StemCell Therapy

Commensurate with technological improvements and the subsequent rise in genetic research and genetics-based tests and treatments, the term personalized medicine is being used more frequently. But what does it mean? This explainer outlines what personalized medicine is, its advantages and some concerns that have been raised in relation to it.

Also known as precision medicine, personalized medicine is a rapidly expanding field of practice that uses an individuals genetic profile to guide decisions about the prevention, diagnosis, and treatment of disease.

Although the concept of personalized medicine began in the 1990s, following advances in DNA sequencing technology, it pretty much remained just a concept and was rarely applied. Since then, continued advances have generated enormous amounts of new information. The discovery of genes, proteins, and pathways has enabled studies into the genetics underlying both rare and common diseases and aided the identification of new drug targets.

Whats good about personalized medicine

For a long time, the practice of medicine has largely been reactive, waiting for the onset of disease before treating or curing it. But were all unique in terms of genetic makeup, environment, and lifestyle factors. Our growing understanding of genetics and genomics the study of all of a persons genes and how they drive health, disease and treatment in individual people offers an opportunity to step away from a one size fits all approach based on broad population averages and adopt an individualized approach.

In addition to advances in the field of genomics, developments in the fields of science and technology play a crucial role in personalized medicine. For example, the development of high-resolution analytics, biotech research and chemistry, and the ability to decipher molecular structures, signaling pathways, and protein interactions that underpin the mechanisms of gene expression.

Personalized medicine is about more than prescribing the best drugs, although thats a large part of it. Proponents say it would shift medicines emphasis from reaction to prevention, better predict disease susceptibility and improve diagnosis, produce more effective drugs and reduce adverse side effects, and eliminate the inefficiency and cost of adopting a trial-and-error approach to healthcare.

Goetz & Schork

Weve already seen personalized medicine positively impact patient care in patients with diseases like breast cancer, melanoma, and cardiovascular disease. And the use of patient-derived cell and organoid avatars as disease models to identify beneficial treatments provides truly personalized medicine to individual patients. Then theres CRISPR, technology that allows genetic material to be added, removed, or altered at particular locations in the genome as a direct way of treating genetic and other conditions.

Concerns about personalized medicine

Despite its numerous benefits, the adoption of a personalized medicine approach raises several issues. For it to reach peak efficiency, a lot of genomic data must be collected from a large and diverse section of the population, and its critical that participants privacy and confidentiality are protected. Privacy issues extend to the collection, storage and sharing of that information.

Extensive changes to the healthcare system, including ethical changes, are likely needed to overcome the ethical obstacles of personalized medicine use, including knowledge gap and informed consent, privacy and confidentiality, and the availability of healthcare services. Social benefit versus science development and individual benefit needs to be considered and balanced. And there are concerns that data collected might be used unethically, such as insurance companies not offering some policies to people with a certain genetic predispositions.

Legally, a physician is negligent when they fail to follow generally accepted practice. In personalized medicine, when the interpretation of genetic information is at issue, there may be no generally accepted practice or standard. It begs the question, at what point does clinical genetic knowledge become a standard of practice?

Cost is another relevant factor. While the expense associated with large-scale DNA sequencing is decreasing, its still expensive. And drugs developed based on molecular or genetic variations are likely to be costly. Further, massive amounts of data require massive infrastructure changes, including changes to the mechanisms of data collection, storage and sharing, all of which require investment.

Will personalized medicine happen?

Personalized medicine is already happening in the form of things like CRISPR, mRNA vaccines and the large-scale genome sequencing of newborns. Its the kind of future that was envisioned when the Human Genome Project was first completed 20 years ago, and it certainly has its benefits.

However, the widespread adoption of personalized medicine may prove more difficult than first imagined. In addition to the concerns already mentioned, there needs to be a change in public attitudes and the way medical professionals, patients and health regulators view the approach. It may require a new approach to the way drugs are tested and a willingness to embrace risk.

Nonetheless, the potential benefits are so great and the march of technology and knowledge so inexorable that it is a near certainty that personalized medicine will continue to develop and become standard in healthcare systems at some point in the future it's just a matter of how fast the advances are made and how soon the aforementioned hurdles can be overcome as to exactly when this might be.

Originally posted here:
Personalized medicine: The pros, cons and concerns - New Atlas

Abstract

The convergence of artificial intelligence (AI) and precision medicine promises to revolutionize health care. Precision medicine methods identify phenotypes of patients with lesscommon responses to treatment or unique healthcare needs. AI leverages sophisticated computation and inference to generate insights, enables the system to reason and learn, and empowers clinician decision making through augmented intelligence. Recent literature suggests that translational research exploring this convergence will help solve the most difficult challenges facing precision medicine, especially those in which nongenomic and genomic determinants, combined with information from patient symptoms, clinical history, and lifestyles, will facilitate personalized diagnosis and prognostication.

In a recent National Academy of Medicine report about the current and future state of artificial intelligence (AI) in health care, the authors noted unprecedented opportunities to augment the care of specialists and the assistance that AI provides in combating the realities of being human (including fatigue and inattention) and the risks of machine error. Importantly, the report notes that whereas care must be taken with the use of these technologies, much promise exists.1 The digitization of healthrelated data and the rapid uptake in technology are fueling transformation and progress in the development and use of AI in healthcare.2, 3, 4 However, multimodal data integration, security, federated learning (which requires fundamental advances in areas, such as privacy, largescale machine learning, and distributed optimization), model performance, and bias may pose challenges to the use of AI in health care.5

Three main principles for successful adoption of AI in health care include data and security, analytics and insights, and shared expertise. Data and security equate to full transparency and trust in how AI systems are trained and in the data and knowledge used to train them. As humans and AI systems increasingly work together, it is essential that we trust the output of these systems.

Analytics and insights equate to purpose and people where augmented intelligence and actionable insights support what humans do, not replace them. AI can combine input from multiple structured and unstructured sources, reason at a semantic level, and use these abilities in computer vision, reading comprehension, conversational systems, and multimodal applications to help health professionals make more informed decisions (e.g., a physician making a diagnosis, a nurse creating a care plan, or a social services agency arranging services for an elderly citizen). Shared expertise equates to our complementary relationship with AI systems, which are trained by and are supporting human professionals, leading to workforce change, which leads to new skills. The ability to create cuttingedge AI models and build highquality business applications requires skilled experts with access to the latest hardware.

A vast amount of untapped data could have a great impact on our healthyet it exists outside medical systems.6 Our individual health is heavily influenced by our lifestyle, nutrition, our environment, and access to care. These behavioral and social determinants and other exogenous factors can now be tracked and measured by wearables and a range of medical devices. These factors account for about 60% of our determinants of health (behavioral, socioeconomical, physiological, and psychological data), our genes account for about 30%, and last our actual medical history accounts for a mere 10%.6 Over the course of our lifetimes, we will each generate the equivalent of over 300 million books of personal and healthrelated data that could unlock insights to a longer and healthier life.7

The phenomenon of big data can be described using the five Vs: volume, velocity, variety, veracity, and value. Volume refers to the vast amount of complex and heterogenous data, which makes data sets too large to store and analyze using traditional database technology. Velocity refers to the speed at which new data are generated and moves around. Variety refers to the different types of structured, semistructured, and unstructured data, such as social media conversations and voice recordings. Veracity refers to the certainty, accuracy, relevance, and predictive value of the data. Value refers to the conversion of data into business insights. Whereas the volume, variety, velocity, and veracity of data are contributing to the increasing complexity of data management and workloadscreating a greater need for advanced analytics to discover insightsmobile devices have made technology more consumable, creating user demand for interactive tools for visual analytics.

Big data analytics and AI are increasingly becoming omnipresent across the entire spectrum of health care, including the 5 Ps spanning: payer, provider, policy maker/government, patients, and product manufacturers. Up to 10% of global health care expenditure is due to fraud and abuse and AIbased tools help mitigate fraud, waste, and abuse in payer programs.8 Reliable identification of medical coding errors and incorrect claims positively impacts payers, providers, and governments by saving inordinate amounts of money, time, and efforts.9 As an example, IBM DataProbe, an AIbased business intelligence tool, was able to detect and recover US $41.5million in feeforservice payments over a 2year period in Medicaid fraud for Iowa Medicaid Enterprise.10 In the provider space, AI is used for evidencebased clinical decision support,11 detection of adverse events, and the usage of electronic health record (EHR) data to predict patients at risk for readmission.12 Healthcare policymakers and government use AIbased tools to control and predict infections and outbreaks. An example is FINDER, a machinelearned model for realtime detection of foodborne illness using anonymous and aggregated web search and location data.13 Another example is the integrated data hub and caremanagement solution using IBM Connect360 and IBM Watson Care Manager that Sonoma County, California government agencies used to transform health and healthcare for socially disadvantaged groups and other displaced individuals during a time of communitywide crisis.14 This solution enabled integration of siloed data and services into a unified citizen status view, identification of clinical and social determinants of health from structured and unstructured sources, construction of algorithms to match clients with services, and streamlining of care coordination during the 2017 and 2019 Sonoma County wildfires. With the advent of the global pandemic coronavirus disease 2019 (COVID19) in early 2020, such a model can be used to predict atrisk populations, and potentially provide additional risk information to clinicians caring for atrisk patients.15 The use of AI for patients and life sciences/healthcare products are addressed extensively in the sections that follow.

AI is not, however, the only datadriven field impacting health and health care. The field of precision medicine is providing an equal or even greater influence than AI on the direction of health care16 and has been doing so for more than a decade.17 Precision medicine aims to personalize care for every individual. This goal requires access to massive amounts of data, such as data collected through the United Kingdoms UK Biobank and the All of Us project, coupled with a receptive health care ecosystem willing to abandon the conventional approach to care in favor of a more highly individualized strategy. The convergence of these fields will likely accelerate the goals of personalized care and tightly couple AI to healthcare providers for the foreseeable future. In the sections that follow, we will briefly summarize the capabilities of existing AI technology, describe how precision medicine is evolving, and, through a series of examples, demonstrate the potentially transformative effect of AI on the rate and increasing breadth of application for precision medicine.

The past 10years have seen remarkable growth and acceptance of AI in a variety of domains and in particular by healthcare professionals. AI provides rich opportunities for designing intelligent products, creating novel services, and generating new business models. At the same time, the use of AI can introduce social and ethical challenges to security, privacy, and human rights.1

AI technologies in medicine exist in many forms, from the purely virtual (e.g., deeplearningbased health information management systems and active guidance of physicians in their treatment decisions) to cyberphysical (e.g., robots used to assist the attending surgeon and targeted nanorobots for drug delivery).18 The power of AI technologies to recognize sophisticated patterns and hidden structures has enabled many imagebased detection and diagnostic systems in healthcare to perform as well or better than clinicians, in some cases.19 AIenabled clinical decisionsupport systems may reduce diagnostic errors, augment intelligence to support decision making, and assist clinicians with EHR data extraction and documentation tasks.20 Emerging computational improvements in natural language processing (NLP), pattern identification, efficient search, prediction, and biasfree reasoning will lead to further capabilities in AI that address currently intractable problems.21, 22

Advances in the computational capability of AI have prompted concerns that AI technologies will eventually replace physicians. The term augmented intelligence, coined by W.R. Ashby in the 1950s,23 may be a more apt description of the future interplay among data, computation, and healthcare providers and perhaps a better definition for the abbreviation AI in healthcare. A version of augmented intelligence, described in the literature in Friedmans fundamental theorem of biomedical informatics,24 relates strongly to the role of AI in health care (depicted in Figure1). Consistent with Friedmans description of augmented intelligence, Langlotz at Stanford stated that Radiologists who use AI will replace radiologists who dont.25

A version of the Friedmans fundamental theorem of informatics describing the impact of augmented intelligence. The healthcare system with AI will be better than the healthcare system without it. AI, artificial intelligence.

An AI system exhibits four main characteristics that allow us to perceive it as cognitive: understanding, reasoning, learning, and empowering.26 An AI system understands by reading, processing, and interpreting the available structured and unstructured data at enormous scale and volume. An AI system reasons by understanding entities and relationships, drawing connections, proposing hypotheses, deriving inferences, and evaluating evidence that allows it to recognize and interpret the language of health and medicine. An AI system learns from human experts and realworld cases by collecting feedback, learning from outcomes at all levels and granularities of the system, and continuing to improve over time and experience. An AI system empowers and interacts clinicians and users by providing a more integrated experience in a variety of settings, combining dialog, visualization, collaboration, and delivering previously invisible data and knowledge into actionable insights. In contrast, humans excel at common sense, empathy, morals, and creativity.

Augmenting human capabilities with those provided by AI leads to actionable insights in areas such as oncology,27 imaging,28 and primary care.29 For example, a breast cancer predicting algorithm, trained on 38,444 mammogram images from 9,611 women, was the first to combine imaging and EHR data with associated health records. This algorithm was able to predict biopsy malignancy and differentiate between normal and abnormal screening results. The algorithm can be applied to assess breast cancer at a level comparable to radiologists, as well has having the potential to substantially reduce missed diagnoses of breast cancer.30 This combined machinelearning and deeplearning model trained on a dataset of linked mammograms and health records may assist radiologists in the detection of breast cancer as a second reader.

The field of precision medicine is similarly experiencing rapid growth. Precision medicine is perhaps best described as a health care movement involving what the National Research Council initially called the development of a New Taxonomy of human disease based on molecular biology, or a revolution in health care triggered by knowledge gained from sequencing the human genome.31 The field has since evolved to recognize how the intersection of multiomic data combined with medical history, social/behavioral determinants, and environmental knowledge precisely characterizes health states, disease states, and therapeutic options for affected individuals.32 For the remainder of this paper, we will use the term precision medicine to describe the health care philosophy and research agenda described above, and the term personalized care to reflect the impact of that philosophy on the individual receiving care.

Precision medicine offers healthcare providers the ability to discover and present information that either validates or alters the trajectory of a medical decision from one that is based on the evidence for the average patient, to one that is based upon individuals unique characteristics. It facilitates a clinicians delivery of care personalized for each patient. Precision medicine discovery empowers possibilities that would otherwise have been unrealized.

Advances in precision medicine manifest into tangible benefits, such as early detection of disease33 and designing personalized treatments are becoming more commonplace in health care.34 The power of precision medicine to personalize care is enabled by several data collection and analytics technologies. In particular, the convergence of highthroughput genotyping and global adoption of EHRs gives scientists an unprecedented opportunity to derive new phenotypes from realworld clinical and biomarker data. These phenotypes, combined with knowledge from the EHR, may validate the need for additional treatments or may improve diagnoses of disease variants.

Perhaps the most wellstudied impact of precision medicine on health care today is genotypeguided treatment. Clinicians have used genotype information as a guideline to help determine the correct dose of warfarin.35 The Clinical Pharmacogenetics Implementation Consortium published genotypebased drug guidelines to help clinicians optimize drug therapies with genetic test results.36 Genomic profiling of tumors can inform targeted therapy plans for patients with breast or lung cancer.34 Precision medicine, integrated into healthcare, has the potential to yield more precise diagnoses, predict disease risk before symptoms occur, and design customized treatment plans that maximize safety and efficiency. The trend toward enabling the use of precision medicine by establishing data repositories is not restricted to the United States; examples from Biobanks in many countries, such as the UK Biobank,37 BioBank Japan,38 and Australian Genomics Health Alliance39 demonstrate the power of changing attitudes toward precision medicine on a global scale.

Although there is much promise for AI and precision medicine, more work still needs to be done to test, validate, and change treatment practices. Researchers face challenges of adopting unified data formats (e.g., Fast Healthcare Interoperability Resources), obtaining sufficient and high quality labeled data for training algorithms, and addressing regulatory, privacy, and sociocultural requirements.

AI and precision medicine are converging to assist in solving the most complex problems in personalized care. Figure2 depicts five examples of personalized healthcare dogma that are inherently challenging but potentially amenable to progress using AI.40, 41, 42

Dimensions of synergy between AI and precision medicine. Both precision medicine and artificial intelligence (AI) techniques impact the goal of personalizing care in five ways: therapy planning using clincal, genomic or social and behavioral determinants of health, and risk prediction/diagnosis, using genomic or other variables.

Genomeinformed prescribing is perhaps one of the first areas to demonstrate the power of precision medicine at scale.43 However, the ability to make realtime recommendations hinges on developing machinelearning algorithms to predict which patients are likely to need a medication for which genomic information. The key to personalizing medications and dosages is to genotype those patients before that information is needed.44

This use case was among the earliest examples of the convergence between AI and precision medicine, as AI techniques have proven useful for efficient and highthroughput genome interpretation.45 As noted recently by Zou and colleagues,46 deep learning has been used to combine knowledge from the scientific literature with findings from sequencing to propose 3D protein configurations, identify transcription start sites, model regulatory elements, and predict gene expression from genotype data. These interpretations are foundational to identifying links among genomic variation and disease presentation, therapeutic success, and prognosis.

In medulloblastoma, the emergence of discrete molecular subgroups of the disease following AImediated analysis of hundreds of exomes, has facilitated the administration of the right treatment, at the right dosage, to the right cohort of pediatric patients.47 Although conventional treatment of this disease involved multimodal treatment, including surgery, chemotherapy, and whole brain radiation, precision genomics has enabled treatment of the wingless tumor subgroup, which is more common in children, with chemotherapy aloneobviating the need for radiation.48 Avoiding radiotherapy is particularly impactful for mitigating potential neurocognitive sequelae and secondary cancers from wholebrain radiation among disease survivors.49, 50

The initial successful paradigm of AI in imaging recognition has also given rise to radiogenomics. Radiogenomics, as a novel precision medicine research field, focuses on establishing associations between cancer imaging features and gene expression to predict a patients risk of developing toxicity following radiotherapy.51, 52, 53 For example, Chang et al. proposed a framework of multiple residual convolutional neural networks to noninvasively predict isocitrate dehydrogenase genotype in grades IIIV glioma using multiinstitutional magnetic resonance imaging datasets. Besides, AI has been used in discovering radiogenomic associations in breast cancer,52 liver cancer,54 and colorectal cancer.53 Currently, limited data availability remains the most formidable challenge for AI radiogenomics.51

Knowing the response to therapy can help clinicians choose the right treatment plan. AI demonstrates potential applications in this area. For example, McDonald et al. trained a support vector machine using patients gene expression data to predict their response to chemotherapy. Their data show encouraging outcomes across multiple drugs.55 Sadanandam et al. proposed approaches of discovering patterns in gene sequences or molecular signatures that are associated with better outcomes following nontraditional treatment. Their findings may assist clinicians in selecting a treatment that is most likely to be effective.56 Although tremendous progress has been made using AI techniques and genomics to predict treatment outcome, more prospective and retrospective clinical research and clinical studies still need to be conducted to generate the data that can then train the algorithms.

Incorporating environmental considerations into management plans require sufficient personal and environmental information, which may affect a patients risk for a poor outcome, knowledge about care alternatives, and conditions under which each alternative may be optimal.

One such example has been the challenge of identifying homelessness in some patients.57, 58, 59 These patients may require care in varying locations over a short period, requiring frequent reassessments of patient demographic data. Related issues, such as transportation, providing medications that require refrigeration, or using diagnostic modalities that require electricity (for monitoring), need to be modified accordingly.

Another environmental consideration is the availability of expertise in remote locations, including the availability of trained professionals at the point of need. AI has provided numerous examples of augmenting diagnostic capabilities in resourcepoor locations, which may translate into better patient classification and therefore more personalized therapy planning. Examples include the use of deep learning to identify patients with malaria60 and cervical cancer,61 as well as predicting infectious disease outbreaks,62 environmental toxin exposure,63 and allergen load.64

Finally, in addition to genomic considerations and social determinants of health, clinical factors are imperative to successful therapy planning. Age, comorbidities, and organ function in particular predicate treatment considerations and AI has emerged as a central pillar in stratifying patients for therapy. In one study, machine learning classifiers were used to analyze 30 comorbidities to identify critically ill patients who will require prolonged mechanical ventilation and tracheostomy placement.65 Other studies have used AI algorithms to analyze bedside monitored adverse events and other clinical parameters to predict organ dysfunction and failure.66, 67

Actress Angelina Jolies response to her inheritance of the BRCA gene illustrates the potential impact of more advanced genomic information on disease risk and prevention options.68 This case is not novel; the case of Woodie Guthrie and Huntingtons disease disclosed a similar conundrum for health care.69 Although the ethics of genetic testing without a clear cure continues to be debated, the broad availability of genetic information offered by nextgeneration sequencing and directtoconsumer testing renders personalized prevention and management of serious diseases a reality.70

Cardiovascular medicine is an area with a long history of embracing predictive modeling to assess patient risk.71 Recent work has uncovered methods to predict heart failure72 and other serious cardiac events in asymptomatic individuals.73 When combined with personalized prevention strategies,74, 75 these models may positively impact disease incidence and sequela. Complex diseases, such as cardiovascular disease, often involve the interplay among gender, genetic, lifestyle, and environmental factors. Integrating these attributes requires attention to the heterogeneity of the data.76 AI approaches that excel at discovering complex relationships among a large number of factors provide such opportunities. A study from Vanderbilt demonstrated early examples of combining EHR and genetic data, with positive results in cardiovascular disease prediction.77 AIenabled recognition of phenotype features through EHR or images and matching those features with genetic variants may allow faster genetic disease diagnosis.78 For example, accurate and fast diagnosis for seriously ill infants that have a suspected genetic disease can be attained by using rapid wholegenome sequencing and NLPenabled automated phenotyping.79

Automated speech analytics have benefited from improvements in the technical performance of NLP, understanding, and generation. Automated speech analytics may provide indicators for assessment and detection of earlystage dementia, minor cognitive impairment, Parkinsons disease, and other mental disorders.80, 81, 82, 83 Efforts also are underway to detect changes in mental health using smartphone sensors.84

AIassisted monitoring may also be used in realtime to assess the risk of intrapartum stress during labor, guiding the decision of cesarean section vs. normal vaginal deliveries, in an effort to decrease perinatal complications and stillbirths.85 This exemplifies realtime AIassisted monitoring of streaming data to reduce manual error associated with human interpretation of cardiotocography data during childbirth.

AI is also being used in the detection and characterization of polyps in colonoscopy.86 Wider adaptation of AI during endoscopy may lead to a higher rate of benign adenoma detection and reduction of cost and risk for unwarranted polypectomy.87 AImediated image analysis aimed at improving disease risk prediction and diagnosis will likely continue to increase in use for detection of diabetic retinopathy88 and metastasis in cancer,89 as well as for identification of benign melanoma.90 AIbased image analysis has become a part of a directtoconsumer diagnostic tool for anemia as well.91

The widespread use of home monitoring and wearable devices has long been accompanied by the expectation that collected data could help detect disease at an earlier state. Indeed, these advances have fueled new, noninvasive, wearable applications for monitoring and detecting specific health conditions, such as diabetes, epilepsy, pain management, Parkinsons disease, cardiovascular disease, sleep disorders, and obesity.92 Digital biomarkers are expected to facilitate remote disease monitoring outside of the physical confines of a hospital setting and can support decentralized clinical trials.93 Wearable tools that provide continuous multidimensional measurements of preselected biomarkers would enable the detection of minimum residual disease and monitor disease progression.94 In the field of cancer care, evolving technology using wearable devices continuously analyzes circulating tumor cells to screen for relapsed disease.95

We have observed increasing efforts to implement AI in precision medicine to perform tasks such as disease diagnosis, predicting risk, and treatment response. Although most of these studies showed promising experimental results, how AI improved health care is not fully demonstrated. In reality, the success of transforming an AI system to a realworld application not only depends on the accuracy but also relies on the capability of working accurately in a reliable, safe, and generalizable manner.5 For example, the difference among institutions in coding definitions, report formats, or cohort diversity, may result in a model trained using onesite data to not work well in another site (https://www.bmj.com/content/368/bmj.l6927). Here, we highlighted three main challenges that would impact the success of transitions to realworld healthcare.

Fairness and bias. The health data can be biased while building and processing the dataset (e.g., a lack of diverse sampling, missing values, and imputation methods; https://datasociety.net/library/fairnessinprecisionmedicine/). An AI model trained on the data might amplify the bias and make nonfavorable decisions toward a particular group of people characterized by age, gender, race, geographic, or economic level. Such unconscious bias may harm clinical applicability and health quality. Thus, it is crucial to detect and mitigate the bias in data and models. Some potential solutions include improving the diversity of the data, such as the All of Us program that aimed to recruit participants with diverse backgrounds. AI communities also proposed several techniques to fight against bias (https://arxiv.org/abs/1908.09635). IBM has developed an online toolkit (AI Fairness 360) that implemented a comprehensive set of fairness metrics to help researchers examine the bias among datasets and models, and algorithms to mitigate bias in classifiers (https://doi.org/10.1147/JRD.2019.2942287). However, fairness and protected attributes are closely related to the domain context and applications. More work is needed in biomedical research to define and explore the fairness and bias in AI models trained with historical patient data. To address the challenge, a collaborative effort that involves the AI and biomedical community is needed.

Socioenvironmental factors. The environmental factors and workflows where the AI model would be deployed may impact model performance and clinical efficacy. A recent prospective study carried out by Google Health evaluated an AI system for screening diabetic retinopathy in a real clinical environment. The AI system was developed to augment diabetic retinopathy screening by providing intime assessment, before this the process may take several weeks. Despite a specialistlevel accuracy (>90% sensitivity and specificity) achieved on retrospective patient data; however, the system has undergone unexpected challenges when applied to Thailand clinics (https://doi.org/10.1145/3313831.3376718). For example, the variety of conditions and workflows in clinics impaired the quality of the images that did not meet the system' high standards, resulting in a high rejection rate of images. The unstable internet connection restricted the processing speed of the AI models and caused a longer waiting time for the patients. Travel and travel costs may deter participants from remaining in the study. Such prospective studies highlighted the importance of validating the AI models in the clinical environment and considering an iteration loopthat collects users feedback as new input for learning and system improvement96 before applying the AI system widely. Of note, in healthcare, obtaining such feedback would take a long time at a high cost. It may take a longer time to evaluate a therapys effect and associated longterm health outcomes than what is required to validate whether a product is appealing to a customer. There is a need to explore other ways to facilitate creation of highperforming AI systems, for example, generating synthetic data that carries similar distributions and variances as the realworld data, or leveraging a simulated environment. Early examples by groups, such as Baowaly and colleagues,89 demonstrate much promise, but more AI research efforts are needed.

Data safety and privacy. Data is crucial to an AIdriven system. As AI and precision medicine are converging, data (e.g., genomics, medical history, behaviors, and social data that covers peoples daily lives) will be increasingly collected and integrated. Individuals concerns for data privacy are closely related to trust when they use AIenabled services. Building a safe and wellcontrolled ecosystem for data storage, management, and sharing is essential, requiring new technology adoptions, and collaborations, as well as the creation of new regulations and business models.

The training of AI methods and validation of AI models using large data sets prior to applying the methods to personal data may address many of the challenges facing precision medicine today. The cited examples reinforce the importance of another potential use of augmented intelligence, namely that of the role of technology in the hands of consumers to help communicate justintime risk or as an agent of behavior change. Although most studies to date are small and the data are limited, the ability to identify atrisk patients will translate into personalized care when identification is combined with strategies to notify and intervene. Researchers are actively pursuing the use of mobile apps, wearables, voice assistants, and other technology to create personspecific interfaces to intelligent systems. A review of these approaches is beyond the scope of this paper.

Active research in both AI and precision medicine is demonstrating a future where healthrelated tasks of both medical professionals and consumers are augmented with highly personalized medical diagnostic and therapeutic information. The synergy between these two forces and their impact on the healthcare system aligns with the ultimate goal of prevention and early detection of diseases affecting the individual, which could ultimately decrease the disease burden for the public at large, and, therefore, the cost of preventable health care for all.

This work was funded by a partnership between IBM Watson Health and Vanderbilt University Medical Center.

Drs. Weeraratne, Rhee, and Snowdon are employed by IBM Watson Health. All other authors declared no competing interests for this work.

The authors thank Karlis Draulis for his assistance with the figures.

Go here to read the rest:
Precision Medicine, AI, and the Future of Personalized Health Care

Why precision medicine results in more effective health care, treatment plans  The Business Journals

View original post here:
Why precision medicine results in more effective health care, treatment plans - The Business Journals

Comprehensive Genomic Profiling at Diagnosis Extends Survival in Patients with Advanced Cancer  Inside Precision Medicine

Read more from the original source:
Comprehensive Genomic Profiling at Diagnosis Extends Survival in Patients with Advanced Cancer - Inside Precision Medicine

More Precise Classifications of NonClear Cell RCC Are Required to Improve Personalized Treatment  OncLive

See the original post here:
More Precise Classifications of NonClear Cell RCC Are Required to Improve Personalized Treatment - OncLive

Bahrain aims to provide residents with personalized healthcare  Healthcare IT News

Go here to read the rest:
Bahrain aims to provide residents with personalized healthcare - Healthcare IT News

Precision Medicine Market Is Expected To Reach Revenue Of  GlobeNewswire

Read this article:
Precision Medicine Market Is Expected To Reach Revenue Of - GlobeNewswire

New Graduates Leverage Genomics Education in Clinical and Research Settings  University of Colorado Anschutz Medical Campus

Read the original:
New Graduates Leverage Genomics Education in Clinical and Research Settings - University of Colorado Anschutz Medical Campus

Editors Note: This is the second article in a two-part series exploring the promise and limitations of the field of personalized medicine. The first part focused on advances and innovation in the field.

In the mid-1990s, researchers identified two gene mutations that are key to predicting genetic susceptibility to breast cancer: BRCA1 and BRCA2. In 1996, the BRCA1/2 mutation screening became the first genetic test for cancer risk available as a clinical service.

This genetic screening was an early innovation in a field that has come to be known as personalized medicine, which can be applied across a variety of medical specialties. Its defining characteristic is that a patients health care team takes into consideration a wide range of factors such as genetics, lifestyle, diet, specifics of disease presentation, and living environment when deciding on an individualized prevention or treatment plan.

With the advent of personalized medicine, including genetic screening as well as more targeted cancer drugs and therapies, the death rate for breast cancer in the United States declined by 43% from 1989 to 2020, according to the American Cancer Society (ACS). But even as mortality from breast cancer has decreased overall, there are statistics that highlight inequities in outcomes. Despite Black women having a lower incidence of breast cancer than non-Hispanic White women, Black women of all ages die from breast cancer at a 40% higher rate than non-Hispanic White women, and Black women under 50 years old die of breast cancer at twice the rate of non-Hispanic White women in the same age category.

Research shows that Black women get the BRCA1/2 screening less often than White women, at least in part because it is offered to them less frequently. One 2017 study found that, of women under 50 years old diagnosed with invasive breast cancer in Florida, 85.7% of the White women in the study were referred for genetic testing, while only 37% of the Black women were.

This is just one example of the inequities that some medical researchers and health equity advocates say severely limit the benefits of personalized medicine, even as technology advances.

[Personalized medicine] products are informative and are having an impact in certain communities, but its not equitable across all communities, says Rick Kittles, PhD, senior vice president for research at Morehouse School of Medicine, a historically Black medical college (HBCU) in Atlanta.

In the United States, people who are Black, Hispanic or Latino, American Indian or Alaska Native, people with low incomes, people who are uninsured or underinsured, and those who live in rural areas, as well as others who have been marginalized, face multiple barriers to personalized medicine. These barriers include a lack of inclusion of diverse genetics in research, the high cost of genetic testing and technology used in personalized medicine, and a lack of awareness of and education about personalized medicine among health care providers outside of urban medical centers. Some sociologists hypothesize that advances in medical innovation may, in fact, exacerbate existing inequities because people with economic and educational advantage are more likely to access care that improves lives and reduces mortality, while those from marginalized communities are left behind.

Its a problem that several academic medical centers are seeking to address with a range of strategies, from expanding personalized medicine research at HBCU medical schools to engaging community partners for research recruitment.

The field of human genetics has grown exponentially since the 2003 completion of the Human Genome Project, an international research effort that mapped the gene pairs that make up human DNA. The endeavor found that all humans share 99.9% of the same genome, with the other 0.1% accounting for all genetic diversity among individuals. Within that 0.1% are the wide variety of heritable traits, from physical characteristics to genetic mutations that cause or increase risk for certain diseases.

And yet, in the more than 6,000 genome-wide association studies (when researchers scan the genomes of large populations to try to identify genetic variations associated with diseases) that have been published internationally over the last two decades, 90% of all people analyzed were of European descent, according to a 2023 article in the Human Molecular Genetics journal.

This means that researchers have very little understanding of heritable disease risk for the vast majority of the worlds population when it differs from the variations seen in people of European descent.

Kittles, who is a genetic epidemiologist by trade, joined Morehouse in 2022 to lead the medical schools expanding efforts to advance medical research focusing on the inclusion of people from groups that have historically been excluded from clinical research and underserved in health care.

Among his faculty recruits is Melissa B. Davis, PhD, a genetics researcher focused on racial disparities in cancer who will lead the schools new Institute of Genomic Medicine. Davis previous work includes identifying two genes found in women of African ancestry that may increase their likelihood of developing an aggressive form of breast cancer, much like the BRCA1/2 gene.

For women of color who get tested [for BRCA 1/2], the benefit of that test is not equitable and in many cases the tests come back unknown, Kittles says. Thats because those variants [that are found in people of African descent] are not in databases Its a glaring, prime example of where we are in precision medicine right now.

The research expansion at Morehouse is funded by an $11.5 million grant from the Chan Zuckerberg Initiative (CZI, created by Facebook founder Mark Zuckerberg and his wife, Priscilla Chan) and is part of the charitable foundations larger Accelerate Precision Health program. CZI has granted equal funds to each of the nations three other HBCU medical schools: Charles Drew University College of Medicine in Los Angeles; Howard University College of Medicine in Washington, D.C.; and Meharry Medical College in Nashville.

When we think about the science we want to support, [we ask,] Who does the science? What science is being done? Who does the science serve? says Bil Clemons, PhD, science program officer for Diversity, Equity, and Inclusion in Science for CZI. Fundamentally, Is the science that were doing inclusive of everyone?

Most of the funding from CZI has gone to hiring faculty at HBCU medical schools to bolster their capacity to expand their research footprint over time, but its also funded the creation of new programs to train genetic counselors at Charles Drew University College of Medicine.

Kittles says that CZIs funding is instrumental to advancing research into genetic diversity and health disparities at HBCU medical schools, particularly because these institutions have often been overlooked for federal and philanthropic funding in the past.

That creates a disparity that not only limits the research impact of those institutions, but also the health of the communities that they serve, says Kittles. So much so that while all HBCUs have strong teaching experience, the development of research has been hampered because of the lack of funding and the ability to bring in talent who want to do research. The sustainability of research is limited because of that history.

In turn, thats set back progress in reducing health disparities, especially in Black communities and other communities of color, Kittles says, because HBCU medical schools tend to have more trust and access to those communities than many other medical centers.

Many academic medical centers historically have had a very strong disconnect with disparate communities, Kittles explains. The bulk of their research and the bulk of their patients are not diverse And so, when they do research, theyre limited in terms of their touch.

In addition to the efforts at the HBCU medical schools, dozens of medical centers are participating in the National Institutes of Health (NIH) All of Us research program, the goal of which is to build one of the largest and most diverse health databases in the world.

The All of Us program is studying patients social determinants of health, a phrase that refers to the various factors such as environment, socioeconomic status, access to healthy food, and access to health care that can affect health.

The NIH has funded and partnered with more than a dozen organizations to expand their reach into the communities that are historically underrepresented in biomedical research, including the American Association on Health and Disability; the National Alliance for Hispanic Health; and the National Baptist Convention, USA Inc.; among others. These organizations use their connections within marginalized communities to enroll and retain participants in the program. As of September 2021, the partners had helped enroll more than 400,000 participants, 80% of whom are from communities that are historically underrepresented in research. The study aims to provide a holistic picture of health by collecting samples of blood, urine, and saliva; physical measurements; electronic health records; health and family medical histories; information about lifestyles and communities; and data from wearable technologies, such as smartwatches, according to the NIH.

And while this and other endeavors are a step forward, Kittles says that all academic medical centers have a responsibility to resolve inequities in their own communities in order to truly make progress in advancing accessibility to personalized medicine.

In my career, Ive been at resource-rich [institutions], and resource-poor [institutions], and what I call community-rich and community-poor. Some had strong relationships with the community, and others had no trust from communities around them, says Kittles. When we talk about health equity, there has to be a commitment that goes beyond the window dressings and the social media tags that you see Part of that is bringing individuals into the institution that represent the communities that you want to benefit.

Here is the original post:
Personalized medicine is coming, but who will have access to it?

Also called: precision medicine or individualized medicine(Showmore)

personalized medicine, field of medicine in which decisions concerning disease prevention, diagnosis, and treatment are tailored to individual patients based on information derived from genetic and genomic data. Personalized medicine centres on the concept that information about a patients genes and genome allows physicians to make more informed and effective decisions about a patients care.This idea essentially is an extension of conventional medicine, in which one strategy is applied across all patients, without tailoring to personal genetic and genomic information.

The concept of personalized medicine, although not novel at the time, materialized in the 1990s, following advances in DNA sequencing technology, including automation and increased throughput. Out of those advances came efforts such as the Human Genome Project (HGP; 19902003), in which sequences of more than three billion base pairs of the human genome were elucidated and made available to researchers worldwide. Likewise, the International HapMap Project (200210), which identified genetic variations that contribute tohuman disease, provided researchers with the information needed to associate gene variants with specific diseases and disorders.

Those advances cast light on phenomena in medicine that had been observed for yearsfor example, that certain drugs are more effective in some patients and that, in response to certain medications, some patients experience unusually severe side effects. Progress in understanding the molecular factors underlying the influence of individual genetic constitution on disease and therapeutics was greatly aided by developments in pharmacogenetics and pharmacogenomicsthe study of genetic causes behind differences in how individuals respond to drugs and the study of how multiple variations within the genome affect responses to drug treatments, respectively. Using data derived from pharmacogenetics and pharmacogenomics, researchers were able to develop more objective and accurate tests fordisease diagnosis and for predicting how patients would respond to therapeutic agents. In some cases, researchers found, using genetic and other molecular data to inform diagnosis and treatment, that the development or outcome of certain diseases could be modified.

The emergence of personalized medicine was further facilitated by developments in the area of health information technology, which entails electronic processing and storage of patient data, and in the clinical uptake of personalized medicine, particularly through translational and clinical research. Advances in those areasespecially the implementation of electronic health records (EHRs), which store data on patient history, medications, test results, anddemographicswere critical to the integration of data derived from genetics and genomics research and clinical settings.

Personalized medicine is used in various ways to facilitate the prevention, diagnosis, and treatment of disease. For example, physicians can use information on family history of disease to assess a patients risk for a disease. In certain instances, family history can be used to determine whether a patient should undergo genetic testing and, based on that information, whether the individual would benefit from specific preventive measures. In the case of individuals with a family history of Lynch syndrome (a cause of hereditary colorectal cancer), for instance, detection of the causative mutation through genetic testing can be used to inform decisions about screening. For persons who carry the mutation, frequent and routine screening for evidence of precancerous lesions in the colon allows for early disease detection, which can be a lifesaving measure. Similarly, tests capable of detecting mutations in multiple genes at one time can assist in the early diagnosis of hereditary forms of breast cancer, ovarian cancer, and prostate cancer.

The term personalized medicine is sometimes considered to be synonymous with targeted therapy, a form of treatment centred on the use of drugs that target specific molecules involved in regulating the growth and spread of cancer.Among the first successful targeted therapies was the anticancerdrug imatinib, which istailored to patients with chronic myelogenousleukemia(CML) who carry anenzymecalled BCR-ABLtyrosinekinase, a protein produced by a cytogenetic abnormality known as the Philadelphiachromosome. Imatinib blocks the proliferation of CML cells that possess themutated kinase, effectively reversing the abnoramalitys cancerous effects.

Special offer for students! Check out our special academic rate and excel this spring semester!

Another example of personalized medicine applied to therapeutics is the use of genotyping to identify variations in enzymes that alter a patients sensitivity to the commonly prescribed anticoagulant drug warfarin. Information about variations in warfarin-metabolizing enzymes can be used to help guide decisions about the amount of the drug that a patient needs to receive in order to achieve the desired effect.

Personalized medicine faces significant challenges. For example, compared with the HGP reference sequence of the human genome, each individual persons genome houses roughly three to five million variations. Thus, attributing disease causation or therapeutic response to a given genetic variant requires careful analysis and interpretation across multiple disciplines. Moreover, genomes vary across geographic and ethnic populations and are influenced by environmental factors; thus, an individual variation identified within a given population may have very different impacts on disease in another population, based on ethnic or geographic factors.

Technological issues also continue to challenge advances in personalized medicine. The structure of EHR data, for example, can impact its utility. Access to and analysis of genomic data in EHRs may be limited by the presentation of genomic test results as a summary that includes relevant observations but excludes raw data and by the lack of information on details such as patient lifestyle and behaviour, which are essential to the accurateinterpretation of genomic information.

Various ethical issues are associated with personalized medicine. Of particular concern is that the majority of genomic studies historically have focused on populations of European descent, with significant underrepresentation of racial and ethnic minorities. This unevenness in representation can impact algorithms used to guide decisions about drug selection and dosing regimens, potentially resulting in ineffective treatment and poorer outcomes for patients whose genetic backgrounds and lifestyles differ from more thoroughly studied groups.

Other ethical issues surround privacy and security concerns, mainly involving the use of EHRs. For example, a breachin an EHR system could result in the release of personal information and health data as well as information about health care providers.Personalized medicine also carries high costs and therefore is potentially inaccessible for patients who lack health insurance and financially out of reach for less-developed countries with limited health resources.

Read more here:
Personalized medicine | Definition, Origins, Examples, & Ethical ...

In 2022, personalized medicines (PM) made up more than 34% of new therapeutics approved by the Food and Drug Administration (FDA). PM is defined by the Personalized Medicines Coalition as the use of diagnostic tests to determine which medical treatments will work best for each patient or [the] use [of] medical interventions to alter molecular mechanisms that impact health. This emerging approach to healthcare is growing rapidly and having an important impact on patients, practitioners, and health systems. While many healthcare providers have already discovered the value of PM for their patients, others may remain unsure or unconvinced. The primary rationale for using PM is that the standard-of-care therapy may work well for some patients, but for others, it may have lower efficacy or higher risk for side effects due to patients biological differences. , This is the subset of patients who could benefit the most from PM.

At USP, the Healthcare Quality & Safety (HQS) Center of Excellence has developed a science-based roadmap for personalized medicine that considers how USP can evolve standards to address new modalities of medicine and close important gaps in these treatments that help ensure quality patient care. Waypoints on this roadmap include examining established standards, building collaborations with key stakeholders, identifying volunteers for future DTx standards work at USP, holding roundtable discussions, drafting Stimuli articles, and developing workplan focus areas for the HQS Expert Committees. USP has a long-standing history of healthcare standards including those for compounding preparations that are tailored to meet the unique needs of patients who may not otherwise have access to their medications. Currently, USP is exploring other specialized areas within PM, including pharmacogenomics, digital therapeutics, and 3D-printed medications to name a few.

Pharmacogenomics

Pharmacogenomics (PGx) is the study of how a patients genes can affect drug therapy. From a sample of saliva, cheek cells, or blood, scientists can extract a patients DNA and sequence it to understand how that individuals genes are similar to or different from genes of other patients. The individuals genetic results are then considered by healthcare providers, in combination with other data about the patient and their medical condition, to select the most appropriate drug therapy for them.

Based on decades of research with populations around the globe, healthcare providers can now use PGx to make predictions of an individuals personalized response to medications. For example, PGx can help predict the amount of drug available in the patients body, which can determine both its therapeutic effect(s) and likelihood to cause side effects. This is based on the observation that multiple people who take the same dose of the same medication may metabolize, transport, bind, or otherwise interact with drugs differently, leading to different amounts of drug in their bodies.

PGx presents opportunities for USP to collaborate with key stakeholders who are already developing PGx standards and guidelines and to apply its standards-setting process to establish alignment and consistency in PGx standards. Specifically, some of the standards and guidelines that USP could help develop are the 1) naming of genetic biomarkers and PGx terminology, 2) labeling of medicines to incorporate PGx information, 3) incorporation of PGx into healthcare information technology such as electronic health records and clinical decision support, and 4) diversification of clinical trials so that PGx information is not limited to patients of common ancestries, such as those of European ancestry. USP is well-positioned to engage a wide audience while increasing the reliability of and confidence in the utilization of PGx. This will help support PGx implementation, including payment and reimbursement, both nationally and globally.

Digital Therapeutics

Digital therapeutics (DTx) are defined by the International Organization for Standardization as health software intended to treat or alleviate a disease, disorder, condition, or injury by generating and delivering a medical intervention that has a demonstrable positive therapeutic impact on a patients health. The DTx landscape is evolving rapidly, with more than 40 prescription digital therapeutics already available in the U.S. DTx is projected to have a cumulative annual growth rate of up to 25.4% in the U.S. market by 2030, and this growth is not limited to the U.S., as the global DTx market is expected to expand by 31.6% by 2027. Due to this burgeoning expansion, DTx products are increasingly in need of robust standards to underpin the creation of high-quality products and the delivery of comprehensive care on a large scale.

USP is actively engaged in researching the landscape of DTx and has started an investigation into opportunities for DTx standards to improve the quality of care provided to patients. USP has identified areas for potential standards that include establishing, or supporting the establishment of, global DTx definitions including outcomes used in clinical studies of DTx products. These potential standards may also include key aspects such as security and privacy, promoting awareness and education among healthcare providers and patients, adopting consistent labeling practices, setting standard technology proficiency requirements, addressing issues related to data integrity and code authenticity to deter counterfeit DTx products, facilitating interoperability among the various clinical systems used in healthcare around the world, and integrating DTx into healthcare delivery systems such as existing software and devices.

These elements would create a comprehensive framework for seamlessly integrating DTx within the healthcare industry, as well as for its development and regulation. USP is also maintaining awareness of the current position of DTx, understanding the potential role of standards in DTx product growth, identifying factors that can expedite progress, recognizing and addressing barriers, and determining the competencies needed to navigate the ever-changing, technology-driven market.

What comes next

USP is currently engaging with stakeholder leaders to develop Stimuli Articles related to its research involving potential standards for PGx and DTx. These future articles will feature in the USP-PF and will solicit public comment to promote stakeholder engagement. In the meantime, USP encourages interested parties to reach out for more information as USP approaches its new 2025-2030 cycle.

For further information about USPs work on personalized medicine, visit our webpage, sign up for the HQS newsletter, or contact:

Blaine GroatEmail: blaine.groat@usp.org

Yasmin HaidarbaigiEmail: Yasmin.haidarbaigi@usp.org

__________________________________

i Personalized Medicine Coalition. Personalized Medicine at FDA: the Scope & Significance of Progress in 2022.report.pdf (personalizedmedicinecoalition.org) Accessed November 10, 2023. Personalized Medicine Coalition.ii Personalized Medicine 101.https://www.personalizedmedicinecoalition.org/personalized-medicine-101/. Accessed November 6, 2023.iii Schork, N. Personalized medicine: Time for one-person trials. Nature 520, 609611 (2015).https://doi.org/10.1038/520609a.iv US Department of Health and Human Services. National Action Plan for Adverse Drug Event Prevention.Washington (DC): 2014; pharmacogenomics working group whose mission is to develop a 56.v International Organization for Standardization. "Health Informatics Personalized Digital Health DigitalTherapeutics Health Software Systems." ISO/TR 11147:Edition 1, 2023, https://www.iso.org/obp/ui/#iso:std:iso:tr:11147:ed-1:v1:en.vi Liesch J, Murphy D, Singh V. Under Pressure: Prescription Digital Therapeutics - How an analysis of the U.S. PDTlandscape indicates mounting pressure for a make-or-break next 3 years. Blue Matter. 2022.vii Digital Therapeutics Market Size, Share, and Analysis Report. Grand View Research, 2023. Accessed viahttps://www.grandviewresearch.com/industry-analysis/digital-therapeutics-market on Sep 05, 2023.viii Digital Therapeutics Market Revenue Forecast: Latest Industry Updates. Markets and Markets, 2023. Accessed via https://www.marketsandmarkets.com/Market-Reports/digital-therapeutics-market-51646724.html on Sep 05, 2023.

Link:
Innovating for Individual Care: The Impact of USP on Personalized Medicine

Live Cell Encapsulation Market To Reach USD 313.3 Million at a CAGR of 4% in 2032  EIN News

Continued here:
Live Cell Encapsulation Market To Reach USD 313.3 Million at a CAGR of 4% in 2032 - EIN News

Cancer Therapeutics and Biotherapeutics Market is estimated to be US$ 506.8 billion by 2032 with a CAGR of  EIN News

Go here to read the rest:
Cancer Therapeutics and Biotherapeutics Market is estimated to be US$ 506.8 billion by 2032 with a CAGR of - EIN News

Regenerative Therapies Market is Set to Grow at a CAGR of 8.7% by 2033, Propelled by Advancements in  EIN News

Continue reading here:
Regenerative Therapies Market is Set to Grow at a CAGR of 8.7% by 2033, Propelled by Advancements in - EIN News

Gene Therapy Market Size (USD 46.5 Bn by 2030): A Growing Industry and Its Impact on Healthcare Systems  EIN News

View original post here:
Gene Therapy Market Size (USD 46.5 Bn by 2030): A Growing Industry and Its Impact on Healthcare Systems - EIN News

Over the past two decades scientists have been working steadily to unravel the complex interplay of the microbiome, human health, and disease.

Our understanding of the microbiomeor the community of microorganisms that reside in the human bodyhas been greatly advanced thanks to the improvements in next-generation sequencing techniques, with methods such as amplicon and shotgun metagenomic sequencing allowing scientists to sequence microbial genetic material directly from a sample without having to culture and grow microbes in the lab. These methods have generated orders of magnitude more data than were available two decades ago. The explosion of data has led to a clearer understanding on the link between diet, the microbiome, and health; how early exposures shape future health outcomes; and even associations between disease states and microbiome composition.

Researchers have made great strides over the past 15 years in trying to understand how organisms may be contributing to disease, says Cynthia Sears, professor of medicine and oncology at Johns Hopkins University. She adds that for a subject like the microbiome, which is complex, diverse, and even varies between people, that is a pretty short timeline of discovery.

The next step is to bring this knowledge into the clinic. The race is on to develop prognostics, diagnostics, and even therapeutics with microbiome data. Many predict that next year will see the first FDA-approved microbiome-based therapeutics targeted against C. difficile, and others are rapidly developing diagnostics against a range of diseases, including cancer and irritable bowel syndrome. While there is much promise in the field, there are also considerable challenges.

One of the most active areas of research in the microbiome field is the effort to better understand the link between nutrition, gut microbes, and health. In 2011, a seminal study was published by researchers from the University of Pennsylvania that showed diet was directly related to gut microbiota composition. Researchers found that people could be grouped into categories, called enterotypes, based on the species and relative proportions of microbes in their gut. In addition, they showed that a shift in diet, from low fat/high fiber to high fat/low fiber, led to a significant change in the composition of their gut microbes. In some cases the changes happened within as little as 24 hours.

A few years later, researchers from the University of Pittsburgh furthered this line of research linking diet to colon cancer risk in African Americans. They wanted to understand why African Americans living in the U.S. had higher rates of colon cancer than Africans living in rural South Africa despite both groups being of African descent. In the study, the researchers replaced high fiber/low fat South African diet with the high fat/low fiber American diet. After only two weeks, they observed marked changes in inflammation and metabolic risk factors in the South African cohort. One change in particular was worrisome. The South African group seemed to lose a specific type of gut microbe that synthesizes the chemical butyrate, known to break down fiber and confer a lower risk of colon cancer. The butyrate levels in the South Africans plunged by as much as 2.5 times. The results demonstrated how changes in diet could impact disease risk through the microbiome.

These studies showed that changes brought on by diet can be quite quick and have a big impact on your metabolism, says Sears. We all have the capacity to improve health through our diet, she adds.

Another active area of investigation is early exposure to germs and development of disease later in life. The incidence of asthma and allergies has risen sharply over the past several decades as the population has shifted to an industrialized lifestyle, with better sanitation and medicines like antibiotics. Some postulate that cleaner living has reduced childrens exposure to germs and thus negatively impacted our immune systems.

Studies of children in Amish and Hutterite communities provide some of the best evidence for this idea. The two groups share a genetic ancestry, but the Amish have continued their traditional farming practices while the Hutterites have adopted a more modern and industrialized lifestyle. It has long been known that children who grow up on traditional farms, like the Amish, have a much lower incidence of asthma than the general population. In fact, asthma is four times lower among the Amish compared to their Hutterite counterparts. In addition, high concentrations of bacterial endotoxins, which are inflammation-producing sugars shed from bacterial cells, have been found in Amish households. And in studies using mice, inhalation of these endotoxins seemed sufficient to ward off asthma and allergies. Now, next-generation sequencing studies in urban American children have found an explanation in the microbiome.

In a study published in 2017, researchers collected and analyzed stool samples from 300 newborns in Detroit, Michigan, and followed the babies for several years. They were looking for signatures in the microbiome that could be linked to later developing asthma or allergies. After four years of follow up they found three distinct microbial signatures, each incurring a different risk profile for later developing asthma. The highest risk group had a low abundance of certain bacterial strains, a high abundance of fungi, and microbial metabolic outputs that cause inflammation.

Studies like this are just the beginning. In January of 2020, researchers at the Chinese University of Hong Kong announced one of the largest studies to date that will investigate these links. They launched a new study that aims to recruit 100,000 mother-baby pairs in the Greater Bay area in China to be followed for more than seven years. They hope to better characterize healthy and disease-promoting microbiomes. They also hope to develop biomarkers and identify risk factors for diseases like irritable bowel syndrome, obesity, and other immune system-related conditions.

One very active area of investigation is the prognostic value of the microbiome for new personalized cancer treatments like immunotherapies. Despite being one of the most promising new approaches to tackle cancer, immunotherapies dont work for everyone. Factors in the cancers genome, like the mutation burden, or even the hosts immune system can interfere with the success of the treatments, and many scientists are looking for ways to expand the population for which immunotherapies are effective.

Various studies have shown that the composition of the microbiome can have an impact on the efficacy of treatment, and there are dozens of clinical trials recruiting patients now to look for a link between microbiome and immunotherapy. Many hope that altering the microbiome concentration could be a way to improve current therapies. Researchers at the University Health Network in Toronto, for example, have launched a clinical trial that is evaluating the efficacy of gut microbes taken from healthy donors and given to cancer patients. The microbes are delivered in oral pill form, which is a safer alternative to fecal microbiota transplants alongside immunotherapy. If the trial is successful, it could mean that many more patients will be eligible for immunotherapy. It could also indicate that themicrobiome could be a tool for pronostic applications.

In 2020, research was published in the journal Nature showing the cancer diagnostic potential of the microbiome. A group of researchers from University of California San Diego made a surprising discovery: blood and tissue harbor their own communities of microbes. The finding overturned a long held belief that the blood is a sterile environment, consisting only of blood cells, platelets, and plasma. Furthermore, they showed that the specific composition of microbes could be linked to the tumor type a patient may harbor.

The team, led by Rob Knight from the University of California San Diego, collected tens of thousands of tumor and blood samples from patients with 33 different types of cancer. Using next-generation sequencing and machine-learning algorithms to analyze the DNA, they found that they could distinguish between healthy individuals and those with cancer just by looking at the composition of the microbial DNA in the blood. Furthermore, using the DNA signatures in the blood they could also tell the difference between tumor types. Some relationships were expected, such as the presence of human papillomavirus (HPV) and cervical, head, and neck cancers and Helicobacter pylori and stomach cancer, but the specificity of the community found beside one of these microbes were totally new associations.

It was a fantastic discovery, according to Sandrine Miller-Montgomery, co-author of the study and CEO of Micronoma, a microbiome-driven liquid biopsy start-up that was launched by the team who published the report. The microbiome in the breast tissue was completely different from the one in the colon tissue in the same way that the microbiome in the ocean differs from the microbiome in a prairie, and this was transpiring in the blood of the patients, making it possible to develop a minimally invasive clinical diagnostic tool, she said. Sears calls the paper a tour-de-force in trying to define the microbiome of different cancer types.

And research from other groups supports the findings as well. In January of 2022, the microbiome was added to the famous list of cancer hallmarks. The Hallmarks of Cancer, originally published in 2000, was meant to highlight the various things that need to happen for a normal cell to turn into a cancer cell. Other hallmarks have to do with genetic mutations, tissue invasion, and metastasis, for example. The addition of the microbiome to this list is a sign that has come of age as a significant contributor to disease.

And now the race is on to bring these advances into the clinic. In 2019, Knight, Miller-Mongomery, and their colleagues founded a startup called Micronoma, which hopes to develop blood-based assays that detect cancer microbial signatures in the blood. First up in their pipeline is a blood test for lung cancer. Lung cancer is the leading cause of cancer deaths and one of the reasons is that it is often caught too late, when it is difficult to treat. Micronoma hopes to develop a blood-based assay for early detection for this type of cancer and then later move on to others.

I think it is a really cool approach, says Sven Borchmann, cancer researcher at the University of Cologne in Germany. Borchmanns lab focuses on developing liquid biopsies. Borchmann published a study in 2021 detailing new links between bacteria, viruses, and cancer. Borchmann found many microbial species that were not known to colonize humans in his cohort of cancer patients, for example those with chronic lymphocytic leukemia.

Borchmann says there are still many challenges in using this data as a diagnostic. Chief among them are contamination and sensitivity. The test has to be extremely sensitive to pick up the minuscule amounts of circulating genetic material in the blood. He thinks instead that it would be a good tool to measure remission or even to match tumor types to personalized therapies.

Sears also worries about sensitivity with such tests and thinks this work and others like it are a very positive step forward but there are still many challenges in bringing microbiome-based assays to the clinic. In order to push the field forward, Sears advocates for larger and more rigorous studies. She says that studies today suffer from poor study designs that gather data at a single point in time, rather than through many time points over many years. She also says that the sample collection techniques vary greatly from study to study. In addition, DNA sequencing methods, sample storage procedures, and DNA extraction protocols are not standardized and often not fully reported in the studies. These discrepancies and omissions make it difficult to interpret and validate results across studies, she says.

Researchers at Harvard University showed just how variable the research could be when they sent microbiome samples to 15 different high-quality laboratories for analysis and found major discrepancies in results. They blamed the variation on the differing DNA extraction, sample prep, and bioinformatics techniques used. The bottom line is that very good labs can analyze samples and come up with different results because of their processes, she says. In order to correct these issues, Sears and others advocate for more transparency and standardization across the field.

Late last year, a consortium of microbiome researchers published a paper in Nature that outlined a checklist of reporting guidelines for microbiome research, called the STORMS checklist. It is a 17-item list that hopes to encourage more transparency and peer-review in the publishing of these papers.

Levi Waldron, epidemiologist at the CUNY Graduate School of Public Health and Health Policy in New York and senior author of the paper, says that microbiome research lacks standardization and oftentimes the reporting on the methods and analysis vary from one lab to another. The field is still so heterogenous that it can be hard to find the most basic things in a paper, he says. Waldron says that reviewing the papers is difficult because it is hard to check all the details that should be reported to make it a replicable paper. Waldron co-founded a consortium of researchers in 2020 that are working to standardize microbiome research in hopes of making the studies easier to evaluate and replicate.

I am very supportive of that paper, says Sears. She thinks that in order for the microbiome to make its way into the clinic it will need to tighten its reporting. She compares it to guidelines used in clinical trials called Consolidated Standards of Reporting Trials or CONSORT. The guidelines are a 25-item checklist of reporting guidelines that requires researchers to report on how the trial was designed, analyzed, and interpreted. Much like the STORMS checklist, it is meant to ensure transparency and it facilitate peer review.

The checklist is meant to encourage good habits in the field and urges scientists to report on the DNA extraction kit that was used, for example, and also on statistical methods used to avoid confounding factors. The STORMS consortium is actively working with journals to enact these guidelines for all studies. Some of the Nature journals have adopted these guidelines and others are considering them.

Despite the lack of guidelines and standardization, microbiome-based therapeutics are pushing ahead. There are at least three companies developing microbiome-based treatments for the intractable intestinal infection, C. difficile. The infection, most often occurring after use of antibiotics in hospitalized patients, affects roughly half a million people every year and is extremely difficult to treat. Antibiotics arent very effective against the infection and many patients end up with chronic and severe illness.

Fecal microbiota transplants have been very effective in fighting C. difficile in patients who dont respond to other treatments. And now, the FDA allows fecal transplants for patients with recurrent C. difficile but the method still comes with many limitations, such as difficulty in quality control.

In September, Seres Therapeutics completed its submission process for an application to the FDA for a product called SER-109, which is an oral therapeutic that consists of purified Firmicutes spores. Firmicutes is a butyrate-producing bacteria that resides in the gut and is known as a healthy microbe. The product is designed to repair the disrupted microbiome in patients with C. difficile.

C. diff takes advantage of the disrupted microbiome to cause disease, according to Casey Theriot, C. difficile researcher and associate professor at North Carolina State University. If you are going to create a therapeutic that targets and leverages the microbiome, I think you start here. she says.

Theriot is hopeful these therapies will take off but also cautions that there is still much work to do. She cautions that next-generation sequencing tools are great at telling scientists which microbes are present, but it doesnt necessarily tell you what they are doing. If youre looking at the metagenome of a gut, itll just tell you which bacteria and genes are present, but it wont tell you if which genes will eventually be made into proteins and or molecules, she says.

Theriot thinks its critical to use metabolomics, in addition to sequencing platforms, in order to understand the mechanism of how certain bacteria, or lack thereof, contributes to disease.

Tools like this, she says, will help scientists parse the complicated interplay between bacterial composition, metabolism, and disease and aid the development of therapeutics in more complicated diseases like irritable bowel syndrome.

Overcoming these challenges will be essential for the microbiome to come of age in clinical applications, but the field is closer than ever to realizing its potential. I think we are at that moment, where perhaps we can make all this information much more usable in the world of medicine, if we just take a little more time and care to document exactly what were doing, says Sears.

Monique Brouillette is a freelance journalist who covers science and health.

Here is the original post:
Translating the Microbiome - Inside Precision Medicine

Integrated approaches can help enhance recruitment plans.

Oncology accounts for 27% of all clinical trials conducted since 2017. Compared to other therapeutic areas, oncology trials are more resource-intensive and less efficient, requiring an average of 16 clinical sites to enroll a median of just 31 patients per study. In fact, average enrollment duration for oncology trials is two times longer than for all diseases combined (22 months vs. 11 months).1

Yet research interest remains high, with oncology accounting for 45% of all planned studies from Q4 2021 to Q4 2024. Nearly half of these are Phase II studies and almost 40% include countries in North America, while 35% are being conducted in Asia.2 As of July 2022, among the 19,700 new drugs in the pipeline, 6,731 (34%) are for cancer. This robust development activity spans over 20,000 organizations across 116 countries. Interestingly, 72% of these oncology trials are sponsored by companies outside of the top 50 pharmaceutical organizations and 67% are for rare and orphan disease indications.1

Among the more than 1,500 potential oncology biomarkers that have been identified in the preclinical setting, approximately 700 are involved in the active or planned clinical trials in oncology. Over 60% of these studies are for immuno-oncology drugs, with the remainder for targeted therapies.3

The rise of personalized medicine has been driven by biomarkers, which have enabled researchers to understand the science behind mechanism of action and have been used to target recruitment. More than one-third of all drugs approved by FDA since 2000 have been personalized medicines, demonstrating that biomarker-driven approaches help optimize treatment impact and improve patient outcomes.4 In fact, a recent analysis of 9,704 development programs from 2011 to 2020 found that trials employing preselection biomarkers have a two-fold higher likelihood of approval, driven by a nearly 50% Phase II success rate.3

The value of biomarkers is not limited to the clinical trial setting. Rather, biomarkers play a critical role throughout drug discovery and development, bridging preclinical and clinical studies. Incorporating biomarkers into programs requires careful choreographyfrom collecting biological samples and analyzing them in decentralized or specialty labs to generating data that will be integrated with other clinical information to support decision-making. It may also require a broad spectrum of logistics and laboratory management capabilities for handling a range of sample types.

Table 1 below provides a sampling of FDA-approved biomarker-driven therapies. A key challenge of integrating biomarkers into development programs is selecting the right biomarker. Often, the frequency of the biomarker of interest is very low. The same or similar biomarker may be present in multiple tumor types at varying frequencies, as is the case with HER2 amplifications in breast and gastric cancer. Biomarker frequency may also differ among races and ethnicitiesit may also change as the disease progresses. For example, EGFR exon 20 T790M alterations increase in frequency in patients with non-small cell lung cancer who have become resistant to previous lines of therapy. Consequently, selecting the right biomarker is akin to finding a needle in a haystack.

Precision for Medicine was involved in an oncology cell therapy study, where eligibility was based on the expression of two biomarkers. The first biomarker was expression of human leukocyte antigen (HLA)-A*02:01 and the second was a tumor type expressing a certain cell receptor on at least 80% of cancer cells. Precision for Medicine performed an analysis and found that the prevalence of HLA-A*02:01 varied among geographic regions, with a prevalence of 38.5% to 53.8% in Europe and 16.8% to 47.5% in North America (see Figure 1 below). Based on this finding, we recommended conducting this clinical trial in Europe.

Analysis of the expression of the cell receptor of interest showed that expression levels varied not only by tumor type, but even by subtype or demographic (see Table 2 below).

We used these findings to project the number of patients and samples that would need to be screened in order to enroll 36-40 study participants. Our assumptions were that 30% of patients screened would have HLA-A*02:01 expression and 10% of those patients would have 80% biomarker expression, and 50% of those would meet all the inclusion criteria for the study.

Based on these assumptions, it was determined that HLA analysis would need to be performed on approximately 2,500 blood samples and immunohistochemistry would need to be performed on about 750 tumor tissue samples to reach the enrollment target.

To increase the efficiency of this study, the Precision for Medicine team implemented various strategies for streamlining the recruitment process:

As oncology clinical research evolves toward personalized treatment of patients in niche populations, a biomarker-driven approach to drug discovery and development is required. With new biological targets frequently having a low level of prevalence, it is important for researchers and developers to look for more innovative approaches to patient identification.

Esther Mahillo, Vice President, Operational Strategy and Feasibility, Precision for Medicine

Read the rest here:
Enhancing Enrollment in Biomarker-Driven Oncology and Rare Disease Trials - Applied Clinical Trials Online

DUBLIN--(BUSINESS WIRE)--The "Cancer/Tumor Profiling Market Research Report by Technology, Cancer Type, Biomarker Type, Application, Region - Global Forecast to 2027 - Cumulative Impact of COVID-19" report has been added to ResearchAndMarkets.com's offering.

The Global Cancer/Tumor Profiling Market size was estimated at USD 9,872.31 million in 2021, USD 11,656.24 million in 2022, and is projected to grow at a CAGR 18.32% to reach USD 27,094.28 million by 2027.

Competitive Strategic Window:

The Competitive Strategic Window analyses the competitive landscape in terms of markets, applications, and geographies to help the vendor define an alignment or fit between their capabilities and opportunities for future growth prospects. It describes the optimal or favorable fit for the vendors to adopt successive merger and acquisition strategies, geography expansion, research & development, and new product introduction strategies to execute further business expansion and growth during a forecast period.

FPNV Positioning Matrix:

The FPNV Positioning Matrix evaluates and categorizes the vendors in the Cancer/Tumor Profiling Market based on Business Strategy (Business Growth, Industry Coverage, Financial Viability, and Channel Support) and Product Satisfaction (Value for Money, Ease of Use, Product Features, and Customer Support) that aids businesses in better decision making and understanding the competitive landscape.

Market Share Analysis:

The Market Share Analysis offers the analysis of vendors considering their contribution to the overall market. It provides the idea of its revenue generation into the overall market compared to other vendors in the space. It provides insights into how vendors are performing in terms of revenue generation and customer base compared to others. Knowing market share offers an idea of the size and competitiveness of the vendors for the base year. It reveals the market characteristics in terms of accumulation, fragmentation, dominance, and amalgamation traits.

The report provides insights on the following pointers:

1. Market Penetration: Provides comprehensive information on the market offered by the key players

2. Market Development: Provides in-depth information about lucrative emerging markets and analyze penetration across mature segments of the markets

3. Market Diversification: Provides detailed information about new product launches, untapped geographies, recent developments, and investments

4. Competitive Assessment & Intelligence: Provides an exhaustive assessment of market shares, strategies, products, certification, regulatory approvals, patent landscape, and manufacturing capabilities of the leading players

5. Product Development & Innovation: Provides intelligent insights on future technologies, R&D activities, and breakthrough product developments

The report answers questions such as:

1. What is the market size and forecast of the Global Cancer/Tumor Profiling Market?

2. What are the inhibiting factors and impact of COVID-19 shaping the Global Cancer/Tumor Profiling Market during the forecast period?

3. Which are the products/segments/applications/areas to invest in over the forecast period in the Global Cancer/Tumor Profiling Market?

4. What is the competitive strategic window for opportunities in the Global Cancer/Tumor Profiling Market?

5. What are the technology trends and regulatory frameworks in the Global Cancer/Tumor Profiling Market?

6. What is the market share of the leading vendors in the Global Cancer/Tumor Profiling Market?

7. What modes and strategic moves are considered suitable for entering the Global Cancer/Tumor Profiling Market?

Market Dynamics

Drivers

Restraints

Opportunities

Challenges

Companies Mentioned

For more information about this report visit https://www.researchandmarkets.com/r/1qunge

Read the rest here:
Global Cancer/Tumor Profiling Market Research Report to 2027 - Increasing Demand for Personalized Medicine Presents Opportunities -...

A Cambridge biotech that wants to develop tailor-made medicines for cancers and diseases of the immune system said Thursday it has raised $168 million in venture funding, bringing the startups total capital to $386 million after 16 months of recruiting investors.

Odyssey Therapeutics will use the money to advance a portfolio of drugs to treat solid tumors and immune disorders, such as inflammatory bowel disease, rheumatoid arthritis, and lupus, according to the startups founder and chief executive, Gary D. Glick. The firm of 160 employees is housed in temporary quarters on Binney Street in Kendall Square and plans to move to Sleeper Street in Bostons Seaport District in early December.

Odyssey has eight drug development programs that rely on the cutting-edge approach known as precision medicine. In contrast to the one-size-fits-all design of most drugs, precision medicine, sometimes called personalized medicine, takes into account differences in peoples genes, environments, and lifestyles when creating medications.

The startup is using artificial intelligence and machine learning, among other tools, to discover and develop drugs. And it is focusing on serious, common diseases with few, if any, effective treatments, unlike the Massachusetts biotechs working on medicines for rare disorders drugs that can carry six- and even seven-figure price tags.

Were not looking to market and commercialize in what has historically been thought of as rare or orphan diseases, Glick said.

The latest funding round, Odysseys second, was led by General Catalyst, a 22-year-old Cambridge-based venture capital firm. At least eight investors from the first round ponied up more money for the second, as did a number of new investors, which further validates our approach to therapeutic development, Glick said.

Glick is a serial biotech investor and former longtime chemistry professor at the University of Michigan who founded Boston-based Scorpion Therapeutics and previously co-founded Lycera, another privately held biotech, in Ann Arbor, Mich.

The chair of Odysseys board is Dr. Jeffrey Leiden, the former CEO of Boston-based Vertex Pharmaceuticals, who now serves as executive chairman of its board. Glick said he has known Leiden since around 2009, when the latter was managing director of Clarus Ventures and chaired Lyceras board. He considers Leiden a mentor, saying, There really is no better biotech executive than Jeff.

Leiden said Odyssey has quickly attracted investors because it possesses the three Ts: the team, the targets, and the technologies. It boasts seasoned drug hunters from Novartis and other companies, has pinpointed a promising raft of drug targets, and is deploying technologies ranging from artificial intelligence to the engineering of so-called small molecule drugs medicines made of small chemical molecules and typically dispensed as pills.

I get asked to join the boards of lots of companies, Leiden said. I said yes to this one, he added, because its a very unusual company.

Jonathan Saltzman can be reached at jonathan.saltzman@globe.com.

View post:
Cambridge biotech raises $168 million to fight cancer and other diseases - The Boston Globe

Medical oncologist Nina DAbreo, MD, an assistant professor in the Department of Medicine at NYU Long Island School of Medicine, treats people with varying stages of breast cancer. She also sees and counsels women with benign but high-risk breast disease, regarding options to lower their risk.

As medical director of the Breast Medical Oncology Program at Perlmutter Cancer Center at NYU Langone HospitalLong Island, Dr. DAbreo is involved in the creation and implementation of programs that can help at every stage of the continuum of breast cancer carefrom diagnosis to survivorship.

She discusses how she decided to become a doctor, how treatment for breast cancer has changed, and more.

I grew up in Mumbai, India, and my mother was a biology teacher, so science was her thing. She had wanted to become a physician, and for many reasons that wasnt feasible. Growing up, I was good at math, science, and biology, but math and physics were my strong suit. I thought eventually I would study engineering and work in an information technologybased job. My mother, however, wanted me to become a physician, and I was adamant that I wouldnt do that.

In India, the way higher education is structured, scores matter in qualifying exams. And I had scores that qualified me to get into one of the very prestigious medical schools there. Unknown to me at the time, my parents put in an application for medical school, for which, lo and behold, I got an interview. I promised them I would attend a few medical school classes and see how it would go. I attended engineering school for three months and medical school right after, just to see what it was like. After the first anatomy exam, I was hooked. There is a lot of physics involved with the human body. Pharmacology was fantastic, and physiology was really what drew me in. So, after the first three months, we had a short exam and I did well on that. I gave up my admission to engineering school and decided to go with medicine.

The most important advance is personalizing therapy, in which we refine treatment to suit an individual patients tumor biology. We have moved away from the one-size-fits-all style of treatment, which has been the history of breast cancer treatment through the ages, from the times of radical mastectomies to, finally, when medical oncology evolved. Traditionally, we gave a lot of chemotherapy to all patients. We still do this for some patients for the right reasons, but now we are personalizing therapy and tailoring it to fit both the patients biology and their clinical condition. This means that in some cases we add treatment when required and in some cases, we de-escalate. Personalized treatment has evolved in the 14 years that I have been in practice as a medical oncologist, and it continues to be refined.

I am the principal investigator in the NYU Langone network for two exciting cooperative group-led trials looking to optimize how we treat HER2-positive breast cancer. This is an aggressive form of the disease, for which we typically give patients multi-agent chemotherapy in combination with HER2-blocking antibodies before or after surgery. CompassHER2-pCR is a de-escalation trial led by the Eastern Cooperative Oncology Group that is using pathological complete response (pCR) to a single chemotherapy with HER2-targeted drugs given before surgery, to appropriately minimize the use of additional chemotherapy for patients who dont need it.

The second part of that trial, called CompassHER2 RD, is looking at optimizing treatment after surgery. This is for people who do not achieve a complete response and have residual disease (RD). Typically these patients would receive a HER2-targeted drug called TDM-1. In this trial, they can be escalated to TDM-1 in combination with another oral HER2-blocking drug. These are great examples of tailoring therapy based on tumor response so we dont over- or under-treat anyone.

Another area that I am intrigued by is using non-pharmacological approaches to improve cancer care. I think there is a growing interest in the idea that exercise is medicine. Not only can exercise make patients feel better, it can also improve cancer outcomes. We are developing an investigator-initiated project in collaboration with colleagues who are experts in the field of oncological rehabilitation at Perlmutter Cancer Center as well as another academic center, looking at adding exercise for patients with early-stage estrogen receptorpositive breast cancer. These patients will receive a short course of exercise before surgery to see whether that will ultimately impact their cancer outcomes. So this is another way of optimizing cancer therapy, but using exercisea nontoxic, non-pharmacological interventionin combination with hormonal therapy.

There are many success stories that are gratifying both professionally and personally.

Eight years ago, I treated a pregnant patient who was diagnosed with locally advanced HER2-positive breast cancer. At the time, she was underinsured and had trouble finding medical care. With our medical oncology team and our gynecologist we were able to successfully get her through her pregnancy. We now see her with her daughter, who was born right after the treatment, in the clinic. Watching her child grow over the years is extremely gratifying to me. For us, each follow-up visit is a sign of how far weve come and how we were able to, as a team, bring this patient successfully through a time of crisis.

Another story thats professionally gratifying concerns a patient who participated in a clinical trial that looked at using adjuvant therapy, which is treatment after surgery, in patients with triple-negative breast cancer. This was an escalation trial in which immunotherapy was added for patients who had residual disease after receiving chemotherapy and surgery. We were one of the few sites on Long Island offering the trial when it opened.

She came to us from Memorial Sloan Kettering Cancer Center (MSKCC). Even though this patient didnt have access to the trial at the time at MSKCC, her oncologist was able to direct her and she traveled to us and was successfully enrolled. She is now about three years out and is doing very, very well. It may not sound like a big deal, but it was a big leap of faith for this patient to leave MSKCC and come to us for treatment. That experience illustrated how oncology care is truly collaborative. Patients are able to find resources, and we are able to assist them thanks to an excellent network of support.

One advance is in patients who receive estrogen-driven therapy for long durations. We know that some patients receive therapy for 10 years, but there are technologies that might help us assess who among those patients really needs extended therapy. One way to do this is by analyzing circulating tumor DNA and identifying markers in the blood that can then predict whether the cancer is likely to recur. This is an evolving field. There are some applications of this science already in the clinic, for example, in colon cancer, and I think this will also be applicable to breast cancer to further refine how long we treat patients and when to change therapy.

Newer antibodydrug conjugates that are less toxic and use smaller doses of chemotherapy bound to a targeted drug are also on the horizon and may replace traditional chemotherapy.

A fascinating area is personalized vaccines. Breast cancer is also one of the tumor types where using the patients own immune system in many ways, including chimeric antigen receptor (CAR) T cells, is an area of promise. This is particularly relevant in patients with cancers like triple-negative breast cancer, where there are fewer effective treatments.

The one thing that has remained constant, regardless of all the advancements in treatment, is that we are still personalizing cancer care. Its not just that the treatments are personalized, but when someone comes to Perlmutter Cancer Center, they will be cared for by a very personal team, one that they can contact at any time. Our physicians, nurse practitioners, nurses, and medical assistants know the patient, and they become part of a family. This very one-on-one approach is what patients can expect when they see us. Like any other major cancer center, we have the ability to offer technology and cutting-edge trials, but its all done in a very personalized fashion.

The story that I shared about the patient who brings her daughter to clinic visits illustrates this. Patients become part of the family here. When she and her daughter come in, you can see that the entire team that treated her is happy to see her. We take that extra step of integrating the entire team in the patients care. We are responsive and available to patients, and we take great pride in making sure that the patients queries are answered in a timely fashion. While people can receive the same treatment anywhere, its this team approach with personal involvement that sets us apart.

Visit link:
Perlmutter Cancer Center Medical Oncologist Provides Personalized Care to People with Breast Cancer - NYU Langone Health