Replies to post #478821 on NorthWest Biotherapeutics Inc (NWBO)

[pgsd]

Thanks flipper, very interesting. Looking forward to what they finally disclose :-)

[flipper44]

The L.O.R. tells me all I need to know. :-)

[sentiment_stocks]

Interesting stuff... lots of it went over my head, but looking back to what Dr. Brem stated at the end of the NYAS presentation:

Yeah, I wanted to answer David’s (Reardon) question… and congratulate Paul Mulholland, the Northwest Bio team. Without being self-congratulatory… full disclosure… I’m one of the seventy authors, and we’ve been waiting for an advance like this for a long time.

David, in addition to the external controls and the numbers, we have some unpublished data from (garbled Penn?) and Dr. Mulholland’s team has been looking at radiological markers, and we’ll present that in June. That will obviously add to the story in terms of using each patient as a control, and having another dimension of efficacy. Thank you.

So it looks like UPenn developed some sort of prediction model based on a variety of radiomics and genomics whereby they could then predict how long a GBM patient might live based on their own individual set of the data they were measuring (age, amount of resection, type of tumor, etc., etc.). So, in a sense, as Dr. Brem stated, each person represented their own control... e.g., based on this, this and this, that patient would be predicted to live "this" long. Following this assumption, if they applied this model to the actual P3 DCVax-L patients, one could compare the predicted OS to the actual OS.

It kind of reminds me of the Wheler method used to measure the DCVax-Direct patients as described in their September 29, 2016 PR.
https://nwbio.com/nw-bio-presents-further-dcvax-direct-phase-i-trial-information-on-individual-patient-survival-at-ny-cancer-immunotherapy-conference/

It looks like target genes played some sort of role in the formula described in your linked paper:

Target genes comprised: TP53, PTEN, ATRX, EGFR, VEGF, PDGFRA, PIK3CA, PIK3R1, NF1, PDL1, CTLA4, HIF1A, MDM4, RB1, STAT1, CD70, CIC, FUBP1, CDK4, ACADL, TRIM26, SMAD1, ARID2, CDKN1B, CREBZF, DNMT3A, EPHB2, ERRFI1, FGFR2, GIGYF2, KDR, KRAS, MAP3K1, MET, NIPBL, NOTCH2, NRAS, NTRK1, PTBP1, PTPN11, SETD2, SMARCB1, TP63, WRN. The hotspots included IDH1 p.R132, IDH2 p.R172, BRAF p.V600, H3F3A p.K28, TERT (chr5:1295151–1295315).

This May 2020 PMC Cells article "Understanding Glioblastoma Biomarkers: Knocking a Mountain with a Hammer", describes in deep detail some of the biomarkers found in GBM stem cells, as well as how they might interact given certain conditions (hypoxia, chemo), as well as how they might evolve or transform. While it's quite a blur for my brain to take in now, I thought I'd at least note the biomarkers detailed in the article for future reference, should they come up.

CD133
CD44
CD15
CD70 (CD27L)
S100A4
ALDH1A3
NANOG
OCT-4
SOX-2
NESTIN

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7291262/

I hope we are able to learn more details in June about what Dr. Brem was referencing.