Preprint: Characterization of the dual roles of senescent-like T cells that arise during healthy and unhealthy ageing
www.biorxiv.org/content/10.1...
@drsianh.bsky.social
NEW: The doctor behind breakthrough Parkinson’s research was among the scientists purged from the National Institutes of Health, the US’s leading medical research agency. www.wired.com/story/doctor...
Decoding sequence determinants of gene expression in diverse cellular and disease states [updated]
Predicts gene expression from DNA via single-cell data; reveals regulation & variant effects.
Enhancing CAR-T cell activity prediction via fine-tuning protein language models with generated CAR sequences [new]
Fine-tuning ProtLM predicts CAR-T activity. Augmented CAR seqs enable task-specific adaptation.
The CDC has literally been gutted today.
There’s no need for “April Fools” when reality is a nightmare for every human on this planet.
Thousand of scientists fired.
Entire departments erased.
1/
Happy to share a blog post I wrote on our new computational approach LogiCAR Designer, which identifies logic-gated antigen circuits for precise, next-generation CAR therapies. 🎯🧬
mlandbio.substack.com/p/from-singl...
22/ Check out our bioRxiv manuscript for even more insights :) www.biorxiv.org/content/10.1...
21/ And to our amazing co-authors who made this possible: Alexandra Harris, Huaitian Liu, Andrew Martinez, Saugato Rahman Dhruba, Binbin Wang, Padma Sheila Rajagopal, Sanju Sinha, Aravind Srinivasan, Simon Knott, Shahin Sayed, Francis Makokha, Chi-Ping Day, Gretchen Gierach, Stefan Ambs.
20/ It’s a pleasure to co-lead this work with Tiangen Chang. Huge thank you to my mentors Eytan Ruppin and Alejandro Schäffer for their invaluable guidance.
19/ Our ultimate vision is to realize rationally designed, intelligent cell therapies.
18/ While we focused on breast cancer, LogiCAR designer can be readily applied to any cancer type and even beyond - such as to autoimmune conditions or aging-related pathologies, where targeted cell therapies show increasing promise.
17/ In sum, LogiCAR designer offers a data-driven framework to facilitate the rational design of safer and more effective CAR immunotherapies for cancer, addressing the fundamental challenges of both inter- and intra-tumor heterogeneity.
16/ Personalized LogiCAR circuits could deliver precision-engineered CAR therapies with unprecedented efficacy by addressing each patient's unique tumor heterogeneity.
15/ Strikingly, personalized LogiCAR circuits provide estimated tumor-targeting efficacy tantamount to complete clinical response in 76% of patients and at least partial response for all patients! If achieved clinically, these response rates would revolutionize cancer treatment.
14/ However, even optimized shared circuits can't achieve optimal targeting for all patients. Focusing on our diverse cohort 1, we studied LogiCAR designer's ability to identify individualized CAR circuits *optimized to each patient's tumor.*
13/ But, can we improve upon the estimated 16% complete response rate? We try another strategy: matching each patient to the best possible general LogiCAR circuit. This strategy can boost the predicted complete response rate to 23%.
12/ The results are promising: e.g., our top general circuit 'GABRP OR PRLR OR VTCN1' could achieve minimal response in 85% of patients, partial response in 50% of patients, and complete response in 16% of patients - far outperforming existing approaches.
11/ How might LogiCAR circuits translate to patient outcomes? We mapped efficacy to treatment responses. In oncology, tumor radius reductions (10%, 30%) yield volume reductions (27%, 66%) - defining minimal and partial responses by RECIST. We define >99% volume reduction as complete response.
10/ Remarkably, LogiCAR-identified circuits maintained their superior performance in two independent validation cohorts: 1) a multi-ethnic 82-patient cohort spanning all breast cancer subtypes that we generated here
at the NCI, and 2) a 35-patient TNBC cohort at Cedars Sinai.
9/ We first optimized LogiCAR designer on the 15 public discovery cohorts to identify "shared circuits" across patients. The results were striking: LogiCAR-identified circuits outperform clinical CAR targets and previously identified circuits from two state-of-the-art studies.
8/ For safety evaluation, we used ~700k cells across 31 healthy tissues from the Human Protein Atlas. In our optimization process, we require LogiCAR-identified circuits to meet a stringent safety threshold (set to >90% of healthy cells spared).
7/ To test LogiCAR designer on a large scale, we assembled a first-of-its-kind breast cancer dataset comprising ~2 million cells (>620k tumor cells) from 342 patient samples, consisting of 15 public cohorts and 2 in-house cohorts.
6/ LogiCAR designer is highly efficient. Runtime scales linearly with gene combination size vs. exponentially with exhaustive search. Convergence is independent of input size. For 3-gene circuits, LogiCAR runs in <1 hour on a typical laptop vs. >450 days for exhaustive search.
5/ LogiCAR designer uses a genetic algorithm to discover near-optimal antigen circuits with unprecedented scale and efficiency. It scales to combinations of up to five genes - a feat not previously accomplished to our knowledge.
4/ To address this challenge, we developed LogiCAR designer: a computational framework that identifies logic-gated antigen combinations from single-cell data. It optimizes for circuits that target the majority of cancer cells while sparing healthy tissues as much as possible.
3/ Given this, we asked: can we systematically harness patient tumor single-cell data to identify logic-gated antigen combinations (i.e., “circuits”) for designing CAR therapies that precisely target cancer cells while sparing healthy tissues?
2/ To overcome these challenges, researchers are developing next-gen CAR designs targeting multiple antigens with Boolean logic gates (AND, OR, NOT). These circuits improve efficacy by overcoming heterogeneity, and safety via increased specificity [Williams et al., Science '21].
1/ CAR therapies have yielded tremendous clinical success, especially against malignancies of B-cell origin. However, their success remains limited in solid tumors when using single-antigen targets due to tumor antigen heterogeneity and off-tumor toxicities.
Can we engineer smarter CAR-T cells that target cancer with precise logic? 🎯🧬
So excited to share the heart of my PhD work:
🌟 LogiCAR designer, a framework that identifies logic-gated multi-antigen circuits for next-generation cell therapies 🧩🧵
www.biorxiv.org/content/10.1...
Single-cell-guided identification of logic-gated antigen combinations for designing effective and safe CAR therapy [new]
Finds logic-gated antigen combos in single-cell data for safer CAR-T, exceeding shared methods.
