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Advances in immunotherapy including immune checkpoint inhibitors (ICIs) have transformed the standard of care of many cancers including melanoma. However, the benefit of ICIs is hampered by immune related adverse events (irAEs). The frequency of irAEs varies between ICI treatments and cancer types. These irAEs represent autoimmune reactions that can affect diverse organs, with distinct biology, onset, and severity from their de novo autoimmune disease counterparts. Some irAEs could persist and develop into chronic comorbidities, and a subset of severe irAEs could led to death. Yet, little is known about the underlying molecular mechanisms and no biomarkers are currently available to predict irAEs after ICI treatment. We aimed to identify soluble predictive biomarkers of irAE by non-invasive means using high-throughput Olink Proteomics assay. We collected plasma from 31 melanoma patients before ICI treatment. 25 patients developed severe irAE (grade 3 or above) at one or more organ system including hepatobiliary (n=9), endocrine (n=8), gastrointestinal (n=6), skin and subcutaneous tissue (n=6), musculoskeletal and connective tissue (n=5), blood and lymphatic (n=4) and respiratory (n=1) after receiving ICI, whereas 6 patients have no severe irAEs. The relative expression of 1,472 probes corresponding to 1,463 proteins as part of the Olink Cardiometabolic, Neurology, Inflammation and Oncology panels were quantified as Normalized Protein eXpresion (NPX) on a log-2 scale. Two-tailed T-test was performed in comparing the severe irAEs vs no irAE groups to determine the difference proteins and p < 0.05 and FDR < 0.1 was deemed to be significant. Similarly, we performed organ-specific irAEs using the same approach. Proteins were assessed by enrichment analysis using EnrichR. Exploratory analysis revealed elevated expression of CD4 and CD8 cell markers such as IL7R, S100A4, GZMA and GZMB in patients that developed severe irAEs. More broadly, pathway enrichment analysis suggest chemokines and cytokine receptor related pathways were enriched in these patients implicating the role of immune system and inflammatory mediators as potential biomarkers of irAE. In particular, the expression of some interleukin family members appears to be a good predictor of irAE toxicity. Interestingly, our analysis revealed irAE at various organ tissues were associated with different set of proteins suggesting an organ specific protein signature. In conclusion, we demonstrated the potential utility of soluble protein in the peripheral blood measurement at baseline prior to ICI treatment for prediction of irAE risk. This finding will be evaluated in a larger independent cohort. Citation Format: Muhammad Zaki Fadlullah Wilmot, Magdalena Kovacsovics, Xuechen Wang, Samuel Coleman, John Marsiglio, Berit Gibson, Yoko DeRose, Qin Zhou, Annaleah Larson, John Hyngstrom, Ben Haaland, Siwen Hu-Lieskovan, Aik Choon Tan. Proteomic study reveals predictive biomarkers of immune-related adverse events in melanoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 2144.

Objective: High-throughput metabolomics assays can generate thousands of biomarker measurements and provide novel opportunities for prognostic modeling in colorectal cancer (CRC) research. The high dimensionality of metabolic data brings unforeseen statistical challenges and traditional variable selection methods may not perform well due to simultaneous challenges of computational expediency, statistical accuracy, and algorithmic stability. The Iterative Sure Independence Screening (ISIS) has demonstrated superior theoretical properties in handling such situations, and may be a viable alternative. Methods: In a prospective study of 77 newly diagnosed CRC patients (stage I-IV), pre-surgical urinary samples were analyzed on a gas chromatography-mass spectrometry platform. After exclusion of metabolites with >30% coefficient of variation, 168 metabolites remained for statistical analysis. Raw measures were processed following a standard normalization pipeline. The primary outcome was overall survival (OS) as measured from date of cancer diagnosis. In addition to metabolomics data, the predictor set included baseline clinical characteristics, such as age, sex, body mass index, tumor site, tumor stage, and receipt of neo-adjuvant and/or adjuvant treatment. We applied the ISIS method with Lasso penalty on a Cox regression model (ISIS-Lasso) to identify features associated with OS. Cox models with either Lasso regularization or backward selection were also considered as competing methods. The performance of the models was assessed through two standard performance matrices: Uno's time-dependent Area Under the Curve (tAUC) and Brier's score, both with resample-based validation. Results: Based on bootstrapped tAUC curves, we demonstrated that the screening step in ISIS can significantly improve model performance, since its predicted mean (and median) AUC are larger across clinically relevant follow-up time points (2 and 5 years after diagnosis) relative to the corresponding measures from the other two models. The prediction error based on Brier's score with 0.632+ bootstrap from ISIS-Lasso was also noticeably lower compared to the model from backward selections. Based on the ISIS-Lasso model, we identified two features, that were predictive of OS in CRC patients: tumor stage and cystine. When fixing all other clinical measures, patients with early stage (I-III) had 52% lower risk of death, compared to late stage (IV); 1 standard deviation of increase of cystine level was associated with 62% increased risk of death. Conclusion: We have demonstrated the feasibility and effectiveness of an ISIS-based method to improve selection of prognostic models derived from metabolomics data. This may be especially useful for studies with moderate sample sizes. We have identified cystine as a potentially important prognostic biomarker. Citation Format: Tengda Lin, Biljana Gigic, Kenneth Boucher, Ben Haaland, David Liesenfeld, Robert Owen, Petra Schrotz-King, Jourgen Boehm, Anita Peoples, Augustin Scalbert, Martin Schneider, Jane Figueiredo, William Grady, Christopher Li, David Shibata, Erin Siegel, Adetunji Toriola, Alexis Ulrich, Neli Ulrich, Jincheng Shen, Jennifer Ose. Application of iterative sureindependence screening to improve urinary metabolomics-based prediction of survival in colorectal cancer patients [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 892.