PURPOSE: Biomarkers on the basis of tumor-infiltrating lymphocytes (TIL) are potentially valuable in predicting the effectiveness of immune checkpoint inhibitors (ICI). However, clinical application remains challenging because of methodologic limitations and laborious process involved in spatial analysis of TIL distribution in whole-slide images (WSI). METHODS: We have developed an artificial intelligence (AI)–powered WSI analyzer of TIL in the tumor microenvironment that can define three immune phenotypes (IPs): inflamed, immune-excluded, and immune-desert. These IPs were correlated with tumor response to ICI and survival in two independent cohorts of patients with advanced non–small-cell lung cancer (NSCLC). RESULTS: Inflamed IP correlated with enrichment in local immune cytolytic activity, higher response rate, and prolonged progression-free survival compared with patients with immune-excluded or immune-desert phenotypes. At the WSI level, there was significant positive correlation between tumor proportion score (TPS) as determined by the AI model and control TPS analyzed by pathologists ( P < .001). Overall, 44.0% of tumors were inflamed, 37.1% were immune-excluded, and 18.9% were immune-desert. Incidence of inflamed IP in patients with programmed death ligand-1 TPS at < 1%, 1%-49%, and ≥ 50% was 31.7%, 42.5%, and 56.8%, respectively. Median progression-free survival and overall survival were, respectively, 4.1 months and 24.8 months with inflamed IP, 2.2 months and 14.0 months with immune-excluded IP, and 2.4 months and 10.6 months with immune-desert IP. CONCLUSION: The AI-powered spatial analysis of TIL correlated with tumor response and progression-free survival of ICI in advanced NSCLC. This is potentially a supplementary biomarker to TPS as determined by a pathologist.

Ahn JS, et al. J Breast Cancer. 2023 Oct;26:e45. https://doi.org/10.4048/jbc.2023.26.e45

PURPOSE: In the treatment of non–small-cell lung cancer (NSCLC) with a driver mutation, the role of anti–PD-(L)1 antibody after tyrosine kinase inhibitor (TKI) remains unclear. This randomized, open-label, multicenter, phase III study evaluates the efficacy of atezolizumab plus bevacizumab, paclitaxel, and carboplatin (ABCP ) in EGFR- or ALK-rearranged or translocated NSCLC upon progression on TKI therapy. MATERIALS AND METHODS: We compared the clinical efficacy of ABCP followed by maintenance therapy with atezolizumab plus bevacizumab with pemetrexed plus carboplatin or cisplatin (PC) followed by pemetrexed maintenance. The primary end point was progression-free survival (PFS). RESULTS: A total of 228 patients with activating EGFR mutation (n = 215) or ALK translocation (n = 13) were enrolled from 16 sites in the Republic of Korea and randomly assigned at 2:1 ratio to either ABCP (n = 154) or PC arm (n = 74). The median follow-up duration was 26.1 months (95% CI, 24.7 to 28.2). Objective response rates (69.5% v 41.9%, P < .001) and median PFS (8.48 v 5.62 months, hazard ratio [HR], 0.62 [95% CI, 0.45 to 0.86]; P = .004) were significantly better in the ABCP than PC arm. PFS benefit increased as PD-L1 expression increased, with an HR of 0.47, 0.41, and 0.24 for PD-L1 ≥1%, ≥10%, and ≥50%, respectively. Overall survival was similar between ABCP and PC arm (20.63 v 20.27 months, HR, 1.01 [95% CI, 0.69 to 1.46]; P = .975). The safety profile of the ABCP arm was comparable with that previously reported, with no additional safety signals, but higher rates of treatment-related adverse events were observed compared with the PC arm. CONCLUSION: To our knowledge, this study is the first randomized phase III study to demonstrate the clinical benefit of anti–PD-L1 antibody in combination with bevacizumab and chemotherapy in patients with EGFR- or ALK-rearranged or translocated NSCLC who have progressed on relevant targeted therapy.

Background The inflamed immune phenotype (IIP), defined by enrichment of tumor-infiltrating lymphocytes (TILs) within intratumoral areas, is a promising tumor-agnostic biomarker of response to immune checkpoint inhibitor (ICI) therapy. However, it is challenging to define the IIP in an objective and reproducible manner during manual histopathologic examination. Here, we investigate artificial intelligence (AI)-based immune phenotypes capable of predicting ICI clinical outcomes in multiple solid tumor types. Methods Lunit SCOPE IO is a deep learning model which determines the immune phenotype of the tumor microenvironment based on TIL analysis. We evaluated the correlation between the IIP and ICI treatment outcomes in terms of objective response rates (ORR), progression-free survival (PFS), and overall survival (OS) in a cohort of 1,806 ICI-treated patients representing over 27 solid tumor types retrospectively collected from multiple institutions. Results We observed an overall IIP prevalence of 35.2% and significantly more favorable ORRs (26.3% vs 15.8%), PFS (median 5.3 vs 3.1 months, HR 0.68, 95% CI 0.61 to 0.76), and OS (median 25.3 vs 13.6 months, HR 0.66, 95% CI 0.57 to 0.75) after ICI therapy in IIP compared with non-IIP patients, respectively (p<0.001 for all comparisons). On subgroup analysis, the IIP was generally prognostic of favorable PFS across major patient subgroups, with the exception of the microsatellite unstable/mismatch repair deficient subgroup. Conclusion The AI-based IIP may represent a practical, affordable, clinically actionable, and tumor-agnostic biomarker prognostic of ICI therapy response across diverse tumor types.

Rationale: The clinical application of biomarkers reflecting tumor immune microenvironment is hurdled by the invasiveness of obtaining tissues despite its importance in immunotherapy. We developed a deep learning-based biomarker which noninvasively estimates a tumor immune profile with fluorodeoxyglucose positron emission tomography (FDG-PET) in lung adenocarcinoma (LUAD).

Purpose: Although programmed death 1/programmed death ligand 1 (PD-1/PD-L1) inhibitors are promising agents for recurrent or metastatic nasopharyngeal carcinoma (NPC), PD-1/PD-L1 inhibitor monotherapy has shown modest efficacy. This study evaluated the efficacy and safety of nivolumab plus gemcitabine in patients with NPC who failed prior platinum-based chemotherapy. Patients and Methods: This is a phase II, multicenter, open-label, single-arm study. Patients with recurrent or metastatic NPC received nivolumab 3 mg/kg and gemcitabine 1,250 mg/m2 every 2 weeks until disease progression or intolerable toxicity. The primary endpoint was progression-free survival (PFS). The secondary endpoints included objective response rate (ORR), overall survival (OS), and safety. To identify potential biomarkers, whole-exome sequencing, whole-transcriptome sequencing, and immune phenotype analysis based on Lunit SCOPE IO, an artificial intelligence–powered spatial tumor-infiltrating lymphocyte analyzer, were performed. Results: Thirty-six patients were enrolled between June 2018 and June 2019. The ORR was 36.1% and disease control rate was 97.2%. With median follow-up of 22.0 months, median PFS was 13.8 months [95% confidence interval (CI), 8.6–16.8 months]. Median OS was not reached, and OS rate at 6 months was 97.0% (95% CI, 80.4%–99.6%). The grade ≥3 treatment-related adverse events were hypertension (2.8%) and anemia (2.8%). In multivariate analysis of mutation of chromatin modifier gene, tumor mutational burden (≥ 2.1 mut/Mb), and somatic copy-number alteration (SCNA) level, the group with high SCNA (> 3 points; HR, 7.0; 95% CI, 1.3–37.9; P = 0.02) had independently associated with poor PFS. Immune phenotype analysis showed that tumors with high proportion of immune-excluded immune phenotype was significantly correlated with poor PFS (HR, 4.4; 95% CI, 1.2–16.2; P = 0.018). Conclusions: Nivolumab plus gemcitabine showed promising efficacy with favorable toxicity profiles in patients with advanced NPC in whom platinum-based combination chemotherapy failed.