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About Parse

Designing the Strategy to Defeat Cancer

Unravel Tumor Complexity Through Single Cell Sequencing

Cancer arises from diverse, evolving cell populations shaped by genetic and environmental factors. Single cell sequencing provides a high-resolution view into these dynamics, capturing each cell’s unique role in cancer initiation, progression, and adaptation. By revealing rare subpopulations and diverse cellular states, researchers can better understand tumor heterogeneity and reimagine the future of cancer research.

Decoding Cancer from Tumor Initiation to Treatment

Tracing the mutations to
mechanistic consequences

Identifying upstream and
downstream pathway targets

Tracking Tumor
Clonal Expansion

Profiling the Tumor Heterogeneity
and Its Microenvironment

Characterize Stem Cells
and Cell Type Plasticity

Mechanisms of Local
Invasion and Metastasis

Identifying
Novel Targets

Mechanisms of
Action Investigations

Characterizing
Resistant Populations

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Suppressing Recurrence in Sonic Hedgehog Subgroup Medulloblastoma Using the OLIG2 Inhibitor CT-179

Li et al., Nat Commun. 2025

In this paper, researchers explored medulloblastoma tumor heterogeneity, demonstrated the therapeutic potential of CT-179 and revealed possible resistance mechanisms in treated tumors by using scRNA-seq. The team examined the impact of the OLIG2 inhibitor CT-179 on SHH subgroup medulloblastoma, uncovering significant shifts in tumor cell populations, with a decrease in proliferative cells and an increase in differentiating cells, driving tumor cells to exit the cell cycle and promote neural differentiation. They also identified distinct clusters of OLIG2-expressing cells, encompassing both quiescent and actively proliferating subtypes. However, they uncovered the upregulation of proliferation-related genes in some tumor cells, suggesting a potential mechanism of resistance.

Tahoe-100M: A Giga-Scale Single-Cell Perturbation Atlas for Context-Dependent Gene Function and Cellular Modeling

Zhang et al., Preprint. 2025

The Tahoe-100M dataset, profiling over 100 million cells across 50 cancer cell lines and 1,100 perturbations to build predictive models of cell behavior. By growing cell lines in spheroids and treating them with small molecules at varying concentrations, they minimized batch effects while creating the largest single-cell atlas to date. Using this dataset, they mapped drug responses by mechanism of action, revealing diverse cellular reactions even within drug classes. Further analysis of the RAS/RAF pathway demonstrated how genotype influences differential gene expression, offering insights into biomarkers and treatment resistance. This dataset paves the way for AI-driven biological predictions, enhancing drug discovery by not only determining if a drug works but also uncovering the mechanisms behind its effects.

Spatially Resolved Analysis of Pancreatic Cancer Identifies Therapy-Associated Remodeling of the Tumor Microenvironment

Shiau et al., Nat Genet 2024

In this paper, researchers analyzed over 700,000 cells from pancreatic ductal adenocarcinoma tumors following neoadjuvant therapy, uncovering treatment-associated changes in tumor composition and architecture. They observed a reduction in malignant cells, disrupted glandular organization, and an increase in inflammatory cancer-associated fibroblasts. The study revealed subtype-specific associations and shifts in ligand–receptor signaling, including enriched IL-6 family signaling in treated tumors. Functional assays showed IL-6 promoted chemoresistance, suggesting a key role in therapy response and
resistance.

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