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Not had a chance to listen to the recording fully as I have been at a bio manufacturing conference all week.
Will give it a listen later on the train.
But the cancer type Lindy spoke about that people missed was hepatocellular carcinoma. An aggressive cancer of the liver.
Complete response means that treatments finished as there is no more detectable disease. It does not however mean the patient is cured - hence why normally people say 5 years cancer free for most cancers (10 for some other cancer types) is the point you would be considered cured.
Either way it’s great news for the patient.
Yup! Imagine finding you you have stage 3/4 cancer and then being told ‘you know the trial you signed up to….well because of that you've achieved a complete response’
No wonder recruitment is on track. Wouldnt surprise me if the SCIB1+ trial is recruited in record speed.
Chester - in response to this, no that answer didn't satisfy me. Whilst it explains the discrepancy in data with ORR percentages etc, it doesn't explain why the data presented on each is different. A swimmer chart should be updated as you go along, with the 'lanes' getting longer, but the datapoints that have come before not being updated with each passing week. That's what I think I was getting at - the data seems to have been revised. I'm curious why.
Yes and no - because the target patient population that SCLP therapies treat are not classified as orphan or rare diseases I don't think currently we would. A large push for this change is the fact that many orphan/rare diseases lack any therapeutic development because the current framework means that trials are nigh on impossible, to get enough numbers together to generate meaningful results.
Having said that, where it could help is if SCLP therapies could be demonstrated to target some of those rare diseases where there is no clinical development going on. So for instance, if moditope/immunobody shows effectiveness in their current trials, but these could be pivoted to really rare diseases i could see expedited approval happening.
I think where SCLP can really benefit from this, is the fact that the Immunobody platform is plug and play. So similarly to mRNA vaccines being tweaked for different Covid-19 variants, I could see the DNA plasmid vaccine being approved for one indication, then relatively swiftly being approved for a rare disease indication with minor changes to the DNA backbone and epitopes it targetted without having to go through the full trials process.
Look at earlier versions of immunobody, developed for melanoma, but was used for two paediatric indications (most likely on compassionate use terms). https://www.dailymail.co.uk/health/article-1278836/Holy-Grail-cancer-vaccine-blasts-tumours-weeks-hailed-huge-leap-fighting-disease.html.
Would be keen to hear what others thoughts on this are.
19th September update said ‘Initial data from 11 patients showed an 82% objective response rate (ORR) to treatment, which is better than 70% ORR that the trial was configured to show.’
So where has this 6 patients giving 83% come from? I appreciate that this link is an August presentation, so maybe they had only just got scans by that date from 6 patients, but an explanation would be good to clear up any confusion.
Nope - I've heard nothing back to the two emails I have sent over the past few weeks.
Maybe somebody attending the AGM can raise the lack of response to shareholders via their internet page. I sadly am unable to attend as I'm away at a conference.
I would have to agree with Jamboian.
Providing clarity prevents hearsay. A single line in the results RNS saying 'due to the delay in publishing results, the company believes that it would be appropriate to not reschedule the previously analysts call and instead provide a further opportunity for update at the AGM'. Doesn't have to go into any more detail than that, but at least it stops shareholders, investors and other parties from second guessing themselves or the rumour mill starting up again.
For context - this is approvals to start trials. Not cutting the length of time the trials run themselves. So organisations would still need to run trials for a number of years or to get the appropriately powered number of patients. But these changes are designed to help get trials started sooner rather than be delayed.
So for us, it would hopefully cut the time for things like Modi-2 trial to start quicker.
If you’re any good at coding or know anyone who is, check out pi-hole. It’s a raspberry pi based ad blocker which removes every single advert from your wifi network on every device connected to it. Works wonders and makes the viewing experience of everything from LSE to YouTube etc so much more pleasant.
Downside is it needs some knowledge of coding to get installed. But well worth it if you can get over that hurdle.
I'll give you an example. During my PhD, I was generating UV spectra for samples. This was 100s of scans per sample at different wavelengths, with 600 datapoints per scan and each scan resulted in a separate spreadsheet.
To analyse the data, you had to take scan 1, copy all 600 datapoints into a new spreadsheet column. Then copy the data from scan 2 spreadsheet into column 2 of that new spreadsheet etc. Until you got all the data in columns in a single spreadsheet.
I remember looking at the data and thinking 'there is this really interesting signal appearing around the 280nm wavelength'. It's very small but looks interesting.
I repeated the experiment and couldn't work out why I was no longer seeing that same signal. Despite the sample being the same, the setup being the same. I spent the whole afternoon repeating it and trying to work out what this signal was coming from. I looked through all the individual spreadsheets and didn't see any issue with the data that was being recorded - so not a machine error.
In anger, I went back and revisited my original compiled spreadsheet. Turns out around the 280nm 'signal' I discovered an error I had introduced. I had accidentally made an error when copying and pasting my data into the spreadsheet and unknown to me had not copied the whole dataset from one of the scans which resulted in my dataset moving up a few cells. Then continued pasting the rest of the datasets without discovering this mistake. This resulted in a 'signal' appearing around the 280nm mark which happens to coincide with UV absorbance of two amino acids (Tryptophan and tyrosine). So a whole afternoon was wasted trying to repeat an experiment to find out what was causing this signal....when actually it was a plotting error caused by yours truely.
It's easy done, especially when dealing with large quantities of data.
Dracula - firstly, IMHO no question is stupid! So ask away.
Mistake in print is completely possible - depending on how data is reported to the company it may be copied into your statistics software. So copying and pasting errors can happen. Especially if you have multiple people plotting the same graphs in different spreadsheets - its very easy for humans to make mistakes. I know for one during my PhD, I was working with such large datasets, that it was easy to introduce errors and you had to be on the ball to catch them.
In this situation, I do not think that a patients cancer is acting eratically. Somebody will have had to make those scans, interpret them, input that data and then plot that data. The scan wont lie, but interpretation, data inputting and plotting introduces a chance of human error. Especially if you are going back and re-measuring lesion reduction or somebody else within the team is revisiting data.
Violin
Yes I agree entirely, but it really is important for me as an investor and as a scientist to look at dat with a critical eye. I'll fire off an email and see whether I get a response. There may be some very logical reason behind it, but if it's not flagged then we're still in the dark.
Seems that LSE doesnt allow the less than sign!
Apologies.
Here should be the full post.
I am confused.
If you compare the 19th September data (https://www.scancell.co.uk/Data/Sites/1/media/docspres/230919-phase-2-scope-trial.pdf) on slide 6, with the data that has been presented on the October poster (https://www.scancell.co.uk/Data/Sites/1/media/publications/posters/2023/sitc_scope-study-poster_october-23_final.pdf) there are three patients who don't match previously published data. Confusingly, the data presented on these two do not use consistent colour schemes for the patients which makes this slightly more confusing to compare (I'll refer to colour as September:October)
Patient 4 (Green:Grey). September had a 50% drop in lesion size at point of scan (12 weeks). October this data is presented as having had an additional unscheduled scan less than 12 weeks with no change in lesion size at this point. But then a drop.
Patient 7: (Red:Green) - September had a ~55% lesion decrease at week 12, then a further drop to ~70% by week 25. Additional scans have shown 40% drop sometime before week 12, followed by static at week 19 before a further drop to ~70% by week 25.
Patient 8: Orange:Yellow - September had a ~80% drop at the 12 week scan. Followed by no further improvement at week 19. Yet in the October data, this shows the patient had a ~10% drop at the 12 week scan, followed by a drop to ~80% by week 19.
Patient 11: Teal:Orange - September showed 95% lesion decrease at week 12, slight growth to week 19, then another drop to week 25. Yet the October data shows a 95% lesion decrease at week 12, static til week 19, then slight increase in size to week 25.
My curiosity therefore is two fold - why is the data not consistent. Is this interpretations changing or data being revisted? I can understand being sent further scan data that may have been taken outside of the trial and not reported at the time. But why is presented data changing? The key one for me is patient 8. Why was it reported previously that they had a massive reduction in lesion, for this to then be revised to hardly anything at first scan? Is this clinician error or is this a plotting error?
Sorry for some reason this was half deleted
I am confused.
If you compare the 19th September data (https://www.scancell.co.uk/Data/Sites/1/media/docspres/230919-phase-2-scope-trial.pdf) on slide 6, with the data that has been presented on the October poster (https://www.scancell.co.uk/Data/Sites/1/media/publications/posters/2023/sitc_scope-study-poster_october-23_final.pdf) there are three patients who don't match previously published data. Confusingly, the data presented on these two do not use consistent colour schemes for the patients which makes this slightly more confusing to compare (I'll refer to colour as September:October)
Patient 4 (Green:Grey). September had a 50% drop in lesion size at point of scan (12 weeks). October this data is presented as having had an additional unscheduled scan