At this year’s American Society of Clinical Oncology (ASCO) Annual Meeting, the atmosphere inside Chicago’s McCormick Place reflected more than the scale of one of oncology’s most important global gatherings: it captured the accelerating transformation of cancer care.

As thousands of clinicians, researchers, industry leaders, and innovators came together to discuss the future of oncology, one message was unmistakable. Precision oncology is no longer defined only by the discovery of more targeted therapies; it is increasingly defined by whether the healthcare ecosystem can turn those advances into timely, actionable decisions for the right patients.

That positions pathology as more than central to precision oncology. It is becoming one of its emerging clinical and commercial control points – the place where therapeutic innovation either becomes actionable or fails to reach patients. As therapies become more targeted, combination-based, and biomarker dependent, the challenge is not only discovering new medicines, but ensuring that the right patients can be identified quickly, accurately, consistently, and with enough tissue preserved to guide the next decision.

Tissue samples, diagnostic images, biomarker data, molecular findings, and laboratory workflows are no longer supporting components of cancer care; they are the operating infrastructure on which precision oncology depends.

Here, we summarize the key ASCO 2026 discussions on precision oncology directions. Together, they suggest that the next leap in cancer care will be shaped not only by what science discovers, but by how effectively pathology connects those discoveries to clinical practice.

Precision medicine is moving the control point into pathology

A decade ago, many oncology trials and treatment strategies were designed around broader patient populations (1). Today, that model is rapidly shifting. Increasingly, clinical trials are built around biomarkers, molecular signatures, and tumor-specific characteristics that can help determine which patients are most likely to benefit from a particular therapy (2). This reflects the broader evolution of oncology: the more precisely a tumor can be profiled, the better clinicians can match patients to targeted medicines, immunotherapies, antibody-drug conjugates, and emerging therapeutic combinations.

This shift makes pathology a strategic discipline for the next era of oncology. The pathologist’s role is expanding beyond diagnosis to become an integrator of morphology, biomarkers, molecular data, and AI-supported insight. In practice, that means helping clinicians understand not only what a tumor is, but which therapeutic options, clinical trials, and testing pathways may be most relevant for that patient. For senior pathology leaders, the opportunity is to define the laboratory as a clinical decision engine and a strategic partner in treatment selection, trial access, and real-world adoption of precision medicine (3).

Imperative 1: Extract more insight from every sample

One of the most important implications of this precision oncology era is the increasing pressure on tissue specimens. Advances in screening and earlier cancer detection are enabling diagnosis at smaller tumor sizes and earlier disease stages. At the same time, the therapeutic landscape continues to expand, with many treatment options requiring biomarker-driven patient selection. This creates a practical challenge: clinicians need more actionable information from specimens that are often smaller, more limited, and increasingly difficult to spare (4).

In this context, digital pathology and AI-enabled image analysis have the potential to be transformative. Hematoxylin and eosin (H&E)-stained slides are already a routine component of pathology assessment (5). If AI can extract additional predictive, prognostic, or stratifying insights from these images, it could help clinicians make more informed decisions about which downstream tests to prioritize. In tissue-constrained settings, the ability to derive greater value from existing slides may become increasingly important.

The opportunity is not to replace established molecular or immunohistochemistry workflows, but to make them smarter and more efficient. AI may help triage cases, identify patterns that are difficult to detect consistently by eye, and support a more rational testing strategy (6). For pathology laboratories facing rising complexity, limited staffing, and growing demand, the goal is not speed for its own sake, but speed with confidence: faster access to insight without compromising quality, consistency, or clinical trust.

Imperative 2: Connect multimodal data into an actionable patient profile

Another major theme from ASCO 2026 was the continued rise of combination strategies. Oncology is moving beyond single-agent paradigms toward increasingly sophisticated regimens. This may combine immunotherapies, targeted therapies, antibody-drug conjugates, chemotherapy, and other modalities. These approaches may improve outcomes, but they also make treatment selection more complex.

The more treatment options clinicians have, the more important it becomes to understand the underlying biology of each patient’s tumor. A therapy that is highly effective in one biomarker-defined population may offer less benefit in another. As a result, diagnostic confidence, biomarker availability, and turnaround time become essential components of patient care. The laboratory must be able to support increasingly nuanced clinical decisions without introducing delays that could affect treatment initiation.

This raises an important question for the field: how can pathology keep pace with the accelerating complexity of oncology while maintaining speed, confidence, and consistency? Increasingly, the answer will require connected workflows that bring morphology, immunohistochemistry, molecular results, clinical context, and AI-supported analysis into a more complete patient profile. The pathologist is uniquely positioned to help synthesize these inputs into actionable insight – not as a passive recipient of data, but as the specialist who can interpret biological complexity in the context of patient care. 

The most complex challenges in cancer care will not be solved by any single tool, test, or institution in isolation; they will require integrated approaches that bring clinicians, pathologists, technologists, researchers, and biopharma partners together around the needs of each patient.

Imperative 3: Scale validated workflows consistently across laboratories

ASCO 2026 discussions underscored that AI is no longer viewed only as an experimental or academic concept. Increasingly, AI is being discussed as an enabling layer for oncology – supporting risk stratification, treatment selection, trial design, patient identification, and operational workflow. In digital pathology, the relevance is particularly strong. Images contain rich biological information, and AI has the potential to help turn that information into clinically useful insights.

For laboratories, this could mean faster prioritization of cases, more consistent analysis, and better support for increasingly complex biomarker strategies. For clinicians and patients, it could mean more timely access to the information needed to choose the most appropriate treatment. And for biopharma, the implications are equally significant. Biomarker strategy, tissue stewardship, assay scalability, and real-world laboratory adoption must be designed in parallel with drug development, not after clinical success.

Pathology networks and workflows should be viewed as part of the therapy-development platform itself – not merely downstream diagnostic execution. The promise of AI in pathology will ultimately depend on whether it can be validated responsibly, integrated into real-world workflows, and scaled across laboratories in ways that support – not complicate – clinical decision-making.

A defining moment in pancreatic cancer

Among the most discussed moments at ASCO 2026 was the presentation of phase 3 data for daraxonrasib, an oral multi-selective RAS inhibitor being studied in previously treated metastatic pancreatic ductal adenocarcinoma. The findings were striking: median overall survival was reported at 13.2 months compared with 6.7 months for standard-of-care chemotherapy (7). The presentation received a standing ovation – a testament to the significance of progress in a disease area where meaningful therapeutic advances have historically been rare.

The importance of this story extends well beyond a single drug. It reflects the broader promise of precision oncology: when the underlying biology of a tumor can be understood and therapeutically targeted, even cancers long regarded as among the most challenging to treat may become increasingly manageable. It also underscores the critical role of diagnostic pathways in identifying the right patients, characterizing disease with greater precision, and enabling access to emerging treatment options.

The daraxonrasib story highlights another important shift in the oncology ecosystem. Transformative innovation is no longer the sole domain of the largest pharmaceutical companies. Smaller, highly focused, and agile organizations are increasingly driving scientific breakthroughs, leveraging novel technologies, targeted development strategies, and data-driven approaches to accelerate progress. As artificial intelligence and computational methods become more deeply integrated into discovery, drug development, and clinical trial design, future advances in oncology are likely to depend less on individual organizations and more on interconnected networks of innovators working across disciplines and sectors.

What this means for the future of cancer care

There’s a broader transformation in cancer care and a clearer set of imperatives for the field. First, laboratories must extract more insight from every sample as tissue becomes more limited and testing demands expand. Second, pathology must connect multimodal data – morphology, biomarkers, molecular findings, images, clinical context, and AI-supported insight – into an actionable patient profile. Third, validated workflows must scale consistently across laboratories so that precision oncology is not confined to leading academic centers, but can reach patients reliably across care settings.

Together, these imperatives elevate pathology from a diagnostic service to a strategic infrastructure layer for oncology innovation. The laboratory is where precision medicine becomes actionable. It is where tissue is interpreted, biomarkers are assessed, images are analyzed, molecular data are contextualized, and the information needed for treatment decisions begins to emerge. Strengthening this environment through digital pathology, responsible AI, shared expertise, and connected workflows should be viewed not simply as a technology initiative, but as a clinical and commercial imperative. Ultimately, breakthroughs will reach patients faster, more consistently, and with greater confidence when pathology is built into the precision-oncology ecosystem from the beginning.

The optimism coming out of ASCO 2026 was grounded in real progress: more targeted therapies, more sophisticated trial designs, more meaningful biomarker strategies, and new hope in historically difficult cancers. But the meeting also underscored a practical reality. Scientific breakthroughs reach patients only when healthcare systems can translate them into timely, reliable, and accessible care.

That translation depends heavily on pathology. As oncology becomes more precise, pathology must become more connected, computational, and data-driven – and pathologists must be recognized as strategic partners in how therapies are developed, selected, and delivered. The opportunity ahead is to help laboratories move faster with confidence, extract more value from every sample, and support clinicians with the insights needed to deliver the right treatment to the right patient at the right time. The next precision-oncology winners will not necessarily be those with the best standalone drug, diagnostic test, or algorithm. They will be the organizations that can build an integrated ecosystem connecting biopharma, pathology, technology, and clinical practice at scale, turning promising science into real-world impact for patients.

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