Oligometastatic non-small cell lung cancer: Impact of local and contemporary systemic treatment approaches on clinical outcome.

Oligometastatic (OMD) non-small cell lung cancer (NSCLC) is a distinct but heterogeneous entity. Current guidelines recommend systemic therapy and consolidation with local ablative therapy (LAT). However, evidence regarding the optimal choice of multimodal treatment approaches is lacking, in particular with respect to the integration of immunotherapy. This real-world study identified 218 patients with OMD NSCLC (2004-2023, prespecified criteria: ≤5 metastases in ≤2 organ systems) from three major German comprehensive cancer centers. Most patients had one (72.5%) or two (17.4%) metastatic lesions in a single (89.9%) organ system. Overall survival (OS) was significantly longer with a single metastatic lesion (HR 0.54, p = .003), and female gender (HR 0.4, p < .001). Median OS of the full cohort was 27.8 months, with 29% survival at 5 years. Patients who had completed LAT to all NSCLC sites, typically excluding patients with early progression, had a median OS of 34.4 months (37.7% 5-year OS rate) with a median recurrence-free survival (RFS) of 10.9 months (13.3% at 5 years). In those patients, systemic treatment as part of first-line therapy was associated with doubling of RFS (12.3 vs. 6.4 months, p < .001). Despite limited follow-up of patients receiving chemo-immunotherapy (EU approval 2018/2019), RFS was greatly improved by adding checkpoint inhibitors to chemotherapy (HR 0.44, p = .008, 2-year RFS 51.4% vs. 15.1%). In conclusion, patients with OMD NSCLC benefitted from multimodality approaches integrating systemic therapy and local ablation of all cancer sites. A substantial proportion of patients achieved extended OS, suggesting a potential for cure that can be further augmented with the addition of immunotherapy.

Spatial single-cell profiling and neighbourhood analysis reveal the determinants of immune architecture connected to checkpoint inhibitor therapy outcome in hepatocellular carcinoma.

BACKGROUND: The determinants of the response to checkpoint immunotherapy in hepatocellular carcinoma (HCC) remain poorly understood. The organisation of the immune response in the tumour microenvironment (TME) is expected to govern immunotherapy outcomes but spatial immunotypes remain poorly defined. OBJECTIVE: We hypothesised that the deconvolution of spatial immune network architectures could identify clinically relevant immunotypes in HCC. DESIGN: We conducted highly multiplexed imaging mass cytometry on HCC tissues from 101 patients. We performed in-depth spatial single-cell analysis in a discovery and validation cohort to deconvolute the determinants of the heterogeneity of HCC immune architecture and develop a spatial immune classification that was tested for the prediction of immune checkpoint inhibitor (ICI) therapy. RESULTS: Bioinformatic analysis identified 23 major immune, stroma, parenchymal and tumour cell types in the HCC TME. Unsupervised neighbourhood detection based on the spatial interaction of immune cells identified three immune architectures with differing involvement of immune cells and immune checkpoints dominated by either CD8 T-cells, myeloid immune cells or B- and CD4 T-cells. We used these to define three major spatial HCC immunotypes that reflect a higher level of intratumour immune cell organisation: depleted, compartmentalised and enriched. Progression-free survival under ICI therapy differed significantly between the spatial immune types with improved survival of enriched patients. In patients with intratumour heterogeneity, the presence of one enriched area governed long-term survival.

Dissecting AI-based mutation prediction in lung adenocarcinoma: A comprehensive real-world study.

INTRODUCTION: Molecular profiling of lung cancer is essential to identify genetic alterations that predict response to targeted therapy. While deep learning shows promise for predicting oncogenic mutations from whole tissue images, existing studies often face challenges such as limited sample sizes, a focus on earlier stage patients, and insufficient analysis of robustness and generalizability. METHODS: This retrospective study evaluates factors influencing mutation prediction accuracy using the large Heidelberg Lung Adenocarcinoma Cohort (HLCC), a cohort of 2356 late-stage FFPE samples. Validation is performed in the publicly available TCGA-LUAD cohort. RESULTS: Models trained on the larger HLCC cohort generalized well to the TCGA dataset for mutations in EGFR (AUC 0.76), STK11 (AUC 0.71) and TP53 (AUC 0.75), in line with the hypothesis that larger cohort sizes improve model robustness. Variation in performance due to pre-processing and modeling choices, such as mutation variant calling, affected EGFR prediction accuracy by up to 7 %. DISCUSSION: Model explanations suggest that acinar and papillary growth patterns are critical for the detection of EGFR mutations, whereas solid growth patterns and large nuclei are indicative of TP53 mutations. These findings highlight the importance of specific morphological features in mutation detection and the potential of deep learning models to improve mutation prediction accuracy. CONCLUSION: Although deep learning models trained on larger cohorts show improved robustness and generalizability in predicting oncogenic mutations, they cannot replace comprehensive molecular profiling. However, they may support patient pre-selection for clinical trials and deepen the insight in genotype-phenotype relationships.

Tumour mutational burden: clinical utility, challenges and emerging improvements.

Tumour mutational burden (TMB), defined as the total number of somatic non-synonymous mutations present within the cancer genome, varies across and within cancer types. A first wave of retrospective and prospective research identified TMB as a predictive biomarker of response to immune-checkpoint inhibitors and culminated in the disease-agnostic approval of pembrolizumab for patients with TMB-high tumours based on data from the Keynote-158 trial. Although the applicability of outcomes from this trial to all cancer types and the optimal thresholds for TMB are yet to be ascertained, research into TMB is advancing along three principal avenues: enhancement of TMB assessments through rigorous quality control measures within the laboratory process, including the mitigation of confounding factors such as limited panel scope and low tumour purity; refinement of the traditional TMB framework through the incorporation of innovative concepts such as clonal, persistent or HLA-corrected TMB, tumour neoantigen load and mutational signatures; and integration of TMB with established and emerging biomarkers such as PD-L1 expression, microsatellite instability, immune gene expression profiles and the tumour immune contexture. Given its pivotal functions in both the pathogenesis of cancer and the ability of the immune system to recognize tumours, a profound comprehension of the foundational principles and the continued evolution of TMB are of paramount relevance for the field of oncology.

Multicenter Real-World Analysis of Combined MET and EGFR Inhibition in Patients With Non-Small Cell Lung Cancer and Acquired MET Amplification or Polysomy After EGFR Inhibition.

PURPOSE: MET amplification is a common resistance mechanism to EGFR inhibition in EGFR-mutant non-small cell lung cancer (NSCLC). Several trials showed encouraging results with combined EGFR and MET inhibition (EGFRi/METi). However, MET amplification has been inconsistently defined and frequently included both polysomy and true amplification. METHODS: This is a multicenter, real-world analysis in patients with disease progression on EGFR inhibition and MET copy number gain (CNG), defined as either true amplification (MET to centromere of chromosome 7 ratio [MET-CEP7] ≥ 2) or polysomy (gene copy number ≥ 5, MET-CEP7 < 2). RESULTS: A total of 43 patients with MET CNG were included, 42 of whom were detected by FISH. Twenty-three, 7, and 14 received EGFRi/METi, METi, and SoC, respectively. Patients in the EGFRi/METi cohort exhibited a superior real-world clinical benefit rate, defined as stable disease or better, of 82% (95% confidence interval [CI], 60-95) compared to METi (29%, 4-71) and SoC (50%, 23-77). Median real-world progression-free survival was longer with EGFRi/METi with 9.8 vs. 4.3 months with METi (hazard ratio [HR], 0.19, 95% CI, 0.06-0.57) and 3.7 months with SoC (0.41, 0.18-0.91), respectively. Overall survival was numerically improved. Interaction analysis with treatment and type of CNG (amplification vs. polysomy) suggests that differences were exclusively driven by MET-amplified patients receiving EGFRi/METi (HR for OS, 0.09, 0.01-0.54). CONCLUSION: In this real-world study, EGFRi/METi showed clinical benefit over METi and SoC. Future studies should focus on the differential impact of the type of MET CNG with a focus on true MET amplification as predictor of response.

Digital spatial profiling identifies the tumor center as a topological niche in prostate cancer characterized by an upregulation of BAD.

Prostate cancer is characterized by a high degree of intratumoral heterogeneity. However, little is known about the spatial distribution of cancer cells with respect to specific functional characteristics and the formation of spatial niches. Here, we used digital spatial profiling (DSP) to investigate differences in protein expression in the tumor center versus the tumor periphery. Thirty-seven regions of interest were analyzed for the expression of 47 proteins, which included components of the PI3K-AKT, MAPK, and cell death signaling pathways as well as immune cell markers. A total of 1739 data points were collected from five patients. DSP identified the BCL-2 associated agonist of cell death (BAD) protein as the most significantly upregulated protein in the tumor center. BAD upregulation was confirmed by conventional immunohistochemistry, which furthermore showed a phosphorylation of BAD at serine 112 indicating its inactivation. Knockdown of BAD in prostate cancer cells in vitro led to decreased cell viability and colony growth. Clinically, high BAD expression was associated with a shorter time to biochemical recurrence in 158 mostly high-risk prostate cancer patients. Collectively, our results suggest that the tumor center is a topological niche with high BAD expression that may drive prostate cancer progression.

Immune landscape of renal cell carcinoma with metastasis to the pancreas.

INTRODUCTION: Clear cell Renal Cell Carcinoma (ccRCC) has a poor prognosis once metastatic. However, certain metastatic sites have been reported to have a different impact on the patient prognosis. For example, patients with pancreatic metastases have a much more favorable prognosis than those with metastases to other organs. The biological basis for this observation remains poorly understood. The aim of this study was to characterize the immune landscape of pancreatic metastases and the corresponding primary tumors in order to identify possible immunological features that correlate with disease biology. PATIENTS AND METHODS: A detailed assessment of immune cell populations was performed using a total of 1,700 microscopic images from ccRCCs from 11 patients, their corresponding pancreatic metastases and ccRCCs from 10 patients without pancreatic metastases. Tumor specimens were stained for CD45, CD8, CD163 and FOXP3 and the densities of the respective immune cells were assessed semiquantitatively in the intratumoral and extratumoral compartment. Multispectral imaging was performed in selected tumors. RESULTS: We found that pancreatic metastases show the lowest intratumoral infiltration with CD8+ cytotoxic T lymphocytes of all tumor specimens analyzed. The frequency of CD8+ lymphocytes was on 1.9 fold lower in pancreatic metastases (median density 8.3 cells per field of view [FOV] = 1.23 mm2) when compared to the corresponding primary tumor (15.6 cells per FOV, P = 0.0002) and more than 3-fold lower when compared to ccRCCs without pancreatic metastases (27.2 cells per FOV, P = 0.0012). There was also a significantly reduced intratumoral infiltration with immunosuppressive FOXP3+ lymphocytes in pancreatic metastases (2.6 cells per FOV, P = 0.009) and corresponding primary tumors (2 cells per FOV, P = 0.028) when compared to ccRCCs without pancreatic metastases (5.6 cells per FOV). CONCLUSIONS: In this proof-of-concept study, we show that pancreatic metastases of ccRCC present with unique immunological features including a low intratumoral density of CD8+ and FOXP3+ lymphocytes. The low counts of CD8+ and FOXP3+ lymphocytes may reflect less aggressive features of ccRCC with pancreatic metastasis that may result in a more favorable patient prognosis.

ROS1 fusions in resected stage I-III adenocarcinoma: Results from the European Thoracic Oncology Platform Lungscape project.

BACKGROUND: ROS1 fusion is a relatively low prevalence (0.6-2.0%) but targetable driver in lung adenocarcinoma (LUAD). Robust and low-cost tests, such as immunohistochemistry (IHC), are desirable to screen for patients potentially harboring this fusion. The aim was to investigate the prevalence of ROS1 fusions in a clinically annotated European stage I-III LUAD cohort using IHC screening with the in vitro diagnostics (IVD)-marked clone SP384, followed by confirmatory molecular analysis in pre-defined subsets. METHODS: Resected LUADs constructed in tissue microarrays, were immunostained for ROS1 expression using SP384 clone in a ready-to-use kit and Ventana immunostainers. After external quality control, analysis was performed by trained pathologists. Staining intensity of at least 2+ (any percentage of tumor cells) was considered IHC positive (ROS1 IHC + ). Subsequently, ROS1 IHC + cases were 1:1:1 matched with IHC0 and IHC1 + cases and subjected to orthogonal ROS1 FISH and RNA-based testing. RESULTS: The prevalence of positive ROS1 expression (ROS1 IHC + ), defined as IHC 2+/3+, was 4 % (35 of 866 LUADs). Twenty-eight ROS1 IHC + cases were analyzed by FISH/RNA-based testing, with only two harboring a confirmed ROS1 gene fusion, corresponding to a lower limit for the prevalence of ROS1 gene fusion of 0.23 %. They represent a 7 % probability of identifying a fusion among ROS1 IHC + cases. Both confirmed cases were among the only four with sufficient material and H-score ≥ 200, leading to a 50 % probability of identifying a ROS1 gene fusion in cases with an H-score considered strongly positive. All matched ROS1 IHC- (IHC0 and IHC1 + ) cases were also found negative by FISH/RNA-based testing, leading to a 100 % probability of lack of ROS1 fusion for ROS1 IHC- cases. CONCLUSIONS: The prevalence of ROS1 fusion in an LUAD stage I-III European cohort was relatively low. ROS1 IHC using SP384 clone is useful for exclusion of ROS1 gene fusion negative cases.

Prostate cancer risk assessment and avoidance of prostate biopsies using fully automatic deep learning in prostate MRI: comparison to PI-RADS and integration with clinical data in nomograms.

OBJECTIVES: Risk calculators (RCs) improve patient selection for prostate biopsy with clinical/demographic information, recently with prostate MRI using the prostate imaging reporting and data system (PI-RADS). Fully-automated deep learning (DL) analyzes MRI data independently, and has been shown to be on par with clinical radiologists, but has yet to be incorporated into RCs. The goal of this study is to re-assess the diagnostic quality of RCs, the impact of replacing PI-RADS with DL predictions, and potential performance gains by adding DL besides PI-RADS. MATERIAL AND METHODS: One thousand six hundred twenty-seven consecutive examinations from 2014 to 2021 were included in this retrospective single-center study, including 517 exams withheld for RC testing. Board-certified radiologists assessed PI-RADS during clinical routine, then systematic and MRI/Ultrasound-fusion biopsies provided histopathological ground truth for significant prostate cancer (sPC). nnUNet-based DL ensembles were trained on biparametric MRI predicting the presence of sPC lesions (UNet-probability) and a PI-RADS-analogous five-point scale (UNet-Likert). Previously published RCs were validated as is; with PI-RADS substituted by UNet-Likert (UNet-Likert-substituted RC); and with both UNet-probability and PI-RADS (UNet-probability-extended RC). Together with a newly fitted RC using clinical data, PI-RADS and UNet-probability, existing RCs were compared by receiver-operating characteristics, calibration, and decision-curve analysis. RESULTS: Diagnostic performance remained stable for UNet-Likert-substituted RCs. DL contained complementary diagnostic information to PI-RADS. The newly-fitted RC spared 49% [252/517] of biopsies while maintaining the negative predictive value (94%), compared to PI-RADS ≥ 4 cut-off which spared 37% [190/517] (p < 0.001). CONCLUSIONS: Incorporating DL as an independent diagnostic marker for RCs can improve patient stratification before biopsy, as there is complementary information in DL features and clinical PI-RADS assessment. CLINICAL RELEVANCE STATEMENT: For patients with positive prostate screening results, a comprehensive diagnostic workup, including prostate MRI, DL analysis, and individual classification using nomograms can identify patients with minimal prostate cancer risk, as they benefit less from the more invasive biopsy procedure. KEY POINTS: The current MRI-based nomograms result in many negative prostate biopsies. The addition of DL to nomograms with clinical data and PI-RADS improves patient stratification before biopsy. Fully automatic DL can be substituted for PI-RADS without sacrificing the quality of nomogram predictions. Prostate nomograms show cancer detection ability comparable to previous validation studies while being suitable for the addition of DL analysis.

KRAS and TP53 co-mutation predicts benefit of immune checkpoint blockade in lung adenocarcinoma.

BACKGROUND: Predictive biomarkers in use for immunotherapy in advanced non-small cell lung cancer are of limited sensitivity and specificity. We analysed the potential of activating KRAS and pathogenic TP53 mutations to provide additional predictive information. METHODS: The study cohort included 713 consecutive immunotherapy patients with advanced lung adenocarcinomas, negative for actionable genetic alterations. Additionally, two previously published immunotherapy and two surgical patient cohorts were analyzed. Therapy benefit was stratified by KRAS and TP53 mutations. Molecular characteristics underlying KRASmut/TP53mut tumours were revealed by the analysis of TCGA data. RESULTS: An interaction between KRAS and TP53 mutations was observed in univariate and multivariate analyses of overall survival (Hazard ratio [HR] = 0.56, p = 0.0044 and HR = 0.53, p = 0.0021) resulting in a stronger benefit for KRASmut/TP53mut tumours (HR = 0.71, CI 0.55-0.92). This observation was confirmed in immunotherapy cohorts but not observed in surgical cohorts. Tumour mutational burden, proliferation, and PD-L1 mRNA were significantly higher in TP53-mutated tumours, regardless of KRAS status. Genome-wide expression analysis revealed 64 genes, including CX3CL1 (fractalkine), as specific transcriptomic characteristic of KRASmut/TP53mut tumours. CONCLUSIONS: KRAS/TP53 co-mutation predicts ICI benefit in univariate and multivariate survival analyses and is associated with unique molecular tumour features. Mutation testing of the two genes can be easily implemented using small NGS panels.

Deep learning for dual detection of microsatellite instability and POLE mutations in colorectal cancer histopathology.

In the spectrum of colorectal tumors, microsatellite-stable (MSS) tumors with DNA polymerase ε (POLE) mutations exhibit a hypermutated profile, holding the potential to respond to immunotherapy similarly to their microsatellite-instable (MSI) counterparts. Yet, due to their rarity and the associated testing costs, systematic screening for these mutations is not commonly pursued. Notably, the histopathological phenotype resulting from POLE mutations is theorized to resemble that of MSI. This resemblance not only could facilitate their detection by a transformer-based Deep Learning (DL) system trained on MSI pathology slides, but also indicates the possibility for MSS patients with POLE mutations to access enhanced treatment options, which might otherwise be overlooked. To harness this potential, we trained a Deep Learning classifier on a large dataset with the ground truth for microsatellite status and subsequently validated its capabilities for MSI and POLE detection across three external cohorts. Our model accurately identified MSI status in both the internal and external resection cohorts using pathology images alone. Notably, with a classification threshold of 0.5, over 75% of POLE driver mutant patients in the external resection cohorts were flagged as "positive" by a DL system trained on MSI status. In a clinical setting, deploying this DL model as a preliminary screening tool could facilitate the efficient identification of clinically relevant MSI and POLE mutations in colorectal tumors, in one go.

High-throughput molecular assays for inclusion in personalised oncology trials - State-of-the-art and beyond.

In the last decades, the development of high-throughput molecular assays has revolutionised cancer diagnostics, paving the way for the concept of personalised cancer medicine. This progress has been driven by the introduction of such technologies through biomarker-driven oncology trials. In this review, strengths and limitations of various state-of-the-art sequencing technologies, including gene panel sequencing (DNA and RNA), whole-exome/whole-genome sequencing and whole-transcriptome sequencing, are explored, focusing on their ability to identify clinically relevant biomarkers with diagnostic, prognostic and/or predictive impact. This includes the need to assess complex biomarkers, for example microsatellite instability, tumour mutation burden and homologous recombination deficiency, to identify patients suitable for specific therapies, including immunotherapy. Furthermore, the crucial role of biomarker analysis and multidisciplinary molecular tumour boards in selecting patients for trial inclusion is discussed in relation to various trial concepts, including drug repurposing. Recognising that today's exploratory techniques will evolve into tomorrow's routine diagnostics and clinical study inclusion assays, the importance of emerging technologies for multimodal diagnostics, such as proteomics and in vivo drug sensitivity testing, is also discussed. In addition, key regulatory aspects and the importance of patient engagement in all phases of a clinical trial are described. Finally, we propose a set of recommendations for consideration when planning a new precision cancer medicine trial.

PD-L1 as a Urine Biomarker in Renal Cell Carcinoma-A Case Series and Proof-of-Concept Study.

BACKGROUND: Renal cell carcinoma (RCC) is among the most lethal urologic malignancies once metastatic. Current treatment approaches for metastatic RCC (mRCC) involve immune checkpoint inhibitors (ICIs) that target the PD-L1/PD-1 axis. High PD-L1 expression in tumor tissue has been identified as a negative prognostic factor in RCC. However, the role of PD-L1 as a liquid biomarker has not yet been fully explored. Herein, we analyze urine levels of PD-L1 in mRCC patients before and after either ICI therapy or surgical intervention, as well as in a series of patients with treatment-naïve RCC. PATIENTS AND METHODS: The mid-stream urine of patients with mRCC (n = 4) or treatment-naïve RCC, i.e., prior to surgery from two centers (cohort I, n = 49: cohort II, n = 29) was analyzed for PD-L1 by ELISA. The results from cohort I were compared to a control group consisting of patients treated for non-malignant urologic diseases (n = 31). In the mRCC group, urine PD-L1 levels were measured before and after tumor nephrectomy (n = 1) or before and after ICI therapy (n = 3). Exosomal PD-L1 in the urine was analyzed in selected patients by immunoblotting. RESULTS: A strong decrease in urine PD-L1 levels was found after tumor nephrectomy or following systemic treatment with ICIs. In patients with treatment-naïve RCC (cohort I), urine PD-L1 levels were significantly elevated in the RCC group in comparison to the control group (median 59 pg/mL vs. 25.7 pg/mL, p = 0.011). PD-L1 urine levels were found to be elevated, in particular, in low-grade RCCs in cohorts I and II. Exosomal PD-L1 was detected in the urine of a subset of patients. CONCLUSION: In this proof-of-concept study, we show that PD-L1 can be detected in the urine of RCC patients. Urine PD-L1 levels were found to correlate with the treatment response in mRCC patients and were significantly elevated in treatment-naïve RCC patients.

Leveraging Off-Target Reads in Panel Sequencing for Homologous Recombination Repair Deficiency Screening in Tumor.

Targeted tumor only sequencing has become a standard practice in cancer diagnostics. This study aims to develop an approach for robust copy number variant calling in tumor samples using only off-target region (OTR) reads. We also established a clinical use case for homologous recombination deficiency (HRD) score estimation (HRDest) using the sum of telomeric-allelic imbalance and large-scale state transition scores without the need for loss of heterozygosity information. A strong correlation was found between HRD score and the sum of telomeric-allelic imbalance + large-scale state transition in The Cancer Genome Atlas cohort (ρ = 0.99, P < 2.2 × 10-16) and in a clinical in-house cohort of 34 tumors (ρ = 0.9, P = 5.1 × 10-13) comparing whole-exome sequencing and targeted sequencing data. HRDest scores from 1086 clinical cases were compared with The Cancer Genome Atlas data set. There were no significant differences in HRD score distribution within the analyzed tumor types. As a control, commercially available HRD standards were also sequenced, and the HRDest scores obtained from the OTR reads were well within the HRD reference range provided by the manufacturer. In conclusion, OTR reads of tumor-only panel sequencing can be used to determine genome-wide copy number variant profiles and to approximate HRD scores.

Die EU-Verordnung für In-vitro-Diagnostika (IVDR) in der Praxis: Umsetzung und Anwendung - Ergebnisse einer öffentlichen Veranstaltung der Arbeitsgemeinschaft der Wissenschaftlichen Medizinischen Fachgesellschaften (AWMF) im März 2023.

In order to discuss first experiences with the implementation of the EU Regulation on In Vitro Diagnostic Medical Devices (IVDR) about one year after its entry into force, the German Association of the Scientific Medical Societies (AWMF e.V.) organized a full-day public webinar. Overall, it became clear that the implementation of the IVDR still poses significant challenges for laboratory medicine and pathology. Corrections at the political level and implementation with a sense of proportion are required. Before the long-term goal of the IVDR, i.e. the increase in patient safety, can be realized, the prevention of disadvantages for patients due to gaps in care must be strived for in the medium term by all parties involved.

High Concordance of Different Assays in the Determination of Homologous Recombination Deficiency-Associated Genomic Instability in Ovarian Cancer.

PURPOSE: Poly(ADP-ribose) polymerase inhibitors (PARPi) have shown promising clinical results in the treatment of ovarian cancer. Analysis of biomarker subgroups consistently revealed higher benefits for patients with homologous recombination deficiency (HRD). The test that is most often used for the detection of HRD in clinical studies is the Myriad myChoice assay. However, other assays can also be used to assess biomarkers, which are indicative of HRD, genomic instability (GI), and BRCA1/2 mutation status. Many of these assays have high potential to be broadly applied in clinical routine diagnostics in a time-effective decentralized manner. Here, we compare the performance of a multitude of alternative assays in comparison with Myriad myChoice in high-grade serous ovarian cancer (HGSOC). METHODS: DNA from HGSOC samples was extracted from formalin-fixed paraffin-embedded tissue blocks of cases previously run with the Myriad myChoice assay, and GI was measured by multiple molecular assays (CytoSNP, AmoyDx, Illumina TSO500 HRD, OncoScan, NOGGO GISv1, QIAseq HRD Panel and whole genome sequencing), applying different bioinformatics algorithms. RESULTS: Application of different assays to assess GI, including Myriad myChoice, revealed high concordance of the generated scores ranging from very substantial to nearly perfect fit, depending on the assay and bioinformatics pipelines applied. Interlaboratory comparison of assays also showed high concordance of GI scores. CONCLUSION: Assays for GI assessment not only show a high concordance with each other but also in correlation with Myriad myChoice. Thus, almost all of the assays included here can be used effectively to assess HRD-associated GI in the clinical setting. This is important as PARPi treatment on the basis of these tests is compliant with European Medicines Agency approvals, which are methodologically not test-bound.