PM_Lect.17_ Personalized Medicine in Ophthalmology.pdf

Personalized Medicine in Ophthalmology Expected Learning outcomes Identify the different eye diseases Understand the personalized approach to Ophthalmology. Challenges and advantages in the personalized approach to diagnosis Role of the diagnostic imaging in precision medicine Eye diseases and condi...

Personalized Medicine in Ophthalmology Expected Learning outcomes Identify the different eye diseases Understand the personalized approach to Ophthalmology. Challenges and advantages in the personalized approach to diagnosis Role of the diagnostic imaging in precision medicine Eye diseases and conditions Age related macular degeneration Cataract Diabetic eye disease Glaucoma Dry eye Low vision The retina is the light-sensing tissue that resides in the back of your eye. It is responsible for relaying images to your brain. A retinal disorder or disease can affect vision to the point of blindness. Common retinal conditions include floaters, macular degeneration, diabetic eye disease, retinal detachment, and retinitis pigments. Macular Degeneration Age-related macular degeneration (AMD or ARMD) is deterioration of the macula, which is the small central area of the retina of the eye that controls visual acuity. diagnosed as either dry (non-neovascular) or wet (neovascular). Neovascular refers to growth of new blood vessels in an area, such as the macula, where they are not supposed to be. Treatments for macular degeneration depend on whether the disease is in its early-stage, dry form or in the more advanced, wet form that can lead to serious vision loss. No FDA-approved treatment exist yet for dry macular degeneration, although nutritional intervention may help prevent its progression to the wet form. For wet AMD, treatments aimed at stopping abnormal blood vessel growth Antioxidant supplement are recommended that can slow the progression, blocking unhealthy blood vessel development. Diabetic Eye Disease Those with diabetes are more susceptible to retinal damage. They notice blurry vision, double vision, floaters or dark spots, pressure or pain in at least one eye, trouble with peripheral vision, flashing lights, or rings. Laser surgery is a treatment that can help a person suffering from diabetic eye disease. It is also important to note that diabetics are also at increased risk of glaucoma and cataracts. Personalized ophthalmology Stratification of disease, achieved at phenotypic and genotypic levels, is already being applied in many areas of ophthalmology. Inherited eye disease ( IED), considered as a single disease entity relies on deep phenotypic clinical assessment , molecular diagnosis and novel imaging technologies. Dissecting the molecular basis of IED stratifies into different disease subtypes. Phenotypic stratification in genetically heterogeneous monogenic diseases, such as retinitis pigmentosa (RP) enables identification of gene-specific phenotypes. In complex diseases such as age-related macular degeneration (AMD), disease stratification identifies endophenotypes. IRDs are a genetically and phenotypically heterogeneous group of conditions characterized by rod and cone photoreceptor degeneration. Around 250 disease causing genes have been identified and mapped. Patients with IRD can opt for personalized ophthalmology and show the need for widespread genetic testing. NGS technologies are revolutionizing access to molecular diagnosis for many patients with IED characterized by genetic and phenotypic heterogeneity such as inherited retinal diseases (IRD), congenital cataract (CC) and inherited optic nerve disorders. With individualization, a personalized approach enables treatment to be based on an individual's genetic or biomarker profile, ultimately this approach covers all aspects of patient management from optimized genetic counseling and conventional therapies, to trials of novel DNAbased therapies. Advantages of broadly available molecular diagnosis for disease stratification - Precise clinical diagnosis - Establishment of pattern of inheritance - Provision of accurate prognosis - Counseling for facilitated decision making - Construction of disease registries - Early access to personalized treatments - Enhanced access to clinical trials Genetic and phenotypic heterogeneity in inherited retinal diseases (IRD) Retinoblastoma is the commonest malignant ocular tumor of childhood. Retinoblastoma occurs in both heritable ( 25-40%) and non heritable forms ( 60-75%) The most important gene in retinoblastoma is the RB1 tumor suppressor gene. This gene makes a protein (pRb) that helps stop cells from growing too quickly. Each cell normally has two RB1 genes. Mutations in both RB1 alleles are required for tumor development. As long as a retinal cell has at least one functional RB1 gene, it will not form a retinoblastoma. When both of the RB1 genes are mutated or missing, a cell can grow unchecked. This can lead to further gene changes, which in turn may cause cells to become cancerous. Molecular genetic testing for RB1 mutations in tumor peripheral blood is indicated in affected individuals with newly diagnosed retinoblastoma, to differentiate hereditary from sporadic cases. Genetic testing is part of standard management for at –risk family members. It informs prognosis , intensity of ocular monitoring and risk of disease development. It significantly impacts patient management enabling personalized risks for the individual patient and family, based on the RB1 gene status. Intensive monitoring and early diagnosis of high -risk infants improves prognosis by enabling use of less intensive ocular salvage treatments. Monogenic Macular dystrophies A heterogenous group of disorders, each caused by mutations in a single gene. Incomplete penetrance and variability in disease expression complicate diagnosis. (a) Stargardt disease (STGD1) is the most prevalent juvenile retinal dystrophy. With an autosomal recessive (AR) pattern of inheritance, STGD1 is associated with rapid central vision loss. Mutations in ABCA4 give rise to STGD1. Defective ABCA4 protein leads to the accumulation of A2E lipofuscin in retinal pigment epithelium (RPE) cells, which is toxic in high concentrations. The accumulation of lipofuscin can be seen in and around the macula as yellowish white flecks. (b) Doyne honeycomb retinal degeneration is a dominant macular dystrophy caused by mutations in EFEMP1. Affected individuals present with drusen in the macula and around the edge of the optic nerve head. (c) Sorsby fundus dystrophy results from mutations in TIMP3 and resembles neovascular age-related macular degeneration (AMD) with an age of onset in early adulthood. (d) Best vitelliform macular dystrophy (BVMD) is caused by AD mutations in BEST1, encoding bestrophin-1, a calciumactivated chloride channel. Accumulation of subretinal fluid and vitelliform material originating from the outer photoreceptors is thought to cause RPE overload, leading to photoreceptor and RPE dysfunction. Early onset glaucoma Mutations in six genes (MYOC, PITX2, FOXC1, PAX6, CYP1B1, and LTBP2) have been shown to cause overlapping phenotypes associated with congenital or juvenile glaucoma accounting for around 20% of cases of glaucoma with onset before the age of 40 The impact on clinical care and genetic counseling can be significant for those with a known genetic mutation because appropriate surveillance and timely treatment may prevent or limit sight loss. Detecting mutations helps to determine mode of inheritance - mutations in CYP1B1 and LTBP2 cause recessive traits, whereas glaucoma caused by other known genes may be inherited in a dominant fashion. Personalized therapeutic options based on the effect of specific mutations on disease pathophysiology could benefit individuals with glaucoma AMD AMD is the commonest cause of blindness and is classified in two major forms, neovascular, or ‘wet’ AMD, and ‘dry’ or non-neovascular AMD. The end-stage event in ‘wet’ AMD is choroidal neovascularization (CNV), an outgrowth of blood vessels from the choroid into the subretinal and intraretinal spaces leading to rapid loss of vision. In ‘dry’ AMD, geographic atrophy of the macula results from progressive retinal pigment epithelium (RPE) atrophy. Neovascular AMD is responsible for 80% of severe vision loss and current treatments rely on pharmacological inhibition of vascular endothelial growth factor (VEGF)-A activity. Identification of genetic risk factors involved in AMD provides important insights into disease pathogenesis. Family-based linkage studies, genome-wide association studies (GWAS) studies and candidate gene approaches identified major common variants conferring AMD risk on chromosomes 1q31 and 10q26 within, complement factor H (CFH) (Y402H) and the age-related maculopathy susceptibility 2 (ARMS2) and adjacent high-temperature requirement factor (HTRA1/PRSS11) genes. Complement factor H (CFH) is the main regulator of the alternative complement pathway and multiple independent genetic studies have now showed that dysfunction of the complement system is a key factor in AMD development. AMD is associated with risk variants in several other complement-related proteins including CFH-related proteins 1 and 3, factor B/C2, C3 and complement factor I. Involvement of the complement system has been confirmed in immunohistochemical and proteomic studies on human donor eyes, and studies of blood complement levels in patients. The two main common short nucleotide polymorphism (SNP) associations on chromosomes 1q31 and 10q26 provide the greatest genetic contribution to AMD development risk. Genetic variants in AMD account for its high heritability (approximately 70% of total risk). Fifty percentage of the genetic risk is believed to be attributable to common variants – those highlighted in CFH (Y402H) and ARMS2/HTRA1 are major contributors to AMD risk and pathogenesis. Challenges in personalized approaches to diagnosis Increasing access to NGS and conventional sequencing technologies has provided opportunities for improved clinical diagnosis and personalized interventions. Genetic testing is particularly valuable when a treatment and counseling plan is dictated by the presence of a disease-causing mutation - IRD, early onset glaucoma, and inherited optic neuropathies. Choice of diagnostic platform Molecular diagnosis of genetic eye disease should be performed ideally with the most cost effective and simple modality available, and NGS should not be a stand-alone technology for advancing genomic medicine. Fluorescent in situ hybridization (FISH) and microarrays to detect copy number variations will remain important in the investigation of the dysmorphic or developmentally delayed infant with an ocular phenotype. For monogenic disorders, conventional sequencing will contribute to specific clinical diagnoses where there is accurate phenotypic data. Other retinal conditions with a characteristic phenotype include Doyne syndrome, where mutation screening of the affected gene can be performed. Technological challenges of NGS The increasing pace of NGS technologies presents enormous challenges in terms of data processing, storage and management, hindering translation from sequence data into clinical practice. The amount of data generated using NGS demands a sophisticated computing infrastructure with skilled IT and bioinformatics staff to maintain and run NGS analysis tools. Also, the cost of managing, storing and analyzing NGS data is currently a deterrent for adoption of NGS in many clinical settings. NGS data interpretation The ability to understand the effects of identified variants on disease causation poses the greatest challenge to integration of NGS into clinical practice. Interpretation of disease-causing variants has been carried out on a case-by-case basis, often in a research setting. The analysis is based on software programs with information-based models that aim to predict pathogenicity of sequence changes. As the number of genes mutated in eye disease increases, there is a need for testing guidelines and the necessary counseling infrastructure to order genetic tests and provide mechanism-specific treatments for their patients. Need for accurate phenotyping : Disease stratification will require accurate phenotyping alongside molecular diagnostics. Databases collecting clinical phenotypic information along with gene specific variants and functional information are required to enable diagnostic context-specific data interpretation. Drivers for adoption of personalized approaches : The need within medical research for translational outputs. The development of targeted therapies will continue to challenge the ethical principles used in the selection of patients as candidates for novel medical therapeutics with each new intervention assessed ; considering disease pathophysiology and mechanism of action of each tested intervention. Many RP (Retinitis pigmentosa) patients are still diagnosed when they have mid to late-stage disease with peripheral vision loss because of photoreceptor degeneration. The success of clinical trials may depend on identifying patients as early as possible to maximize the efficacy of treatment requiring changes in diagnostic processes as well as the introduction of asymptomatic individuals into clinical trials. Main difference between the classic and the deep learning ones. Deep learning models can extract features and classify from the input directly, hence allowing for fully “end-toend” learning. The application of AI, and machine learning in retina images , is dominated by supervised learning tasks. three principal use-case scenarios has been identified. Diagnostic imaging Diagnostic imaging is currently the highest and most efficient application of AI-based analyses and will likely further expand as imaging modalities become advance and multi-modal. Next-generation OCT assessment Optical coherence tomography (OCT) is a noninvasive imaging method that uses reflected light to create pictures of the back of your eye – monitor diabetes-related retinopathy and glaucoma. OCT is the backbone of diagnostic assessment of retinal diseases. Most current commercial systems provide basic software for retinal thickness measurements. Generally, most clinical decisions are made based on subjective clinician interpretation of imaging features (eg, the presence of fluid). Higher-order OCT analysis (using machine-learning augmentation) can provide new opportunities for targeted layer/zone specific interrogation and pathology-specific segmentation (eg, fluid volume assessment). These analysis systems enable new opportunities for disease characterization. Advances in the segmentation of CFP and OCT show comparable performance between human graders and machine learning algorithms. CFP- Colour fundus photography Precision medicine and image-guided metrics will help optimize diagnostic efficiency, risk stratify patients more quickly and accurately, and determine treatment risk and benefit. Artificial Intelligence (AI) has the potential to be used in patient care, in automated detection of disease and characterization (such as in severity grading), enabling next-generation disease interrogation, creating opportunities not possible previously; enhancing current analysis platforms to improve efficiency and accuracy of workflow; and opportunities in pattern recognition and phenotype classification using pharmacogenomics or imaging genomics to understand underlying diseases. There is an urgent need to bridge the gap between proof-of-concept development and real-life patient benefit with high quality clinical validation and implementation science through AI To fulfill the potential of PHC (primary healthcare) in ophthalmology, there is a tremendous amount of work to be done across all stages of artificial intelligence solutions development and unprecedented. Collaboration will be required across health care and technology sectors to achieve this. Any Questions ?