Organ-specific tumor dynamics predict survival of patients with metastatic colorectal cancer.

BACKGROUND: We aim to compare the prognostic value of organ-specific dynamics with the sum of the longest diameter (SLD) dynamics in patients with metastatic colorectal cancer (mCRC). METHODS: All datasets are accessible in Project Data Sphere, an open-access platform. The tumor growth inhibition models developed based on organ-level SLD and SLD were used to estimate the organ-specific tumor growth rates (KGs) and SLD KG. The early tumor shrinkage (ETS) from baseline to the first measurement after treatment was also evaluated. The relationship between organ-specific dynamics, SLD dynamics, and survival outcomes (overall survival, OS; progression-free survival, PFS) was quantified using Kaplan-Meier analysis and Cox regression. RESULTS: This study included 3687 patients from 6 phase III mCRC trials. The liver emerged as the most frequent metastatic site (2901, 78.7 %), with variable KGs across different organs in individual patients (liver 0.0243 > lung 0.0202 > lymph node 0.0127 > other 0.0118 [week-1]). Notably, the dynamics for different organs did not equally contribute to predicting survival outcomes. In liver metastasis cases, liver KG proved to be a superior prognostic indicator for OS and surpasses the predictive performance of SLD, (C-index, liver KG 0.610 vs SLD KG 0.606). A similar result can be found for PFS. Moreover, liver ETS also outperforms SLD ETS in predicting survival. Cox regression analysis confirmed liver KG is the most significant variable in survival prediction. CONCLUSIONS: In mCRC patients with liver metastasis, liver dynamics is the primary prognostic indicator for both PFS and OS. In future drug development for mCRC, greater emphasis should be directed towards understanding the dynamics of liver metastasis development.

MESBC: A novel mutually exclusive spectral biclustering method for cancer subtyping.

Many soft biclustering algorithms have been developed and applied to various biological and biomedical data analyses. However, few mutually exclusive (hard) biclustering algorithms have been proposed, which could better identify disease or molecular subtypes with survival significance based on genomic or transcriptomic data. In this study, we developed a novel mutually exclusive spectral biclustering (MESBC) algorithm based on spectral method to detect mutually exclusive biclusters. MESBC simultaneously detects relevant features (genes) and corresponding conditions (patients) subgroups and, therefore, automatically uses the signature features for each subtype to perform the clustering. Extensive simulations revealed that MESBC provided superior accuracy in detecting pre-specified biclusters compared with the non-negative matrix factorization (NMF) and Dhillon's algorithm, particularly in very noisy data. Further analysis of the algorithm on real datasets obtained from the TCGA database showed that MESBC provided more accurate (i.e., smaller p-value) overall survival prediction in patients with lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) cancers when compared to the existing, gold-standard subtypes for lung cancers (integrative clustering). Furthermore, MESBC detected several genes with significant prognostic value in both LUAD and LUSC patients. External validation on an independent, unseen GEO dataset of LUAD showed that MESBC-derived clusters based on TCGA data still exhibited clear biclustering patterns and consistent, outstanding prognostic predictability, demonstrating robust generalizability of MESBC. Therefore, MESBC could potentially be used as a risk stratification tool to optimize the treatment for the patient, improve the selection of patients for clinical trials, and contribute to the development of novel therapeutic agents.

Image-Based Subtype Classification for Glioblastoma Using Deep Learning: Prognostic Significance and Biologic Relevance.

PURPOSE: To apply deep learning algorithms to histopathology images, construct image-based subtypes independent of known clinical and molecular classifications for glioblastoma, and produce novel insights into molecular and immune characteristics of the glioblastoma tumor microenvironment. MATERIALS AND METHODS: Using whole-slide hematoxylin and eosin images from 214 patients with glioblastoma in The Cancer Genome Atlas (TCGA), a fine-tuned convolutional neural network model extracted deep learning features. Biclustering was used to identify subtypes and image feature modules. Prognostic value of image subtypes was assessed via Cox regression on survival outcomes and validated with 189 samples from the Clinical Proteomic Tumor Analysis Consortium (CPTAC) data set. Morphological, molecular, and immune characteristics of glioblastoma image subtypes were analyzed. RESULTS: Four distinct subtypes and modules (imClust1-4) were identified for the TCGA patients with glioblastoma on the basis of the image feature data. The glioblastoma image subtypes were significantly associated with overall survival (OS; P = .028) and progression-free survival (P = .003). Apparent association was also observed for disease-specific survival (P = .096). imClust2 had the best prognosis for all three survival end points (eg, after 25 months, imClust2 had >7% surviving patients than the other subtypes). Examination of OS in the external validation using the unseen CPTAC data set showed consistent patterns. Multivariable Cox analyses confirmed that the image subtypes carry unique prognostic information independent of known clinical and molecular predictors. Molecular and immune profiling revealed distinct immune compositions of the tumor microenvironment in different image subtypes and may provide biologic explanations for the patterns in patients' outcomes. CONCLUSION: Our image-based subtype classification on the basis of deep learning models is a novel tool to refine risk stratification in cancers. The image subtypes detected for glioblastoma represent a promising prognostic biomarker with distinct molecular and immune characteristics and may facilitate developing novel, individualized immunotherapies for glioblastoma.

Evaluating Prognostic Value of Dynamics of Circulating Lactate Dehydrogenase in Colorectal Cancer Using Modeling and Machine Learning.

Pretreatment serum lactate dehydrogenase (LDH) levels have been associated with poor prognosis in several types of cancer, including metastatic colorectal cancer (mCRC). However, very few models link survival to longitudinal LDH measured repeatedly over time during treatment. We investigated the prognostic value of on-treatment LDH dynamics in mCRC. Using data from two large phase III studies (2L and 3L+ mCRC settings, n = 824 and 210, respectively), we found that integrating longitudinal LDH data with baseline risk factors significantly improved survival prediction. Current LDH values performed best, enhancing discrimination ability (area under the receiver operating characteristic curve) by 4.5~15.4% and prediction accuracy (Brier score) by 3.9~15.0% compared with baseline variables. Combining all longitudinal LDH markers further improved predictive performance. After controlling for baseline covariates and other longitudinal LDH indicators, current LDH levels remained a significant risk factor in mCRC, increasing mortality risk by over 90% (P < 0.001) in 2L patients and 60-70% (P < 0.01) in 3L+ patients per unit increment in current log (LDH). Machine-learning techniques, like functional principal component analysis (FPCA), extracted informative features from longitudinal LDH data, capturing over 99% of variability and allowing prediction of survival. Unsupervised clustering based on the extracted FPCA features stratified patients into three groups with distinct LDH dynamics and survival outcomes. Hence, our approaches offer a valuable and cost-effective way for risk stratification and improves survival prediction in mCRC using LDH trajectories.

A Novel and Efficient Digital Pathology Classifier for Predicting Cancer Biomarkers Using Sequencer Architecture.

In digital pathology tasks, transformers have achieved state-of-the-art results, surpassing convolutional neural networks (CNNs). However, transformers are usually complex and resource intensive. This study developed a novel and efficient digital pathology classifier called DPSeq to predict cancer biomarkers through fine-tuning a sequencer architecture integrating horizontal and vertical bidirectional long short-term memory networks. Using hematoxylin and eosin-stained histopathologic images of colorectal cancer from two international data sets (The Cancer Genome Atlas and Molecular and Cellular Oncology), the predictive performance of DPSeq was evaluated in a series of experiments. DPSeq demonstrated exceptional performance for predicting key biomarkers in colorectal cancer (microsatellite instability status, hypermutation, CpG island methylator phenotype status, BRAF mutation, TP53 mutation, and chromosomal instability), outperforming most published state-of-the-art classifiers in a within-cohort internal validation and a cross-cohort external validation. In addition, under the same experimental conditions using the same set of training and testing data sets, DPSeq surpassed four CNNs (ResNet18, ResNet50, MobileNetV2, and EfficientNet) and two transformer (Vision Transformer and Swin Transformer) models, achieving the highest area under the receiver operating characteristic curve and area under the precision-recall curve values in predicting microsatellite instability status, BRAF mutation, and CpG island methylator phenotype status. Furthermore, DPSeq required less time for both training and prediction because of its simple architecture. Therefore, DPSeq appears to be the preferred choice over transformer and CNN models for predicting cancer biomarkers.

Waist-to-Height Ratio Is a Stronger Mediator in the Association between DASH Diet and Hypertension: Potential Micro/Macro Nutrients Intake Pathways.

Several studies have demonstrated that adhering to the Dietary Approaches to Stop Hypertension (DASH) diet may result in decreased blood pressure levels and hypertension risk. This may be an effect of a reduction in central obesity. In the current study, we explored the mediation role of multiple anthropometric measurements in association with DASH score and hypertension risk, and we investigated potential common micro/macro nutrients that react with the obesity-reduction mechanism. Our study used data from the National Health and Nutrition Examination Survey (NHANES). Important demographic variables, such as gender, race, age, marital status, education attainment, poverty income ratio, and lifestyle habits such as smoking, alcohol drinking, and physical activity were collected. Various anthropometric measurements, including weight, waist circumference, body mass index (BMI), and waist-to-height ratio (WHtR) were also obtained from the official website. The nutrient intake of 8224 adults was quantified through a combination of interviews and laboratory tests. We conducted stepwise regression to filter the most important anthropometric measurements and performed a multiple mediation analysis to test whether the selected anthropometric measurements had mediation effects on the total effect of the DASH diet on hypertension. Random forest models were conducted to identify nutrient subsets associated with the DASH score and anthropometric measurements. Finally, associations between common nutrients and DASH score, anthropometric measurements, and risk of hypertension were respectively evaluated by a logistic regression model adjusting for possible confounders. Our study revealed that BMI and WHtR acted as full mediators between DASH score and high blood pressure levels. Together, they accounted for more than 45% of the variation in hypertension. Interestingly, WHtR was found to be the strongest mediator, explaining approximate 80% of the mediating effect. Furthermore, we identified a group of three commonly consumed nutrients (sodium, potassium, and octadecatrienoic acid) that had opposing effects on DASH score and anthropometric measurements. These nutrients were also found to be associated with hypertension in the same way as BMI and WHtR in univariate regression models. The most important among these nutrients was sodium, which was negatively correlated with the DASH score (ß = -0.53, 95% CI = -0.56~-0.50, p < 0.001) and had a positive association with BMI (ß = 0.04, 95% CI = 0.01~0.07, p = 0.02), WHtR (ß = 0.06, 95% CI = 0.03~0.09, p < 0.001), and hypertension (OR = 1.09, 95% CI = 1.01~1.19, p = 0.037). Our investigation revealed that the WHtR exerts a greater mediating effect than BMI on the correlation between the DASH diet and hypertension. Notably, we identified a plausible nutrient intake pathway involving sodium, potassium, and octadecatrienoic acid. Our findings suggested that lifestyle modifications that emphasize the reduction of central obesity and the attainment of a well-balanced micro/macro nutrient profile, such as the DASH diet, could potentially be efficacious in managing hypertension.

Clearance as an Early Indicator of Efficacy for Therapeutic Monoclonal Antibodies: Circumventing Dose Selection Challenges in Oncology.

BACKGROUND AND OBJECTIVE: The designs of first-in-human (FIH) studies in oncology (e.g., 3 + 3 dose escalation design) usually do not provide a sufficient sample size to determine the dose-response relationship for efficacy. This study aimed to assess the feasibility of using monoclonal antibody (mAb) clearance as a biomarker for efficacy to facilitate the identification of potentially efficacious doses across cancer types and drug targets. METHODS: We performed electronic searches of the Drugs@FDA website, the European Medicines Agency website, and PubMed to identify reports of FIH trials of approved mAbs in oncology. The clearance, half-life, and overall response rate (ORR) data for the mAbs at different dose levels were extracted. RESULTS: Twenty-five approved mAbs were included in this study. As expected, due to the small sample sizes in FIH studies, there was no clear dose-response for ORR. However, we found a clear negative association between mAb clearance and ORR across tumors/drug targets, and a clear negative dose-clearance relationship, with clearance decreasing and saturated at high dose levels. The approved mAb doses (1-25 mg/kg) are approximately 2-fold the saturation doses (1-10 mg/kg). The associated clearance values at the approved doses vary across different cancers and drug targets (0.17-1.56 L/day), while tend to be similar within a disease/drug target. Anti-CD20 mAbs for B-cell lymphomas show a higher clearance (~ 1 L/day) than other cancers and targets (e.g., ~ 0.3 L/day for anti-PD-1). CONCLUSIONS: Clearance of mAbs can be a tumor/drug target-agnostic biomarker for potential anti-tumor activity as clearance decreases with increasing ORR. Our findings shed important insights into target clearance values that may lead to desired efficacy for different cancers and drug targets, which can be used to guide dose selection for the future development of mAbs during FIH oncology studies.

Predicting microsatellite instability and key biomarkers in colorectal cancer from H&E-stained images: achieving state-of-the-art predictive performance with fewer data using Swin Transformer.

Many artificial intelligence models have been developed to predict clinically relevant biomarkers for colorectal cancer (CRC), including microsatellite instability (MSI). However, existing deep learning networks require large training datasets, which are often hard to obtain. In this study, based on the latest Hierarchical Vision Transformer using Shifted Windows (Swin Transformer [Swin-T]), we developed an efficient workflow to predict biomarkers in CRC (MSI, hypermutation, chromosomal instability, CpG island methylator phenotype, and BRAF and TP53 mutation) that required relatively small datasets. Our Swin-T workflow substantially achieved the state-of-the-art (SOTA) predictive performance in an intra-study cross-validation experiment on the Cancer Genome Atlas colon and rectal cancer dataset (TCGA-CRC-DX). It also demonstrated excellent generalizability in cross-study external validation and delivered a SOTA area under the receiver operating characteristic curve (AUROC) of 0.90 for MSI, using the Molecular and Cellular Oncology dataset for training (N = 1,065) and the TCGA-CRC-DX (N = 462) for testing. A similar performance (AUROC = 0.91) was reported in a recent study, using ~8,000 training samples (ResNet18) on the same testing dataset. Swin-T was extremely efficient when using small training datasets and exhibited robust predictive performance with 200-500 training samples. Our findings indicate that Swin-T could be 5-10 times more efficient than existing algorithms for MSI prediction based on ResNet18 and ShuffleNet. Furthermore, the Swin-T models demonstrated their capability in accurately predicting MSI and BRAF mutation status, which could exclude and therefore reduce samples before subsequent standard testing in a cascading diagnostic workflow, in turn reducing turnaround time and costs.

A robust and lightweight deep attention multiple instance learning algorithm for predicting genetic alterations.

Self-attention mechanism-based algorithms are attractive in digital pathology due to their interpretability, but suffer from computation complexity. This paper presents a novel, lightweight Attention-based Multiple Instance Mutation Learning (AMIML) model to allow small-scale attention operations for predicting gene mutations. Compared to the standard self-attention model, AMIML reduces the number of model parameters by approximately 70%. Using data for 24 clinically relevant genes from four cancer cohorts in TCGA studies (UCEC, BRCA, GBM, and KIRC), we compare AMIML with a standard self-attention model, five other deep learning models, and four traditional machine learning models. The results show that AMIML has excellent robustness and outperforms all the baseline algorithms in the vast majority of the tested genes. Conversely, the performance of the reference deep learning and machine learning models vary across different genes, and produce suboptimal prediction for certain genes. Furthermore, with the flexible and interpretable attention-based pooling mechanism, AMIML can further zero in and detect predictive image patches.

Optimization of deep learning models for the prediction of gene mutations using unsupervised clustering.

Deep learning models are increasingly being used to interpret whole-slide images (WSIs) in digital pathology and to predict genetic mutations. Currently, it is commonly assumed that tumor regions have most of the predictive power. However, it is reasonable to assume that other tissues from the tumor microenvironment may also provide important predictive information. In this paper, we propose an unsupervised clustering-based multiple-instance deep learning model for the prediction of genetic mutations using WSIs of three cancer types obtained from The Cancer Genome Atlas. Our proposed model facilitates the identification of spatial regions related to specific gene mutations and exclusion of patches that lack predictive information through the use of unsupervised clustering. This results in a more accurate prediction of gene mutations when compared with models using all image patches on WSIs and two recently published algorithms for all three different cancer types evaluated in this study. In addition, our study validates the hypothesis that the prediction of gene mutations solely based on tumor regions on WSI slides may not always provide the best performance. Other tissue types in the tumor microenvironment could provide a better prediction ability than tumor tissues alone. These results highlight the heterogeneity in the tumor microenvironment and the importance of identification of predictive image patches in digital pathology prediction tasks.

Improving feature extraction from histopathological images through a fine-tuning ImageNet model.

Background: Due to lack of annotated pathological images, transfer learning has been the predominant approach in the field of digital pathology. Pre-trained neural networks based on ImageNet database are often used to extract "off-the-shelf" features, achieving great success in predicting tissue types, molecular features, and clinical outcomes, etc. We hypothesize that fine-tuning the pre-trained models using histopathological images could further improve feature extraction, and downstream prediction performance. Methods: We used 100 000 annotated H&E image patches for colorectal cancer (CRC) to fine-tune a pre-trained Xception model via a 2-step approach. The features extracted from fine-tuned Xception (FTX-2048) model and Image-pretrained (IMGNET-2048) model were compared through: (1) tissue classification for H&E images from CRC, same image type that was used for fine-tuning; (2) prediction of immune-related gene expression, and (3) gene mutations for lung adenocarcinoma (LUAD). Five-fold cross validation was used for model performance evaluation. Each experiment was repeated 50 times. Findings: The extracted features from the fine-tuned FTX-2048 exhibited significantly higher accuracy (98.4%) for predicting tissue types of CRC compared to the "off-the-shelf" features directly from Xception based on ImageNet database (96.4%) (P value = 2.2 × 10-6). Particularly, FTX-2048 markedly improved the accuracy for stroma from 87% to 94%. Similarly, features from FTX-2048 boosted the prediction of transcriptomic expression of immune-related genes in LUAD. For the genes that had significant relationships with image features (P < 0.05, n = 171), the features from the fine-tuned model improved the prediction for the majority of the genes (139; 81%). In addition, features from FTX-2048 improved prediction of mutation for 5 out of 9 most frequently mutated genes (STK11, TP53, LRP1B, NF1, and FAT1) in LUAD. Conclusions: We proved the concept that fine-tuning the pretrained ImageNet neural networks with histopathology images can produce higher quality features and better prediction performance for not only the same-cancer tissue classification where similar images from the same cancer are used for fine-tuning, but also cross-cancer prediction for gene expression and mutation at patient level.

A retrospective analysis using deep-learning models for prediction of survival outcome and benefit of adjuvant chemotherapy in stage II/III colorectal cancer.

PURPOSE: Most of Stage II/III colorectal cancer (CRC) patients can be cured by surgery alone, and only certain CRC patients benefit from adjuvant chemotherapy. Risk stratification based on deep-learning from haematoxylin and eosin (H&E) images has been postulated as a potential predictive biomarker for benefit from adjuvant chemotherapy. However, very limited success has been achieved in using biomarkers, including deep-learning-based markers, to facilitate the decision for adjuvant chemotherapy despite recent advances of artificial intelligence. METHODS: We trained and internally validated CRCNet using 780 Stage II/III CRC patients from Molecular and Cellular Oncology. Independent external validation of the model was performed using 337 Stage II/III CRC patients from The Cancer Genome Atlas (TCGA). RESULTS: CRCNet stratified the patients into high, medium, and low-risk subgroups. Multivariate Cox regression analyses confirmed that CRCNet risk groups are statistically significant after adjusting for existing risk factors. The high-risk subgroup significantly benefits from adjuvant chemotherapy. A hazard ratio (chemo-treated vs untreated) of 0.2 (95% Confidence Interval (CI), 0.05-0.65; P = 0.009) and 0.6 (95% CI 0.42-0.98; P = 0.038) are observed in the TCGA and MCO Fluorouracil-treated patients, respectively. Conversely, no significant benefit from chemotherapy is observed in the low- and medium-risk groups (P = 0.2-1). CONCLUSION: The retrospective analysis provides further evidence that H&E image-based biomarkers may potentially be of great use in delivering treatments following surgery for Stage II/III CRC, improving patient survival, and avoiding unnecessary treatment and associated toxicity, and warrants further validation on other datasets and prospective confirmation in clinical trials.

A forward selection algorithm to identify mutually exclusive alterations in cancer studies.

Mutual exclusivity analyses provide an effective tool to identify driver genes from passenger genes for cancer studies. Various algorithms have been developed for the detection of mutual exclusivity, but controlling false positive and improving accuracy remain challenging. We propose a forward selection algorithm for identification of mutually exclusive gene sets (FSME) in this paper. The method includes an initial search of seed pair of mutually exclusive (ME) genes and subsequently including more genes into the current ME set. Simulations demonstrated that, compared to recently published approaches (i.e., CoMEt, WExT, and MEGSA), FSME could provide higher precision or recall rate to identify ME gene sets, and had superior control of false positive rates. With application to TCGA real data sets for AML, BRCA, and GBM, we confirmed that FSME can be utilized to discover cancer driver genes.

Early M-Protein Dynamics Predicts Progression-Free Survival in Patients With Relapsed/Refractory Multiple Myeloma.

This study aimed to predict long-term progression-free survival (PFS) using early M-protein dynamic measurements in patients with relapsed/refractory multiple myeloma (MM). The PFS was modeled based on dynamic M-protein data from two phase III studies, POLLUX and CASTOR, which included 569 and 498 patients with relapsed/refractory MM, respectively. Both studies compared active controls (lenalidomide and dexamethasone, and bortezomib and dexamethasone, respectively) alone vs. in combination with daratumumab. Three M-protein dynamic features from the longitudinal M-protein data were evaluated up to different time cutoffs (1, 2, 3, and 6 months). The abilities of early M-protein dynamic measurements to predict the PFS were evaluated using Cox proportional hazards survival models. Both univariate and multivariable analyses suggest that maximum reduction of M-protein (i.e., depth of response) was the most predictive of PFS. Despite the statistical significance, the baseline covariates provided very limited predictive value regarding the treatment effect of daratumumab. However, M-protein dynamic features obtained within the first 2 months reasonably predicted PFS and the associated treatment effect of daratumumab. Specifically, the areas under the time-varying receiver operating characteristic curves for the model with the first 2 months of M-protein dynamic data were ~ 0.8 and 0.85 for POLLUX and CASTOR, respectively. Early M-protein data within the first 2 months can provide a prospective and reasonable prediction of future long-term clinical benefit for patients with MM.

Split First Dose Administration of Intravenous Daratumumab for the Treatment of Multiple Myeloma (MM): Clinical and Population Pharmacokinetic Analyses.

INTRODUCTION: Daratumumab, a human immunoglobulin Gκ monoclonal antibody targeting CD38, is approved as monotherapy and in combination with standard-of-care regimens for multiple myeloma. In clinical studies, the median durations of the first, second, and subsequent intravenous infusions of daratumumab were 7.0, 4.3, and 3.4 h, respectively. Splitting the first intravenous infusion of daratumumab over 2 days is an approved alternative dosing regimen to reduce the duration of the first infusion and provide flexibility for patients and healthcare providers. METHODS: The feasibility of splitting the first 16-mg/kg infusion into two separate infusions of 8 mg/kg on Days 1 and 2 of the first treatment cycle was investigated in two cohorts [daratumumab, carfilzomib, and dexamethasone (D-Kd) and daratumumab, carfilzomib, lenalidomide, and dexamethasone (D-KRd)] of the phase 1b MMY1001 study. Additionally, a population pharmacokinetic (PK) analysis and simulations were used to compare the PK profiles of the split first dose regimen with the recommended single first dose regimens of daratumumab in previously approved indications. RESULTS: In MMY1001, following administration of the second half of a split first dose on Cycle 1 Day 2, postinfusion median (range) daratumumab concentrations were similar between split first dose [D-Kd, 254.9 (125.8-435.5) µg/ml; D-KRd, 277.2 (164.0-341.8) µg/ml; combined, 256.8 (125.8-435.5) µg/ml] and single first dose [D-Kd, 319.2 (237.5-394.7) µg/ml]. At the end of weekly dosing, median (range) Cycle 3 Day 1 preinfusion daratumumab concentrations were similar between split first dose [D-Kd, 663.9 (57.7-1110.7) µg/ml; D-KRd, 575.1 (237.9-825.5) µg/ml; combined, 639.2 (57.7-1110.7) µg/ml] and single first dose [D-Kd, 463.2 (355.9-792.9) µg/ml]. The population PK simulations demonstrated virtually identical PK profiles after the first day of treatment for all approved indications and recommended dosing schedules of daratumumab. CONCLUSION: These data support the use of an alternative split first dose regimen of intravenous daratumumab for the treatment of MM. TRIAL REGISTRATION: ClinicalTrials.gov number, NCT01998971.

Pharmacokinetics and Exposure-Response Analyses of Daratumumab in Combination Therapy Regimens for Patients with Multiple Myeloma.

INTRODUCTION: Daratumumab, a human IgG monoclonal antibody targeting CD38, has demonstrated activity as monotherapy and in combination with standard-of-care regimens in multiple myeloma. Population pharmacokinetic analyses were conducted to determine the pharmacokinetics of intravenous daratumumab in combination therapy versus monotherapy, evaluate the effect of patient- and disease-related covariates on drug disposition, and examine the relationships between daratumumab exposure and efficacy/safety outcomes. METHODS: Four clinical studies of daratumumab in combination with lenalidomide/dexamethasone (POLLUX and GEN503); bortezomib/dexamethasone (CASTOR); pomalidomide/dexamethasone, bortezomib/thalidomide/dexamethasone, and bortezomib/melphalan/prednisone (EQUULEUS) were included in the analysis. Using various dosing schedules, the majority of patients (684/694) received daratumumab at a dose of 16 mg/kg. In GEN503, daratumumab was administered at a dose of 2 mg/kg (n = 3), 4 mg/kg (n = 3), 8 mg/kg (n = 4), and 16 mg/kg (n = 34). A total of 650 patients in EQUULEUS (n = 128), POLLUX (n = 282), and CASTOR (n = 240) received daratumumab 16 mg/kg. The exposure-efficacy and exposure-safety relationships examined progression-free survival (PFS) and selected adverse events (infusion-related reactions; thrombocytopenia, anemia, neutropenia, lymphopenia, and infections), respectively. RESULTS: Pharmacokinetic profiles of daratumumab were similar between monotherapy and combination therapy. Covariate analysis identified no clinically important effects on daratumumab exposure, and no dose adjustments were recommended on the basis of these factors. Maximal clinical benefit on PFS was achieved for the majority of patients (approximately 75%) at the 16 mg/kg dose. No apparent relationship was observed between daratumumab exposure and selected adverse events. CONCLUSION: These data support the recommended 16 mg/kg dose of daratumumab and the respective dosing schedules in the POLLUX and CASTOR pivotal studies. FUNDING: Janssen Research & Development.

Influence of Disease and Patient Characteristics on Daratumumab Exposure and Clinical Outcomes in Relapsed or Refractory Multiple Myeloma.

OBJECTIVE: The aim of this study was to understand the influence of disease and patient characteristics on exposure to daratumumab, an immunoglobulin Gκ (IgGκ) monoclonal antibody, and clinical outcomes in relapsed or refractory multiple myeloma (MM). PATIENTS AND METHODS: Baseline myeloma type, albumin levels, renal/hepatic function, age, sex, race, weight, Eastern Cooperative Oncology Group (ECOG) status, refractory status, and number of prior therapies were evaluated using data from two clinical studies-GEN501 (N = 104) and SIRIUS (N = 124). RESULTS: Daratumumab clearance was approximately 110% higher in IgG myeloma patients than non-IgG myeloma patients, leading to significantly lower exposure in IgG myeloma patients based on maximum trough serum concentrations (p < 0.0001). However, the overall response rate was similar for IgG and non-IgG myeloma patients (odds ratio 1.08, 95% confidence interval 0.54-2.17, p = 0.82). For a given exposure, the drug effect was significantly higher (approximately two times) in IgG versus non-IgG patients (p = 0.03). The influence of other patient and disease characteristics on daratumumab exposure was minimal and no significant effect on efficacy was observed (p ≥ 0.1). The incidences of infections and overall grade 3 or higher adverse events in subpopulations were generally consistent with that of the overall population. CONCLUSION: Due to competition with the MM-produced IgG M-protein for neonatal Fc receptor protection from clearance, IgG-based monoclonal antibodies in general may have significantly higher clearance and lower concentrations in IgG MM patients compared with non-IgG MM patients. Careful evaluation of the impact of exposure and patient and disease characteristics on safety and efficacy is warranted for all IgG-based monoclonal antibodies used in MM.

Modeling the Relationship Between Exposure to Abiraterone and Prostate-Specific Antigen Dynamics in Patients with Metastatic Castration-Resistant Prostate Cancer.

BACKGROUND AND OBJECTIVES: Recent analysis revealed strong associations between prostate-specific antigen (PSA) dynamics and overall survival (OS) in metastatic castration-resistant prostate cancer (mCRPC) and supported PSA dynamics as bridging surrogacy endpoints for clinical benefit from treatment with abiraterone acetate plus prednisone. This analysis aimed to investigate the abiraterone exposure-PSA dynamics relationship in mCRPC. METHODS: Abiraterone pharmacokinetics-PSA models were constructed using data from the COU-AA-301 (chemotherapy-pretreated) and COU-AA-302 (chemotherapy-naïve) trials comparing abiraterone acetate 1000 mg/day plus prednisone 5 mg twice daily with prednisone alone in mCRPC. The drug effect-PSA dynamics relationship was modeled as a function of selected pharmacokinetic measures. The influences of baseline demographic variables, laboratory values, and disease status on PSA dynamics were assessed. RESULTS: A tumor growth inhibition model best described PSA dynamics post-treatment with abiraterone acetate. Abiraterone acetate treatment in chemotherapy-pretreated and chemotherapy-naïve patients increased the PSA decay rate (k dec) to the same extent (1.28-fold, 95 % confidence interval [CI] 0.58-1.98; and 0.93-fold, 95 % CI 0.6-1.27, respectively). Lower baseline lactate dehydrogenase and higher baseline testosterone significantly increased k dec. Findings from our analysis suggest a maximum-effect relationship between abiraterone trough concentration and PSA dynamics in both patient populations. The majority of patients had a steady-state trough concentration greater than the estimated half maximal effective concentration. CONCLUSION: The model appropriately described the exposure-response relationship between abiraterone and PSA dynamics in chemotherapy-pretreated and chemotherapy-naïve patients following oral administration of abiraterone acetate.

Pharmacokinetics of Daratumumab Following Intravenous Infusion in Relapsed or Refractory Multiple Myeloma After Prior Proteasome Inhibitor and Immunomodulatory Drug Treatment.

Daratumumab is a CD38 monoclonal antibody recently approved for the treatment of multiple myeloma (MM). We report daratumumab pharmacokinetic data from GEN501, a phase I/II dose-escalation (0.005-24 mg/kg) and dose-expansion (8 or 16 mg/kg) study, and SIRIUS, a phase II study (8 or 16 mg/kg), in relapsed or refractory MM. Noncompartmental analysis was conducted to characterize daratumumab pharmacokinetics, and, in both studies, daratumumab exhibited nonlinear pharmacokinetic characteristics. Decreasing daratumumab clearance with increasing dose suggests saturation of target-mediated clearance at higher dose levels, whereas decreasing clearance over time with repeated dosing may be due to tumor burden reductions as CD38-positive cells are eliminated. These and other pharmacokinetic data analyses support the use of the recommended dose regimen of daratumumab (16 mg/kg weekly for 8 weeks, every 2 weeks for 16 weeks, and every 4 weeks thereafter) to rapidly saturate target-mediated clearance during weekly dosing and maintain saturation when dosing every 2 or 4 weeks.