Unknown

Dataset Information

0

Clinical and molecular features of innate and acquired resistance to anti-PD-1/PD-L1 therapy in lung cancer.


ABSTRACT: The majority of non-small cell lung cancer (NSCLC) patients treated with anti-PD-1/PD-L1 therapy develop either innate or acquired resistance. Across tumor types, the "T cell-inflamed" tumor microenvironment correlates with clinical response to immunotherapy. We hypothesize that clinical characteristics may be predictive of resistance and that "T cell-inflamed" NSCLC tumors can be identified by gene expression profiling.Of 93 patients, 36 (38.7%) had innate resistance and 57 (61.3%) had initial benefit to immunotherapy. Innate resistance was associated with non-smokers (p = 0.013), more involved disease sites (p = 0.011), more prior therapy (p = 0.001), and a lower albumin level (p = 0.014). Among patients with initial benefit, factors associated with subsequent progression-free survival included higher Karnofsky Performance Status (KPS) (p = 0.004) and lower depth of response to anti-PD-1 therapy (p = 0.003). A "T cell-inflamed" microenvironment was identified in 42% of TCGA adenocarcinoma samples versus 21.0% of squamous cell.Specific clinical characteristics appear to be predictive of either innate or acquired resistance to anti-PD-1/PD-L1 therapy. A "T cell-inflamed" tumor was more common in adenocarcinoma than squamous histology.A retrospective review of NSCLC patients treated with anti-PD-1/PD-L1 monotherapy. Patients with innate resistance to anti-PD-1/PD-L1 therapy (defined as progression at first CT evaluation) were compared to patients with initial clinical benefit. Among those with initial clinical benefit, we identified prognostic factors for time to progression (acquired resistance) or death. To further corroborate our findings on limited numbers, immune gene expression profiling of all NSCLC samples from the TCGA database was also pursued.

SUBMITTER: Shah S 

PROVIDER: S-EPMC5796980 | biostudies-literature |

REPOSITORIES: biostudies-literature

Similar Datasets

| S-EPMC8262501 | biostudies-literature
| S-EPMC5502536 | biostudies-literature
| S-EPMC7592056 | biostudies-literature
| S-EPMC9357203 | biostudies-literature
2020-09-24 | GSE154879 | GEO
2022-05-31 | GSE199733 | GEO
| S-EPMC7531287 | biostudies-literature
2022-03-31 | MSV000089180 | MassIVE
| S-EPMC10140599 | biostudies-literature
| S-EPMC8110980 | biostudies-literature