1 00:00:00,420 --> 00:00:10,960 I think it's sort of like. 2 00:00:10,960 --> 00:00:19,750 Consultant in medical oncology and a senior researcher in experimental cancer therapeutics based at the Churchill Hospital. 3 00:00:19,750 --> 00:00:21,640 He came from from Leeds, 4 00:00:21,640 --> 00:00:31,120 having previously trained in Southampton to deal with Agent Harris at the whim and then transferred his number by popular demand, 5 00:00:31,120 --> 00:00:35,530 I'm told, and then was appointed as a consultant here three years ago. 6 00:00:35,530 --> 00:00:40,540 So we're going to hear a bit about the translational research that he's setting up with a breast unit. 7 00:00:40,540 --> 00:00:46,510 Some of you will know that breast, as you said, has moved on significantly over the last five years. 8 00:00:46,510 --> 00:00:54,370 And it's really good to have the science behind that as a collaboration between oncology and surgery. 9 00:00:54,370 --> 00:00:57,890 Simon, thanks very much. 10 00:00:57,890 --> 00:01:05,950 And so as it's just been alluded to, my interest is in drug development, predominantly and particularly in the breast cancer space. 11 00:01:05,950 --> 00:01:11,500 And so today I'm going to present a project that we ran a little while ago, 12 00:01:11,500 --> 00:01:17,140 which is essentially a clinical pharmacodynamic study to try and understand the effects of metformin. 13 00:01:17,140 --> 00:01:25,270 Most of you will have heard of that drug. One of the most commonly prescribed treatments worldwide and treatment for diabetes. 14 00:01:25,270 --> 00:01:31,120 We wanted to understand this pharmacodynamic effects on breast cancer metabolism. 15 00:01:31,120 --> 00:01:34,870 So initially, I'll give you a bit of background about why we were interested in metformin. 16 00:01:34,870 --> 00:01:39,280 So my firm has been around for a bit of history. 17 00:01:39,280 --> 00:01:46,060 I've always been around for a long time, is actually derived from French lilac and mediaeval herbalists in the Middle Ages. 18 00:01:46,060 --> 00:01:50,020 Use it and use this to treat diabetes hundreds of years ago, 19 00:01:50,020 --> 00:01:59,890 but it was first synthesised in the mid-20th century and then licenced for the treatment of type two to type two diabetes in the 1950s in the UK, 20 00:01:59,890 --> 00:02:10,270 later in the US. And now it is the most widely prescribed oral Angie hypoglycaemic drug worldwide 21 00:02:10,270 --> 00:02:15,790 and insisting it reduces all cause mortality for patients with type two diabetes. 22 00:02:15,790 --> 00:02:23,930 So that doesn't just include mortality in the context of diabetic complications. 23 00:02:23,930 --> 00:02:34,040 But there's growing interest in metformin as anti-cancer effects. This was sparked by a series of epidemiological studies, 24 00:02:34,040 --> 00:02:40,160 the first of which was published in The BMJ in 2005 that showed that diabetics 25 00:02:40,160 --> 00:02:47,750 on metformin had a reduced cancer incidence than diabetics on other treatments. 26 00:02:47,750 --> 00:02:54,530 And it's now been over 50 studies worldwide and a number of meta analysis. 27 00:02:54,530 --> 00:03:01,670 Depending on which one you look at. The risk reduction is in the order of 30 percent later. 28 00:03:01,670 --> 00:03:09,020 Metronidazole suggests it may be a bit less than that, but but but it seems to be quite a strong link because of that. 29 00:03:09,020 --> 00:03:16,790 There have now been a number of clinical studies which have tried to understand the effects of metformin on cancer cell proliferation. 30 00:03:16,790 --> 00:03:25,790 Typically, these have been pre surgical in those studies in which patients have been given a small or a short course of metformin two to eight weeks, 31 00:03:25,790 --> 00:03:32,870 depending on the study, and they've had biopsies beforehand and then a sample taken at surgery afterwards. 32 00:03:32,870 --> 00:03:37,250 And these are mainly been in breast, prostate and then mutual cancer. 33 00:03:37,250 --> 00:03:49,730 And and in a number of these studies, they've shown that metformin can reduce Ki 67 expression so well validated marker of cell proliferation. 34 00:03:49,730 --> 00:03:57,020 Some of these studies suggest that the effect may be greater in the obese population, but that is a little controversial still. 35 00:03:57,020 --> 00:04:05,300 But just to show the interest in metformin, the potential of repurposing this drug as a cancer therapy is now over 100 clinical trials worldwide. 36 00:04:05,300 --> 00:04:14,870 Some of those fall into the prevention space. Others are looking at using metformin is an adjuvant therapy following surgery to prevent recurrence. 37 00:04:14,870 --> 00:04:22,690 And then some are also looking at the potential of using metformin advanced disease. 38 00:04:22,690 --> 00:04:27,440 So despite that, despite all this interest in forming and hundreds, if not thousands, 39 00:04:27,440 --> 00:04:39,380 of preclinical publications that have been that have assessed its mechanistic mechanistic effects in cancer, 40 00:04:39,380 --> 00:04:47,900 there's still controversy as to the mechanism of action at clinical doses in tumour cells. 41 00:04:47,900 --> 00:05:00,860 We know that metformin is an inhibitor of complex one, so complex one is a core member of the electron transport chain in mitochondria. 42 00:05:00,860 --> 00:05:05,060 And if you inhibit complex one one it, expect an energy stress. 43 00:05:05,060 --> 00:05:10,310 However, those findings have only been that's only really been observed in in preclinical studies, 44 00:05:10,310 --> 00:05:16,980 typically using doses that are much higher than peak plasma level in patients. 45 00:05:16,980 --> 00:05:28,680 But this is, I guess, the canonical hypothesis that metformin canonical view that metformin inhibits complex one leading to an energy stress, 46 00:05:28,680 --> 00:05:37,140 an increase in the AMP or ATP two ATP ratio that leads to activation of africanised high impact kinases, 47 00:05:37,140 --> 00:05:46,770 the key regulator of energy homeostasis in cells and the downstream effects of activation of AMP 48 00:05:46,770 --> 00:05:55,110 companies to inhibit a number of anabolic pathways that are absolutely key to cell proliferation. 49 00:05:55,110 --> 00:06:01,260 So, for example, fatty acid synthesis or protein synthesis. 50 00:06:01,260 --> 00:06:07,920 However, others take the view that it's probably metformin effects on host metabolism so patients metabolism have the greatest, 51 00:06:07,920 --> 00:06:15,980 greatest influence on on kind of effect on all tumour cells, as it were. 52 00:06:15,980 --> 00:06:26,210 We know that there's good evidence now, Ray, that metformin probably does have an anti mitochondrial effect in hepatocytes leading to MPIC activation. 53 00:06:26,210 --> 00:06:34,160 This leads to inhibition of if you Konya Genesis, and this is why you see falling glucose and insulin levels in patients that 54 00:06:34,160 --> 00:06:40,100 you would expect to reduce stimulation of the PI3K pathway in tumour cells, 55 00:06:40,100 --> 00:06:48,140 which is a key regulator of cell proliferation and anabolic metabolism and in all cells. 56 00:06:48,140 --> 00:06:55,270 And is and is already a target for therapy in cancer treatment. 57 00:06:55,270 --> 00:07:00,220 So it's a number of outstanding questions, as I've alluded to, there's still a session. 58 00:07:00,220 --> 00:07:05,380 How does it work in consumer sales? What effect is it having on tumour cells, if any, 59 00:07:05,380 --> 00:07:14,650 as all the preclinical studies relevant where they've used doses of metformin that are typically 100 to a thousand times? 60 00:07:14,650 --> 00:07:19,840 People assume that we're based in in in vitro and in vivo models. 61 00:07:19,840 --> 00:07:24,160 How should we select patients for future clinical trials? 62 00:07:24,160 --> 00:07:30,490 What biomarkers might there be to make sure that clinical trials are properly designed? 63 00:07:30,490 --> 00:07:37,420 What in what state should we be metformin as a clinical therapy in the context of cancer? 64 00:07:37,420 --> 00:07:42,940 Should this be a preventative treatment, perhaps in diabetic patients or obese patients? 65 00:07:42,940 --> 00:07:50,890 Should it be an active treatment that we give following surgery alongside perhaps chemotherapy and other standard treatments? 66 00:07:50,890 --> 00:07:54,030 Well, should we be using an advanced disease? 67 00:07:54,030 --> 00:08:05,410 And lastly, how maybe should we be combining that form in with other treatments to get synthetic lethality or an additive effect, at least? 68 00:08:05,410 --> 00:08:09,460 So this is the these are the sort of the research objectives of the clinical study, such as firstly, 69 00:08:09,460 --> 00:08:14,080 to characterise the effects of metformin on breast cancer metabolism and also to 70 00:08:14,080 --> 00:08:19,260 assess the potential ways that we could select patients for future clinical trials. 71 00:08:19,260 --> 00:08:25,290 This is a design. In total, we recruited 41 patients to the study. 72 00:08:25,290 --> 00:08:30,590 We recruited patients that had an untreated primary breast cancer. 73 00:08:30,590 --> 00:08:37,640 It's essentially pristine, so they haven't been touched by any terapie before just off the diagnosis. 74 00:08:37,640 --> 00:08:43,040 These patients who are going to go on to have neoadjuvant pre surgical chemotherapy. 75 00:08:43,040 --> 00:08:54,200 We gave them a two week window therapeutic window of metformin, and either side of that window, we carried out a series of pharmacy atomic assays, 76 00:08:54,200 --> 00:09:05,030 novel imaging with a dynamic, a pet CT scan, a series of research, biopsies for immunohistochemistry transcriptomics and metabolomics. 77 00:09:05,030 --> 00:09:10,130 And then lastly, we also took research blood samples because we wanted to understand the effects of metformin 78 00:09:10,130 --> 00:09:16,370 on host metabolism so that we could then relate that back to the effects on the tumour. 79 00:09:16,370 --> 00:09:21,590 Once they completed that two week course of metformin, they went on to have that chemotherapy. 80 00:09:21,590 --> 00:09:25,790 And if they chose to continue the metformin alongside before having definitive surgery. 81 00:09:25,790 --> 00:09:38,110 But essentially the study was over after that last month and access following the two week and the initial two run in with with metformin alone. 82 00:09:38,110 --> 00:09:45,700 So one of the first activities we carried out was just to look at the kinetic. 83 00:09:45,700 --> 00:09:51,970 Character, genetic analysis of metformin in our patients, and we saw that there was a relatively wide spread of metformin levels, 84 00:09:51,970 --> 00:10:04,270 peak plasma levels is about two hours per dose, but this was consistent with with the literature from our pharmacokinetic studies in diabetic space. 85 00:10:04,270 --> 00:10:12,430 But interestingly, because we had tumour samples, we were able to therefore also assess the levels of metformin using mass spec in the tumour 86 00:10:12,430 --> 00:10:16,840 samples and then determine whether there was any relationship with serum plasma levels. 87 00:10:16,840 --> 00:10:20,780 And there was there was a significant correlation. 88 00:10:20,780 --> 00:10:29,050 Now you might say, well, that's obvious, but actually a lot of investigators think that the main determinants of the level of metformin, 89 00:10:29,050 --> 00:10:38,110 the tumours, expression of certain key transporters to get metformin into the cell, one of the main one is resolved one. 90 00:10:38,110 --> 00:10:42,550 But here we see actually that at least to a certain extent, 91 00:10:42,550 --> 00:10:48,610 serum level of metformin seems to be important in terms of the amounts you get into the to the tumour. 92 00:10:48,610 --> 00:10:55,780 And that's important because therefore dose escalation studies of metformin in the cancer context might be might be sensible. 93 00:10:55,780 --> 00:11:02,710 We can in cancer patients, I'm afraid we were happy to accept, accept higher levels of toxicity than many other diseases. 94 00:11:02,710 --> 00:11:09,820 So. And there are studies that are going on and looking at that, 95 00:11:09,820 --> 00:11:17,120 particularly in terms of the functional effects of metformin on the cell lung cancer cells. 96 00:11:17,120 --> 00:11:20,960 So moving on to one of the causes of this study, 97 00:11:20,960 --> 00:11:30,410 and that is to understand whether we saw any change in 18F uptake on pet CT imaging following metformin treatment. 98 00:11:30,410 --> 00:11:37,580 And I'll just give you a bit of background as to why we're interested in pets as a modality in this space. 99 00:11:37,580 --> 00:11:47,360 But I'm probably just preaching to people that already understand it, and I'll give a brief overview anyway. 100 00:11:47,360 --> 00:11:58,880 So as you're aware, this is the EFG is essentially a glucose analogue regulating glucose analogue and is used routinely in the clinical setting, 101 00:11:58,880 --> 00:12:09,770 particularly to stage cancer patients. Although there are other applications because tumours will take to take up more glucose than benign tissues, 102 00:12:09,770 --> 00:12:18,890 typically of other certain tissues of his brain and liver also have take up a lot of glucose. 103 00:12:18,890 --> 00:12:29,210 And this is really because tumour cells need a lot of glucose based carbon to proliferate to use for fatty acid synthesis, 104 00:12:29,210 --> 00:12:35,510 amino acid synthesis, nucleus nucleotides insists. 105 00:12:35,510 --> 00:12:50,540 So when one carries out pet CT or fuses a pet scan, which will image the uptake of EFG into tissues and to a CT scan that delineates the anatomy. 106 00:12:50,540 --> 00:12:59,210 And so you can see here this is a lung cancer patient that you've got a very average tumour in this patient's lung. 107 00:12:59,210 --> 00:13:04,970 But you can also use this technique to monitor response to therapy so you can see post chemotherapy. 108 00:13:04,970 --> 00:13:09,010 They've had an excellent metabolic response. 109 00:13:09,010 --> 00:13:17,800 But why are you interested in this technique in the context of its possible understanding, dynamic response to metformin? 110 00:13:17,800 --> 00:13:25,760 Well, as I mentioned before, we one hypothesis is that metformin inhibits complex one in the mitochondria and it's induces long distress. 111 00:13:25,760 --> 00:13:27,790 You then get AMPK activation. 112 00:13:27,790 --> 00:13:39,610 A key consequence of an activation is that you get upregulation of peak one and click fall to glucose transporters on on cells. 113 00:13:39,610 --> 00:13:43,240 And so you will see increased glucose uptake. 114 00:13:43,240 --> 00:13:47,000 Potentially, you should see increased glucose uptake as a consequence of that. 115 00:13:47,000 --> 00:13:55,330 So if you are seeing a mitochondria effect from metformin that has any sort of cause any sort of significant energy stress, 116 00:13:55,330 --> 00:14:08,900 this is one of the first sequelae that you should see. So firstly, we carried out a static analysis, so you got allies data in different ways. 117 00:14:08,900 --> 00:14:17,180 This would be what you doing standardly in the clinic. So you give a dose of radiation EFG, you take an image forty five, 60 minutes afterwards, 118 00:14:17,180 --> 00:14:22,830 depending on your protocol, and you just get that snapshot of uptake of Asian FTT at that point. 119 00:14:22,830 --> 00:14:31,010 And so in this here, we did not see any change in Asian energy uptake in our primary tumours. 120 00:14:31,010 --> 00:14:35,360 So it's a little disappointing because one of the first notices we carried out, 121 00:14:35,360 --> 00:14:41,910 but thankfully Fergus Gleason suggested we also carried out cinematic imaging and we had a lot. 122 00:14:41,910 --> 00:14:50,150 We had a quartette of medical engineers in the departments here able to help carry out this modelling and analysis for us. 123 00:14:50,150 --> 00:14:57,500 So we thought that iPads, instead of just taking that one, scan forty five 60 minutes after that, 124 00:14:57,500 --> 00:15:05,060 given the dose of SD instead, you take a series of images over that time period, 125 00:15:05,060 --> 00:15:06,860 many more at the beginning, 126 00:15:06,860 --> 00:15:20,330 and you can therefore carry out a kinetic analysis to understand changing or the change in rates of uptake over a period of time. 127 00:15:20,330 --> 00:15:21,080 In this context, 128 00:15:21,080 --> 00:15:31,310 you then have to also integrate an input function that gives you a readout of change in activity of EFG in the bloodstream over that period of time. 129 00:15:31,310 --> 00:15:37,730 Because obviously that will change is going to slow rate each site great any time. 130 00:15:37,730 --> 00:15:48,990 So we in this in our study, we use the left ventricle, which is well described as as a good way of doing this. 131 00:15:48,990 --> 00:15:57,750 Once you carry out the analysis, you can then come up with a variable CFG, which describes the rate of interest EFG phosphorylation. 132 00:15:57,750 --> 00:16:02,190 And this has been shown to be in a number of other studies to be more sensitive 133 00:16:02,190 --> 00:16:12,680 to changes in EFG uptake than just standard SUV max use in the clinical setting. 134 00:16:12,680 --> 00:16:24,560 So this is this is the dynamic. The results of the dynamic nation after picked us is we actually say overall, we saw actually an increase in CFG, 135 00:16:24,560 --> 00:16:33,170 so an increase in the flux of FTE into the primary tumour following metformin treatment. 136 00:16:33,170 --> 00:16:35,390 So this is quite exciting. 137 00:16:35,390 --> 00:16:44,210 You can see in this patient here, we had one of the higher, greater increases in fatigue uptake that the patients pre metformin, 138 00:16:44,210 --> 00:16:50,780 we can't see any axillary nodes two weeks later itself, which may or may not be. 139 00:16:50,780 --> 00:16:58,370 We think it's probably a consequence of the fact that metformin has increased refugee uptake into into the night into those nights. 140 00:16:58,370 --> 00:17:09,860 So potentially that for dynamic ageing fatigue, perhaps it could be a dynamic biomarker of metformin effect on breast cancer metabolism, 141 00:17:09,860 --> 00:17:17,020 although this is not a technique that you can take into the clinic anytime soon. 142 00:17:17,020 --> 00:17:25,870 So besides the imaging essays, we also carried out tissue based assays alongside, which makes this study a lot more powerful. 143 00:17:25,870 --> 00:17:33,820 So first of all, we carried out metabolomic profiling using spent with a collaborator in Cambridge and looked 144 00:17:33,820 --> 00:17:40,180 at a series of different metabolites that we thought might change in the mitochondrial space. 145 00:17:40,180 --> 00:17:48,680 And these. Before metabolites, where there was a significant difference in level following metformin treatment, 146 00:17:48,680 --> 00:17:58,520 so gluconate to short chain short chain kinases are still promising and proportional cleansing and citrulline. 147 00:17:58,520 --> 00:18:08,120 So. This is a bit speculative, but I thought I just sort of show you why these changes might be might be relevant, 148 00:18:08,120 --> 00:18:18,080 so I still can't see an issue and not commenting on both mitochondrial metabolites. 149 00:18:18,080 --> 00:18:28,010 I still saw contain is a key way by which carbons can be pulled from citrate and proportional carnitine 150 00:18:28,010 --> 00:18:36,110 is a breakdown product of a fatty acid option that sits where the meat based upon accounting comes from. 151 00:18:36,110 --> 00:18:43,250 And we saw lower levels of these two metabolites, possibly in the case of mitochondrial interference. 152 00:18:43,250 --> 00:18:54,440 We also saw a decrease in Citrulline. The situation is the only metabolite of the urea cycle that is synthesised within mitochondria. 153 00:18:54,440 --> 00:19:01,190 And so again, this suggests this possibly alludes to a mitochondrial effect of metformin. 154 00:19:01,190 --> 00:19:06,500 And lastly, we saw an increase in gluconate. And this is really speculative. 155 00:19:06,500 --> 00:19:15,290 But this possibly reflects something of glucose carbons away from glycolysis growth going 156 00:19:15,290 --> 00:19:22,490 further down the glycolysis pathway because they can't be utilised by the mitochondria. 157 00:19:22,490 --> 00:19:29,850 As I say, speculation read bit, but but did that change in the metabolite levels linked to our imaging assay? 158 00:19:29,850 --> 00:19:31,010 So yes, they did. 159 00:19:31,010 --> 00:19:43,410 There was actually a strong correlation between a positive correlation between changing as technology intervals and changing its energy flux. 160 00:19:43,410 --> 00:19:47,460 Moving on to the transcriptomic analysis, we carry it out. 161 00:19:47,460 --> 00:19:55,120 Transcript chairman, I seek. And when we carried out an initial pathway analysis, 162 00:19:55,120 --> 00:20:02,480 one of the striking things was that there is a large number of pathways associated with metabolism that were either upregulated or downregulated, 163 00:20:02,480 --> 00:20:07,610 as you can see in the circles on the left in the left panel. 164 00:20:07,610 --> 00:20:12,670 Even more striking was that there were a great number of mitochondrial pathways associated with 165 00:20:12,670 --> 00:20:18,560 mitochondrial metabolism that were significantly upregulated following metformin treatment. 166 00:20:18,560 --> 00:20:27,060 And many of these were the most significantly upregulated pathways that we saw on pathway analysis. 167 00:20:27,060 --> 00:20:33,120 Who then look to integrate the transcriptome of data back to other all their other assays. 168 00:20:33,120 --> 00:20:41,190 The first thing we do was carry out hierarchical clustering, looking at four key pathways or clustering genes, 169 00:20:41,190 --> 00:20:47,820 looking at four key pathways that we might expect to be modulated in the context of of mitochondrial interference, 170 00:20:47,820 --> 00:20:58,740 oxidative phosphorylation, TCA cycle glycolysis and Spartacus and glutamate metabolism and aspartate and glutamate are a key amino acids. 171 00:20:58,740 --> 00:21:09,510 This importance in providing all their synthesis and utilisation are key with 172 00:21:09,510 --> 00:21:14,940 regards to certain resistance mechanisms related to mitochondrial interference, 173 00:21:14,940 --> 00:21:21,960 and it has been shown in metformin and in preclinical studies. But anyway, what was what was he? 174 00:21:21,960 --> 00:21:36,690 There is a group of eight patients who had global activation of number of genes to annotate to these pathways, 175 00:21:36,690 --> 00:21:44,420 and we called this group the Oxytocin Transcriptional Response Group. 176 00:21:44,420 --> 00:21:53,180 We then went on to see if there was to relate that back to our other assets, and here we saw that for the experts transcriptional response group. 177 00:21:53,180 --> 00:21:58,610 If anything, actually we didn't see any change in EFG flux, 178 00:21:58,610 --> 00:22:03,750 but rather it was the other patients where we were more likely to see an increase in EFG Flux. 179 00:22:03,750 --> 00:22:08,640 So we term these guys the AFG Response Group. 180 00:22:08,640 --> 00:22:16,500 When we looked at some of our metabolites, we also saw that there was a discrepancy picture here between the two groups, 181 00:22:16,500 --> 00:22:20,070 so the OK for the Oxford transcription was not sure. 182 00:22:20,070 --> 00:22:23,940 We saw a marked decrease in this. 183 00:22:23,940 --> 00:22:27,300 We saw a decrease in our style casting that was following that forward. 184 00:22:27,300 --> 00:22:38,890 But this was not really observed on a general level for the desktop, for the FG Response Group. 185 00:22:38,890 --> 00:22:42,610 So we just looked at sort of a key set of genes at this point. 186 00:22:42,610 --> 00:22:49,210 Annotated to mitochondrial metabolic pathways. 187 00:22:49,210 --> 00:22:55,570 We then went on to look at the whole transcriptome. And when we carried out hierarchical clustering of the whole transcriptome, 188 00:22:55,570 --> 00:23:07,620 we again saw that the focus was transcriptional response group clustered together. 189 00:23:07,620 --> 00:23:16,740 When we looked at clustering of genes, the fold change of clustering, of change of genes that are encoded by mitochondrial DNA. 190 00:23:16,740 --> 00:23:29,060 Once again, the oxytocin transcriptional response we've clustered together, giving giving us greater evidence that we were seeing a real effect here. 191 00:23:29,060 --> 00:23:36,500 But how might this reflect back on possible therapeutic effects? 192 00:23:36,500 --> 00:23:48,300 So we used a messaging approach. And observe that for the Oxford Transcriptional Response Group, if anything, 193 00:23:48,300 --> 00:23:55,230 there was an increase in expression of genes that made up a well validated proliferation messaging and suggesting, 194 00:23:55,230 --> 00:24:00,690 if anything, that these guys were resistant to treatment. 195 00:24:00,690 --> 00:24:07,500 Whereas there are a number of patients in the EFG response group who had a decrease in expression of this proliferation, 196 00:24:07,500 --> 00:24:17,130 Mataji suggesting they this they may well be more sensitive to metformin. 197 00:24:17,130 --> 00:24:21,670 I alluded to the importance of glutamine and the spotlight on previous slides, 198 00:24:21,670 --> 00:24:29,430 so we know this glutamine utilisation of glutamine carbons because they can avoid the TCA 199 00:24:29,430 --> 00:24:36,540 cycle of our process of reductive oxidation and thereby be used for a spot in System B. 200 00:24:36,540 --> 00:24:50,730 Fatty Acids insists that utilisation of glutamine is a key resistance pathway to mitochondrial interference hypoxia as it happens and also metformin. 201 00:24:50,730 --> 00:24:58,860 And so we wanted to to hone down on on some of the genes involved in glutamine metabolism and then also a aspartate metabolism, 202 00:24:58,860 --> 00:25:04,830 particularly with a view to possibly targeting these genes to see if we see them. 203 00:25:04,830 --> 00:25:10,170 So in the future clinical studies or targeting these pathways in future 204 00:25:10,170 --> 00:25:16,280 clinical studies to try and get additive benefit alongside metformin treatment. 205 00:25:16,280 --> 00:25:26,840 And we saw that firstly, there were a number of genes with increased expression that a key checkpoints, 206 00:25:26,840 --> 00:25:33,290 these pathways in particular are citrate dehydrogenase of rejected car selection pathway and 207 00:25:33,290 --> 00:25:40,400 then a large number of genes had increased expression that regulate the aspartate male shuttle. 208 00:25:40,400 --> 00:25:53,690 But also it seemed that there was a markedly increased expression, particularly in the air group versus the EFG Response Group, 209 00:25:53,690 --> 00:25:58,280 suggesting that certainly these pathways were activated, at least in some patients. 210 00:25:58,280 --> 00:26:04,310 And therefore there may be an opportunity to target these pathways in combination with metformin and 211 00:26:04,310 --> 00:26:11,690 there their already drugs out there that target our citrate dehydrogenase in coming into the clinic. 212 00:26:11,690 --> 00:26:21,420 And there are those being developed to target target aspartate metabolism. 213 00:26:21,420 --> 00:26:26,760 We also related the expression of certain genes back to our imaging data. 214 00:26:26,760 --> 00:26:27,930 And so, for example, 215 00:26:27,930 --> 00:26:41,760 here we we saw that expression of glued to one key glucose transporter had a strong positive correlation with changing 18 EFG flux, 216 00:26:41,760 --> 00:26:56,080 which is what you might expect. So earlier, I mentioned that we also there's also a separate hypothesis that suggests that the. 217 00:26:56,080 --> 00:27:02,440 Considers this possibly metformin as effects on host metabolism, 218 00:27:02,440 --> 00:27:12,580 rather than the direct effects on the tumour cells that are most important for driving any final chemical effects of metformin in the cancer context. 219 00:27:12,580 --> 00:27:19,990 And certainly so we measured we took blood samples pre and post metformin in our Windows study. 220 00:27:19,990 --> 00:27:29,210 And in these non-diabetic, they had to be had to have a fasting glucose to get into the study. 221 00:27:29,210 --> 00:27:33,810 That's a term that they were not diabetic. 222 00:27:33,810 --> 00:27:43,950 We saw in this group of patients that there was a decrease in a number of metabolic host metabolic markers for metformin treatment, 223 00:27:43,950 --> 00:27:50,100 including glucose, insulin, C-peptide, which is an excellent marker of insulin secretion and then higher, 224 00:27:50,100 --> 00:27:58,140 which is a function of education, it's good that determines of insulin sensitivity. 225 00:27:58,140 --> 00:28:03,300 There were dramatic decreases you might see in a diabetic patients, but there was they were strongly significant. 226 00:28:03,300 --> 00:28:16,460 They were small but strongly significant, consistent. So we then used our transcriptomic data to try and see if there was a relationship here. 227 00:28:16,460 --> 00:28:30,380 And we wanted to understand whether the change in expression. Of genes correlated with the different. 228 00:28:30,380 --> 00:28:38,180 The change in our different assays, including 18 G.G. Flux C peptides, 229 00:28:38,180 --> 00:28:47,360 says good measure of circulating insulin levels and one of the key metabolites we measured in the tumour style carnitine. 230 00:28:47,360 --> 00:29:01,250 And what we observed was that there were a great deal, a large number of genes that whose changing expression significantly. 231 00:29:01,250 --> 00:29:07,760 Correlated with change in both Asian refugee flux and also stock policy. 232 00:29:07,760 --> 00:29:13,310 But this was not the case when we realised when we looked at C-peptide, 233 00:29:13,310 --> 00:29:22,320 and this is highly significant, suggesting that the change in these two things are linked. 234 00:29:22,320 --> 00:29:29,910 From this data, we think we think this basic data gives a good indication that we are seeing a direct effect of metformin 235 00:29:29,910 --> 00:29:39,120 on tumours have this and it's not related to the key driver is not the effects on circulating insulin. 236 00:29:39,120 --> 00:29:44,820 Of course, we can't determine mechanism. Absolutely. 237 00:29:44,820 --> 00:29:51,000 In a study like this, which is that we have to make certain inferences from from the data. 238 00:29:51,000 --> 00:30:00,800 But I think this was a very intriguing finding. So in conclusion, for him, it increases refugee flux into pouring breast tumours. 239 00:30:00,800 --> 00:30:09,830 This is consistent with the upregulation of glycolysis or at least glucose transport into cells, tumour cells. 240 00:30:09,830 --> 00:30:17,420 And this would be consistent with a metformin having a direct mitochondrial effect. 241 00:30:17,420 --> 00:30:25,970 Metformin reduces short chain cell carnitine levels and regulates multiple mitochondrial pathways that the transcriptome level in forms of cancer. 242 00:30:25,970 --> 00:30:31,970 And we describe two metabolic response patterns that may define sensitivity to metformin. 243 00:30:31,970 --> 00:30:39,680 So if you want to read it about this a bit more, it's came out in cell metabolism actually about four weeks ago, 244 00:30:39,680 --> 00:30:46,340 and it's open access, so you have to pay anything to look at next steps. 245 00:30:46,340 --> 00:30:50,960 So we there's some intriguing data published by a number of groups. 246 00:30:50,960 --> 00:31:01,310 This is the the the seminal paper from about four years ago that shows that if you knock out certain genes in complex one, 247 00:31:01,310 --> 00:31:08,480 there's certain subjects of complex one. You can markedly change or reduce the sensitivity of tumours to two metformin. 248 00:31:08,480 --> 00:31:14,660 And then there's quite a lot of data to suggest that mutations in complex one 249 00:31:14,660 --> 00:31:21,290 really do significantly alter sensitivity to various mitochondrial insults, 250 00:31:21,290 --> 00:31:30,630 such as hypoxia or low glucose levels in vitro and in vivo. 251 00:31:30,630 --> 00:31:38,640 And so we've now got some funding, a whole exome sequencing, so that we will be able to assay the mitochondrial mutational burden, 252 00:31:38,640 --> 00:31:45,930 but particularly focus on complex mutations using tiny samples from the study and 253 00:31:45,930 --> 00:31:54,840 then relate back to that back to the metabolic response that I've just shown. 254 00:31:54,840 --> 00:31:59,310 And that might give us some baseline biomarkers that we can then test in future. 255 00:31:59,310 --> 00:32:08,340 Trials are actually working with a group that has completed an achievement study in assessing breast cancer, 256 00:32:08,340 --> 00:32:17,700 which the five years of metformin to three and a half thousand patients will often receive metformin, the other half randomised to placebo. 257 00:32:17,700 --> 00:32:21,240 And so they have very good long term outcome data in that setting. 258 00:32:21,240 --> 00:32:28,080 And potentially if we can show that our metabolic response patterns are relevant related to the 259 00:32:28,080 --> 00:32:36,390 mitochondrial mutational burden using their banks translational samples that they took from it in. 260 00:32:36,390 --> 00:32:44,110 In that study, we could then potentially look at that assay in context or relevant clinical context. 261 00:32:44,110 --> 00:32:48,910 So I just like to thank everyone that was involved, particularly Agent Harris, 262 00:32:48,910 --> 00:32:55,450 who was instrumental in setting up the city, the Oxford Cancer Imaging Centre. 263 00:32:55,450 --> 00:33:08,290 You and Joel Finit is now moved to Liverpool, carried out with the analysis apprenticeship, offer science and reach and carry out match analysis, 264 00:33:08,290 --> 00:33:14,380 mainly for the transcriptome data that also help with some of the metabolomics analysis as well. 265 00:33:14,380 --> 00:33:21,100 And then Christine and Eduardo carried out the next step. 266 00:33:21,100 --> 00:33:31,820 And lastly, these are our funders who is quite a lot of fun, and you can imagine a study like this, and they are very grateful. 267 00:33:31,820 --> 00:33:41,970 So they're not making. OK, any questions?