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V. Krishnan et al.
Despite the barriers, recent advances in technological and computational platforms are enabling the interroga- tion of patient samples on an unprecedented scale, and are being translated into robust technical assays on patient material that are reproducible in clinical laborato- ries.15 Such advances may eventually result in the identifi- cation of pretreatment biomarkers that not only predict TKI resistance but suggest alternative non-BCR-ABL1-tar- geting therapies to pre-empt the emergence of clinical resistance. Accordingly, it is timely to review the results of GE studies using primary patient material annotated for clinical outcomes, and assess how genetic and epige- netic factors associated with treatment outcome con- tribute to GE signatures. In doing so, it is also important to develop models incorporating the interplay between genetic and epigenetic factors, and determine how best to use the resulting GE outputs to understand and predict CML drug resistance and transformation. Finally, it is incumbent on the CML community to outline the practi- cal steps needed for the clinical development of GE-based biomarkers in CML.
Gene expression signatures associated with resistance to tyrosine kinase inhibitors
Since the beginning of the TKI era, a variety of diagnos- tic material from CP patients has been used to discover TKI-resistance GE signatures (Table 1). Here, we review the key conclusions from these studies.
Gene expression using peripheral blood
In the earliest research by Kaneta et al.16 and McLean et al.17 microarray studies were conducted on blood from imatinib responders and non-responders. Apart from CBLB, which was downregulated in responders, there was no overlap between the two datasets. De Lavallade et al. conducted microarray studies on peripheral blood mononuclear cells to identify a 105-gene set that was enriched in imatinib non-responders, comprising mainly genes in cell cycle and DNA repair pathways.18 However, the GE signature could be validated only in an imatinib- treated cohort but not in a cohort treated with interferon- a. As a targeted approach, the expression of 21 genes asso- ciated with TKI responses and disease progression was studied by Zhang et al.19 Increased PTGS1 expression was the only gene that differentiated primary imatinib-resis- tant patients from responders, while 15 genes distin- guished CP from BC. Twelve genes distinguished ima- tinib-responsive from secondary imatinib-resistant CML without BCR-ABL1 mutations, of which LYN, JAK2, PTPN22 and CEBPA downregulation was shared with BC samples. The study concluded that at least some features of secondary imatinib resistance overlap with BC transfor- mation.
More recently, Kok et al. conducted microarray-based analysis on diagnostic blood from 96 CP patients from the TIDEL-II trial to predict failure of early molecular response,20 which correlates with inferior long-term out- comes.21,22 Three hundred sixty-five differentially expressed genes were identified which were enriched for ‘cell cycle’ and ‘stemness’ (MYC, HOXA9, b-catenin) but depleted for ‘immune-response’ categories in the group with early molecular response failure. A binary classifica- tion model was built to predict early molecular response
failure based on 17 genes and the signature was validated in an independent cohort. Of these, eight genes IGFBP2, SRSF11, BAX, CDKN1B, BNIP3L, FZD7, PRSS57, and RPS28 intersected with findings of previous CML TKI- resistance and progression studies. This study demon- strated that GE information from diagnostic samples could predict events long in the future, including major molecular response (MMR) at 24 months, MR4.5 at 5 years, and BC transformation.
Gene expression using bone marrow
Independently, a series of studies used unselected bone marrow for comparisons of GE between groups of patients with different treatment responses. Frank et al. identified a 128 GE signature associated with imatinib resistance, specifically in an interferon-a pre-treated cohort. Differentially expressed genes were involved in apoptosis (CASP9, TRAP1), DNA repair (MSH3, DDB2), oxidative stress protection (GSS, PON2, VNN1) and cen- trosomes (ID1).23 Villuendas et al.24 identified 46 differen- tially expressed genes of which a six-gene prediction score (BIRC4, FZD7, IKBKB, IL-7R, TNC, VWF) that correlated with imatinib resistance after interferon-a failure devel- oped. Differentially expressed genes were involved in cell adhesion (TNC and SCAM-1), drug metabolism (COX1 or PTGS1), protein tyrosine kinases (MKNK1), and phos- phatases (BTK and PTPN22). Notably, the MKNK1/2 kinases have been shown by two independent groups to be involved in BC transformation.25,26 In contrast to the prior studies, Crossman et al. found no differentially expressed genes between the imatinib responder cate- gories. The use of mixed peripheral blood and bone mar- row samples, unselected white blood cells and a heteroge- neous cohort of patients in late CP and heavily pre-treat- ed, were suggested as potential reasons for the negative results.27 The important conclusion was that GE compar- isons should be made on purified CD34+ cells. Indeed, in a meta-analysis comparing six published GE studies in CML, DDX11, MSH5, and RAB11FIP3 were the only genes coincident between any two of the studies.28 The small differences in differential GE between responder groups, different GE platforms, different statistical meth- ods and different sources of cells profiled were suggested reasons for the poor intersection. The disappointing results from unselected peripheral blood and bone mar- row provided the impetus to isolate and study CD34+ frac- tions.
Gene expression using CD34+ cells
McWeeney et al. were the first group to use CD34+ cells from diagnostic bone marrow.11 Cell adhesion genes were upregulated in imatinib-resistant patients suggesting that CD34+ cells may establish more adhesive interactions with the bone marrow milieu. The enrichment for b- catenin binding targets suggested activated Wnt/b-catenin signaling in imatinib-resistant patients, a feature shared with CD34+ progenitors from BC.26,29 The authors conclud- ed that primary resistance to imatinib might reflect more advanced disease progression. A 75-probe minimal gene classifier predicted 88% of responders and 83% of non- responders in a validation cohort. Importantly, the authors of this paper compared their GE signatures to those pre- dicting early BC transformation, as discussed below, and provided an important resource for validation and com- parison of other CD34+-based GE datasets.
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haematologica | 2022; 107(2)