Biomedical subjects

Nikolaus Schultz

Publications and source records attributed to Nikolaus Schultz.

8 recordsLinked to original sources

PubMed2026

A Clinically Integrated Pediatric Patient-Derived Xenograft Program Enables Evaluation of Cohort and Patient-Specific Biology and Therapeutic Strategies.

UNLABELLED: Preclinical translational research has increasingly utilized patient-derived xenograft (PDX) models for mechanistic and experimental therapeutic studies, yet most existing models have been developed from adult cancer types. We describe the establishment of a PDX program to expand the availability of pediatric-specific PDXs for preclinical research and enable studies of pediatric cancer histologies, including ultrarare diseases. Processes for PDX generation were integrated into established clinical workflows to facilitate universal model generation. Methodologies for tissue procurement, processing, and cryopreservation were optimized to enable intra- and interinstitutional PDX model generation. Over a 6-year span, 388 PDX tumor models representing more than 40 diagnoses were generated, including ultrarare tumors and longitudinal models established from pretherapy, posttherapy, and relapse tumors from the same patient. Genomic characterization of these PDXs demonstrates excellent concordance and recapitulation of molecular alterations of the source tumor. Successful PDX generation was enhanced from relapsed samples, was higher in sarcomas compared with other solid tumor types, and was a negative prognosticator for clinical outcome. With a broad portfolio of molecularly annotated models, we demonstrate utility for validating cross-histology biomarker-driven therapeutic strategies by demonstrating antitumor activity of an MAT2A inhibitor in MTAP-deficient PDXs. Universal model creation also allows for experimental validation of therapeutic hypotheses on a patient-specific basis, as we describe the characterization of a novel RAF1 fusion (EPB41L2::RAF1) in an osteosarcoma PDX. Development of a diverse collection of pediatric PDX models enables hypothesis-driven and cross-histology studies that expand our understanding of cancer biology and aid ongoing drug prioritization efforts in rare tumors. SIGNIFICANCE: A clinically integrated, genomically annotated pediatric PDX portfolio supported by systematic benchmarking of model generation facilitates exploratory biomarker-driven and patient-specific translational studies.

Humans↗

PubMed2026

Molecular and Clinical Determinants of Targeted Therapy Treatment in Biliary Tract Cancer.

PURPOSE: Actionable genomic alterations occur in all anatomic subsets of biliary tract cancer; however, targeted therapies have not shown a survival advantage over cytotoxics, and resistance mechanisms require further characterization. EXPERIMENTAL DESIGN: We analyzed a prospectively maintained cohort of 1,254 patients with histologically confirmed biliary tract cancer who underwent molecular profiling using an FDA-authorized targeted next-generation sequencing (NGS) assay. We defined actionable alterations across anatomic subsets, compared outcomes with targeted therapy versus cytotoxics, and evaluated genomic correlates of resistance using longitudinal samples. RESULTS: Overall, 59% of patients harbored at least one OncoKB alteration, and 32.2% (intrahepatic 40%, extrahepatic 15%, and gallbladder 22%) had a level 1/2 alteration. Emerging targets included KRAS alterations (17%), MTAP deletions (12.8%), MDM2 amplification (6.5%), and MET amplification (1.5%). Targeted therapy was associated with improved progression-free survival but not overall survival. Co-occurring TP53/RAS pathway and SMAD4 alterations were associated with inferior outcomes in IDH1/FGFR2-and ERBB2-driven tumors, respectively. Longitudinal profiling demonstrated ERBB2 loss in ERBB2-driven tumors, whereas IDH-, FGFR-, BRAF-, and NTRK-driven tumors retained the primary oncogenic driver. Acquired resistance was associated with alterations in RAS, MEK, MET, MYC, and CDKN2A. CONCLUSIONS: This comprehensive molecular profiling study illustrates the real-world utility and limitations of targeted NGS of biliary tract cancer and affirms the use of precision medicine in patients with these diseases. Genomic heterogeneity and therapeutic resistance observed in this study has the potential to inform ongoing drug development efforts for biliary tract cancer.

Humans↗

PubMed2026

Target Antigen Identification for Antibody Drug Conjugate Therapy in Biliary Tract Cancer.

BACKGROUND: Data on antibody-drug conjugates (ADCs) target expression prevalence, intertumoral heterogeneity, genomic concordance, and its effect on clinical outcomes is limited in biliary tract cancers (BTC). METHODS: Resected primary BTC specimens, and when available, matched metastatic samples were assembled into tissue microarrays and tested for CLDN18.2, c-MET, Nectin-4, TROP2, and HER2 expression by immunohistochemistry (IHC). A subset underwent targeted next-generation sequencing using MSK-IMPACT (NCT01775072). Exploratory associations of target expression with clinicopathologic parameters, genomic alterations, recurrence-free (RFS), and overall (OS) survival were evaluated. RESULTS: 65 patients with resected BTC and 18 paired metastatic sites were identified-43% extrahepatic cholangiocarcinoma, 40% intrahepatic cholangiocarcinoma, and 17% gallbladder cancer. All evaluated target antigens were expressed; percent positivity and H-score ≥200 were: TROP2 (83%, 26%), c-MET (75%, 26%), Nectin-4 (66%, 35%), and CLDN18.2 (46%, 7.7%). HER2 overexpression occurred in 3.1% of tumors. Overall agreement among paired primary and metastatic samples on calling either positive or negative ranged from 43% to 75% with the highest observed for HER2 [75%; κ=0.29 (95%CI: -0.32 to 0.91)] and TROP2 (71%; κ not available) and lowest for c-MET, CLDN18.2, and Nectin-4. Frequently altered genes included TP53 (36%), SMAD4 (27%), ELF3 (21%). We observed no significant association between target antigen expression with genomics, RFS, or OS. CONCLUSIONS: BTC displays frequent but heterogeneous expression of multiple ADC targets. These hypothesis generating findings suggest inherent complexity of target protein quantification, target threshold determination, and target sampling discordance. Future studies will be required to refine our understanding the utlitiy of ADCs in BTC.

Journal Article↗

PubMed2026

Integrated clinicogenomic analysis reveals the evolution and metastatic tropisms of advanced colorectal cancer.

We performed an integrated clinical and genomic analysis of over 7,000 consecutively sequenced colorectal cancer (CRC) samples to comprehensively characterize genetic drivers and metastatic tropisms of CRC. We find that genomic evolutionary changes, such as clonal mutations and oncogenic mutant allelic imbalance, selectively enhance the impact of recurrent oncogenic alterations. We identify the relative timing of organ-specific metastasis, showing sequential metastatic progression in microsatellite stable CRC with brain and adrenal metastases as late events; metastatic sites that cluster together, such as lung, bone, and brain metastases; and genomic events that enhance or decrease risk for each metastatic site, with WNT pathway activation as overall protective while RAS pathway activation increased risk for spread to all metastatic sites. Our data suggest that despite the heterogeneity in CRC, genomic evolution increases the impact of recurrent alterations, and integrating information about tumor primary location and genomics can be used to predict organ-specific metastasis risk.

Humans↗

PubMed2024

Pan-cancer analysis of biallelic inactivation in tumor suppressor genes identifies KEAP1 zygosity as a predictive biomarker in lung cancer.

The canonical model of tumor suppressor gene (TSG)-mediated oncogenesis posits that loss of both alleles is necessary for inactivation. Here, through allele-specific analysis of sequencing data from 48,179 cancer patients, we define the prevalence, selective pressure for, and functional consequences of biallelic inactivation across TSGs. TSGs largely assort into distinct classes associated with either pan-cancer (Class 1) or lineage-specific (Class 2) patterns of selection for biallelic loss, although some TSGs are predominantly monoallelically inactivated (Class 3/4). We demonstrate that selection for biallelic inactivation can be utilized to identify driver genes in non-canonical contexts, including among variants of unknown significance (VUSs) of several TSGs such as KEAP1. Genomic, functional, and clinical data collectively indicate that KEAP1 VUSs phenocopy established KEAP1 oncogenic alleles and that zygosity, rather than variant classification, is predictive of therapeutic response. TSG zygosity is therefore a fundamental determinant of disease etiology and therapeutic sensitivity.

Kelch-Like ECH-Associated Protein 1↗

PubMed2024

Clinicogenomic predictors of survival and intracranial progression after stereotactic radiosurgery for colorectal cancer brain metastases.

OBJECTIVE: Brain metastases (BM) from colorectal cancer (CRC) are associated with dismal prognosis. When BM-directed therapy is considered, better methods are needed to identify patients at risk of poor oncological outcomes in order to optimize patient selection for closer surveillance or escalated therapy. The authors sought to identify clinicogenomic predictors of survival and intracranial disease progression after CRC BM have been treated with stereotactic radiosurgery (SRS). METHODS: Patients with newly diagnosed CRC BM treated with SRS between 2009 and 2022 who had next-generation genomic sequencing data available were included. Frameless SRS was delivered in 1-5 fractions, alone or after neurosurgical resection. Outcomes included overall survival (OS) and intracranial progression (IP), evaluated per patient treated with SRS, and local progression (LP), evaluated per BM. Associations between baseline clinicogenomic features and outcomes were evaluated with Cox regression and competing risk regression, with death as a competing risk. RESULTS: This analysis included 123 patients with 299 BM. At BM diagnosis, 111 patients (90%) had progressive extracranial disease, and 79 patients (64%) had ≥ 3 sites of extracranial metastasis. The median (IQR) number of BM was 2 (1-3) per patient. The median (IQR) biologically effective dose (BED) was 51.3 (51.3-65.1) Gy, corresponding to a prescription of 27 Gy in 3 fractions. OS, IP, and LP estimates at 1 year after SRS were 36%, 55%, and 12%, respectively. OS was independently associated with progressive extracranial disease (HR 4.26, 95% CI 1.63-11.2, p = 0.003) and ≥ 3 extracranial metastatic sites (HR 1.84, 95% CI 1.12-3.01, p = 0.02). LP was less likely when BM received BED ≥ 51.3 Gy (HR 0.24, 95% CI 0.07-0.78, p = 0.02), independent of BM diameter (HR 1.21/cm, 95% CI 0.8-1.84, p = 0.4). IP was independently associated with genomic alterations; TP53 driver alterations were associated with higher risk of IP (HR 2.71, 95% CI 1.26-5.79, p = 0.01), whereas MYC pathway alterations were associated with lower risk (HR 0.15, 95% CI 0.03-0.68, p = 0.01). CONCLUSIONS: The authors identified clinicogenomic features associated with adverse outcomes after SRS for CRC BM. Progressive and extensive extracranial metastases predicted worse OS. Insufficient SRS doses predicted greater risk of LP. Wild-type TP53 and alterations in the MYC pathway were independently associated with lower risk of IP. Patients at high risk of IP may be considered for closer surveillance or escalated therapy.

Humans↗

PubMed2024

Molecular and Clinical Determinants of Acquired Resistance and Treatment Duration for Targeted Therapies in Colorectal Cancer.

PURPOSE: Targeted therapies have improved outcomes for patients with metastatic colorectal cancer, but their impact is limited by rapid emergence of resistance. We hypothesized that an understanding of the underlying genetic mechanisms and intrinsic tumor features that mediate resistance to therapy will guide new therapeutic strategies and ultimately allow the prevention of resistance. EXPERIMENTAL DESIGN: We assembled a series of 52 patients with paired pretreatment and progression samples who received therapy targeting EGFR (n = 17), BRAF V600E (n = 17), KRAS G12C (n = 15), or amplified HER2 (n = 3) to identify molecular and clinical factors associated with time on treatment (TOT). RESULTS: All patients stopped treatment for progression and TOT did not vary by oncogenic driver (P = 0.5). Baseline disease burden (≥3 vs. <3 sites, P = 0.02), the presence of hepatic metastases (P = 0.02), and gene amplification on baseline tissue (P = 0.03) were each associated with shorter TOT. We found evidence of chromosomal instability (CIN) at progression in patients with baseline MAPK pathway amplifications and those with acquired gene amplifications. At resistance, copy-number changes (P = 0.008) and high number (≥5) of acquired alterations (P = 0.04) were associated with shorter TOT. Patients with hepatic metastases demonstrated both higher number of emergent alterations at resistance and enrichment of mutations involving receptor tyrosine kinases. CONCLUSIONS: Our genomic analysis suggests that high baseline CIN or effective induction of enhanced mutagenesis on targeted therapy underlies rapid progression. Longer response appears to result from a progressive acquisition of genomic or chromosomal instability in the underlying cancer or from the chance event of a new resistance alteration.

Humans↗

PubMed2024

Deep-Learning Model for Tumor-Type Prediction Using Targeted Clinical Genomic Sequencing Data.

UNLABELLED: Tumor type guides clinical treatment decisions in cancer, but histology-based diagnosis remains challenging. Genomic alterations are highly diagnostic of tumor type, and tumor-type classifiers trained on genomic features have been explored, but the most accurate methods are not clinically feasible, relying on features derived from whole-genome sequencing (WGS), or predicting across limited cancer types. We use genomic features from a data set of 39,787 solid tumors sequenced using a clinically targeted cancer gene panel to develop Genome-Derived-Diagnosis Ensemble (GDD-ENS): a hyperparameter ensemble for classifying tumor type using deep neural networks. GDD-ENS achieves 93% accuracy for high-confidence predictions across 38 cancer types, rivaling the performance of WGS-based methods. GDD-ENS can also guide diagnoses of rare type and cancers of unknown primary and incorporate patient-specific clinical information for improved predictions. Overall, integrating GDD-ENS into prospective clinical sequencing workflows could provide clinically relevant tumor-type predictions to guide treatment decisions in real time. SIGNIFICANCE: We describe a highly accurate tumor-type prediction model, designed specifically for clinical implementation. Our model relies only on widely used cancer gene panel sequencing data, predicts across 38 distinct cancer types, and supports integration of patient-specific nongenomic information for enhanced decision support in challenging diagnostic situations. See related commentary by Garg, p. 906. This article is featured in Selected Articles from This Issue, p. 897.

Humans↗