Elizabeth Grogan, PhD | AVP, Medical Strategy, Oncology, Medical Communications
Oncology is moving quickly, with each year bringing new insights that change how we understand and treat cancer. Advances in treatments, testing, and data are converging, moving the field toward early detection and more tailored treatments. The challenge now is making sure these advancements are used effectively in real-world patient care.
Advances in several key areas are shaping the future of oncology. As we head into another ASCO season, each new wave of data continues to sharpen our understanding and application of these areas in clinical practice.
Antibody-drug conjugates (ADCs) are moving into standard of care across tumor types and into earlier lines of therapy. New targets are bringing biomarkers to the forefront of conversations about patient selection, while the success of ADCs such as trastuzumab deruxtecan in HER2 low and ultra-low expressing tumors has sparked debate about the relationship between target expression and efficacy.
Beyond HER2, ADCs targeting antigens such as FRa and Nectin-4 are advancing rapidly through clinical development, reflecting a broadening of both targets and tumor types. Next-generation designs, including dual payloads, bispecific ADCs, and radioconjugates were among the fastest-growing categories at this year’s AACR, underscoring the pace of innovation. As new data are presented at major congresses such as ASCO, further readouts on next-generation ADCs and novel targets will continue to define how these therapies are best positioned and sequenced.
Community oncologists often manage a broad range of tumor types and may have less time to keep up with the rapid influx of new data, making it increasingly difficult to navigate the growing number of ADC options. Companies should support clear differentiation of ADC design, efficacy, and safety profiles, and translate evolving biomarker definitions into clinically actionable education for oncologists.
Immunotherapy is entering a new phase that is becoming more complex and ambitious. Combination therapies, including bispecific antibodies paired with ADCs, are showing promise in hematologic malignancies, with mosunetuzumab plus polatuzumab vedotin demonstrating high complete remission rates in mantle cell lymphoma. Similarly, initial data from the LOTIS-7 trial with glofitamab plus loncastuximab tesirine demonstrate promising response rates in diffuse large B-cell lymphoma, with full data expected later this year. To support broader use of these combinations, particularly in community settings, it will be essential for oncologists to have a clear understanding of how to recognize and manage treatment-related adverse events.
There is also growing interest in bringing CAR-T cell therapies into solid tumors such as glioblastoma and certain gastric cancers, where early results are encouraging but still evolving. Advancements in manufacturing, including universal CAR-T platforms and in vivo CAR-T, are broadening access. At the same time, efforts to move CAR-T into outpatient and community-based settings are gaining momentum, with the potential to expand access but requiring new care models to support delivery.
Immuno-oncology is no longer centered around just one type of treatment. Instead, it has become a much broader and more crowded space, with many different options becoming available. As a result, it is not just about efficacy, but also about durability, safety, and accessibility. For oncologists, this growing number of options makes treatment sequencing more challenging.
Emerging technologies, such as the Elephas elive™ platform, are beginning to provide more functional insights into how individual tumors respond to immunotherapy, offering the potential to better guide treatment selection and sequencing.
Oncologists need complex mechanisms translated into clear, clinically relevant narratives to ensure emerging therapies are understood, appropriately positioned, and integrated into patient care.
Precision oncology is now an established part of clinical practice, with biomarker-driven strategies expanding beyond initial treatment selection into ongoing disease monitoring and decision-making. Technologies such as liquid biopsy and circulating tumor DNA (ctDNA) are being used to track disease status in real time, enabling earlier and more informed adjustments to treatment.
Multi-cancer early detection tests, such as GRAIL’s Galleri® and Exact Sciences’ Cancerguard® emerging blood-based tests, are expanding the role of liquid biopsy beyond treatment monitoring into earlier detection, signaling a shift toward identifying cancer before it becomes clinically apparent.
Molecular signals are also used to inform oncologists earlier when treatment changes may be needed. The phase 3 BREAKWATER study showed that early ctDNA clearance is a strong predictor of clinical benefit and overall survival in patients with BRAF V600E-mutant metastatic colorectal cancer, demonstrating the potential of molecular monitoring to identify high-risk patients earlier than conventional imaging.
Precision oncology is becoming an ongoing process of monitoring and adjusting treatment over time. Companies can assist oncologists in this process by supporting education on biomarker testing, clarifying the clinical relevance of emerging endpoints such as MRD, and helping translate increasingly complex datasets into clear, actionable insights.
AI is moving from novelty to a more integrated part of patient care, increasingly being used to support routine decision-making. In practice, oncologists and their multidisciplinary partners are already engaging with AI‑enabled tools such as Tempus, which integrates genomic and clinical data to help match patients to targeted therapies and clinical trials, and PathAI’s AISight, which helps to improve diagnostic accuracy and standardize pathology workflows.
Recent studies reinforce this shift. A deep learning model trained on over 13,000 CT scans of renal masses outperformed radiologists in distinguishing benign from malignant disease, while AI-driven survival prediction models in renal cell carcinoma have demonstrated accuracy rates exceeding 90%, supporting more individualized treatment planning. Interest in these approaches continues to grow, with multiple sessions at ASCO focused on applying these tools in patient care.
AI can help make decisions more consistent and support earlier intervention, but it also introduces new challenges. Key concerns include a lack of transparency in how models generate outputs, limited external validation across diverse patient populations, and the risk of bias if training data are not representative. There are also practical concerns about workflow integration, data interoperability, and legal responsibility when AI-informed decisions are used in clinical care.
Ultimately, the next phase of advancements in oncology will not be defined solely by new treatments, but by how well those advances are understood and applied in clinical practice. As competition grows and there is greater focus on cost, value, and real-world impact, effective data communication will become even more important.
If you are navigating these challenges, our team welcomes the opportunity to connect and explore how we can support evidence generation, data interpretation, and impactful scientific engagement. Get in touch today to learn more.