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  • American Association for Cancer Research (AACR)  (2)
  • Akhave, Neal  (2)
  • Cascone, Tina  (2)
  • Fujimoto, Junya  (2)
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  • American Association for Cancer Research (AACR)  (2)
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  • 1
    In: Cancer Research, American Association for Cancer Research (AACR), Vol. 81, No. 13_Supplement ( 2021-07-01), p. 619-619
    Abstract: Introduction: Differences in the host immune environment are thought to mediate heterogeneous treatment responses in non-small cell lung cancer (NSCLC). Unlike individual platform analyses, integrative analysis of multi-platform profiling allows for the discovery of novel interactions that expand our understanding of the disease. Utilizing the ImmunogenomiC prOfiling of NSCLC patient cohort (ICON), a prospective multi-omics protocol of operable early-stage NSCLC tumors with integrated immune, genomic, and clinical data, we hypothesized that multi-platform analyses would identify differences in the immune-genomic landscape that are associated with disease recurrence. Methods: Tumor and tumor-adjacent uninvolved lung was collected at resection; blood was collected before and after surgery. Tissue samples underwent WES, RNAseq, TCR sequencing (TCRseq), multiplex immunofluorescence (mIF), and RPPA profiling; tissue and blood (PBMC) samples were analyzed by flow cytometry. An integrated, inter-modality network was built using Spearman correlations between measurement pairs from different data modalities. Multivariate analysis was performed to adjust for stage and histology. Results: A total of 89 treatment-naïve patients with Stage 1-3 resected NSCLC (Squamous: 19; Non-squamous: 70) and 24 months of follow-up were analyzed (recurrence N = 24; no recurrence N = 65). The data network includes over 4,000 measurements linked by over 50,000 correlations. InfoMap, a community detection approach, was used to extract sub-network modules, which were used to contextualize the results of multivariate analysis. Tumors from patients with recurrence demonstrated decreased immune cell infiltration and activation including decreased cytotoxic CD8 T-cells (CD8+PD1+; fold-change (FC) = 0.898, p = 0.018; flow cytometry), decreased T-cell clonality (FC = 0.954, p = 0.017; TCRseq), and decreased tumor-associated macrophages (CD68+PD-L1+; FC = 0.426, p = 0.011; mIF). Furthermore, circulating CD8+ICOS+ activated T cells were decreased in patients with recurrence suggesting an impaired systemic anti-tumor immune response (FC = 0.552, p = 0.042; PBMC Flow). Finally, tumor-adjacent uninvolved lungs showed distinct T-cell phenotypes with accumulation of inactive CD8 T-cells (CD8+PD1-TIM3-) in patients with recurrence and increased populations of activated CD8 T-cells (CD8+PD1+) in patients without recurrence. Conclusion: Integrative multi-omic analysis suggests preserved anti-tumor immune surveillance in patients who are disease-free after 2 years from surgical resection with curative intent for treatment of NSCLC relative to patients with disease recurrence. Further analysis is ongoing to interrogate genomic and immune variables that are associated with disease recurrence. Citation Format: Neal Akhave, Stephanie Schmidt, Alexandre Reuben, Tina Cascone, Jianhua Zhang, Jun Li, Junya Fujimoto, Lauren A. Byers, Beatriz Sanchez-Espiridion, Lixia Diao, Jing Wang, Lorenzo Federico, Marie-Andree Forget, Daniel J. McGrail, Annikka Weissferdt, Shiaw-Yih Lin, Younghee Lee, Carmen Behrens, Ignacio I. Wistuba, Andrew Futreal, Ara Vaporciyan, Boris Sepesi, John V. Heymach, Chantale Bernatchez, Cara Haymaker, Jianjun Zhang, Christopher A. Bristow, Marcelo V. Negrao, Don L. Gibbons. Integrated multi-platform profiling of early-stage non-small cell lung cancer identifies relationship between disease recurrence and decreased native immune response in treatment-naïve resected NSCLC [abstract] . In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 619.
    Type of Medium: Online Resource
    ISSN: 0008-5472 , 1538-7445
    RVK:
    RVK:
    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2021
    detail.hit.zdb_id: 2036785-5
    detail.hit.zdb_id: 1432-1
    detail.hit.zdb_id: 410466-3
    Location Call Number Limitation Availability
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  • 2
    In: Molecular Cancer Therapeutics, American Association for Cancer Research (AACR), Vol. 20, No. 12_Supplement ( 2021-12-01), p. P009-P009
    Abstract: Background: The ImmunogenomiC prOfiling of Non-small cell lung cancer (NSCLC) Project (ICON) represents an ambitious undertaking to comprehensively characterize immuno-genomic diversity in NSCLC across diverse platforms. The depth and breadth of this cohort presented a unique opportunity to develop a specialized method for multi-platform data integration and exploration, which can be broadly applied to forthcoming large-scale patient profiling studies. Such a holistic approach can unlock insights for therapeutic targets, biomarkers, and treatment plans by providing a more complete view of phenomena driving disease pathogenesis and evolution. Purpose: We developed a novel shared nearest neighbors (SNN) approach to create an integrated network of ICON’s multi-platform data and identified collections of closely related measurements within the resulting network tied to noteworthy patient characteristics, including recurrence and oncogenotype. Methods: The ICON dataset is derived from tumor and normal lung tissue samples collected from 150 patients at time of resection as well as blood samples collected then and at intervals during the year following. Tissue samples underwent RNA-sequencing (RNA-seq), whole exome sequencing, T-cell receptor sequencing, multiplex immunofluorescence for immune cells, and reverse phase protein array profiling; flow cytometry for immune cells was performed on tissue and blood samples. From these data, the ICON data network was built using an integrative approach based on the SNN algorithm in which genes were linked on the basis of their shared top correlates in orthogonal datasets. Results: The ICON data network currently includes over 20,000 genes linked by over 500,000 connections derived from correlations between RNA-seq and orthogonal platforms. We captured established associations between cancer-related genes and examined these along with new ones in the network. To do so, we used the InfoMap algorithm to extract more interpretable sub-networks, termed modules, from the ICON data network. Single sample gene set enrichment scores for each module were used in multivariate analysis to highlight modules linked to clinical characteristics of interest. As an example, we found modules significantly tied to disease recurrence. The most notable of these was strongly associated with metabolic pathways, and other modules associated with platelets and ion channels were also identified. The metabolic pathway module is being explored as a prognostic biomarker, underscoring the opportunites enabled by mining the network. Conclusions: Through the framework developed, we identified modules in the ICON data network significantly associated with important patient characteristics like recurrence and oncogenotype. We are validating the gene sets identified as potential biomarkers and are developing an interactive application to facilitate further mining of the network. Taken together, our SNN network-building approach enables the integration and exploration of patient data from diverse platforms. Citation Format: Stephanie T. Schmidt, Neal Akhave, Alexandre Reuben, Tina Cascone, Jianhua Zhang, Jun Li, Junya Fujimoto, Lauren A. Byers, Beatriz Sanchez-Espiridion, Lixia Diao, Jing Wang, Lorenzo Federico, Marie-Andree Forget, Daniel J McGrail, Annikka Weissferdt, Shiaw-Yih Lin, Younghee Lee, Natalie Vokes, Carmen Behrens, Ignacio I. Wistuba, Andrew Futreal, Ara Vaporciyan, Boris Sepesi, John V. Heymach, Chantale Bernatchez, Cara Haymaker, Jianjun Zhang, Christopher A. Bristow, Timothy P. Heffernan, Marcelo V. Negrao, Don L. Gibbons. A shared nearest neighbors approach for integrated, multi-platform networks and its application to the exploration of multiomics data from early-stage non-small cell lung cancers [abstract]. In: Proceedings of the AACR-NCI-EORTC Virtual International Conference on Molecular Targets and Cancer Therapeutics; 2021 Oct 7-10. Philadelphia (PA): AACR; Mol Cancer Ther 2021;20(12 Suppl):Abstract nr P009.
    Type of Medium: Online Resource
    ISSN: 1535-7163 , 1538-8514
    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2021
    detail.hit.zdb_id: 2062135-8
    SSG: 12
    Location Call Number Limitation Availability
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