The SNaPshot melanoma screen can detect 43 point mutations in 6 genes relevant to targeted therapy in melanoma.

*<p>SNaPshot assays in bold text were previously published <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0035309#pone.0035309-DiasSantagata1" target="_blank">[23]</a>.</p

Spectrum of mutations in the first 150 melanomas genotyped in the molecular diagnostic lab.

*<p>CSD – chronic sun damage.</p>#<p>This CTNNB1 mutation (CTNNB1 S45P) occurred concurrently with an NRAS Q61L mutation.</p

Influence of tumor genotype on subsequent treatment in patients with metastatic melanoma.

*<p>This CTNNB1 mutation (CTNNB1 S45P) occurred concurrently with an NRAS Q61L mutation.</p

Melanoma SNaPshot screen (v1.0).

<p>A, five multiplexed panels can detect the mutational status of twenty gene loci. Each peak color represents a particular nucleotide at that locus. The gene name, amino acid, and nucleotide are labeled above each peak. An “(R)” after the nucleotide denotes a reverse extension primer. B, pan-positive control for melanoma SNaPshot screen. Peaks are labeled as described in A. C, SNaPshot sensitivity measurement using cell line DNA carrying known mutations. Numbers indicate the arbitrary fluorescence units of WT (panel 1: green, panels 2, 3: blue) and mutant (panel 1: blue, panels 2, 3: green) peaks. Solid arrows indicate mutant peaks and dotted arrows show background peaks. Background peaks in the negative controls (far right panel) are indicated by their peak height and a star (*).</p

Gene mutation frequency in melanoma and predicted sensitivities to targeted.

<p>Gene mutation frequency in melanoma and predicted sensitivities to targeted.</p

Table_1_Rapid identification of inflammatory arthritis and associated adverse events following immune checkpoint therapy: a machine learning approach.docx

IntroductionImmune checkpoint inhibitor-induced inflammatory arthritis (ICI-IA) poses a major clinical challenge to ICI therapy for cancer, with 13% of cases halting ICI therapy and ICI-IA being difficult to identify for timely referral to a rheumatologist. The objective of this study was to rapidly identify ICI-IA patients in clinical data and assess associated immune-related adverse events (irAEs) and risk factors.MethodsWe conducted a retrospective study of the electronic health records (EHRs) of 89 patients who developed ICI-IA out of 2451 cancer patients who received ICI therapy at Northwestern University between March 2011 to January 2021. Logistic regression and random forest machine learning models were trained on all EHR diagnoses, labs, medications, and procedures to identify ICI-IA patients and EHR codes indicating ICI-IA. Multivariate logistic regression was then used to test associations between ICI-IA and cancer type, ICI regimen, and comorbid irAEs.ResultsLogistic regression and random forest models identified ICI-IA patients with accuracies of 0.79 and 0.80, respectively. Key EHR features from the random forest model included ICI-IA relevant features (joint pain, steroid prescription, rheumatoid factor tests) and features suggesting comorbid irAEs (thyroid function tests, pruritus, triamcinolone prescription). Compared to 871 adjudicated ICI patients who did not develop arthritis, ICI-IA patients had higher odds of developing cutaneous (odds ratio [OR]=2.66; 95% Confidence Interval [CI] 1.63-4.35), endocrine (OR=2.09; 95% CI 1.15-3.80), or gastrointestinal (OR=2.88; 95% CI 1.76-4.72) irAEs adjusting for demographics, cancer type, and ICI regimen. Melanoma (OR=1.99; 95% CI 1.08-3.65) and renal cell carcinoma (OR=2.03; 95% CI 1.06-3.84) patients were more likely to develop ICI-IA compared to lung cancer patients. Patients on nivolumab+ipilimumab were more likely to develop ICI-IA compared to patients on pembrolizumab (OR=1.86; 95% CI 1.01-3.43).DiscussionOur machine learning models rapidly identified patients with ICI-IA in EHR data and elucidated clinical features indicative of comorbid irAEs. Patients with ICI-IA were significantly more likely to also develop cutaneous, endocrine, and gastrointestinal irAEs during their clinical course compared to ICI therapy patients without ICI-IA.</p

Distribution of mutations in the first 150 tumors genotyped in the molecular diagnostic lab.

<p>Left: distribution of all mutations. Right: distribution of V600 mutations. See <b><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0035309#pone.0035309.s014" target="_blank">Table S10</a></b> for more details.</p