Abstract Management of multiple primary tumours is an increasing clinical burden as the population of cancer survivors rises globally. Despite standard-of-care histological, radiological and panel sequencing diagnostics, identifying multiple primary cancers remains clinically challenging. We characterize multiple primary tumours in the TRACERx study. We integrate baseline and follow up clinical data, centrally-reviewed histopathological assessments with whole-exome sequencing (WES) to identify patients with multiple primary tumours from lung and other sites. Where multi-region sample collection was available, we assessed the genomic relatedness of multiple lesions from the same patient through shared clonal mutations. 205 (24% of 844) TRACERx patients had clinically-diagnosed or genomically-discovered multiple primary cancers. Of these, 82 patients had a prior cancer diagnosis, 56 patients had synchronous primaries at study enrollment, and 95 developed new primaries during follow up. Lung was the most common organ site for additional primaries, with 98 patients having two or more lung primary tumours, including the lung tumour profiled for the TRACERx study. High resolution tumour WES revealed clinical misclassification in 21 of 138 patients (15.2%) with multiple sequenced lesions. At baseline, clinical classification favoured relatedness between lesions: two patients diagnosed with intrapulmonary metastases actually had independent primary lung cancers. In addition, eight patients with lesions staged histopathologically as single tumours, some with microscopic evidence of histological heterogeneity, were confirmed through WES to be collision tumours composed of 2 to 3 independent lineages. One patient was discovered to have had synchronous lung primaries, undetectable clinically, through WES sequencing of lymph node metastases. During follow up, clinical classification tended to favour independent primary designation, with 6 of 10 discrepancies due to clinically-diagnosed metachronous primary tumours found through WES to be metastases. Together, the TRACERx multiple primary cohort represents one of the largest multi-region cohorts of multiple primary lung cancers analyzed to date. These data demonstrate the clinical utility of WES in diagnosing independent primary cancers. Ongoing analysis will investigate the association between history of multiple primary cancers and germline cancer susceptibility and patient outcome. Deep, multi-region molecular characterization, available for 58 patients with multiple primary lung cancers, will also allow detailed exploration of the molecular mechanisms driving the evolution of multiple primary cancers, informing potential biomarkers to highlight patients with elevated risk of secondary primary tumours and improve subsequent clinical management. Citation Format: Lydia Y. Liu, Charlotte Grieco, Adriana Salcedo, Takahiro Karasaki, Cristina Naceur-Lombardelli, Oliver Shutkever, Ariana Huebner, Carlos Martinez Ruiz, Sara Waise, Aino-Maija Leppä, Olivia Lucas, Tej Pandya, Selvaraju Veeriah, Sophia Ward, Kerstin Haase, Alexander A. Azizi, Woody Z. Zhang, Emma Hazelwood, Alexandar M. Frankell, TRACERx Consortium, Allan Hackshaw, Nicholas McGranahan, David A. Moore, Mariam Jamal-Hanjani, Charles Swanton. Clinico-genomic characteristics of multiple primary cancers in TRACERx [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 6793.

Supplementary Figure 1 shows cell viability assays of various pediatric cancer cell lines.

Supplementary Figure 10 shows the IHC markers tested in MNA and NMNA NB PDXs.

Supplementary Figure 3 shows western blots of DNA damage response markers in two pediatric cancer cell lines.

Supplementary Table 5 lists the quantifications of IHC markers in EWS and OS PDX.

Supplementary Figure 9 shows the IHC markers tested in OS, ARMS and ERMS PDXs.

Supplementary Figure 8 shows the IHC markers tested in EWS PDXs.

Supplementary Table 2 lists characteristics of PDX origin.

Supplementary Figure 13 depicts the correlation of baseline IHC marker expression to treatment response.

Supplementary Figure 5 shows all tumor volume curves of the PDXs used in this study.

Supplementary Table 4 lists the quantifications of IHC markers in RMS PDX.

Supplementary Table 3 shows statistics for all tumor volume curves.

Supplementary Figure 11 displays the quantification of each IHC marker in each PDX tested.

Supplementary Figure 7 depicts the overall survival of each pediatric cancer entity under elimusertib treatment.

Supplementary Figure 12 shows changes in pHH3, yH2AX and Clc3 expression in PDXs of different entities.

Supplementary Table 6 lists the quantifications of IHC markers in NB PDX.

Supplementary Table 1 shows AUC and IC50 values for all pediatric cancer cell lines tested.

Supplementary Figure 6 displays the body weight curves of all PDX-bearing mice over the course of treatment.

Supplementary Figure 4 shows immunofluorescence stainings of R-loops and their quanitfication in pediatric cancer cell lines.

Supplementary Figure 2 demonstrates an increase of sub-G1 fragments upon elimusertib treatment as measured using FACS.