Supplementary Notes, Supplementary Figures, and Supplementary Table Legends

Abstract Understanding and comparing tumor evolutionary histories is fundamental to cancer genomics, with direct implications for tracking subclonal population dynamics, treatment resistance, and tumor heterogeneity. Clonal trees, widely used to model tumor progression, are rooted, unordered trees in which each node represents a subclone labeled by a set of distinct mutations. Various principled and efficient methods have been developed for inferring clonal trees from either bulk or single-cell sequencing data. However, no existing computational approach offers a method that is both efficient and principled to fully align clonal trees and to compare their subclonal architectures, which limits the robustness of any downstream analysis based on inferred clonal trees. We introduce omlta, the optimal multi-label tree alignment of two clonal trees, which removes the minimum number of mutation labels, so that the remaining trees are isomorphic. Computing omlta is NP-hard. Here, we present a fixed-parameter tractable algorithm to compute the omlta, with a running time of O(L^3 log L 2^k) where L is the number of mutation labels shared between the input trees and k is the minimum possible number of mutation labels that need to be removed for the alignment - which we call omltd, the optimal multi-label tree edit distance. Our approach provides an exponentially better (in k) asymptotic runtime than the state-of-the-art algorithm by Akutsu et al. for computing the classic tree alignment and edit distance, concepts similar to what omlta/omltd optimizes on clonal trees. We applied omlta to 126 multi-sample bulk-sequencing data from the TRACERx study on non-small cell lung cancers by comparing clonal trees inferred by CONIPHER and PairTree. Despite the theoretically exponential runtime, we could compute the tree alignment for each tumor quickly, often within seconds. The omltd between CONIPHER and PairTree clonal trees on the same tumor varies substantially across tumors and the distances are negatively associated with the mean cancer cell fraction among mutations. For the tumors characterized by mutations with low cancer cell fractions, it is thus advisable not to use a single tree, but rather the alignment of multiple alternative trees, so that downstream inferences are informed only by robustly placed mutations. We further evaluated our algorithm on an in-house melanoma sample with clonal trees inferred by PhISCS and ScisTree, highlighting the utility of omlta on trees inferred from single-cell sequencing data. On these datasets, our algorithm completed all analyses in practical wall-clock times and showed that it can identify common evolutionary trajectories among clonal trees representing (i) distinct tumors, (ii) distinct samples from the same tumor, (iii) distinct sequencing data from the same sample. Additional supplementary results demonstrate the robustness of our approach in comparison to alternatives on simulated data. Citation Format: Jacob Gilbert, Chih Hao Wu, Marina Knittel, Alejandro Schaffer, Salem Malikić, S. Cenk Sahinalp. Identifying robust subclonal structures through tumor progression tree alignment [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 6898.

Current CAR-T therapies for solid tumors are limited by suboptimal target selection, with single antigens failing to address tumor heterogeneity and causing on-target, off-tumor toxicities. Systematic identification of multi-target combinations using logic gates (AND, OR, NOT) represents an untapped approach for discovering safer, more effective CAR immunotherapy targets. We developed LogiCAR designer, a genetic algorithm-based framework that systematically screens 2,758 cell surface proteins to identify optimal 1-5 gene logic-gated target combinations (i.e., ‘circuits’) from single-cell transcriptomics data. Applied to the largest breast cancer single-cell dataset (∼2 million cells, >620k tumor cells from 342 patients across 17 cohorts), we optimized tumor-targeting efficacy while maintaining safety across 689,601 normal cells from 31 Human Protein Atlas tissues. Comprehensive target validation included RNA and protein expression profiling across major human tissues and tumor microenvironment specificity analysis. Our systematic approach identified novel cell surface targets with superior profiles compared to current clinical candidates. The top 3-gene circuit ('GABRP | PRLR | VTCN1') achieved 60% tumor-targeting efficacy—234% higher than the best clinical trial targets. Newly discovered single targets (ELAPOR1, PRLR, BAMBI, LDLRAD3) demonstrated consistent tumor-specific expression (>2-fold tumor vs. non-tumor cells, p<0.05) across all datasets, unlike current clinical targets. Safety profiling revealed that many existing targets (BSG, EPCAM, TACSTD2) showed concerning expression in critical normal tissues, while our identified circuits maintained superior safety profiles. Finally, shared circuits were still ineffective for some patients, prompting development of personalized approaches. Individualized target combinations addressed intratumor heterogeneity: in our new 82-patient multi-ethnicity cohort, personalized circuits reached 98% mean efficacy with 76% of patients achieving complete response-equivalent targeting. This work establishes the first systematic molecular target discovery pipeline for multi-antigen therapeutic design, identifying previously unrecognized targets with superior efficacy-safety profiles. Our approach transforms target selection from empirical to data-driven, providing a foundation for next-generation precision therapeutics across cancer types. Sanna Madan, Tian-Gen Chang, Andrew Martinez, Alexandra R. Harris, Huaitian Liu, Saugato R. Dhruba, Binbin Wang, Padma S. Rajagopal, Sanju Sinha, Aravind Srinivasan, Simon R. V. Knott, Shahin Sayed, Francis Makokha, Chi-Ping Day, Gretchen L. Gierach, Stefan Ambs, Alejandro A. Schäffer, Eytan Ruppin. Systematic discovery of logic-gated cell surface targets for enhanced solid tumor CAR therapy through single-cell transcriptomics [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference on Molecular Targets and Cancer Therapeutics; 2025 Oct 22-26; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2025;24(10 Suppl):Abstract nr C042.

Chimeric antigen receptor (CAR) T-cell therapy has transformed the treatment of hematological malignancies. However, its application to solid tumors remains constrained by antigen heterogeneity and on-target, off-tumor toxicities. One critical emerging approach to combat this challenge is logic-gated multi-antigen targeting for both enhanced specificity and efficacy. To address these challenges, we developed a novel genetic algorithm, termed LogiCAR, and applied it to analyze more than a dozen different breast cancer (BC) patient cohorts of single-cell (scRNA-seq) and single-nucleus (snRNA-seq) transcriptomics data from patient tumors and the Tabula Sapiens healthy reference tissue atlas. This approach identifies logical combinations of surface antigens that are selectively expressed in cancer cells vs. noncancerous cells by using the logical operators "AND, " "OR, " and "NOT." The efficacy of a given logic-gated antigen combination is defined as the proportion of tumor cells it targets, while its safety is defined as the proportion of non-tumor cells estimated to be spared by that combination in the tumor microenvironment (TME) and twenty-four Tabula Sapiens atlas healthy tissues. As a benchmark, we first evaluated the single-cell-derived safety and efficacy scores of single-antigen clinical CAR targets. Overall, single-antigen clinical targets have strong safety profiles when evaluated on the Tabula Sapiens cohort. However, when tested on our sc/snRNA-seq BC cohorts, they have low efficacy scores. As benchmarks against which to compare our newly identified combination LogiCARs, we identified EPCAM as the overall most efficacious target (mean patient efficacy = 0.43), and EGFR as the lowest overall safety target (mean patient safety = 0.76).When tested on more than a dozen breast cancer cohorts, including a new multi-ethnicity snRNA-seq cohort of 82 breast cancer patients that we have generated at the NCI, the LogiCAR algorithm identified numerous triplet antigen combinations that outperform the clinically approved/tested single-antigen targets in efficacy measures, while maintaining high safety, displaying the potential to overcome both intra- and intertumor heterogeneity. The top LogiCAR combinations identified across all patients exhibited strong performance across cohorts, and strikingly, across all breast cancer subtypes (HR+, HER2+, and TNBC). LogiCAR successfully identifies highly efficacious and safe combinations in breast cancer, promising to facilitate the clinical development of logic-gated CAR circuits. Sanna Madan, Tiangen Chang, Alexandra Harris, Huaitian Liu, Saugato Rahman Dhruba, Sanju Sinha, Sheila Rajagopal, Aravind Srinivasan, Stefan Ambs, Chi-Ping Day, Alejandro Schaffer, Eytan Ruppin. Single-cell-guided identification of logic-gated combinatorial antigens for effective and safe CAR therapy design [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 3752.

Abstract The advancement of chimeric antigen receptor (CAR) T-cell therapy has been groundbreaking in the treatment of hematological malignancies. However, its application in solid tumors is constrained by antigen variability and unintended off-tumor, on-target toxicities. Addressing these issues, we introduce a genetic algorithm to analyze single-cell transcriptomics data from patient tumors. This method pinpoints sets of surface antigens exclusive to cancer cells, utilizing the logical operators "AND," "OR," and "NOT." Our technique scales to gene combinations of any size and exhibits robust performance across diverse parameter settings. When applied to five breast cancer datasets, this algorithm successfully uncovered triplet antigen combinations superior to current clinical single antigen targets in distinguishing between cancerous and healthy cells. This strategy offers a promising pathway toward the design of more selective and safe CAR therapies. Citation Format: Sanna Madan, Tiangen Chang, Binbin Wang, Saugato Rahman Dhruba, Alejandro A. Schäffer, Eytan Ruppin. Single cell guided identification of logic-gated cell surface combinations for selective and safe CAR therapy design [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr LB245.

Abstract Base editing encompasses techniques that efficiently alter specific nucleotides at the DNA or RNA level. Initially explored for inherited diseases, these techniques hold promise for addressing various genetically driven disorders caused by single nucleotide variants (SNVs). The precise programmability of base editors (BEs) for specific sequences allows customization for rare genetic variants, tailoring them to individual patients within affordability and delivery constraints. Cancer stems from the accumulation of mutations. However, the relevance of BEs in cancer therapy is doubted due to the limited types of mutations they can address within tumors. Yet, their untapped potential in the realm of cancer treatment invites exploration. BEs utilize a modified form of a deaminase enzyme to catalyze the conversion of one nucleotide to another by removing an amino group. A 'classic' BE consists of a deaminase, a Cas nuclease, and a guide RNA (gRNA) ensuring target specificity through Watson-Crick base pairing. An alternative RNA BE, known as Endogenous-ADAR, involves designing a gRNA to recruit native Adenosine Deaminases Acting on RNA (ADAR), responsible for extensive A-to-I(G) editing in mammals. Due to its reliance on ADAR capabilities, Endogenous-ADAR exclusively targets G>A SNVs at the RNA level. Notably, RNA editing, occurring before splicing, provides the flexibility to target all gene regions. Also, editing the RNA sequence is considered 'safer' in the event of an off-target error. Our objective is to systematically explore the potential of Endogenous-ADAR for cancer prevention and treatment. Our first approach evaluates Endogenous-ADAR's potential to revert germline mutations in cancer predisposition genes (CPGs) for cancer prevention. Focusing on CPGs recommended for pediatric genetic testing by NCBI, associated with cancer predisposition disorders, our findings indicate that among the 2,820 SNVs examined, 566 (20%) are suitable for Endogenous-ADAR. Remarkably, 88% of correctable variants show no off-target sites, indicating safe therapeutic targets. Further examining pathogenic high-penetrance germline variants in adults, 48 (20%) of the 239 SNVs examined are correctable using Endogenous-ADAR. Second, we estimate the potential to correct cancer driver mutations. Examining 5,913 driver mutations from 2,010 patients representing 36 cancer types, which underwent whole genome sequencing (WGS) in the PCWAG project, we find that 955 (16%) are suitable for Endogenous-ADAR. In 729 (36%) of patients, at least one driver mutation is correctable, and in 91 (5%), all known driver mutations are correctable. Aligned with the swift incorporation of WGS in oncology, our systematic exploration portrays a favorable landscape of correctable mutations in cancer. This points to the future possibility of leveraging the unique capabilities of base editing, particularly Endogenous-ADAR, for clinical cancer risk/prevention and treatment outcomes. Citation Format: Rona Merdler-Rabinowicz, Ariel Dadush, Sumeet Patiyal, Padma Sheila Rajagopal, Gulzar Daya, Alejandro Schäffer, Eli Eisenberg, Eytan Ruppin, Erez Y. Levanon. A systematic evaluation of the therapeutic potential of Endogenous-ADAR base editors in cancer prevention and treatment [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr LB007.

Small-cell lung cancer (SCLC) is the most fatal form of lung cancer. Intratumoral heterogeneity, marked by neuroendocrine (NE) and non-neuroendocrine (non-NE) cell states, defines SCLC, but the cell-extrinsic drivers of SCLC plasticity are poorly understood. To map the landscape of SCLC tumor microenvironment (TME), we apply spatially resolved transcriptomics and quantitative mass spectrometry-based proteomics to metastatic SCLC tumors obtained via rapid autopsy. The phenotype and overall composition of non-malignant cells in the TME exhibit substantial variability, closely mirroring the tumor phenotype, suggesting TME-driven reprogramming of NE cell states. We identify cancer-associated fibroblasts (CAFs) as a crucial element of SCLC TME heterogeneity, contributing to immune exclusion, and predicting exceptionally poor prognosis. Our work provides a comprehensive map of SCLC tumor and TME ecosystems, emphasizing their pivotal role in SCLC's adaptable nature, opening possibilities for reprogramming the TME-tumor communications that shape SCLC tumor states.

Abstract Introduction: Gliomas, including aggressive forms such as glioblastoma (GBM) and lower-grade gliomas (LGG), present formidable challenges for treatment. Since the late 1980’s it has been known that loss of chromosome 10 (10 loss) and gain of chromosome 7 (7 gain) often co-occur in GBM. The reasons for this frequently co-occurring event are not very clear, with some prior studies speculating on a few driver genes as culprits. In this study, we aim to study and understand the phenomenon of 10 loss and 7 gain in gliomas in a systematic, chromosome-wide level. Methods: Our analysis consists of three main analyses, that bring independent sources of evidence to address our research question: a) Analyzing data from Progenetix, we developed rigorous probabilistic models that elucidate the probability dynamics of 10 loss and 7 gain in gliomas. b) Next, by analyzing hundreds of genomic and transcriptomic samples from TCGA brain cancer patients, and cell lines, we identified a category of clinically relevant genetic interactions termed 'synthetic rescues'. Synthetic rescue (DU-SR) interactions refer to a form of functional interplay in which the detrimental impact on cell fitness caused by the inactivation of a specific gene (termed ‘vulnerable gene’) is compensated by upregulation of another gene (termed ‘rescuer gene’). The enrichment of rescuer genes on 7 for the vulnerable genes on 10 was then tested. c) Utilizing large-scale in vitro essentiality screens via CRISPR technology in central nervous system (CNS) cancer cell lines, we sought to investigate the fitness effects of different possible sequences of chromosomal 10 and 7 loss and gains. Results: The main results emerging from this three-pronged analysis are as follows: 1) We find that 7 gain after 10 loss is a more likely event than the opposite order of loss of 10 after 7 gain whose probability is explained by a random chance model. 2) We show that loss of 10 is enabled by the cells’ ability to readily mitigate its loss, specifically through rescue interactions with genes lying on the amplified 7 chromosome. 3) These findings in the analysis of patients’ tumors are further reinforced by an independent investigation in IDH wild-type CNS cancer cell line essentiality screens. Conclusions: The co-occurrence of 10 loss and 7 gain in gliomas is the most frequent loss-gain co-aneuploidy pair occurring in human cancer. Our analysis presents a new multi-pronged approach to analyze co-occurring aneuploidy events in cancer, showing the long-contemplated chromosomal co-occurrence events in gliomas, a mystery that has persisted for decades, is likely to arise from genetic rescue interactions between many genes that lie on those chromosomes. From a future translational standpoint, it additionally points to key rescuer genes that may be potentially targeted. Citation Format: Nishanth Ulhas Nair, Alejandro A. Schäffer, Michael E. Gertz, Kuoyuan Cheng, Avinash Das Sahu, Gil Leor, Eldad D. Shulman, Kenneth D. Aldape, Uri Ben-David, Eytan Ruppin. Why does the loss of chromosomes 10 and the gain of 7 co-occur in gliomas [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2263.

The 2015 American College of Medical Genetics and Genomics and the Association for Molecular Pathology sequence variant classification publication established a standard employed internationally to guide laboratories in variant assessment. Those recommendations included both pathogenic (PP1) and benign (BS4) criteria for evaluating the inheritance patterns of variants, but details of how to apply those criteria at appropriate evidence levels were sparse. Several publications have since attempted to provide additional guidance but anecdotally, this issue is still challenging.

Abstract The co-occurrence of chromosome 10 loss and chromosome 7 gain in gliomas is the most frequent loss–gain co-aneuploidy pair in human cancers. This phenomenon has been investigated since the late 1980s without resolution. Expanding beyond previous gene-centric studies, we investigated the co-occurrence in a genome-wide manner, taking an evolutionary perspective. Mining of large-scale tumor aneuploidy data confirmed the previous finding of a small-scale longitudinal study that the most likely order is chromosome 10 loss, followed by chromosome 7 gain. Extensive analysis of genomic and transcriptomic data from both patients and cell lines revealed that this co-occurrence can be explained by functional rescue interactions that are highly enriched on chromosome 7, which could potentially compensate for any detrimental consequences arising from the loss of chromosome 10. Transcriptomic data from various normal, noncancerous human brain tissues were analyzed to assess which tissues may be most predisposed to tolerate compensation of chromosome 10 loss by chromosome 7 gain. The analysis indicated that the preexisting transcriptomic states in the cortex and frontal cortex, where gliomas arise, are more favorable than other brain regions for compensation by rescuer genes that are active on chromosome 7. Collectively, these findings suggest that the phenomenon of chromosome 10 loss and chromosome 7 gain in gliomas is orchestrated by a complex interaction of many genes residing within these two chromosomes and provide a plausible reason why this co-occurrence happens preferentially in cancers originating in certain regions of the brain. Significance: Increased expression of multiple rescuer genes on the gained chromosome 7 could compensate for the downregulation of several vulnerable genes on the lost chromosome 10, resolving the long-standing mystery of this frequent co-occurrence in gliomas.

Tailoring optimal treatment for individual cancer patients remains a significant challenge. To address this issue, we developed PERCEPTION (PERsonalized Single-Cell Expression-Based Planning for Treatments In ONcology), a precision oncology computational pipeline. Our approach uses publicly available matched bulk and single-cell (sc) expression profiles from large-scale cell-line drug screens. These profiles help build treatment response models based on patients’ sc-tumor transcriptomics. PERCEPTION demonstrates success in predicting responses to targeted therapies in cultured and patient-tumor-derived primary cells, as well as in two clinical trials for multiple myeloma and breast cancer. It also captures the resistance development in patients with lung cancer treated with tyrosine kinase inhibitors. PERCEPTION outperforms published state-of-the-art sc-based and bulk-based predictors in all clinical cohorts. PERCEPTION is accessible at https://github.com/ruppinlab/PERCEPTION . Our work, showcasing patient stratification using sc-expression profiles of their tumors, will encourage the adoption of sc-omics profiling in clinical settings, enhancing precision oncology tools based on sc-omics. Sinha and colleagues present PERCEPTION, a precision oncology computational pipeline that can predict the response and resistance of patients by analyzing single-cell transcriptomic data from their tumor samples.

Abstract The aging of the immune system has profound implications for individual immune responses, yet precise quantification of immune age remains a challenge. Analyzing single-cell peripheral blood mononuclear cells (PBMC) transcriptomics data from 981 healthy individuals, we developed IMMClock (IMMune Clock), a human immune age clock derived from single cell transcriptomics. IMMClock is the first to accurately assess the age of three major immune cell types, CD8+ T cells, CD4+ T cells and NK cells, at the individual level. Testing the ability of these clocks to capture the dynamic aging process, we show that they reliably predict individuals' chronological ages across seven publicly available single-cell transcriptomics datasets and the large Framingham bulk transcriptomics cohort. IMMClock identifies cell type specific age-related pathways including apoptosis, interferon gamma response, and cytokine response. The application of IMMClock reveals several associations between immune aging of different cell-types and key clinical phenotypes during aging: it recapitulates the established observation of higher immune age in males compared to females and uncovers higher immune age in CD8+ T cells of smokers and individuals with chronic illnesses including cancer. Analyzing cancer patients’ data, IMMClock readings strongly correlate with CD8+ T cell exhaustion levels, and, quite strikingly, highlights an elevated immune age in the tumor microenvironment compared to blood. Citation Format: Yael Gurevich-Schmidt, Kun Wang, Di Wu, Sanna Madan, Vishaka Gopalan, Sanju Sinha, Binbin Wang, Saugato Rahman Dhruba, Alejandro A. Schäffer, Eytan Ruppin. Cell-type-specific transcriptomic immune aging clocks reveal clinically relevant associations with chronic illnesses including cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1411.

( Acta Obstet Gynecol Scand . 2023;102:457–464) Preterm birth is a common and impactful complication of pregnancy and is defined as the delivery of a fetus between 20 and 37 weeks of gestation. It can lead to further complications and even developmental problems for the child that can be long term. Standard treatment for symptoms of preterm labor involves tocolytic drugs to stop contractions and delay delivery long enough for interventions to occur, minimizing effects on the infant. Although many different drugs are used, the efficacy and side effects of many are not well characterized or understood. This study was designed to assess the effectiveness and side effects of a combination of drugs: β 2 -agonist terbutaline and magnesium sulfate (MgSO 4 ). This was done by examining the combined administration of the drugs in an isolated organ bath and performing in vivo smooth muscle electromyographic studies in pregnant rats.

Abstract Bispecific antibodies have emerged as promising candidates for cancer treatment, prompting the necessity for a comprehensive understanding of their potential efficacy and toxicity across various cancer types. Leveraging single cell sequencing data, we systematically evaluated the efficacy and toxicity of all cell surface gene pairs across multiple cancers. First, we found that gene pairs involved in successful bispecific antibodies phase I/II clinical trials exhibited significantly higher efficacy scores and lower toxicity scores than gene pairs in those that failed, underscoring the utility of our method in selecting high-quality bispecific pairs. Second, to facilitate the identification of new pairs, we established baseline efficacy and toxicity scores based on successful phase I/II clinical trials, employing these scores to assess the potential of novel combinations. We systematically identified both cancer type-specific and pan-cancer candidate new bispecific antibody pairs. This extensive analysis not only provides valuable insights into the potential of bispecific antibodies to broaden the spectrum of cancer treatments, but also yields a highly confident list of candidate gene pairs capable of constructing effective bispecific antibodies. Citation Format: Binbin Wang, Sanna Madan, Alejandro Schaffer, Eytan Ruppin. Unveiling the potential of bispecific antibodies: A comprehensive analysis of efficiency, toxicity, and repurposing in cancer treatment [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 871.

Background: Synthetic lethality (SL) denotes a genetic interaction between two genes whose co-inactivation is detrimental to cells. Since the seminal paper of Hartwell and colleagues has raised the possibility that SL can be used to devise highly selective cancer treatments, it has been one of the promising approaches for precision oncology and drug discovery. Many different avenues have so far been explored to bring this idea to the clinic. As 25 years have passed by now, we take stock and systematically and comprehensively chart the landscape of SL-based preclinical research and clinical trials. Approach and Key Findings: We systematically mined both public and commercial databases to curate the preclinical and clinical landscape of the SL-based oncology studies. Our analysis shows that the number of SL oncology studies is rapidly growing since the first identification of the well-known BRCA-PARP synthetic lethality axis. Importantly, we find that the success rate of SL oncology trials is significantly higher than non-SL-based trials. While more than 70% of SL-oncology trials involve genes in the most-studied DNA damage response (DDR) pathways, the fraction of SL trials involving non-DDR pathways keeps growing since 2009. We further charted the landscape of SL triplets, which is a promising future higher-order extension of the conventional pairwise SL interactions. We find that only about 8% of preclinically validated SL triplets were clinically tested in trials, providing new opportunities for more refined clinical trial design. Our analysis suggests that emerging opportunities in SL oncology may arise from metabolic and paralogous interactions, disease-agnostic biomarkers, context-specific combinations against treatment resistance, artificial intelligence and data science approaches, and multi-omics patient stratification signatures. Significance: Taken together, our findings testify that a considerable potential benefits of SL based approaches are yet untapped, reinforcing the belief that the synthetic lethality approach may serve as a key driver of precision oncology going forward. Citation Format: Joo Sang Lee, Youngmin Chung, Ashwin V. Kammula, Alejandro A. Schaffer, Eytan Ruppin. A silver jubilee for synthetic lethality in cancer treatment: where do we stand [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 946.

Abstract Chimeric antigen receptor (CAR) T-cell therapy has demonstrated significant efficacy in treating hematological cancers but faces challenges in treating solid tumors due to antigen heterogeneity and off-tumor, on-target expression in normal tissues. To combat these challenges, we developed a novel genetic algorithm approach to analyze patient tumor single-cell transcriptomics data. This algorithm identifies combinations of cell surface antigens specific to cancer cells by incorporating "AND," "OR," and "NOT" logic. Our method is generalizable to combinations of any size and demonstrates rapid convergence across different parameter choices. When applied to breast cancer datasets, our algorithm identified optimal combinations of triplet antigens that outperformed clinically tested single target antigens in discriminating tumor cells from non-tumor cells across patient samples. In sum, our approach may guide the design of logic-gated CAR therapies aimed at multiple target antigens, thereby increasing the safety and selectivity of these therapies. Citation Format: Sanna Madan, Tiangen Chang, Binbin Wang, Alejandro A. Schäffer, Eytan Ruppin. Single cell transcriptomics guided identification of antigen combinations for the design of logic-gated CAR therapies [abstract]. In: Proceedings of the AACR-NCI-EORTC Virtual International Conference on Molecular Targets and Cancer Therapeutics; 2023 Oct 11-15; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2023;22(12 Suppl):Abstract nr LB_B06.

Recent studies exploring the impact of methylation in tumor evolution suggest that while the methylation status of many of the CpG sites are preserved across distinct lineages, others are altered as the cancer progresses. Since changes in methylation status of a CpG site may be retained in mitosis, they could be used to infer the progression history of a tumor via single-cell lineage tree reconstruction. In this work, we introduce the first principled distance-based computational method, Sgootr, for inferring a tumor's single methylation lineage tree and jointly identifying lineage-informative CpG sites which harbor changes in methylation status that are retained along the lineage. We apply Sgootr on single-cell bisulfite-treated whole genome sequencing data of multi-regionally-sampled tumor cells from 9 metastatic colorectal cancer patients, as well as multi-regionally-sampled single-cell reduced-representation bisulfite sequencing data from a glioblastoma patient. We demonstrate that the tumor lineages constructed reveal a simple model underlying tumor progression and metastatic seeding. A comparison of Sgootr against alternative approaches shows that Sgootr can construct lineage trees with fewer migration events and more in concordance with the sequential-progression model of tumor evolution, with a running time a fraction of that used in prior studies. Lineage-informative CpG sites identified by Sgootr are in inter-CpG island (CGI) regions, as opposed to intra-CGIs, which have been the main regions of interest in genomic methylation-related analyses.