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. 2026 Mar 5:e260127.
doi: 10.1001/jamaoncol.2026.0127. Online ahead of print.

Genomic Therapy Matching in Rare and Refractory Cancers

Frank P Lin  1   2   3 , Subotheni Thavaneswaran  1   2   3   4 , John P Grady  2   3 , Christine E Napier  2 , Maya Kansara  2   3 , Lucille Sebastian  1 , Damien Kee  5   6   7 , Jayesh Desai  6 , Milita Zaheed  2   3   8 , Sarah Chinchen  1 , Samantha R Oakes  2   3   9 , James Blackburn  2   3 , Hamish S Scott  10   11 , Anthony Glover  2   3   12 , Stephen B Fox  13 , David Goldstein  3   14 , Paul Leo  15 , Benhur Amanuel  16   17   18 , Antony Mersiades  1   19 , Michael Millward  20   21 , Michael P Brown  11   22 , Michail Charakidis  22 , Adrian M J Pokorny  4   23   24 , Paul Craft  25 , David Espinoza  1 , Peter Grimison  12   26 , Rosemary Harrup  27 , Anthony M Joshua  2   3   4 , Ken O'Byrne  28   29 , Chee Khoon Lee  1   30 , Mark J Cowley  3   31 , Mandy L Ballinger  2   3   32 , John Simes  1   25 , David M Thomas  2   3   32 ; Australian Molecular Screening and Therapeutic program investigators and contributors
Collaborators, Affiliations

Genomic Therapy Matching in Rare and Refractory Cancers

Frank P Lin et al. JAMA Oncol. .

Abstract

Importance: The clinical utility of matching therapies to genomic biomarkers based on varying levels of evidence remains uncertain, particularly for patients with rare and refractory cancers.

Objective: To assess whether a tiered, evidence-based framework for matching genomic biomarkers to therapies is associated with differential overall survival in patients with advanced solid tumors.

Design, setting, and participants: This multicenter cohort study was conducted within the Molecular Screening and Therapeutic program, a nationwide precision oncology program in Australia. Patients aged 18 years and older with advanced, refractory solid tumors and adequate Eastern Cooperative Oncology Group Performance Status were enrolled from June 2016 to December 2021, with follow-up through July 2022. Data were analyzed from July 2022 to July 2024.

Exposures: Systemic therapy following comprehensive genomic profiling. Therapies were classified as matched or unmatched using the TOPOGRAPH (Therapy-Oriented Precision Oncology Guidelines for Recommending Anticancer Pharmaceuticals) knowledge base, which stratifies biomarker-drug pairs by level of evidence (tiers 1-3A, prospective trial evidence; tiers 3B/4, investigational/repurposed).

Main outcome and measures: The primary outcome was overall survival from date of molecular profiling results. The hypothesis was tested using a time-dependent multivariable Cox proportional hazards model, adjusted for age, Eastern Cooperative Oncology Group Performance Status, cancer type, prior therapy, and prior receipt of matched therapy.

Results: Of 3383 patients (mean [SD] age 57.1 [14.3] years; 1792 [53.0%] female), 1270 (37.5%) had a clinically active (tiers 1-3A) biomarker. Among patients with a tier 1 to 3A biomarker receiving treatment, those receiving matched therapy had a longer median overall survival than those receiving unmatched therapy (21.2 months [95% CI, 17.1-26.8 months] vs 12.8 months [95% CI, 11.7-13.9 months]; adjusted hazard ratio [aHR], 0.60; 95% CI, 0.44-0.82; P = .001). In contrast, among patients receiving therapy matched to investigational evidence (tiers 3B/4), there was not an associated survival benefit compared with unmatched therapy (14.5 months [95% CI, 12.6-18.4 months] vs 12.8 months [95% CI, 12.0-14.7 months]; aHR, 1.04; 95% CI, 0.84-1.29; P = .71). Patients who received therapies repurposed from other cancer types based solely on a biomarker and lacking direct evidence (tier 3B) did not experience longer survival compared with those receiving unmatched therapy (13.6 months [95% CI, 8.0-16.8 months] vs 12.5 months [95% CI, 11.3-13.5 months]; aHR, 1.40; 95% CI, 1.00-1.96; P = .047).

Conclusions and relevance: In this cohort study of patients with advanced solid tumors, matching therapies to genomic biomarkers was associated with improved survival only when supported by prospective clinical trial evidence. These findings support using an evidence-based framework to prioritize genomically guided therapies.

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Conflict of interest statement

Conflict of Interest Disclosures: Dr Kee reported personal fees from Bristol Myers Squibb, Merck Sharp & Dohme, and Novartis during the conduct of the study. Prof Goldstein reported grants from AstraZeneca; institutional support for clinical trial activity from Bayer, Merck, Pfizer, and Amplicare; other support from Sun BioPharma (Data and Safety Monitoring Board); and personal fees from Takeda and AstraZeneca outside the submitted work. Dr Mersiades reported personal fees from The Limbic and conference registration support from Merck Sharp & Dohme outside the submitted work. Prof Brown reported honoraria paid to institution from Bristol Myers Squibb and Merck Sharp & Dohme during the conduct of the study. Dr Harrup reported personal fees from BeiGene and other support from Genentech and Roche outside the submitted work. Dr O’Byrne reported advisory/consulting fees from Amgen, AstraZeneca, BeOne, Boehringer Ingelheim, Bristol Myers Squibb, Immutep, Johnson & Johnson, MSD, Novartis, Pfizer/Seagan, Race Oncology, Roche/Genentech, Takeda, and TriStar, as well as speakers bureau fees from Amgen, AstraZeneca, BeOne, Boehringer Ingelheim, Bristol Myers Squibb, Merck Group, MSD, Pfizer/Seagan, Roche/Genentech, Sanofi, and Takeda. Prof Lee reported grants from AstraZeneca, Roche, Amgen, and Merck KGA, as well as personal fees from AstraZeneca, MSD, Boehringer Ingelheim, Daiichi Sankyo, GlaxoSmithKline, Gilead, Glenmark, Janssen Oncology, Novartis, and Pfizer during the conduct of the study. Dr Simes reported institutional research grants from the National Health and Medical Research Council, the Medical Research Future Fund, AstraZeneca, Roche, and MSD during the conduct of the study, as well as institutional research grants from Bayer, Pfizer, and Amgen outside the submitted work. Prof Thomas reported personal fees from Omico; nonfinancial support from AstraZeneca, Pfizer, and Illumina; and grants from Roche and Sun Pharma during the conduct of the study, as well as grants from Microba and Tesselate Bio and funding to Omico from MSD, Eli Lilly, BeiGene, Boehringer Ingelheim, and Servier outside the submitted work. No other disclosures were reported.

Figures

Figure 1.
Figure 1.. Cohort Flow Diagram and Genomic Matching Tier Definitions
Prognostic evaluations were conducted for both cohorts A and B. In cohort B, tier-matched analyses were also conducted to assess the potential treatment effect of genomically matched therapies, stratified by tiers. Further exploratory analyses were conducted to identify potential genomic alterations that may be predictive of differential treatment effects. Therapies, together with their genomic profiling results and cancer type, were searched against the TOPOGRAPH (Therapy-Oriented Precision Oncology Guidelines for Recommending Anticancer Pharmaceuticals) knowledge base. This knowledge base comprises triples of biomarker variants, cancer type, and drug, all linked by supporting evidence. A therapy is considered matched if any drug from its regimen class is present in the TOPOGRAPH curation, mirroring the clinical strategy used in therapy selection based on genomic biomarker testing. CGP indicates comprehensive genomic profiling; FDA, US Food and Drug Administration; MoST, Molecular Screening and Therapeutic program; MTB, molecular tumor board; PBS, Pharmaceutical Benefits Scheme; TGA, Therapeutic Goods Administration.
Figure 2.
Figure 2.. Kaplan-Meier Curves Among Distinct Prognostic Groups Stratified by Matching Status in Patients With Advanced Cancers Undergoing Genomic Profiling
This figure presents the analysis of overall survival in cohorts A and B from the time of genomic profiling, by whether the genomic profiles were matched to any TOPOGRAPH (Therapy-Oriented Precision Oncology Guidelines for Recommending Anticancer Pharmaceuticals) tier or stratified. Overall survival is measured from the date of genomic profiling. The forest plot showing adjusted hazard ratios of prognostic groups is shown in eFigure 2 in Supplement 1.
Figure 3.
Figure 3.. Kaplan-Meier Curves Among the Tier-Matched Analysis in Cohort B Comparing Survival Outcomes
A, Difference in overall survival between the matched (tiers 1-4) and unmatched therapy groups (adjusted hazard ratio, 0.76; 95% CI, 0.70-0.90; P < .001). B, Analysis of patients whose highest genomically matched TOPOGRAPH (Therapy-Oriented Precision Oncology Guidelines for Recommending Anticancer Pharmaceuticals) tier was in the clinically active group (tiers 1-3) (adjusted hazard ratio, 0.60; 95% CI, 0.50-0.80; P < .001). C, Analysis of the investigational therapy tier group (tiers 3B/ 4) (adjusted hazard ratio, 0.83; 95% CI, 0.70-1.00; P = .10).
Figure 4.
Figure 4.. Forest Plots Showing the Unadjusted and Adjusted Hazard Ratios (HRs) from the Tier-Matched Analysis in Cohort B, Comparing Time-to-Matched Therapy With Time-to-Unmatched Therapy in Regression Models
HRs were adjusted for the time to initiation of the most active subsequent therapies, age, Eastern Cooperative Oncology Group Performance Status at consent, cancer type, prior lines of therapy, and whether the patient had previously received a therapy that matched the corresponding TOPOGRAPH (Therapy-Oriented Precision Oncology Guidelines for Recommending Anticancer Pharmaceuticals) tier or tier group.
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