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Raw RNA-sequencing data for deriving BCR sequences from the NIH U01 project: Finding the optimal balance of immunotherapy efficacy and toxicity.

ABSTRACT: IrAEs convey substantial morbidity, incur considerable costs, and in some cases may preclude further use of immune checkpoint blockades. Recent studies indicate that (1) up to 80% of individuals on ICIs experience some form of irAEs; (2) ~35% of patients require corticosteroid treatments to mitigate these events; and (3) up to 20% terminate their therapy due to irAEs. As immunotherapy use extends from major centers to smaller, isolated, and less experienced community sites, predicting the development of irAEs and incorporation of this prediction into ICI treatment considerations becomes extremely valuable. Despite the substantial efforts invested in machine learning models for predicting ICI treatment responses, few studies have focused on predictive modeling for irAEs. We address this unmet need by leveraging the unique patient resources collected at UTSW and developing a BCR-based biomarker for irAEs. Moreover, irAEs can happen as short as a few weeks or as long as a few years after ICI treatment. We deployed our model longitudinally to track if the evolution of autoreactive BCRs align with the timings of irAEs.

ORGANISM(S): Homo sapiens

PROVIDER: GSE296826 | GEO | 2025/05/12

REPOSITORIES: GEO

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Project description:Immunotherapies, including immune checkpoint inhibitors (ICI), have revolutionized the treatment of many cancers, producing significant improvements in survival in patients with many different cancers. However, the intended anti-tumor effects also result in a unique form of autoimmunity, known as immune-related adverse events (irAEs), which have emerged as a limiting factor for many immunotherapies. Cutaneous irAEs (cirAEs), the most frequently occurring ICI–related toxicities, have been associated with improved efficacy and survival but, in their severe forms, require systemic steroids and have in cases led to premature ICI discontinuation and fatality. There is a need for both robust biomarkers and adequate models that effectively predict which patients who develop irAEs may have improved outcomes. Using a microfluidic droplet technology that generates "mini" patient-derived organoids called MicroOrganoSpheres (MOSTM), we successfully generated skin MOS from skin biopsy samples in both healthy skin and tumor-involved skin that sustain the original patient skin immune microenvironment over three weeks. Using this model, we assessed skin cell toxicity and cytokine release in response to ICI and targeted therapies. Clinical responses were largely consistent with the skin and tumor MOS assay readouts, indicating that MOS recapitulates the potential association between skin irAEs and efficacy. Matched pairs of patient melanoma and skin MOS showed good concordance with patient outcome indicating that MOS recapitulates the potential association between skin irAEs and efficacy. Enrichment of genes associated with atopic dermatitis, vitiligo, and psoriatic dermatitis were observed in MOS generated from skin of an ICI-sensitive patient compared to the MOS generated from skin of an ICI-resistant patient. These results indicated that the skin MOS platform can potentially predict the dermatological toxicity of ICI. As the MOS assay can be completed within 12 days after biopsy acquisition, this novel technology may enable personalized medicine approaches by prediction of cirAEs and efficacy for individual patients.

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.

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Raw RNA-sequencing data for deriving BCR sequences from the NIH U01 project: Finding the optimal balance of immunotherapy efficacy and toxicity.

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