May 16, 2024

On the Verge: How Multi-Omics Can Unleash the Full Potential of Cell and Gene Therapy

Image courtesy of Sapient Bioanalytics.

By Sapient Bioanalytics

Ten percent of all FDA novel approvals in 2023 were cell and gene therapies, representing a landmark year for progress in these fields. The momentum continues to build today, with more than 4,000 gene, cell, and RNA therapies currently under development as targeted treatments not only for cancer but a myriad of other disease indications—including those once considered intractable.

While cell- and gene-based treatments hold immense potential, their development is not without challenges. What makes these drug modalities most promising is their ability to specifically target genes or cells associated with a disease, providing delivery precision to maximize therapeutic efficacy while minimizing off-target effects. Yet, there is still much variation in target engagement and drug response across patients, as well as in adverse events experienced from drug exposure. Such variability can stem from the therapy itself, in terms of the viral vectors or cells used, and/or from patient-specific factors including tumor/disease biology or immune status. Deciphering and addressing this heterogeneity is essential to advance more gene and cell therapies to market that can deliver patients a predictable clinical response.

Pushing through complexity: how biomarker strategies in cell and gene therapy are evolving

Biomarkers have an important role to play in improving our understanding of the multifactorial phenotypes and processes contributing to gene or cell therapy efficacy or toxicity. Many development programs already employ biomarkers to varying extent, such as using genetic markers to stratify patient populations with a disease-associated gene mutation or profiling biomarkers of immune response to assess the therapeutic mechanisms of chimeric antigen receptor T-cell (CAR-T) therapies.

The field is finding, however, that with the compounding complexity in drug, disease, and patient population composition, single biomarkers can have limited utility, particularly when it comes to predicting therapeutic response. There is a need to evolve to multi-omics biomarker strategies that can more fully elucidate the intricate interactions of host, disease, and drug effects on cellular pathways, the immune system, and tissue microenvironments.

Armed with integrated layers of insight, we can optimize the reproducibility of gene and cell therapy benefits within distinct and across diverse patient populations.

Advances in view: discovery tools enabling multi-omics at unprecedented speed and scale

Bioanalytical constraints limiting analytical speed have, until recent years, largely precluded the implementation of multi-omics biomarker strategies in cell and gene therapy programs. Now, with the emergence of high throughput discovery tools, including high-throughput mass spectrometry, metabolites, lipids, and proteins can be comprehensively profiled within a single sample—and at a speed that enables numerous samples to be processed at a time. Large-scale multi-omics analyses can be performed efficiently in multiple liquid and tissue biomatrices to support novel discovery of robust biomarker signatures associated with disease mechanisms, target engagement, and drug response.

Importantly, these analyses go beyond the genome, meaning cell and gene therapy developers no longer need to rely on largely static genetic biomarkers alone. Metabolites, lipids, and proteins are dynamically modulated by endogenous and exogenous influences, including disease processes and drug exposure. They can capture new or complementary information that may provide greater predictive value of patients likely to be responsive to a particular therapy, as well as enable earlier detection of drug-induced toxicity or drug resistance over time.

With such advances in multi-omics technologies, biological insights are more accessible than ever to gene and cell therapy developers looking to accelerate and optimize their therapeutic pipelines. They just need to know where to look for a partner that can help them take full advantage of this data.

The key to continued progress: multi-omics data and actionable insight

Sapient, a leading biomarker discovery organization providing bespoke at-scale services for metabolomics, lipidomics, and proteomics data generation and analysis, is enabling cell and gene therapy innovators at all phases of development to leverage the power of next-generation multi-omics for therapeutic advancement. Our mass spectrometry-based approaches are unparalleled in their ability to probe the breadth of dynamic biomarkers in a single biosample—capturing >15,000 metabolites and lipids and >10,000 proteins and post-translational modifications (PTMs). Studies can span from nontargeted discovery screenings for identification of novel biomarkers to precision omics focused on targeted, quantitative measurement of specific proteins or small molecules of interest.

The high-throughput capacity of our technologies mean we can make these measures in thousands, or even tens of thousands, of samples at a time. However, we understand that data generation alone is not enough to inform critical therapeutic decisions. This data needs to be integrated and interpreted to derive actionable insights that enable better target selection, patient stratification, treatment response monitoring, and safety profiling. Only then can biomarker-driven development progress accelerate.

As part of our workflows, we provide integrative analysis of our multi-dimensional omics data with other preclinical, clinical, and phenotypic information to identify key cell and gene therapy biomarkers and map their associations for greater biological context. We can also cross-validate biomarker findings using our Human Biology Database with multi-omics measures already collected in >100,000 biosamples, evaluating whether the biomarker behaves similarly in a broader or more diverse population.

While there are many ways our multi-omics workflow can be applied to advance biomarker strategies in cell and gene therapy development programs, several examples are highlighted below.

Cell therapy: Cell surface proteomics for CAR-T therapies

CAR-T therapies are engineered to target tumor-associated antigens (TAAs) on the surface of tumor cells. To optimize binding for treatment efficacy and limited off-target effects, it is imperative to identify and target TAAs that are differentially and abundantly expressed on tumors compared to healthy tissues.

By leveraging our mass spectrometry-based Cell Surface Proteomics method, Sapient can comprehensively characterize up to 1,000 cell surface proteins, including with quantification of their copy numbers. This allows CAR-T developers to prioritize TAAs as attractive targets and supports discovery of novel TAAs for CAR-T therapeutic targeting.

Gene therapy: Measuring gene knockout or knockdown effects in rare disease

Gene therapies targeting rare disease often work by replacing, modifying, or inactivating the disease-causing gene, with the goal of regulating gene expression to restore proper physiological conditions. While established biomarkers in the rare disease field are very limited, Sapient can develop bespoke assays to identify and quantify proteins, metabolites, and lipids that are altered in patient samples or cell and tissue models following gene knockout or knockdown.

Proteomic profiling can be used broadly characterize changes in protein abundances or PTMs within the proteome, providing insight into the downstream effects of gene perturbation, or to measure specific proteins of interest. Metabolomics can provide insight into the metabolic alterations occurring after gene knockout or knockdown. By using these biomarkers to monitor gene expression patterns following treatment, we can confirm if the target gene is being regulated, to what level of efficacy, and elucidate potential off-target effects.

Cell and gene therapy: Discovery TE and PD biomarkers

Heterogeneity in necessary target engagement (TE) is a key obstacle to achieving higher rates of therapeutic response across patients exposed to cell and gene therapy. Multi-omics biomarker strategies incorporating dynamic biomarkers, which reflect the physiologic changes that occur in response to drug exposure, can help to decipher this heterogeneity and predict treatment response to support patient selection for clinical trials.

Sapient’s discovery multi-omics approaches can broadly profile metabolites, lipids, and proteins that read out TE, to confirm if the therapy is effectively binding to its intended target. These markers can also associate with pharmacological activity and provide a measure of pharmacodynamic (PD) response following TE. Their dynamic nature makes them ideal for assessing TE at different drug doses, helping to optimize cell or gene therapy targeting for efficacy with minimizing potential toxicities.

Bridging the gap from promise to clinical impact: Multi-omics for the future of cell and gene therapy

The cell and gene therapy fields are rapidly advancing, but there are still hurdles to overcome to realize the incredible therapeutic potential represented in pipelines today. We must develop a deeper understanding of the complex interactions occurring at and across the drug, disease, and individual patient level to better address the variability we still see commonly in therapeutic efficacy and safety.

Multi-omics biomarker strategies will be integral to bridging this gap, by unlocking unprecedented insights into the molecular underpinnings of disease mechanisms and drug response. Through these analyses we can uncover new disease subtypes, therapeutic targets, and predictive biomarkers that facilitate the optimization of cell and gene therapies, aligning the right drug with patients most likely to benefit based on their unique health status and disease biology.