{"id":"W2982501627","doi":"10.1093/jnci/djz208","title":"Unraveling Triple-Negative Breast Cancer Tumor Microenvironment Heterogeneity: Towards an Optimized Treatment Approach","year":2019,"lang":"en","type":"article","venue":"JNCI Journal of the National Cancer Institute","topic":"Cancer Immunotherapy and Biomarkers","field":"Medicine","cited_by":204,"is_retracted":false,"has_abstract":true,"ca_institutions":"Centre Hospitalier de l’Université de Montréal; McGill University","funders":"KU Leuven; McGill University; Breast Cancer Research Foundation","keywords":"Biology; Immune system; Tumor microenvironment; Stromal cell; Cancer research; Phenotype; Breast cancer; Transcriptome; Triple-negative breast cancer; Comparative genomic hybridization; Androgen receptor; Gene expression profiling; Cancer; Immunology; Gene; Gene expression; Genetics; Chromosome; Prostate cancer","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008248696,0.0004258144,0.001152137,0.0009194236,0.0002023176,0.001210214,0.0005071913,0.0003407713,0.0008213581],"category_scores_gemma":[0.0006550093,0.0001599214,0.0005712844,0.0006638786,0.0002373481,0.0006280742,0.0007335183,0.0009836433,0.0003049812],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004238359,"about_ca_system_score_gemma":0.0008900838,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005341799,"about_ca_topic_score_gemma":0.001093614,"domain_scores_codex":[0.999751,0.00005996969,0.00001447338,0.00006938101,0.00006132734,0.00004377107],"domain_scores_gemma":[0.9997357,0.00006532297,0.00007732138,0.00002704907,0.00005476239,0.00003983057],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0008603004,0.00036163,0.07646369,0.002589028,0.0004083641,0.0005524447,0.0002293053,0.01046063,0.4771207,0.001989242,0.004724943,0.4242398],"study_design_scores_gemma":[0.0003210635,0.003861469,0.3516551,0.001568778,0.002941298,0.007633968,0.002739773,0.1209447,0.3238647,0.02244698,0.1617627,0.0002595285],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6515387,0.1700916,0.1619156,0.006566725,0.0002582078,0.0004014138,0.003560839,0.0009230425,0.00474399],"genre_scores_gemma":[0.8812985,0.04027611,0.07170933,0.001235445,0.0003164418,0.0003136596,0.003643408,0.0001102415,0.001096889],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001210214,"threshold_uncertainty_score":0.004362404,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0455643746623005,"score_gpt":0.3265562538542315,"score_spread":0.280991879191931,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}