Deciphering radiological stable disease to immune checkpoint inhibitors.

BACKGROUND: 'Stable disease (SD)' as per RECIST is a common but ambiguous outcome in patients receiving immune checkpoint inhibitors (ICIs). This study aimed to characterize SD and identify the subset of patients with SD who are benefiting from treatment. Understanding SD would facilitate drug development and improve precision in correlative research. PATIENTS AND METHODS: A systematic review was carried out to characterize SD in ICI trials. SD and objective response were compared to proliferation index using The Cancer Genome Atlas gene expression data. To identify a subgroup of SD with outcomes mirroring responders, we examined a discovery cohort of non-small-cell lung cancer (NSCLC). Serial cutpoints of two variables, % best overall response and progression-free survival (PFS), were tested to define a subgroup of patients with SD with similar survival as responders. Results were then tested in external validation cohorts. RESULTS: Among trials of ICIs (59 studies, 14 280 patients), SD ranged from 16% to 42% in different tumor types and was associated with disease-specific proliferation index (ρ = -0.75, P = 0.03), a proxy of tumor kinetics, rather than relative response to ICIs. In a discovery cohort of NSCLC [1220 patients, 313 (26%) with SD to ICIs], PFS ranged widely in SD (0.2-49 months, median 4.9 months). The subset with PFS >6 months and no tumor growth mirrored partial response (PR) minor (overall survival hazard ratio 1.0) and was proposed as the definition of SD responder. This definition was confirmed in two validation cohorts from trials of NSCLC treated with durvalumab and found to apply in tumor types treated with immunotherapy in which depth and duration of benefit were correlated. CONCLUSIONS: RECIST-defined SD to immunotherapy is common, heterogeneous, and may largely reflect tumor growth rate rather than ICI response. In patients with NSCLC and SD to ICIs, PFS >6 months and no tumor growth may be considered 'SD responders'. This definition may improve the efficiency of and insight derivable from clinical and translational research.

COVID-19 in patients with lung cancer.

BACKGROUND: Patients with lung cancers may have disproportionately severe coronavirus disease 2019 (COVID-19) outcomes. Understanding the patient-specific and cancer-specific features that impact the severity of COVID-19 may inform optimal cancer care during this pandemic. PATIENTS AND METHODS: We examined consecutive patients with lung cancer and confirmed diagnosis of COVID-19 (n = 102) at a single center from 12 March 2020 to 6 May 2020. Thresholds of severity were defined a priori as hospitalization, intensive care unit/intubation/do not intubate ([ICU/intubation/DNI] a composite metric of severe disease), or death. Recovery was defined as >14 days from COVID-19 test and >3 days since symptom resolution. Human leukocyte antigen (HLA) alleles were inferred from MSK-IMPACT (n = 46) and compared with controls with lung cancer and no known non-COVID-19 (n = 5166). RESULTS: COVID-19 was severe in patients with lung cancer (62% hospitalized, 25% died). Although severe, COVID-19 accounted for a minority of overall lung cancer deaths during the pandemic (11% overall). Determinants of COVID-19 severity were largely patient-specific features, including smoking status and chronic obstructive pulmonary disease [odds ratio for severe COVID-19 2.9, 95% confidence interval 1.07-9.44 comparing the median (23.5 pack-years) to never-smoker and 3.87, 95% confidence interval 1.35-9.68, respectively]. Cancer-specific features, including prior thoracic surgery/radiation and recent systemic therapies did not impact severity. Human leukocyte antigen supertypes were generally similar in mild or severe cases of COVID-19 compared with non-COVID-19 controls. Most patients recovered from COVID-19, including 25% patients initially requiring intubation. Among hospitalized patients, hydroxychloroquine did not improve COVID-19 outcomes. CONCLUSION: COVID-19 is associated with high burden of severity in patients with lung cancer. Patient-specific features, rather than cancer-specific features or treatments, are the greatest determinants of severity.

Clinical and molecular correlates of PD-L1 expression in patients with lung adenocarcinomas.

BACKGROUND: Programmed death-ligand 1 (PD-L1) expression is the only FDA-approved biomarker for immune checkpoint inhibitors (ICIs) in patients with lung adenocarcinoma, but sensitivity is modest. Understanding the impact of molecular phenotype, clinical characteristics, and tumor features on PD-L1 expression is largely unknown and may improve prediction of response to ICI. PATIENTS AND METHODS: We evaluated patients with lung adenocarcinoma for whom PD-L1 testing and targeted next-generation sequencing (using MSK-IMPACT) was performed on the same tissue sample. Clinical and molecular features were compared across PD-L1 subgroups to examine how molecular phenotype associated with tumor PD-L1 expression. In patients treated with anti-PD-(L)1 blockade, we assessed how these interactions impacted efficacy. RESULTS: A total of 1586 patients with lung adenocarcinoma had paired PD-L1 testing and targeted next-generation sequencing. PD-L1 negativity was more common in primary compared to metastatic samples (P < 0.001). The distribution of PD-L1 expression (lymph nodes enriched for PD-L1 high; bones predominantly PD-L1 negative) and predictiveness of PD-L1 expression on ICI response varied by organ. Mutations in KRAS, TP53, and MET significantly associated with PD-L1 high expression (each P < 0.001, Q < 0.001) and EGFR and STK11 mutations associated with PD-L1 negativity (P < 0.001, Q = 0.01; P = 0.001, Q < 0.001, respectively). WNT pathway alterations also associated with PD-L1 negativity (P = 0.005). EGFR and STK11 mutants abrogated the predictive value of PD-L1 expression on ICI response. CONCLUSION: PD-L1 expression and association with ICI response vary across tissue sample sites. Specific molecular features are associated with differential expression of PD-L1 and may impact the predictive capacity of PD-L1 for response to ICIs.