Analysis of genetic variants in myeloproliferative neoplasms using a 22-gene next-generation sequencing panel

Background The Philadelphia (Ph)-negative myeloproliferative neoplasms (MPNs), namely essential thrombocythaemia (ET), polycythaemia vera (PV) and primary myelofibrosis (PMF), are a group of chronic clonal haematopoietic disorders that have the propensity to advance into bone marrow failure or acute myeloid leukaemia; often resulting in fatality. Although driver mutations have been identified in these MPNs, subtype-specific markers of the disease have yet to be discovered. Next-generation sequencing (NGS) technology can potentially improve the clinical management of MPNs by allowing for the simultaneous screening of many disease-associated genes. Methods The performance of a custom, in-house designed 22-gene NGS panel was technically validated using reference standards across two independent replicate runs. The panel was subsequently used to screen a total of 10 clinical MPN samples (ET n = 3, PV n = 3, PMF n = 4). The resulting NGS data was then analysed via a bioinformatics pipeline. Results The custom NGS panel had a detection limit of 1% variant allele frequency (VAF). A total of 20 unique variants with VAFs above 5% (4 of which were putatively novel variants with potential biological significance) and one pathogenic variant with a VAF of between 1 and 5% were identified across all of the clinical MPN samples. All single nucleotide variants with VAFs ≥ 15% were confirmed via Sanger sequencing. Conclusions The high fidelity of the NGS analysis and the identification of known and novel variants in this study cohort support its potential clinical utility in the management of MPNs. However, further optimisation is needed to avoid false negatives in regions with low sequencing coverage, especially for the detection of driver mutations in MPL.

Multi-omic data helps improve prediction of personalised tumor suppressors and oncogenes

The progression of tumorigenesis starts with a few mutational and structural driver events in the cell. Various cohort-based computational tools exist to identify driver genes but require a large number of samples to produce reliable results. Many studies use different methods to identify driver mutations/genes from mutations that have no impact on tumour progression; however, a small fraction of patients show no mutational events in any known driver genes. Current unsupervised methods map somatic and expression data onto a network to identify the perturbation in the network. Our method is the first machine learning model to classify genes as tumour suppressor gene (TSG), oncogene (OG) or neutral, thus assigning the functional impact of the gene in the patient. In this study, we develop a multi-omic approach, PIVOT (Personalised Identification of driVer OGs and TSGs), to train on experimentally or computationally validated mutational and structural driver events. Given the lack of any gold standards for the identification of personalised driver genes, we label the data using four strategies and, based on classification metrics, show gene-based labelling strategies perform best. We build different models using SNV, RNA, and multi-omic features to be used based on the data available. Our models trained on multi-omic data improved predictions compared to mutation and expression data, achieving an accuracy >0.99 for BRCA, LUAD and COAD datasets. We show network and expression-based features contribute the most to PIVOT. Our predictions on BRCA, COAD and LUAD cancer types reveal commonly altered genes such as TP53, and PIK3CA, which are predicted drivers for multiple cancer types. Along with known driver genes, our models also identify new driver genes such as PRKCA, SOX9 and PSMD4. Our multi-omic model labels both CNV and mutations with a more considerable contribution by CNV alterations. While predicting labels for genes mutated in multiple samples, we also label rare driver events occurring in as few as one sample. We also identify genes with dual roles within the same cancer type. Overall, PIVOT labels personalised driver genes as TSGs and OGs and also identifies rare driver genes. PIVOT is available at https://github.com/RamanLab/PIVOT.

A Preclinical Model of Cutaneous Melanoma Based on Reconstructed Human Epidermis

Malignant melanoma is among the tumor entities with the highest increase of incidence worldwide. To elucidate melanoma progression and develop new effective therapies, rodent models are commonly used. While these do not adequately reflect human physiology, two-dimensional cell cultures lack crucial elements of the tumor microenvironment. To address this shortcoming, we have developed a melanoma skin equivalent based on an open-source epidermal model. Melanoma cell lines with different driver mutations were incorporated into these models forming distinguishable tumor aggregates within a stratified epidermis. Although barrier properties of the skin equivalents were not affected by incorporation of melanoma cells, their presence resulted in a higher metabolic activity indicated by an increased glucose consumption. Furthermore, we re-isolated single cells from the models to characterize the proliferation state within the respective model. The applicability of our model for tumor therapeutics was demonstrated by treatment with a commonly used v-raf murine sarcoma viral oncogene homolog B (BRAF) inhibitor vemurafenib. This selective BRAF inhibitor successfully reduced tumor growth in the models harboring BRAF-mutated melanoma cells. Hence, our model is a promising tool to investigate melanoma development and as a preclinical model for drug discovery.

Increased prevalence of clonal hematopoiesis of indeterminate potential amongst people living with HIV

People living with human immunodeficiency virus (PLWH) have significantly increased risk for cardiovascular disease in part due to inflammation and immune dysregulation. Clonal hematopoiesis of indeterminate potential (CHIP), the age-related acquisition and expansion of hematopoietic stem cells due to leukemogenic driver mutations, increases risk for both hematologic malignancy and coronary artery disease (CAD). Since increased inflammation is hypothesized to be both a cause and consequence of CHIP, we hypothesized that PLWH have a greater prevalence of CHIP. We searched for CHIP in multi-ethnic cases from the Swiss HIV Cohort Study (SHCS, n = 600) and controls from the Atherosclerosis Risk in the Communities study (ARIC, n = 8111) from blood DNA-derived exome sequences. We observed that HIV is associated with a twofold increase in CHIP prevalence, both in the whole study population and in a subset of 230 cases and 1002 matched controls selected by propensity matching to control for demographic imbalances (SHCS 7%, ARIC 3%, p = 0.005). We also observed that ASXL1 is the most commonly mutated CHIP-associated gene in PLWH. Our results suggest that CHIP may contribute to the excess cardiovascular risk observed in PLWH.

Splice-switching of the insulin receptor pre-mRNA alleviates tumorigenic hallmarks in rhabdomyosarcoma

Rhabdomyosarcoma (RMS) is an aggressive pediatric tumor with a poor prognosis for metastasis and recurrent disease. Large-scale sequencing endeavors demonstrate that Rhabdomyosarcomas have a dearth of precisely targetable driver mutations. However, IGF-2 signaling is known to be grossly altered in RMS. The insulin receptor (IR) exists in two alternatively spliced isoforms, IR-A and IR-B. The IGF-2 signaling molecule binds both its innate IGF-1 receptor as well as the insulin receptor variant A (IR-A) with high affinity. Mitogenic and proliferative signaling via the canonical IGF-2 pathway is, therefore, augmented by IR-A. This study shows that RMS patients express increased IR-A levels compared to control tissues that predominantly express the IR-B isoform. We also found that Hif-1α is significantly increased in RMS tumors, portraying their hypoxic phenotype. Concordantly, the alternative splicing of IR adapts to produce more IR-A in response to hypoxic stress. Upon examining the pre-mRNA structure of the gene, we identified a potential hypoxia-responsive element, which is also the binding site for the RNA-binding protein CUG-BP1 (CELF1). We designed Splice Switching Oligonucleotides (SSO) against this binding site to decrease IR-A levels in RMS cell lines and, consequently, rescue the IR-B expression levels. SSO treatment resulted in a significant reduction in cell proliferation, migration, and angiogenesis. Our data shows promising insight into how impeding the IGF-2 pathway by reducing IR-A expression mitigates tumor growth. It is evident that Rhabdomyosarcomas use IR alternative splicing as yet another survival strategy that can be exploited as a therapeutic intervention in conjunction with already established anti-IGF-1 receptor therapies.

Pervasive conditional selection of driver mutations and modular epistasis networks in cancer

Cancer driver mutations often display mutual exclusion or co-occurrence, underscoring the key role of epistasis in carcinogenesis. However, estimating the magnitude of epistatic interactions and their quantitative effect on tumor evolution remains a challenge. We developed a method to quantify COnditional SELection on the Excess of Nonsynonymous Substitutions (Coselens) in cancer genes. Coselens infers the number of drivers per gene in different partitions of a cancer genomics dataset using covariance-based mutation models and determines whether coding mutations in a gene affect selection for drivers in any other gene. Using Coselens, we identified 296 conditionally selected gene pairs across 16 cancer types in the TCGA dataset. Conditional selection accounts for 25-50% of driver substitutions in tumors with >2 drivers. Conditionally co-selected genes form modular networks, whose structures challenge the traditional interpretation of within-pathway mutual exclusivity and across-pathway synergy, suggesting a more complex scenario, where gene-specific across-pathway interactions shape differentiated cancer subtypes.

Association of mutation signature effectuating processes with mutation hotspots in driver genes and non-coding regions

Cancer driving mutations are difficult to identify especially in the non-coding part of the genome. Here, we present sigDriver, an algorithm dedicated to call driver mutations. Using 3813 whole-genome sequenced tumors from International Cancer Genome Consortium, The Cancer Genome Atlas Program, and a childhood pan-cancer cohort, we employ mutational signatures based on single-base substitution in the context of tri- and penta-nucleotide motifs for hotspot discovery. Knowledge-based annotations on mutational hotspots reveal enrichment in coding regions and regulatory elements for 6 mutational signatures, including APOBEC and somatic hypermutation signatures. APOBEC activity is associated with 32 hotspots of which 11 are known and 11 are putative regulatory drivers. Somatic single nucleotide variants clusters detected at hypermutation-associated hotspots are distinct from translocation or gene amplifications. Patients carrying APOBEC induced PIK3CA driver mutations show lower occurrence of signature SBS39. In summary, sigDriver uncovers mutational processes associated with known and putative tumor drivers and hotspots particularly in the non-coding regions of the genome.

CCND3 is indispensable for the maintenance of B-cell acute lymphoblastic leukemia

The D-type cyclins (CCND1, CCND2, and CCND3) in association with CDK4/6 are known drivers of cell cycle progression. We reported previously that inactivation of FOXO1 confers growth arrest and apoptosis in B-ALL, partially mediated by subsequent depletion of CCND3. Given that previously the canonical MYC target CCND2 has been considered to play the major role in B-ALL proliferation, further investigation of the role of FOXO1 in CCND3 transcription and the role of CCND3 in B-ALL is warranted. In this study, we demonstrated that CCND3 is essential for the proliferation and survival of B-ALL, independent of the mutational background. Respectively, its expression at mRNA level exceeds that of CCND1 and CCND2. Furthermore, we identified FOXO1 as a CCND3-activating transcription factor in B-ALL. By comparing the effects of CCND3 depletion and CDK4/6 inhibition by palbociclib on B-ALL cells harboring different driver mutations, we found that the anti-apoptotic effect of CCND3 is independent of the kinase activity of the CCND3-CDK4/6 complex. Moreover, we found that CCND3 contributes to CDK8 transcription, which in part might explain the anti-apoptotic effect of CCND3. Finally, we found that increased CCND3 expression is associated with the development of resistance to palbociclib. We conclude that CCND3 plays an essential role in the maintenance of B-ALL, regardless of the underlying driver mutation. Moreover, downregulation of CCND3 expression might be superior to inhibition of CDK4/6 kinase activity in terms of B-ALL treatment.

Cancer-driving mutations are enriched in genic regions intolerant to germline variation

Large reference datasets of protein-coding variation in human populations have allowed us to determine which genes and genic sub-regions are intolerant to germline genetic variation. There is also a growing number of genes implicated in severe Mendelian diseases that overlap with genes implicated in cancer. Here, we hypothesized that mitotically mutable genic sub-regions that are intolerant to germline variation are enriched for cancer-driving mutations. We introduce a new metric, OncMTR, which uses 125,748 exomes in the gnomAD database to identify genic sub-regions intolerant to germline variation but enriched for hematologic somatic variants. We demonstrate that OncMTR can significantly predict driver mutations implicated in hematologic malignancies. Divergent OncMTR regions were enriched for cancer-relevant protein domains, and overlaying OncMTR scores on protein structures identified functionally important protein residues. Finally, we performed a rare variant, gene-based collapsing analysis on an independent set of 394,694 exomes from the UK Biobank and find that OncMTR dramatically improves genetic signals for hematologic malignancies. Our web app enables easy visualization of OncMTR scores for each protein-coding gene (https://astrazeneca-cgr-publications.github.io/OncMTR-Viewer/).