Sapanisertib

1 KIT-dependent and -independent genomic heterogeneity of resistance in

2 gastrointestinal stromal tumors – TORC1/2 inhibition as salvage strategy

3Thomas Mühlenberg1,2, Julia Ketzer1,2, Michael C. Heinrich3, Susanne Grunewald1,2, Adrian Marino-
Enriquez4, Marcel Trautmann5, Wolfgang Hartmann5, Eva Wardelmann5, Jürgen Treckmann6, Karl
4Worm7, Stefanie Bertram7, Thomas Herold2,7, Hans-Ulrich Schildhaus8, Hanno Glimm2,9, AlbrechtStenzinger2,10, Benedikt Brors2,11, Peter Horak2,12, Peter Hohenberger13, Stefan Fröhling2,12, Jonathan A. Fletcher4, Sebastian Bauer1,2

filiations:

1Dept. of Medical Oncology, Sarcoma Center, West German Cancer Center, University Duisburg-

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Essen, Medical School, Essen, Germany
2German Cancer Consortium (DKTK), Heidelberg, Germany
3Portland VA Health Care System, Knight Cancer Institute, Oregon Health and Science University,Portland, Oregon, USA

4Dept. of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston,Massachusetts, USA

5Gerhard Domagk Institute of Pathology, University Hospital Münster, Münster, Germany
6Dept. of Visceral and Transplant Surgery, Sarcoma Center, West German Cancer Center,University Duisburg-Essen, Medical School, Essen, Germany

7Institute of Pathology, University Hospital of Essen, University of Duisburg-Essen, Germany
8Institute of Pathology, Universitätsmedizin Göttingen, Göttingen, Germany
9Department of Translational Oncology, National Center for Tumor Diseases (NCT) Dresden,

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Dresden University Hospital, Dresden, Germany
10Institute of Pathology, Heidelberg University Hospital, Heidelberg, Germany
11Dept of Applied Bioinformatics, German Cancer Research Center (DKFZ), Heidelberg University,Heidelberg, Germany

12Department of Translational Oncology, National Center for Tumor Diseases (NCT) Heidelberg,

German Cancer Research Center (DKFZ), Heidelberg University Hospital, Heidelberg, Germany 13 Mannheim, University Medical Center, Mannheim, Germany

30Running title: Sapanisertib in genetically heterogeneous GIST

31Keywords: Soft-tissue sarcoma; GIST; imatinib resistance; sapanisertib; Clinical drug resistance;
32Novel mechanisms of drug resistance

33Financial support: This work was supported by funds from the fundraising event “Sarkomtour”
34(www.sarkomtour.de; S. Bauer). Whole-exome/genome and RNA sequencing were funded by the
35DKTK Joint Funding Program (S. Fröhling). Further support was received from the GIST Cancer
36Research Fund (M.C. Heinrich), and VA Merit Review Grant (M.C. Heinrich, 2I01BX000338-05).

37Corresponding authors:
38Dr. Thomas Mühlenberg Dr. Sebastian Bauer
39Universitätsklinikum Essen Universitätsklinikum Essen
40Innere Klinik (Tumorforschung) Innere Klinik (Tumorforschung)
41Hufelandstraße 55 Hufelandstraße 55
42D- 45147 Essen D- 45147 Essen
43Phone: +49 201 / 723-85097 Phone: +49 201 / 723-2014
44Fax: +49 201 / 723-3112 Fax: +49 201 / 723-3112

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[email protected]
[email protected]

47The authors declare no potential conflicts of interest.

48No. of Figures: 3; No. of Tables: 4; Word count: ~5000

1Abstract:
2Sporadic gastrointestinal stromal tumors (GIST), characterized by activating mutations of KIT or
3PDGFRA, favorably respond to KIT inhibitory treatment but eventually become resistant. The
4development of effective salvage treatments is complicated by the heterogeneity of KIT
5secondary resistance mutations. Recently, additional mutations that independently activate KIT-
6downstream signaling have been found in pretreated patients – adding further complexity to the
7scope of resistance. We collected genotyping data for KIT from tumor samples of pretreated
8GIST, providing a representative overview on the distribution and incidence of secondary KIT
9mutations (n=80). Analyzing next generation sequencing data of 109 GIST, we found that 18%
10carried mutations in KIT-downstream signaling intermediates (NF1/2, PTEN, RAS, PIK3CA,
11TSC1/2, AKT, BRAF) potentially mediating resistance to KIT inhibitors. Notably, we found no
12apparent other driver mutations in refractory cases that were analyzed by whole exome/genome
13sequencing (13/109). Employing CRISPR/Cas9 methods, we generated a panel of GIST cell
14lines harboring mutations in KIT, PTEN, KRAS, NF1, and TSC2. We utilized this panel to
15evaluate sapanisertib, a novel mTOR kinase inhibitor, as a salvage strategy. Sapanisertib had
16potent antiproliferative effects in all cell lines, including those with KIT-downstream mutations.
17Combinations with KIT- or MEK- inhibitors completely abrogated GIST-survival signaling and
18displayed synergistic effects. Our isogenic cell line panel closely approximates the genetic
19heterogeneity of resistance observed in heavily pretreated GIST patients. With the clinical
20development of novel, broad spectrum KIT inhibitors, emergence of non-KIT-related resistance
21may require combination treatments with inhibitors of KIT-downstream signaling such as mTOR

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or MEK.

1Introduction
2Gastrointestinal stromal tumors (GIST) are the most common sarcomas of the GI tract and are
3characterized by activating mutations of the KIT or PDGFRA receptor tyrosine kinases (1,2).
4Most patients respond to the KIT/PDGFRA inhibitor imatinib (IM) but eventually progress due to
5secondary resistance mutations in KIT (3,4). Second and third line KIT-inhibitors have limited
6clinical benefit and only for a subset of patients (5-7). The development of effective salvage
7treatments is hampered by the heterogeneity of resistance mutations in KIT often observed
8within a single patient (8-10).
9The various KIT mutations in GIST activate the PI3K/AKT/mTOR and RAS/RAF/MAPK signaling
10pathways, and these same pathways are also activated in other GIST subtypes, even in so-
11called wild type GIST (11). Recently, activating mutations in these signaling cascades, including
12PI3K, KRAS, PTEN, and NF1, have been shown to emerge in later treatment lines, representing
13resistance mechanisms that cannot likely be addressed using direct KIT-inhibition approaches
14(11,12). Therefore, novel treatment strategies beyond the direct inhibition of KIT may become a
15crucial factor in GIST treatment in the near future. However, preclinical models recapitulating this
16heterogeneity of resistance, which would alleviate research towards this goal, do not exist yet.
17We used the CRISPR/Cas9 (Clustered Regularly Interspaced Short Palindromic Repeats/
18CRISPR associated protein 9) system as a novel powerful tool to generate new GIST cell line
19models (13).
20Inhibitors of KIT-downstream signaling intermediates (PI3K, mTOR, MEK) have been evaluated
21in GIST for several years but have not transitioned to advanced clinical development.
22Rapamycin-analogues targeting mTOR have only been examined in refractory GIST in
23combination with imatinib – which could not reverse imatinib-resistance. However, next-
24generation mTOR kinase inhibitors have not yet been tested. Sapanisertib (INK0128, MLN0128,
25TAK-228) represents a novel class of ATP-competitive mTOR inhibitors, specifically inhibiting
26mTOR kinase in both mTOR complexes (mTORC) 1 and 2 (14). The compound has been shown
27to be a more potent inhibitor of mTOR signaling than rapamycin, one that can overcome intrinsic
28and acquired resistance to rapamycin, and is well tolerated in vivo and in early clinical trials (15-
2918).
30Here, we sought to generate a GIST cell line panel comprising KIT-dependent and –independent
31mechanisms of resistance to current KIT-inhibitors, approximating the clinical situation in vitro.
32We then utilized this panel to evaluate the efficacy of sapanisertib alone and in combination with
33KIT- or MEK-inhibition.

1Methods
2Patients
3All patients were previously diagnosed as GIST patients by routine pathological review. In a
4retrospective study we gathered all consecutive sequencing data available (Sanger + NGS) for
5GIST patients who underwent routine molecular-pathology review in Essen. Furthermore we
6compiled panel NGS data of routine molecular-pathology review from sarcoma centers of
7Göttingen and Münster, Germany and Portland, OR, USA, as well as WES/WGS data from GIST
8patients who participated in the DKTK MASTER program (19) in Heidelberg. Due to the nature
9of these data (i.e. anonymized molecular pathology review only, except for the DKTK MASTER
10cohort), no clinical data, or sequencing raw data of these patients are available and no written
11informed consent could be requested or given. In this study exclusively anonymized sequencing
12data were analyzed, allowing no inference to patient identity and medical history except for
13diagnosis. The study was approved by the institutional review board (IRB; ethics committee) of
14the Medical School of the University of Duisburg-Essen was conducted in accordance with the
15Declaration of Helsinki. For the DKTK MASTER cohort, all patients provided written informed
16consent under a protocol approved by the ethics committee of Heidelberg University, and the

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study was conducted in accordance with the Declaration of Helsinki.

19Illumina – panel sequencing of tumor samples (63 patients)
20Multiplex PCR and purification was performed with the GeneRead DNAseq Custom Panel V2,
21GeneRead DNAseq Panel PCR Kit V2 (QIAgen) and Agencourt® AMPure® XP Beads
22(Beckmann). A total amount of 44ng DNA was used to perform multiplex PCR (four primer pools
23with 11ng each). Library preparation was performed using NEBNext Ultra DNA Library Prep Set
24for Illumina (New England Biolabs; NEB), according to the manufacturer’s recommendations
25applying 24 different indices per run. The pooled library was sequenced on MiSeq (Illumina;
262×150 bases paired-end run) and analyzed by the Biomedical Genomics Workbench (CLC Bio,
27QIAgen). Within the CLC Cancer Research Workbench demultiplexed paired-end sequencing
28data was mapped to human genome (version hg19). A local realignment was performed to reach
29better alignment quality, especially for regions with small insertions or deletions. All reads which
30were mapped outside of targeted-regions were deleted after the mapping process. In a filtering-
31step all reference-variants and variants found in dbSNP common, 1000 genome project and
32HapMap were deleted. An allele-frequency of minimum 2% and coverage of at least 100
33mapped-reads were applied. Samples with less than 50% of mapped bases against hg19 were
34categorized as not analyzable. For deviations from this protocol, as performed for patients from

1 Münster and Göttingen, and detailed information on sequencing panels see Supplemental

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Material.

4Ion Torrent – panel sequencing of tumor samples (33 patients)
5Targeted sequence analysis was performed with a custom AmpliSeq panel (Life Technologies,
6Grand Island, NY) that includes 24 genes (AKT1, AKT2, AKT3, ATM, BRAF, CDKN2A, HRAS,
7KIT, KRAS, MAP2K1, NF1, NRAS, PDGFRA, PIK3CA, PTEN, PTPN11, RB1, SDHA, SDHAF1,
8SDHAF2, SDHB, SDHC, SDHD, TP53). Sequencing was carried out on an Ion Torrent PGM
9instrument, and Torrent Suite Software v3.2 was used for sequence alignment and variant

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calling (20).

13Whole exome/genome and RNA sequencing (13 patients)
14DNA was extracted from 13 GIST samples from patients participating in the DKTK MASTER
15program (19) along with corresponding peripheral blood or adjacent normal tissue and subjected
16to whole exome/genome and RNA sequencing as described previously (21,22). Reads were
17mapped to the 1000 Genomes Phase II assembly of the human reference genome (NCBI build
1837.1). Genome sequencing data were aligned using Burrows-Wheeler Aligner, BWA (version
190.6.2 or 0.7.15). BAM files were sorted with SAMtools (version 0.1.19)(23), and duplicates were
20marked with Picard tools (version 1.125). Single-nucleotide variants (SNVs) and small
21insertions/deletions (indels) were analyzed using a previously reported bioinformatics workflow
22(24). Copy number variants (CNVs) were extracted from the whole exome sequencing samples
23with the help of CNVkit (version 0.8.3.dev0)(25) and from the whole genome sequencing (WGS)
24data using our in-house CNV calling pipeline ACEseq (26). Structural variants were detected in
25whole exome sequencing (WES) data using CREST (27). All events were annotated with
26RefSeq genes using BEDTools (28). RNA sequencing (RNA-Seq) data generated on the HiSeq
272500 platform were processed as described before (24), RNA-Seq data generated on the HiSeq
284000 platform were aligned using STAR 2.5.1b (29). Relative RNA expression of 467 predefined
29cancer relevant genes, compared to median RPKM (Reads Per Kilobase Million) in a cohort of
30149 diverse cancer patients, is reported. Overexpressed genes with RPKM fold change > 10 and
31Z-score > 1 and underexpressed genes with RPKM fold change < 0.25 and Z-score <-1 were
32evaluated. Sequencing data were deposited in the European Genome-phenome Archive under

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accession EGAS00001003405.

35CRISPR/Cas9 mediated gene editing

1To generate GIST-T1/V654A, GIST-T1/D816A, and GIST-T1/G12R suitable guide sequences
2targeting KIT exon 13 and 17, and KRAS exon 2, respectively, were identified using the online
3tool CRISPOR (30) (www.crispor.org). For both KIT exons two adjacent guides were selected. A
4forward oligo containing the t7 RNA polymerase promoter sequence and the respective guide
5sequence, as well as a reverse oligo containing the generic single guide RNA sequence were
6purchased at MWG Eurofins. For guide RNA design we employed “FE-modified” sequences
7described by Chen et al. (31). The t7 RNA polymerase DNA template was generated by a fill-in
8PCR using Q5 high fidelity DNA polymerase (NEB), running 10 cycles of 62°C / 20sec and 72°C
9/ 2min. sgRNA was then transcribed with t7 RNA polymerase (NEB), according to
10manufacturer’s instructions and precipitated by phenol/chlorophorm extraction, using standard
11protocols.
12GIST-T1 cells were seeded in a T25 flask at low density and after 72h of growth cells were
13trypsinized, washed and resuspended in electroporation buffer (Buffer SF, Lonza). 105 cells
14were mixed with 0.5-0.7µl Recombinant Cas9 (20µM; NEB), 0.5-0.7µl in vitro transcribed sgRNA
15(20µM) per guide, and 0.5-0.7µl single stranded DNA template (ssODN (MWG Eurofins);
16500µM), carrying the desired mutation to be introduced via the homology directed repair (HDR)
17pathway. Cells were electroporated using the program DN100 on the Amaxa Nucleofector 4D
18(Lonza). On the next day the bulk cells were selected with IM 100nM until outgrowth of a
19resistant population was achieved. Sanger sequencing confirmed heterozygous mutations KIT
20V654A and D816A, and KRAS G12R, as well as silent mutations induced by the HDR templates.
21In the attempt to generate GIST-T1/G12R we furthermore generated a subline harboring a
22heterozygous 23bp deletion in KRAS (GIST-T1-KRAS) starting in exon 2, codon 9, causing a
23frameshift and premature stop codon within the exon. Interestingly, these cells showed strong
24activation of RAS downstream signaling (see results). For a detailed list of sequences see

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Supplemental Material.

27CRISPR/Cas9 mediated gene knock out
28For the generation of T1-PTEN and T1-TSC2, suitable guide sequences against PTEN, TSC2,
29with sticky-end overhangs were ordered from MWG Eurofins. Oligos were annealed, and cloned
30into the “lentiCRISPR v2” vector (Addgene Plasmid #52961), according to the published protocol
31(13). For PTEN knockout the resulting plasmid was transfected by nucleofection as described
32above. 24h after transfection cells were selected with puromycin 2µM for 96h. For TSC2
33knockout the plasmid was transduced after lentiviral Packaging, according to standard protocols.
345d after transfection cells were selected with puromycin 2µM for 96h. Cells were then further
35selected with IM 100nM until outgrowth of a resistant population. For NF1 knockout cell (T1-

1NF1), lentiviral constructs encoding SpCas9 (pXPR_BRD001, Broad Institute) and an anti-NF1
2guide RNA (pXPR_BRD003) were transduced by two consecutive lentiviral infections, followed
3by one week of 2µM of puromycin selection, and further selection with IM 100nM until outgrowth
4of a resistant population. Knock out was confirmed by western blot and next generation

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sequencing. For a detailed list sequences see Supplemental Material.

7Cell lines
8Apart from the cell lines described above, further IM-sensitive (GIST-T1, GIST882, GIST430)
9and IM-resistant (GIST430/654, GIST-T1-D816E, GIST48B) cell lines were studied. GIST-T1
10and GIST882 were established from human, untreated, metastatic GISTs and carry primary
11activating mutations in exons 11(V560_Y578del) and 13 (K642E), respectively. GIST430/654
12was established from a GIST that had progressed, after initial clinical response during IM
13therapy and harbors a primary activating mutation in exon 11 (51bp del V560-Y578) and
14secondary resistance mutation in exon 13 (V654A). GIST430 is an IM-sensitive subline, with
15only the primary KIT exon 11 (51bp del V560-Y578) mutation but no secondary resistance
16mutation, derived from the same GIST culture as the GIST430/654 line. GIST48B, despite
17retaining the activating KIT mutation in all cells, expresses KIT transcript and protein at
18essentially undetectable levels. GIST-T1 was established by Takahiro Taguchi (Kochi University,
19Kochi, Japan). GIST882 were cultured in RPMI1640 containing 15% FBS and 1% Pen/Strep
20(Gibco). All other cell lines were cultured in IMDM containing 10% FBS and 1% Pen/Strep. Cell
21lines are regularly authenticated by sequencing of endogenous mutations in KIT, confirmation of
22KIT-expression, and response to KIT inhibitor treatment. In the course of this study all cell lines
23were regularly tested for mycoplasma contamination by PCR and by MycoAlert Mycoplasma

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Detection Kit (Lonza).

26Reagents and Antibodies
27Sapanisertib, imatinib, sunitinib, ponatinib, trametinib and everolimus (RAD001) were purchased
28form Selleck chemicals. A primary polyclonal rabbit antibody against KIT was purchased from
29Dako. A monoclonal mouse antibody against beta-actin was purchased from Sigma. All other
30primary and secondary antibodies used in this study were purchased from Cell Signaling

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Technologies.

33Western Blot
34Cells were plated in six-well plates and on the next day treated with different inhibitors or vehicle
35control. After 24h of treatment lysis buffer (1% NP-40, 50mM Tris-HCl pH 8.0, 100mM sodium

1fluoride, 30mM sodium pyrophosphate, 2mM sodium molybdate, 5mM EDTA and 2mM sodium
2vanadate; freshly adding 0.1% 10mg/mL aprotinin and leupeptin as well as 1% 100mM PMSF
3and 200mM sodium vanadate) was added, and cells were scraped off and then lysed while
4rotating for 1h at 4°C. Lysates were centrifuged at 4°C for 30min at 18,000rcf and protein
5concentration was determined using the Bio-Rad Protein Assay (Bio-Rad Laboratories). Protein
6concentration was adjusted to 2µg/µl (if not otherwise specified), SDS-loading buffer (0.5M Tris-
7HCl pH 6.7, 10% SDS, 2.5% DTT, 50% Glycerol, and 0.05% bromophenol blue) was added and
8lysates were incubated for 5min at 95°C. Equal amounts of Protein (30µg per lane, if not
9otherwise specified) were separated on SDS-PAGE Gels (NuPAGE 4-12%, Life Technologies)
10and blotted onto nitrocellulose-membranes (GE Healthcare/Amersham-Biosciences). After
11blocking with Net-G buffer (1.5M NaCl, 50mM EDTA, 500mM Tris, 0.5% Tween 20 and 0.4%
12gelatine) membranes were incubated at 4°C overnight with the respective primary antibody.
13After washing (Net-G), membranes were incubated for 2h at room temperature with a secondary
14antibody (in Net-G) and washed again. Changes in protein expression and phosphorylation as
15visualized by chemiluminescence were captured and quantified using a FUJI LAS3000 system
16with Science Lab 2001 ImageGauge 4.0 software (Fujifilm Medial Systems). Usually 2–4
17gels/membranes were prepared from the same experiment/lysates, to enable clean stains of
18proteins with similar or nearby molecular weight as well as stains of total proteins and their
19phosphorylated counterparts. Membranes were consecutively stained with different antibodies of
20different molecular weights. Beta-Actin served as loading control for each membrane and a

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representative stain is shown.

23Sulforhodamin B assay
24Cell viability was evaluated by Sulforhodamin B (SRB; Sigma-Aldrich) assay after 72h of
25treatment, as previously described (32). Cells were treated with increasing concentrations of
26DMSO-dissolved compounds, sapanisertib, trametinib, imatinib, sunitinib, regorafenib, ponatinib.
27Mean values were normalized to DMSO-solvent control and the mean standard error was
28calculated. All experiments were carried out in triplicate/quadruplicate cultures at least twice and

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a representative example is shown.

31Dose-combination studies
32For dose-finding experiments 1000 cells/well were seeded in white 384-well plates (Greiner)
33using the Multidrop (Thermo Scientific) and allowed to attach overnight. Respective compounds
34were added in duplicates or triplicates using the digital dispenser Tecan D300e, and normalized
35to identical solvent volumes. After 72h Cell Titer Glo (Promega) reagent was added according to

1manufacturer’s instructions. Luminescence was measured on the Tecan Spark M10. The
2combinatorial index (CI) was calculated according to the method by Chou-Talalay (33), using
3CalcuSyn Software (BioSoft). For confirmation the most effective combinations were then
4evaluated in triplicates in 96-well plates using the SRB assay.

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Results

3GIST patients display heterogeneity of resistance to KIT-inhibition
4First we reviewed the in-house sequencing data bases for mutations in GIST patients found
5during routine clinical testing in our centers. We thus compiled a cohort of 80 patients with
6secondary resistance mutations in KIT, 11 of which displayed more than one such mutation in
7the particular examined biopsies (Figure 1). Most of these were point mutations in exon 13
8(V654A) or exon 17 (involving codons 820, 822, or 823). However, we also found less common
9mutations in amino acids D677, C809, and S840.
10Next, we queried our next generation sequencing patient data (n=109) for mutations in KIT
11downstream effectors, such as NF1, N/H/KRAS, BRAF, PTEN, PIK3CA, AKT, and TSC1/2. We
12found that a subset of patients (18%; 20/109) displayed such downstream mutations, potentially
13causing resistance to KIT/PDGFRA-inhibitors (Tables 1 and 2). Notably, 10% (3/31) of patients
14with primary PDGFRA mutations displayed mutations in downstream signaling intermediates,
15indicating a similar incidence of these events as in KIT mutated GIST. Except for the BRAF-
16mutated cases, all patients with downstream effector mutations also displayed activating
17mutations in KIT or PDGFRA, most of which were accompanied by secondary resistance
18mutations (13/18; Tables 1 and 2).
19Thirteen mostly heavily pretreated (median treatment lines = 4) patients with TKI-resistant GIST
20were recruited into the DKTK MASTER molecular stratification program (19) in which tumors are
21analyzed by whole exome/genome and RNA sequencing to identify clinically actionable
22aberrations. In these datasets, we identified primarily alterations in genes that are involved in the
23PI3K or MAPK signaling pathways (Table 2). Notably, no driver mutations apart from KIT and its
24downstream pathways were detected (Supplemental Figure S1). Furthermore, RNA-Seq data
25revealed mostly lineage-specific markers among the highest ranking transcripts (KIT, PDGFRA,

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ETV1; Table 2).

28Novel cell line panel approximates genetic heterogeneity of GIST patients
29To expand our panel of models recapitulating the heterogeneity of resistance in GIST we applied
30CRISPR/Cas9 mediated gene editing and knockout. We thus induced heterozygous point
31mutations of KIT in exon 13 (V654A) and exon 17 (D816A), respectively, in addition to the
32endogenous primary exon 11 mutation in GIST-T1. As expected, these cells displayed a much
33higher resistance to IM as well as an inhibitory profile towards the approved second- and third
34line KIT-inhibitors sunitinib (SU) and regorafenib (RE), matching their respective secondary
35mutations (Table 3).

1To investigate the effect of oncogenic mutations in KIT-downstream signaling intermediates on
2KIT-inhibitor sensitivity, we generated further GIST-T1 sublines: T1/G12R-HOM/HET harboring
3homozygous and heterozygous G12R mutations in KRAS, respectively, as well as T1-KRAS
4with a heterozygous KRAS deletion (see methods; Figure 2). The cell lines T1-PTEN, T1-TSC2,
5and T1-NF1 carry homozygous knockout of PTEN, TSC2, and NF1, respectively. While TSC2
6and NF1 deficient cells were completely resistant to KIT inhibition, cells with loss of PTEN
7displayed some sensitivity to IM treatment, which still efficiently inhibits KIT-dependent MEK
8signaling (Table 3). However, these cells would still continue to grow, albeit slower, at high IM
9concentrations (10µM; Suppl. Figure S2). Interestingly the heterozygous mutation of KRASG12R
10did not cause a notable increase in tolerance to KIT inhibition, while cells carrying homozygous
11KRASG12R were, similar to T1-KRAS, partly resistant (Suppl. Figure S3). As depicted in Table 3,
12cell lines harboring mutations in KIT or its downstream signaling intermediates are highly

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resistant to all currently approved GIST treatments.

15Sapanisertib has antiproliferative effects in IM-sensitive and IM-resistant GIST cell lines
16We then sought to evaluate the therapeutic potential of KIT-downstream inhibition using the
17mTOR kinase inhibitor sapanisertib. In cell viability assays after 3 days of treatment, sapanisertib
18displayed IC50 values between 20nM (GIST430/654) and 70nM (T1-G12R) (Figure 3A).
19Sensitivity towards sapanisertib was independent of secondary mutations, sensitivity to IM, and
20KIT expression (GIST48B). Strikingly, cell lines harboring KIT-independent resistance mutations

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in KRAS, PTEN, TSC2 and NF1 were similarly sensitive to sapanisertib treatment.

23Sapanisertib efficiently abrogates mTORC1/2 signaling
24To elucidate the effects of sapanisertib on intracellular signaling we conducted western blot
25experiments in GIST cell lines of different origins. We observed a dose-dependent inhibition of
26ribosomal protein S6 phosphorylation (pS6; as marker for mTOR activation) starting at 1 – 5nM
27with complete inhibition at 50-100nM in all cell lines (Figure 2b). Notably, in this concentration
28range sapanisertib mediated inhibition of mTORC1 led to a strong inhibition of AKT
29phosphorylation (pAKT), followed by loss of 4E-BP1 phosphorylation (p4E-BP1). In contrast,
3020nM everolimus (RAD001), while also potently inhibiting pS6, did not inhibit pAKT or p4e-BP1,
31but instead increased their phosphorylation levels. Interestingly, sapanisertib treatment,
32especially at higher concentrations, led to a dose dependent increase of ERK1/2

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phosphorylation (pERK; Figure 2b).

1Co-treatment inhibits feedback induction of MEK/ERK-signaling

2mTOR inhibition alone appeared to leave the cells with an escape route via MEK/ERK driven
3survival, as indicated by induction of pERK1/2 (Figure 2b). Therefore, we combined sapanisertib
4either with KIT-inhibition or with the clinical MEK inhibitor trametinib (Figure 3). Combinations
5with KIT-inhibitors displayed the strongest effects in cell lines with KIT-downstream mutations,
6which was to be expected as GIST-T1-PTEN and KRAS-mutated sublines displayed residual
7sensitivity to IM alone (Table 3, Suppl. figures S2 and S3). Strikingly, in GIST-T1-TSC2,
8combination of sapanisertib and IM completely abrogated S6 and 4E-BP1 phosphorylation, even
9at sapanisertib doses as low as 10nM, whereas IM alone did not inhibit S6 and 4E-BP1
10phosphorylation. These cells displayed reduced baseline KIT-expression which increased after
11IM-treatment (Figures 2 and 3). Combinations of sapanisertib with trametinib completely

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abrogated both KIT-downstream signaling axes in all investigated cell lines.

14Combinational treatment with KIT- or MEK-inhibitors displays synergistic effects
15To further elucidate the potential of combinational treatment, we next conducted multi-dose
16combination proliferation experiments with sapanisertib, combined with either KIT-inhibition or
17trametinib. Cells were treated with 5-100nM of each inhibitor and with each possible combination
18of two drugs. We then calculated the combinatorial Index (CI) for each combination according to
19the Chou-Talalay method, which describes synergy at CI < 1, additivity at CI = 1, and
20antagonism at CI > 1. We found that concentrations of sapanisertib between 25nM – 100nM
21yielded the strongest combinatorial effects in all cell lines (Table 4). In most cell lines
22combinations with IM yielded at best moderate additive effects (CI: 0.5 – 0.58) at low
23concentrations of IM (50-200nM). However, in TSC2 deficient cells the combination displayed a
24strong synergy signified by the lowest CI-value of 0.26 (Table 4). In the sublines harboring
25secondary KIT mutations (V654A and D816A) cells were treated with sunitinib and ponatinib,
26respectively. These combinations also had moderate synergistic effects.
27Combinations of sapanisertib and trametinib showed strong synergistic effects in all examined
28cell lines (lowest CI values: 0.126 – 0.37; Table 3; Suppl. Figure S3). Notably, the lowest CI-
29value overall of 0.126 was obtained in TSC2 deficient cells when sapanisertib 25nM was
30combined with trametinib 200nM. However, even at lower concentrations of trametinib 25nM and
3150nM, the combination with sapanisertib 25nM had strong synergistic effects (CI-values 0.33
32and 0.19, respectively; Suppl. Figure S3). All calculated CI-values, as well as the results of the

33
34
35
cytotoxicity experiments they were generated from are depicted in Supplemental Figure S3.

1Discussion

2Activating mutations of KIT or PDGFRA are the oncogenic hallmarks of GIST that lead to ligand-
3independent downstream activation of the PI3K and RAS/RAF/MAPK signaling cascades (2,34).
4Inhibition of KIT by imatinib abrogates KIT phosphorylation and consequently signaling through
5these pathways. While most GISTs exhibit long-lasting responses to KIT-inhibitor therapy the
6majority of patients eventually progress. Secondary mutations in KIT have been identified as the
7main mechanism of resistance in resection specimens of patients failing imatinib (35). This is
8accompanied by re-activation of PI3K and RAS/RAF/MAPK signaling. Notably, these two
9pathways seem to be required for GIST homeostasis and proliferation regardless of the
10presence of KIT or PDGFRA mutations – thus defining crucial tissue-specific pathways.
11Therefore, in a broader sense, GIST could be defined not by KIT activation itself, but rather by
12conjoined activation of its downstream signaling intermediates (11).
13Therapeutic success in GIST is therefore not defined only by successful inhibition of KIT but, in
14extension, by abrogation of KIT downstream signaling. Sustained therapeutic KIT inhibition is
15confounded by the heterogeneity of secondary KIT IM-resistance mutations, while activity of
16therapies targeting KIT downstream signaling might depend on the types of mutations activating
17these downstream pathways. Very recently, genomic activation of KIT-downstream pathways
18has even been observed in treatment-naïve GIST, further underscoring the clinical relevance of
19KIT-independent mutations within these pathways (12,36).
20In an international collaborative effort we interrogated the pathology databases of several GIST
21centers, and thus compiled a representative spectrum and incidence of secondary KIT mutations
22in a large cohort of TKI-refractory GIST. Interestingly, we found a lower incidence of exon 14
23mutations compared to reports from the early 2000s (7,35). This may be reflective of the
24availability of additional KIT inhibitors in recent years which effectively inhibit the exon 14
25gatekeeper mutations. Also, there could be technical bias, as KIT exon 14 was not sequenced in
26the early years of routine pathological diagnosis in all centers, these patients may have been
27falsely classified as not carrying a secondary KIT mutation. Our data indicate the need for more
28potent KIT-inhibitors, with activity against the full spectrum of resistance mutations. Of note,
29latest generation KIT-inhibitors show broader and more specific inhibitory profiles (37,38)
30although no drug has yet been shown to be equally and universally potent against all KIT
31mutations.
32Recently, next generation panel sequencing was introduced into routine pathological analysis in
33many centers, covering genes whose activation or loss could confer resistance apart from
34secondary KIT mutations. We were thus able show the incidence of non-KIT mutations in
35random samples of TKI-resistant GIST specimens sent for genotyping. In a fraction larger than

1expected, these comprise KIT-downstream signaling intermediates in the PI3K/AKT/mTOR and
2RAS/RAF/MAPK pathways. Of note, most mutations of tumor suppressor genes appeared
3heterozygous in this cohort, which might result from homozygous mutations present in only a
4subset of the neoplastic cells, which would not be surprising given that these were generally
5secondary KIT/PDGFRA-inhibitor resistance mutations. Another possibility is that inactivating
6mutations in the remaining tumor suppressor allele were often present but undetected, which
7can occur in the instance of large indels or promoter-region mutations. We also cannot exclude
8the possibility that some inactivations were truly heterozygous, and that haploinsufficiency in this
9context is biologically meaningful.
10Although, this “tertiary resistance” has rarely been reported yet our results underscore the
11importance and functional relevance of mutations in these pathways (11,12). We speculate that
12these mutations are frequently not tested for or even not added to routine pathology reports – ,
13despite the availiability of the data, e.g. when only KIT sequencing is requested. Given the
14advent of broader KIT inhibitors, we expect that these novel mechanisms represent a clinically
15relevant cause of treatment resistance, as GIST cells are crucially depending on the PI3K and
16RAS/RAF-MAPK pathways. Future studies, employing plasma sequencing with sequencing
17panels optimized for GIST will most likely reveal these mutations to be more common than
18previously expected.
19To our surprise, comprehensive DNA and RNA sequencing in a subset of patients revealed no
20other apparent driver mutations that may have replaced KIT signaling as the dominant
21oncogenic pathway. This finding underscores the requirement for concomitant activation of PI3K
22and MAPK signaling GIST cell homeostasis. Inhibiting KIT downstream signaling may therefore
23prove to be a necessary, effective, and actionable strategy.
24To help validate our hypotheses, we expanded our panel of GIST cell lines, to model
25mechanisms of the complex heterogeneity observed in the clinic. Employing CRISPR/Cas9
26methods we induced specific point mutations in KIT, and KRAS and thus show for the first time
27that precise genomic editing in GIST cell lines is possible and is a valuable tool to generate
28clinically relevant models. Up to now, KIT-inhibitor studies for GIST were often conducted in
29Ba/F3 cells, lacking the GIST specific cellular background (39). In fact, perturbations of GIST-
30specific KIT-downstream signaling are probably not optimally modelled in those systems. To
31date, virtually no in vitro studies have been conducted in GIST harboring mutations in signaling
32intermediates downstream of KIT (40).
33Furthermore, our newly generated cell lines will not only enable improved inhibitor research but
34may also yield relevant insights into GIST biology. Of note, we observed that KIT expression
35decreased in GIST-T1 sublines with mutations in KRAS, NF1, and TSC2, but not PTEN.

1Inhibition of mTOR upon sapanisertib treatment subsequently increased the levels of total KIT
2(Figures 2, 3). Especially in cells lacking TSC2, this reactivation also reinstated KIT-dependence
3and thus sensitivity to IM treatment (Figure 3, Table 4). These findings indicate that KIT-
4independent mutations may supplant the role of KIT and may impact KIT expression levels. Loss
5of KIT has been occasionally observed in clinical specimens and also in GIST cell lines grown in
6vitro (41). In this context our modified sublines may serve as informative models to better
7understand the feedback-regulation of KIT by its main intracellular signaling pathways. We were
8furthermore surprised to find that, at least in our GIST-T1 derived cell line model, the
9heterozygous mutation of KRASG12R was not able to confer KIT-inhibitor resistance. We assume
10that GIST-T1 is particularly dependent on the RAS/RAF/MEK signal so that the activation of a
11single allele does not compensate for the complete block of the upstream KIT-dependent signal.
12In other cancers with KRAS-mediated mechanisms of TKI-resistance very little data is published
13on the zygosity of secondary mutations. Notably, Serrano et al. recently reported a KRASG12R
14mutated resistant GIST clone bearing a hemizygous mutation (12).
15Targeting mTOR has been a strategy in clinical trials based on the observation that PI3K-
16activation is a signaling hallmark in GIST regardless of the presence of secondary resistance
17mutations (42). However, clinical success may have been hampered by pharmacological
18interactions as well as the selection of a refractory treatment setting, in which imatinib was
19unlikely to inhibit clones with secondary resistance mutations (43). For imatinib-resistant clones,
20mTOR inhibition (everolimus) alone is most likely not sufficient to fully control tumor growth and
21it may require a combination with a broader KIT-inhibitor or with inhibitors of MEK. Based on
22preclinical findings in GIST (44), imatinib in combination with MEK is currently being tested in the
23clinic (NCT01991379). Another approach based on the in vivo studies by van Looy et. al (45)
24recently looked at the combination of imatinib and the PI3K-inhibitor Alpelisib (BYL719).
25Unfortunately, the results of this study are not yet available.
26In contrast to everolimus, sapanisertib inhibits not only mTORC1, but also mTORC2 (46). This
27distinct inhibitory profile, causing a strong decrease of AKT- as well as 4E-BP1-phosphorylation,
28may yield superior clinical efficacy. Notably, compared to other rapalogs, which also inhibit
29mTORC2, sapanisertib has been shown to only cause grade 1 and 2 hyperglycemia and only in
30a subset of patients (17,47). We now report that sapanisertib has strong anti-proliferative effects
31in IM-sensitive and IM-resistant cell lines, including KIT-negative GIST. As Slotkin et al. have
32shown sapanisertib has antitumor effects in a panel of bone and soft tissue sarcoma cell lines
33and xenograft models (48). In their study, cell lines displaying in vitro IC50 values similar to the
34ones described herein were also inhibited in vivo at a dose of 1mg/kg/day. Furthermore,
35effective concentrations for this drug against the IM-resistant GIST models are well within the

1range of clinically achievable plasma levels (17,18). Currently sapanisertib is investigated in
2several clinical phase 2 trials, in entities including lung cancer, acute lymphoblastic leukemia and
3soft tissue sarcomas (clinicaltrials.gov).
4However, RAS/RAF/MAPK signaling, unperturbed by sapanisertib, is similarly important for
5GIST cell proliferation and survival (42). Combinations of sapanisertib with approved KIT
6inhibitors display moderate synergistic effects and may represent a feasible clinical strategy,
7which warrants further investigation. To date, MEK inhibitors show clinical toxicity profiles
8requiring careful management in combination therapies (49,50). We found strong synergistic
9effects when combining sapanisertib with trametinib, which was to be expected as this
10combination inhibits the two major routes of GIST proliferation and survival. These strong
11combinational effects might allow for dose reduction of one or both drugs which may reduce side
12effects and thus become more attractive to patients, especially to those with non-KIT resistance
13mutations. Although it is possible that the particular prototypic compounds or their combinations
14selected for our in vitro studies may display in vivo toxicity, we are convinced that inhibition of
15the two major oncogenic signaling axes in GIST will prove to be a clinically feasible treatment
16option. We speculate that in a disease exceptionally dependent on these pathways, such as
17GIST, the therapeutic window of such drug combinations may be even more favorable than in
18other cancers.
19In summary, our data strongly underscore the need for comprehensive sequencing of KIT as
20well as of KIT-related signaling molecules that may contribute to KIT-inhibitor resistance in GIST.
21With the advent of more potent KIT-inhibitory molecules we hypothesize that mutations of genes
22coding for KIT-downstream signaling intermediates will become more prevalent. Future
23treatment strategies, both in untreated and pretreated GIST may benefit from integrating potent
24inhibitors of these pathways. The novel cell lines presented herein may provide meaningful

25
26
27
28
29
30
models for the validation of such new drug combinations.

31Acknowledgments
32The authors sincerely thank Miriam Christoff for the expert technical assistance.

1
2

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1
2

Tables

3
4
Table 1

Patient Gene AA-change AF [%] KIT/PDGFRA mutation
1AKT3 F27I 13 PDGFRA – D842V + V658A
2BRAF V600E 60 WT
3BRAF V600E 32 WT
4KRAS G13D 36 KIT – e11 + N822K
5PIK3CA H1065Y 33 KIT – e11 + N822K
6PIK3CA H1047R 21 KIT – e11
7PIK3CA H1047R 81 KIT – e11
8* NF1 M1981V 5 KIT – e11 + Y823E
9* NF1 I719fs 32 KIT – e11 + D820Y + A829P
10PTEN I122S 78 KIT – e11
11TSC1 E479del 46 PDGFRA – D842V
12TSC2 A1719T 58 PDGFRA – D842V
5Table 1. Non-KIT mutations, with potential for causing KIT-inhibitor resistance. Next generation
6sequencing data (Illumina panel, Ion Torrent panel, WES, WGS) was analysed for mutations in KIT-
7downstream signaling intermediates. Total NGS pts. n=109. LOH = loss of heterozygosity; AA = amino

8
9
10
acid; AF = allelic frequency; * = patient also appears in Table 2.

Table 2

RNA-expression
(HIGH: foldchange > 10 + ZScore > 1; LOW: foldchange < 0.25 + ZScore <-1)

1
IM; SU; RE; Pazo
11 A829P
-
- LOH
LOH (TSC2)
HIGH: KIT; PTCH1; FOXL2; EPHA3; ETV1; FGFR1; LMO2; JAK3; AFF3; SOCS1; PDGFRA LOW: SDC4

2*
IM; RE
11 Y823E M1981V LOH -
-
HIGH: FOXL2; KIT; TLX1; NTRK2; EPHA3; USP6; PTCH1; GPC3; MYB; LMO2; WNK2; DDR2; ETV1 LOW: SMAD4; SDC4; DDR1

3 IM; SU
11 A829P
-
LOH -
-
HIGH: KIT; NTRK2; GPC3; NRG3; CARD11; ETV1; AFF3; MYB; EPHA3; BCL2; DDR2; PBX1; ZBTB16 LOW: PLK2; PTPRJ; NRAS; RAD54B; RB1; SDC4; DDR1; RUNX1

IM; SU;
4
IM+BYL719; RE; Pona; SU+Sirolimus ; Pazo; Cabozantinib

11
V654A
T670I
D677A

LOH -

HIGH: KIT; RSPO2; ETV1; EPHA3; PTCH1; AFF3; LMO2; DDR2; SOCS1; FGFR1; CARD11 LOW: STL; RUNX1; DDR1; SDC4
5
IM; SU; Pazo; RE; Pona; Nilo

11 D816E N822K

LOH

LOH LOH

LOH (TSC1)
HIGH: RSPO2; FOXL2; HOXA11; ETV1; IL2
LOW: EWSR1; EP300; MAF; SUFU; BRD4; ELF4; MAP2K1; TPM4; MSN; ERC1; TRAF7; GAK; STIL; TOP1; CIC; EXT1; SET; TPM3; CUX1; PTPRK; ATP1A1; CLIP1; SMARCB1; KIF5B; PPFIBP1; RUNX1; SMARCA4; PTPRJ; NF2; SRC; MKL1; THRAP3; NOTCH2; WHSC1; DCTN1; IDH2; NOTCH1; TAF15; BCR; LASP1; SDC4; HIP1; DDR1; MYH9

6
IM; SU; RE; Pazo; SU+Sirolimus
11 V654A D820E
-
-
-
-
HIGH: CD79B; FEV; IDO2; HIST1H3B
LOW: MLLT6; BCL9; FANCE; NT5C2; KRAS; TPM3; SF3B1; TCEA1; RIT1; MAF; RAB35; CCDC6; ARID1A; CDC73; XPO1; NUMA1; GAK; TCF12; KDM6A; MED12; SET; CBL; CBLB; CRTC1; CDKN1B; RB1; USP11; MYH9; MLLT1; NIN; CHEK1; KTN1; RASA1; BCL10; MRE11A; SMARCA4; FBXO11; ATRX; RAD21; PMS1; TOP1; KIF5B; TPM4; SUZ12; ELL; CIC; STIL; RNF2; SFPQ; KDM5C; GOPC; STAG2; FIP1L1; PHF6; SRC; ATF1; TCF3; DEK; PTPN12; HDAC2; CBFB; RALGDS; MSH2; FUBP1; DDR1; PLK1; AURKA; RAD51AP1; MLF1; CDK1; PSIP1; NOTCH1; ELF4; RUNX1; HIST1H4I

7*
IM; SU
9
D820Y
A829P
I719fs LOH -
LOH (TSC1)
HIGH: KIT; FOXL2; EPHA3; ETV1; CARD11; MYB; AFF3; SOCS1; ZNF521; LMO2; JAK3 LOW: HIP1; PPFIBP1; PTPRK; STIL; PTPRF; SDC4; STL; DDR1

IM; SU; RE;

8
Pona; Nilo; SU+Sirolimus ; Pazo
11 R634L D820G
-
-
-
LOH (TSC1)
HIGH: KIT; HOXA11; MYCN; ETV1; AFF3; LMO2; FGF2; FGFR1
LOW: HIP1; ELF4; NOTCH1; PTPRJ; PLK2; RUNX1; MAF; SDC4; FOXO1

9
IM; Masitinib; SU; IM; RE
11 Y823C
-
LOH -
-
HIGH: KIT; HOXA11; HOXA9; NRG3; ETV1; HLF; NRG2; AFF3; FGF1; WNK2; LMO2; DDR2; CDH11 LOW: SDC4; RUNX1

IM; IM (800);

10
SU; Nilo; RE; Pazo; SU+Sirolimus
9 D816E
-
LOH -
-
HIGH: RSPO2; ALK; KIT; HOXA13; FOXL2; NRG2; HOXA11; EPHA3; SOCS1; ETV1; HOXA9; AFF3; ZNF521 LOW: HIP1; RUNX1; SDC4

11IM 18 – - – - n.a.
12IM; SU 11 D820Y – LOH – - n.a.

13
IM; SU; RE; IM
9
-
-
-
-
HIGH: KIT; CD79B; EPHA3; ETV1; LMO2; ERG; HOXA11; SOCS1; JAK3; NRG4; BRIP1; MLLT11; BCL2 LOW: STL; RUNX1; SDC4; DDR1

Table 2. Genetic and transcriptional abberations identified by whole exome/genome nad RNA sequencing. 13 patients were analyzed (WES/WGS) for mutations in KIT-downstream signaling intermediates. 11/13 patients were additionally analyzed by RNA-Seq and the transcripts with highest and lowest abundance are shown,

ordered from highest to lowest foldchange compare to control. LOH = loss of heterozygosity; IM = imatinib; SU = sunitinib; RE = regorafenib; PO = ponatinib; Pazo = pazopanib; Nilo = nilotinib; n.a. = not available; * = patient also appears in Table 1.

1Table 3

cell line IC50[nM]
IM SU RE Sap
GIST-T1 35 9 30 35
T1-G12R-HOM n.r. n.r. 1000 70
T1-G12R-HET 30 25 90 50
T1-KRAS n.r. 1500 450 45
T1-NF1 n.r. 6000 1100 50
T1-PTEN 150 25 150 50
T1-TSC2 n.r. n.r. 900 35
T1-V654A 850 30 300 25
T1-D816A 700 5000 150 50
T1-D816E 850 3000 200 45
GIST430 25 5 15 35
GIST430/654 250 25 50 20
GIST882 300 75 350 40
GIST48B n.r. 6000 10000 50
2Table 3. Antiproliferative effects of sapanisertib compared to current KIT-inhibitors.
3IC50 values from cell viability assays (SRB) after 72h of treatment with increasing doses of imatinib (IM,

4
5

18
sunitinib (SU), regorafenib (RE) and sapanisertib (Sap).

1
2

3
Table 4

cell line Sap + IM Sap + Tram
lowest CI Sap
[nM] IM
[nM] Fa lowest
CI Sap
[nM] Tram
[nM] Fa
GIST-T1 0.52 25 50 0.72 0.19 25 25 0.65
T1/G12R-HOM 0.56 25 50 0.44 0.27 50 50 0.66
T1-KRAS 0.5 50 100 0.55 0.37 50 50 0.62
T1-PTEN 0.5 50 200 0.74 0.27 25 50 0.67
T1-NF1 0.58 50 100 0.54 0.13 25 100 0.69
T1-TSC2 0.26 50 200 0.77 0.13 25 200 0.79
T1/V654A 0.45 25 50* 0.71 0.19 50 25 0.71
T1/D816A 0.37 50 25* 0.75 0.21 25 50 0.66
4Table 4. Combinatorial indexes obtained from combination studies. In combination studies,
5increasing doses of IM or Tram (12.5nM – 200nM) were combined with 25, 50, or 100nM or sapanisertib.
6After 3 days cell survival was measured by SRB and the Fraction affected (Fa) and combinatorial index
7(CI) were calculated. For each cell line the best combination, as signified by the lowest CI value is

8
9
10

19
displayed. * = For T1-V654A and T1-D816A IM was substituted with SU and ponatinib, respectively.

1Figure legends

2Figure 1: Resistance mutations in KIT found in GIST patients. Distribution of secondary KIT mutations
3in KIT exons 13, 14, 17, and 18 (mixed sanger and NGS; n=80). black = point mutation; red = patients
4(n=11) presented >1 resistance mutation in the same or in subsequent biopsies; , blue = frame shift
5mutation with STOP at c.813; green = in-frame InDel (823_G827delIns5); letters indicate the respective

6
7
amino acid change.
8Figure 2. Western blot analyses of the novel isogenic GIST-T1 sublines panel and other GIST cell
9lines. A. Effects of 24h IM 100nM treatment of KIT and KIT-dependent signaling in the novel GIST-T1
10subline panel compared to parental GIST-T1. For direct comparison between T1/G12R-HOM/HET, and
11T1-KRAS, T1-KRAS appears twice in this panel (first and last column) and cell lysates were prepared from
12two independent experiments. B. Sapanisertib dose-response studies in IM-sensitive and IM-resistant

13
14
GIST cell lines after 24h of treatment in comparison to RAD001 (everolimus) and IM.

15Figure 3. Western Blot analyses of sapanisertib (SAap) in combination with trametinib and KIT-
16inhibitors. In IM-sensitive GIST-T1 and IM-resistant GIST-T1 sublines, sapanisertib was combined with
17either MEK-inhibition (trametinib) or KIT-inhibition (imatinib, ponatinib) for 24h and effects on KIT-related

18
19

20
signaling were examined.

Author Manuscript Published OnlineFirst on July 15, 2019; DOI: 10.1158/1535-7163.MCT-18-1224 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited.

Figure 1

I 3 K 9 K 5 K 6

Y 11 Y 2
Y
Y

D4
10
E
E
10 Y
6 K
D7 D

8
7
2
1
G 1 E
E
G
G
G
V
V
K
K
K
K
K
K
K
K
D
D
D
D
D
D
D
D

G 4 Y K D
E9 Y K D 11

I 8 E

A 8

G 3
E5 E
E
H
Y
Y
Y
Y
K
K
K
K
D
E
C
N

N
exon
V654A
13
T670 D677
14
C809 D816 D820 N822 Y823
17
A829P S840
18

A
T1-
GIST-T1 PTEN KRAS TSC2
T1-
GIST-T1 V654A D816A NF1
T1/G12R
GIST-T1 HOM HET

T1-
KRAS

IM 100nM – + – + – + – + – + – + – + – + – + – + – + – +

pKIT
Y703

KIT

pAKT
S473

AKT

pMAPK
T202/Y204

MAPK

pS6
S235/236

p4E-BP1
S65

4E-BP1
Actin

Author Manuscript Published OnlineFirst on July 15, 2019; DOI: 10.1158/1535-7163.MCT-18-1224 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited.

Sapanisertib Sapanisertib Sapanisertib

pKIT
Y703
Sapanisertib

KIT

pAKT
S473
AKT

pMAPK
T202/Y204
MAPK

pS6
S235/236
S6

p4E-BP1
S65
4E-BP1

Actin

GIST-T1 GIST882 GIST430/654 GIST48B

Figure 3
Author Manuscript Published OnlineFirst on July 15, 2019; DOI: 10.1158/1535-7163.MCT-18-1224 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited.
pKIT
Y703
KIT

pAKT
S473

AKT

pMAPK
T202/Y204

MAPK

pS6
S235/236
S6

p4E-BP1
S65
4E-BP1

Actin

GIST-T1 GIST-T1/D816A GIST-T1/V654A

pKIT
Y703
KIT

pAKT
S473
AKT

pMAPK T202/Y204
MAPK

pS6
S235/236
S6

p4E-BP1
S65
4E-BP1

Actin
T1-PTEN T1/G12R-HOM T1-TSC2

Author Manuscript Published OnlineFirst on July 15, 2019; DOI: 10.1158/1535-7163.MCT-18-1224 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited.

KIT-dependent and -independent genomic heterogeneity of resistance in gastrointestinal stromal tumors – TORC1/2 inhibition as salvage strategy
Thomas Mühlenberg, Julia Ketzer, Michael C Heinrich, et al.
Mol Cancer Ther Published OnlineFirst July 15, 2019.

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