Towards overtreatment-free immunotherapy: Using genomic scars to select treatment beneficiaries in lung cancer
Donker, H. C.; van Es, B.; Tamminga, M.; Lunter, G. A.; van Kempen, L. C. L. T.; Schuuring, E.; Hiltermann, T. J. N.; Groen, H. J. M.
Show abstract
In advanced non-small cell lung cancer (NSCLC), response to immunotherapy is difficult to predict from pre-treatment information. Given the toxicity of immunotherapy and its financial burden on the healthcare system, we set out to identify patients for whom treatment is effective. To this end, we used mutational signatures from DNA mutations in pre-treatment tissue. Single base substitutions, doublet base substitutions, indels, and copy number alteration signatures were analysed in m = 101 patients (the discovery set). We found that tobacco smoking signature (SBS4) and thiopurine chemotherapy exposure-associated signature (SBS87) were linked to durable benefit. Combining both signatures in a machine learning model separated patients with a progression-free survival hazard ratio of [Formula] on the cross-validated discovery set and [Formula] on an independent external validation set (m = 56). This paper demonstrates that the fingerprints of mutagenesis, codified through mutational signatures, select advanced NSCLC patients who may benefit from immunotherapy, thus potentially reducing unnecessary patient burden.
Matching journals
The top 9 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Personalized Cancer Therapy Prioritization Based on Driver Alteration Co-occurrence Patterns 95%
- Novel temporal and spatial patterns of metastatic colonization from rapid-autopsy tumor biopsies 94%
- Burden of tumor mutations, neoepitopes, and other variants are dubious predictors of cancer immunotherapy response and overall survival 94%
Similar papers in this journal
- Kinome focused CRISPR-Cas9 screens in African ancestry patient-derived breast cancer organoids identifies essential kinases and synergy of EGFR and FGFR1 inhibition. 93%
- Division of labor between YAP and TAZ in non-small cell lung cancer 93%
- Regulation of PD1 signaling is associated with prognosis in glioblastoma multiforme 92%
Similar papers in this journal
- The Epithelial and Stromal Immune Microenvironment in Gastric Cancer: A Comprehensive Analysis Reveals Prognostic Factors with Digital Cytometry 92%
- Propagated circulating tumor cells uncovers the rople of NFκB and COP1 in metastasis 92%
- Sex biases in cancer and autoimmune disease incidence are strongly positively correlated with mitochondrial gene expression across human tissues 92%
Similar papers in this journal
- Estimating tumor mutational burden from RNA-sequencing without a matched-normal sample 96%
- Evolutionary signatures of human cancers revealed via genomic analysis of over 35,000 patients 96%
- Machine learning-based tissue of origin classification for cancer of unknown primary diagnostics using genome-wide mutation features 95%
Similar papers in this journal
- Enhanced prediction of breast cancer patient response to chemotherapy by integrating deconvolved expression patterns of immune, stromal and tumor cells 94%
- Single-cell Transcriptome Profiling of Post-treatment and Treatment-naive Colorectal Cancer: Insights into Putative Mechanisms of Chemoresistance 94%
- Integrative analysis of patient-derived tumoroids and ex vivo organoid modeling of ARID1A loss in bladder cancer reveals therapeutic molecular targets 92%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.