Dosiomics-Based Prediction of Radiation-Induced Valvulopathy after Childhood Cancer.

Valvular Heart Disease (VHD) is a known late complication of radiotherapy for childhood cancer (CC), and identifying high-risk survivors correctly remains a challenge. This paper focuses on the distribution of the radiation dose absorbed by heart tissues. We propose that a dosiomics signature could provide insight into the spatial characteristics of the heart dose associated with a VHD, beyond the already-established risk induced by high doses. We analyzed data from the 7670 survivors of the French Childhood Cancer Survivors' Study (FCCSS), 3902 of whom were treated with radiotherapy. In all, 63 (1.6%) survivors that had been treated with radiotherapy experienced a VHD, and 57 of them had heterogeneous heart doses. From the heart-dose distribution of each survivor, we extracted 93 first-order and spatial dosiomics features. We trained random forest algorithms adapted for imbalanced classification and evaluated their predictive performance compared to the performance of standard mean heart dose (MHD)-based models. Sensitivity analyses were also conducted for sub-populations of survivors with spatially heterogeneous heart doses. Our results suggest that MHD and dosiomics-based models performed equally well globally in our cohort and that, when considering the sub-population having received a spatially heterogeneous dose distribution, the predictive capability of the models is significantly improved by the use of the dosiomics features. If these findings are further validated, the dosiomics signature may be incorporated into machine learning algorithms for radiation-induced VHD risk assessment and, in turn, into the personalized refinement of follow-up guidelines.

The risk of valvular heart disease in the French Childhood Cancer Survivors' Study: Contribution of dose-volume histogram parameters.

BACKGROUND AND PURPOSE: Valvular Heart Disease (VHD) is a known complication of childhood cancer after radiotherapy treatment. However, the dose-volume-effect relationships have not been fully explored. MATERIALS AND METHODS: We obtained individual heart Dose Volume Histograms (DVH) for survivors of the French Childhood Cancer Survivors Study (FCCSS) who had received radiotherapy. We calculated the Mean Dose to the Heart (MHD) in Gy, as well as the heart DVH parameters (Vd Gy, which represents the percentage of heart volume receiving at least d Gy), fixing the thresholds to 0.1 Gy, 5 Gy, 20 Gy, and 40 Gy. We analyzed them furtherly in the subpopulation of the cohort that was treated with a dose lower than 5 Gy (V0.1Gy|V5Gy=0%), 20 Gy (V5Gy|V20Gy=0%), and 40 Gy (V20Gy|V40Gy=0%), respectively. We investigated their role in the occurrence of a VHD in this population-based observational cohort study using the Cox proportional hazard model, adjusting for age at cancer diagnosis and chemotherapy exposure. RESULTS: Median follow-up was 30.6 years. Eighty-one patients out of the 7462 (1 %) with complete data experienced a severe VHD (grade ≥ 3). The risk of VHD increased along with the MHD, and it was associated with high doses to the heart (V40Gy < 50 %, hazard ratio (HR) = 7.96, 95 % CI: 4.26-14.88 and V20Gy|V40Gy=0% >50 %, HR = 5.03, 95 % CI: [2.35-10.76]). Doses 5-20 Gy to more than 50 % (V5Gy|V20Gy=0% >50 %) of the heart induced a marginally non-significant estimated risk. We also observed a remarkable risk increase with attained age. CONCLUSIONS: Our results provide new insight into the VHD risk that may impact current treatments and long-term follow-up of childhood cancer survivors.

Inferring the initiation and development of myeloproliferative neoplasms.

The developmental history of blood cancer begins with mutation acquisition and the resulting malignant clone expansion. The two most prevalent driver mutations found in myeloproliferative neoplasms-JAK2V617F and CALRm-occur in hematopoietic stem cells, which are highly complex to observe in vivo. To circumvent this difficulty, we propose a method relying on mathematical modeling and statistical inference to determine disease initiation and dynamics. Our findings suggest that CALRm mutations tend to occur later in life than JAK2V617F. Our results confirm the higher proliferative advantage of the CALRm malignant clone compared to JAK2V617F. Furthermore, we illustrate how mathematical modeling and Bayesian inference can be used for setting up early screening strategies.

Comprehensive landscape of immune-checkpoints uncovered in clear cell renal cell carcinoma reveals new and emerging therapeutic targets.

Clear cell renal cell carcinoma (ccRCC) constitutes the most common renal cell carcinoma subtype and has long been recognized as an immunogenic cancer. As such, significant attention has been directed toward optimizing immune-checkpoints (IC)-based therapies. Despite proven benefits, a substantial number of patients remain unresponsive to treatment, suggesting that yet unreported, immunosuppressive mechanisms coexist within tumors and their microenvironment. Here, we comprehensively analyzed and ranked forty-four immune-checkpoints expressed in ccRCC on the basis of in-depth analysis of RNAseq data collected from the TCGA database and advanced statistical methods designed to obtain the group of checkpoints that best discriminates tumor from healthy tissues. Immunohistochemistry and flow cytometry confirmed and enlarged the bioinformatics results. In particular, by using the recursive feature elimination method, we show that HLA-G, B7H3, PDL-1 and ILT2 are the most relevant genes that characterize ccRCC. Notably, ILT2 expression was detected for the first time on tumor cells. The levels of other ligand-receptor pairs such as CD70:CD27; 4-1BB:4-1BBL; CD40:CD40L; CD86:CTLA4; MHC-II:Lag3; CD200:CD200R; CD244:CD48 were also found highly expressed in tumors compared to adjacent non-tumor tissues. Collectively, our approach provides a comprehensible classification of forty-four IC expressed in ccRCC, some of which were never reported before to be co-expressed in ccRCC. In addition, the algorithms used allowed identifying the most relevant group that best discriminates tumor from healthy tissues. The data can potentially assist on the choice of valuable immune-therapy targets which hold potential for the development of more effective anti-tumor treatments.