A prediction model for response to immune checkpoint inhibition in advanced melanoma

Predicting who will benefit from treatment with immune checkpoint inhibition (ICI) in patients with advanced melanoma is challenging. We developed a multivariable prediction model for response to ICI, using routinely available clinical data including primary melanoma characteristics. We used a population-based cohort of 3525 patients with advanced cutaneous melanoma treated with anti-PD-1-based therapy. Our prediction model for predicting response within 6 months after ICI initiation was internally validated with bootstrap resampling. Performance evaluation included calibration, discrimination and internal–external cross-validation. Included patients received anti-PD-1 monotherapy (n = 2366) or ipilimumab plus nivolumab (n = 1159) in any treatment line. The model included serum lactate dehydrogenase, World Health Organization performance score, type and line of ICI, disease stage and time to first distant recurrence—all at start of ICI—, and location and type of primary melanoma, the presence of satellites and/or in-transit metastases at primary diagnosis and sex. The over-optimism adjusted area under the receiver operating characteristic was 0.66 (95% CI: 0.64–0.66). The range of predicted response probabilities was 7%–81%. Based on these probabilities, patients were categorized into quartiles. Compared to the lowest response quartile, patients in the highest quartile had a significantly longer median progression-free survival (20.0 vs 2.8 months; P &lt;.001) and median overall survival (62.0 vs 8.0 months; P &lt;.001). Our prediction model, based on routinely available clinical variables and primary melanoma characteristics, predicts response to ICI in patients with advanced melanoma and discriminates well between treated patients with a very good and very poor prognosis.</p

A prediction model for response to immune checkpoint inhibition in advanced melanoma

Predicting who will benefit from treatment with immune checkpoint inhibition (ICI) in patients with advanced melanoma is challenging. We developed a multivariable prediction model for response to ICI, using routinely available clinical data including primary melanoma characteristics. We used a population-based cohort of 3525 patients with advanced cutaneous melanoma treated with anti-PD-1-based therapy. Our prediction model for predicting response within 6 months after ICI initiation was internally validated with bootstrap resampling. Performance evaluation included calibration, discrimination and internal-external cross-validation. Included patients received anti-PD-1 monotherapy (n = 2366) or ipilimumab plus nivolumab (n = 1159) in any treatment line. The model included serum lactate dehydrogenase, World Health Organization performance score, type and line of ICI, disease stage and time to first distant recurrence-all at start of ICI-, and location and type of primary melanoma, the presence of satellites and/or in-transit metastases at primary diagnosis and sex. The over-optimism adjusted area under the receiver operating characteristic was 0.66 (95% CI: 0.64-0.66). The range of predicted response probabilities was 7%-81%. Based on these probabilities, patients were categorized into quartiles. Compared to the lowest response quartile, patients in the highest quartile had a significantly longer median progression-free survival (20.0 vs 2.8 months; P < .001) and median overall survival (62.0 vs 8.0 months; P < .001). Our prediction model, based on routinely available clinical variables and primary melanoma characteristics, predicts response to ICI in patients with advanced melanoma and discriminates well between treated patients with a very good and very poor prognosis

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Patients with resistance to thyroid hormone (RTH) exhibit elevated thyroid hormone levels and inappropriate thyrotropin (thyroid-stimulating hormone, or TSH) production. The molecular basis of this disorder resides in the dominant inhibition of endogenous thyroid hormone receptors (TRs) by a mutant receptor. To determine the relative contributions of pituitary versus hypothalamic resistance to the dysregulated production of thyroid hormone in these patients, we developed a transgenic mouse model with pituitary-specific expression of a mutant TR (∆337T). The equivalent mutation in humans is associated with severe generalized RTH. Transgenic mice developed profound pituitary resistance to thyroid hormone, as demonstrated by markedly elevated baseline and non-triodothyronine (T 3 )-suppressible serum TSH and pituitary TSH-β mRNA. Serum thyroxine (T 4 ) levels were only marginally elevated in transgenic mice and thyrotropin-releasing hormone (TRH) gene expression in the paraventricular hypothalamus was downregulated. After TRH administration, T 4 concentrations increased markedly in transgenic, but not in wild-type mice. Transgenic mice rendered hypothyroid exhibited a TSH response that was only 30% of the response observed in wild-type animals. These findings indicate that pituitary expression of this mutant TR impairs both T 3 -mediated suppression and T 3 -independent activation of TSH production in vivo. The discordance between basal TSH and T 4 levels and the reversal with TRH administration demonstrates that resistance at the level of both the thyrotroph and the hypothalamic TRH neurons are required to elevate thyroid hormone levels in patients with RTH