There have also been conflicting reports regarding the relationship between tumour macrophage infiltrate and survival in patients with breast cancer (Mohammed et al, 2012b). Five out of ten studies, that examined the relationship between macrophage infiltrate and survival, reported an inverse association whereas five others reported no association (Mohammed et al, 2012b). More recently, study by Mahmoud et al (2012) reported that high CD68+ macrophage infiltrate was significantly associated with poorer recurrence-free and cancer-specific survival. However, in multivariate analaysis, this was not independently significant. In contrast, in this study a high tumour macrophage infiltrate was independently associated with improved survival in patients with ER-negative breast cancer. The reasons for such discrepances using similar methodology are not clear, however, there is some evidence that macrophage markers, such as CD68+ may be expressed by other non-myeloid tissues in cancer (Gottfried et al, 2008).
Analysis of the association between TILs and clinicopathologic variables was performed by using SPSS version 19.0 and R 2.11.1. Because the distributions of the outcome variable (BCSS) were not normal in the study cohort, non-parametric Wilcoxon testing was used to check the bivariate relationship between BCSS and TILs and other potential confounding variables, including age at diagnosis, grade, tumor size, involvement of lymph nodes, LVI, and intrinsic subtypes. Chi-squared testing was used to check the relationship between TILs and those potential confounding variables. For survival analysis, the event under study was death from breast cancer. BCSS time was defined as the number of years between the date of diagnosis of breast cancer and the date of death attributable to breast cancer. Survival time was censored at the time a patient died from another cause or when the follow-up period ended. For univariate survival analyses, the Kaplan-Meier function analysis was performed to estimate probabilities of BCSS. Log-rank testing was used to assess differences in BCSS among different subgroups. For multivariate survival analyses, Cox proportional hazards regression models were built to estimate the TIL hazard ratio (HR), which was adjusted by the potential confounding variables on the basis of the partial maximum likelihood estimation. Smoothed, rescaled Schoenfeld residual plots were performed to test proportional hazards assumptions. Only cases with sufficient information for all covariates were included in the multivariate analysis. Wald statistics were used to test the significance of individual coefficients. Interactions between TILs and some covariables were checked by building Cox regression models for different levels of those variables and comparing HRs of TILs. All of the tests were two-sided at a significance level of 0.05. Supplementary analyses were also performed by using relapse-free survival as an outcome variable; relapse-free survival time was defined as the number of years between the date of diagnosis of breast cancer and the date of any type of relapse, including local, regional, and distant relapses of the disease.
Studies on TILs in breast cancer have come to inconsistent conclusions. We believe that one of the underlying reasons could be inconsistency in defining and measuring TILs. Some research considered only the presence of peritumoral stromal lymphocytes [65, 66], and many considered all T lymphocytes (which might include larger numbers of regulatory T cells that could in some cases reflect immune suppression instead of activation). In our study, specific immunohistochemistry was used with a mouse monoclonal anti-human CD8 antibody to detect cytotoxic effector CD8+ TILs in intratumoral and stromal locations for each tumor tissue core. We evaluated the reliability of repeated scoring by the same scorer and between different scorers, and it was demonstrated that our visual CD8+ TILs scoring was highly reliable (Figure S4 of Additional file 6). Analyses with intratumoral, stromal, and total CD8+ TILs were conducted, and consistent results were obtained. We also did analyses using relapse-free survival as an outcome and obtained results similar to those using BCSS as the outcome (Figures S5 and S6 of Additional files 7 and 8 and Tables S5 to S7 of Additional file 4). Thus, we are confident that the identification and quantification of TILs and the assessment of the association of TILs with clinical outcome in breast cancer are reliable and valid in this study. One potential limitation of our methods is that TMAs may not adequately represent breast tumor heterogeneity. Several studies nevertheless have shown that findings from TMAs are consistent with those from full-face tissue sections [67, 68]. Although we observed a trend to a favorable prognostic effect of CD8 TILs in the HER2+/ER- subgroup (and this trend is consistent with a gene expression study ), the effect was not statistically significant in our univariate or multivariate analyses. Research with more power particularly for this subgroup needs to be done to draw a more definitive conclusion among HER2+ cases. We were not able to measure changes in immune response induced by chemotherapy, as all of the tissue samples were collected before patients received systemic therapy. Further studies would need to be conducted to assess the interaction of TILs with chemotherapy, ideally in randomized trials.
Additional file 4: Supplemental tables. Table S1 showed the distributions of CD8+ sTIL and tTIL in relation to patient and tumor characteristics. Table S2 showed the hazard ratios (HRs) of sTIL and tTIL in the whole cohort with multivariate Cox regression analysis, adjusted by age at diagnosis, tumor grade and size, lymph node status, lymphovascular invasion, and intrinsic subtype. Table S3 showed the HRs of sTIL and tTIL in triple negative (TNP), core basal (CBP), and five negative (5NP) breast cancer intrinsic subgroups in multivariate analysis. Table S4 showed the HRs of iTIL, sTIL and tTIL in patients without adjuvant systemic therapy (AST) and with chemotherapy in multivariate analysis. Table S5 showed HRs of iTIL in the whole cohort with univariate and mulvariate analysis, using relapse-free survival (RFS) as the outcome variable. Tables S6 and S7 showed the HRs of iTIL in different intrinsic subgroups with multivariate Cox regression analysis using RFS as the outcome variable. (PDF 108 KB)
Additional file 8: Relapse-free survival (RFS) by iTIL in different breast cancer intrinsic subgroups. Kaplan-Meier function survival analysis of association between iTIL and RFS in: (A) luminal A, (B) luminal B, (C) HER2+/ER-, (D) Triple negative, (E) core basal, and (F) five negative subgroups. (PDF 644 KB)
The constructs evaluated in investigating association between psychosocial factors and cancer survival has varied between studies, and factors related to quality of life (QOL) have shown contradictory results. We investigated the effect of socioeconomic and early QOL and psychological factors on disease-free time and survival in localized prostate cancer.
After controlling for biological prognostic factors, age, and adjuvant hormonal therapies, moderate and high socioeconomic status and an increased level of pain predicted longer survival, whereas an increased level of prostate-area symptoms and fatigue and, especially, reports of no/few physical symptoms were predictors of a shorter survival time. A longer PSA-relapse-free time was predicted by Cognitive Avoidance/Denial coping, whereas problems in social functioning, hopelessness, and an excellent self-reported QOL predicted a shorter PSA-relapse-free time.
In the present study, on the basis of the model and experiences from our previous studies, we investigated the baseline and early predictors of disease-free and overall survival times in prostate cancer patients of all ages and treated with external beam radiotherapy in Finland. We hypothesized that specific factors in the psychological stress processes (patterns of coping, anger expression traits, non-cancer life events), and components of patient-reported QOL evaluated at the time of primary treatment are associated with disease-free time and survival in localized prostate cancer.
Cancer and treatment data (staging, Gleason classification, neo-adjuvant hormonal therapy, and adjuvant hormonal therapy; Table 1) and survival time and disease-free time data (date of diagnosis, date of death, date of PSA-verification of recurrence of the cancer) were obtained from hospital medical records.
We investigated both overall survival and disease-free times because they are very different outcomes in cancer types with good prognoses, such as prostate cancer, and may thus be predicted by different mechanisms; we found that they were indeed predicted by different factors. The potential predictors were simultaneously investigated to account for their relative impact, and were either derived from a theoretical model  or were previously demonstrated to exhibit an association with cancer survival [4, 20, 33]. Most of the predictor variables were subscales of established validated questionnaires. The radiotherapy was currently going on in all patients. The analyses were carefully adjusted for age, known biological prognostic factors, and treatment.Footnote 3
Second, NO is essential to the innate immunological response to pathogens as indicated by extensive studies of innate immunity involving monocytes and macrophages . NO is a free radical and has immediate antimicrobial effects including disruption of bacterial target structures and inhibition of bacterial metabolism [9,10,11]. NO is generated by three tissue-specific NO synthases (NOS) termed endothelial (eNOS), neural (nNOS) and inducible NOS (iNOS), the latter being expressed in immune tissue . Two non-proteinogenic amino acids inhibit NO formation via interference with the NOS substrate arginine: high concentration of symmetric dimethylarginine (SDMA) inhibits cellular arginine uptake and asymmetric dimethylarginine (ADMA) competes for catalytic substrate conversion . SDMA is metabolized in the kidneys by alanine-glyoxylate aminotransferase 2 (AGXT2) and is closely associated with renal function . This is one reason why SDMA may serve as a reliable marker for renal failure [15, 16]. ADMA levels are controlled by two cleaving enzymes, dimethylarginase-dimethylalaminohydrolase-1 and 2 (DDAH1 and 2), of which DDAH2 is predominantly expressed in immune cells [15, 17]. Interestingly, in studies of polymicrobial sepsis in mice, global knockout of DDAH2 is associated with 12% 120-h survival compared to 53% survival in wild-type animals, underlining the important immunosuppression effect of ADMA in sepsis . Further, we have shown that plasma ADMA levels are increased whereas DDAH2 expression in peripheral blood monocytes (PBMC) is reduced in patients with sepsis . 2b1af7f3a8