Abstract Linking glioblastoma (GBM) evolution to clinical progression is challenged by multiple factors, including tumor location for repeated sample collection, and short patient survival. In a single individual, we collected and analysed samples from 11 operations distributed across 31 months of multi-relapsing and multifocal GBM, including terminal leptomeningeal progression. All samples shared genomic ancestry of the retinoblastoma protein 1 (RB1) and neurofibromin 1 (NF1) mutations while advanced progression and extracranial metastases featured mutations of tuberous sclerosis complex 2 (TSC2) , PBRM1 , CD22 and Fanconi anemia supplementation group I ( FANCI ), correlated with clinical resistance to immunotherapies and DNA-damaging agents. Single-cell analytics revealed distinct yet reversible shifts in response to the precision medicine arsenal. GBM parenchymal dissemination and extracranial progression were associated with strengthening of neuron-like cell phenotypes. Our multidimensional study describes GBM evolution over a never reported time scale, and provides a valuable resource linking genetic, molecular, cellular and clinical progressions. Statement of significance: We assembled multidimensional omics data of mutlirelapsing GBM uncovering the cascade of mutations associated with GBM progression, transcriptomic response to therapies and evolution of phenotypic cell states that ultimately lead to extracranial progression. This unique resource sheds light on GBM evolution at the genetic, transcriptomic, cellular and clinical levels.

“Just Accepted” papers have undergone full peer review and have been accepted for publication in Radiology: Artificial Intelligence. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content. Purpose To construct and evaluate the performance of a machine learning model for bone segmentation using whole-body CT images. Materials and Methods In this retrospective study, whole-body CT scans (June 2010 to January 2018) from 90 patients (mean age, 61 ± [SD] 9 years; 45 male, 45 female) with multiple myeloma were manually segmented using 60 labels and subsegmented into cortical and trabecular bone. Segmentations were verified by board-certified radiology and nuclear medicine physicians. The impacts of isotropy, resolution, multiple labeling schemes, and postprocessing were assessed. Model performance was assessed on internal and external test datasets ( n = 362 scans) and benchmarked against the TotalSegmentator segmentation model. Performance was assessed using Dice similarity coefficient (DSC), normalized surface distance (NSD), and manual inspection. Results Skellytour achieved consistently high segmentation performance on the internal dataset (DSC: 0.94, NSD: 0.99) and two external datasets (DSC: 0.94, 0.96, NSD: 0.999, 1.0), outperforming TotalSegmentator on the first two datasets. Subsegmentation performance was also high (DSC: 0.95, NSD: 0.995). Skellytour produced finely detailed segmentations, even in low density bones. Conclusion The study demonstrates that Skellytour is an accurate and generalizable bone segmentation and subsegmentation model for CT data and is available as a Python package via GitHub ( https://github.com/cpwardell/Skellytour ). Published under a CC BY 4.0 license.

Introduction: Multiple Myeloma (MM) is a heterogeneous disease making it difficult to accurately predict the disease course in individual patients. Staging systems in MM have evolved, firstly the International Staging System (ISS), then incorporation of genomic aberrations with revised-ISS. Recently, the IMWG agreed on consensus genomic staging of high-risk MM. However, no scoring system captures the nuances of the genetic landscape of each MM patient. In the era of genomic profiling, this information should be utilised to predict disease behaviour, which can be achieved through computational modelling. We recently developed and validated a model signalling network within B cells, which predicts pathway activation and protein abundance in B cells. We hypothesised that inclusion of genetic mutations from MM as parameter changes in this model could enable the creation of patient specific virtual cells that could improve disease stratification. Methods: Genetic data for 53 newly diagnosed MM patients, enrolled to the NCRI Myeloma XI was collected. The cohort were phenotypically high risk, relapsing within 30 months of maintenance randomisation. All patients achieved at least a partial response prior to relapse. Whole exome sequencing was conducted at presentation and relapse. All genetic mutations (SNPs, copy number variants, translocations) were verified for their oncogenic potential with OncoKB. Each mutation in each patient was mapped to a model parameter to create a set of 53 unique patient models in silico (e.g. gain of one copy of BCL2 increased the parameter representing BCL2 expression by 50%). To determine the apoptotic, and proliferative signalling state of the patient models 6-hour simulation was performed and the predicted abundance of cytoplasmic Smac, cytochrome C and cadherin-1 was stored. Patients were grouped according to their predicted signalling state, namely anti-apoptotic (AA, n=16), pro-proliferative (PP, n=11), anti-apoptotic and pro-proliferative (AAPP, n=15) and non-proliferative or apoptotic (NAP, n=11). We compared progression-free survival (PFS) in days between different groups using Cox regression for Hazard ratios (HR) and Log-Rank test for p-values. Results: As a benchmark we first evaluated the ISS in our cohort. ISS II versus ISS I was associated with a significantly worse PFS (HR 2.27 95% CI 1.07-4.8, p=0.032) as well as ISS III (HR 2.45 CI 1.13-5.31 p=0.024). However, ISS II was not significantly different from ISS III, likely due to the phenotypic high-risk cohort assessed. High-risk lesions del(17p), gain(1q), del(1p), (t(4;14), t(14;16), t(14;20) and neutral lesions, del(13q), HRD, t(6;14), t(11;14) and t(MYC), were all non-significant predictors of progression (p>0.05). Maintenance strategy (lenalidomide vs observation) was also not a significant predictor of outcome, consistent with the phenotypic behaviour. We then applied the model-driven signalling state stratification (AA, PP, AAPP, NAP) and found model stratification alone significantly predicted PFS (p=0.047). The PP group was associated with the longest PFS and the NAP group had the shortest (HR 3.33 CI 1.37-8.11, p=0.008). No HR genetic lesions were noted in the PP group, compared to 2/11 (18%), 4/16 (25%) and 7/15 (47%) for the NAP, AA and AAPP groups respectively. To incorporate modelling with existing prognostic tools we combined all good prognosis patient groups (ISS I [n=13], and PP patients [n=11]) into one group and compared it to the rest. This stratification method identified 9/53 (17%) patients with high ISS (II/III), but good prognosis predicted through modelling, and assigned them to the low-risk group. The remaining high risk patients (n=29) had a significantly higher HR of 2.21 for shorter PFS (CI 1.24-3.94, p=0.007). Conclusion:We show that computational modelling can be utilised to stratify MM patients into both good and poor prognostic groups in ways that improve on previous staging systems. Furthermore, this has been illustrated within a phenotypical high-risk cohort, emphasising its ability to uncover distinct subgroups in previously hard to classify patients. This approach provides the foundation for a novel approach to improving treatment decisions, developing personalised approaches such as treatment intensification based on the full genetic profile of an individual patient. Characterising the signalling state in a larger cohort and predicting response to treatment is underway.

Multiple myeloma (MM) is associated with a debilitating bone disease that poses significant therapeutic challenges. MM bone disease is characterized by increased bone resorption and suppression of osteoblasts, which hinders the repair of damaged bone. Sclerostin, an antagonist of Wnt signaling, is elevated in MM patients, and its inhibition with a neutralizing antibody (Scl-ab) has been shown to restore osteoblast function in mouse models of MM. However, it remains unclear whether Scl-ab can promote skeletal repair, enable effective tumor control when combined with anti-cancer agents, or improve bone health in MM patients. To investigate these knowledge gaps, we used preclinical MM mouse models and patient-derived samples. We also characterize the impact of Scl-ab on cancer and osteoblastic cells isolated from mouse models through bulk and single-cell RNA sequencing. Lastly, we performed a retrospective analysis of the efficacy of Scl-ab to improve bone health in patients with MM in remission. Scl-ab promoted skeletal repair and enabled tumor suppression by an anti-cancer agent in various animal models of established MM bone disease. MM tumors suppressed Wnt signaling and decreased the number of osteoblasts and osteo-CAR cells, and treatment with Scl-ab reversed these effects. Treatment with Scl-ab increased bone mass and repaired bone in patients with MM in remission, even when combined with maintenance chemotherapy. Our findings highlight the potent bone-healing effects of Scl-ab and its potential as an adjuvant to anti-cancer therapy, offering a promising approach to improve clinical outcomes and the quality of life for MM patients.

Abstract Despite the advances in precision oncology, genomic approaches have not significantly improved outcomes for high grade gliomas (HGGs) as once was anticipated. Recurrent HGG pose even greater therapeutic challenge. The integration of functional precision medicine (FPM), in which drugs screens are performed on live tissue, into clinical routine practice presents a promising alternative by offering personalized treatments based on the unique characteristics of the patient’s tumor. Here, we present our institutional experience of integrating a spheroid-based drug screening assay (3D PredictTM Glioma) in the treatment management of HGG patients to evaluate its feasibility and efficacy as a therapeutic tool in a clinical setting. We also sought to determine factors that related to assay success. Tissue from intracranial lesions of patients with presumed HGG and planned surgical resection at our institution were collected for ex vivo 3D cell culture and challenged with 12 drug agents to determine patient-specific response parameters. The impact of potential variables, such as ki67, tumor pathology, and shipment timing, on the assay’s success was evaluated. Clinical correlation was established between ex vivo response and clinical response in HGG patients. 57 samples, including 24 upfront HGGs, 32 recurrent HGGs, and one medulloblastoma, were sent for spheroid formation and drug testing. In total, 57.9% (33/57) or 23% (13/57) of the assays were successful and resulted in complete or partial functional profiling data, respectively. We conducted a multivariable analysis of our cohort to determine which variables (i.e. tumor volume sent, ki-67 proliferation index, tumor percentage determined by DNA sequencing by Tempus Lab, recurrent status, sample shipment time) were predictive of assay success. The only variable significantly associated with assay success was tumor percentage with samples comprised of >70% tumor leading to assay success, 40%-70% leading to 60% chance of success, and <40% having 0% success. Among 46 patients with partial or complete assay success, 26 patients (56.5%) had a targetable mutation and 17/26 yielded a moderate or full response to one or more of their corresponding targeted drugs in the assay. Treatments were newly administered or altered from prior treatments in 40/57 patients (70%) based on FPM results. Our study demonstrates the technical feasibility of incorporating FPM approaches in the development of treatment regimens for patients with HGG. Success was correlated to tumor percentage present in the sample which indicates intraoperative communication between surgeon and pathologist can lead to improved assay results. Our institutional protocol demonstrates that Integrating FPM into routine clinical practice is possible and can serve as a valuable guide in the refinement of therapeutic recommendations for HGG patients. Citation Format: Grace Guzman, Sahana Bettadapura, William Jeremy Shelton-Correa, Taylor Brooks, Melissa Rayner, Christopher Wardell, Analiz Rodriguez. Integration of functional precision medicine assay for high grade glioma management: A single institution experience [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 953.

Brain tumors and genomics have a long-standing history given that glioblastoma was the first cancer studied by the cancer genome atlas. The numerous and continuous advances through the decades in sequencing technologies have aided in the advanced molecular characterization of brain tumors for diagnosis, prognosis, and treatment. Since the implementation of molecular biomarkers by the WHO CNS in 2016, the genomics of brain tumors has been integrated into diagnostic criteria. Long-read sequencing, also known as third generation sequencing, is an emerging technique that allows for the sequencing of longer DNA segments leading to improved detection of structural variants and epigenetics. These capabilities are opening a way for better characterization of brain tumors. Here, we present a comprehensive summary of the state of the art of third-generation sequencing in the application for brain tumor diagnosis, prognosis, and treatment. We discuss the advantages and potential new implementations of long-read sequencing into clinical paradigms for neuro-oncology patients.

Supplementary Table 2 from Gender Disparities in the Tumor Genetics and Clinical Outcome of Multiple Myeloma

Supplementary Table 2 from Gender Disparities in the Tumor Genetics and Clinical Outcome of Multiple Myeloma

OBJECTIVE:Central nervous system (CNS) manifestations of hematologic malignancies are uncommon and often have a poor prognosis. As hematologic neoplasms are typically chemotherapy- and radiotherapy-sensitive, surgical resection is usually not indicated; thus, opportunities for in-depth characterization of CNS hematologic tumors are limited. Here, we report four cases of rare intracranial hematologic tumors requiring surgical intervention, allowing for histopathologic and genomic characterization.METHODS:The clinical course, genetic perturbations, and histopathological features are described for a case of 1) primary marginal zone B-cell lymphoma of the dura as well as cases of brain metastases of 2) cutaneous T-cell lymphoma, 3) acute myeloid leukemia/myeloid sarcoma, and 4) multiple myeloma. Targeted DNA sequencing, fluorescence in situ hybridization, cytogenetic analysis, flow cytometry and immunohistochemical staining were used to assess the lesions.RESULT:Molecular and histopathological characterizations of four unusual presentations of hematolymphoid diseases involving the CNS are presented. Genetic abnormalities were identified in each lesion, including chromosomal aberrations and single nucleotide variants resulting in missense or nonsense mutations in oncogenes.CONCLUSIONS:Our case series provides insight into unique pathological phenotypes of hematologic neoplasms with atypical CNS involvement. We offer targets for future studies by identifying potentially pathogenic genetic variants in these lesions, as the full implications of the novel molecular abnormalities described remain unclear.

Supplementary Table 1 from Gender Disparities in the Tumor Genetics and Clinical Outcome of Multiple Myeloma

Background: Pulmonary Sclerosing Pneumocytoma (PSP) is a rare tumor of the lung with a low malignant potential that primarily affects females. Initial studies of PSP focused primarily on analyzing features uncovered using conventional X-ray or CT imaging. In recent years, because of the widespread use of next-generation sequencing (NGS), the study of PSP at the molecular-level has emerged.Methods: Analytical approaches involving genomics, radiomics, and pathomics were performed. Genomics studies involved both DNA and RNA analyses. DNA analyses included the patient’s tumor and germline tissues and involved targeted panel sequencing and copy number analyses. RNA analyses included tumor and adjacent normal tissues and involved studies covering expressed mutations, differential gene expression, gene fusions and molecular pathways. Radiomics approaches were utilized on clinical imaging studies and pathomics techniques were applied to tumor whole slide images.Results: A comprehensive molecular profiling endeavor involving over 50 genomic analyses corresponding to 16 sequencing datasets of this rare neoplasm of the lung were generated along with detailed radiomic and pathomic analyses to reveal insights into the etiology and molecular behavior of the patient’s tumor. Driving mutations (AKT1) and compromised tumor suppression pathways (TP53) were revealed. To ensure the accuracy and reproducibility of this study, a software infrastructure and methodology known as NPARS, which encapsulates NGS and associated data, open-source software libraries and tools including versions, and reporting features for large and complex genomic studies was used.Conclusion: Moving beyond descriptive analyses towards more functional understandings of tumor etiology, behavior, and improved therapeutic predictability requires a spectrum of quantitative molecular medicine approaches and integrations. To-date this is the most comprehensive study of a patient with PSP, which is a rare tumor of the lung. Detailed radiomic, pathomic and genomic molecular profiling approaches were performed to reveal insights regarding the etiology and molecular behavior. In the event of recurrence, a rational therapy plan is proposed based on the uncovered molecular findings.

Supplementary Table 3 from Gender Disparities in the Tumor Genetics and Clinical Outcome of Multiple Myeloma

Supplementary Methods, References, Legends for Table 1 and Figure 1 from Gender Disparities in the Tumor Genetics and Clinical Outcome of Multiple Myeloma

The accumulation of mutations in cancer driver genes, such as tumor suppressors or proto-oncogenes, affects cellular homeostasis. Disturbances in the mechanism controlling proliferation cause significant augmentation of cell growth and division due to the loss of sensitivity to the regulatory signals. Nowadays, an increasing number of cases of liver cancer are observed worldwide. Data provided by the International Cancer Genome Consortium (ICGC) have indicated many alterations within gene sequences, whose roles in tumor development are not well understood. A comprehensive analysis of liver cancer (virus-associated hepatocellular carcinoma) samples has identified new and rare mutations in B-Raf proto-oncogene (BRAF) in Japanese HCC patients, as well as BRAF V600E mutations in French HCC patients. However, their function in liver cancer has never been investigated. Here, using functional analysis and next generation sequencing, we demonstrate the tumorigenic effect of BRAF V600E on hepatocytes (THLE-2 cell line). Moreover, we identified genes such as BMP6, CXCL11, IL1B, TBX21, RSAD2, MMP10, and SERPIND1, which are possibly regulated by the BRAF V600E-mediated, mitogen-activated protein kinases/extracellular signal-regulated kinases (MAPK/ERK) signaling pathway. Through several functional assays, we demonstrate that BRAF L537M, D594A, and E648G mutations alone are not pathogenic in liver cancer. The investigation of genome mutations and the determination of their impact on cellular processes and functions is crucial to unraveling the molecular mechanisms of liver cancer development.

Deciphering genomic architecture is key to identifying novel disease drivers and understanding the mechanisms underlying myeloma initiation and progression. In this work, using the CoMMpass dataset, we show that structural variants (SV) occur in a nonrandom fashion throughout the genome with an increased frequency in the t(4;14), RB1, or TP53 mutated cases and reduced frequency in t(11;14) cases. By mapping sites of chromosomal rearrangements to topologically associated domains and identifying significantly upregulated genes by RNAseq we identify both predicted and novel putative driver genes. These data highlight the heterogeneity of transcriptional dysregulation occurring as a consequence of both the canonical and novel structural variants. Further, it shows that the complex rearrangements chromoplexy, chromothripsis and templated insertions are common in MM with each variant having its own distinct frequency and impact on clinical outcome. Chromothripsis is associated with a significant independent negative impact on clinical outcome in newly diagnosed cases consistent with its use alongside other clinical and genetic risk factors to identify prognosis.

Introduction: Autologous stem cell transplant (ASCT) followed by maintenance lenalidomide remains standard of care for newly diagnosed eligible myeloma patients. An understanding of how these treatments impact the genetic profile of the myeloma clone at relapse are lacking. We have previously shown that depth of response impacts clonal evolution at relapse. Here we explore the impact of melphalan conditioned ASCT as an additional factor in inducing genetic change. We used whole exome sequencing (WES) data from paired samples for a series of 56 newly diagnosed patients treated in the NCRI Myeloma XI Trial. Methods: The Myeloma XI trial recruited patients to both ASCT and non-ASCT pathways. Within both pathways patients were randomised to an induction regime of either thalidomide or lenalidomide with cyclophosphamide and dexamethasone. Following induction, and ASCT if eligible, patients were further randomised to observation or lenalidomide maintenance. WES was undertaken on presentation and relapse malignant plasma cells (122x, n=22 ASCT, 34 non-ASCT). The mutational load and profile, structural aberrations, and evolutionary mechanism leading to relapse was determined. The series of patients were all phenotypically high risk, defined as relapse within 30 months of treatment initiation. The median PFS from maintenance randomisation was 19 months. Best response prior to relapse was determined for all; CR/nCR = 50% ASCT, 39% non-ASCT, VGPR/PR = 50% ASCT, 61% non-ASCT. Results: In the patients who had undergone ASCT there was a significant increase in non-synonymous (NS) mutations between presentation and relapse (32 vs 49, p=0.005). This was not seen in the non-ASCT patients (43 vs 44, p=0.53). When breaking down the mutational load according to response, only ASCT patients achieving a CR/nCR had a significant increase in the NS mutational load from 28 to 58 (p=0.003). We profiled 24 mutations known to be recurrent in myeloma. To infer possible clonal evolution, we determined whether there was a change in the profile of the mutations at relapse i.e. gain of new mutations at relapse or loss of mutations noted at presentation. In the ASCT patients 55% (12/22) had a change in the profile of these mutations, compared to only 29% (10/34) non-ASCT patients (p=0.09). Depth of response did not impact on this in the ASCT patients. In the non-ASCT patients, there was a difference between response groups, with 62% (8/13) of patients in the CR/nCR having a change in the profile, compared to 10% (2/21) of the VGPR/PR patients (p=0.002). We assessed high-risk structural lesions. Acquisition of +1q during the disease course was noted as a new event in 18% of ASCT and 9% of non-ASCT patients (p=0.41). There was no impact on +1q change according to depth of response. Acquired tMYC was seen as a new event at relapse only in non-ASCT patients (n=3) with no apparent difference due to response. The tumour suppressor gene (TSG) regions of copy number loss -1p, -12, -14 and -17p were reviewed. ASCT patients were more likely to have a change in the profile of TSG deletions at relapse, with 41% of patients either gaining or losing evidence of a lesion, compared to 9% of the non-ASCT patients (p=0.007). Depth of response did not impact on the profile of TSG deletions. Branching evolution was the dominant pathway leading to relapse, noted in 77% (17/22) of ASCT patients and 58% (20/34) non-ASCT patients (p=0.25). There was no impact on depth of response and evolutionary pathway according to response, although as per our previous findings, stable evolution was confined only to patients achieving a VGPR/PR. Patients in the trial are randomised to lenalidomide maintenance or observation. There was no impact of maintenance strategy on the mutational profiles at relapse, in both the ASCT and non-ASCT patient groups. Conclusion We show that newly diagnosed, phenotypically high-risk patients who have undergone ASCT have a greater mutational load and significant change in the profile of TSG deletions at relapse, suggestive of clonal evolution. A deep response (CR/nCR) also led to a significant increase in the mutation load in ASCT patients and a change in the profile of recurrent mutations in non-ASCT patients, consistent with our previous work. We conclude that although depth of response is a key determinant of genetic evolution, exposure to the high dose melphalan may also play a role, consistent with the presence of mutational signatures described previously.

Abstract Evidence has accumulated regarding the association of some types of long noncoding RNA (lncRNAs) with severity and progression of multiple myeloma (MM). In this study, we explore the expression of novel lncRNA in different molecular subtypes of MM and examine their correlation with the prognosis of the patient. Whole transcriptome RNA sequencing of 643 newly diagnosed MM samples was performed. De novo and reference guided transcript assembly pipelines were used for RNA-seq data processing and discovery of novel lncRNAs in MM. We identified 8,556 potentially novel lncRNA transcripts expressed in patients with MM. Of these, 1,264 novel transcripts showed significant differential expression between the different molecular subtypes of MM. Through bioinformatic analysis, we identify their potential targets and roles in MM. Functional enrichment analysis of nearby coexpressed genes was used to predict involved pathways. The function was also inferred by comparing the k-mer content with known lncRNAs. Two of the novel lncRNAs had a significant association with progression free survival and/or overall survival. In conclusion, we identified many novel lncRNAs, describe their expression pattern among different genetic subtypes of MM and provide evidence of their potential role in the pathogenesis, progression, and prognosis of the disease.

OBJECTIVES/GOALS: A functional precision medicine platform to identify therapeutic targets for a glioblastoma patient with Li Fraumeni syndrome was performed. Comparative transcriptomics identified druggable targets and patient derived organoids and a 3D-PREDICT drug screening assay was used to validate the pipeline and identify further therapeutic targets. METHODS/STUDY POPULATION: A comparative transcriptomics pipeline was used to identify druggable genes that are uniquely overexpressed in our patient of interest relative to a cancer compendium of 12,747 tumor RNA sequencing datasets including 200 GBMs. Mini-ring patient derived organoid-based drug viability assays were performed to validate the comparative transcriptomics data. Additionally, a spheroid-based drug screening assay (3D-PREDICT) was performed and used to identify further therapeutic targets. RESULTS/ANTICIPATED RESULTS: Using comparative transcriptomics STAT1 and STAT2 were found to be significantly overexpressed in our patient, indicating ruxolitinib, a Janus kinase 1 and 2 inhibitor, as a potential therapy. Druggable pathways predicted using comparative transcriptomics corresponded with ruxolitinib sensitivity in a panel of patient derived organoids screened with this compound. Cells from the LFS patient were among the most sensitive to ruxolitinib compared to patient-derived cells with lower STAT1 and STAT2 expression levels. Additionally, 3D-PREDICT screening identified the mTOR inhibitor everolimus as a potential candidate. These two targeted therapies were selected for our patient and resulted in radiographic disease stability. DISCUSSION/SIGNIFICANCE: This research illustrates the use of comparative transcriptomics to identify druggable pathways irrespective of actionable DNA mutations present. Our results are promising and serve to highlight the importance of functional precision medicine in tailoring treatment regimes to specific patients.