Dataset Information

Genetic dependencies associated with transcription factor activities in human cancer cell lines [SLAM-seq]

ABSTRACT: Transcription factors (TFs) are important mediators of aberrant transcriptional programs in cancer cells. In this study, we focused on TF activity (TFa) as a new biomarker for cell line-selective anti-proliferative effects, in that high TFa predicts sensitivity to loss-of-function of a given gene (i.e., genetic dependencies (GD)). Our linear regression-based framework identified 3,043 pan-cancer and 3,952 cancer-type-specific candidate TFa-GD associations from cell line data, which were then cross-examined for impact on survival in patient data. One of the most prominent biomarkers was TEAD1 activity, whose associations with its predicted GDs were validated through experimental evidence as proof of concept. Overall, these TFa-GD pairs represent an attractive resource for identifying innovative, biomarker-driven hypotheses for drug discovery programs in oncology.

ORGANISM(S): Homo sapiens

PROVIDER: GSE254799 | GEO | 2024/04/29

REPOSITORIES: GEO

ACCESS DATA

Similar Datasets

Project description:This ordinary differential equation model simulating the mechanisms that govern cancer-immune dynamics and their role in tumor responses to immunotherapy is described by the publication: Creemers JHA, Lesterhuis WJ, Mehra N, et al. "A tipping point in cancer-immune dynamics leads to divergent immunotherapy responses and hampers biomarker discovery." Journal for ImmunoTherapy of Cancer 2021;9:e002032. doi:10.1136/jitc-2020-002032 Comment: Simulation parameters in supplementary table 1 mismatch with figure 1 in manuscript. Therefore, to clarify, the following parameter values were used: Reproduction of Fig. 1(C), xi = 0.0005 Reproduction of Fig. 1(D), xi = 0.00025 Abstract: Background: Predicting treatment response or survival of cancer patients remains challenging in immuno-oncology. Efforts to overcome these challenges focus, among others, on the discovery of new biomarkers. Despite advances in cellular and molecular approaches, only a limited number of candidate biomarkers eventually enter clinical practice. Methods: A computational modeling approach based on ordinary differential equations was used to simulate the fundamental mechanisms that dictate tumor-immune dynamics and to investigate its implications on responses to immune checkpoint inhibition (ICI) and patient survival. Using in silico biomarker discovery trials, we revealed fundamental principles that explain the diverging success rates of biomarker discovery programs. Results: Our model shows that a tipping point—a sharp state transition between immune control and immune evasion—induces a strongly non-linear relationship between patient survival and both immunological and tumor-related parameters. In patients close to the tipping point, ICI therapy may lead to long-lasting survival benefits, whereas patients far from the tipping point may fail to benefit from these potent treatments. Conclusion: These findings have two important implications for clinical oncology. First, the apparent conundrum that ICI induces substantial benefits in some patients yet completely fails in others could be, to a large extent, explained by the presence of a tipping point. Second, predictive biomarkers for immunotherapy should ideally combine both immunological and tumor-related markers, as a patient’s distance from the tipping point can typically not be reliably determined from solely one of these. The notion of a tipping point in cancer-immune dynamics helps to devise more accurate strategies to select appropriate treatments for patients with cancer.

Dataset Information

Genetic dependencies associated with transcription factor activities in human cancer cell lines [SLAM-seq]

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure