Privacy and Accountability in Black-Box Medicine

Black-box medicine—the use of big data and sophisticated machine learning techniques for health-care applications—could be the future of personalized medicine. Black-box medicine promises to make it easier to diagnose rare diseases and conditions, identify the most promising treatments, and allocate scarce resources among different patients. But to succeed, it must overcome two separate, but related, problems: patient privacy and algorithmic accountability. Privacy is a problem because researchers need access to huge amounts of patient health information to generate useful medical predictions. And accountability is a problem because black-box algorithms must be verified by outsiders to ensure they are accurate and unbiased, but this means giving outsiders access to this health information. This article examines the tension between the twin goals of privacy and accountability and develops a framework for balancing that tension. It proposes three pillars for an effective system of privacy-preserving accountability: substantive limitations on the collection, use, and disclosure of patient information; independent gatekeepers regulating information sharing between those developing and verifying black-box algorithms; and information-security requirements to prevent unintentional disclosures of patient information. The article examines and draws on a similar debate in the field of clinical trials, where disclosing information from past trials can lead to new treatments but also threatens patient privacy

Updates in Pharmacogenetics of Non-Small Cell Lung Cancer

Though significant clinical advances have been made, lung cancer remains the most lethal, with a low 5-year survival rate. The variability in patient response towards therapy is substantial and is associated with lung cancer’s genomic landscape. Pharmacogenetic studies have deciphered many clinically relevant associations between tumor genetic alterations and their influences on drug efficacy, toxicity sensitivity and overall outcomes of cancer treatment. Biomarkers are tools in the arsenal that can help in the prediction, prognosis, diagnosis and follow-up of cancer treatment. Bulk and single-cell next-generation sequencing of large patient cohorts have generated a better understanding of the genetic underpinnings of lung cancer, and opening up personalized therapeutic opportunities. Immunotherapy and personalized medicine are providing hope for lung cancer patients. This review highlights the genetic alterations and important lung cancer biomarkers. The pharmacogenetic associations, personalized immunotherapy and challenges associated with effective therapy are also discussed. Pharmacogenetics and pharmacogenomics can open up new vistas for optimized, personalized NSCLC treatment

Metastatic Breast Cancer: Biomolecular Characterization and Targeted Therapy

Metastasis is a complex process that remains a major challenge in the clinical management of cancer, because most cancer-related deaths are attributed to disseminated disease rather than the primary tumor. Despite the significant advances in the prediction of prognosis, and therapeutic management of primary breast cancers, coupled with the substantial improvement in our understanding of the molecular determinants of metastasis, breast cancer relapse and death rates remain unacceptably high. The aim of the research presented in this thesis was to characterize the biomolecular heterogeneity of breast cancer across tumor progression stages and to identify novel biomarkers and therapeutic strategies which may improve prognostication and personalization of therapy for women diagnosed with metastatic breast cancer. By analysis of tumor biopsies collected at different stages of disease progression, we showed that, in general, the phenotype of the primary tumor is typically conserved during tumor progression. However, in a clinically relevant number of cases, a phenotypic drift in biomarkers and tumor molecular subtypes occurs longitudinally with disease progression, with a change to a more aggressive phenotype being associated with an inferior clinical outcome. We also uncovered that breast cancer liver metastases are transcriptionally different from metastases in other anatomical sites and identified candidate liver metastasis-selective genes with the potential to specifically predict liver metastatic relapse and more generally, the time to any recurrence in early stage breast cancer. Furthermore, we demonstrated that co-targeting of PARP1 and PI3K may represent an improved and specific treatment strategy for BRCA1 deficient breast cancers. The results we present continue to emphasize the clinical significance of breast cancer heterogeneity and highlight possible ways to improve the accuracy of predicting prognosis and effectively treating patients with metastatic disease, a step towards achieving the promise of personalized cancer management and overcoming the clinical burden of metastatic breast cancer

Development of a minimally invasive molecular biomarker for early detection of lung cancer

The diagnostic evaluation of ever smokers with pulmonary nodules represents a growing clinical challenge due to the implementation of lung cancer screening. The high false-positive rate of screening frequently results in the use of unnecessary invasive procedures in patients who are ultimately diagnosed as benign, clearly highlighting the need for additional diagnostic approaches. We previously derived and validated a bronchial epithelial gene-expression biomarker to detect lung cancer in ever smokers. However, bronchoscopy is not always chosen as a diagnostic modality. Given that bronchial and nasal epithelial gene-expression are similarly altered by cigarette smoke exposure, we sought to determine if cancer-associated gene-expression might also be detectable in the more readily accessible nasal epithelium. Nasal epithelial brushings were prospectively collected from ever smokers undergoing diagnostic evaluation for lung cancer in the AEGIS-1 (n=375) and AEGIS-2 (n=130) clinical trials and gene-expression profiled using microarrays. The computational framework used to discover biomarkers in these data was formalized and implemented in an open-source R-package. We identified 535 genes in the nasal epithelium of AEGIS-1 patients whose expression was associated with lung cancer status. Using matched bronchial gene-expression data from a subset of these patients, we found significantly concordant cancer-associated gene-expression alterations between the two airway sites. A nasal lung cancer classifier derived in the AEGIS-1 cohort that combined clinical factors and nasal gene-expression had significantly higher AUC (0.81) and sensitivity (0.91) than the clinical-factor model alone in independent samples from the AEGIS-2 cohort. These results support that the airway epithelial field of lung cancer-associated injury extends to the nose and demonstrates the potential of using nasal gene-expression as a non-invasive biomarker for lung cancer detection. The framework for deriving this biomarker was generalized and implemented in an open-source R-package. The package provides a computational pipeline to compare biomarker development strategies using microarray data. The results from this pipeline can be used to highlight the optimal model development parameters for a given dataset leading to more robust and accurate models. This package provides the community with a novel and powerful tool to facilitate biomarker discovery in microarray data

Personalized medicine: potential, barriers and contemporary issues

Trabalho Final de Mestrado Integrado, Ciências Farmacêuticas, 2020, Universidade de Lisboa, Faculdade de Farmácia.A medicina personalizada (PM), é um modelo de prática médica personalizada a cada doente por meio da identificação de características individuais, como a sua informação genética, histórico familiar e estilo de vida, e ganhou relevância significativa nas últimas décadas à medida que os avanços tecnológicos permitiram a compreensão das diferenças biológicas entre indivíduos. Além disso, a necessidade de uma abordagem mais econômica é considerada vital pelas várias entidades envolvidas nos cuidados de saúde. Na verdade, existem muitos potenciais benefícios da PM, incluindo a minimização do risco de toxicidade a determinados medicamentos e o aumento da eficácia dos mesmos, contribuindo assim para a sustentabilidade do sistema de saúde, e facilitando a descoberta de novas moléculas com ação terapêutica benéfica. Infelizmente, existem também muitas barreiras à sua implementação, nomeadamente aquelas relacionadas com custos, com a complexidade dos dados envolvidos, com a qualidade da evidências clínica, e a necessidade de educação e formação e de novas políticas regulatórias, barreiras estas que têm limitado a tradução clínica deste modelo médico para os cuidados de saúde atuais. Nesta dissertação, pretendemos abordar as características que ajudam a moldar a PM, o seu impacto na prática clínica, e as barreiras que precisam de ser superadas para demonstrar o valor deste modelo clinico inovador. Esperamos ter abordado uma série de questões que destacam o potencial impacto benéfico da PM, tendo em consideração a heterogeneidade da doença e a variabilidade genética inter-individual, a importância da segurança nas análises preditivas e da identificação de biomarcadores de eficácia, a relevância da farmacogenómica, as mudanças necessárias no desenho dos ensaios clínico, fatores que no seu conjunto permitirão o desenvolvimento de uma abordagem clínica mais adaptativa. Embora o impacto da PM possa já ser confirmado através de vários exemplos descritos nesta monografia, há várias etapas a serem realizadas para construir um modelo médico mais robusto. Esses esforços são descritos neste trabalho, bem como o papel vital dos Farmacêuticos, É ainda apresentada uma abordagem esquemática para a implementação a PM na prática clínica atual. O investimento em investigação e educação, novas políticas regulatórias, a aposta em novas técnicas de análise de big data, infraestruturas tecnológicas inovadoras, e alterações de padrões daindústria farmacêutica permitirão melhorar a qualidade de vida da população através da PM.Personalized medicine (PM), which refers to providing tailored medical treatment to individual patients through the identification of common features, including their genetics, inheritance, and lifestyle, has gained significant relevance over the last decades as technological breakthroughs have allowed for the understanding of biological differences between individuals. Moreover, the need for a more cost-effective approach has also been deemed vital by the various stakeholders involved in health care. Indeed, there are many potential benefits of PM, including minimizing the risk of drug toxicity and increasing the efficacy of the drugs used, contributing to the sustainability of the healthcare system, and facilitating drug discovery and development programs. Unfortunately, there are also many barriers such as cost, complexity, high quality evidence requirements, the need for further education and regulatory policies, which have limited the clinical translation of this medical model to current healthcare. In this dissertation we aimed to discuss on the characteristics that help shape PM, its perceived impact on clinical practice, and the barriers that have to be resolved in order to demonstrate the value of this innovative model. We hope that have addressed a number of issues that highlight the potential beneficial impact of PM, taking in consideration disease heterogeneity and genetic variability, the importance of predictive safety and efficacy biomarkers, the weight of Pharmacogenomics, and the importance of changes in the design of clinical trials that will enable a more adaptive clinical approach. Although the impact of PM is already in place to some degree, there are several steps to be made in order to build a more robust medical model. These efforts are described in this work, as well as the vital role of Pharmacists, and a schematic approach is proposed to implement PM into the current clinical practice. Research and Education investment, regulatory policies, big data analysis, technology infrastructures, and industry standards must be revised and change with the goal of securing patients’ quality of life through PM