Australian study finds genomic therapy matching improves survival, but only when backed by strong clinical evidence

Precision oncology has promised a future in which cancer therapies are matched to the molecular profile of a patient’s tumor, replacing one-size-fits-all chemotherapy with targeted treatments. Regulatory agencies have responded as the FDA has approved a growing list of tumor-agnostic drugs, therapies indicated by biomarker rather than cancer type, and multigene panel testing is now routine for patients with advanced disease. Findings from a study in JAMA Oncology published earlier this month suggested prioritizing genomically guided therapies if the strength of the evidence supports doing so.

The study led by Frank P. Lin, M.B.Ch.B., Ph.D., from the Garvan Institute of Medical Research, Sydney, New South Wales, Australia, and colleagues from the Molecular Screening and Therapeutic (MoST) program, a nationwide precision oncology initiative in Australia, evaluated 3,383 patients with advanced, refractory solid tumors who underwent comprehensive genomic profiling between 2016 and 2021. The researchers observed a persistent inconsistency across precision medicine programs. A percentage of studies of biomarker-guided therapy have reported improved disease control, others have shown negligible benefit, largely because of wide variability in how genomic findings are classified and acted upon.

To address this challenge, the MoST investigators applied the TOPOGRAPH framework, a curated evidence-based system that classifies biomarker-drug pairs into hierarchical tiers based on the strength of supporting prospective, biomarker-linked data. The highest tiers (1 through 3A) represent therapies backed by regulatory approval or positive results from clinical trials. Lower tiers cover drugs repurposed from other cancer types based on a shared biomarker (tier 3B) or those supported only by preclinical or early-phase data (tier 4). Lastly, tier R2 included therapies that suspected to be inactive in a cancer type.

The standard of care for patients with advanced cancers who have exhausted conventional treatments typically involves sequential single-agent chemotherapy, enrollment in clinical trials, or best supportive care. Genomic profiling has emerged as a tool to identify additional therapeutic options in this setting, but until now, it has been unclear how much the strength of the underlying evidence matters when selecting a matched therapy.

Among patients whose tumors harbored biomarkers supported by prospective trial evidence (tiers 1–3A), those who received a matched therapy had a median overall survival of 21.2 months, compared with 12.8 months for those receiving unmatched therapy, a 40% reduction in the risk of death after adjusting for confounders. The magnitude of benefit was similar whether the drug was already approved by regulators or backed by strong phase 2 data.

In contrast, patients who received therapies matched only to investigational-level evidence showed no survival advantage. Notably, those treated with drugs repurposed from other cancer types solely based on a shared genomic marker, a common clinical practice, fared no better, and potentially worse, than those receiving standard unmatched therapy.

“The observation of lack of survival difference regarding drug repurposing (tier 3B) was particularly noteworthy, as inferring treatments based on biomarkers alone from evidence extrapolated from a noncognate cancer type is commonly used in the absence of histotype-specific trials or regulatory approvals,” wrote Lin and his colleagues. “Off-label repurposing of drugs should be discouraged outside of clinical trials or at least prompt a discussion with the patient about options not informed by genomics to avoid ineffective treatment.”

For managed care decision makers, these results offer a data-driven rationale for structuring coverage policies around evidence tiering. Rather than treating all genomically matched therapies as equivalent, payers can use frameworks like TOPOGRAPH to differentiate between matches supported by robust trial data and those based on extrapolation or preclinical rationale alone. This distinction is especially relevant as off-label prescribing of targeted therapies continues to expand.

The study also raises important access questions. Although 37.5% of patients carried a biomarker linked to a high-confidence therapy, only 3.4% received a matched treatment, a gap the investigators attribute in part to drug access barriers. For payers evaluating precision oncology programs, the implication is twofold. The results suggest coverage frameworks should prioritize evidence-supported matches where clinical benefit is demonstrated while also identifying structural barriers that prevent eligible patients from receiving effective care.

As the precision medicine landscape continues to evolve, tiered evidence systems could serve as a shared language between oncologists and payers, supporting informed treatment decisions while directing resources toward therapies that are most likely to improve patient outcomes.