Sanna Madan

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

Doctor Behind Award-Winning Parkinson’s Research Among Scientists Purged From NIH Leading scientists at the National Institutes of Health, the US’s leading medical research agency, were swept up Tuesday in the Trump administration's latest firing blitz.

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

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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

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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...

Single-cell-guided identification of logic-gated antigen combinations for designing effective and safe CAR therapy Chimeric antigen receptor (CAR) T-cell therapy has revolutionized the treatment of hematological malignancies. However, its application in solid tumors remains limited because single targets are unlik...

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

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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.