Immunotherapy Master Trial for Advanced Cancers

Understanding cancer as a pathological condition, both clinically and biologically, has profoundly shaped the evolution of therapeutic strategies in oncology. Over the past decade, the emergence of immune checkpoint-targeted cancer immunotherapies has triggered a major paradigm shift. These treatments not only integrate fundamental insights from cancer and immune cell biology into clinical practice, but also demonstrate that targeting the immune system can, in many cases, provide superior and more durable outcomes compared with approaches focused solely on cancer cells.

Despite this transformative progress, the success rate of novel anticancer agents in clinical development remains unacceptably low, with failure rates exceeding 90%. One key contributor to this persistent inefficiency is the continued enrollment of patients in clinical trials based on the flawed assumption that individuals with the same cancer histology and stage are biologically and functionally equivalent. After more than a decade of intensive clinical and translational research, it has become evident that this assumption does not reflect biological reality.

First, within any given tumor indication and disease stage, there is marked inter-individual biological variability. This heterogeneity arises from multiple, interconnected factors, including somatic genetic and epigenetic alterations within cancer cells, inherited germline polymorphisms affecting immune regulation, environmental influences that shape the composition and function of innate and adaptive immune cells, and variability in the magnitude and quality of tumor antigen-specific immune responses. Together, these elements generate distinct tumor-host ecosystems that profoundly influence therapeutic responsiveness.

Second, common biological features can be shared across different cancer indications and confer similar levels of treatment efficacy irrespective of tissue of origin. For example, tumors characterized by microsatellite instability-high (MSI-H) status or high tumor mutational burden (TMB-H) demonstrate objective response rates of approximately 30% across multiple cancer types when treated with immune checkpoint inhibitors. These observations have challenged the traditional histology-based framework and paved the way for tumor-agnostic therapeutic approvals.

Third, the efficacy of immune checkpoint-targeted therapies depends far more on the tumor microenvironment and host-related factors than on cancer histology itself. Critical determinants of response include tumor-infiltrating lymphocytes, the presence of tertiary lymphoid structures, PD-L1 expression, tumor mutational burden, and other genomic features, as well as host parameters such as lactate dehydrogenase levels, metastatic burden (notably liver metastases), systemic inflammation, and the gut microbiome. These tumor and host characteristics also explain why the majority of patients still fail to benefit from immune checkpoint blockade.

Collectively, these insights indicate that clinical outcomes in oncology would be substantially improved by better orientation and stratification of patients based on their individual tumor and host biological profiles. However, the diagnostic and molecular screening techniques currently used in routine clinical practice are poorly suited to this task. Many lack sufficient sensitivity or specificity-particularly when assessing protein expression on defined cellular subsets-and are associated with long turnaround times that are incompatible with real-time clinical decision-making.

Compounding this issue, clinical drug development in oncology traditionally begins with first-in-human phase I trials conducted in patients with relapsing or refractory advanced, often metastatic, disease. When efficacy is not demonstrated in this late-stage setting, drug development programs are frequently terminated under the assumption that the therapy would not perform better in earlier disease stages. Emerging evidence, however, clearly shows that the biology of early-stage cancers differs fundamentally from that of advanced disease. Consequently, therapies with limited activity in metastatic settings may exhibit substantial efficacy in localized or early-stage disease, highlighting a critical limitation of current development paradigms.

Against this backdrop, the oncology field would greatly benefit from strategies capable of characterizing patients' cancer biology in real time. Such approaches should enable accurate assessment of pharmacodynamic parameters, including target expression, target occupancy, and target engagement, while supporting biomarker-driven therapeutic stratification. In this context, master protocols offer a powerful and efficient framework for clinical research.

A master protocol provides a unified structure for the simultaneous evaluation of multiple investigational therapies within a single overarching clinical framework. Guided by predefined mechanistic hypotheses and tailored endpoints, this approach ensures consistency across operational, regulatory, and methodological aspects while streamlining review processes, facilitating site participation, and accelerating study implementation. By eliminating redundancies, master protocols optimize resource utilization and expedite patient access to innovative therapies.

The inherent adaptability of a master protocol, supported by individual sub-protocols, allows for the rapid initiation or closure of specific investigations without disrupting ongoing studies. The integration of innovative trial designs and adaptive analytical methods, including Bayesian decision rules, maximizes knowledge generation by leveraging data across related sub-protocols and incorporating relevant historical information. This strategy enables meaningful clinical investigation with fewer patients while maintaining robust scientific rigor.

Additional advantages of master protocols include centralized governance to ensure patient safety and methodological consistency, standardized systems and processes to enhance operational efficiency, uniform study language across sub-protocols and informed consent documents, simplified registration and recruitment procedures, centralized data and informatics infrastructure, and the flexibility to adapt investigational priorities based on emerging safety and efficacy signals. Together, these features support faster and more reliable generation of early-stage data to inform subsequent confirmatory studies.

Importantly, while many immune- and tumor-targeted therapies enter clinical development with strong preclinical rationale, sponsors and investigators often lack real-time confirmation that enrolled patients actually express the intended therapeutic targets. This represents a major limitation of evidence-based drug development in oncology. Routine diagnostic tools-including immunohistochemistry on formalin-fixed or frozen tissues and bulk DNA or RNA sequencing-are poorly sensitive or specific, provide limited spatial or cellular resolution, and are associated with long turnaround times of several weeks to months. Moreover, these methods cannot adequately address critical questions related to target saturation and engagement following treatment. Routine blood tests offer only coarse information on systemic immune and inflammatory status and fail to capture the complexity of the immune landscape.

Recent data demonstrate that novel approaches based on fresh biological samples can more accurately define the biological context of individual patients and better predict responses to cancer immunotherapies. Ultrasensitive, multiplexed cytokine profiling in patient plasma can identify baseline levels of interleukin-6 or interleukin-8 associated with primary resistance to PD-(L)1 blockade. Similar analyses performed on supernatants from freshly collected tumor biopsies reveal key cytokines and soluble factors linked to treatment efficacy at baseline and during therapy. In parallel, multiparametric flow cytometry applied to fresh whole blood or dissociated tumor tissues enables precise identification of cellular subsets and quantification of protein expression levels relevant to immunotherapy response. These techniques require minimal pre-analytical processing and can deliver actionable results on the day of sample acquisition, making them uniquely suited for real-time clinical application.

The PORTRAIT method (Profile in Onco-immunology for a Rapid Treatment Research Adapted to Immunity and Tumor) has been developed to address these unmet needs. By integrating advanced cellular and molecular analyses of fresh tumor and blood samples, PORTRAIT provides oncologists and patients with timely, relevant information on tumor and immune biology. This enables informed treatment selection and dynamic assessment of therapeutic impact on both the disease and the host.

Within this framework, the METAREM master protocol will apply baseline PORTRAIT profiling across its sub-protocols using freshly collected tumor biopsies and blood samples from cancer patients. Multicolor flow cytometry of fresh mononuclear cells from blood and tumor tissues will be used for targeted biomarker screening, while plasma and tumor secretomes will be analyzed for cytokines, chemokines, and soluble factors. PORTRAIT analyses will be conducted at baseline prior to initiation of a new line of therapy and, when specified, at defined on-treatment time points.

For investigational immunotherapeutic agents whose predictive biomarkers are not yet established, PORTRAIT data will not be used for prospective patient selection but instead will support retrospective discovery of biomarkers associated with therapeutic efficacy. Through this approach, METAREM aims to enable biologically informed patient stratification, accelerate the development of effective immunotherapies, and ultimately improve outcomes across the cancer continuum.