This small proof-of-concept study can give us a glimpse into the future of cancer therapy.
Let’s unpack the details & relevance of this study👇🧵
First things first:
Here’s the link to the paper, which has been made public by @Nature in an unedited form (before official publication), due to its perceived immediate relevance to the research & clinical cancer communities.
Most cancerous lesions have mutations in the 🧬of their cells.
Cancer cells (literally) display small pieces of cellular mutated proteins (called peptides) on their surface.
These peptides, called neoantigens, are important therapeutic targets.
How neoantigens are targeted:
1. major histocompatibility complex (MHC) molecules present a given neoantigen on the cell’s surface.
2. if any T cell can recognize the neoantigen via its receptor (TCR=T cell receptor), then it binds to it.
3. T cell destroys 🤺the mutated cell.
Many challenges to this process exist, including the fact that the binding TCR-MHC (or HLA, as human MHC=HLA) is very specific.
The human gene loci encoding the HLA are diverse, meaning that multiple different HLA phenotypes exist (each person has their own 🧬configuration 🧩).
Currently, most therapies are limited to patients with very few HLA phenotypes & are unavailable for the rest.
However: TCRs that bind to multiple HLA alleles could recognize neoantigens better & kill cancer cells more efficiently.
Hence: TCR engineering has huge potential🔮
Current TCR engineering approaches rely mostly on recombinanat viral vectors,which are hard to scale.
In turn, #CRISPR would facilitate simultaneous & more efficient knockouts of the endogenous TCR chains while inserting the transgenic TCR under the physiologic TCR promoter 🧬✂️
B. Solution💡proposed in this work:
An efficient method part of Adoptive Cell Therapy (immunotherapy) to:
1. Identify expressed tumor mutations using bioinformatics pipelines 💻.
2. Prioritize neoantigen-peptide HLA candidate pairs per patient using bioinformatics & synthesize them (up to 104 per patient, corresponding to 34 unique HLAs).
3. Isolate & enrich CD8 Tcells from the patient’s blood.
4. Stain these T cells for the library created in step 2👆to isolate rare blood-circulating Tcells predicted to both recognize patient-specific antigens & bind to their HLA complexes (median/patient:8 TCRs for 5 antigens).
5. CRISPR engineer 🧬✂️ healthy donor CD8 & CD4 Tcells to mimic the identified rare cells by knocking out the endogenous TCR beta chain & inserting the transgenic TCR alpha & beta genes in the endogenous locus.
6. Test newly assembled neoTCR candidates for specificity of HLA binding (57% ok) & select confirmed neoTCRs for clinical manufacturing (max 3/patient).
Caution: verifying is a must,as previously found antigens can be lost (bc of high mutability, sublonality,loss of HLA allele).
7. Put the expression of the neoTCR constructs under endogenous promoter regulation & infuse 💉cells in patients (across patients, they make up from 2% to 47% of live cell product).
8. Characterize how the engineered neoTCR T cells work inside patients & assess efficacy.
C. Results 🏥: How do neoTCRs act in patients?
- they exist intra-tumorally, in close contact with cancer cells. Also present in metastases at higher freq. than baseline native T cells w same antigen-specific TCR
- their phenotype shifts from effectors to stem & central memory
However‼️
The given T cell dose was low ➡️ limited in-vivo expansion ➡️ low chance of clinical efficiency/benefit.
Disease outcome:
- only minor side effects
- 11/16 patients: disease progression
- 5/16 patients: stable disease at first tumor assessment (day 28 post-infusion)
Additional current hurdles 🏔️
1. Time⏳: the road to clinical implementation is very slow.
Screening ➡️ TCR discovery: median 167 days
+
Enrollment ➡️ dosing (product manufacturing): median 102 days
2. Cost💰: this therapy is very expensive per patient, hence cannot be scaled.
How to increase 📈 neoTCR efficacy?
- strengthen conditioning chemotherapy
- add IL-2
- increase neoTCR positive cells & decrease WT cells in final product
How to decrease 📉 time & cost?
- automation, e.g. establish existing libraries of common HLA alleles-neoantigens pairs.
D. Relevance🔮: why is this study important?
1. One of the most complex therapies ever attempted!
2. First time CRISPR-engineered TCRs are tested in human solid tumors
3. Probably a main therapy of the future & opens way to new clinical routes
4. Proof of biomedical progress
Takeaways ✍️
1. This small phase 1 study is about the feasibility & safety of non-viral precision genome engineering 🧬✂️ to generate personalized T cells with neoantigen-specific TCRs for Adoptive Cell Therapy.
Such an approach is much more efficient than virus-based vectors.
2. The therapy is safe, but also of limited clinical benefit, very lengthy, expensive & complex.
3. In the same time, it is an amazing 🤩 evidence of tens of years of research & clinical progress across many areas s.a. immunotherapy, sequencing, genome editing & bioinformatics.
4. There’s tremendous room for improvement! Now is the time to join forces & push forward to create version 2.0 of genetically engineered fighters🤺 to fight against a huge evil.
We don’t yet know how this fight ends. But a new road to walk this hard path has just been found 🌄
• • •
Missing some Tweet in this thread? You can try to
force a refresh
New🔥 #DataScience#Bioinformatics resource: 850,000‼️ #scRNAseq cells from 226 samples across 10 cancer types draw a map of the tumor microenvironment, in particular fibroblasts.
Let’s see👇what are the main contributions of this work & what this means for #cancer#Genomics🧵
But first, some background.
Cancers are (unfortunately) complex ecosystems,consisting of various types of cells.
Malignant cells represent only a fraction of the tumor. The rest is made of the tumor microenvironment/TME (fibroblasts + immune cells), with complicated dual roles.
Understanding the essence of this duality is key in understanding why most cancer therapies fail.
TME cells are plastic & can easily change states.
The same TME cells can either promote or suppress tumor development, depending on very subtle factors totally not well understood.
2. Further, this next tutorial walks us through graphs & GNNs in an intuitive manner, while also going quite deep into the specific mathematical terminology of the field.
It’s that time of the year🎃when students are deciding if to apply for #PhDpositions👻
Even though #Academia is far from perfect:doing a #PhD is valuable.
It develops unique & important skills that will stay with you forever
These PhD skills are totally☹️not discussed enough👇
1. Ability to work interdependently🔁(independently & with others).
Doing a PhD requires individual planning, as well as collaborating and working within teams. The ability to dive really deep into both these areas *simultaneously* is one of the defining features of a PhD.
2. Self-motivation & drive
Doing a PhD can also be hard & frustrating😮💨. You'll sometimes find yourself working alone, with no clear goals on the project, and no end in sight.
Making it work somehow nurtures your ability to motivate yourself when faced with hard situations.
1.STELLAR🇺🇸 @jure: a cell type annotation & discovery atlas-type framework
2.NCEM🇪🇺 @fabian_theis: an approach to infer cellular communication patterns
Deep dive below🧵
But first, some background.
Spatial molecular biology has actually been around since the 70s. @lpachter's wonderful book-like article "Museum of Spatial Transcriptomics" comprehensively discusses history, tech & methodology advances in the past 50 years. nature.com/articles/s4159…
Nevertheless, recent advances in single cell molecular technologies (brought by e.g. @10xGenomics & @AkoyaBio) have facilitated the high-throughout profiling of (groups of) single cells in their tissue context across embryogenesis, normal tissue development & disease progression.
When interpreting #Bioinformatics results,don't cherry-pick your gene/pathway results!
Don't only discuss/analyze the specific hits that support your hypothesis
Let the data speak for what it is.
Here's hands-on advice on how to NOT overinterpret🧵
1. Filter & sort your list:
Most people use cutoffs for adjusted p-value, but few do so for effect size. While the 2 are correlated, some genes/pathways do show significant, but minuscule, changes among conditions.
‼️Don't overinterpret such changes!Nature♥️large effect sizes
2. Sort your genes/paths by effect size.
3. Do NOT cherry-pick your favorite gene, but with random rank in the list,to center your story on.
Think of it as the Olympics:you are mostly interested in top candidates. Never heard of them? Obscure hits?
Today's amazing science dives deep into the 2 strongest #cancer modulators: evolution & immune defense.
First-ever detailed temporal evolutionary trajectories for 600,000 B cell lymphoma immune cells #scRNAseq & #scTCRSeq of 32 patients during immunotherapy with 2 CAR-T drugs 🧵
First, what is chimeric antigen receptor (CAR) T cell therapy?
It is an immunotherapy in which the patient's own immune cells are genetically engineered ex-vivo to recognize, attack & kill tumor cells. Then they are infused back into the patient, ready to fight the enemy!🤺 2/13
Immunotherapies have revolutionized cancer treatment & are among the most promising future approaches.
However: response rates, even if varying across cancers, remain limited, with e.g. 50% response in lymphomas.
Why such therapies fail for the other half remains a mystery 3/13