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.