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.

Surgery and radiation are the current standard treatments for cervical cancer. However, there is no effective therapy for metastatic or recurrent cases, necessitating the identification of therapeutic targets. In order to create preclinical models for screening potential therapeutic targets, we established 14 patient-derived xenograft (PDX) models of cervical cancers using subrenal implantation methods. Serially passaged PDX tumors retained the histopathologic and genomic features of the original tumors. Among the 9 molecularly profiled cervical cancer patient samples, a HER2-amplified tumor was detected by array comparative genomic hybridization and targeted next-generation sequencing. We confirmed HER2 overexpression in the tumor and serially passaged PDX. Co-administration of trastuzumab and lapatinib in the HER2-overexpressed PDX significantly inhibited tumor growth compared to the control. Thus, we established histopathologically and genomically homologous PDX models of cervical cancer using subrenal implantation. Furthermore, we propose HER2 inhibitor-based therapy for HER2-amplified cervical cancer refractory to conventional therapy.

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