Abstract
High PD-L1 expression (≥50%) is a routine biomarker but is incompletely predictive, with response rates to PD-1 monotherapy only 35-45% in patients with lung cancer. Beyond PD-L1, additional individual pre-treatment variables, including clinical (smoking history, BMI), genomic (TMB, STK11, EGFR), and laboratory features (baseline dNLR), individually associate with response but have not been comprehensively examined in combination. We hypothesized that a multifactorial model incorporating routinely available clinical, pathologic, and genomic variables could improve prediction of response in high PD-L1 patients receiving first line anti-PD-(L)1 monotherapy.
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