Biology subjects

Weigelt, B.

Publications and source records attributed to Weigelt, B..

14 recordsLinked to original sources

bioRxiv2026

Coexistent PTEN and PIK3CA alterations hyperactivate mTORC1 signaling in endometrial cancers and cause their selective sensitivity to mTORC1 inhibition

In approximately half of endometrial carcinoma (EC), PTEN loss-of-function and activating PI3K mutants coexist. Unlike cells with either single mutation, PTEN/PIK3CA coexistent alterations result in elevated membrane phosphatidylinositol (3,4,5)-trisphosphate (PIP3) levels and mTORC1 hyperactivation, rendering PI3K or AKT inhibition ineffective in blocking mTORC1 activity and tumor growth. The bi-steric mTORC1 kinase inhibitor, RMC-6272, suppresses mTORC1 activity and cell growth by reducing protein translation and cell cycle progression. In vivo, RMC-6272, but not PI3K inhibitors, effectively suppressed mTORC1 and growth of EC PDXs with coexistent PTEN/PIK3CA lesions. These findings are consistent with a phase I trial of bi-steric mTORC1 inhibitor RMC-5552, showing anti-tumor activity in patients with EC. PDXs with KRAS co-mutations regrew after RMC-6272 treatment, which was prevented by the addition of the RAS(ON) multi-selective inhibitor RMC-7977. Overall, these data suggest that mTORC1 hyperactivation drives ECs with coexistent PTEN/PIK3CA mutations, explain the limited antitumor activity of PI3K and AKT inhibitors, and support clinical evaluation of mTORC1 inhibitors as potential therapy for EC. SignificanceWe have found the mechanistic consequences of PTEN/PIK3CA co-alterations in endometrial tumors and that these mutations result in a profound hyperactivation of mTORC1 signaling. Single mutant tumors are sensitive to PI3K inhibition but those with both mutations are insensitive to PI3K or AKT inhibition but are exquisitely dependent on mTORC1 kinase. This provides strong preclinical rationale for targeting mTORC1, alone or combined with RAS inhibition (in RAS co-mutant tumors), as an effective therapeutic strategy.

cancer biology↗

bioRxiv2025

Integrated histopathologic modeling of detailed tumor subtypes and actionable biomarkers

Accurate cancer subtyping with accompanying molecular characterization is critical for precision oncology. While machine learning approaches have been applied to both digital pathology and cancer genomics, previous work has been limited in sample size and has typically aggregated granular cancer subtypes into coarse groupings, likely obfuscating informative molecular and prognostic associations and phenotypic variation of more detailed tumor subtypes. Accordingly, we collated 378,123 hematoxylin and eosin (H&E)-stained whole-slide images (WSIs) with matched targeted DNA clinical sequencing results and OncoTree detailed cancer subtypes from a real-world cohort of 71,142 patients. Using this scaled, granular dataset and a cancer subtype knowledge graph, we developed Mosaic: a family of calibrated machine learning models using H&E WSI embeddings to classify tumors and identify molecular phenotypes across 163 detailed subtypes. The cancer subtyping module (Aeon) achieved an area under the receiver operating characteristic curve (AUROC) of 0.992 overall, with 161/163 subtypes reaching an AUROC [≥] 0.90 and improved performance over a state-of-the-art genomics-based classifier. The genomic inference module (Paladin) achieved an AUROC [≥] 0.80 for 167 pairs of detailed subtypes and genomic targets. We further used the learned histopathologic representations to i) identify key associations of the histopathologic embeddings with clinical biomarkers; ii) identify unsupervised sub-clusters of tumors with genomic determinants of tumor phenotype; iii) specify granular diagnoses for cancers of unknown primary, evaluated by genomic associations and expected clinical outcome distributions; iv) annotate functional significance for variants of uncertain significance (VUS); and v) identify cases that mimic the phenotypic effect of known DNA variants on H&E in the absence of detectable DNA alterations. Taken together, this work advances our understanding of phenotypic variation of granular tumor subtypes, their relevance to enhanced diagnostics, and their potential utility in risk stratification with multimodal machine learning in cancer.

cancer biology↗

bioRxiv2024

Tracking clonal evolution of drug resistance in ovarian cancer patients by exploiting structural variants in cfDNA

Drug resistance is the major cause of therapeutic failure in high-grade serous ovarian cancer (HGSOC). Yet, the mechanisms by which tumors evolve to drug resistant states remains largely unknown. To address this, we aimed to exploit clone-specific genomic structural variations by combining scaled single-cell whole genome sequencing with longitudinally collected cell-free DNA (cfDNA), enabling clonal tracking before, during and after treatment. We developed a cfDNA hybrid capture, deep sequencing approach based on leveraging clone-specific structural variants as endogenous barcodes, with orders of magnitude lower error rates than single nucleotide variants in ctDNA (circulating tumor DNA) detection, demonstrated on 19 patients at baseline. We then applied this to monitor and model clonal evolution over several years in ten HGSOC patients treated with systemic therapy from diagnosis through recurrence. We found drug resistance to be polyclonal in most cases, but frequently dominated by a single high-fitness and expanding clone, reducing clonal diversity in the relapsed disease state in most patients. Drug-resistant clones frequently displayed notable genomic features, including high-level amplifications of oncogenes such as CCNE1, RAB25, NOTCH3, and ERBB2. Using a population genetics Wright-Fisher model, we found evolutionary trajectories of these features were consistent with drug-induced positive selection. In select cases, these alterations impacted selection of secondary lines of therapy with positive patient outcomes. For cases with matched single-cell RNA sequencing data, pre-existing and genomically encoded phenotypic states such as upregulation of EMT and VEGF were linked to drug resistance. Together, our findings indicate that drug resistant states in HGSOC pre-exist at diagnosis and lead to dramatic clonal expansions that alter clonal composition at the time of relapse. We suggest that combining tumor single cell sequencing with cfDNA enables clonal tracking in patients and harbors potential for evolution-informed adaptive treatment decisions.

cancer biology↗

bioRxiv2024

APOBEC3 mutagenesis drives therapy resistance in breast cancer

Acquired genetic alterations commonly drive resistance to endocrine and targeted therapies in metastatic breast cancer1-7, however the underlying processes engendering these diverse alterations are largely uncharacterized. To identify the mutational processes operant in breast cancer and their impact on clinical outcomes, we utilized a well-annotated cohort of 3,880 patient samples with paired tumor-normal sequencing data. The mutational signatures associated with apolipoprotein B mRNA-editing enzyme catalytic polypeptide-like 3 (APOBEC3) enzymes were highly prevalent and enriched in post-treatment compared to treatment-naive hormone receptor-positive (HR+) cancers. APOBEC3 mutational signatures were independently associated with shorter progression-free survival on antiestrogen plus CDK4/6 inhibitor combination therapy in patients with HR+ metastatic breast cancer. Whole genome sequencing (WGS) of breast cancer models and selected paired primary-metastatic samples demonstrated that active APOBEC3 mutagenesis promoted resistance to both endocrine and targeted therapies through characteristic alterations such as RB1 loss-of-function mutations. Evidence of APOBEC3 activity in pre-treatment samples illustrated a pervasive role for this mutational process in breast cancer evolution. The study reveals APOBEC3 mutagenesis to be a frequent mediator of therapy resistance in breast cancer and highlights its potential as a biomarker and target for overcoming resistance.

cancer biology↗

bioRxiv2024

Multimodal histopathologic models stratify hormone receptor-positive early breast cancer

For patients with hormone receptor-positive, early breast cancer without HER2 amplification, multigene expression assays including Oncotype DX (R) recurrence score (RS) have been clinically validated to identify patients who stand to derive added benefit from adjuvant cytotoxic chemotherapy. However, cost and turnaround time have limited its global adoption despite recommendation by practice guidelines. We investigated if routinely available hematoxylin and eosin (H&E)-stained pathology slides could act as a surrogate triaging data substrate by predicting RS using machine learning methods. We trained and validated a multimodal transformer model, Orpheus, using 6,203 patients across three independent cohorts, taking both H&E images and their corresponding synoptic text reports as input. We showed accurate inference of recurrence score from whole-slide images (r = 0.63 (95% C.I. 0.58 - 0.68); n = 1,029), the raw text of their corresponding reports (r = 0.58 (95% C.I. 0.51 - 0.64); n = 972), and their combination (r = 0.68 (95% C.I. 0.64 - 0.73); n = 964) as measured by Pearsons correlation. To predict high-risk disease (RS>25), our model achieved an area under the receiver operating characteristic curve (AUROC) of 0.89 (95% C.I. 0.83 - 0.94), and area under the precision recall curve (AUPRC) of 0.64 (95% C.I. 0.60 - 0.82), compared to 0.49 (95% C.I. 0.36 - 0.64) for an existing nomogram based on clinical and pathologic features. Moreover, our model generalizes well to external international cohorts, effectively identifying recurrence risk (r = 0.61, p < 10-4, n = 452; r = 0.60, p < 10-4, n = 575) and high-risk status (AUROC = 0.80, p < 10-4, AUPRC = 0.68, p < 10-4, n = 452; AUROC = 0.83, p < 10-4, AUPRC = 0.73, p < 10-4, n = 575) from whole-slide images. Probing the biologic underpinnings of the model decisions uncovered tumor cell size heterogeneity, immune cell infiltration, a proliferative transcription program, and stromal fraction as correlates of higher-risk predictions. We conclude that at an operating point of 94.4% precision and 33.3% recall, this model could help increase global adoption and shorten lag between resection and adjuvant therapy.

pathology↗

bioRxiv2023

Spatial molecular profiling of mixed invasive ductal-lobular breast cancers reveals heterogeneity in intrinsic molecular subtypes, oncogenic signatures and actionable driver mutations

Mixed invasive ductal and lobular carcinoma (MDLC) is a rare histologic subtype of breast cancer displaying both E-cadherin positive ductal and E-cadherin negative lobular morphologies within the same tumor, posing challenges with regard to anticipated clinical management. It remains unclear whether these distinct morphologies also have distinct biology and risk of recurrence. Our spatially-resolved transcriptomic, genomic, and single-cell profiling revealed clinically significant differences between ductal and lobular tumor regions including distinct intrinsic subtype heterogeneity (e.g., MDLC with TNBC/basal ductal and ER+/luminal lobular regions), distinct enrichment of senescence/dormancy and oncogenic (ER and MYC) signatures, genetic and epigenetic CDH1 inactivation in lobular, but not ductal regions, and single-cell ductal and lobular sub-populations with unique oncogenic signatures further highlighting intra-regional heterogeneity. Altogether, we demonstrated that the intra-tumoral morphological/histological heterogeneity within MDLC is underpinned by intrinsic subtype and oncogenic heterogeneity which may result in prognostic uncertainty and therapeutic dilemma. SignificanceMDLC displays both ductal and lobular tumor regions. Our multi-omic profiling approach revealed that these morphologically distinct tumor regions harbor distinct intrinsic subtypes and oncogenic features that may cause prognostic uncertainty and therapeutic dilemma. Thus histopathological/molecular profiling of individual tumor regions may guide clinical decision making and benefit patients with MDLC, particularly in the advanced setting where there is increased reliance on next generation sequencing.

cancer biology↗

bioRxiv2023

Long-term breast cancer response to CDK4/6 inhibition defined by TP53-mediated geroconversion

Inhibition of CDK4/6 kinases has led to improved outcomes in breast cancer. Nevertheless, only a minority of patients experience long-term disease control. Using a clinically-annotated cohort of patients with metastatic HR+ breast cancer, we identified TP53 loss (28.8%) and MDM2 amplification (6.7%) to be associated with lack of long-term disease control. Human breast cancer models revealed that p53 loss did not affect CDK4/6 activity or G1-blockade, but instead promoted drug-insensitive p130 phosphorylation by CDK2. Persistence of phospho-p130 prevented DREAM complex assembly, enabling cell cycle reentry and tumor progression. Inhibitors of CDK2 could overcome p53 loss, leading to geroconversion and manifestation of senescence phenotypes. Complete inhibition of both CDK4/6 and CDK2 kinases appears to be necessary to facilitate long-term response across genomically-diverse HR+ breast cancers.

cancer biology↗

bioRxiv2023

Interferon response and epigenetic modulation by SMARCA4 mutations drive ovarian tumor immunogenicity

Cell-intrinsic mechanisms of immunogenicity in ovarian cancer (OC) are not well understood. The presence of damaging mutations in the SWI/SNF chromatin remodeling complex, such as the SMARCA4 (BRG1) catalytic subunit, has been associated with improved response to ICB, however the mechanism by which this occurs is unclear. The aim of this current study was to examine the alterations in tumor cell-intrinsic and extrinsic immune signaling caused by SMARCA4 loss. Using OC models with loss-of-function mutations in SMARCA4, we found that SMARCA4 loss resulted in increased cancer cell-intrinsic immunogenicity, characterized by upregulation of long-terminal RNA repeats such as endogenous retroviruses, increased expression of interferon-stimulated genes, and upregulation of antigen presentation machinery. Notably, this response was dependent on IRF3 signaling, but was independent of the type I interferon receptor. Mice inoculated with cancer cells bearing SMARCA4 loss demonstrated increased activation of cytotoxic T cells and NK cells in the tumor microenvironment as well as increased infiltration with activated dendritic cells. These results were recapitulated when animals bearing SMARCA4-proficient tumors were treated with a BRG1 inhibitor, suggesting that modulation of chromatin remodeling through targeting SMARCA4 may serve as a strategy to reverse immune evasion in OC.

cancer biology↗

bioRxiv2022

Epigenetic dysregulation from chromosomal transit in micronuclei

Chromosomal instability (CIN) and epigenetic alterations are characteristics of advanced and metastatic cancers [1-4], yet whether they are mechanistically linked is unknown. Here we show that missegregation of mitotic chromosomes, their sequestration in micronuclei [5, 6], and subsequent micronuclear envelope rupture [7] profoundly disrupt normal histone post-translational modifications (PTMs), a phenomenon conserved across humans and mice as well as cancer and non-transformed cells. Some of the changes to histone PTMs occur due to micronuclear envelope rupture whereas others are inherited from mitotic abnormalities prior to micronucleus formation. Using orthogonal techniques, we show that micronuclei exhibit extensive differences in chromatin accessibility with a strong positional bias between promoters and distal or intergenic regions. Finally, we show that inducing CIN engenders widespread epigenetic dysregulation and that chromosomes which transit in micronuclei experience durable abnormalities in their accessibility long after they have been reincorporated into the primary nucleus. Thus, in addition to genomic copy number alterations, CIN can serve as a vehicle for epigenetic reprogramming and heterogeneity in cancer.

cancer biology↗

bioRxiv2021

Geometric Network Analysis Provides Prognostic Information in Patients with High Grade Serous Carcinoma of the Ovary Treated with Immune Checkpoint Inhibitors

PurposeNetwork analysis methods can potentially quantify cancer disturbances in gene networks without introducing fitted parameters or variable selection. A new network curvature-based method is introduced to provide an integrated measure of variability within cancer gene networks. The method is applied to high grade serous ovarian cancers (HGSOCs) to predict response to immune checkpoint inhibitors (ICIs), and to rank key genes associated with prognosis. MethodsCopy number alterations (CNAs) from targeted and whole exome sequencing data were extracted for HGSOC patients (n = 45) treated with ICIs. CNAs at a gene level were represented on a protein-protein interaction network to define patient-specific networks with a fixed topology. A version of Ollivier-Ricci curvature was used to identify genes that play a potentially key role in response to immunotherapy and further to stratify patients at high risk of mortality. Overall survival (OS) was defined as the time from the start of ICI treatment to either death or last follow-up. Kaplan-Meier analysis with log-rank test was performed to assess OS between the high and low curvature classified groups. ResultsThe network curvature analysis stratified patients at high risk of mortality with p=0.00047 in Kaplan-Meier analysis. Genes with high curvature were in accordance with CNAs relevant to ovarian cancer. ConclusionNetwork curvature using CNAs has the potential to be a novel predictor for OS in HGSOC patients treated with immunotherapy.

cancer biology↗

bioRxiv2021

The impact of mutational processes on structural genomic plasticity in cancer cells

Structural genome alterations are determinants of cancer ontogeny and therapeutic response. While bulk genome sequencing has enabled delineation of structural variation (SV) mutational processes which generate patterns of DNA damage, we have little understanding of how these processes lead to cell-to-cell variations which underlie selection and rates of accrual of different genomic lesions. We analysed 309 high grade serous ovarian and triple negative breast cancer genomes to determine their mutational processes, selecting 22 from which we sequenced >22,000 single cell whole genomes across a spectrum of mutational processes. We show that distinct patterns of cell-to-cell variation in aneuploidy, copy number alteration (CNA) and segment length occur in homologous recombination deficiency (HRD) and fold-back inversion (FBI) phenotypes. Widespread aneuploidy through induction of HRD through BRCA1 and BRCA2 inactivation was mirrored by continuous whole genome duplication in HRD tumours, contrasted with early ploidy fixation in FBI. FBI tumours exhibited copy number distributions skewed towards gains, widespread clone-specific variation in amplitude of high-level amplifications, often impacting oncogenes, and break-point variability consistent with progressive genomic diversification, which we termed serriform structural variation (SSV). SSVs were consistent with a CNA-based molecular clock reflecting a continual and distributed process across clones within tumours. These observations reveal previously obscured genome plasticity and evolutionary properties with implications for cancer evolution, therapeutic targeting and response.

cancer biology↗

bioRxiv2021

Immunogenicity of a public neoantigen derived from mutated PIK3CA

Public neoantigens (NeoAgs) represent an elite class of shared cancer-specific epitopes derived from recurrent mutations in driver genes that are restricted by prevalent HLA alleles. Here, we report on a high-throughput platform combining single-cell transcriptomic and T cell receptor (TCR) sequencing to establish whether mutant (Mut) PIK3CA, among the most common genomically altered driver oncogenes, generates an immunogenic public NeoAg. Using this method, we developed a library of TCRs that recognize an endogenously processed neoepitope containing a common PIK3CA hotspot mutation that is restricted by HLA-A*03:01. Mechanistically, immunogenicity to this public NeoAg arises primarily from enhanced stability of the neopeptide/HLA complex caused by a preferred HLA anchor substitution. Structural studies indicated that the HLA-bound neopeptide presents a relatively "featureless" surface dominated by the peptides backbone. To overcome the challenge of binding such an epitope with high specificity and affinity, we discovered that a lead TCR clinical candidate engages the neopeptide through an extended interface aided by an unusually long {beta}-chain complementarity-determining region 3 (CDR3{beta}) loop. In a pan-cancer cohort of patients with diverse malignancies that express the PIK3CA public NeoAg, we observed spontaneous immunogenicity, NeoAg clonal conservation, and in a limited number of cases, evidence of targeted immune escape. Together, these results establish the immunogenic potential of Mut PIK3CA, creating a framework for off-the-shelf immunotherapies targeting this public NeoAg.

immunology↗

bioRxiv2018

Unraveling Tumor-Immune Heterogeneity in Advanced Ovarian Cancer Uncovers Immunogenic Effect of Chemotherapy

In metastatic cancer, the role of heterogeneity at the tumor-immune microenvironment, its molecular underpinnings and clinical relevance remain largely unexplored. To understand tumor-immune dynamics at baseline and upon chemotherapy treatment, we performed unbiased pathway and cell type-specific immunogenomics analysis of treatment-naive (38 samples from 8 patients) and paired chemotherapy treated (80 paired samples from 40 patients) high-grade serous ovarian cancer (HGSOC) samples. Whole transcriptome analysis and image-based quantification of T cells from treatment-naive tumors revealed ubiquitous variability in immune signaling and distinct immune microenvironments co-existing within the same individuals and within tumor deposits at diagnosis. To systematically explore cell type composition of the tumor microenvironment using bulk mRNA, we derived consensus immune and stromal cell gene signatures by intersecting state-of-the-art deconvolution methods, providing improved accuracy and sensitivity when compared to HGSOC immunostaining and leukocyte methylation data sets. Cell-type deconvolution and pathway analyses revealed that Myc and Wnt signaling associate with immune cell exclusion in untreated HGSOC. To evaluate the effect of chemotherapy on the intrinsic tumor-immune heterogeneity, we compared site-matched and site-unmatched tumors before and after neoadjuvant chemotherapy. Transcriptomic and T-cell receptor sequencing analyses showed that site-matched samples had increased cytotoxic immune activation and oligoclonal expansion of T cells after chemotherapy, which was not seen in site-unmatched samples where heterogeneity could not be accounted for. These results demonstrate that the tumor-immune interface in advanced HGSOC is intrinsically heterogeneous, and thus requires site-specific analysis to reliably unmask the impact of therapy on the tumor-immune microenvironment.

cancer biology↗

bioRxiv2018

Reliability of Whole-Exome Sequencing for Assessing Intratumor Genetic Heterogeneity

Multi-region sequencing is used to detect intratumor genetic heterogeneity (ITGH) in tumors. To assess whether genuine ITGH can be distinguished from sequencing artifacts, we whole-exome sequenced (WES) three anatomically distinct regions of the same tumor with technical replicates to estimate technical noise. Somatic variants were detected with three different WES pipelines and subsequently validated by high-depth amplicon sequencing. The cancer-only pipeline was unreliable, with about 69% of the identified somatic variants being false positive. Even with matched normal DNA where 82% of the somatic variants were detected reliably, only 36%-78% were found consistently in technical replicate pairs. Overall 34%-80% of the discordant somatic variants, which could be interpreted as ITGH, were found to constitute technical noise. Excluding mutations affecting low mappability regions or occurring in certain mutational contexts was found to reduce artifacts, yet detection of subclonal mutations by WES in the absence of orthogonal validation remains unreliable.

genomics↗