Clinical Researcher—August 2024 (Volume 38, Issue 4)

PEER REVIEWED

Deepika Khedekar, MPharm

 

 

 

This article delves into the role of multi-omics in enhancing cancer clinical trials, highlighting its promise against the backdrop of frequent trial failures encountered in oncology research and limited success of precision medicine. Multi-omics, by analyzing genetic, proteomic, and metabolomic data, aims to personalize cancer treatment, addressing obstacles like data inconsistency, tumor diversity, and therapy resistance. However, integrating multi-omics into cancer trials presents significant challenges, including variance in data regulations, complex data analysis methodologies, ethical hurdles around patient consent, and logistical hurdles in trial management. This discussion advocates for improved trial designs, effective data handling, and robust patient safety measures. It emphasizes the need for collaborative efforts across the scientific community to navigate these challenges and to enable holistic integration of multi-omics in cancer clinical trials, thereby advancing precision oncology.

The Global Cancer Crisis: Reevaluating the Clinical Trial Landscape

Ten million people die each year due to cancer.{1} Our efforts to treat cancer on a global scale —through radiation, chemotherapy, surgery, and targeted therapies—have often felt like drops in the ocean. Research conducted by MIT reveals that about 97% of trials in oncology end in failure.{2} This daunting statistic not only highlights the immense challenge at hand, but also raises critical questions about the trajectory of our current cancer research methodologies and, more importantly, about the precision drugs which have been long thought as the final frontier for cancer.

Foundations of Precision Medicine in Oncology

Precision medicine at its core is the idea of tailoring treatments to fit an individual’s unique genetic profile. It involves the detailed analysis of a patient’s tumor at the molecular level, to identify specific genetic mutations or biomarkers that drive the growth and spread of cancer. This comprehensive molecular profiling enables oncologists to select treatments that are most likely to be effective against the unique characteristics of each patient’s cancer. While precision medicine has been around for awhile, one might ask about what their success rate has been so far, and what kinds of challenges are being faced in clinical trials targeting cancer through precision medicine.

Milestones and Roadblocks in Targeted Cancer Therapy Studies

In the realm of precision oncology, there have been remarkable achievements that showcase the potential of targeted treatments. Notably, Imatinib has shown a 95% response rate in treating chronic myeloid leukemia patients, extending their quality of life by an average of nine years.{3} Similarly, Venetoclax has been effective in 80% of cases involving chronic lymphocytic leukemia , and CAR-T therapy, specifically Tisagenlecleucel, has demonstrated a 62% remission rate at 24 months for patients with acute lymphoblastic leukemia.{3}

Despite these advances, the broader impact of precision medicine on cancer care remains modest. A JAMA Oncology study highlighted that only about 8% of cancer patients are eligible for precision medicines, with merely 5% likely to benefit from them.{3} This underscores a significant gap between the potential and the actual reach of precision oncology, pointing to the need for broader application and accessibility of such treatments in the oncology field. This discrepancy raises a crucial question about what kinds of obstacles are hindering the holistic implementation of precision medicine in clinical trials for oncology.

Barriers to Success in Precision Oncology Clinical Trials

Data Fragmentation

Precision medicine trials in oncology are facing several hurdles that make personalized cancer treatment a complex goal to achieve. First off, the lack of standardized genetic testing across the more than 150 research sites working on this front in the U.S. leads to significant inconsistencies in the data we rely on to develop precision treatments.{4} This fragmentation is a significant barrier to advancing precision medicine, as it hampers our ability to draw definite conclusions from these clinical trials. This diversity underscores a pressing need for standardized sequencing methods and data formats to ensure reliability and comparability of results across different platforms and studies.

However, data fragmentation is not the only challenge we face in precision oncology trials, since the tumor profiles are as diverse as the data themselves.

Tumor Heterogeneity and Drug Resistance

One of the primary challenges in precision oncology is the heterogeneity of tumors. Tumor heterogeneity refers to the variation found within a single tumor, as well as between tumors in different patients, in terms of genetic, epigenetic, and phenotypic characteristics. This diversity is not only present among the different cells within a single tumor (intra-tumor heterogeneity), but also across multiple tumors within the same patient (inter-tumor heterogeneity) and among similar tumor types across different patients. This inherent heterogeneity of tumors, which can vary significantly between primary and metastatic sites within the same patient, makes it challenging to evaluate the efficacy of targeted treatments in oncology trials.

Furthermore, the rapid development of resistance to targeted therapies is a pressing concern. As tumors evolve, they can become resistant to treatments that were initially effective, leading to late-stage failures in precision oncology trials. Compounding these issues, the sheer volume of data generated by genetic testing requires sophisticated data analysis methodologies, yet the existing infrastructure for bioinformatics analysis often struggles to keep pace.

Traditional Trial Designs Limiting Precision Oncology

The design of the vast majority of precision oncology trials still follows a traditional format that does not fully embrace the holistic principles of precision medicine. This conventional approach, coupled with the slow development of new drugs compared to the rate of genetic target discovery, restricts the availability of innovative treatments for evaluation in these trials. Finally, precision oncology trials are heavily dependent on data sharing principles, and yet this is the same principle that has been entangled in a web of legal, ethical, and technical hurdles.

Overcoming these challenges is key to unlocking the full potential of precision medicine in oncology trials, making it a critical area of focus for researchers and clinicians alike. This is where multi-omics might come in handy, but what exactly is multi-omics?

Multi-Omics: A Cornerstone for Precision Cancer Trials

In precision-focused cancer clinical trials, multi-omics represents the comprehensive analysis of the genes (genomics), proteins (proteomics), and metabolites (metabolomics) within a cancer cell or tumor environment. This integrated approach provides a holistic view of the tumor’s molecular landscape which is essential for developing personalized treatments. By leveraging multi-omics, researchers can match therapeutic strategies with the specific biological context of each patient’s oncology profile, potentially leading to more effective and targeted interventions.

Further, multi-omics has the potential to address the below-mentioned challenges we see in precision oncology trials.

Overcoming Tumor Heterogeneity

The presence of tumor heterogeneity in precision oncology trials means that a treatment found to be effective for one part of the tumor, or one patient’s tumor, might not be effective for others. This complexity can lead to difficulties in predicting how different patients will respond to the same treatment, making the design and execution of clinical trials a challenging task. This is where multi-omics can help.

For instance, in trials for novel breast cancer treatments, multi-omics allows for the precise categorization of breast tumors into specific subtypes based on genetic mutations and deeper insights into active biological pathways. Consequently, this enables the identification of cohorts of patients whose tumor profile resonates with the molecular signature targeted by the therapy under investigation, allowing for treatments to be specifically tailored to the molecular characteristics of this cohort.

Such a level of resonance addresses key challenges in precision oncology trials, notably improving patient selection and enhancing treatment efficacy, and thereby improving the success rate in these trials. Here, multi-omics not only accelerates the development of effective treatments, but also minimizes the time and resources spent on less promising therapeutic paths in these trials.

Still, tumor heterogeneity is not the only core challenge for precision oncology trials—we must also consider the problems raised by drug resistance.

Identifying and Overcoming Drug Resistance

Drug resistance remains a significant obstacle in cancer trials, often leading to the failure of initially effective therapies. This phenomenon occurs when the cancer cells undergo genetic mutations that enable them to survive and proliferate despite the presence of therapeutic agents designed to inhibit or kill them. Multiple cancer studies have shown that it often contributes to the 95% failure rate that we see in oncology trials.{5}

Multi-omics offers a pathway to understanding and overcoming this challenge. Consider a trial for a new lung cancer drug where patients begin to show resistance after initial success. This phenomenon can lead to late-stage failures when drugs no longer work as expected in these trials. Multi-omics analysis can be employed to investigate the resistance mechanisms at play.

For example, genomic sequencing might reveal mutations in the cancer cells that deactivate the drug’s target. Proteomic analysis could uncover alternative signaling pathways the tumor exploits to survive. By integrating these insights, researchers can identify biomarkers indicative of emerging resistance. This enables the adaptation of trial protocols to include combination therapies designed to block both the primary target and the alternative pathways identified, offering a strategic approach to circumvent drug resistance in these trials.

Refining Clinical Trial Design with Multi-Omics

Finally, multi-omics data can help us transform the design of precision-focused cancer clinical trials. By analyzing the genomic data of tumors within the given cohort of patients, researchers could identify specific mutations that are predictive of a positive response to the treatment being tested. Proteomics could further differentiate patients based on the protein expression profiles associated with those mutations, while metabolomics might offer additional clues about a tumor’s environment that influence drug efficacy.

This stratification allows for the design of a trial where only patients with the molecular profile likely to respond are enrolled. Additionally, multi-omics can monitor for early signs of treatment efficacy or emerging resistance, enabling real-time adjustments to treatment plans. This not only increases the trial’s chance of success, but also accelerates the development of personalized treatment strategies.

By integrating genomic, proteomic, and metabolomic data, multi-omics enables researchers to not only improve the accuracy in assessing long-term therapeutic effectiveness, but also increases the likelihood of trial success by ensuring treatments remain effective against evolving tumor profiles. This helps us build the next generation of robust models for precision oncology trials.

Integrating Multi-Omics into Precision Cancer Trials

While multi-omics presents a promising avenue for enhancing precision-focused cancer clinical trials, its adoption comes with a set of complex challenges spanning clinical trial execution, patient considerations, data regulation, ethics, and safety.

Clinical Trial Execution Challenges

The sheer complexity of integrating genomic, proteomic, and metabolomic data necessitates powerful analytical tools and an advanced level of expertise, posing substantial logistical and financial challenges. How can a trial efficiently process and analyze millions of bytes of data within a reasonable timeframe and budget, especially in oncology, where the rate of progression of the disease is exponential? Moreover, adapting trial designs to accommodate the complex insights provided by multi-omics data demands a modern techno-clinical infrastructure that many institutions may not have in place today, potentially limiting the speed and scope of these trials.

Patient-Centric Considerations: Consent and Incidental Findings

From the perspective of patient engagement, integrating multi-omics raises important questions about consent and the handling of incidental findings. For instance, in a breast cancer trial let’s say we employed a multi-omics approach to customize treatment plans based on the genetic, proteomic, and metabolomic profiles of individual patients. However, this comprehensive analysis may inadvertently reveal genetic markers that not only inform the current treatment strategy, but also indicate susceptibility to other hereditary conditions, such as mutations in the BRCA1 or BRCA2 genes, which significantly increase the risk of breast and ovarian cancers.{6}

How should researchers disclose this information to the participant without adding unnecessary stress while ensuring they are fully aware of the findings?

The challenge begins with ensuring informed consent is truly informed. Traditional consent forms might not adequately capture the breadth of potential discoveries multi-omics analyses can unearth, including those with implications beyond the immediate focus of the trial. This raises critical questions about how to effectively communicate the possible outcomes of such analyses to participants, ensuring they understand the potential for findings that could affect their health in ways unrelated to the cancer being treated.

These scenarios underscore the need for clear communication and ethical guidelines to navigate the balance between research objectives and patient rights in these trials employing multi-omics.

Data Regulation, Ethics, and the Path to Collaboration

The management of the extensive datasets generated by multi-omics analyses presents another layer of complexity—navigating complex data protection regulations and ethical considerations, especially when data are shared across international borders.

For instance, a multi-omics cancer trial spanning several European countries will need to tackle the challenges related to compliance with the General Data Protection Regulation (GDPR). The GDPR rightly sets stringent standards for the processing of personal data, including genetic, proteomic, and metabolomic information, which are central to multi-omics trials. The challenge is, each European country has its own national legislation that implements GDPR, tailored to its specific legal and cultural context.{7} This variation poses a substantial challenge for the management and operation of clinical trials that span these jurisdictions.

Addressing these challenges, initiatives like the Cancer Cell Line Encyclopedia (CCLE) offer a blueprint for standardizing and centralizing anonymized data in precision oncology.{8} The CCLE, a collaboration among the Broad Institute, the Novartis Institutes for Biomedical Research, and the Genomics Institute of the Novartis Research Foundation, has detailed the genetic and pharmacological profiles of more than 1,000 human cancer cell lines.{8,9} This resource includes fully anonymized and standardized transcriptomic, epigenomic, proteomic, and metabolomic datasets, and is freely available to the oncology research community.

This model demonstrates how anonymizing and standardizing data can mitigate privacy concerns and facilitate data sharing in compliance with regulations like GDPR. It underscores the importance of collaboration among stakeholders to harmonize data handling, protecting patient data integrity and streamlining data flow throughout a trial’s lifecycle. Thus, researchers must design trials with robust informed consent processes and secure data protocols that comply with GDPR across all involved jurisdictions, ensuring participant data protection against unauthorized access or transfer.

Safety Concerns with Targeted Therapies

Finally, the application of targeted therapies derived from multi-omics analyses introduces safety considerations that must not be overlooked. The precision of these therapies, while a boon for treatment efficacy, requires a thorough understanding of potential side effects and interactions with existing medications. For example, a therapy targeting a specific genetic mutation in colorectal cancer might inadvertently affect other cellular processes, raising concerns about adverse events (AEs) and serious adverse events (SAEs) in these trials.

This scenario underscores the necessity for comprehensive clinical validation and diligent monitoring throughout the trial phase. The identification and management of AEs and SAEs is paramount to maintaining participant safety and the integrity of the trial. Rigorous preclinical studies and early-phase trials are essential to anticipate potential off-target effects and interactions with conventional treatments. Moreover, once a trial is underway, continuous monitoring and real-time reporting mechanisms for AEs and SAEs ensure swift response strategies to mitigate risks to participants.

Conclusion

In conclusion, multi-omics presents an unprecedented opportunity to advance precision oncology clinical trials, promising to significantly improve the personalization of cancer treatment. This approach, however, introduces a spectrum of challenges, including data integration complexities, ethical concerns, regulatory hurdles, and patient safety concerns. The question then arises: How can we effectively leverage the potential of multi-omics while ensuring the integrity and safety of clinical trial processes?

It necessitates a concerted effort from a broad coalition of stakeholders—researchers, clinicians, ethicists, and regulatory authorities—each committed to navigating these challenges with diligence and foresight. Pushing the boundaries of what’s possible in cancer treatment through a unified effort while holding patient welfare as our north star is how we’ll truly make a difference in these trials—for we’re not just navigating the complexities of precision medicine; we’re redefining the very essence of cancer treatment for generations to come.

References

1. World Health Organization. Cancer Key Facts.
2. Wong CH, Siah KW, Lo AW. 2019. Estimation of clinical trial success rates and related parameters. Biostatistics 20(2):366.
3. Cutler DM. 2020. Early Returns from the Era of Precision Medicine. JAMA 323(2):109–10. doi:10.1001/jama.2019.20659
4. Fountzilas E, Tsimberidou AM. 2018. Overview of precision oncology trials: challenges and opportunities. Expert Rev Clin Pharmacol 11(8):797–804. doi:10.1080/17512433.2018.1504677
5. Rezayatmand, H, Razmkhah M, Razeghian-Jahromi I. 2022. Drug resistance in cancer therapy: the Pandora’s Box of cancer stem cells. Stem Cell Res Ther 13(181) https://doi.org/10.1186/s13287-022-02856-6
6. Centers for Disease Control and Prevention. The BRCA1 and BRCA2 Genes.
7. Vlahou A, Hallinan D, Apweiler R, et al. 2021. Data Sharing Under the General Data Protection Regulation: Time to Harmonize Law and Research Ethics? Hypertension 77(4):1029–35. doi:10.1161/HYPERTENSIONAHA.120.16340
8. Nature Portfolio. Big picture oncology through multi-omics.
9. Ghandi M, Huang FW, Jané-Valbuena J, et al. 2019. Next-generation characterization of the Cancer Cell Line Encyclopedia. Nature 569(7757):503–8. doi:10.1038/s41586-019-1186-3

Deepika Khedekar, MPharm, (deepika.khedekar@gmail.com) is a Centralized Clinical Lead with IQVIA Inc., Mumbai, India.