Intertumoral Heterogeneity Research Articles

Abstract B040: Cell-free DNA captures inter- and intra-patient heterogeneity in advanced bladder cancer

Abstract Metastatic bladder cancer (BLCA) is an aggressive disease complicated by the emergence of variant histological subtypes. The UW/Fred Hutch metastatic BLCA rapid autopsy program is a first-of-its-kind program collecting cell-free DNA (cfDNA) and matched normal tissue, primary tumor, and metastatic tumor samples from advanced BLCA patients, with a focus on these variant subtype tumors. This dataset, consisting of 20 patients with up to five metastases per patient, is a unique opportunity to assess how cfDNA is able to capture heterogeneity across BLCA. We assessed both inter-patient heterogeneity between BLCA subtypes and inter-tumoral heterogeneity within each patient using epigenetic and genomic profiling. Using nucleosome profiling, we identified the activity of lineage markers and transcription factors that distinguish healthy individuals from BLCA patients, and between BLCA subtypes. Furthermore, we conducted a detailed analysis of whole genome (30X) and targeted (4,000X) cfDNA sequencing in their ability to capture the evolutionary history of tumor mutations. Detection of tumor mutations by cfDNA is highly dependent on clonality, with >90% of founder tumor mutations captured by either sequencing method, while capture is decreased for either subclonal mutations or those private to a single metastasis. Importantly, deleterious mutations in BLCA driver genes had close to 100% detection using targeted panel sequencing, even for subclonal mutations. These findings lay the groundwork for the use of cfDNA in clinical BLCA decision-making, including tracking the emergence of pathogenic, targetable mutations and variant subtypes. Citation Format: Samantha L Schuster, Pushpa Itagi, Sonali Arora, Patricia C Galipeau, Thomas W Persse, Michael Yang, Allie Kreitman, Alan Min, Funda Vakar-Lopez, John K Lee, Petros Grivas, Robert B Montgomery, Jonathan L Wright, Andrew C Hsieh, Hung- Ming Lam, Gavin Ha. Cell-free DNA captures inter- and intra-patient heterogeneity in advanced bladder cancer [abstract]. In: Proceedings of the AACR Special Conference: Liquid Biopsy: From Discovery to Clinical Implementation; 2024 Nov 13-16; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(21_Suppl):Abstract nr B040.

Melanoma Cells from Different Patients Differ in Their Sensitivity to Alpha Radiation-Mediated Killing, Sensitivity Which Correlates with Cell Nuclei Area and Double Strand Breaks

Background/Objective: In this study, for the first time, we examined and compared the sensitivity of four patient-derived cutaneous melanoma cell lines to alpha radiation in vitro and analyzed it in view of cell nucleus area and the formation of double-strand breaks (DSB). Melanoma cells sensitivity to alpha radiation was compared to photon radiation effects. Furthermore, we compared the sensitivity of the melanoma cells to squamous cell carcinoma. Methods: Human melanoma cell lines YDFR.C, DP.C, M12.C, and M16.C, and the squamous cell carcinoma cell line, CAL 27, were irradiated in vitro using Americium-241 as alpha-particle source. Cells were irradiated with doses of 0 to 2.8 gray (Gy). Cell viability, DNA DSB, and nuclear size were measured. Results: 1. Alpha radiation caused death and proliferation arrest of all four melanoma cell lines, but inter-tumor heterogeneity was observed. 2. The most sensitive cell line (DP.C) had a significantly larger nucleus area (408 µm2) and the highest mean number of DSB per cell (9.61) compared to more resistant cells. 3. The most resistant cell, M16.C, had a much lower nucleus area (236.99 µm2) and DSB per cell (6.9). 4. Alpha radiation was more lethal than photon radiation for all melanoma cells. 5. The SCC cell, CAL 27, was more sensitive to alpha radiation than all melanoma cells but had a similar number of DSB (6.67) and nucleus size (175.49 µm2) as the more resistant cells. 6. The cytotoxic effect of alpha radiation was not affected by proliferation arrest after serum starvation. 7. Killing of cells by alpha radiation was marginally elevated by ATR or topoisomerase 1 inhibition. Conclusions: This study demonstrates that various human melanoma cells can be killed by alpha radiation but exhibit variance in sensitivity to alpha radiation. Alpha radiation applied using the Intra-tumoral Diffusing alpha-emitters Radiation Therapy (Alpha DaRT) methodology may serve as an efficient treatment for human melanoma.

An insight to PDAC tumor heterogeneity across pancreatic subregions using computed tomography images

Pancreatic Ductal Adenocarcinoma (PDAC) is an exceptionally deadly form of pancreatic cancer with an extremely low survival rate. From diagnosis to treatment, PDAC is highly challenging to manage. Studies have demonstrated that PDAC tumors in distinct regions of the pancreas exhibit unique characteristics, influencing symptoms, treatment responses, and survival rates. Gaining insight into the heterogeneity of PDAC tumors based on their location in the pancreas can significantly enhance overall management of PDAC. Previous studies have explored PDAC tumor heterogeneity across pancreatic subregions based on their genetic and molecular profiles through biopsy-based histologic assessment. However, biopsy examinations are highly invasive and impractical for large populations. Abdominal imaging, such as Computed Tomography (CT) offers a completely non-invasive means to evaluate PDAC tumor heterogeneity across pancreatic subregions and an opportunity to correlate image feature of tumors with treatment outcome and monitoring. In this study, we explored the inter-tumor heterogeneity in PDAC tumors across three primary pancreatic subregions: the head, body, and tail. Utilizing contrast-enhanced abdominal CT scans and a thorough radiomic analysis of PDAC tumors, several morphological and textural tumor features were identified to be notably different between tumors in the head and those in the body and tail regions. To validate the significance of the identified features, a machine learning ML model was trained to automatically classify PDAC tumors into their respective regions i.e. head or body/tail subregion using their CT features. The study involved 200 CT abdominal scans, with 100 used for radiomic analysis and model training, and the remaining 100 for model testing. The ML model achieved an average classification accuracy, sensitivity, and specificity of 87%, 86%, and 88% on the testing scans respectively. Evaluating the heterogeneity of PDAC tumors across pancreatic subregions provides valuable insights into tumor composition and has the potential to enhance diagnosis and personalize treatment based on tumor characteristics and location.

EXTH-16. UNBIASED DISCOVERY OF SYNERGISTIC VULNERABILITIES AGAINST GLIOBLASTOMA: AN INNOVATIVEIN VIVO CRISPR PROFILING PLATFORM TO UNLEASH THE POWER OF TUMOR-TARGETED CYTOKINE THERAPY

Abstract BACKGROUND Glioblastoma poses a formidable challenge due to its intricate intra- and intertumoral heterogeneities and its associated immunosuppressive milieu. The standards of care have limited activity, and no major advances have been made in decades. One promising avenue is tumor-targeted cytokine-based therapies, particularly L19-TNF, which is undergoing clinical trials in Europe and the US for patients with newly diagnosed and recurrent glioblastoma. However, there is still an opportunity to find safer and more tolerable combination partners to improve the efficacy of L19-TNF immunotherapy. To address this challenge, we employed a cutting-edge in vivo CRISPR screening approach to identify synergistic vulnerabilities with a focus on druggable targets. METHODS We systematically investigated the dependencies of tumor growth and key signaling pathways under the pressure of L19-TNF immunotherapy in the CT-2A orthotopic murine glioma model. We used an innovative in vivo CRISPR screening approach that enables timed sgRNA activation to mitigate off-target effects and overcome orthotopic tumor cell implantation challenges, such as the bottleneck of cell engraftment. RESULTS We first conducted a genome-wide in vivo screen and identified 400 top-scoring candidate genes, which we subsequently tested again in a targeted in vivo screen to refine our drug selection process. Through this, we uncovered novel and previously unexplored druggable targets for glioblastoma. Out of these, we validated four promising target-specific drugs in preclinical glioblastoma models in downstream in vivo experiments. In addition, we investigated modes of action and resistance. CONCLUSION Our research marks a significant advancement in pursuing novel treatment strategies for glioblastoma. We have unveiled a range of synergistic combinations and paved the way for the rational design of combinatory treatment approaches to maximize the therapeutic efficacy of L19-TNF immunotherapy, laying the groundwork for future clinical translation.

IMMU-46. CROSS-TALK OF INNATE IMMUNE CELLS IN GLIOBLASTOMA PATHOPHYSIOLOGY: UNDERSTANDING NLRP3 AND NLRP12

Abstract Glioblastoma (GBM) are malignant gliomas with an average survival of less than 14 months after diagnosis, despite surgery, chemotherapy, and radiotherapy. Tumor survival is aided by the heterogeneous cell populations in the tumor microenvironment. Two crucial innate immune cells of the brain, namely microglia and macrophages, favor a tumor-promoting microenvironment IN GBM. The nucleotide-binding domain leucine-rich repeat-containing receptor (NLR) family in astrocytes and microglia cells recognizes pathogen- and damage-associated molecular patterns that induce inflammation. NLRP3, upon activation, leads to cleavage and activation of Caspase-1, causing the proteolytic cleavage-mediated activation and conversion of pro-IL-1ß and pro-IL-18 to IL-1ß and IL-18, leading to pyroptosis. NLRP12 downregulates inflammatory responses in microglia and macrophages by regulating the NF-kB pathway. NLRP3 and NLRP12 have tumor-promoting and anti-tumour functions in various cancers. NLRP12 is a prognostic marker for glioblastoma, and its increased expression correlates with poor survival in GBM patients. However, the cellular and molecular mechanisms of NLRP3 and NLRP12 in GBM pathophysiology are largely unknown. This study aims to understand the effect of NLRP3 and NLRP12 expression in GBM, to interpret the pathophysiology, and to help develop future targeted therapeutic interventions. To understand the baseline expression of NLRP3 and NLRP12 in cell lines and patient-derived GBM cells, PCR, western blotting, and Immunocytochemistry were performed comparing WT and nlrp3-/- and nlrp12-/- cells. Differential expression of NLRP3 and NLRP12 in patient-derived cells can be compared to GBM, astrocyte, and microglia cell lines. This may be the underlying cause for intra and inter-tumor heterogeneity and resultant differential therapy outcomes. To understand cell-to-cell communication in GBM-like tumor microenvironments, heterocellular 3D organoids, and patient-derived tumor organoids were developed. Cell viability in 3D patient-derived tumor organoids decreased by up to 10% with Doxorubicin and 40% with Carboplatin treatment decreases compared to untreated controls. This suggests the scope of developing drugs targeting inflammation regulatory genes.

EPCO-36. INTEGRATIVE DEEP-LEARNING AND PHARMACOGENOMIC PROFILING OF MALIGNANT GLIOMAS USING PATIENT-DERIVED TUMOR CELLS

Abstract Malignant glioma stands as a formidable cancer with limited therapeutic options due to many factors including pronounced genetic heterogeneity, the blood-brain barrier, among others. Precision medicine, tailoring treatment based on a patient’s unique molecular profiles, is a fitting approach for addressing intra- and inter-tumor heterogeneity. Previously, we have shown the advantage of using patient-derived tumor cells (PDCs) to evaluate drug efficacy across various cancer types. Unlike cancer cell lines, which significantly differ from primary tumors, PDCs capture the molecular features of primary tumors, showing predictive clinical responses in a retrospective study. Building on these encouraging results, we have expanded PDC-based drug screening to specifically target malignant gliomas. We have established an initial cohort of over 500 malignant glioma PDCs (~ 370 patients) and screened more than 100 drugs. We have complemented our cohort with molecular profiles, including genomic and transcriptomic data to infer the potential pharmacogenomic associations of malignant gliomas. We constructed a multi-modal deep-learning model that seamlessly integrates molecular features and drug structure to predict the drug sensitivity. By employing an explainable machine learning method, we retrospectively analyzed the model to investigate the potential molecular biomarkers and their contribution to drug sensitivity. Collectively, our study demonstrates the efficacy of using patient-derived tumor cells (PDCs) combined with deep-learning models to predict drug sensitivity in malignant gliomas. This integrative approach can potentially refine personalized treatment strategies and improve clinical outcomes for glioma patients.

DDDR-05. SPATIALLY RESOLVED DRUG SENSITIVITY PROFILING IN GLIOBLASTOMA

Abstract BACKGROUND Glioblastoma is the most common malignant primary brain tumor in adults, characterized by poor overall survival and an urgent need for more effective treatment strategies. Macroscopic heterogeneity, as observed through MR imaging, divides the tumor into distinct regions: a tumor-cell dense contrast-enhancing tumor core and a non-contrast-enhancing infiltration zone that appears hyperintense on T2/FLAIR imaging. The infiltration zone contains both infiltrating tumor cells and non-malignant cells from the local brain microenvironment. Although genomic and transcriptomic differences between the tumor core and the infiltration zone have been reported, functional differences, such as differential drug sensitivities, remain largely unknown. METHODS To investigate this, we collected region-specific glioblastoma patient tissue samples from both the contrast-enhancing tumor core and the non-contrast-enhancing but 5-aminolevulinic acid (5-ALA) fluorescent infiltration zone during surgery. Using pharmacoscopy, a microscopy-based single-cell drug screening platform, we assessed drug responses across regions and patients. RESULTS To determine whether drug responses within a single tumor region were more similar compared to responses across different regions we collected multiple tissue samples from each tumor region in seven patients. We identified region-specific drug responses for temozolomide and lomustine, among other drugs, in some of the patients. However, drug sensitivities were not consistent within the same region across different patients. Thus, across patients, the region-specific drug responses were obscured by patient heterogeneity. To address intra- and inter-tumor drug response heterogeneity, we developed a complementarity score based on region-specific drug response data of 60 drugs across 23 paired samples. The complementarity score identified promising drug combinations that demonstrated strong anti-tumor activity across tumor regions and patients. CONCLUSIONS Monotherapy approaches have largely failed in glioblastoma, despite extensive molecular profiling efforts aimed at identifying tumor-specific vulnerabilities. Identifying drug combinations that synergistically target the locoregional heterogeneity of glioblastoma may open up new avenues of personalized therapy.

EPCO-57. INTEGRATED ANALYSIS OF BULK AND SINGLE-CELL RNA SEQUENCING DATA OF PRIMARY AND METASTATIC PEDIATRIC MEDULLOBLASTOMA

Abstract Medulloblastoma (MB) is a highly aggressive and the most common brain tumor in childhood. MB presents a high intertumoral heterogeneity, with at least four molecular subgroups identified (SHH, WNT, Group 3, and Group 4). Metastatic MB, or Leptomeningeal Disease (LMD), is predominantly found in the MB Group 3 type. Although LMD represents a main clinical challenge, its molecular mechanisms remain poorly characterized. Recent research has shown that primary and MB metastasis diverge dramatically. Our work has focused on establishing therapy naïve Group 3 Patient-Derived Xenografts models of primary and metastatic Medulloblastoma to conduct transcriptomic profiling at the bulk and single-nuclei RNAseq levels to identify genetic drivers/pathways that sustain leptomeningeal disease compartment. Our results show various signaling pathways enriched across LMD models, such as MYC targets, unfolded protein response, and fatty acid metabolism. Using single-sample GSEA analysis (ssGSEA) and deconvolution approaches, we have also identified that our PDXes models retain neoplastic subpopulations previously identified in MB single-cell sequencing studies. Similarly, we have identified slight differences in cell subpopulation proportions between primary and leptomeningeal compartments. Our single-nuclei studies have confirmed these results and differentially expressed genes previously found in bulk RNAseq analyses. These results suggest the presence of cell populations enriched in the metastatic compartment with an aberrant transcription phenotype and adaptations in fatty acid metabolism to survive the leptomeningeal space. In conclusion, our work supports the notion that primary and LMD retain subpopulation clusters present in MB Group 3 tumors with different proportions in the metastatic compartment shown by bulkRNAseq deconvolution and single nuclei RNA analysis. We also show that primary and LMD are transcriptionally different, with various enriched pathways in the metastatic compartment and shared among LMD Group 3 PDX models. Through these approaches, we aim to elucidate the genetic dependencies of metastatic Medulloblastoma that will help for targeted therapies.

PATH-21. SINGEL CELL TRANSCRIPTOMICS OF SPINAL EPENDYMOMA SUBTYPES IDENTIFIES INTRATUMORAL HETEROGENEITY

Abstract Spinal ependymomas comprise glial neoplasms that are classified by histological and molecular features. Two of the respective types, myxopapillary ependymomas (MPE) and spinal ependymomas (SP-EPN) are largely understudied. For each of these, our group identified clinically relevant subtypes with poorer (MPE-A; SP-EPN-A) and more favorable progression-free survival (MPE-B; SP-EPN-B) employing global methylation and transcriptomic profiling. As cellular origin and pathogenesis of these tumors are largely unknown, therapeutic options for patients with unfavorable subtypes are unavailable. We performed single cell transcriptomic sequencing of 12 formalin-fixed and paraffin-embedded spinal ependymomas including all MPE and SP-EPN subtypes and obtained a median of 10,091 cells per tumor after quality control. Uniform Manifold Approximation and Projection (UMAP) analysis revealed intertumoral heterogeneity as most samples formed separate clusters with tumors of the same subtype located in proximity. The expression of literature-defined neoplastic consensus cell states was examined for each cell of the data set and revealed intratumoral heterogeneity. Tumors expressed predominantly two programs, being a ciliated (ependymal-like) cell state (61% of all neoplastic cells) and the astrocytic state (31 % of all neoplastic cells). Interestingly, third and fourth most abundant states (mesenchymal and hypoxia) were almost exclusively expressed in MPE-A and SP-EPN-A. Furthermore, distinct clones of chromosomal copy number variations were identified across all tumors. These clones can be distinguished in UMAP representation of MPE single cell transcriptomes and to a lesser extend in SP-EPN. Next, we sought to characterize the resemblance of integrated tumor subtypes to normal spinal cord cell populations and observed a high similarity to human spinal cord ependymal cells. All spinal ependymoma subtypes display intratumoral heterogeneity with respect to the transcriptomic cell state resemblance of the neoplastic cells. Together with the transcriptomic similarity to ependymal and astrocytic cells, our data suggest the tumor origin within the astro-ependymal lineage.

DDDR-34. A SYSTEMS MEDICINE PLATFORM FOR INDIVIDUALIZED TREATMENT OF GLIOBLASTOMA

Abstract Glioblastoma is an aggressive brain cancer characterized by complex intertumoral heterogeneity. Yet, no methods have successfully stratified patients for effective individualized treatments. We aimed to use a systems medicine approach to individualize treatments for patients. From 27 patients undergoing surgery for either a newly diagnosed or a progressive glioblastoma, we collected biopsies and cultured the cells as spheroids. Drug sensitivity testing for >500 drugs from the FDA-approved oncology collection was performed. Drug efficacy was quantified using a modified area under the dose-response curve (Drug Sensitivity Score, DSS), and individualized by normalizing the DSS to the drug response in bone marrow cells from healthy donors (selective DSS, sDSS). All samples were assayed by WES, WGS, and RNA-sequencing. We found that glioblastomas can be stratified into three DSS groups. These were associated with expression of genes both known (e.g. ALDH1A1), and not known (e.g. VRK2) to be linked to drug resistance. By unsupervised clustering of sDSS, we found a robust patient stratification primarily driven by drug sensitivity to four groups of targets: i) EGFR, ii) FGFR/PDGFR/VEGFR, iii) MDM2 and iv) MEK/ERK. We found differentially expressed genes (N = 20-90 genes, varying by group) between the responders and non-responders in each group. We explored differential pathway activity between the responders and non-responders in each group to correlate drug responses to transcriptional networks to inform underlying disease mechanisms. The transcriptional networks further provide a molecular basis for drug predictions for additional treatment prioritizations. We conclude that our platform can stratify glioblastomas into subgroups, inform the underlying biology of drug sensitivity, and prioritize treatments for patients. Clinical utility is currently under evaluation in a formal trial.

Genomic, epigenomic and transcriptomic inter- and intra-tumor heterogeneity in desmoid tumors.

Desmoid tumors are bland fibroblastic tumors that do not metastasize but have a high rate of local recurrence. Previously published studies proposed two different transcriptomic signatures to predict relapse. Molecular heterogeneity has been well established in high-grade sarcomas but little is known about molecular variability within locally aggressive tumors such as desmoids. We performed transcriptomic profiling of 31 specimens from 20 primary desmoid tumors to identify genes predictive of relapse. We also performed multi-omic analysis including DNA methylation, copy number alterations, point mutations and gene expression on 24 specimens from different regions of primary and recurrent desmoid tumors from 3 patients (7-9 specimens per patient). We observed highly variable expression of transcriptomic prognostic signatures, both in patients who did or did not progress. Signatures associated with favorable and unfavorable outcome were detected in different regions within the same tumor. Further multi-omic studies showed remarkable intra- and inter-tumor heterogeneity of genomic, epigenomic and transcriptomic patterns. The transcriptomic profiles showed the highest degree of variability within tumors and between primary and recurrent tumors from the same patient. This study shows an unexpected degree of intra- and inter-tumor heterogeneity in desmoid tumors. Our analysis indicates that molecular analysis of a single tumor biopsy may underestimate the magnitude of molecular alterations in desmoid tumors. Our study also shows that recurrent desmoid tumors acquire multiple new molecular alterations. Thus, molecular heterogeneity is an important consideration in drug development and validation of prognostic and predictive biomarkers for desmoid tumors.

Fluorescence Lifetime Imaging Enables In vivo Quantification of PD-L1 Expression and Inter-tumoral Heterogeneity.

Patient selection for cancer immunotherapy requires precise, quantitative readouts of biomarker expression in intact tumors that can be reliably compared across multiple subjects over time. The current clinical standard biomarker for assessing immunotherapy response is programmed death-ligand-1 (PD-L1) expression, typically quantified using immunohistochemistry. This method, however, only provides snapshots of PD-L1 expression status in microscopic regions of ex vivo specimens. While various targeted probes have been investigated for in vivo imaging of PD-L1, non-specific probe accumulation within the tumor microenvironment (TME) has hindered accurate quantification, limiting the utility for preclinical and clinical studies. Here, we demonstrated that in vivo time-domain (TD) fluorescence imaging of an anti-PD-L1 antibody tagged with the near-infrared fluorophore IRDye 800CW (αPDL1-800) can yield quantitative estimates of baseline tumor PD-L1 heterogeneity across untreated mice, as well as variations in PD-L1 expression in mice undergoing clinically relevant anti-PD1 treatment. The fluorescence lifetime (FLT) of PD-L1 bound αPDL1-800 was significantly longer than the FLT of nonspecifically accumulated αPDL1-800 in the TME. This FLT contrast allowed quantification of PD-L1 expression across mice both in superficial breast tumors using planar FLT imaging and in deep-seated liver tumors (>5 mm depth) using the asymptotic TD algorithm for fluorescence tomography. These findings suggest that fluorescence lifetime imaging can accelerate the preclinical investigation and clinical translation of new immunotherapy treatments by enabling robust quantification of receptor expression across subjects.

Cell death in glioblastoma and the central nervous system.

Glioblastoma is the commonest and deadliest primary brain tumor. Glioblastoma is characterized by significant intra- and inter-tumoral heterogeneity, resistance to treatment and dismal prognoses despite decades of research in understanding its biological underpinnings. Encompassed within this heterogeneity and therapy resistance are severely dysregulated programmed cell death pathways. Glioblastomas recapitulate many neurodevelopmental and neural injury responses; in addition, glioblastoma cells are composed of multiple different transformed versions of CNS cell types. To obtain a greater understanding of the features underlying cell death regulation in glioblastoma, it is important to understand the control of cell death within the healthy CNS during homeostatic and neurodegenerative conditions. Herein, we review apoptotic control within neural stem cells, astrocytes, oligodendrocytes and neurons and compare them to glioblastoma apoptotic control. Specific focus is paid to the Inhibitor of Apoptosis proteins, which play key roles in neuroinflammation, CNS cell survival and gliomagenesis. This review will help in understanding glioblastoma as a transformed version of a heterogeneous organ composed of multiple varied cell types performing different functions and possessing different means of apoptotic control. Further, this review will help in developing more glioblastoma-specific treatment approaches and will better inform treatments looking at more direct brain delivery of therapeutic agents.

Recent advances in immunotherapy for small cell lung cancer.

This review aims to provide an overview of recent advances in immunotherapy for small cell lung cancer (SCLC), with a focus on the current status of immune checkpoint inhibitors (ICIs), novel combination strategies, and key biomarkers. The integration of ICIs into standard chemotherapy has established them as the first-line treatment for extensive-stage SCLC (ES-SCLC). The ADRIATIC trial further demonstrated the efficacy of ICI maintenance therapy in limited-stage SCLC. Additionally, combining radiotherapy with ICIs has shown promising synergistic effects, including the abscopal and radscopal effects. Ongoing investigations into the combination of ICIs with targeted therapies, such as antiangiogenic agents and DNA damage response inhibitors, have yielded encouraging preliminary results. Notably, the novel therapeutic agent tarlatamab, the first bispecific DLL3-directed CD3 T-cell engager, has recently received FDA approval for second-line treatment of ES-SCLC. Advances in omics technologies have shed light on the intra-tumor and inter-tumor heterogeneity of SCLC, leading to the identification of new molecular subtypes and biomarkers, thereby paving the way for precision medicine. Despite the improved outcomes associated with immunotherapy in SCLC, the overall clinical benefit remains modest. Further preclinical and clinical studies are essential to identify optimal treatment regimens and enhance therapeutic efficacy.

Comprehensive mapping of somatotroph pituitary neuroendocrine tumour heterogeneity using spatial and single-cell transcriptomics.

Pituitary neuroendocrine tumours (PitNETs) are common intracranial tumours that are highly heterogeneous with unpredictable growth patterns. The driver genes and mechanisms that are crucial for tumour progression in somatotroph PitNETs are poorly understood. In this study, we performed integrative spatial transcriptomics (ST) and single-cell RNA sequencing (scRNA-seq) analysis on somatotroph tumours and normal pituitary samples to comprehensively characterize the differences in cellular characteristics. By analyzing combined copy number variations (CNVs), tumour tissues were divided into two regions, which included the CNVhigh and CNVlow areas. The protumour genes DLK1 and RCN1 were highly expressed in the CNVhigh area, which might be related to tumour progression and could be targeted for precision therapy. We also found that the transforming growth factor beta signalling pathway participated in tumour progression and identified heterogeneity in the expression profiles of key genes. We assessed the intertumoral and intratumoral heterogeneity in somatotroph PitNETs and emphasized the importance of individualized treatment. In summary, we visualized the cellular distribution and transcriptional differences in normal pituitary and somatotroph PitNETs by ST and scRNA-seq for the first time. This study provides a strong theoretical foundation to comprehensively understand the crucial mechanisms involved in tumour progression and develop new strategies to treat somatotroph PitNETs. The first-ever visualization of cellular distributions in normal and tumor pituitary tissues. The inter- and intra-tumoral transcriptomic heterogeneity of somatotroph PitNETs was comprehensively revealed. Identification of potential protumor factors and critical signaling pathways, opening new avenues for therapeutic intervention.

ScTML: a pan-cancer single-cell landscape of multiple mutation types.

Investigating mutations, including single nucleotide variations (SNVs), gene fusions, alternative splicing and copy number variations (CNVs), is fundamental to cancer study. Recent computational methods and biological research have demonstrated the reliability and biological significance of detecting mutations from single-cell transcriptomic data. However, there is a lack of a single-cell-level database containing comprehensive mutation information in all types of cancer. Establishing a single-cell mutation landscape from the huge emerging single-cell transcriptomic data can provide a critical resource for elucidating the mechanisms of tumorigenesis and evolution. Here, we developed scTML (http://sctml.xglab.tech/), the first database offering a pan-cancer single-cell landscape of multiple mutation types. It includes SNVs, insertions/deletions, gene fusions, alternative splicing and CNVs, along with gene expression, cell states and other phenotype information. The data are from 74 datasets with 2 582 633 cells, including 35 full-length (Smart-seq2) transcriptomic single-cell datasets (all publicly available data with raw sequencing files), 23 datasets from 10X technology and 16 spatial transcriptomic datasets. scTML enables users to interactively explore multiple mutation landscapes across tumors or cell types, analyze single-cell-level mutation-phenotype associations and detect cell subclusters of interest. scTML is an important resource that will significantly advance deciphering intra-tumor and inter-tumor heterogeneity, and how mutations shape cell phenotypes.

Bihemispheric IDH-Wildtype Glioblastoma with Unilateral FGFR3-TACC3 Fusion in Patient with Breast Cancer History: A Case Report and Literature Review

Abstract Introduction/Objective Adult IDH-wild type glioblastomas comprise a heterogeneous spectrum of neoplasms, characterized by poor prognosis and resistance to the chemoradiotherapy. For WHO integrated diagnosis classification and to establish treatment options, molecular analysis is necessary. Herein, we present a case of a glioblastoma, IDH-wildtype with involvement of both cerebral hemispheres and an uncharacteristic in-frame fusion limited to one hemisphere. Methods/Case Report The patient is a 52-year-old woman with past medical history of metastatic breast cancer who presented with headache and worsening facial pain. Brain magnetic resonance imaging brain demonstrated a right frontal lobe lesion with vasogenic edema. The patient was treated with Gamma Knife radiosurgery. Scans revealed enlargement and a new left frontal lobe lesion. Bihemispheric tumor resection and GammaTile placement was undertaken. Results (if a Case Study enter NA) Histologically, the tumor was composed of hypercellular astrocytes, microvascular proliferation, and necrosis with no evidence of metastatic disease. Immunohistochemistically, neoplastic cells showed glial fibrillary acidic protein expression and elevated Ki-67 proliferation index. Molecular examination revealed in the right hemisphere lesion post-radiation (1) FGFR3::TACC3 (exon 17::exon 11) fusion and (2) TERT promoter, while the left hemisphere lesion which did not receive radiation showed clinically relevant variants in (1) TERT promoter, (2) TP53, (3) NF1, and (3) PIK3CA without identification of a fusion. An integrated diagnosis of glioblastoma, IDH- wildtype, CNS WHO grade 4 was established bilaterally. Conclusion The fibroblast growth factor receptor 3 (FGFR3)-transforming acidic coiled-coil 3 (TACC3) fusion is identified as a rare molecular feature in glioblastomas with an estimated prevalence of 4%. Clinical trials suggest that F3T3-positive tumors have a better prognosis than those without the translocation. The identification of the oncogenic FGFR3-TACC3 fusion highlights the possibility of identifying patients potentially responsive to targeted therapy with FGFR kinase inhibitors. The significance of the molecular differences in the right and left hemisphere lesions is uncertain; glioblastomas are heterogeneous tumors with intra-and intertumoral heterogeneity but it may suggest independent origins.

The Evolutionary Forest of Pancreatic Cancer.

The genomic features of pancreatic ductal adenocarcinoma (PDAC) have been well described, yet the evolutionary contexts within which those features occur remains unexplored. We studied the genome landscapes, phylogenies and clonal compositions of 91 PDACs in relation to clinicopathologic features. There was no difference in the number of driver mutations or the evolutionary timing that each mutation occurred. High truncal density, a metric of the accumulation of somatic mutations in the lineage that gave rise to each PDAC, was significantly associated with worse overall survival. Polyclonal, monoclonal or mixed polyclonal/monoclonal metastases were identified across the cohort highlighting multiple forms of inter-tumoral heterogeneity. Advanced stage and treated PDACs had higher odds of being polyclonal, whereas oligometastatic PDACs had fewer driver alterations, a lower fractional allelic loss and increased likelihood of being monoclonal. In sum, our findings reveal novel insights into the dynamic nature of the PDAC genome beyond established genetic paradigms.

Pan-cancer characterization of cellular senescence reveals its inter-tumor heterogeneity associated with the tumor microenvironment and prognosis

Cellular senescence (CS) is characterized by the irreversible cell cycle arrest and plays a key role in aging and diseases, such as cancer. Recent years have witnessed the burgeoning exploration of the intricate relationship between CS and cancer, with CS recognized as either a suppressing or promoting factor and officially acknowledged as one of the 14 cancer hallmarks. However, a comprehensive characterization remains absent from elucidating the divergences of this relationship across different cancer types and its involvement in the multi-facets of tumor development. Here we systematically assessed the cellular senescence of over 10,000 tumor samples from 33 cancer types, starting by defining a set of cancer-associated CS signatures and deriving a quantitative metric representing the CS status, called CS score. We then investigated the CS heterogeneity and its intricate relationship with the prognosis, immune infiltration, and therapeutic responses across different cancers. As a result, cellular senescence demonstrated two distinct prognostic groups: the protective group with eleven cancers, such as LIHC, and the risky group with four cancers, including STAD. Subsequent in-depth investigations between these two groups unveiled the potential molecular and cellular mechanisms underlying the distinct effects of cellular senescence, involving the divergent activation of specific pathways and variances in immune cell infiltrations. These results were further supported by the disparate associations of CS status with the responses to immuno- and chemo-therapies observed between the two groups. Overall, our study offers a deeper understanding of inter-tumor heterogeneity of cellular senescence associated with the tumor microenvironment and cancer prognosis.

A hybrid meta-heuristic framework with ensemble deep learning for multi-functional simultaneous optimized automatic intensity-modulated radiotherapy planning

Intensity-modulated radiotherapy (IMRT) is one of the main treatments for patients with cancer, and its treatment planning holds significant importance. Compared to manual treatment planning, automatic treatment planning (ATP) is expected to improve plan quality and efficiency, which is of great clinical importance. However, treatment planning is an intractable “black art” that calls for intuition and heuristics from medical dosimetrists. There are two main issues with the current ATP methods: (i) they rely strongly on prior clinical experience and are not sufficiently scalable, and (ii) they use a single-functional optimization concept that ignores delivery accuracy information, which lowers the delivery accuracy of the generated ATP plans. To address these issues, this paper presents a novel multi-functional simultaneous optimized automatic treatment planning (MFSO-ATP) method. First, to address the issues of high reliance and insufficient scalability, an evolutionary strategy (ES) and simulated annealing (SA)-based hybrid meta-heuristic trial-and-error (ESSA-HMTE) framework is designed to automatically adjust optimization parameters without human intervention. Second, to address the issue of delivery accuracy deterioration, an ensemble deep learning-based gamma passing rate (GPR) prediction (EnDL-GPR) framework was constructed and trained to predict the plan delivery accuracy a priori. The ESSA-HMTE framework effectively fuses dosimetric quality information and delivery accuracy a priori information to simultaneously optimize the two, ensuring delivery accuracy while achieving automatic planning. We tested the MFSO-ATP method using multiple diseases with high inter-tumor heterogeneity. The results showed that the proposed method can improve the quality and delivery accuracy of IMRT plans, reduce the percentage of quality assurance failures, is highly scalable, and has good clinical application prospects.