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Data already delayed from Q4 '23 to this quarter. My gut feeling says the results are poor but will see.
Hey loser. Check out TPTI
Presentation is tomorrow
will go match higher soon
nice 1.40s
ACHL...HERE WE GO...PIVOT UP AHEAD AT 1.63
ACHL...NEXT BIO BEAST READY T BEAST
https://finviz.com/quote.ashx?t=ACHL&p=d
$ACHL Q1 catalyst and trading well below cash of $3.5 I think. I like risk reward here because of negative EV (trading under cash value)
— Pharmdca (@Pharmdca) February 20, 2024
Took a position last week around 0.98
This stock has so much cash but these guys can’t keep their stock up. I think I will be exiting this situation. I think they will might loose their NASD listing as well. Brutal
How can this management let this stock trade down under a dollar when they have approximately 140 million cash in the bank. They should look for a new CEO this deal.
True, but they still have quite some way to go when it comes to manufacturing, with dose, reactivity, and phenotype all playing a role https://www.science.org/doi/10.1126/science.abb9847
More targets better than less. Frameshift mutations matter the most. Targets not enough if T cells getting exhausted anyway. They need a process similar to LuminICE
One implication is that strategies that attempt to enhance (subclonal) neoantigen burden alone could fail.
(OT): In this paper the authors also reanalysed human clinical trial data of colorectal and gastric cancers with MMRd and show that clonal, but not subclonal, neoantigen burden is predictive of response to immune checkpoint blockade (anti-PD-1) https://www.nature.com/articles/s41588-023-01499-4
Targeting Clonal Neoantigens in Cancer https://ir.achillestx.com/static-files/d8cbf4d2-0364-4cb6-8a1e-4728edcf8f4d
New Immunogenicity Prediction Application of its AI-Powered PELEUS™ Platform Uniquely Identifies the Most Potent T Cell Antigens https://finance.yahoo.com/news/achilles-therapeutics-immunogenicity-prediction-application-110000181.html
“HLA loss is an important mechanism of immune escape in cancer, and neoantigens that are only presented by HLA alleles that are lost in a tumor are unlikely to represent effective therapeutic targets. By determining which specific HLA alleles might have been lost, we can focus therapeutic efforts on neoantigens that are much more likely to elicit a response,” said Dr Sergio Quezada, Chief Scientific Officer of Achilles Therapeutics. “Crucially, the method described in the patent uses sequencing data that is commonly available for tumors, so both the neoantigen identification and the detection of HLA loss can be performed from the same sequencing data. We are currently using this technology within our PELEUSTM bioinformatics platform for research purposes and look forward to determining how to best use it to guide decisions in our clinical trials and beyond.”
The method described in the patent relies on the calculation of an allele-specific copy number for HLA alleles by aligning sequence data to a patient specific reference rather than a standard genomic reference. The patent covers research performed by Achilles co-founder Prof. Charlie Swanton and his academic team, first published in Cell 2017 https://www.cell.com/cell/fulltext/S0092-8674(17)31185-6
https://finance.yahoo.com/news/achilles-therapeutics-announces-grant-us-110000522.html
''During the development of our clonal neoantigen T cell therapy (cNeT) for treatment of solid tumours, we identified and screened circa 10,000 clonal neoantigens for T cell reactivity using cells grown from tumour infiltrating lymphocytes. Using this unique data, we developed and validated an AI method for predicting neoantigen immunogenicity, significantly outperforming existing tools. This technology has broad applicability for optimising target selection across all types of personalised neoantigen therapies.'' https://www.immuno-oncologyeurope.com/t-cell-therapy
A virtual panel will take place on Thursday, Oct 13, at 12:30pm ET / 5:30pm UK and will be moderated by LifeSci Partners Managing Director, Neil Canavan. The panel will highlight neoantigens as valuable targets in immunotherapy, neoantigen discovery, selection, and prediction, and tumour-infiltrating lymphocyte therapies as a platform for mobilising neoantigen reactive T-cells https://lifesci.rampard.com/WebcastingAppv5/Events/eventsDispatcher.jsp?Y2lk=MjA0Ng==
AI model will be great for diagnosis, chronic diseases mgt., not treating cancer progressing rapidly.
The data model is not biased compared to hospital protocols which are profit driven. Just look at the covid horse paste/do nothing fiasco. How many elderlies could have been saved with their parasites wiped out?
I do think AI is overhyped, but not machine or deep learning https://aacrjournals.org/cancerdiscovery/article/12/2/372/678469/Functional-Precision-Medicine-Provides-Clinical
But the key is how each company plans to continue to enhance their platform(s).
Deep learning is hype. AI is modeling if you understand how neural networks work.
PELEUS was designed and trained on validated data from the TRACERx study. It is the largest longitudinal patient data set with 3,200 tumour multi-regions (both primary and met sites) collected from 795 NSCLC patients over five years.
The predicted clonal neoantigens are further validated prospectively with patient samples (over 120 for different types to date). Also, deep learning algorithms are being used to enhance the prediction of neoantigen immunogenicity. So I think this will improve as times goes on. The next update will be in 2H with higher-dose cNeT (process two) and in combo with an anti-PD-1 (both process one and two).
bounced off the low from Mar 07 2022 2.7301
amazing how these things work
#RobotTrading
It took 40 years for neural network to do skin deep facial recognition. from 2X2 to 300X300 just to solve 5 holes visualization. It will take multiple 3000X3000 to see anything in TME.
5055 - Precise segmentation of growth patterns in TRACERx lung adenocarcinoma https://www.abstractsonline.com/pp8/#!/10517/presentation/18433
5636 - V-delta-1 T cells are resident in the human lung and associate with survival in patients with non-small cell lung cancer in the TRACERx Study https://www.abstractsonline.com/pp8/#!/10517/presentation/18670
5710 - Identification of convergent gene repression mechanisms through integrative genomic and DNA methylation analysis in TRACERx https://www.abstractsonline.com/pp8/#!/10517/presentation/18766
5818 - Features of cancer cachexia in non-small cell lung cancer: Insights from the prospective TRACERx study https://www.abstractsonline.com/pp8/#!/10517/presentation/18790
6080 - The heterogeneity and evolution of lung neuroendocrine tumors within TRACERx https://www.abstractsonline.com/pp8/#!/10517/presentation/18959
6091 - Evolutionary characterisation of lung adenocarcinoma pathological subtypes in TRACERx https://www.abstractsonline.com/pp8/#!/10517/presentation/18970
6113 - Spatially resolved biomarker detection on single cells in TRACERx using multiplex imaging https://www.abstractsonline.com/pp8/#!/10517/presentation/18032
6217 - TP53 loss with whole genome doubling mediates heterogeneous intra-patient therapy response in EGFR-driven lung adenocarcinoma: A TRACERx study https://www.abstractsonline.com/pp8/#!/10517/presentation/19053
LB504 - Automated grading of growth patterns in lung adenocarcinoma-from TRACERx to LATTICe-A https://www.abstractsonline.com/pp8/#!/10517/presentation/19990
Lung cancer evolution and its prognostic impact https://www.abstractsonline.com/pp8/#!/10517/presentation/435
692 - Patient-derived co-cultures of TRACERx lung cancer organoids and autologous T-cells reveal heterogeneity in immune evasion between cancer subclones https://www.abstractsonline.com/pp8/#!/10517/presentation/13698
645 - Heterogeneity of immunotherapy biomarkers in the TRACERx non-small cell lung cancer multi-region lung cancer cohort study https://www.abstractsonline.com/pp8/#!/10517/presentation/12564
674 - Tracking the transcriptome of lung cancer-associated fibroblasts within the TRACERx lung study from patient to culture models reveals phenotypic plasticity and instructive cues from cancer cells https://www.abstractsonline.com/pp8/#!/10517/presentation/13235
1211 / 7 - Allele-specific copy-number based deconvolution of bulk tumour RNA sequencing data from the TRACERx study https://www.abstractsonline.com/pp8/#!/10517/presentation/15832
1394 / 25 - Pervasive allele specific transcriptional repression of the class I and II HLA genes in TRACERx non-small cell lung cancer https://www.abstractsonline.com/pp8/#!/10517/presentation/14639
1591 / 6 - Convergence of antigenic diversity and Tex-cell stability fatally constrains immune surveillance in non-small cell lung cancer (TRACERx) and HIV-1 infection (Protocol C) https://www.abstractsonline.com/pp8/#!/10517/presentation/14124
1603 / 18 - Genomic transcriptomic evolution in TRACERx lung cancer and metastasis https://www.abstractsonline.com/pp8/#!/10517/presentation/14137
1699 / 16 - SignaTree: A tool to identify evolutionary trajectories of the activity of mutational processes in TRACERx https://www.abstractsonline.com/pp8/#!/10517/presentation/14141
1926 / 5 - Machine learning-enhanced image and spatial analytic pipelines for imaging mass cytometry applied to the TRACERx non-small cell lung cancer study https://www.abstractsonline.com/pp8/#!/10517/presentation/14797
2197 - Targeted cancer therapy induces APOBEC fueling the evolution of drug resistance https://www.abstractsonline.com/pp8/#!/10517/presentation/13694
2144 - Holistic sampling of clonal dynamics using cfDNA in lung TRACERx https://www.abstractsonline.com/pp8/#!/10517/presentation/12566
2784 / 18 - A robust copy number based clonality classification for multiple pulmonary tumors in routine pathology practice by shallow next generation sequencing of formalin fixed clinical specimens https://www.abstractsonline.com/pp8/#!/10517/presentation/12713
3043 / 1 - Exploring the microbial landscape of NSCLC through TRACERx https://www.abstractsonline.com/pp8/#!/10517/presentation/11523
3096 / 3 - Multi-region patient-derived xenograft models from non-small cell lung cancer patients enrolled in lung TRACERx https://www.abstractsonline.com/pp8/#!/10517/presentation/14356
LB153 / 6 - Clinical relevance of spatial intermixing of growth patterns and immune infiltration in lung adenocarcinoma-from TRACERx to LATTICe-A https://www.abstractsonline.com/pp8/#!/10517/presentation/20375
3626 - Genomic evolutionary patterns in matched pre-invasive and invasive lung disease in TRACERx https://www.abstractsonline.com/pp8/#!/10517/presentation/16334
3609 - Defining extrinsic and intrinsic mechanisms of immune evasion in TRACERx using imaging mass cytometry https://www.abstractsonline.com/pp8/#!/10517/presentation/13896
3792 / 5 - TRACERx: Mapping the evolution of metastases in non-small cell lung cancer https://www.abstractsonline.com/pp8/#!/10517/presentation/14147
The company will host a KOL webcast & fireside chat moderated by Joe Catanzaro on April 14 at 10:30am ET with Drs. Charlie Swanton, Sergio Quezada and Iraj Ali to discuss the TRACERx study and highlight select AACR posters and presentations (of thirty-one).
ADXS also found numerous frame shift mutations common on tumor types, which resulted in long peptides in their HOT vaccine. Are clonal neoantigens same as frame shift mutations?
No, but the data its trained on is designed to distinguish tumour DNA from non-tumour DNA and clonal from sub-clonal neoantigens*. For any clonal sequences, they create peptides and use them for (patient-derived) DC co-culture, before T-cells (both CD4+ and CD8+) are added, reactivates are identified, and then selective expansion of them.
In 2H there will be additional low-dose mono data, higher-dose (Process 2) mono data, and combo data (plus Opdivo in melanoma, NSCLC is slightly behind).
* https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.1001906 https://www.nejm.org/doi/full/10.1056/NEJMoa1616288 https://www.nature.com/articles/s41586-019-1032-7 https://www.nature.com/articles/nature22364
Computer prediction has not worked for ADXS and many other bios
PELEUS was designed and trained using TRACERx data. That was longitudinal across five years from over 780 NSCLC patients (deep sequencing, multi-region and multi-time points, with over 3,000 tumour samples). After DNA seq they use it to compare tumour DNA to healthy DNA and select clonal sequences for use in dendritic cell co-culture.
As for VELOS, process two generates products with increased clonal neoantigen reactivities, as well as higher doses.
Looking at sitc poster, 2 pts were dosed at about 50 mil and 200 mil. cells. PELEUS seems to be the bigger issue even if the updated process can boost the dosage.
New PR https://www.globenewswire.com/news-release/2021/12/09/2349042/0/en/Achilles-Therapeutics-Presents-Positive-Data-at-ESMO-I-O-Congress-2021-on-High-Dose-Manufacturing-Process-for-Precision-T-Cell-Therapies-Targeting-Clonal-Neoantigens.html
Poster https://ir.achillestx.com/static-files/3fd3b623-b2ce-4cf1-a63a-6493ff90117a
ACHL is making progress while GNCA is running out of breath.
From the PR: ''The data we presented today continue to illustrate the differentiated profile of our cNeT product and overall platform that builds on standard TIL therapy by leveraging clonal neoantigen targeting to deliver a more precise and potent product,'' said Dr Iraj Ali, Chief Executive Officer of Achilles. ''The ability to reliably detect and quantify our active component is a key differentiator of our world-class technology that is unique in the field and which we believe will be critical for the successful development of TIL-based therapies.''
Engraftment Kinetics, Quantification and Tracking of cNeT in CHIRON and THETIS
Data presented from the first eight patients dosed across the first-in-human PI/IIa CHIRON and THETIS trials confirm the ability of Achilles' VELOS manufacturing process to generate fit, polyclonal cNeT that can target multiple cancer neoantigens present on all tumour cells. Achilles' platform can detect, quantify and track the patient-specific cNeT during manufacturing and post patient administration, allowing for extensive product characterization and immune-monitoring.
At the data cut-off for this presentation, five patients with melanoma (THETIS) and three patients with NSCLC (CHIRON) had received their cNeT infusion. The median age of the cohort was 57 years and patients had received a median of 2.5 lines of prior therapy. 88% (7 of 8) of the cNeT products dosed targeted multiple clonal neoantigens present on all tumor cells. In these seven products, the number of individual reactivities ranged from two to twenty-eight and cNeT were detected in the blood of 71% (5 of 7) of the patients following infusion at time points up to six weeks post dosing. Best response in the eight dosed patients was stable disease in 63% (5 of 8) in this initial, low-dose cohort generated using VELOS Process 1. The tolerability profile was generally similar to that of standard TIL products that have not been enriched for cNeT reactivity, with none of the higher-grade adverse events more commonly associated with the use of higher doses of interleukin-2 (IL-2). There were no suspected unexpected serious adverse reactions reported since the previous update on the first six patients earlier in 2021. Overall, in the cohort there were three events of cytokine release syndrome and one ICANS event deemed to be possibly related to cNeT treatment. A previously disclosed case of encephalopathy was subsequently deemed unlikely related to cNeT treatment following an Independent Data Safety Monitoring Committee review.
''The encouraging data from this low-dose cohort are important as they show how the Achilles platform can answer potency questions, gives a first look at mechanism of action in a TIL product, and adds confidence to now move to higher cell doses,'' said Dr Samra Turajlic, THETIS Chief Investigator, Royal Marsden NHS Foundation Trust, London, UK. ''I look forward to exploring higher median doses from VELOS Process 2 manufacturing that should more predictably be in the anticipated therapeutic range, based on work done with other cell therapies. As we move to higher cNeT doses I expect improved cellular engraftment, both in terms of peak expansion and durability, and hope to see greater evidence of anti-tumour activity.''
The median cNeT dose in patients in this low-dose, Process 1 cohort was 14.2 million cNeT, which is in line with previous updates. VELOS Process 1 manufacturing generated doses between 0.1 million and 287 million cNeT. cNeT reactivity, defined as the percentage of clonal neoantigen-reactive cells in the final dosed products, ranged from 5% to 77%. As the dataset expands and matures, these metrics of detection and expansion will be correlated with product, clinical and genomic characteristics to determine variables associated with peripheral cNeT dynamics and clinical response.
VELOS™ Process 2 Manufacturing
Achilles' VELOS Process 2 manufacturing generated an 18-fold increase in cNeT compared to Process 1 in this proof-of-concept study. The increased cNeT contained multiple polyclonal reactivities and key phenotypic features associated with high cell fitness and reduced cell exhaustion. VELOS Process 2 improves upon Process 1 by introducing additional culture media supplementation and an expansion-boosting stimulation cocktail during the co-culture period, without adding any time to the overall manufacturing process. Complementary GMP scale manufacturing data from Process 2 will be presented at the ESMO Immuno-Oncology Congress taking place December 8-11, 2021. This GMP scale manufacturing is identical to the process for Achilles' clinical studies and formed the basis of the Company's regulatory submissions to move the ongoing clinical studies to Process 2.
''We are thrilled to see that our Process 2 generated such a robust increase in cells while maintaining a highly functional phenotype and we look forward to treating patients with higher doses manufactured using VELOS Process 2,'' said Dr Sergio Quezada, Chief Scientific Officer of Achilles. ''Based on our experience with other cell therapies, we are confident that Process 2 will deliver doses able to elicit detectable clinical activity.''
Achilles' proprietary potency assay enabled the quantification of the proportion of tumour reactive cNeT within the expanded TIL population. Both processes generated CD4+ and CD8+ cells able to recognize clonal neoantigens. Process 2 delivered a polyclonal product with a median of five neoantigen reactivities (range 3 to 18) detected per patient. The immunophenotype of cNeT generated by Process 1 and 2 was largely similar, with the majority of the cells bearing an effector memory phenotype. Cells generated from both processes are also functionally similar as determined by their ability to secrete INF-?, IL-2 and TNF-a in response to polyclonal stimulus.
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