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Mathematical prediction of clinical outcomes in advanced cancer patients treated with checkpoint inhibitor immunotherapy.

ABSTRACT: We present a mechanistic mathematical model of immune checkpoint inhibitor therapy to address the oncological need for early, broadly applicable readouts (biomarkers) of patient response to immunotherapy. The model is built upon the complex biological and physical interactions between the immune system and cancer, and is informed using only standard-of-care CT. We have retrospectively applied the model to 245 patients from multiple clinical trials treated with anti-CTLA-4 or anti-PD-1/PD-L1 antibodies. We found that model parameters distinctly identified patients with common (n = 18) and rare (n = 10) malignancy types who benefited and did not benefit from these monotherapies with accuracy as high as 88% at first restaging (median 53 days). Further, the parameters successfully differentiated pseudo-progression from true progression, providing previously unidentified insights into the unique biophysical characteristics of pseudo-progression. Our mathematical model offers a clinically relevant tool for personalized oncology and for engineering immunotherapy regimens.

SUBMITTER: Butner JD

PROVIDER: S-EPMC7190324 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature

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Mathematical prediction of clinical outcomes in advanced cancer patients treated with checkpoint inhibitor immunotherapy.

Butner Joseph D JD Elganainy Dalia D Wang Charles X CX Wang Zhihui Z Chen Shu-Hsia SH Esnaola Nestor F NF Pasqualini Renata R Arap Wadih W Hong David S DS Welsh James J Koay Eugene J EJ Cristini Vittorio V

Science advances 20200429 18

We present a mechanistic mathematical model of immune checkpoint inhibitor therapy to address the oncological need for early, broadly applicable readouts (biomarkers) of patient response to immunotherapy. The model is built upon the complex biological and physical interactions between the immune system and cancer, and is informed using only standard-of-care CT. We have retrospectively applied the model to 245 patients from multiple clinical trials treated with anti-CTLA-4 or anti-PD-1/PD-L1 anti ...[more]

PMID: 32426472

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Project description:Background Given the increasing number of available immunotherapeutic agents, more patients are presenting after failing immunotherapy in need of new treatment options. In this study, we investigated the clinical outcomes of patients treated with sequential immunotherapy. Methods We performed a retrospective review of 90 advanced stage cancer patients treated on immunotherapy-based phase 1 clinical trials at Winship Cancer Institute from 2009 to 2017. We included 49 patients with an immune checkpoint inhibitor (ICI)-indicated histology. Patients were analyzed based on whether they had received prior ICI. Clinical outcomes were overall survival (OS), progression-free survival (PFS), and clinical benefit (best response of complete response, partial response, or stable disease). Univariate analysis (UVA) and multivariate analysis (MVA) were performed using Cox proportional hazard or logistic regression model. Covariates included age, liver metastases, number of prior lines of therapy, histology, and Royal Marsden Hospital (RMH) risk group. Results The most common histologies were melanoma (61%) and lung/head and neck cancers (37%). More than half of patients (n?=?27, 55%) received at least one ICI prior to trial enrollment: ten received anti-PD-1, two received anti-CTLA-4, five received anti-PD-1/CTLA-4 combination, and ten received multiple ICI. In MVA, ICI-naïve patients had significantly longer OS (HR: 0.22, CI: 0.07-0.70, p?=?0.010) and trended towards higher chance of CB (HR: 2.52, CI: 0.49-12.97, p?=?0.268). Patients who received prior ICI had substantially shorter median OS (10.9 vs 24.3 months, p?=?0.046) and PFS (2.8 vs. 5.1 months, p?=?0.380) than ICI-naïve patients per Kaplan-Meier estimation. Within the ICI-naïve group, 78% (7 of 9) of patients who received prior interleukin (IL-2) or interferon gamma (IFN?) experienced disease control for at least 6 months, compared to a disease control rate of 15% (2 of 13) in patients who had received chemotherapy, targeted therapy, or no prior treatment. Conclusions ICI-naïve patients may experience improved clinical outcomes on immunotherapy-based phase 1 clinical trials than patients who have received prior ICI. This may be particularly true for patients who received prior IL-2 or IFN?. Further development of immunotherapy combination therapies is needed to improve clinical outcomes of these patients. These results should be validated in a larger study.

Project description:Background and aimsImmune checkpoint inhibitors (ICI) are increasingly used in cancer therapy. Elevated liver enzymes frequently occur in patients treated with ICI but evaluation is poorly described. We sought to better understand causes of liver enzyme elevation, investigation and management.MethodsPatients treated with anti-PD-1, PDL-1 or CTLA-4 therapy in Phase I/II clinical trials between August 2012 and December 2018 were included. Clinical records of patients with significant liver enzyme elevations were retrospectively reviewed.ResultsOf 470 ICI-treated patients, liver enzyme elevation occurred in 102 (21.6%), attributed to disease progression (56; 54.9%), other drugs/toxins (7; 6.9%), other causes (22; 21.6%) and ICI immunotoxicity (17; 16.7%; 3.6% of total cohort). Immunotoxicity was associated with higher peak ALT than other causes of enzyme elevation (N = 17; M = 217, 95% CI 145-324 for immunotoxicity, N = 103; M = 74, 95% CI 59-92 for other causes; ratio of means 0.34, 95% CI 0.19-0.60, p = <0.001) and higher ALT:AST ratio (M = 1.27, 95% CI 0.78-2.06 for immunotoxicity, M = 0.69, 95% CI 0.59-0.80 for other causes, ratio of means 0.54, 95% CI 0.36-0.82, p = 0.004). Immunotoxicity was more often seen in patients with prior CPI exposure (41.2% of immunotoxicity vs 15.9% of patients without, p = 0.01), anti-CTLA-4 -containing ICI treatments (29.4% of immunotoxicity vs 6.8% of patients without, p = <0.001) and other organ immunotoxicity (76.5% of immunotoxicity vs 19.2% of patients without, p = <0.001). Cause for enzyme elevation was established in most patients after non-invasive investigation. Liver biopsy was reserved for four patients with atypical treatment response.ConclusionsLiver enzyme elevation is common in patients receiving ICI, but often has a cause other than immunotoxicity. A biochemical signature with higher ALT and ALT/AST ratio, a history of prior ICI exposure and other organ immunotoxicities may help to identify patients at a higher likelihood of immunotoxicity. Liver biopsy can be safely deferred in most patients. We propose an approach to diagnostic evaluation in patients with liver enzyme elevations following ICI exposure.

Project description:Tremelimumab is an antibody that blocks CTLA-4 and demonstrates clinical efficacy in a subset of advanced melanoma patients. An unmet clinical need exists for blood-based response-predictive gene signatures to facilitate clinically effective and cost-efficient use of such immunotherapeutic interventions.Peripheral blood samples were collected in PAXgene® tubes from 210 treatment-naïve melanoma patients receiving tremelimumab in a worldwide, multicenter phase III study (discovery dataset). A central panel of radiologists determined objective response using RECIST criteria. Gene expression for 169 mRNA transcripts was measured using quantitative PCR. A 15-gene pre-treatment response-predictive classifier model was identified. An independent population (N = 150) of refractory melanoma patients receiving tremelimumab after chemotherapy enrolled in a worldwide phase II study (validation dataset). The classifier model, using the same genes, coefficients and constants for objective response and one-year survival after treatment, was applied to the validation dataset.A 15-gene pre-treatment classifier model (containing ADAM17, CDK2, CDKN2A, DPP4, ERBB2, HLA-DRA, ICOS, ITGA4, LARGE, MYC, NAB2, NRAS, RHOC, TGFB1, and TIMP1) achieved an area under the curve (AUC) of 0.86 (95% confidence interval 0.81 to 0.91, p < 0.0001) for objective response and 0.6 (95% confidence interval 0.54 to 0.67, p = 0.0066) for one-year survival in the discovery set. This model was validated in the validation set with AUCs of 0.62 (95% confidence interval 0.54 to 0.70 p = 0.0455) for objective response and 0.68 for one-year survival (95% confidence interval 0.59 to 0.75 p = 0.0002).To our knowledge, this is the largest blood-based biomarker study of a checkpoint inhibitor, tremelimumab, which demonstrates a validated pre-treatment mRNA classifier model that predicts clinical response. The data suggest that the model captures a biological signature representative of genes needed for a robust anti-cancer immune response. It also identifies non-responders to tremelimumab at baseline prior to treatment.

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Mathematical prediction of clinical outcomes in advanced cancer patients treated with checkpoint inhibitor immunotherapy.

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Mathematical prediction of clinical outcomes in advanced cancer patients treated with checkpoint inhibitor immunotherapy.

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