The inflamed immune phenotype (IIP): A clinically actionable artificial intelligence (AI)-based biomarker predictive of immune checkpoint inhibitor (ICI) outcomes across >16 primary tumor types.

Authors

null

Jeanne Shen

Department of Pathology, Stanford University School of Medicine, Stanford, CA

Jeanne Shen , Yoon-La Choi , Taebum Lee , Hyojin Kim , Young Kwang Chae , Benjamin Dulken , Stephanie Bogdan , Maggie Huang , George A. Fisher Jr., Sehhoon Park , Se-Hoon Lee , Jun Eul Hwang , Jin-haeng Chung , Leeseul Kim , Seunghwan Shin , Yoojoo Lim , Heon Song , Sergio Pereira , Chan-Young Ock

Organizations

Department of Pathology, Stanford University School of Medicine, Stanford, CA, Department of Pathology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea, Department of Pathology, Chonnam National University Medical School, Gwangju, South Korea, Department of Pathology, Seoul National University Bundang Hospital, Seongnam, South Korea, Northwestern University, Chicago, IL, Center for Artificial Intelligence in Medicine & Imaging, Stanford University School of Medicine, Stanford, CA, UC Davis Health, Davis, CA, Department of Medicine, Stanford University School of Medicine, Stanford, CA, Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea, Division of Hematology-Oncology, Department of Internal Medicine, Chonnam National University Medical School, Hwasun, South Korea, AMITA health Saint Francis Hospital Evanston, Evanston, IL, Lunit Inc., Seoul, South Korea

Research Funding

Other

Background: The IIP, defined by enriched intratumoral tumor-infiltrating lymphocytes (TIL), is a potential tumor-agnostic biomarker of responsiveness to ICI therapy. Here, we validate the IIP, as assessed by Lunit SCOPE IO, an AI-powered spatial TIL analyzer that runs on routine H&E-stained whole-slide images (WSI), for clinical outcome prediction in a large, multi-center international cohort of ICI-treated patients, demonstrating its utility as a practical biomarker to guide ICI treatment planning. Methods: Lunit SCOPE IO was developed using 17,849 H&E WSI of multiple cancer types, annotated by 104 board-certified pathologists (13.5 x 109µm2 area and 6.2 x 106 TIL). IIP+ tumors were defined as those with ≥ 20% of all 1 mm2 tumor tiles in a WSI classified as having a high intratumoral TIL density. We evaluated the correlation between IIP and ICI treatment outcomes (overall response rate (ORR) and progression-free survival (PFS), assessed by RECIST v1.1) in a real-world dataset of 1,806 patients ( > 16 primary tumor types) retrospectively collected from Stanford University Medical Center, Samsung Medical Center, Chonnam National University Hospital, Seoul National University Bundang Hospital, and Northwestern University. IIP status was sub-analyzed by PD-L1 22C3 tumor proportion score (TPS, n = 798), microsatellite status, and tumor mutational burden (TMB, n = 130). Results: The IIP+ phenotype (35.2%, 636 of 1,806) was highly enriched in nasopharyngeal carcinoma (68.0%), melanoma (56.3%), renal cell carcinoma (52.9%), and non-small cell lung cancer (NSCLC, 33.7%). The IIP+ proportion by PD-L1 TPS ( < 1% / ≥ 1%) was 21.6% and 40.7%, respectively. While 33.3% of microsatellite unstable (MSI-H) or TMB-high (≥ 10/Mb) tumors were IIP+, a substantial proportion (26.1%) of microsatellite stable (MSS), TMB-low tumors were IIP+. The ORR in IIP+ patients was significantly higher (26.0% vs. 15.8% in IIP-, p < 0.001). Median PFS for IIP+ was 5.3 months (95% CI 4.6-6.9 m), significantly longer than that for IIP- (3.1 m, 95% CI 2.8-3.6 m), with a hazard ratio (HR) of 0.68 (95% CI 0.61-0.76, p < 0.001). The association held after excluding NSCLC patients (n = 909) (HR 0.69, 95% CI 0.59-0.81, p < 0.001). On subgroup analysis, IIP+ correlated significantly with prolonged PFS, regardless of ICI regimen (mono / combo therapy) or PD-L1 TPS ( < 1% / ≥ 1%). Of note, IIP+ was predictive of favorable PFS only in the MSS, TMB-low group (n = 88, HR 0.56, 95% CI 0.33-0.96), but not in the MSI-H or TMB-high groups. Conclusions: The IIP, as evaluated by Lunit SCOPE IO, may represent a practical, clinically-actionable biomarker predictive of favorable ICI treatment outcomes across diverse cancer patient populations, including those with PD-L1 negative, MSS/TMB-low tumors, in whom predictive biomarkers are urgently needed.

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Abstract Details

Meeting

2022 ASCO Annual Meeting

Session Type

Poster Session

Session Title

Developmental Therapeutics—Immunotherapy

Track

Developmental Therapeutics—Immunotherapy

Sub Track

Tissue-Based Biomarkers

Citation

J Clin Oncol 40, 2022 (suppl 16; abstr 2621)

DOI

10.1200/JCO.2022.40.16_suppl.2621

Abstract #

2621

Poster Bd #

276

Abstract Disclosures