Li, Ming

Abstract Acute graft-versus-host disease (GVHD), a serious complication of allogeneic hematopoietic cell transplantation (HCT), frequently involves the skin. Since clinicopathologic features of GVHD can mimic those of certain drug eruptions, accurate diagnosis can be challenging. We aimed to develop a tissue-based molecular assay to improve differentiation of GVHD from vacuolar interface drug eruption in the post-HCT period. After identifying 62 putative tissue-based biomarkers using unbiased molecular methods, we incorporated 26 into a quantitative RT-PCR assay. We tested skin biopsy specimens showing vacuolar interface changes attributable to either acute GVHD (n=67) or drug reaction (n=17), as classified by meticulous review of clinical features and course. Data were incorporated into clinical-molecular fusion diagnostic models. Significant differences in the expression of several genes, including MX1, MNDA, OAS, LIMA1, GSTM5, and SPP1, were detected between the two diagnostic groups (p<0.05). The optimal fusion model, which incorporated quantitative expression of MX1, MNDA, OAS, and GSTM5, as well as the clinical feature of diarrhea, imparted high diagnostic accuracy (receiver operating characteristic area-under-curve: 0.89). This novel tissue-based diagnostic molecular assay and fusion model demonstrates potential to improve distinction of GVHD from drug eruptions in the post-HCT setting. Multicenter validation studies are required.

Abstract Background: Current management of lung nodules is complicated by nontherapeutic resections and missed chances for cure. We hypothesized that a serum proteomic signature may add diagnostic information beyond that provided by combined clinical and radiographic data. Methods: Cohort A included 265 and cohort B 114 patients. Using multivariable logistic regression analysis we calculated the area under the receiver operating characteristic curve (AUC) and quantified the added value of a previously described serum proteomic signature beyond clinical and radiographic risk factors for predicting lung cancer using the integration discrimination improvement (IDI) index. Results: The average computed tomography (CT) measured nodule size in cohorts A and B was 37.83 versus 23.15 mm among patients with lung cancer and 15.82 versus 17.18 mm among those without, respectively. In cohort A, the AUC increased from 0.68 to 0.86 after adding chest CT imaging variables to the clinical results, but the proteomic signature did not provide meaningful added value. In contrast, in cohort B, the AUC improved from 0.46 with clinical data alone to 0.61 when combined with chest CT imaging data and to 0.69 after adding the proteomic signature (IDI of 20% P = 0.0003). In addition, in a subgroup of 100 nodules between 5 and 20 mm in diameter, the proteomic signature added value with an IDI of 15% (P ≤ 0.0001). Conclusions: The results show that this serum proteomic biomarker signature may add value to the clinical and chest CT evaluation of indeterminate lung nodules. Impact: This study suggests a possible role of a blood biomarker in the evaluation of indeterminate lung nodules. Cancer Epidemiol Biomarkers Prev; 21(5); 786–92. ©2012 AACR.