Biomedical subjects

Michael F Berger

Publications and source records attributed to Michael F Berger.

4 recordsLinked to original sources

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

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

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↗