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Desmoplastic small round cell tumor (DSRCT) is an ultra-rare sarcoma with limited treatment options. Here, we show that comprehensive molecular profiling informs diagnosis and individualized therapy in this disease. We report the results of whole-genome/exome, transcriptome, and DNA methylome analyses performed in 30 refractory DSRCT patients, complemented by (phospho)proteomic profiling in nine, within a nationwide precision oncology program. In eight patients (27%), DSRCT was diagnosed only after molecular profiling. Although DSRCTs have “quiet” genomes, 28 patients (93%) received 107 molecular-based management recommendations, including assessment of clinical trial eligibility in 17 (57%). Most recommendations are informed by overexpression of tyrosine kinases, SSTR3/5, and CLDN6, detected in 45%, 33%, and 20% of cases, respectively. Thirteen patients (46%) received recommended therapies, yielding disease control in eight (62%), including three long-lasting responses to pazopanib and trastuzumab deruxtecan, the latter administered based on ERBB2 overexpression in the absence of aberrant ERBB2 kinase activation. These findings demonstrate that multi-omics profiling provides clinically actionable insights for DSRCT management. Desmoplastic small round cell tumor (DSRCT), a very rare and understudied sarcoma, presents serious challenges for both diagnosis and treatment. Here, the authors employ multi-omics profiling on 30 refractory DSRCT patients to improve the diagnosis and identify potentially actionable targets for individualized DSRCT treatment.

Precision cancer medicine aims to improve patient outcomes by providing individually tailored recommendations for clinical management based on the evaluation of biological disease profiles in multidisciplinary molecular tumor boards (MTBs). The quality of MTB decisions depends on the comprehensive, reliable, and reproducible interpretation of increasingly complex molecular data. We developed and implemented, as part of a multicenter precision oncology program, the Knowledge Connector (KC), a decision support system that integrates individual patients’ molecular and clinical data with world knowledge to generate and document MTB recommendations. The KC supports data curation, database integration, and discussion based on multiomics data and provides an interface for creating a cross-institutional knowledge base. Furthermore, it extracts relevant biomarker-drug associations and increases the efficacy of data interpretation in a clinically relevant manner by reducing reliance on external sources and optimizing inter-curator concordance. Our results demonstrate that the KC is a versatile tool that supports medical decision-making in MTBs, thus enabling the scalability of precision cancer medicine. Integrating complex multi-omics data for individual patient decision making can be challenging. Here, the authors develop Knowledge Connector as a decision support system to generate and document Molecular Tumor Board recommendations and support medical decision-making.

As new generations of targeted therapies emerge and tumor genome sequencing discovers increasingly comprehensive mutation repertoires, the functional relationships of mutations to tumor phenotypes remain largely unknown. Here, we measured ex vivo sensitivity of 246 blood cancers to 63 drugs alongside genome, transcriptome, and DNA methylome analysis to understand determinants of drug response. We assembled a primary blood cancer cell encyclopedia data set that revealed disease-specific sensitivities for each cancer. Within chronic lymphocytic leukemia (CLL), responses to 62% of drugs were associated with 2 or more mutations, and linked the B cell receptor (BCR) pathway to trisomy 12, an important driver of CLL. Based on drug responses, the disease could be organized into phenotypic subgroups characterized by exploitable dependencies on BCR, mTOR, or MEK signaling and associated with mutations, gene expression, and DNA methylation. Fourteen percent of CLLs were driven by mTOR signaling in a non-BCR-dependent manner. Multivariate modeling revealed immunoglobulin heavy chain variable gene (IGHV) mutation status and trisomy 12 as the most important modulators of response to kinase inhibitors in CLL. Ex vivo drug responses were associated with outcome. This study overcomes the perception that most mutations do not influence drug response of cancer, and points to an updated approach to understanding tumor biology, with implications for biomarker discovery and cancer care.

Background Metastatic uveal melanoma (mUM) is a rare malignancy and is different from metastatic cutaneous melanoma (mCM) in tumor characteristics and efficacy to immunotherapies. Tumor-specific biomarkers are required for mUM patients to monitor early disease progression on immunotherapies. Methods We investigated clinical characteristics such as liver tumor burden and routine blood tumor markers, including lactate dehydrogenase (LDH) and transaminases in patients with mUM and with liver metastasized cutaneous melanoma (LmCM), treated with immune checkpoint inhibitors (ICIs) between May 2013-February 2024. In addition, we analyzed soluble cMET (scMET) in serum samples from these patients along with a cohort of mCM patients without liver metastases (nLmCM) using ELISA. Circulating tumor DNA (ctDNA) in the plasma was analyzed using digital droplet PCR (ddPCR) in mUM patients receiving immunotherapies. scMET, ctDNA, and LDH combination was used to simultaneously monitor disease progression in ICI and tebentafusp-receiving mUM patients. Results Sixty-nine patients with mUM and seventy-six patients with LmCM were treated with either anti-PD1 monotherapy ( n  = 69, 48%) or ipi + nivo combination therapy ( n  = 76, 52%). Irrespective of the type of melanoma and type of immunotherapy, patients with liver metastasis size greater than 8cm experienced rapid disease progression. ICI-treated mUM patients with increased LDH, aspartate aminotransferase (AST), alanine transaminase (ALT), scMET, ctDNA, and rapidly growing tumors were significantly associated with treatment resistance and shorter progression-free and overall survival ( p  < 0.05). scMET (AUC: 0.82) outperforms LDH (AUC: 0.77) and S100 (0.68) in predicting one-year overall survival in these patients. A validation set with LmCM and nLmCM patient samples showed that increased scMET is likely a mUM-specific feature and does not predict ICI outcomes in LmCM or nLmCM patients ( p  > 0.05). Moreover, monitoring ctDNA and scMET in mUM patients under ICIs or tebentafusp treatment revealed the potential for early detection of disease progression. Conclusion Soluble cMET might serve as a tumor-specific biomarker to predict clinical outcomes in mUM patients. A combinational assessment of scMET and ctDNA in mUM patients’ blood offers a highly sensitive potential approach to monitor early disease progression under immunotherapies with ICI or tebentafusp.

Genomic analyses of chronic lymphocytic leukemia (CLL) identified somatic mutations and associations of clonal diversity with adverse outcomes. Clonal evolution likely has therapeutic implications but its dynamic is less well studied. We studied clonal composition and prognostic value of seven recurrently mutated driver genes using targeted next-generation sequencing in 643 CLL patients and found higher frequencies of mutations in TP53 (35 vs. 12%, p  < 0.001) and SF3B1 (20 vs. 11%, p  < 0.05) and increased number of (sub)clonal ( p  < 0.0001) mutations in treated patients. We next performed an in-depth evaluation of clonal evolution on untreated CLL patients (50 “progressors” and 17 matched “non-progressors”) using a 404 gene-sequencing panel and identified novel mutated genes such as AXIN1 , SDHA , SUZ12 , and FOXO3 . Progressors carried more mutations at initial presentation (2.5 vs. 1, p  < 0.0001). Mutations in specific genes were associated with increased ( SF3B1 , ATM , and FBXW7 ) or decreased progression risk ( AXIN1 and MYD88 ). Mutations affecting specific signaling pathways, such as Notch and MAP kinase pathway were enriched in progressive relative to non-progressive patients. These data extend earlier findings that specific genomic alterations and diversity of subclones are associated with disease progression and persistence of disease in CLL and identify novel recurrently mutated genes and associated outcomes.

Myxoid liposarcomas (MLS), malignant tumors of adipocyte origin, are driven by the FUS‐DDIT3 fusion gene encoding an aberrant transcription factor. The mechanisms whereby FUS‐DDIT3 mediates sarcomagenesis are incompletely understood, and strategies to selectively target MLS cells remain elusive. Here we show, using an unbiased functional genomic approach, that FUS‐DDIT3‐expressing mesenchymal stem cells and MLS cell lines are dependent on YAP1, a transcriptional co‐activator and central effector of the Hippo pathway involved in tissue growth and tumorigenesis, and that increased YAP1 activity is a hallmark of human MLS. Mechanistically, FUS‐DDIT3 promotes YAP1 expression, nuclear localization, and transcriptional activity and physically associates with YAP1 in the nucleus of MLS cells. Pharmacologic inhibition of YAP1 activity impairs the growth of MLS cells in vitro and in vivo . These findings identify overactive YAP1 signaling as unifying feature of MLS development that could represent a novel target for therapeutic intervention. Synopsis The transcriptional co‐activator YAP1 is essential in myxoid liposarcoma (MLS), an aggressive soft‐tissue tumor. This study reveals a link between aberrant YAP1 signaling and the FUS DDIT3 fusion oncoprotein that drives MLS. Pharmacologic YAP1 inhibition impairs MLS growth in vitro and in vivo . YAP1, encoding a transcriptional co activator that is inhibited by the Hippo pathway, was identified by RNA interference screening as an essential gene in mesenchymal stem cells expressing FUS‐DDIT3. FUS‐DDIT3‐expressing MLS cell lines and MLS patient specimens exhibited increased YAP1 activity, and YAP1 suppression in MLS cells caused proliferation arrest, senescence, and apoptosis. FUS DDIT3 induced the expression and nuclear localization of YAP1 and its downstream effectors. FUS‐DDIT3 and YAP1 physically associated in the nucleus of MLS cells. MLS cells were sensitive to pharmacologic blockade of YAP1 activity. Graphical Abstract The transcriptional co‐activator YAP1 is essential in myxoid liposarcoma (MLS), an aggressive soft‐tissue tumor. This study reveals a link between aberrant YAP1 signaling and the FUS DDIT3 fusion oncoprotein that drives MLS. Pharmacologic YAP1 inhibition impairs MLS growth in vitro and in vivo .

Progression Free Survival Ratio (PFSratio), as defined as the ratio between PFS on investigational treatment (PFS2) and PFS on the last prior therapy (PFS1), is a popular endpoint in precision oncology (PO) studies. In this work, five methodologies for PFSratio-based trial analysis (count-based, Kaplan Meier, Kernel-based Kaplan Meier, parametric and midrank) and two for trial design (GBVE and Weibull) are benchmarked. The Kernel-based Kaplan Meier analysis is most recommended, as it handles informative censoring and does not require PFS1/PFS2 distribution assumptions. Sample size and power calculation methods perform best when applied to settings with expected high PFS1/PFS2 correlation and median ratio. Analysis of five clinical trials (MOSCATO 01, WINTHER, MASTER, SHIVA and POG570) from >800 patients revealed an overall weak PFS1/PFS2 correlation (Kendall’s τ range 0.17-0.35), and an asymptotically unbiased median S PFSratio ( δ =1.3) = 33% by means of the Kernel-based analysis, while other methods considerably deviated in studies with censoring rate>10%. This methodology is implemented in the PROPHETS R package and Shiny app.

Analysis of selected cancer genes has become an important tool in precision oncology but cannot fully capture the molecular features and, most importantly, vulnerabilities of individual tumors. Observational and interventional studies have shown that decision-making based on comprehensive molecular characterization adds significant clinical value. However, the complexity and heterogeneity of the resulting data are major challenges for disciplines involved in interpretation and recommendations for individualized care, and limited information exists on how to approach multilayered tumor profiles in clinical routine. We report our experience with the practical use of data from whole-genome or exome and RNA sequencing and DNA methylation profiling within the MASTER (Molecularly Aided Stratification for Tumor Eradication Research) program of the National Center for Tumor Diseases (NCT) Heidelberg and Dresden and the German Cancer Research Center (DKFZ). We cover all relevant steps of an end-to-end precision oncology workflow, from sample collection, molecular analysis, and variant prioritization to assigning treatment recommendations and discussion in the molecular tumor board. To provide insight into our approach to multidimensional tumor profiles and guidance on interpreting their biological impact and diagnostic and therapeutic implications, we present case studies from the NCT/DKFZ molecular tumor board that illustrate our daily practice. This manual is intended to be useful for physicians, biologists, and bioinformaticians involved in the clinical interpretation of genome-wide molecular information.

Biliary tract cancers (BTCs) have poor prognosis and limited therapeutic options. The impact of O6‐methylguanine‐DNA methyltransferase (MGMT) inactivation in advanced BTC patients is not established. We investigated the prevalence, prognostic, and predictive impact of MGMT inactivation in two multicenter cohorts. MGMT inactivation was assessed through PCR and immunohistochemistry (IHC) in an Italian cohort; the results were then externally validated using RNA sequencing (RNA‐seq) data from the BTC subcohort of the Molecularly Aided Stratification for Tumor Eradication Research (MASTER) precision oncology program of the National Center for Tumor Diseases Heidelberg and the German Cancer Consortium. Among 164 Italian cases, 18% presented MGMT promoter hypermethylation (> 14%) and 73% had negative MGMT protein expression. Both were associated with worse overall survival (OS; HR 2.31; P < 0.001 and HR 1.99, P = 0.012, respectively). In the MASTER cohort, patients with lower MGMT mRNA expression showed significantly poorer OS (median OS [mOS] 20.4 vs 31.7 months, unadjusted HR 1.89; P = 0.043). Our results suggest that MGMT inactivation is a frequent epigenetic alteration in BTC, with a significant prognostic impact, and provide the rationale to explore DNA‐damaging agents in MGMT‐inactivated BTCs. The impact of MGMT inactivation in patients with advanced biliary tract cancer (BTC) is not established. We investigated its prevalence, prognostic, and predictive impact, assessed through methylation‐specific PCR and IHC, in a large Italian cohort. Then we evaluated MGMT inactivation through RNA‐seq and DNA methylation analysis in an independent German cohort. Overall, MGMT inactivation or reduced expression consistently resulted in poorer survival.

Precision oncology offers new cancer treatment options, yet sequencing methods vary in type and scope. In this study, we compared whole-exome/whole-genome (WES/WGS) and transcriptome sequencing (TS) with broad panel sequencing by resequencing the same tumor DNA and RNA as well as normal tissue DNA for germline assessment, from 20 patients with rare or advanced tumors, who were originally sequenced by WES/WGS ± TS within the DKFZ/NCT/DKTK MASTER program from 2015 to 2020. Molecular analyses resulted in a median number of 2.5 (gene panel) to 3.5 (WES/WGS ± TS) treatment recommendations per patient. Our results showed that approximately half of the therapy recommendations (TRs) of both sequencing programs were identical, while approximately one-third of the TRs in WES/WGS ± TS relied on biomarkers not covered by the panel. Eight of 10 molecularly informed therapy implementations were supported by the panel, the remaining two were based on biomarkers absent from the panel, highlighting the potential additional clinical benefit of WGS and TS.