Additional file 1: of Pan-cancer adaptive immune resistance as defined by the Tumor Inflammation Signature (TIS): results from The Cancer Genome Atlas (TCGA)

Figure S7. Distribution of TIS scores in stage IV disease. TIS scores are shown for all TCGA patients with stage 4 disease. Cancer types are ordered by median TIS score in all patients, identical to Fig. 1. (PDF 30 kb

Additional file 2: of Pan-cancer adaptive immune resistance as defined by the Tumor Inflammation Signature (TIS): results from The Cancer Genome Atlas (TCGA)

Figure S1. Meta-analysis of TIS score with objective response rates. [21, 50, 57–106]. (PDF 6 kb

Additional file 5: of Pan-cancer adaptive immune resistance as defined by the Tumor Inflammation Signature (TIS): results from The Cancer Genome Atlas (TCGA)

Figure S4. GO terms with the strongest negative association with TIS. Color shows GSA score. (PDF 16 kb

Additional file 4: of Pan-cancer adaptive immune resistance as defined by the Tumor Inflammation Signature (TIS): results from The Cancer Genome Atlas (TCGA)

Figure S2. In order to assess whether/how cancer cell of origin could directly affect the expression of TIS genes, the observed expression level for each gene versus the expected expression level based on total TIS score was evaluated. Specifically, for each algorithm gene, a linear mixed model (LMM) was fit predicting the gene’s log2 expression from TIS score and cancer type, with cancer type modelled as a random effect. The LMM’s variance term for cancer type was compared to each gene’s marginal variance across TCGA datasets. (PDF 4 kb

Additional file 6: of Pan-cancer adaptive immune resistance as defined by the Tumor Inflammation Signature (TIS): results from The Cancer Genome Atlas (TCGA)

Table S1. For each cancer type, GO terms with negative associations with TIS, defined as a GSA score below − 1. (CSV 48 kb

Additional file 9: of Pan-cancer adaptive immune resistance as defined by the Tumor Inflammation Signature (TIS): results from The Cancer Genome Atlas (TCGA)

Figure S5. Association between TIS score and breast cancer survival. Breast cancers were divided in 4 subsets based on their TIS scores. Kaplan-Meier curves and confidence intervals are shown for each subset. (PDF 17 kb

Additional file 3: of Pan-cancer adaptive immune resistance as defined by the Tumor Inflammation Signature (TIS): results from The Cancer Genome Atlas (TCGA)

Figure S6. Distribution of TIS scores within two MSI status categories and three cancer types. Points show individual samples’ TIS scores. (PDF 31 kb

Functional Pathway Analysis Using SCNP of <em>FLT3</em> Receptor Pathway Deregulation in AML Provides Prognostic Information Independent from Mutational Status

<div><p>FMS-like tyrosine kinase 3 receptor (<i>FLT3</i>) internal tandem duplication (ITD) mutations result in constitutive activation of this receptor and have been shown to increase the risk of relapse in patients with acute myeloid leukemia (AML); however, substantial heterogeneity in clinical outcomes still exists within both the ITD mutated and unmutated AML subgroups, suggesting alternative mechanisms of disease relapse not accounted by <i>FLT3</i> mutational status. Single cell network profiling (SCNP) is a multiparametric flow cytometry based assay that simultaneously measures, in a quantitative fashion and at the single cell level, both extracellular surface marker levels and changes in intracellular signaling proteins in response to extracellular modulators. We previously reported an initial characterization of FLT3 ITD-mediated signaling using SCNP. Herein SCNP was applied sequentially to two separate cohorts of samples collected from elderly AML patients at diagnosis. In the first (training) study, AML samples carrying unmutated, wild-type <i>FLT3</i> (<i>FLT3</i> WT) displayed a wide range of induced signaling, with a fraction having signaling profiles comparable to <i>FLT3</i> ITD AML samples. Conversely, the <i>FLT3</i> ITD AML samples displayed more homogeneous induced signaling, with the exception of patients with low (<40%) mutational load, which had profiles comparable to <i>FLT3</i> WT AML samples. This observation was then confirmed in an independent (verification) cohort. Data from the second cohort were also used to assess the association between SCNP data and disease-free survival (DFS) in the context of <i>FLT3</i> and nucleophosmin (<i>NPM1</i>) mutational status among patients who achieved complete remission (CR) to induction chemotherapy. The combination of SCNP read outs together with <i>FLT3 and NPM1</i> molecular status improved the DFS prediction accuracy of the latter. Taken together, these results emphasize the value of comprehensive functional assessment of biologically relevant signaling pathways in AML as a basis for the development of highly predictive tests for guidance of post-remission therapy.</p> </div

Additional file 1: of Validation of biomarkers to predict response to immunotherapy in cancer: Volume II — clinical validation and regulatory considerations

Publications by the SITC Immune Biomarker Task Force. (DOCX 14.1 kb

Additional file 1: of Validation of biomarkers to predict response to immunotherapy in cancer: Volume I — pre-analytical and analytical validation

Publications by the SITC Immune Biomarker Task Force. (DOCX 14.1 kb