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Background A number of papers have proposed or evaluated the delayed-start design as an alternative to the standard two-arm parallel group randomized clinical trial (RCT) design in the field of rare disease. However the discussion is felt to lack a sufficient degree of consideration devoted to the true virtues of the delayed start design and the implications either in terms of required sample-size, overall information, or interpretation of the estimate in the context of small populations. Objectives To evaluate whether there are real advantages of the delayed-start design particularly in terms of overall efficacy and sample size requirements as a proposed alternative to the standard parallel group RCT in the field of rare disease. Methods We used a real-life example to compare the delayed-start design with the standard RCT in terms of sample size requirements. Then, based on three scenarios regarding the development of the treatment effect over time, the advantages, limitations and potential costs of the delayed-start design are discussed. Results We clarify that delayed-start design is not suitable for drugs that establish an immediate treatment effect, but for drugs with effects developing over time, instead. In addition, the sample size will always increase as an implication for a reduced time on placebo resulting in a decreased treatment effect. Conclusions A number of papers have repeated well-known arguments to justify the delayed-start design as appropriate alternative to the standard parallel group RCT in the field of rare disease and do not discuss the specific needs of research methodology in this field. The main point is that a limited time on placebo will result in an underestimated treatment effect and, in consequence, in larger sample size requirements compared to those expected under a standard parallel-group design. This also impacts on benefit-risk assessment.

The analysis of histopathology images with artificial intelligence aims to enable clinical decision support systems and precision medicine. The success of such applications depends on the ability to model the diverse patterns observed in pathology images. To this end, we present Virchow, the largest foundation model for computational pathology to date. In addition to the evaluation of biomarker prediction and cell identification, we demonstrate that a large foundation model enables pan-cancer detection, achieving 0.95 specimen-level area under the (receiver operating characteristic) curve across nine common and seven rare cancers. Furthermore, we show that with less training data, the pan-cancer detector built on Virchow can achieve similar performance to tissue-specific clinical-grade models in production and outperform them on some rare variants of cancer. Virchow’s performance gains highlight the value of a foundation model and open possibilities for many high-impact applications with limited amounts of labeled training data. Trained on 1.5 million whole-slide images from 100,000 patients, a pathology foundation model is shown to improve performance of specialized models in detection of rare cancers.

There are limited published data on the burden of rare cancers in the United States. By using data from the North American Association of Central Cancer Registries and the Surveillance, Epidemiology, and End Results program, the authors provide information on incidence rates, stage at diagnosis, and survival for more than 100 rare cancers (defined as an incidence of fewer than 6 cases per 100,000 individuals per year) in the United States. Overall, approximately 20% of patients with cancer in the United States are diagnosed with a rare cancer. Rare cancers make up a larger proportion of cancers diagnosed in Hispanic (24%) and Asian/Pacific Islander (22%) patients compared with non-Hispanic blacks (20%) and non-Hispanic whites (19%). More than two-thirds (71%) of cancers occurring in children and adolescents are rare cancers compared with less than 20% of cancers diagnosed in patients aged 65 years and older. Among solid tumors, 59% of rare cancers are diagnosed at regional or distant stages compared with 45% of common cancers. In part because of this stage distribution, 5-year relative survival is poorer for patients with a rare cancer compared with those diagnosed with a common cancer among both males (55% vs 75%) and females (60% vs 74%). However, 5-year relative survival is substantially higher for children and adolescents diagnosed with a rare cancer (82%) than for adults (46% for ages 65-79 years). Continued efforts are needed to develop interventions for prevention, early detection, and treatment to reduce the burden of rare cancers. Such discoveries can often advance knowledge for all cancers.

Chordoma is a rare bone cancer that is aggressive, locally invasive, and has a poor prognosis. Chordomas are thought to arise from transformed remnants of notochord and have a predilection for the axial skeleton, with the most common sites being the sacrum, skull base, and spine. The gold standard treatment for chordomas of the mobile spine and sacrum is en-bloc excision with wide margins and postoperative external-beam radiation therapy. Treatment of clival chordomas is unique from other locations with an enhanced emphasis on preservation of neurological function, typified by a general paradigm of maximally safe cytoreductive surgery and advanced radiation delivery techniques. In this Review, we highlight current standards in diagnosis, clinical management, and molecular characterisation of chordomas, and discuss current research.

Background Individuals with rare diseases may face challenges that are different from those experienced in more common medical conditions. A wide range of different rare conditions has resulted in a myriad of studies investigating the specificities of the diagnosis in focus. The shared psychological experiences of individuals with a rare condition, however, have not been reviewed systematically. Methods We performed a systematic review, including qualitative studies on adults, published between 2000 and 2016. Papers including more than one rare genetic or nongenetic diagnosis were included. Studies based on single diagnoses were excluded except for four specific conditions: hemophilia (bleeding disorder), phenylketonuria (metabolic disorder), Fabry disease (lysosomal storage disorder), and epidermolysis bullosa (skin disorder). Results The review identified 21 studies. Findings were synthesized and categorized according to three main themes: (1) Consequences of living with a rare disorder, (2) Social aspects of living with a rare disorder, and (3) Experiences with the health care system. Findings point to several unique challenges, such as the psychological, medical, and social consequences of a lack of knowledge about the condition in health care and social settings. Conclusion The findings highlight the need for more research on the shared psychological and social impact of living with a rare diagnosis across conditions, in order to identify risk factors and inform clinical practice. Individuals with rare diseases may face challenges that are different from those experienced in more common medical conditions. We performed a systematic review, including qualitative studies on adults, published between 2000 and 2016. The findings highlight the need for more research on the shared psychological and social impact of living with a rare diagnosis across conditions, in order to identify risk factors and inform clinical practice.

Rare genetic diseases are typically caused by a single gene defect. Despite this clear causal relationship between genotype and phenotype, identifying the pathobiological mechanisms at various levels of biological organization remains a practical and conceptual challenge. Here, we introduce a network approach for evaluating the impact of rare gene defects across biological scales. We construct a multiplex network consisting of over 20 million gene relationships that are organized into 46 network layers spanning six major biological scales between genotype and phenotype. A comprehensive analysis of 3,771 rare diseases reveals distinct phenotypic modules within individual layers. These modules can be exploited to mechanistically dissect the impact of gene defects and accurately predict rare disease gene candidates. Our results show that the disease module formalism can be applied to rare diseases and generalized beyond physical interaction networks. These findings open up new venues to apply network-based tools for cross-scale data integration. Despite the clear causal relationship between genotype and phenotype in rare diseases, identifying the pathobiological mechanisms at various levels of biological organization remains a practical and conceptual challenge. Here, the authors introduce a network approach for evaluating the impact of rare gene defects across biological scales.

Rare cell populations play a pivotal role in the initiation and progression of diseases such as cancer. However, the identification of such subpopulations remains a difficult task. This work describes CellCnn, a representation learning approach to detect rare cell subsets associated with disease using high-dimensional single-cell measurements. Using CellCnn, we identify paracrine signalling-, AIDS onset- and rare CMV infection-associated cell subsets in peripheral blood, and extremely rare leukaemic blast populations in minimal residual disease-like situations with frequencies as low as 0.01%. While rare cell subpopulations frequently make the difference between health and disease, their detection remains a challenge. Here, the authors devise CellCnn, a representation learning approach to detecting such rare cell populations from high-dimensional single cell data, and, among other examples, demonstrate its capacity for detecting rare leukaemic blasts in minimal residual disease.

Despite advances in high-throughput sequencing that have revolutionized the discovery of gene defects in rare Mendelian diseases, there are still gaps in translating individual genome variation to observed phenotypic outcomes. While we continue to improve genomics approaches to identify primary disease-causing variants, it is evident that no genetic variant acts alone. In other words, some other variants in the genome (genetic modifiers) may alleviate (suppress) or exacerbate (enhance) the severity of the disease, resulting in the variability of phenotypic outcomes. Thus, to truly understand the disease, we need to consider how the disease-causing variants interact with the rest of the genome in an individual. Here, we review the current state-of-the-field in the identification of genetic modifiers in rare Mendelian diseases and discuss the potential for future approaches that could bridge the existing gap.

Around 5% of the population is affected by a rare genetic disease, yet most endure years of uncertainty before receiving a genetic test. A common feature of genetic diseases is the presence of multiple rare phenotypes that often span organ systems. Here, we use diagnostic billing information from longitudinal clinical data in the electronic health records (EHRs) of 2,286 patients who received a chromosomal microarray test, and 9,144 matched controls, to build a model to predict who should receive a genetic test. The model achieved high prediction accuracies in a held-out test sample (area under the receiver operating characteristic curve (AUROC), 0.97; area under the precision–recall curve (AUPRC), 0.92), in an independent hospital system (AUROC, 0.95; AUPRC, 0.62), and in an independent set of 172,265 patients in which cases were broadly defined as having an interaction with a genetics provider (AUROC, 0.9; AUPRC, 0.63). Patients carrying a putative pathogenic copy number variant were also accurately identified by the model. Compared with current approaches for genetic test determination, our model could identify more patients for testing while also increasing the proportion of those tested who have a genetic disease. We demonstrate that phenotypic patterns representative of a wide range of genetic diseases can be captured from EHRs to systematize decision-making for genetic testing, with the potential to speed up diagnosis, improve care and reduce costs. Machine learning of electronic health records identifies individuals with a high suspicion of a wide range of genetic diseases and prioritizes those individuals for genetic testing.

Empirical evidence on depression and anxiety in patients with rare diseases is scarce but can help improve comprehensive treatment. The objectives of this study were to investigate the frequency of depression and anxiety in this heterogeneous population and to examine aspects associated with increased psychopathology. N = 300 patients with 79 different rare diseases (female:80%, age:M = 44.3(12.8), range:16-74 years) participated in a cross-sectional online study. We determined the percentages of patients reporting elevated depression (PHQ-9) and anxiety (GAD-7) scores. We calculated two linear regressions with depression and anxiety as outcomes. Predictor variables were diagnosis-related aspects (diagnosis assigned to ICD-10 chapter, visibility of symptoms, time since diagnosis, comorbid diseases), perceived somatic-symptom-severity (PHQ-15), illness-perceptions (consequences, control, identity, concern, understanding and treatment control; B-IPQ-R), coping mechanisms (constructive attitudes, active engagement in life) and social support (heiQ). We controlled for gender, age and depression or anxiety depending on the outcome. 42% of the patients (95%CI [36.41%,47.59%]) reported depression scores indicating moderately or severely elevated symptom levels. Regarding anxiety, this applies to 23% (95%CI [18.54%,28.06%]). Variables significantly associated with depression were higher perceived somatic-symptom-severity (B = 0.41,p < .001), less control (B = .17,p < .05), lower levels of concern (B = -0.32,p < .01) and less constructive attitudes (B = -1.40,p < .001). No diagnosis-related variables were associated with depression. Variables significantly associated with anxiety were diseases of the circulatory system compared to congenital malformations (B = 1.88,p < .05), less consequences (B = -0.32,p < .05) and more concern (B = -0.32,p < .01). The data reveal first insights into depression and anxiety in patients with different rare diseases. High percentages of patients showed clinically relevant symptom burden. No diagnosis-related differences were found in depression while anxiety seems to be particularly frequent in patients with rare diseases of the circulatory system. Besides perceived somatic symptom severity, cognitive appraisal seems to be linked to depression. Supporting patients in coping with their disease may help reduce psychopathology and therefore improve overall health.