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Supplementary table S1. Cell cycle genes Supplementary table S2. Cross-table of concordance between Ki67 immunohistochemical staining and gene expression signature classifications in Cohorts 1 and 2 Supplementary table S3. Clinico-pathological characteristics of patient subgroups in cohort 1 Supplementary table S4. Change in C-index when adding gene expression signatures to Ki67/IHC for All, ER+/LN-, ER+/LN-, ER- patients in Cohorts 1 and 2. Supplementary Table S5. Clinico-pathological characteristics of patient subgroups in cohort 2

Supplementary Figure 1 CONSORT diagram, TEX cohort Supplementary Figure 2 Long term post-relapse BCSS for gene expression signatures Supplementary Figure 3 Short term post-relapse BCSS for gene expression signatures Supplementary Figure 4 Long and short term post-relapse BCSS Kaplan-Meier curves for PAM50 at specific metastatic sites

Supplementary Table 1. Primary tumour characteristics of patients included in the translational TEX trial Supplementary Table 2. Metastatic signature subtype split by primary tumour PR status and adjuvant therapy (N = 109) Supplementary Table 3. Long and short-term univariate Cox regression analysis for all signatures, post-relapse BCSS as clinical endpoint Supplementary Table 4. Long and short-term Likelihood ratios for all signatures in metastases from All, lymph nodes and other sites combined groups

Purpose: Transcriptional pathway activity and the molecular subtypes of breast cancer metastases have been shown to significantly influence patient postrelapse survival. Here, we further determine the relevance of clinically employed gene signatures in the advanced breast cancer (ABC) setting.Experimental Design: Sufficient RNA for expression profiling was obtained from distant metastatic or inoperable loco-regional relapse tissue by fine-needle aspiration from 109 patients of the Swedish TEX clinical trial. Gene signatures (GGI, 70 gene, recurrence score, cell-cycle score, risk of recurrence score, and PAM50) were applied to all metastases, and their relationship to long- (5-year) and short-term (1.5-year) postrelapse survival at all and locoregional lymph nodes (n = 40) versus other metastatic sites (n = 69) combined was assessed using Kaplan–Meier and/or multivariate Cox regression analyses.Results: The majority of metastases were classified into intermediate or high-risk groups by all signatures, and a significant association was found between metastatic signature subgroups and primary tumor estrogen receptor status and histologic grade (P < 0.05). When considering all sites of metastasis, only PAM50 was statistically significant in Kaplan–Meier analysis (Log-rank P = 0.008 and 0.008 for long- and short-term postrelapse breast cancer–specific survival, respectively). This significance remained in both uni- and multivariate models when restricting analyses to lymph node metastases only, and a similar trend was observed in other metastatic sites combined, but did not reach formal significance.Conclusions: Our findings are the first to demonstrate that the PAM50 signature can provide prognostic information from the lymph node metastases of ABC patients. Clin Cancer Res; 23(23); 7225–31. ©2017 AACR.

Supplementary figure S1: Survival analysis (Kaplan-Meier estimates) with Breast cancer specific survival (BCSS) as end point for All patients of Cohort 1. Supplementary figure S2: Flowchart showing simplified results from Cohort 1. Supplementary figure S3: Additional prognostic value of gene expression signatures beyond Ki67/IHC, Cohort 1. Supplementary figure S4: Additional prognostic value of Ki67/IHC beyond gene expression signatures, Cohort 1. Supplementary figure S5: Additional prognostic value of gene expression signatures beyond Ki67/IHC, Cohort 2. Supplementary figure S6: Additional prognostic value of Ki67/IHC beyond gene expression signatures, Cohort 2.

All supplementary materials and methods

Purpose: Gene signatures and Ki67 stratify the same breast tumor into opposing good/poor prognosis groups in approximately 20% of patients. Given this discrepancy, we hypothesized that the combination of a clinically relevant signature and IHC markers may provide more prognostic information than either classifier alone.Experimental Design: We assessed Ki67 alone or combined with ER, PR and HER2 (forming IHC subtypes), and the research versions of the Genomic Grade Index, 70-gene, cell-cycle score, recurrence score (RS), and PAM50 signatures on matching TMA/whole tumor sections and microarray data in two Swedish breast cancer cohorts of 379 and 209 patients, with median follow-up of 12.4 and 12.5 years, respectively. First, we fit Cox proportional hazards models and used the change in likelihood ratio (Δ LR) to determine the additional prognostic information provided by signatures beyond that of (i) Ki67 and (ii) IHC subtypes. Second and uniquely, we then assessed whether signatures could compete well with pathology-based IHC classifiers by calculating the additional prognostic information of Ki67/IHC subtypes beyond signatures.Results: In cohort 1, only RS and PAM50 provided additional prognostic information beyond Ki67 and IHC subtypes (Δ LR-χ2 Ki67: RS = 12.8, PAM50 = 20.7, IHC subtypes: RS = 12.9, PAM50 = 11.7). Conversely, IHC subtypes added prognostic information beyond all signatures except PAM50. Similar results were observed in cohort 2.Conclusions: RS and PAM50 provided more prognostic information than the IHC subtypes in all breast cancer patients; however, the IHC subtypes did not add any prognostic information to PAM50. Clin Cancer Res; 23(24); 7512–20. ©2017 AACR.

BACKGROUND: B lymphocytes have multifaceted functions in the tumour microenvironment, and their prognostic role in human cancers is controversial. Here we aimed to identify tumour microenvironmental factors that influence the prognostic effects of B cells. METHODS: We conducted a gene expression analysis of 3585 patients for whom the clinical outcome information was available. We further investigated the clinical relevance for predicting immunotherapy response. RESULTS: We identified a novel B cell-related gene (BCR) signature consisting of nine cytokine signalling genes whose high expression could diminish the beneficial impact of B cells on patient prognosis. In triple-negative breast cancer, higher B cell abundance was associated with favourable survival only when the BCR signature was low (HR = 0.68, p = 0.0046). By contrast, B cell abundance had no impact on prognosis when the BCR signature was high (HR = 0.93, p = 0.80). This pattern was consistently observed across multiple cancer types including lung, colorectal, and melanoma. Further, the BCR signature predicted response to immune checkpoint blockade in metastatic melanoma and compared favourably with the established markers. CONCLUSIONS: The prognostic impact of tumour-infiltrating B cells depends on the status of cytokine signalling genes, which together could predict response to cancer immunotherapy.

Analysis of DNA methylation is a valuable tool to understand disease progression and is increasingly being used to create diagnostic and prognostic clinical biomarkers. While conversion of cytosine to 5-methylcytosine (5mC) commonly results in transcriptional repression, further conversion to 5-hydroxymethylcytosine (5hmC) is associated with transcriptional activation. Here we perform the first study integrating whole-genome 5hmC with DNA, 5mC, and transcriptome sequencing in clinical samples of benign, localized, and advanced prostate cancer. 5hmC is shown to mark activation of cancer drivers and downstream targets. Furthermore, 5hmC sequencing revealed profoundly altered cell states throughout the disease course, characterized by increased proliferation, oncogenic signaling, dedifferentiation, and lineage plasticity to neuroendocrine and gastrointestinal lineages. Finally, 5hmC sequencing of cell-free DNA from patients with metastatic disease proved useful as a prognostic biomarker able to identify an aggressive subtype of prostate cancer using the genes TOP2A and EZH2, previously only detectable by transcriptomic analysis of solid tumor biopsies. Overall, these findings reveal that 5hmC marks epigenomic activation in prostate cancer and identify hallmarks of prostate cancer progression with potential as biomarkers of aggressive disease. Significance: In prostate cancer, 5-hydroxymethylcytosine delineates oncogene activation and stage-specific cell states and can be analyzed in liquid biopsies to detect cancer phenotypes. See related article by Wu and Attard, p. 3880

BackgroundPan-cancer genomic analyses based on the magnitude of pathway activity are currently lacking. Focusing on the cell cycle, we examined the DNA mutations and chromosome arm-level aneuploidy within tumours with low, intermediate and high cell cycle activity.Patients and methodsMatching mRNA, gene mutational status, chromosomal arm-level aberrations and clinico-pathological data was assembled from pan-cancer studies of 9,515 patients with 32 different cancers. Cell cycle activity was estimated from mRNA data using the cell cycle score (CCS) signature. Barplots were used to visualise mutation and chromosomal aberration frequency within CCS subgroups. Kaplan-Meier and multivariable Cox-regression analyses were used to determine survival differences between CCS subgroups.ResultsCell cycle activity varied broadly across and within all cancers. TP53 and PIK3CA mutations were common in all CCS subgroups but with increasing frequency as cell cycle activity levels increased (P < 0.001). Mutations in BRAF and gains in 16p were less frequent CCS high tumours (P < 0.001). In Kaplan-Meier analysis, patients whose tumours were CCS Low had a longer PFI relative to intermediate or high (P < 0.001) and this significance remained in multivariable analysis (CCS intermediate: HR = 1.37; 95% CI 1.17 – 1.60, CCS high: 1.54; 1.29 – 1.84, CCS Low = Ref).ConclusionsCell cycle activity varies across and within cancers and whilst similar DNA alterations can be found at all activity levels, some notable exceptions exist. These data also demonstrate that independent prognostic information can be derived on a pan-cancer level from a simple measure of cell cycle activity.

Genomic alterations resulting in loss of control over the cell cycle is a fundamental hallmark of human malignancies. Whilst pan-cancer studies have broadly assessed tumour genomics and their impact on oncogenic pathways, analyses taking the baseline signalling levels in normal tissue into account are lacking. To this end, we aimed to reclassify the cell cycle activity of tumours in terms of their tissue of origin and determine the DNA mutations, chromosome arm-level changes and signalling pathways driving cell cycle activity. Combining normal tissue and pan-cancer data from over 13,000 samples we demonstrate that tumours of gynaecological origin show the highest levels of baseline corrected cell cycle activity, partially owing to hormonal signalling and gene expression changes. We also show that normal and tumour tissues can be separated into groups (quadrants) of low/high cell cycle activity and propose the novel hypothesis of an upper limit on these activity levels in tumours.

Purpose: Gene signatures and Ki67 stratify the same breast tumor into opposing good/poor prognosis groups in approximately 20% of patients. Given this discrepancy, we hypothesized that the combination of a clinically relevant signature and IHC markers may provide more prognostic information than either classifier alone. Experimental Design: We assessed Ki67 alone or combined with ER, PR and HER2 (forming IHC subtypes), and the research versions of the Genomic Grade Index, 70-gene, cell-cycle score, recurrence score (RS), and PAM50 signatures on matching TMA/whole tumor sections and microarray data in two Swedish breast cancer cohorts of 379 and 209 patients, with median follow-up of 12.4 and 12.5 years, respectively. First, we fit Cox proportional hazards models and used the change in likelihood ratio (Δ LR) to determine the additional prognostic information provided by signatures beyond that of (i) Ki67 and (ii) IHC subtypes. Second and uniquely, we then assessed whether signatures could compete well with pathology-based IHC classifiers by calculating the additional prognostic information of Ki67/IHC subtypes beyond signatures. Results: In cohort 1, only RS and PAM50 provided additional prognostic information beyond Ki67 and IHC subtypes (Δ LR-χ2 Ki67: RS = 12.8, PAM50 = 20.7, IHC subtypes: RS = 12.9, PAM50 = 11.7). Conversely, IHC subtypes added prognostic information beyond all signatures except PAM50. Similar results were observed in cohort 2. Conclusions: RS and PAM50 provided more prognostic information than the IHC subtypes in all breast cancer patients; however, the IHC subtypes did not add any prognostic information to PAM50. Clin Cancer Res; 23(24); 7512–20. ©2017 AACR.

Purpose: Transcriptional pathway activity and the molecular subtypes of breast cancer metastases have been shown to significantly influence patient postrelapse survival. Here, we further determine the relevance of clinically employed gene signatures in the advanced breast cancer (ABC) setting. Experimental Design: Sufficient RNA for expression profiling was obtained from distant metastatic or inoperable loco-regional relapse tissue by fine-needle aspiration from 109 patients of the Swedish TEX clinical trial. Gene signatures (GGI, 70 gene, recurrence score, cell-cycle score, risk of recurrence score, and PAM50) were applied to all metastases, and their relationship to long- (5-year) and short-term (1.5-year) postrelapse survival at all and locoregional lymph nodes (n = 40) versus other metastatic sites (n = 69) combined was assessed using Kaplan–Meier and/or multivariate Cox regression analyses. Results: The majority of metastases were classified into intermediate or high-risk groups by all signatures, and a significant association was found between metastatic signature subgroups and primary tumor estrogen receptor status and histologic grade (P < 0.05). When considering all sites of metastasis, only PAM50 was statistically significant in Kaplan–Meier analysis (Log-rank P = 0.008 and 0.008 for long- and short-term postrelapse breast cancer–specific survival, respectively). This significance remained in both uni- and multivariate models when restricting analyses to lymph node metastases only, and a similar trend was observed in other metastatic sites combined, but did not reach formal significance. Conclusions: Our findings are the first to demonstrate that the PAM50 signature can provide prognostic information from the lymph node metastases of ABC patients. Clin Cancer Res; 23(23); 7225–31. ©2017 AACR.

Soft tissue artifact (STA) is the main source of error in kinematic estimation of human movements based on skin markers. Our objective was to determine the components of marker displacements that best describe STA of the shoulder and arm (i.e. clavicle, scapula and humerus). Four participants performed arm flexion and rotation, a daily-life and a sports movement. Three pins with reflective markers were inserted into the clavicle, scapula and humerus. In addition, up to seven skin markers were stuck on each segment. STA was described with a modal approach: individual marker displacements or marker-cluster (i.e. translations, rotations, homotheties and stretches) relative to the local segment coordinate system defined by markers secured to the pins. The modes were then ranked according to the percentage of total STA energy that they explained. Both individual skin marker displacements and marker-cluster geometrical transformations were task-, location-, segment- and subject-specific. However, 85% of the total STA energy was systematically explained by the rigid transformations (i.e. translations and rotations of the marker-cluster). In conclusion, large joint dislocations and limited efficiency of least squares bone pose estimators are expected for the computation of upper limb joint kinematics from skin markers. Future developments shall consider the rigid transformations of marker-clusters in the implementation of an STA model to reduce its effects on kinematics estimation.