Nilotinib-induced bone marrow CD34+/lin-Ph+ cells early clearance in newly diagnosed CP-Chronic Myeloid Leukemia: Final report of the PhilosoPhi34 study

Chronic Myeloid Leukemia is a clonal disorder characterized by the presence of the Ph‐chromosome and the BCR‐ABL tyrosine‐kinase (TK). Target‐therapy with Imatinib has greatly improved its outcome. Deeper and faster responses are reported with the second‐generation TKI Nilotinib. Sustained responses may enable TKI discontinuation. However, even in a complete molecular response, some patients experience disease recurrence possibly due to persistence of quiescent leukemic CD34+/lin−Ph+ stem cells (LSCs). Degree and mechanisms of LSCs clearance during TKI treatment are not clearly established. The PhilosoPhi34 study was designed to verify the in‐vivo activity and timecourse of first‐line Nilotinib therapy on BM CD34+/lin−Ph+ cells clearance. Eighty‐seven CP‐CML patients were enrolled. BM cells were collected and tested for Ph+ residual cells, at diagnosis, 3, 6 and 12 months of treatment. FISH analysis of unstimulated CD34+/lin− cells in CCyR patients were positive in 8/65 (12.3%), 5/71 (7%), 0/69 (0%) evaluable tests, respectively. Per‐Protocol analysis response rates were as follows: CCyR 95% at 12 months, MR4.5 31% and 46% at 12 and 36 months, respectively. An exploratory Gene Expression Profiling (GEP) study of CD34+/lin− cells was performed on 30 patients at diagnosis and after, on 79 patients at diagnosis vs 12 months of nilotinib treatment vs 10 healthy subjects. Data demonstrated some genes significantly different expressed: NFKBIA, many cell cycle genes, ABC transporters, JAK‐STAT signaling pathway (JAK2). In addition, a correlation between different expression of some genes (JAK2, OLFM4, ICAM1, NFKBIA) among patients at diagnosis and their achievement of an early and deeper MR was observed.

Reduced CD62L Expression on T Cells and Increased Soluble CD62L Levels Predict Molecular Response to Tyrosine Kinase Inhibitor Therapy in Early Chronic-Phase Chronic Myelogenous Leukemia

Purpose

Immunologic surveillance of minimal residual disease in chronic myelogenous leukemia (CML) may be relevant for long-term control or cure of CML. Little is known about immune-modulatory effects of nilotinib in vivo, potentially predicting response to therapy.

Patients and Methods

A prospective and comprehensive flow cytometry–based immunomonitoring program paralleled the ENEST1st clinical study, investigating 52 nilotinib-naïve patients with chronic-phase CML. Data were verified in independent validation cohorts.

Results

T cells of patients with CML at diagnosis expressed low l-selectin (CD62L) levels, which was not a result of proportional aberrations of T-cell subsets. Low numbers of CD62L-expressing CD4+ and CD8+ T cells correlated with higher Sokal score, increased spleen size, and high leukocyte and peripheral-blood blast counts. At month 6 during nilotinib therapy, CD62L expression returned to levels of healthy individuals. The level of CD62L loss on T cells directly correlated with the extent of soluble CD62L (sCD62L) elevation. In parallel, the proteolytic activity of tumor necrosis factor α–converting enzyme (TACE; ADAM17, CD156b), the metalloproteinase shedding CD62L, was increased at diagnosis and significantly decreased during nilotinib treatment. High CD62L+ expression on both CD4+ and CD8+ T cells and, vice versa, low sCD62L levels at CML diagnosis were linked to superior molecular responses. These findings were corroborated in independent validation cohorts.

Conclusion

We demonstrate the prognostic impact of CD62L shedding from T cells and increased sCD62L plasma levels at CML diagnosis on molecular response to tyrosine kinase inhibitor therapy in early chronic-phase CML. Functionally, decreased CD62L may be a consequence of increased TACE-mediated CD62L cleavage and potentially impairs immune-cell function. Larger prospective studies are ongoing to confirm the prognostic relevance of this finding.

Genetic basis for the increased expression of vacuolar H+ translocating ATPase genes upon imatinib treatment in human lymphoblastoid cells

PURPOSE

The role of v-ATPases in cancer biology is being increasingly recognized. Yeast studies indicate that the tyrosine kinase inhibitor imatinib may interact with the v-ATPase genes and alter the course of cancer progression. Data from humans in this regard are lacking.

METHODS

We constructed 55 lymphoblastoid cell lines from pedigreed, cancer-free human subjects and treated them with IC20 concentration of imatinib mesylate. Using these cell lines, we (i) estimated the heritability and differential expression of 19 genes encoding several subunits of the v-ATPase protein in response to imatinib treatment; (ii) estimated the genetic similarity among these genes; and (iii) conducted a high-density scan to find cis-regulating genetic variation associated with differential expression of these genes.

RESULTS

We found that the imatinib response of the genes encoding v-ATPase subunits is significantly heritable and can be clustered to identify novel drug targets in imatinib therapy. Further, five of these genes were significantly cis-regulated and together represented nearly half-log fold change in response to imatinib (p = 0.0107) that was homogenous (p = 0.2598).

CONCLUSIONS

Our results proffer support to the growing view that personalized regimens using proton pump inhibitors or v-ATPase inhibitors may improve outcomes of imatinib therapy in various cancers.

Co-clustering phenome-genome for phenotype classification and disease gene discovery

Understanding the categorization of human diseases is critical for reliably identifying disease causal genes. Recently, genome-wide studies of abnormal chromosomal locations related to diseases have mapped >2000 phenotype–gene relations, which provide valuable information for classifying diseases and identifying candidate genes as drug targets. In this article, a regularized non-negative matrix tri-factorization (R-NMTF) algorithm is introduced to co-cluster phenotypes and genes, and simultaneously detect associations between the detected phenotype clusters and gene clusters. The R-NMTF algorithm factorizes the phenotype–gene association matrix under the prior knowledge from phenotype similarity network and protein–protein interaction network, supervised by the label information from known disease classes and biological pathways. In the experiments on disease phenotype–gene associations in OMIM and KEGG disease pathways, R-NMTF significantly improved the classification of disease phenotypes and disease pathway genes compared with support vector machines and Label Propagation in cross-validation on the annotated phenotypes and genes. The newly predicted phenotypes in each disease class are highly consistent with human phenotype ontology annotations. The roles of the new member genes in the disease pathways are examined and validated in the protein–protein interaction subnetworks. Extensive literature review also confirmed many new members of the disease classes and pathways as well as the predicted associations between disease phenotype classes and pathways.