Clinicopathologic predictors of renal outcomes in light chain cast nephropathy: a multicenter retrospective study

Light chain cast nephropathy (LCCN) in multiple myeloma often leads to severe and poorly reversible acute kidney injury. Severe renal impairment influences the allocation of chemotherapy and its tolerability; it also affects patient survival. Whether renal biopsy findings add to the clinical assessment in predicting renal and patient outcomes in LCCN is uncertain. We retrospectively reviewed clinical presentation, chemotherapy regimens, hematologic response, and renal and patient outcomes in 178 patients with biopsy-proven LCCN from 10 centers in Europe and North America. A detailed pathology review, including assessment of the extent of cast formation, was performed to study correlations with initial presentation and outcomes. Patients presented with a mean estimated glomerular filtration rate (eGFR) of 13 ± 11 mL/min/1.73 m2, and 82% had stage 3 acute kidney injury. The mean number of casts was 3.2/mm2 in the cortex. Tubulointerstitial lesions were frequent: acute tubular injury (94%), tubulitis (82%), tubular rupture (62%), giant cell reaction (60%), and cortical and medullary inflammation (95% and 75%, respectively). Medullary inflammation, giant cell reaction, and the extent of cast formation correlated with eGFR value at LCCN diagnosis. During a median follow-up of 22 months, mean eGFR increased to 43 ± 30 mL/min/1.73 m2. Age, β2-microglobulin, best hematologic response, number of cortical casts per square millimeter, and degree of interstitial fibrosis/tubular atrophy (IFTA) were independently associated with a higher eGFR during follow-up. This eGFR value correlated with overall survival, independently of the hematologic response. This study shows that extent of cast formation and IFTA in LCCN predicts the quality of renal response, which, in turn, is associated with overall survival.info:eu-repo/semantics/publishedVersio

Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen

The effectiveness of most cancer targeted therapies is short-lived. Tumors often develop resistance that might be overcome with drug combinations. However, the number of possible combinations is vast, necessitating data-driven approaches to find optimal patient-specific treatments. Here we report AstraZeneca’s large drug combination dataset, consisting of 11,576 experiments from 910 combinations across 85 molecularly characterized cancer cell lines, and results of a DREAM Challenge to evaluate computational strategies for predicting synergistic drug pairs and biomarkers. 160 teams participated to provide a comprehensive methodological development and benchmarking. Winning methods incorporate prior knowledge of drug-target interactions. Synergy is predicted with an accuracy matching biological replicates for >60% of combinations. However, 20% of drug combinations are poorly predicted by all methods. Genomic rationale for synergy predictions are identified, including ADAM17 inhibitor antagonism when combined with PIK3CB/D inhibition contrasting to synergy when combined with other PI3K-pathway inhibitors in PIK3CA mutant cells.Peer reviewe