Early-phase Oncology Trials Research Articles

A Bayesian Dynamic Model-Based Adaptive Design for Oncology Dose Optimization in Phase I/II Clinical Trials.

With the development of targeted therapy, immunotherapy, and antibody-drug conjugates (ADCs), there is growing concern over the "more is better" paradigm developed decades ago for chemotherapy, prompting the US Food and Drug Administration (FDA) to initiate Project Optimus to reform dose optimization and selection in oncology drug development. For early-phase oncology trials, given the high variability from sparse data and the rigidity of parametric model specifications, we use Bayesian dynamic models to borrow information across doses with only vague order constraints. Our proposed adaptive design simultaneously incorporates toxicity and efficacy outcomes to select the optimal dose (OD) in Phase I/II clinical trials, utilizing Bayesian model averaging to address the uncertainty of dose-response relationships and enhance the robustness of the design. Additionally, we extend the proposed design to handle delayed toxicity and efficacy outcomes. We conduct extensive simulation studies to evaluate the operating characteristics of the proposed method under various practical scenarios. The results demonstrate that the proposed designs have desirable operating characteristics. A trial example is presented to demonstrate the practical implementation of the proposed designs.

REDOMA: Bayesian random-effects dose-optimization meta-analysis using spike-and-slab priors.

The rise of cutting-edge precision cancer treatments has led to a growing significance of the optimal biological dose (OBD) in modern oncology trials. These trials now prioritize the consideration of both toxicity and efficacy simultaneously when determining the most desirable dosage for treatment. Traditional approaches in early-phase oncology trials have conventionally relied on the assumption of a monotone relationship between treatment efficacy and dosage. However, this assumption may not hold valid for novel oncology therapies. In reality, the dose-efficacy curve of such treatments may reach a plateau at a specific dose, posing challenges for conventional methods in accurately identifying the OBD. Furthermore, achieving reliable identification of the OBD is typically not possible based on a single small-sample trial. With data from multiple phase I and phase I/II trials, we propose a novel Bayesian random-effects dose-optimization meta-analysis (REDOMA) approach to identify the OBD by synthesizing toxicity and efficacy data from each trial. The REDOMA method can address trials with heterogeneous characteristics. We adopt a curve-free approach based on a Gamma process prior to model the average dose-toxicity relationship. In addition, we utilize a Bayesian model selection framework that uses the spike-and-slab prior as an automatic variable selection technique to eliminate monotonic constraints on the dose-efficacy curve. The good performance of the REDOMA method is confirmed by extensive simulation studies.

RBMA: A robust Bayesian Model Averaging Method for phase II basket trials based on informative mixture priors

Oncology drug research in the last few decades has been driven by the development of targeted agents. In the era of targeted therapies, basket trials are often used to test the antitumor activity of a novel treatment in multiple indications sharing the same genomic alteration. As patient population are further fragmented into biomarker-defined subgroups in basket trials, novel statistical methods are needed to facilitate cross-indication learning to improve the statistical power in basket trial design. Here we propose a robust Bayesian model averaging (rBMA) technique for the design and analysis of phase II basket trials. We consider the posterior distribution of each indication (basket) as the weighted average of three different models which only differ in their priors (enthusiastic, pessimistic and non-informative). The posterior weights of these models are determined based on the effect of the experimental treatment in all the indications tested. In early phase oncology trials, different binary endpoints might be chosen for different indications (objective response, disease control or PFS at landmark times), which makes it even more challenging to borrow information across indications. Compared to previous approaches, the proposed method has the flexibility to support cross-indication learning in the presence of mixed endpoints. We evaluate and compare the performance of the proposed rBMA approach to competing approaches in simulation studies. R scripts to implement the proposed method are available at https://github.com/xwang317/rBMA.

Why Oncology Global Safety Teams Should Develop the Safety Section of the Study's Target Product Profile (TPP).

Oncology Global Safety Teams (GSTs) are not universally tasked with the development of the risk section of the products target product profile (TPP). This fact makes little sense since the GST is tasked by the company to identify, analyze, and mitigate a product's risks. The TPP, in essence, establishes boundaries for go/no-go decisions around a product or products in combination treatment. Involvement of the Oncology GST in producing a well-researched and evidenced based TPP safety section allows the team to develop knowledge around the drug(s) studied or added to a study arm. The increased use of umbrella and platform studies for early-phase oncology trials allows an excellent resource for the use of clinical data to estimate the risk of developmental drugs combined to treat a given oncology indication. To shorten time to marketing, companies are including developmental products with novel mechanisms early within their development cycles. Antibody drug conjugates (ADCs) and bi-directional antibodies are a few examples of products combined in arms of a platform or umbrella study early and with only immature clinical data available. This article will share a novel analytical approach for safety teams to develop a well thought-out and defendable safety section to the TPP. Strategies to estimate the risks associated with combination therapies will be brought forward. The advantages of having the safety team involved early in the benefit/risk, go/no-go decisions for a study or the addition of a study arm will be detailed. The early development of a well-documented TPP will enhance chances of a successful product submission.

Adaptive Bayesian information borrowing methods for finding and optimizing subgroup-specific doses.

In precision oncology, integrating multiple cancer patient subgroups into a single master protocol allows for the simultaneous assessment of treatment effects in these subgroups and promotes the sharing of information between them, ultimately reducing sample sizes and costs and enhancing scientific validity. However, the safety and efficacy of these therapies may vary across different subgroups, resulting in heterogeneous outcomes. Therefore, identifying subgroup-specific optimal doses in early-phase clinical trials is crucial for the development of future trials. In this article, we review various innovative Bayesian information-borrowing strategies that aim to determine and optimize subgroup-specific doses. Specifically, we discuss Bayesian hierarchical modeling, Bayesian clustering, Bayesian model averaging or selection, pairwise borrowing, and other relevant approaches. By employing these Bayesian information-borrowing methods, investigators can gain a better understanding of the intricate relationships between dose, toxicity, and efficacy in each subgroup. This increased understanding significantly improves the chances of identifying an optimal dose tailored to each specific subgroup. Furthermore, we present several practical recommendations to guide the design of future early-phase oncology trials involving multiple subgroups when using the Bayesian information-borrowing methods.

1845P Exclusion of patients with psychiatric disorders from early phase oncology trials: A limited access to innovation?

1845P Exclusion of patients with psychiatric disorders from early phase oncology trials: A limited access to innovation?

Evaluating hybrid controls methodology in early-phase oncology trials: A simulation study based on the MORPHEUS-UC trial.

Phase Ib/II oncology trials, despite their small sample sizes, aim to provide information for optimal internal company decision-making concerning novel drug development. Hybrid controls (a combination of the current control arm and controls from one or more sources of historical trial data [HTD]) can be used to increase statistical precision. Here we assess combining two sources of Roche HTD to construct a hybrid control in targeted therapy for decision-making via an extensive simulation study. Our simulations are based on the real data of one of the experimental arms and the control arm of the MORPHEUS-UC Phase Ib/II study and two Roche HTD for atezolizumab monotherapy. We consider potential complications such as model misspecification, unmeasured confounding, different sample sizes of current treatment groups, and heterogeneity among the three trials. We evaluate two frequentist methods (with both Cox and Weibull accelerated failure time [AFT] models) and three different commensurate priors in Bayesian dynamic borrowing (with a Weibull AFT model), and modifications within each of those, when estimating the effect of treatment on survival outcomes and measures of effect such as marginal hazard ratios. We assess the performance of these methods in different settings and the potential of generalizations to supplement decisions in early-phase oncology trials. The results show that the proposed joint frequentist methods and noninformative priors within Bayesian dynamic borrowing with no adjustment on covariates are preferred, especially when treatment effects across the three trials are heterogeneous. For generalization of hybrid control methods in such settings, we recommend more simulation studies.

How to expedite clinical development of oncology agents: overcoming complexities of early-phase oncology trials for ATMPs

How to expedite clinical development of oncology agents: overcoming complexities of early-phase oncology trials for ATMPs

A Multi-Arm Two-Stage (MATS) design for proof-of-concept and dose optimization in early-phase oncology trials

The Project Optimus initiative by the FDA's Oncology Center of Excellence is widely viewed as a groundbreaking effort to change the status quo of conventional dose-finding strategies in oncology. Unlike in other therapeutic areas where multiple doses are evaluated thoroughly in dose ranging studies, early-phase oncology dose-finding studies are characterized by the practice of identifying a single dose, such as the maximum tolerated dose (MTD) or the recommended phase 2 dose (RP2D). Following the spirit of Project Optimus, we propose an Multi-Arm Two-Stage (MATS) design for proof-of-concept (PoC) and dose optimization that allows the evaluation of two selected doses from a dose-escalation trial. The design assesses the higher dose first across multiple indications in the first stage, and adaptively enters the second stage for an indication if the higher dose exhibits promising anti-tumor activities. In the second stage, a randomized comparison between the higher and lower doses is conducted to achieve PoC and dose optimization. A Bayesian hierarchical model governs the statistical inference and decision making by borrowing information across doses, indications, and stages. Our simulation studies show that the proposed MATS design yield desirable performance. An R Shiny application has been developed and made available at https://matsdesign.shinyapps.io/mats/.

Abstract 5532: Increasing racial and ethnic diversity of phase 1 solid tumor clinical trials through enrollment at a safety net hospital

Abstract Background: Racial minorities are under-represented in oncology clinical trials. African Americans and Hispanic/Latinos account for 13% and 18% of the US population, respectively, but in 2018 these groups combined were only 8% of oncology trial subjects. Disproportionate enrollment can limit how generalizable trial outcomes may be and can perpetuate healthcare disparities. The majority of clinical trial enrollment occurs at large academic cancer centers that may not reflect the diverse general cancer patient population in the US. Los Angeles County/University of Southern California Medical Center (LAC+USC) is a large safety net hospital that serves a racially and ethnically diverse patient population. Methods: We developed a database of all adult patients (pts) enrolled in phase I solid tumor trials at LAC+USC from 2015 - 2022. The data collected includes baseline demographics (age, gender, race, ethnicity, primary language, tumor histology), type of therapeutic intervention, radiologic response per RECIST 1.1, duration of treatment, and survival. Progression-free survival was defined as time from enrollment in the study until progression, discontinuation of treatment, or death. Disease control rate (DCR) was defined as pts with best response of CR, PR, or SD. Results: 120 pts with solid tumors were enrolled in phase 1 trials at LAC+USC from 2015-2022. Median age was 58 (range 26-78); 58% female; ECOG 0-1 (99%), 2 (1%). 73 patients (61%) were Hispanic, 27 (23%) Asian/Pacific Islander, 9 (8%) non-Hispanic (NH) White, 5 (4%) were NH Black, and 6 (5%) were unknown. 70% of pts had a primary language other than English: 56% Spanish. The most common tumor types were colorectal (16%), hepatocellular (13%), non-small cell lung (12%), head and neck (11%), and ovarian (11%). Trial interventions included chemotherapy (19%), immunotherapy (35%), targeted therapy (54%), and 10% required biomarker selection. The disease control rate (DCR) was 58/120 (48%): 2 pts had CR, 9 pts had PR, and 47 pts had SD. 4-month progression-free survival (PFS) was 42/120 (35%), and 6-month PFS was 27/120 (23%). 90-day survival was confirmed in 101/120 (84%) of patients. Discussion: A racially diverse population can be successfully enrolled in early phase oncology trials at a safety net hospital. Patient populations that are underrepresented nationally in clinical trials had robust enrollment and comprised the majority of participation (88%) in this setting. Almost half of patients derived clinical benefit (48% DCR) from trial participation. Creating an infrastructure to enroll patients to phase 1 clinical trials at a safety net hospital may be a successful strategy to improve racial and ethnic diversity. Citation Format: Varun Roy, Esther Lee, Ming Li, Xiomara Menendez, Elena Nieves, Rebecca Umayam, Nicole Jensen, Lorraine Martinez, Diana Hanna, Jorge Nieva, Anthony El-Khoueiry, Jacob Thomas. Increasing racial and ethnic diversity of phase 1 solid tumor clinical trials through enrollment at a safety net hospital. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5532.

Project Optimus, an FDA initiative: Considerations for cancer drug development internationally, from an academic perspective.

Modern cancer therapeutics are increasingly targeted, bringing the promise of new and improved activity, alongside better tolerability. However, while many are indeed resulting in dramatic improvements in disease control and patient survival, short- and long-term tolerability has not always accompanied it. The choice of dose and schedule is often in the upper range of the therapeutic window, driven by the maximum tolerated dose (MTD) model of previous cytotoxic agents. There is increasing recognition that this needs to change, by taking a more holistic approach to determine the optimal dose for desired biological effects and tolerability early in clinical development. In the US, the FDA's Oncology Centre of Excellence is addressing this via the Project Optimus initiative: aiming to reform dose optimisation studies so that they can demonstrate the most appropriate dose selection. Early clinical development will need to demonstrate the dose-exposure, -pharmacodynamic, -toxicity and -activity relationships, including randomised evaluations for dose selection. Regulatory agencies outside the US are similarly exploring this. Along with Australia, Brazil, Canada, Israel, Singapore and Switzerland, the UK participates in Project Orbis, a collaborative program with the FDA to accelerate patient access to new cancer medicines through coordinated regulatory review. Close alignment with Project Optimus will be important internationally and will require changes across industry, including for academic units and small biotech. We discuss our perspective on the implications, and opportunities, for early phase oncology trials as a uniquely charity-funded drug development facility, the Centre for Drug Development within the Cancer Research UK charity.

Improving early phase oncology clinical trial design: The case for finding the optimal biological dose.

Historically early phase oncology drug development programmes have been based on the belief that "more is better". Furthermore, rule-based study designs such as the "3 + 3" design are still often used to identify the MTD. Phillips and Clark argue that newer Bayesian model-assisted designs such as the BOIN design should become the go to designs for statisticians for MTD finding. This short communication goes one stage further and argues that Bayesian model-assisted designs such as the BOIN12 which balances risk-benefit should be included as one of the go to designs for early phase oncology trials, depending on the study objectives. Identifying the optimal biological dose for future research for many modern targeted drugs, immunotherapies, cell therapies and vaccine therapies can save significant time and resources.

Do Early Phase Oncology Trials Predict Clinical Efficacy in Subsequent Biomarker-Enriched Phase III Randomized Trials?

Promising early phase trial results of biomarker-targeted therapies have occasionally led to regulatory approval. We examined if early phase trials were predictive of efficacy in randomized controlled trials (RCTs) with matching treatment settings. Cancer drug RCTs conducted between January 2006 and March 2021 were identified through Clinicaltrials.gov. Biomarker-enriched RCTs and associated matching early phase trials were included. Trial pairs were compared using objective response rate (ORR) and progression-free survival (PFS). We examined whether early phase trials results were associated with RCT results using logistic regression. The search yielded 2157 unique RCTs and 27 RCTs pairing with early phase trials were included. Based on average difference of trial pairs, ORR was similar (1.6%; 95% confidence interval (CI) - 2.5 to 5.6, p = 0.50) and median PFS was higher in early phase trials (2.0months; 95% CI 0.9-3.0, p < 0.05). On an individual pair basis, there was large variability in difference for ORR (range - 23.9 to 20.2%) and median PFS (range - 0.8 to 7.4months). The probability of the RCT meeting its primary endpoint is 95% (95% prediction interval (PI) 72.8-99.3%) when the early phase trial ORR is 77.7%. Overall, in early phase trials, ORR has minimal bias and median PFS appears to be slightly overestimated. Substantial variability between trials suggests early phase trial results may be inconsistent with subsequent RCT. Early phase trial results may be associated with RCTs meeting their primary endpoint when ORR is very high; however, caution must be exercised when using early phase trials as representative of RCTs.

Early-Phase Oncology Trials: Why So Many Designs?

The past 30 years have seen a considerable effort on the part of statisticians to improve the design and accuracy of early-phase oncology trials. Some of this effort has been rewarded via successful implementation in actual trials, yet it would be fair to say that among clinicians, there remains some reluctance to fully embrace more efficient model-based approaches. One reason for such reticence is the difficulty in understanding exactly what is being offered by more modern designs. Although it is generally accepted that these designs offer improvements over the old standard 3 + 3 design, a new question has then to be addressed: How should we decide among the new proposals which one is the best for our purpose? In this study, we recall 15 designs that are currently proposed and in use. We show that among these 15 designs, many are operationally identical. These 15 designs reduce to three broad classes of designs. This review helps summarize their properties and differences and highlights that certain designs require ad hoc modifications to ensure satisfactory performance.

Improving early phase oncology clinical trial design: A case study.

This short communication presents a first in human Bayesian Optimal Interval design case study. The study design and associated operating characteristics are discussed, together with study amendments proposed whilst the study was ongoing. Simulations investigating the impact of the amendments on the operating characteristics of the study design are presented. Lessons learnt from the case study, including providing practical advice when designing smarter early phase oncology trials to identify the maximum tolerate dose are also summarised. It is argued that model-assisted designs are simple to implement, flexible and perform significantly better than the commonly used "3 + 3" design, and thus should become the go to design for statisticians when limited information is known about the dose toxicity curve.

Oncology clinical trial termination trends: Analysis across the prior 20 years.

e13602 Background: Terminations of oncology clinical trials are an unavoidable potential outcome when novel anti-cancer therapeutics are evaluated, yet are a significant burden on cancer patients, institutions, regulators and the industry as a whole. Overall, approximately 1 in 5 trials are terminated – the four main reasons are poor enrollment (PE), business decisions, lack of efficacy, and toxicity. The objective of our study is to describe trends in trial terminations. Methods: We searched Citeline® Trialtrove database for oncology, phase 1-3, industry sponsored trials, started 1Jan2000-31Dec2019 listed as completed or terminated. Data collection was performed in Jan2022. For analysis purposes, they were grouped based on start year into four 5-year periods: I-2000-2004, II-2005-2009, III-2010-2014, IV-2015-2019. Undetermined phase, phase 4 and non-oncology trials were excluded. Parameters collected: phase, reason for termination (more than one could be listed), sponsor (academic involvement; Top20 pharma as determined by Scrip 100 annually). Results: A total of 21,125 trials met search criteria, of which 4,596 (22%) were indicated as terminated. Termination rate increased from period I to II (17% to 24%) and remained at a steady 22% in periods III, IV. When evaluating the rate of termination according to phase, over periods I-II-III-IV we observed an increase in the rate of termination of phase 1/2 trials - 10%, 17%, 19% and 20% respectively. Across other phases, the change in termination rates was similar to change in their proportional representation per period. In terms of reason for termination, PE remained a major reason overall, 1275 trials (28%) were terminated due to poor enrollment with a low rate of 13% in period I, followed by 37%, 29%, 21% in periods II, III, IV. Of the trials that were terminated due to PE, 51% had academic, and 73% had Top20 involvement. This is a higher representation that their respective involvement in trials. Conclusions: According to the results of our analysis, the proportion of oncology industry-sponsored terminated trials remained high over the last 20 years, with early phases (1, 1/2 &amp; 2) being the most affected. PE accounted for almost 1/3 of the overall terminations, with a possible trend of decrease. In terms sponsor type, there appeared to be a higher number of trials terminated due to PE with Top20, and with academic involvement, compared to their representation in the overall numbers. It is yet to be determined whether innovation in oncology early phase drug development, such as biomarker driven trials and seamless phase transitions will impact termination rates. Further analysis is ongoing focusing on early phase oncology trials.[Table: see text]

POSA95 Exploring the Role of Response to Treatment in Oncology Technology Appraisals

Expedited access programmes for new anticancer medicines have increased the reliance on early-phase clinical trials to inform reimbursement decisions in HTA bodies such as the National Institute for Health and Care Excellence (NICE). Early-phase trials lack mature data on overall survival (OS) and other time-to-event endpoints commonly used to inform economic modelling. The result is increased uncertainty in cost-effectiveness estimates. Early-phase oncology trials are often powered on response endpoints however, therefore the Objectives: of this study are to assess the prevalence of response in clinical trials used in NICE cancer technology appraisals (TAs), and how response is incorporated in economic modelling.

Controlled backfill in oncology dose-finding trials

The use of backfill in early phase dose-finding trials is a relatively recent practice. It consists of assigning patients to dose levels below the level where the study is at. The main reason for backfilling is to collect additional pharmacokinetic, pharmacodynamic and response data, in order to assess whether a plateau may exist on the dose-efficacy curve. This is a possibility in oncology with molecularly targeted agents or immunotherapy. Recommending for further study a dose level lower than the maximum tolerated dose could be supported in such situations. How to best allocate backfill patients to dose levels is not yet established. In this paper we propose to randomise backfill patients below the dose level where the study is at. A refinement of this would be to stop backfilling to lower dose levels when these show insufficient efficacy compared to higher levels, starting at dose level 1 and repeating this process sequentially. At study completion, data from all patients (both backfill patients and dose-finding patients) is used to estimate the dose-response curve. The fit from a change point model is compared to the fit of a monotonic model to identify a potential plateau. Using simulations, we show that this approach can identify the plateau on the dose-response curve when such a plateau exists, allowing the recommendation of a dose level lower than the maximum tolerated dose for future studies. This contribution provides a methodological framework for backfilling, from the perspective of both design and analysis in early phase oncology trials.

Bayesian modeling of a bivariate toxicity outcome for early phase oncology trials evaluating dose regimens.

Most phase I trials in oncology aim to find the maximum tolerated dose (MTD) based on the occurrence of dose limiting toxicities (DLT). Evaluating the schedule of administration in addition to the dose may improve drug tolerance. Moreover, for some molecules, a bivariate toxicity endpoint may be more appropriate than a single endpoint. However, standard dose‐finding designs do not account for multiple dose regimens and bivariate toxicity endpoint within the same design. In this context, following a phase I motivating trial, we proposed modeling the first type of DLT, cytokine release syndrome, with the entire dose regimen using pharmacokinetics and pharmacodynamics (PK/PD), whereas the other DLT (DLTo) was modeled with the cumulative dose. We developed three approaches to model the joint distribution of DLT, defining it as a bivariate binary outcome from the two toxicity types, under various assumptions about the correlation between toxicities: an independent model, a copula model and a conditional model. Our Bayesian approaches were developed to be applied at the end of the dose‐allocation stage of the trial, once all data, including PK/PD measurements, were available. The approaches were evaluated through an extensive simulation study that showed that they can improve the performance of selecting the true MTD‐regimen compared to the recommendation of the dose‐allocation method implemented. Our joint approaches can also predict the DLT probabilities of new dose regimens that were not tested in the study and could be investigated in further stages of the trial.

Abstract 153: Development of a workflow to handle the quality control and analysis of Olink protein biomarker data in early phase oncology clinical trials

Abstract INTRODUCTION: Olink is a high-throughput targeted biomarker platform based on Proximity Extension Assay (PEA) technology using oligonucleotide-labeled antibody pairs. At AbbVie RWC, we have primarily utilized the 96-plex Immune-Oncology (IO) panel measuring 92 proteins to support our early-phase oncology clinical trials. As we analyzed batches of specimens from ongoing trials, we set out to develop a workflow to control data quality and streamline analysis. METHODS: Prior to sample shipment to Olink, we randomize sample position onto plates to ensure key factors such as dose and cohort are balanced within and across plates. This prevents plate or location effects from becoming a confounder. If the study specimens are analyzed in more than one batch, subsequent batches require bridging samples from the first batch to allow us to detect and remove batch effects if necessary. Our workflow thus identifies bridging samples and recommends the optimal number. Upon receiving the data, we evaluate its quality by calculating inter- and intra-plate coefficients of variation (CVs) and visualizing it in multiple ways. The next step in our workflow is analysis, including pharmacodynamic (PD) analysis and association with clinical response. In the PD analysis, we identify biomarkers that significantly change across time and/or exhibit a dose effect. To help interpret the results, enrichment analysis is performed to identify biological processes enriched in the significantly modulated biomarkers. Heatmaps and volcano plots are also generated. For analysis of association with clinical response, we examine how each biomarkers' baseline value and modulation over time associates with progression free survival (PFS) and best overall response (BOR). RESULTS: We successfully created a workflow that handles quality control and analysis of Olink protein biomarker data. The workflow starts from experimental design quality control. Upon receiving the data, we conduct data quality assessment and examination and removal of potential batch effects. Finally, we perform analysis of the data to identify potential biomarkers of interest. We used elements of this workflow on data from M15-891 (ABBV-181) and have now applied the full workflow to multiple batches of data from three early-phase oncology trials (M15-862 (ABBV-927), M16-074 (ABBV-368) and M19-345 (ABBV-151)), with plans to incorporate this methodology into several additional upcoming trials. CONCLUSIONS: We have developed a comprehensive workflow that manages Olink quality control and analysis and can potentially be implemented to all early-phase oncology clinical trials using Olink. It allows us to standardize and streamline Olink data handling and provide robust and efficient analyses that enable rapid biomarker data interpretation, hypothesis generation, and decision making for our trials. Citation Format: Claire J. Guo, Mary Saltarelli, Stacie Lambert, Hua Fang, Chun Zhang. Development of a workflow to handle the quality control and analysis of Olink protein biomarker data in early phase oncology clinical trials [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 153.