THE ROLE OF POTASSIUM CHANNELS IN THE PATHOGENESIS OF GASTROINTESTINAL CANCERS AND THERAPEUTIC POTENTIAL

Voltage sensitive potassium channels play an important role in controlling membrane potential and ionic homeostasis in the gut and have been implicated in gastrointestinal (GI) cancers. Through large scale analysis of 1594 patients with GI cancers coupled with in vitro models we find KCNQ family genes are mutated in ~30% of patients, and play therapeutically targetable roles in GI cancer growth. KCNQ1 and KCNQ3 mediate the WNT pathway and MYC to increase proliferation, and its resultant effects on cadherins junctions. This also highlights novel roles for KCNQ3 in non-excitable tissues. We additionally discover that activity of KCNQ3 sensitises cancer cells to existing potassium channel inhibitors, and that inhibition of KCNQ activity reduces proliferation of GI cancer. These findings reveal a novel and exploitable role for potassium channels in the advancement of human cancer, and highlight that supplemental treatments for GI cancers may exist through KCNQ inhibitors. SIGNIFICANCE KCNQ channels modulate the WNT pathway and MYC signalling, and drive growth of gastrointestinal cancers. Available drugs modulate these pathways and offer therapeutic potential in gastrointestinal cancer.

We also find application of amitriptyline has a potent inhibitory effect on growth in 4 0 0 OE33 cells, but that this effect is also present in FLO1. This suggests that 4 0 1 amitriptyline likely also acts through mechanisms other than KCNQ3 to reduce 4 0 2 growth rate. To confirm that linopirdine and amitriptyline mechanism of action 4 0 3 involves inhibition of KCNQ3 we also performed RNA sequencing on OE33 cells 4 0 4 exposed to 100mg/ml of each drug. There is a strong overlap in the differentially proposed mechanisms. We also find cadherins junctions are amongst the most 4 1 3 enriched GO molecular functions in both instances (Figure S6A, B). Finally, to 4 1 4 confirm a reduction in MYC/WNT signalling in OE33 exposed to drugs, differential Relative confluence plots for OE33 (left) and FLO1 (right) cell lines exposed to biological processes enriched in ctrl vs linopirdine exposed KCNQ3 OE OE33 cells 4 2 4 (purple) and ctrl vs amitriptyline exposed KCNQ3 OE OE33 cells. C) REVIGO 4 2 5 clustered GO biological process terms associated with overlap between ctrl vs 4 2 6 linopirdine and amitriptyline exposed KCNQ3 OE OE33 cells. D) Fold change of 4 2 7 MYC, CCND1, CDH1, and E2F1 in ctrl vs linopirdine exposed (purple) and ctrl vs 4 2 8 amitriptyline exposed (orange) KCNQ3 OE OE33 cells. * represents q value < 0.05.  There is emerging evidence that ion channels play a role in many, and potentially all Through integration of data at the patient, cell, and protein structural levels, coupled with in vitro models we show that KCNQ genes and protein products contribute to 4 4 9 cancer phenotype and are a potential therapeutic target. We show that a large polarity. We propose a mechanism for KCNQ3 activity in GI cancer, whereby it to the activation of WNT and MYC signalling, as well as changing cellular polarity 4 6 0 and morphology ( Figure 6D). Finally, we demonstrate that KCNQ family members 4 6 1 are a viable drug target with the use of already available therapeutic compounds that have not yet been actioned against cancer, but have been FDA approved for other uses. This is particularly interesting in the case of KCNQ3 -as it is often recurrently  therapeutic window, as is thought to be the case with hERG inhibitors(41). That the 4 8 0 KCNQ2/3 specific inhibitor linopirdine shows no effect in FLO1 cell lines, but they are 4 8 1 potently inhibited by the broader acting amitriptyline indicates a key role for a number 4 8 2 of other proteins that may be therapeutic targets in GI cancer, but also opens up the  TCGA level 3 data was downloaded using Firebrowse (RNAseq) or cBioportal (copy 4 9 3 number alteration, mutation and clinical data)(42). COSMIC data(19) was 4 9 4 downloaded from cancer.sanger.ac.uk (version 92). We subset mutations into those 4 9 5 only found in gastrointestinal tissue, defined as those where the primary site is in one 4 9 6 of the following categories: "large_intestine", "small_intestine", 4 9 7 "gastrointestinal_tract_(site_indeterminate)", "oesophagus", "stomach". Oncoprint was generated using the oncoprint library in R(43). Copy number 5 0 0 alterations were determined as follows -relative copy number for each gene was Genes were defined as deleted if total copy number == 0 OR relative copy number < 5 0 5 -1, genes were defined as amplified if relative copy number > 1.

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For calculating the expected vs observed mutational distribution, exon data was 5 1 5 downloaded from the Ensembl Biomart (ensembl.org/biomart). Ensembl 96, 5 1 6 hg38.p12 was selected, and data downloaded for chromosomes 1-22, X, and Y. We used bedtools(47) to sort data and overlapping exons were merged. To sort data we 5 1 8 used the following command: Merging was performed using: The R library Deconstructsigs(48) was used to generate the mutational signature for 5 2 3 all COSMIC mutations in KCNQ genes within GI tissue for these exons. The 5 2 4 mutational spectrum was normalised using the mutational signature to generate the 5 2 5 expected relative mutation rate for each possible missense mutation. This was then  Mutational clustering was calculated using the NMC clustering method from the R COSMIC database for GI cancers were considered. The top 5 mutational clusters  tail -n +2 human_exon_bed_1-Y.txt | cut -f 1,2,3 | bedtools sort -i stdin > human_exon_bed_file_1-Y_sorted.bed bedtools merge -i human_exon_bed_file_1-Y_sorted.bed > human_exon_bed_file_1-Y_merged.bed Cox proportional hazards was performed using the python library lifelines (49).

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Patients were labelled with their cancer origin (OAC, OSCC, STAD, COADREAD),   Homology modelling was performed using the template structure 5VMS from the mutate_model.py script available on the modeller website.

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For simulations of homology models in AT we used the charmm36 forcefield (55). In structures (CG-MD) with an elastic network in the martiniv2 forcefield(57) and self- was converted back to atomistic detail using the CG2AT method (58). The AT 5 6 1 system was neutralised with counterions, and additional ions added up to a total 5 6 2 NACL concentration of 0.05 mol/litre. The system was minimized using the steepest 5 6 3 descents algorithm until maximum force Fmax of the system converged. Equilibration was performed using NVT followed by NPT ensembles for 100 ps each with the protein backbone restrained. We used the Verlet cutoff scheme with PME 5 6 6 electrostatics, and treated the box as periodic in the X, Y, and Z planes. Simulations were run for 200ns of unrestrained molecular dynamics. Root mean square deviation 5 6 8 (RMSD) was calculated for structures using the g_rmsdist command in GROMACS.

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CG simulations of single helices were performed as described previously (59).

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Models of single helices were generated and converted to MARTINI coarse grained 5 7 1 structures. Helices were then inserted into POPC bilayers and simulated for short 5 7 2 (100ns) simulations for 100 repeats of each sequence. Pore analysis was performed using the algorithm HOLE (60). Pore profile was 5 7 5 visualised using Visual Molecular Dynamics (VMD) (61).
3 1 cells were transduced with the lentivirus, sub-cultured and selected for mCherry bright 6 0 2 cells using FACS, respectively, to generate stable Cas9 expressing cell lines. We then designed four sgRNA sequences that targeting exon2 and exon3 of 6 0 5 KCNQ1, which were shared by both known KCNQ1 variants, using an online tool exon3. sgRNA sequences were cloned into a backbone plasmid pKLV2-   lentivirus to generate KCNQ1 knockout cell lines, which were later sub-cultured and 1% agarose gel and purified using Qiagen Gel Extraction Kit, and then sent for  To generate KCNQ3 overexpressing lentiviral plasmid, KCNQ3 fragment was cloned  The KCNQ3 PCR product was purified using Qiagen QIAquick PCR Purification Kit, 6 4 1 and then cloned into a backbone plasmid with a EGFP tag, pUltra, a gift from  Proliferation assay . . P h a n N N ,  K  e  l  l  y  G  L  ,  K  u  e  h  A  J  ,  B  r  e  n  n  a  n  M  S  ,  O  '  C  o  n  n  o  r  L  ,  M  i  l  l  a  L  ,  e  t  a  l  .  A  n  i  n  d  u  c  i  b  l  e  l  e  n  t  i  v  i  r  a  l  8  7  6   g  u  i  d  e  R  N  A  p  l  a  t  f  o  r  m  e  n  a  b  l  e  s  t  h  e  i  d  e  n  t  i  f  i  c  a  t  i  o  n  o  f  t  u  m  o  r  -e  s  s  e  n  t  i  a  l  g  e  n  e  s  a  n  d  t  u  m  o  r  -p  r  o  m  o  t  i  n  g  8  7  7   m  u  t  a  t  i  o  n  s  i  n  v  i  v