Plasma-Derived Cell-Free DNA as a Biomarker for Early Detection, Prognostication, and Personalized Treatment of Urothelial Carcinoma.

Bladder cancer (BC) is one of the most common malignancies in the United States, with over 80,000 new cases and 16,000 deaths each year. Urothelial carcinoma (UC) is the most common histology and accounts for 90% of cases. BC management is complicated by recurrence rates of over 50% in both muscle-invasive and non-muscle-invasive bladder cancer. As such, the American Urological Association (AUA) recommends that patients undergo close surveillance during and after treatment. This surveillance is in the form of cystoscopy or imaging tests, which can be invasive and costly tests. Considering this, there have been recent pushes to find complements to bladder cancer surveillance. Cell-free DNA (CfDNA), or DNA released from dying cells, and circulating tumor DNA (ctDNA), or mutated DNA released from tumor cells, can be analyzed to detect and characterize the molecular characteristics of tumors. Research has shown promising results for ctDNA use in the BC care realm. A PubMed literature review was performed finding studies discussing cfDNA and ctDNA in BC detection, prognostication, and monitoring for recurrence. Keywords used included bladder cancer, cell-free DNA, circulating tumor DNA, urothelial carcinoma, and liquid biopsy. Studies show that ctDNA can serve as prognostic indicators of both early- and late-stage BC, aid in risk stratification prior to major surgery, assist in detection of disease progression and metastatic relapse, and can assess patients who may respond to immunotherapy. The benefit of ctDNA is not confined to BC, as studies have also suggested its promise as a biomarker for neoadjuvant chemotherapy in upper-tract UC. However, there are some limitations to ctDNA that require improvements in ctDNA-specific detection methods and BC-specific mutations before widespread utilization can be achieved. Further prospective, randomized trials are needed to elucidate the true potential ctDNA has in advancements in BC care.

Mismatch repair protein MLH1 suppresses replicative stress in BRCA2-deficient breast tumors.

Loss of BRCA2 (breast cancer 2) is lethal for normal cells. Yet it remains poorly understood how, in BRCA2 mutation carriers, cells undergoing loss of heterozygosity overcome the lethality and undergo tissue-specific neoplastic transformation. Here, we identified mismatch repair gene mutL homolog 1 (MLH1) as a genetic interactor of BRCA2 whose overexpression supports the viability of Brca2-null cells. Mechanistically, we showed that MLH1 interacts with Flap endonuclease 1 (FEN1) and competes to process the RNA flaps of Okazaki fragments. Together, they restrained the DNA2 nuclease activity on the reversed forks of lagging strands, leading to replication fork (RF) stability in BRCA2-deficient cells. In these cells, MLH1 also attenuated R-loops, allowing the progression of stable RFs, which suppressed genomic instability and supported cell viability. We demonstrated the significance of their genetic interaction by the lethality of Brca2-mutant mice and inhibition of Brca2-deficient tumor growth in mice by Mlh1 loss. Furthermore, we described estrogen as inducing MLH1 expression through estrogen receptor α (ERα), which might explain why the majority of BRCA2 mutation carriers develop ER-positive breast cancer. Taken together, our findings reveal a role of MLH1 in relieving replicative stress and show how it may contribute to the establishment of BRCA2-deficient breast tumors.

Transcriptional state dynamics lead to heterogeneity and adaptive tumor evolution in urothelial bladder carcinoma.

Intra-tumor heterogeneity contributes to treatment failure and poor survival in urothelial bladder carcinoma (UBC). Analyzing transcriptome from a UBC cohort, we report that intra-tumor transcriptomic heterogeneity indicates co-existence of tumor cells in epithelial and mesenchymal-like transcriptional states and bi-directional transition between them occurs within and between tumor subclones. We model spontaneous and reversible transition between these partially heritable states in cell lines and characterize their population dynamics. SMAD3, KLF4 and PPARG emerge as key regulatory markers of the transcriptional dynamics. Nutrient limitation, as in the core of large tumors, and radiation treatment perturb the dynamics, initially selecting for a transiently resistant phenotype and then reconstituting heterogeneity and growth potential, driving adaptive evolution. Dominance of transcriptional states with low PPARG expression indicates an aggressive phenotype in UBC patients. We propose that phenotypic plasticity and dynamic, non-genetic intra-tumor heterogeneity modulate both the trajectory of disease progression and adaptive treatment response in UBC.

Denoising sparse microbial signals from single-cell sequencing of mammalian host tissues.

Existing genomic sequencing data can be used to study host-microbiome ecosystems, however distinguishing signals originating from truly present microbes versus contaminating species and artifacts is a substantial and often prohibitive challenge. Here we show that emerging sequencing technologies definitely capture reads from present microbes. We developed SAHMI, a computational resource to identify truly present microbial nucleic acids and filter contaminants and spurious false-positive taxonomic assignments from standard transcriptomic sequencing of mammalian tissues. In benchmark studies, SAHMI correctly identifies known microbial infections present in diverse tissues, and we validate SAHMI's enrichment for correctly classified, truly present species using multiple orthogonal computational experiments. The application of SAHMI to single-cell and spatial genomic data thus enables co-detection of somatic cells and microorganisms and joint analysis of host-microbiome ecosystems.

Netrin-1 feedforward mechanism promotes pancreatic cancer liver metastasis via hepatic stellate cell activation, retinoid, and ELF3 signaling.

The biology of metastatic pancreatic ductal adenocarcinoma (PDAC) is distinct from that of the primary tumor due to changes in cell plasticity governed by a distinct transcriptome. Therapeutic strategies that target this distinct biology are needed. We detect an upregulation of the neuronal axon guidance molecule Netrin-1 in PDAC liver metastases that signals through its dependence receptor (DR), uncoordinated-5b (Unc5b), to facilitate metastasis in vitro and in vivo. The mechanism of Netrin-1 induction involves a feedforward loop whereby Netrin-1 on the surface of PDAC-secreted extracellular vesicles prepares the metastatic niche by inducing hepatic stellate cell activation and retinoic acid secretion that in turn upregulates Netrin-1 in disseminated tumor cells via RAR/RXR and Elf3 signaling. While this mechanism promotes PDAC liver metastasis, it also identifies a therapeutic vulnerability, as it can be targeted using anti-Netrin-1 therapy to inhibit metastasis using the Unc5b DR cell death mechanism.

Association between SMAD4 Mutations and GATA6 Expression in Paired Pancreatic Ductal Adenocarcinoma Tumor Specimens: Data from Two Independent Molecularly-Characterized Cohorts.

Several molecular biomarkers have been identified to guide induction treatment selection for localized pancreatic ductal adenocarcinoma (PDAC). SMAD4 alterations and low GATA6 expression/modified "Moffitt" basal-like phenotype have each been associated with inferior survival uniquely for patients receiving 5-FU-based therapies. SMAD4 may directly regulate the expression of GATA6 in PDAC, pointing to a common predictive biomarker. To evaluate the relationship between SMAD4 mutations and GATA6 expression in human PDAC tumors, patients with paired SMAD4 mutation and GATA6 mRNA expression data in the TCGA and CPTAC were identified. In 321 patients (TCGA: n = 180; CPTAC: n = 141), the rate of SMAD4 alterations was 26.8%. The rate of SMAD4 alteration did not vary per tertile of normalized GATA6 expression (TCGA: p = 0.928; CPTAC: p = 0.828). In the TCGA, SMAD4 alterations and the basal-like phenotype were each associated with worse survival (log rank p = 0.077 and p = 0.080, respectively), but their combined presence did not identify a subset with uniquely inferior survival (p = 0.943). In the CPTAC, the basal-like phenotype was associated with significantly worse survival (p < 0.001), but the prognostic value was not influenced by the combined presence of SMAD4 alterations (p = 0.960). SMAD4 alterations were not associated with poor clinico-pathological features such as poor tumor grade, advanced tumor stage, positive lymphovascular invasion (LVI), or positive perineural invasion (PNI), compared with SMAD4-wildtype. Given that SMAD4 mutations were not associated with GATA6 expression or Moffitt subtype in two independent molecularly characterized PDAC cohorts, distinct biomarker-defined clinical trials are necessary.

Hierarchical and automated cell-type annotation and inference of cancer cell of origin with Census.

MOTIVATION: Cell-type annotation is a time-consuming yet critical first step in the analysis of single-cell RNA-seq data, especially when multiple similar cell subtypes with overlapping marker genes are present. Existing automated annotation methods have a number of limitations, including requiring large reference datasets, high computation time, shallow annotation resolution, and difficulty in identifying cancer cells or their most likely cell of origin. RESULTS: We developed Census, a biologically intuitive and fully automated cell-type identification method for single-cell RNA-seq data that can deeply annotate normal cells in mammalian tissues and identify malignant cells and their likely cell of origin. Motivated by the inherently stratified developmental programs of cellular differentiation, Census infers hierarchical cell-type relationships and uses gradient-boosted \decision trees that capitalize on nodal cell-type relationships to achieve high prediction speed and accuracy. When benchmarked on 44 atlas-scale normal and cancer, human and mouse tissues, Census significantly outperforms state-of-the-art methods across multiple metrics and naturally predicts the cell-of-origin of different cancers. Census is pretrained on the Tabula Sapiens to classify 175 cell-types from 24 organs; however, users can seamlessly train their own models for customized applications. AVAILABILITY AND IMPLEMENTATION: Census is available at Zenodo https://zenodo.org/records/7017103 and on our Github https://github.com/sjdlabgroup/Census.

Integrated metabolic and genetic analysis reveals distinct features of human differentiated thyroid cancer.

BACKGROUND: Differentiated thyroid cancer (DTC) affects thousands of lives worldwide each year. Typically, DTC is a treatable disease with a good prognosis. Yet, some patients are subjected to partial or total thyroidectomy and radioiodine therapy to prevent local disease recurrence and metastasis. Unfortunately, thyroidectomy and/or radioiodine therapy often worsen(s) quality of life and might be unnecessary in indolent DTC cases. On the other hand, the lack of biomarkers indicating a potential metastatic thyroid cancer imposes an additional challenge to managing and treating patients with this disease. AIM: The presented clinical setting highlights the unmet need for a precise molecular diagnosis of DTC and potential metastatic disease, which should dictate appropriate therapy. MATERIALS AND METHODS: In this article, we present a differential multi-omics model approach, including metabolomics, genomics, and bioinformatic models, to distinguish normal glands from thyroid tumours. Additionally, we are proposing biomarkers that could indicate potential metastatic diseases in papillary thyroid cancer (PTC), a sub-class of DTC. RESULTS: Normal and tumour thyroid tissue from DTC patients had a distinct yet well-defined metabolic profile with high levels of anabolic metabolites and/or other metabolites associated with the energy maintenance of tumour cells. The consistency of the DTC metabolic profile allowed us to build a bioinformatic classification model capable of clearly distinguishing normal from tumor thyroid tissues, which might help diagnose thyroid cancer. Moreover, based on PTC patient samples, our data suggest that elevated nuclear and mitochondrial DNA mutational burden, intra-tumour heterogeneity, shortened telomere length, and altered metabolic profile reflect the potential for metastatic disease. DISCUSSION: Altogether, this work indicates that a differential and integrated multi-omics approach might improve DTC management, perhaps preventing unnecessary thyroid gland removal and/or radioiodine therapy. CONCLUSIONS: Well-designed, prospective translational clinical trials will ultimately show the value of this integrated multi-omics approach and early diagnosis of DTC and potential metastatic PTC.

Integrated metabolic and genetic analysis reveals distinct features of primary differentiated thyroid cancer and its metastatic potential in humans.

Differentiated thyroid cancer (DTC) affects thousands of lives worldwide every year. Typically, DTC is a treatable disease with a good prognosis. Yet, some patients are subjected to partial or total thyroidectomy and radioiodine therapy to prevent local disease recurrence and metastasis. Unfortunately, thyroidectomy and/or radioiodine therapy often worsen(s) the quality of life and might be unnecessary in indolent DTC cases. This clinical setting highlights the unmet need for a precise molecular diagnosis of DTC, which should dictate appropriate therapy. Here we propose a differential multi-omics model approach to distinguish normal gland from thyroid tumor and to indicate potential metastatic diseases in papillary thyroid cancer (PTC), a sub-class of DTC. Based on PTC patient samples, our data suggest that elevated nuclear and mitochondrial DNA mutational burden, intratumor heterogeneity, shortened telomere length, and altered metabolic profile reflect the potential for metastatic disease. Specifically, normal and tumor thyroid tissues from these patients had a distinct yet well-defined metabolic profile with high levels of anabolic metabolites and/or other metabolites associated with the energy maintenance of tumor cells. Altogether, this work indicates that a differential and integrated multi-omics approach might improve DTC management, perhaps preventing unnecessary thyroid gland removal and/or radioiodine therapy. Well-designed, prospective translational clinical trials will ultimately show the value of this targeted molecular approach. TRANSLATIONAL RELEVANCE: In this article, we propose a new integrated metabolic, genomic, and cytopathologic methods to diagnose Differentiated Thyroid Cancer when the conventional methods failed. Moreover, we suggest metabolic and genomic markers to help predict high-risk Papillary Thyroid Cancer. Both might be important tools to avoid unnecessary surgery and/or radioiodine therapy that can worsen the quality of life of the patients more than living with an indolent Thyroid nodule.

Tumor microbiome links cellular programs and immunity in pancreatic cancer.

Microorganisms are detected in multiple cancer types, including in putatively sterile organs, but the contexts in which they influence oncogenesis or anti-tumor responses in humans remain unclear. We recently developed single-cell analysis of host-microbiome interactions (SAHMI), a computational pipeline to recover and denoise microbial signals from single-cell sequencing of host tissues. Here we use SAHMI to interrogate tumor-microbiome interactions in two human pancreatic cancer cohorts. We identify somatic-cell-associated bacteria in a subset of tumors and their near absence in nonmalignant tissues. These bacteria predominantly pair with tumor cells, and their presence is associated with cell-type-specific gene expression and pathway activities, including cell motility and immune signaling. Modeling results indicate that tumor-infiltrating lymphocytes closely resemble T cells from infected tissue. Finally, using multiple independent datasets, a signature of cell-associated bacteria predicts clinical prognosis. Tumor-microbiome crosstalk may modulate tumorigenesis in pancreatic cancer with implications for clinical management.

Inference on spatial heterogeneity in tumor microenvironment using spatial transcriptomics data.

In the tumor microenvironment (TME), functional interactions among tumor, immune, and stromal cells and the extracellular matrix play key roles in tumor progression, invasion, immune modulation, and response to treatment. Intratumor heterogeneity is ubiquitous not only at the genetic and transcriptomic levels but also in the composition and characteristics of TME. However, quantitative inference on spatial heterogeneity in the TME is still limited. Here, we propose a framework to use network graph-based spatial statistical models on spatially annotated molecular data to gain insights into modularity and spatial heterogeneity in the TME. Applying the framework to spatial transcriptomics data from pancreatic ductal adenocarcinoma samples, we observed significant global and local spatially correlated patterns in the abundance score of tumor cells; in contrast, immune cell types showed dispersed patterns in the TME. Hypoxia, EMT, and inflammation signatures contributed to intra-tumor spatial variations. Spatial patterns in cell type abundance and pathway signatures in the TME potentially impact tumor growth dynamics and cancer hallmarks. Tumor biopsies are integral to the diagnosis and clinical management of cancer patients; our data suggest that owing to intra-tumor non-genetic spatial heterogeneity, individual biopsies may underappreciate the extent of clinically relevant, functional variations across geographic regions within tumors.

Loss of the BRCA1-PALB2 interaction accelerates p53-associated tumor development in mice.

The BRCA1-PALB2-BRCA2 axis, or the BRCA pathway, plays key roles in genome stability maintenance and suppression of breast and several other cancers. Due to frequent p53 mutations in human BRCA1 breast cancers and mouse mammary tumors from Brca1, Brca2 and Palb2 conditional knockout models, it is often thought that p53 inactivation accelerates BRCA1/2 and PALB2-associated tumorigenesis. Here, we studied tumor development in mice with a mutation in Palb2 that disengages the PALB2-BRCA1 interaction in different Trp53 backgrounds. Rather than mammary tumors, Palb2 and Trp53 compound mutant mice developed, with greatly reduced latencies, lymphomas and sarcomas that are typically associated with germline Trp53 inactivation. Whole exome sequencing failed to identify any significant differences in genomic features between the same tumor types of Trp53 single mutant and Palb2;Trp53 compound mutant mice. These results suggest that loss of the BRCA pathway accelerates p53-associated tumor development, possibly without altering the fundamental tumorigenic processes.

KMT2C-deficient tumors have elevated APOBEC mutagenesis and genomic instability in multiple cancers.

The histone methyltransferase KMT2C is among the most frequently mutated epigenetic modifier genes in cancer and plays an essential role in MRE11-dependent DNA replication fork restart. However, the effects of KMT2C deficiency on genomic instability during tumorigenesis are unclear. Analyzing 9,663 tumors from 30 cancer cohorts, we report that KMT2C mutant tumors have a significant excess of APOBEC mutational signatures in several cancer types. We show that KMT2C deficiency promotes APOBEC expression and deaminase activity, and compromises DNA replication speed and delays fork restart, facilitating APOBEC mutagenesis targeting single stranded DNA near stalled forks. APOBEC-mediated mutations primarily accumulate during early replication and tend to cluster along the genome and also in 3D nuclear domains. Excessive APOBEC mutational signatures in KMT2C mutant tumors correlate with elevated genome maintenance defects and signatures of homologous recombination deficiency. We propose that KMT2C deficiency is a likely promoter of APOBEC mutagenesis, which fosters further genomic instability during tumor progression in multiple cancer types.

Clustered 8-Oxo-Guanine Mutations and Oncogenic Gene Fusions in Microsatellite-Unstable Colorectal Cancer.

PURPOSE: Colorectal carcinomas (CRCs) with microsatellite-instability (MSI) are enriched for oncogenic kinase fusions (KFs), including NTRK1, RET, and BRAF, but the mechanism underlying this finding is unclear. METHODS: The genomic profiles of 32,218 advanced CRC tumor specimens were analyzed to assess the fusion breakpoints of oncogenic alterations including KFs in microsatellite-stable and microsatellite-unstable CRC. Genomic contexts of such alterations were analyzed to obtain mechanistic insights. RESULTS: Genomic analysis demonstrated that oncogenic fusion breakpoints in MSI tumors do not preferentially involve repetitive or low-complexity sequences. Instead, their junction regions showed pronounced guanine and cytosine bias and elevated mutation frequency at G:C contexts. Elevated mutation frequency at G:C bases in relevant introns predicted prevalence of associated oncogenic fusions in MSI CRCs. CRCs harboring mismatch repair signatures had enrichment of butyrate-producing microbial species, reported to be associated with induction of 8-oxoguanine lesions in the intestine. CONCLUSION: Detailed analysis of breakpoints in MSI-associated KFs support a model in which inefficient repair and/or processing of microbiome-induced clustered 8-oxoguanine damage in MSI CRC contributes to the increased incidence of specific oncogenic fusions.

Reconstructing physical cell interaction networks from single-cell data using Neighbor-seq.

Cell-cell interactions are the fundamental building blocks of tissue organization and multicellular life. We developed Neighbor-seq, a method to identify and annotate the architecture of direct cell-cell interactions and relevant ligand-receptor signaling from the undissociated cell fractions in massively parallel single cell sequencing data. Neighbor-seq accurately identifies microanatomical features of diverse tissue types such as the small intestinal epithelium, terminal respiratory tract, and splenic white pulp. It also captures the differing topologies of cancer-immune-stromal cell communications in pancreatic and skin tumors, which are consistent with the patterns observed in spatial transcriptomic data. Neighbor-seq is fast and scalable. It draws inferences from routine single-cell data and does not require prior knowledge about sample cell-types or multiplets. Neighbor-seq provides a framework to study the organ-level cellular interactome in health and disease, bridging the gap between single-cell and spatial transcriptomics.

Signatures Beyond Oncogenic Mutations in Cell-Free DNA Sequencing for Non-Invasive, Early Detection of Cancer.

Early detection of cancer saves lives, but an effective detection strategy in public health settings requires a delicate balance - periodic screening should neither miss rapidly progressing disease nor fail to detect rare tumors at unusual locations; on the other hand, even a modest false positive rate carries risks of over-diagnosis and over-treatment of relatively indolent non-malignant disease. Genomic profiling of cell-free DNA from liquid biopsy using massively parallel sequencing is emerging as an attractive, non-invasive screening platform for sensitive detection of multiple types of cancer in a single assay. Genomic data from cell-free DNA can not only identify oncogenic mutation status, but also additional molecular signatures related to potential tissue of origin, the extent of clonal growth, and malignant disease states. Utilization of the full potential of the molecular signatures from cfDNA sequencing data can guide clinical management strategies for targeted follow-ups using imaging or molecular marker-based diagnostic platforms and treatment options.

Mutational signatures associated with exposure to carcinogenic microplastic compounds bisphenol A and styrene oxide.

Microplastic pollutants in oceans and food chains are concerning to public health. Common plasticizing compounds Bisphenol-A (BPA) and Styrene-7,8-Oxide (SO) are now labeled as carcinogens. We show that BPA and SO cause deoxyribonucleic acid damage and mutagenesis in human cells, and analyze the genome-wide point mutation and genomic rearrangement patterns associated with BPA and SO exposure. A subset of the single- and doublet base substitutions shows mutagenesis near or at guanine, consistent with these compounds' preferences to form guanosine adducts. Presence of other mutational signatures suggest additional mutagenesis probably due to complex effects of BPA and SO on diverse cellular processes. Analyzing data for 19 cancer cohorts, we find that tumors of digestive and urinary organs show relatively high similarity in mutational profiles, and the burden of such mutations increases with age. Even within the same cancer type, proportions of corresponding mutational patterns vary among the cohorts from different countries, as does the amount of microplastic waste in ocean waters. BPA and SO are relatively mild mutagens, and other environmental agents can also potentially generate similar, complex mutational patterns in cancer genomes. Nonetheless, our findings call for systematic evaluation of public health consequences of microplastic exposure worldwide.

Drivers of dynamic intratumor heterogeneity and phenotypic plasticity.

Cancer is a clonal disease, i.e., all tumor cells within a malignant lesion trace their lineage back to a precursor somatic cell that acquired oncogenic mutations during development and aging. And yet, those tumor cells tend to have genetic and nongenetic variations among themselves-which is denoted as intratumor heterogeneity. Although some of these variations are inconsequential, others tend to contribute to cell state transition and phenotypic heterogeneity, providing a substrate for somatic evolution. Tumor cell phenotypes can dynamically change under the influence of genetic mutations, epigenetic modifications, and microenvironmental contexts. Although epigenetic and microenvironmental changes are adaptive, genetic mutations are usually considered permanent. Emerging reports suggest that certain classes of genetic alterations show extensive reversibility in tumors in clinically relevant timescales, contributing as major drivers of dynamic intratumor heterogeneity and phenotypic plasticity. Dynamic heterogeneity and phenotypic plasticity can confer resistance to treatment, promote metastasis, and enhance evolvability in cancer. Here, we first highlight recent efforts to characterize intratumor heterogeneity at genetic, epigenetic, and microenvironmental levels. We then discuss phenotypic plasticity and cell state transition by tumor cells, under the influence of genetic and nongenetic determinants and their clinical significance in classification of tumors and therapeutic decision-making.

Requirement of Bccip for the Regeneration of Intestinal Progenitors.

BCCIP was originally identified as a BRCA2 and CDKN1A/p21 interaction protein. Although a partial loss of BCCIP function is sufficient to trigger genomic instability and tumorigenesis, complete deletion of BCCIP is lethal to cells. Using Rosa26-CreERT2 mouse models, we found that induced Bccip deletion in adult mice caused an acute intestinal epithelial denudation that cannot be relieved by co-deletion of Trp53. The critical role of Bccip in intestine epithelial renewal was verified with a Villin-CreERT2 mouse model. The epithelium degeneration was associated with a rapid loss of the proliferative capability of the crypt progenitor cells in vivo, lack of crypt base columnar stem cell markers, and a failure of in vitro crypt organoid growth. RNA-Seq analysis of freshly isolated intestinal crypt cells showed that Bccip deletion caused an overwhelming down-regulation of genes involved in mitotic cell division but an up-regulation of genes involved in apoptosis and stress response to microbiomes. Our data not only indicate that intestinal epithelium is the most sensitive tissue to whole-body deletion of Bccip but also point to Bccip as a novel and critical factor for the proliferation of the intestinal progenitors. These findings have significant implications for understanding why a hypomorphic loss of BCCIP functions is more relevant to tumorigenesis.