Research graph
References from Automated measurement of CD8+ T-cell distribution at the tumor epithelial–stromal interface is associated with NSCLC immunotherapy benefit and transcriptomic pathways. Local targets link to admitted publications; unresolved targets remain external evidence.
Immune checkpoint inhibitors for lung cancer treatment: a review
2020 · External reference
Signatures of T cell dysfunction and exclusion predict cancer immunotherapy response
10.1038/s41591-018-0136-1 · 2018 · External reference
PD-1 and PD-L1 checkpoint signaling inhibition for cancer immunotherapy: mechanism, combinations, and clinical outcome
10.3389/fphar.2017.00561 · 2017 · External reference
Predictive correlates of response to the anti-PD-L1 antibody MPDL3280A in cancer patients
10.1038/nature14011 · 2014 · External reference
Pembrolizumab versus chemotherapy for PD-L1–positive non–small-cell lung cancer
10.1056/nejmoa1606774 · 2016 · External reference
Cancer immunotherapy update: FDA-approved checkpoint inhibitors and companion diagnostics
10.1208/s12248-021-00574-0 · 2021 · External reference
PD-L1 expression as a predictive biomarker in cancer immunotherapy
10.1158/1535-7163.mct-14-0983 · 2015 · External reference
FDA approval summary: pembrolizumab, atezolizumab, and cemiplimab-rwlc as single agents for first-line treatment of advanced/metastatic PD-L1 high NSCLC
10.1158/1078-0432.ccr-21-3844 · 2022 · External reference
Analytical concordance of PD-L1 assays utilizing antibodies from FDA-approved diagnostics in advanced cancers: a systematic literature review
2021 · External reference
Heterogeneity of PD-L1 expression and relationship with biology of NSCLC
10.21873/anticanres.12662 · 2018 · External reference
Next generation predictive biomarkers for immune checkpoint inhibition
10.1007/s10555-016-9652-y · 2017 · External reference
The role of PD-L1 expression as a predictive biomarker: an analysis of all US Food and Drug Administration (FDA) approvals of immune checkpoint inhibitors
10.1186/s40425-019-0768-9 · 2019 · External reference
Mechanisms of resistance to immune checkpoint inhibitors
10.1038/bjc.2017.434 · 2018 · External reference
The immune contexture and immunoscore in cancer prognosis and therapeutic efficacy
10.1038/s41568-020-0285-7 · 2020 · External reference
The association between CD8+ tumor-infiltrating lymphocytes and the clinical outcome of cancer immunotherapy: a systematic review and meta-analysis
10.1016/j.eclinm.2021.101134 · 2021 · External reference
Pitfalls in machine learning-based assessment of tumor-infiltrating lymphocytes in breast cancer: a report of the International Immuno-Oncology Biomarker Working Group on Breast Cancer
10.1002/path.6155 · 2023 · External reference
Cytokine and chemokine signals of T-cell exclusion in tumors
10.3389/fimmu.2020.594609 · 2020 · External reference
Three-dimensional segregation of supramolecular activation clusters in T cells
10.1038/25764 · 1998 · External reference
Cytotoxic T cells are able to efficiently eliminate cancer cells by additive cytotoxicity
10.1038/s41467-021-25282-3 · 2021 · External reference
The where, the when, and the how of immune monitoring for cancer immunotherapies in the era of checkpoint inhibition
10.1158/1078-0432.ccr-15-1507 · 2016 · External reference
Cancer immune evasion through loss of MHC class I antigen presentation
10.3389/fimmu.2021.636568 · 2021 · External reference
T cell exclusion, immune privilege, and the tumor microenvironment
10.1126/science.aaa6204 · 2015 · External reference
Automated tumor immunophenotyping predicts clinical benefit from anti-PD-L1 immunotherapy
2023 · External reference
Spatial interplay patterns of cancer nuclei and tumor-infiltrating lymphocytes (TILs) predict clinical benefit for immune checkpoint inhibitors
2022 · External reference
Computational image features of immune architecture is associated with clinical benefit and survival in gynecological cancers across treatment modalities
10.1136/jitc-2021-003833 · 2022 · External reference
Integrated digital pathology and transcriptome analysis identifies molecular mediators of T-cell exclusion in ovarian cancer
10.1038/s41467-020-19408-2 · 2020 · External reference
A prognostic and predictive computational pathology immune signature for ductal carcinoma in situ: retrospective results from a cohort within the UK/ANZ DCIS trial
10.1016/s2589-7500(24)00116-x · 2024 · External reference
A method for normalizing histology slides for quantitative analysis
2009 · External reference
Cellpose: a generalist algorithm for cellular segmentation
10.1038/s41592-020-01018-x · 2020 · External reference
Cellpose 2.0: how to train your own model
10.1038/s41592-022-01663-4 · 2022 · External reference
Unresolved reference
1994 · External reference
Is there a difference between T- and B-lymphocyte morphology?
10.1117/1.3275471 · 2009 · External reference
Tumour-infiltrating lymphocyte density is associated with favourable outcome in patients with advanced non–small cell lung cancer treated with immunotherapy
10.1016/j.ejca.2020.10.017 · 2021 · External reference
Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles
10.1073/pnas.0506580102 · 2005 · External reference
Conserved pan-cancer microenvironment subtypes predict response to immunotherapy
10.1016/j.ccell.2021.04.014 · 2021 · External reference
Quantitative set analysis for gene expression: a method to quantify gene set differential expression including gene-gene correlations
10.1093/nar/gkt660 · 2013 · External reference
Nonparametric estimation from incomplete observations
10.1080/01621459.1958.10501452 · 1958 · External reference
Evaluating the yield of medical tests
10.1001/jama.1982.03320430047030 · 1982 · External reference
lifelines: survival analysis in Python
10.21105/joss.01317 · 2019 · External reference
The efficacy and safety of anti-PD-1/PD-L1 antibodies for treatment of advanced or refractory cancers: a meta-analysis
10.18632/oncotarget.12230 · 2016 · External reference
Analysis of response rate with ANTI PD1/PD-L1 monoclonal antibodies in advanced solid tumors: a meta-analysis of randomized clinical trials
10.18632/oncotarget.24283 · 2018 · External reference
Immunotherapy in lung cancer: current landscape and future directions
10.3389/fimmu.2022.823618 · 2022 · External reference
Selecting the optimal immunotherapy regimen in driver-negative metastatic NSCLC
10.1038/s41571-021-00520-1 · 2021 · External reference
Digital pathology and artificial intelligence
10.1016/s1470-2045(19)30154-8 · 2019 · External reference
Artificial intelligence in digital pathology — time for a reality check
10.1038/s41571-025-00991-6 · 2025 · External reference
Cytotoxic CD8+ T cells in cancer and cancer immunotherapy
10.1038/s41416-020-01048-4 · 2021 · External reference
Immunogradient indicators for antitumor response assessment by automated tumor-stroma interface zone detection
10.1016/j.ajpath.2020.01.018 · 2020 · External reference
CD8+ cell density gradient across the tumor epithelium–stromal interface of non-muscle invasive papillary urothelial carcinoma predicts recurrence-free survival after BCG immunotherapy
10.3390/cancers15041205 · 2023 · External reference
Establishing standardized immune phenotyping of metastatic melanoma by digital pathology
10.1038/s41374-021-00653-y · 2021 · External reference
Overcoming solid-tumor barriers: armored CAR-T cell therapy
10.1016/j.trecan.2025.08.009 · 2025 · External reference
Tumour-infiltrating lymphocyte therapy landscape: prospects and challenges
2025 · External reference
What clinicians want: contextualizing explainable machine learning for clinical end use
2019 · External reference
