Peptidome profiling of human serum of uveal melanoma patients based on magnetic bead fractionation and mass spectrometry

Applications of artificial intelligence and bioinformatics methodologies in the analysis of ocular biofluid markers: a scoping review

PURPOSE

This scoping review summarizes the applications of artificial intelligence (AI) and bioinformatics methodologies in analysis of ocular biofluid markers. The secondary objective was to explore supervised and unsupervised AI techniques and their predictive accuracies. We also evaluate the integration of bioinformatics with AI tools.

METHODS

This scoping review was conducted across five electronic databases including EMBASE, Medline, Cochrane Central Register of Controlled Trials, Cochrane Database of Systematic Reviews, and Web of Science from inception to July 14, 2021. Studies pertaining to biofluid marker analysis using AI or bioinformatics were included.

RESULTS

A total of 10,262 articles were retrieved from all databases and 177 studies met the inclusion criteria. The most commonly studied ocular diseases were diabetic eye diseases, with 50 papers (28%), while glaucoma was explored in 25 studies (14%), age-related macular degeneration in 20 (11%), dry eye disease in 10 (6%), and uveitis in 9 (5%). Supervised learning was used in 91 papers (51%), unsupervised AI in 83 (46%), and bioinformatics in 85 (48%). Ninety-eight papers (55%) used more than one class of AI (e.g. > 1 of supervised, unsupervised, bioinformatics, or statistical techniques), while 79 (45%) used only one. Supervised learning techniques were often used to predict disease status or prognosis, and demonstrated strong accuracy. Unsupervised AI algorithms were used to bolster the accuracy of other algorithms, identify molecularly distinct subgroups, or cluster cases into distinct subgroups that are useful for prediction of the disease course. Finally, bioinformatic tools were used to translate complex biomarker profiles or findings into interpretable data.

CONCLUSION

AI analysis of biofluid markers displayed diagnostic accuracy, provided insight into mechanisms of molecular etiologies, and had the ability to provide individualized targeted therapeutic treatment for patients. Given the progression of AI towards use in both research and the clinic, ophthalmologists should be broadly aware of the commonly used algorithms and their applications. Future research may be aimed at validating algorithms and integrating them in clinical practice.

A Fibrinogen Alpha Fragment Mitigates Chemotherapy-Induced MLL Rearrangements

Rearrangements in the Mixed Lineage Leukemia breakpoint cluster region (MLLbcr) are frequently involved in therapy-induced leukemia, a severe side effect of anti-cancer therapies. Previous work unraveled Endonuclease G as the critical nuclease causing initial breakage in the MLLbcr in response to different types of chemotherapeutic treatment. To identify peptides protecting against therapy-induced leukemia, we screened a hemofiltrate-derived peptide library by use of an enhanced green fluorescent protein (EGFP)-based chromosomal reporter of MLLbcr rearrangements. Chromatographic purification of one active fraction and subsequent mass spectrometry allowed to isolate a C-terminal 27-mer of fibrinogen α encompassing amino acids 603 to 629. The chemically synthesized peptide, termed Fα27, inhibited MLLbcr rearrangements in immortalized hematopoietic cells following treatment with the cytostatics etoposide or doxorubicin. We also provide evidence for protection of primary human hematopoietic stem and progenitor cells from therapy-induced MLLbcr breakage. Of note, fibrinogen has been described to activate toll-like receptor 4 (TLR4). Dissecting the Fα27 mode-of action revealed association of the peptide with TLR4 in an antagonistic fashion affecting downstream NFκB signaling and pro-inflammatory cytokine production. In conclusion, we identified a hemofiltrate-derived peptide inhibitor of the genome destabilizing events causing secondary leukemia in patients undergoing chemotherapy.

Update on uveal melanoma: Translational research from biology to clinical practice (Review)

Uveal melanoma is the most common type of intraocular cancer with a low mean annual incidence of 5‑10 cases per million. Tumours are located in the choroid (90%), ciliary body (6%) or iris (4%) and of 85% are primary tumours. As in cutaneous melanoma, tumours arise in melanocytes; however, the characteristics of uveal melanoma differ, accounting for 3‑5% of melanocytic cancers. Among the numerous risk factors are age, sex, genetic and phenotypic predisposition, the work environment and dermatological conditions. Management is usually multidisciplinary, including several specialists such as ophthalmologists, oncologists and maxillofacial surgeons, who participate in the diagnosis, treatment and complex follow‑up of these patients, without excluding the management of the immense emotional burden. Clinically, uveal melanoma generates symptoms that depend as much on the affected ocular globe site as on the tumour size. The anatomopathological study of uveal melanoma has recently benefited from developments in molecular biology. In effect, disease classification or staging according to molecular profile is proving useful for the assessment of this type of tumour. Further, the improved knowledge of tumour biology is giving rise to a more targeted approach to diagnosis, prognosis and treatment development; for example, epigenetics driven by microRNAs as a target for disease control. In the present study, the main epidemiological, clinical, physiopathological and molecular features of this disease are reviewed, and the associations among all these factors are discussed.