Tumor growth rates derived from data for patients in a clinical trial correlate strongly with patient survival: a novel strategy for evaluation of clinical trial data

Bridging the gap between two-stage and joint models: The case of tumor growth inhibition and overall survival models

Many clinical trials generate both longitudinal biomarker and time-to-event data. We might be interested in their relationship, as in the case of tumor size and overall survival in oncology drug development. Many well-established methods exist for analyzing such data either sequentially (two-stage models) or simultaneously (joint models). Two-stage modeling (2stgM) has been challenged (i) for not acknowledging that biomarkers are endogenous covariable to the survival submodel and (ii) for not propagating the uncertainty of the longitudinal biomarker submodel to the survival submodel. On the other hand, joint modeling (JM), which properly circumvents both problems, has been criticized for being time-consuming, and difficult to use in practice. In this paper, we explore a third approach, referred to as a novel two-stage modeling (N2stgM). This strategy reduces the model complexity without compromising the parameter estimate accuracy. The three approaches (2stgM, JM, and N2stgM) are formulated, and a Bayesian framework is considered for their implementation. Both real and simulated data were used to analyze the performance of such approaches. In all scenarios, our proposal estimated the parameters approximately as JM but without being computationally expensive, while 2stgM produced biased results.

Predicting overall survival from tumor dynamics metrics using parametric statistical and machine learning models: application to patients with RET-altered solid tumors

In oncology drug development, tumor dynamics modeling is widely applied to predict patients' overall survival (OS) via parametric models. However, the current modeling paradigm, which assumes a disease-specific link between tumor dynamics and survival, has its limitations. This is particularly evident in drug development scenarios where the clinical trial under consideration contains patients with tumor types for which there is little to no prior institutional data. In this work, we propose the use of a pan-indication solid tumor machine learning (ML) approach whereby all three tumor metrics (tumor shrinkage rate, tumor regrowth rate and time to tumor growth) are simultaneously used to predict patients' OS in a tumor type independent manner. We demonstrate the utility of this approach in a clinical trial of cancer patients treated with the tyrosine kinase inhibitor, pralsetinib. We compared the parametric and ML models and the results showed that the proposed ML approach is able to adequately predict patient OS across RET-altered solid tumors, including non-small cell lung cancer, medullary thyroid cancer as well as other solid tumors. While the findings of this study are promising, further research is needed for evaluating the generalizability of the ML model to other solid tumor types.

Assessment of PSA responses and changes in the rate of tumor growth (g-rate) with immune checkpoint inhibitors in US Veterans with prostate cancer

We examined data from US Veterans with prostate cancer (PC) to assess disease response to immune checkpoint inhibitors (ICI) as monotherapy or combined with abiraterone or enzalutamide to assess ICI efficacy in the real-world. We queried the VA corporate data warehouse (CDW) to identify Veterans with a diagnosis of PC who received ICI for any malignancy and had ≥1 PSA measurement while receiving ICI. To evaluate ICI monotherapy, we restricted analysis to Veterans who had not received LHRH agonists/antagonists, PC-directed medical therapy, or radiation/extirpative surgery of the bladder/prostate within and preceding the duration of ICI administration. For ICI combination analysis, we identified Veterans who received abiraterone or enzalutamide for PC while on ICI. We calculated rates of tumor (PSA) growth (g-rates), comparing them to a 1:2 matched reference cohort. We identified 787 Veterans with PC and ≥1 PSA measurement while receiving an ICI. Median duration of ICI therapy was 155 days. 223 Veterans received ICI monotherapy, with only 17(8%) having a reduction in PSA (median decline = 43%). 12 (5%) had PSA declines >30% (PSA30) which included 6 (3%) who had PSA reductions greater than 50% (PSA50). Median g-rates for ICI plus abiraterone (n = 20) or enzalutamide (n = 31) were 0.000689/d-1 and 0.002819/d-1, respectively, and were statistically insignificant compared to g-rates of matched cohorts receiving abiraterone (g = 0.000925/d-1, P = 0.73) or enzalutamide (g = 0.001929/d-1, P = 0.58) alone. Our data align with clinical trial data in PC, demonstrating limited benefit from ICI monotherapy and predicting no survival benefit from simultaneous abiraterone or enzalutamide with an ICI using g-rate.

Society for Immunotherapy of Cancer (SITC) recommendations on intratumoral immunotherapy clinical trials (IICT): from premalignant to metastatic disease

BACKGROUND

Intratumorally delivered immunotherapies have the potential to favorably alter the local tumor microenvironment and may stimulate systemic host immunity, offering an alternative or adjunct to other local and systemic treatments. Despite their potential, these therapies have had limited success in late-phase trials for advanced cancer resulting in few formal approvals. The Society for Immunotherapy of Cancer (SITC) convened a panel of experts to determine how to design clinical trials with the greatest chance of demonstrating the benefits of intratumoral immunotherapy for patients with cancers across all stages of pathogenesis.

METHODS

An Intratumoral Immunotherapy Clinical Trials Expert Panel composed of international key stakeholders from academia and industry was assembled. A multiple choice/free response survey was distributed to the panel, and the results of this survey were discussed during a half-day consensus meeting. Key discussion points are summarized in the following manuscript.

RESULTS

The panel determined unique clinical trial designs tailored to different stages of cancer development-from premalignant to unresectable/metastatic-that can maximize the chance of capturing the effect of intratumoral immunotherapies. Design elements discussed included study type, patient stratification and exclusion criteria, indications of randomization, study arm determination, endpoints, biological sample collection, and response assessment with biomarkers and imaging. Populations to prioritize for the study of intratumoral immunotherapy, including stage, type of cancer and line of treatment, were also discussed along with common barriers to the development of these local treatments.

CONCLUSIONS

The SITC Intratumoral Immunotherapy Clinical Trials Expert Panel has identified key considerations for the design and implementation of studies that have the greatest potential to capture the effect of intratumorally delivered immunotherapies. With more effective and standardized trial designs, the potential of intratumoral immunotherapy can be realized and lead to regulatory approvals that will extend the benefit of these local treatments to the patients who need them the most.

Synergistic Drug Combinations Promote the Development of Resistance in Acute Myeloid Leukemia

Combination therapy is an important part of cancer treatment and is often employed to overcome or prevent drug resistance. Preclinical screening strategies often prioritize synergistic drug combinations; however, studies of antibiotic combinations show that synergistic drug interactions can accelerate the emergence of resistance because resistance to one drug depletes the effect of both. In this study, we aimed to determine whether synergy drives the development of resistance in cancer cell lines using live-cell imaging. Consistent with prior models of tumor evolution, we found that when controlling for activity, drug synergy is associated with increased probability of developing drug resistance. We demonstrate that these observations are an expected consequence of synergy: the fitness benefit of resisting a drug in a combination is greater in synergistic combinations than in nonsynergistic combinations. These data have important implications for preclinical strategies aiming to develop novel combinations of cancer therapies with robust and durable efficacy.

SIGNIFICANCE

Preclinical strategies to identify combinations for cancer treatment often focus on identifying synergistic combinations. This study shows that in AML cells combinations that rely on synergy can increase the likelihood of developing resistance, suggesting that combination screening strategies may benefit from a more holistic approach rather than focusing on drug synergy. See related commentary by Bhola and Letai, p. 81. This article is featured in Selected Articles from This Issue, p. 80.

Analysis of data from the PALOMA-3 trial confirms the efficacy of palbociclib and offers alternatives for novel assessment of clinical trials

PURPOSE

There remains a need for novel therapies for patients with metastatic breast cancer (MBC). We explore the use of a novel biomarker of survival that could potentially expedite the testing of novel therapies.

METHODS

We applied a tumor regression-growth model to radiographic measurement data from 393 women with MBC enrolled in PALOMA-3 examining efficacy of palbociclib in disease that had progressed on previous endocrine therapy. 261 and 132 women were randomized to fulvestrant plus palbociclib or placebo, respectively. We estimated rates of regression (d) and growth (g) of the sensitive and resistant fractions of tumors, respectively. We compared the median g of both arms. We examined the relationship between g and progression-free and overall survival (OS).

RESULTS

As in other tumors, g is a biomarker of OS. In PALOMA-3, we found significant differences in g among patients with tumors sensitive to endocrine therapy but not amongst resistant tumors, emulating clinical trial results. Subgroup analysis found favorable g values in visceral metastases treated with palbociclib. Palbociclib efficacy demonstrated by slower g values was evident early in the trial, twelve weeks after the first 28 patients had been enrolled.

CONCLUSION

Values of g, estimated using data collected while a patient is enrolled in a clinical trial is an excellent biomarker of OS. Our results correlate with the survival outcomes of PALOMA-3 and argue strongly for using g as a clinical trial endpoint to help inform go/no-go decisions, improve trial efficiency, and deliver novel therapies to patients sooner.

Starting and stopping cancer drugs: The need for randomized trials

Precision oncology has gained widespread popularity over the past decade, and increasingly oncologists strive to provide the right treatment to the right patient. To date, precision efforts have focused on the specific mutational target(s), food/ drug interactions, functional oncology, or dose of drug given. Moreover, the tumor and blood samples of hundreds of thousands of patients with cancer have been sequenced in the United States alone with the goal of identifying and prescribing the most precise treatment. Despite this broad consideration of precision oncology, one neglected aspect of precision oncology is identifying the optimal start time and stopping point for cancer therapies. Is it possible to improve overall survival (OS) or quality of life for patients with more precise initiation and discontinuation of therapy? In this commentary, we review the historical basis to initiate, discontinue or switch therapies. We emphasize that largely these time points were selected arbitrarily, and subsequently constrained by historical accident. We highlight randomized efforts to better elucidate the time points in starting or stopping therapy. Finally, we provide suggestions for a research agenda on precision timing of anti-cancer drugs.

A comparison of statistical methods for animal oncology studies

In pre-clinical oncology studies, tumor-bearing animals are treated and observed over a period of time in order to measure and compare the efficacy of one or more cancer-intervention therapies along with a placebo/standard of care group. A data analysis is typically carried out by modeling and comparing tumor volumes, functions of tumor volumes, or survival. Data analysis on tumor volumes is complicated because animals under observation may be euthanized prior to the end of the study for one or more reasons, such as when an animal's tumor volume exceeds an upper threshold. In such a case, the tumor volume is missing not-at-random for the time remaining in the study. To work around the non-random missingness issue, several statistical methods have been proposed in the literature, including the rate of change in log tumor volume and partial area under the curve. In this work, an examination and comparison of the test size and statistical power of these and other popular methods for the analysis of tumor volume data is performed through realistic Monte Carlo computer simulations. The performance, advantages, and drawbacks of popular statistical methods for animal oncology studies are reported. The recommended methods are applied to a real data set.

Tumor Growth Rate Informs Treatment Efficacy in Metastatic Pancreatic Adenocarcinoma: Application of a Growth and Regression Model to Pivotal Trial and Real-World Data

BACKGROUND

Methods for screening agents earlier in development and strategies for conducting smaller randomized controlled trials (RCTs) are needed.

METHODS

We retrospectively applied a tumor growth model to estimate the rates of growth of pancreatic cancer using radiographic tumor measurements or serum CA 19-9 values from 3033 patients with stages III-IV PDAC who were enrolled in 8 clinical trials or were included in 2 large real-world data sets.

RESULTS

g correlated inversely with OS and was consistently lower in the experimental arms than in the control arms of RCTs. At the individual patient level, g was significantly faster for lesions metastatic to the liver relative to those localized to the pancreas. Regardless of regimen, g increased toward the end of therapy, often by over 3-fold.

CONCLUSIONS

Growth rates of PDAC can be determined using radiographic tumor measurement and CA 19-9 values. g is inversely associated with OS and can differentiate therapies within the same trial and across trials. g can also be used to characterize changes in the behavior of an individual's PDAC, such as differences in the growth rate of lesions based on metastatic site, and the emergence of chemoresistance. We provide examples of how g can be used to benchmark phase II and III clinical data to a virtual reference arm to inform go/no go decisions and consider novel trial designs to optimize and accelerate drug development.

Preoperative Factors Associated with Intraoperative Maximum Arterial Pressures in Patients with Pheochromocytoma and Paraganglioma

BACKGROUND

Surgery for pheochromocytoma and paraganglioma (PPGL) can lead to life-threatening complications, such as intraoperative hypertensive crises, even when adequate doses of preoperative α-receptor blockades are administered.

OBJECTIVES

The aim of this study was to identify preoperative factors associated with intraoperative maximum arterial pressure (AP) in patients with PPGL.

METHODS

We retrospectively reviewed the cases of 61 PPGL patients who underwent surgical resection in our hospital between 2006 and 2020. The primary outcome was intraoperative maximum AP as a single index for continuous variables. Simple and multiple linear regression model were used for statistical analysis.

RESULTS

The median maximum systolic AP during surgery was 165 mmHg (interquartile range: 150 - 180 mmHg). Log24-h urinary-fractionated metanephrine (MN) and normetanephrine (NMN) was correlated with intraoperative maximum AP (R-squared = 0.218, P < 0.001). Multiple regression analyses showed that diabetes mellitus, one or more of the classic triad, and log24-h urinary-fractionated MN and NMN were independent factors associated with intraoperative maximum AP.

CONCLUSIONS

Patients with PPGL accompanied by diabetes mellitus, one or more of the classic triad, and high log 24-h urinary-fractionated MN and NMN values may be at risk for hypertensive crises during surgery regardless of whether preoperative α-receptor blockades are used. Clinicians should manage these patients more carefully and effectively.

Translation of Prognostic and Pharmacodynamic Biomarkers from Trial to Non-trial Patients with Metastatic Castration-resistant Prostate Cancer Treated with Docetaxel

AIMS

We conducted a pooled analysis of four randomised controlled trials and a non-trial retrospective dataset to study the changes in serum prostate-specific antigen (PSA) concentrations during treatment and its impact on survival in men treated with docetaxel for metastatic castration-resistant prostate cancer. We also compared the outcomes and pre-treatment prognostic factors between trial and non-trial patients.

MATERIALS AND METHODS

Data were obtained from four randomised controlled trials and a non-trial cohort from a tertiary cancer centre. The PSA kinetics covariates chosen were absolute value (PSAT), best percentage change (BPCH) and tumour growth rate (K). The association between the covariates collected and overall survival was assessed within a Cox proportional hazards model. How well a covariate captured the difference between trial and non-trial patients was assessed by reporting on models with or without trial status as a covariate.

RESULTS

We reviewed individual datasets of 2282 patients. The median overall survival for trial patients was 20.4 (95% confidence interval 19.6-22.2) months and for the non-trial cohort was 12.4 (10.7-14.7) months (P < 0.001). Of the pre-treatment factors, we found that only lactate dehydrogenase fully captured the difference in prognosis between the trial and non-trial cohorts. All PSA kinetic metrics appeared to be prognostic in both the trial and non-trial patients. However, the effect size was reduced in non-trial versus trial patients (interaction P < 0.001). Of the time-dependent covariates, we found that BPCH best captured the difference between trial and non-trial patient prognosis.

CONCLUSIONS

The analysis presented here highlights how data from open-source trial databases can be combined with emerging clinical practice databases to assess differences between trial versus non-trial patients for particular treatments. These results highlight the importance of developing prognostic models using both pre-treatment and time-dependent biomarkers of new treatments.

Early prediction of clinical response to checkpoint inhibitor therapy in human solid tumors through mathematical modeling

Background:

Checkpoint inhibitor therapy of cancer has led to markedly improved survival of a subset of patients in multiple solid malignant tumor types, yet the factors driving these clinical responses or lack thereof are not known. We have developed a mechanistic mathematical model for better understanding these factors and their relations in order to predict treatment outcome and optimize personal treatment strategies.

Methods:

Here, we present a translational mathematical model dependent on three key parameters for describing efficacy of checkpoint inhibitors in human cancer: tumor growth rate (α), tumor-immune infiltration (Λ), and immunotherapy-mediated amplification of anti-tumor response (µ). The model was calibrated by fitting it to a compiled clinical tumor response dataset (n = 189 patients) obtained from published anti-PD-1 and anti-PD-L1 clinical trials, and then validated on an additional validation cohort (n = 64 patients) obtained from our in-house clinical trials.

Results:

The derived parameters Λ and µ were both significantly different between responding versus nonresponding patients. Of note, our model appropriately classified response in 81.4% of patients by using only tumor volume measurements and within 2 months of treatment initiation in a retrospective analysis. The model reliably predicted clinical response to the PD-1/PD-L1 class of checkpoint inhibitors across multiple solid malignant tumor types. Comparison of model parameters to immunohistochemical measurement of PD-L1 and CD8+ T cells confirmed robust relationships between model parameters and their underlying biology.

Conclusions:

These results have demonstrated reliable methods to inform model parameters directly from biopsy samples, which are conveniently obtainable as early as the start of treatment. Together, these suggest that the model parameters may serve as early and robust biomarkers of the efficacy of checkpoint inhibitor therapy on an individualized per-patient basis.

Funding:

We gratefully acknowledge support from the Andrew Sabin Family Fellowship, Center for Radiation Oncology Research, Sheikh Ahmed Center for Pancreatic Cancer Research, GE Healthcare, Philips Healthcare, and institutional funds from the University of Texas M.D. Anderson Cancer Center. We have also received Cancer Center Support Grants from the National Cancer Institute (P30CA016672 to the University of Texas M.D. Anderson Cancer Center and P30CA072720 the Rutgers Cancer Institute of New Jersey). This research has also been supported in part by grants from the National Science Foundation Grant DMS-1930583 (ZW, VC), the National Institutes of Health (NIH) 1R01CA253865 (ZW, VC), 1U01CA196403 (ZW, VC), 1U01CA213759 (ZW, VC), 1R01CA226537 (ZW, RP, WA, VC), 1R01CA222007 (ZW, VC), U54CA210181 (ZW, VC), and the University of Texas System STARS Award (VC). BC acknowledges support through the SER Cymru II Programme, funded by the European Commission through the Horizon 2020 Marie Skłodowska-Curie Actions (MSCA) COFUND scheme and the Welsh European Funding Office (WEFO) under the European Regional Development Fund (ERDF). EK has also received support from the Project Purple, NIH (U54CA210181, U01CA200468, and U01CA196403), and the Pancreatic Cancer Action Network (16-65-SING). MF was supported through NIH/NCI center grant U54CA210181, R01CA222959, DoD Breast Cancer Research Breakthrough Level IV Award W81XWH-17-1-0389, and the Ernest Cockrell Jr. Presidential Distinguished Chair at Houston Methodist Research Institute. RP and WA received serial research awards from AngelWorks, the Gillson-Longenbaugh Foundation, and the Marcus Foundation. This work was also supported in part by grants from the National Cancer Institute to SHC (R01CA109322, R01CA127483, R01CA208703, and U54CA210181 CITO pilot grant) and to PYP (R01CA140243, R01CA188610, and U54CA210181 CITO pilot grant). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Prediction of overall survival in patients across solid tumors following atezolizumab treatments: A tumor growth inhibition-overall survival modeling framework

The objectives of the study were to use tumor size data from 10 phase II/III atezolizumab studies across five solid tumor types to estimate tumor growth inhibition (TGI) metrics and assess the impact of TGI metrics and baseline prognostic factors on overall survival (OS) for each tumor type. TGI metrics were estimated from biexponential models and posttreatment longitudinal data of 6699 patients. TGI‐OS full models were built using parametric survival regression by including all significant baseline covariates from the Cox univariate analysis followed by a backward elimination step. The model performance was evaluated for each trial by 1000 simulations of the OS distributions and hazard ratios (HR) of the atezolizumab‐containing arms versus the respective controls. The tumor growth rate estimate was the most significant predictor of OS across all tumor types. Several baseline prognostic factors, such as inflammatory status (C‐reactive protein, albumin, and/or neutrophil‐to‐lymphocyte ratio), tumor burden (sum of longest diameters, number of metastatic sites, and/or presence of liver metastases), Eastern Cooperative Oncology Group performance status, and lactate dehydrogenase were also highly significant across multiple studies in the final multivariate models. TGI‐OS models adequately described the OS distribution. The model‐predicted HRs indicated good model performance across the 10 studies, with observed HRs within the 95% prediction intervals for all study arms versus controls. Multivariate TGI‐OS models developed for different solid tumor types were able to predict treatment effect with various atezolizumab monotherapy or combination regimens and could be used to support design and analysis of future studies.

A meta-analysis of tumour response and relapse kinetics based on 34,881 patients: A question of cancer type, treatment and line of treatment

PURPOSE

Cancer disease burden is commonly assessed radiologically in solid tumours in support of response assessment via the RECIST criteria. These longitudinal data are amenable to mathematical modelling and these models characterise the initial tumour size, initial tumour shrinkage in responding patients and rate of regrowth as patient's disease progresses. Knowing how these parameters vary between patient populations and treatments would inform translational modelling approaches from non-clinical data as well as clinical trial design.

EXPERIMENTAL DESIGN

Here a meta-analysis of reported model parameter values is reported. Appropriate literature was identified via a PubMed search and the application of text-based clustering approaches. The resulting parameter estimates are examined graphically and with ANOVA.

RESULTS

Parameter values from a total of 80 treatment arms were identified based on 80 trial arms containing a total of 34,881 patients. Parameter estimates are generally consistent. It is found that a significant proportion of the variation in rates of tumour shrinkage and regrowth are explained by differing cancer and treatment: cancer type accounts for 66% of the variation in shrinkage rate and 71% of the variation in reported regrowth rates. Mean average parameter values by cancer and treatment are also reported.

CONCLUSIONS

Mathematical modelling of longitudinal data is most often reported on a per clinical trial basis. However, the results reported here suggest that a more integrative approach would benefit the development of new treatments as well as the further optimisation of those currently used.

Tumor Growth Rate Estimates Are Independently Predictive of Therapy Response and Survival in Recurrent High-Grade Serous Ovarian Cancer Patients

Simple Summary

While latest evidence suggests that some patients with recurrent high-grade serous ovarian cancer may profit from reinduction with platinum-based chemotherapy regimens, the selection of patients who are likely to respond remains difficult. The present study therefore aimed to adapt a mathematical model, which used frequently available laboratory values to estimate growth rates of recurring tumors as an objectifiable surrogate of both therapy response and patient survival. After clinical validation, the model may help to personalize treatment strategies and thereby increase survival of affected patients.

Abstract

This study aimed to assess the predictive value of tumor growth rate estimates based on serial cancer antigen-125 (CA-125) levels on therapy response and survival of patients with recurrent high-grade serous ovarian cancer (HGSOC). In total, 301 consecutive patients with advanced HGSOC (exploratory cohort: n = 155, treated at the Medical University of Vienna; external validation cohort: n = 146, from the Ovarian Cancer Therapy–Innovative Models Prolong Survival (OCTIPS) consortium) were enrolled. Tumor growth estimates were obtained using a validated two-phase equation model involving serial CA-125 levels, and their predictive value with respect to treatment response to the next chemotherapy and the prognostic value with respect to disease-specific survival and overall survival were assessed. Tumor growth estimates were an independent predictor for response to second-line chemotherapy and an independent prognostic factor for second-line chemotherapy use in both univariate and multivariable analyses, outperforming both the predictive (second line: p = 0.003, HR 5.19 [1.73–15.58] vs. p = 0.453, HR 1.95 [0.34–11.17]) and prognostic values (second line: p = 0.042, HR 1.53 [1.02–2.31] vs. p = 0.331, HR 1.39 [0.71–2.27]) of a therapy-free interval (TFI) < 6 months. Tumor growth estimates were a predictive factor for response to third- and fourth-line chemotherapy and a prognostic factor for third- and fourth-line chemotherapy use in the univariate analysis. The CA-125-derived tumor growth rate estimate may be a quantifiable and easily assessable surrogate to TFI in treatment decision making for patients with recurrent HGSOC.

Two decades of research toward the treatment of locally advanced and metastatic pancreatic cancer: Remarkable effort and limited gain

Pancreatic ductal adenocarcinoma (PDAC) is an aggressive malignancy that is diagnosed at the locally advanced or metastatic stage in approximately 80% of cases. Relative to other tumor types, progress in the treatment of this disease has been painfully slow. While agents targeting DNA repair have proven successful in a subset of patients, the majority of PDACs do not exhibit validated molecular targets. Hence, conventional chemotherapy remains at the forefront of therapy for this disease. In this review, we study two decades of efforts to improve upon the gemcitabine backbone - 67 phase II and III trials enrolling 16,446 patients - that culminated in the approvals of gemcitabine/nab-paclitaxel (Gem/NabP) and FOLFIRINOX. Today, these remain gold standards for the first-line treatment of locally advanced unresectable and metastatic PDAC, while ongoing efforts focus on improving upon the Gem/NabP backbone. Because real world data often do not reflect the data of randomized controlled trials (RCTs), we also summarize the retrospective evidence comparing the efficacy of Gem/NabP and FOLFIRINOX in the first-line setting - 29 studies reporting a median overall survival of 10.7 and 9.1 months for FOLFIRINOX and Gem/NabP, respectively. These values are surprisingly comparable to those reported by the pivotal RCTs at 11.1 and 8.5 months. Finally, there is a paucity of RCT data regarding the efficacy of second-line therapy. Hence, we conclude this review by summarizing the data that ultimately demonstrate a small but significant survival benefit of second-line therapy with Gem/NabP or FOLFIRINOX. Collectively, these studies describe the long journey, the steady effort, and the myriad lessons to be learned from 20 years of PDAC trials to inform strategies for success in clinical trials moving forward.

Novel Tumor Growth Rate Analysis in the Randomized CLARINET Study Establishes the Efficacy of Lanreotide Depot/Autogel 120 mg with Prolonged Administration in Indolent Neuroendocrine Tumors

Introduction

Tumor quantity while receiving cancer therapy is the sum of simultaneous regression of treatment‐sensitive and growth of treatment‐resistant fractions at constant rates. Exponential rate constants for tumor regression/decay (d) and growth (g) can be estimated. Previous studies established g as a biomarker for overall survival; g increases after treatment cessation, can estimate doubling times, and can assess treatment effectiveness in small cohorts by benchmarking to large reference data sets. Using this approach, we analyzed data from the clinical trial CLARINET, evaluating lanreotide depot/autogel 120 mg/4 weeks (LAN) for treatment of neuroendocrine tumors (NETs).

Methods and Materials

Computed tomography imaging data from 97 LAN‐ and 101 placebo‐treated patients from CLARINET were analyzed to estimate g and d.

Results

Data from 92% of LAN‐ and 94% of placebo‐treated patients could be fit to one of the equations to derive g and d (p < .001 in most data sets). LAN‐treated patients demonstrated significantly slower g than placebo recipients (p = .00315), a difference of 389 days in doubling times. No significant difference was observed in d. Over periods of LAN administration up to 700 days, g did not change appreciably. Simulated analysis with g as the endpoint showed a sample size of 48 sufficient to detect a difference in median g with 80% power.

Conclusion

Although treatment of NETs with LAN can affect tumor shrinkage, LAN primarily slows tumor growth rather than accelerates tumor regression. Evidence of LAN efficacy across tumors was identified. The growth‐retarding effect achieved with LAN was sustained for a prolonged period of time.

Implications for Practice

The only curative treatment for neuroendocrine tumors (NETs) is surgical resection; however, because of frequent late diagnosis, this is often impossible. Because of this, treatment of NETs is challenging and often aims to reduce tumor burden and delay progression. A novel method of analysis was used to examine data from the CLARINET trial, confirming lanreotide depot/autogel is effective at slowing tumor growth and extending progression‐free survival. By providing the expected rate and doubling time of tumor growth early in the course of treatment, this method of analysis has the potential to guide physicians in their management of patients with NETs.

Treatment of neuroendocrine tumors is challenging, mainly aiming to reduce tumor burden and delay disease progression. This article reports on the kinetics of tumor growth using a novel method of analysis and data from the CLARINET study.

Confounding factors in exposure-response analyses and mitigation strategies for monoclonal antibodies in oncology

Dose selection and optimization is an important topic in drug development to maximize treatment benefits for all patients. While exposure-response (E-R) analysis is a useful method to inform dose-selection strategy, in oncology, special considerations for prognostic factors are needed due to their potential to confound the E-R analysis for monoclonal antibodies. The current review focuses on 3 different approaches to mitigate the confounding effects for monoclonal antibodies in oncology: (i) Cox-proportional hazards modelling and case-matching; (ii) tumour growth inhibition-overall survival modelling; and (iii) multiple dose level study design. In the presence of confounding effects, studying multiple dose levels may be required to reveal the true E-R relationship. However, it is impractical for pivotal trials in oncology drug development programmes. Therefore, the strengths and weaknesses of the other 2 approaches are considered, and the favourable utility of tumour growth inhibition-overall survival modelling to address confounding in E-R analyses is described. In the broader scope of oncology drug development, this review discusses the downfall of the current emphasis on E-R analyses using data from single dose level trials and proposes that development programmes be designed to study more dose levels in earlier trials.

Clone Wars: Quantitatively Understanding Cancer Drug Resistance

A key aim of early clinical development for new cancer treatments is to detect the potential for efficacy early and to identify a safe therapeutic dose to take forward to phase II. Because of this need, researchers have sought to build mathematical models linking initial radiologic tumor response, often assessed after 6 to 8 weeks of treatment, with overall survival. However, there has been mixed success of this approach in the literature. We argue that evolutionary selection pressure should be considered to interpret these early efficacy signals and so optimize cancer therapy.

Enhanced Detection of Treatment Effects on Metastatic Colorectal Cancer with Volumetric CT Measurements for Tumor Burden Growth Rate Evaluation

PURPOSE

Mathematical models combined with new imaging technologies could improve clinical oncology studies. To improve detection of therapeutic effect in patients with cancer, we assessed volumetric measurement of target lesions to estimate the rates of exponential tumor growth and regression as treatment is administered.

EXPERIMENTAL DESIGN

Two completed phase III trials were studied (988 patients) of aflibercept or panitumumab added to standard chemotherapy for advanced colorectal cancer. Retrospectively, radiologists performed semiautomated measurements of all metastatic lesions on CT images. Using exponential growth modeling, tumor regression (d) and growth (g) rates were estimated for each patient's unidimensional and volumetric measurements.

RESULTS

Exponential growth modeling of volumetric measurements detected different empiric mechanisms of effect for each drug: panitumumab marginally augmented the decay rate [tumor half-life; d [IQR]: 36.5 days (56.3, 29.0)] of chemotherapy [d: 44.5 days (67.2, 32.1), two-sided Wilcoxon P = 0.016], whereas aflibercept more significantly slowed the growth rate [doubling time; g = 300.8 days (154.0, 572.3)] compared with chemotherapy alone [g = 155.9 days (82.2, 347.0), P ≤ 0.0001]. An association of g with overall survival (OS) was observed. Simulating clinical trials using volumetric or unidimensional tumor measurements, fewer patients were required to detect a treatment effect using a volumetric measurement-based strategy (32-60 patients) than for unidimensional measurement-based strategies (124-184 patients).

CONCLUSIONS

Combined tumor volume measurement and estimation of tumor regression and growth rate has potential to enhance assessment of treatment effects in clinical studies of colorectal cancer that would not be achieved with conventional, RECIST-based unidimensional measurements.

Differences in physical function across cancer recovery phases: Findings from the 2015 National Health Interview Survey

Introduction

Recent cancer survivors (<2 years post-diagnosis) report poorer general health and physical weakness compared to long-term cancer survivors (≥2 years post-diagnosis), but differences in functional limitations are unknown. It is unclear which daily tasks are more difficult for recent versus long-term survivors. We aimed to examine differences in functional performances across cancer recovery phases as potential targets for functional impairment screening.

Method

The cohort consisted of adults with a cancer history in the 2015 National Health Interview Survey (n=2372). Multivariate logistic regression models were used to estimate the odds of having difficulty in health-related outcomes across the cancer recovery phases (recent versus long-term).

Results

Most subjects were long-term survivors (84.9%). Recent survivors were more likely to have difficulty in work, mobility-related daily tasks and social participation compared to long-term survivors. No differences were found in basic activities of daily living, cognition and emotional functioning between the groups.

Conclusion

While recent cancer survivors were independent in basic daily tasks, they had difficulties in performing daily tasks that required a high level of physical function. Clinicians, especially occupational therapists, should prioritize evaluating physical functioning to guide intervention planning for recent cancer survivors.

Artificial Intelligence and Mechanistic Modeling for Clinical Decision Making in Oncology

The amount of "big" data generated in clinical oncology, whether from molecular, imaging, pharmacological, or biological origin, brings novel challenges. To mine efficiently this source of information, mathematical models able to produce predictive algorithms and simulations are required, with applications for diagnosis, prognosis, drug development, or prediction of the response to therapy. Such mathematical and computational constructs can be subdivided into two broad classes: biologically agnostic, statistical models using artificial intelligence techniques, and physiologically based, mechanistic models. In this review, recent advances in the applications of such methods in clinical oncology are outlined. These include machine learning applied to big data (omics, imaging, or electronic health records), pharmacometrics and quantitative systems pharmacology, as well as tumor kinetics and metastasis modeling. Focus is set on studies with high potential of clinical translation, and particular attention is given to cancer immunotherapy. Perspectives are given in terms of combinations of the two approaches: "mechanistic learning."

A Review of Mathematical Models for Tumor Dynamics and Treatment Resistance Evolution of Solid Tumors

Increasing knowledge of intertumor heterogeneity, intratumor heterogeneity, and cancer evolution has improved the understanding of anticancer treatment resistance. A better characterization of cancer evolution and subsequent use of this knowledge for personalized treatment would increase the chance to overcome cancer treatment resistance. Model‐based approaches may help achieve this goal. In this review, we comprehensively summarized mathematical models of tumor dynamics for solid tumors and of drug resistance evolution. Models displayed by ordinary differential equations, algebraic equations, and partial differential equations for characterizing tumor burden dynamics are introduced and discussed. As for tumor resistance evolution, stochastic and deterministic models are introduced and discussed. The results may facilitate a novel model‐based analysis on anticancer treatment response and the occurrence of resistance, which incorporates both tumor dynamics and resistance evolution. The opportunities of a model‐based approach as discussed in this review can be of great benefit for future optimizing and personalizing anticancer treatment.

Cancer drug development: The missing links

IMPACT STATEMENT

The success rate for cancer drugs which enter into phase 1 clinical trials is utterly less. Why the vast majority of drugs fail is not understood but suggests that pre-clinical studies are not adequate for human diseases. In 1975, as per the Tufts Center for the Study of Drug Development, pharmaceutical industries expended 100 million dollars for research and development of the average FDA approved drug. By 2005, this figure had more than quadrupled, to $1.3 billion. In order to recover their high and risky investment cost, pharmaceutical companies charge more for their products. However, there exists no correlation between drug development cost and actual sale of the drug. This high drug development cost could be due to the reason that all patients might not respond to the drug. Hence, a given drug has to be tested in large number of patients to show drug benefits and obtain significant results.

Neutral evolution of drug resistant colorectal cancer cell populations is independent of their KRAS status

Emergence of tumor resistance to an anti-cancer therapy directed against a putative target raises several questions including: (1) do mutations in the target/pathway confer resistance? (2) Are these mutations pre-existing? (3) What is the relative fitness of cells with/without the mutation? We addressed these questions in patients with metastatic colorectal cancer (mCRC). We conducted an exhaustive review of published data to establish a median doubling time for CRCs and stained a cohort of CRCs to document mitotic indices. We analyzed published data and our own data to calculate rates of growth (g) and regression (d, decay) of tumors in patients with CRC correlating these results with the detection of circulating MT-KRAS DNA. Additionally we estimated mathematically the caloric burden of such tumors using data on mitotic and apoptotic indices. We conclude outgrowth of cells harboring intrinsic or acquired MT-KRAS cannot explain resistance to anti-EGFR (epidermal growth factor receptor) antibodies. Rates of tumor growth with panitumumab are unaffected by presence/absence of MT-KRAS. While MT-KRAS cells may be resistant to anti-EGFR antibodies, WT-KRAS cells also rapidly bypass this blockade suggesting inherent resistance mechanisms are responsible and a neutral evolution model is most appropriate. Using the above clinical data on tumor doubling times and mitotic and apoptotic indices we estimated the caloric intake required to support tumor growth and suggest it may explain in part cancer-associated cachexia.

Role of Antigen Spread and Distinctive Characteristics of Immunotherapy in Cancer Treatment

Immunotherapy is an important breakthrough in cancer. US Food and Drug Administration-approved immunotherapies for cancer treatment (including, but not limited to, sipuleucel-T, ipilimumab, nivolumab, pembrolizumab, and atezolizumab) substantially improve overall survival across multiple malignancies. One mechanism of action of these treatments is to induce an immune response against antigen-bearing tumor cells; the resultant cell death releases secondary (nontargeted) tumor antigens. Secondary antigens prime subsequent immune responses (antigen spread). Immunotherapy-induced antigen spread has been shown in clinical studies. For example, in metastatic castration-resistant prostate cancer patients, sipuleucel-T induced early immune responses to the immunizing antigen (PA2024) and/or the target antigen (prostatic acid phosphatase). Thereafter, most patients developed increased antibody responses to numerous secondary proteins, several of which are expressed in prostate cancer with functional relevance in cancer. The ipilimumab-induced antibody profile in melanoma patients shows that antigen spread also occurs with immune checkpoint blockade. In contrast to chemotherapy, immunotherapy often does not result in short-term changes in conventional disease progression end points (eg, progression-free survival, tumor size), which may be explained, in part, by the time taken for antigen spread to occur. Thus, immune-related response criteria need to be identified to better monitor the effectiveness of immunotherapy. As immunotherapy antitumor effects take time to evolve, immunotherapy in patients with less advanced cancer may have greater clinical benefit vs those with more advanced disease. This concept is supported by prostate cancer clinical studies with sipuleucel-T, PSA-TRICOM, and ipilimumab. We discuss antigen spread with cancer immunotherapy and its implications for clinical outcomes.

TARP vaccination is associated with slowing in PSA velocity and decreasing tumor growth rates in patients with Stage D0 prostate cancer

T-cell receptor alternate reading frame protein (TARP) is a 58-residue protein over-expressed in prostate and breast cancer. We investigated TARP peptide vaccination's impact on the rise in PSA (expressed as Slope Log(PSA) or PSA Doubling Time (PSADT)), validated tumor growth measures, and tumor growth rate in men with Stage D0 prostate cancer. HLA-A*0201 positive men were randomized to receive epitope-enhanced (29-37-9V) and wild-type (27-35) TARP peptides administered as a Montanide/GM-CSF peptide emulsion or as an autologous peptide-pulsed dendritic cell vaccine every 3 weeks for a total of five vaccinations with an optional 6th dose of vaccine at 36 weeks based on immune response or PSADT criteria with a booster dose of vaccine for all patients at 48 and 96 weeks. 41 patients enrolled with median on-study duration of 75 weeks at the time of this analysis. Seventy-two percent of patients reaching 24 weeks and 74% reaching 48 weeks had a decreased Slope Log(PSA) compared to their pre-vaccination baseline (p = 0.0012 and p = 0.0004 for comparison of overall changes in Slope Log(PSA), respectively). TARP vaccination also resulted in a 50% decrease in median tumor growth rate (g): pre-vaccine g = 0.0042/day, post-vaccine g = 0.0021/day (p = 0.003). 80% of subjects exhibited new vaccine-induced TARP-specific IFNγ ELISPOT responses but they did not correlate with decreases in Slope Log(PSA). Thus, vaccination with TARP peptides resulted in significant slowing in PSA velocity and reduction in tumor growth rate in a majority of patients with PSA biochemical recurrence.

Using the SAEM algorithm for mechanistic joint models characterizing the relationship between nonlinear PSA kinetics and survival in prostate cancer patients

Joint modeling is increasingly popular for investigating the relationship between longitudinal and time-to-event data. However, numerical complexity often restricts this approach to linear models for the longitudinal part. Here, we use a novel development of the Stochastic-Approximation Expectation Maximization algorithm that allows joint models defined by nonlinear mixed-effect models. In the context of chemotherapy in metastatic prostate cancer, we show that a variety of patterns for the Prostate Specific Antigen (PSA) kinetics can be captured by using a mechanistic model defined by nonlinear ordinary differential equations. The use of a mechanistic model predicts that biological quantities that cannot be observed, such as treatment-sensitive and treatment-resistant cells, may have a larger impact than PSA value on survival. This suggests that mechanistic joint models could constitute a relevant approach to evaluate the efficacy of treatment and to improve the prediction of survival in patients.

Integrating Immunotherapies in Prostate Cancer

In recent years, immunotherapy has emerged as a viable and promising treatment for prostate cancer. Beyond sipulecuel-T, phase III trials are evaluating multiple vaccine and immune-based therapies in men with this disease. Evidence suggests that many of these therapies are effective at augmenting immune responses and slowing tumor growth rates. Yet prospective data evaluating these responses as surrogates for survival are still needed. In the absence of validated intermediate markers of response, growing data suggests that patients with more indolent disease are more likely to benefit from immunotherapies. In order to further optimize immunotherapy use, ongoing trials are evaluating its combination with traditional as well as other immune-based treatments. Preliminary data from these trials are promising and are shedding new light on this area.

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.

Disease Progression/Clinical Outcome Model for Castration-Resistant Prostate Cancer in Patients Treated With Eribulin

Frameworks that associate cancer dynamic disease progression models with parametric survival models for clinical outcome have recently been proposed to support decision making in early clinical development. Here we developed such a disease progression clinical outcome model for castration-resistant prostate cancer (CRPC) using historical phase II data of the anticancer agent eribulin. Disease progression was captured using the dynamics of prostate-specific antigen (PSA). For clinical outcome, overall survival (OS) was used. The model for PSA dynamics comprised parameters for baseline PSA (23.2 ng/ml, relative standard error (RSE) 16.5%), growth rate (0.00879 day(-1), RSE 12.6%), drug effect (0.241 µg·h·l(-1) day(-1), RSE 32.6%), and resistance development (0.0113 day(-1), RSE 44.3%). OS was modeled according to a Weibull distribution. Predictors for survival included model-predicted PSA time to nadir (TTN), PSA growth rate, Eastern Cooperative Oncology Group (ECOG) score, and baseline PSA. The developed framework can be considered to support informative design and analysis of drugs developed for CRPC.

The effect of intrinsic and acquired resistances on chemotherapy effectiveness

Although chemotherapy is one of the most common treatments for cancer, it can be only partially successful. Drug resistance is the main cause of the failure of chemotherapy. In this work, we present a mathematical model to study the impact of both intrinsic (preexisting) and acquired (induced by the drugs) resistances on chemotherapy effectiveness. Our simulations show that intrinsic resistance could be as dangerous as acquired resistance. In particular, our simulations suggest that tumors composed by even a small fraction of intrinsically resistant cells may lead to an unsuccessful therapy very quickly. Our results emphasize the importance of monitoring both intrinsic and acquired resistances during treatment in order to succeed and the importance of doing more experimental and genetic research in order to develop a pretreatment clinical test to avoid intrinsic resistance.

Therapies with diverse mechanisms of action kill cells by a similar exponential process in advanced cancers

Successful cancer treatments are generally defined as those that decrease tumor quantity. In many cases, this decrease occurs exponentially, with deviations from a strict exponential being attributed to a growing fraction of drug-resistant cells. Deviations from an exponential decrease in tumor quantity can also be expected if drugs have a nonuniform spatial distribution inside the tumor, for example, because of interstitial pressure inside the tumor. Here, we examine theoretically different models of cell killing and analyze data from clinical trials based on these models. We show that the best description of clinical outcomes is by first-order kinetics with exponential decrease of tumor quantity. We analyzed the total tumor quantity in a diverse group of clinical trials with various cancers during the administration of different classes of anticancer agents and in all cases observed that the models that best fit the data describe the decrease of the sensitive tumor fraction exponentially. The exponential decrease suggests that all drug-sensitive cancer cells have a single rate-limiting step on the path to cell death. If there are intermediate steps in the path to cell death, they are not rate limiting in the observational time scale utilized in clinical trials--tumor restaging at 6- to 8-week intervals. On shorter time scales, there might be intermediate steps, but the rate-limiting step is the same. Our analysis, thus, points to a common pathway to cell death for cancer cells in patients. See all articles in this Cancer Research section, "Physics in Cancer Research."

Optimizing oncology therapeutics through quantitative translational and clinical pharmacology: challenges and opportunities

Despite advances in biomedical research that have deepened our understanding of cancer hallmarks, resulting in the discovery and development of targeted therapies, the success rates of oncology drug development remain low. Opportunities remain for objective dose selection informed by exposure-response understanding to optimize the benefit-risk balance of novel therapies for cancer patients. This review article discusses the principles and applications of modeling and simulation approaches across the lifecycle of development of oncology therapeutics. Illustrative examples are used to convey the value gained from integration of quantitative clinical pharmacology strategies from the preclinical-translational phase through confirmatory clinical evaluation of efficacy and safety.

Continuing a cancer treatment despite tumor growth may be valuable: sunitinib in renal cell carcinoma as example

BACKGROUND

The US FDA and the EMA have approved seven agents for the treatment of renal cell carcinoma, primarily based on differences in progression-free survival (PFS). Because PFS is an arbitrary endpoint we hypothesized that an analysis would demonstrate the growth rate of tumors remained constant at the time of RECIST-defined disease progression.

METHODS

We previously estimated the growth (g) and regression (d) rates and the stability of g using data from the Phase III trial comparing sunitinib and interferon.

RESULTS

Sufficient data were available and rate constants statistically valid in 321 of 374 patients randomized to sunitinib. Median d was 0•0052 days(-1); in 53 patients no tumor growth was recorded. Median g was 0•00082 days(-1) and was stable for a median of 275 days on therapy, remaining stable beyond 300, 600 and 900 days in 122, 65 and 27 patients, respectively. A possible increase in g while receiving sunitinib could be discerned in only 18 of 321 patients. Given a median g of 0•00082 days(-1) the estimated median time to a second progression were sunitinib continued past RECIST-defined progression was 7.3 months. At 100, 200, and 300 days after starting therapy, an estimated 47%, 27%, and 13% of tumor remains sunitinib sensitive and could explain a RECIST-defined response to a new TKI.

CONCLUSION

Prolonged stability of g with sunitinib suggests continued sunitinib beyond RECIST-defined progression may provide a beneficial outcome. Randomized trials in patients whose disease has "progressed" on sunitinib are needed to test this hypothesis.

The VEGF inhibitor axitinib has limited effectiveness as a therapy for adrenocortical cancer

CONTEXT

Adrenocortical carcinoma (ACC) is a rare malignancy with a poor prognosis in need of more effective treatment options. Published evidence indicates many ACCs express the vascular endothelial growth factor receptor (VEGFR), suggesting inhibiting vascular endothelial growth factor signaling could potentially impact tumor growth.

OBJECTIVE

The objective of the study was to determine the antitumor efficacy of axitinib (AG-013736), a potent, selective inhibitor of VEGFR1, -2, and -3.

DESIGN

This was a phase II, open-label trial using a two-stage design.

PATIENTS

Thirteen patients with metastatic ACC previously treated with at least one chemotherapy regimen with or without mitotane participated in the study.

INTERVENTION

Starting axitinib dose was 5 mg orally twice daily. Dose escalations were permitted if the administered dose was tolerable.

RESULTS

Thirteen patients were enrolled. Dose escalation was possible in seven patients, but the majority could not tolerate a dose higher than the starting 5 mg, twice-daily dose for prolonged periods of time. All patients experienced known grade 1/2 toxicities, and 10 of 13 patients had at least one grade 3/4 adverse event. No patient tumor could be scored as a Response Evaluation Criteria in Solid Tumors response, although the growth rate on therapy compared with that prior to starting axitinib was reduced in 4 of the 13 patients. The median progression-free survival was 5.48 months, and the median overall survival was longer than 13.7 months.

CONCLUSION

Axitinib has limited effectiveness in ACC. Together with 48 patients previously reported who received either sorafenib or sunitinib, a total of 61 ACC patients have now been treated with a VEGFR tyrosine kinase inhibitor without an objective Response Evaluation Criteria in Solid Tumors response. Future trials in ACC should look to other targets for possible active agents.

Simplifying the complexity of resistance heterogeneity in metastasis

The main goal of treatment regimens for metastasis is to control growth rates, not eradicate all cancer cells. Mathematical models offer methodologies that incorporate high-throughput data with dynamic effects on net growth. The ideal approach would simplify, but not over-simplify, a complex problem into meaningful and manageable estimators that predict the response of a patient to specific treatments. We explore here three fundamental approaches with different assumptions concerning resistance mechanisms in which the cells are categorized into either discrete compartments or described by a continuous range of resistance levels. We argue in favor of modeling resistance as a continuum, and demonstrate how integrating cellular growth rates, density-dependent versus exponential growth, and intratumoral heterogeneity improves predictions concerning the resistance heterogeneity of metastases.

Prostate-specific antigen growth rate constant after first-line cytotoxic chemotherapy in metastatic castration-resistant prostate cancer: a monoinstitutional experience

OBJECTIVE

Validation in clinical practice, after first-line chemotherapy (CT) of metastatic castration-resistant prostate cancer (PC), of prostate-specific antigen growth rate constant logarithm (PSA-G), calculated by a formula developed by Stein et al. in comparison with PSA decrease (PSA-D), calculated as recommended by PCWG2.

PATIENTS AND METHODS

This study is a retrospective monoinstitutional assessment of PSA-G and PSA-D after 12 weeks from the beginning of first-line cytotoxic CT in 49 patients with metastatic castration-resistant PC treated from 2006 to 2011, and whose pre-CT PSA and post-CT PSA determinations have been measured at specific time points. The 12-week PSA was measured at 80 to 91 days from the beginning of CT.

RESULTS

PSA-G exhibited a significant correlation with overall survival by Mann-Whitney U test and by linear regression, whereas PSA-D did only at the first test. After multivariate analysis, PSA-G was the only posttreatment measure to predict overall survival.

CONCLUSION

PSA-G appears a reliable surrogate end point after first-line cytotoxic CT outside of clinical trials. A cutoff value of PSA-G post-CT higher than-2.4 could be considered suggestive for moving to another treatment.

Modeling tumor growth kinetics after treatment with pazopanib or placebo in patients with renal cell carcinoma

PURPOSE

The purpose of this study is to characterize tumor growth kinetics in patients with renal cell carcinoma (RCC) after treatment with pazopanib or placebo and to identify predictive patient-specific covariates.

METHODS

Different tumor growth models that included patient-specific covariates were fit to tumor growth data from Phase 2 (n = 220) and Phase 3 (n = 423) clinical trials using nonlinear mixed-effects modeling. Logistic regression was used to determine whether individual model parameters or covariates were related to occurrence of new lesions.

RESULTS

A modified Wang model that included a quadratic growth term and a mixture model adequately described the data. Patients in Group 1 (93 %) showed treatment-dependent tumor shrinkage followed by treatment-independent regrowth. Patients in Group 2 (7 %) showed treatment-independent tumor shrinkage that did not regrow. In Group 1, pazopanib 800 mg increased the tumor shrinkage rate by 267 % compared to placebo. Baseline tumor size was dependent on baseline hemoglobin, baseline lactate dehydrogenase, study, and prior nephrectomy. Logistic regression analysis showed that prior radiotherapy, baseline tumor size, tumor shrinkage rate, tumor regrowth rate, study, and treatment (P < 0.01 for all) were all important predictors of new lesions. Patients treated with placebo were approximately twice as likely to develop new lesions than patients treated with pazopanib.

CONCLUSIONS

Mathematical modeling of tumor growth kinetics can quantify the effect of anticancer therapies. Pazopanib 800 mg was shown to be an effective treatment for RCC that increased the tumor shrinkage rate by 267 % compared with placebo and reduced the likelihood of developing new lesions.

Strategies for optimizing the clinical impact of immunotherapeutic agents such as sipuleucel-T in prostate cancer

Sipuleucel-T is a therapeutic cancer vaccine that has shown improved survival in men with metastatic castration-resistant prostate cancer. As a first-in-class agent, it has been met with both fan-fare and controversy. A broad review of immune-based therapies may reveal the delayed clinical impact of sipuleucel-T to be a class effect. As new strategies of immune-based therapy are developed, their effects can be optimized through better understanding of how they affect disease differently from more standard therapeutics. Furthermore, combination therapy with agents that can either work synergistically with immune-activating therapies or deplete immune-regulating cells may result in more vigorous immune responses and improved clinical outcomes. In addition, therapeutic vaccines may be ideal candidates to safely combine with standard-of-care therapies because of their nonoverlapping toxicity profile. The ultimate role of immunotherapy may not be to supplant standard therapies, but rather to work in concert with them to maximize clinical benefit for patients.

Demystifying immunotherapy in prostate cancer: understanding current and future treatment strategies

Immunotherapy has emerged as a viable therapeutic option for patients with prostate cancer. There are multiple potential strategies that use the immune system, including therapeutic cancer vaccines that are designed to stimulate immune cells to target antigens expressed by cancer cells. Sipuleucel-T is a vaccine currently approved for the treatment of minimally symptomatic metastatic prostate cancer, whereas the vaccine PSA-TRICOM and the immune-checkpoint inhibitor ipilimumab are in phase III testing. Although there are no short-term changes in disease progression or available biomarkers to assess response, these agents appear to improve survival. One hypothesis suggests that this apparent paradox can be explained by the growth-moderating effects of these treatments, which do not cause tumor size to diminish, but rather stall or slow their growth rate over time. For this reason, the use of immunotherapy earlier in the disease process is being investigated. Another approach is to block immune-regulatory mechanisms mediated by the molecules cytotoxic T lymphocyte antigen 4 and programmed cell death protein 1. Additional future strategies will combine immunotherapy with other standard therapies, potentially enhancing the latter's clinical impact and thereby improving both time to progression and overall survival due to the combined effects of both treatments. Prospective trials are currently evaluating these hypotheses and will ultimately serve to optimize immunotherapy in the treatment of prostate cancer.

Therapeutic cancer vaccines in prostate cancer: the quest for intermediate markers of response

Despite recent advances in cancer immunotherapy, no prospectively validated intermediate biomarkers exist to predict response. These biomarkers are highly desirable given modern immunotherapy's paradoxical pattern of clinical benefit; that is, improvement in overall survival without short-term change in progression. Immunotherapy clinical trials have evaluated biomarkers that may correlate with clinical outcomes. Many of them are performed on peripheral blood to evaluate the systemic response, such as tumor-targeted humoral and cellular immunity, and cytokine responses. Accumulating evidence suggests that immune infiltrates in tumors may suggest evidence for the therapy's mechanism of action, and have greater potential for providing prognostic and predictive information. In addition, a non-immunologic biomarker, such as tumor growth kinetics, may explain this paradoxical pattern of clinical benefit, and predict survival in patients treated with an immunotherapy. Prospective assessment and validation of these and other intermediate markers would be required to better understand their potential clinical role.

Cancer chemotherapy: a critical analysis of its 60 years of history

Chemotherapy has already proven widely effective in the treatment of cancer, occupying a prominent place in the current therapeutic arsenal. However, in recent years, there has been a plateau in the evolution of the clinical results obtained with this modality treatment. In some cases, the limitations of chemotherapy observed during the early days still apply. These facts forced us to do a thorough analysis of what happened in the past 60years. We have observed that each major advance obtained in this field was based on empirical clinical observations. We thus believe that the current results of old or new agents can only be improved by understanding the natural history of each specific cancer subtype at the clinical level and by overcoming the physiological barriers involved in chemotherapy failure. This strategy will surely allow us to enlarge the list of curable cancers by chemotherapy.