Since the Human Genome Project finished in 2003, our understanding of the genetic drivers of cancer has accelerated biomarker-based therapy development dramatically. While precision medicine is not new to oncology, recent developments suggest it is here to stay. More than 40% of oncology approvals over the past five years have biomarker-based indications, with non-small cell lung cancer (NSCLC) alone having at least ten actionable biomarkers with FDA-approved treatment choices. The past two decades have seen a three-fold increase in biomarker-targeted oncology trials. 

Despite the precision medicine revolution, companies have been slow to adopt commercialization approaches and business processes attuned to the nuances of this field. Many pharma organizations still design clinical trials using a traditional “all comer” approach and continue to use the same sales, marketing and data acquisition, analytics and insights approaches they use for therapies targeting broader patient pools. We believe that needs to change. 

Precision medicine has led to radical improvements in patient outcomes. In NSCLC, for example, anti-EGFR and anti-ALK tyrosine kinase inhibitors have doubled clinical responses and now represent the standard of care for patients harboring the relevant mutations. While targeting patient sub-segments has shown clinical benefit, its impact on company revenues has been modest. That’s because, despite smaller patient pools, we see companies anchoring prices to existing all-comer cancer therapies. This practice limits the potential financial impact for biomarker-driven therapies.

Alternatively, rare disease companies can (and, we believe, should) set the price of precision therapies higher to account for their smaller patient pools. To justify continued investment in developing these therapies, companies launching biomarker-driven therapies must challenge conventional pricing strategies and benchmarks.

Optimizing the development and commercial investment in biomarker-driven therapies requires a delicate balancing act. Despite the comparatively modest opportunity, commercializing these assets typically requires more work than it does for non-biomarker-driven ones, given the testing dynamics required to identify suitable treatment candidates. That’s because testing for biomarkers introduces logistical complexities, which increases potential leakage points through which eligible patients may “spill out” of the funnel before being prescribed a treatment. These additional complexities include engaging a wider stakeholder universe, such as pathologists and lab directors, to ensure awareness and pull-through of the right biomarker testing practices as well as supporting the operational logistics of test ordering and reimbursement. Each of these additional activities requires companies to decide if, how and with what resources it will address them.

Precision medicine commercialization also introduces the element of companion diagnostics, an additional test used to assess a patient’s candidacy for a given therapy. The right assay and partner can play a major role in product uptake and success, and many companies are, for the first time, having to think about formalizing and operationalizing a companion diagnostics strategy, roadmap and operational plan.

Lack of experience in developing precision therapies can lead to lower uptake. A ZS analysis of launch performance has found that products from manufacturers with prior companion diagnostics launch experience have stronger uptake than products from players who don’t. Companies with experience in this space recognize biomarker testing as a prerequisite for uptake and thus make efforts around testing, including companion diagnostics, foundational to their commercialization models.

Some organizations have even created new roles, such a diagnostics liaisons, to engage with pathologists well before product launch—both to understand the market and prepare physicians for the introduction of a new therapy. Others have expanded their medical science liaison (MSL) teams and trained them to help educate healthcare providers on both the diagnostic and therapeutic aspects of their products. While approaches may differ, one thing is clear: Smart companies are making incremental investments to address the additional complexities inherent to precision medicine because commercial success depends on it.

Wider adoption and insurance coverage of blood and non-tissue-based cancer screening will decrease the number of de novo metastatic patients and thus result in pharma companies using outdated forecast assumptions. Over time, earlier-lines therapies and procedures will rise, while later-line interventions will correspondingly fall. This shift will vary by cancer type and will require companies to refine epidemiology models to account for the changes in disease incidence and progression, as seen in Figure 5. 

With the comparatively modest candidate pool for precision medicines, commercialization teams must make a series of choices about where and how to invest. This is particularly challenging in precision medicine, since companies must do more—plugging additional leakage points and coordinating new stakeholders, for example—with lower upside potential. In this catch-22, teams must consider:

  • Area of focus—prioritize commercial efforts with the highest potential given a revenue-constrained environment
  • Proactivity vs. reactivity—pressure test the threshold for proactive versus reactive patient engagement
  • Keeping in house vs. outsourcing—to retain flexibility and scalability, be selective about which activities to keep in house and which ones to outsource

Development and commercialization of precision medicine-based therapies is different than for traditional therapies, so forecasting for them must be tailored accordingly. To adapt traditional processes to precision medicine, we recommend forecasters take four key actions:

  1. Challenge your pricing assumptions. As a strategic partner, work with your cross-functional team to understand the rationale behind pricing decisions. Confirm the team is using appropriate benchmarks and considerations, not anchoring to current standard-of-care prices—especially if those are for all-comer therapies. Communicate the overall financial impact of product success using multiple price scenarios to support decision-making.
  2. Keep your forecast fresh. Testing practices and behaviors change rapidly. Molecular profiling continues to evolve, which can affect forecasts due to differences in specificity and sensitivity of different assays. Ensure your insights and analytics function has adopted practices to test assumptions related to all steps of the patient journey, especially for the most dynamic areas, such as testing.
  3. Invest in real-world data (RWD). Using publicly available biomarker frequency data can affect forecast results by a factor of roughly 10, which may cast doubt on overall forecast reliability. Be sure to triangulate your sources for indication incidence and biomarker prevalence data using RWD such as electronic health and medical records, as well as data partnerships with diagnostics labs and others to ensure confidence in prevalence inputs.
  4. Account for your incremental investments. Successfully commercializing precision medicine requires incremental investments. Ensure these are accurately accounted for when calculating gross to net and net present value. Investments in companion diagnostics partnerships, biomarker and lab data and additional field teams can rise into the millions of dollars, but they are foundational to enabling a strong commercial model.

Precision medicine represents a confounding paradox: While it has delivered major impact on the lives of cancer patients, it has simultaneously complexified the standard commercialization playbook for many pharmaceutical companies. As treatments evolve, the methods pharma uses to calculate the size of opportunities—and how they choose to go after them—must evolve in step. Forecasting teams must play a critical role. By elevating how their organizations commercialize these therapies, they will ensure more accurate forecasts—which in turn will lead to savvier investments, a more sustainable business model for precision medicine and a clear runway for even more life-saving cancer therapies.


The authors would like to thank Vinod Nair and Mimi Traylor-Knowles for their invaluable contributions to this piece.