Mutations in the IFNγ-JAK-STAT Pathway Causing Resistance to Immune Checkpoint Inhibitors in Melanoma Increase Sensitivity to Oncolytic Virus Treatment
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Bibliographic record
Abstract
Abstract Purpose: Next-generation sequencing studies and CRISPR-Cas9 screens have established mutations in the IFNγ-JAK-STAT pathway as an immune checkpoint inhibitor (ICI) resistance mechanism in a subset of patients with melanoma. We hypothesized ICI resistance mutations in the IFNγ pathway would simultaneously render melanomas susceptible to oncolytic virus (OV) therapy. Experimental Design: Cytotoxicity experiments were performed with a number of OVs on a matched melanoma cell line pair generated from a baseline biopsy and a progressing lesion with complete JAK2 loss from a patient that relapsed on anti-PD-1 therapy, in melanoma lines following JAK1/2 RNA interference (RNAi) and pharmacologic inhibition and in Jak2 knockout (KO) B16-F10 mouse melanomas. Furthermore, we estimated the frequency of genetic alterations in the IFNγ-JAK-STAT pathway in human melanomas. Results: The melanoma line from an anti-PD-1 progressing lesion was 7- and 22-fold more sensitive to the modified OVs, herpes simplex virus 1 (HSV1-dICP0) and vesicular stomatitis virus (VSV-Δ51), respectively, compared with the line from the baseline biopsy. RNAi, JAK1/2 inhibitor studies, and in vivo studies of Jak2 KOs B16-F10 melanomas revealed a significant increase in VSV-Δ51 sensitivity with JAK/STAT pathway inhibition. Our analysis of The Cancer Genome Atlas data estimated that approximately 11% of ICI-naïve cutaneous melanomas have alterations in IFNγ pathway genes that may confer OV susceptibility. Conclusions: We provide mechanistic support for the use of OVs as a precision medicine strategy for both salvage therapy in ICI-resistant and first-line treatment in melanomas with IFNγ-JAK-STAT pathway mutations. Our study also supports JAK inhibitor–OV combination therapy for treatment-naïve melanomas without IFN signaling defects. See related commentary by Kaufman, p. 3278
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it