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

Assessing olaparib efficacy in U.S. Veterans with metastatic prostate cancer utilizing a time-indifferent g-rate method ideal for real-world analyses

BACKGROUND

We aimed to assess real-world efficacy of the PARP inhibitor, olaparib, in US Veterans with metastatic prostate cancer (mPC) by leveraging the national data repository and evaluate a novel approach to assess treatment efficacy in tumors considered rare or harboring rare mutations.

METHODS

Included Veterans had 1) mPC with somatic or germline alterations/mutations in genes involved in homologous recombination repair (HRR), 2) received olaparib monotherapy as well as a novel hormonal therapy/androgen receptor pathway inhibitors (NHT/ARPI), and/or chemotherapy, and 3) estimable rates of tumor growth (g-rate) using PSA values obtained while receiving treatment. Previous work has shown an excellent inverse correlation of g-rate with survival. Using g-rate, we determined tumor doubling time (DT) and DT ratios (DT on olaparib/DT on prior medication). We postulated that a DT ratio≥ 1 was associated with benefit.

FINDINGS

We identified 139 Veterans, including 42 Black males with tumors harboring mutations/alterations in HRR genes who received olaparib: BRCA2 (50), ATM (32), BRCA1 (10), other mutations (47). 62/139 (45%) of all and 21/42 (50%) of Black Veterans had DT ratios ≥1, including 31, 10, 2, and 19 with BRCA2, ATM, BRCA1, and other mutations, respectively (p = 0.006). Median survival with DT ratios ≥1 was superior, being 24.5 vs. 11.4 months for DT ratio <1 (p = 0.01, HR 0.50, 95% CI 0.29-0.85). Benefit from olaparib, defined as DT ratio ≥1, was not observed for germline status, starting PSA value, number of prior therapies, or immediate prior therapy. Compared to matched cohorts, tumors in the olaparib cohort had shorter DTs with enzalutamide in first line (367 vs. 884 days; p = 0.0043).

INTERPRETATION

Using equations indifferent to timing of assessments ideal for real-world efficacy analyses, we showed DT ratio ≥1 representing slower tumor growth on olaparib relative to the prior therapy correlates with improved survival. Olaparib efficacy in Veterans with mPC harboring mutations/alterations in HRR genes emulates clinical trial results. Black men had comparable results. Compared to matched cohorts, in first line, enzalutamide was less efficacious in tumors harboring mutations/alterations in HRR genes.

FUNDING

American Society of Clinical Oncology Conquer Cancer Foundation (ASCO CCF), the Blavatnik Family Foundation and the Prostate Cancer Foundation (PCF).

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.

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.

Depth of Radiographic Response and Time to Tumor Regrowth Predicts Overall Survival Following Anti-VEGF Therapy in Recurrent Glioblastoma

PURPOSE

Antiangiogenic therapies are known to cause high radiographic response rates due to reduction in vascular permeability resulting in a lower degree of contrast extravasation. In this study, we investigate the prognostic ability for model-derived parameters describing enhancing tumor volumetric dynamics to predict survival in recurrent glioblastoma treated with antiangiogenic therapy.

EXPERIMENTAL DESIGN

N = 276 patients in two phase II trials were used as training data, including bevacizumab ± irinotecan (NCT00345163) and cabozantinib (NCT00704288), and N = 74 patients in the bevacizumab arm of a phase III trial (NCT02511405) were used for validation. Enhancing volumes were estimated using T1 subtraction maps, and a biexponential model was used to estimate regrowth (g) and regression (d) rates, time to tumor regrowth (TTG), and the depth of response (DpR). Response characteristics were compared to diffusion MR phenotypes previously shown to predict survival.

RESULTS

Optimized thresholds occurred at g = 0.07 months-1 (phase II: HR = 0.2579, P = 5 × 10-20; phase III: HR = 0.2197, P = 5 × 10-5); d = 0.11 months-1 (HR = 0.3365, P < 0.0001; HR = 0.3675, P = 0.0113); TTG = 3.8 months (HR = 0.2702, P = 6 × 10-17; HR = 0.2061, P = 2 × 10-5); and DpR = 11.3% (HR = 0.6326, P = 0.0028; HR = 0.4785, P = 0.0206). Multivariable Cox regression controlling for age and baseline tumor volume confirmed these factors as significant predictors of survival. Patients with a favorable pretreatment diffusion MRI phenotype had a significantly longer TTG and slower regrowth.

CONCLUSIONS

Recurrent glioblastoma patients with a large, durable radiographic response to antiangiogenic agents have significantly longer survival. This information is useful for interpreting activity of antiangiogenic agents in recurrent glioblastoma.

Support to early clinical decisions in drug development and personalised medicine with checkpoint inhibitors using dynamic biomarker-overall survival models

Longitudinal models of biomarkers such as tumour size dynamics capture treatment efficacy and predict treatment outcome (overall survival) of a variety of anticancer therapies, including chemotherapies, targeted therapies, immunotherapies and their combinations. These pharmacological endpoints like tumour dynamic (tumour growth inhibition) metrics have been proposed as alternative endpoints to complement the classical RECIST endpoints (objective response rate, progression-free survival) to support early decisions both at the study level in drug development as well as at the patients level in personalised therapy with checkpoint inhibitors. This perspective paper presents recent developments and future directions to enable wider and robust use of model-based decision frameworks based on pharmacological endpoints.

Modeling Tumor Growth Using Partly Conditional Survival Models: A Case Study in Colorectal Cancer

PURPOSE

There are multiple approaches to modeling the relationship between longitudinal tumor measurements obtained from serial imaging and overall survival. Many require strong assumptions that are untestable and debatable. We illustrate how to apply a novel, more flexible approach, the partly conditional (PC) survival model, using images acquired during a phase III, randomized clinical trial in colorectal cancer as an example.

METHODS

PC survival approaches were used to model longitudinal volumetric computed tomography data of 1,025 patients in the completed VELOUR trial, which evaluated adding aflibercept to infusional fluorouracil, leucovorin, and irinotecan for treating metastatic colorectal cancer. PC survival modeling is a semiparametric approach to estimating associations of longitudinal measurements with time-to-event outcomes. Overall survival was our outcome. Covariates included baseline tumor burden, change in tumor burden from baseline to each follow-up time, and treatment. Both unstratified and time-stratified models were investigated.

RESULTS

Without making assumptions about the distribution of the tumor growth process, we characterized associations between the change in tumor burden and survival. This change was significantly associated with survival (hazard ratio [HR], 1.04; 95% CI, 1.02 to 1.05; P < .001), suggesting that aflibercept works at least in part by altering the tumor growth trajectory. We also found baseline tumor size prognostic for survival even when accounting for the change in tumor burden over time (HR, 1.02; 95% CI, 1.01 to 1.02; P < .001).

CONCLUSION

The PC modeling approach offers flexible characterization of associations between longitudinal covariates, such as serially assessed tumor burden, and survival time. It can be applied to a variety of data of this nature and used as clinical trials are ongoing to incorporate new disease assessment information as it is accumulated, as indicated by an example from colorectal cancer.

External validation of a tumor growth inhibition-overall survival model in non-small-cell lung cancer based on atezolizumab studies using alectinib data

BACKGROUND

A modeling framework was previously developed to simulate overall survival (OS) using tumor growth inhibition (TGI) data from six randomized phase 2/3 atezolizumab monotherapy or combination studies in non-small-cell lung cancer (NSCLC). We aimed to externally validate this framework to simulate OS in patients with treatment-naive advanced anaplastic lymphoma kinase (ALK)-positive NSCLC in the alectinib ALEX study.

METHODS

TGI metrics were estimated from a biexponential model using longitudinal tumor size data from a Phase 3 study evaluating alectinib compared with crizotinib in patients with treatment-naive ALK-positive advanced NSCLC. Baseline prognostic factors and TGI metric estimates were used to predict OS.

RESULTS

286 patients were evaluable (at least baseline and one post-baseline tumor size measurements) out of 303 (94%) followed for up to 5 years (cut-off: 29 November 2019). The tumor growth rate estimate and baseline prognostic factors (inflammatory status, tumor burden, Eastern Cooperative Oncology Group performance status, race, line of therapy, and sex) were used to simulate OS in ALEX study. Observed survival distributions for alectinib and crizotinib were within model 95% prediction intervals (PI) for approximately 2 years. Predicted hazard ratio (HR) between alectinib and crizotinib was in agreement with the observed HR (predicted HR 0.612, 95% PI 0.480-0.770 vs. 0.625 observed HR).

CONCLUSION

The TGI-OS model based on unselected or PD-L1 selected NSCLC patients included in atezolizumab trials is externally validated to predict treatment effect (HR) in a biomarker-selected (ALK-positive) population included in alectinib ALEX trial suggesting that TGI-OS models may be treatment independent.

Tumor Dynamic Model-Based Decision Support for Phase Ib/II Combination Studies: A Retrospective Assessment Based on Resampling of the Phase III Study IMpower150

PURPOSE

Model-based tumor growth inhibition (TGI) metrics are increasingly incorporated into go/no-go decisions in early clinical studies. To apply this methodology to new investigational combinations requires independent evaluation of TGI metrics in recently completed Phase III trials of effective immunotherapy.

PATIENTS AND METHODS

Data were extracted from IMpower150, a positive, randomized, Phase III study of first-line therapy in 1,202 patients with non-small cell lung cancer. We resampled baseline characteristics and longitudinal sum of longest diameters of tumor lesions of patients from both arms, atezolizumab+ bevacizumab+chemotherapy (ABCP) versus BCP, to mimic Phase Ib/II studies of 15 to 40 patients/arm with 6 to 24 weeks follow-up. TGI metrics were estimated using a bi-exponential TGI model. Effect sizes were calculated as TGI metrics geometric mean ratio (GMR), objective response rate (ORR) difference (d), and progression-free survival (PFS), hazard ratio (HR) between arms. Correct and incorrect go decisions were evaluated as the probability to achieve desired effect sizes in ABCP versus BCP and BCP versus BCP, respectively, across 500 replicated subsamples for each design.

RESULTS

For 40 patients/24 weeks follow-up, correct go decisions based on probability tumor growth rate (KG) GMR <0.90, dORR >0.10, and PFS HR <0.70 were 83%, 69%, and 58% with incorrect go decision rates of 4%, 12%, and 11%, respectively. For other designs, the ranking did not change with TGI metrics consistently overperforming RECIST endpoints. The predicted overall survival (OS) HR was around 0.80 in most of the scenarios investigated.

CONCLUSIONS

Model-based estimate of KG GMR is an exploratory endpoint that informs early clinical decisions for combination studies.

A comprehensive regulatory and industry review of modeling and simulation practices in oncology clinical drug development

Exposure-response (E-R) analyses are an integral component in the development of oncology products. Characterizing the relationship between drug exposure metrics and response allows the sponsor to use modeling and simulation to address both internal and external drug development questions (e.g., optimal dose, frequency of administration, dose adjustments for special populations). This white paper is the output of an industry-government collaboration among scientists with broad experience in E-R modeling as part of regulatory submissions. The goal of this white paper is to provide guidance on what the preferred methods for E-R analysis in oncology clinical drug development are and what metrics of exposure should be considered.

Imaging endpoints for clinical trial use: a RECIST perspective

Twenty years after its initial introduction, Response Evaluation Criteria in Solid Tumors (RECIST) remains today a unique standardized tool allowing uniform objective evaluation of response in solid tumors in clinical trials across different treatment indications. Several attempts have been made to update or replace RECIST, but none have realized the general traction or uptake seen with RECIST. This communication provides an overview of some challenges faced by RECIST in the rapidly changing oncology landscape, including the incorporation of PET with ¹⁸F-fluorodeoxyglucose tracer as a tool for response assessment and the validation of criteria for use in trials involving immunotherapeutics. The latter has mainly been slow due to lack of data sharing. Work is ongoing to try to address this.We also aim to share our view as statistician representatives on the RECIST Working Group on what would be needed to validate new imaging endpoints for clinical trial use, with a specific focus on RECIST. Whether this could lead to an update of RECIST or replace RECIST altogether, depends on the changes being proposed. The ultimate goal remains to have a well defined, repeatable, confirmable and objective standard as provided by RECIST today.

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.

The 28th Annual Prostate Cancer Foundation Scientific Retreat report

BACKGROUND

The 28th Annual Prostate Cancer Foundation (PCF) Scientific Retreat was held virtually over 4 days, on October 28-29 and November 4-5, 2021.

METHODS

The Annual PCF Scientific Retreat is a leading global scientific conference that focuses on first-in-field, unpublished, and high-impact basic, translational, and clinical prostate cancer research, as well as research from other fields with high probability for impacting prostate cancer research and patient care.

RESULTS

Primary areas of research discussed at the 2021 PCF Retreat included: (i) prostate cancer disparities; (ii) prostate cancer survivorship; (iii) next-generation precision medicine; (iv) PSMA theranostics; (v) prostate cancer lineage plasticity; (vi) tumor metabolism as a cancer driver and treatment target; (vii) prostate cancer genetics and polygenic risk scores; (viii) glucocorticoid receptor biology in castration-resistant prostate cancer (CRPC); (ix) therapeutic degraders; (x) new approaches for immunotherapy in prostate cancer; (xi) novel technologies to overcome the suppressive tumor microenvironment; and (xii) real-world evidence and synthetic/virtual control arms.

CONCLUSIONS

This article provides a summary of the presentations from the 2021 PCF Scientific Retreat. We hope that sharing this knowledge will help to improve the understanding of the current state of research and direct new advances in prostate cancer research and care.

Novel biomarkers in NSCLC: Radiomic analysis, kinetic analysis, and circulating tumor DNA

Current radiographic methods of measuring treatment response for patients with nonsmall cell lung cancer have significant limitations. Recently, new modalities using standard of care images or minimally invasive blood-based DNA tests have gained interest as methods of evaluating treatment response. This article highlights three emerging modalities: radiomic analysis, kinetic analysis and serum-based measurement of circulating tumor DNA, with a focus on the clinical evidence supporting these methods. Additionally, we discuss the possibility of combining these modalities to develop a robust biomarker with strong correlation to clinically meaningful outcomes that could impact clinical trial design and patient care. At Last, we focus on how these methods specifically apply to a Veteran population.

Tumor Growth Rate After Nadir Is Associated With Survival in Patients With EGFR-Mutant Non-Small-Cell Lung Cancer Treated With Epidermal Growth Factor Receptor Tyrosine Kinase Inhibitor

PURPOSE

To investigate the association between tumor volume growth rate after the nadir and survival in patients with EGFR-mutant advanced non-small-cell lung cancer (NSCLC) treated with erlotinib.

MATERIALS AND METHODS

Seventy-one patients with EGFR-mutant advanced NSCLC treated with erlotinib were studied for computed tomography tumor volume kinetics during therapy. The tumor growth rate after nadir was obtained using a previously published analytic module for longitudinal volume tracking to study its relationship with overall survival (OS).

RESULTS

The median tumor volume for the cohort was 19,842 mm3 at baseline and 4,083 mm3 at nadir. The median time to nadir was 6.2 months. The tumor growth rate after nadir for logeV (the natural logarithm of tumor volume measured in mm3) was 0.11/mo on average for the cohort (SE: 0.014), which was very similar to the previously validated reference value of 0.12/mo to define slow and fast tumor growth. The OS of 48 patients with slow tumor growth (≤ 0.12/mo) was significantly longer compared with 23 patients with fast tumor growth (> 0.12/mo; median OS: 37.8 v 25.0 months; P = .0012). In Cox models, tumor growth rate was also associated with survival (regression coefficient: 3.9903; P = .0024; faster rate leads to increased hazards), after adjusting for time to nadir (regression coefficient: -0.0863; P = .0008; longer time to nadir leads to decreased hazards) and smoking history.

CONCLUSION

In patients with EGFR-mutant advanced NSCLC treated with erlotinib, slower tumor growth rates after nadir were associated with longer OS, providing a rationale for using tumor growth rates to guide precision therapy for lung cancer.

Tumor Growth Rate After Nadir Is Associated With Survival in Patients With EGFR-Mutant Non-Small-Cell Lung Cancer Treated With Epidermal Growth Factor Receptor Tyrosine Kinase Inhibitor

PURPOSE

To investigate the association between tumor volume growth rate after the nadir and survival in patients with EGFR-mutant advanced non-small-cell lung cancer (NSCLC) treated with erlotinib.

MATERIALS AND METHODS

Seventy-one patients with EGFR-mutant advanced NSCLC treated with erlotinib were studied for computed tomography tumor volume kinetics during therapy. The tumor growth rate after nadir was obtained using a previously published analytic module for longitudinal volume tracking to study its relationship with overall survival (OS).

RESULTS

The median tumor volume for the cohort was 19,842 mm³ at baseline and 4,083 mm³ at nadir. The median time to nadir was 6.2 months. The tumor growth rate after nadir for log_eV (the natural logarithm of tumor volume measured in mm³) was 0.11/mo on average for the cohort (SE: 0.014), which was very similar to the previously validated reference value of 0.12/mo to define slow and fast tumor growth. The OS of 48 patients with slow tumor growth (≤ 0.12/mo) was significantly longer compared with 23 patients with fast tumor growth (> 0.12/mo; median OS: 37.8 v 25.0 months; P = .0012). In Cox models, tumor growth rate was also associated with survival (regression coefficient: 3.9903; P = .0024; faster rate leads to increased hazards), after adjusting for time to nadir (regression coefficient: -0.0863; P = .0008; longer time to nadir leads to decreased hazards) and smoking history.

CONCLUSION

In patients with EGFR-mutant advanced NSCLC treated with erlotinib, slower tumor growth rates after nadir were associated with longer OS, providing a rationale for using tumor growth rates to guide precision therapy for lung cancer.

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.