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...

19/ Our ultimate vision is to realize rationally designed, intelligent cell therapies.

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.

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...

Incredible experience at the
NCI HTAN Data Jamboree! Grateful to work with a brilliant team to develop HTANalyzer, a multi-agent LLM that helps researchers query and analyze HTAN spatial transcriptomics data through natural language queries. Check out our project github: github.com/NCI-HTAN-Jam...