Biology subjects

Frederick, D. T.

Publications and source records attributed to Frederick, D. T..

13 recordsLinked to original sources

bioRxiv2026

Context-specific genetic interaction mapping reveals combinatorial KRAS/FGFR dependence in pancreatic cancer

Oncogenic KRAS drives >90% of pancreatic ductal adenocarcinoma (PDAC), and the pan-RAS inhibitor daraxonrasib doubles overall survival, but adaptive and acquired resistance limit the depth and duration of responses. To systematically map combination vulnerabilities, we developed RAS+, a digenic CRISPR knockout library testing 10,296 pairwise perturbations across oncogenic signaling pathways, and screened 12 cell lines, revealing genotype- and lineage-specific interactions. In KRASmut PDAC, the most differentially effective gene pair was KRAS and FRS2, an adapter coupling FGFR signaling to RAS. Combined KRAS/pan-FGFR inhibition was synergistic and cytotoxic, eradicating tumor cells and overcoming resistance in vitro and deepening and prolonging regressions in vivo. Single-cell analysis identified cancer-associated fibroblasts (CAFs) as a primary source of FGF ligands in PDAC, and CAF conditioned media or individual FGFs promoted resistance. These findings define a stroma-to-tumor adaptive FGFR circuit limiting the effects of KRAS inhibition. Thus, combined KRAS/FGFR blockade may be a strategy to improve responses.

cancer biology↗

bioRxiv2024

Identification of potent biparatopic antibodies targeting FGFR2 fusion driven cholangiocarcinoma.

Translocations involving FGFR2 gene fusions are common in cholangiocarcinoma and predict response to FGFR kinase inhibitors. However, the rate and durability of response are limited due to the emergence of resistance, typically involving acquired FGFR2 kinase domain mutations, and to sub-optimal dosing, relating to drug adverse effects. Here, we report the development of biparatopic antibodies targeting the FGFR2 extracellular domain (ECD), as candidate therapeutics. Biparatopic antibodies can overcome drawbacks of standard bivalent monoparatopic antibodies, which often show poor inhibitory or even agonist activity against oncogenic receptors. We show that oncogenic transformation by FGFR2 fusions requires an intact ECD. Moreover, by systematically generating biparatopic antibodies that target distinct epitope pairs along the FGFR2 ECD, we identified antibodies that effectively block signaling and malignant growth driven by FGFR2-fusions. Importantly, these antibodies demonstrate efficacy in vivo, synergy with FGFR inhibitors, and activity against FGFR2 fusions harboring kinase domain mutations. Thus, biparatopic antibodies may serve as new treatment options for patients with FGFR2-altered cholangiocarcinoma. SummaryWe identify biparatopic FGFR2 antibodies that are effective against FGFR2 fusion driven cholangiocarcinoma.

cancer biology↗

bioRxiv2024

A Comprehensive Proteogenomic and Spatial Analysis of Innate and Acquired Resistance of Metastatic Melanoma to Immune Checkpoint Blockade Therapies

While a subset of patients with metastatic melanoma achieves durable responses to immune checkpoint blockade (ICB) therapies, the majority ultimately exhibit either innate or acquired resistance to these treatments. However, the molecular mechanisms underlying resistance to ICB therapies remain elusive and are warranted to elucidate. Here, we comprehensively investigated the tumor and tumor immune microenvironment (TIME) of paired pre- and post-treatment tumor specimens from metastatic melanoma patients who were primary or secondary resistance to anti-CTLA-4 and/or anti-PD-1/PD-L1 therapies. Differentially expressed gene (DEG) analysis and single-sample gene set enrichment analysis (ssGSEA) with transcriptomic data identified cell cycle and c-MYC signaling as pathway-based resistance signatures. And weighted gene co-expression network analysis (WGCNA) revealed the activation of a cross-resistance meta-program involving key signaling pathways related to tumor progression in ICB resistant melanoma. Moreover, spatially-resolved, image-based immune monitoring analysis by using NanoStrings digital spatial profiling (DSP) and Cyclic Immunofluorescence (CyCIF) showed infiltration of suppressive immune cells in the tumor microenvironment of melanoma with resistance to ICB therapies. Our study reveals the molecular mechanisms underlying resistance to ICB therapies in patients with metastatic melanoma by conducting such integrated analyses of multi-dimensional data, and provides rationale for salvage therapies that will potentially overcome resistance to ICB therapies. Statement of translational relevanceThis study paves the way for the creation of innovative therapeutic strategies, aimed at subverting resistance to immune checkpoint blockade (ICB) therapies in metastatic melanoma patients. By unraveling the specific molecular mechanisms underlying resistance, scientists can design effective alternative treatments that target pathways such as pathways associated with cell cycle dysregulation and c-MYC signaling. Furthermore, through the application of advanced immune monitoring techniques such as NanoString Digital Spatial Profiling (DSP) and Cyclic Immunofluorescence (CyCIF), this study has significantly enriched our understanding of the tumor microenvironment. This enhanced characterization facilitates the discovery of potential biomarkers that may forecast a patients response to ICB treatment. Ultimately, these advancements could potentially refine patient outcomes and foster the development of more personalized cancer treatments in the future.

genetics↗

bioRxiv2024

Mature and migratory dendritic cells promote immune infiltration and response to anti-PD-1 checkpoint blockade in metastatic melanoma

Immune checkpoint inhibitors (ICIs) have heralded a remarkable shift in cancer care, significantly extending survival for advanced cancer patients. However, despite their remarkable clinical successes, a substantial majority of patients fail to achieve a lasting response to ICIs. To address this challenge and gain insights into the complex landscape of the tumor microenvironment (TME), we conducted an extensive analysis using single-cell RNA sequencing (scRNA; [~]216K cells across 39 samples) and single-nucleus transposase-accessible chromatin sequencing (snATAC; [~]43K cells from 15 samples) in a metastatic melanoma cohort. This systematic approach delineates 14 distinct cell types and 55 cell subtypes, including the identification of 15 transcriptional hallmarks of malignant cells. Through correlation analysis of cell subtype proportions, we unveiled six distinct clusters associated with varying tumor responses. Particularly intriguing was the identification of the mature DC enriched in immunoregulatory molecules (mregDC) subtype exhibiting correlations with naive T and B cells, forming an anti-tumor program that underscores the importance of multiple immune cell types in mediating anti-tumor immunity. Notably, we found that mregDC abundance represents a good prognostic predictor of progression-free survival (PFS) in the context of ICI treatment, and when combined with the TCF7+/- CD8 T cell ratio, it reliably predicts patient PFS across treatments beyond ICI. We validated our findings using an independent cohort of 274 ICI-treated melanoma samples analyzed using tissue-level expression. We next compared mregDCs and conventional dendritic cell types 1 and 2 (cDC1 and cDC2) using transcriptome signature, differentiation trajectory, interactome, cytokine milieu, and epigenome landscape analyses. This comparative analysis shed light on the unique attributes of mregDCs within the TME. Finally, we investigated cell type/subtype-specific genes, pathways, immune response enrichment, and ligand-receptor interactions closely associated with the proportions of mregDCs within the TME. These molecular and cellular insights, with their critical roles in enhancing the immune response against cancer, offer valuable prospects for predicting the efficacy of ICI regimens, and potentially guiding the selection of rational combinatorial therapies.

cancer biology↗

bioRxiv2022

Genomic heterogeneity and ploidy identify patients with intrinsic resistance to PD-1 blockade in metastatic melanoma

While the introduction of immune checkpoint blockade (ICB) has dramatically improved clinical outcomes for patients with advanced melanoma, a significant proportion of patients develop resistance to therapy, and mechanisms of resistance are poorly elucidated in most cases. Further, while combination ICB has higher response rates and improved progression free survival compared to single agent therapy in the front line setting, there is significantly increased toxicity with combination ICB, and biomarkers to identify patients who would disproportionately benefit from combination therapy vs aPD-1 ICB are poorly characterized. To understand resistance mechanisms to single vs combination ICB therapy, we analyze whole-exome-sequencing (WES) of pre-treatment tumor and matched normals of 4 cohorts (n=140) of previously ICB-naive aPD-1 ICB treated patients. We find that high intratumoral genomic heterogeneity and low ploidy identify patients with intrinsic resistance to aPD-1 ICB. Comparing to a melanoma cohort from a pre-targeted therapy and ICB time period ("untreated" cohort), we find that genomic heterogeneity specifically predicts response and survival in the ICB treated cohorts, but not in the untreated cohort, while ploidy is also prognostic of overall survival in the "untreated" (by targeted therapy or ICB) group. To establish clinically actionable predictions, we optimize a simple decision tree using genomic ploidy and heterogeneity to identify with high confidence (90% PPV) a subset of patients with intrinsic resistance to and significantly worse survival on aPD1 ICB treatment. We then validate this model in independent cohorts, and further show that a significant proportion of patients predicted to have intrinsic resistance to single agent aPD-1 ICB respond to combination ICB, which suggests that nominated patients may benefit disproportionately from combination ICB. We further show that the features and predictions of the model are independent of known clinical features and previously nominated molecular biomarkers. These findings highlight the clinical and biological importance of genomic heterogeneity and ploidy, and sets a concrete framework towards clinical actionability, broadly advancing precision medicine in oncology.

cancer biology↗

bioRxiv2022

Whole genome error-corrected sequencing for sensitive circulating tumor DNA cancer monitoring

Circulating cell-free DNA (ccfDNA) sequencing for low-burden cancer monitoring is limited by sparsity of circulating tumor DNA (ctDNA), the abundance of genomic material within a plasma sample, and pre-analytical error rates due to library preparation, and sequencing errors. Sequencing costs have historically favored the development of deep targeted sequencing approaches for overcoming sparsity in ctDNA detection, but these techniques are limited by the abundance of ccfDNA in samples, which imposes a ceiling on the maximal depth of coverage in targeted panels. Whole genome sequencing (WGS) is an orthogonal approach to ctDNA detection that can overcome the low abundance of ccfDNA by supplanting sequencing depth with breadth, integrating signal across the entire tumor mutation landscape. However, the higher cost of WGS limits the practical depth of coverage and hinders broad adoption. Lower sequencing costs may thus allow for enhanced ctDNA cancer monitoring via WGS. We therefore applied emerging lower-cost WGS (Ultima Genomics, 1USD/Gb) to plasma samples at [~]120x coverage. Copy number and single nucleotide variation profiles were comparable between matched Ultima and Illumina datasets, and the deeper WGS coverage enabled ctDNA detection at the parts per million range. We further harnessed these lower sequencing costs to implement duplex error-corrected sequencing at the scale of the entire genome, demonstrating a [~]1,500x decrease in errors in the plasma of patient-derived xenograft mouse models, and error rates of [~]10-7 in patient plasma samples. We leveraged this highly de-noised plasma WGS to undertake cancer monitoring in the more challenging context of resectable melanoma without matched tumor sequencing. In this context, duplex-corrected WGS allowed us to harness known mutational signature patterns for disease monitoring without matched tumors, paving the way for de novo cancer monitoring.

genomics↗

bioRxiv2022

Proteasomal downregulation of the pro-apoptotic MST2 pathway contributes to BRAF inhibitor resistance in melanoma

The RAS-RAF-MEK-ERK pathway is hyperactivated in almost all malignant melanomas, and mutations in BRAF or NRAS account for most of these cases. BRAF inhibitors are highly efficient for treating patients with BRAFV600E mutations, but tumours frequently acquire resistance within a few months. Multiple resistance mechanisms have been identified, due to mutations or network adaptations that revive ERK signalling. We have previously shown that RAF proteins inhibit the MST2 proapoptotic pathway in a kinase independent fashion. Here, we have investigated the role of the MST2 pathway in mediating resistance to BRAF inhibitors. We show that the BRAFV600E mutant protein, but not the wildtype BRAF protein, strongly binds to MST2 and inhibits MST2 pro-apoptotic signalling. Downregulation of MST2 reduces BRAF inhibitor induced apoptosis. In BRAF inhibitor resistant cell lines MST2 pathway proteins are downregulated by ubiquitination and subsequent proteasomal degradation rendering cells refractory to MST2 pathway induced apoptosis. Restoration of apoptosis can be achieved by increasing MST2 pathway protein expression using proteasome inhibitors. In summary, we show that the MST2 pathway plays a role in the acquisition of BRAF inhibitor resistance in melanoma.

molecular biology↗

bioRxiv2022

Plasma proteomic biomarkers identify non-responders and reveal biological insights about the tumor microenvironment in melanoma patients after PD1 blockade

Most patients treated with immune checkpoint blockade (ICB) do not have durable treatment responses. Therefore, there is a critical need to identify early non-invasive biomarkers of response. We performed plasma proteomic analysis (>700 proteins) at three timepoints on 174 metastatic melanoma patients treated with ICB. We leverage independent training and testing cohorts to build a predictor of immunotherapy response that outperforms several tissue-based approaches. We found 217 differentially expressed proteins between ICB responders (R) and non-responders (NR), including a co-regulated module of proteins enriched in certain NR patients. By analyzing single-cell RNA-sequencing data of tumor biopsies from 32 patients, we dissected the relative contribution of cells in the tumor to proteins in circulation. The majority of proteins in the co-regulated NR module derived from tumor and myeloid cells. Amongst myeloid cells, we identified a subset of tumor-associated macrophages (TAMs) with a suppressive phenotype that expressed high levels of the co-regulated NR module, thus suggesting they are key drivers of non-response signatures. Together, our data demonstrates the utility of plasma proteomics in biomarker discovery and in understanding the biology of host response to tumors.

cancer biology↗

bioRxiv2022

Machine learning guided signal enrichment for ultrasensitive plasma tumor burden monitoring

In solid tumor oncology, circulating tumor DNA (ctDNA) is poised to transform care through accurate assessment of minimal residual disease (MRD) and therapeutic response monitoring. To overcome the sparsity of ctDNA fragments in low tumor fraction (TF) settings and increase MRD sensitivity, we previously leveraged genome-wide mutational integration through plasma whole genome sequencing (WGS). We now introduce MRD-EDGE, a composite machine learning-guided WGS ctDNA single nucleotide variant (SNV) and copy number variant (CNV) detection platform designed to increase signal enrichment. MRD-EDGE uses deep learning and a ctDNA-specific feature space to increase SNV signal to noise enrichment in WGS by 300X compared to our previous noise suppression platform MRDetect. MRD-EDGE also reduces the degree of aneuploidy needed for ultrasensitive CNV detection through WGS from 1Gb to 200Mb, thereby expanding its applicability to a wider range of solid tumors. We harness the improved performance to track changes in tumor burden in response to neoadjuvant immunotherapy in non-small cell lung cancer and demonstrate ctDNA shedding in precancerous colorectal adenomas. Finally, the radical signal to noise enrichment in MRD-EDGE enables de novo mutation calling in melanoma without matched tumor, yielding clinically informative TF monitoring for patients on immune checkpoint inhibition.

genomics↗

bioRxiv2021

Microenvironmental correlates of immune checkpoint inhibitor response in human melanoma brain metastases revealed by T cell receptor and single-cell RNA sequencing

Melanoma-derived brain metastases (MBM) represent an unmet clinical need due to central nervous system (CNS) progression as a frequent, end-stage site of disease. Immune checkpoint inhibition (ICI) represents a clinical opportunity against MBM; however, the MBM tumor microenvironment (TME) has not been fully elucidated in the context of ICI. To dissect unique MBM-TME elements and correlates of MBM-ICI response, we collected 32 fresh MBM and performed single cell RNA sequencing of the MBM-TME and T cell receptor clonotyping on T cells from MBM and matched blood and extracranial lesions. We observed myeloid phenotypic heterogeneity, most notably multiple distinct neutrophil states including an IL-8 expressing population that correlated with malignant cell epithelial-to-mesenchymal transition. Additionally, we observe significant relationships between intracranial T cell phenotypes and the distribution of T cell clonotypes intracranially and peripherally. We found that the phenotype, clonotype, and overall number of MBM-infiltrating T cells were associated with response to ICI, suggesting that ICI-responsive MBMs interact with peripheral blood in a manner similar to extracranial lesions. These data demonstrate unique features of the MBM-TME, which may represent potential targets to improve clinical outcomes for patients with MBM.

cancer biology↗

bioRxiv2021

Combined tumor and immune signals from genomes or transcriptomes predict outcomes of checkpoint inhibition in melanoma

Cancer immunotherapy with checkpoint blockade (CPB) leads to improved outcomes in melanoma and other tumor types, but a majority of patients do not respond. High tumor mutation burden (TMB) and high levels of tumor-infiltrating T cells have been associated with response to immunotherapy, but integrative models to predict clinical benefit using DNA or RNA alone have not been comprehensively explored. We sequenced DNA and RNA from melanoma patients receiving CPB, and aggregated previously published data, yielding whole exome sequencing data for 189 patients and bulk RNA sequencing data for 178 patients. Using these datasets, we derived genomic and transcriptomic factors that predict overall survival (OS) and response to immunotherapy. Using whole-exome DNA data alone, we calculated T cell burden (TCB) and B cell burden (BCB) based on rearranged TCR/Ig DNA sequences and found that patients whose melanomas have high TMB together with either high TCB or high BCB survived longer and had higher response rates as compared to patients with either low TMB or TCB/BCB. Next, using bulk RNA-Seq data, differential expression analysis identified 83 genes associated with high or low OS. By combining pairs of immune-expressed genes with tumor-expressed genes, we identified three gene pairs associated with response and survival (Bonferroni P<0.05). All three gene pair models were validated in an independent cohort (n=180) (Bonferroni P<0.05). The best performing gene pair model included the lymphocyte-expressed MAP4K1 (Mitogen- Activated Protein Kinase Kinase Kinase Kinase 1) combined with the transcription factor TBX3 (T-Box Transcription Factor 3) which is overexpressed in poorly differentiated melanomas. We conclude that RNA-based (MAP4K1&TBX3) or DNA-based (TCB&TMB) models combining immune and tumor measures improve predictions of outcome after checkpoint blockade in melanoma.

genomics↗

bioRxiv2021

Multicellular immune hubs and their organization in MMRd and MMRp colorectal cancer

Immune responses to cancer are highly variable, with mismatch repair-deficient (MMRd) tumors exhibiting more anti-tumor immunity than mismatch repair-proficient (MMRp) tumors. To understand the rules governing these varied responses, we transcriptionally profiled 371,223 cells from colorectal tumors and adjacent normal tissues of 28 MMRp and 34 MMRd patients. Analysis of 88 cell subsets and their 204 associated gene expression programs revealed extensive transcriptional and spatial remodeling across tumors. To discover hubs of interacting malignant and immune cells, we identified expression programs in different cell types that co-varied across patient tumors and used spatial profiling to localize coordinated programs. We discovered a myeloid cell-attracting hub at the tumor-luminal interface associated with tissue damage, and an MMRd-enriched immune hub within the tumor, with activated T cells together with malignant and myeloid cells expressing T-cell-attracting chemokines. By identifying interacting cellular programs, we thus reveal the logic underlying spatially organized immune-malignant cell networks.

cancer biology↗

bioRxiv2019

Plasma-derived exosomal analysis and deconvolution enables prediction and tracking of melanoma checkpoint blockade response

PurposeImmune checkpoint inhibitors (ICI) have demonstrated promising therapeutic benefit although a majority will not respond. Here we identify and validate predictive biomarkers from plasma-derived exosomes that allow non-invasive monitoring of tumor intrinsic and host immune status and prediction of ICI success.\n\nExperimental DesignTranscriptomic profiling of peripheral blood bulk exosomes and tumors from a discovery cohort of 50 patients with metastatic melanoma treated with ICI was undertaken; a further validation cohort of 30 patients was utilized to validate findings from the discovery cohort. We designed a Bayesian probabilistic model to partition bulk exosomes into tumor-specific and non-tumor-specific proportions.\n\nResultsExosomal RNA signatures exhibit significant correlations with tumor transcriptomes. Exosomal profiles reflect several key biological drivers of ICI resistance or melanoma progression, exhibit significantly differentially expressed genes and pathways, and correlate with and are predictive of clinical response to therapy. Our deconvolution model estimates contributions from tumor and non-tumor sources, enabling more precise interpretation of differentially-expressed genes and pathways. Exosomal RNA-seq mutational information can be used to segregate responders and non-responders.\n\nConclusionsPeripheral blood-derived exosomes can serve as a non-invasive biomarker to jointly probe tumor-intrinsic and immune changes to ICI, and can potentially function as predictive markers of ICI responsiveness and a monitoring tool for tumor persistence and immune activation.\n\nStatement of SignificanceWe use transcriptomic analysis of bulk, non-selected, peripheral blood derived exosomes to reveal both tumor-intrinsic and immune-derived signatures predictive of early response to immune checkpoint inhibitor therapy. We develop a novel computational model to classify exosomal transcripts into tumor and non-tumor components and establish relevance in immune checkpoint blockade therapy. We show that tumor driver load from RNA-seq mutational calls are significantly different between responders and non-responders.

cancer biology↗