Discordant CD34+ cell results in peripheral blood and hematopoietic progenitor cell-apheresis product: implications for clinical decisions and impact on patient treatment.

CASE REPORT: A 50-year-old male with T-cell lymphoma presented for autologous peripheral blood stem cell transplantation. After granulocyte-colony-stimulating factor (G-CSF) mobilization, his peripheral blood CD34+ cell count was 166 × 10(6) /L on the day before collection, which predicted a high yield of CD34+ cells in the apheresis product. The first two collections had yields much lower than expected, triggering an investigation and changes to the apheresis collection methods since mobilization appeared adequate from the peripheral CD34+ values. RESULTS: Changes to the apheresis collection variables and instrumentation did not improve the yields in the next three collections. The laboratory investigation demonstrated that there was an interfering substance in the patient's plasma that was causing falsely high peripheral blood CD34+ cell values and that the low CD34+ cell yields in the collections were consistent with the actual peripheral blood CD34+ cell value. Based on this finding and after discussion with the clinical service, the patient then received plerixafor to increase the number of circulating CD34+ cells before Collections 6 and 7. The patient went on to achieve the target CD34+ cell dose and subsequently underwent a successful autologous transplant with full hematopoietic engraftment. CONCLUSION: This case demonstrates the importance of timely and critical review of laboratory results in the context of the specific patient. This case exemplifies how diligent review of laboratory results and open communication among the various teams can positively affect patient outcomes.

Immune monitoring using the predictive power of immune profiles.

BACKGROUND: We have developed a novel approach to categorize immunity in patients that uses a combination of whole blood flow cytometry and hierarchical clustering. METHODS: Our approach was based on determining the number (cells/µl) of the major leukocyte subsets in unfractionated, whole blood using quantitative flow cytometry. These measurements were performed in 40 healthy volunteers and 120 patients with glioblastoma, renal cell carcinoma, non-Hodgkin lymphoma, ovarian cancer or acute lung injury. After normalization, we used unsupervised hierarchical clustering to sort individuals by similarity into discreet groups we call immune profiles. RESULTS: Five immune profiles were identified. Four of the diseases tested had patients distributed across at least four of the profiles. Cancer patients found in immune profiles dominated by healthy volunteers showed improved survival (p < 0.01). Clustering objectively identified relationships between immune markers. We found a positive correlation between the number of granulocytes and immunosuppressive CD14(+)HLA-DR(lo/neg) monocytes and no correlation between CD14(+)HLA-DR(lo/neg) monocytes and Lin(-)CD33(+)HLA-DR(-) myeloid derived suppressor cells. Clustering analysis identified a potential biomarker predictive of survival across cancer types consisting of the ratio of CD4(+) T cells/µl to CD14(+)HLA-DR(lo/neg) monocytes/µL of blood. CONCLUSIONS: Comprehensive multi-factorial immune analysis resulting in immune profiles were prognostic, uncovered relationships among immune markers and identified a potential biomarker for the prognosis of cancer. Immune profiles may be useful to streamline evaluation of immune modulating therapies and continue to identify immune based biomarkers.