) and gene expression (RNA-seq) using Similarity Network Fusion
(SNF)12. Each data type was first modelled individually as a separate network,
3 bioRxiv preprint doi: https://doi.org/10.1101/370726; this version posted July 17, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-ND 4.0 International license.
followed by fusion of the three networks to generate a combined network most stably
supported by all three data types (Figure 1d,e). Spectral clustering on the fused
network-based analysis identified three clusters revealing novel and distinct GSC
subtypes (C1, C2, C3), supported overall by all three data types (Figure 1e) but with
sample proximity within individual clusters better supported by specific data types.
For example, sample proximity in the fused network within C1 and C3 is most
strongly supported by chromatin accessibility and gene expression, while sample
similarity within C2 is driven by DNA methylation and chromatin accessibility (Figure
1e). Comparing the final clusters obtained through the combined SNF to those
generated from individual data types revealed that gene expression and DNA
methylation largely support the final clustering, but fail to replicate the fused SNF-
defined clusters (Figure 1f). However, chromatin accessibility alone is sufficient to
recapitulate the fused SNF-defined subtypes. For comparison, we mapped the
commonly reported13 expression-based mesenchymal, classical, neuronal, and
proneural classification derived from bulk GBM tumours onto the 27 GSCs. Although
all four bulk GBM tumour signatures are represented in our cohort of GSCs, multiple
signatures were significantly enriched within each GSC and within each subtype
(Figure 1f). In addition, this expression-based classification of the GSCs does not
match the expression-only SNF clusters of GSCs (Figure 1f), further highlighting the
inadequacy of these signatures to subtype GSCs. Taken together, these results
suggest that chromatin accessibility underlies the biological variation between these
novel GSC subtypes, providing a unique data type to delineate the mechanisms
driving the specific identity of each cluster.
To that end, we first assessed the subtype-specific coverage of chromatin
accessibility across our cohort through a saturation analysis of the ATAC-seq data.
4 bioRxiv preprint doi: https://doi.org/10.1101/370726; this version posted July 17, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-ND 4.0 International license.
Using a self-starting nonlinear regression model, we predicted a 93%, 88%, and 71%
saturation for the detection of regions of accessible chromatin across GSCs from the
C1 (n=13), C2 (n=9), and C3 (n=5) subtypes, respectively (Figure 1g). Considering
that accessible chromatin provides binding sites for transcription factors to regulate
gene expression, we then performed DNA recognition motif enrichment analysis
across subtypes to uncover regulators of GSC identity. We identified enriched DNA
recognition motifs using HOMER14 on regions exclusively accessible in each of the
three GSC subtypes and grouped them into families with CIS-BP15 (Figure 2a and
Supplementary Figure 2). Interestingly, the most enriched DNA recognition motif
families in each subtype were either depleted or showed only low-level enrichment in
the other subtypes. The DNA recognition motifs for the interferon-regulatory factor
(IRF) and Cys2-His2 zinc finger (C2H2 ZF) transcription factor families were
enriched in the C1 subtype (Figure 2a). Regulatory factor X (RFX) and basic helix-
loop-helix (bHLH) binding motifs were enriched in the C2 subtype, and the DNA
recognition motif for the Forkhead family of transcription factors was enriched in the
C3 subtype (Figure 2a). We subsequently tested the subtype-specific essentiality for
growth of each transcription factor through genome-wide CRISPR-Cas9 screens in
GSC lines representative of each subtype (Figure 2b,c) and validated their
expression (Supplementary figure 3). Together, these analyses identified six
transcription factors across the three GSC subtypes whose DNA recognition motif is
exclusively enriched in a given subtype, that are expressed in GSCs from that
subtype, and are exclusively essential for the proliferation of GSCs in that subtype:
SP1 in C1, ASCL1, OLIG2, AHR, and NPAS3 in C2, and FOXD1 in C3 (Figure 2c).
These transcription factors were previously associated with crucial roles in
GBM and/or GSC function. C1-associated SP1 is involved in cellular differentiation
5 bioRxiv preprint doi: https://doi.org/10.1101/370726; this version posted July 17, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-ND 4.0 International license.
and growth, apoptosis, response to DNA damage, chromatin remodelling16, and the
stimulation of TERT expression17 in cancer stem cells, and increases stemness and
invasion in GBM18. Although SP1 is essential for growth in only one of the three C1
samples tested, other members of the SP1 regulatory network are found to be
exclusively essential in the other two C1 samples, suggesting the SP1 network as a
whole is the key regulator of this GSC subtype. Of the C2-enriched essential
transcription factors, OLIG2 is a known GSC marker19, while ASCL1 is a critical
regulator of GSC differentiation11. FOXD1, enriched in C3, is a known pluripotency
regulator and determinant of tumourigenicity in GSCs where it regulates the
transcriptional activity of the aldehyde dehydrogenase ALDH1A3, an established
functional marker for mesenchymal GSCs20,21. In addition, all six transcription factors
display significantly higher expression in GBM compared to normal brain (Figure 2d),
further supporting their function as key regulators of tumour initiation and
development.
Our data were generated on patient-derived GSC lines plated as mixed
populations and enriched for self-renewal and tumour-initiating cells over several
passages (Figure 3a). However, GBM tumours are reported to contain
heterogeneous GSC populations8,10,22, that could potentially correspond to a
hierarchy of stem cell states. To assess the heterogeneity in GSC subtypes of the
GBM tumours in our cohort, we delineated the subtypes of clonal populations
derived from single cells initially sorted out from a fresh bulk tumour mixed
population using cell-surface markers (Figure 3a). In total, we used six clonal
populations derived from the same tumour region as the matched G498 GSC, four
for G551, and six for G648. We then performed unsupervised hierarchical clustering
using the ATAC-seq signal of the individual clones along with our cohort of 27 GSCs,
6 bioRxiv preprint doi: https://doi.org/10.1101/370726; this version posted July 17, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-ND 4.0 International license.
excluding the G498, G551 and G648 GSCs. These were removed to avoid any
patient-specific bias in the clustering (Figure 3b-d). The G498 and G551 GSCs
classify within the C1 subtype (Figure 1d-f). However, only 60% (n=10) of their
matched clones clustered within C1 (3 of 6 clones matched to G498, and 3 of 4
matched to G551) (Figure 3b,d), while 40% clustered within the C2 subtype. Clones
matched to G648 all clustered within C2, the same subtype as G648 (Figure 3c). The
scarcity of C3 subtype GSC lines and available tumour tissue prevented the
derivation of clonal populations from the original bulk matched tumours. Together,
these data suggest that adult GBM tumours can rely on GSCs from more than one
subtype.
Therapeutic options for GBM patients are currently extremely limited, and the
subdivision of GSCs into the functional subtypes presented here offers new
possibilities to identify effective compounds. We thus first re-analysed a published
drug screen performed on GSCs8 to identify compounds with subtype-specific
effectiveness. Additional GSC populations were tested with nine of the more
promising compounds: they had been tested in the original screen on populations
from least one of the GSC subtypes and had shown no impact on the proliferation of
normal HFNS cells8. In total, two drugs showed no impact on any of the subtypes
(Supplementary Figure 4), and, as expected, we identified several compounds with
subtype-biased effects, such as ML-9, DL-Cycloserine, Phorbol 12−myristate
13−acetate, and Tetraethylthiuram disulfide (Figure 4a).
Unexpectedly, however, three compounds inhibited proliferation by over 20%
across all GSC subtypes. Two of these are neurotransmitter signalling disruptors,
7 bioRxiv preprint doi: https://doi.org/10.1101/370726; this version posted July 17, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-ND 4.0 International license.
shown to bind to a variety of receptors and neurotransmitter uptake channels:
SB224289 hydrochloride and GBR-2909 dihydrochloride (Figure 4b). The only
neurotransmitter-related gene expressed across more than 75% of the GSC
populations and known to be bound by both compounds is the serotonin receptor 5-
HT2 (Figure 4c), identifying serotonin signalling as a likely common pathway for
targeting in all GSC subtypes. As neither of the identified compounds are clinically
available, we tested the approved antipsychotic drug perphenazine on representative
lines of each subtype. Perphenazine, with affinity to multiple serotonin receptors23,
has been previously shown to have no effect on the viability of normal cells up to
concentrations of 50 µM24. However, end-point viability assays demonstrate that
perphenazine has a strong negative proliferative effect on members of all three GSC
subtypes (Figure 4d,g,i). Limiting dilution assays additionally revealed that, after two
weeks of treatment, perphenazine significantly reduces the sphere-forming capacity
of GSCs from all three subtypes (Figure 4e,f,h,i,k,l and Supplementary Figure 5).
Through chromatin-based subtyping we have thus identified both compounds with
subtype-biased efficacy and a clinically-approved drug that impacts the self-renewal
capacity of GSCs in a subtype-agnostic manner.
Taken together, our work identifies three distinct GSC subtypes based on
chromatin accessibility, DNA methylation, and gene expression data. Each GSC
subtype relies on exclusive networks of essential transcription factors. Notably, our
clonal analysis demonstrates that multiple GSC subtypes are present within
individual tumours and we demonstrate the efficacy of perphenazine, a clinically-
approved drug, in decreasing the self-renewal capacity of all GSCs.
ACKNOWLEDGEMENTS
8 bioRxiv preprint doi: https://doi.org/10.1101/370726; this version posted July 17, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-ND 4.0 International license.
We would like to thank the Princess Margaret Genomics Centre for their assistance
in this study, and Aude Gerbaud for her help with figure formatting. This work was
supported by the Princess Margaret Cancer Foundation and SU2C Canada Cancer
Stem Cell Dream Team Research Funding (SU2C-AACR-DT-19-15) provided by the
Government of Canada through Genome Canada and the Canadian Institute of
Health Research, with supplemental support from the Ontario Institute for Cancer
Research, through funding provided by the Government of Ontario. Stand Up To
Cancer Canada is a Canadian Registered Charity (Reg. # 80550 6730 RR0001).
Research Funding is administered by the American Association for Cancer Research
International - Canada, the Scientific Partner of SU2C Canada. This study was also
conducted with the support of the Ontario Institute for Cancer Research through
funding provided by the Government of Ontario for the Brain Cancer Translational
Research Initiative. M.L. holds an Investigator Award from the Ontario Institute for
Cancer Research and a Canadian Institutes of Health Research (CIHR) New
Investigator Award. P.G is supported by a CIHR Fellowship (MFE 338954). PBD is
also supported by CIHR, OICR, the Terry Fox Research Institute, and the Hospital
for Sick Children Foundation, Jessica’s Footprint, Hopeful Minds, and the Bresler
Family. PBD holds a Garron Chair in Childhood Cancer Research at the Hospital for
Sick Children. S.A. is supported by the CIHR, the Terry Fox Institute and the
Canadian Cancer Society.
AUTHOR CONTRIBUTIONS
ML, PG, and PBD conceptualized and designed the study assisted by BHK, SA, and
FJC. PG conducted the genomics experiments and designed and/or implemented
most of the computational and statistical approaches. MMK performed all the tissue
9 bioRxiv preprint doi: https://doi.org/10.1101/370726; this version posted July 17, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-ND 4.0 International license.
culture and in vitro drug screens, with the help of NR, XC, and MM under the
supervision of PD. FMGC performed SNF, under the supervision of MDT. GM and
SA contributed the essentiality screen data. SAMT contributed to the computational
analysis, under supervision of BHK and ML. The manuscript was written by PG and
ML with input from all other authors.
COMPETING FINANCIAL INTERESTS
The authors declare no competing financial interests
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FIGURE LEGENDS
13 bioRxiv preprint doi: https://doi.org/10.1101/370726; this version posted July 17, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-ND 4.0 International license.
Figure 1 GSCs cluster into three distinct groups. a) Distribution of all ATAC-seq
peaks across the 27 GSC samples. b) Distribution of ATAC-seq peaks shared
between GSCs and HFNS across the 27 GSC samples. c) Distribution of GSC-
exclusive ATAC-seq peaks across the 27 GSC samples. d) Sample networks
determined by SNF on individual data types, modelled using Cytoscape. e) Fused
SNF network, using DNA methylation (Methylation), gene expression (Expression),
and chromatin accessibility (ATAC). f) Sample similarity as determined by SNF
(central heatmap, darker blue = more similar, lighter blue = less similar); clustering of
individual data types compared to the combined network (left, right and bottom);
mapping of expression-determined GBM subtypes using GSEA signatures (top): any
displayed signature was found significantly associated with that sample and
signatures are ordered from top to bottom in order of increasing GSEA enrichment
score. g) Saturation curves of accessible chromatin regions for all 27 GSCs and
individual subtypes.
Figure 2 GSC subtypes are regulated by subtype-specific essential TFs. a) Motif
family enrichment in each cluster; log2(Fold Enrichment) > 0.5 threshold selected
based on the distribution of values in each cluster (Supplementary Figure 3). b)
Schematic of drop-out essentiality screen using GSCs stably expressing Cas9 and
gRNA libraries. c) z-score distribution of key essential genes in each cluster. Red
line corresponds to the empirically determined threshold for essentiality in each
tested line. d) Expression levels of key transcription factors in tumour and normal
samples, analysed and displayed using GEPIA25.
14 bioRxiv preprint doi: https://doi.org/10.1101/370726; this version posted July 17, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-ND 4.0 International license.
Figure 3 GSCs from multiple clusters are present in individual tumours. a)
Schematic of the derivation of lines and clones from patient tumours. b) Results of
hierarchical clustering of each of the six clones matched to G498 with the 24 GSCs.
The G498 line clusters with C1, while three of the six matched clones cluster within
C2. c) Results of hierarchical clustering of each of the six clones matched to G648
with the 24 GSCs. The G648 line clusters with C2, and all six matched clones cluster
within C2. d) Results of hierarchical clustering of each of the four clones matched to
G551 with the 24 GSCs. The G551 line clusters with C1, while 1 of the four matched
clones clusters within C2.
Figure 4 Serotonin signalling as a common target among GSC subtypes. a) Drug
screen of 165 compounds on GSC populations. b) Binding targets of SB224289 and
GBR-12909. Edge width represents binding affinity between the drug and its targets,
while colour represents the evidence source for the interaction; green= experimental
evidence, others = predicted interactions from text-mining, co-expression, etc. c)
Expression of neurotransmitter-related genes across the 27 GSC populations. d)
Dose response curve for perphenazine in C1 lines. e-f) Limiting dilution assay with
DMSO or perphenazine (5 μM) treatment in C1 lines. g) Dose response curve for
perphenazine in C2 lines. h-i) Limiting dilution assay with DMSO or perphenazine (5
μM) treatment in C2 lines. j) Dose response curve for perphenazine in C3 lines. k-l)
Limiting dilution assay with DMSO or perphenazine (5 μM) treatment in C3 lines.
ONLINE METHODS
Patient samples and cell culture
15 bioRxiv preprint doi: https://doi.org/10.1101/370726; this version posted July 17, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-ND 4.0 International license.
All tissue samples were obtained following informed consent from patients, and all
experimental procedures were performed in accordance with the Research Ethics
Board at The Hospital for Sick Children (Toronto, Canada). Approval to pathological
data was obtained from the respective institutional review boards. Primary tissue
samples were dissociated in artificial cerebrospinal fluid followed by treatment with
enzyme cocktail at 37°C. GSC lines were grown as adherent monolayer cultures in
serum-free medium as previously described26. Briefly, cells were grown adherently
on culture plates coated with poly-L-ornithine and laminin. Serum-free NS cell self-
renewal media (NS media) consisted of Neurocult NS-A Basal media, supplemented
with 2 mmol/L L-glutamine, N2 and B27 supplements, 75 μg/mL bovine serum
albumin, 10 ng/mL recombinant human EGF (rhEGF), 10 ng/mL basic fibroblast
growth factor (bFGF), and 2 μg/mL heparin. Single cell-derived clonal populations
were generated by staining for cell surface markers CD15 and human specific
CD133/1 followed by FACS live sorting. Single cells from four populations (CD
negative, CD15 positive, CD133 positive CD15/CD133 double positive) were
collected and expanded in serum-free conditions8.
Chemical Screen and Secondary Screen/Dose Response Curve
Cells were screened with the LOPAC library (Sigma) at the High-Throughput
Screening Division, formerly known as the SMART Laboratory, at the Lunenfeld-
Tanenbaum Research Institute. Cells were seeded in laminin coated 384-well plates
at a density of 2000 cells per well and chemicals were added at a concentration of 5
M and incubated for five days at 37°C. Cell viability was assessed by measuring
Alamar Blue incorporation as per the manufacturer’s protocol (Invitrogen). Percent
growth inhibition was calculated relative to DMSO treated control wells. The potency
16 bioRxiv preprint doi: https://doi.org/10.1101/370726; this version posted July 17, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-ND 4.0 International license.
of hits from the primary screen were re-tested at 1 M and 5 M or in a 9-point 2-fold
dilution series ranging from 50 M-0.2 M concentrations.
In Vitro Limiting Dilution Assay
Cells were plated in serial dilutions on non-adherent 96-well plates and in six
biological replicates under NS conditions. Serial dilutions ranged from 2000 cells to 3
cells per well. After 7 and 14 days of plating with chemical or vehicle, each well was
scored for the presence or absence of neurosphere formation. Data was plotted and
tested for inequality in frequency between multiple groups and tested for adequacy
of the single-hit model using Extreme Limiting Dilution Analysis (ELDA) software.
ATAC-seq
ATAC-seq was used to profile the accessible chromatin landscape of 27 GSC lines
and 17 clonal lines. 50,000 cells were processed from each sample as previously
described27. The resulting libraries were sequenced with 50 bp single-end reads
which were mapped to hg19. Reads were filtered to remove duplicates, unmapped
or poor quality (Q <30) reads, mitochondrial reads, chrY reads, and those
overlapping the ENCODE blacklist. Following alignment, accessible chromatin
regions/peaks were called using MACS2. Default parameters were used except for
the following: --keep-dup all -B --nomodel --SPMR -q 0.05 --slocal 6250 --llocal 6250.
The signal intensity was calculated as the fold enrichment of the signal per million
reads in a sample over a modelled local background using the bdgcmp function in
MACS2.
A given chromatin region was considered exclusive to one of the clusters if it was
called as a peak in any of the cluster’s samples using a q-value filter of 0.05 and was
17 bioRxiv preprint doi: https://doi.org/10.1101/370726; this version posted July 17, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-ND 4.0 International license.
not called as a peak in any of the other samples using a q-value filter of 0.2, in order
to ensure stringency of exclusivity.
The ATAC-seq saturation analysis was performed by randomizing the order of
samples, and successively calculating the number of additional peaks discovered
with the addition of each new sample. This process was repeated 10,000 times and
averaged. A self-starting non-linear regression model was then fitted to the data to
estimate the level of saturation reached.
In the clonal analysis, we mapped the signal of all clones to the full catalogue of
peaks identified in the 27 GSC lines. For each clustering, the signal matrix for the 24
GSCs and the 1 clonal population was quantile normalised before clustering.
Data have been deposited at GEO (GSE109399).
DNA Methylation arrays
Bisulphite conversion of the DNA for methylation profiling was performed using the
EZ DNA Methylation kit (Zymo Research) on 500 ng genomic DNA from all 27
samples. Conversion efficiency was quantitatively assessed by quantitative PCR
(qPCR). The Illumina Infinium MethylationEPIC BeadChips were processed as per
manufacturer’s recommendations. The R package ChAMP v2.6.428 was used to
process and analyse the data.
Data have been deposited at GEO (GSE109399).
RNA-seq
RNA was extracted from GSC lines using the Qiagen RNeasy Plus kit. RNA sample
quality was measured by Qubit (Life Technologies) for concentration and by Agilent
Bioanalyzer for RNA integrity. All samples had RIN above 9. Libraries were prepared
18 bioRxiv preprint doi: https://doi.org/10.1101/370726; this version posted July 17, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-ND 4.0 International license.
using the TruSeq Stranded mRNA kit (Illumina). Two hundred nanograms from each
sample were purified for polyA tail containing mRNA molecules using poly-T oligo
attached magnetic beads, then fragmented post-purification. The cleaved RNA
fragments were copied into first strand cDNA using reverse transcriptase and
random primers. This is followed by second strand cDNA synthesis using RNase H
and DNA Polymerase I. A single “A” base was added and adapter ligated followed by
purification and enrichment with PCR to create cDNA libraries. Final cDNA libraries
were verified by the Agilent Bioanalyzer for size and concentration quantified by
qPCR. All libraries were pooled to a final concentration of 1.8nM, clustered and
sequenced on the Illumina NextSeq500 as a pair-end 75 cycle sequencing run using
v2 reagents to achieve a minimum of ~40 million reads per sample. Reads were
aligned to hg19 using the STAR aligner v2.4.2a 29 and transcripts were quantified
using RSEM v1.2.2130.
Data are being deposited at EGA.
Motif Enrichment
Regions exclusively accessible in one of the GSC subtypes and not the others were
used as input sequences for the motif enrichment, while the full ATAC-seq catalogue
served as the background set when running HOMER v4.7 to detect enrichments of
transcription factor binding motifs. Enriched motifs were then grouped into families
based on similarities in DNA-binding domains using the CIS-BP database15. Each
family was assigned the fold-enrichment value of the most enriched motif within the
family.
19 bioRxiv preprint doi: https://doi.org/10.1101/370726; this version posted July 17, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-ND 4.0 International license.
The transcription factors whose motifs were found enriched in C1-exclusive
accessible regions, were run together through GSEA31, and the gene set
corresponding to genes potentially regulated by SP1 was identified as significantly
enriched (GSEA gene set GGGCGGR_SP1_Q6).
Gene essentiality screen
Illumina sequencing reads from genome-wide TKOv1 CRISPR screens in patient-
derived GSCs were mapped using MAGECK32 and analysed using the BAGEL
algorithm with version 2 reference core essential genes/non-essential genes33,34.
Resultant raw Bayes Factor (BF) statistics were used to determine essentiality of
transcription factor genes using a minimum BF of 3 and a 5% FDR cut-off.
Similarity Network Fusion
The Similar Network Fusion (SNF) method was run the 27 cell lines using gene
expression, DNA methylation and ATAC-seq data. The SNF method does not
require any prior feature selection so we used the full matrix of gene expression
(20753 genes), the full matrix of methylation data (Beta values, 629309 probes) and
the ATAC-seq peaks matrix (255890 peaks). We used the SNFtool R package
(v2.2.0) with the parameters K = 10, alpha = 0.4, T = 20, as determined through
empirical testing. Spectral clustering implemented in the SNFtool package was run
on the SNF fused similarity matrix to obtain the groups corresponding to k=2 to 12.
We identified the top associated genes and methylation probes and ATAC-seq
peaks that have the largest agreement with the final fused network structure. To do
so we computed the Normalized Mutual Information (NMI) score (as part of the
SNFtool package) for each feature (i.e each gene, methylation probe and ATAC-seq
20 bioRxiv preprint doi: https://doi.org/10.1101/370726; this version posted July 17, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-ND 4.0 International license.
peak). For each feature, we constructed a patient network based on the feature
alone and subsequently used spectral clustering. We then compared the result of the
resultant clustering to the one obtained from the whole fused similarity matrix by
computing the NMI score as previously described12. As mentioned in this paper, a
score of 1 indicates the strongest feature and shows that the network of patients
based on the given feature leads to the same groups as the fused network. A score
of 0 means that there is no agreement between the groups that can be derived from
the feature and the fused network groups.
21 Figure 1 bioRxiv preprint doi: https://doi.org/10.1101/370726; this version posted July 17, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder,100 who has granted bioRxiv a license to display the preprint100 in perpetuity. It is made available under 100 a b aCC-BY-ND 4.0 International license. c GSC accessible regions All GSC accessible regions 80 shared with HFNS 80 GSC-exclusive accessible regions 80
60 60 60 le Regions (%) le Regions (%) le Regions (%) 40 40 40 Accessib Accessib Accessib
20 20 20
0 0 0 27 24 21 18 15 12 9 7 5 3 1 27 24 21 18 15 12 9 7 5 3 1 27 24 21 18 15 12 9 7 5 3 1 Number of Samples Number of Samples Number of Samples Percentage Cumulative Percentage Percentage Cumulative Percentage Percentage Cumulative Percentage f d 3 e p y 2 t
Mesenchymal e
G 5 7 7 Proneural Neural Classical b
p
u
y 1 S esenchymal ATAC M t e _
e b
p
G 5 6 6 M GSEA_Subtype4 A u
y lassical C E S t G 7 9 9 C1 cor S _ b
N C C C C M C G A
u
nt S eural G 5 5 1 N E S
G 4 9 8 S _
C2 N N N N N
P P P P C P C N N N M C N N N N A G
G 7 0 5 roneural P E
G 5 8 4
G 7 0 2 S N N N N N N
G 4 8 9 C3
GSEA M N M C P C P C P P P C M N M M M P M M M M M G
Enrichme r e 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 3 3 3 3 3 t
G 7 2 9
G 4 1 1 s G 5 9 4 u
G 7 0 6 l G551 G571 G361 G411 G549 G719 G729 G489 G799 G498 G583 G523 G648 G613 G620 G800 G705 G702 G566 G706 G564 G577 G584 G594 G797 G744 G567 C
G 3 6 1 _
G 8 0 0 C A
G 5 6 7 T
G 5 4 9
G 5 7 1
G 7 9 7 G 6 4 8 A
G 6 2 0
G 6 1 3
G 5 6 4
G 5 2 3
G 7 1 9
G 5 8 3
G 7 4 4
G 5 7 Methylation 7
G 7 9 9
G 5 6 6
G 5 5 1
G 4 9 8
G 7 0 2
G 4 8 G 9 5 8 4
G 7 0 5
G 7 0 6
G 5 9 4 G 7 2 9
G 4 1 1
G 8 0 0
G 3 6 1
G 6 4 8
G 5 4 9
G 5 7 1
G 6 2 0 G 7 9 7
G 5 6 7
G 5 6 4
G 6 1 3
G 5 2 3
G 7 1 9
G 5 8 3
G 7 4 4
Expression G 5 7 7
G 5 6 6
G 7 9 9 ATAC Expression
G 5 5 1
G 4 9 8
G 7 0 5
2 0
G 5 8 4
7 G
G 4 8 9
G 7 2 9
G 4 1 1
G 5 9 4
G 7 0 6
G 3 6 1
G 8 0 0 Methylation
G 5 6 7
G 5 7 1 G 5 4 9
G 7 9 7
G 6 4 8
G 6 2 0
G 6 1 3
G 5 6 4 Methylation
G 5 2 3
G 7 1 9 G 5 8 3 C1
G 7 4 4 Saturation C2 g C3 e ATAC Expression 250000
7 G 5 7
6 G 5 6
9 G 7 9 200000
1 G 5 5
8 G 4 9
5 G 7 0
4 G 5 8
2 G 7 0
9 G 4 8 150000 peaks
9 G 7 2 1 G 4 1
4 G 5 9
6 G 7 0
1 G 3 6
0 G 8 0
ATAC All samples 100000
7 G 5 6
1 G 5 7
9 G 5 4
7 G 7 9
8 G 6 4 C1
0 G 6 2
6 1 3 C2 G 50000
4 G 5 6 C3
3 G 5 2
9 G 7 1 71% 88% 93% 92%
3 G 5 8
4 G 7 4 10 20 30 40 Number of samples Figure 2 C1 C2 C3 a C1 C2 C3 Nuclear receptor Nuclear receptor Nuclear receptor Forkhead Forkhead Forkhead bioRxivEts preprint doi: https://doi.org/10.1101/370726Ets; this version posted July 17, 2018. The copyright holderEts for this preprint (which was not certifiedT−box by peer review) is the author/funder, who hasT−box granted bioRxiv a license to display the preprintT− inbox perpetuity. It is made available under SMAD SMADaCC-BY-ND 4.0 International license. SMAD RFX RFX RFX Homeodomain,POU Homeodomain,POU Homeodomain,POU Homeodomain Homeodomain Homeodomain CUT,Homeodomain CUT,Homeodomain CUT,Homeodomain CSL CSL CSL bHLH bHLH bHLH TEA TEA TEA 4.5Sox Sox Sox Runt Runt Runt Rel Rel Rel IRF IRF IRF GATA GATA GATA C2H2 ZF C2H2 ZF C2H2 ZF AP−2 AP−2 AP−2
−1.0−0.50.00.5 1.01.5 −1.0−0.50.00.5 1.01.5 −1.0−0.50.00.5 1.01.5 Log2(Fold Enrichment) Log2(Fold Enrichment) Log2(Fold Enrichment) b c GSC-Cas9 + 3 3 3 TKOv1 gRNA-library
2 2 2 3.6 SP1
GBM Illumina OLIG2 sequencing 1 1 1 4 T0 FOXD1 FOXD1
1 FOXD1 ASCL1 AHR SP1 ASCL1 Compare gRNA abundance 0 0 0 OLIG2 & NPAS3 TF Motif Families OLIG2 NPAS3 identify essential genes NPAS3 SP1 ASCL1 AHR
AHR Illumina −1 −1 −1 14 Doublings sequencing Gene Essentiality z − score −2 −2 −2 2.7 −3 G564 (C1) −3 G440 (C1) −3 G432 (C1) 5 SP1 C2 essentials Essentials in d SP1 AHR ASCL1 NPAS3 OLIG2 FOXD1 other clusters GBM GBM GBM GBM GBM GBM C3 essentials 5.4 6 7 7 10 5.4 SP1-regulated 1 2 * * * * * * OLIG2 6 3 ASCL1 3 9 OLIG2 AHR 6 6 NPAS3 4.5 5 4.5 8 4 2 2 5 5 7 3.6 4 3.6 AHR FOXD1 NPAS3 6 2 ASCL1 1 1 4 4 AHR
2.7 3 5 2.7 ASCL1 1.8 OLIG2 SP1 FOXD1 FOXD1 3 3 0 0 0 4 6 Log2(TPM+1) NPAS3 1.8 2 1.8 3 2 2 −2 −1 SP1 −1 SP1
2
0.9 1 0.9 Gene Essentiality z − score 1 1 Transcripts Per Million (TPM) Per Transcripts Transcripts Per Million (TPM) Per Transcripts Transcripts Per Million (TPM) Per Transcripts Transcripts Per Million (TPM) Per Transcripts Transcripts Per Million (TPM) Per Transcripts Transcripts Per Million (TPM) Per Transcripts 1 −4 −2 −2
0 0 0 0 0 0 Tumor (TCGA, n=163) Normal (TCGA + GTEx, n=207) −6 −3 −3 T (n=163)N (n=207) G472 (C2) G523 (C2) G361 (C3) T (n=163)N (n=207) T (n=163)N (n=207) T (n=162)N (n=207) T (n=161)N (n=207) T (n=163)N (n=207) Figure 3 a
GSC bioRxiv preprint doi: https://doi.org/10.1101/370726; this version posted July 17, 2018.enrichment The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-ND 4.0 International license.
b c
G620 G706 G620 G620 G706 G567 G706 G706
G567 G361 G567 G567 G702 G620 G702 G702 G800 G702 G800 G800 G361 G489 G411 G800 G361 G489 G361 G489 G498 1 G489 G648 1 G648 4 G411 G744 G613 G799 G411 G799 G411 G799
G613 G523 G705 G744 G613 G744 G613 G744 G799 G523 G523 G523 G705 G564 G583 G564 G705 G564 G705 G564 G549 G549 G549 G549 G583 G498 5 G583 G583 G594 G594 G594 G594 G571 G584 G571 G584 G571 G584 G571 G584 G797 G797 G797 G797
G729 G729 G729 G729 G719 G719 G719 G719
G566 G566 G566 G566
G577 G577 G577 G577
G620 G706 G620
G706 G567 G523 G706 G744 G567 G361 G567 G702 G620 G702 G800 G702 G567 G800 G648 2 G361 G498 2 G411 G800 G706 G361 G489 G620 G489 G489 G361 G799 G702 G411 G799 G613 G799 G411 G744 G411 G800 G744 G744 G523 G613 G705 G613 G489 G613 G523 G523 G799 G648 5 G705 G583 G705 G564 G594 G705 G564 G564 G584 G583 G549 G549 G583 G549 G583 G549 G594 G797 G594 G594 G571 G584 G729 G571 G584 G571 G584 G797 G498 6 G564 G797 G577 G797 G729 G729 G729 G719 G571 G719 G719
G566 G719 G566 G566
G577 G566 G577 G577
G706 G620 G620 G620 G567 G706 G706 G706
G361 G567 G567 G567 G620 G702 G702 G702 G702 G800 G800 G800 G411 G800 G361 G498 7 G361 G489 G361 G489 G489 G489 G799 G799 G613 G799 G411 G799 G411 G744 G411 G744 G744 G705 G613 G744 G613 G523 G613 G523 G523 G523 G648 3 G648 6 G583 G564 G705 G564 G705 G564 G705 G564 G571 G549 G583 G549 G583 G549 G583 G549 G498 3 G594 G594 G594 G719 G594 G571 G584 G571 G584 G571 G584 G584 C1 G797 G797 C1 G797 G797 G729 G729 G729 G566 C2 G719 G719 C2 G719 G577 G566 G566 G566 G729 C3 G577 G577 C3 G577
d
G620 G706 G706 G706 G706 G567 G567 G567
G361 G361 G361 G567 G620 G620 G702 G620 G702 G702 G800 G702 G800 G800 G361 G551 1 G411 G800 G411 G411 G489 G489 G489 G489 G613 G799 G613 G799 G411 G744 G613 G799 G744 G744 G523 G744 G705 G705 G613 G705 G523 G523 G799 G523 G583 G564 G583 G564 G705 G583 G564 G577 G571 G549 G571 G549 G583 G549 G566 G594 G594 G571 G719 G584 G719 G584 G797 G797 G594 G551 3 G729 G729 G571 G584 G719 G564 C1 G577 G577 G797 G549 G729 G594 G566 G566 G719 G729 C2 G566 G797
G577 G584 C3 G551 DP2 G551 DP3 Figure 4 bioRxiv preprint doi: https://doi.org/10.1101/370726; this version posted July 17, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-ND 4.0 International license. a b c Neurotransmitters and receptors 6 Subtype CYP2E1 ● HTR1B ● ● CYP3A4 ● ● ●● SB224289 ● C1 HTR2C 10 ●● ●● SB 224289 hydrochloride ● hydrochloride ● ●● ● ●●● ●● ●● ●● 5 HTR2A ● ● ● C2 ● ● ● Amiodarone hydrochloride SIGMAR1 ● ●● ● ● ● ● ● ● ● ● ● GBR-12909 ●● ● ●● ●● 8 ●● ●● GBR−12909 dihydrochloride ●● ● ● ● dihydrochloride CYP2C8C3 ●●● ●● 6 HTR2B ●● ● ●● ● Subtype ● ●● ● ● ● ●● ●● ● HTR1D ●●●●●●● ●● ● SKF 96365 ● ●● ● 4 HFNSC1 ● ● ● ●●● SB 224289 hydrochloride ●● ● ● ● ● GBR-2909 dihydro. 6 ● ● ● 5 C2 ●● ● ● MLAmiodarone−7 hydrochloride ● ● ● ● ●● HRH1 ● ● ● ●● GBR−12909 dihydrochloride C3 ●● ● ●● ●● ●● ● ● ●●●● ML−9 ● ●● ●● ●● ● ●●
Log2(FPKM) ● SKF 96365 ● ● ● ●● ● ●● ● ●● 4 HFNS ● ● ● ● ● 3 ● ● ●● ● ●● 4 ●● ● ●● ● ● ● ● ● ● ● ●● ML−7 ● ●●● ●● ●● ●● ● DL−Cycloserine ● ● ● ● ●● ● ● ● ● ● ●● ADORA3 SB224289 hydro. ● ●● ● ● ● ML−9 ● ● ● ● 3 ● ● ●● SLC6A3 ● ● ●● ● ●●● ●● ●● ● FluphenazineDLGrowth−Cycloserine 2HCl ● ● ●● ● ● ● ● ● ● ●●● ● ● ●● ● 2 ● ●● ● ● ● ● ●● ● FluphenazineInhibition 2HCl (%) 2 ● ●● ● Phorbol 12−myristate 13−acetate ● ● ● SNCA ● ●● ● Phorbol 12−myristate6 13−acetate 2 ● ●● ● Subtype ● ●● ● Tetraethylthiuram80-100 disulfide C1 0 ● ●● ●● ● ●●●● ●●●● ●● 5−Iodotubercidin5 SLC6A4 5−Iodotubercidin60-80 C2 1 Astemizole C3 SLC6A2 CGP−74514A hydrochloride Astemizole 40-604 HFNS APP ZACN PINK1 CNIH3 GRIA4 Chelerythrine chloride LYPD1 HTR2B ANXA9 MEF2C CHRNE SHISA9 ZNF219 GRIN3A
CGP−74514A hydrochloride CHRNA3 Cycloheximide20-403 Genes ChelerythrineEllipticine chloride G498_rep2 pvalue=1.75567035220541e−12 G498_rep2 pvalue=1.75567035220541e6 −12 Ifenprodil tartrate Subtype 0-202 G498_rep2 pvalue=1.75567035220541e−12 G564_rep1 pvalue=0.0022211354101052 CycloheximideIvermectin d e f EllipticineMevastatin No Data C1 MG624 1 G498 Group DMSO G564 Group DMSO 120 IfenprodilN,N−Diehtyl tartrate−2−[4−(phenylmethyl)phenoxy]ethanamine p=1.76x10-12Group Perphenazine 5 C2 p=2.2x10-3 Group Perphenazine IvermectinNiclosamide NSC 95397 100 C3 MevastatinRo 25−6981 hydrochloride Rotenone MG624SB 216641 hydrochloride 80 4 HFNS N,NSM−−21Diehtyl maleate−2− [4((+)−(phenylmethyl)phenoxy]ethanamine−Tropanyl 2−(4−chlorophenoxy)butanoate) Temozolomide 60 NiclosamideTrifluoperazine dihydrochloride Tropanyl−3,5−dimethylbenzoate NSC 95397 40 3 log fraction nonresponding log fraction Aminolevulinic acid G564 nonresponding log fraction RoBortezomib 25−6981 hydrochloride (%) Viability G584 6 CabazitaxelSubtype 20 G498 RotenoneSubtype G440 ) Cladribine 0uMSBClofarabine 216641 hydrochlorideC1 All Subtypes 2 Dactinomycin 0 Log(fraction non-responding) Log(fraction 5 SMDasatinib−21 maleateC2 ((+)−Tropanyl 2−(4−chlorophenoxy)butanoate) non-responding) Log(fraction − 2.5 2.0 1.5 1.0 0.5 0.0 0.195uM − 2.5 2.0 1.5 1.0 0.5 0.0 TemozolomideDaunorubicin HCl Docetaxel C3 DMSO0.1 1 10 0 500 1000 1500 2000 0 20 40 60 80 100 120 6 Subtype μ 1 PerphenazineC1 ( M) 0.39uMSBDoxorubicin 224289 hydrochloride HCl Numberdose (number of of cells) cells dose (number of cells) AmiodaroneTrifluoperazine hydrochloride dihydrochloride 5 C2 G498_rep2 pvalue=1.75567035220541e−12 G498_rep2 pvalue=1.75567035220541eNumber−12 of cells 4 GBR−12909 dihydrochlorideHFNS C3 SKF 96365 HFNS 4 HFNS MLEtoposide−7 G489_rep1 pvalue=2.12766126456371e−10 ML−9 i G523_rep2 pvalue=0.0101938683184333 3 DLTropanyl−Cycloserine−3,5−dimethylbenzoate FluphenazineEverolimus 2HCl g h 0.78uMPhorbol 12−myristate 13−acetate 2 Tetraethylthiuram disulfide nonresponding log fraction 5Gemcitabine−Iodotubercidin HCl nonresponding log fraction 1 AstemizoleAminolevulinic acid 100 CGP−74514A hydrochloride Group DMSO Group DMSO Chelerythrine chloride G489 G523 CycloheximideIxabepilone 3 Ellipticine .5625uMIfenprodil tartrate -10 Group Perphenazine Group Perphenazine IvermectinBortezomibMitomycin C Mevastatin p=2.1x10 p=0.01 MG624 N,NMitoxantrone−Diehtyl−2−[4−(phenylmethyl)phenoxy]ethanamine Niclosamide 80 NSCCabazitaxel 95397 Ro 25−6981 hydrochloride 3.125uMRotenonePaclitaxel SB 216641 hydrochloride SM−21 maleate ((+)−Tropanyl 2−(4−chlorophenoxy)butanoate) Temozolomide 2 TrifluoperazineCladribinePlicamycin dihydrochloride Tropanyl−3,5−dimethylbenzoate Aminolevulinic acid BortezomibRapamycin 6.25uMCabazitaxel 60 CladribineClofarabine ClofarabineRomidepsin Dactinomycin Dasatinib DaunorubicinTeniposide HCl DocetaxelDactinomycin Doxorubicin HCl 12.5uM1 Etoposide EverolimusTopotecan HCl Gemcitabine HCl 40 Ixabepilone MitomycinDasatinibValrubicin C Mitoxantrone Paclitaxel Plicamycin 25uMRapamycinVinblastine sulfate RomidepsinDaunorubicin HCl Teniposide − 2.5 2.0 1.5 1.0 0.5 0.0 Topotecan HCl nonresponding log fraction Vincristine sulfate nonresponding log fraction
Valrubicin − 2.5 2.0 1.5 1.0 0.520 0.0 Vinblastine sulfate Vincristine sulfate VinorelbineDocetaxel tartrate 50uM(+)−Bromocriptine methanesulfonate (%) Viability N6−Cyclohexyladenosine Nitrendipine(+)−Bromocriptine methanesulfonate (−)−Cyanopindolol hemifumarate 0 500 1000 1500 2000 (Doxorubicin−)−N−Phenylcarbamoyleseroline HCl 0G523 500 1000 1500 2000 (−)−Naproxen sodium (+)N6−IsoproterenolCyclohexyladenosine HCl (Isoprenaline hydrochloride)0 3−CPMT (3a−[(4−Chlorophenyl)phenylmethoxy]tropane hydrochloride)G489 5−Aminovaleric acid hydrochloride 5EtoposideNitrendipine−Carboxamidotryptamine maleate 5'Fluorouracil All Subtypes 6−Methyl−2−(phenylethynyl)pyridine hydrocloride dose (number of cells) 7(−−)Nitroindazole−Cyanopindolol hemifumarate dose (number of cells) Acrichine
Allopurinol non-responding) Log(fraction alpha,beta−Methylene adenosine 5'−triphosphate dilithium − 2.5 2.0 1.5 1.0 0.5 0.0 − 2.5 2.0 1.5 1.0 0.5 0.0 Everolimus non-responding) Log(fraction Altretamine(−)−N−Phenylcarbamoyleseroline −20 Amifostine Anastrozole Arsenic(−)−Naproxen trioxide sodium AzacitidineGemcitabine HCl Bendamustine HCl DMSO0.1 1 10 0 50 100 150 200 250 0 20 40 60 80 100 120 Benzydamine(+)−Isoproterenol HCl (Isoprenaline hydrochloride) Bleomycin BNTX maleate Busulfan Perphenazine (μM) dose (number of cells) CapecitabineIxabepilone3−CPMT (3a−[(4−Chlorophenyl)phenylmethoxy]tropane hydrochloride) Number of cells dose (number of cells) Carboplatin G498_rep2 pvalue=1.75567035220541e−12 Number of cells Carmustine G498_rep2 pvalue=1.75567035220541e−12 Celecoxib5−Aminovaleric acid hydrochloride Chlorambucil CisplatinMitomycin C G361_rep1 pvalue=2.32435524138765e−27 G411_rep1 pvalue=1.14549758948815e−33 Crizotinib5−Carboxamidotryptamine maleate Cyclophosphamide l log fraction nonresponding log fraction Cyclothiazide k nonresponding log fraction Cytarabine HCl j DacarbazineMitoxantrone5'Fluorouracil Decitabine Dexrazoxone 100 Erlotinib6−Methyl HCl−2−(phenylethynyl)pyridine hydrocloride Group DMSO Group DMSO Estramustine disodium phosphate G361 G411 chloride) Exemestane FloxuridinePaclitaxel Fludarabine7−Nitroindazole -27 Group Perphenazine -33Group Perphenazine Fluorouracil p=2.3x10 p=1.1x10 Fulvestrant GBRAcrichine 12935 2HCl GBRPlicamycin−13069 dihydrochloride Gefitinib Hydroxyurea 80 IfosfamideAllopurinol Imatinib ImiquimodRapamycin Irinotecanalpha,beta −HClMethylene adenosine 5'−triphosphate dilithium L−162,313 L−741,742 HCl LAltretamine−Canavanine sulfate Lapatinib LenalidomideRomidepsin Letrozole 60 LidocaineAmifostine N−ethyl bromide quaternary salt Lomustine, CCNU LY−165,163 MDLTeniposideAnastrozole−72222 Megestrol acetate Melphalan Mercaptopurine MethotrexateArsenic trioxide Methoxsalen MitotaneTopotecan HCl 40 NAzacitidine−Acetylprocainamide hydrochloride N−Bromoacetamide N−Phenylanthranilic acid NANBendamustine−190 hydrobromide HCl NelarabineValrubicin Nilotinib − 2.5 2.0 1.5 1.0 0.5 0.0 Nitrogen mustard − 2.5 2.0 1.5 1.0 0.5 0.0 OxaliplatinBenzydamine
Pazopanib HCl nonresponding log fraction PemetrexedVinblastine sulfate 20 nonresponding log fraction I PentostatinBleomycin Pipobroman Pirfenidone PNUBNTX 96415E maleate (%) Viability Pralatrexate 0 500 10000 1500 500 2000 1000 1500 2000 ProcarbazineVincristine sulfate R(−)−Propylnorapomorphine HCl R(+)Busulfan−SKF−81297 G361 Raloxifene HCl RS 17053 hydrochloride 0 RSVinorelbineCapecitabine 39604 hydrochloride tartrate G411 Sorafenib SR 2640 dose (number of cells) dose (number of cells) Streptozocin All Subtypes SunitinibCarboplatin Tamoxifen citrate Thalidomide(+)−Bromocriptine methanesulfonate ThioguanineCarmustine Thioridazine HCl Log(fraction non-responding) Log(fraction Thiotepa −20 non-responding) Log(fraction TranylcypromineCelecoxib hydrochloride − 2.5 2.0 1.5 1.0 0.5 0.0 Tretinoin N6−Cyclohexyladenosine − 2.5 2.0 1.5 1.0 0.5 0.0 Triethylenemelamine U−74389G maleate UracilChlorambucil mustard DMSO0.1 1 10 0 500 1000 1500 2000 Vandetanib 0 500 1000 1500 2000 VemurafenibNitrendipine VorinostatCisplatin Zolendronic acid μ C1 C2 C3 HFNS Perphenazine ( M) dose (number of cells) Number of cells dose (number of cells) (Crizotinib−)−Cyanopindolol hemifumarate Number of cells Cyclophosphamide log fraction nonresponding log fraction (Cyclothiazide−)−N−Phenylcarbamoyleseroline nonresponding log fraction (Cytarabine−)−Naproxen HCl sodium Dacarbazine (+)Decitabine−Isoproterenol HCl (Isoprenaline hydrochloride) Dexrazoxone 3Erlotinib−CPMT HCl (3a−[(4−Chlorophenyl)phenylmethoxy]tropane hydrochloride) 5Estramustine−Aminovaleric disodium acid hydrochloride phosphate Exemestane 5Floxuridine−Carboxamidotryptamine maleate 5'FluorouracilFludarabine Fluorouracil 6Fulvestrant−Methyl−2−(phenylethynyl)pyridine hydrocloride GBR 12935 2HCl − 2.5 2.0 1.5 1.0 0.5 0.0 7GBR−Nitroindazole−13069 dihydrochloride − 2.5 2.0 1.5 1.0 0.5 0.0 AcrichineGefitinib Hydroxyurea 0 500 10000 1500 500 2000 1000 1500 2000 AllopurinolIfosfamide alphaImatinib beta−Methylene adenosine 5'−triphosphate dilithium dose (number of cells) dose (number of cells)