Cancer Evolution Research Articles

Clonal Lineage Tracing with Somatic Delivery of Recordable Barcodes Reveals Migration Histories of Metastatic Prostate Cancer.

The patterns by which primary tumors spread to metastatic sites remain poorly understood. Here, we define patterns of metastatic seeding in prostate cancer using a novel injection-based mouse model-EvoCaP (Evolution in Cancer of the Prostate), featuring aggressive metastatic cancer to bone, liver, lungs, and lymph nodes. To define migration histories between primary and metastatic sites, we used our EvoTraceR pipeline to track distinct tumor clones containing recordable barcodes. We detected widespread intratumoral heterogeneity from the primary tumor in metastatic seeding, with few clonal populations instigating most migration. Metastasis-to-metastasis seeding was uncommon, as most cells remained confined within the tissue. Migration patterns in our model were congruent with human prostate cancer seeding topologies. Our findings support the view of metastatic prostate cancer as a systemic disease driven by waves of aggressive clones expanding their niche, infrequently overcoming constraints that otherwise keep them confined in the primary or metastatic site. Significance: Defining the kinetics of prostate cancer metastasis is critical for developing novel therapeutic strategies. This study uses CRISPR/Cas9-based barcoding technology to accurately define tumor clonal patterns and routes of migration in a novel somatically engineered mouse model (EvoCaP) that recapitulates human prostate cancer using an in-house developed analytical pipeline (EvoTraceR).

The Role of TGFBR3 in the Development of Lung Cancer.

The Transforming Growth Factor-β (TGF-β) mediates embryonic development, maintains cellular homeostasis, regulates immune function, and is involved in a wide range of other biological processes. TGF-β superfamily signaling pathways play an important role in cancer development and can promote or inhibit tumorigenesis. Type III TGF-β receptor (TGFBR3) is a co-receptor in the TGF-β signaling pathway, which often occurs with reduced or complete loss of expression in many cancer patients and can act as a tumor suppressor gene. The reduction or deletion of TGFBR3 is more pronounced compared to other elements in the TGF-β signaling pathway. In recent years, lung cancer is one of the major malignant tumors that endanger human health, and its prognosis is poor. Recent studies have reported that TGFBR3 expression decreases to varying degrees in different types of lung cancer, both at the tissue level and at the cellular level. The invasion, metastasis, angiogenesis, and apoptosis of lung cancer cells are closely related to the expression of TGFBR3, which strengthens the inhibitory function of TGFBR3 in the evolution of lung cancer. This article reviews the mechanism of TGFBR3 in lung cancer and the influencing factors associated with TGFBR3. Clarifying the physiological function of TGFBR3 and its molecular mechanism in lung cancer is conducive to the diagnosis and treatment of lung cancer.

Genomic characteristics and evolution of Multicentric Esophageal and gastric Cardiac Cancer

BackgroundEsophageal carcinoma (EC) and gastric cardiac adenocarcinoma (GCA) have high incidence rates in the Chaoshan region of South China. Multifocal esophageal and cardiac cancer (MECC) is commonly observed in this region in clinical practice. However, the genomic characteristics of MECC remains unclear.Materials and methodsIn this study, a total of 2123 clinical samples of EC and GCA were analyzed to determine the frequency of multifocal tumors, as well as their occurrence sites and pathological types. Cox proportional hazards regression was used to model the relationship between age, sex, and tumor state concerning survival in our analysis of the cohort of 541 patients with available follow-up data. We performed whole-genome sequencing on 20 tumor foci and 10 normal samples from 10 MECC patients to infer clonal structure on 6 MECC patients to explore genome characteristics.ResultThe MECC rate of EC and GCA was 5.65% (121 of 2123). Age and sex were potential factors that may influence the risk of MECC (p < 0.001). Furthermore, MECC patients showed worse survival compared with single tumor patients. We found that 12 foci from 6 patients were multicentric origin model (MC), which exhibited significant heterogeneity of variations in paired foci and had an increased number of germline mutations in immune genes compared to metastatic model. In MC cases, different lesions in the same patient were driven by distinct mutation and copy number variation (CNV) events. Although TP53 and other driver mutation genes have a high frequency in the samples, their mutation sites show significant heterogeneity in paired tumor specimens. On the other hand, CNV genes exhibited higher concordance in paired samples, especially in the amplification of oncogenes and the deletion of tumor suppressor genes.ConclusionsThe extent of inter-tumor heterogeneity suggests both monoclonal and polyclonal origins of MECC, which could provide insight into the genome diversity of MECC and guide clinical implementation.

A modelling framework for cancer ecology and evolution.

Cancer incidence increases rapidly with age, typically as a polynomial. The somatic mutation theory explains this increase through the waiting time for enough mutations to build up to generate cells with the full set of traits needed to grow without control. However, lines of evidence ranging from tumour reversion and dormancy to the prevalence of presumed cancer mutations in non-cancerous tissues argue that this is not the whole story, and that cancer is also an ecological process, and that mutations only lead to cancer when the systems of control within and across cells have broken down. Aging thus has two effects: the build-up of mutations and the breakdown of control. This paper presents a mathematical modelling framework to unify these theories with novel approaches to model the mutation and diversification of cell lineages and of the breakdown of the layers of control both within and between cells. These models correctly predict the polynomial increase of cancer with age, show how germline defects in control accelerate cancer initiation, and compute how the positive feedback between cell replication, ecology and layers of control leads to a doubly exponential growth of cell populations.

Homopolymer switches mediate adaptive mutability in mismatch repair-deficient colorectal cancer

Mismatch repair (MMR)-deficient cancer evolves through the stepwise erosion of coding homopolymers in target genes. Curiously, the MMR genes MutS homolog 6 (MSH6) and MutS homolog 3 (MSH3) also contain coding homopolymers, and these are frequent mutational targets in MMR-deficient cancers. The impact of incremental MMR mutations on MMR-deficient cancer evolution is unknown. Here we show that microsatellite instability modulates DNA repair by toggling hypermutable mononucleotide homopolymer runs in MSH6 and MSH3 through stochastic frameshift switching. Spontaneous mutation and reversion modulate subclonal mutation rate, mutation bias and HLA and neoantigen diversity. Patient-derived organoids corroborate these observations and show that MMR homopolymer sequences drift back into reading frame in the absence of immune selection, suggesting a fitness cost of elevated mutation rates. Combined experimental and simulation studies demonstrate that subclonal immune selection favors incremental MMR mutations. Overall, our data demonstrate that MMR-deficient colorectal cancers fuel intratumor heterogeneity by adapting subclonal mutation rate and diversity to immune selection.

Eco-evolutionary Guided Pathomic Analysis to Predict DCIS Upstaging.

Cancers evolve in a dynamic ecosystem. Thus, characterizing cancer's ecological dynamics is crucial to understanding cancer evolution and can lead to discovering novel biomarkers to predict disease progression. Ductal carcinoma in situ (DCIS) is an early-stage breast cancer characterized by abnormal epithelial cell growth confined within the milk ducts. Although there has been extensive research on genetic and epigenetic causes of breast carcinogenesis, none of these studies have successfully identified a biomarker for the progression and/or upstaging of DCIS. In this study, we show that ecological habitat analysis of hypoxia and acidosis biomarkers can significantly improve prediction of DCIS upstaging. First, we developed a novel eco-evolutionary designed approach to define habitats in the tumor intra-ductal microenvironment based on oxygen diffusion distance in our DCIS cohort of 84 patients. Then, we identify cancer cells with metabolic phenotypes attributed to their habitat conditions, such as the expression of CA9 indicating hypoxia responding phenotype, and LAMP2b indicating a hypoxia-induced acid adaptation. Traditionally these markers have shown limited predictive capabilities for DCIS upstaging, if any. However, when analyzed from an ecological perspective, their power to differentiate between indolent and upstaged DCIS increased significantly. Second, using eco-evolutionary guided computational and digital pathology techniques, we discovered distinct spatial patterns of these biomarkers and used the distribution of such patterns to predict patient upstaging. The patterns were characterized by both cellular features and spatial features. With a 5-fold validation on the biopsy cohort, we trained a random forest classifier to achieve the area under curve(AUC) of 0.74. Our results affirm the importance of using eco-evolutionary-designed approaches in biomarkers discovery studies in the era of digital pathology by demonstrating the role of eco-evolution dynamics in predicting cancer progression.

Machine learning on multiple epigenetic features reveals H3K27Ac as a driver of gene expression prediction across patients with glioblastoma.

Cancer cells show remarkable plasticity and can switch lineages in response to the tumor microenvironment. Cellular plasticity drives invasiveness and metastasis and helps cancer cells to evade therapy by developing resistance to radiation and cytotoxic chemotherapy. Increased understanding of cell fate determination through epigenetic reprogramming is critical to discover how cancer cells achieve transcriptomic and phenotypic plasticity. Glioblastoma is a perfect example of cancer evolution where cells retain an inherent level of plasticity through activation or maintenance of progenitor developmental programs. However, the principles governing epigenetic drivers of cellular plasticity in glioblastoma remain poorly understood. Here, using machine learning (ML) we employ cross-patient prediction of transcript expression using a combination of epigenetic features (ATAC-seq, CTCF ChIP-seq, RNAPII ChIP-seq, H3K27Ac ChIP-seq, and RNA-seq) of glioblastoma stem cells (GSCs). We investigate different ML and deep learning (DL) models for this task and build our final pipeline using XGBoost. The model trained on one patient generalizes to another one suggesting that the epigenetic signals governing gene transcription are consistent across patients even if GSCs can be very different. We demonstrate that H3K27Ac is the epigenetic feature providing the most significant contribution to cross-patient prediction of gene expression. In addition, using H3K27Ac signals from patients-derived GSCs, we can predict gene expression of human neural crest stem cells suggesting a shared developmental epigenetic trajectory between subpopulations of these malignant and benign stem cells. Our cross-patient ML/DL models determine weighted patterns of influence of epigenetic marks on gene expression across patients with glioblastoma and between GSCs and neural crest stem cells. We propose that broader application of this analysis could reshape our view of glioblastoma tumor evolution and inform the design of new epigenetic targeting therapies.

Multistate Gene Cluster Switches Determine the Adaptive Mitochondrial and Metabolic Landscape of Breast Cancer.

Adaptive metabolic switches are proposed to underlie conversions between cellular states during normal development as well as in cancer evolution. Metabolic adaptations represent important therapeutic targets in tumors, highlighting the need to characterize the full spectrum, characteristics, and regulation of the metabolic switches. To investigate the hypothesis that metabolic switches associated with specific metabolic states can be recognized by locating large alternating gene expression patterns, we developed a method to identify interspersed gene sets by massive correlated biclustering and to predict their metabolic wiring. Testing the method on breast cancer transcriptome datasets revealed a series of gene sets with switch-like behavior that could be used to predict mitochondrial content, metabolic activity, and central carbon flux in tumors. The predictions were experimentally validated by bioenergetic profiling and metabolic flux analysis of 13C-labeled substrates. The metabolic switch positions also distinguished between cellular states, correlating with tumor pathology, prognosis, and chemosensitivity. The method is applicable to any large and heterogeneous transcriptome dataset to discover metabolic and associated pathophysiological states. Significance: A method for identifying the transcriptomic signatures of metabolic switches underlying divergent routes of cellular transformation stratifies breast cancer into metabolic subtypes, predicting their biology, architecture, and clinical outcome.

TP53 somatic evolution in cervical liquid-based cytology and blood from individuals with and without ovarian cancer and BRCA1 or BRCA2 germline mutations.

Somatic TP53 mutations are prevalent in normal tissue but little is known about their association with cancer risk. Cervical liquid-based cytology (LBC), commonly known as Pap test, provides an accessible gynecological sample to test the value of TP53 somatic mutations as a biomarker for high-grade serous ovarian cancer (HGSC), a cancer type mostly driven by TP53 mutations. We used ultra-deep duplex sequencing to analyze TP53 mutations in LBC and blood samples from 70 individuals (30 with and 40 without HGSC) undergoing gynecologic surgery, 30 carrying BRCA1 or BRCA2 germline pathogenic variants (BRCApv). Only 30% of the tumor mutations were found in LBC samples. However, TP53 pathogenic mutations were identified in nearly all LBC and blood samples, with only 5.4% of mutations in LBC (20/368) also found in the corresponding blood sample. TP53 mutations were more abundant in LBC than in blood and increased with age in both sample types. BRCApv carriers with HGSC had more TP53 clonal expansions in LBC than BRCApv carriers without cancer. Our results show that, while not useful for direct cancer detection, LBC samples capture TP53 mutation burden in the gynecological tract, presenting potential value for cancer risk assessment in individuals at higher hereditary risk for ovarian cancer.

Neotelomeres and telomere-spanning chromosomal arm fusions in cancer genomes revealed by long-read sequencing

Alterations in the structure and location of telomeres are pivotal in cancer genome evolution. Here, we applied both long-read and short-read genome sequencing to assess telomere repeat-containing structures in cancers and cancer cell lines. Using long-read genome sequences that span telomeric repeats, we defined four types of telomere repeat variations in cancer cells: neotelomeres where telomere addition heals chromosome breaks, chromosomal arm fusions spanning telomere repeats, fusions of neotelomeres, and peri-centromeric fusions with adjoined telomere and centromere repeats. These results provide a framework for the systematic study of telomeric repeats in cancer genomes, which could serve as a model for understanding the somatic evolution of other repetitive genomic elements.

Neutral selection and clonal expansion during the development of colon cancer metastasis.

Intratumour heterogeneity has been shown to play a role in the malignant progression of cancer. The clonal evolution in primary cancer has been well studied, however, that in metastatic tumorigenesis is not fully understood. In this study, we established human colon cancer-derived organoids and investigated clonal dynamics during liver metastasis development by tracking barcode-labelled subclones. Long-term subclone co-cultures showed clonal drift, with a single subclone becoming dominant in the cell population. Interestingly, the selected subclones were not always the same, suggesting that clonal selection was not based on cell intrinsic properties. Furthermore, liver tumours developed by co-transplantation of organoid subclones into the immunodeficient mouse spleen showed a progressive drastic reduction in clonal diversity, and only one or two subclones predominated in the majority of large metastatic tumours. Importantly, selections were not limited to particular subclones but appeared to be random. A trend towards a reduction in clonal diversity was also found in liver metastases of multiple colour-labelled organoids of mouse intestinal tumours. Based on these results, we propose a novel mechanism of metastasis development, i.e. a subclone population of the disseminated tumour cells in the liver is selected by neutral selection during colonization and constitutes large metastatic tumours.

OTHR-15. POSTMORTEM TISSUE BIOBANKING AND COLLABORATIVE EFFORTS: A SINGLE INSTITUTION REPORT

Abstract BACKGROUND Post-mortem tissue donation holds significant value to researchers, clinicians and bereaved families however it is an underutilized tool. Initiatives in pediatric neuro-oncology allow new insights into tumors such as DIPG and is becoming more widespread. Barriers include treating physicians lack of knowledge, lack of resources, religious beliefs and more. Postmortem tissue gives insight into the evolving biology of cancer and failed treatments. Postmortem tissue donation offers some understanding to families and empower them to help others. Surgical tissue collection used for diagnosis, leaves limited amounts for research. METHODS Since 2012, we have contributed to pediatric cancer research by offering postmortem donation to 48 patients. We have consented and facilitated 35 autopsies. In 2020, Arnold Palmer Hospital for Children became a lead institution for the COG’s Project:EveryChild. In 2021, we became a Center of Excellence for Gift From A Child. Our local Translational Protocol, IRB-approved, allowing us to collect and store our surgical and postmortem samples. To date, we have collected over 839 postmortem samples from 35 patients. In our Translational Laboratory, we have access to 207 postmortem samples from 13 patients. Through collaborative research initiatives, we have successfully sent over 368 samples from 11 patients and 14 parents to GFAC. Through Project:EveryChild we have shared 218 samples from 7 patients. RESULTS To explore the quality of postmortem tissues, we tested the integrity of our samples through DNA and RNA sequencing. The DNA quality is excellent across specimens and equivalent to surgical specimens however the RNA quality from autopsy specimens has degraded 7.5% RINe factor &amp;gt; 8 (excellent) and 38% RINe factor &amp;gt; 6 (mildly degraded) compared to surgical specimens, 85% with RINe factor &amp;gt; 8, 14.3% RINe factor &amp;gt; 6. CONCLUSION The platform of postmortem tissue donation can lead to further advancements in understanding the evolution and resistance of cancer.

GCT-22. MEDICAL AND SURGICAL VARIABLES ASSOCIATED WITH RELAPSE IN CENTRAL NERVOUS SYSTEM (CNS) NONGERMINOMATOUS GERM CELL TUMORS (NGGCTS): SECONDARY ANALYSIS OF STRATUM 1 OF THE CHILDREN’S ONCOLOGY GROUP ACNS1123 PHASE 2 CLINICAL TRIAL

Abstract BACKGROUND ACNS1123 was a Phase 2 trial that evaluated the survival benefit of whole ventricular irradiation (WVI) instead of craniospinal irradiation in children with localized CNS NGGCTs who achieved a complete/partial radiographic response (CR/PR) to induction chemotherapy with negative tumor markers (beta-HCG/AFP) in serum and cerebrospinal fluid (CSF). Patients who did not achieve a CR/PR were considered inevaluable and removed from study therapy post-induction. METHODS In unplanned, exploratory analyses, we evaluated the association of age &amp;lt;15/&amp;gt;15-years at diagnosis, tumor location (pineal/suprasellar/bifocal/ventricular), tumor markers (beta-HCG/AFP) in serum and cerebrospinal fluid (CSF) at baseline (&amp;gt;/&amp;lt;1000), and rate of decline during induction in CR/PR and &amp;lt;CR/PR patients. Since most evaluable patients with relapse experienced distant failure in the spine, we assessed the impact of surgical variables, such as endoscopic third ventriculostomy (ETV)/biopsy/resection/combination, on spinal relapse in patients with or without disease progression. RESULTS 66 of 107 eligible patients achieved CR/PR with negative tumor markers while 41 patients did not. Bifocal tumors appeared to demonstrate a higher hazard for disease progression compared to suprasellar tumors (Hazard Ratio=5.4; p=0.0279). Otherwise, we did not find an association with progression-free-survival for any of the analyzed medical variables. Of 66 evaluable patients who received WVI, eight patients experienced relapse, with all patients demonstrating disease in the spine. No significant association was detected between any surgical variable (ETV/biopsy/resection) and spinal failure among evaluable patients who did or did not relapse; however, we had very limited power for these analyses due to very few events. CONCLUSION Current reliance on conventional parameters appears inadequate to identify biomarkers of response as well as relapse. Innovative approaches such as comprehensive genomic profiling of primary and recurrence tumor specimens could inform somatic clonal evolution and potential targeted therapies.

Exploring the Role of Vitamin D, Vitamin D-Dependent Proteins, and Vitamin D Receptor Gene Variation in Lung Cancer Risk.

Lung cancer has an unfavorable prognosis with a rate of low overall survival, caused by the difficulty of diagnosis in the early stages and resistance to therapy. In recent years, there have been new therapies that use specific molecular targets and are effective in increasing the survival chances of advanced cancer. Therefore, it is necessary to find more specific biomarkers that can identify early changes in carcinogenesis and allow the earliest possible treatment. Vitamin D (VD) plays an important role in immunity and carcinogenesis. Furthermore, the vitamin D receptor (VDR) regulates the expression of various genes involved in the physiological functions of the human organism. The genes encoding the VDR are extremely polymorphic and vary greatly between human populations. To date, there are significant associations between VDR polymorphism and several types of cancer, but the data on the involvement of VDR polymorphism in lung cancer are still conflicting. Therefore, in this review, our aim was to investigate the relationship between VDR single-nucleotide polymorphisms in humans and the degree of risk for developing lung cancer. The studies showcased different gene polymorphisms to be associated with an increased risk of lung cancer: TaqI, ApaI, BsmI, FokI, and Cdx2. In addition, there is a strong positive correlation between VD deficiency and lung cancer development. Still, due to a lack of awareness, the assessment of VD status and VDR polymorphism is rarely considered for the prediction of lung cancer evolution and their clinical applicability, despite the fact that studies have shown the highest risk for lung cancer given by TaqI gene polymorphisms and that VDR polymorphisms are associated with more aggressive cancer evolution.

Breast organoid suspension cultures maintain long-term estrogen receptor expression and responsiveness.

Organoid cultures offer a powerful technology to investigate many different aspects of development, physiology, and pathology of diverse tissues. Unlike standard tissue culture of primary breast epithelial cells, breast organoids preserve the epithelial lineages and architecture of the normal tissue. However, existing organoid culture methods are tedious, difficult to scale, and do not robustly retain estrogen receptor (ER) expression and responsiveness in long-term culture. Here, we describe a modified culture method to generate and maintain organoids as suspension cultures in reconstituted basement membrane (™Matrigel). This method improves organoid growth and uniformity compared to the conventional Matrigel dome embedding method, while maintaining the fidelity of the three major epithelial lineages. Using this adopted method, we are able to culture and passage purified hormone sensing (HS) cells that retain ER responsiveness upon estrogen stimulation in long-term culture. This culture system presents a valuable platform to study the events involved in initiation and evolution of ER-positive breast cancer.

Machine Learning-Assisted Direct RNA Sequencing with Epigenetic RNA Modification Detection via Quantum Tunneling.

RNA sequence information holds immense potential as a drug target for diagnosing various RNA-mediated diseases and viral/bacterial infections. Massively parallel complementary DNA (c-DNA) sequencing helps but results in a loss of valuable information about RNA modifications, which are often associated with cancer evolution. Herein, we report machine learning (ML)-assisted high throughput RNA sequencing with inherent RNA modification detection using a single-molecule, long-read, and label-free quantum tunneling approach. The ML tools achieve classification accuracy as high as 100% in decoding RNA and 98% for decoding both RNA and RNA modifications simultaneously. The relationships between input features and target values have been well examined through Shapley additive explanations. Furthermore, transmission and sensitivity readouts enable the recognition of RNA and its modifications with good selectivity and sensitivity. Our approach represents a starting point for ML-assisted direct RNA sequencing that can potentially decode RNA and its epigenetic modifications at the molecular level.

Threshold-awareness in adaptive cancer therapy.

Although adaptive cancer therapy shows promise in integrating evolutionary dynamics into treatment scheduling, the stochastic nature of cancer evolution has seldom been taken into account. Various sources of random perturbations can impact the evolution of heterogeneous tumors, making performance metrics of any treatment policy random as well. In this paper, we propose an efficient method for selecting optimal adaptive treatment policies under randomly evolving tumor dynamics. The goal is to improve the cumulative "cost" of treatment, a combination of the total amount of drugs used and the total treatment time. As this cost also becomes random in any stochastic setting, we maximize the probability of reaching the treatment goals (tumor stabilization or eradication) without exceeding a pre-specified cost threshold (or a "budget"). We use a novel Stochastic Optimal Control formulation and Dynamic Programming to find such "threshold-aware" optimal treatment policies. Our approach enables an efficient algorithm to compute these policies for a range of threshold values simultaneously. Compared to treatment plans shown to be optimal in a deterministic setting, the new "threshold-aware" policies significantly improve the chances of the therapy succeeding under the budget, which is correlated with a lower general drug usage. We illustrate this method using two specific examples, but our approach is far more general and provides a new tool for optimizing adaptive therapies based on a broad range of stochastic cancer models.

The Glasgow Microenvironment Score: an exemplar of contemporary biomarker evolution in colorectal cancer.

Colorectal cancer remains a leading cause of mortality worldwide. Significant variation in response to treatment and survival is evident among patients with similar stage disease. Molecular profiling has highlighted the heterogeneity of colorectal cancer but has had limited impact in daily clinical practice. Biomarkers with robust prognostic and therapeutic relevance are urgently required. Ideally, biomarkers would be derived from H&E sections used for routine pathological staging, have reliable sensitivity and specificity, and require minimal additional training. The biomarker targets would capture key pathological features with proven additive prognostic and clinical utility, such as the local inflammatory response and tumour microenvironment. The Glasgow Microenvironment Score (GMS), first described in 2014, combines assessment of peritumoural inflammation at the invasive margin with quantification of tumour stromal content. Using H&E sections, the Klintrup-Mäkinen (KM) grade is determined by qualitative morphological assessment of the peritumoural lymphocytic infiltrate at the invasive margin and tumour stroma percentage (TSP) calculated in a semi-quantitative manner as a percentage of stroma within the visible field. The resulting three prognostic categories have direct clinical relevance: GMS 0 denotes a tumour with a dense inflammatory infiltrate/high KM grade at the invasive margin and improved survival; GMS 1 represents weak inflammatory response and low TSP associated with intermediate survival; and GMS 2 tumours are typified by a weak inflammatory response, high TSP, and inferior survival. The prognostic capacity of the GMS has been widely validated while its potential to guide chemotherapy has been demonstrated in a large phase 3 trial cohort. Here, we detail its journey from conception through validation to clinical translation and outline the future for this promising and practical biomarker.

Mortalin promotes the evolution of androgen-independent prostate cancer through Wnt/β-catenin signaling pathway

Prostate cancer (PC) is a major global health concern affecting male individuals. Among its variants, androgen-independent prostate cancer exhibits slow progression and lacks effective treatment targets, rendering it insensitive to hormone therapy. Recent reports have highlighted the significance of Mortalin, an important oncogene, in tumor migration and invasion through various signaling pathways. Experimental evidence from in-vivo and in-vitro studies indicate upregulated expression of Mortalin in prostate cancer tissues. Moreover, it has been shown to regulate the epithelial-mesenchymal transition (EMT) process via the Wnt/β-catenin signaling pathway, thereby promoting prostate cancer proliferation and metastasis. These findings suggest that Mortalin may serve as a promising novel immunotherapeutic target for prostate cancer.

Molecular recording using DNA Typewriter.

Recording molecular information to genomic DNA is a powerful means of investigating topics ranging from multicellular development to cancer evolution. With molecular recording based on genome editing, events such as cell divisions and signaling pathway activity drive specific alterations in a cell's DNA, marking the genome with information about a cell's history that can be read out after the fact. Although genome editing has been used for molecular recording, capturing the temporal relationships among recorded events in mammalian cells remains challenging. The DNA Typewriter system overcomes this limitation by leveraging prime editing to facilitate sequential insertions to an engineered genomic region. DNA Typewriter includes three distinct components: DNA Tape as the 'substrate' to which edits accrue in an ordered manner, the prime editor enzyme, and prime editing guide RNAs, which program insertional edits to DNA Tape. In this protocol, we describe general design considerations for DNA Typewriter, step-by-step instructions on how to perform recording experiments by using DNA Typewriter in HEK293T cells, and example scripts for analyzing DNA Typewriter data ( https://doi.org/10.6084/m9.figshare.22728758 ). This protocol covers two main applications of DNA Typewriter: recording sequential transfection events with programmed barcode insertions by using prime editing and recording lineage information during the expansion of a single cell to many. Compared with other methods that are compatible with mammalian cells, DNA Typewriter enables the recording of temporal information with higher recording capacities and can be completed within 4-6 weeks with basic expertise in molecular cloning, mammalian cell culturing and DNA sequencing data analysis.