Chan Zuckerberg Biohub New York

Abstract Broad-spectrum RAS inhibition has the potential to benefit roughly a quarter of human patients with cancer whose tumours are driven by RAS mutations 1,2 . RMC-7977 is a highly selective inhibitor of the active GTP-bound forms of KRAS, HRAS and NRAS, with affinity for both mutant and wild-type variants 3 . More than 90% of cases of human pancreatic ductal adenocarcinoma (PDAC) are driven by activating mutations in KRAS 4 . Here we assessed the therapeutic potential of RMC-7977 in a comprehensive range of PDAC models. We observed broad and pronounced anti-tumour activity across models following direct RAS inhibition at exposures that were well-tolerated in vivo. Pharmacological analyses revealed divergent responses to RMC-7977 in tumour versus normal tissues. Treated tumours exhibited waves of apoptosis along with sustained proliferative arrest, whereas normal tissues underwent only transient decreases in proliferation, with no evidence of apoptosis. In the autochthonous KPC mouse model, RMC-7977 treatment resulted in a profound extension of survival followed by on-treatment relapse. Analysis of relapsed tumours identified Myc copy number gain as a prevalent candidate resistance mechanism, which could be overcome by combinatorial TEAD inhibition in vitro. Together, these data establish a strong preclinical rationale for the use of broad-spectrum RAS-GTP inhibition in the setting of PDAC and identify a promising candidate combination therapeutic regimen to overcome monotherapy resistance.

BACKGROUND: Gastroenteropancreatic neuroendocrine tumors (GEP-NETs) are rare neoplasms with an increasing annual incidence and prevalence. Many are metastatic at presentation or recur following surgical resection and require systemic therapy, for which somatostatin analogs such as octreotide or lanreotide comprise typical first-line therapies. Nonetheless, treatment options remain limited. Epigenetic processes such as histone modifications have been implicated in malignant transformation and progression. In this study, we evaluated the anti-proliferative effects of a histone deacetylase (HDAC) inhibitor, entinostat, which was computationally predicted to show anti-cancer activity, as confirmed in in vitro and in vivo models of GEP-NETs. METHODS: This was a phase II study to evaluate the efficacy and safety of entinostat in patients with relapsed or refractory abdominal NETs. The primary objective was to estimate the objective response rate to entinostat. Additionally, with each patient as his/her own control we estimated the rates of tumor growth prior to enrollment on study and while receiving entinostat. Patients received 5 mg entinostat weekly until disease progression or intolerable toxicity. The dose could be changed to 10 mg biweekly for patients who did not experience grade ≥ 2 treatment-related adverse events (AEs) in cycle 1, but was primarily administered at the starting 5 mg weekly dose. RESULTS: The study enrolled only 5 patients due to early termination by the drug sponsor. The first patient that enrolled had advanced disease and died within days of enrollment before follow-up imaging due to a grade 5 AE unrelated to study treatment and was considered non-evaluable. Best RECIST response for the remaining 4 patients was stable disease (SD) with time on study of 154+, 243, 574, and 741 days. With each patient as his/her own control, rates of tumor growth on entinostat were markedly reduced with rates 17%, 20%, 33%, and 68% of the rates prior to enrollment on study. Toxicities possibly or definitely related to entinostat included grade 2/3 neutrophil count decrease [2/4 (50%)/ 2/4 (50%)], grade 3 hypophosphatemia [1/4, (25%)], grade 1/2 fatigue [1/4 (25%)/ 2/4 (50%)], and other self-limiting grade 1/2 AEs. CONCLUSION: In the treatment of relapsed or refractory abdominal NETs, entinostat 5 mg weekly led to prolonged SD and reduced the rate of tumor growth by 32% to 83% with an acceptable safety profile (ClinicalTrials.gov Identifier: NCT03211988).

Aberrant signaling pathway activity is a hallmark of tumorigenesis and progression, which has guided targeted inhibitor design for over 30 years. Yet, adaptive resistance mechanisms, induced by rapid, context-specific signaling network rewiring, continue to challenge therapeutic efficacy. Leveraging progress in proteomic technologies and network-based methodologies, we introduce Virtual Enrichment-based Signaling Protein-activity Analysis (VESPA)-an algorithm designed to elucidate mechanisms of cell response and adaptation to drug perturbations-and use it to analyze 7-point phosphoproteomic time series from colorectal cancer cells treated with clinically-relevant inhibitors and control media. Interrogating tumor-specific enzyme/substrate interactions accurately infers kinase and phosphatase activity, based on their substrate phosphorylation state, effectively accounting for signal crosstalk and sparse phosphoproteome coverage. The analysis elucidates time-dependent signaling pathway response to each drug perturbation and, more importantly, cell adaptive response and rewiring, experimentally confirmed by CRISPR knock-out assays, suggesting broad applicability to cancer and other diseases.

Abstract Despite high rates of post-surgical recurrence in men with high-risk localized prostate cancer (PCa), there is currently no role for neoadjuvant therapy. Tumor infiltrating regulatory T cells (TI-Tregs) limit the antitumor effects of presurgical androgen deprivation therapy (ADT). Therefore, we designed a neoadjuvant clinical trial to test whether Treg depletion via a non-fucosylated anti-CTLA-4 antibody (BMS-986218) is feasible and augments response to ADT. In this single-center, two-arm, open-label study, 24 men with high-risk localized PCa were randomized to ADT with or without BMS-986218 prior to radical prostatectomy. Treatment was well tolerated and feasible. Mechanistic studies indicated BMS-986218 depleted TI-Tregs by engaging CD16a/FCGR3A on tumor macrophages, modulated dendritic cells (DCs), and augmented T cell priming. Depth of Treg depletion and increased DC frequencies were quantitatively associated with improved clinical outcome. Overall, this study supports the feasibility and biological activity of neoadjuvant immunotherapy with ADT + Fc- enhanced anti-CTLA-4 in high-risk localized PCa. Statement of Significance Next-generation antibodies targeting CTLA-4 have been engineered for enhanced tumor Treg depletion in patients, yet their mechanisms of action remain incompletely defined. We performed the first single cell multi-omic correlative analyses of response to a glycoengineered anti-CTLA-4 antibody and defined mechanisms associated with clinical outcome in patients with high-risk localized prostate cancer.

BACKGROUND: Diffuse midline glioma (DMG) is the most aggressive primary brain tumor in children. All previous studies examining the role of systemic agents have failed to demonstrate a survival benefit; the only standard of care is radiation therapy (RT). Successful implementation of radiosensitization strategies in DMG remains an essential and promising avenue of investigation. We explore the use of Napabucasin, an NAD(P)H quinone dehydrogenase 1 (NQO1)-bioactivatable reactive oxygen species (ROS)-inducer, as a potential therapeutic radiosensitizer in DMG. METHODS: In this study, we conduct in vitro and in vivo assays using patient-derived DMG cultures to elucidate the mechanism of action of Napabucasin and its radiosensitizing properties. As penetration of systemic therapy through the blood-brain barrier (BBB) is a significant limitation to the success of DMG therapies, we explore focused ultrasound (FUS) and convection-enhanced delivery (CED) to overcome the BBB and maximize therapeutic efficacy. RESULTS: Napabucasin is a potent ROS-inducer and radiosensitizer in DMG, and treatment-mediated ROS production and cytotoxicity are dependent on NQO1. In subcutaneous xenograft models, combination therapy with RT improves local control. After optimizing targeted drug delivery using CED in an orthotopic mouse model, we establish the novel feasibility and survival benefit of CED of Napabucasin concurrent with RT. CONCLUSIONS: As nearly all DMG patients will receive RT as part of their treatment course, our validation of the efficacy of radiosensitizing therapy using CED to prolong survival in DMG opens the door for exciting novel studies of alternative radiosensitization strategies in this devastating disease while overcoming limitations of the BBB.

Abstract IgA vasculitis (IgAV) is a pediatric disease with skin and systemic manifestations. Here, we conducted genome, transcriptome, and proteome-wide association studies in 2,170 IgAV cases and 5,928 controls, generated IgAV-specific maps of gene expression and splicing from blood of 255 pediatric cases, and reconstructed myeloid-specific regulatory networks to define disease master regulators modulated by the newly identified disease driver genes. We observed significant association at the HLA - DRB1 (OR=1.55, P=1.1×10 −25 ) and fine-mapped specific amino-acid risk substitutions in DRβ1. We discovered two novel non-HLA loci: FCAR (OR=1.51, P=1.0×10 −20 ) encoding a myeloid IgA receptor FcαR, and INPP5D (OR=1.34, P=2.2×10 −09 ) encoding a known inhibitor of FcαR signaling. The FCAR risk locus co-localized with a cis-eQTL increasing FCAR expression; the risk alleles disrupted a PRDM1 binding motif within a myeloid enhancer of FCAR . Another risk locus was associated with a higher genetically predicted levels of plasma IL6R. The IL6R risk haplotype carried a missense variant contributing to accelerated cleavage of IL6R into a soluble form. Using systems biology approaches, we prioritized IgAV master regulators co-modulated by FCAR , INPP5D and IL6R in myeloid cells. We additionally identified 21 shared loci in a cross-phenotype analysis of IgAV with IgA nephropathy, including novel loci PAID4, WLS , and ANKRD55 .

Embryo size, specification, and homeostasis are regulated by a complex gene regulatory and signaling network. Here we used gene expression signatures of Wnt-activated mouse embryonic stem cell (mESC) clones to reverse engineer an mESC regulatory network. We identify NKX1-2 as a novel master regulator of preimplantation embryo development. We find that Nkx1-2 inhibition reduces nascent RNA synthesis, downregulates genes controlling ribosome biogenesis, RNA translation, and transport, and induces severe alteration of nucleolus structure, resulting in the exclusion of RNA polymerase I from nucleoli. In turn, NKX1-2 loss of function leads to chromosome missegregation in the 2- to 4-cell embryo stages, severe decrease in blastomere numbers, alterations of tight junctions (TJs), and impairment of microlumen coarsening. Overall, these changes impair the blastocoel expansion-collapse cycle and embryo cavitation, leading to altered lineage specification and developmental arrest.

Splicing factors control exon inclusion in messenger RNAs, shaping transcriptome and proteome diversity. Their catalytic activity is regulated by multiple layers, making single-omic measurements on their own fall short in identifying which splicing factors underlie a phenotype. Here, we posit that splicing factor activity can be estimated from changes in exon inclusion. To test this hypothesis, we benchmarked methods for constructing splicing factor→exon networks and estimating splicing factor activity. We found that combining RNA-seq perturbation-based networks with VIPER (Virtual Inference of Protein Activity by Enriched Regulon analysis) accurately captures splicing factor activation as modulated by multiple regulatory layers. This approach integrates splicing factor regulation into a single score derived solely from exon inclusion signatures, allowing functional interpretation of heterogeneous conditions. As a proof of concept, we identify recurrent cancer splicing programs, revealing oncogenic- and tumor suppressor-like splicing factors missed by conventional methods. These programs correlate with patient survival and key cancer hallmarks: initiation, proliferation, and immune evasion. Altogether, we show splicing factor activity can be accurately estimated from exon inclusion changes, enabling comprehensive analyses of splicing regulation with minimal data requirements.

A bstract The ever-increasing availability of large-scale single-cell profiles presents an opportunity to develop foundation models to capture cell properties and behavior. However, standard language models such as transformers benefits from sequentially structured data with well defined absolute or relative positional relationships, while single cell RNA data have orderless gene features. Molecular-interaction graphs, such as gene regulatory networks (GRN) or protein-protein interaction (PPI) networks, offer graph structure-based models that effectively encode both non-local gene token dependencies, as well as potential causal relationships. We introduce GREmLN ( G ene R egulatory Em bedding-based L arge N eural model), a foundation model that leverages graph signal processing to embed gene token graph structure directly within its attention mechanism, producing biologically informed single cell specific gene embeddings. Our model faithfully captures transcriptomics landscapes and achieves superior performance relative to state-of-the-art baselines on cell type annotation, graph structure understanding, and fine-tuned reverse perturbation prediction tasks. It offers a unified and interpretable framework for learning high-capacity foundational representations that capture complex, long-range regulatory dependencies from high-dimensional single-cell transcriptomic data. Moreover, the incorporation of graph-structured inductive biases enables more parameter-efficient architectures and accelerates training convergence.

Understanding how the chromatin state of a cell influences its future behavior is a major challenge throughout biology. However, most chromatin profiling methods are limited to endpoint assays. Here, we present LagTag, a method for recovery of earlier and endpoint chromatin states in the same mammalian cells. In this approach, transient expression of bacterial adenine methyltransferase fusions records the DNA binding profiles of chromatin-associated proteins of interest at earlier timepoints. Subsequent tagmentation and sequencing recovers the earlier chromatin profile from adenine methylation profiles, alongside endpoint profiles of endogenous chromatin-associated proteins. We verified that LagTag profiles aligned with those from established methods in mouse and human cells. More importantly, LagTag was able to record and recover dynamic chromatin state transitions during mouse embryonic stem cell differentiation, capturing transcriptional signatures from pre- and post-differentiation timepoints within the same cell population. LagTag thus provides a foundation for temporally resolved chromatin profiling.

The impact of SARS-CoV-2 in the lung has been extensively studied, yet the molecular regulators of host-cell programs hijacked by the virus in distinct human airway epithelial cell populations remain poorly understood. Some of the reasons include overreliance on transcriptomic profiling and use of nonprimary cell systems. Here we report a network-based analysis of single-cell transcriptomic profiles able to identify master regulator (MR) proteins controlling SARS-CoV-2-mediated reprogramming in pathophysiologically relevant human ciliated, secretory, and basal cells. This underscored chromatin remodeling, endosomal sorting, ubiquitin pathways, as well as proviral factors identified by CRISPR assays as components of the viral-host response in these cells. Large-scale drug perturbation screens revealed 11 candidate drugs able to invert the entire MR signature activated by SARS-CoV-2. Leveraging MR analysis and perturbational profiles of human primary cells represents an innovative approach to investigate pathogen-host interactions in multiple airway conditions for drug prioritization.

Abstract The Human Cancer Models Initiative (HCMI) has developed 665 novel cancer models, including organoids and matched parental tumors, providing a significant addition to existing cancer model resources. To evaluate the transcriptional relatedness of HCMI models and tumors to the Cancer Cell Line Encyclopedia (CCLE) and The Cancer Genome Atlas (TCGA), we used the Celligner algorithm to align transcriptomic profiles across datasets, removing systematic biases while preserving intrinsic biological variability. HCMI models and tumors both exhibited high transcriptional fidelity to TCGA tumors, clustering closely with their respective tumor types in the Celligner-aligned space. Pairwise Euclidean distances showed complementary strengths between HCMI and CCLE models. For example, HCMI models demonstrated significantly closer alignment to TCGA tumors in glioblastoma, breast cancer, and ovarian cancer, while CCLE models performed comparably or better in colorectal cancer. Notably, aligning HCMI tumors to TCGA confirmed their strong transcriptional relatedness, validating the fidelity of these models to their original tumor states. Combining HCMI and CCLE datasets further enhanced the total transcriptional representation across the diversity of TCGA tumors, underscoring the complementary roles of these resources. The HCMI collection uniquely includes rare cancer types such as nephroblastoma, desmoid tumors, and ampulla of Vater carcinoma, which are absent in CCLE. Celligner analysis confirmed that these rare HCMI models faithfully retained transcriptional features of their corresponding TCGA tumors. This expands opportunities to study rare and clinically challenging cancers that have been underrepresented in preclinical models. These findings demonstrate the value of integrating HCMI, CCLE, and TCGA datasets. Together, they form a complementary and robust compendium for studying tumor biology, enabling improved cancer modeling and advancing precision oncology. Citation Format: Dina ElHarouni, Mushriq Al-Jazrawe, Seongmin Choi, Merve Dede, Toshinori Hinoue, Sean A. Misek, Heeju Noh, Luca Zanella, Moony Tseng, Hayley E. Francies, Priya Sridevi, Rachana Agarwal, Cindy W. Kyi, Julyann Perez-Mayoral, Megan J. Stine, Eva Tonsing-Carter, James M. Clinton, The HCMI Network, Peter W. Laird, Calvin J. Kuo, Olivier Elemento, David L. Spector, Andrew D. Cherniack, Kyle Ellrott, Martin L. Ferguson, Rameen Beroukhim, Katherine A. Hoadley, Nicolas Robine, Andrew McPherson, Mathew J. Garnett, David A. Tuveson, Andrea Califano, Paul T. Spellman, Keith L. Ligon, Daniela S. Gerhard, Louis M. Staudt, Jesse S. Boehm. Integrating HCMI models and tumors with CCLE and TCGA: Advancing cancer modeling and precision oncology [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 6608.

Abstract Cell function studies primarily focus on measuring overall molecular abundances while often overlooking critical clues—including protein modifications and molecular interaction networks—that critically determine the functional properties of the cell. In prior work, we introduced a suite of methods to reveal context-specific transcription factor-gene regulatory networks, kinase-substrate networks, and protein interaction networks and leveraged them to gain deeper insights into transcriptional regulation and signal transduction. However, the complex interdependencies between these networks are still elusive. To address this challenge, we introduce a multi-omics framework, aimed at harnessing measured or inferred protein activity in context-specific networks, which yields deeper functional insights into mechanisms underlying molecular phenotypes, compared to protein abundance alone. As proof of concept, we utilized progressively differentiated instances of HeLa CCL2 and Kyoto cell lines to explore the role of protein complexes and interactions in cell doubling time and susceptibility to Salmonella Typhimurium infection. Notably, this analysis underscores the pivotal role of protein interaction networks in linking molecular profiles to phenotypic outcomes, thus providing a highly generalizable framework for multi-omics dataset analysis.