Review of phase II trial designs used in studies of molecular targeted agents: outcomes and predictors of success in phase III

Clinical and pathological predictors of engraftment for patient-derived xenografts in lung adenocarcinoma

Patient-derived xenografts (PDXs) are increasingly utilized in preclinical drug efficacy studies due to their ability to retain the molecular, histological, and drug response characteristics of patient tumors. This study aimed to investigate the factors influencing the successful engraftment of PDXs. Lung adenocarcinoma PDXs were established using freshly resected tumor tissues obtained through surgery. Radiological data of pulmonary nodules from this PDX cohort were analyzed, categorizing them into solid tumors and tumors with ground-glass opacity (GGO) based on preoperative CT images. Gene mutation status was obtained from next generation sequencing data and MassARRAY panel. A total of 254 resected primary lung adenocarcinomas were utilized for PDX establishment, with successful initial engraftment in 58 cases (22.8 %); stable engraftment defined as at least three serial passages was observed in 43 cases (16.9 %). The stable engraftment rates of PDXs from solid tumors and tumors with GGO were 22.1 % (42 of 190 cases) and 1.6 % (1 of 64 cases), respectively (P < 0.001). Adenocarcinomas with advanced stage, poor differentiation, solid histologic subtype, and KRAS or TP53 gene mutations were associated with stable PDX engraftment. Avoiding tumors with GGO features could enhance the cost-effectiveness of establishing PDX models from early-stage resected lung adenocarcinomas.

Patient eligibility for trials with imaging response assessment at the time of molecular tumor board presentation

PURPOSE

To assess the eligibility of patients with advanced or recurrent solid malignancies presented to a molecular tumor board (MTB) at a large precision oncology center for inclusion in trials with the endpoints objective response rate (ORR) or duration of response (DOR) based on Response Evaluation Criteria in Solid Tumors (RECIST version 1.1).

METHODS

Prospective patients with available imaging at the time of presentation in the MTB were included. Imaging data was reviewed for objectifiable measurable disease (MD) according to RECIST v1.1. Additionally, we evaluated the patients with MD for representativeness of the identified measurable lesion(s) in relation to the overall tumor burden.

RESULTS

262 patients with different solid malignancies were included. 177 patients (68%) had MD and 85 (32%) had non-measurable disease (NMD) at the time point of MTB presentation in accordance with RECIST v1.1. MD was not representative of the overall tumor burden in eleven patients (6%). The main reasons for NMD were lesions with longest diameter shorter than 10 mm (22%) and non-measurable peritoneal carcinomatosis (18%). Colorectal cancer and malignant melanoma displayed the highest rates of MD (> 75%). In contrast, gastric cancer, head and neck malignancies, and ovarian carcinoma had the lowest rates of MD (< 55%). In case of MD, the measurable lesions were representative of the overall tumor burden in the vast majority of cases (94%).

CONCLUSION

Approximately one third of cancer patients with advanced solid malignancies are not eligible for treatment response assessment in trials with endpoints ORR or DOR at the time of MTB presentation. The rate of patients eligible for trials with imaging endpoints differs significantly based on the underlying malignancy and should be taken under consideration during the planning of new precision oncology trials.

New Anticancer Drugs: Reliably Assessing "Value" While Addressing High Prices

Countries face challenges in paying for new drugs. High prices are driven in part by exploding drug development costs, which, in turn, are driven by essential but excessive regulation. Burdensome regulation also delays drug development, and this can translate into thousands of life-years lost. We need system-wide reform that will enable less expensive, faster drug development. The speed with which COVID-19 vaccines and AIDS therapies were developed indicates this is possible if governments prioritize it. Countries also differ in how they value drugs, and generally, those willing to pay more have better, faster access. Canada is used as an example to illustrate how "incremental cost-effectiveness ratios" (ICERs) based on measures such as gains in "quality-adjusted life-years" (QALYs) may be used to determine a drug's value but are often problematic, imprecise assessments. Generally, ICER/QALY estimates inadequately consider the impact of patient crossover or long post-progression survival, therapy benefits in distinct subpopulations, positive impacts of the therapy on other healthcare or societal costs, how much governments willingly might pay for other things, etc. Furthermore, a QALY value should be higher for a lethal or uncommon disease than for a common, nonlethal disease. Compared to international comparators, Canada is particularly ineffective in initiating public funding for essential new medications. Addressing these disparities demands urgent reform.

Comparison of efficacy discrepancy between early-phase clinical trials and phase III trials of PD-1/PD-L1 inhibitors

BACKGROUND

Phase III clinical trials are pivotal for evaluating therapeutics, yet a concerning failure rate has been documented, particularly impacting oncology where accelerated approvals of immunotherapies are common. These failures are predominantly attributed to a lack of therapeutic efficacy, indicating overestimation of results from phase II studies. Our research aims to systematically assess overestimation in early-phase trials involving programmed cell death-1 (PD-1)/programmed cell death-ligand 1(PD-L1) inhibitors compared with phase III trials and identify contributing factors.

METHODS

We matched 51 pairs of early-phase and phase III clinical trials from a pool of over 9,600 PD-1/PD-L1 inhibitor trials. The matching criteria included identical treatment regimens, cancer types, treatment lines, and biomarker enrichment strategies. To assess overestimation, we compared the overall response rates (ORR) between early-phase and phase III trials. We established independent variables related to eligibility criteria, and trial design features of participants to analyze the factors influencing the observed discrepancy in efficacy between the two phases through univariable and multivariable logistic analyses.

RESULT

Early-phase trial outcomes systematically overestimated the subsequent phase III results, yielding an odds ratio (OR) comparing ORR in early-phase versus phase III: 1.66 (95% CI: 1.43 to 1.92, p<0.05). This trend of inflated ORR was consistent across trials testing PD-1/PD-L1 monotherapies and combination therapies involving PD-1/PD-L1. Among the examined factors, the exclusion of patients with autoimmune diseases was significantly associated with the disparity in efficacy between early-phase trials and phase III trials (p=0.023). We calculated a Ward statistic of 2.27 to validate the effectiveness of the model.

CONCLUSION

These findings underscore the tendency of overestimation of efficacy in early-phase trials involving immunotherapies. The observed differences could be attributed to variations in the inclusion of patients with autoimmune disorders in early-phase trials. These insights have the potential to inform stakeholders in the future development of cancer immunotherapies.

BASIC: A Bayesian adaptive synthetic-control design for phase II clinical trials

BACKGROUND

Randomized controlled trials are considered the gold standard for evaluating experimental treatments but often require large sample sizes. Single-arm trials require smaller sample sizes but are subject to bias when using historical control data for comparative inferences. This article presents a Bayesian adaptive synthetic-control design that exploits historical control data to create a hybrid of a single-arm trial and a randomized controlled trial.

METHODS

The Bayesian adaptive synthetic control design has two stages. In stage 1, a prespecified number of patients are enrolled in a single arm given the experimental treatment. Based on the stage 1 data, applying propensity score matching and Bayesian posterior prediction methods, the usefulness of the historical control data for identifying a pseudo sample of matched synthetic-control patients for making comparative inferences is evaluated. If a sufficient number of synthetic controls can be identified, the single-arm trial is continued. If not, the trial is switched to a randomized controlled trial. The performance of The Bayesian adaptive synthetic control design is evaluated by computer simulation.

RESULTS

The Bayesian adaptive synthetic control design achieves power and unbiasedness similar to a randomized controlled trial but on average requires a much smaller sample size, provided that the historical control data patients are sufficiently comparable to the trial patients so that a good number of matched controls can be identified in the historical control data. Compared to a single-arm trial, The Bayesian adaptive synthetic control design yields much higher power and much smaller bias.

CONCLUSION

The Bayesian adaptive synthetic-control design provides a useful tool for exploiting historical control data to improve the efficiency of single-arm phase II clinical trials, while addressing the problem of bias when comparing trial results to historical control data. The proposed design achieves power similar to a randomized controlled trial but may require a substantially smaller sample size.

TMBserval: a statistical explainable learning model reveals weighted tumor mutation burden better categorizing therapeutic benefits

A high tumor mutation burden (TMB) is known to drive the response to immune checkpoint inhibitors (ICI) and is associated with favorable prognoses. However, because it is a one-dimensional numerical representation of non-synonymous genetic alterations, TMB suffers from clinical challenges due to its equal quantification. Since not all mutations elicit the same antitumor rejection, the effect on immunity of neoantigens encoded by different types or locations of somatic mutations may vary. In addition, other typical genomic features, including complex structural variants, are not captured by the conventional TMB metric. Given the diversity of cancer subtypes and the complexity of treatment regimens, this paper proposes that tumor mutations capable of causing various degrees of immunogenicity should be calculated separately. TMB should therefore, be segmented into more exact, higher dimensional feature vectors to exhaustively measure the foreignness of tumors. We systematically reviewed patients' multifaceted efficacy based on a refined TMB metric, investigated the association between multidimensional mutations and integrative immunotherapy outcomes, and developed a convergent categorical decision-making framework, TMBserval (Statistical Explainable machine learning with Regression-based VALidation). TMBserval integrates a multiple-instance learning concept with statistics to create a statistically interpretable model that addresses the broad interdependencies between multidimensional mutation burdens and decision endpoints. TMBserval is a pan-cancer-oriented many-to-many nonlinear regression model with discrimination and calibration power. Simulations and experimental analyses using data from 137 actual patients both demonstrated that our method could discriminate between patient groups in a high-dimensional feature space, thereby rationally expanding the beneficiary population of immunotherapy.

Assessment and Monitoring of Response to Systemic Treatment in Advanced Hepatocellular Carcinoma: Current Insights

Advanced hepatocellular carcinoma (HCC) management has become more complex as novel therapies have been proven effective. After sorafenib, the approval of other multikinase inhibitors (MKIs) and immune checkpoints inhibitors (ICIs) has considerably increased the number of systemic therapies available. Therefore, careful assessment and monitoring of response to systemic treatment are essential to identify surrogate endpoints of overall survival (OS) in clinical trials and reliable tools to gauge treatment benefit in clinical practice. Progression-free survival (PFS) and objective response rate (ORR) are early informative parameters of efficacy that are not influenced by further lines of therapy. However, none of them has shown sufficient surrogacy to be recommended in place of OS in phase 3 trials. With such a wealth of therapeutic options, the prime intent of tumor assessments is no longer limited to identifying progressive disease to spare ineffective treatments to non-responders. Indeed, the early detection of responders could also help tailor treatment sequencing. Tumor assessment relies on the Response Evaluation Criteria for Solid Tumors (RECIST), which are easy to interpret – being based on dimensional principles – but could misread the activity of targeted agents. The HCC-specific modified RECIST (mRECIST), considering both the MKI-induced biological modifications and some of the cirrhosis-induced liver changes, better capture tumor response. Yet, mRECIST could not be considered a standard in advanced HCC. Further prognosticators including progression patterns, baseline and on-treatment liver function deterioration, and baseline alpha-fetoprotein (AFP) levels and AFP response have been extensively evaluated for MKIs. However, limited information is available for patients receiving ICIs and regarding their predictive role. Finally, there is increasing interest in incorporating novel imaging techniques which go beyond sizes and novel serum biomarkers in the advanced HCC framework. Hopefully, multiparametric models grouping dimensional and functional radiological parameters with biochemical markers will most precisely reflect treatment response.

Identifying and Mitigating Potential Biases in Predicting Drug Approvals

INTRODUCTION

Machine learning models are increasingly applied to predict the drug development outcomes based on intermediary clinical trial results. A key challenge to this task is to address various forms of bias in the historical drug approval data.

OBJECTIVE

We aimed to identify and mitigate the bias in drug approval predictions and quantify the impacts of debiasing in terms of financial value and drug safety.

METHODS

We instantiated the Debiasing Variational Autoencoder, the state-of-the-art model for automated debiasing. We trained and evaluated the model on the Citeline dataset provided by Informa Pharma Intelligence to predict the final drug development outcome from phase II trial results.

RESULTS

The debiased Debiasing Variational Autoencoder model achieved better performance (measured by the [Formula: see text] score 0.48) in predicting the drug development outcomes than its un-debiased baseline ([Formula: see text] score 0.25). It had a much higher true-positive rate than baseline (60% vs 15%), while its true-negative rate was slightly lower (88% vs 99%). The Debiasing Variational Autoencoder distinguished between drugs developed by large pharmaceutical firms and those by small biotech companies. The model prediction is strongly influenced by multiple factors such as prior approval of the drug for another indication, whether the trial meets the positive/negative endpoints, and the year when the trial is completed. We estimate that the debiased model generates financial value for the drug developer in six major therapeutic areas, with a range of US$763-1,365 million.

CONCLUSIONS

Our analysis shows that debiasing improves the financial efficiency of late-stage drug development. From the pharmacovigilance perspective, the debiased model is more likely to identify drugs that are both safe and effective. Meanwhile, it may predict a higher probability of success for drugs with potential adverse effects (because of its lower true-negative rate), thus it must be used with caution to predict the development outcomes of drug candidates currently in the pipeline.

A Primer on RECIST 1.1 for Oncologic Imaging in Clinical Drug Trials

Drug discovery and approval in oncology is mediated by the use of imaging to evaluate drug efficacy in clinical trials. Imaging is performed while patients receive therapy to evaluate their response to treatment. Response criteria, specifically Response Evaluation Criteria in Solid Tumors version 1.1 (RECIST 1.1), are standardized and can be used at different time points to classify response into the categories of complete response, partial response, stable disease, or disease progression. At the trial level, categorical responses for all patients are summated into image-based trial endpoints. These outcome measures, including objective response rate (ORR) and progression-free survival (PFS), are characteristics that can be derived from imaging and can be used as surrogates for overall survival (OS). Similar to OS, ORR and PFS describe the efficacy of a drug. U.S. Food and Drug Administration (FDA) regulatory approval requires therapies to demonstrate direct evidence of clinical benefit, such as improved OS. However, multiple programs have been created to expedite drug approval for life-threatening illnesses, including advanced cancer. ORR and PFS have been accepted by the FDA as adequate predictors of OS on which to base drug approval decisions, thus substantially shortening the time and cost of drug development (1). Use of imaging surrogate markers for drug approval has become increasingly common, accounting for more than 90% of approvals through the Accelerated Approval Program and allowing for use of many therapies which have altered the course of cancer. Keywords: Oncology, Tumor Response  RSNA, 2021.

Kinomic profiling of tumour xenografts derived from patients with non-small cell lung cancer confirms their fidelity and reveals potentially actionable pathways

INTRODUCTION

High fidelity between non-small cell lung cancer (NSCLC) primary tumours and patient-derived tumour xenografts (PDTXs) is of paramount relevance to spur their application. Extensive proteomic and kinomic analysis of these preclinical models are missing and may inform about their functional status, in terms of phosphopeptides and hyperactive signalling pathways.

METHODS

We investigated tumour xenografts derived from patients with NSCLC to identify hyperactive signalling pathways. Fresh tumour fragments from 81 NSCLC surgical samples were implanted in Nod/Scid/Gamma mice, and engrafted tumours were compared with primary specimens by morphology, immunohistochemistry, gene mutation analyses, and kinase activity profiling. Four different tyrosine and serine/threonine kinase inhibitors were tested against primary tumour and PDTX lysates using the PamGene peptide microarray platform.

RESULTS

The engraftment rate was 33%, with successful engraftment being more associated with poor clinical outcomes. Genomic profiles led to the recognition of hotspot mutations, some of which were initially undetected in donor samples. Kinomic analyses showed that characteristics of primary tumours were retained in PDTXs, and tyrosine kinase inhibitors (TKIs) responses of individual PDTX lines were either expected, based on the genetic status, or alternatively defined suitable targets unpredictable by single-genome fingerprints.

CONCLUSIONS

Collectively, PDTXs mostly resembled their parental NSCLC. Combining genomic and kinomic analyses of tumour xenografts derived from patients with NSCLC, we identified patients' specific targetable pathways, confirming PDTXs as a preclinical tool for biomarker identification and therapeutic algorithm'' improvement.

Comparing the Efficacy of Cancer Therapies between Subgroups in Basket Trials

The need to test anticancer drugs in multiple indications has been addressed by basket trials, which are Phase I or II clinical trials involving multiple tumor subtypes and a single master protocol. Basket trials typically involve few patients per type, making it challenging to rigorously compare responses across types. We describe the use of permutation testing to test for differences among subgroups using empirical null distributions and the Benjamini-Hochberg procedure to control for false discovery. We apply the approach retrospectively to tumor-volume changes and progression-free survival in published basket trials for neratinib, larotrectinib, pembrolizumab, and imatinib and uncover examples of therapeutic benefit missed by conventional binomial testing. For example, we identify an overlooked opportunity for use of neratinib in lung cancers carrying ERBB2 Exon 20 mutations. Permutation testing can be used to design basket trials but is more conservatively introduced alongside established approaches to enrollment such as Simon’s two-stage design.

Basket clinical trials simultaneously test a single drug in multiple tumor subtypes, but statistical challenges limit the comparison of responses across subtypes. We describe a rigorous approach to permutation testing using empirical null distributions that can identify previously overlooked opportunities for use of targeted therapy in genetically defined cancer subtypes.

Three-Dimensional Neural Network to Automatically Assess Liver Tumor Burden Change on Consecutive Liver MRIs

BACKGROUND

Tumor response to therapy is often assessed by measuring change in liver lesion size between consecutive MRIs. However, these evaluations are both tedious and time-consuming for clinical radiologists.

PURPOSE

In this study, we sought to develop a convolutional neural network to detect liver metastases on MRI and applied this algorithm to assess change in tumor size on consecutive examinations.

METHODS

We annotated a data set of 64 patients with neuroendocrine tumors who underwent at least two consecutive liver MRIs with gadoxetic acid. We then developed a 3-D neural network using a U-Net architecture with ResNet-18 building blocks that first detected the liver and then lesions within the liver. Liver lesion labels for each examination were then matched in 3-D space using an iterative closest point algorithm followed by Kuhn-Munkres algorithm.

RESULTS

We developed a deep learning algorithm that detected liver metastases, co-registered the detected lesions, and then assessed the interval change in tumor burden between two multiparametric liver MRI examinations. Our deep learning algorithm was concordant in 91% with the radiologists' manual assessment about the interval change of disease burden. It had a sensitivity of 0.85 (95% confidence interval (95% CI): 0.77; 0.93) and specificity of 0.92 (95% CI: 0.87; 0.96) to classify liver segments as diseased or healthy. The mean DICE coefficient for individual lesions ranged between 0.73 and 0.81.

CONCLUSIONS

Our algorithm displayed high agreement with human readers for detecting change in liver lesions on MRI, offering evidence that artificial intelligence-based detectors may perform these tasks as part of routine clinical care in the future.

Quantitative magnetic resonance imaging (q-MRI) for the assessment of soft-tissue sarcoma treatment response: a narrative case review of technique development

Soft-tissue sarcomas are a heterogeneous class of tumors that exhibit varying degrees of cellularity and cystic degeneration in response to neoadjuvant chemotherapy. This creates unique challenges in the radiographic assessment of treatment response when relying on conventional markers such as tumor diameter (RECIST criteria). In this case series, we provide a narrative discussion of technique development for whole tumor volume quantitative magnetic resonance imaging (q-MRI), highlighting cases from a small pilot study of 8 patients (9 tumors) pre- and post-neoadjuvant chemotherapy. One of the methods of q-MRI analysis (the "constant-cutoff" technique) was able to predict responders versus non-responders based on percent necrosis and viable tumor volume calculations (p = 0.05), respectively. Our results suggest that q-MRI of whole tumor volume contrast enhancement may have a role in tumor response assessment, although further validation is needed.

Efficacy gap between phase II and subsequent phase III studies in oncology

Aims

There is a trend for more flexibility in timing of evidence generation in relation to marketing authorization, including the option to complete phase III trials after authorization or not at all. This paper investigated the relation between phase II and III clinical trial efficacy in oncology.

Methods

All oncology drugs approved by the European Medicines Agency (2007–2016) were included. Phase II and phase III trials were matched based on indication and treatment and patient characteristics. Reported objective response rates (ORR), median progression‐free survival (PFS) and median overall survival (OS) were analysed through weighted mixed‐effects regression with previous treatment, treatment regimen, blinding, randomization, marketing authorization type and cancer type as covariates.

Results

A total of 81 phase II‐III matches were identified including 252 trials. Mean (standard deviation) weighted difference (phase III minus II) was −4.2% (17.4) for ORR, 2.1 (6.7) months for PFS and −0.3 (5.1) months for OS, indicating very small average differences between phases. Differences varied substantially between individual indications: from −46.6% to 47.3% for ORR, from −5.3 to 35.9 months for PFS and from −13.3 to 10.8 months for OS. All covariates except blinding were associated with differences in effect sizes for at least 1 outcome.

Conclusions

The lack of marked average differences between phases may encourage decision‐makers to regard the quality of design and total body of evidence instead of differentiating between phases of clinical development. The large variability emphasizes that replication of study findings remains essential to confirm efficacy of oncology drugs and discern variables associated with demonstrated effects.

ePAD: An Image Annotation and Analysis Platform for Quantitative Imaging

Medical imaging is critical for assessing the response of patients to new cancer therapies. Quantitative lesion assessment on images is time-consuming, and adopting new promising quantitative imaging biomarkers of response in clinical trials is challenging. The electronic Physician Annotation Device (ePAD) is a freely available web-based zero-footprint software application for viewing, annotation, and quantitative analysis of radiology images designed to meet the challenges of quantitative evaluation of cancer lesions. For imaging researchers, ePAD calculates a variety of quantitative imaging biomarkers that they can analyze and compare in ePAD to identify potential candidates as surrogate endpoints in clinical trials. For clinicians, ePAD provides clinical decision support tools for evaluating cancer response through reports summarizing changes in tumor burden based on different imaging biomarkers. As a workflow management and study oversight tool, ePAD lets clinical trial project administrators create worklists for users and oversee the progress of annotations created by research groups. To support interoperability of image annotations, ePAD writes all image annotations and results of quantitative imaging analyses in standardized file formats, and it supports migration of annotations from various propriety formats. ePAD also provides a plugin architecture supporting MATLAB server-side modules in addition to client-side plugins, permitting the community to extend the ePAD platform in various ways for new cancer use cases. We present an overview of ePAD as a platform for medical image annotation and quantitative analysis. We also discuss use cases and collaborations with different groups in the Quantitative Imaging Network and future directions.

MRI Assessment of Hepatocellular Carcinoma after Local-Regional Therapy: A Comprehensive Review

Nearly 80% of cirrhotic patients diagnosed with hepatocellular carcinoma (HCC) are not eligible for surgical resection and instead undergo local-regional treatment. After therapy for HCC, patients undergo imaging surveillance to assess treatment efficacy and identify potential sites of progressive tumor elsewhere within the liver. Accurate interpretation of posttreatment imaging is essential for guiding further management decisions, and radiologists must understand expected treatment-specific imaging findings for each of the local-regional therapies. Of interest, expected imaging findings seen after radiation-based therapies (transarterial radioembolization and stereotactic body radiation therapy) are different than those seen after thermal ablation and transarterial chemoembolization. Given differences in expected posttreatment imaging findings, the current radiologic treatment response assessment algorithms used for HCC (modified Response Evaluation Criteria in Solid Tumors classification, European Association for the Study of Liver Diseases criteria, and Liver Imaging and Reporting Data System Treatment Response Algorithm) must be applied cautiously for radiation-based therapies in which persistent arterial phase hyperenhancement in the early posttreatment period is common and expected. This article will review the concept of tumor response assessment for HCC, the forms of local-regional therapy for HCC, and the expected posttreatment findings for each form of therapy. Keywords: Abdomen/GI, Liver, MR-Imaging, Treatment Effects, Tumor Response © RSNA, 2020.

A Review of Perspectives on the Use of Randomization in Phase II Oncology Trials

Historically, phase II oncology trials assessed a treatment's efficacy by examining its tumor response rate in a single-arm trial. Then, approximately 25 years ago, certain statistical and pharmacological considerations ignited a debate around whether randomized designs should be used instead. Here, based on an extensive literature review, we review the arguments on either side of this debate. In particular, we describe the numerous factors that relate to the reliance of single-arm trials on historical control data and detail the trial scenarios in which there was general agreement on preferential utilization of single-arm or randomized design frameworks, such as the use of single-arm designs when investigating treatments for rare cancers. We then summarize the latest figures on phase II oncology trial design, contrasting current design choices against historical recommendations on best practice. Ultimately, we find several ways in which the design of recently completed phase II trials does not appear to align with said recommendations. For example, despite advice to the contrary, only 66.2% of the assessed trials that employed progression-free survival as a primary or coprimary outcome used a randomized comparative design. In addition, we identify that just 28.2% of the considered randomized comparative trials came to a positive conclusion as opposed to 72.7% of the single-arm trials. We conclude by describing a selection of important issues influencing contemporary design, framing this discourse in light of current trends in phase II, such as the increased use of biomarkers and recent interest in novel adaptive designs.

Predictive Nomograms for Synchronous Liver and Lung Metastasis in Colon Cancer

BACKGROUND

The risk of distant metastasis may be estimated using predictive nomograms. The purpose of this study is to develop nomograms that may assess the risk of synchronous metastasis in patients with colon cancer.

METHODS

A retrospective analysis of the Surveillance Epidemiology and End Results database between 2010 and 2014. Logistic regression was performed to identify factors associated with synchronous liver and lung metastasis.

RESULTS

Overall, 117,934 patients with colon cancer (59,076 [50.1%] males, mean age 68.3 ± 13.7 years) were included, of which 16,135 (13.7%) had liver metastasis and 4601 (3.9%) had lung metastasis at diagnosis. Age, sex, race, tumor location, tumor grade, CEA levels, perineural invasion, and T and N stage were associated with the presence of liver metastasis. Age, sex, race, tumor location, tumor grade, CEA levels, perineural invasion, T stage, N stage, and presence of liver metastasis were associated with the presence of lung metastasis. These variables were used to construct predictive nomograms. The c-indexes for both predictive models were 0.97.

CONCLUSIONS

In this study, we constructed predictive nomograms for the presence of synchronous liver and lung metastasis in patients with colon cancer that may be used to quantitatively assess the risk of synchronous metastatic disease.

Tumor Growth Rate as a Validated Early Radiological Biomarker Able to Reflect Treatment-Induced Changes in Neuroendocrine Tumors: The GREPONET-2 Study

PURPOSE

Tumor growth rate (TGR) represents the percentage change in tumor volume per month (%/m). Previous results from the GREPONET study showed that TGR measured after 3 months (TGR_3m) of starting systemic treatment (ST) or watch and wait (WW) was an early biomarker predicting progression-free survival (PFS) in neuroendocrine tumors (NET).

EXPERIMENTAL DESIGN

Patients from 7 centers with advanced grade (G) 1/2 NETs from the pancreas (P)/small bowel (SB) initiating ST/WW were eligible. Computed tomography (CT)/MRI performed at prebaseline, baseline, and 3(±1) months of study entry were retrospectively reviewed. Aim-1: explore treatment-induced changes in TGR (ΔTGR_3m-BL; paired T test), and Aim-2: validate TGR_3m (<0.8%/m vs. ≥0.8%/m) as an early biomarker in an independent cohort (Kaplan-Meier/Cox regression).

RESULTS

Of 785 patients screened, 127 were eligible. Mean (SD) TGR₀ and TGR_3m were 5.4%/m (14.9) and -1.4%/m (11.8), respectively. Mean (SD) ΔTGR_3m-BL paired-difference was -6.8%/m (19.3; P < 0.001). Most marked ΔTGR_3m-BL [mean (SD)] were identified with targeted therapies [-11.3%/m (4.7); P = 0.0237] and chemotherapy [-7.9%/m (3.4); P = 0.0261]. Multivariable analysis confirmed the absence of previous treatment (OR = 4.65; 95% CI, 1.31-16.52; P = 0.018) and low TGR_3m (continuous variable; OR 1.09; 95% CI, 1.01-1.19; P = 0.042) to be independent predictors of radiologic objective response. When the multivariable survival analysis for PFS (Cox regression) was adjusted to grade (P = 0.004) and stage (P = 0.017), TGR_3m ≥ 0.8 (vs. <0.8) maintained its significance as a prognostic factor (P < 0.001), whereas TGR₀ and ΔTGR_3m-BL did not. TGR_3m ≥ 0.8%/m was confirmed as an independent prognostic factor for PFS [external validation; Aim-2; multivariable HR 2.21 (95% CI, 1.21-3.70; P = 0.003)].

CONCLUSIONS

TGR has a role as a biomarker for monitoring response to therapy for early identification of treatment-induced changes and for early prediction of PFS and radiologic objective response.

Randomized trials and endpoints in advanced HCC: Role of PFS as a surrogate of survival

Hepatocellular carcinoma (HCC) is a major cause of cancer-related mortality worldwide. Around half of patients with HCC will receive systemic therapies during their life span. The pivotal positive sorafenib trial and regulatory approval in 2007 was followed by a decade of negative studies with drugs leading to marginal antitumoral efficacy, toxicity, or trials with a lack of enrichment strategies. This trend has changed over the last 2 years with several compounds, such as lenvatinib (in first-line) and regorafenib, cabozantinib, ramucirumab and nivolumab (in second-line), showing clinical benefit. These successes came at a cost of increasing the complexity of decision-making, and ultimately, impacting the design of future clinical trials. Nowadays, life expectancy with single active agents has surpassed the threshold of 1 year and sequential strategies have provided encouraging outcomes. Overall survival (OS) remains the main endpoint in phase III investigations, but as in other solid tumours, there is a clear need to define surrogate endpoints that both reliably recapitulate survival benefits and can be assessed before additional efficacious drugs are administered. A thorough analysis of 21 phase III trials published in advanced HCC demonstrated a moderate correlation between progression-free survival (PFS) or time to progression (TTP) and OS (R = 0.84 and R = 0.83, respectively). Nonetheless, the significant differences in PFS identified in 7 phase III studies only correlated with differences in OS in 3 cases. In these cases, the hazard ratio (HR) for PFS was ≤0.6. Thus, this threshold is herein proposed as a potential surrogate endpoint of OS in advanced HCC. Conversely, PFS with an HR between 0.6-0.7, despite significance, was not associated with better survival, and thus these magnitudes are considered uncertain surrogates. In the current review, we discuss the reasons for positive or negative phase III trials in advanced HCC, and the strengths and limitations of surrogate endpoints (PFS, TTP and objective response rate [ORR]) to predict survival.

Assessment of the response of hepatocellular carcinoma to interventional radiology treatments

According to Barcelona Clinic Liver Cancer (BCLC) guidelines, interventional radiology procedures are valuable treatment options for many hepatocellular carcinomas (HCCs) that are not amenable to resection or transplantation. Accurate assessment of the efficacy of therapies at earlier stages enables completion of treatment, optimal follow-up and to prevent potentially unnecessary treatments, side effects and costly failure. The goal of this review is to summarize and describe the radiological strategies that have been proposed to predict survival and to stratify HCC responses after interventional radiology therapies. New techniques currently in development are also described.

Opportunities and challenges of implementing Pharmacogenomics in cancer drug development

Cancer drug development is a time and resources consuming process. Around 90% of drugs entering clinical trials fail due to lack of efficacy and/or safety issues, more often after conspicuous research and economic efforts. Part of the discarded drugs might be beneficial only in a subgroup of the study patients, and some adverse events might be prevented by identifying those patients more vulnerable to toxicities. The implementation of pharmacogenomic biomarkers allows the categorization of patients, to predict efficacy and toxicity and to optimize the drug development process. Around seventy FDA approved drugs currently present one or more genetic biomarker to keep in consideration, and with the progress of Precision Medicine tailoring therapies on individuals' genomic landscape promises to become a new standard of cancer care. In the current article we review the role of pharmacogenomics in cancer drug development, underlying the advantages and challenges of their implementation.

Predictors of Success of Phase II Pediatric Oncology Clinical Trials

BACKGROUND

There are limited data to predict which novel childhood cancer therapies are likely to be successful. To help rectify this, we sought to identify the factors that impact the success of phase II clinical trials for pediatric malignancies.

MATERIALS AND METHODS

We examined the impact of 24 preclinical and trial design variables for their influence on 132 phase II pediatric oncology clinical trials. Success was determined by an objective assessment of patient response, with data analyzed using Fisher's exact test, Pearson's chi-square test, and logistic regression models.

RESULTS

Trials that evaluated patients with a single histological cancer type were more successful than those that assessed multiple different cancer types (68% vs. 47%, 27%, and 17% for 1, 2-3, 4-7, and 8+; p < .005). Trials on liquid or extracranial solid tumors were more successful than central nervous system or combined trials (70%, 60%, 38%, and 24%; p < .005), and trials of combination therapies were more successful than single agents (71% vs. 28%; p < .005). Trials that added therapies to standard treatment backbones were more successful than trials testing novel therapies alone or those that incorporated novel agents (p < .005), and trials initiated based on the results of adult studies were less likely to succeed (p < .05). For 61% of trials (80/132), we were unable to locate any relevant preclinical findings to support the trial. When preclinical studies were carried out (52/132), there was no evidence that the conduct of any preclinical experiments made the trial more likely to succeed (p < .005).

CONCLUSION

Phase II pediatric oncology clinical trials that examine a single cancer type and use combination therapies have the highest possibility of clinical success. Trials building upon a standard treatment regimen were also more successful. The conduct of preclinical experiments did not improve clinical success, emphasizing the need for a better understanding of the translational relevance of current preclinical testing paradigms.

IMPLICATIONS FOR PRACTICE

To improve the clinical outcomes of phase II childhood cancer trials, this study identified factors impacting clinical success. These results have the potential to impact not only the design of future clinical trials but also the assessment of preclinical studies moving forward. This work found that trials on one histological cancer type and trials testing combination therapies had the highest possibility of success. Incorporation of novel therapies into standard treatment backbones led to higher success rates than testing novel therapies alone. This study found that most trials had no preclinical evidence to support initiation, and even when preclinical studies were available, they did not result in improved success.

Imaging evaluation of sorafenib for treatment of advanced hepatocellular carcinoma

Sorafenib, which is a novel targeted agent, plays an important role in treating advanced hepatocellular carcinoma (HCC) through its antiangiogenic and antiproliferative effects. However, conventional morphology-based radiographic evaluation systems may underestimate the efficacy of sorafenib in HCC due to a lack of apparent tumor shrinkage or altered tumor morphology in many cases. This calls for the development of more accurate imaging methods for evaluating sorafenib. The introduction of tumor burden measurements based on viability and other evolving imaging approaches for assessing therapeutic effects are promising for overcoming some of the limitations of the morphology-based criteria. In this review, we summarize various imaging methods that are used to assess treatment responses of advanced HCC to sorafenib. Imaging markers predictive of prognosis in advanced HCC after treatment with sorafenib are also included and discussed.

Evaluation of the quality of the reporting of phase II clinical trials in oncology: A systematic review

OBJECTIVE

To describe the current state of knowledge concerning the quality of reporting in phase II clinical trials in oncology and to describe the various methods published allowing this quality evaluation.

METHODS

databases including MEDLINE and COCHRANE were searched. Reviews and meta-analyses analyzing the quality of the reporting of phase II trials in oncology were included. Descriptive analysis of the results was performed.

RESULTS

Thirteen publications were retained. Only 2 publications adopted a systematic approach of evaluation of the quality of reporting by overall scores. The Key Methodological Score (KMS), proposed by Grellety et al., gathering 3 items, seemed adapted for such an evaluation. A score of 3/3 was found in 16.1% of the 156 phase II trials analysed by this score. The other reviews used a qualitative analysis to evaluate the reporting, via an analysis of a single criterion, generally the statistical plan of the study. This item was considered as having been correctly reported in less than 50% of the analysed articles.

CONCLUSION

The quality of reporting in phase II trials in oncology is a field that has been investigated very little (13 publications). When it is studied, the estimated level of quality is not satisfactory, whatever the method employed. The use of an overall score of evaluation is a path which should be pursued, in order to get reliable results. It also seems necessary to propose strong recommendations, which would create a consensus for the methodology and the reporting of these studies.

Pre-treatment tumour perfusion parameters and initial RECIST response do not predict long-term survival outcomes for patients with head and neck squamous cell carcinoma treated with induction chemotherapy

Objectives

Previously, we showed that pre-treatment tumour plasma perfusion (F_p) predicts RECIST response to induction chemotherapy (ICT) in locoregionally advanced head and neck squamous cell carcinoma (HNSCC). The aim here was to determine whether the pre-treatment tumour F_p estimate, changes in tumour F_p or RECIST response post 2 cycles of ICT were prognostic for long-term survival outcomes.

Methods

A prospective study enrolled patients with high stage HNSCC treated with docetaxel (T), cisplatin (P) and 5-fluorouracil (F) (ICT) followed by synchronous cisplatin and intensity modulated radiotherapy. Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) before and after two cycles of ICT was used to measure F_p and RECIST response.

Results

Forty-two patients were recruited and 37 underwent two scans. The median follow-up was 36 (range 23–49) months. Pre-treatment tumour F_p (stratified by median) was not prognostic for overall survival (p = 0.42), disease specific survival (p = 0.20) and locoregional control (p = 0.64). Neither change in tumour F_p nor RECIST response post two cycles of ICT was prognostic for any outcome (p>0.21).

Conclusion

DCE-MRI parameters do not predict long-term survival outcomes following ICT and RECIST response to ICT may not be an appropriate endpoint to determine early efficacy of a treatment in HNSCC patients.

Quality of reporting in oncology phase II trials: A 5-year assessment through systematic review

BACKGROUND

Phase II clinical trials are a cornerstone of the development in experimental treatments They work as a "filter" for phase III trials confirmation. Surprisingly the attrition ratio in Phase III trials in oncology is significantly higher than in any other medical specialty. This suggests phase II trials in oncology fail to achieve their goal. Objective The present study aims at estimating the quality of reporting in published oncology phase II clinical trials.

DATA SOURCES

A literature review was conducted among all phase II and phase II/III clinical trials published during a 5-year period (2010-2015).

STUDY ELIGIBILITY CRITERIA

All articles electronically published by three randomly-selected oncology journals with Impact-Factors>4 were included: Journal of Clinical Oncology, Annals of Oncology and British Journal of Cancer.

INTERVENTION

Quality of reporting was assessed using the Key Methodological Score.

RESULTS

557 articles were included. 315 trials were single-arm studies (56.6%), 193 (34.6%) were randomized and 49 (8.8%) were non-randomized multiple-arm studies. The Methodological Score was equal to 0 (lowest level), 1, 2, 3 (highest level) respectively for 22 (3.9%), 119 (21.4%), 270 (48.5%) and 146 (26.2%) articles. The primary end point is almost systematically reported (90.5%), while sample size calculation is missing in 66% of the articles. 3 variables were independently associated with reporting of a high standard: presence of statistical design (p-value <0.001), multicenter trial (p-value = 0.012), per-protocol analysis (p-value <0.001).

LIMITATIONS

Screening was mainly performed by a sole author. The Key Methodological Score was based on only 3 items, making grey zones difficult to translate.

CONCLUSIONS & IMPLICATIONS OF KEY FINDINGS

This literature review highlights the existence of gaps concerning the quality of reporting. It therefore raised the question of the suitability of the methodology as well as the quality of these trials, reporting being incomplete in the corresponding articles.

Tumor response assessment: comparison between unstructured free text reporting in routine clinical workflow and computer-aided evaluation based on RECIST 1.1 criteria

PURPOSE

Standardized computer-aided tumor response assessment is common in clinical trials. In contrast, unstructured free text reporting (UFTR) is common in daily routine. Therefore, this study aimed to discern and quantify differences between UFTR and computer-aided standardized tumor response evaluation based on RECIST 1.1 criteria (RECIST), serving as gold standard, in clinical workflow.

METHODS

One-hundred consecutive patients with cancer eligible for RECIST 1.1 evaluation, who received five follow-up CTs of the trunk, were retrospectively included. All UFTRs were assigned to RECIST response categories [complete response, partial response (PR), stable disease (SD), progressive disease (PD)]. All CTs were re-evaluated using dedicated software (mint lesion™) applying RECIST 1.1. The accordance in tumor response ratings was analyzed using Cohen's kappa.

RESULTS

At the first follow-up, 47 cases were rated differently with an SD underrepresentation and a PR and PD overrepresentation in UFTR. In the subsequent follow-ups, categorical differences were seen in 38, 44, 37, and 44%. Accordance between UFTR and RECIST was fair to moderate (Cohen's kappa: 0.356, 0.477, 0.390, 0.475, 0.376; always p < 0.001). Differences were mainly caused by the rating of even small tumor burden changes as PD or PR in UFTR or by comparison to the most recent prior CT scan in UFTR instead of comparison to nadir or baseline.

CONCLUSIONS

Significant differences in tumor response ratings were detected comparing UFTR and computer-aided standardized evaluation based on RECIST 1.1. Thus, standardized reporting should be implemented in daily routine workflow.

Volumetric Versus Unidimensional Measures of Metastatic Colorectal Cancer in Assessing Disease Response

INTRODUCTION

The purpose of this study was to compare unidimensional (1D/linear) and volumetric (3D) measures of metastatic colorectal cancer (mCRC) at computed tomography (CT) for predicting clinical outcome.

PATIENTS AND METHODS

Analysis of CT images in 105 patients (mean age, 59 years; range, 25-81 years; 45 women, 60 men) receiving treatment for mCRC was performed. Both unidimensional and volumetric measures were obtained on index lesions at 3 time points (baseline/midpoint/post-therapy; mean interval, 4.1 months; median, 3.7 months) by 3 readers using a semi-automated technique. Measurements were summed and compared using best overall response across the 3 time points. Patient response was categorized based on Response Evaluation Criteria In Solid Tumors (RECIST) 1.1 thresholds for unidimensional and volume measures (CR, complete response; PR, partial response; SD, stable disease; PD, progressive disease). Survival data was correlated (mean follow-up, 19.9 ± 17.1 months; median, 14.7 months). Intra/interobserver variability and reproducibility of 1D and 3D measures was assessed. Cox survival and Kaplan-Meier models were constructed and compared.

RESULTS

Cox models and Kaplan-Meier curves for unidimensional versus volumetric assessment were very similar in appearance. Both 1D and 3D measurements effectively separated PD from the SD/PR groups, but neither separated SD from PR well. Volumetric measures showed comparable intra/interobserver variability on Bland-Altman analysis to unidimensional measures across readers using a semi-automated measurement technique. Metastatic site (lung, liver, node, other) did not seem to impact measurement reproducibility.

CONCLUSIONS

Although CT volumetric assessment of metastatic colorectal cancer is fairly reproducible by reader and site using a semi-automated technique, the ability to stratify progressive disease from other disease response categories in terms of survival was similar to unidimensional measurement.

Molecular heterogeneity of non-small cell lung carcinoma patient-derived xenografts closely reflect their primary tumors

Availability of lung cancer models that closely mimic human tumors remains a significant gap in cancer research, as tumor cell lines and mouse models may not recapitulate the spectrum of lung cancer heterogeneity seen in patients. We aimed to establish a patient-derived tumor xenograft (PDX) resource from surgically resected non-small cell lung cancer (NSCLC). Fresh tumor tissue from surgical resection was implanted and grown in the subcutaneous pocket of non-obese severe combined immune deficient (NOD SCID) gamma mice. Subsequent passages were in NOD SCID mice. A subset of matched patient and PDX tumors and non-neoplastic lung tissues were profiled by whole exome sequencing, single nucleotide polymorphism (SNP) and methylation arrays, and phosphotyrosine (pY)-proteome by mass spectrometry. The data were compared to published NSCLC datasets of NSCLC primary and cell lines. 127 stable PDXs were established from 441 lung carcinomas representing all major histological subtypes: 52 adenocarcinomas, 62 squamous cell carcinomas, one adeno-squamous carcinoma, five sarcomatoid carcinomas, five large cell neuroendocrine carcinomas, and two small cell lung cancers. Somatic mutations, gene copy number and expression profiles, and pY-proteome landscape of 36 PDXs showed greater similarity with patient tumors than with established cell lines. Novel somatic mutations on cancer associated genes were identified but only in PDXs, likely due to selective clonal growth in the PDXs that allows detection of these low allelic frequency mutations. The results provide the strongest evidence yet that PDXs established from lung cancers closely mimic the characteristics of patient primary tumors.

Veliparib in combination with whole-brain radiation therapy for patients with brain metastases from non-small cell lung cancer: results of a randomized, global, placebo-controlled study

Veliparib is a potent, orally bioavailable, poly (adenosine diphosphate-ribose) polymerase (PARP) inhibitor that crosses the blood-brain barrier and has been shown to potentiate the effects of radiation in preclinical and early clinical studies. This phase 2, randomized, global study evaluated the efficacy and safety of veliparib in combination with whole-brain radiation therapy (WBRT) in patients with brain metastases from non-small cell lung cancer (NSCLC). Three-hundred and seven patients with brain metastases from NSCLC were randomized 1:1:1 to WBRT (30 Gy in 10 fractions) plus 50 mg veliparib twice daily (BID; n = 103), 200 mg veliparib BID (n = 102), or placebo BID (n = 102). Treatment began within 28 days of diagnosis. Tumor response and safety were assessed; the primary endpoint was overall survival (OS). Patients who received ≥1 dose of treatment were included in the safety analysis. All randomized patients were included in the efficacy endpoint analyses. Patient characteristics were well balanced between treatment arms. Median OS was 185 days for patients treated with WBRT plus placebo and 209 days for WBRT plus veliparib (50 or 200 mg). No statistically significant differences in OS, intracranial response rate, and time to clinical or radiographic progression between any of the treatment arms were noted. No differences were observed in adverse events (all grades) across treatment arms; nausea, fatigue, alopecia, and headache were the most commonly reported. No new safety signals were identified for veliparib. A significant unmet need for therapies that improve the outcomes of patients with brain metastases from NSCLC remains.

Failures in Phase III: Causes and Consequences

Phase III randomized controlled trials (RCT) in oncology fail to lead to registration of new therapies more often than RCTs in other medical disciplines. Most RCTs are sponsored by the pharmaceutical industry, which reflects industry's increasing responsibility in cancer drug development. Many preclinical models are unreliable for evaluation of new anticancer agents, and stronger evidence of biologic effect should be required before a new agent enters the clinical development pathway. Whenever possible, early-phase clinical trials should include pharmacodynamic studies to demonstrate that new agents inhibit their molecular targets and demonstrate substantial antitumor activity at tolerated doses in an enriched population of patients. Here, we review recent RCTs and found that these conditions were not met for most of the targeted anticancer agents, which failed in recent RCTs. Many recent phase III RCTs were initiated without sufficient evidence of activity from early-phase clinical trials. Because patients treated within such trials can be harmed, they should not be undertaken. The bar should also be raised when making decisions to proceed from phase II to III and from phase III to marketing approval. Many approved agents showed only better progression-free survival than standard treatment in phase III trials and were not shown to improve survival or its quality. Introduction of value-based pricing of new anticancer agents would dissuade the continued development of agents with borderline activity in early-phase clinical trials. When collaborating with industry, oncologists should be more critical and better advocates for cancer patients.

Optimizing clinical benefit with targeted treatment in mRCC: "Tumor growth rate" as an alternative clinical endpoint

Tumor growth rate (TGR), usually defined as the ratio between the slope of tumor growth before the initiation of treatment and the slope of tumor growth during treatment, between the nadir and disease progression, is a measure of the rate at which tumor volume increases over time. In patients with metastatic renal cell carcinoma (mRCC), TGR has emerged as a reliable alternative parameter to allow a quantitative and dynamic evaluation of tumor response. This review presents evidence on the correlation between TGR and treatment outcomes and discusses the potential role of this tool within the treatment scenario of mRCC. Current evidence, albeit of retrospective nature, suggests that TGR might represent a useful tool to assess whether treatment is altering the course of the disease, and has shown to be significantly associated with progression-free survival and overall survival. Therefore, TGR may represent a valuable endpoint for clinical trials evaluating new molecularly targeted therapies. Most importantly, incorporation of TGR in the assessment of individual patients undergoing targeted therapies may help clinicians decide if a given agent is no longer able to control disease growth and whether continuing therapy beyond RECIST progression may still produce clinical benefit.

Correlating Surrogate Endpoints with Overall Survival at the Individual Patient Level in BRAFV600E-Mutated Metastatic Melanoma Patients Treated with Vemurafenib

PURPOSE

Surrogate endpoints are needed that correlate with overall survival (OS). We analyzed individual patient tumor data from a phase III trial of vemurafenib versus dacarbazine (BRIM3) to identify criteria for tumor measures that correlated with OS. Correlates were validated using a separate data set from a phase II trial of vemurafenib (BRIM2).

EXPERIMENTAL DESIGN

Deidentified tumor measurements and OS data from BRIM3 and from BRIM2 were analyzed. Target tumor measurement data and nontarget tumor data were available from pretreatment, weeks 6,12, and every 9 weeks thereafter. In the BRIM3 data set, associations of OS with both early tumor response (first 12 weeks) and time to progression (TTP) were assessed. Different definitions of response and progression were explored. Findings were validated using the BRIM2 data set.

RESULTS

Thresholds of early response were explored ranging from any degree of tumor shrinkage to 100% tumor shrinkage. Correlation was weak at all thresholds tested. TTP, however, was more strongly correlated with OS. The strongest correlation was seen when progression was defined as ≥50% increase in the sum of tumor diameters or appearance of new tumors. This was confirmed by similar analyses in the BRIM2 cohort.

CONCLUSIONS

TTP defined as ≥50% increase in the sum of tumor diameters or appearance of new tumors was more strongly associated with OS than early tumor shrinkage in melanoma patients treated with RAF inhibitor. In future trials, consideration should be given to replacing response rate with TTP or PFS as preferable clinical endpoints in early-phase studies.

Correlation of single arm versus randomised phase 2 oncology trial characteristics with phase 3 outcome

BACKGROUND AND AIM

The primary aim of this study was to determine whether randomised phase 2 (RP2) trials predict phase 3 trial outcome better than single arm phase 2 (SAP2) studies. Although theoretical superiority of RP2 trials has been postulated, no empiric studies have been conducted.

METHODS

Published phase 3 trials testing systemic cancer therapy were identified through a Medline search. Those of superiority design, which cited phase 2 trials supporting the experimental arm, were included. Trial design and outcome details were extracted. Statistical analysis was performed using the Generalized Estimating Equation method correlating phase 2 features with phase 3 outcome, accounting for any phase 3 duplication.

RESULTS

Of 189 eligible phase 3 trials, 18.5% were in haematological malignancies and 81.5% in solid tumors. The primary outcome was positive in 79 (41.8%). These were supported by 336 phase 2 trials (range 1-9 per phase 3 trial) including 66 RP2 trials. Positive phase 2 outcome, randomised or not, correlated with positive phase 3 outcome (p=0.03). RP2 studies were not superior to SAP2 studies at predicting phase 3 study success. Phase 2 trial features not predictive of phase 3 outcome included primary endpoint, sponsorship, sample size, similarity in patient population and therapy.

CONCLUSIONS

RP2 studies were not superior to SAP2 trials at predicting phase 3 study success. Further research into phase 2 trial design is required given the added resources required to conduct RP2 studies and the lack of empiric evidence supporting superiority over single arm studies.

Optimal combination of antiangiogenic therapy for hepatocellular carcinoma

The success of sorafenib in prolonging survival of patients with hepatocellular carcinoma (HCC) makes therapeutic inhibition of angiogenesis a component of treatment for HCC. To enhance therapeutic efficacy, overcome drug resistance and reduce toxicity, combination of antiangiogenic agents with chemotherapy, radiotherapy or other targeted agents were evaluated. Nevertheless, the use of antiangiogenic therapy remains suboptimal regarding dosage, schedule and duration of therapy. The issue is further complicated by combination antiangiogenesis to other cytotoxic or biologic agents. There is no way to determine which patients are most likely respond to a given form of antiangiogenic therapy. Activation of alternative pathways associated with disease progression in patients undergoing antiangiogenic therapy has also been recognized. There is increasing importance in identifying, validating and standardizing potential response biomarkers for antiangiogenesis therapy for HCC patients. In this review, biomarkers for antiangiogenesis therapy including systemic, circulating, tissue and imaging ones are summarized. The strength and deficit of circulating and imaging biomarkers were further demonstrated by a series of studies in HCC patients receiving radiotherapy with or without thalidomide.

Mixed response and time-to-event endpoints for multistage single-arm phase II design

BACKGROUND

The objective of phase II cancer clinical trials is to determine if a treatment has sufficient activity to warrant further study. The efficiency of a conventional phase II trial design has been the object of considerable debate, particularly when the study regimen is characteristically cytostatic. At the time of development of a phase II cancer trial, we accumulated clinical experience regarding the time to progression (TTP) for similar classes of drugs and for standard therapy. By considering the time to event (TTE) in addition to the tumor response endpoint, a mixed-endpoint phase II design may increase the efficiency and ability of selecting promising cytotoxic and cytostatic agents for further development.

METHODS

We proposed a single-arm phase II trial design by extending the Zee multinomial method to fully use mixed endpoints with tumor response and the TTE. In this design, the dependence between the probability of response and the TTE outcome is modeled through a Gaussian copula.

RESULTS

Given the type I and type II errors and the hypothesis as defined by the response rate (RR) and median TTE, such as median TTP, the decision rules for a two-stage phase II trial design can be generated. We demonstrated through simulation that the proposed design has a smaller expected sample size and higher early stopping probability under the null hypothesis than designs based on a single-response endpoint or a single TTE endpoint.

CONCLUSIONS

The proposed design is more efficient for screening new cytotoxic or cytostatic agents and less likely to miss an effective agent than the alternative single-arm design.

Disease specific productivity of american cancer hospitals

CONTEXT

Research-oriented cancer hospitals in the United States treat and study patients with a range of diseases. Measures of disease specific research productivity, and comparison to overall productivity, are currently lacking.

HYPOTHESIS

Different institutions are specialized in research of particular diseases.

OBJECTIVE

To report disease specific productivity of American cancer hospitals, and propose a summary measure.

METHOD

We conducted a retrospective observational survey of the 50 highest ranked cancer hospitals in the 2013 US News and World Report rankings. We performed an automated search of PubMed and Clinicaltrials.gov for published reports and registrations of clinical trials (respectively) addressing specific cancers between 2008 and 2013. We calculated the summed impact factor for the publications. We generated a summary measure of productivity based on the number of Phase II clinical trials registered and the impact factor of Phase II clinical trials published for each institution and disease pair. We generated rankings based on this summary measure.

RESULTS

We identified 6076 registered trials and 6516 published trials with a combined impact factor of 44280.4, involving 32 different diseases over the 50 institutions. Using a summary measure based on registered and published clinical trails, we ranked institutions in specific diseases. As expected, different institutions were highly ranked in disease-specific productivity for different diseases. 43 institutions appeared in the top 10 ranks for at least 1 disease (vs 10 in the overall list), while 6 different institutions were ranked number 1 in at least 1 disease (vs 1 in the overall list).

CONCLUSION

Research productivity varies considerably among the sample. Overall cancer productivity conceals great variation between diseases. Disease specific rankings identify sites of high academic productivity, which may be of interest to physicians, patients and researchers.

Volumetric evaluation of hepatic tumors: multi-vendor, multi-reader liver phantom study

PURPOSE

To compare liver lesion volume measurement on multiple 3D software platforms using a liver phantom.

METHODS

An anthropomorphic phantom constructed with ten liver lesions of varying size, attenuation, and shape with known volume and long axis measurement was scanned (120 kVp, 80-440 smart mA, NI 12). DICOM data were uploaded to five commercially available 3D visualization systems and manual tumor volume was obtained by three-independent readers. Accuracy and reproducibility of linear and volume measurements were compared. The two most promising systems were then compared with an additional prototype system by two readers using both manual and semi-automated measurement with similar comparison between linear and volume measures. Measurements were performed on 5- and 1.25-mm data sets. Inter- and intra-observer variability was also assessed.

RESULTS

Overall mean % volume error on the five commercially available software systems (averaging all ten liver lesions among all three readers) was 8.0% ± 7.5%, 13.7% ± 11.2%, 14.2% ± 15.2%, 16.4% ± 14.8 %, and 16.9% ± 13.8%, varying almost twofold across vendor. Moderate inter-observer variability was present. Volume measurement was slightly more accurate than linear measurement, but linear measurement was more reproducible across readers and systems. On the two "best" systems, the manual measurement method was more accurate than the automated method (p = 0.001). The prototype system demonstrated superior semi-automated assessment, with a mean % volume error of 5.3% ± 4.1% (vs. 17.8% ± 11.1% and 31.5% ± 19.7%, p < 0.001), with improved inter- and intra-observer variability.

CONCLUSIONS

Accuracy and reproducibility of volume assessment of liver lesions varies significantly by vendor, which has important implications for clinical use.

Outcomes and endpoints in trials of cancer treatment: the past, present, and future

Cancer treatment should allow patients to live better or longer lives, and ideally, both. Trial endpoints should show clinically meaningful improvements in patient survival or quality of life. Alternative endpoints such as progression-free survival, disease-free survival, and objective response rate have been used to identify benefit earlier, but their true validity as surrogate endpoints is controversial. In this Review we discuss the measurement, assessment, and benefits and limitations of trial endpoints in use for cancer treatment. Many stakeholders are affected, including regulatory agencies, industry partners, clinicians, and most importantly, patients. In an accompanying Review, reflections from individual stakeholders are incorporated into a discussion of what the future holds for clinical trial endpoints and design.

Automated tracking of quantitative assessments of tumor burden in clinical trials

THERE ARE TWO KEY CHALLENGES HINDERING EFFECTIVE USE OF QUANTITATIVE ASSESSMENT OF IMAGING IN CANCER RESPONSE ASSESSMENT: 1) Radiologists usually describe the cancer lesions in imaging studies subjectively and sometimes ambiguously, and 2) it is difficult to repurpose imaging data, because lesion measurements are not recorded in a format that permits machine interpretation and interoperability. We have developed a freely available software platform on the basis of open standards, the electronic Physician Annotation Device (ePAD), to tackle these challenges in two ways. First, ePAD facilitates the radiologist in carrying out cancer lesion measurements as part of routine clinical trial image interpretation workflow. Second, ePAD records all image measurements and annotations in a data format that permits repurposing image data for analyses of alternative imaging biomarkers of treatment response. To determine the impact of ePAD on radiologist efficiency in quantitative assessment of imaging studies, a radiologist evaluated computed tomography (CT) imaging studies from 20 subjects having one baseline and three consecutive follow-up imaging studies with and without ePAD. The radiologist made measurements of target lesions in each imaging study using Response Evaluation Criteria in Solid Tumors 1.1 criteria, initially with the aid of ePAD, and then after a 30-day washout period, the exams were reread without ePAD. The mean total time required to review the images and summarize measurements of target lesions was 15% (P < .039) shorter using ePAD than without using this tool. In addition, it was possible to rapidly reanalyze the images to explore lesion cross-sectional area as an alternative imaging biomarker to linear measure. We conclude that ePAD appears promising to potentially improve reader efficiency for quantitative assessment of CT examinations, and it may enable discovery of future novel image-based biomarkers of cancer treatment response.

Challenges relating to solid tumour brain metastases in clinical trials, part 1: patient population, response, and progression. A report from the RANO group

Therapeutic outcomes for patients with brain metastases need to improve. A critical review of trials specifically addressing brain metastases shows key issues that could prevent acceptance of results by regulatory agencies, including enrolment of heterogeneous groups of patients and varying definitions of clinical endpoints. Considerations specific to disease, modality, and treatment are not consistently addressed. Additionally, the schedule of CNS imaging and consequences of detection of new or progressive brain metastases in trials mainly exploring the extra-CNS activity of systemic drugs are highly variable. The Response Assessment in Neuro-Oncology (RANO) working group is an independent, international, collaborative effort to improve the design of trials in patients with brain tumours. In this two-part series, we review the state of clinical trials of brain metastases and suggest a consensus recommendation for the development of criteria for future clinical trials.