Clinical trial design in the era of precision medicine

Integrating AI in fighting advancing Alzheimer: diagnosis, prevention, treatment, monitoring, mechanisms, and clinical trials

The application of artificial intelligence (AI) in neurology is a growing field offering opportunities to improve accuracy of diagnosis and treatment of complicated neuronal disorders, plus fostering a deeper understanding of the aetiologies of these diseases through AI-based analyses of large omics data. The most common neurodegenerative disease, Alzheimer's disease (AD), is characterized by brain accumulation of specific pathological proteins, accompanied by cognitive impairment. In this review, we summarize the latest progress on the use of AI in different AD-related fields, such as analysis of neuroimaging data enabling early and accurate AD diagnosis; prediction of AD progression, identification of patients at higher risk and evaluation of new treatments; improvement of the evaluation of drug response using AI algorithms to analyze patient clinical and neuroimaging data; the development of personalized AD therapies; and the use of AI-based techniques to improve the quality of daily life of AD patients and their caregivers.

Evolving assessment pathways for precision oncology medicines to improve patient access: a tumor-agnostic lens

BACKGROUND

Genomic and molecular alterations are increasingly important in cancer diagnosis, and scientific advances are opening new treatment avenues. Precision oncology (PO) uses a patient's genomic profile to determine optimal treatment, promising fewer side effects and higher success rates. Within PO, tumor-agnostic (TA) therapies target genomic alterations irrespective of tumor location. However, traditional value frameworks and approval pathways pose challenges which may limit patient access to PO therapies.

OBJECTIVES

This study describes challenges in assessing PO and TA medicines, explores possible solutions, and provides actionable recommendations to facilitate an iterative life-cycle assessment of these medicines.

METHODS

After reviewing the published literature, we obtained insights from key stakeholders and European experts across a range of disciplines, through individual interviews and an industry workshop. The research was guided and refined by an international expert committee through 2 sounding board meetings.

RESULTS

The current challenges faced by PO and TA medicines are multiple and can be demonstrated through real-world examples of the current barriers and opportunities. A life-cycle approach to assessment should be taken, including key actions at the early stages of evidence generation, regulatory and reimbursement stage, as well as payment and adoption solutions that make use of the evolving evidence base. Working toward these solutions to maximize PO medicine value is a shared responsibility and stands to benefit all stakeholders.

CONCLUSIONS

Our call to action is to expand access to comprehensive genomic testing, foster a learning health care system, enable fast and equitable access to cost-effective treatments, and ultimately improve health outcomes.

Digital biomarkers: 3PM approach revolutionizing chronic disease management - EPMA 2024 position

Non-communicable chronic diseases (NCDs) have become a major global health concern. They constitute the leading cause of disabilities, increased morbidity, mortality, and socio-economic disasters worldwide. Medical condition-specific digital biomarker (DB) panels have emerged as valuable tools to manage NCDs. DBs refer to the measurable and quantifiable physiological, behavioral, and environmental parameters collected for an individual through innovative digital health technologies, including wearables, smart devices, and medical sensors. By leveraging digital technologies, healthcare providers can gather real-time data and insights, enabling them to deliver more proactive and tailored interventions to individuals at risk and patients diagnosed with NCDs. Continuous monitoring of relevant health parameters through wearable devices or smartphone applications allows patients and clinicians to track the progression of NCDs in real time. With the introduction of digital biomarker monitoring (DBM), a new quality of primary and secondary healthcare is being offered with promising opportunities for health risk assessment and protection against health-to-disease transitions in vulnerable sub-populations. DBM enables healthcare providers to take the most cost-effective targeted preventive measures, to detect disease developments early, and to introduce personalized interventions. Consequently, they benefit the quality of life (QoL) of affected individuals, healthcare economy, and society at large. DBM is instrumental for the paradigm shift from reactive medical services to 3PM approach promoted by the European Association for Predictive, Preventive, and Personalized Medicine (EPMA) involving 3PM experts from 55 countries worldwide. This position manuscript consolidates multi-professional expertise in the area, demonstrating clinically relevant examples and providing the roadmap for implementing 3PM concepts facilitated through DBs.

Disparities in clinical trial enrollment among patients with gastrointestinal cancer relative to minority-serving and safety-netting hospitals

BACKGROUND

For results to be generalizable to all patients with cancer, clinical trials need to include a diverse patient demographic that is representative of the general population. We sought to characterize the effect of receiving care at a minority-serving hospital (MSH) and/or safety-net hospital on clinical trial enrollment among patients with gastrointestinal (GI) malignancies.

METHODS

Adult patients with GI cancer who underwent oncologic surgery and were enrolled in institutional-/National Cancer Institute-funded clinical trials between 2012 and 2019 were identified in the National Cancer Database. Multivariable regression was used to assess the relationship between MSH and safety-net status relative to clinical trial enrollment.

RESULTS

Among 1,112,594 patients, 994,598 (89.4%) were treated at a non-MSH, whereas 117,996 (10.6%) were treated at an MSH. Only 1857 patients (0.2%) were enrolled in a clinical trial; most patients received care at a non-MSH (1794 [96.6%]). On multivariable analysis, the odds of enrollment in a clinical trial were markedly lower among patients treated at an MSH vs non-MSH (odds ratio [OR], 0.32; 95% CI, 0.22-0.46). In addition, even after controlling for receipt of care at MSH, Black patients remained at lower odds of enrollment in a clinical trial than White patients (OR, 0.57; 95% CI, 0.45-0.73; both P < .05).

CONCLUSION

Overall, clinical trial participation among patients with GI cancer was extremely low. Patients treated at an MSH and high safety-net burden hospitals and Black individuals were much less likely to be enrolled in a clinical trial. Efforts should be made to improve trial enrollment and address disparities in trial representation.

Anticancer drugs: How to select small molecule combinations?

Small molecules are at the forefront of anticancer therapies. Successive treatments with single molecules incur drug resistance, calling for combination. Here, we explore the tough choices oncologists face - not just which drugs to use but also the best treatment plans, based on factors such as target proteins, pathways, and gene expression. We consider the reality of cancer's disruption of normal cellular processes, highlighting why it's crucial to understand the ins and outs of current treatment methods. The discussion on using combination drug therapies to target multiple pathways sheds light on a promising approach while also acknowledging the hurdles that come with it, such as dealing with pathway crosstalk. We review options and provide examples and the mechanistic basis, altogether providing the first comprehensive guide to combinatorial therapy selection.

Targeting PI3K/AKT/mTOR signaling to overcome drug resistance in cancer

This review comprehensively explores the challenge of drug resistance in cancer by focusing on the pivotal PI3K/AKT/mTOR pathway, elucidating its role in oncogenesis and resistance mechanisms across various cancer types. It meticulously examines the diverse mechanisms underlying resistance, including genetic mutations, feedback loops, and microenvironmental factors, while also discussing the associated resistance patterns. Evaluating current therapeutic strategies targeting this pathway, the article highlights the hurdles encountered in drug development and clinical trials. Innovative approaches to overcome resistance, such as combination therapies and precision medicine, are critically analyzed, alongside discussions on emerging therapies like immunotherapy and molecularly targeted agents. Overall, this comprehensive review not only sheds light on the complexities of resistance in cancer but also provides a roadmap for advancing cancer treatment.

High-throughput molecular assays for inclusion in personalised oncology trials - State-of-the-art and beyond

In the last decades, the development of high-throughput molecular assays has revolutionised cancer diagnostics, paving the way for the concept of personalised cancer medicine. This progress has been driven by the introduction of such technologies through biomarker-driven oncology trials. In this review, strengths and limitations of various state-of-the-art sequencing technologies, including gene panel sequencing (DNA and RNA), whole-exome/whole-genome sequencing and whole-transcriptome sequencing, are explored, focusing on their ability to identify clinically relevant biomarkers with diagnostic, prognostic and/or predictive impact. This includes the need to assess complex biomarkers, for example microsatellite instability, tumour mutation burden and homologous recombination deficiency, to identify patients suitable for specific therapies, including immunotherapy. Furthermore, the crucial role of biomarker analysis and multidisciplinary molecular tumour boards in selecting patients for trial inclusion is discussed in relation to various trial concepts, including drug repurposing. Recognising that today's exploratory techniques will evolve into tomorrow's routine diagnostics and clinical study inclusion assays, the importance of emerging technologies for multimodal diagnostics, such as proteomics and in vivo drug sensitivity testing, is also discussed. In addition, key regulatory aspects and the importance of patient engagement in all phases of a clinical trial are described. Finally, we propose a set of recommendations for consideration when planning a new precision cancer medicine trial.

Decoding the neurotoxic effects of propofol: insights into the RARα-Snhg1-Bdnf regulatory cascade

The potential neurotoxic effects of propofol, an extensively utilized anesthetic, underline the urgency to comprehend its influence on neuronal health. Insights into the role of the retinoic acid receptor-α, small nucleolar RNA host gene 1, and brain-derived neurotrophic factor (RARα-Snhg1-Bdnf) network can offer significant advancements in minimizing these effects. The study targets the exploration of the RARα and Snhg1 regulatory network's influence on Bdnf expression in the realm of propofol-induced neurotoxicity. Harnessing the Gene Expression Omnibus (GEO) database and utilizing JASPAR and RNA-Protein Interaction Prediction (RPISeq) database for projections, the study embarks on an in-depth analysis employing both in vitro and in vivo models. The findings draw a clear link between propofol-induced neurotoxicity and the amplification of RAR signaling pathways, impacting hippocampal development and apoptosis and leading to increased RARα and Snhg1 and decreased Bdnf. Propofol is inferred to accentuate neurotoxicity by heightening RARα and Snhg1 interactions, culminating in Bdnf suppression.NEW & NOTEWORTHY This study aimed to decode propofol's neurotoxic effects on the regulatory cascade, provide insights into the RARα-Snhg1-Bdnf interaction, apply extensive validation techniques, provide a detailed analysis and exploration of propofol's neurotoxicity, and offer a comprehensive approach to understanding molecular interactions.

Using Bayesian statistics in confirmatory clinical trials in the regulatory setting: a tutorial review

Bayesian statistics plays a pivotal role in advancing medical science by enabling healthcare companies, regulators, and stakeholders to assess the safety and efficacy of new treatments, interventions, and medical procedures. The Bayesian framework offers a unique advantage over the classical framework, especially when incorporating prior information into a new trial with quality external data, such as historical data or another source of co-data. In recent years, there has been a significant increase in regulatory submissions using Bayesian statistics due to its flexibility and ability to provide valuable insights for decision-making, addressing the modern complexity of clinical trials where frequentist trials are inadequate. For regulatory submissions, companies often need to consider the frequentist operating characteristics of the Bayesian analysis strategy, regardless of the design complexity. In particular, the focus is on the frequentist type I error rate and power for all realistic alternatives. This tutorial review aims to provide a comprehensive overview of the use of Bayesian statistics in sample size determination, control of type I error rate, multiplicity adjustments, external data borrowing, etc., in the regulatory environment of clinical trials. Fundamental concepts of Bayesian sample size determination and illustrative examples are provided to serve as a valuable resource for researchers, clinicians, and statisticians seeking to develop more complex and innovative designs.

[Precision oncology and molecular tumor boards]

Precision oncology is a field of personalized medicine in which tumor biology forms the basis for tailored treatments. The preferred approach currently applied in clinical practice is based on the concept of malignant tumors as genetic diseases that are caused by mutations in oncogenes and tumor suppressors. On the one hand, these can be targeted by molecular drugs, while on the other hand, next-generation sequencing allows for comprehensive analysis of all relevant aberrations, thus enabling the matching of appropriate treatments across entities based on molecular information. Rational molecular therapies are developed and annotated with supporting evidence by molecular tumor boards, which have been established at various academic centers in recent years. Advancing precision oncology to a new standard of care requires improved applicability of personalized molecular therapies and thorough scientific evaluation of precision oncology programs.

Precision Endpoints for Contemporary Precision Oncology Trials

SUMMARY

Traditional endpoints such as progression-free survival and overall survival do not fully capture the pharmacologic and pharmacodynamic effects of a therapeutic intervention. Incorporating mechanism-driven biomarkers and validated surrogate proximal endpoints can provide orthogonal readouts of anti-tumor activity and delineate the relative contribution of treatment components on an individual level, highlighting the limitation of solely relying on aggregated readouts from clinical trials to facilitate go/no-go decisions for precision therapies.

Integration of liposomal irinotecan in the first-line treatment of metastatic pancreatic cancer: try to do not think about the white bear

The approval of novel therapeutic agents remains widely reliant on evidence derived from large phase III randomized controlled trials. Liposomal irinotecan (ONIVYDE®) stands out as the only drug that has demonstrated improved survival both as a first-line therapy in combination with oxaliplatin and 5-fluorouracil/leucovorin (5FU/LV) (NALIRIFOX) compared to the standard gemcitabine plus nab-paclitaxel in the NAPOLI3 trial, and as a second-line treatment in combination with 5FU/LV compared to the standard 5FU/LV in the NAPOLI1 trial. However, just as the white bear of the Dostoevsky's paradox, the judgment of these results is invariably distracted by the intrusive thought of how different they might be if compared to similar regimens containing standard-free irinotecan as FOLFIRINOX or FOLFIRI, respectively. Here, we present and thoroughly discuss the evidence encompassing the pharmacologic, preclinical, and clinical development of liposomal irinotecan that can dispel any intrusive thoughts and foster a rational and well-considered judgment of this agent and its potential integration into the therapeutic strategies for pancreatic ductal adenocarcinoma.

A Review of Practice-Changing Therapies in Oncology in the Era of Personalized Medicine

In the past decade, a lot of insight was gathered into the composition of the host and tumor factors that promote oncogenesis and treatment resistance. This in turn has led to the ingenious design of multiple new classes of drugs, which have now become the new standards of care in cancer therapy. These include novel antibody-drug conjugates, chimeric antigen receptor T cell therapies (CAR-T), and bispecific T cell engagers (BitTE). Certain host factors, such as the microbiome composition, are also emerging not only as biomarkers for the response and toxicity to anti-cancer therapies but also as potentially useful tools to modulate anti-tumor responses. The field is slowly moving away from one-size-fits-all treatment options to personalized treatments tailored to the host and tumor. This commentary aims to cover the basic concepts associated with these emerging therapies and the promises and challenges to fight cancer.

A review on advancements in the application of starch-based nanomaterials in biomedicine: Precision drug delivery and cancer therapy

The burgeoning field of starch-based nanomaterials in biomedical applications has perceived notable progressions, with a particular emphasis on their pivotal role in precision drug delivery and the inhibition of tumor growth. The complicated challenges in current biomedical research require innovative approaches for improved therapeutic outcomes, prompting an exploration into the possible of starch-based nanomaterials. The conceptualization of this review emerged from recognizing the need for a comprehensive examination of the structural attributes, versatile properties, and mechanisms underlying the efficiency of starch-based nanomaterials in inhibiting tumor growth and enabling targeted drug delivery. This review delineates the substantial growth in utilizing starch-based nanomaterials, elucidating their small size, high surface-volume ratio, and biocompatibility, predominantly emphasizing their possible to actively recognize cancer cells, deliver anticancer drugs, and combat tumors efficiently. The investigation of these nanomaterials encompasses to improving biocompatibility and targeting specific tissues, thereby contributing to the evolving landscape of precision medicine. The review accomplishes by highlighting the auspicious strategies and modern developments in the field, envisioning a future where starch-based nanomaterials play a transformative role in molecular nanomaterials, evolving biomedical sciences. The translation of these advancements into clinical applications holds the potential to revolutionize targeted drug delivery and expand therapeutic outcomes in the realm of precision medicine.

Evolution of Precision Oncology, Personalized Medicine, and Molecular Tumor Boards

With multiple molecular targeted therapies available for patients with cancer that correspond to a specific genetic alteration, the selection of the best treatment is essential to ensure therapeutic efficacy. Molecular tumor boards (MTBs) play a key role in this process to deliver personalized medicine to patients with cancer in a multidisciplinary manner. Historically, personalized medicine has been offered to patients with advanced cancer, but the incorporation of molecular targeted therapies and immunotherapy into the perioperative setting requires clinicians to understand the role of the MTB. Evidence is accumulating to support feasibility and survival benefit in patients treated with matched therapy.

Natural products for combating multidrug resistance in cancer

Cancer cells frequently develop resistance to chemotherapeutic therapies and targeted drugs, which has been a significant challenge in cancer management. With the growing advances in technologies in isolation and identification of natural products, the potential of natural products in combating cancer multidrug resistance has received substantial attention. Importantly, natural products can impact multiple targets, which can be valuable in overcoming drug resistance from different perspectives. In the current review, we will describe the well-established mechanisms underlying multidrug resistance, and introduce natural products that could target these multidrug resistant mechanisms. Specifically, we will discuss natural compounds such as curcumin, resveratrol, baicalein, chrysin and more, and their potential roles in combating multidrug resistance. This review article aims to provide a systematic summary of recent advances of natural products in combating cancer drug resistance, and will provide rationales for novel drug discovery.

Tumor-agnostic baskets to N-of-1 platform trials and real-world data: Transforming precision oncology clinical trial design

Choosing the right drug(s) for the right patient via advanced genomic sequencing and multi-omic interrogation is the sine qua non of precision cancer medicine. Traditional cancer clinical trial designs follow well-defined protocols to evaluate the efficacy of new therapies in patient groups, usually identified by their histology/tissue of origin of their malignancy. In contrast, precision medicine seeks to optimize benefit in individual patients, i.e., to define who benefits rather than determine whether the overall group benefits. Since cancer is a disease driven by molecular alterations, innovative trial designs, including biomarker-defined tumor-agnostic basket trials, are driving ground-breaking regulatory approvals and deployment of gene- and immune-targeted drugs. Molecular interrogation further reveals the disruptive reality that advanced cancers are extraordinarily complex and individually distinct. Therefore, optimized treatment often requires drug combinations and N-of-1 customization, addressed by a new generation of N-of-1 trials. Real-world data and structured master registry trials are also providing massive datasets that are further fueling a transformation in oncology. Finally, machine learning is facilitating rapid discovery, and it is plausible that high-throughput computing, in silico modeling, and 3-dimensional printing may be exploitable in the near future to discover and design customized drugs in real time.

Hot topics in allergen immunotherapy, 2023: Current status and future perspective

The importance of allergen immunotherapy (AIT) is multifaceted, encompassing both clinical and quality-of-life improvements and cost-effectiveness in the long term. Key mechanisms of allergen tolerance induced by AIT include changes in memory type allergen-specific T- and B-cell responses towards a regulatory phenotype with decreased Type 2 responses, suppression of allergen-specific IgE and increased IgG1 and IgG4, decreased mast cell and eosinophil numbers in allergic tissues and increased activation thresholds. The potential of novel patient enrolment strategies for AIT is taking into account recent advances in biomarkers discoveries, molecular allergy diagnostics and mobile health applications contributing to a personalized approach enhancement that can increase AIT efficacy and compliance. Artificial intelligence can help manage and interpret complex and heterogeneous data, including big data from omics and non-omics research, potentially predict disease subtypes, identify biomarkers and monitor patient responses to AIT. Novel AIT preparations, such as synthetic compounds, innovative carrier systems and adjuvants, are also of great promise. Advances in clinical trial models, including adaptive, complex and hybrid designs as well as real-world evidence, allow more flexibility and cost reduction. The analyses of AIT cost-effectiveness show a clear long-term advantage compared to pharmacotherapy. Important research questions, such as defining clinical endpoints, biomarkers of patient selection and efficacy, mechanisms and the modulation of the placebo effect and alternatives to conventional field trials, including allergen exposure chamber studies are still to be elucidated. This review demonstrates that AIT is still in its growth phase and shows immense development prospects.

Preclinical Development of Antisense Oligonucleotides to Rescue Aberrant Splicing Caused by an Ultrarare ABCA4 Variant in a Child with Early-Onset Stargardt Disease

Precision medicine is rapidly gaining recognition in the field of (ultra)rare conditions, where only a few individuals in the world are affected. Clinical trial design for a small number of patients is extremely challenging, and for this reason, the development of N-of-1 strategies is explored to accelerate customized therapy design for rare cases. A strong candidate for this approach is Stargardt disease (STGD1), an autosomal recessive macular degeneration characterized by high genetic and phenotypic heterogeneity. STGD1 is caused by pathogenic variants in ABCA4, and amongst them, several deep-intronic variants alter the pre-mRNA splicing process, generally resulting in the insertion of pseudoexons (PEs) into the final transcript. In this study, we describe a 10-year-old girl harboring the unique deep-intronic ABCA4 variant c.6817-713A>G. Clinically, she presents with typical early-onset STGD1 with a high disease symmetry between her two eyes. Molecularly, we designed antisense oligonucleotides (AONs) to block the produced PE insertion. Splicing rescue was assessed in three different in vitro models: HEK293T cells, fibroblasts, and photoreceptor precursor cells, the last two being derived from the patient. Overall, our research is intended to serve as the basis for a personalized N-of-1 AON-based treatment to stop early vision loss in this patient.

Generating and using real-world data: A worthwhile uphill battle

The precision oncology paradigm challenges the feasibility and data generalizability of traditional clinical trials. Consequently, an unmet need exists for practical approaches to test many subgroups, evaluate real-world drug value, and gather comprehensive, accessible datasets to validate novel biomarkers. Real-world data (RWD) are increasingly recognized to have the potential to fill this gap in research methodology. Established applications of RWD include informing disease epidemiology, pharmacovigilance, and healthcare quality assessment. Currently, concerns regarding RWD quality and comprehensiveness, privacy, and biases hamper their broader application. Nonetheless, RWD may play a pivotal role in supplementing clinical trials, enabling conditional reimbursement and accelerated drug access, and innovating trial conduct. Moreover, purpose-built RWD repositories may support the extension or refinement of drug indications and facilitate the discovery and validation of new biomarkers. This perspective explores the potential of leveraging RWD to advance oncology, highlights its benefits and challenges, and suggests a path forward in this evolving field.

Identification of potentially actionable genetic variants in epithelial ovarian cancer: a retrospective cohort study

Ovarian cancer is the most lethal gynecologic malignancy, mainly due to late-stage diagnosis, frequent recurrences, and eventually therapy resistance. To identify potentially actionable genetic variants, sequencing data of 351 Belgian ovarian cancer patients were retrospectively captured from electronic health records. The cohort included 286 (81%) patients with high-grade serous ovarian cancer, 17 (5%) with low-grade serous ovarian cancer, and 48 (14%) with other histotypes. Firstly, an overview of the prevalence and spectrum of the BRCA1/2 variants highlighted germline variants in 4% (11/250) and somatic variants in 11% (37/348) of patients. Secondly, application of a multi-gene panel in 168 tumors revealed a total of 214 variants in 28 genes beyond BRCA1/2 with a median of 1 (IQR, 1-2) genetic variant per patient. The ten most often altered genes were (in descending order): TP53, BRCA1, PIK3CA, BRCA2, KRAS, ERBB2 (HER2), TERT promotor, RB1, PIK3R1 and PTEN. Of note, the genetic landscape vastly differed between the studied histotypes. Finally, using ESCAT the clinical evidence of utility for every genetic variant was scored. Only BRCA1/2 pathogenic variants were classified as tier-I. Nearly all patients (151/168; 90%) had an ESCAT tier-II variant, most frequently in TP53 (74%), PIK3CA (9%) and KRAS (7%). In conclusion, our findings imply that although only a small proportion of genetic variants currently have direct impact on ovarian cancer treatment decisions, other variants could help to identify novel (personalized) treatment options to address the poor prognosis of ovarian cancer, particularly in rare histotypes.

From osimertinib to preemptive combinations

Here, I suggest that while first-line osimertinib extends median progression-free survival (PFS) in EGFR-mutant lung cancer compared to first-generation TKIs, it reduces individual PFS in 15-20% of patients compared to first-generation TKIs. Since detecting a single resistant cell before treatment is usually impossible, osimertinib must be used in all patients as a first-line treatment, raising median PFS overall but harming some. The simplest remedy is a preemptive combination (PC) of osimertinib and gefitinib. A comprehensive PC (osimertinib, afatinib/gefitinib, and capmatinib) could dramatically increase PFS for 80% of patients compared to osimertinib alone, without harming anyone. This article also explores PCs for MET-driven lung cancer.

Addressing disparities in cancer clinical trials: a roadmap to more equitable accrual

The Georgia Center for Oncology Research and Education (Georgia CORE) and the Georgia Society of Clinical Oncology (GASCO) held a one-day summit exploring opportunities and evidence-based interventions to address disparities in cancer clinical trials. The purpose of the summit was to identify clear and concise recommendations aimed at decreasing clinical trial accrual disparities in Georgia for rural and minority populations. The summit included expert presentations, panel discussions with leaders from provider organizations throughout Georgia, and breakout sessions to allow participants to critically discuss the information presented. Over 120 participants attended the summit. Recognizing the need for evidence-based interventions to improve clinical trial accrual among rural Georgians and persons of color, summit participants identified four key areas of focus that included: improving clinical trial design, providing navigation for all, enhancing public education and awareness of cancer clinical trials, and identifying potential policy and other opportunities. A comprehensive list of takeaways and action plans was developed in the four key areas of focus with the expectation that implementation of the strategies that emerged from the summit will enhance cancer clinical trial accrual for all Georgians.

New clinical trial design in precision medicine: discovery, development and direction

In the era of precision medicine, it has been increasingly recognized that individuals with a certain disease are complex and different from each other. Due to the underestimation of the significant heterogeneity across participants in traditional "one-size-fits-all" trials, patient-centered trials that could provide optimal therapy customization to individuals with specific biomarkers were developed including the basket, umbrella, and platform trial designs under the master protocol framework. In recent years, the successive FDA approval of indications based on biomarker-guided master protocol designs has demonstrated that these new clinical trials are ushering in tremendous opportunities. Despite the rapid increase in the number of basket, umbrella, and platform trials, the current clinical and research understanding of these new trial designs, as compared with traditional trial designs, remains limited. The majority of the research focuses on methodologies, and there is a lack of in-depth insight concerning the underlying biological logic of these new clinical trial designs. Therefore, we provide this comprehensive review of the discovery and development of basket, umbrella, and platform trials and their underlying logic from the perspective of precision medicine. Meanwhile, we discuss future directions on the potential development of these new clinical design in view of the "Precision Pro", "Dynamic Precision", and "Intelligent Precision". This review would assist trial-related researchers to enhance the innovation and feasibility of clinical trial designs by expounding the underlying logic, which be essential to accelerate the progression of precision medicine.

Diversity in Oncology Clinical Trials: Current Landscape for Industry-Sponsored Clinical Trials in Asia

INTRODUCTION

There has been a growing recognition on the importance of diversity in clinical trials. Existing research has highlighted a significant demographic imbalance. Amidst this renewed focus on diversity, it is crucial to acknowledge that Asia comprises over half of the world's population. Given the region's demographic significance, we sought to compare various characteristics and growth rates for trials with sites in Asia against those without any sites in Asia.

METHODS

We performed comprehensive analyses of industry-sponsored phase 2 and 3 oncology trials registered at Clinicaltrials.gov, using drugs or biologics as investigational agents and executed between 1 January 2018 and 31 December 2022. We applied the compound annual growth rate (CAGR) as an analytical tool to track the trial growth rates over this 5-year period.

RESULTS

We identified 894 industry-sponsored phase 2 and 3 cancer studies with available study location data. Out of these, 415 trials (46.42%) had study sites in Asia. Notably, these trials with sites in Asia were also more likely to be phase 3 trials (39.76% vs 6.47%, p < 0.001), include female and paediatric populations, and be randomised trials. Interestingly, lung and stomach cancers were more commonly studied in these trials, while myeloma was less commonly studied. The number of trial sites for liver cancer was not significantly higher for Asia, even though the incidence of the disease is much higher in this region. Despite an overall declining trend in the number of clinical trials in the last 5 years, we observed a transitional positive increase in the CAGR from 2020 to 2021 for trials with sites in Asia. However, East Asia, specifically China, exhibited a disproportionate overrepresentation in these trials.

CONCLUSIONS

There are notable characteristics of clinical trials with sites in Asia. Comprehending these disparities may aid in the strategic planning to enhance a balanced representation of ethnicities in trials.

From genomic spectrum of NTRK genes to adverse effects of its inhibitors, a comprehensive genome-based and real-world pharmacovigilance analysis

Introduction: The discovery of neurotrophic tyrosine receptor kinase (NTRK) gene fusions has facilitated the development of precision oncology. Two first-generation NTRK inhibitors (larotrectinib and entrectinib) are currently approved for the treatment of patients with solid tumors harboring NTRK gene fusions. Nevertheless, comprehensive NTRK profiling at the pan-cancer genomic level and real-world studies pertaining to the adverse events of NTRK inhibitors are lacking. Methods: We characterize the genome of NTRK at the pan-cancer level through multi-omics databases such as The Cancer Genome Atlas (TCGA). Through the FDA Adverse Event Reporting System (FAERS) database, we collect reports of entrectinib and larotrectinib-induced adverse events and perform a pharmacovigilance analysis using various disproportionality methods. Results: NTRK1/2/3 expression is lower in most tumor tissues, while they have higher methylation levels. NTRK gene expression has prognostic value in some cancer types, such as breast invasive carcinoma (BRCA). The cancer type with highest NTRK alteration frequency is skin cutaneous melanoma (SKCM) (31.98%). Thyroid carcinoma (THCA) has the largest number of NTRK fusion cases, and the most common fusion pair is ETV6-NTRK3. Adverse drug events (ADEs) obtained from the FAERS database for larotrectinib and entrectinib are 524 and 563, respectively. At the System Organ Class (SOC) level, both drugs have positive signal value for "nervous system disorder". Other positive signals for entrectinib include "cardiac disorders", "metabolism and nutrition disorders", while for larotrectinib, it is "hepatobiliary disorders". The unexpected signals are also listed in detail. ADEs of the two NTRK inhibitors mainly occur in the first month. The median onset time of ADEs for entrectinib and larotrectinib was 16 days (interquartile range [IQR] 6-86.5) and 44 days ([IQR] 7-136), respectively. Conclusion: Our analysis provides a broad molecular view of the NTRK family. The real-world adverse drug event analysis of entrectinib and larotrectinib contributes to more refined medication management.

Omics-based molecular classifications empowering in precision oncology

BACKGROUND

In the past decades, cancer enigmatical heterogeneity at distinct expression levels could interpret disparities in therapeutic response and prognosis. It built hindrances to precision medicine, a tactic to tailor customized treatment informed by the tumors' molecular profile. Single-omics analysis dissected the biological features associated with carcinogenesis to some extent but still failed to revolutionize cancer treatment as expected. Integrated omics analysis incorporated tumor biological networks from diverse layers and deciphered a holistic overview of cancer behaviors, yielding precise molecular classification to facilitate the evolution and refinement of precision medicine.

CONCLUSION

This review outlined the biomarkers at multiple expression layers to tutor molecular classification and pinpoint tumor diagnosis, and explored the paradigm shift in precision therapy: from single- to multi-omics-based subtyping to optimize therapeutic regimens. Ultimately, we firmly believe that by parsing molecular characteristics, omics-based typing will be a powerful assistant for precision oncology.

Cancer Evolution: A Multifaceted Affair

Cancer cells adapt and survive through the acquisition and selection of molecular modifications. This process defines cancer evolution. Building on a theoretical framework based on heritable genetic changes has provided insights into the mechanisms supporting cancer evolution. However, cancer hallmarks also emerge via heritable nongenetic mechanisms, including epigenetic and chromatin topological changes, and interactions between tumor cells and the tumor microenvironment. Recent findings on tumor evolutionary mechanisms draw a multifaceted picture where heterogeneous forces interact and influence each other while shaping tumor progression. A comprehensive characterization of the cancer evolutionary toolkit is required to improve personalized medicine and biomarker discovery.

SIGNIFICANCE

Tumor evolution is fueled by multiple enabling mechanisms. Importantly, genetic instability, epigenetic reprogramming, and interactions with the tumor microenvironment are neither alternative nor independent evolutionary mechanisms. As demonstrated by findings highlighted in this perspective, experimental and theoretical approaches must account for multiple evolutionary mechanisms and their interactions to ultimately understand, predict, and steer tumor evolution.

Measuring Progress in Precision Oncology

SUMMARY

In this issue of Cancer Discovery, Suehnholz and colleagues describe their efforts to quantify the gradual yet steady progress of precision oncology by surveying the regulatory approvals of targeted cancer therapies, and thus the actionability of corresponding molecular alterations in clinical practice, over more than 20 years. Their work also suggests a relationship between the discovery of candidate therapeutic targets through comprehensive tumor profiling and molecularly guided cancer drug development. See related article by Suehnholz et al., p. 49 (5).

Transformative Potential of AI in Healthcare: Definitions, Applications, and Navigating the Ethical Landscape and Public Perspectives

Artificial intelligence (AI) has emerged as a crucial tool in healthcare with the primary aim of improving patient outcomes and optimizing healthcare delivery. By harnessing machine learning algorithms, natural language processing, and computer vision, AI enables the analysis of complex medical data. The integration of AI into healthcare systems aims to support clinicians, personalize patient care, and enhance population health, all while addressing the challenges posed by rising costs and limited resources. As a subdivision of computer science, AI focuses on the development of advanced algorithms capable of performing complex tasks that were once reliant on human intelligence. The ultimate goal is to achieve human-level performance with improved efficiency and accuracy in problem-solving and task execution, thereby reducing the need for human intervention. Various industries, including engineering, media/entertainment, finance, and education, have already reaped significant benefits by incorporating AI systems into their operations. Notably, the healthcare sector has witnessed rapid growth in the utilization of AI technology. Nevertheless, there remains untapped potential for AI to truly revolutionize the industry. It is important to note that despite concerns about job displacement, AI in healthcare should not be viewed as a threat to human workers. Instead, AI systems are designed to augment and support healthcare professionals, freeing up their time to focus on more complex and critical tasks. By automating routine and repetitive tasks, AI can alleviate the burden on healthcare professionals, allowing them to dedicate more attention to patient care and meaningful interactions. However, legal and ethical challenges must be addressed when embracing AI technology in medicine, alongside comprehensive public education to ensure widespread acceptance.

My battle with cancer. Part 1

In January 2023, diagnosed with numerous metastases of lung cancer in my brain, I felt that I must accomplish a mission. If everything happens for a reason, my cancer, in particular, I must find out how metastatic cancer can be treated with curative intent. This is my mission now, and the reason I was ever born. In January 2023, I understood the meaning of life, of my life. I was born to write this article. In this article, I argue that monotherapy with targeted drugs, even when used in sequence, cannot cure metastatic cancer. However, preemptive combinations of targeted drugs may, in theory, cure incurable cancer. Also, I share insights on various topics, including rapamycin, an anti-aging drug that can delay but not prevent cancer, through my personal journey.

Understanding the Termination of Urologic Cancer Clinical Trials: Insights and Challenges

PURPOSE

Clinical trials are valuable evidence for managing urologic malignancies. Early termination of clinical trials is associated with a waste of resources and may substantially affect patient care. We sought to study the termination rate of urologic cancer clinical trials and identify factors associated with trial termination.

METHODS

A cross-sectional search of ClinicalTrials.gov identified completed and terminated kidney, prostate, and bladder cancer clinical trials started. Trials were assessed for reasons for termination. Multivariable analyses were conducted to determine the significant factors associated with the termination.

RESULTS

Between 2000 and 2020, 9,145 oncology clinical trials were conducted, of which 11.30% (n = 1,033) were urologic cancer clinical trials. Of the urologic cancer clinical trials, 25.38% (n = 265) were terminated, with low patient accrual being the most common reason for termination, 52.9% (n = 127). Multivariable analysis showed that only the university funding source odds ratio (OR) of 2.20 (95% CI, 1.45 to 3.32), single-center studies OR of 2.11 (95% CI, 1.59 to 2.81), and sample size of <50 were significant predictors of clinical trial termination OR of 5.26 (95% CI, 3.85 to 7.69); all P values are <.001.

CONCLUSION

The termination rate of urologic cancer clinical trials was 25%, with low accrual being the most frequently reported reason. Trials funded by a university, single-center trials, and small trials (sample size <50) were associated with early termination. A better understanding of these factors might help researchers, funding agencies, and other stakeholders prioritize resource allocations for multicenter trials that aim to recruit a sufficient number of patients.

Alternative Tissue Fixation Protocols Dramatically Reduce the Impact of DNA Artifacts, Unraveling the Interpretation of Clinical Comprehensive Genomic Profiling

Formalin-fixed paraffin-embedded (FFPE) samples represent the cornerstone of tissue-based analysis in precision medicine. Targeted next-generation sequencing panels are routinely used to analyze a limited number of genes to guide treatment decision-making for advanced-stage patients. The number and complexity of genetic alterations to be investigated are rapidly growing; in several instances, a comprehensive genomic profiling analysis is needed. The poor quality of genetic material extracted from FFPE samples may impact the feasibility/reliability of sequencing data. We sampled 9 colorectal cancers to allow 4 parallel fixations: (1) neutral buffered formalin (NBF), (2) acid-deprived formalin fixation (ADF), (3) precooled ADF (coldADF), and (4) glyoxal acid free (GAF). DNA extraction, fragmentation analysis, and sequencing by 2 large next-generation sequencing panels (OCAv3 and TSO500) followed. We comprehensively analyzed library and sequencing quality controls and the quality of sequencing results. Libraries from coldADF samples showed significantly longer reads than the others with both panels. ADF-derived and coldADF-derived libraries showed the lowest level of noise and the highest levels of uniformity with the OCAv3 panel, followed by GAF and NBF samples. The data uniformity was confirmed by the TSO500 results, which also highlighted the best performance in terms of the total region sequenced for the ADF and coldADF samples. NBF samples had a significantly smaller region sequenced and displayed a significantly lower number of evaluable microsatellite loci and a significant increase in single-nucleotide variations compared with other protocols. Mutational signature 1 (aging and FFPE artifact related) showed the highest (37%) and lowest (17%) values in the NBF and coldADF samples, respectively. Most of the identified genetic alterations were shared by all samples in each lesion. Five genes showed a different mutational status across samples and/or panels: 4 discordant results involved NBF samples. In conclusion, acid-deprived fixatives (GAF and ADF) guarantee the highest DNA preservation/sequencing performance, thus allowing more complex molecular profiling of tissue samples.

Dem-Aging: autophagy-related pathologies and the "two faces of dementia"

Neuronal ceroid lipofuscinosis (NCL) is an umbrella term referring to the most frequent childhood-onset neurodegenerative diseases, which are also the main cause of childhood dementia. Although the molecular mechanisms underlying the NCLs remain elusive, evidence is increasingly pointing to shared disease pathways and common clinical features across the disease forms. The characterization of pathological mechanisms, disease modifiers, and biomarkers might facilitate the development of treatment strategies.The DEM-AGING project aims to define molecular signatures in NCL and expedite biomarker discovery with a view to identifying novel targets for monitoring disease status and progression and accelerating clinical trial readiness in this field. In this study, we fused multiomic assessments in established NCL models with similar data on the more common late-onset neurodegenerative conditions in order to test the hypothesis of shared molecular fingerprints critical to the underlying pathological mechanisms. Our aim, ultimately, is to combine data analysis, cell models, and omic strategies in an effort to trace new routes to therapies that might readily be applied in the most common forms of dementia.

The Buzz Surrounding Precision Medicine: The Imperative of Incorporating It into Evidence-Based Medical Practice

Precision medicine (PM), through the integration of omics and environmental data, aims to provide a more precise prevention, diagnosis, and treatment of disease. Currently, PM is one of the emerging approaches in modern healthcare and public health, with wide implications for health care delivery, public health policy making formulation, and entrepreneurial endeavors. In spite of its growing popularity and the buzz surrounding it, PM is still in its nascent phase, facing considerable challenges that need to be addressed and resolved for it to attain the acclaim for which it strives. In this article, we discuss some of the current methodological pitfalls of PM, including the use of big data, and provide a perspective on how these challenges can be overcome by bringing PM closer to evidence-based medicine (EBM). Furthermore, to maximize the potential of PM, we present real-world illustrations of how EBM principles can be integrated into a PM approach.

Genomics in Clinical trials for Breast Cancer

Breast cancer (B.C.) still has increasing incidences and mortality rates globally. It is known that B.C. and other cancers have a very high rate of genetic heterogeneity and genomic mutations. Traditional oncology approaches have not been able to provide a lasting solution. Targeted therapeutics have been instrumental in handling the complexity and resistance associated with B.C. However, the progress of genomic technology has transformed our understanding of the genetic landscape of breast cancer, opening new avenues for improved anti-cancer therapeutics. Genomics is critical in developing tailored therapeutics and identifying patients most benefit from these treatments. The next generation of breast cancer clinical trials has incorporated next-generation sequencing technologies into the process, and we have seen benefits. These innovations have led to the approval of better-targeted therapies for patients with breast cancer. Genomics has a role to play in clinical trials, including genomic tests that have been approved, patient selection and prediction of therapeutic response. Multiple clinical trials in breast cancer have been done and are still ongoing, which have applied genomics technology. Precision medicine can be achieved in breast cancer therapy with increased efforts and advanced genomic studies in this domain. Genomics studies assist with patient outcomes improvement and oncology advancement by providing a deeper understanding of the biology behind breast cancer. This article will examine the present state of genomics in breast cancer clinical trials.

The efficacy of immunotherapy and chemoimmunotherapy in patients with advanced rare tumors: A Turkish oncology group (TOG) study

INTRODUCTION

The advances in immune checkpoint inhibitors (ICIs) were relatively slow in rare tumors. Therefore, we conducted a multi-center study evaluating the efficacy of ICI monotherapy and the combination of ICIs with chemotherapy (CT) in patients with advanced rare tumors.

METHODS

In this retrospective cohort study, we included 93 patients treated with ICIs for NCI-defined rare tumors from the 12 cancer centers in Turkey. The primary endpoints were the overall response (ORR) and disease control rate (DCR).

RESULTS

The cohort's median age was 56, and 53.8% of the patients were male. The most frequent diagnosis was sarcoma (29%), and 81.7% of the patients were previously treated with at least one line of systemic therapy in the advanced stage. The ORR and DCR were 36.8% and 63.2%, respectively. The germ cell tumors had the lowest ORR (0%), while the Merkel cell carcinoma had the highest ORR to ICIs (57.1%). Patients treated with ICI + ICI or ICI plus chemotherapy combinations had higher ORR (55.2% vs. 27.6%, p = 0.012) and DCR (82.8% vs. 53.4%, p = 0.008). The median OS was 13.47 (95% CI: 7.79-19.15) months, and the six and 12-month survival rates were 71% and 52%. The median duration of response was 16.59 months, and the 12-month progression-free survival rate was 66% in responders. The median time-to-treatment failure was 5.06 months (95% CI: 3.42-6.71). Three patients had high-grade irAEs with ICIs (grade 3 colitis, grade 3 gastritis, and grade 3 encephalitis in one patient each).

CONCLUSION

We observed over 30% ORR and a 13-month median OS in patients with rare cancers treated with ICI monotherapy or ICI plus CT combinations. The response rates to ICIs or ICIs plus CT significantly varied across different tumor types. Responding patients had over 2 years of survival, highlighting a need for further trials with ICIs for patients with rare tumors.

Navigating the Immune Maze: Pioneering Strategies for Unshackling Cancer Immunotherapy Resistance

Cancer immunotherapy has ushered in a transformative era in oncology, offering unprecedented promise and opportunities. Despite its remarkable breakthroughs, the field continues to grapple with the persistent challenge of treatment resistance. This resistance not only undermines the widespread efficacy of these pioneering treatments, but also underscores the pressing need for further research. Our exploration into the intricate realm of cancer immunotherapy resistance reveals various mechanisms at play, from primary and secondary resistance to the significant impact of genetic and epigenetic factors, as well as the crucial role of the tumor microenvironment (TME). Furthermore, we stress the importance of devising innovative strategies to counteract this resistance, such as employing combination therapies, tailoring immune checkpoints, and implementing real-time monitoring. By championing these state-of-the-art methods, we anticipate a paradigm that blends personalized healthcare with improved treatment options and is firmly committed to patient welfare. Through a comprehensive and multifaceted approach, we strive to tackle the challenges of resistance, aspiring to elevate cancer immunotherapy as a beacon of hope for patients around the world.

BMC3PM: bioinformatics multidrug combination protocol for personalized precision medicine and its application in cancer treatment

BACKGROUND

In recent years, drug screening has been one of the most significant challenges in the field of personalized medicine, particularly in cancer treatment. However, several new platforms have been introduced to address this issue, providing reliable solutions for personalized drug validation and safety testing. In this study, we developed a personalized drug combination protocol as the primary input to such platforms.

METHODS

To achieve this, we utilized data from whole-genome expression profiles of 6173 breast cancer patients, 312 healthy individuals, and 691 drugs. Our approach involved developing an individual pattern of perturbed gene expression (IPPGE) for each patient, which was used as the basis for drug selection. An algorithm was designed to extract personalized drug combinations by comparing the IPPGE and drug signatures. Additionally, we employed the concept of drug repurposing, searching for new benefits of existing drugs that may regulate the desired genes.

RESULTS

Our study revealed that drug combinations obtained from both specialized and non-specialized cancer medicines were more effective than those extracted from only specialized medicines. Furthermore, we observed that the individual pattern of perturbed gene expression (IPPGE) was unique to each patient, akin to a fingerprint.

CONCLUSIONS

The personalized drug combination protocol developed in this study offers a methodological interface between drug repurposing and combination drug therapy in cancer treatment. This protocol enables personalized drug combinations to be extracted from hundreds of drugs and thousands of drug combinations, potentially offering more effective treatment options for cancer patients.

Multifactor assessment of ovarian cancer reveals immunologically interpretable molecular subtypes with distinct prognoses

Background

Ovarian cancer (OC) is a highly heterogeneous and malignant gynecological cancer, thereby leading to poor clinical outcomes. The study aims to identify and characterize clinically relevant subtypes in OC and develop a diagnostic model that can precisely stratify OC patients, providing more diagnostic clues for OC patients to access focused therapeutic and preventative strategies.

Methods

Gene expression datasets of OC were retrieved from TCGA and GEO databases. To evaluate immune cell infiltration, the ESTIMATE algorithm was applied. A univariate Cox analysis and the two-sided log-rank test were used to screen OC risk factors. We adopted the ConsensusClusterPlus algorithm to determine OC subtypes. Enrichment analysis based on KEGG and GO was performed to determine enriched pathways of signature genes for each subtype. The machine learning algorithm, support vector machine (SVM) was used to select the feature gene and develop a diagnostic model. A ROC curve was depicted to evaluate the model performance.

Results

A total of 1,273 survival-related genes (SRGs) were firstly determined and used to clarify OC samples into different subtypes based on their different molecular pattern. SRGs were successfully stratified in OC patients into three robust subtypes, designated S-I (Immunoreactive and DNA Damage repair), S-II (Mixed), and S-III (Proliferative and Invasive). S-I had more favorable OS and DFS, whereas S-III had the worst prognosis and was enriched with OC patients at advanced stages. Meanwhile, comprehensive functional analysis highlighted differences in biological pathways: genes associated with immune function and DNA damage repair including CXCL9, CXCL10, CXCL11, APEX, APEX2, and RBX1 were enriched in S-I; S-II combined multiple gene signatures including genes associated with metabolism and transcription; and the gene signature of S-III was extensively involved in pathways reflecting malignancies, including many core kinases and transcription factors involved in cancer such as CDK6, ERBB2, JAK1, DAPK1, FOXO1, and RXRA. The SVM model showed superior diagnostic performance with AUC values of 0.922 and 0.901, respectively. Furthermore, a new dataset of the independent cohort could be automatically analyzed by this innovative pipeline and yield similar results.

Conclusion

This study exploited an innovative approach to construct previously unexplored robust subtypes significantly related to different clinical and molecular features for OC and a diagnostic model using SVM to aid in clinical diagnosis and treatment. This investigation also illustrated the importance of targeting innate immune suppression together with DNA damage in OC, offering novel insights for further experimental exploration and clinical trial.

Artificial intelligence revolutionizing drug development: Exploring opportunities and challenges

By harnessing artificial intelligence (AI) algorithms and machine learning techniques, the entire drug discovery process stands to undergo a profound transformation, offering a myriad of advantages. Foremost among these is the ability of AI to conduct swift and efficient screenings of expansive compound libraries, significantly augmenting the identification of potential drug candidates. Moreover, AI algorithms can prove instrumental in predicting the efficacy and safety profiles of candidate compounds, thus endowing invaluable insights and reducing reliance on extensive preclinical and clinical testing. This predictive capacity of AI has the potential to streamline the drug development pipeline and enhance the success rate of clinical trials, ultimately resulting in the emergence of more efficacious and safer therapeutic agents. However, the deployment of AI in drug discovery introduces certain challenges that warrant attention. A primary hurdle entails the imperative acquisition of high-quality and diverse data. Furthermore, ensuring the interpretability of AI models assumes critical importance in securing regulatory endorsement and cultivating trust within scientific and medical communities. Addressing ethical considerations, including data privacy and mitigating bias, represents an additional momentous challenge, requiring assiduous navigation. In this review, we provide an intricate and comprehensive overview of the multifaceted challenges intrinsic to conventional drug development paradigms, while simultaneously interrogating the efficacy of AI in effectively surmounting these formidable obstacles.

An explainable machine learning-based phenomapping strategy for adaptive predictive enrichment in randomized clinical trials

Randomized clinical trials (RCT) represent the cornerstone of evidence-based medicine but are resource-intensive. We propose and evaluate a machine learning (ML) strategy of adaptive predictive enrichment through computational trial phenomaps to optimize RCT enrollment. In simulated group sequential analyses of two large cardiovascular outcomes RCTs of (1) a therapeutic drug (pioglitazone versus placebo; Insulin Resistance Intervention after Stroke (IRIS) trial), and (2) a disease management strategy (intensive versus standard systolic blood pressure reduction in the Systolic Blood Pressure Intervention Trial (SPRINT)), we constructed dynamic phenotypic representations to infer response profiles during interim analyses and examined their association with study outcomes. Across three interim timepoints, our strategy learned dynamic phenotypic signatures predictive of individualized cardiovascular benefit. By conditioning a prospective candidate's probability of enrollment on their predicted benefit, we estimate that our approach would have enabled a reduction in the final trial size across ten simulations (IRIS: -14.8% ± 3.1%, pone-sample t-test = 0.001; SPRINT: -17.6% ± 3.6%, pone-sample t-test < 0.001), while preserving the original average treatment effect (IRIS: hazard ratio of 0.73 ± 0.01 for pioglitazone vs placebo, vs 0.76 in the original trial; SPRINT: hazard ratio of 0.72 ± 0.01 for intensive vs standard systolic blood pressure, vs 0.75 in the original trial; all simulations with Cox regression-derived p value of  < 0.01 for the effect of the intervention on the respective primary outcome). This adaptive framework has the potential to maximize RCT enrollment efficiency.

Perspectives and challenges of small molecule inhibitor therapy for FLT3-mutated acute myeloid leukemia

Acute myeloid leukemia (AML) is a heterogeneous clonal disease characterized overall by an aggressive clinical course. The underlying genetic abnormalities present in leukemic cells contribute significantly to the AML phenotype. Mutations in FMS-like tyrosine kinase 3 (FLT3) are one of the most common genetic abnormalities identified in AML, and the presence of these mutations strongly influences disease presentation and negatively impacts prognosis. Since mutations in FLT3 were identified in AML, they have been recognized as a valid therapeutic target resulting in decades of research to develop effective small molecule inhibitor treatment that could improve outcome for these patients. Despite the approval of several FLT3 inhibitors over the last couple of years, the treatment of patients with FLT3-mutated AML remains challenging and many questions still need to be addressed. This review will provide an up-to-date overview of our current understanding of FLT3-mutated AML and discuss what the current status is of the available FLT3 inhibitors for the day-to-day management of this aggressive disease.

Protein-based nutritional strategies to manage the development of diabetes: evidence and challenges in human studies

Type 2 diabetes mellitus (T2DM) is one of the most prevalent diseases in modern society, governed by both genetic and environmental factors, such as nutritional habits. This metabolic disorder is characterized by insulin resistance, which is related to high blood glucose levels, implying negative health effects in humans, hindering the healthy ageing of people. The relationship between food and health is clear, and the ingestion of specific nutrients modulates some physiological processes, potentially implying biologically relevant changes, which can translate into a health benefit. This review aims to summarize human studies published in which the purpose was to investigate the effect of protein ingestion (in native state or as hydrolysates) on human metabolism. Overall, several studies showed how protein ingestion might induce a decrease of glucose concentration in the postprandial state (area under the curve), although it is highly dependent on the source and the dose. Other studies showed no biological effects upon protein consumption, mostly with fish-derived products. In addition, the major challenges and perspectives in this research field are highlighted, suggesting the future directions, towards which scientists should focus on. The dietary intake of proteins has been proven to likely exert a beneficial effect on diabetes-related parameters, which can have a biological relevance in the prevention and pre-treatment of diabetes. However, the number of well-designed human studies carried out to date to demonstrate the effects of specific proteins or protein hydrolysates in vivo is still scarce.

A Strategy Utilizing Protein-Protein Interaction Hubs for the Treatment of Cancer Diseases

We describe a strategy for the development of a rational approach of neoplastic disease therapy based on the demonstration that scale-free networks are susceptible to specific attacks directed against its connective hubs. This strategy involves the (i) selection of up-regulated hubs of connectivity in the tumors interactome, (ii) drug repurposing of these hubs, (iii) RNA silencing of non-druggable hubs, (iv) in vitro hub validation, (v) tumor-on-a-chip, (vi) in vivo validation, and (vii) clinical trial. Hubs are protein targets that are assessed as targets for rational therapy of cancer in the context of personalized oncology. We confirmed the existence of a negative correlation between malignant cell aggressivity and the target number needed for specific drugs or RNA interference (RNAi) to maximize the benefit to the patient's overall survival. Interestingly, we found that some additional proteins not generally targeted by drug treatments might justify the addition of inhibitors designed against them in order to improve therapeutic outcomes. However, many proteins are not druggable, or the available pharmacopeia for these targets is limited, which justifies a therapy based on encapsulated RNAi.

An explainable machine learning-based phenomapping strategy for adaptive predictive enrichment in randomized controlled trials

Randomized controlled trials (RCT) represent the cornerstone of evidence-based medicine but are resource-intensive. We propose and evaluate a machine learning (ML) strategy of adaptive predictive enrichment through computational trial phenomaps to optimize RCT enrollment. In simulated group sequential analyses of two large cardiovascular outcomes RCTs of (1) a therapeutic drug (pioglitazone versus placebo; Insulin Resistance Intervention after Stroke (IRIS) trial), and (2) a disease management strategy (intensive versus standard systolic blood pressure reduction in the Systolic Blood Pressure Intervention Trial (SPRINT)), we constructed dynamic phenotypic representations to infer response profiles during interim analyses and examined their association with study outcomes. Across three interim timepoints, our strategy learned dynamic phenotypic signatures predictive of individualized cardiovascular benefit. By conditioning a prospective candidate's probability of enrollment on their predicted benefit, we estimate that our approach would have enabled a reduction in the final trial size across ten simulations (IRIS: -14.8% ± 3.1%, pone-sample t-test=0.001; SPRINT: -17.6% ± 3.6%, pone-sample t-test<0.001), while preserving the original average treatment effect (IRIS: hazard ratio of 0.73 ± 0.01 for pioglitazone vs placebo, vs 0.76 in the original trial; SPRINT: hazard ratio of 0.72 ± 0.01 for intensive vs standard systolic blood pressure, vs 0.75 in the original trial; all with pone-sample t-test<0.01). This adaptive framework has the potential to maximize RCT enrollment efficiency.

Precision oncology medicines and the need for real world evidence acceptance in health technology assessment: Importance of patient involvement in sustainable healthcare

Precision oncology has made remarkable strides in improving clinical outcomes, offering hope to patients with historically difficult-to-treat, as well as rare or neglected cancers. However, despite rapid advancement, precision oncology has reached a critical juncture, where patient access to these life-saving medicines may be hampered by strict requirements by Health Technology Assessment (HTA) bodies for randomised controlled trials (RCTs) for assessing new medicines against appropriate comparator. The very nature of precision oncology-matching a tumour's unique molecular alterations to targeted therapies predicted to elicit response-can make the use of RCTs very difficult, as only a very small number of patients might qualify for a given therapy within a traditional clinical trial setting. Real-world evidence (RWE) has been accepted for regulatory decision-making but has yet to reach widespread acceptance by HTA bodies. As the oncology treatment landscape has evolved towards favouring the concept of precision oncology, there is a growing need for flexibility in the way HTA bodies evaluate new medicines. We must acknowledge that current assessment methodologies can limit access to life-changing medicines for many patients who have no alternative options and that a growing number of precision oncology medicines with proven clinical benefits in rare tumours cannot be reasonably evaluated using traditional methodologies. The objectives of this paper are to advocate a change in mindset regarding best practices in drug assessment models and to propose alternative approaches when considering indications for which RWE is the most compelling data source available.

BCL2 Inhibition Reveals a Dendritic Cell-Specific Immune Checkpoint That Controls Tumor Immunosurveillance

We developed a phenotypic screening platform for the functional exploration of dendritic cells (DC). Here, we report a genome-wide CRISPR screen that revealed BCL2 as an endogenous inhibitor of DC function. Knockout of BCL2 enhanced DC antigen presentation and activation as well as the capacity of DCs to control tumors and to synergize with PD-1 blockade. The pharmacologic BCL2 inhibitors venetoclax and navitoclax phenocopied these effects and caused a cDC1-dependent regression of orthotopic lung cancers and fibrosarcomas. Thus, solid tumors failed to respond to BCL2 inhibition in mice constitutively devoid of cDC1, and this was reversed by the infusion of DCs. Moreover, cDC1 depletion reduced the therapeutic efficacy of BCL2 inhibitors alone or in combination with PD-1 blockade and treatment with venetoclax caused cDC1 activation, both in mice and in patients. In conclusion, genetic and pharmacologic BCL2 inhibition unveils a DC-specific immune checkpoint that restrains tumor immunosurveillance.

SIGNIFICANCE

BCL2 inhibition improves the capacity of DCs to stimulate anticancer immunity and restrain cancer growth in an immunocompetent context but not in mice lacking cDC1 or mature T cells. This study indicates that BCL2 blockade can be used to sensitize solid cancers to PD-1/PD-L1-targeting immunotherapy. This article is featured in Selected Articles from This Issue, p. 2293.

Exploring challenges in the management and treatment of inclusion body myositis

PURPOSE OF REVIEW

This review provides an overview of the management and treatment landscape of inclusion body myositis (IBM), while highlighting the current challenges and future directions.

RECENT FINDINGS

IBM is a slowly progressive myopathy that predominantly affects patients over the age of 40, leading to increased morbidity and mortality. Unfortunately, a definitive cure for IBM remains elusive. Various clinical trials targeting inflammatory and some of the noninflammatory pathways have failed. The search for effective disease-modifying treatments faces numerous hurdles including variability in presentation, diagnostic challenges, poor understanding of pathogenesis, scarcity of disease models, a lack of validated outcome measures, and challenges related to clinical trial design. Close monitoring of swallowing and respiratory function, adapting an exercise routine, and addressing mobility issues are the mainstay of management at this time.

SUMMARY

Addressing the obstacles encountered by patients with IBM and the medical community presents a multitude of challenges. Effectively surmounting these hurdles requires embracing cutting-edge research strategies aimed at enhancing the management and treatment of IBM, while elevating the quality of life for those affected.