ASXL1 promotes adrenocortical carcinoma and is associated with chemoresistance to EDP regimen
Liang Wang1,*, Yinfeng Lyu2,*, Yuqing Li2, Kunping Li2, Hui Wen2, Chenchen Feng2, Ning Li3
1Department of Urology, Tianjin Medical University General Hospital, Tianjin 300052, P.R. China
2Department of Urology, Huashan Hospital, Fudan University, Shanghai 200040, P.R. China
3Department of Urology, Fourth Affiliated Hospital of China Medical University, Shenyang 100032, Liaoning Province, P.R. China
*Equal contribution
Correspondence to: Chenchen Feng, Ning Li; email: drfengchenchen@163.com, https://orcid.org/0000-0002-1854-356x; air- nick@hotmail.com, https://orcid.org/0000-0003-3420-0206
Keywords: adrenocortical carcinoma, ASXL1, chemoresistance
Received: July 19, 2021
Accepted: September 2, 2021
Published: September 18, 2021
Copyright: @ 2021 Wang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
ABSTRACT
Adrenocortical carcinoma (ACC) is a rare but aggressive disease that lacks definitive treatment. We aim to evaluate role of ASXL1 in ACC and exploit its therapeutic merits therein. We performed in silico reproduction of datasets of the Cancer Genome Atlas (TCGA), GDSC (Genomics of Drug Sensitivity in Cancer) and Human Protein Atlas using platforms of cBioPortal, UALCAN, NET-GE, GSEA and GEPIA. Validation in ACC was performed in tissue, in vitro and in vivo using the NCI-H295R and SW-13 cells. ASXL1 was gained in over 50% of ACC cases with its mRNA overexpressed in DNA gained cases. ASXL1 overexpression was associated with recurrence and worsened prognosis in ACC. ASXL1 gain was associated with resistance to etoposide, doxorubicin and cisplatin (EDP). ASXL1 expression was positively correlated with FSCN1 expression. Targeting ASXL1 significantly impaired fitness of ACC cells, which could be in part rescued by FSCN1 overexpression. Targeting FSCN1 however could not rescue resistance to EDP induced by ASXL1 overexpression. Targeting ASXL1 sensitized ACC cells to EDP regimen but constitutive ASXL3 overexpression in SW-13 cells could induce resistance upon prolonged treatment. Functional gain of ASXL1 was common in ACC and exerted pro-tumorigenic and
INTRODUCTION
Adrenocortical carcinoma (ACC) originated from adrenal cortex cells, as a rare neoplasm with annual incidence of 0.7~2 per million [1], is highly aggressive and is prone to metastasize. Lack of heralding symptoms often render patients with ACC diagnosed at a late stage. Mitotane, as the only FDA approved specific drug for ACC, together with surgery, chemo- and radio-therapy forms a multi- modality treatment scheme for ACC at present. However, for stage IV patients catalogued by traditional European Network for the Study of Adrenal Tumors (ENSAT) classified method, their 5-years survival could merely reach 6% - 13% [2]. In general, current treatment for stage III-IV ACC is disappointing.
Current clinical practice guidelines on the management of adrenocortical carcinoma was issued by European Society of Endocrinology (ESE) in 2018. In terms of drug treatment, based on the results of the FIRM-ACT trial, the guidelines recommend the utilization of mitotane +EDP (etoposide, Adriamycin, cisplatin combination chemotherapy) as the most effective remedy for advanced ACC patients who are not amenable to local treatment [3].
Recent high throughput sequencing studies revealed some genomic mutations remarks that be identified as “drive events” of ACC tumorigenesis including mutations in IGF-2, TP53, CTNNB1, MEN1 and PRKAR1A. Somatic mutations and fusions of these
genes mostly relate to continuous activation of WNT/B- catenin pathway which cause the overexpression of corresponding growth factors and inactivation of tumor suppressor TP53. In the meantime, there is a dearth of studies on the impacts of copy number variation (CNV) in ACC. Assie, Fernandez, Lerario et al. reported partial or complete deletion of chromosome 1p, 17p, 22p, 22q, 2q and 11q by comparative genomics via the microsatellite method loss of heterozygosity and allele imbalance in ACC [4-6]. However, insightful mechanistic analyses are still lacking.
The Cancer Genome Atlas (TCGA) project of ACC demonstrated unprecedented genomic and genetic panorama of the rare cancer which substantially promoted researches on ACC from bench to bedside [7]. However, many copy number variance (CNV) in ACC revealed in TCGA still lacked insightful functional investigation. In the current study, we set off to evaluate role of 20q gain in ACC and identified Additional sex combs like transcriptional regulator 1 (ASXL1) as target gene that could mediate chemoresistance with the aim of deepening the understanding of biology of ACC.
MATERIALS AND METHODS
In silico analysis
TCGA dataset was used to examine expression of ASXLs in ACC. Using the cBioPortal platform, we reproduced ACC dataset of TCGA. Gain of function was defined as over-expression whose z-score was of “Exp >= 2” and copy number gain or amplification. we plotted cases with gain of function of ASXLs using the OncoPrint function of cBioPortal. Enriched genes at mutation and expression level in cases with gain of function of ASXL1 was generated at cBioPortal and were then submitted to NET-GE analytical platform for functional annotation. Survival analysis and correlation of gene expression were generated using cBioPortal and GEPIA, respectively.
Immunohistochemistry (IHC)
An in-house tissue microarray containing 54 paraffinized sections of ACC sample was used. All samples were surgically removed ranging from 1990 to 2019 from authors’ institutes. Informed consent was acquired in all patients and the study was approved by local ethics committee. Briefly, chip was first sliced, mounted, and subsequently deparaffinized. Tumors on xenograft models were prepared in the same manner. Sections were dehydrated with alcohol and hydrogen peroxide was applied for blockade. The sodium citrate buffer
solution was prepared for antigen retrieval with microwave. The non-fat milk was applied for non- specific antigen blockade. Primary antibodies against ASXL1 (ThermoFisher, PA5-71475, 1:50) and ASXL3 (Novus Biologicals, NBP2-14791, 1:50) were applied overnight. Secondary antibody and DAB was subsequently applied and the section was counter- stained with hematoxylin. Production intensity and extensity of staining was first calculated. Intensity score ranged from 0 to 3 representing from no staining to brown. Extensity score was as follows: 0 for negative, 1 for 1-25% cells stained, 2 for 26%-50%, 3 for 51-75% and 4 for 76%-100%. A final score of 0-4, 5-8 and 9-12 corresponded to IHC score of 0, 1 and 2 respectively. Sections were all de-identified and only information on cancer subtype, TNM stage, and tumor grade were available. The whole experiment conformed to Declaration of Helsinki.
Cell line and RNA interference
The NCI-H295R and SW-13 cells were obtained from the National Experimental Cell Resource Sharing Platform of China. Cell lines were cultured with RPMI 1640 medium supplemented 10% bovine serum. shRNAs targeting ASXL1 and FSCN1 were selected from GPP (https://portals.broadinstitute.org/ gpp/public/) as follows: TRCN0000379665 (shRNA#1) and TRCN0000285358 (shRNA#2) for ASXL1; TRCN0000123042 and TRCN0000123040 for ASXL3. A standard non-lipofectamine transfection protocol was followed and puromycin- resistant clone was selected. Green-fluorescent protein (GFP) was integrated for efficacy determination. Treatment of EDP was performed as previously reported [8].
RNA isolation and quantitative PCR (qPCR)
We used TRIzol reagent (Invitrogen) to extract the total RNA of the cells according to the instructions of the manufacturer. PrimeScrip RT Master Mix (TakaRa) was employed to perform reverse transcription reactions of RNA samples. For determining the expression levels of cDNA, SYBR Premix ExTaq II (TaKaRa) was used to conduct quantitative real-time polymerase chain reaction (qRT-PCR) analyses according to manufacturer’s protocols. The internal control in this experiment was GAPDH. Ct method was used to calculate the relative abundance of mRNA after normalization. Primers used were as follows: ASXL1, Forward 5’- CGCGCCTGGTATTAGAAAACT-3’; Reverse 5’- GCATCCTTCTTGAGCGTGAAAAG-3’; FSCN1, Forward 5’- CCAGGGTATGGACCTGTCTG-3’; Reverse 5’- GTGTGGGTACGGAAGGCAC-3’.
Luciferase activity assay
NCI-H295R cells were co-transfected with promoter firefly luciferase of target genes and plasmids of gene of interest using Lipofectamine Reagent (Invitrogen). Thirty- six hours later, luciferase activity was measured using the Dual-Luciferase Reporter Assay System (Promega) according to the manufacturer’s protocol. Luciferase activity was normalized to Renilla luciferase activity.
Transwell and wound-healing assays
For transwell migration assay, with polycarbonate membrane as separation, DMEM and 10% FBS served as nutrient solution in outer chamber, while 7 x 104 tumor cells per well were put into the inner chamber. After 16 hours, the migrated cells were stained by crystal violet and counted by microscope. For invasion assay, transwell inserts (Costar) coated with Matrigel (BD Biosciences)/fibronectin (BD Biosciences) was utilized. To perform the wound healing assay, cell suspension was cultured 16-24 hours as monolayer cells. Scratch the cells with the head of pipetting gun and add the 5-Fu solution incubating for 24 hours, and change the 10% FBS for 24 hours. Inverted microscope was used for observation and image capture.
Xenograft murine model
Tumor cells were cultured in DMEM medium. Extract 100 ul of the mixed cell suspension with 1 ml syringe and inoculated subcutaneously on the right hind limb of the right back of the nude mice. On the 12th day of inoculation, the tumor volume of all nude mice was > 100mm3. The spirit, diet, defecation and activity of the nude mice were observed daily. Application of EDP-M regimen was strictly according to previous report [9]. The mass of the transplanted tumor was weighed and the long diameter (A) and short diameter (B) of the transplanted tumor were measured with a vernier caliper every 3 days from the 3rd day of inoculation. The mean volume of the transplanted tumor was calculated according to the volume formula V=1/2 (AxB2), and the average value was obtained and the curve of tumor growth was plotted. On the 60th day after inoculation, the nude mice were sacrificed and the tumor was removed, and the morphology, texture and activity of the transplanted tumor were observed. Endpoints included tumor reaching cut-off volume of 2000 mm3 or necrosis. Animal experiments were approved by Department of Laboratory Animal Science of Fudan University.
Statistical analyses
Statistical analysis for in silico studies were automatically performed with the platforms used, as
aforementioned. Statistical analysis for in vitro assays and in vivo experiments were performed using the Prism Graphpad 9.0 for Mac. Comparisons between two groups were studied using the Mann-Whitney test for non-parametric variants and using the Student’s t test for parametric variants. IC50 for drug treatment was interpolated and fitted with sigmoidal curve. The survival data was presented using the Kaplan-Meier curve and compared using the Log-rank test. The P value of <. 05 was accepted as significant.
RESULTS
CNV of ASXL1 was first queried across cancer types and ACC was amongst top cancers that harbored ASXL1 gain (Figure 1A). We then focused on TCGA ACC cohort and found gain-of-function was the predominant alteration in all 3 ASXLs (Figure 1A). Of note, gain of ASXL1 occurred in ~57% of ACC cases (Figure 1B). We further plotted mRNA expression against copy number in all 3 ACXLs and found only ASXL1 showed increased mRNA expression along with increased copy number, indicating functional output of the gene (Figure 1B). Overexpression of AXSL1 conferred both worsened overall and progression-free survival in ACC patients (Figure 1C). Enrichment analysis showed CTNNB1 mutation, one of the established driver events in ACC, was significantly enriched in ASXL1-gained cases (Figure 1D, 1E). Gene expression enrichment analysis also showed significantly enriched drug metabolism pathway (Figure 1F) and WNT signaling (Figure 1G).
We then pursued drug metabolism function of ASXL1 gain. By querying cytoband that encompassed ASXL1 that was amplified in GDSC dataset, we showed that ASXL1 gain was associated with significant resistance to a variety of compounds in both GDSC1 and GDSC2 pools (Figure 2A). Amongst the compounds were three chemo-agents (EDP) that consisted of the first line chemotherapy of ACC (Figure 2B). ASXL1 functioned in chromatin remodeling and regulates the expression of many genes. To identify target gene(s) of ASXL1 we performed analysis of the ChIP-Atlas and found FSCN1 ranking top 2 in binding affinity to ASXL1 (Figure 2C). Of note, FSCN1 was reported to play important pro- tumorigenic role in ACC [10]. Reproduction of TCGA ACC cohort showed significant linear positive correlation between expressions of ASXL1 and FSCN1 (Figure 2D). Interestingly, this correlation did not exist in normal adrenal tissue (Figure 2E). Analysis of clinicopathological parameters in TCGA ACC cohort showed ASXL1 expression was significantly higher in tumors with incomplete removal, with marginal significance in tumors of higher stage and progressed disease upon adjuvant treatment (Supplementary Table 1).
In our IHC cohort, we validated linear correlation of expression of ASXL1 and FSCN1 using IHC scores (r = 0.823, P <0.001) (Figure 2F) and showed AXSL1 expression being significantly higher in tumors with advanced staging and in nodal positive cases with marginal significance (Table 1). We then used an alternative strategy to validate ASXL1/FSCN1 axis by plotting drug sensitivity profile against expressions of ASXL1 and of FSCN1 in the CTRP platform that incorporated multiple datasets. We showed austocystin D being the top hit in both queries (Figure 2G).
We then evaluated effect of ASXL1 in vitro in 2 ACC cell lines. Silencing of ASXL1 significantly inhibited proliferation in both cell lines (Figure 3A). ASXL1- KD also significantly inhibited colony formation (Figure 3B). Silencing of ASXL1 significantly lowered population in G1phase and increased population in S phase in both cell lines (Figure 3C).
ASXL1-KD also led to increased early and late apoptosis in NCI-H295R cell but showed no effect on apoptosis in SW-13 cells (Figure 3D). For cell motility, we showed that ASXL1-KD significantly decreased cell invasion (Figure 3E) and wound healing (Figure 3F) in both cell lines. In the rescue assays, we first found that transcriptional activity being significantly increased in both cell lines with ASXL1-OE (Figure 4A). FSCN1 silencing restored promotion of proliferation and cell migration by ASXL1-OE in both ACC cell lines (Figure 4B, 4C). However, FSCN1 silencing did not alter cell cycle profile and apoptosis interfered by ASXL1-OE in either cell line (Figure 4D, 4E). We then examined drug sensitivity profile of EDP. We found both ASXL1 and FSCN1 silencing decreased IC50 of doxorubicin in both ACC cell lines (Figure 4F). Nonetheless, only ASXL1-KD but not FSCN1-KD could cold lower IC50 of cisplatin (Figure 4G).
A
ASXL1
B
ASXL1 57%
80%
ASXL2 19%
Amplification
Gain
mRNA High
No alterations
Acoration Frequency
60%
ASXL3 4%
ns
40%
Amplification
Gain
ns
ns
20%
4
GNA data
>
Ulerine Carcinosarcoma (TCGA, Firehose Legacy)
Encghageal Carcinoma (TOGA, Frshose Legacy) Colorectal Adenocarcinoma (TCGA, Fishone Legacy)
Stomach Adenocarcinoma (TCGA, Firehose Legacy) Bladder Uruthelal Carcinoma (TOGA, Firehose Legacy)
Lung Squamous Cell Carcinoma (TCGA, Parsbose Legacy)
Crelan Serous Cystadenocarcinoma (TCGA, Firehose Legacy)
Alebocorsica Caronoma (TCGA, Firehose Legacy)
Skin Cutaneous Melanoma (TCGA, Feshose Legacy)
Cervical Squamous Cell Carcinoma and Enducervical (TCOA, Firehose Legacy)
Heut and tech Squamous Cell Carcinoma (TCGA, Firehose Legacy)
Lung Adenocarcinoma (TCGA, Firehose Legacy) Choosestoma Musiforme (TODA, Frehese Legacy?
Breast invasive Carcinoma (TOGA, Firehose Legacy) time Legacy)
Sarcoma (TCOA, Prstyse Legacy)
Chopped nome Kidney Renal Papillary Cell Carcinoma (TCGA, Feshose Legacy)
done (TCOA Firehose Legacy)
Kidney Cheomophobe (TOGA, Firshose Legse) Testicular Gen Cel Cancer (TCGA, Feshose Legacy)
Liver Hepatocellular Carcinoma (TCGA, Firehose Lagaoy) Vermogen wwwwww Cartonoma (YOGA, Firehose Legacy)
Compareznome (TCGA, Fishose Legacy)
Kidney Ranać Chear Cell Carcinoma (TOGA, Frehose Legacy)
altaloma (TCGA, Firehose Legacy)
Lymphoid Neoplasm Difuse Large B-cel tamphoma (TCGA, Firehose Legacy)
Brain Lawer Grade Glioma (TCGA, F Uvedl Melanoma (TCGA, Firehose Legacy)
haymoma (TOGA, Firsthose Legacy)
Thyroid Carcinoma (TCGA, Firehose Legacy) :www roma (TOGA, Firehose Legacy;
heochromocytoma and Paragangliome (TCDA, Frahose Legacy)
Acute Myeloid Leukamnis (TOGA, Firehose Legacy)
ASXL1 expression (RSEM)
ASXL2 expression (RSEM)
ASXL3 expression (RSEM)
·
·
A
Shallow Dip Del
Gain
Amp
Deep Del
Shallow Dip Del
Gain
Shallow Dip Del
Gain
Amp
C
D
Mutation
Expression
100%
CTNNB1
ASXL1
90%
6.0
10
Disease-free
80%
ASXL1 unaltered
q < 0.05
q <0.05
ASXL1 unaltered
Overall
8
-log10 p-Value
4.0
Significance -+
-log10 p-Value
Significance →
6
ASXL1 overexpressed
ASXL1
4
30%
overexpressed
2.0
20%
20%
2
P = 6.596e-4
10%
P = 0.012
10 20 30 40 50 60 70 80 90 100 tỉ0 120 130 500 150 Disease-free survival (Months)
10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 Overall survival (Months)
+- Unaltered group
Altered group -+
- Unaltered group
4 Altered group - 2
-10
0
10
-4
-2
0
Log Ratio
Log Ratio
E
F
G
q < 0.05
Enrichment plot: KEGG_WNT_SIGNALING_PATHWAY
Altered group
hs200053
9.35
Enrichment score (ES)
0.25
50-
Carbohydrate metabolism
Ascorbate and aldarate metabolism
0.20
Mutation frequency (%)
Unaltered group
40-
hsa00140
Homo sapiens Metabolism
- Lipid metabolism
0.05
1.00
Metabolism
Steroid hormone biosynthesis
3.00
0.50
30-
-
hs#00983
Ranked list matic (Signal@tuoisa)
20-
Xenobiotics biodegradation and metabolism
Drug metabolism
- other
# 2
enzymes
2
10-
#
R-HSA-1430728 Metabolism
R-HSA-211859 Biological oxidations
R-HSA-156588 Glucuronidation
.
0
Homo sapiens Pathways
R-HSA-156580 Phase II conjugation
0
2.000 4000 0000 0000 10.000 12:000 14:000 18:000 18 006 28,200
Rank in Ordered Dataset
CTNNB1*
ZFPM1
LRIG1
ZNF517
MUC5B
GARS
IRX3
ASPDH
ERCC2
SALL3
Enrichment profile - Hits
Ranking metric scores
Similarly, FSCN1-KD showed minimal effect on sensitivity of etoposide whereas ASXL1-KD increased sensitivity (Figure 4H).
We next evaluated role of ASXL1 in vivo. Both silencing of ASXL1 and EDP-M regimen significantly inhibited tumor growth of NCI-H295R cells, whereas addition of ASXL-KD further inhibited tumor growth (Figure 5A). Survival analysis showed significantly prolonged survival in group with ASXL1-KD and EDP- M (Figure 5B). Interestingly, whereas tumors in SW-13 xenograft models grew at a similar rate to NCI-H295R, SW-13 tumors showed trend towards acquired insensitivity upon prolonged treatment starting on day 39 (Figure 5C). Combination of EDP-M and ASXL-KD also showed similar trend although the combination group showed most potent inhibition (Figure 5C, 5D). As ASXL3 was an important paralog of ASXL1 [11] and ASXL3 was constitutively overexpressed in SW-13 cells, we evaluated ASXL3 expression in both cells and
found EDP could only induced increased ASXL3 expression in SW-13 cells (Figure 5E). IHC in harvested tumors also showed decreased ASXL1 expression in ASXL1-KD group and strong ASXL3 expression in SW-13 cells (Figure 5F).
DISCUSSION
Stage IV ACC confers a 5-year overall survival of 0% and novel treatment modality is at urgent need [12]. Our study highlights both the protumorigenic role and chemoresistance of ASXL1 gain in ACC. Normal ASXL1 plays a role in embryonic development. ASXL1 mutations are observed primarily in myelodysplastic syndromes, but they are also observed in colorectal and endometrial cancer [13, 14]. ASXL1 is altered in 4.42% of all cancers with colon adenocarcinoma, lung adenocarcinoma, breast invasive ductal carcinoma, acute myeloid leukemia, and myelodysplastic syndromes. The most common alterations in ASXL1 is
A
ASXL1 Gain GDSC1
C
ASXL1
SRX367491
D
E
₹
Average
293
ARMY-820
#2 FSCN1
-6
4-43-41
1500
p-value =0.00058
164 142-70
R=0.38
p-value = 0.083
.
R = 0.15
-5
A204ETT
-
-
log10(p-value)
6
Target genes
log2(FSCN1 TPM)
log2(FSCN1 TPM)
.
-
-3
1400
-
-2
.
2
-
-
1300
0
1
2
+
.
.
0
2
3
4
5
0
¥
2
3
4
5
—
IC50 Effect
—
log2(ASXL1 TPM)
log2(ASXL1 TPM)
ASXL1 Gain GDSC2
F
4
AXSL1 (2’)
FSCN1 (2’)
AXSL1 (0’)
FSCN1 (0’)
log10(p-value)
4
2
-1
0
1
IC50 Effect
B
G
Pearson correlation of AUC
Austocystin D
urinary tract
-
stomach
1
central nervous system
(z-score)
Pearson correlation of AUC
Austocystin D
-
cabral nervous system
thyroid
4
A
-
salivary gland
salivary gland
ASILI GER
-
(z-score)
FSCNI GEX
4
2.
TX
$
4
4-
upper sorodigestive tract
upper serodigestive tract
ASXL1 (mRNA expression)
ER
.
4%
large intestine
hoematicsietic and lyrrohoid Batut
FSCN1 (mRNA expression)
tuatocystin DAUG
haematopoietic and lymphoid tissue
| Dataset | Drug | P-value | FDR% |
|---|---|---|---|
| GDSC1 | Doxorubicin | 0.00398 | 22.6 |
| GDSC2 | Doxorubicin | 0.0119 | 9.38 |
| GDSC1 | Cisplatin | 0.000469 | 3.48 |
| GDSC2 | Cisplatin | 0.00194 | 7.18 |
| GDSC1 | Etoposide | 0.000638 | 15.6 |
Figure 2. Expressions of ASXL1 and FSCN1 was positively correlated. Reproduced from the GDSC dataset, shown were volcano plots of drug sensitivity of gain of cytoband encompassing ASXL1 in pan-cancer cell lines from (A) GDSC1 and GDSC2 repositories and (B) retrieved P values for Doxorubicin, Etoposide and Cisplatin; (C) Reproduced from ChIP-Atlas, shown was heatmap ranked by binding score of ASXL1 from high to low in different model cells (human 293 cells), each row representing one gene; (D) Reproduced from the TCGA adrenocortical carcinoma (ACC) dataset, shown was expression correlation between ASXL1 and FSCN1 in ACC samples; (E) Reproduced from the GTEx dataset, shown was expression correlation between ASXL1 and FSCN1 in normal adrenal samples; (F) Representative IHC image of ASXL1 and FSCN1 staining in ACC samples with numbers indicating IHC score; (G) Reproduced from The Cancer Therapeutics Response Portal, shown were box plots of correlation coefficients between area under control and gene expression in cancer cells.
| IHC score (non-parametric) | ||||
|---|---|---|---|---|
| N | Median | SE | ||
| Clinical Stage | ||||
| I | 14 | 1 | 0.256776296 | |
| II | 20 | 2 | 0.160591014 | |
| III | 7 | 2 | 0.260820265 | |
| IV | 13 | 2 | 0.307692308 | |
| P value | 0.005 | |||
| Atypical Mitotic Figures | ||||
| Present | 25 | 2 | 0.2212088 | |
| Absent | 29 | 2 | 0.160155352 | |
| P value | 0.417 | |||
| Clinical Status 3 Mo Post-Op | ||||
| Present | 17 | 2 | 0.260588899 | |
| Absent | 37 | 2 | 0.15525845 | |
| P value | 0.611 | |||
| Lymph node | ||||
| N0 | 42 | 2 | 0.160065041 | |
| N1 | 12 | 2 | 0.166666667 | |
| P value | 0.074 | |||
| T stage | ||||
| T1 | 14 | 1 | 0.256776296 | |
| T2 | 25 | 2 | 0.144683563 | |
| T3 | 6 | 2 | 0.25819889 | |
| T4 | 9 | 2 | 0.323941772 | |
| P value | 0.0002 | |||
A
12
H295R
11
SW-13
B
H295R
100-
100
SW-13
NC
10.
Cell Count/Fold
10
ASXL1-KD1
Cell Count/Fold
ONWAGON00
NC
9
ASXL1-KD1
Colony formation
80
7
Colony formation
80
3
ASXL1-KD2
ASXL1-KD2
60
60
**
**
**
**
40
**
**
40
20
O
20
e
0
0
0
0
day1
day2
day3
day4
day5
day1
day2
day3
day4
day5
NC
KD1
NC
KD1
ASXL1
ASXL1
C
H295R
D
H295R
E
NC
ASXL1
H295R
SW-13
60
$1
NC
2.75-
150
**
NC
Percentage (%)
ASXL1
50
**
2.50
**
NC
ASXL1
KD1
KD
Apoptosis
Cell invasion (% of NC)
2.25
NC
100
T
40
2.00
1.75
KD
=
30
.
1.50
KD1
KD1
1.25
**
50
1.00
**
**
20
0.75
0.50
T
T
10
0.25
0
G1
S
G2/M
Early
Late
S
H295R
SW-13
APC
SW-13
14
NC
SW-13
NC
F
H295R
65- 60-
9
ns
YR
80-
ASXL1
Percentage (%)
55
**
8
50
ASXL1
PFKFB4
**
Wound healing (%)
NC
ah
₹
45
NC
% Apoptosis
7
NC
60
KD1
40
KD
KD
35
30-
KD1
6
₹
KD1
5
40
25
20-
4
**
**
8
15-
20
-
6h
10
5
T
3
Y
0
G1
S
G2/M
2
Early
Late
APC
H295R
SW13
mutation (2.62%) followed by amplification (0.67%). As a driver event in several cancers, ASXL1 status has been recognized as an inclusion criteria in cancer trials targeting hematological malignancies such as acute myeloid leukemia, myelodysplastic syndromes, acute myeloid leukemia arising from previous
myelodysplastic syndrome, chronic myelomonocytic leukemia, and secondary acute myeloid leukemia [15].
Our study has been the first to identify the role of ASXL1 in ACC. Though ASXL1 is solely amplified in 1 case in ACC, CN gain occurred in over half of cases
A
B
H295R
SW13
FSCN1 Luciferase Activity (% of AvCON)
1000-
AvCON
AvASXL1
30-
**
AvCON
40
AvCON
T
800-
Cell count/Fold
AvASXL1
Su
Cell count/Fold
30
AvASXL1
**
600
20
shFSCN1
shFSCN1
su
:
AvASXL1+shFSCN1
20
AvASXL1+shFSCN1
400
10
200
10
0
H295R
SW-13
0
0
day1
day2
day3
day4
day5
day1
day2
day3
day4
day5
C
300-
**
D
S
Cell migration (% of AvCON
G2/M
H295R
S
E
G1
G2/M
SW-13
Live
H295R
T
Live
SW-13
AvCON
G1
Apoptotic
**
Cell cycle phases (% of Cells)
100
Z
AvASXL1
I
T
Cell cycle phases (% of Cells)
100
£
I
I
¥
Apoptosis (% of Apoptotic cells)
100
I
I
Apoptotic
1
1
Apoptosis (% of Apoptotic cells)
100
I
T
I
I
200
A
T
shFSCN1
-
AvASXL1+shFSCN1
I
I
I
I
100
ns
T
ns
I
I
-
50
T
z
50
50
-
50
-
0
H295R
SW-13
-
-
I
I
I
I
AvCON
AvASXL1
shFSCN1
AvASXL1+shFSCN1
*
ns
I
I
I
.
T
ns
0
AvCON
AvASXL1
shFSCN1
AvASXL1+shFSCN1
0
AvCON
AvASXL1
shFSCN1
AvASXL1+shFSCN1
0
AvCON
AvASXL1
shFSCN1
AvASXL1+shFSCN1
0
AvCON
AvASXL1
shFSCN1
AvASXL1+shFSCN1
F
G
H
150-
H295R
150
H295R
100
H295R
shFSCN1 (0.02356)
shAXSL1 (0.07285)
% Inhibition
shFSCN1 (1.225)
% Inhibition
:
shAXSL1(0.08943)
% Inhibition
80
shFSCN1(0.6566)
100
#
shAXSL1(0.086)
¥
100
shCON (0.7548)
shCON (1.324)
60
shCON (0.641)
Doxorubicin
Cisplatin
Etoposide
40
50
50
20
-6
-4
-2
0
2
-6
-4
-2
0
2
-6
-4
-2
0
2
Log-Dose (UM)
Log-Dose (UM)
Log-Dose (uM)
150
150-
SW-13
150
SW-13
SW-13
shFSCN1 (0.04674)
shFSCN1 (1.224)
shFSCN1 (0.5353)
% Inhibition
shAXSL1 (0.04568)
% Inhibition
100
shAXSL1
(0.19)
:
% Inhibition
:
100
100
shAXSL1 (0.2063)
shCON (0.5836)
:
shCON (1.259)
shCON (0.6267)
Doxorubicin
Cisplatin
Etoposide
50
50
50
-6
-4
-2
0
2
-6
-4
-2
0
2
-6
-4
-2
0
2
Log-Dose (UM)
Log-Dose (M)
Log-Dose (M)
and the increased mRNA expression following CN gain is indicative of gain of function of the gene. This is also consolidated by its prognostic value at mRNA level. Of note, TCGA cohort shows ASXL1 is associated with incomplete resection and our cohort indicates ASXL1 is associated with advanced stage. Both findings suggest that ASXL1 gain confers growth advantage to treatment-naïve ACC.
Being the first and only phase III randomized controlled trial, the FIRM-ACT trial provided by far the most solid evidence for chemotherapy regimen. The EDP-M regimen is now recognized as the first line therapy for advanced ACC [16]. Immune checkpoint inhibitors (ICIs) that reinforced last defense in many solid tumors only show moderate effect in ACC and are currently used under salvage and trial settings [17]. In fact, most
A
3000
H295R
B
H295R
Tumor Vol (mm3)
2500
shCON+Veh
100
shASXL1+Veh
2000
shCON+EDP-M
% Survival
shCON+Veh
shASXL1+EDP-M
shASXL1+Veh
1500
50
shCON+EDP-M
shASXL1+EDP-M
1000
P < 0.0001
500
*
0
0
20
40
0
Days elapsed
60
6
11
14
18
21
25
28
32
35
39
42
46
49
53
56
60
C
D
3000-
SW-13
shCON+Veh
SW-13
Tumor Vol (mm3)
2500
shASXL1+Veh
100
2000
shCON+EDP-M
shASXL1+EDP-M
% Survival
shCON+Veh
1500
shASXL1+Veh
50
shCON+EDP-M
1000
shASXL1+EDP-M
500
P < 0.0001
0
0
6
0
20
40
60
11
14
18
21
25
28
32
35 39
42
46
49
53
56
60
Days elapsed
E
800-
ASXL3 expression (% of shCON in CON)
F
shCON
600
shASXL1
shCON
shAXSL1
ASXL1
ASXLI
ASXL3
400
200
-
0
CON
EDP
CON
EDP
H295R
SW-13
H295R
SW-13
trials trying to transplant agents effective in other cancers into ACC yield frustrating outcomes [18]. The nature of multi-resistance and rare disease renders drug development stumbling over decades.
Genetic landscape of ACC revealed by TGCA triggers various translational studies on ACC and our findings in the current study also takes advantage of the CNV data of TCGA. We for the first time report association between EDP sensitivity and CN of a certain gene. Gain of function in both ASXL1 and FSCN1 plays critical role in mediating resistance. Interestingly, most positive findings in NCI-H295R cells has also been observed in SW-13 cells that is now considered to originate from small cell carcinoma of adrenal gland. Furthermore, overexpression ASXL3 in SW-13 cells suggests that crosstalk may exist in ASXL family to facilitate resistance. Nonetheless, we find that gain of function of ASXL3 occurs in only 4% of ACC cases, further supporting different cell context of SW-13 and NCI- H295R. The interplay between FSCN1 and ASXL1 has not been reported before. However, overactive FSCN1 has been reported to be pro-tumorigenic in a variety of cancers [19-21]. Detailed mechanism of how FSCN1 mediate ASXL1 expression in the presence of ASXL1 copy number gain, another driver incentive, would be interesting.
Our study also has limitations. Although ASXL1 appears to be a promising target in ACC, we failed to test compound(s) that target ASXL1. Using gene- compound sensitivity dataset, we have identified austocystin D as a potential agent. However, the compound is not commercially available. Austocystin D is an organic heteropentacyclic compound isolated from Aspergillus and Aspergillus ustus and has been identified as a potent cytotoxic agent with in vivo antitumor activity and selectivity for cells expressing the multidrug resistance transporter MDR1, which was a characteristic of ACC as well [22-24]. Selective cytotoxic action of austocystin D arises from its selective activation by cytochrome P450 (CYP) enzymes in specific cancer cell lines, leading to induction of DNA damage in cells and in vitro [25]. The pattern of cytotoxicity of austocystin D was distinct from doxorubicin and etoposide putting austocystin D fairly promising in overcoming chemoresistance. Of note, as mitotane is a potent inducer of CYP3A4 activity which may have reduced the blood levels of doxorubicin and etoposide, both of which being metabolized by CYP3A4 [26, 27], monotherapy of austocystin D may in theory overcome this drawback. Production of austocystin D is now in progress by our group according to reported protocol and we look forward to testing the compound in ACC. Recent studies report that ACC cells are highly susceptible to
ferroptosis, potentiating novel drug combination in this aggressive disease [28-30].
To sum up, we have shown that ASXL1 gain is common in ACC. Gain of function of ASXL1 confers worsened prognosis and promotes tumor growth of ACC. ASXL1 overexpression also induces chemoresistance to EDP regimen. Targeting ASXL1 hold promise to combat this rare but aggressive disease.
AUTHOR CONTRIBUTIONS
LW, YLyu and CF performed experiments. NL and CF designed the study. LW and YLi, KL and HW wrote the manuscript. All authors read and approved the final manuscript.
CONFLICTS OF INTEREST
The authors declare that they have no conflicts of interest.
FUNDING
This study was sponsored in part by National Natural Science Foundation of China (Grant No. 81874123).
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SUPPLEMENTARY MATERIALS
Supplementary Table
Please browse Full Text version to see the data of Supplementary Table 1.
Supplementary Table 1. Association of ASXL1 expression and clinicopathological parameters in TCGA ACC cohort.