Age at onset as stratifier in idiopathic Parkinson’s disease – effect of ageing and polygenic risk score on clinical phenotypes

Several phenotypic differences observed in Parkinson’s disease (PD) patients have been linked to age at onset (AAO). We endeavoured to find out whether these differences are due to the ageing process itself by using a combined dataset of idiopathic PD (n = 430) and healthy controls (HC; n = 556) excluding carriers of known PD-linked genetic mutations in both groups. We found several significant effects of AAO on motor and non-motor symptoms in PD, but when comparing the effects of age on these symptoms with HC (using age at assessment, AAA), only positive associations of AAA with burden of motor symptoms and cognitive impairment were significantly different between PD vs HC. Furthermore, we explored a potential effect of polygenic risk score (PRS) on clinical phenotype and identified a significant inverse correlation of AAO and PRS in PD. No significant association between PRS and severity of clinical symptoms was found. We conclude that the observed non-motor phenotypic differences in PD based on AAO are largely driven by the ageing process itself and not by a specific profile of neurodegeneration linked to AAO in the idiopathic PD patients.

Hematopoietic Prostaglandin D2 Synthase Controls Tfh/Th2 Communication and Limits Tfh Antitumor Effects

T follicular helper (Tfh) cells are a subset of CD4+ T cells essential in immunity and have a role in helping B cells produce antibodies against pathogens. However, their role during cancer progression remains unknown. The mechanism of action of Tfh cells remains elusive because contradictory data have been reported on their protumor or antitumor responses in human and murine tumors. Like Tfh cells, Th2 cells are also involved in humoral immunity and are regularly associated with tumor progression and poor prognosis, mainly through their secretion of IL4. Here, we showed that Tfh cells expressed hematopoietic prostaglandin D2 (PGD2) synthase in a pSTAT1/pSTAT3-dependent manner. Tfh cells produced PGD2, which led to recruitment of Th2 cells via the PGD2 receptor chemoattractant receptor homologous molecule expressed on Th type 2 cells (CRTH2) and increased their effector functions. This cross-talk between Tfh and Th2 cells promoted IL4-dependent tumor growth. Correlation between Th2 cells, Tfh cells, and hematopoietic PGD2 synthase was observed in different human cancers and associated with outcome. This study provides evidence that Tfh/Th2 cross-talk through PGD2 limits the antitumor effects of Tfh cells and, therefore, could serve as a therapeutic target.

Evaluation of tumor immune contexture among intrinsic molecular subtypes helps to predict outcome in early breast cancer

Background

The prognosis of early breast cancer is linked to clinic-pathological stage and the molecular characteristics of intrinsic tumor cells. In some patients, the amount and quality of tumor-infiltrating immune cells appear to affect long term outcome. We aimed to propose a new tool to estimate immune infiltrate, and link these factors to patient prognosis according to breast cancer molecular subtypes.

Methods

We performed in silico analyses in more than 2800 early breast cancer transcriptomes with corresponding clinical annotations. We first developed a new gene expression deconvolution algorithm that accurately estimates the quantity of immune cell populations (tumor immune contexture, TIC) in tumors. Then, we studied associations between these immune profiles and relapse-free and overall survival among the different intrinsic molecular subtypes of breast cancer defined by PAM50 classification.

Results

TIC estimates the abundance of 15 immune cell subsets. Both myeloid and lymphoid subpopulations show different spread among intrinsic molecular breast cancer subtypes. A high abundance of myeloid cells was associated with poor outcome, while lymphoid cells were associated with favorable prognosis. Unsupervised clustering describing the 15 immune cell subsets revealed four subgroups of breast tumors associated with distinct patient survival, but independent from PAM50. Adding this information to clinical stage and PAM50 strongly improves the prediction of relapse or death.

Conclusions

Our findings make it possible to refine the survival stratification of early patients with breast cancer by incorporating TIC in addition to PAM50 and clinical tumor burden in a prognostic model validated in training and validation cohorts.

Artificial intelligence-guided tissue analysis combined with immune infiltrate assessment predicts stage III colon cancer outcomes in PETACC08 study

OBJECTIVE

Diagnostic tests, such as Immunoscore, predict prognosis in patients with colon cancer. However, additional prognostic markers could be detected on pathological slides using artificial intelligence tools.

DESIGN

We have developed a software to detect colon tumour, healthy mucosa, stroma and immune cells on CD3 and CD8 stained slides. The lymphocyte density and surface area were quantified automatically in the tumour core (TC) and invasive margin (IM). Using a LASSO algorithm, DGMate (DiGital tuMor pArameTErs), we detected digital parameters within the tumour cells related to patient outcomes.

RESULTS

Within the dataset of 1018 patients, we observed that a poorer relapse-free survival (RFS) was associated with high IM stromal area (HR 5.65; 95% CI 2.34 to 13.67; p<0.0001) and high DGMate (HR 2.72; 95% CI 1.92 to 3.85; p<0.001). Higher CD3+ TC, CD3+ IM and CD8+ TC densities were significantly associated with a longer RFS. Analysis of variance showed that CD3+ TC yielded a similar prognostic value to the classical CD3/CD8 Immunoscore (p=0.44). A combination of the IM stromal area, DGMate and CD3, designated 'DGMuneS', outperformed Immunoscore when used in estimating patients' prognosis (C-index=0.601 vs 0.578, p=0.04) and was independently associated with patient outcomes following Cox multivariate analysis. A predictive nomogram based on DGMuneS and clinical variables identified a group of patients with less than 10% relapse risk and another group with a 50% relapse risk.

CONCLUSION

These findings suggest that artificial intelligence can potentially improve patient care by assisting pathologists in better defining stage III colon cancer patients' prognosis.

Combination of tissues analysis and immune infiltrate in localized colon cancer using artificial intelligence in PETACC8 study

3574

Background: We used artificial intelligence to perform tissue classification and count CD3 and CD8 in each subclass and determined their role in outcome prediction in PETACC8 cohort of stage III colon cancer treated with FOLFOX or FOLFOX plus cetuximab. Methods: We developed artificial intelligence aimed to detect tumor, healthy mucosa, stroma and immune cells on whole slide of CD3 and CD8 staining. The invasive margin (IM) was also automatically determined. Using a lasso algorithm, the software was able to detect digital parameters within the tumor core (TC) which were related to patients’ outcome (variable called DGMate for DiGital tuMor pArameTErs). CD3 and CD8 lymphocytes density were also quantified automatically by the software in TC and at IM. Associations with disease-free survival (DFS) were evaluated by multivariable Cox regression adjusting for age, T/N stage, sidedness, KRAS/BRAF, DNA mismatch repair (MMR). Results: On 1220 samples collected, data could be generated for 1018 patients. We observed that a high IM stromal area and a high DGMate were associated with a poorer DFS [HR 5.65 (95% CI, 2.34, 13.67), p < 0.0001; HR 2.72 (95% IC, 1.92, 3.85), p<0.001 respectively for the continuous variable]. A higher density of CD3+ TC, CD3+ IM and CD8+ TC were significantly associated with a longer DFS (HR 0.75 (95% IC, .66, .87), p<0.0001; HR 0.78 (95% IC, .68, .88), p<0.0001; HR 0.83 (95% IC, .71, .96), p=0.01). All these immune variables were significantly correlated with each other. ANOVA test demonstrated that CD3+ TC gave a similar prognostic value compared to the classical CD3/CD8 immunoscore (p=0.44). The combination of IM stromal area, DGMate and CD3 outperformed the classical CD3/CD8 immunoscore to estimate patients’ prognosis (C-index= 0.601 vs 0.578, p-value=0.04). Adding this new variable to classical clinical prognostic parameters we generated a nomogram which predicted the risk of relapse of stage III colon cancer with a stronger predictive value compared to clinical parameters or the immunoscore. Conclusions: We propose a new fully automated method of whole slide analysis using a software based on artificial intelligence which classify tissue and determine tumor and immune parameters on one single slide stained with CD3 antibody. This valuable strategy outperforms immunoscore and clinical outcome prediction models.

Cell lines and immune classification of glioblastoma define patient's prognosis

Background

Prognostic markers for glioblastoma are lacking. Both intrinsic tumour characteristics and microenvironment could influence cancer prognostic. The aim of our study was to generate a pure glioblastoma cell lines and immune classification in order to decipher the respective role of glioblastoma cell and microenvironment on prognosis.

Methods

We worked on two large cohorts of patients suffering from glioblastoma (TCGA, n = 481 and Rembrandt, n = 180) for which clinical data, transcriptomic profiles and outcome were recorded. Transcriptomic profiles of 129 pure glioblastoma cell lines were clustered to generate a glioblastoma cell lines classification. Presence of subtypes of glioblastoma cell lines and immune cells was determined using deconvolution.

Results

Glioblastoma cell lines classification defined three new molecular groups called oncogenic, metabolic and neuronal communication enriched. Neuronal communication-enriched tumours were associated with poor prognosis in both cohorts. Immune cell infiltrate was more frequent in mesenchymal classical classification subgroup and metabolic-enriched tumours. A combination of age, glioblastoma cell lines classification and immune classification could be used to determine patient’s outcome in both cohorts.

Conclusions

Our study shows that glioblastoma-bearing patients can be classified based on their age, glioblastoma cell lines classification and immune classification. The combination of these information improves the capacity to address prognosis.

RAS status and neoadjuvant chemotherapy impact CD8+ cells and tumor HLA class I expression in liver metastatic colorectal cancer

Background

T lymphocytes and HLA expression on tumor cell both influence prognostic of localized colorectal cancer, but their role following chemotherapy in patients with liver metastatic colorectal cancer (mCRC) was not addressed.

Methods

One hundred fourteen patients treated in curative intend of liver mCRC were included in this retrospective study. Patients were either untreated or treated with neoadjuvant therapy containing an anti-EGFR, bevacizumab or oxaliplatin. Immune densities were quantified in the tumor core and in invasive margin of metastases, using Qupath software or a pathologist’s quantification. CD8, NKp46, Foxp3, CD163, HLA, PD-L1 were analyzed and were correlated with progression free survival (PFS) and overall survival (OS) using multivariable Cox proportional hazards models.

Results

In the whole cohort only a high CD8+ cells infiltrate, a high HLA-I expression and wild-type RAS/RAF status were associated with a better overall survival in both univariate and multivariate model. Moreover, CD8+ cells immune infiltrate at invasive margin combined to HLA expression in cancer cell could increase patient’s outcome prediction. RAS status but not immune cell infiltrate was associated with HLA expression on tumor cells. In comparison to untreated patients, neoadjuvant chemotherapy induced CD8+ cells recruitment and increased PD-L1 staining in immune infiltrates only for WT RAS patients. In this context, anti-EGFR and oxaliplatin based chemotherapy are the most powerful to induce CD8+ cells mobilization within the metastatic site.

Conclusions

While CD8 infiltrate and HLA expression appear to be prognostic for mCRC, CD8 and PD-L1 infiltrate are enhanced by neoadjuvant chemotherapy in mCRC under RAS status dependence.

Electronic supplementary material

The online version of this article (10.1186/s40425-018-0438-3) contains supplementary material, which is available to authorized users.