NOJAH: NOt Just Another Heatmap for genome-wide cluster analysis

Non-B DNA-informed mutation burden as a marker of treatment response and outcome in cancer

BACKGROUND

Genomic instability is crucial in tumorigenesis, with Tumour Mutation Burden (TMB) being a biomarker to indicate therapeutic effectiveness, particularly in immunotherapy. However, TMB is not always a reliable predictor and displays heterogeneity. Non-B DNA, susceptible to mutations, play a significant role in cancer development, indicating their potential merit when combined with mutation for enhanced markers in cancer.

METHODS

We assessed mutations and non-B DNA interplay as biomarkers. Our methodology quantifies tumour mutations and their co-localization with non-B DNA, using survival and drug sensitivity assessments for clinical relevance.

RESULTS

We introduce two novel markers, 'nbTMB' (non-B-informed tumour mutation burden) and 'mlTNB' (mutation-localised tumour non-B burden). In case studies: (1) nbTMB informs on survival heterogeneity among TMB-high patients undergoing immunotherapy whereas TMB is unable to further differentiate; (2) nbTMB informs on altered cisplatin sensitivity among ovarian cancer cell lines whereas TMB is unable to differentiate; and (3) mlTNB informs on survival heterogeneity among early-stage pancreatic cancer progressors in whom other markers of genomic instability fail to differentiate.

CONCLUSIONS

These novel markers offer a nuanced approach to enhance our understanding of treatment responses and outcomes in cancer, underscoring the need for a comprehensive exploration of the interplay between non-B and B-DNA features.

Heat stress tolerance in wheat seedling: Clustering genotypes and identifying key traits using multivariate analysis

Elevated atmospheric heat is considered as one of the bottlenecks for global wheat production. Screening potential wheat genotypes against heat stress and selecting some suitable indicators to assist in understanding thermotolerance could be crucial for sustaining wheat cultivation. Accordingly, 80 diverse bread wheat genotypes were evaluated in controlled lab condition by imposing a week-long heat stress (35/25 °C D/N) at the seedling stage. The response of heat stress was evaluated using multivariate analysis techniques on 20 morpho-physiological traits. Results showed significant variations in the studied traits due to the imposition of heat stress. Eleven seedling traits that contributed significantly to the genotypic variability were identified using principal component analysis (PCA). A substantial correlation between most of the selected seedling attributes was observed. Hierarchical cluster analysis identified three distinct clusters among the tested wheat genotypes. Cluster 1, consisting of 33 genotypes, exhibited the highest tolerance to heat stress, followed by Cluster 2 (18 genotypes) with moderate tolerance and Cluster 3 (29 genotypes) showing susceptibility. Linear discriminant analysis (LDA) approved that nearly 93 % of the wheat genotypes were appropriately ascribed to each cluster. The squared distance analysis confirmed the distinct nature of the clusters. Using multi-trait genotype-ideotype distance index (MGIDI), all 12 identified tolerant genotypes (BG-30, BD-468, BG-24, BD-9908, BG-32, BD-476, BD-594, BD-553, BD-488, BG-33, BD-495, and AS-10627) originated from Cluster 1. Selection gain in MGIDI analysis, broad-sense heritability, and multiple linear regression analysis together identified shoot and root dry and fresh weights, chlorophyll contents (a and total), shoot tissue water content, root-shoot dry weight ratio, and efficiency of photosystem II (PS II) as the most vital discriminatory factors explaining heat stress tolerance of 80 wheat genotypes. The identified genotypes with superior thermotolerance would offer resourceful genetic tools for breeders to improve wheat yield in warmer regions. The traits found to have greater contribution in explaining heat stress tolerance will be equally important in prioritizing future research endeavors.

Differential expression analysis identifies a prognostically significant extracellular matrix-enriched gene signature in hyaluronan-positive clear cell renal cell carcinoma

Hyaluronan (HA) accumulation in clear cell renal cell carcinoma (ccRCC) is associated with poor prognosis; however, its biology and role in tumorigenesis are unknown. RNA sequencing of 48 HA-positive and 48 HA-negative formalin-fixed paraffin-embedded (FFPE) samples was performed to identify differentially expressed genes (DEG). The DEGs were subjected to pathway and gene enrichment analyses. The Cancer Genome Atlas Kidney Renal Clear Cell Carcinoma (TCGA-KIRC) data and DEGs were used for the cluster analysis. In total, 129 DEGs were identified. HA-positive tumors exhibited enhanced expression of genes related to extracellular matrix (ECM) organization and ECM receptor interaction pathways. Gene set enrichment analysis showed that epithelial-mesenchymal transition-associated genes were highly enriched in the HA-positive phenotype. A protein-protein interaction network was constructed, and 17 hub genes were discovered. Heatmap analysis of TCGA-KIRC data identified two prognostic clusters corresponding to HA-positive and HA-negative phenotypes. These clusters were used to verify the expression levels and conduct survival analysis of the hub genes, 11 of which were linked to poor prognosis. These findings enhance our understanding of hyaluronan in ccRCC.

Loss of p53 enhances the tumor-initiating potential and drug resistance of clonogenic multiple myeloma cells

Tumor relapse and drug resistance are major factors that limit the curability of multiple myeloma (MM). New regimens have improved overall MM survival rates, but patients with high-risk features continue to have inferior outcomes. Chromosome 17p13 deletion (del17p) that includes the loss of the TP53 gene is a high-risk cytogenetic abnormality and is associated with poor clinical outcomes owing to relatively short remissions and the development of pan-drug resistant disease. Increased relapse rates suggest that del17p enhances clonogenic growth, and we found that the loss of p53 increased both the frequency and drug resistance of tumor-initiating MM cells (TICs). Subsequent RNA sequencing (RNA-seq) studies demonstrated significant activation of the Notch signaling pathway and upregulation of inhibitor of DNA binding (ID1/ID2) genes in p53-knock out (p53-KO) cells. We found that the loss of ID1 or HES-1 expression or treatment with a gamma-secretase inhibitor (GSI) significantly decreased the clonogenic growth of p53-KO but not p53 wild-type cells. GSI treatment in a small set of MM specimens also reduced the clonogenic growth in del17p samples but not in non-del17p samples. This effect was specific as overexpression of the Notch intracellular domain (NICD) rescued the effects of GSI treatment. Our study demonstrates that the Notch signaling and ID1 expression are required for TIC expansion in p53-KO MM cells. These findings also suggest that GSI may be specifically active in patients with p53 mutant MM.

Eukaryotic initiation factor 6 repression mitigates atherosclerosis progression by inhibiting macrophages expressing Fasn

Atherosclerosis, a chronic inflammatory disease that often leads to myocardial infarction and stroke, is mainly caused by lipid accumulation. Eukaryotic initiation factor 6 (Eif6) is a rate-limiting factor in protein translation of transcription factors necessary for lipogenesis. To determine whether Eif6 affects atherosclerosis, Eif6+/- mice were crossed into Apoe-/- background. Apoe-/-/Eif6+/- mice on high fat diet showed significant reduction in atherosclerotic lesions and necrotic core content in aortic root sections in comparison with Apoe-/- mice. RNA-Seq was used to investigate the effect of Eif6 in aorta. Deficiency of Eif6 shows broad effect on cell metabolism. Expression of genes for fatty acid synthesis including Fatty acid synthase (Fasn), Elovl3, Elovl6 and Acaca are down-regulated in aortas. Importantly, Fasn is decreased in macrophages. Results suggest that Eif6 deficiency may decrease atherosclerosis through inhibition of Fasn and lipids metabolism in macrophages.

Using MMRFBiolinks R-Package for Discovering Prognostic Markers in Multiple Myeloma

Multiple myeloma (MM) is the second most frequent hematological malignancy in the world although the related pathogenesis remains unclear. Gene profiling studies, commonly carried out through next-generation sequencing (NGS) and Microarrays technologies, represent powerful tools for discovering prognostic markers in MM. NGS technologies have made great leaps forward both economically and technically gaining in popularity. As NGS techniques becomes simpler and cheaper, researchers choose NGS over microarrays for more of their genomic applications. However, Microarrays still provide significant benefits with respect to NGS. For instance, RNA-Seq requires more complex bioinformatic analysis with respect to Microarray as well as it lacks of standardized protocols for analysis. Therefore, a synergy between the two technologies may be well expected in the future. In order to take up this challenge, a valid tool for integrative analysis of MM data retrieved through NGS techniques is MMRFBiolinks, a new R package for integrating and analyzing datasets from the Multiple Myeloma Research Foundation (MMRF) CoMMpass (Clinical Outcomes in MM to Personal Assessment of Genetic Profile) study, available at MMRF Researcher Gateway (MMRF-RG), and at the National Cancer Institute Genomic Data Commons (NCI-GDC) Data Portal. Instead of developing a completely new package from scratch, we decided to leverage TC-GABiolinks, an R/Bioconductor package, because it provides some useful methods to access and analyze MMRF-CoMMpass data. An integrative analysis workflow based on the usage of MMRFBiolinks is illustrated.In particular, it leads towards a comparative analysis of RNA-Seq data stored at GDC Data Portal that allows to carry out a Kaplan Meier (KM ) Survival Analysis and an enrichment analysis for a Differential Gene Expression (DGE) gene set.Furthermore, it deals with MMRF-RG data for analyzing the correlation between canonical variants and treatment outcome as well as treatment class. In order to show the potential of the workflow, we present two case studies. The former deals with data of MM Bone Marrow sample types available at GDC Data Portal. The latter deals with MMRF-RG data for analyzing the correlation between canonical variants in a gene set obtained from the case study 1 and the treatment outcome as well as the treatment class.

MMRFBiolinks: an R-package for integrating and analyzing MMRF-CoMMpass data

In order to understand the mechanisms underlying the onset and the drug responses in multiple myeloma (MM), the second most frequent hematological cancer, the use of appropriate bioinformatic tools for integrative analysis of publicly available genomic data is required. We present MMRFBiolinks, a new R package for integrating and analyzing datasets from the Multiple Myeloma Research Foundation (MMRF) CoMMpass (Clinical Outcomes in MM to Personal Assessment of Genetic Profile) study, available at MMRF Researcher Gateway (MMRF-RG), and from the National Cancer Institute Genomic Data Commons (NCI-GDC) Data Portal. The package provides several methods for integrative analysis (array-array intensity correlation, Kaplan-Meier survival analysis) and visualization (response to treatments plot) of MMRF data, for performing an easily comprehensible analysis workflow. MMRFBiolinks extends the TCGABiolinks package by providing 13 new functions to analyze MMRF-CoMMpass data: six dealing with MMRF-RG data and seven with NCI-GDC data. As validation of the tool, we present two cases studies for searching, downloading and analyzing MMRF data. The former presents a workflow for identifying genes involved in survival depending on treatment. The latter presents an analysis workflow for analyzing the Best Overall (BO) response through correlation plots between the BO Response with respect to treatments, time, duration of treatment and annotated variants, as well as through Kaplan-Meier survival curves. The case studies demonstrate how MMRFBiolinks is able of overcoming the limitations of the analysis tools available at NCI-GDC and MMRF-RG, facilitating and making more comprehensive the retrieval, downloading and analysis of MMRF data.

Pathogenic genetic variations of C. acnes are associated with clinically relevant orthopedic shoulder infections

Many surgeons continue to face the clinical dilemma of interpreting a positive aspiration or unexpected positive Cutibacterium acnes (C. acnes) culture. There are factors that complicate the interpretation of positive cultures including variations in both frequency of false positive cultures and virulence properties. As indices of virulence, hemolytic strains, from previously confirmed clinically infected shoulders, were compared with non-hemolytic isolates determined to be contaminants, by RNA-sequencing (RNA-Seq). Six C. acnes isolates from patients who underwent revision total shoulder arthroplasty (TSA) were identified based on previously described infection criteria. Three C. acnes isolates from each group underwent RNA-Seq. Differential gene expression analysis, principal component analysis (PCA), and heatmap analysis were used to determine the gene variation and patterning between the definite infection and probable contaminant isolates. Differential gene expression analysis identified genes that were differentially expressed between the isolates classified as definite infection and isolates classified as probable contaminants. PCA using a 500 gene subset of identified genes was able to find combinations of these genes that separated out the definite infection and probable contaminants isolates. The heatmap demonstrated similar gene expression in the three Definite Infections isolates, and significantly different expression when compared with the probable contaminant isolates. Clinical significance: C. acnes revision TSA isolates classified as definite infection and probable contaminant demonstrated a similar gene expression pattern to each respective group and different gene expression pattern when compared between groups. These findings indicate distinct differences in C. acnes strains associated with clinically relevant orthopedic TSA infections.

Efficacy based ginger fingerprinting reveals potential antiproliferative analytes for triple negative breast cancer

Ginger (Zingiber officinale) is one of the most widely consumed dietary supplements worldwide. Its anticancer potential has been demonstrated in various studies. However, ginger roots obtained from different geographical locations showed extensive variability in their activities, mainly due to differences in the levels of bioactive compounds. Here we evaluated the effect of these differences on the anticancer activity of ginger by performing efficacy-based fingerprinting. We characterized the fingerprint profiles of 22 ginger samples using liquid chromatography-mass spectroscopy, followed by a principal component analysis (PCA) and pearson correlation analysis. We also evaluated the anti-proliferative effects (IC50) of these samples on triple-negative breast cancer cells using the MTT assays. The supervised PCA identified a subset of analytes whose abundance strongly associated with the IC50 values of the ginger extracts, providing a link between ginger extract composition and in vitro anticancer efficacy. This study demonstrated that variation in the ginger fingerprint profiles resulting from differences in their chemical composition could have a significant impact on efficacy and bioactivity of ginger extracts. Also, this first-of-a-kind efficacy-based fingerprinting approach proposed here can identify potent anticancer candidates from the ginger fingerprint without the need for isolating individual components from the extracts.

Epigenetically heterogeneous tumor cells direct collective invasion through filopodia-driven fibronectin micropatterning

Tumor heterogeneity drives disease progression, treatment resistance, and patient relapse, yet remains largely underexplored in invasion and metastasis. Here, we investigated heterogeneity within collective cancer invasion by integrating DNA methylation and gene expression analysis in rare purified lung cancer leader and follower cells. Our results showed global DNA methylation rewiring in leader cells and revealed the filopodial motor MYO10 as a critical gene at the intersection of epigenetic heterogeneity and three-dimensional (3D) collective invasion. We further identified JAG1 signaling as a previously unknown upstream activator of MYO10 expression in leader cells. Using live-cell imaging, we found that MYO10 drives filopodial persistence necessary for micropatterning extracellular fibronectin into linear tracks at the edge of 3D collective invasion exclusively in leaders. Our data fit a model where epigenetic heterogeneity and JAG1 signaling jointly drive collective cancer invasion through MYO10 up-regulation in epigenetically permissive leader cells, which induces filopodia dynamics necessary for linearized fibronectin micropatterning.

Host Genome Variation is Associated with Neurocognitive Outcome in Survivors of Pediatric Medulloblastoma

Host genome analysis is a promising source of predictive information for long-term morbidity in cancer survivors. However, studies on genetic predictors of long-term outcome, particularly neurocognitive function following chemoradiation in pediatric oncology, are limited. Here, we evaluated variation in host genome of long-term survivors of medulloblastoma and its association with neurocognitive outcome. Whole-genome sequencing was conducted on peripheral blood of long-term survivors of pediatric medulloblastoma who also completed neuropsychological testing. Cognitively impaired and less impaired survivors did not differ in exposure to chemoradiation therapy or age at treatment. Unsupervised consensus clustering yielded two distinct variant clusters that were significantly associated with neurocognitive outcome. Interestingly, 34 of the 36 significant variants were found in noncoding DNA regions with unknown regulatory function. A separate unsupervised cluster analysis of variants within DNA repair genes identified discrete variant groups that were not associated with neurocognitive outcome, suggesting that variations in genes corresponding to a single functional group may be insufficient to predict long-term outcome alone. These findings are supportive of the presence of a genetic diathesis for treatment-related neurocognitive morbidity in medulloblastoma that may be driven by variation in noncoding regulatory elements.