Integrating digital gait data with metabolomics and clinical data to predict outcomes in Parkinson's disease.

Parkinson's disease (PD) presents diverse symptoms and comorbidities, complicating its diagnosis and management. The primary objective of this cross-sectional, monocentric study was to assess digital gait sensor data's utility for monitoring and diagnosis of motor and gait impairment in PD. As a secondary objective, for the more challenging tasks of detecting comorbidities, non-motor outcomes, and disease progression subgroups, we evaluated for the first time the integration of digital markers with metabolomics and clinical data. Using shoe-attached digital sensors, we collected gait measurements from 162 patients and 129 controls in a single visit. Machine learning models showed significant diagnostic power, with AUC scores of 83-92% for PD vs. control and up to 75% for motor severity classification. Integrating gait data with metabolomics and clinical data improved predictions for challenging-to-detect comorbidities such as hallucinations. Overall, this approach using digital biomarkers and multimodal data integration can assist in objective disease monitoring, diagnosis, and comorbidity detection.

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.

Correction: Prognostic and predictive role of CD8 and PD-L1 determination in lung tumor tissue of patients under anti-PD-1 therapy.

Since the publication of this paper, the authors noticed that Corentin Richard was not credited as contributing equally to the paper. He should be considered as a joint first author with Jean-David Fumet.

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.

Bevacizumab-based Chemotherapy for Poorly-differentiated Neuroendocrine Tumors.

AIM: To assess and report the efficacy of and tolerance to bevacizumab-based chemotherapy in treatment outcome of metastatic poorly differentiated neuroendocrine tumors. PATIENTS AND METHODS: From 2007 to 2018, 11 consecutive patients with metastatic poorly differentiated neuroendocrine treated in first- or second-line with bevacizumab-based chemotherapies were included in this monocentric retrospective cohort. Tumor response was evaluated by computed tomographic scans. RESULTS: Administered treatment included 5-fluorouracil and irinotecan (FOLFIRI) bevacizumab, 5-fluorouracil and oxaliplatin (FOLFOX) bevacizumab and 5-fluorouracil, oxaliplatin and irinotecan (FOLFIRINOX) bevacizumab for four, two and five patients, respectively. Three were treated in first-line and eight in second-line after cisplatin-etoposide regimen. Using Response Evaluation Criteria in Solid Tumors, partial response was observed for seven patients, and stable disease for one patient, giving a response rate of 63.6% (95% confidence interval=35.2-92.1%) and disease control rate of 72.7% (95% confidence interval=46.6-99.0%). All patients had died by the time of analysis, median progression-free survival was 14 months, and median overall survival was 15.3 months. Observed toxicity with such protocols was classical with 10 grade 3-4 toxic events, including three of hematological toxicity, three of infection, and three of digestive toxicity. CONCLUSION: Bevacizumab-based chemotherapy gave surprising efficacy and safety in first-or second-line treatment for metastatic poorly differentiated neuroendocrine tumor in this retrospective cohort. Prospective randomized trials of such therapy are warranted.

Prognostic and predictive role of CD8 and PD-L1 determination in lung tumor tissue of patients under anti-PD-1 therapy.

BACKGROUND: No study has evaluated the predictive and prognostic role of CD8 and PD-L1 coexpression in non-small-cell lung cancer (NSCLC). METHODS: We analyzed RNA sequencing and/or immunohistochemistry staining in NSCLC patients from The Cancer Genome Atlas (n = 1016), and 34 metastatic NSCLC samples not treated by immunotherapy as prognostic cohorts. As predictive aspect of CD8 and PD-L1, we used 85 NSCLC patients treated with anti-PD-1. Two validation cohorts were used including 44 NSCLC patients treated with anti-PD-1 and an external cohort with different tumor types. RESULTS: In prognostic cohorts, high CD8A expression was associated with longer OS (p = 0.02), while high CD274 mRNA was associated with poor prognosis (p = 0.05). In predictive cohort, high CD8 expression and CD8A mRNA were associated with longer progression-free survival (PFS) (p = 0.0002). There was no significant association between PD-L1 expression and PFS while high CD274 mRNA was associated with longer PFS (p = 0.009). A combination of CD8A and CD274 was highly predictive of outcome. These results were confirmed in the validation cohorts. This two-genes signature demonstrated similar results compared to gold standard signatures. CONCLUSION: CD8 represents both a prognostic and predictive factor of outcomes, while PD-L1 share different prognostic and predictive roles.