Molecular testing is essential in precision oncology. Whole-genome sequencing (WGS) provides a tumor-agnostic solution for detecting an increasingly complex range of DNA-based biomarkers. Here we present real-world data from 888 patients to demonstrate the clinical utility of routine, paired tumor-normal WGS diagnostics for solid cancers in a comprehensive cancer center. WGS succeeded in 89% of cases with a median turnaround time of 6 working days. Potentially actionable biomarkers were identified in 73% of patients, including biomarkers for reimbursed (27%) and experimental (63%) therapies. Within 1 year, 40% and 19% of patients, respectively, started biomarker-informed treatment, which was associated with a 31% longer median overall survival (+96 days) compared with patients not receiving such therapy. Among patients without prior systemic therapy, biomarker-informed treatment yielded significantly longer overall survival (median not reached) than non-biomarker-informed therapy (427 days) or no systemic therapy (214 days). In cancers of unknown primary (n = 123), WGS contributed to diagnostic solution or detected biomarker-driven reimbursed treatment options in 67%, with 68% starting tumor-type-specific therapy. Clinically relevant pathogenic germline variants were identified in 6.5% of patients. Overall, WGS-based diagnostics had clinical consequences for 41% of tested patients, providing a versatile tool for routine clinical practice in solid oncology.

Schematic overview of the study. Patients underwent a biopsy of a metastasis for PDO culture and WGS, before starting a new line of systemic treatment. Predictors of successful PDO establishment were identified: male sex, increased LDH, biopsy in academic hospitals, optimized culture conditions, and experience. A total of 42 PDOs were screened for standard-of-care treatments, and the patient received standard systemic treatment. PDO response was correlated with patient response, and the association between PDO response and survival was assessed. (Created in BioRender. Roodhart, J. [2025] https://BioRender.com/by1f1uq.)

Survival of patients with metastatic castration-resistant prostate cancer (mCRPC) depends on the site of metastatic dissemination. Patients with mCRPC were prospectively included in the CPCT-02 metastatic site biopsy study. We evaluated whole genome sequencing (WGS) of 378 mCRPC metastases to understand the genetic traits that affect metastatic site distribution. Our findings revealed that RB1, PIK3CA, JAK1, RNF43, and TP53 mutations are the most frequent genetic determinants associated with site selectivity for metastatic outgrowth. Furthermore, we explored mutations in the non-coding genome and found that androgen receptor (AR) chromatin binding sites implicated in metastatic prostate cancer differ in mutation frequencies between metastatic sites, converging on pathways that impact DNA repair. Notably, liver and visceral metastases have a higher tumor mutational load (TML) than bone and lymph node metastases, independent of genetic traits associated with neuroendocrine differentiation. We found that TML is strongly associated with DNA mismatch repair (MMR)-deficiency features in these organs. Our results revealed gene mutations that are significantly associated with metastatic site selectivity and that frequencies of non-coding mutations at AR chromatin binding sites differ between metastatic sites. Immunotherapeutics are thus far unsuccessful in unselected mCRPC patients. We found a higher TML in liver and visceral metastases compared to bone and lymph node metastases. As immunotherapeutics response is associated with mutational burden, these findings may assist in selecting mCRPC patients for immunotherapy treatment based on organs affected by metastatic disease. NCT01855477.

Clinical variables related to PDO establishment success: CEA, LDH, mutational status, sex, primary tumor sidedness, prior chemotherapeutic treatment, metastatic site of the biopsy, WGS success, and aggregated biopsy color (brown, pink, red, and white). CEA, carcinoembryonic antigen; mut, mutant.

Genomic landscape of 36 tumors and/or PDOs derived from patients with mCRC. Top, microsatellite stability, tumor mutational load, tumor mutational burden, and whether the sequencing was performed on the PDOs or the tumor of origin; bottom, somatic driver mutations. MS, microsatellite status; MSI, microsatellite instability; MSS, microsatellite stability; Seq, sequencing; TML, tumor mutational load.

Cancer in Adolescents and Young Adults (AYAs) represents a unique biological intersection, encompassing malignancies typical of both paediatric and older adults, as well as age-specific cancers. Yet, diagnostic and treatment approaches are not tailored to AYAs, potentially overlooking age-specific molecular characteristics. This review examines genomic differences between AYAs and paediatric and older patients, emphasising trends in cancer driver genes and identifying knowledge gaps that limit our understanding of AYA cancer biology. Across studies and cancer types, the AYA age range varies but is typically defined as 15-39 years. Remarkably, for nearly half of cancers diagnosed in AYAs, little or no literature exists on genomic differences or similarities compared to other age groups. Common cancers like testicular cancer and gynaecological cancer are underrepresented, while other typical AYA cancers such as thyroid cancer entirely lack comparative literature. In contrast, less common AYA cancers such as lung cancer and colorectal cancer are investigated extensively. Although genomic differences are reported across cancer types and in pan-cancer analyses, findings are often not generalisable to relevant subtypes or inconsistent, and insights gained are limited by the use of single-gene assays or small gene panels. This review reveals an imbalance between the global incidence of AYA cancers and the scope of comparative genomic studies, as well as a lack of broader comprehensive molecular profiling. Addressing these gaps through broader genomic research, particularly in underexplored cancers, is essential to determine whether AYA tumour biology is indeed distinctive, and how age-appropriate, genomics-driven precision oncology should be delivered.

Background Glioblastoma is most commonly reported in the second (pediatric form) and seventh (adult form) decade of life. Pathogenic germline variants (PGVs) and its association to late onset glioblastoma remains unclear. This study aimed to investigate the genetic predisposition to adult glioblastoma. Methods We performed an in-depth analysis of whole genome sequencing (WGS) data of tumor-normal tissue pairs of 98 glioma WHO grade 4 patients for potential presence of PGVs, in a comprehensive set of 170 genes associated with cancer predisposition. All candidate pathogenic events were also assessed for second-hit somatic events. Results In 11 patients (11%), PGVs were observed that were considered relevant by clinical experts in the context of glioblastoma. In these patients, 13 PGVs were found in genes known for a strong association with familial glioblastoma (MSH6 (3x), PMS2 (5x), MSH2, TP53, NF1 and BRCA1) or with medulloblastoma (SUFU). In eight of these patients (73%) causality was supported by a second (somatic) event and/or a matching genome-wide mutational signature. Conclusions Germline predisposition does also play a role in the development of adult glioblastoma, with mismatch repair deficiency being the main mechanism. Our results do illustrate benefits of tumor-normal WGS for glioblastoma patients and their relatives beyond the identification of potentially actionable mutations for therapy guidance. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement Nothing to report ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: Patient consent was based on a broad consent intending publicly available access-controlled data for academic cancer research related requests. For this study, a Data Access Request (https://www.hartwigmedicalfoundation.nl/en/data/data-access-request/ DR-310) was signed to obtain the genome-wide germline and somatic data. All samples were de-identified and keys between study number and patient number were stored solely locally in the hospitals. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes The underlying research are partly facilitated by Hartwig Medical Foundation and the Center for Personalized Cancer Treatment (CPCT) which have generated, analysed and made available data for this research. Data can be requested via https://www.hartwigmedicalfoundation.nl/en/data/data-access-request/. Hartwig Medical Foundation is willing to share with external qualified researchers access to patient-level data and supporting clinical documents. These requests are reviewed and approved by an independent review committee on the basis of scientific merit. All data provided are anonymized to respect the privacy of patients who have participated in the study, in line with applicable laws and regulations.

Tumors arise from uncontrolled cell proliferation driven by mutations in genes that regulate stem cell renewal and differentiation. Intestinal tumors, however, retain some hierarchical organization, maintaining both cancer stem cells (CSCs) and cancer differentiated cells (CDCs). This heterogeneity, coupled with cellular plasticity enabling CDCs to revert to CSCs, contributes to therapy resistance and relapse. Using genetically encoded fluorescent reporters in human tumor organoids, combined with our machine-learning-based cell tracker, CellPhenTracker, we simultaneously traced cell-type specification, metabolic changes, and reconstructed cell lineage trajectories during tumor organoid development. Our findings reveal distinctive metabolic phenotypes in CSCs and CDCs. We find that lactate regulates tumor dynamics, suppressing CSC differentiation and inducing dedifferentiation into a proliferative CSC state. Mechanistically, lactate increases histone acetylation, epigenetically activating MYC. Given that lactate's regulation of MYC depends on the bromodomain-containing protein 4 (BRD4), targeting cancer metabolism and BRD4 inhibitors emerge as a promising strategy to prevent tumor relapse.

Whole genome sequencing (WGS) provides complete genetic information in one test, supporting the shift towards individualized metastatic colorectal cancer (mCRC) treatment. Although WGS is validated as a diagnostic test, the potential clinical implications for mCRC remain unknown. We evaluated the clinical consequences of WGS in 96 mCRC patients. Clinically actionable biomarkers were identified by a molecular biologist and medical oncologist, with added value defined as biomarkers undetected by standard diagnostics. We evaluated how these biomarkers informed treatment decisions. We used patient-derived organoids (PDOs) to test drug sensitivity to MET, MEK, and CDK4/6 inhibitors, translating genomic findings into functional evidence. WGS yields biomarkers with clinical implications in 81% of patients, with 49% (N = 47/96) identified by WGS that were not detected by guideline-based diagnostics, and 40% (N = 38/96) not detected by applied diagnostics. The proportion of patients receiving biomarker-based treatment has increased from 11% to at least 24% by WGS. PDOs with actionable biomarkers showed clear differential response to different biomarker-based treatments. WGS enables considerably more personalized therapeutic interventions and represents a promising approach in advancing precision oncology for mCRC patients. PDO pre-screening can refine therapy by identifying (in)effective treatments in a patient-specific context, to accelerate the development of personalized treatment.

The costs of cancer therapies are rising rapidly across the globe, with novel therapies like targeted treatment and immunotherapies as major contributors, but their effectiveness can be low or uncertain due to limited post market surveillance. Reliable biomarkers to identify patients highly unlikely to respond to cancer therapies represent an increasingly important clinical and societal need, as they could prevent unnecessary treatments, reduce side effects, and alleviate pressure on healthcare systems. Here, we developed a robust statistical framework and applied it to whole-genome and transcriptome sequencing data of cancer patients (n = 2,596) with advanced disease. Our approach systematically identified known and potentially novel genomic and transcriptomic biomarkers of non-response, such as immune evasion driver events in skin melanoma patients treated with anti-PD-1 checkpoint inhibitors, and KRAS G12 mutations in metastatic colorectal cancer patients treated with different chemotherapy regimens. Despite the identification of these promising non-response signals, an analytical power analysis revealed that for most treatments and/or cancer types the cohort sizes are underpowered. Our results underscore the promises and the urgent need for expanding response-annotated real-world comprehensive genomics datasets to enable robust biomarker identification and validation. ### Competing Interest Statement The authors have declared no competing interest.

The germline genetic susceptibility to adult glioblastoma remains unclear. With the option of broad molecular testing, it is crucial that clinicians are aware of the a priori probability of finding germline predisposition in glioblastoma patients. Here, we studied the genetic predisposition to adult glioblastoma using paired tumor-normal WGS data in an unselected, average cohort of 92 glioma WHO grade 4 patients. In 10 patients (11%), 12 Pathogenic Germline Variants (PGVs) were found in genes strongly associated with familial glioblastoma (MSH6 (3x), PMS2 (5x), MSH2, NF1, BRCA1) or medulloblastoma (SUFU). In six of these patients (60%), causality was supported by a second (somatic) event and/or a matching genome-wide mutational signature. Thus, germline predisposition does play a role in the development of adult glioblastoma, with mismatch repair deficiency being the main mechanism. Our results also highlight the benefits of tumor-normal WGS for glioblastoma patients and their families, beyond identifying actionable mutations for therapy.

Circulating tumor DNA (ctDNA) is the most frequently utilized liquid biopsy. High sensitivity is critical for minimal residual disease detection to inform treatment response, risk stratification and molecular relapse. Whole-genome-based, tumor-informed strategy is increasingly being used in ctDNA detection in adult cancers but not in pediatric cancers. We designed a personalized ctDNA assay which utilized paired tumor-normal whole genome sequencing data of individual patient to design a panel of ∼400 patient-specific tumor biomarkers. 83 patients with high-risk solid tumors from the ZERO Childhood Cancer Precision Medicine Program in Australia who had 2 or more serial liquid biopsy samples collected were selected for analysis. The cohort consisted of 54 sarcoma, 16 neuroblastoma and 13 other solid tumors. 303 liquid biopsy samples were analyzed for plasma ctDNA, ranging from 2 - 7 samples per patient. The detection limit was between 10^-4 and 10^-5 ctDNA fraction for 90% of the samples and between 10^-3 and 10^-4 for the remaining 10%. 73 (88%) patients had at least one blood sample with detectable ctDNA. ctDNA detection rate was 96% in newly diagnosed disease, with ctDNA fraction ranging from 0.01 to 99% (median 36%), 88% in off-treatment relapse (median 3.6%; range: 0.011 – 77%) and 85% in refractory/progressive disease (PD) (median 0.8%; range: 0.37 – 81%). For patients with complete response (CR), 89% had undetectable ctDNA. In the remaining 11% with CR and detectable ctDNA, majority (71%) experienced relapse. ctDNA levels were significantly associated with disease burden and treatment response. Patients with metastatic disease at enrolment had significantly higher ctDNA fraction (median 8.2%; range: 0 – 99%) than those with localized disease (median 0.06%; range: 0 – 42%) (P<0.05). The median ctDNA fraction corresponding to CR was 0% (range: 0 – 4.0%), 0.0095% (range: 0 – 4.7%) for partial response, 0.022% (range: 0 – 23%) for stable disease and 5.6% (range: 0 – 82%) for PD (P<0.0001). Most importantly, ctDNA predicted clinical outcomes. Patients with early clearance of ctDNA between 60 and 120 days from start of treatment, compared with those with detectable ctDNA, had significantly better PFS (5-yr PFS 47% vs 21%; P<0.01) and OS (5-yr OS 54% vs 37%; P<0.05). Patients with undetectable ctDNA at the end of treatment (+/-42 days from completing treatment), compared with those with persistence ctDNA, also had significantly better PFS (5-yr PFS 50% vs 0%; P<0.0001) and OS (5-yr OS 66% vs 17%; P<0.001). This ultrasensitive personalized ctDNA assay with a detection limit between 10^-4 and 10^-5 ctDNA fraction is highly correlative of disease burden and treatment response. Early clearance of ctDNA and persistence of ctDNA at end of the treatment both correlated with clinical outcome. The assay will be evaluated prospectively to determine its clinical utility in complementing treatment response evaluation, detecting early recurrence and predicting outcome in pediatric cancers. Loretta M.S. Lau, Rob Salomon, Wenhan Chen, Charles Shale, Mojgan Toumari, Wenyan Li, Jingwei Chan, Sajad Razavi-Bazaz,, Louise Cui, Sam El-Kamand, Marie Wong, Edwin Cuppen, Michelle Haber, Vanessa Tyrrell, Paul G Ekert, David S Ziegler, Peter Priestley, Mark J Cowley. Whole-genome-based, tumor-informed circulating tumor DNA detection correlates with treatment response and survival of pediatric solid tumor [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Discovery and Innovation in Pediatric Cancer— From Biology to Breakthrough Therapies; 2025 Sep 25-28; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2025;85(18_Suppl_2):Abstract nr A001-PR009.

Correlations between patient response (best RECIST response, size change of all target lesions, and the biopsied lesion) and PDO response (normalized GRAUC per treatment in A–C and AUC in D–F). ROC curves showing the performance of the PDO classification based on GRAUC. The curve plots the true-positive rate (sensitivity) against the false-positive rate (1 − specificity). The AUC indicates the model’s ability to distinguish between responders and nonresponders to 5-FU and oxaliplatin, defined by size change of all target lesions (G) and the biopsied lesion (H). Kaplan–Meier PFS (I) and OS (J) curves of patients stratified by PDO sensitivity to 5-FU and oxaliplatin, based on normalized GRAUC (cutoff = 0.63). Censored events are indicated by vertical bars on the corresponding curve. The table underneath each plot denotes the numbers at risk. Log-rank test–based P value is shown. SN-38, active metabolite of irinotecan.

PURPOSE:Accurately predicting treatment response in metastatic colorectal cancer (mCRC) is critical to avoid unnecessary toxicity and improve patient outcomes. Patient-derived organoids (PDO) are promising models, but larger prospective studies are needed to confirm their predictive value. EXPERIMENTAL DESIGN:Patients with mCRC underwent a metastatic biopsy for PDO establishment, before starting new systemic treatment. Predictors of PDO establishment were identified. PDOs were incubated with a seven-drug panel, including the patient's treatment, to determine drug sensitivity as measured by CyQUANT cell viability [area under the nonfitted "curve" of the raw viability or growth rate inhibition values (AUC and GRAUC) and concentration that gives half-maximal viability or GR inhibition (IC50 and GR50)]. Patient response was measured by size change of biopsied and all target lesions. The diagnostic performance was evaluated by positive predictive value, negative predictive value, and area under the ROC curve. Additionally, the association between PDO response and survival was assessed. RESULTS:A total of 232 patients were included, and 205 biopsies were obtained. PDO establishment success increased from 22% to 75%, yielding 52% overall. Male sex, increased lactate dehydrogenase, biopsy in academic hospitals, optimized culture conditions, and experience were related to PDO establishment success. In this interim analysis, focused on oxaliplatin-based doublet chemotherapy, 42 PDOs were screened. PDO drug sensitivity significantly correlated with the response of the biopsied lesion (R = 0.41-0.49, P < 0.011) and all target lesions (R = 0.54-0.60, P < 0.001) for all treatments combined. The 5-fluorouracil and oxaliplatin PDO screens demonstrated high predictive accuracy (positive predictive value: 0.78; negative predictive value: 0.80; area under the ROC curve: 0.78-0.88) and were associated with progression-free survival and overall survival (P = 0.016 and 0.049). CONCLUSIONS:We identified predictors for successful mCRC PDO establishment and validated that PDOs can accurately predict patient outcomes during systemic treatment, specifically with 5-fluorouracil and oxaliplatin.

A, Heatmap of the drug sensitivity per PDO for different types of systemic treatment. The columns show the normalized GRAUC (left) and AUC (right), respectively. Potential other treatment options for intermediate or high 5-FU and oxaliplatin–resistant PDOs, as well as intermediate or high 5-FU and oxaliplatin–sensitive but 5-FU and irinotecan–resistant PDOs, are shown in black bars. PDOs that were not screened are shown in gray. B, Sensitivity (GRAUC) to doublet and triplet chemotherapy showing increased sensitivity with the addition of irinotecan. C, Sensitivity (normalized GRAUC) to panitumumab for PDOs categorized according to the tumor’s mutational status and primary tumor sidedness (RAS-mutant, BRAF-mutant, and/or right-sided; RAS/BRAF-WT and left-sided. Boxplots show the minimum, median, maximum, upper and lower quartiles, and individual data points. iri, irinotecan; oxa, oxaliplatin; TT, trifluridine/tipiracil.

Baseline characteristics of the study population, with key demographic, clinical, and molecular details from regular diagnostic sequencing.