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Diversity and Health Disparity Issues Need to be Addressed for GWAS and Precision Medicine Studies

Curator: Stephen J. Williams, PhD

 

 

From the POLICY FORUM ETHICS AND DIVERSITY Section of Science

Ethics of inclusion: Cultivate trust in precision medicine

 See all authors and affiliations

Science  07 Jun 2019:
Vol. 364, Issue 6444, pp. 941-942
DOI: 10.1126/science.aaw8299

Precision medicine is at a crossroads. Progress toward its central goal, to address persistent health inequities, will depend on enrolling populations in research that have been historically underrepresented, thus eliminating longstanding exclusions from such research (1). Yet the history of ethical violations related to protocols for inclusion in biomedical research, as well as the continued misuse of research results (such as white nationalists looking to genetic ancestry to support claims of racial superiority), continue to engender mistrust among these populations (2). For precision medicine research (PMR) to achieve its goal, all people must believe that there is value in providing information about themselves and their families, and that their participation will translate into equitable distribution of benefits. This requires an ethics of inclusion that considers what constitutes inclusive practices in PMR, what goals and values are being furthered through efforts to enhance diversity, and who participates in adjudicating these questions. The early stages of PMR offer a critical window in which to intervene before research practices and their consequences become locked in (3).

Initiatives such as the All of Us program have set out to collect and analyze health information and biological samples from millions of people (1). At the same time, questions of trust in biomedical research persist. For example, although the recent assertions of white nationalists were eventually denounced by the American Society of Human Genetics (4), the misuse of ancestry testing may have already undermined public trust in genetic research.

There are also infamous failures in research that included historically underrepresented groups, including practices of deceit, as in the Tuskegee Syphilis Study, or the misuse of samples, as with the Havasupai tribe (5). Many people who are being asked to give their data and samples for PMR must not only reconcile such past research abuses, but also weigh future risks of potential misuse of their data.

To help assuage these concerns, ongoing PMR studies should open themselves up to research, conducted by social scientists and ethicists, that examines how their approaches enhance diversity and inclusion. Empirical studies are needed to account for how diversity is conceptualized and how goals of inclusion are operationalized throughout the life course of PMR studies. This is not limited to selection and recruitment of populations but extends to efforts to engage participants and communities, through data collection and measurement, and interpretations and applications of study findings. A commitment to transparency is an important step toward cultivating public trust in PMR’s mission and practices.

From Inclusion to Inclusive

The lack of diverse representation in precision medicine and other biomedical research is a well-known problem. For example, rare genetic variants may be overlooked—or their association with common, complex diseases can be misinterpreted—as a result of sampling bias in genetics research (6). Concentrating research efforts on samples with largely European ancestry has limited the ability of scientists to make generalizable inferences about the relationships among genes, lifestyle, environmental exposures, and disease risks, and thereby threatens the equitable translation of PMR for broad public health benefit (7).

However, recruiting for diverse research participation alone is not enough. As with any push for “diversity,” related questions arise about how to describe, define, measure, compare, and explain inferred similarities and differences among individuals and groups (8). In the face of ambivalence about how to represent population variation, there is ample evidence that researchers resort to using definitions of diversity that are heterogeneous, inconsistent, and sometimes competing (9). Varying approaches are not inherently problematic; depending on the scientific question, some measures may be more theoretically justified than others and, in many cases, a combination of measures can be leveraged to offer greater insight (10). For example, studies have shown that American adults who do not self-identify as white report better mental and physical health if they think others perceive them as white (1112).

The benefit of using multiple measures of race and ancestry also extends to genetic studies. In a study of hypertension in Puerto Rico, not only did classifications based on skin color and socioeconomic status better predict blood pressure than genetic ancestry, the inclusion of these sociocultural measures also revealed an association between a genetic polymorphism and hypertension that was otherwise hidden (13). Thus, practices that allow for a diversity of measurement approaches, when accompanied by a commitment to transparency about the rationales for chosen approaches, are likely to benefit PMR research more than striving for a single gold standard that would apply across all studies. These definitional and measurement issues are not merely semantic. They also are socially consequential to broader perceptions of PMR research and the potential to achieve its goals of inclusion.

Study Practices, Improve Outcomes

Given the uncertainty and complexities of the current, early phase of PMR, the time is ripe for empirical studies that enable assessment and modulation of research practices and scientific priorities in light of their social and ethical implications. Studying ongoing scientific practices in real time can help to anticipate unintended consequences that would limit researchers’ ability to meet diversity recruitment goals, address both social and biological causes of health disparities, and distribute the benefits of PMR equitably. We suggest at least two areas for empirical attention and potential intervention.

First, we need to understand how “upstream” decisions about how to characterize study populations and exposures influence “downstream” research findings of what are deemed causal factors. For example, when precision medicine researchers rely on self-identification with U.S. Census categories to characterize race and ethnicity, this tends to circumscribe their investigation of potential gene-environment interactions that may affect health. The convenience and routine nature of Census categories seemed to lead scientists to infer that the reasons for differences among groups were self-evident and required no additional exploration (9). The ripple effects of initial study design decisions go beyond issues of recruitment to shape other facets of research across the life course of a project, from community engagement and the return of results to the interpretation of study findings for human health.

Second, PMR studies are situated within an ecosystem of funding agencies, regulatory bodies, disciplines, and other scholars. This partly explains the use of varied terminology, different conceptual understandings and interpretations of research questions, and heterogeneous goals for inclusion. It also makes it important to explore how expectations related to funding and regulation influence research definitions of diversity and benchmarks for inclusion.

For example, who defines a diverse study population, and how might those definitions vary across different institutional actors? Who determines the metrics that constitute successful inclusion, and why? Within a research consortium, how are expectations for data sharing and harmonization reconciled with individual studies’ goals for recruitment and analysis? In complex research fields that include multiple investigators, organizations, and agendas, how are heterogeneous, perhaps even competing, priorities negotiated? To date, no studies have addressed these questions or investigated how decisions facilitate, or compromise, goals of diversity and inclusion.

The life course of individual studies and the ecosystems in which they reside cannot be easily separated and therefore must be studied in parallel to understand how meanings of diversity are shaped and how goals of inclusion are pursued. Empirically “studying the studies” will also be instrumental in creating mechanisms for transparency about how PMR is conducted and how trade-offs among competing goals are resolved. Establishing open lines of inquiry that study upstream practices may allow researchers to anticipate and address downstream decisions about how results can be interpreted and should be communicated, with a particular eye toward the consequences for communities recruited to augment diversity. Understanding how scientists negotiate the challenges and barriers to achieving diversity that go beyond fulfilling recruitment numbers is a critical step toward promoting meaningful inclusion in PMR.

Transparent Reflection, Cultivation of Trust

Emerging research on public perceptions of PMR suggests that although there is general support, questions of trust loom large. What we learn from studies that examine on-the-ground approaches aimed at enhancing diversity and inclusion, and how the research community reflects and responds with improvements in practices as needed, will play a key role in building a culture of openness that is critical for cultivating public trust.

Cultivating long-term, trusting relationships with participants underrepresented in biomedical research has been linked to a broad range of research practices. Some of these include the willingness of researchers to (i) address the effect of history and experience on marginalized groups’ trust in researchers and clinicians; (ii) engage concerns about potential group harms and risks of stigmatization and discrimination; (iii) develop relationships with participants and communities that are characterized by transparency, clear communication, and mutual commitment; and (iv) integrate participants’ values and expectations of responsible oversight beyond initial informed consent (14). These findings underscore the importance of multidisciplinary teams that include social scientists, ethicists, and policy-makers, who can identify and help to implement practices that respect the histories and concerns of diverse publics.

A commitment to an ethics of inclusion begins with a recognition that risks from the misuse of genetic and biomedical research are unevenly distributed. History makes plain that a multitude of research practices ranging from unnecessarily limited study populations and taken-for-granted data collection procedures to analytic and interpretive missteps can unintentionally bolster claims of racial superiority or inferiority and provoke group harm (15). Sustained commitment to transparency about the goals, limits, and potential uses of research is key to further cultivating trust and building long-term research relationships with populations underrepresented in biomedical studies.

As calls for increasing diversity and inclusion in PMR grow, funding and organizational pathways must be developed that integrate empirical studies of scientific practices and their rationales to determine how goals of inclusion and equity are being addressed and to identify where reform is required. In-depth, multidisciplinary empirical investigations of how diversity is defined, operationalized, and implemented can provide important insights and lessons learned for guiding emerging science, and in so doing, meet our ethical obligations to ensure transparency and meaningful inclusion.

References and Notes

  1. C. P. Jones et al Ethn. Dis. 18496 (2008).
  2. C. C. GravleeA. L. NonC. J. Mulligan
  3. S. A. Kraft et al Am. J. Bioeth. 183 (2018).
  4. A. E. Shields et al Am. Psychol. 6077 (2005).

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Cancer initiatives

Larry H. Bernstein, MD, FCAP, Curator

LPBI

 

Updated 4/12/2019

AACR 2016: Biden Calls for Overhauling Cancer Research Incentives

http://www.genengnews.com/gen-news-highlights/aacr-2016-biden-calls-for-overhauling-cancer-research-incentives/81252636/

 

The first priority cited by the vice president was data sharing. Biden defended the concept as essential to advancing the process of cancer research and countered a January 21 New England Journal of Medicine editorial in which editor-in-chief Jeffrey Drazen, M.D., contended that data sharing could breed data “parasites.”

Four days later, Dr. Drazen clarified NEJM’s position by adding that with “appropriate systems” in place, “we will require a commitment from authors to make available the data that underlie the reported results of their work within 6 months after we publish them.”

Other priorities Biden said should serve as the basis of new incentives:

  • Involve patients in clinical trial design—Raising awareness of trials, and allowing patients to participate in how they are designed and conducted, could help address the difficulty of recruiting patients for studies. Only 4% of cancer patients are involved in a trial, he said.
  • “Let scientists do science”—Biden contrasted unfavorably NIH’s roughly 1-year process for decisions on grants to that of the Prostate Cancer Foundation, which limits grant applications to 10 pages and decides on those funding requests within 30 days: “Why is it that it takes multiple submissions and more than a year to get an answer from us?” Biden said.
  • Encourage grants from younger researchers—Biden decried the current professional system under which younger researchers are sidetracked for years doing administrative work in labs before they can pursue their own research grants: “It’s like asking Derek Jeter to take several years off to sell bonds to build Yankee Stadium,” the VP quipped.
  • Measure progress by outcomes—Rather than the quantity of research papers generated by grants, Biden said, “what you propose and how it affects patients, it seems to me, should be the basis of whether you continue to get the grant.”
  • Promote open-access publication of results—Biden criticized academic publishing’s reliance on paid-subscription journals that block content behind paywalls and which own data for up to a year. He contrasted that system with the Bill and Melinda Gates Foundation’s stipulation that the research it funds be published in an open-access journal and be freely available once published.
  • Reward verification—Research that verifies results through replication should be encouraged, Biden said, which acknowledging that few people now get such funding.

Biden recalled how following Beau’s diagnosis with cancer, he and his wife Jill Biden, Ed.D., who introduced the VP at the AACR event, “had access to the best doctors in the world.”

“The more we talked to them, the more we understood that we are on the cusp of a real inflection point in the fight against cancer.”

Updated 4/12/2019

Pediatric Cancer Initiatives

Data Sharing for Pediatric Cancers: President Trump Announces Pledge to Fight Childhood Cancer Will Involve Genomic Data Sharing Effort

In the journal Science, Drs. Olena Morozova Vaske ( and David Haussler University of California, Santa Cruz) recently wrote an editorial entitled “Data Sharing for Pediatric Cancers“, in which they discuss the implications of President Trump’s intentions to increase funding for pediatric cancers with a corresponding effort for genomic data sharing.  Also discussed is the current efforts on pediatric genomic data sharing as well as some opinions on coordinating these efforts on a world-wide scale to benefit the patients, researchers, and clinicians.

The article is found below as it is a very good read on the state of data sharing in the pediatric cancer field and offers some very good insights in designing such a worldwide system to handle this data sharing, including allowing patients governance over their own data.

Last month, in a conference call held by the U.S. Department of Health and Human Services and National Institutes of Health (NIH), it was revealed that a large focus of President Trump’s pledge to fund childhood cancer research will be genomic data sharing. Although the United States has only 5% of the world’s pediatric cancer cases, it has disproportionately more resources and access to genomic information compared to low-income countries. We hope that the spotlight on genomic data sharing in the United States will galvanize the world’s pediatric cancer community to elevate genomic data sharing to a level where its full potential can finally be realized.

Pediatric cancers are rare, affecting 50 to 200 children per million a year worldwide. Thus, with 16 different major types and many subtypes, no cancer center encounters large cohorts of patients with the same diagnosis. To advance their understanding of particular cancer subtypes, pediatric oncologists must have access to data from similar cases at other centers. Because subtypes of pediatric cancer are rare, assembling large cohorts is a limiting factor in clinical trials as well. Here, too, data sharing is the first critical step.

Typically, pediatric cancers don’t have the number of mutations that make immunotherapies effective, and only a few subtypes have recurrent mutations that can be used to develop gene-targeted therapies. However, the abnormal expression level of genes gives a vivid picture of genetic misregulation, and just sharing this information would be a huge step forward. Using gene expression and mutation data, analysis of genetic misregulation in different pediatric cancer subtypes could point the way to new treatments.

A major challenge in genomic data sharing is the patient’s young age, which frequently precludes an opportunity for informed consent. Compounding this, the rarity of subtypes requires the aggregation of patients from multiple jurisdictions, raising barriers to assembling large representative data sets. A greater percentage of children than adults with cancer participate in research studies, and children often participate in multiple studies. However, this means that data collected on individual children may be found at multiple institutions, creating difficulties if there are no standards for data sharing.

To enable effective sharing of genomic and clinical data, the Global Alliance for Genomics and Health has developed the Key Implications for Data Sharing (KIDS) framework for pediatric genomics. The recommendations include involving children in the data-sharing decision-making process and imposing an ethical obligation on data generators to provide children and parents with the opportunity to share genomic and clinical information with researchers. Although KIDS guidelines are not legally binding, they could inform policy development worldwide.

To advance the sharing culture, along with the NIH, pediatric cancer foundations such as the St. Baldrick’s Foundation and Alex’s Lemonade Stand Foundation have incorporated genomic data-sharing requirements into their grants processes. Researchers and clinicians around the world have created dozens of pediatric cancer genomic databases and portals, but pulling these together into a larger network is problematic, especially for patients with data at more than one institution, as patient identifiers are stripped from shared data. However, initiatives like the Children’s Oncology Group’s Project Every Child and the European Network for Cancer Research in Children and Adolescents’ Unified Patient Identity may resolve this issue.

We urge the creators of pediatric cancer genomic resources to collaborate and build a real-time federated data-sharing system, and hope that the new U.S. initiative will inspire other countries to link databases rather than just create new siloed regional resources. The great advances in information technology and life sciences in the last decades have given us a new opportunity to save our children from the scourge of cancer. We must resolve to use them.

Source: Olena Morozova Vaske and David Haussler.  Science; 363(6432): 1125 (2019). Data sharing for pediatric cancers. 

NIH-NCI Initiative: International collaboration to create new cancer models to accelerate research

LIVE 1:45 pm – 3:10 pm 4/25/2016 Forum Opening, A War or Moonshot: Where Do We Stand? Creating a Disruptive Cancer Pipeline @2016 World Medical Innovation Forum: CANCER, April 25-27, 2016, Westin Hotel, Boston

Will President Obama’ s Cancer Immunotherapy Colloquium (dubbed Moonshot) mean Government is Fully Behind the War on Cancer or have we heard this before?

Exome Aggregation Consortium (ExAC), generated the largest catalogue so far of variation in human protein-coding regions: Sequence data of 60,000 people, NOW is a publicly accessible database

Healthcare conglomeration to access Big Data and lower costs

 

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Larry H Bernstein, MD, FCAP
Pharmaceutical Intelligence

UPDATED 4/23/2020:  New Design for Phase 1 pediatric oncology trials to expedite dose escalation studies.

Clinical Trials Revisited

https://pharmaceuticalintelligence.com/2013/04/03/clinical-trials-revisit/

Cancer Clinical Trials of Tomorrow

Advances in genomics and cancer biology will alter the design of human cancer studies

By Tomasz M. Beer | April 1, 2013   The Scientist
We stand on the cusp of significant change in the fundamental structure of cancer clinical trials, as the emphasis begins to shift from large-scale studies of relatively unselected patients to smaller studies testing more narrowly targeted therapies in molecularly characterized populations.
The previous (and still current) generation of trials established the cancer treatment standards used today. Trials that demonstrated the value of combination chemotherapy in the adjuvant treatment of breast cancer are an excellent example. Meticulous development of treatment regimens through Phase 1 and Phase 2 trials, followed by large-scale comparisons of the new regimens to established treatment protocols, have defined the modern practice of oncology for the last 4 decades. Future cancer clinical trials will be very different from those of the past, adopting a more personalized, sometimes called “precision,” approach.
It is, of course, not entirely true that past clinical trials did not include efforts to target treatments to the right patients. Where possible, targeted therapies are already being implemented. Using the presence of endocrine receptors to guide endocrine therapy for breast cancer was one of the first forays into molecular selection of patients. Unfortunately, the ability to select subgroups of patients for study has been severely curtailed by a still-limited knowledge of human cancer biology.
This is rapidly changing, however, thanks to advances in genomics and comprehensive cancer biology research over the last decade. Large-scale efforts, such as The Cancer Genome Atlas, are comprehensively defining many of the crucial molecular characteristics of human malignancies by illuminating genetic alterations that are clinically and biologically important, and which, by virtue of their functional roles, are viable targets for cancer treatment. At the same time, the ability to design small-molecule inhibitors of specific cancer targets is rapidly accelerating. In 2011, two new agents exemplified the power of these trends: crizotinib was approved for the treatment of lung cancers that harbor a specific mutation in the ALK gene, and vemurafenib was approved for the treatment of melanomas with a specific BRAF mutation. In both cases, the drugs were approved along with companion diagnostic tests that identify patients with the target mutation, who are therefore likely to benefit from treatment.

Smaller, more precise trials ahead

Clinical trials are being transformed by these trends. It will not happen overnight, as the knowledge of cancer biology and the availability of targeted agents are uneven. Unselected populations of patients will still be studied, but it is inevitable that there will be a rise in the number of trials that incorporate molecular tumor testing prior to treatment, with treatment selection informed by the molecular features of each individual’s cancer. Such personalized trials have the potential to yield better outcomes by increasing the probability of response and to employ less toxic therapies by increasingly targeting cancer-specific functions, rather than normal proliferative functions.
To the extent that targeted therapies will prove more effective when given to selected patients, clinical trials should get dramatically smaller. Trial size is largely driven by how effective the treatment is expected to be, so fewer participants are needed when the therapeutic benefit is larger. But the promise of smaller trials will not to be universal; for example, when two targeted agents are compared to one another in the same molecularly selected population, the differences in efficacy may be small and larger trials will be required.
As approaches to cancer treatment advance, there will need to be continual engagement with patients and with cancer survivors.
Furthermore, smaller trials may not necessarily move faster or be easier to complete, as they will require the “right patients,” who may be hard to find. Many of the mutations that represent promising targets are present in a minority of tumors. Today, molecular characterization of tumors is often done as part of the screening process for each trial. Many, and sometimes most, of the patients prove ineligible, making this approach frustrating and difficult to carry out. A better avenue of attack would be to make comprehensive molecular characterization of tumors a routine part of establishing a patient’s eligibility for a range of therapies. With the plummeting cost of genomic analysis, one can envision a day in the near future when a complete cancer genome (and perhaps other molecular evaluations) becomes a standard component of an initial diagnostic evaluation. Patients will be armed with molecular information about their own tumors, and thus able to make more-informed decisions about standard and investigational therapies that match the mutations driving their cancer.

New challenges

The road to personalized and targeted treatment strategies will offer new challenges. For rare targets that are present in a minority of cases across many different types of cancers, one will have to consider clinical trials that include a number of different cancers. There are many design pitfalls to such trials, chiefly the additional clinical and molecular heterogeneity introduced by the inclusion of more than one cancer type. Despite these challenges, it will inevitably make sense in some settings to select patients who share a particular tumor biology, regardless of the tissue of origin.
Another major challenge is how to combine targeted therapies to improve clinical outcomes. To date, targeted therapies have not been able to cure advanced solid tumors. Clinical benefits, while sometimes quite impressive when compared to marginally effective treatments, still fall far short. It stands to reason that redundant survival and growth pathways enable tumors to overcome therapies that inhibit a single target. The simultaneous inhibition of relevant redundant pathways may yield dramatically better results, but will also dramatically increase the complexity of molecularly personalized clinical trials.
As approaches to cancer treatment advance, there will need to be continual engagement with patients and with cancer survivors. Fewer than 5 percent of adult cancer patients participate in a clinical trial. To carry out meaningful clinical trials in the future, that number must increase. This will be most important for treatments that target relatively rare mutations; a large number of potential volunteers will have to be screened to identify a sufficient number who harbor the relevant target. To succeed, we must partner with a much larger fraction of cancer patients.
Designing and executing future cancer clinical trials will not be easy, but physician-scientists are armed with a fast-growing body of omics-informed knowledge with which to surmount these hurdles.
Tomasz M. Beer is deputy director of the Knight Cancer Institute and a professor of medicine at Oregon Health & Science University in Portland. He is the coauthor of Cancer Clinical Trials: A Commonsense Guide to Experimental Cancer Therapies and Clinical Trials. Written for people living with cancer, the book is accompanied by a blog (www.cancer-clinical-trials.com) that seeks to disseminate knowledge about clinical trials.

Tags

tumor suppression, tumor heterogeneity, genetics & genomics, disease/medicine, clinical trials, chemotherapy, cancer genomics and cancer

UPDATED 4/23/2020:  New Design for Phase 1 pediatric oncology trials to expedite dose escalation studies.

 

REVIEW

Ushering in the next generation of precision trials for pediatric cancer

Steven G. DuBois, Laura B. Corson, Kimberly Stegmaier, Katherine A. Janeway

Science  15 Mar 2019:Vol. 363, Issue 6432, pp. 1175-1181 DOI: 10.1126/science.aaw4153

 

Abstract

Cancer treatment decisions are increasingly based on the genomic profile of the patient’s tumor, a strategy called “precision oncology.” Over the past few years, a growing number of clinical trials and case reports have provided evidence that precision oncology is an effective approach for at least some children with cancer. Here, we review key factors influencing pediatric drug development in the era of precision oncology. We describe an emerging regulatory framework that is accelerating the pace of clinical trials in children as well as design challenges that are specific to trials that involve young cancer patients. Last, we discuss new drug development approaches for pediatric cancers whose growth relies on proteins that are difficult to target therapeutically, such as transcription factors.

Some terms from the bibliography:

3+3 design: A commonly used rule-based design for phase 1 clinical trials in which patients are enrolled in cohorts of three patients, and decisions to increase or decrease the dose level for the next three participants are based on toxicities observed in those three patients.

 

Basket trial: A precision oncology trial design in which patients with many different cancer types are enrolled, the tumor is tested for a set of biomarkers of interest, and then patients are assigned to one of several clinical trial subprotocols based on the presence of a biomarker corresponding to a particular molecularly targeted therapy.

 

Bayesian model–based trial designs: A broad class of trial designs that use data known before the trial as well as data obtained during the conduct of the trial to adapt trial parameters as more information becomes available

Continual reassessment method: One example of a Bayesian model–based trial design in which an initial mathematical model of the relationship between drug dose and probability of unacceptable toxicity is continually updated as new information becomes available to assign subsequent patients to a dose anticipated to have an unacceptable toxicity rate below a set rate.

First-in-child trial: The first clinical trial of a specific agent to include a pediatric population, traditionally considered patients <18 years of age.

 

Rolling 6 design: A variation of the 3+3 design in which up to six participants may be enrolled to a dosing cohort before enrollment pauses to assess toxicity.

Safety run-in: An initial component of a phase 2 or phase 3 trial in which a small group of patients are treated with a previously untested regimen to evaluate toxicity before opening the trial to a larger group of participants.

Umbrella trial: A precision oncology trial design in which patients with a specific cancer type are enrolled, tumor is tested for a set of biomarkers of interest, and then patients are assigned to one of several clinical trial subprotocols based on the presence of a biomarker corresponding to a particular molecularly targeted therapy.

 

In this review article, DuBois et al describe new paradigms for pediatric precision oncology trial design and how these designs should be contrasted with the old models and differentiate from the design for these types of trials in the adult.  As the genomic landscape of pediatric tumors is becoming clearer (12) the authors noticed two themes which are becoming evident:

  1. Pediatric cancers harbor certain genomic mutations rarely seen in adult cancers
  2. Pediatric cancers share some genomic alterations and mutational gene signatures with adult tumors

However there is only a small number of pediatric clinical trials to investigate if specific genetic mutations predict outcome to a given personalized therapy.

            Thus, there an urgent need for precision clinical trials in pediatric cancers.

Several reviews have described numerous ongoing and recently completed trials however most are phase 1 dose escalation trials including basket trials and umbrella trials but based on previous data from adult trials using the same precision drug.  For example, pediatric trials involving the TRK inhibitor laratrectinib in tumors harboring a NTRK fusion gene or a pediatric crizotinib trial for pediatric glioblastomas having an ALK fusion protein have shown great success yet most of the early phase 1 work was based on adults or carried out in a way that does not take advantage of the new regulatory framework designed to expedite new drugs for adult precision medicines.

Speeding up the early phase trials in pediatric cancers: new trial design paradigms

Dose escalation phase I trials have, traditionally been the starting point for clinical development of new pediatric anticancer drugs however these first in child trials have seriously lagged their adult counterparts by many years.  These trials relied on the standard 3 x 3  or rolling six trial design, and doses escalated until a pediatric MTD  (maximum tolerated dose) was achieved.  In recent years new precision medicine pediatric trial design has been adopted to expedite the process, based on the fundamental shift in thinking that many new oncology agents will not have a true MTD when tested in adults.

Doses in phase 1 trials for targeted therapies like those in precision medicine are usually escalated based on considerations other than toxicity, like pharmacodynamics or biomarker analysis.  A pediatric phase 1 dose escalation trial may require more subjects than an adult trial.  But

although these newer approaches to early-phase trial design more efficiently establish a pediatric dose, they do little to advance our understanding of with patients are most likely to benefit from a new therapy.

Thus the need for good biomarkers to be included early on in these initial trial designs.  For example, Dana Farber’s first in child clinical trial NCT03654716, a Phase 1 Study of the Dual MDM2/MDMX Inhibitor ALRN-6924 in Pediatric Cancer (as a possible treatment for resistant (refractory) solid tumor, brain tumor, lymphoma or leukemia), are reducing the time children are waiting for entry into a trial, as unselected patients can enroll and the biomarker, increased MDM2 expression is used to determine those patients who go on to phase 2 dose escalation. In other cases, such as NCI Children’s Oncology Group basket trials, they have completely supplanted formal phase 1 trial design and instead incorporated molecularly targeted therapies based on adult doses but adjusted for patient size.  The use of combinations with traditional therapies in trial design is also helping to speed up the process for enrollment.  The authors also suggest that tumor profiling is pertinent however should be put in trial design so the costs to patients can be covered by the trial funds.

 

Figure 1Fig. 1 Evolution of precision trials for pediatric cancer.

Illustration: Kellie Holoski/Science

Source: Ushering in the next generation of precision trials for pediatric cancer BY STEVEN G. DUBOIS, LAURA B. CORSON, KIMBERLY STEGMAIER, KATHERINE A. JANEWAY SCIENCE 15 MAR 2019 : 1175-1181 https://science.sciencemag.org/content/363/6432/1175

 

  1. S. N. Gröbner, B. C. Worst, J. Weischenfeldt, I. Buchhalter, K. Kleinheinz, V. A. Rudneva, P. D. Johann, G. P. Balasubramanian, M. Segura-Wang, S. Brabetz, S. Bender, B. Hutter, D. Sturm, E. Pfaff, D. Hübschmann, G. Zipprich, M. Heinold, J. Eils, C. Lawerenz, S. Erkek, S. Lambo, S. Waszak, C. Blattmann, A. Borkhardt, M. Kuhlen, A. Eggert, S. Fulda, M. Gessler, J. Wegert, R. Kappler, D. Baumhoer, S. Burdach, R. Kirschner-Schwabe, U. Kontny, A. E. Kulozik, D. Lohmann, S. Hettmer, C. Eckert, S. Bielack, M. Nathrath, C. Niemeyer, G. H. Richter, J. Schulte, R. Siebert, F. Westermann, J. J. Molenaar, G. Vassal, H. Witt, B. Burkhardt, C. P. Kratz, O. Witt, C. M. van Tilburg, C. M. Kramm, G. Fleischhack, U. Dirksen, S. Rutkowski, M. Frühwald, K. von Hoff, S. Wolf, T. Klingebiel, E. Koscielniak, P. Landgraf, J. Koster, A. C. Resnick, J. Zhang, Y. Liu, X. Zhou, A. J. Waanders, D. A. Zwijnenburg, P. Raman, B. Brors, U. D. Weber, P. A. Northcott, K. W. Pajtler, M. Kool, R. M. Piro, J. O. Korbel, M. Schlesner, R. Eils, D. T. W. Jones, P. Lichter, L. Chavez, M. Zapatka, S. M. Pfister, ICGC PedBrain-Seq Project, ICGC MMML-Seq Project, The landscape of genomic alterations across childhood cancers. Nature 555, 321–327 (2018). 10.1038/nature25480pmid:29489754

 

2.  X. Ma, Y. Liu, Y. Liu, L. B. Alexandrov, M. N. Edmonson, C. Gawad, X. Zhou, Y. Li, M. C. Rusch, J. Easton, R. Huether, V. Gonzalez-Pena, M. R. Wilkinson, L. C. Hermida, S. Davis, E. Sioson, S. Pounds, X. Cao, R. E. Ries, Z. Wang, X. Chen, L. Dong, S. J. Diskin, M. A. Smith, J. M. Guidry Auvil, P. S. Meltzer, C. C. Lau, E. J. Perlman, J. M. Maris, S. Meshinchi, S. P. Hunger, D. S. Gerhard, J. Zhang, Pan-cancer genome and transcriptome analyses of 1,699 paediatric leukaemias and solid tumours. Nature 555, 371–376 (2018). 10.1038/nature25795pmid:29489755

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