Unlocking the Power of Immunotherapy: Navigating the Frontiers of Computational Immuno-Oncology
Immuno-oncology has revolutionized the treatment of cancer, with groundbreaking advancements like immune checkpoint inhibitors and CAR T-cell therapy becoming standard care across various cancer types. However, the majority of patients still do not experience durable clinical benefits, underscoring the urgent need to further optimize and expand these life-saving treatments.
Enter computational immuno-oncology (compIO) – an innovative, interdisciplinary field that leverages biomedical data science to accelerate the development of effective and safe immunotherapies. By harnessing the power of advanced analytics, machine learning, and AI, compIO holds the key to unlocking new insights into the complex interplay between cancer and the immune system.
In this article, we’ll explore ten critical challenges and opportunities in the fast-pacing world of compIO. From optimizing clinical trial design to predicting and mitigating adverse events, we’ll delve into the cutting-edge strategies and collaborative efforts driving progress in this transformative field. So, let’s dive in and discover how computational approaches are poised to reshape the future of cancer care.
Optimizing Clinical Trial Design
The design of immuno-oncology (IO) clinical trials is crucial for determining the most effective and safe treatment strategies. Given the intricate interactions between the immune system and cancer, advanced computational and analytical methodologies have become indispensable in accelerating drug development and fine-tuning trial parameters – from preclinical studies to post-marketing phase IV trials.
Quantitative systems pharmacology models, coupled with pharmacokinetic (PK) and pharmacodynamic analyses, are now routinely used to simulate and predict patient responses to candidate IO drugs. These methods are also being increasingly applied in the preclinical phase and to inform regulatory interactions in clinical research.
To optimize efficacy and minimize severe immune-related adverse events (irAEs) in trials, computational algorithms have been employed to simulate scenarios across combinations of different treatment modalities. The proper assessment of immunogenicity endpoints is also recognized as crucial in this process.
Traditional phase I dose-escalation trials, which assume the highest dose meeting safety criteria will be the most effective, have proven inadequate in the IO setting due to the non-linear relationship between dose and efficacy or toxicity. New models that account for and continually reassess the safety of both long-term and short-term irAEs are essential to identify the safest and most effective dose.
Cost-effective and expeditious phase II and phase I/II trial designs have become a priority, partly due to the need for receiving rapid FDA approval. Adaptive randomization schemes, which can reweigh treatment allocations, are now being employed in the phase II setting for IO.
For large-scale phase III randomized trials, the design must consider delays in effects relative to progression-free survival (PFS) and overall survival (OS). Modeling delayed PFS and OS beyond the median, or employing time-varying treatment effect estimates, may be appropriate when modeling IO drug outcomes.
Precision medicine approaches, such as large basket trials that leverage immune profile testing and other companion diagnostics to assign agents to subjects, are increasingly important in the IO space. This has been demonstrated in the iMATCH trial, which uses biological mechanisms of resistance to categorize tumors, and the MyPathway phase IIa trial, which uses tumor mutational burden (TMB) as a predictor for the response to atezolizumab.
Throughout the entire drug development pipeline, from preclinical studies to phase III/IV trials, computational methodologies play a central role in accelerating medicines to market. These strategies are crucial for optimizing trial design, predicting responses, and informing regulatory interactions – ultimately paving the way for more effective and safer IO treatments.
Predicting and Monitoring Immune-Related Adverse Events
Patients undergoing IO treatment are vulnerable to developing immune-related adverse events (irAEs) because the treatment itself can compromise the immune system, making it harder for the body to fight off infections or inflammation. Common irAEs include cytokine release syndrome, neurotoxicity, pneumonitis, and rash, among others. These conditions increase the disease burden and, in rare cases, can lead to fatality.
For example, immunotherapy-induced pneumonitis is a type of lung inflammation that can cause symptoms such as coughing, shortness of breath, and chest pain; if untreated, it can lead to serious complications like respiratory failure. Hyperprogressive disease (HPD) is another concerning phenomenon, wherein cancer grows at an accelerated rate after the initiation of IO treatment, leading to worse outcomes and reduced survival rates.
Computational strategies to predict and monitor irAEs have become a priority in IO. Forecasting irAE incidences may help doctors adjust treatment plans to potentially prevent the worsening of the disease or design alternative therapies for patients.
Radiomics from CT scans has been used to identify IO-induced pneumonitis and to predict treatment response and pneumo-toxicity from programmed cell death-1 pathway inhibition in patients with non-small cell lung cancer. Radiomics can also distinguish between radiation- and IO-induced pneumonitis.
Identifying HPD is critical because it can influence the decision to continue or discontinue immunotherapy treatment, as well as the choice of alternative therapies that may be more effective for the patient. By analyzing radiologic images, AI algorithms have been demonstrated for predicting the risk of irAE such as pneumonitis and HPD.
Integrating clinical and radiomic parameters from baseline, pretreatment CT scans could facilitate the identification of HPD in patients with lung cancer being treated with immunotherapy. The combination of radiomic parameters representing texture patterns of lung nodules and features related to vessel tortuosity of a nodule could distinguish between responders, non-responders, and hyperprogressors in immunotherapy.
In addition to CT scans, recent studies have reported a correlation between specific tumor microenvironment (TME) characteristics, such as TIL density and irAE, with evidence suggesting organ-specific co-occurrence of toxicity. Predictive modeling has also shown promise in anticipating drug combinations that minimize the risk of irAEs.
However, accurately predicting and understanding the incidence of irAE remains challenging due to the complex nature of human immune responses, which vary significantly according to individual genetic backgrounds and environmental factors present across different hospital settings. Enhancing computational algorithms and acquiring training data that encompasses diverse patient demographics and clinical contexts should be priorities for the field.
Addressing Disparities in Immuno-Oncology
Disparities in immuno-oncology (IO) are a significant concern for equitable and effective cancer care, leading to differences in trial accrual, treatment outcomes, and survival rates. These disparities are influenced by a variety of factors such as socioeconomic status, geographic location, race, ethnicity, sex, and underlying health conditions.
To reduce health inequities and ensure that the benefits of IO treatments are accessible to all patient populations, it is essential to understand and address these factors. Disparities can lead to unequal access to healthcare, resulting in higher rates of disease and mortality in certain populations. This is also true for risk calculators developed to identify individual patient risk.
For example, a report in JAMA Oncology indicated that the Oncotype DX assay, a 21-gene expression test designed to help identify which estrogen receptor positive, patients with lymph node negative breast cancer would benefit from adjuvant chemotherapy, was not prognostic when used in black women.
AI could help alleviate the issue of health disparities, especially in oncology, by providing more accurate and personalized diagnoses and treatments. However, to achieve this, an intentional focus is needed to ensure biases are not incorporated into AI algorithms. AI could also assist in identifying potential morphologic and molecular differences between populations, allowing for the creation of more specific and accurate population-tailored risk prediction models.
Financial toxicity, the economic burden experienced by patients with cancer due to the high cost of cancer treatment, can also lead to financial distress, psychological stress, and potentially compromise the quality of life for patients and their families. AI can play a crucial role in mitigating financial toxicity by analyzing routine data to identify patients who are likely to benefit from specific therapies, thereby enhancing treatment efficacy and reducing unnecessary financial strain.
Addressing disparities in IO requires a multifaceted approach that includes policy changes, community engagement, and tailored healthcare strategies. Future efforts should focus on generating more data from molecular profiling and improving clinical trials accrual for under-represented populations, implementing education and outreach programs to increase awareness of IO treatments, building trust between doctors and the community, and developing policies that promote health equity.
Integrating Multimodal Data and Overcoming Heterogeneity
Addressing computational and clinical challenges in IO requires multimodality data and proper strategies to identify meaningful signals without introducing bias. Multistudy and multimodal data integration is a pivotal aspect of compIO across diverse research domains.
Careful integration of data from preclinical and clinical IO studies can enable cross-trial correlative analyses for reverse translation and biological discovery, as well as cross-assay studies that use multimodal data types for enhanced inference, which can guide biomarker development strategies.
Data integration across clinical trials is necessary because IO trials and the associated correlative analyses from trials and concomitant model systems are typically too small for biomarker identification, particularly with datasets that contain errors or inconsistencies. Noise in data can also be introduced by variability in sample collection and processing methods across different clinical sites, as well as differences in patient demographics, disease characteristics, and treatment regimens among clinical trials.
Thoughtful data integration across clinical trials helps advance knowledge so that further studies are statistically powered to identify underlying molecular commonalities and differences between responding and non-responding patient populations. Such data reuse requires careful statistical and analytic approaches, including conditioning on treatment regimens, harmonized clinical data elements and outcome associations, and standardization of molecular analysis pipelines.
Similarly, computational innovations have enabled significant advances in understanding immune responses to cancer through the integration of data from different assays, such as gene expression profiling, flow cytometry, and imaging. Transfer learning approaches can be used for annotating descriptions of immune cell function across reference atlases, which facilitates the standardized annotation of single-cell datasets, and this need extends to the tracking of lymphoid cell state changes in the periphery across ICI studies.
Likewise, advances in deep learning (DL) are enabling the multimodal fusion of data types across many domains simultaneously, such as genomics, transcriptomics, and pathology images, and these efforts may yield novel IO insights. However, significant investments are necessary to harmonize data types across contexts as well as to innovate new methods for learning from complex, yet complementary, data types.
Harnessing the Power of Artificial Intelligence
With the exponential growth in data volume and the complexity of cancer-immune biology, AI has transformed the methodologies we use to conduct research. AI offers immense potential to enhance clinical trial design, improve data integration and fusion, and predict irAEs as well as therapy responses in IO.
Traditionally, FDA-approved tissue clinical biomarkers for solid tumors, such as programmed death-ligand 1, microsatellite instability, and TMB, have been routinely used for patient stratification in ICI treatment. Transcriptomic-based biomarkers have also been widely recognized for predicting ICI response, with available tests like NanoString’s tumor inflammation signature (TIS) gene expression assay measuring suppressed adaptive immunity.
Despite these advances, the accuracy of existing biomarkers is often limited, and their performance varies by cancer type and study cohort. Techniques such as dynamical network biomarkers represent a promising approach by modeling multimodal and/or longitudinal data. Unlike traditional biomarkers, which are often static and measured at a single time point, dynamical network biomarkers focus on capturing the dynamic changes in biological networks over time, potentially leading to improved reliability and reproducibility of biomarkers.
Radiology and pathology are areas where AI has demonstrated considerable progress in IO. AI algorithms have been used to analyze radiologic images and identify tumor characteristics, such as size, shape, and texture, and assess treatment responses. Radiomics, a technique that extracts quantitative features from images, has shown promise in predicting the response to immunotherapy in patients with melanoma.
In pathology, AI-driven analysis of histopathological images has enabled the identification of immune cells, including location and density, predicting the response to immunotherapy. For instance, one study used a neural network to analyze histopathological images from patients with melanoma, finding that DL could accurately predict the response to immunotherapy based on the density and location of immune cells in the TME.
AI algorithms can also analyze large-scale genomic data, such as DNA sequencing data, to identify genetic mutations that may affect the response to immunotherapy. A study employed a convolutional neural network to combine genomic and clinical features to stratify patients with non-small cell lung cancer according to their response to immunotherapies.
However, the practical integration of AI into clinical practice remains uncertain due, in part, to regulatory and accountability concerns. Strategies that make such biases more visible, accountable, and quantifiable should be prioritized, along with the development of AI solutions capable of effectively correcting for bias and validation of their performance in real-world datasets.
Spatial Profiling and Systems Immunology
High-resolution, high-throughput spatial technologies offer a unique advantage in dissecting the tumor-immune ecosystem, systems immunity, and host-environmental interactions, ultimately linking these insights back to clinical outcomes.
With co-detection by indexing (CODEX), tissue-based cyclic immunofluorescence microscopy, and multiplexed ion beam imaging, researchers can now scale the number of traditionally measured biomarkers from 10 or fewer to upwards of 40–100 antibodies simultaneously for a single FFPE slide of tumor tissue. This expansion in data capturing capabilities is critical for in-depth investigations of cell-cell communications within and between distinct tumor niches.
Studies have used these technologies to identify unique subsets of cell populations associated with patients’ responses to ICIs and CAR T-cell therapy. On account of further achievements, single-molecule localization imaging by photoactivated localization microscopy (PALM) and stochastic optical reconstruction microscopy (STORM) can now provide intracellular levels of resolution for target transcripts, proteins, or metabolites of interest, as well as spatial data on subcellular organelles such as lysosomes and extracellular vesicles.
Computational reconstruction of tissues from sequentially sectioned H&E-stained images has revealed tumor-lymphocyte interfaces that contribute to cancer progression. Because FFPE tissues are commonly stored by biorepositories, there exists a vast resource that can be used for high-resolution spatial analyses, such as CODEX, Visium, GeoMX, CosMX, and other MICCCS (Multiplex Immuno-Cohort Characterization in Cancer Studies) technologies.
While spatial omics and imaging technologies offer invaluable biological insights, their clinical scalability is hindered by cost considerations. In contrast, H&E staining is a routine and readily available process in clinical practice, applicable to almost all patients without additional processing. Strategies that harness AI for stain normalization as well as create spatially aware biomarkers and elucidate tumor heterogeneity in the context of therapy response should be the next direction for clinical translation efforts.
Advancing Antigen Discovery and T-Cell Engineering
Cancer immunotherapies such as ICIs are designed to unleash antitumor T cells, which in turn mediate antitumor immunity that provides long-term protection. However, open questions remain about the mechanisms that lead to a successful antitumor T-cell response: What determines the fate trajectories of therapeutically relevant T cells? How can we systematically identify tumor-reactive T cells and their cellular phenotypes?
Insights into these questions will help characterize the specificity and strength of endogenous antitumor T-cell responses during immunotherapy, which may lead to significant advances in vaccine and immunotherapy engineering. Despite the increasing number of high-throughput, standardized technologies to experimentally identify tumor-antigen-specific T cells, experiments are costly and few studies to date have successfully identified neoantigen-specific T cells.
In individuals with cancer, only a modest number of neoantigen-specific T cells are detected in the TME; this is confounded by the presence of bystander T cells with viral specificities. Remarkably, neoantigen-specific T cells exhibit congruent transcriptional programs across cancer studies, supporting the joint analysis of existing single-cell transcriptomic datasets paired with matching TCR data as a basis to more effectively uncover transcriptomic and TCR sequence similarities that underlie tumor-reactive T cells.
Advances in AI for protein structure prediction have radically transformed the field of protein optimization and de novo design. While significant progress has been made in ML-based methods for T-cell antigen specificity prediction, the complexity and variability of antigen interactions continue to present substantial challenges.
Unsupervised ML methods have been developed to annotate T-cell antigen specificity based on cross-referencing with the known TCR-epitope databases or by calculating similarities between TCR sequence properties and/or transcriptional profiles in single cells. Supervised methods, including DL models, have been developed to use TCR-antigen pairs as training data to predict epitope specificity, achieving some success in model training but with diminished predictability for unseen antigens.
One of the major barriers in building ML models to predict T-cell antigen specificity is the scarcity of paired TCR-antigen specificity data. Publicly available TCR-epitope databases contain an abundance of viral antigen specificity data, and thus, bias can be introduced when using these data for
