Background

The development of clinical -omic biomarkers for predicting patient prognosis has mostly focused on multi-gene models. However, several studies have described significant weaknesses of multi-gene biomarkers. Indeed, some high-profile reports have even indicated that multi-gene biomarkers fail to consistently outperform simple single-gene ones. Given the continual improvements in -omics technologies and the availability of larger, better-powered datasets, we revisited this "single-gene hypothesis" using new techniques and datasets.

Results

By deeply sampling the population of available gene sets, we compare the intrinsic properties of single-gene biomarkers to multi-gene biomarkers in twelve different partitions of a large breast cancer meta-dataset. We show that simple multi-gene models consistently outperformed single-gene biomarkers in all twelve partitions. We found 270 multi-gene biomarkers (one per ~11,111 sampled) that always made better predictions than the best single-gene model.

Conclusions

The single-gene hypothesis for breast cancer does not appear to retain its validity in the face of improved statistical models, lower-noise genomic technology and better-powered patient cohorts. These results highlight that it is critical to revisit older hypotheses in the light of newer techniques and datasets.

Background

Biomarkers are a key component of precision medicine. However, full clinical integration of biomarkers has been met with challenges, partly attributed to analytical difficulties. It has been shown that biomarker reproducibility is susceptible to data preprocessing approaches. Here, we systematically evaluated machine-learning ensembles of preprocessing methods as a general strategy to improve biomarker performance for prediction of survival from early breast cancer.

Results

We risk stratified breast cancer patients into either low-risk or high-risk groups based on four published hypoxia signatures (Buffa, Winter, Hu, and Sorensen), using 24 different preprocessing approaches for microarray normalization. The 24 binary risk profiles determined for each hypoxia signature were combined using a random forest to evaluate the efficacy of a preprocessing ensemble classifier. We demonstrate that the best way of merging preprocessing methods varies from signature to signature, and that there is likely no 'best' preprocessing pipeline that is universal across datasets, highlighting the need to evaluate ensembles of preprocessing algorithms. Further, we developed novel signatures for each preprocessing method and the risk classifications from each were incorporated in a meta-random forest model. Interestingly, the classification of these biomarkers and its ensemble show striking consistency, demonstrating that similar intrinsic biological information are being faithfully represented. As such, these classification patterns further confirm that there is a subset of patients whose prognosis is consistently challenging to predict.

Conclusions

Performance of different prognostic signatures varies with pre-processing method. A simple classifier by unanimous voting of classifications is a reliable way of improving on single preprocessing methods. Future signatures will likely require integration of intrinsic and extrinsic clinico-pathological variables to better predict disease-related outcomes.

Rationale

During embryogenesis, hematopoietic cells appear in the myocardium prior to the initiation of coronary formation. However, their role is unknown.

Objective

Here we investigate whether pre-existing hematopoietic cells are required for the formation of coronary vasculature.

Methods and results

As a model of for hematopoietic cell deficient animals, we used Runx1 knockout embryos and Vav1-cre; R26-DTA embryos, latter of which genetically ablates 2/3 of CD45(+) hematopoietic cells. Both Runx1 knockout embryos and Vav1-cre; R26-DTA embryos revealed disorganized, hypoplastic microvasculature of coronary vessels on section and whole-mount stainings. Furthermore, coronary explant experiments showed that the mouse heart explants from Runx1 and Vav1-cre; R26-DTA embryos exhibited impaired coronary formation ex vivo. Interestingly, in both models it appears that epicardial to mesenchymal transition is adversely affected in the absence of hematopoietic progenitors.

Conclusion

Hematopoietic cells are not merely passively transported via coronary vessel, but substantially involved in the induction of the coronary growth. Our findings suggest a novel mechanism of coronary growth.

Replicative immortality is a hallmark of cancer, and can be achieved through telomere lengthening and maintenance. Although the role of telomere length in cancer has been well studied, its association to genomic features is less well known. Here, we report the telomere lengths of 392 localized prostate cancer tumours and characterize their relationship to genomic, transcriptomic and proteomic features. Shorter tumour telomere lengths are associated with elevated genomic instability, including single-nucleotide variants, indels and structural variants. Genes involved in cell proliferation and signaling are correlated with tumour telomere length at all levels of the central dogma. Telomere length is also associated with multiple clinical features of a tumour. Longer telomere lengths in non-tumour samples are associated with a lower rate of biochemical relapse. In summary, we describe the multi-level integration of telomere length, genomics, transcriptomics and proteomics in localized prostate cancer.

During mammalian embryogenesis, de novo hematopoiesis occurs transiently in multiple anatomical sites including the yolk sac, dorsal aorta, and heart tube. A long-unanswered question is whether these local transient hematopoietic mechanisms are essential for embryonic growth. Here, we show that endocardial hematopoiesis is critical for cardiac valve remodeling as a source of tissue macrophages. Colony formation assay from explanted heart tubes and genetic lineage tracing with the endocardial specific Nfatc1-Cre mouse revealed that hemogenic endocardium is a de novo source of tissue macrophages in the endocardial cushion, the primordium of the cardiac valves. Surface marker characterization, gene expression profiling, and ex vivo phagocytosis assay revealed that the endocardially derived cardiac tissue macrophages play a phagocytic and antigen presenting role. Indeed, genetic ablation of endocardially derived macrophages caused severe valve malformation. Together, these data suggest that transient hemogenic activity in the endocardium is indispensable for the valvular tissue remodeling in the heart.

Tissue clearing methods promise to provide exquisite three-dimensional imaging information; however, there is a need for simplified methods for lower resource settings and for non-fluorescence based phenotyping to enable light microscopic imaging modalities. Here we describe the simplified CLARITY method (SCM) for tissue clearing that preserves epitopes of interest. We imaged the resulting tissues using light sheet microscopy to generate rapid 3D reconstructions of entire tissues and organs. In addition, to enable clearing and 3D tissue imaging with light microscopy methods, we developed a colorimetric, non-fluorescent method for specifically labeling cleared tissues based on horseradish peroxidase conversion of diaminobenzidine to a colored insoluble product. The methods we describe here are portable and can be accomplished at low cost, and can allow light microscopic imaging of cleared tissues, thus enabling tissue clearing and imaging in a wide variety of settings.

Radioresistance remains a significant obstacle in the treatment of Prostate Cancer (PCa). To simulate the clinical scenario of irradiation resistance (IRR), we created DU145-IRR PCa cell lines by treatment with 2 Gy daily IR for 59 fractions. DU145-IRR cells acquired an aggressive phenotype as evidenced by increased clonogenic survival, tumorigenic potential and invasiveness. We performed transcriptome profiling to discover dysregulated genes in DU145-IRR cells and identified the long non-coding RNA (lncRNA), Urothelial carcinoma-associated 1 (UCA1). We first investigated the role of UCA1 in radiation response and found that UCA1 abundance was significantly higher in DU145-IRR cells compared to control cells. UCA1 siRNA-knockdown reversed the aggressive phenotype and significantly increased sensitivity to IR. UCA1 depletion inhibited growth, induced cell cycle arrest at the G2/M transition and decreased activation of the pro-survival Akt pathway. We then studied the clinical significance of UCA1 expression in two independent cohorts of PCa patients: MSKCC (130 patients) and CPC-GENE (209 patients). UCA1 over-expression was associated with decreased 5-year disease-free survival in MSKCC patients (HR = 2.9; p = 0.007) and a trend toward lower biochemical recurrence-free survival in CPC-GENE patients (HR = 2.7; p = 0.05). We showed for the first time that UCA1 depletion induces radiosensitivity, decreases proliferative capacity and disrupts cell cycle progression, which may occur through altered Akt signaling and induced cell cycle arrest at the G2/M transition. Our results indicate that UCA1 might have prognostic value in PCa and be a potential therapeutic target.

Purpose

The third-generation EGFR inhibitor, osimertinib, is the first mutant-selective inhibitor that has received regulatory approval for the treatment of patients with EGFR-mutant lung cancer. Despite the development of highly selective third-generation inhibitors, acquired resistance remains a significant clinical challenge. Recently, we and others have identified a novel osimertinib resistance mutation, G724S, which was not predicted in in vitro screens. Here, we investigate how G724S confers resistance to osimertinib.Experimental Design: We combine structure-based predictive modeling of G724S in combination with the 2 most common EGFR-activating mutations, exon 19 deletion (Ex19Del) and L858R, with in vitro drug-response models and patient genomic profiling.

Results

Our simulations suggest that the G724S mutation selectively reduces osimertinib-binding affinity in the context of Ex19Del. Consistent with our simulations, cell lines transduced with Ex19Del/G724S demonstrate resistance to osimertinib, whereas cells transduced with L858R/G724S are sensitive to osimertinib. Subsequent clinical genomic profiling data further suggest G724S occurs with Ex19Del but not L858R. Furthermore, we demonstrate that Ex19Del/G724S retains sensitivity to afatinib, but not to erlotinib, suggesting a possible therapy for patients at the time of disease relapse.

Conclusions

Altogether, these data suggest that G724S is an allele-specific resistance mutation emerging in the context of Ex19Del but not L858R. Our results fundamentally reframe the problem of targeted therapy resistance from one focused on the "drug-resistance mutation" pair to one focused on the "activating mutation-drug-resistance mutation" trio. This has broad implications across clinical oncology.

Multiparametric magnetic resonance imaging (mpMRI) has transformed the management of localized prostate cancer by improving identification of clinically significant disease at diagnosis. Approximately 20% of primary prostate tumors are invisible to mpMRI, and we hypothesize that this invisibility reflects fundamental molecular properties of the tumor. We therefore profiled the genomes and transcriptomes of 40 International Society of Urological Pathology grade 2 tumors: 20 mpMRI-invisible (Prostate Imaging-Reporting and Data System [PI-RADS] v2 <3) and 20 mpMRI-visible (PI-RADS v2 5) tumors. mpMRI-visible tumors were enriched in hallmarks of nimbosus, an aggressive pathological, molecular, and microenvironmental phenomenon in prostate cancer. These hallmarks included genomes with increased mutation density, a higher prevalence of intraductal carcinoma/cribriform architecture pathology, and altered abundance of 102 transcripts, including overexpression of noncoding RNAs such as SCHLAP1. Multiple small nucleolar RNAs (snoRNAs) were identified, and a snoRNA signature synergized with nimbosus hallmarks to discriminate visible from invisible tumors. These data suggest a confluence of aggressive molecular and microenvironmental phenomena underlie mpMRI visibility of localized prostate cancer. PATIENT SUMMARY: We examined the correlation between tumor biology and magnetic resonance imaging (MRI) visibility in a group of patients with low- intermediate-risk prostate cancer. We observed that MRI findings are associated with biological features of aggressive prostate cancer.

INTRODUCTION: Lenz microphthalmia syndrome (LMS) is a genetically heterogeneous X-linked disorder characterised by microphthalmia/anophthalmia, skeletal abnormalities, genitourinary malformations, and anomalies of the digits, ears, and teeth. Intellectual disability and seizure disorders are seen in about 60% of affected males. To date, no gene has been identified for LMS in the microphthalmia syndrome 1 locus (MCOPS1). In this study, we aim to find the disease-causing gene for this condition. METHODS AND RESULTS: Using exome sequencing in a family with three affected brothers, we identified a mutation in the intron 7 splice donor site (c.471+2T→A) of the N-acetyltransferase NAA10 gene. NAA10 has been previously shown to be mutated in patients with Ogden syndrome, which is clinically distinct from LMS. Linkage studies for this family mapped the disease locus to Xq27-Xq28, which was consistent with the locus of NAA10. The mutation co-segregated with the phenotype and cDNA analysis showed aberrant transcripts. Patient fibroblasts lacked expression of full length NAA10 protein and displayed cell proliferation defects. Expression array studies showed significant dysregulation of genes associated with genetic forms of anophthalmia such as BMP4, STRA6, and downstream targets of BCOR and the canonical WNT pathway. In particular, STRA6 is a retinol binding protein receptor that mediates cellular uptake of retinol/vitamin A and plays a major role in regulating the retinoic acid signalling pathway. A retinol uptake assay showed that retinol uptake was decreased in patient cells. CONCLUSIONS: We conclude that the NAA10 mutation is the cause of LMS in this family, likely through the dysregulation of the retinoic acid signalling pathway.