Resensitising proteasome inhibitor-resistant myeloma with sphingosine kinase 2 inhibition.

The introduction of the proteasome inhibitor bortezomib into treatment regimens for myeloma has led to substantial improvement in patient survival. However, whilst bortezomib elicits initial responses in many myeloma patients, this haematological malignancy remains incurable due to the development of acquired bortezomib resistance. With other patients presenting with disease that is intrinsically bortezomib resistant, it is clear that new therapeutic approaches are desperately required to target bortezomib-resistant myeloma. We have previously shown that targeting sphingolipid metabolism with the sphingosine kinase 2 (SK2) inhibitor K145 in combination with bortezomib induces synergistic death of bortezomib-naïve myeloma. In the current study, we have demonstrated that targeting sphingolipid metabolism with K145 synergises with bortezomib and effectively resensitises bortezomib-resistant myeloma to this proteasome inhibitor. Notably, these effects were dependent on enhanced activation of the unfolded protein response, and were observed in numerous separate myeloma models that appear to have different mechanisms of bortezomib resistance, including a new bortezomib-resistant myeloma model we describe which possesses a clinically relevant proteasome mutation. Furthermore, K145 also displayed synergy with the next-generation proteasome inhibitor carfilzomib in bortezomib-resistant and carfilzomib-resistant myeloma cells. Together, these findings indicate that targeting sphingolipid metabolism via SK2 inhibition may be effective in combination with a broad spectrum of proteasome inhibitors in the proteasome inhibitor resistant setting, and is an approach worth clinical exploration.

Targeted gene panels identify a high frequency of pathogenic germline variants in patients diagnosed with a hematological malignancy and at least one other independent cancer.

The majority of studies assessing the contribution of pathogenic germline variants (PGVs) to cancer predisposition have focused on patients with single cancers. We analyzed 45 known cancer predisposition genes (CPGs) in germline samples of 202 patients with hematological malignancies (HMs) plus one or more other independent cancer managed at major tertiary medical centers on two different continents. This included 120 patients with therapy-related myeloid neoplasms (t-MNs), where the HM occurred after cytotoxic treatment for a first malignancy, and 82 patients with multiple cancers in which the HM was not preceded by cytotoxic therapy (MC-HM). Using American College of Medical Genetics/Association for Molecular Pathology variant classification guidelines, 13% of patients had PGVs, most frequently identified in CHEK2 (17% of PGVs), BRCA1 (13%), DDX41 (13%), and TP53 (7%). The frequency of PGVs in MC-HM was higher than in t-MN, although not statistically significant (18 vs. 9%; p = 0.085). The frequency of PGVs in lymphoid and myeloid HM patients was similar (19 vs. 17.5%; p > 0.9). Critically, patients with PGVs in BRCA1, BRCA2 or TP53 did not satisfy current clinical phenotypic criteria for germline testing. Our data suggest that a personal history of multiple cancers, one being a HM, should trigger screening for PGVs.

RUNX1-mutated families show phenotype heterogeneity and a somatic mutation profile unique to germline predisposed AML.

First reported in 1999, germline runt-related transcription factor 1 (RUNX1) mutations are a well-established cause of familial platelet disorder with predisposition to myeloid malignancy (FPD-MM). We present the clinical phenotypes and genetic mutations detected in 10 novel RUNX1-mutated FPD-MM families. Genomic analyses on these families detected 2 partial gene deletions, 3 novel mutations, and 5 recurrent mutations as the germline RUNX1 alterations leading to FPD-MM. Combining genomic data from the families reported herein with aggregated published data sets resulted in 130 germline RUNX1 families, which allowed us to investigate whether specific germline mutation characteristics (type, location) could explain the large phenotypic heterogeneity between patients with familial platelet disorder and different HMs. Comparing the somatic mutational signatures between the available familial (n = 35) and published sporadic (n = 137) RUNX1-mutated AML patients showed enrichment for somatic mutations affecting the second RUNX1 allele and GATA2. Conversely, we observed a decreased number of somatic mutations affecting NRAS, SRSF2, and DNMT3A and the collective genes associated with CHIP and epigenetic regulation. This is the largest aggregation and analysis of germline RUNX1 mutations performed to date, providing a unique opportunity to examine the factors underlying phenotypic differences and disease progression from FPD to MM.

Aberrant RAG-mediated recombination contributes to multiple structural rearrangements in lymphoid blast crisis of chronic myeloid leukemia.

Blast crisis of chronic myeloid leukemia is associated with poor survival and the accumulation of genomic lesions. Using whole-exome and/or RNA sequencing of patients at chronic phase (CP, n = 49), myeloid blast crisis (MBC, n = 19), and lymphoid blast crisis (LBC, n = 20), we found 25 focal gene deletions and 14 fusions in 24 patients in BC. Deletions predominated in LBC (83% of structural variants). Transcriptional analysis identified the upregulation of genes involved in V(D)J recombination, including RAG1/2 and DNTT in LBC. RAG recombination is a reported mediator of IKZF1 deletion. We investigated the extent of RAG-mediated genomic lesions in BC. Molecular hallmarks of RAG activity; DNTT-mediated nucleotide insertions and a RAG-binding motif at structural variants were exclusively found in patients with high RAG expression. Structural variants in 65% of patients in LBC displayed these hallmarks compared with only 5% in MBC. RAG-mediated events included focal deletion and novel fusion of genes associated with hematologic cancer: IKZF1, RUNX1, CDKN2A/B, and RB1. Importantly, 8/8 patients with elevated DNTT at CP diagnosis progressed to LBC by 12 months, potentially enabling early prediction of LBC. This work confirms the central mutagenic role of RAG in LBC and describes potential clinical utility in CML management.

The Genomic Landscape of Sporadic Prolactinomas.

Somatic GNAS and USP8 mutations have been implicated in sporadic somatotrophinomas and corticotrophinomas, respectively. However, no genes are known to be recurrently mutated in sporadic prolactinomas. The prevalence of copy number variants (CNV), which is emerging as a mechanism of tumorigenesis in sporadic pituitary adenomas in general, is also unclear in prolactinomas. To characterize the genetic events underpinning sporadic prolactinomas, we performed whole exome sequencing of paired tumor and germline DNA from 12 prolactinoma patients. We observed recurrent large-scale CNV, most commonly in the form of copy number gains. We also identified sequence variants of interest in 15 genes. This included the DRD2, PRL, TMEM67, and MLH3 genes with plausible links to prolactinoma formation. Of the 15 genes of interest, CNV was seen at the gene locus in the corresponding tumor in 10 cases, and pituitary expression of eight genes was in the top 10% of tissues. However, none of our shortlisted somatic variants appeared to be classical driver mutations as no variant was found in more than one tumor. Future directions of research include mechanistic studies to investigate how CNV may contribute to prolactinoma formation, larger studies of relevant prolactinoma subsets according to clinical characteristics, and additional genetic investigations for aberrations not captured by whole exome sequencing.

The mutational burden of therapy-related myeloid neoplasms is similar to primary myelodysplastic syndrome but has a distinctive distribution.

Therapy-related myeloid neoplasms (T-MN) are poorly characterized secondary hematological malignancies following chemotherapy/radiotherapy exposure. We compared the clinical and mutational characteristics of T-MN (n = 129) and primary myelodysplastic syndrome (P-MDS, n = 108) patients. Although the somatic mutation frequency was similar between T-MN and P-MDS patients (93% in both groups), the pattern was distinct. TP53 mutations were more frequent in T-MN (29.5 vs. 7%), while spliceosomal complex mutations were more common in P-MDS (56.5 vs. 25.6%). In contrast to P-MDS, the ring sideroblasts (RS) phenotype was not associated with better survival in T-MN, most probably due to genetic association with TP53 mutations. SF3B1 was mutated in 96% of P-MDS with ≥15% RS, but in only 32% T-MN. TP53 mutations were detected in 92% T-MN with ≥15% RS and SF3B1 wild-type cases. Interestingly, T-MN and P-MDS patients with "Very low" or "Low" Revised International Prognostic Scoring System (IPSS-R) showed similar biological and clinical characteristics. In a Cox regression analysis, TP53 mutation was a poor prognostic factor in T-MN, independent of IPSS-R cytogenetics, disease-modifying therapy, and NRAS mutation. Our data have direct implications for T-MN management and provide evidence that, in addition to conventional disease parameters, mutational analysis should be incorporated in T-MN risk stratification.

ASXL1 and BIM germ line variants predict response and identify CML patients with the greatest risk of imatinib failure.

Scoring systems used at diagnosis of chronic myeloid leukemia (CML), such as Sokal risk, provide important response prediction for patients treated with imatinib. However, the sensitivity and specificity of scoring systems could be enhanced for improved identification of patients with the highest risk. We aimed to identify genomic predictive biomarkers of imatinib response at diagnosis to aid selection of first-line therapy. Targeted amplicon sequencing was performed to determine the germ line variant profile in 517 and 79 patients treated with first-line imatinib and nilotinib, respectively. The Sokal score and ASXL1 rs4911231 and BIM rs686952 variants were independent predictors of early molecular response (MR), major MR, deep MRs (MR4 and MR4.5), and failure-free survival (FFS) with imatinib treatment. In contrast, the ASXL1 and BIM variants did not consistently predict MR or FFS with nilotinib treatment. In the imatinib-treated cohort, neither Sokal or the ASXL1 and BIM variants predicted overall survival (OS) or progression to accelerated phase or blast crisis (AP/BC). The Sokal risk score was combined with the ASXL1 and BIM variants in a classification tree model to predict imatinib response. The model distinguished an ultra-high-risk group, representing 10% of patients, that predicted inferior OS (88% vs 97%; P = .041), progression to AP/BC (12% vs 1%; P = .034), FFS (P < .001), and MRs (P < .001). The ultra-high-risk patients may be candidates for more potent or combination first-line therapy. These data suggest that germ line genetic variation contributes to the heterogeneity of response to imatinib and may contribute to a prognostic risk score that allows early optimization of therapy.

BAM-matcher: a tool for rapid NGS sample matching.

UNLABELLED: The standard method used by high-throughput genome sequencing facilities for detecting mislabelled samples is to use independently generated high-density SNP data to determine sample identity. However, as it has now become commonplace to have multiple samples sequenced from the same source, such as for analysis of somatic variants using matched tumour and normal samples, we can directly use the genotype information inherent in the sequence data to match samples and thus bypass the need for additional laboratory testing. Here we present BAM-matcher, a tool that can rapidly determine whether two BAM files represent samples from the same biological source by comparing their genotypes. BAM-matcher is designed to be simple to use, provides easily interpretable results, and is suitable for deployment at early stages of data processing pipelines. AVAILABILITY AND IMPLEMENTATION: BAM-matcher is licensed under the Creative Commons by Attribution license, and is available from: https://bitbucket.org/sacgf/bam-matcher SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. CONTACT: paul.wang@sa.gov.au.