NorthWest Biotherapeutics Inc (NWBO)

[sentiment_stocks]

Well not necessarily. But I remember transcribing this section from a recent interview Dr. Liau had along with Dr. Cobb last year where she indicates that it is the IDH1 wild-type tumors that do best with DCVax-L, and these tend to be a sub classified into the mesenchymal, classical, and neural subtypes. I'm not sure how large a population this IDH wild-type is, or whether it would be considered "feasible or practical" to conduct a clinical trial with them alone, or whether it's even considered a "molecular alteration" since it's not mutated. Perhaps you have some thoughts on this?

Here is what LL said...

48:10
But some of our data from our immunotherapy trials though, actually have shown immune therapy actually may be more beneficial in the, you know, mesenchymal subgroup of glioblastomas, which actually tend to be IDH1 mutation negative… the IDH1 wild-type tumors. And potentially, these tumors do worse because they have more mutations, and they tend to be more aggressive. That being said, the fact that they have more mutations may actually make them more susceptible to immunotherapy because they have more targets. You know, there are mutations that the immune system can target. - Linda Liau

In contrast IDH-wildtype glioblastoma tends to be subclassified into mesenchymal, classical, and neural transcriptional subtypes.

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4347445/

I guess this type of tumor does not have a mutation of the IDH-gene, or the IDH mutant type (IDH1 and IDH2), that often begin as the low-grade diffuse gliomas such as an astrocytoma or oligodendroglioma.

The following article indicates that the IDH mutated tumors are 80% more likely to have MGMT methylation; whereas only 60% of IDH-wild type (wt) have it. Tumors with normal, non mutated IDH genes are the much more aggressive types and the prognosis is typically poor, as LL indicated in her discussion transcribed above.

I found this of interest as well...

If no immunohistochemical reactivity is detected, it is likely but not certain that the tumour is IDH wild-type. This can only be established with formal genotyping, e.g. using pyrosequencing. In practice, however, this is not always done, both because it is expensive and in some patient groups (e.g. elderly patients with GBM) almost always negative (i.e. almost all GBMs in elderly patients are IDH wild-type).

https://radiopaedia.org/articles/isocitrate-dehydrogenase-idh

This trial was not initially set up to measure the more recently identified sub-types. But you will remember that it was set up to measure MGMT methylation status.

From the protocol:

MGMT methylation status is a predictive factor for Temodar responsiveness, and is used for stratification of the randomization.

and

Randomization is performed using an IWRS system, and is initiated upon a randomization request form submitted by the site followed by review of all eligibility criteria. The randomization is stratified by site and by MGMT methylation status(methylated: yes/no).

and

15.8. ANALYSIS OF COVARIATES
The associations of 1) MGMT methylation status, 2) clinical site, 3) RPA classification (a composite score based on age, performance status and extent of resection) and 4) temozolomide usage with PFS and OS will be assessed with Cox proportional hazards regression models All randomized patients in the main cohort will be included in the analysis.

It would seem that adjustments can be made for covariates in randomized controlled trials (RCTs) and these can result in substantial increases in power when the covariates are decidedly predictive.

Adjustment for baseline covariates in the analysis of randomized controlled trials (RCTs) can lead to a substantial increase in power when the covariates are highly prognostic.

One of the main advantages of covariate adjustment is that it can lead to increased power. For continuous outcomes, this occurs because the covariates help to explain some of the variation in outcomes between patients, leading to smaller standard errors (SEs) for the treatment effect. The amount by which the SE is reduced depends on the correlation between the covariates and the outcome; the higher the correlation, the larger the increase in power.

Randomization ensures that, on average, both known and unknown covariates are well balanced between treatment groups. However, randomization does not guarantee balance; in any individual trial, there may be large imbalances in important prognostic covariates between treatment groups merely by chance. Any such imbalance can give an unfair advantage to one treatment group over another if not accounted for in the analysis. Therefore, prespecifying that important baseline covariates are included in the analysis will help to ensure that any chance imbalances between treatment groups in these covariates will not affect treatment effect estimates.

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4022337/

I'd imagine the details of how the powering could be adjusted according to perhaps a highly predictive covariate is likely located in the Statistical Analysis Plan (SAP) which was never accidentally leaked for that brief two hour period back in February 2014.