A gene signature for immune subtyping of desert, excluded, and inflamed ovarian tumors

Problem The current tumor immunology paradigm emphasizes the role of the immune tumor microenvironment and distinguishes several histologically and transcriptionally different immune tumor subtypes. However, the experimental validation of such classification is so far limited to selected cancer type...

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Veröffentlicht in:American journal of reproductive immunology (1989) 2020-07, Vol.84 (1), p.e13244-n/a
Hauptverfasser: Mlynska, Agata, Vaišnorė, Ramunė, Rafanavičius, Vytautas, Jocys, Simonas, Janeiko, Julija, Petrauskytė, Monika, Bijeikis, Simas, Cimmperman, Piotras, Intaitė, Birutė, Žilionytė, Karolina, Barakauskienė, Aušrinė, Meškauskas, Raimundas, Paberalė, Emilija, Pašukonienė, Vita
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container_title American journal of reproductive immunology (1989)
container_volume 84
creator Mlynska, Agata
Vaišnorė, Ramunė
Rafanavičius, Vytautas
Jocys, Simonas
Janeiko, Julija
Petrauskytė, Monika
Bijeikis, Simas
Cimmperman, Piotras
Intaitė, Birutė
Žilionytė, Karolina
Barakauskienė, Aušrinė
Meškauskas, Raimundas
Paberalė, Emilija
Pašukonienė, Vita
description Problem The current tumor immunology paradigm emphasizes the role of the immune tumor microenvironment and distinguishes several histologically and transcriptionally different immune tumor subtypes. However, the experimental validation of such classification is so far limited to selected cancer types. Here, we aimed to explore the existence of inflamed, excluded, and desert immune subtypes in ovarian cancer, as well as investigate their association with the disease outcome. Method of study We used the publicly available ovarian cancer dataset from The Cancer Genome Atlas for developing subtype assignment algorithm, which was next verified in a cohort of 32 real‐world patients of a known tumor subtype. Results Using clinical and gene expression data of 489 ovarian cancer patients in the publicly available dataset, we identified three transcriptionally distinct clusters, representing inflamed, excluded, and desert subtypes. We developed a two‐step subtyping algorithm with COL5A2 serving as a marker for separating excluded tumors, and CD2, TAP1, and ICOS for distinguishing between inflamed and desert tumors. The accuracy of gene expression–based subtyping algorithm in a real‐world cohort was 75%. Additionally, we confirmed that patients bearing inflamed tumors are more likely to survive longer. Conclusion Our results highlight the presence of transcriptionally and histologically distinct immune subtypes among ovarian tumors and emphasize the potential benefit of immune subtyping as a clinical tool for treatment tailoring. Using gene expression data of 489 ovarian cancer patients from TCGA dataset, we identified three transcriptionally distinct clusters, representing inflamed, excluded, and desert subtypes, which presence was then confirmed in a cohort of 32 real‐world ovarian cancer patients. We developed a two‐step gene subtyping algorithm with COL5A2 serving as a marker for separating excluded tumors, and CD2, TAP1, ICOS for distinguishing between inflamed and desert tumors. We confirmed that patients bearing inflamed tumors are more likely to survive longer.
doi_str_mv 10.1111/aji.13244
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However, the experimental validation of such classification is so far limited to selected cancer types. Here, we aimed to explore the existence of inflamed, excluded, and desert immune subtypes in ovarian cancer, as well as investigate their association with the disease outcome. Method of study We used the publicly available ovarian cancer dataset from The Cancer Genome Atlas for developing subtype assignment algorithm, which was next verified in a cohort of 32 real‐world patients of a known tumor subtype. Results Using clinical and gene expression data of 489 ovarian cancer patients in the publicly available dataset, we identified three transcriptionally distinct clusters, representing inflamed, excluded, and desert subtypes. We developed a two‐step subtyping algorithm with COL5A2 serving as a marker for separating excluded tumors, and CD2, TAP1, and ICOS for distinguishing between inflamed and desert tumors. The accuracy of gene expression–based subtyping algorithm in a real‐world cohort was 75%. Additionally, we confirmed that patients bearing inflamed tumors are more likely to survive longer. Conclusion Our results highlight the presence of transcriptionally and histologically distinct immune subtypes among ovarian tumors and emphasize the potential benefit of immune subtyping as a clinical tool for treatment tailoring. Using gene expression data of 489 ovarian cancer patients from TCGA dataset, we identified three transcriptionally distinct clusters, representing inflamed, excluded, and desert subtypes, which presence was then confirmed in a cohort of 32 real‐world ovarian cancer patients. We developed a two‐step gene subtyping algorithm with COL5A2 serving as a marker for separating excluded tumors, and CD2, TAP1, ICOS for distinguishing between inflamed and desert tumors. 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However, the experimental validation of such classification is so far limited to selected cancer types. Here, we aimed to explore the existence of inflamed, excluded, and desert immune subtypes in ovarian cancer, as well as investigate their association with the disease outcome. Method of study We used the publicly available ovarian cancer dataset from The Cancer Genome Atlas for developing subtype assignment algorithm, which was next verified in a cohort of 32 real‐world patients of a known tumor subtype. Results Using clinical and gene expression data of 489 ovarian cancer patients in the publicly available dataset, we identified three transcriptionally distinct clusters, representing inflamed, excluded, and desert subtypes. We developed a two‐step subtyping algorithm with COL5A2 serving as a marker for separating excluded tumors, and CD2, TAP1, and ICOS for distinguishing between inflamed and desert tumors. 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However, the experimental validation of such classification is so far limited to selected cancer types. Here, we aimed to explore the existence of inflamed, excluded, and desert immune subtypes in ovarian cancer, as well as investigate their association with the disease outcome. Method of study We used the publicly available ovarian cancer dataset from The Cancer Genome Atlas for developing subtype assignment algorithm, which was next verified in a cohort of 32 real‐world patients of a known tumor subtype. Results Using clinical and gene expression data of 489 ovarian cancer patients in the publicly available dataset, we identified three transcriptionally distinct clusters, representing inflamed, excluded, and desert subtypes. We developed a two‐step subtyping algorithm with COL5A2 serving as a marker for separating excluded tumors, and CD2, TAP1, and ICOS for distinguishing between inflamed and desert tumors. The accuracy of gene expression–based subtyping algorithm in a real‐world cohort was 75%. Additionally, we confirmed that patients bearing inflamed tumors are more likely to survive longer. Conclusion Our results highlight the presence of transcriptionally and histologically distinct immune subtypes among ovarian tumors and emphasize the potential benefit of immune subtyping as a clinical tool for treatment tailoring. Using gene expression data of 489 ovarian cancer patients from TCGA dataset, we identified three transcriptionally distinct clusters, representing inflamed, excluded, and desert subtypes, which presence was then confirmed in a cohort of 32 real‐world ovarian cancer patients. We developed a two‐step gene subtyping algorithm with COL5A2 serving as a marker for separating excluded tumors, and CD2, TAP1, ICOS for distinguishing between inflamed and desert tumors. 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subjects Algorithms
Gene expression
Genomes
immune markers
immunologic subtyping
Inflammation
Ovarian cancer
Patients
Transcription
Tumors
tumor‐infiltrating lymphocytes
title A gene signature for immune subtyping of desert, excluded, and inflamed ovarian tumors
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