Making the investigational oncology pipeline more efficient and effective: are we headed in the right direction?

Statistical plots in oncologic imaging, a primer for neuroradiologists

The simplest approach to convey the results of scientific analysis, which can include complex comparisons, is typically through the use of visual items, including figures and plots. These statistical plots play a critical role in scientific studies, making data more accessible, engaging, and informative. A growing number of visual representations have been utilized recently to graphically display the results of oncologic imaging, including radiomic and radiogenomic studies. Here, we review the applications, distinct properties, benefits, and drawbacks of various statistical plots. Furthermore, we provide neuroradiologists with a comprehensive understanding of how to use these plots to effectively communicate analytical results based on imaging data.

Objective response rate assessment in oncology: Current situation and future expectations

The tumor objective response rate (ORR) is an important parameter to demonstrate the efficacy of a treatment in oncology. The ORR is valuable for clinical decision making in routine practice and a significant end-point for reporting the results of clinical trials. World Health Organization and Response Evaluation Criteria in Solid Tumors (RECIST) are anatomic response criteria developed mainly for cytotoxic chemotherapy. These criteria are based on the visual assessment of tumor size in morphological images provided by computed tomography (CT) or magnetic resonance imaging. Anatomic response criteria may not be optimal for biologic agents, some disease sites, and some regional therapies. Consequently, modifications of RECIST, Choi criteria and Morphologic response criteria were developed based on the concept of the evaluation of viable tumors. Despite its limitations, RECIST v1.1 is validated in prospective studies, is widely accepted by regulatory agencies and has recently shown good performance for targeted cancer agents. Finally, some alternatives of RECIST were developed as immune-specific response criteria for checkpoint inhibitors. Immune RECIST criteria are based essentially on defining true progressive disease after a confirmatory imaging. Some graphical methods may be useful to show longitudinal change in the tumor burden over time. Tumor tissue is a tridimensional heterogenous mass, and tumor shrinkage is not always symmetrical; thus, metabolic response assessments using positron emission tomography (PET) or PET/CT may reflect the viability of cancer cells or functional changes evolving after anticancer treatments. The metabolic response can show the benefit of a treatment earlier than anatomic shrinkage, possibly preventing delays in drug approval. Computer-assisted automated volumetric assessments, quantitative multimodality imaging in radiology, new tracers in nuclear medicine and finally artificial intelligence have great potential in future evaluations.

Trial Reporting in Immuno-Oncology (TRIO): An American Society of Clinical Oncology-Society for Immunotherapy of Cancer Statement

PURPOSE

To develop recommendations for clinical trial reporting that address the unique efficacy, toxicity, and combination and sequencing aspects of immuno-oncology (IO) treatments.

METHODS

ASCO and the Society for Immunotherapy of Cancer (SITC) convened a working group that consisted of practicing medical oncologists, immunologists, clinical researchers, biostatisticians, and representatives from industry and government to develop Trial Reporting in Immuno-Oncology (TRIO) recommendations. These recommendations are based on expert consensus, given that existing data to support evidence-based recommendations are limited.

CONCLUSION

The TRIO recommendations are intended to improve the reporting of IO clinical trials and thus provide more complete evidence on the relative benefits and risks of an IO therapeutic approach. Given the rapid expansion of the number of IO clinical trials and ongoing improvements to the evidence base supporting the use of IO treatments in clinical care, these recommendations will likely need regular revision as the IO field develops.

Trial Reporting in Immuno-Oncology (TRIO): an American society of clinical oncology-society for immunotherapy of cancer statement

Purpose

To develop recommendations for clinical trial reporting that address the unique efficacy, toxicity, and combination and sequencing aspects of immuno-oncology (IO) treatments.

Methods

ASCO and the Society for Immunotherapy of Cancer (SITC) convened a working group that consisted of practicing medical oncologists, immunologists, clinical researchers, biostatisticians, and representatives from industry and government to develop Trial Reporting in Immuno-Oncology (TRIO) recommendations. These recommendations are based on expert consensus, given that existing data to support evidence-based recommendations are limited.

Conclusion

The TRIO recommendations are intended to improve the reporting of IO clinical trials and thus provide more complete evidence on the relative benefits and risks of an IO therapeutic approach. Given the rapid expansion of the number of IO clinical trials and ongoing improvements to the evidence base supporting the use of IO treatments in clinical care, these recommendations will likely need regular revision as the IO field develops.

Clinical research in small genomically stratified patient populations

The paradigm of early drug development in cancer is shifting from 'histology-oriented' to 'molecularly oriented' clinical trials. This change can be attributed to the vast amount of tumour biology knowledge generated by large international research initiatives such as The Cancer Genome Atlas (TCGA) and the use of next generation sequencing (NGS) techniques developed in recent years. However, targeting infrequent molecular alterations entails a series of special challenges. The optimal molecular profiling method, the lack of standardised biological thresholds, inter- and intra-tumor heterogeneity, availability of enough tumour material, correct clinical trials design, attrition rate, logistics or costs are only some of the issues that need to be taken into consideration in clinical research in small genomically stratified patient populations. This article examines the most relevant challenges inherent to clinical research in these populations. Moreover, perspectives from the Academia point of view are reviewed as well as initiatives to be taken in forthcoming years.

Innovations in Clinical Trial Design in the Era of Molecular Profiling

Historically, cancer has been studied, and therapeutic agents have been evaluated based on organ site, clinical staging, and histology. The science of molecular profiling has expanded our knowledge of cancer at the cellular and molecular level such that numerous subtypes are being described based on biomarker expression and genetic mutations rather than traditional classifications of the disease. Drug development has experienced a concomitant revolution in response to this knowledge with many new targeted therapeutic agents becoming available, and this has necessitated an evolution in clinical trial design. The traditional, large phase II and phase III adjuvant trial models need to be replaced with smaller, shorter, and more focused trials. These trials need to be more efficient and adaptive in order to quickly assess the efficacy of new agents and develop new companion diagnostics. We are now seeing a substantial shift from the traditional multiphase trial model to an increase in phase II adjuvant and neoadjuvant trials in earlier-stage disease incorporating surrogate endpoints for long-term survival to assess efficacy of therapeutic agents in shorter time frames. New trial designs have emerged with capabilities to assess more efficiently multiple disease types, multiple molecular subtypes, and multiple agents simultaneously, and regulatory agencies have responded by outlining new pathways for accelerated drug approval that can help bring effective targeted therapeutic agents to the clinic more quickly for patients in need.

Phase I Cancer Trials and Palliative Care: Antagonism, Irrelevance, or Synergy?

This article synthesizes the presentations and conclusions of an international symposium on Phase 1 oncology trials, palliative care, and ethics held in 2014. The purpose of the symposium was to discuss the intersection of three independent trends that unfolded in the past decade. First, large-scale reviews of hundreds of Phase I trials have indicated there is a relatively low risk of serious harm and some prospect of clinical benefit that can be meaningful to patients. Second, changes in the design and analysis of Phase I trials, the introduction of "targeted" investigational agents that are generally less toxic, and an increase in Phase I trials that combine two or more agents in a novel way have changed the conduct of these trials and decreased fears and apprehensions about participation. Third, the field of palliative care in cancer has expanded greatly, offering symptom management to late-stage cancer patients, and demonstrated that it is not mutually exclusive with disease-targeted therapies or clinical research. Opportunities for collaboration and further research at the intersection of Phase 1 oncology trials and palliative care are highlighted.

Current and Evolving Methods to Visualize Biological Data in Cancer Research

Although the measurements of clinical outcomes for cancer treatments have become diverse and complex, there remains a need for clear, easily interpreted representations of patients' experiences. With oncology trials increasingly reporting non-time-to-event outcomes, data visualization has evolved to incorporate parameters such as responses to therapy, duration and degree of response, and novel representations of underlying tumor biology. We review both commonly used and newly developed methods to display outcomes in oncology, with a focus on those that have evolved to represent complex datasets.

Revisiting dosing regimen using PK/PD modeling: the MODEL1 phase I/II trial of docetaxel plus epirubicin in metastatic breast cancer patients

The MODEL1 trial is the first model-driven phase I/II dose-escalation study of densified docetaxel plus epirubicin administration in metastatic breast cancer patients, a regimen previously known to induce unacceptable life-threatening toxicities. The primary objective was to determine the maximum tolerated dose of this densified regimen. Study of the efficacy was a secondary objective. Her2-negative, hormone-resistant metastatic breast cancer patients were treated with escalating doses of docetaxel plus epirubicin every 2 weeks for six cycles with granulocyte colony stimulating factor support. A total of 16 patients were treated with total doses ranging from 85 to 110 mg of docetaxel plus epirubicin per cycle. Dose escalation was controlled by a non-hematological toxicity model. Dose densification was guided by a model of neutrophil kinetics, able to optimize docetaxel plus epirubicin dosing with respect to pre-defined acceptable levels of hematological toxicity while ensuring maximal efficacy. The densified treatment was safe since hematological toxicity was much lower compared to previous findings, and other adverse events were consistent with those observed with this regimen. The maximal tolerated dose was 100 mg given every 2 weeks. The response rate was 45 %; median progression-free survival was 10.4 months, whereas 54.6 months of median overall survival was achieved. The optimized docetaxel plus epirubicin dosing regimen led to fewer toxicities associated with higher efficacy as compared with standard or empirical densified dosing. This study suggests that model-driven dosage adjustment can lead to improved efficacy-toxicity balance in patients with cancer when several anticancer drugs are combined.

The Neoadjuvant Model Is Still the Future for Drug Development in Breast Cancer

The many improvements in breast cancer therapy in recent years have so lowered rates of recurrence that it is now difficult or impossible to conduct adequately powered adjuvant clinical trials. Given the many new drugs and potential synergistic combinations, the neoadjuvant approach has been used to test benefit of drug combinations in clinical trials of primary breast cancer. A recent FDA-led meta-analysis showed that pathologic complete response (pCR) predicts disease-free survival (DFS) within patients who have specific breast cancer subtypes. This meta-analysis motivated the FDA's draft guidance for using pCR as a surrogate endpoint in accelerated drug approval. Using pCR as a registration endpoint was challenged at ASCO 2014 Annual Meeting with the presentation of ALTTO, an adjuvant trial in HER2-positive breast cancer that showed a nonsignificant reduction in DFS hazard rate for adding lapatinib, a HER-family tyrosine kinase inhibitor, to trastuzumab and chemotherapy. This conclusion seemed to be inconsistent with the results of NeoALTTO, a neoadjuvant trial that found a statistical improvement in pCR rate for the identical lapatinib-containing regimen. We address differences in the two trials that may account for discordant conclusions. However, we use the FDA meta-analysis to show that there is no discordance at all between the observed pCR difference in NeoALTTO and the observed HR in ALTTO. This underscores the importance of appropriately modeling the two endpoints when designing clinical trials. The I-SPY 2/3 neoadjuvant trials exemplify this approach.

Joint modeling tumor burden and time to event data in oncology trials

The tumor burden (TB) process is postulated to be the primary mechanism through which most anticancer treatments provide benefit. In phase II oncology trials, the biologic effects of a therapeutic agent are often analyzed using conventional endpoints for best response, such as objective response rate and progression-free survival, both of which causes loss of information. On the other hand, graphical methods including spider plot and waterfall plot lack any statistical inference when there is more than one treatment arm. Therefore, longitudinal analysis of TB data is well recognized as a better approach for treatment evaluation. However, longitudinal TB process suffers from informative missingness because of progression or death. We propose to analyze the treatment effect on tumor growth kinetics using a joint modeling framework accounting for the informative missing mechanism. Our approach is illustrated by multisetting simulation studies and an application to a nonsmall-cell lung cancer data set. The proposed analyses can be performed in early-phase clinical trials to better characterize treatment effect and thereby inform decision-making.

Optimal design of trials to demonstrate the utility of genomically-guided therapy: Putting Precision Cancer Medicine to the test

The new age of Precision Cancer Medicine, with specific biomarkers being used to direct targeted agents, generally concerns only a subset of patients within a certain histopathologically defined tumor type. This paradigm is challenged by the need to perform widespread molecular screening in certified laboratories, with results available to clinicians within reasonable timeframe. Tumor heterogeneity and clonal evolution must be considered in the decision making process. Adaptive and innovative clinical trial designs exploring predictive algorithms and reconsideration of traditional efficacy endpoints are required to rapidly translate scientific discoveries into patient care. Furthermore, international collaboration in cancer research and open discussions on the availability of investigational agents will likely redefine the drug development and approval process in the coming years.

Accelerated approval and breakthrough therapy designation: oncology drug development on speed?

Recent advances in biotechnology have led to discoveries resulting in major improvements in the therapy of refractory malignancies, although most advanced cancers remain incurable. Thus, there is global consensus around the need to streamline the drug approval process for effective agents. Accelerated Approval and Breakthrough Therapy Designation hold the promise of making new treatments available sooner through the use of smaller studies using intermediate endpoints. Here, we consider the inherent limitations of smaller studies and discuss the strategies for hastening oncology drug development while maintaining high-efficacy standards.

Targeted therapies in neuroendocrine tumors (NET): clinical trial challenges and lessons learned

In the past 3 years, we have witnessed the completion of four randomized phase III studies in neuroendocrine tumors and the approval of two new drugs, everolimus and sunitinib, for the treatment of patients with well-differentiated pancreatic neuroendocrine tumors. These studies demonstrate a shift from case series and single-arm studies toward prospective, randomized controlled clinical trials and evidence-based therapy in the neuroendocrine tumor field. However, the clinical development of these agents also highlights the potential challenges awaiting other new drugs in this area. Herein, we discuss the strengths and weaknesses of the most recent phase II and phase III neuroendocrine tumor studies and discuss how limitations inherent in current trial design can lead to potential pitfalls. We also discuss how trial design can be improved, with the hope of increasing the number of drugs successfully developed to treat patients with neuroendocrine tumors.

Oncology drug development and approval of systemic anticancer therapy by the U.S. Food and Drug Administration

BACKGROUND

Regulatory approval of oncology drugs is the cornerstone of the development process and approval characteristics shape eventual utilization. Approval trends and characteristics provide valuable information for drug developers and regulators and ultimately affect clinicians and patients.

METHODS

Indication characteristics were tabulated for drugs approved by the U.S. Food and Drug Administration (FDA) for systemic therapy of malignancies from 1949 through October 2011. Variables included time to approval, initial/supplemental indication, tumor type, stage of disease, specification of protein expression or genetic information, drug class, trial design, concomitant agent, trial size, and endpoint.

RESULTS

A total of 121 unique anticancer agents, including 242 unique indications, were approved. The number of trials for each indication has decreased; however, trial size has increased and more randomized controlled trials have been performed. Trial designs have increasingly used time-to-event endpoints and rarely have used symptom-based primary endpoints. Approvals have been primarily single agent, with less emphasis on palliative treatments and increasing emphasis on advanced disease stages and requirements for prior therapy. Molecular specifications in labels have increased, but they are present in less than 30% of recent indications and are not associated with shorter approval times.

CONCLUSION

Approval of oncology agents is occurring in increasingly more challenging settings, suggesting gaps between eventual practice and development in potentially suboptimal indications. Molecular specifications promise to enhance development, yet widespread use in label indications has not yet been achieved.

Survival prediction in everolimus-treated patients with metastatic renal cell carcinoma incorporating tumor burden response in the RECORD-1 trial

BACKGROUND

The phase 3 RECORD-1 study demonstrated clinical benefit of everolimus over placebo (median progression-free survival: 4.9 mo compared with 1.9 mo, p<0.001) in treatment-resistant patients with metastatic renal cell carcinoma (mRCC). However, the Response Evaluation Criteria in Solid Tumors (RECIST) objective response rate was low.

OBJECTIVE

To explore the potential role of tumor burden response to everolimus in predicting patient survival.

DESIGN, SETTING, AND PARTICIPANTS

RECORD-1 patients with at least two tumor assessments (baseline and weeks 2-14) were included (n=246).

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

A multivariate Cox proportional hazard model was used to assess the impact of various prognostic factors on overall survival (OS). Components of RECIST progression were explored using univariate Cox regression.

RESULTS AND LIMITATIONS

The baseline sum of longest tumor diameters (SLD) and progression at weeks 2-14 were prognostic factors of OS by multivariate analysis. Univariate analysis at weeks 2-14 demonstrated that growth of nontarget lesions and appearance of new lesions were predictive of OS (p<0.001). This retrospective analysis used data from one arm of one trial; patients in the placebo arm were excluded because of confounding effects when they crossed over to everolimus.

CONCLUSIONS

This analysis identified baseline SLD as a predictive factor of OS, and the appearance of a new lesion or progression of a nontarget lesion at first assessment after baseline also affects OS in patients with mRCC treated with everolimus.

Proof of Concept to Clinical Confirmation: Evolving Clinical Trial Designs for Targeted Agents

No single therapy benefits the majority of patients in the practice of oncology as responses differ even among patients with similar tumor types. The variety of response to therapy witnessed while treating our patients supports the concept of personalized medicine using patients' genomic and biologic information and their clinical characteristics to make informed decisions about their treatment. Personalized medicine relies on identification and confirmation of biologic targets and development of agents to target them. These targeted agents tend to focus on subsets of patients and provide improved clinical outcomes. The continued success of personalized medicine will depend on the expedited development of new agents from proof of concept to confirmation of clinical efficacy.

From modules to medicine: How modular domains and their associated networks can enable personalized medicine

Unveiling of cancer genomes is unleashing new therapeutic strategies for cancer. With cancer parts lists in hand, new approaches to personalized medicine can be developed by understanding the assembly of cancer machines using modular domains in proteins and their associated networks. Using the Src-homology-2 (SH2) domain as an example, new profiling approaches can discern global patterns of tyrosine phosphorylation in cancer cells that can enable molecular cancer medicine. Identifying and quantifying protein-protein interactions also has the potential to subtype tumors and guide clinical decision making. These approaches should extend the impact of genomics through viewing the architecture of cancer systems and improve predictions of patient outcome and therapeutic response, as well as guide combination therapy approaches that attack cancer systems.

Development and preliminary results of the Financial Incentive Coercion Assessment questionnaire

Financial incentives are often used in research, yet no measure exists to determine whether they lead to perceptions of coercion in subjects. We present a preliminary evaluation of a recently developed Financial Incentive Coercion Assessment (FICA) questionnaire. FICA measures perceived coercion specifically related to payment for participation in a research study. Two hundred sixty-six subjects were recruited from a large randomized controlled trial; 152 returned for a 6-month follow-up and completed the FICA. Approximately 30% of participants reported the major reason for participating was "for the money," but less than 5% felt that the financial incentives were coercive. FICA results are consistent with levels of perceived coercion using an alternative measure. Initial assessment of responses on the FICA suggests that it may provide a novel approach to measuring perceived coercion from financial incentives in research. Future work will refine the FICA and analyze its psychometric properties.

Delivering affordable cancer care in high-income countries

The burden of cancer is growing, and the disease is becoming a major economic expenditure for all developed countries. In 2008, the worldwide cost of cancer due to premature death and disability (not including direct medical costs) was estimated to be US$895 billion. This is not simply due to an increase in absolute numbers, but also the rate of increase of expenditure on cancer. What are the drivers and solutions to the so-called cancer-cost curve in developed countries? How are we going to afford to deliver high quality and equitable care? Here, expert opinion from health-care professionals, policy makers, and cancer survivors has been gathered to address the barriers and solutions to delivering affordable cancer care. Although many of the drivers and themes are specific to a particular field-eg, the huge development costs for cancer medicines-there is strong concordance running through each contribution. Several drivers of cost, such as over-use, rapid expansion, and shortening life cycles of cancer technologies (such as medicines and imaging modalities), and the lack of suitable clinical research and integrated health economic studies, have converged with more defensive medical practice, a less informed regulatory system, a lack of evidence-based sociopolitical debate, and a declining degree of fairness for all patients with cancer. Urgent solutions range from re-engineering of the macroeconomic basis of cancer costs (eg, value-based approaches to bend the cost curve and allow cost-saving technologies), greater education of policy makers, and an informed and transparent regulatory system. A radical shift in cancer policy is also required. Political toleration of unfairness in access to affordable cancer treatment is unacceptable. The cancer profession and industry should take responsibility and not accept a substandard evidence base and an ethos of very small benefit at whatever cost; rather, we need delivery of fair prices and real value from new technologies.

Variability of lung tumor measurements on repeat computed tomography scans taken within 15 minutes

PURPOSE

We use changes in tumor measurements to assess response and progression, both in routine care and as the primary objective of clinical trials. However, the variability of computed tomography (CT) -based tumor measurement has not been comprehensively evaluated. In this study, we assess the variability of lung tumor measurement using repeat CT scans performed within 15 minutes of each other and discuss the implications of this variability in a clinical context.

PATIENTS AND METHODS

Patients with non-small-cell lung cancer and a target lung lesion ≥ 1 cm consented to undergo two CT scans within a period of minutes. Three experienced radiologists measured the diameter of the target lesion on the two scans in a side-by-side fashion, and differences were compared.

RESULTS

Fifty-seven percent of changes exceeded 1 mm in magnitude, and 33% of changes exceeded 2 mm. Median increase and decrease in tumor measurements were +4.3% and -4.2%, respectively, and ranged from 23% shrinkage to 31% growth. Measurement changes were within ± 10% for 84% of measurements, whereas 3% met criteria for progression according to Response Evaluation Criteria in Solid Tumors (RECIST; ≥ 20% increase). Smaller lesions had greater variability of percent measurement change (P = .005).

CONCLUSION

Apparent changes in tumor diameter exceeding 1 to 2 mm are common on immediate reimaging. Increases and decreases less than 10% can be a result of the inherent variability of reimaging. Caution should be exercised in interpreting the significance of small changes in lesion size in the care of individual patients and in the interpretation of clinical trial results.

Value and cancer care: toward an equitable future

Health care expenses in the United States are increasing inexorably. At the current rate of growth, it is anticipated that 20% of the gross national product will consist of health-related expenditures within the next decade. Cancer is the second leading cause of death in the United States, and it is increasing in prevalence because of the aging of the population and the limited number of successful prevention strategies. As the biological characteristics of cancer come into sharper focus, targeted therapies are being developed that offer the promise of increased clinical benefit with fewer toxicities than are associated with conventional treatment. Although spectacular successes are infrequent with this approach, to date, the majority of targeted therapies are modestly effective at best, and extremely costly. This observation suggests that a broadly acceptable definition of value in a cancer therapeutic agent is not at hand, but is sorely needed from the vantage points of the patient and society. A corollary issue of enormous import is how to equitably distribute the health care dollar in the service of achieving the greatest good for the greatest number. Although cancer is responsible for only 5% of the health care budget, its cost is increasing and it can be viewed as paradigmatic when contemplating the problem of equity in health care. Here, a number of concepts are discussed that focus on this goal and its implications for the cancer patient and society at large.