Ibrutinib for therapy of steroid-refractory chronic graft vs. host disease: A multicenter real-world analysis.

To examine activity of ibrutinib in steroid-refractory chronic GVHD (SR-cGVHD) after FDA approval, we conducted a multicenter retrospective study. Data were standardly collected (N=270 from 19 centers). Involved organs included skin (75%), eye (61%), mouth (54%), joint/fascia (47%), GI (26%), lung (27%), liver (19%), genital (7%), other (4.

Melphalan Dose in Combination With Fludarabine Affects Gastrointestinal Toxicity and Graft-Versus-Host Disease After Allogeneic Transplantation in Acute Myeloid Leukemia and Myelodysplastic Syndromes.

Fludarabine (Flu) and melphalan (Mel) reduced-intensity conditioning is frequently used for allogenic hematopoietic cell transplant (allo-HCT) in patients with acute myeloid leukemia (AML) and myelodysplastic syndromes (MDS). However, there is limited evidence on the impact of Mel dosing on toxicities and clinical outcomes of allo-HCT. We retrospectively compared 8/8 HLA-matched donor allo-HCT outcomes of 345 patients with AML or MDS receiving total Mel dose of 100 mg/m (Mel-100, n = 62) versus 140 mg/m (Mel-140, n = 283) in combination with Flu.

15 years of patient-reported outcomes in clinical trials leading to GU cancer drug approvals: a systematic review on the quality of data reporting and analysis.

Background: Standardized, high-quality PRO data reporting is crucial for patient centered care in the field of oncology, especially in clinical trials that establish standard of care. This study evaluated PRO endpoint design, conduct and reporting methods in FDA approved drugs for GU malignancies.

Methods: A systematic review of the FDA archives identified GU cancer drug approvals from Feb 2007 to July 2022.

Prediction-oriented prognostic biomarker discovery with survival machine learning methods.

Identifying novel and reliable prognostic biomarkers for predicting patient survival outcomes is essential for deciding personalized treatment strategies for diseases such as cancer. Numerous feature selection techniques have been proposed to address the high-dimensional problem in constructing prediction models. Not only does feature selection lower the data dimension, but it also improves the prediction accuracy of the resulted models by mitigating overfitting.

Efficient gradient boosting for prognostic biomarker discovery.

Motivation: A gradient boosting decision tree (GBDT) is a powerful ensemble machine-learning method that has the potential to accelerate biomarker discovery from high-dimensional molecular data. Recent algorithmic advances, such as extreme gradient boosting (XGB) and light gradient boosting (LGB), have rendered the GBDT training more efficient, scalable and accurate. However, these modern techniques have not yet been widely adopted in discovering biomarkers for censored survival outcomes, which are key clinical outcomes or endpoints in cancer studies.

A Comprehensive, Multi-Modal Strategy to Mitigate Alzheimer's Disease Risk Factors Improves Aspects of Metabolism and Offsets Cognitive Decline in Individuals with Cognitive Impairment.

Background: Alzheimer's disease (AD) is a chronic condition that progresses over time. While several therapeutic approaches have been developed, none have substantially altered disease progression. One explanation is that the disease is multi factorial.

The Association of Mutation with Tumor Mutation Burden and Its Prognostic Implications in Cutaneous Melanoma.

Background: is a mucin marker that is frequently mutated in melanoma, but whether mutations could be useful as a surrogate biomarker for tumor mutation burden (TMB) remains unclear.

Methods: This study rigorously evaluates the mutation as a clinical biomarker in cutaneous melanoma by utilizing genomic and clinical data from patient samples from The Cancer Genome Atlas (TCGA) and two independent validation cohorts. We further extended the analysis to studies with patients treated with immunotherapies.