Tumour Size and Overall Survival in a Cohort of Patients with Unifocal Glioblastoma: A Uni- and Multivariable Prognostic Modelling and Resampling Study.

Published models inconsistently associate glioblastoma size with overall survival (OS). This study aimed to investigate the prognostic effect of tumour size in a large cohort of patients diagnosed with GBM and interrogate how sample size and non-linear transformations may impact on the likelihood of finding a prognostic effect. In total, 279 patients with a IDH-wildtype unifocal WHO grade 4 GBM between 2014 and 2020 from a retrospective cohort were included. Uni-/multivariable association between core volume, whole volume (CV and WV), and diameter with OS was assessed with (1) Cox proportional hazard models +/- log transformation and (2) resampling with 1,000,000 repetitions and varying sample size to identify the percentage of models, which showed a significant effect of tumour size. Models adjusted for operation type and a diameter model adjusted for all clinical variables remained significant (p = 0.03). Multivariable resampling increased the significant effects (p < 0.05) of all size variables as sample size increased. Log transformation also had a large effect on the chances of a prognostic effect of WV. For models adjusted for operation type, 19.5% of WV vs. 26.3% log-WV (n = 50) and 69.9% WV and 89.9% log-WV (n = 279) were significant. In this large well-curated cohort, multivariable modelling and resampling suggest tumour volume is prognostic at larger sample sizes and with log transformation for WV.

A study demonstrating users' preference for the adapted-REQUITE patient-reported outcome questionnaire over PRO-CTCAE® in patients with lung cancer.

Introduction: The use of patient-reported outcomes (PROs) has been shown to enhance the accuracy of symptom collection and improve overall survival and quality of life. This is the first study comparing concordance and patient preference for two PRO tools: Patient-Reported Outcomes version of the Common Terminology Criteria for Adverse Events (PRO-CTCAE®) and the adapted-REQUITE Lung Questionnaire. Materials and Methods: Patients with lung cancer were recruited to the study while attending outpatient clinics at a tertiary cancer centre. Clinician-reported outcomes were generated through initial patient assessment with CTCAE v4.03. Participants then completed the PRO-CTCAE® and adapted-REQUITE questionnaires. Concordance between the 2 questionnaires was assessed by calculating Pearson correlation coefficient. PRO-CTCAE® and CTCAE concordance was demonstrated by calculating Pearson correlation coefficient from the linear predictors of an ordinal logistic regression. P-values were also calculated. Results: Out of 74 patients approached, 65 provided written informed consent to participate in the study. 63 (96.9%) patients completed both PRO-CTCAE® and adapted-REQUITE questionnaires. Pearson correlation coefficient between PRO tools was 0.8-0.83 (p <.001). Correlation between CTCAE and PRO-CTCAE® ranged between 0.66-0.82 (p <.001). Adapted-REQUITE and CTCAE correlation was higher for all symptoms ranging between 0.79-0.91 (p <.001). Acceptable discrepancies within one grade were present in 96.8%-100% of symptom domains for REQUITE and in 92.1%-96.8% for all domains in the PRO-CTCAE®. 54% of the total participant cohort favored the adapted-REQUITE questionnaire due to reduced subjectivity in the questions and ease of use. Conclusion: The adapted-REQUITE questionnaire has shown a superior correlation to clinician-reported outcomes and higher patient preference than the PRO-CTCAE®. The results of this study suggest the use of the REQUITE questionnaire for patients with lung cancer in routine clinical practice.

Hierarchical cluster analysis and nonlinear mixed-effects modelling for candidate biomarker detection in preclinical models of cancer.

BACKGROUND: Preclinical models of cancer can be of translational benefit when assessing how different biomarkers are regulated in response to particular treatments. Detection of molecular biomarkers in preclinical models of cancer is difficult due inter-animal variability in responses, combined with limited accessibility of longitudinal data. METHODS: Nonlinear mixed-effects modelling (NLME) was used to analyse tumour growth data based on expected tumour growth rates observed 7 days after initial doses (DD7) of Radiotherapy (RT) and Combination of RT with DNA Damage Response Inhibitors (DDRi). Cox regression was performed to confirm an association between DD7 and survival. Hierarchical Cluster Analysis (HCA) was then used to identify candidate biomarkers impacting responses to RT and RT/DDRi and these were validated using NLME. RESULTS: Cox regression confirmed significant associations between DD7 and survival. HCA of RT treated samples, combined with NLME confirmed significant associations between DD7 and Cluster specific CD8+ Ki67 MFI, as well as DD7 and cluster specific Natural Killer cell density in RT treated mice. CONCLUSION: Application of NLME, as well as HCA of candidate biomarkers may provide additional avenues to assess the effect of RT in MC38 syngeneic tumour models. Additional studies would need to be conducted to confirm association between DD7 and biomarkers in RT/DDRi treated mice.

Long-Term Outcomes After Concurrent Once- or Twice-Daily Chemoradiation in Limited-Stage Small Cell Lung Cancer: A Brief Report From the CONVERT Trial.

PURPOSE: CONVERT was a phase 3 international randomized clinical trial comparing once-daily (OD) and twice-daily (BD) radiation therapy (RT). This updated analysis describes the 6.5-year outcomes of these regimens delivered with conformal techniques. METHODS AND MATERIALS: CONVERT (NCT00433563) randomized patients 1:1 between OD RT (66 Gy/33 fractions/6.5 weeks) and BD RT (45 Gy/30 fractions/3 weeks), both delivered with concurrent cisplatin/etoposide. Three-dimensional conformal RT was mandatory, intensity-modulated RT was permitted, and elective nodal irradiation was not allowed. Prophylactic cranial irradiation was delivered at the discretion of treating clinicians. RT treatment planning was subject to central quality assurance. RESULTS: Five hundred forty-seven patients were recruited at 73 centers. The median follow-up for the surviving cohort (n = 164) was 81.2 months. The median survival for the OD and BD arms were 25.4 months (95% CI, 21.1-30.9) and 30.0 months (95% CI, 25.3-36.5; hazard ratio, 1.13; 95% CI, 0.92-1.38; P = .247). Performance status and tumor volume were associated with survival on multivariate analysis. No treatment-related deaths occurred subsequent to the initial analysis performed in 2017. Regarding late toxicity, 7 patients in the OD arm developed grade 3 esophagitis, 4 of which went on to develop stricture or fistulation, compared with no patients in the BD arm. Grade 3 pulmonary fibrosis occurred in 2 and 3 patients in the OD and BD arms, respectively. CONCLUSIONS: As the CONVERT trial did not demonstrate the superiority of OD RT and this regimen had a slightly worse toxicity profile after 80 months of follow-up, 45 Gy BD should remain the standard of care in limited stage small cell lung cancer.

A hypoxia biomarker does not predict benefit from giving chemotherapy with radiotherapy in the BC2001 randomised controlled trial.

BACKGROUND: BC2001 showed combining chemotherapy (5-FU + mitomycin-C) with radiotherapy improves loco-regional disease-free survival in patients with muscle-invasive bladder cancer (MIBC). We previously showed a 24-gene hypoxia-associated signature predicted benefit from hypoxia-modifying radiosensitisation in BCON and hypothesised that only patients with low hypoxia scores (HSs) would benefit from chemotherapy in BC2001. BC2001 allowed conventional (64Gy/32 fractions) or hypofractionated (55Gy/20 fractions) radiotherapy. An exploratory analysis tested an additional hypothesis that hypofractionation reduces reoxygenation and would be detrimental for patients with hypoxic tumours. METHODS: RNA was extracted from pre-treatment biopsies (298 BC2001 patients), transcriptomic data generated (Affymetrix Clariom-S arrays), HSs calculated (median expression of 24-signature genes) and patients stratified as hypoxia-high or -low (cut-off: cohort median). PRIMARY ENDPOINT: invasive loco-regional control (ILRC); secondary overall survival. FINDINGS: Hypoxia affected overall survival (HR = 1.30; 95% CI 0.99-1.70; p = 0.062): more uncertainty for ILRC (HR = 1.29; 95% CI 0.82-2.03; p = 0.264). Benefit from chemotherapy was similar for patients with high or low HSs, with no interaction between HS and treatment arm. High HS associated with poor ILRC following hypofractionated (n = 90, HR 1.69; 95% CI 0.99-2.89 p = 0.057) but not conventional (n = 207, HR 0.70; 95% CI 0.28-1.80, p = 0.461) radiotherapy. The finding was confirmed in an independent cohort (BCON) where hypoxia associated with a poor prognosis for patients receiving hypofractionated (n = 51; HR 14.2; 95% CI 1.7-119; p = 0.015) but not conventional (n = 24, HR 1.04; 95% CI 0.07-15.5, p = 0.978) radiotherapy. INTERPRETATION: Tumour hypoxia status does not affect benefit from BC2001 chemotherapy. Hypoxia appears to affect fractionation sensitivity. Use of HSs to personalise treatment needs testing in a biomarker-stratified trial. FUNDING: Cancer Research UK, NIHR, MRC.

Explaining in-vitro to in-vivo efficacy correlations in oncology pre-clinical development via a semi-mechanistic mathematical model.

In-vitro to in-vivo correlations (IVIVC), relating in-vitro parameters like IC50 to in-vivo drug exposure in plasma and tumour growth, are widely used in oncology for experimental design and dose decisions. However, they lack a deeper understanding of the underlying mechanisms. Our paper therefore focuses on linking empirical IVIVC relations for small-molecule kinase inhibitors with a semi-mechanistic tumour-growth model. We develop an approach incorporating parameters like the compound's peak-trough ratio (PTR), Hill coefficient of in-vitro dose-response curves, and xenograft-specific properties. This leads to formulas for determining efficacious doses for tumor stasis under linear pharmacokinetics equivalent to traditional empirical IVIVC relations, but enabling more systematic analysis. Our findings reveal that in-vivo xenograft-specific parameters, specifically the growth rate (g) and decay rate (d), along with the average exposure, are generally more significant determinants of tumor stasis and effective dose than the compound's peak-trough ratio. However, as the Hill coefficient increases, the dependency of tumor stasis on the PTR becomes more pronounced, indicating that the compound is more influenced by its maximum or trough values rather than the average exposure. Furthermore, we discuss the translation of our method to predict population dose ranges in clinical studies and propose a resistance mechanism that solely relies on specific in-vivo xenograft parameters instead of IC50 exposure coverage. In summary, our study aims to provide a more mechanistic understanding of IVIVC relations, emphasizing the importance of xenograft-specific parameters and PTR on tumor stasis.

Randomized Phase 3 Trial of the Hypoxia Modifier Nimorazole Added to Radiation Therapy With Benefit Assessed in Hypoxic Head and Neck Cancers Determined Using a Gene Signature (NIMRAD).

PURPOSE: Tumor hypoxia is an adverse prognostic factor in head and neck squamous cell carcinoma (HNSCC). We assessed whether patients with hypoxic HNSCC benefited from the addition of nimorazole to definitive intensity modulated radiation therapy (IMRT). METHODS AND MATERIALS: NIMRAD was a phase 3, multicenter, placebo-controlled, double-anonymized trial of patients with HNSCC unsuitable for concurrent platinum chemotherapy or cetuximab with definitive IMRT (NCT01950689). Patients were randomized 1:1 to receive IMRT (65 Gy in 30 fractions over 6 weeks) plus nimorazole (1.2 g/m2 daily, before IMRT) or placebo. The primary endpoint was freedom from locoregional progression (FFLRP) in patients with hypoxic tumors, defined as greater than or equal to the median tumor hypoxia score of the first 50 patients analyzed (≥0.079), using a validated 26-gene signature. The planned sample size was 340 patients, allowing for signature generation in 85% and an assumed hazard ratio (HR) of 0.50 for nimorazole effectiveness in the hypoxic group and requiring 66 locoregional failures to have 80% power in a 2-tail log-rank test at the 5% significance level. RESULTS: Three hundred thirty-eight patients were randomized by 19 centers in the United Kingdom from May 2014 to May 2019, with a median follow-up of 3.1 years (95% CI, 2.9-3.4). Hypoxia scores were available for 286 (85%). The median patient age was 73 years (range, 44-88; IQR, 70-76). There were 36 (25.9%) locoregional failures in the hypoxic group, in which nimorazole + IMRT did not improve FFLRP (adjusted HR, 0.72; 95% CI, 0.36-1.44; P = .35) or overall survival (adjusted HR, 0.96; 95% CI, 0.53-1.72; P = .88) compared with placebo + IMRT. Similarly, nimorazole + IMRT did not improve FFLRP or overall survival in the whole population. In total (N = 338), 73% of patients allocated nimorazole adhered to the drug for ≥50% of IMRT fractions. Nimorazole + IMRT caused more acute nausea compared with placebo + IMRT (Common Terminology Criteria for Adverse Events version 4.0 G1+2: 56.6% vs 42.4%, G3: 10.1% vs 5.3%, respectively; P < .05). CONCLUSIONS: Addition of the hypoxia modifier nimorazole to IMRT for locally advanced HNSCC in older and less fit patients did not improve locoregional control or survival.

Training and external validation of pre-treatment FDG PET-CT-based models for outcome prediction in anal squamous cell carcinoma.

OBJECTIVES: The incidence of anal squamous cell carcinoma (ASCC) is increasing worldwide, with a significant proportion of patients treated with curative intent having recurrence. The ability to accurately predict progression-free survival (PFS) and overall survival (OS) would allow for development of personalised treatment strategies. The aim of the study was to train and external test radiomic/clinical feature derived time-to-event prediction models. METHODS: Consecutive patients with ASCC treated with curative intent at two large tertiary referral centres with baseline FDG PET-CT were included. Radiomic feature extraction was performed using LIFEx software on the pre-treatment PET-CT. Two distinct predictive models for PFS and OS were trained and tuned at each of the centres, with the best performing models externally tested on the other centres' patient cohort. RESULTS: A total of 187 patients were included from centre 1 (mean age 61.6 ± 11.5 years, median follow up 30 months, PFS events = 57/187, OS events = 46/187) and 257 patients were included from centre 2 (mean age 62.6 ± 12.3 years, median follow up 35 months, PFS events = 70/257, OS events = 54/257). The best performing model for PFS and OS was achieved using a Cox regression model based on age and metabolic tumour volume (MTV) with a training c-index of 0.7 and an external testing c-index of 0.7 (standard error = 0.4). CONCLUSIONS: A combination of patient age and MTV has been demonstrated using external validation to have the potential to predict OS and PFS in ASCC patients. CLINICAL RELEVANCE STATEMENT: A Cox regression model using patients' age and metabolic tumour volume showed good predictive potential for progression-free survival in external testing. The benefits of a previous radiomics model published by our group could not be confirmed on external testing. KEY POINTS: • A predictive model based on patient age and metabolic tumour volume showed potential to predict overall survival and progression-free survival and was validated on an external test cohort. • The methodology used to create a predictive model from age and metabolic tumour volume was repeatable using external cohort data. • The predictive ability of positron emission tomography-computed tomography-derived radiomic features diminished when the influence of metabolic tumour volume was accounted for.

Radius additivity score: a novel combination index for tumour growth inhibition in fixed-dose xenograft studies.

The effect of combination therapies in many cancers has often been shown to be superior to that of monotherapies. This success is commonly attributed to drug synergies. Combinations of two (or more) drugs in xenograft tumor growth inhibition (TGI) studies are typically designed at fixed doses for each compound. The available methods for assessing synergy in such study designs are based on combination indices (CI) and model-based analyses. The former methods are suitable for screening exercises but are difficult to verify in in vivo studies, while the latter incorporate drug synergy in semi-mechanistic frameworks describing disease progression and drug action but are unsuitable for screening. In the current study, we proposed the empirical radius additivity (Rad-add) score, a novel CI for synergy detection in fixed-dose xenograft TGI combination studies. The Rad-add score approximates model-based analysis performed using the semi-mechanistic constant-radius growth TGI model. The Rad-add score was compared with response additivity, defined as the addition of the two response values, and the bliss independence model in combination studies derived from the Novartis PDX dataset. The results showed that the bliss independence and response additivity models predicted synergistic interactions with high and low probabilities, respectively. The Rad-add score predicted synergistic probabilities that appeared to be between those predicted with response additivity and the Bliss model. We believe that the Rad-add score is particularly suitable for assessing synergy in the context of xenograft combination TGI studies, as it combines the advantages of CI approaches suitable for screening exercises with those of semi-mechanistic TGI models based on a mechanistic understanding of tumor growth.

Skipping a pillar does not make for strong foundations: Pharmacokinetic-pharmacodynamic reasoning behind the shape of dose-response relationships in oncology.

Dose-response analysis is often applied to the quantification of drug-effect especially for slowly responding disease end points where a comparison is made across dose levels after a particular period of treatment. It has long been recognized that exposure - response is more appropriate than dose-response. However, trials necessarily are designed as dose-response experiments. Second, a wide range of functional forms are used to express relationships between dose and response. These considerations are also important for clinical development because pharmacokinetic (PK; and variability) plus pharmacokinetic-pharmacodynamic modeling may allow one to anticipate the shape of the dose-response curve and so the trial design. Here, we describe how the location and steepness of the dose response is determined by the PKs of the compound being tested and its exposure-response relationship in terms of potency (location), efficacy (maximum effect) and Hill coefficient (steepness). Thus, the location (50% effective dose [ED50 ]) is dependent not only on the potency (half-maximal effective concentration) but also the compound's PKs. Similarly, the steepness of the dose response is shown to be a function of the half-life of the drug. It is also shown that the shape of relationship varies dependent on the assumed time course of the disease. This is important in the context of drug-discovery where the in vivo potencies of compounds are compared as well as when considering an analysis of summary data (for example, model-based meta-analysis) for clinical decision making.

Mixed effects modeling of radiotherapy in combination with immune checkpoint blockade or inhibitors of the DNA damage response pathway.

Dosage optimization to maximize efficacy and minimize toxicity is a potential issue when administering radiotherapy (RT) in combination with immune checkpoint blockade (ICB) or inhibitors of the DNA Damage Response Pathway (DDRi) in the clinic. Preclinical models and mathematical modeling can help identify ideal dosage schedules to observe beneficial effects of a tri-therapy. The aim of this study is to describe a mathematical model to capture the impact of RT in combination with inhibitors of the DNA Damage Response Pathway or blockade of the immune checkpoint protein - programmed death ligand 1 (PD-L1). This model describes how RT mediated activation of antigen presenting cells can induce an increase in cytolytic T cells capable of targeting tumor cells, and how combination drugs can potentiate the immune response by inhibiting the rate of T cell exhaustion. The model was fitted using preclinical data, where MC38 tumors were treated in vivo with RT alone or in combination with anti-PD-L1 as well as with either olaparib or the ataxia telangiectasia mutated (ATM) inhibitor-AZD0156. The model successfully described the observed data and goodness-of-fit, using visual predictive checks also confirmed a successful internal model validation for each treatment modality. The results demonstrated that the anti-PD-L1 effect in combination with RT was maximal in vivo and any additional benefit of DDRi at the given dosage and schedule used was undetectable. Model fit results indicated AZD0156 to be a more potent DDRi than olaparib. Simulations of alternative doses indicated that reducing efficacy of anti-PD-L1 by 68% would potentially provide evidence for a benefit of ATM inhibition in combination with ICB and increase the relative efficacy of tri-therapy.

Quantitative Systems Pharmacology and Machine Learning: A Match Made in Heaven or Hell?

As pharmaceutical development moves from early-stage in vitro experimentation to later in vivo and subsequent clinical trials, data and knowledge are acquired across multiple time and length scales, from the subcellular to whole patient cohort scale. Realizing the potential of this data for informing decision making in pharmaceutical development requires the individual and combined application of machine learning (ML) and mechanistic multiscale mathematical modeling approaches. Here we outline how these two approaches, both individually and in tandem, can be applied at different stages of the drug discovery and development pipeline to inform decision making compound development. The importance of discerning between knowledge and data are highlighted in informing the initial use of ML or mechanistic quantitative systems pharmacology (QSP) models. We discuss the application of sensitivity and structural identifiability analyses of QSP models in informing future experimental studies to which ML may be applied, as well as how ML approaches can be used to inform mechanistic model development. Relevant literature studies are highlighted and we close by discussing caveats regarding the application of each approach in an age of constant data acquisition. SIGNIFICANCE STATEMENT: We consider when best to apply machine learning (ML) and mechanistic quantitative systems pharmacology (QSP) approaches in the context of the drug discovery and development pipeline. We discuss the importance of prior knowledge and data available for the system of interest and how this informs the individual and combined application of ML and QSP approaches at each stage of the pipeline.

Bladder-Sparing Treatment With Radical Dose Radiotherapy Is an Effective Alternative to Radical Cystectomy in Patients With Clinically Node-Positive Nonmetastatic Bladder Cancer.

PURPOSE: Bladder-sparing trimodal therapy (TMT) is an alternative to radical cystectomy (RC) according to international guidelines. However, there are limited data to guide management of nonmetastatic clinically node-positive bladder cancer (cN+ M0 BCa). We performed a multicenter retrospective analysis of survival outcomes in node-positive patients to inform practice. METHODS: Data from patients diagnosed with cN+ M0 BCa were collected from participating UK Oncology centers offering both TMT and RC. Overall survival (OS) and progression-free survival (PFS) outcomes were collected with details of treatment and clinical factors. RESULTS: A total of 287 patients with cN+ M0 BCa were included in the survival analysis. Median OS across all patients was 1.55 years (95% CI, 1.35 to 1.82 years). Receiving radical treatments was associated with improved OS (hazard ratio [HR], 0.32; 95% CI, 0.23 to 0.44; P < .001) compared with receiving palliative treatment. Radically treated patients (n = 163) received RC (n = 76) or radical dose radiotherapy (RT, n = 87); choice of radical treatment showed no association with OS (HR, 0.94; 95% CI, 0.63 to 1.41; P = .76) or PFS (HR, 0.74; 95% CI, 0.50 to 1.08; P = .12) on multivariable analysis. CONCLUSION: Patient cohorts with cN+ M0 BCa had equivalent survival outcomes whether treated with surgery or radical RT. Given the known morbidities of RC-in a patient group with poor survival-this study confirms that bladder-sparing TMT treatment should be a treatment option available to all patients with cN+ M0 BCa.

Impact of prostate radiotherapy on survival outcomes in clinically node-positive prostate cancer: A multicentre retrospective analysis.

PURPOSE: To evaluate clinical outcomes for cN1M0 prostate cancer treated with varied modalities. MATERIALS AND METHODS: Men with radiological stage cN1M0 prostate cancer on conventional imaging, treated from 2011-2019 with various modalities across four centres in the UK were included. Demographics, tumour grade and stage, and treatment details were collected. Biochemical and radiological progression-free survival (bPFS, rPFS) and overall survival (OS) were estimated using Kaplan Meier analyses. Potential factors impacting survival were tested with univariable log-rank test and multivariable Cox-proportional hazards model. RESULTS: Total 337 men with cN1M0 prostate cancer were included, 47% having Gleason grade group 5 disease. Treatment modalities included androgen deprivation therapy (ADT) in 98.9% men, either alone (19%) or in combinations including prostate radiotherapy (70%), pelvic nodal radiotherapy (38%), docetaxel (22%), or surgery (7%). At median follow up of 50 months, 5-year bPFS, rPFS, and OS were 62.7%, 71.0%, and 75.8% respectively. Prostate radiotherapy was associated with significantly higher bPFS (74.1% vs 34.2%), rPFS (80.7% vs 44.3%) and OS (86.7% vs 56.2%) at five years (log rank p < 0.001 each). On multivariable analysis including age, Gleason grade group, tumour stage, ADT duration, docetaxel, and nodal radiotherapy, benefit of prostate radiotherapy persisted for bPFS [HR 0.33 (95% CI 0.18-0.62)], rPFS [HR 0.25 (0.12-0.51)], and OS [HR 0.27 (0.13-0.58)] (p < 0.001 each). Impact of nodal radiotherapy or docetaxel was not established due to small subgroups. CONCLUSION: Addition of prostate radiotherapy to ADT in cN1M0 prostate cancer yielded improved disease control and overall survival independent of other tumour and treatment factors.

Radiation in Combination with Immune Checkpoint Blockade and DNA Damage Response Inhibitors in Mice: Dosage Optimization in MC38 Syngeneic Tumors via Modelling and Simulation.

Clinical trials assessing the impact of radiotherapy (RT) in combination with DNA damage response pathway inhibitors (DDRis) and/or immune checkpoint blockade are currently ongoing. However, current methods for optimizing dosage and schedule are limited. A mathematical model was developed to capture the impacts of RT in combination with DDRi and/or anti-PD-L1 [immune checkpoint inhibitor (ICI)] on tumor immune interactions in the MC38 syngeneic tumor model. The model was fitted to datasets that assessed the impact of RT in combination with the DNA protein kinase inhibitor (DNAPKi) AZD7648. The model was further fitted to datasets from studies that were used to assess both RT/ICI combinations as well as RT/ICI combinations followed by concurrent administration of the poly ADP ribose polymerase inhibitor (PARPi) olaparib. Nonlinear mixed-effects modeling was performed followed by internal validation with visual predictive checks (VPC). Simulations of alternative dosage regimens and scheduling were performed to identify optimal candidate dosage regimens of RT/DNAPKi and RT/PARPi/ICI. Model fits and VPCs confirmed a successful internal validation for both datasets and demonstrated very small differences in the median, lower, and upper percentile values of tumor diameters between RT/ICI and RT/PARPi/ICI, which indicated that the triple combination of RT/PARPi/ICI at the given dosage and schedule does not provide additional benefit compared with ICI in combination with RT. Simulation of alternative dosage regimens indicated that lowering the dosage of ICI to between 2 and 4 mg/kg could induce similar benefits to the full dosage regimen, which could be of translational benefit. SIGNIFICANCE STATEMENT: This work provides a mixed-effects model framework to quantify the effects of combination radiotherapy/DNA damage response pathway inhibitors/immune checkpoint inhibitors in preclinical tumor models and identify optimal dosage regimens, which could be of translational benefit.

Technical development and validation of a clinically applicable microenvironment classifier as a biomarker of tumour hypoxia for soft tissue sarcoma.

BACKGROUND: Soft tissue sarcomas (STS) are rare, heterogeneous tumours and biomarkers are needed to inform management. We previously derived a prognostic tumour microenvironment classifier (24-gene hypoxia signature). Here, we developed/validated an assay for clinical application. METHODS: Technical performance of targeted assays (Taqman low-density array, nanoString) was compared in 28 prospectively collected formalin-fixed, paraffin-embedded (FFPE) biopsies. The nanoString assay was biologically validated by comparing to HIF-1α/CAIX immunohistochemistry (IHC) in clinical samples. The Manchester (n = 165) and VORTEX Phase III trial (n = 203) cohorts were used for clinical validation. The primary outcome was overall survival (OS). RESULTS: Both assays demonstrated excellent reproducibility. The nanoString assay detected upregulation of the 24-gene signature under hypoxia in vitro, and 16/24 hypoxia genes were upregulated in tumours with high CAIX expression in vivo. Patients with hypoxia-high tumours had worse OS in the Manchester (HR 3.05, 95% CI 1.54-5.19, P = 0.0005) and VORTEX (HR 2.13, 95% CI 1.19-3.77, P = 0.009) cohorts. In the combined cohort, it was independently prognostic for OS (HR 2.24, 95% CI 1.42-3.53, P = 0.00096) and associated with worse local recurrence-free survival (HR 2.17, 95% CI 1.01-4.68, P = 0.04). CONCLUSIONS: This study comprehensively validates a microenvironment classifier befitting FFPE STS biopsies. Future uses include: (1) selecting high-risk patients for perioperative chemotherapy; and (2) biomarker-driven trials of hypoxia-targeted therapies.

Association between radiotherapy protocol variations and outcome in the CONVERT trial.

Background: Radiotherapy quality assurance (QA) is integral to radiotherapy delivery. Here we report comprehensive contouring, dosimetry, and treatment delivery QA, describe protocol compliance, and detail the impact of protocol variations on acute grade ≥3 toxicity, progression free survival (PFS), and overall survival (OS) in the phase III CONVERT trial. Materials/Methods: Radiotherapy planning data from one hundred randomly selected patients were requested. Members of the CONVERT Trial Management Group (TMG) recontoured the heart, lung, and spinal cord organs at risk (OAR) according to the trial guideline. The existing radiotherapy plan were re-applied to the new structures and the new dosimetric data were recollected. Compliance with radiotherapy QA components were recorded and radiotherapy QA components were pooled into protocol variations: acceptable, acceptable variation, and unacceptable variation. Univariable analysis with a Cox proportional hazards model established the relationship between protocol variations and patient outcome. Results: Ninety-three cases were submitted for retrospective radiotherapy QA review. Demographics of the radiotherapy QA cohort (n=93) matched the non-QA (n=450) cohort. 97.8% of gross tumour volume (GTV) contours were protocol compliant. OAR contours were non-compliant in 79.6% instances of the heart, 37.6% lung, and 75.3% spinal cord. Of the non-compliant heart contours, 86.5% and 2.7% had contours caudal and cranial to the protocol-defined heart borders. 10.8% did not include the pericardial sac and 2.7% did not include the anterior aspect of the pericardium. Eleven (11.8%) submissions exceeded protocol-defined dosimetric heart constraints; six of which were only noted on the application of protocol-compliant contours. Unacceptable variations were not associated with an increase in grade 3 toxicity (p=0.808), PFS (p=0.232), or OS (p=0.743). Conclusion: Non-protocol compliant heart contours were associated with increased dose delivered to the heart OAR, with 11.8 % of submitted heart structures exceeding protocol-defined constraints. In this QA cohort of patients with small cell lung cancer, unacceptable variations were not associated with acute grade ≥3 toxicity, PFS, or OS. Radiotherapy QA remains the cornerstone of high-quality radiotherapy delivery and should be embedded into clinical trial and non-clinical trial practice; clinical trials should report standardised radiotherapy QA parameters alongside trial outcomes.

Determining bioequivalence possibilities of long acting injectables through population PK modelling.

Long acting injectables (LAI) products are a popular intervention for treating a number of chronic conditions, with their long drug release reducing the administration frequency and thus improving patient adherence. The extended release, however, can provide a major challenge to bioequivalence (BE) testing since the long absorption half-life results in a long washout period, meaning that a traditional BE study can be many months or years in length. The unique PK profile for LAI products also means that it is critical to have appropriate metrics to summarise the plasma concentration profile. In this work, we use paliperidone as a case study to demonstrate how Population PK modelling can be utilised to explore sensitivity of summary metrics to different products. We also determine a range of products that are bioequivalent after both multiple dosing and single dosing. Finally, we show how the modelling can be used in a (virtual) PK study as an alternative approach to determining bioequivalence. This work demonstrates the potential for Population PK modelling in bioequivalence assessment, opening doors to more streamlined product development.