Background: There is tremendous potential for genome sequencing to improve clinical diagnosis and care once it becomes routinely accessible, but this will require formalizing research methods into clinical best practices in the areas of sequence data generation, analysis, interpretation and reporting. The CLARITY Challenge was designed to spur convergence in methods for diagnosing genetic disease starting from clinical case history and genome sequencing data. DNA samples were obtained from three families with heritable genetic disorders and genomic sequence data were donated by sequencing platform vendors. The challenge was to analyze and interpret these data with the goals of identifying disease-causing variants and reporting the findings in a clinically useful format. Participating contestant groups were solicited broadly, and an independent panel of judges evaluated their performance. Results: A total of 30 international groups were engaged. The entries reveal a general convergence of practices on most elements of the analysis and interpretation process. However, even given this commonality of approach, only two groups identified the consensus candidate variants in all disease cases, demonstrating a need for consistent fine-tuning of the generally accepted methods. There was greater diversity of the final clinical report content and in the patient consenting process, demonstrating that these areas require additional exploration and standardization. Conclusions: The CLARITY Challenge provides a comprehensive assessment of current practices for using genome sequencing to diagnose and report genetic diseases. There is remarkable convergence in bioinformatic techniques, but medical interpretation and reporting are areas that require further development by many groups.

Following up on previous work demonstrating the involvement of the transcription factor forkhead box M1 (FOXM1) in the biology and outcome of a high-risk subset of newly diagnosed multiple myeloma (nMM), this study evaluated whether FOXM1 gene expression may be further upregulated upon tumor recurrence in patients with relapsed multiple myeloma (rMM). Also assessed was the hypothesis that increased levels of FOXM1 diminish the sensitivity of myeloma cells to commonly used myeloma drugs, such as the proteasome inhibitor bortezomib (Bz) and the DNA intercalator doxorubicin (Dox). FOXM1 message was evaluated in 88 paired myeloma samples from patients with nMM and rMM, using gene expression microarrays as measurement tool. Sources of differential gene expression were identified and outlier analyses were performed using statistical methods. Two independent human myeloma cell lines (HMCLs) containing normal levels of FOXM1 (FOXM1^N) or elevated levels of lentivirus-encoded FOXM1 (FOXM1^Hi) were employed to determine FOXM1-dependent changes in cell proliferation, survival, efflux-pump activity, and drug sensitivity. Levels of retinoblastoma (Rb) protein were determined with the assistance of Western blotting. Upregulation of FOXM1 occurred in 61 of 88 (69%) patients with rMM, including 4 patients that exhibited > 20-fold elevated expression peaks. Increased FOXM1 levels in FOXM1^Hi myeloma cells caused partial resistance to Bz (1.9–5.6 fold) and Dox (1.5–2.9 fold) in vitro, using FOXM1^N myeloma as control. Reduced sensitivity of FOXM1^Hi cells to Bz was confirmed in vivo using myeloma-in-mouse xenografts. FOXM1-dependent regulation of total and phosphorylated Rb agreed with a working model of myeloma suggesting that FOXM1 governs both chromosomal instability (CIN) and E2F-dependent proliferation, using a mechanism that involves interaction with NIMA related kinase 2 (NEK2) and cyclin dependent kinase 6 (CDK6), respectively. These findings enhanced our understanding of the emerging FOXM1 genetic network in myeloma and provided preclinical support for the therapeutic targeting of the FOXM1-NEK2 and CDK4/6-Rb-E2F pathways using small-drug CDK and NEK2 inhibitors. Clinical research is warranted to assess whether this approach may overcome drug resistance in FOXM1^Hi myeloma and, thereby, improve the outcome of patients in which the transcription factor is expressed at high levels.

Background: A better understanding of the quality of cellular immune responses directed against molecularly defined targets will guide the development of TB diagnostics and identification of molecularly defined, clinically relevant M.tb vaccine candidates. Methods: Recombinant proteins (n = 8) and peptide pools (n = 14) from M. tuberculosis (M.tb) targets were used to compare cellular immune responses defined by IFN-γ and IL-17 production using a Whole Blood Assay (WBA) in a cohort of 148 individuals, i.e. patients with TB + (n = 38), TB- individuals with other pulmonary diseases (n = 81) and individuals exposed to TB without evidence of clinical TB (health care workers, n = 29). Results: M.tb antigens Rv2958c (glycosyltransferase), Rv2962c (mycolyltransferase), Rv1886c (Ag85B), Rv3804c (Ag85A), and the PPE family member Rv3347c were frequently recognized, defined by IFN-γ production, in blood from healthy individuals exposed to M.tb (health care workers). A different recognition pattern was found for IL-17 production in blood from M.tb exposed individuals responding to TB10.4 (Rv0288), Ag85B (Rv1886c) and the PPE family members Rv0978c and Rv1917c. Conclusions: The pattern of immune target recognition is different in regard to IFN-γ and IL-17 production to defined molecular M.tb targets in PBMCs from individuals frequently exposed to M.tb. The data represent the first mapping of cellular immune responses against M.tb targets in TB patients from Honduras.

The heterogeneity and tumourigenicity of metastatic melanoma is attributed to a cancer stem cell model, with CD133 considered to be a cancer stem cell marker in melanoma as well as other tumours, but its role has remained controversial. We iteratively sorted CD133+ and CD133- cells from 3 metastatic melanoma cell lines, and observed tumourigenicity and phenotypic characteristics over 7 generations of serial xeno-transplantation in NOD/SCID mice. We demonstrate that iterative sorting is required to make highly pure populations of CD133+ and CD133- cells from metastatic melanoma, and that these two populations have distinct characteristics not related to the cancer stem cell phenotype. In vitro, gene set enrichment analysis indicated CD133+ cells were related to a proliferative phenotype, whereas CD133- cells were of an invasive phenotype. However, in vivo, serial transplantation of CD133+ and CD133- tumours over 7 generations showed that both populations were equally able to initiate and propagate tumours. Despite this, both populations remained phenotypically distinct, with CD133- cells only able to express CD133 in vivo and not in vitro. Loss of CD133 from the surface of a CD133+ cell was observed in vitro and in vivo, however CD133- cells derived from CD133+ retained the CD133+ phenotype, even in the presence of signals from the tumour microenvironment. We show for the first time the necessity of iterative sorting to isolate pure marker-positive and marker-negative populations for comparative studies, and present evidence that despite CD133+ and CD133- cells being equally tumourigenic, they display distinct phenotypic differences, suggesting CD133 may define a distinct lineage in melanoma.