Next-generation clinical trials: Novel strategies to address the challenge of tumor molecular heterogeneity

Elevated expression of ELK1 promotes breast cancer cell growth and correlates with poor prognosis of breast cancer patients

Background

Breast cancer is the most common tumor in women and poses a serious threat to women's physical and mental health. The ETS-like gene 1 (ELK1), upregulated in various malignancies, serves as a transcription regulatory factor. This study primarily investigates the biological functions and prognostic significance of ELK1 in breast cancer.

Materials and methods

The authors conducted an analysis of ELK1 expression in breast cancer and adjacent tissues using data from The Cancer Genome Atlas (TCGA), and validated these findings with clinical specimens. Additionally, the authors employed siRNA transfection, proliferation and apoptosis assays to elucidate the roles of ELK1 in breast cancer cells. Furthermore, we assessed the correlations between ELK1 expression and the tumor microenvironment, as well as tumor-infiltrating immune cells (TIICs), utilizing the ESTIMATE and CIBERSORT algorithms. Finally, we used Kaplan-Meier plots and COX regressions to identify prognostic factors, and developed a predictive alignment diagram to evaluate the prognostic significance of ELK1 in breast cancer.

Results

A marked increase in ELK1 expression is evident in breast cancer tissues (P<0.01). Experimental findings demonstrate that silencing ELK1 suppresses proliferation and promotes apoptosis in breast cancer cells. ELK1 plays a pivotal role in regulating the immune microenvironment of breast cancer. Furthermore, the alignment diagram indicates that ELK1 may serve as an independent prognostic factor for breast cancer patients.

Conclusion

The authors' study reveals that ELK1 exhibits a high expression level in breast cancer tissues and is associated with disease progression and poor prognosis.

Genipin's potential as an anti-cancer agent: from phytochemical origins to clinical prospects

This comprehensive review delves into the multifaceted aspects of genipin, a bioactive compound derived from medicinal plants, focusing on its anti-cancer potential. The review begins by detailing the sources and phytochemical properties of genipin, underscoring its significance in traditional medicine and its transition into contemporary cancer research. It then explores the intricate relationship between genipin's chemical structure and its observed anti-cancer activity, highlighting the molecular underpinnings contributing to its therapeutic potential. This is complemented by a thorough analysis of preclinical studies, which investigates genipin's efficacy against various cancer cell lines and its mechanisms of action at the cellular level. A crucial component of the review is the examination of genipin's bioavailability and pharmacokinetics, providing insights into how the compound is absorbed, distributed, metabolized, and excreted in the body. Then, this review offers a general and updated overview of the anti-cancer studies of genipin and its derivatives based on its basic molecular mechanisms, induction of apoptosis, inhibition of cell proliferation, and disruption of cancer cell signaling pathways. We include information that complements the genipin study, such as toxicity data, and we differentiate this review by including commercial status, disposition, and regulation. Also, this review of genipin stands out for incorporating information on proposals for a technological approach through its load in nanotechnology to improve its bioavailability. The culmination of this information positions genipin as a promising candidate for developing novel anti-cancer drugs capable of supplementing or enhancing current cancer therapies.

Incorporating Molecular Data Into Treatment Decision Making in Gastroesophageal and Pancreaticobiliary Cancers: Timing and Strategies

Gastroesophageal (GE) and pancreatobiliary (PB) cancers represent a significant clinical challenge. In this context, it is critical to understand the key molecular targets within these malignancies including how they are assayed for as well as the clinical actionability of these targets. Integrating biomarkers into the standard of care presents a critical avenue for refining treatment paradigms. This review aims to explore these complexities, offering insights into the optimal sequencing of chemotherapy and targeted therapies and their utility in the management of GE and PB cancers. The timely integration of promising investigational therapies into clinical practice has broader implications around strategies for future clinical trial designs, which would pave the way for advancements in the management of GE and PB cancers. This review provides guidance in navigating the evolving landscape of GE and PB cancer care, which ultimately will drive forward progress in the field and lead to improved patient outcomes.

Predicting clinical progression trajectories of early Alzheimer's disease patients

BACKGROUND

Models for forecasting individual clinical progression trajectories in early Alzheimer's disease (AD) are needed for optimizing clinical studies and patient monitoring.

METHODS

Prediction models were constructed using a clinical trial training cohort (TC; n = 934) via a gradient boosting algorithm and then evaluated in two validation cohorts (VC 1, n = 235; VC 2, n = 421). Model inputs included baseline clinical features (cognitive function assessments, APOE ε4 status, and demographics) and brain magnetic resonance imaging (MRI) measures.

RESULTS

The model using clinical features achieved R2 of 0.21 and 0.31 for predicting 2-year cognitive decline in VC 1 and VC 2, respectively. Adding MRI features improved the R2 to 0.29 in VC 1, which employed the same preprocessing pipeline as the TC. Utilizing these model-based predictions for clinical trial enrichment reduced the required sample size by 20% to 49%.

DISCUSSION

Our validated prediction models enable baseline prediction of clinical progression trajectories in early AD, benefiting clinical trial enrichment and various applications.

Global cluster analysis and network visualization in organoids in cancer research: a scientometric mapping from 1991 to 2021

Objective

In the last three decades, there has been a surge in research on cancer organoids using 3D culture technologies, which has resulted in the development of physiological human cancer models. This study aims to provide an overview of the global trends and frontiers in research on cancer organoids.

Methods

A total of 3189 publications on organoids in cancer research from 1991 to 2021 were collected from the Science Citation Index-Expanded (SCIE) of Web of Science (WoS). Bibliometric methods such as the R package "Bibliometrix," Citespace, and VOS viewer software were employed to investigate and visualize bibliographic coupling, co-citation, co-authorship, and co-occurrence trends, as well as publication trends in the field of organoids in cancer research.

Results

From 1991 to 2021, there has been a significant increase in publications on cancer organoids, with most articles being from North America, Eastern Asia, and Western Europe. The USA had the highest number of publications, citations, prolific authors, and research funding globally. Cancers was the journal with the most publications, while Nature had the best total link strength. Harvard University were the most contributive institutions. The global research in this field could be classified into five clusters: chemotherapy study, organoids for drug screening, different models, molecular mechanism study, and organoid construction. These areas are expected to remain hotspots for future research.

Conclusions

The number of publications on organoids in cancer research is expected to increase based on current global trends.

Intraoperative Imaging in Hepatopancreatobiliary Surgery

Hepatopancreatobiliary surgery belongs to one of the most complex fields of general surgery. An intricate and vital anatomy is accompanied by difficult distinctions of tumors from fibrosis and inflammation; the identification of precise tumor margins; or small, even disappearing, lesions on currently available imaging. The routine implementation of ultrasound use shifted the possibilities in the operating room, yet more precision is necessary to achieve negative resection margins. Modalities utilizing fluorescent-compatible dyes have proven their role in hepatopancreatobiliary surgery, although this is not yet a routine practice, as there are many limitations. Modalities, such as photoacoustic imaging or 3D holograms, are emerging but are mostly limited to preclinical settings. There is a need to identify and develop an ideal contrast agent capable of differentiating between malignant and benign tissue and to report on the prognostic benefits of implemented intraoperative imaging in order to navigate clinical translation. This review focuses on existing and developing imaging modalities for intraoperative use, tailored to the needs of hepatopancreatobiliary cancers. We will also cover the application of these imaging techniques to theranostics to achieve combined diagnostic and therapeutic potential.

Biophysical determinants of cancer organotropism

Metastasis remains the leading cause of cancer lethality. The 'seed/soil' hypothesis provides the framework to explain this cancer phenomenon where the concept of organotropism has been in part mechanistically explained by the properties of the tumor cells and their compatibility with the stromal environment of the distal site. The 'mechanical' hypothesis counters that non-random seeding is driven solely by the circulation patterns and vascular networks of organ systems. We incorporate concepts of mechanobiology and revisit the two hypotheses to provide additional insights into the mechanisms that regulate organ selection during metastatic outgrowth. We focus on the latter stages of the metastatic cascade and examine the role of the endothelium in regulating organ selectivity.

Patient-Derived Multiple Myeloma 3D Models for Personalized Medicine-Are We There Yet?

Despite the wide variety of existing therapies, multiple myeloma (MM) remains a disease with dismal prognosis. Choosing the right treatment for each patient remains one of the major challenges. A new approach being explored is the use of ex vivo models for personalized medicine. Two-dimensional culture or animal models often fail to predict clinical outcomes. Three-dimensional ex vivo models using patients' bone marrow (BM) cells may better reproduce the complexity and heterogeneity of the BM microenvironment. Here, we review the strengths and limitations of currently existing patient-derived ex vivo three-dimensional MM models. We analyze their biochemical and biophysical properties, molecular and cellular characteristics, as well as their potential for drug testing and identification of disease biomarkers. Furthermore, we discuss the remaining challenges and give some insight on how to achieve a more biomimetic and accurate MM BM model. Overall, there is still a need for standardized culture methods and refined readout techniques. Including both myeloma and other cells of the BM microenvironment in a simple and reproducible three-dimensional scaffold is the key to faithfully mapping and examining the relationship between these players in MM. This will allow a patient-personalized profile, providing a powerful tool for clinical and research applications.

Design and analysis of umbrella trials: Where do we stand?

Background

The efficiencies that master protocol designs can bring to modern drug development have seen their increased utilization in oncology. Growing interest has also resulted in their consideration in non-oncology settings. Umbrella trials are one class of master protocol design that evaluates multiple targeted therapies in a single disease setting. Despite the existence of several reviews of master protocols, the statistical considerations of umbrella trials have received more limited attention.

Methods

We conduct a systematic review of the literature on umbrella trials, examining both the statistical methods that are available for their design and analysis, and also their use in practice. We pay particular attention to considerations for umbrella designs applied outside of oncology.

Findings

We identified 38 umbrella trials. To date, most umbrella trials have been conducted in early phase settings (73.7%, 28/38) and in oncology (92.1%, 35/38). The quality of statistical information available about conducted umbrella trials to date is poor; for example, it was impossible to ascertain how sample size was determined in the majority of trials (55.3%, 21/38). The literature on statistical methods for umbrella trials is currently sparse.

Conclusions

Umbrella trials have potentially great utility to expedite drug development, including outside of oncology. However, to enable lessons to be effectively learned from early use of such designs, there is a need for higher-quality reporting of umbrella trials. Furthermore, if the potential of umbrella trials is to be realized, further methodological research is required.

Advancing microfluidic diagnostic chips into clinical use: a review of current challenges and opportunities

Microfluidic diagnostic (μDX) technologies miniaturize sensors and actuators to the length-scales that are relevant to biology: the micrometer scale to interact with cells and the nanometer scale to interrogate biology's molecular machinery. This miniaturization allows measurements of biomarkers of disease (cells, nanoscale vesicles, molecules) in clinical samples that are not detectable using conventional technologies. There has been steady progress in the field over the last three decades, and a recent burst of activity catalyzed by the COVID-19 pandemic. In this time, an impressive and ever-growing set of technologies have been successfully validated in their ability to measure biomarkers in clinical samples, such as blood and urine, with sensitivity and specificity not possible using conventional tests. Despite our field's many accomplishments to date, very few of these technologies have been successfully commercialized and brought to clinical use where they can fulfill their promise to improve medical care. In this paper, we identify three major technological trends in our field that we believe will allow the next generation of μDx to have a major impact on the practice of medicine, and which present major opportunities for those entering the field from outside disciplines: 1. the combination of next generation, highly multiplexed μDx technologies with machine learning to allow complex patterns of multiple biomarkers to be decoded to inform clinical decision points, for which conventional biomarkers do not necessarily exist. 2. The use of micro/nano devices to overcome the limits of binding affinity in complex backgrounds in both the detection of sparse soluble proteins and nucleic acids in blood and rare circulating extracellular vesicles. 3. A suite of recent technologies that obviate the manual pre-processing and post-processing of samples before they are measured on a μDX chip. Additionally, we discuss economic and regulatory challenges that have stymied μDx translation to the clinic, and highlight strategies for successfully navigating this challenging space.

A Contextual-Bandit-Based Approach for Informed Decision-Making in Clinical Trials

Clinical trials are conducted to evaluate the efficacy of new treatments. Clinical trials involving multiple treatments utilize the randomization of treatment assignments to enable the evaluation of treatment efficacies in an unbiased manner. Such evaluation is performed in post hoc studies that usually use supervised-learning methods that rely on large amounts of data collected in a randomized fashion. That approach often proves to be suboptimal in that some participants may suffer and even die as a result of having not received the most appropriate treatments during the trial. Reinforcement-learning methods improve the situation by making it possible to learn the treatment efficacies dynamically during the course of the trial, and to adapt treatment assignments accordingly. Recent efforts using multi-arm bandits, a type of reinforcement-learning method, have focused on maximizing clinical outcomes for a population that was assumed to be homogeneous. However, those approaches have failed to account for the variability among participants that is becoming increasingly evident as a result of recent clinical-trial-based studies. We present a contextual-bandit-based online treatment optimization algorithm that, in choosing treatments for new participants in the study, takes into account not only the maximization of the clinical outcomes as well as the patient characteristics. We evaluated our algorithm using a real clinical trial dataset from the International Stroke Trial. We simulated the online setting by sequentially going through the data of each participant admitted to the trial. Two bandits (one for each context) were created, with four choices of treatments. For a new participant in the trial, depending on the context, one of the bandits was selected. Then, we took three different approaches to choose a treatment: (a) a random choice (i.e., the strategy currently used in clinical trial settings), (b) a Thompson sampling-based approach, and (c) a UCB-based approach. Success probabilities of each context were calculated separately by considering the participants with the same context. Those estimated outcomes were used to update the prior distributions within the bandit corresponding to the context of each participant. We repeated that process through the end of the trial and recorded the outcomes and the chosen treatments for each approach. We also evaluated a context-free multi-arm-bandit-based approach, using the same dataset, to showcase the benefits of our approach. In the context-free case, we calculated the success probabilities for the Bernoulli sampler using the whole clinical trial dataset in a context-independent manner. The results of our retrospective analysis indicate that the proposed approach performs significantly better than either a random assignment of treatments (the current gold standard) or a multi-arm-bandit-based approach, providing substantial gains in the percentage of participants who are assigned the most suitable treatments. The contextual-bandit and multi-arm bandit approaches provide 72.63% and 64.34% gains, respectively, compared to a random assignment.

Patient-derived Tumour Organoids: A Bridge between Cancer Biology and Personalised Therapy

Patient-derived tumour organoids (PDOs) have revolutionised our understanding of cancer biology and the applications of personalised therapies. These advancements are principally ascribed to the ability of PDOs to consistently recapitulate and maintain the genomic, proteomic and morphological characteristics of parental tumours. Given these characteristics, PDOs (and their extended biobanks) are a representative preclinical model eminently suited to translate relevant scientific findings into personalized therapies rapidly. Here, we summarise recent advancements in PDOs from the perspective of cancer biology and clinical applications, focusing on the current challenges and opportunities of reconstructing and standardising more sophisticated PDO models. STATEMENT OF SIGNIFICANCE: Patient-derived tumour organoids (PDOs), three-dimensional (3D) self-assembled organotypic structures, have revolutionised our understanding of cancer biology and the applications of personalised therapies. These advancements are principally ascribed to the ability of PDOs to consistently recapitulate and maintain the genomic, proteomic and morphological characteristics of parental tumours. Given these characteristics, PDOs (and their extended biobanks) are a representative preclinical model eminently suited to translate relevant scientific findings into personalized therapies rapidly.

Operational complexity versus design efficiency: challenges of implementing a phase IIa multiple parallel cohort targeted treatment platform trial in advanced breast cancer

BACKGROUND

Platform trial designs are used increasingly in cancer clinical research and are considered an efficient model for evaluating multiple compounds within a single disease or disease subtype. However, these trial designs can be challenging to operationalise. The use of platform trials in oncology clinical research has increased considerably in recent years as advances in molecular biology enable molecularly defined stratification of patient populations and targeted therapy evaluation. Whereas multiple separate trials may be deemed infeasible, platform designs allow efficient, parallel evaluation of multiple targeted therapies in relatively small biologically defined patient sub-populations with the promise of increased molecular screening efficiency and reduced time for drug evaluation. Whilst the theoretical efficiencies are widely reported, the operational challenges associated with these designs (complexity, cost, regulatory, resource) are not always well understood. MAIN: In this commentary, we describe our practical experience of the implementation and delivery of the UK plasmaMATCH trial, a platform trial in advanced breast cancer, comprising an integrated screening component and multiple parallel downstream mutation-directed therapeutic cohorts. plasmaMATCH reported its primary results within 3 years of opening to recruitment. We reflect on the operational challenges encountered and share lessons learnt to inform the successful conduct of future trials. Key to the success of the plasmaMATCH trial was well co-ordinated stakeholder engagement by an experienced clinical trials unit with expert methodology and trial management expertise, a federated model of clinical leadership, a well-written protocol integrating screening and treatment components and including justification for the chosen structure and intentions for future adaptions, and an integrated funding model with streamlined contractual arrangements across multiple partners. Findings based on our practical experience include the importance of early engagement with the regulators and consideration of a flexible resource infrastructure to allow adequate resource allocation to support concurrent trial activities as adaptions are implemented in parallel to the continued management of patient safety and data quality of the ongoing trial cohorts.

CONCLUSION

Platform trial designs allow the efficient reporting of multiple treatment cohorts. Operational challenges can be overcome through multidisciplinary engagement, streamlined contracting processes, rationalised protocol and database design and appropriate resourcing.

Development of iPSC-based clinical trial selection platform for patients with ultrarare diseases

A "Leap-of-Faith" approach is used to treat patients with previously unknown ultrarare pathogenic mutations, often based on evidence from patients having dissimilar but more prevalent mutations. This uncertainty reflects the need to develop personalized prescreening platforms for these patients to assess drug efficacy before considering clinical trial enrollment. In this study, we report an 18-year-old patient with ultrarare Leigh-like syndrome. This patient had previously participated in two clinical trials with unfavorable responses. We established an induced pluripotent stem cell (iPSC)-based platform for this patient, and assessed the efficacy of a panel of drugs. The iPSC platform validated the safety and efficacy of the screened drugs. The efficacy of three of the screened drugs was also investigated in the patient. After 3 years of treatment, the drugs were effective in shifting the metabolic profile of this patient toward healthy control. Therefore, this personalized iPSC-based platform can act as a prescreening tool to help in decision-making with respect to patient's participation in future clinical trials.

Clinical translation of patient-derived tumour organoids- bottlenecks and strategies

Multiple three-dimensional (3D) tumour organoid models assisted by multi-omics and Artificial Intelligence (AI) have contributed greatly to preclinical drug development and precision medicine. The intrinsic ability to maintain genetic and phenotypic heterogeneity of tumours allows for the reconciliation of shortcomings in traditional cancer models. While their utility in preclinical studies have been well established, little progress has been made in translational research and clinical trials. In this review, we identify the major bottlenecks preventing patient-derived tumour organoids (PDTOs) from being used in clinical setting. Unsuitable methods of tissue acquisition, disparities in establishment rates and a lengthy timeline are the limiting factors for use of PDTOs in clinical application. Potential strategies to overcome this include liquid biopsies via circulating tumour cells (CTCs), an automated organoid platform and optical metabolic imaging (OMI). These proposed solutions accelerate and optimize the workflow of a clinical organoid drug screening. As such, PDTOs have the potential for potential applications in clinical oncology to improve patient outcomes. If remarkable progress is made, cancer patients can finally benefit from this revolutionary technology.

Next-generation cancer organoids

Organotypic models of patient-specific tumours are revolutionizing our understanding of cancer heterogeneity and its implications for personalized medicine. These advancements are, in part, attributed to the ability of organoid models to stably preserve genetic, proteomic, morphological and pharmacotypic features of the parent tumour in vitro, while also offering unprecedented genomic and environmental manipulation. Despite recent innovations in organoid protocols, current techniques for cancer organoid culture are inherently uncontrolled and irreproducible, owing to several non-standardized facets including cancer tissue sources and subsequent processing, medium formulations, and animal-derived three-dimensional matrices. Given the potential for cancer organoids to accurately recapitulate the intra- and intertumoral biological heterogeneity associated with patient-specific cancers, eliminating the undesirable technical variability accompanying cancer organoid culture is necessary to establish reproducible platforms that accelerate translatable insights into patient care. Here we describe the current challenges and recent multidisciplinary advancements and opportunities for standardizing next-generation cancer organoid systems.

Multiomic analysis identifies CPT1A as a potential therapeutic target in platinum-refractory, high-grade serous ovarian cancer

Resistance to platinum compounds is a major determinant of patient survival in high-grade serous ovarian cancer (HGSOC). To understand mechanisms of platinum resistance and identify potential therapeutic targets in resistant HGSOC, we generated a data resource composed of dynamic (±carboplatin) protein, post-translational modification, and RNA sequencing (RNA-seq) profiles from intra-patient cell line pairs derived from 3 HGSOC patients before and after acquiring platinum resistance. These profiles reveal extensive responses to carboplatin that differ between sensitive and resistant cells. Higher fatty acid oxidation (FAO) pathway expression is associated with platinum resistance, and both pharmacologic inhibition and CRISPR knockout of carnitine palmitoyltransferase 1A (CPT1A), which represents a rate limiting step of FAO, sensitize HGSOC cells to platinum. The results are further validated in patient-derived xenograft models, indicating that CPT1A is a candidate therapeutic target to overcome platinum resistance. All multiomic data can be queried via an intuitive gene-query user interface (https://sites.google.com/view/ptrc-cell-line).

Sputum-Based Tumor Fluid Biopsy: Isolation and High-Throughput Single-Cell Analysis of Exfoliated Tumor Cells for Lung Cancer Diagnosis

Timely and effective diagnosis is of great significance for improving the survival rate of lung cancer patients. Although histopathology is the main diagnostic tool among the existing methods for lung cancer diagnosis, it is not suitable for high-risk groups, early lung cancer patients, patients with advanced-stage disease, and other situations wherein tumor tissues cannot be obtained. In view of this, we proposed an innovative lung cancer diagnosis method employing for the first time a microfluidic technology for high-efficiency isolation and high-throughput single-cell analysis of exfoliated tumor cells (ETCs) in sputum. This method fully combines the advantages of traditional sputum cytology and microfluidic technology and realizes the diagnosis of lung cancer by using a small amount of repeatable ETCs instead of the tumor tissue. This method is expected to provide a practical strategy for the non-invasive detection of lung cancer patients and lung cancer screening for high-risk groups.

Treatment allocation strategies for umbrella trials in the presence of multiple biomarkers: A comparison of methods

Umbrella trials are an innovative trial design where different treatments are matched with subtypes of a disease, with the matching typically based on a set of biomarkers. Consequently, when patients can be positive for more than one biomarker, they may be eligible for multiple treatment arms. In practice, different approaches could be applied to allocate patients who are positive for multiple biomarkers to treatments. However, to date there has been little exploration of how these approaches compare statistically. We conduct a simulation study to compare five approaches to handling treatment allocation in the presence of multiple biomarkers – equal randomisation; randomisation with fixed probability of allocation to control; Bayesian adaptive randomisation (BAR); constrained randomisation; and hierarchy of biomarkers. We evaluate these approaches under different scenarios in the context of a hypothetical phase II biomarker-guided umbrella trial. We define the pairings representing the pre-trial expectations on efficacy as linked pairs, and the other biomarker-treatment pairings as unlinked. The hierarchy and BAR approaches have the highest power to detect a treatment-biomarker linked interaction. However, the hierarchy procedure performs poorly if the pre-specified treatment-biomarker pairings are incorrect. The BAR method allocates a higher proportion of patients who are positive for multiple biomarkers to promising treatments when an unlinked interaction is present. In most scenarios, the constrained randomisation approach best balances allocation to all treatment arms. Pre-specification of an approach to deal with treatment allocation in the presence of multiple biomarkers is important, especially when overlapping subgroups are likely.

Matched sequential tumor molecular profiling in solid malignancies may impact clinical practice

OBJECTIVES

To determine if performing repeat tumor molecular profiling in solid malignancies over time can identify new findings that impact clinical care.

METHODS

All patients with a solid malignancy and more than 1 tumor molecular analysis were identified at a single institution. Each test report was examined to identify the genomic alterations. Chart review was performed to determine subsequent therapies following each test result and the impact of tumor profiling on clinical practice.

RESULTS

At a single institution, 110 patients were identified with having more than 1 tumor molecular analysis, with 98 subjects having test results available for review. Eighty-seven patients had differences in reported results at the time of subsequent analysis. These differences may reflect changes in tumor biology, be attributed to intra-patient or intra-tumor heterogeneity or be due to technical updates of the next generation sequencing platforms. Among the 98 subjects with solid tumors, the median time between tests was 10 months (range 0.5-66 months), with the majority of tests performed at the time of disease progression or recurrence. In this population, a total of 30 patients received targeted therapies that were associated with actionable findings on any tumor molecular analysis. Of these, 6 patients had new genomic findings identified on sequential testing that affected treatment.

CONCLUSIONS

The future of cancer care must include precision medicine approaches. Evolution of next generation sequencing has contributed to this effort. Results of this single institution study summarize the reported findings on tumor molecular testing and suggest that subsequent testing may impact clinical care in a subset of patients. While only 6% of patients in this study saw a change in treatment based on new findings on sequential testing reports, this approach may be more clinically relevant in the future with the development of novel targeted therapies. This may be especially significant in a patient population that has progressed on standard therapies and where treatment options are limited.

Personalized Antibodies for Gastroesophageal Adenocarcinoma (PANGEA): A Phase II Study Evaluating an Individualized Treatment Strategy for Metastatic Disease

The one-year and median overall survival (mOS) rates of advanced gastroesophageal adenocarcinomas (GEA) are ∼50% and <12 months, respectively. Baseline spatial and temporal molecular heterogeneity of targetable alterations may be a cause of failure of targeted/immunooncologic therapies. This heterogeneity, coupled with infrequent incidence of some biomarkers, has resulted in stalled therapeutic progress. We hypothesized that a personalized treatment strategy, applied at first diagnosis then serially over up to three treatment lines using monoclonal antibodies combined with optimally sequenced chemotherapy, could contend with these hurdles. This was tested using a novel clinical expansion-platform type II design with a survival primary endpoint. Of 68 patients by intention-to-treat, the one-year survival rate was 66% and mOS was 15.7 months, meeting the primary efficacy endpoint (one-sided P = 0.0024). First-line response rate (74%), disease control rate (99%), and median progression-free survival (8.2 months) were superior to historical controls. The PANGEA strategy led to improved outcomes warranting a larger randomized study. SIGNIFICANCE: This study highlights excellent outcomes achieved by individually optimizing chemotherapy, biomarker profiling, and matching of targeted therapies at baseline and over time for GEA. Testing a predefined treatment strategy resulted in improved outcomes versus historical controls. Therapeutic resistance observed in correlative analyses suggests that dual targeted inhibition may be beneficial.This article is highlighted in the In This Issue feature, p. 211.

Genome Medicine for Brain Tumors: Current Status and Future Perspectives

As a result of rapid progress in genome medicine technologies, such as the evolution of DNA sequencing and the development of molecular targeted drugs, the era of precision cancer medicine has begun. In 2019, a nationwide genome medicine system was established and cancer gene panel sequencing began being covered by national health insurance in Japan. However, patients with brain tumors have not benefited much from genome medicine, even though gliomas contain many potential molecular targets, such as alterations in EGFR, IDH1/2, BRAF, and Histone H3K27. Targeted therapies for these molecules are currently under enthusiastic development; however, such attempts have not yet achieved remarkable success. To date, only a limited number of targeted drugs for brain tumors such as immune checkpoint, neurotrophic tyrosine receptor kinase (NTRK), and Bruton tyrosine kinase (BTK) inhibitors are available, and only in limited cases. Several obstacles remain in the development of drugs to treat brain tumors, including the difficulties in conducting clinical trials because of the relatively rare incidence and in drug delivery through the blood–brain barrier (BBB). Furthermore, general problems for numerous types of cancer, such as tumor heterogeneity, also exist for brain tumors. We hope that overcoming these issues could enable precision genome medicine to be more beneficial for patients with brain tumors such as malignant gliomas. In addition, careful consideration of ethical, legal, and social issues (ELSIs) is important as it is indispensable for maintaining good relationships with patients, which is one of the keys for genome medicine promotion.

Master Protocol Trial Design for Efficient and Rational Evaluation of Novel Therapeutic Oncology Devices

Historically, the gold standard for evaluation of cancer therapeutics, including medical devices, has been the randomized clinical trial. Although high-quality clinical data are essential for safe and judicious use of therapeutic oncology devices, class II devices require only preclinical data for US Food and Drug Administration approval and are often not rigorously evaluated prior to widespread uptake. Herein, we review master protocol design in medical oncology and its application to therapeutic oncology devices, using examples from radiation oncology. Unique challenges of clinical testing of radiation oncology devices (RODs) include patient and treatment heterogeneity, lack of funding for trials by industry and health-care payers, and operator dependence. To address these challenges, we propose the use of master protocols to optimize regulatory, financial, administrative, quality assurance, and statistical efficiency of trials evaluating RODs. These device-specific master protocols can be extrapolated to other devices and encompass multiple substudies with the same design, statistical considerations, logistics, and infrastructure. As a practical example, we outline our phase I and II master protocol trial of stereotactic magnetic resonance imaging–guided adaptive radiotherapy, which to the best of our knowledge is the first master protocol trial to test a ROD. Development of more efficient clinical trials is needed to promote thorough evaluation of therapeutic oncology devices, including RODs, in a resource-limited environment, allowing more practical and rapid identification of the most valuable advances in our field.

Towards a cancer mission in Horizon Europe: recommendations

A comprehensive translational cancer research approach focused on personalized and precision medicine, and covering the entire cancer research-care-prevention continuum has the potential to achieve in 2030 a 10-year cancer-specific survival for 75% of patients diagnosed in European Union (EU) member states with a well-developed healthcare system. Concerted actions across this continuum that spans from basic and preclinical research through clinical and prevention research to outcomes research, along with the establishment of interconnected high-quality infrastructures for translational research, clinical and prevention trials and outcomes research, will ensure that science-driven and social innovations benefit patients and individuals at risk across the EU. European infrastructures involving comprehensive cancer centres (CCCs) and CCC-like entities will provide researchers with access to the required critical mass of patients, biological materials and technological resources and can bridge research with healthcare systems. Here, we prioritize research areas to ensure a balanced research portfolio and provide recommendations for achieving key targets. Meeting these targets will require harmonization of EU and national priorities and policies, improved research coordination at the national, regional and EU level and increasingly efficient and flexible funding mechanisms. Long-term support by the EU and commitment of Member States to specialized schemes are also needed for the establishment and sustainability of trans-border infrastructures and networks. In addition to effectively engaging policymakers, all relevant stakeholders within the entire continuum should consensually inform policy through evidence-based advice.

Challenges and approaches to implementing master/basket trials in oncology

The appetite for cutting-edge cancer research, across medical institutions, scientific researchers, and health care providers, is increasing based on the promise of true breakthroughs and cures with new therapeutics available for investigation. At the same time, the barriers for advancing clinical research are impacting how quickly drug development efforts are conducted. For example, we know now that under a microscope, patients with the same type of cancer and histology might look the same; however, the reality is that most cancers are driven by genomic, transcriptional, and epigenetic changes that make each patient unique. Additionally, the immunologic reaction to different tumor types is distinct among patients. The challenge for researchers developing new therapies today is vastly different than it was in the era of cytotoxics. Today, we must identify a sufficient number of patients harboring a rare mutation or other characteristic and match this to the right therapeutic option. This summary provides a guide to help inform the scientific cancer community about the benefits and challenges of conducting umbrella or basket trials (master trials), and to create a roadmap to help make this new and evolving form of clinical trial design as effective as possible.

Population enrichment for critical care trials: phenotypes and differential outcomes

PURPOSE OF REVIEW

Sepsis and acute respiratory distress syndrome (ARDS) are two heterogenous acute illnesses where numerous RCTs have indeterminate results. We present a narrative review on the recent developments in enriching patient populations for future sepsis and ARDS trials.

RECENT FINDINGS

Many researchers are actively pursuing enrichment strategies to reduce heterogeneity to increase the sensitivity of future trials. Enrichment refers to the use of measurable patient characteristics, known before randomisation, to refine trial populations. Biomarkers could increase the diagnostic certainty of sepsis, whereas chest radiology training to enhance reliability of interpretation and stabilisation period of mechanical ventilation have been considered to increase the diagnostic certainty of ARDS. Clinical and biomarker data analyses identifies four to six sepsis clinical phenotypes and two ARDS clinical phenotypes. Similarly, leukocyte gene expression data identifies two to four sepsis molecular phenotypes. Use of a test-dose identifies ARDS subpopulations who are likely to benefit from higher PEEP. Early-phase trials report how a biomarker that is altered by the intervention, such as lymphocyte count for recombinant interleukin-7 therapy and higher check point inhibitor expression for anti-check point treatments in sepsis, could identify a higher treatment effect population for future trials.

SUMMARY

Enrichment reduces heterogeneity and will enhance the sensitivity of future trials. However, enrichment, even when it identifies more homogenous populations, may not be efficient to deploy in trials or clinical practice.

Precision Medicine Tumor Boards: Clinical Applicability of Personalized Treatment Concepts in Ovarian Cancer

Background: Treating cancer according to its molecular alterations (i.e., targeted treatment, TT) is the goal of precision medicine tumor boards (PTBs). Their clinical applicability has been evaluated for ovarian cancer patients in this analysis. Methods: All consecutive ovarian cancer patients discussed in a PTB at the Medical University of Vienna, Austria, from April 2015 to April 2019 were included (n = 44). Results: In 38/44 (86%) cases, at least one mutation, deletion or amplification was detected. The most frequently altered genes were p53 (64%), PI3K pathway (18%), KRAS (14%), BRCA1 (11%) and BRCA2 (2%). In 31 patients (70%) a TT was recommended. A total of 12/31 patients (39%) received the recommended therapy. Median time from indication for PTB to TT start was 65 days (15–216). Median time to treatment failure was 2.7 months (0.2–13.2). Clinical benefit rate (CBR) was 42%. Reasons for treatment discontinuation were disease progression (42%), poor performance status (PS > 2; 25%), death (17%) or treatment related side effects (8%). In 61% the TT was not administered—mainly due to PS > 2. Conclusion: Even though a TT recommendation can be derived frequently, clinical applicability remains limited due to poor patients’ general condition after exploitation of standard treatment. However, we observed antitumor activity in a substantial number of heavily pretreated patients.

Evaluation of the Association of Perioperative UGT1A1 Genotype-Dosed gFOLFIRINOX With Margin-Negative Resection Rates and Pathologic Response Grades Among Patients With Locally Advanced Gastroesophageal Adenocarcinoma: A Phase 2 Clinical Trial

IMPORTANCE

Patients with locally advanced gastroesophageal adenocarcinoma (ie, stage ≥T3 and/or node positive) have high rates of recurrence despite surgery and adjunctive perioperative therapies, which also have high toxicity profiles. Evaluation of pharmacogenomically dosed perioperative gFOLFIRINOX (fluorouracil, leucovorin, oxaliplatin, and UGT1A1 genotype-directed irinotecan) to optimize efficacy while limiting toxic effects may have value.

OBJECTIVE

To evaluate the coprimary end points of margin-negative (R0) resection rates and pathologic response grades (PRGs) of gFOLFIRINOX therapy among patients with locally advanced gastroesophageal adenocarcinoma.

DESIGN, SETTING, AND PARTICIPANTS

This single-group phase 2 trial, conducted at 2 academic medical centers from February 2014 to March 2019, enrolled 36 evaluable patients with locally advanced adenocarcinoma of the esophagus, gastroesophageal junction, and gastric body. Data analysis was conducted in May 2019.

INTERVENTIONS

Patients received biweekly gFOLFIRINOX (fluorouracil, 2400 mg/m2 over 46 hours; oxaliplatin, 85 mg/m2; irinotecan, 180 mg/m2 for UGT1A1 genotype 6/6, 135 mg/m2 for UGT1A1 genotype 6/7, or 90 mg/m2 for UGT1A1 genotype 7/7; and prophylactic peg-filgastrim, 6 mg) for 4 cycles before and after surgery. Patients with tumors positive for ERBB2 also received trastuzumab (6-mg/kg loading dose, then 4 mg/kg).

MAIN OUTCOMES AND MEASURES

Margin-negative resection rate and PRG.

RESULTS

A total of 36 evaluable patients (27 [78%] men; median [range] age, 66 [27-85] years; 10 [28%] with gastric body cancer; 24 [67%] with intestinal-type tumors; 6 [17%] with ERBB2-positive tumors; 19 [53%] with UGT1A1 genotype 6/6; 16 [44%] with genotype 6/7; and 1 [3%] with genotype 7/7) were enrolled. Of these, 35 (97%) underwent surgery; 1 patient (3%) died after completing neoadjuvant chemotherapy while awaiting surgery. Overall, R0 resection was achieved in 33 of 36 patients (92%); 2 patients (6%) with linitis plastica achieved R1 resection. Pathologic response grades 1, 2, and 3 occurred in 13 patients (36%), 9 patients (25%), and 14 patients (39%), respectively, and PRG 1 was observed in 11 of 24 intestinal-type tumors (46%). Median disease-free survival was 30.1 months (95% CI, 15.0 months to not reached), and median overall survival was not reached (95% CI, 8.3 months to not reached). There were no differences in outcomes by UGT1A1 genotype group. A total of 38 patients, including 2 (5%) with antral tumors, were evaluable for toxic effects. Grade 3 or higher adverse events occurring in 5% or more of patients during the perioperative cycles included diarrhea (7 patients [18%]; 3 of 19 patients [16%] with genotype 6/6; 2 of 16 patients [13%] with genotype 6/7; 2 of 3 patients [67%] with genotype 7/7), anemia (2 patients [5%]), vomiting (2 patients [5%]), and nausea (2 patients [5%]).

CONCLUSIONS AND RELEVANCE

In this study, perioperative pharmacogenomically dosed gFOLFIRINOX was feasible, providing downstaging with PRG 1 in more than one-third of patients and an R0 resection rate in 92% of patients.

TRIAL REGISTRATION

ClinicalTrials.gov Identifier: NCT02366819.

Variety Is the Spice of Life, but Maybe Not in Gastroesophageal Adenocarcinomas

Sanchez-Vega and colleagues prospectively demonstrate that both intra- and intertumoral differential expression of the receptor tyrosine kinases HER2, EGFR, and MET dictate sensitivity to the pan-HER inhibitor afatinib in a phase II trial of trastuzumab-refractory HER2-amplified gastroesophageal adenocarcinomas. Rapid autopsy, tissue-based genomic characterization, and molecular imaging provide complementary information and may aid in selecting optimal patients for targeted monotherapy versus combination approaches in gastroesophageal adenocarcinomas.See related article by Sanchez-Vega et al., p. 199.

Circulating Tumor DNA Sequencing Analysis of Gastroesophageal Adenocarcinoma

PURPOSE

Gastroesophageal adenocarcinoma (GEA) has a poor prognosis and few therapeutic options. Utilizing a 73-gene plasma-based next-generation sequencing (NGS) cell-free circulating tumor DNA (ctDNA-NGS) test, we sought to evaluate the role of ctDNA-NGS in guiding clinical decision-making in GEA.

EXPERIMENTAL DESIGN

We evaluated a large cohort (n = 2,140 tests; 1,630 patients) of ctDNA-NGS results (including 369 clinically annotated patients). Patients were assessed for genomic alteration (GA) distribution and correlation with clinicopathologic characteristics and outcomes.

RESULTS

Treatment history, tumor site, and disease burden dictated tumor-DNA shedding and consequent ctDNA-NGS maximum somatic variant allele frequency. Patients with locally advanced disease having detectable ctDNA postoperatively experienced inferior median disease-free survival (P = 0.03). The genomic landscape was similar but not identical to tissue-NGS, reflecting temporospatial molecular heterogeneity, with some targetable GAs identified at higher frequency via ctDNA-NGS compared with previous primary tumor-NGS cohorts. Patients with known microsatellite instability-high (MSI-High) tumors were robustly detected with ctDNA-NGS. Predictive biomarker assessment was optimized by incorporating tissue-NGS and ctDNA-NGS assessment in a complementary manner. HER2 inhibition demonstrated a profound survival benefit in HER2-amplified patients by ctDNA-NGS and/or tissue-NGS (median overall survival, 26.3 vs. 7.4 months; P = 0.002), as did EGFR inhibition in EGFR-amplified patients (median overall survival, 21.1 vs. 14.4 months; P = 0.01).

CONCLUSIONS

ctDNA-NGS characterized GEA molecular heterogeneity and rendered important prognostic and predictive information, complementary to tissue-NGS.See related commentary by Frankell and Smyth, p. 6893.

Evolving Clinical Utility of Liquid Biopsy in Gastrointestinal Cancers

Room for improvement exists regarding recommendations for screening, staging, therapy selection, and frequency of surveillance of gastrointestinal cancers. Screening is costly and invasive, improved staging demands increased sensitivity and specificity to better guide therapy selection. Surveillance requires increased sensitivity for earlier detection and precise management of recurrences. Peripherally collected blood-based liquid biopsies enrich and analyze circulating tumor cells and/or somatic genomic material, including circulating tumor DNA along with various subclasses of RNA. Such assays have the potential to impact clinical practice at multiple stages of management in gastrointestinal cancers. This review summarizes current basic and clinical evidence for the utilization of liquid biopsy in cancers of the esophagus, pancreas, stomach, colon, and rectum. Technical aspects of various liquid biopsy methodologies and targets are reviewed and evidence supporting current commercially available assays is examined. Finally, current clinical applicability, potential future uses, and pitfalls of applying liquid biopsy to the screening, staging and therapeutic management of these diseases are discussed.

FGFR2-Altered Gastroesophageal Adenocarcinomas Are an Uncommon Clinicopathologic Entity with a Distinct Genomic Landscape

Little is known about the genomic landscape of FGFR2‐altered gastroesophageal adenocarcinomas. This article attempts to bridge that gap, with a focus on concurrent alterations that may affect sensitivity to FGFR2‐directed therapies.

Background.

With the exception of trastuzumab, therapies directed at receptor tyrosine kinases (RTKs) in gastroesophageal adenocarcinomas (GEA) have had limited success. Recurrent fibroblast growth factor receptor 2 (FGFR2) alterations exist in GEA; however, little is known about the genomic landscape of FGFR2‐altered GEA. We examined FGFR2 alteration frequency and frequency of co‐occurring alterations in GEA.

Subjects, Materials, and Methods.

A total of 6,667 tissue specimens from patients with advanced GEA were assayed using hybrid capture‐based genomic profiling. Tumor mutational burden (TMB) was determined on up to 1.1 Mb of sequenced DNA, and microsatellite instability was determined on 95 or 114 loci. Descriptive statistics were used to compare subgroups.

Results.

We identified a total of 269 (4.0%) FGFR2‐altered cases consisting of FGFR2‐amplified (amp; 193, 72% of FGFR2‐altered), FGFR2‐mutated (36, 13%), FGFR2‐rearranged (re; 23, 8.6%), and cases with multiple FGFR2 alterations (17, 6.3%). Co‐occurring alterations in other GEA RTK targets including ERBB2 (10%), EGFR (8%), and MET (3%) were observed across all classes of FGFR2‐altered GEA. Co‐occurring alterations in MYC (17%), KRAS (10%), and PIK3CA (5.6%) were also observed frequently. Cases with FGFR2amp and FGFR2re were exclusively microsatellite stable. The median TMB for FGFR2‐altered GEA was 3.6 mut/mb, not significantly different from a median of 4.3 mut/mb seen in FGFR2 wild‐type samples.

Conclusion.

FGFR2‐altered GEA is a heterogenous subgroup with approximately 20% of FGFR2‐altered samples harboring concurrent RTK alterations. Putative co‐occurring modifiers of FGFR2‐directed therapy including oncogenic MYC, KRAS, and PIK3CA alterations were also frequent, suggesting that pretreatment molecular analyses may be needed to facilitate rational combination therapies and optimize patient selection for clinical trials.

Implications for Practice.

Actionable receptor tyrosine kinase alterations assayed within a genomic context with therapeutic implications remain limited to HER2 amplification in gastroesophageal adenocarcinomas (GEA). Composite biomarkers and heterogeneity assessment are critical in optimizing patients selected for targeted therapies in GEA. Comprehensive genomic profiling in FGFR2‐altered GEA parallels the heterogeneity findings in HER2‐amplified GEA and adds support to the utility of genomic profiling in advanced gastroesophageal adenocarcinomas.

Bemarituzumab with modified FOLFOX6 for advanced FGFR2-positive gastroesophageal cancer: FIGHT Phase III study design

Bemarituzumab is an afucosylated monoclonal antibody against FGFR2b (a FGF receptor) with demonstrated monotherapy clinical activity in patients with late-line gastric cancer whose tumors overexpress FGFR2b (NCT02318329). We describe the rationale and design of the FIGHT trial (NCT03343301), a global, randomized, double-blind, placebo-controlled Phase III study evaluating the role of bemarituzumab in patients with previously untreated, FGFR2b-overexpressing advanced gastroesophageal cancer. Patients are randomized in a blinded fashion to the combination of mFOLFOX6 and bemarituzumab or mFOLFOX6 and placebo. Eligible patients are selected based on the presence of either FGFR2b protein overexpression determined by immunohistochemistry or FGFR2 gene amplification determined by circulating tumor DNA. The primary end point is overall survival, and secondary end points include progression-free survival, objective response rate and safety.

Impact of molecular testing in clinical practice in gynecologic cancers

Background

With the growing understanding of the molecular and genetic profiles of cancers, targeted treatments are increasingly utilized in personalized cancer care. The objective of this study was to determine how these advances have translated into practice by examining how often molecular profiling of gynecological tumors led to treatment changes.

Methods

We identified women with gynecological cancers at our institution who had molecular tumor testing performed from November 2014 to June 2017. Clinicopathologic data were extracted from medical records. We determined (a) if molecular profiling identified actionable targets for which therapy is available, and (b) whether the patient's treatment course changed as a result of molecular profiling. Chi‐square, Wilcoxon rank‐sum, and Fisher's exact tests were used with a P < 0.05 considered statistically significant.

Results

We identified 152 patients with gynecologic cancers who underwent molecular profiling. Of the 152 patients, 116 (76.3%) had actionable mutations identified, with 41 (35.3%) patients having a treatment change. Stratified by cancer type, molecular profiling most frequently identified an actionable target in patients with endometrial cancer (73.6%). Changes in treatment occurred most frequently in patients with endometrial cancer, 22 (56.4%), and ovarian cancers, 16 (39%), as compared to patients with cervical and vulvar cancer (P = 0.02). Of those patients who received a change in treatment, 39 patients (95.1%) received an FDA‐approved therapeutic agent, while two patients (4.8%) were enrolled in a clinical trial.

Conclusion

Molecular profiling in gynecologic cancers often identified at least one actionable mutation; however, only in a minority of these cases was the course of treatment changed. Further studies are needed to elucidate optimal timing for testing to best utilize actionable information.

Master protocol trials in oncology: Review and new trial designs

In oncology, next generation sequencing and comprehensive genomic profiling have enabled the detailed classification of tumors using molecular biology. However, it is unrealistic to conduct phase I-III trials according to each sub-population based on patient molecular subtypes. Common protocols that assess the combination of several molecular markers and their targeted therapies by means of multiple sub-studies are required. These protocols are called "master protocols," and are drawing attention as a next-generation clinical trial design. Recently, several reviews of clinical trials based on the master protocol design have been published, but their definitions of these such trials, including basket, umbrella, and platform trials, were not consistent. Concurrently, the acceleration of the development of new statistical designs for master protocol trials has been underway. This article provides an overview of recent reviews for master protocols, including their statistical design methodologies in Oncology. We also introduce several examples of previous and on-going master protocol trials along with their classifications by some recent studies.

Art and Challenges of Precision Medicine: Interpreting and Integrating Genomic Data Into Clinical Practice

Precision medicine is at the forefront of innovation in cancer care. With the development of technologies to rapidly sequence DNA from tumors, cell-free DNA, proteins, and even metabolites coupled with the rapid decline in the cost of genomic sequencing, there has been an exponential increase in the amount of data generated for each patient diagnosed with cancer. The ability to harness this explosion of data will be critical to improving treatments for patients. Precision medicine lends itself to big data or "informatics" approaches and is focused on storing, accessing, sharing, and studying these data while taking necessary precautions to protect patients' privacy. Major cancer care stakeholders have developed a variety of systems to incorporate precision medicine technologies into patient care as soon as possible and also to provide the ability to store and analyze the omics and clinical data aggregately in the future. Scaling these precision medicine programs within the confines of health care system silos is challenging, and research consortiums are being formed to overcome these limitations. Incorporating and interpreting the results of precision medicine sequencing is complex and rapidly changing, necessitating reliance on a group of experts. This is often performed at molecular tumor boards at large academic and research institutions with available in-house expertise, but alternative models clinical decision support software or of virtual tumor boards potentially expand these advances to almost any patient, regardless of site of care. The promises of precision medicine will be more quickly realized by expanding collaborations to rapidly process and interpret the growing volumes of omics data.

The Omics Revolution Continues: The Maturation of High-Throughput Biological Data Sources

OBJECTIVE

 The aim is to provide a comprehensive review of state-of-the art omics approaches, including proteomics, metabolomics, cell-free DNA, and patient cohort matching algorithms in precision oncology.

METHODS

 In the past several years, the cancer informatics revolution has been the beneficiary of a data explosion. Different complementary omics technologies have begun coming into their own to provide a more nuanced view of the patient-tumor interaction beyond that of DNA alterations. A combined approach is beneficial to the patient as nearly all new cancer therapeutics are designed with an omics biomarker in mind. Proteomics and metabolomics provide us with a means of assaying in real-time the response of the tumor to treatment. Circulating cell-free DNA may allow us to better understand tumor heterogeneity and interactions with the host genome.

RESULTS

 Integration of increasingly available omics data increases our ability to segment patients into smaller and smaller cohorts, thereby prompting a shift in our thinking about how to use these omics data. With large repositories of patient omics-outcomes data being generated, patient cohort matching algorithms have become a dominant player.

CONCLUSIONS

 The continued promise of precision oncology is to select patients who are most likely to benefit from treatment and to avoid toxicity for those who will not. The increased public availability of omics and outcomes data in patients, along with improved computational methods and resources, are making precision oncology a reality.

Emerging Trends in Clinical Research: With Implications for Population Health and Health Policy

Policy Points: Significant advances in clinical medicine that have broader societal relevance may be less accessible to population health researchers and policymakers because of increased specialization within fields. We describe important recent clinical advances and discuss their broader societal impact. These advances include more expansive strategies for disease prevention, the rise of precision medicine, applications of human microbiome research, and new and highly successful treatments for hepatitis C infection. These recent developments in clinical research raise important issues surrounding health care costs and equitable resource allocation that necessitate an ongoing dialogue among the fields of clinical medicine, population health, and health policy.

CONTEXT

Developments in clinical medicine have important implications for population health, and there is a need for interdisciplinary engagement among clinical medicine, the social sciences, and public health research. The aim of this article is to help bridge the divide between these fields by exploring major recent advances in clinical medicine that have important implications for population health.

METHODS

We reviewed the most cited articles published from 2010 to 2015 in 5 high-impact clinical journals and selected 5 randomized controlled trials and 2 related clinical practice guidelines that are broadly relevant to population health and policy.

FINDINGS

We discuss the following themes: (1) expanding indications for drug therapy and the inherent medicalization of the population as highlighted by studies and clinical guidelines supporting lower blood pressure targets or widespread statin use; (2) the tension in nutritional research between quantifying the impact of isolated nutrients and studying specific foods and dietary patterns, for example, the role of the Mediterranean diet in the primary prevention of cardiovascular disease; (3) the issue of high medication costs and the challenge of providing equitable access raised by the development of new and effective treatments for hepatitis C infection; (4) emerging clinical applications of research on the human microbiome as illustrated by fecal transplant to treat Clostridium difficile infections; and (5) the promise and limitations of precision medicine as demonstrated by the rise of novel targeted therapies in oncology.

CONCLUSIONS

These developments in clinical science hold promise for improving individual and population health and raise important questions about resource allocation, the role of prevention, and health disparities.

Targeted Therapies for Targeted Populations: Anti-EGFR Treatment for EGFR-Amplified Gastroesophageal Adenocarcinoma

Previous anti-EGFR trials in unselected patients with gastroesophageal adenocarcinoma (GEA) were resoundingly negative. We identified EGFR amplification in 5% (19/363) of patients at the University of Chicago, including 6% (8/140) who were prospectively screened with intention-to-treat using anti-EGFR therapy. Seven patients received ≥1 dose of treatment: three first-line FOLFOX plus ABT-806, one second-line FOLFIRI plus cetuximab, and three third/fourth-line cetuximab alone. Treatment achieved objective response in 58% (4/7) and disease control in 100% (7/7) with a median progression-free survival of 10 months. Pretreatment and posttreatment tumor next-generation sequencing (NGS), serial plasma circulating tumor DNA (ctDNA) NGS, and tumor IHC/FISH for EGFR revealed preexisting and/or acquired genomic events, including EGFR-negative clones, PTEN deletion, KRAS amplification/mutation, NRAS, MYC, and HER2 amplification, and GNAS mutations serving as mechanisms of resistance. Two evaluable patients demonstrated interval increase of CD3+ infiltrate, including one who demonstrated increased NKp46+, and PD-L1 IHC expression from baseline, suggesting an immune therapeutic mechanism of action. EGFR amplification predicted benefit from anti-EGFR therapy, albeit until various resistance mechanisms emerged.Significance: This paper highlights the role of EGFR inhibitors in EGFR-amplified GEA-despite negative results in prior unselected phase III trials. Using serial ctDNA and tissue NGS, we identified mechanisms of primary and acquired resistance in all patients, as well as potential contribution of antibody-dependent cell-mediated cytotoxicity to their clinical benefit. Cancer Discov; 8(6); 696-713. ©2018 AACR.See related commentary by Strickler, p. 679This article is highlighted in the In This Issue feature, p. 663.

Novel Targeted Therapies for Esophagogastric Cancer

Gastroesophageal cancer (GEC) remains a major cause of cancer-related mortality worldwide. Although the incidence of distal gastric adenocarcinoma (GC) is declining in the United States, proximal esophagogastric junction adenocarcinoma (EGJ) is increasing in incidence. GEC, including GC and EGJ, is treated uniformly in the metastatic setting. Overall survival in the metastatic setting remains poor. Molecular characterization of GEC has identified mutations and copy number variations, along with other oncogenes, biomarkers, and immuno-oncologic checkpoints that may serve as actionable therapeutic targets. This article reviews these key aberrations, their impact on protein expression, therapeutic implications, and clinical directions within each pathway.

Use of Real-World Data for the Research, Development, and Evaluation of Oncology Precision Medicines

Although randomized controlled trials remain the scientific ideal for determining the efficacy and safety of new treatments, they are sometimes insufficient to address the evidentiary requirements of regulators and payers. This is particularly the case when it comes to precision medicines because trials are often small, deliver incomplete insights into outcomes of most interest to policymakers (eg, overall survival), and may fail to address other complex diagnostic and treatment-related questions. Additional methods, both experimental and observational, are increasingly being used to fill critical evidentiary gaps. A number of modified early- and late-phase trial designs have been proposed to better support earlier biomarker validation, patient identification, and selection for regulatory studies, but there is still a need for confirmatory evidence from real-world data sources. These data are usually provided through observational, postapproval, phase IIIB and IV studies, which rely heavily on registries and other electronic data sets—most notably data from electronic health records. It is, therefore, crucial to understand what ethical, practical, and scientific challenges are raised by the use of electronic health records to generate evidence about precision medicines.