Abstract / Summary
Pooling cBioPortal studies into one patient-by-gene matrix is the standard preliminary to pan-cancer analysis, and the natural join key is the patient. Three Memorial Sloan Kettering clonal-hematopoiesis studies deposit blood normals carrying the same patient identifiers as tumors deposited elsewhere, so a patient-keyed merge records a hematopoietic clone as a mutation in that patients solid tumor. We linked 1,282,483 coding calls across 140 genes from 483 studies to patients, classified every call by specimen type, and rebuilt the matrix with and without blood-derived calls. Blood normals supply 1.7% of calls but concentrate sharply: 52.8% of patients flagged DNMT3A-mutant, 21.3% of TET2 and 19.7% of CHEK2 have no tumor-derived call in that gene, against 0.4% for TP53. The affected calls carry a clonal-hematopoiesis signature: DNMT3A supplies 42.0% of blood-normal and 0.61% of tumor calls, at a median allele fraction of 0.4% against 33.4%. The contamination reaches 65 other study cohorts, almost all of them solid-tumor studies, affecting more than one patient in ten in fifteen of them. It is a property of cohort composition rather than disease: across 45 cancer types the distortion tracks the share of patients with a paired blood normal at r = 0.994, and where one disease carries two unharmonized labels the rates differ up to fivefold before correction and agree after it. Correction lowers the pan-cancer DNMT3A rate by 45% and moves survival estimates further than chance removal of the same patients would. Two filter lines, applied before aggregation, remove it.