Abstract
Genomics-driven cancer therapeutics has gained prominence in
personalized cancer treatment. However, its utility in indications
lacking biomarker-driven treatment strategies remains limited. Here we
present a “phenotype-driven precision-oncology” approach, based on the
notion that biological response to perturbations, chemical or genetic,
in ex vivo patient-individualized models can serve as predictive
biomarkers for therapeutic response in the clinic. We generated a
library of “screenable” patient-derived primary cultures (PDCs) for
head and neck squamous cell carcinomas that reproducibly predicted
treatment response in matched patient-derived-xenograft models.
Importantly, PDCs could guide clinical practice and predict tumour
progression in two n = 1 co-clinical trials. Comprehensive “-omics”
interrogation of PDCs derived from one of these models revealed YAP1 as
a putative biomarker for treatment response and survival in ~24% of
oral squamous cell carcinoma. We envision that scaling of the proposed
PDC approach could uncover biomarkers for therapeutic stratification
and guide real-time therapeutic decisions in the future.
__________________________________________________________________
Treatment response in patient-derived models may serve as a biomarker
for response in the clinic. Here, the authors use paired
patient-derived mouse xenografts and patient-derived primary culture
models from head and neck squamous cell carcinomas, including
metastasis, as models for high-throughput screening of anti-cancer
drugs.
Introduction
Precision medicine, which has been largely genomics driven^[72]1–[73]4,
is defined by being able to treat “the right patient, with the right
drug, at the right time”^[74]5, [75]6. Given that tumour heterogeneity
is underrepresented in many preclinical cell line models, it is not
surprising that most drugs fail to demonstrate the efficacy necessary
for clinical application^[76]7–[77]9. Importantly, the use of
conventional pre-established in vivo surrogate models, such as
patient-derived xenografts (PDX), often lack the desired effect on
management as they may not be available in a clinically relevant time
frame. Apart from being cost prohibitive, the utility of such PDX
models are also severely limited by the fact that they cannot be used
to interrogate therapeutics or genetic vulnerabilities in
high-throughput screen (HTS) format^[78]10. Therefore for precision
oncology to be successful, novel disruptive technologies are needed
that can generate “HTS-amenable” real- or accelerated-time
patient-specific ex vivo models, and can also feed information back to
the clinic in a relevant time frame. To fulfil the unmet need for novel
and predictive approaches to allay treatment failure from the outset,
and/or treat recurrences^[79]11, [80]12, here we present an approach
for treatment individualization which is based on phenotypic screening
in patient-derived models. We report the generation of a live biobank
of patient-derived primary cultures (PDCs) representing primary,
metastatic or recurrent tumours obtained from patients with high-risk
head and neck squamous cell carcinomas (HNSCCs). HTS-based phenotypic
screening of the PDCs, and subsequent validation in matched PDX models
for therapeutic vulnerabilities revealed previously unexpected
treatment strategies that could potentially allow drug repurposing, and
the discovery of novel biomarkers for treatment outcome and resistance.
Strikingly, the use of phenotypic screening was translated to the
clinic for two different patients, both of whom elicited robust
favourable responses as part of co-clinical trials. Additionally,
“pan-omics” interrogation of patient-derived models suggested that they
largely retain the molecular signatures and phenotypic characteristics
of the parental tumour, thereby serving as robust assay/discovery
platforms. Altogether results from this study support the hypothesis
that treatment response (phenotype) in real-time patient-derived models
may serve as the best biomarker for response in the clinic, especially
for indications lacking biomarker-guided treatment^[81]13.
Results
PDC models predict therapeutic vulnerabilities in vivo
We established a pipeline to generate PDC and their matched PDX models
(Fig. [82]1a) from pairs of primary and metastatic or recurrent
tumours. To date, the success rate of generating PDXs is ~58.1% (25 SCC
PDX models out of 43 tumour samples from 24 patients), a number with
associated PDCs (Table [83]1). Notably, among these 24 patients, at
least one PDX model was successfully established for each of the 20
patients (~83.3%) (Table [84]1). Short tandem-repeat (STR) profiling
(Supplementary Fig. [85]1a), gene expression (Fig. [86]1b) and targeted
sequencing of ~763 cancer-related genes (POLARIS Xplora panel;
Supplementary Fig. [87]1b and Supplementary Tables [88]1 and [89]2)
demonstrated that while genetic alterations and expression profiles
were distinct for different patients/models (HN137, HN148 and HN124),
each model was highly representative of the original tumour. Three-way
pair-wise comparison of the patient-tumour, PDX and source-matched PDCs
from primary (HN137-Pri) or from lymph node metastasis (HN137-Met,
HN124-Met and HN148-Met) showed high correlation coefficient (R = ~0.9)
with minimal divergence in gene expression (Supplementary Fig. [90]1c)
or genetic alterations (Supplementary Fig. [91]1a).
Fig. 1.
[92]Fig. 1
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Establishing a library of screenable in vitro and in vivo
patient-specific squamous cell carcinoma (SCC) models for
identification of therapeutic vulnerabilities. a Schematic
representation of the pipeline for the generation of
patient-personalized in vitro and in vivo models that can serve as a
screening platform for uncovering therapeutic vulnerabilities. Resected
tumours from human patients were grafted into NSG mice for the
establishment of in vivo patient-derived xenograft (PDX) models, and
for expansion of tumour material. Patient-derived primary culture (PDC)
models were also derived and screened against small molecule libraries
for identification of patient-specific therapeutics. Sections of
patient tumours, as well as freeze-viable PDX and PDC models were
stored in our biobank. Representative images of HN137-Pri and HN137-Met
cultures are shown. b Hierarchical clustering of gene expression
profiles for HN124, HN137 and HN148 paired primary (Pri) and metastatic
(Met) PDXs in duplicates. Scale bar denotes Pearson’s correlation
coefficient r from 0.8 (blue) to 1 (red). c Heat map of selected
anti-cancer compounds exhibiting strong inhibition in at least one of
the PDC lines. The scale represents percentage inhibition of the
compounds, with inhibition score <50% shown in grey. d Selected
molecular signatures (P < 0.05) of genes that show elevated expressions
across the five Met cell lines, some of which appear to be associated
with the selective responses of PDC lines to compounds of same target
classes. e Six independent cohorts of mice (n = 6) bearing
patient-matched PDX in one flank were treated with vehicle (control),
5 mg kg^−1 Flavopiridol (HN120), 40 mg kg^−1 Belinostat (HN148) and
8 mg kg^−1 Docetaxol (HN160). Scale bar, 1 cm. Error bars represent
mean ± s.e.m. Two-tail Student’s t test was carried out between
treatment and control groups on day 9 tumour weight. **P value <0.01
and ***P value <0.001
Table 1.
Patients recruited for this pipeline, patients’ tumour staging, sites
of tumour resection and availability of matched PDXs and PDCs
S/N Patient ID Primary site Met site Stage Primary PDX Met PDX PDC
1 HN120 Tongue Lymph node T4N2b Yes Yes Yes
2 HN124 Retromolar trigone Lymph node T4N1 Yes Yes No
3 HN127 Tongue Lymph node T2N2b Yes Yes No
4 HN132 Tongue Lymph node T2N1 No No No
5 HN137 Floor of mouth Lymph node T2N2c Yes Yes Yes
6 HN144 Alveolar ridge Lymph node T4N2c No Yes No
7 HN145 Retromolar trigone Lymph node T4N1 No No No
8 HN110 Tongue NA rT2 Yes NA No
9 HN148 Alveolar ridge Lymph node T4N2a Yes Yes Yes
10 HN150 Floor of mouth Lymph node T2N2a Yes No No
11 HN154 Floor of mouth Lymph node T4N1 Yes No No
12 HN155 Tongue Lymph node T2N1 Yes No No
13 HN156 Tongue Lymph node T2N2c Yes No No
14 HN158 Alveolar ridge Lymph node T4N2c No No No
15 HN159 Tongue Lymph node T4N2b No Yes Yes
16 HN160 Buccal Lymph node T2N2b No Yes Yes
17 HN164 Tongue Lymph node T2N2a No No ip
18 HN165 Buccal Lymph node rT4N3 Yes No ip
19 HN166 Buccal Lymph node T2N1 No Yes ip
20 HN169 Tongue Lymph node T4N1 Yes No ip
21 HN176 Alveolar ridge Lymph node T4N1 Yes No ip
22 HN177 Esophagus Lymph node T3N1 NA Yes Yes
23 HN173 Buccal Neck rN2b NA Yes ip
24 HN182 Tongue Lymph node T2N2b NA Yes Yes
[94]Open in a new tab
NA, no patient tumour available; ip, PDX generation in progress
PDCs derived from seven patients (HN120, HN137, HN148, HN159, HN160,
HN177 and HN182) were subjected to high-throughput small molecule
screens to uncover pan-cancer as well as patient-specific therapeutic
vulnerabilities (Supplementary Fig. [95]1d). Five of the seven screens
were carried out in “real time” (~6 months) either prior to tumour
recurrence or in parallel with patient treatment in the clinic. These
screens uncovered both shared and patient-specific vulnerabilities
(Fig. [96]1c). For example, HN160 was sensitive to microtubule
inhibitors, whereas HN148 showed a particular susceptibility to HDAC
inhibitors. It was interesting to note that the patients’ mutational
profile of cancer-related genes revealed limited predictive potential
for therapeutic vulnerabilities (Supplementary Fig. [97]1b and
Supplementary Table [98]1). This was expected in indications such as
HNSCC as it lacks clear biomarker-driven treatment options. Instead, we
observed that patient-specific gene expression signatures largely
correlated with drug response, thereby suggesting the potential of
utilizing these signatures to predict pre-disposition to unique drug
sensitivities (Fig. [99]1d), Finally, the vulnerabilities identified
were validated in patient-matched PDX in vivo (Fig. [100]1e and
Supplementary Fig. [101]1e), demonstrating the translatability of
screen hits identified using PDCs.
Differential gene expression predicts patient-specific response
A case study of patient HN137: Among the variety of patient models
generated, gene expression analysis of the paired HN137-Pri and
HN137-Met cells showed the most divergent and interesting differences
despite having highly conserved mutational profile of cancer-associated
genes (Supplementary Fig. [102]1a). Therefore, we focussed on the
HN137-Pri/Met-isogenic models to investigate whether their unique gene
expression signature could confer differential drug response. Pathway
enrichment analysis of differentially expressed genes showed an
upregulation in cellular processes associated with metastasis in the
HN137-Met cells, compared to HN137-Pri. These include the loss of
epithelial and cell-junction-related genes (EpCAM, CDH1, CLDN4 and
CLDN7) and increase in epithelial-mesenchymal transition signatures
(ZEB1/2, VIM, SNAI1 and SERPINE) (Fig. [103]2a, Supplementary
Fig. [104]2a and Supplementary Tables [105]3 and [106]4) (P value
<0.0001), which were validated using quantitative-PCR (qPCR)
(Supplementary Table [107]5).
Fig. 2.
[108]Fig. 2
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Small molecule screen against HN137 PDC revealed patient-specific
therapeutic vulnerabilities validates in vivo. a Differentially
expressed genes (fold-change >2) in HN137-Met vs. HN137-Pri. The heat
map represents (Log[10]) normalized gene expression data in duplicates.
b Schema of the co-culture chemical screen performed in triplicate. c
Representative fluorescence images of cells treated with compounds
specifically targeting either HN137-Pri (bottom left), HN137-Met (top
right) or both (bottom right). Scale bars, 100 μm. d Secondary
validation of HN137-Met-specific cytotoxic compound (YM155),
HN137-Pri-specific cytotoxic compound (gefitinib) and dual cytotoxic
compounds (Velcade and Staurosporine). Experiments were performed at
least twice, in triplicates. Cell viability was determined using
CellTiter-Glo reagent. Triplicate data, error bars represent
mean ± s.d. e Four independent cohorts of male mice (n = 5), bearing
tumours in both flanks from HN137-Pri PDX and HN137-Met PDX, were
treated with vehicle (control), or 25 mg kg^−1 gefitinib (Gef). Note
that the HN137-Pri PDX display greater response to gefitinib compared
to HN137-Met PDX at earlier days (Days 2-6). Error bars represent
mean ± s.e.m. Two-tail Student’s t test was carried out between
HN137-Pri and HN137-Met for days 2-10; N.S.: not significant,
*P < 0.05. f Four independent cohorts of male mice (n = 5), bearing
tumours on both flanks from HN137-Pri PDX and HN137-Met PDX, were
treated with 2 mg kg^−1 of YM155, compared to vehicle (control). A
significant anti-tumour effect was observed for YM155 treatment in
HN137-Met PDX while HN137-Pri PDX did not display significant
sensitivity to YM155. Two-tail Student’s t test was carried out between
HN137-Pri and HN137-Met for days 2-10; *P < 0.05, **P < 0. 01,
***P < 0. 001
We designed a co-culture screen using HN137-Pri and HN137-Met cells to
investigate differential drug response of these two populations.
HN137-Pri and HN137-Met cells were labelled with green fluorescent
protein (GFP) and tdTomato, respectively, seeded together into each
well of a 384-well plate, and screened against a library of anti-cancer
compounds and kinase inhibitors, some of which are FDA approved
(Fig. [110]2b and Supplementary Tables [111]6 and [112]7). Given the
high correlation between the screen replicates (Supplementary
Fig. [113]2c), we identified compounds that displayed pan- and
selective toxicity towards HN137-Pri and HN137-Met, respectively
(Fig. [114]2c and Supplementary Fig. [115]2d). Hierarchical clustering
of top-hit compounds with putative targets revealed that HN137-Pri
cells were more sensitive to EGFR-targeting tyrosine kinase inhibitors
(TKI), whereas HN137-Met were highly sensitive to YM155, an inhibitor
of pro-survival genes, including survivin (BIRC5)^[116]14–[117]17
(Supplementary Fig. [118]2e). These were validated by standard IC[50]
analysis (Fig. [119]2d), where HN137-Pri showed ~80-fold higher
sensitivity to gefitinib compared to HN137-Met (IC[50] 0.183 vs.
16 μM), while HN137-Met showed 10-fold higher sensitivity to YM155
compared to HN137-Pri (IC[50] 14 vs. 140 nM). Importantly, validation
in PDX (Supplementary Fig. [120]2f) showed concordance with in vitro
data: HN137-Pri-PDX showed preferential sensitivity towards gefitinib
as compared to HN137-Met-PDX, especially at earlier time points (days
2-6: P value <0.05) (Fig. [121]2e), while HN137-Met-PDX exhibited
greater susceptibility towards YM155 as compared to HN137-Pri
(Fig. [122]2f), with minimal systemic toxicity (Supplementary
Fig. [123]2g).
PDC/PDX-guided treatment of patients in co-clinical trials
One of the major objectives of this pipeline is the ability to
influence treatment decisions in real time. The patient (HN137)
initially presented with stage IV (T4N2bM0) oral squamous cell
carcinoma (OSCC) was treated with surgery and adjuvant chemo
(cisplatin-based)-radiation therapy. Six months after treatment, he
developed recurrent disease (including dermal and lung metastasis),
suggesting that the patient may have developed resistance to cisplatin
(Fig. [124]3a). We developed cisplatin-resistant HN137-Pri and
HN137-Met lines concurrently while the patient was undergoing adjuvant
therapy. Interestingly, we found that acquisition of cisplatin
resistance does not alter its sensitivity to gefitinib (Fig. [125]3b
and Supplementary Fig. [126]3a), suggesting that the recurrent tumour
could be treated using gefitinib.
Fig. 3.
[127]Fig. 3
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PDC/PDX-guided treatment of patients under two independent n = 1
co-clinical trials. a Timeline for patient HN137 from surgery
(December), adjuvant chemo-radiation therapy, until tumour recurrence
in June. b Graph denoting Log[2](IC[50]) values of HN137-Pri, HN137-Pri
cisplatin resistant (CR), HN137-Met, HN137-Met cisplatin resistant (CR)
cell lines in the presence of gefitinib. c Computed tomography scan
(CT-scan) of recurrent responsive metastatic sites (dermal metastasis
(top panel) and lung metastasis (bottom panel)) in HN137 patient,
before and after treatment with 250 mg per day of gefitinib. Arrows
denote sites of tumours before and after treatment. d Dose response of
HN177-PDC to erlotinib and olaparib. Cell viability was determined
using CellTiter-Glo reagent. Triplicate data, error bars represent
mean ± s.d. e Three independent cohorts of mice (n = 2 for control and
n = 3 for treated) bearing HN177-PDX in one flank were treated with
vehicle control (Ctrl), 50 mg kg^−1 olaparib and 150 mg kg^−1
erlotinib. Error bars represent mean ± s.e.m. Two-tail Student’s t test
was carried out between olaparib and control group (N.S.: not
significant) and between erlotinib and control group **P value <0.01. f
Changes in serum carbohydrate antigen 19-9 (CA 19-9) at initial time of
diagnosis and during the course of treatment. g CT-scan of recurrent
non-responsive lung metastasis in HN137 patient, before and after
treatment with 250 mg day^−1 of gefitinib. Arrows denote sites of
tumours before and after treatment
Based on the therapeutic vulnerability identified using PDCs; their
subsequent validation in matched PDX models; and an ongoing study
exploring a putative biomarker for EGFR-TKI sensitivity^[129]18, a
co-clinical trial was conducted using gefitinib. The patient was
treated with monotherapy of gefitinib (250 mg daily) through the
IMPACT-SG protocol. This resulted in remarkably significant regression
within 6 weeks of treatment (Fig. [130]3c), supporting the ability of
this pipeline to guide patient-specific management of treatment
decisions in real time.
To further bolster this notion of using patient-derived models for
phenotype-guided clinical decisions, we tested this paradigm in an
unrelated case designated HN177. This was a patient who developed
widespread metastasis more than a year after definitive treatment for
T3N1 oesophageal adenosquamous cancer. Patient HN177 experienced tumour
progression on cytotoxic chemotherapy and was hence switched to
erlotinib and olaparib. However, the patient developed adverse side
effects from this combination with no clinical benefit as measured by
no change in tumour markers (Fig. [131]3f). Patient-specific models
generated at the initial presentation of metastases were available for
in vitro (PDC) and in vivo (PDX) testing. These showed that erlotinib
was effective against the tumour, while olaparib elicited no response
(Fig. [132]3d, e and Supplementary Fig. [133]3b). Based on these
results (and biomarker analyses mentioned previously), the patient was
switched to erlotinib monotherapy. This resulted in effective tumour
regression with dramatic reduction in the tumour marker CA19-9
(Fig. [134]3f). Altogether, these observations demonstrate that how
PDCs are able to guide or even alter treatment regimens in real time,
as patients undergo therapy in the clinic.
YAP1—a biomarker for metastasis and gefitinib resistance
Next, we wondered whether the patient-derived models could be utilized
to identify potential biomarkers/mechanisms that may predict tumour
progression under the selection pressure of drugs. For patient HN137,
despite the initial remarkable response, there was persistence of
minimal residual disease in the lungs and dermis, which started to
progress after 6 months of gefitinib monotherapy (Fig. [135]3g).
Intriguingly, our PDC models had predicted that HN137-Met cells could
represent an aggressive subclone that is naturally more resistant to
gefitinib (Fig. [136]2d). Therefore, we sought to utilize our HN137-Met
PDC models to understand the mechanism of resistance to gefitinib.
To determine the context responsible for vulnerabilities identified, we
retroactively analysed available “omics” data focussing on the more
aggressive HN137-Met PDCs, which showed particular resistance to
gefitinib while being sensitivity to YM155 (Fig. [137]2d). Gene
expression profiling revealed that several IAP-family genes were
upregulated in the HN137-Met compared to the primary (Figs. [138]2a and
[139]4a), suggesting a correlation with YM155 sensitivity. Therefore,
we explored mechanisms that could account for this increased
expression.
Fig. 4.
[140]Fig. 4
[141]Open in a new tab
YAP1 expression as a biomarker for differential therapeutic
sensitivity, patient survival, metastatic progression and gefitinib
resistance. a Protein-protein interaction (PPI) network of
differentially expressed genes between HN137-Pri and HN137-Met related
to inhibitor-of-apoptosis (IAPs). The width of the edges is
proportional to the number of evidences supporting the interaction.
Nodes in yellow denotes that the genes that are upregulated in
HN137-Met, whereas blue denotes downregulation. The size of the nodes
is proportional to magnitude of fold change. b Circos plot depicting
global amplification (red) and deletion (blue) in HN137-Pri (inner
track) and HN137-Met (outer track) genomes. Region of amplification
found on chromosome 11 in HN137-Met is shown on the right. c Western
blot for YAP1 expression in HN137-Pri and HN137-Met cells. Asterisk
denotes band depicting low level of YAP1 expression in HN137-Pri cells.
d IHC staining of HN137-Pri and HN137-Met tissue specimens for YAP1
expression. Bottom panels display higher magnification images of
regions marked in black boxes. Positive cells were visualized by DAB
staining. Scale bar, 2 mm (top panel), 100 μm (bottom panel). e
Dose-response curve for HN137-Pri, HN137-Met and HN137-Pri cells stably
overexpressing YAP1 (HN137-Pri YAP1) to YM155 (left panel) and
gefitinib (right panel). Experiments were performed at least twice, in
triplicates. Cell viability was determined using CellTiter-Glo reagent.
Error bars represent mean ± s.d. f Timeline for patient HN137 from
surgery and post-operative chemotherapy (Dec), gefitinib treatment
(June) till development of gefitinib-resistant dermal metastasis. IHC
staining for YAP1 in HN137-Pri, HN137-Met and HN137 gefitinib-resistant
dermal biopsy sample (HN137-Gef-R). Positive cells were visualized by
DAB staining. Scale bar, 100 μm. g Representative IHC staining for YAP1
in (n = 166) OSCC patient set from Iyer et al.^[142]21. Graded
intensities (0, 1+, 2+ and 3+) of YAP1 nuclear staining shown in the
upper right corner (left panel). Scale bar, 100 μm. Kaplan-Meier
survival analysis for YAP1 expression (positive: 1+, 2+, 3+; negative:
0) was done. Overall survival from diagnosis (middle panel) (Log-rank P
value = 0.01), and disease-free survival from treatment (right panel)
(Log-rank P value = 0.04) are depicted in number of days. h An overview
of scaling the proposed “phenotype-driven precision oncology” approach
to identify prognostic molecular signatures for treatment response
Intriguingly, among the upstream transcriptional regulators of IAPs,
Yes-associated protein-1 (YAP1), a known activator of pro-survival
genes^[143]19, was found to be markedly overexpressed in HN137-Met
(Figs. [144]2a and [145]4a). Array-based comparative genomic
hybridization (arrayCGH) demonstrated a significant amplification of
the YAP1 and BIRC3 locus 11q22 (Fig. [146]4b), specifically in
HN137-Met. To assess the chromatin profile of this region, we used
formaldehyde-assisted isolation of regulatory elements coupled with
sequencing (FAIRE-seq) to profile the chromatin landscape in an
unbiased manner. FAIRE-seq analysis revealed de novo opening (thereby
suggesting transcriptional activation) of ~14,000 genomic regions
(Supplementary Fig. [147]4a) in HN137-Met compared to HN137-Pri;
specifically, this included the YAP1 locus 11q22, and several YAP1
target genes (e.g., CYR61 and CTGF) (Supplementary Fig. [148]4b).
Western blots and qPCR confirmed these findings (Fig. [149]4c and
Supplementary Fig. [150]4c, [151]d).
Interestingly, lower levels of YAP1 expression was detected in
HN137-Pri compared to HN137-Met (Fig. [152]4c). Immunohistochemistry
(IHC) staining of HN137 primary and metastatic tumour tissue revealed
small YAP1-high subpopulations within the HN137 primary tumour
(Fig. [153]4d). Conversely, majority of the HN137-Met tumour displayed
uniformly high levels of YAP1. These data suggested that clonal
selection and expansion of YAP1-positive tumour cells occurred during
metastatic progression, likely by activating pro-survival pathways.
YAP1 signalling enhances cell growth and survival^[154]20. Therefore to
determine if this is necessary for the viability of HN137-Met, we
knocked down YAP1 using shRNAs. Downregulation of YAP1 resulted in
reduced cell proliferation (Supplementary Fig. [155]4e), reduced
expression of pro-survival protein BIRC5 and upregulation of apoptotic
markers such as cleaved-caspase3 (Supplementary Fig. [156]4f),
suggesting that YAP1 expression was indeed necessary for survival of
HN137-Met tumour cells. To determine the function of YAP1 in modulating
drug sensitivity, HN137-Pri cells stably overexpressing YAP1 were
generated (Supplementary Fig. [157]4g), and treated with YM155 and
gefitinib. Overexpression of YAP1 in HN137-Pri could sensitize cells to
YM155 (IC[50] 0.07 μM vs. IC[50] 0.2 μM) while conferring a reciprocal
100-fold resistance to gefitinib (IC[50] > 10 vs. IC[50] 0.124 μM)
compared to control cells (Fig. [158]4e). Remarkably, YAP1
overexpression had no effect on modulating response to other classes of
compounds (Supplementary Fig. [159]4h), suggesting that its function in
conferring sensitivity towards YM155 and EGFR-TKIs is indeed specific.
These observations led to the prediction that patient HN137 may relapse
with gefitinib-resistant disease, and that the majority of the
recurrent tumour would comprise of YAP1-positive cells. This was indeed
the case as the patient subsequently developed geftinib-resistant
dermal metastatic lesions ~6 months after initiating gefitinib
treatment (Fig. [160]3d). Importantly, evaluation of a biopsy of the
dermal lesion by IHC with YAP1 antibody revealed that the persistent
gefitinib-resistant tumour was indeed positive for YAP1 expression
(Fig. [161]4f). Unfortunately, treatment with YM155 (which our data
predicts specifically targets this aggressive subclone) (Fig. [162]2d)
was not possible because of the unavailability of this drug for
clinical trials.
To investigate the clinical relevance of YAP1 expression levels in
OSCC, we performed IHC on tissue microarray (TMA) sections
(Supplementary Fig. [163]4i). TMA sections of 166 tumours^[164]21 were
stained and scored for YAP1 expression. Increased expression of YAP1
was observed in 24% (n = 41) of patients (Fig. [165]4g). Survival
analyses on high-risk (stages 3 and 4) patients found within our TMA
cohort showed that high nuclear YAP1 correlated with poorer overall and
disease-free survival (P = 0.01 and 0.04, respectively) (Fig. [166]4g).
Altogether, these observations suggest that YAP1 could be used as a
diagnostic as well as prognostic biomarker for patient stratification,
prediction of gefitinib resistance and metastatic progression.
Discussion
Rapid advances in sequencing technologies and the ability to more
comprehensively interrogate molecular signatures (genomic,
transcriptomic, epigenomic and proteomic) have resulted in a strong
focus in bringing precision medicine to the clinic. This is especially
relevant for cancer research/precision oncology. Over the past several
years, genomics-driven technologies have led to major advances in the
discovery and mechanistic understanding of oncogenic driver mutations,
and pathways that promote cancer progression. However, single
cell/multi-sector tumour sequencing studies, and longitudinal tumour
progression studies have identified an extraordinary degree of tumour
heterogeneity and cellular plasticity, respectively. This may explain
how treatment resistance occurs with either conventional chemo- or
molecularly targeted therapies. Additionally, even if we were to
identify the various driver mutations in individual subclones, not all
genomic alterations are actionable, not to mention that developing
targeted drugs for every private mutation will take its due
course^[167]22. Therefore, while the genomics-driven strategies will
continue to yield insights into tumour biology that may help identify
novel therapeutic targets or patient-selection strategies, the clinical
benefit, especially as it is currently practiced, may be
limited^[168]23, [169]24. In this study, we argue that
“phenotype-driven precision oncology” using patient-personalized models
can provide novel alternative treatment modalities, predictive
strategies and help bring “true precision oncology” into the clinic.
Additionally, such models can serve as ideal surrogates for
retrospective “pan-omics” analyses for the interrogation of mechanisms
of treatment failure, resistance and metastasis.
Patient-specific tumour heterogeneity represents one of the greatest
challenge undermining fidelity of model generation and therapeutics
identification. It appears imperative that we would need to turn
towards PDX models for preclinical drug discovery^[170]25, [171]26 for
increased clinical relevance as they are shown by several groups to
retain the molecular and architectural features of the original patient
tumour^[172]27, [173]28. Indeed, a recent publication demonstrated the
utility of PDX models in predicting human clinical trial drug
response^[174]10, [175]28. However, PDX models are not amenable to HTS,
thereby greatly limiting the number of clinically available drugs that
can be tested for repurposing. Here we propose that a combination of
the PDC/PDX system may provide a cost effective pipeline for
identification of therapeutics vulnerabilities (or preclinical drug
discovery) given the ability to query a larger collection of
drugs/combinations. To this end, we showed that therapeutic strategies
and prediction of clinical response identified using PDCs could be
validated in vivo using patient-matched PDX models (Fig. [176]1f and
Supplementary Fig. [177]1c), suggesting that this platform can identify
hits robustly, with good reproducibility across various models
analogous to studies using organoids and more recently,
explants^[178]28, [179]29.
In vitro models however present their own set of limitations. Tumour
heterogeneity among various tumour sectors could contribute to
inconsistency as the tumour sections used for establishment of models
may not be representative of the tumour in its entirety^[180]30. Once
established, clonal selection can occur during the process of model
generation, culture adaptation and/or during propagation thereby not
retaining the full complexity of the parental tumour^[181]31, [182]32.
However, our primary screen using HN137-PDCs revealed that a class of
molecules targeting EGFR showed similar efficacy against HN137-PDC and
PDX models. In another instance, the HN177-PDC also reflected the
treatment response of HN177 patient. These results suggest that PDCs
can capture to a significant extent, the drug response of the patient
tumour in vivo. This is further supported by genomic characterization
and gene expression analyses that show key molecular signatures, and
oncogenic alterations found in HN137 patient tumours were indeed
conserved in PDXs and corresponding PDCs. Similar observations were
made with other patient models as well. Therefore, we propose that
patient-matched PDCs can potentially serve as predictive models for the
identification of alternative therapeutics, in a clinically relevant
time frame.
HN137 received cisplatin as post-operative adjuvant chemotherapy.
However, tumour recurred, suggesting that cisplatin was not an
effective treatment for this patient. Retrospectively, this observation
correlated well with our in vitro observation where treatment naive
HN137 cells were found to be more resistant to cisplatin as compared to
gefitinib (Supplementary Fig. [183]3a). However, as gefitinib was also
effective against recurrent tumour (presumably cisplatin resistant),
this observation suggested that gefitinib could be effective against
both treatment naive and cisplatin-resistant tumour. Indeed,
cisplatin-resistant PDC models developed in vitro were found to be
sensitive to gefitinib, correlating well with treatment response in the
clinic (Fig. [184]3b, c).
The development of patient-specific in vitro models that are amenable
to genetic manipulation also allowed us to functionally test signatures
that could potentially confer differential therapeutic response. YAP1
amplification has been previously reported in OSCCs^[185]33 and
implicated in conferring cetuximab resistance^[186]34. In this study,
we demonstrate that overexpresison of YAP1 alone in HN137-Pri cells (to
a level similar to those in HN137-Met) was sufficient to confer
resistance to EGFR-targeting TKIs, while also increasing their
sensitivity to IAP inhibitors. Therefore, high levels of YAP1 which
occurs in 24% of OSCCs could serve as potential biomarkers for
gefitinib and cisplatin resistance, and these patients can be directed
for treatment with IAP inhibitors, such as YM155.
One of the most striking observations made from our models was that
they predicted that upregulation or selection of YAP1-positive cells
may be responsible for secondary treatment failure upon gefitinib
administration. HN137-Met and HN137-Pri overexpressing YAP1 displayed
resistance to gefitinib. Indeed, this was demonstrated when patient
HN137, who initially responded to gefitinib treatment, developed
treatment failure, and as predicted, the resistant tumours were
uniformly YAP1 positive. This is particularly significant as it
suggests that personalized ex vivo models can predict the trajectory of
tumour evolution in a patient, even before it actually occurs in the
clinic, thus serving as powerful surrogates to study tumour evolution
under different selection pressures.
The proposed phenotypic approach towards precision oncology using
patient-matched models aims to complement existing genomics-driven
methodologies needed to rapidly and cost effectively translate
phenotypic response data into the clinic. While this platform is
exciting, it awaits further validation using larger patient cohorts.
However, we believe that expanding the repertoire of drug-response
phenotypes by running multiple n = 1 co-clinical trials will facilitate
the development of machine-learning algorithms that may accurately
correlate “omics-data” with empirically obtained phenotypes
(Fig. [187]4h). This may result in the identification of novel
molecular signatures that could serve as biomarkers that are prognostic
of treatment response and progression, especially in indications
lacking molecular stratification or in patients without any treatment
options. Thus, adopting this complementary approach of pairing genomics
with phenotype-driven precision oncology may serve as the best strategy
for identifying effective drugs and improving treatment outcomes.
Methods
Quantitative real-time reverse transcription PCR
RNAs were isolated from cells and tissue using QIAzol (Qiagen) prior to
purification using RNeasy Mini Kit (Qiagen, cat. no. 74106). RNAs were
reverse transcribed using Superscript II reverse transcriptase (Thermo
Fisher, cat. no. 18064-071) and quantitative polymerase chain reaction
using SYBR Green (KAPA, cat. no. KK4602) were carried out in accordance
to the manufacturer’s protocol. Transcripts were normalized to GAPDH
levels. Sequences of primers used for RT-qPCR can be found in
Supplementary Table [188]5.
Compounds
Cisplatin was purchased from Tocris (cat. no. 2251), YM155 was
purchased from Selleckchem (cat. no. S1130) and gefitinib was purchased
from Cayman Chemical (cat. no. 13166). All compounds were dissolved in
DMSO (Sigma-Aldrich, cat. no. D8418). Cisplatin was prepared fresh for
each treatment.
Dose-response IC[50]
Approximately 10,000 cells were seeded per well into a 96-well plate
24 h prior to drug treatment. Drugs were threefold diluted in DMSO and
kept at 1% (v/v) across all drug concentrations and control. Each drug
concentration was tested in triplicate. The viability of cells were
assayed using CellTiter-Glo luminescent cell viability reagent
(Promega, cat. no. G7572). The luminescence signals were detected using
TECAN Infinite M1000 pro multi-mode plate reader using an integration
time of 250 ms. The relative luminescence units from treated wells were
normalized against DMSO control wells and expressed as percentage cell
viability. IC[50] values were calculated using GraphPad Prism software.
Generation and passaging of PDXs
Tumour samples were obtained from patients post surgery after obtaining
informed patient consent in accordance to SingHealth Centralized
Institutional Review Board (CIRB: 2014/2093/B). Tumours were minced
into ~1 mm^3 fragments and suspended in a mixture of 5% Matrigel
(Corning, cat. no. 354234) in DMEM/F12 (Thermo Fisher, cat. no.
10565-018). The tumour fragment mixtures were then implanted
subcutaneously into the left and right flanks of 5–7 weeks old NSG
(NOD.Cg-Prkdc ^scid Il2rg ^tm1Wjl /SzJ) (Jackson Laboratory, stock no.
005557) mice, using 18-gauge needles. Tumours were excised and passaged
when they reached 1.5 cm^3. For passaging, tissues were cut into small
fragment of 1 mm^3 prior to resuspension in 20% Matrigel/DMEM/F12 mix,
before subcutaneous inoculation of tumour fragments into 5–7 weeks old
NSG mice. Protocols for all the animal experiments described were
approved by the A*STAR Biological Resource Centre (BRC) Institutional
Animal Care and Use Committee (IACUC) under protocol #151065.
Derivation of PDC cell lines and cell culture
Tumours were minced prior to enzymatic dissociation using 4 mg mL^−1
collagenase type IV (Thermo Fisher, cat. no. 17104019) in DMEM/F12, at
37 °C for 2 h. Cells were washed using cyclical treatment of pelleting
and resuspension in phosphate-buffered saline (Thermo Fisher, cat. no
14190235) for three cycles. The final cell suspensions were strained
through 70 µm cell strainers (Falcon, cat. no. 352350), prior to
pelleting and resuspension in RPMI (Thermo Fisher, cat. no 61870036),
supplemented with 10% foetal bovine serum (Biowest, cat. no. S181B) and
1% penicillin-streptomycin (Thermo Fisher, cat. no. 15140122). Cells
were kept in a humidified atmosphere of 5% CO[2] at 37 °C. Cell line
identity was authenticated by comparing the STR profile (Indexx
BioResearch) of each cell line to its original tumour. Cells were
routinely screened for mycoplasma contamination using Venor^®GEM
OneStep mycoplasma detection kit (Minerva Biolabs, cat. no. 11-8100).
Drug preparation and in vivo treatment
Gefitinib (Iressa) was prepared by dissolving a 250 mg clinical grade
tablet (AstraZeneca) in sterile water containing 0.05% Tween-80
(Sigma-Aldrich, cat. no. P4780) to a concentration of 10 mg mL^−1 and
administered at a dosage of 25 mg kg^−1 daily via oral gavage. YM155
(Selleckchem, cat. no. S1130) was dissolved in saline to a
concentration of 0.5 mg mL^−1 and administered by intraperitoneal
(i.p.) injection, once every 2 days at 2 mg kg^−1. Flavopiridol (LC
Laboratory, cat. no. A-3499) was dissolved in DMSO to a concentration
of 200 mg mL^−1 before diluting to 5 mg mL^−1 using saline and
administered by i.p. injection, once every 2 days at 5 mg kg^−1.
Belinostat (MedChem Express, cat. no. HY-10225) was dissolved in DMSO
to a concentration of 100 mg mL^−1 before diluting to 5 mg mL^−1 using
solvent containing (2% Tween-80 and 1% DMSO in saline), and
administered at a dosage of 40 mg kg^−1 daily via i.p. injection.
Docetaxol was prepared in accordance to published formulation^[189]35
and administered by i.p. injection, once every 2 days at 8 mg kg^−1.
Olaparib was solubilized in DMSO and diluted to 5 mg mL^−1 with saline
containing 10% (w/v) 2-hydroxy-propyl-beta-cyclodextrin (Sigma, cat.
no. 332607), and administered at 50 mg kg^−1 daily via i.p. erlotinib
was dissolved in 6% captisol (CyDex, Inc., Lenexa, KS) in water, pH 4.5
and administered at 150 mg kg^−1 daily via i.p. Control groups for all
compounds were treated in their corresponding diluent in the absence of
compounds.
PDXs were generated by grafting tumours either on both flanks or singly
as stated. The length and width of tumours were measured by caliper
once every 2 days. Tumour volumes were estimated using the following
modified ellipsoidal formula: Tumour volume = 1/2(length × width^2).
Mice were euthanized when tumours in the control group reaches
2.0 cm^3. The weight of tumour was not directly measured, but were
estimated using volume where the density of tissue was assumed to be
1 g cm^−3. The ratio of the change in treated tumour volume (ΔT) to the
average change in control tumour volume (ΔT/Average ΔC) at each time
point was calculated as follows:
T = Tumour volume of treatment group
ΔT = Tumour volume of drug-treated group on study day−Tumour volume on
initial day of dosing
C = Tumour volume of control group
ΔC = Tumour volume of control group on study day−Tumour volume on
initial day of dosing
Average ΔC = Average change in tumour volume across the control-treated
group.
Lentiviral and RNAi knockdown of genes
Knockdown was performed using shRNAs from MISSION^® shRNA library
(Sigma-Aldrich) cloned into pLKO-1 vector. Overexpression constructs
were obtained from the CCSB-Broad Lentiviral expression Library (cat.
no. OHS6085, OHS6087, OHS6269, OHS6270 and OHS6271). Plasmid encoding
GFP and tdTomato, cloned into pLL3.7 and pLV vector, respectively, was
obtained (gift from Tam Wai Leong). Briefly, lentiviruses were packaged
using HEK293T cells via co-transfection of plasmid of interest, VSVG
and psPAX6 using Lipofectamine 2000 transfection reagent (Thermo
Fisher, 11668500). Viruses collected were applied onto the target cells
for infection in the presence of 4 μg mL^−1 polybrene (Santa Cruz
Biotechnology, cat. no. sc-134220). The target sequences of the shRNAs
are as follow: YAP1 H3 shRNA 5′-GACCAATAGCTCAGATCCTTTC-3′; YAP1 H6
shRNA 5′-GCCACCAAGCTAGATAAAGAA-3′. Cells overexpressing proteins of
interest were selected using 10 μg mL^−1 of Blasticidin (Thermo
Scientific, cat. no. [190]R21001). GFP and tdTomato-positive cells were
FACS sorted.
Immunoblotting
Cells were lysed in the presence of RIPA buffer (Thermo Fisher, cat.
no. 89900) containing protease inhibitor (Calbiochem, cat. no. 539134)
and phosSTOP phosphatase inhibitor cocktail (Roche, cat. no.
04906837001). Lysates were separated on SDS-PAGE gel before blotting
onto polyvinylidene difluoride membrane (Millipore, cat. no.
IPVH00010). Membranes were blocked using Odyssey blocking buffer
(LI-COR, cat. no. 927-40000). Primary antibodies against YAP1 (Abcam,
ab52771, 1:2000), baculoviral IAP repeat containing 5 (Santa Cruz
Biotechnology, sc-17779, 1:500), β-actin (Santa Cruz Biotechnology,
sc-47778, 1:1000), cleaved caspase3 (Cell Signaling Technology, 9664,
1:500), glyceraldehyde 3-phosphate dehydrogenase (Santa Cruz
Biotechnology, sc-25778, 1:1000) and V5 (Santa Cruz Biotechnology,
sc-81594, 1:1000) were used. Proteins were detected and quantified
using secondary antibody (LI-COR, 1:10,000) and imaged on the LI-COR
Odyssey scanner. Uncropped scans of blots are supplied in the
supplementary information.
IHC
Tissue paraffin blocks were sectioned onto polylysine-coated slides and
sent to the Institute of Molecular and Cellular Biology (IMCB) Advanced
Molecular Pathology Laboratory (AMPL) for IHC. Briefly, the
avidin-biotin-peroxidase was performed against YAP1 (Abcam, ab52771,
1:200) for detection. Sections were deparaffinized in xylene and
rehydrated through descending percentage of ethanol. Antigen retrieval
was performed using citrate buffer (pH 6.1) for 40 min at 65 °C before
blocking of endogenous peroxidase activity using 3% solution of
hydrogen peroxidase, for 15 min, at room temperature. Slides were
blocked using 10% goat serum before application of primary antibody
diluted in 10% goat serum. The secondary antibody and HR-peroxidase
complex were added and incubated for 30 min at room temperature before
washing using TBS-T. The peroxidase activity was visualized by applying
diaminobenzidine (DAB) for 5 min at room temperature prior to
counterstaining using haemotoxylin. Slides were then dehydrated before
mounting and visualization.
TCGA data
The gene expression data available for HNSCC was obtained from TCGA
(Nature 2015) using cBioPortal for Cancer Genomics
([191]www.cbioportal.org). A Z-score threshold of 2 was used. Genes
that co-expressed with YAP1 and their Spearman correlation values were
downloaded and plotted using Microsoft Excel.
Scoring of YAP1 expression in TMAs (tissue microarrays)
The immunostained slides were scored by two independent pathologists.
The specific staining of the YAP1 was observed in five high-power
fields (×40). Expression of YAP1 expression was scored as 0, +/−, 1, 2,
3 separately for both nuclear and cytoplasmic staining.
Generation of Kaplan-Meier plots
Standard Kaplan-Meier overall survival and disease-free survival plots
and the significance of YAP1 expression in patient TMA were generated
using SPSS (version 2.0). For the Kaplan-Meier survival analyses, the
overall survival and disease-free survival curves are compared using
the log-rank test.
FAIRE-Seq
The HN137 primary and metastatic cells were cross-linked with 1%
formaldehyde for 10 min, which was followed by nuclear isolation and
sonication^[192]36. The FAIRE sites were recovered by
phenol:chloroform-isomyl-alcohol extraction of open chromatin, which
was quantified and used to generate Illumina next-generation sequencing
libraries for analysis with the Illumina HiSeq-Hi-Output. We employed
D-filter^[193]37 to call FAIRE peaks in the primary and metastatic
cells relative to input control.
Microarray gene expression
Flash frozen human tissue and PDX samples were homogenized in the
presence of QIAzol (Qiagen, cat. no. 79306) using M-tubes (Miltenyi,
cat. no. 130093236) in combination of the gentleMACs dissociator. Cells
were grown to ~80% confluency prior to cell lysis using QIAzol. Total
RNA was purified from human tissue, PDX and cell line using miRNAeasy
kit (QIAGEN, cat. no. 217004). RNAs were sent to genotypic technology
for one-color microarray-based gene expression analysis, carried out
according to the manufacturer’s protocol (Agilent Technologies).
Briefly, 500 ng of total RNA were amplified and labelled using
Quick-Amp labelling kit one-color (Agilent, cat. no. 5190-0442). The
cRNA was hybridized to genotypic’s propriety human whole-genome oligo
DNA microarray, which includes 50,599 probe set covering 36,337
genes/transcripts (Agilent Human GXP_8X60K, AMADID: 039494).
Signal intensity values from all probes were subtracted with background
controls and quantile normalized across samples to correct for batch
variation. Correlation between samples were determined from the Pearson
product-moment correlation coefficient r of all paired probes, and
plotted using R (IDPmisc package).
Probes showing more than twofold change in the expression levels of
metastasis and primary samples were considered as differentially
regulated, and investigated for overrepresentation of molecular
signature gene sets (MSigDB, version 5.1)^[194]38 using Fisher’s exact
test, followed by multiple testing correction using false discovery
rate estimation^[195]39. Significant gene sets were determined at P
value <0.05.
Comparative MSig analysis across the PDC Met lines
A reference expression data set was determined from the average
normalized expression of each probe in the five PDC Met line. Probes
showing more than twofold change in the expression levels of metastasis
and reference data set were considered for elevated expressions, and
investigated for overrepresentation of molecular signature gene sets
using Fisher’s exact test. Significant gene sets were determined at P
value <0.05.
Chemical compound library screens and analysis
Equal number of HN137-Pri GFP and HN137-Met tdTomato cells were seeded
for the co-culture screen and treated 24 h post seeding. A total of
4000 cells per well were seeded into 384-well plates (PerkinElmer, cat.
no. 6007558). Cells were cultured at 37 °C, 5% CO[2] and treated with
anti-cancer (Selleckchem, cat. no. L3000) and anti-kinase (Selleckchem,
cat. no L1200) small molecule libraries 24 h after cell seeding. Cells
were fixed 72 h after treatment using 4% paraformaldehyde for 15 min
prior to measurement of fluorescence intensity using the Tecan M1000
and imaging on Operetta High-Content Imaging System (PerkinElmer).
Drug response of various PDC to 317 compounds (Selleckchem anti-cancer
compound library) were quantified using the inhibition score (I-score).
For each compound c, the I-score was determined as follows:
[MATH: I - scorec=FluorescencesignalcMedianofFluorescencesignalDMSO :MATH]
where the denominator is the median of the fluorescence emitted in
presence of DMSO. Percentage inhibition was determined using
(1–I-score) × 100. Comparison between PDC was performed using
complete-linkage hierarchical clustering with euclidean distances as
distance measure.
GFP (Ex: 488 nm; Em: 507 nm) and RFP (Ex: 554 nm; Em: 581 nm) signals
were measured on TECAN Infinite M1000 pro multi-mode plate reader. Drug
response to 480 compounds (Selleckchem anti-cancer compound library and
kinase inhibitor library) were measured. For each compound c, the
Z-score transformation was applied such that Z = (X [c]−μ)/s.d., where
X [c] refers to the fluorescent signals in presence of compound c, μ
refers to the mean I-scores and s.d. is the standard deviation.
Compounds that scored Z < −2.4 for both RFP and GFP displayed toxicity
towards both HN137-Pri and HN137-Met lines. Compounds that scored (RFP
Z < −1, GFP Z > −0.5) were considered as hits selectively targeting the
HN137-Met, whereas compounds that scored (GFP Z < −1, GFP Z > −0.5)
displayed selectivity towards HN137-Pri.
Comparative genomics hybridization and analysis
Genomic DNA was isolated from both cell lines and tissue samples as per
the manufacturer’s protocol (Qiagen, cat. no. 69504). DNA were sent to
genotypic technology for comparative genomics hybridization analysis
carried out according to their in-house protocol. Briefly, DNAs were
label using Agilent Sure Tag Genomic DNA labelling kit (Agilent, cat.
no. 5190-3399), a kit that uses random primers and exo-klenow fragment
to differentially label genomic DNA samples with different fluorescent
label. The labelled DNAs were hybridized onto genotypic proprietary CGH
array Human DNA 2X400K chip (Agilent, AMADID: G4124A_068045) using
Agilent in situ hybridization kit (cat. no. 5188-6420). After washing,
the microarray slides were scanned and the captured images were
imported into Agilent cytogenomics 2.9.2.4 software for analysis. At
least three consecutive probe sets were used to call a CNV. Abberent
intervals were identified using the aberation detection algorithms
(ADM-2) with a threshold of 6.0, a minimum absolute average log[2]
ratio per region of 0.25 and nesting filter of 100.
The CIRCOS circular genome presentation software^[196]40 was used to
plot the histogram of copy number gains and losses (filtered at three
and above) detected in HN137-Pri and HN137-Met genomes.
Protein-protein interaction
Physical interactions between differentially regulated genes were
integrated from BIOGRID release 3.4.137^[197]41. The network views were
constructed using Cytoscape (version 3.2.1), and arranged using spring
embedded layout^[198]40. A proprietary algorithm (J.L.Y. Koh et al.,
unpublished) was used to determine the sub-network involving key genes
that were used as “seeds” to identify first- and second-degree
neighbouring nodes. A closed neighbourhood was then identified from
these nodes.
Targeted capture panel
The capture panel used in this study is an in-house designed panel
consisting of 763 genes that are clinically and biologically relevant
to cancer. They are derived from a comprehensive literature and
database search and include genes involved in key oncogenic signalling
pathways, oncogenes, tumour suppressor genes and genes from kinase and
chromatin remodeller families. The list of target capture gene panel is
provided (Supplementary Table [199]2). The tumour-normal pairs were
enriched with the Xplora capture panel and sequencing was performed on
the HiSeq platform.
Bioinformatics analysis pipeline
Our bioinformatics pipeline for processing of next-generation
sequencing data and variant discovery utilized best practices as
described by Genome Analyzer toolkit (GATK) (Broad Institute,
[200]https://www.broadinstitute.org/gatk/guide/best-practices.php).
Paired-end sequencing reads were aligned to the human reference genome
NCBI GRC Human Build 37 (hg19) using the Burrows-Wheeler Aligner
(BWA)-MEM (v0.7.9a)^[201]42 and PCR duplicates were removed using
SAMtools (v0.1.8)^[202]43. Base qualities were recalibrated and
realignment around microindels was performed using GATK^[203]44.
Somatic variants within the targeted region were called using MuTect
(v1.1.4)^[204]45 and LoFreq (v0.5.0)^[205]46 with default parameters.
We filtered any variants called by a single method or with an allele
frequency <0.1.
Single nucleotide variations (SNVs) were annotated using Variant Effect
Predictor (VEP, v2.8)^[206]47 for hg19. Gene transcript annotation
databases (CCDS, RefSeq, Ensembl, UCSC Known Genes) were used to
identify transcripts and to determine amino acid changes. Amino acid
changes corresponding to SNVs were annotated according to the largest
transcript of the gene. SNVs that were present in dbSNP (Build 132)
were removed unless they were also present in COSMIC (v52) indicating a
previously confirmed somatic event.
Data availability
Gene expression and CGH array data that support the findings of this
study have been deposited in GEO with the accession code [207]GSE84676.
FAIR-seq data that support the findings of this study have been
deposited in GEO with the accession code [208]GSE92479. Compound
library screen data associated with this study can be accessed via the
Centre for High-throughput Phenomics (CHiP-GIS) portal at
[209]http://chip.gis.a-star.edu.sg/DOWNLOAD/Chia2017/
Electronic supplementary material
[210]Supplementary Information^ (4.8MB, pdf)
[211]Peer Review File^ (506.8KB, pdf)
Acknowledgements