Tumor Genotype Research Articles

Estrogen/Progesterone Receptor Loss, CTNNB1 and KRAS Mutations Are Associated With Local Recurrence or Distant Metastasis in Low-Grade Endometrial Endometrioid Carcinoma.

A subset of endometrial endometrioid carcinomas (EECs) with low-grade histology recur with poor outcomes. Published evidence suggests that poor outcomes may be associated with loss of expression of ER-alpha (ER-α) as well as with β-Catenin-1 ( CTNNB1 ) and Kirsten rat sarcoma viral oncogene homolog ( KRAS ) mutations. This study reports on institutional experience with the incidence of recurrence in low-grade EEC and their association with CTNNB1 and KRAS mutations as well as estrogen/progesterone receptor (ER/PR) expression. Forty-eight (8.5%) out of 568 cases of low-grade EEC with biopsy-proven recurrence were identified; and were analyzed by immunohistochemistry for ER, PR, p53, MMR protein, and mutation analysis for exon 3 of the CTNNB1 and exon 2 of KRAS in relation to recurrence type, local or distant metastasis/recurrence. Twenty-three patients (4%) developed local, and 25 patients (4.4%) developed distant metastases/recurrence. Decreased expression or loss of ER/PR was found in 17/44 (38.6%) patients with recurrence. Eighty-four percent of patients with low-grade EEC and local recurrence had CTNNB1 mutations. Seventy-three percent of patients with distant metastasis/recurrence had KRAS mutations. The association of these mutations with the type of recurrence was statistically significant for both. Five cases with the morphology of low-grade EEC were reclassified as mesonephric-like carcinoma and were universally characterized by distant metastasis/recurrence, loss of ER/PR expression, large tumor size, absence of CTNNB1 mutations, and the presence of KRAS mutations. In low-grade EEC, CTNNB1 and KRAS mutations are associated with local recurrence and distant metastasis/recurrence, respectively, suggesting that these 2 different progression types may be conditioned by tumor genotype. ER/PR immunohistochemistry may be helpful in identifying poor performers in low-grade EEC. Furthermore, identification of the decreased expression or loss of ER/PR in tumors with low-grade histology should prompt consideration of mesonephric-like carcinoma, which is a more aggressive tumor than the low-grade EEC. KRAS mutations were associated with distant metastasis/recurrence in tumors with and without mesonephric-like phenotype.

Addressing Modern Diagnostic Pathology for Patient-Derived Soft Tissue Sarcosphere Models in the Era of Functional Precision Oncology

Responses to therapy often cannot be exclusively predicted by molecular markers, thus evidencing a critical need to develop tools for better patient selection based on relations between tumor phenotype and genotype. Patient-derived cell models could help to better refine patient stratification procedures and lead to improved clinical management. So far, such ex vivo cell models have been used for addressing basic research questions and in preclinical studies. As they now enter the era of functional precision oncology, it is of utmost importance that they meet quality standards to fully represent the molecular and phenotypical architecture of patients’ tumors. Well-characterized ex vivo models are imperative for rare cancer types with high patient heterogeneity and unknown driver mutations. Soft tissue sarcomas account for a very rare, heterogeneous group of malignancies that are challenging from a diagnostic standpoint and difficult to treat in a metastatic setting because of chemotherapy resistance and a lack of targeted treatment options. Functional drug screening in patient-derived cancer cell models is a more recent approach for discovering novel therapeutic candidate drugs. However, because of the rarity and heterogeneity of soft tissue sarcomas, the number of well-established and characterized sarcoma cell models is extremely limited. Within our hospital-based platform we establish high-fidelity patient-derived ex vivo cancer models from solid tumors for enabling functional precision oncology and addressing research questions to overcome this problem. We here present 5 novel, well-characterized, complex-karyotype ex vivo soft tissue sarcosphere models, which are effective tools to study molecular pathogenesis and identify the novel drug sensitivities of these genetically complex diseases. We addressed the quality standards that should be generally considered for the characterization of such ex vivo models. More broadly, we suggest a scalable platform to provide high-fidelity ex vivo models to the scientific community and enable functional precision oncology.

Role of Inflammatory/Immune Response and Cytokine Polymorphisms in the Severity of Chronic Hepatitis C (CHC) before and after Direct Acting Antiviral (DAAs) Treatment.

Host regulatory immune response is involved in the hepatic inflammatory process caused by the hepatitis C virus (HCV). We aimed to determine if HCV clearance with direct-acting antivirals (DAAs) changes the hepatic fibrosis stage, biochemical parameters of liver injury, and inflammatory/immune responses. Sample: 329 chronic hepatitis C (CHC) patients, 134 of them treated with DAAs. Liver fibrosis was evaluated by transient elastography (FibroScan), biochemical and cellular parameters were determined by standard methods, cytokine concentration by enzyme-linked immunoabsorbent assay (ELISA), and genetic polymorphisms by polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) or endpoint genotyping. Before DAA treatment, severe fibrosis or cirrhosis (F3/4) was associated with higher values of tumor necrosis factor-alpha (TNF-α) and genotypes transforming growth factor-beta-509 C/T_CC (TGF-β-509 C/T_CC), interleukine-10-1082 T/C_CC (IL-10-1082 T/C_CC), and IL-10-592 G/T_GT. After DAA treatment, fewer F3/4 patients and lower values of TNF-α were found. Patients with TNF-α-308 G/A_GG and IL-10-592 G/T_GT were at risk for F3/4. Lack of improvement of liver fibrosis was associated with lower baseline values of platelet count for genotypes TNF-α-308 G/A_GG and haplotype TT/GG of IL-10-1082 T/C and IL-10-592 G/T. Our study showed decreased liver fibrosis/inflammation and normalization of liver injury biomarkers after DAA treatment. It also points to the importance of suppressing the pro-inflammatory response by DAAs in the resolution of hepatitis C, contributing to the improvement of liver damage evaluated by transient elastography.

Digital ECMT Cancer Trial Matching Tool: an Open Source Research Application to Support Oncologists in the Identification of Precision Medicine Clinical Trials.

Matching patients with cancer to precision medicine clinical trials on the basis of their tumor genotype has the potential to improve outcomes for patients who have exhausted standard-of-care treatment options. However, the matching process presents a substantial challenge because of the number of clinical trials available. We describe a free, open source research tool designed to extract relevant trial information to support oncologists in the matching process, and we illustrate its utility with recent case studies of patients who were matched to trials using this tool. Trial records are sourced from ClinicalTrials.gov and indexed using natural language processing techniques, including named entity recognition, term normalization, and relationship extraction. Relationships between trials and genetic alterations are assigned scores on the basis of a rule-based system. All data are updated daily. A user interface is provided via R Shiny app. An instance of the trial match tool, configured for UK clinical trials, is hosted by the digital Experimental Cancer Medicine Team (see link in Data Sharing Statement). Users select the relevant cancer type and genetic alteration(s). Matching studies are ranked according to the score assigned for the selected genetic alterations. Results may be downloaded and attached to the patient's health record if desired. The tool is currently being used to support the ongoing TARGET National study, which aims to match up to 6,000 patients to early phase clinical trials. We present three case studies that exemplify relationships between genetic alterations and studies. With increasing numbers of precision medicine treatments and as comprehensive molecular profiling of tumor samples becomes more common, decision support tools are likely to become increasingly important. This work represents an important step toward the development and wider implementation of such systems.

Retrospective Assessment of Complementary Liquid Biopsy on Tissue Single-Gene Testing for Tumor Genotyping in Advanced NSCLC.

Biomarker testing is key for non-small cell lung cancer (NSCLC) management and plasma based next-generation sequencing (NGS) is increasingly characterized as a non-invasive alternative. This study aimed to evaluate the value of complementary circulating tumor DNA (ctDNA) NGS on tissue single-gene testing (SGT). Ninety-one advanced stage NSCLC patients with tumor genotyping by tissue SGT (3 genes) followed by ctDNA (38 genes amplicon panel) were included. ctDNA was positive in 47% (n = 43) and identified a targetable biomarker in 19 patients (21%). The likelihood of positivity on ctDNA was higher if patients had extra-thoracic disease (59%) or were not under active treatment (59%). When compared to SGT, ctDNA provided additional information in 41% but missed a known alteration in 8%. Therapeutic change for targeted therapy based on ctDNA occurred in five patients (5%), while seven patients with missed alterations on ctDNA had EGFR mutations or ALK fusions. The median turnaround time of ctDNA was 10 days (range 6-25), shorter (p = 0.002) than the cumulative delays for the tissue testing trajectory until biomarker availability (13 d; range 7-1737). Overall, the results from this study recapitulate the potential and limitations of ctDNA when used complementarily to tissue testing with limited biomarker coverage.

Determinants of resistance to engineered T cell therapies targeting CD19 in large B cell lymphomas.

Most relapsed/refractory large B cell lymphoma (r/rLBCL) patients receiving anti-CD19 chimeric antigen receptor (CAR19) Tcells relapse. To characterize determinants of resistance, we profiled over 700 longitudinal specimens from two independent cohorts (n= 65 and n= 73) of r/rLBCL patients treated with axicabtagene ciloleucel. A method for simultaneous profiling of circulating tumor DNA (ctDNA), cell-free CAR19 (cfCAR19) retroviral fragments, and cell-free Tcell receptor rearrangements (cfTCR) enabled integration of tumor and both engineered and non-engineered Tcell effector-mediated factors for assessing treatment failure and predicting outcomes. Alterations in multiple classes of genes are associated with resistance, including B cell identity (PAX5 and IRF8), immune checkpoints (CD274), and those affecting the microenvironment (TMEM30A). Somatic tumor alterations affect CAR19 therapy at multiple levels, including CAR19 Tcell expansion, persistence, and tumor microenvironment. Further, CAR19 Tcells play a reciprocal role in shaping tumor genotype and phenotype. We envision these findings will facilitate improved chimeric antigen receptor (CAR) Tcells and personalized therapeutic approaches.

Artificial Intelligence and Precision Medicine: A New Frontier for the Treatment of Brain Tumors.

Brain tumors are a widespread and serious neurological phenomenon that can be life- threatening. The computing field has allowed for the development of artificial intelligence (AI), which can mimic the neural network of the human brain. One use of this technology has been to help researchers capture hidden, high-dimensional images of brain tumors. These images can provide new insights into the nature of brain tumors and help to improve treatment options. AI and precision medicine (PM) are converging to revolutionize healthcare. AI has the potential to improve cancer imaging interpretation in several ways, including more accurate tumor genotyping, more precise delineation of tumor volume, and better prediction of clinical outcomes. AI-assisted brain surgery can be an effective and safe option for treating brain tumors. This review discusses various AI and PM techniques that can be used in brain tumor treatment. These new techniques for the treatment of brain tumors, i.e., genomic profiling, microRNA panels, quantitative imaging, and radiomics, hold great promise for the future. However, there are challenges that must be overcome for these technologies to reach their full potential and improve healthcare.

Molecular profile and clinical features of patients with gliomas using a broad targeted next generation-sequencing panel.

Gliomas are the most common malignant primary brain tumors characterized by poor prognosis. The genotyping of tumors using next generation sequencing (NGS) platforms enables the identification of genetic alterations that constitute diagnostic, prognostic and predictive biomarkers. The present study investigated the molecular profile of 32 tumor samples from 32 patients with high-grade gliomas by implementing a broad 80-gene targeted NGS panel while reporting their clinicopathological characteristics and outcomes. Subsequently, 14 of 32 tumor specimens were also genotyped using a 55-gene NGS panel to validate the diagnostic accuracy and clinical utility of the extended panel. The median follow-up was 19.2 months. In total, 129 genetic alterations including 33 structural variants were identified in 38 distinct genes. Among 96 variants (single nucleotide variants and insertions and deletions), 38 were pathogenic and 58 variants of unknown clinical significance. TP53 was the most frequently mutated gene, followed by PTEN and IDH1 genes. Glioma patients with IDH1 mutant tumors were younger and had significantly longer overall survival compared to patients with wild-type IDH1 tumors. Similarly, tumors with TP53 mutations were more likely observed in younger patients with glioma. Subsequently, a comparison of mutational profiles of samples analyzed by both panels was also performed. Implementation of the comprehensive pan-cancer and the MOL panels resulted in the identification of 37 and 15 variants, respectively. Of those, 13 were common. Comprehensive pan-cancer panel identified 24 additional variants, 22 of which were located in regions that were not targeted by the MOL panel. By contrast, the MOL panel identified two additional variants. Overall, the present study demonstrated that using an extended tumor profile assay instead of a glioma-specific tumor profile panel identified additional genetic changes that may be taken into consideration as potential therapeutic targets for glioma diagnosis and molecular classification.

Crosstalk between tumor acidosis, p53 and extracellular matrix regulates pancreatic cancer aggressiveness.

Pancreatic ductal adenocarcinoma (PDAC) is an extremely aggressive malignancy with minimal treatment options and a global rise in prevalence. PDAC is characterized by frequent driver mutations including KRAS and TP53 (p53), and a dense, acidic tumor microenvironment (TME). The relation between genotype and TME in PDAC development is unknown. Strikingly, when wild type (WT) Panc02 PDAC cells were adapted to growth in an acidic TME and returned to normal pH to mimic invasive cells escaping acidic regions, they displayed a strong increase of aggressive traits such as increased growth in 3-dimensional (3D) culture, adhesion-independent colony formation and invasive outgrowth. This pattern of acidosis-induced aggressiveness was observed in 3D spheroid culture as well as upon organotypic growth in matrigel, collagen-I and combination thereof, mimicking early and later stages of PDAC development. Acid-adaptation-induced gain of cancerous traits was further increased by p53 knockout (KO), but only in specific extracellular matrix (ECM) compositions. Akt- and Transforming growth factor-β (TGFβ) signaling, as well as expression of the Na+ /H+ exchanger NHE1, were increased by acid adaptation. Whereas Akt inhibition decreased spheroid growth regardless of treatment and genotype, stimulation with TGFβI increased growth of WT control spheroids, and inhibition of TGFβ signaling tended to limit growth under acidic conditions only. Our results indicate that a complex crosstalk between tumor acidosis, ECM composition and genotype contributes to PDAC development. The findings may guide future strategies for acidosis-targeted therapies.

High-resolution single-cell atlas reveals diversity and plasticity of tissue-resident neutrophils in non-small cell lung cancer.

Non-small cell lung cancer (NSCLC) is characterized by molecular heterogeneity with diverse immune cell infiltration patterns, which has been linked to therapy sensitivity and resistance. However, full understanding of how immune cell phenotypes vary across different patient subgroups is lacking. Here, we dissect the NSCLC tumor microenvironment at high resolution by integrating 1,283,972 single cells from 556 samples and 318 patients across 29 datasets, including our dataset capturing cells with low mRNA content. We stratify patients into immune-deserted, B cell, Tcell, and myeloid cell subtypes. Using bulk samples with genomic and clinical information, we identify cellular components associated with tumor histology and genotypes. We then focus on the analysis of tissue-resident neutrophils (TRNs) and uncover distinct subpopulations that acquire new functional properties in the tissue microenvironment, providing evidence for the plasticity of TRNs. Finally, we show that a TRN-derived gene signature is associated with anti-programmed cell death ligand 1 (PD-L1) treatment failure.

Genomic Landscaping of Post-Transplant Lymphoproliferative Disorders Using Circulating Tumor DNA

Introduction: Identification of post-transplant lymphoproliferative disorder (PTLD) is a diagnostic challenge. The current diagnostic framework usually require analysis on tissue and imaging through PET/CT scans. Yet, these methods have diagnostic limitations. For instance due to missing information on tumor heterogeneity and relatively low sensitivity of PET/CT scans. Measuring cell free DNA (cfDNA) represents a novel, minimally-invasive method for tumor detection and genotyping. In this study we explored the feasibility of plasma cfDNA tumor genotyping with targeted deep sequencing to detect copy number variation (CNV), single nucleotide variants (SNVs) and Epstein Bar Virus (EBV) sequences, using a new in-house variant calling pipeline. Methods: In this multi-center observational cohort study of 18 patients diagnosed with monomorphic PTLD, we performed deep targeted NGS for SNV detection and low-coverage whole genome sequencing (lcWGS) for CNV detection. NGS was targeted on a panel of genes (n = 39) known to be recurrently mutated in B-cell lymphomas (244 kbp). All patients gave written informed consent. The R package CNAclinic was used to identify genome wide CNAs and fraction of the genome altered (FGA). The ichorCNA package was used to determine the ctDNA fraction in cfDNA samples. SNVs were called using a pipeline, combining 4 (cfDNA) SNV calling tools: Mutect2, LoFreq, VarDict & SiNVICT. Variants were filtered on presumed functional effect (Synonymous-, UTR-, Intron-, 2 kb upstream-, 2 kb downstream variants were removed). A "panel of normals” (variant blacklist) was generated from variants found in cfDNA from healthy controls, which was used to produce a variant blacklist to filter out technical artifacts and SNPs. Finally, variants with a variant allele frequency (VAF) < 1% and variants with an allele frequency (AF) of > 1% in 1000Genomes and/or gnomAD were filtered out. Results: The cohort included 18 PTLD patients. The majority of patients had stage IV disease (n=13, 76%). All of the PTLD cases were histologically proven, of diffuse large B cell lymphoma type and 10/18 patients (55%) were considered to have an EBV-negative tumor. EBV copy number in plasma was performed by qPCR in 12 patients and was considered to be elevated in four patients (>5000 copies/mL). The fraction of reads mapping to the EBV genome was compared between controls (n=8), EBV-negative PTLD (n=10) and EBV-positive PTLD (n=8). The median fraction of EBV reads for controls and EBV-negative PTLD were similar with a fraction of 1.5x10-4 (Q1-Q3: 1.3x10-4-1.6x10-4) and 1.8x10-4 (Q1-Q3: 1.4x10-4-3.9x10-4), respectively (p>0.05, Kruskal-Wallis). In EBV-positive tumors, the median fraction of EBV reads was 3-log higher, 5.3x10-1 (Q1-Q3: 7.3 x10-2-2) (p≤0.05, Kruskal-Wallis) CNVs were detected in cfDNA of 11/18 (61%) patients. Two of the patients in whom no CNVs were detected had initiated therapy at the time of plasma sampling. Median fraction of genome altered was higher in EBV-negative tumors (0.03, Q1-Q3: 2.5x10-4-0.12) than in EBV-positive tumors (5x10-4, Q1-Q3: 0-0.03), but this difference was not significant (p>0.05, Mann-Whitney U) (Figure 1). In the top 15 recurrently mutated genes (described below), across all 18 samples, 180 SNVs were found in total. The median amount of SNVs found in each sample was 11 (range 6-25). The most frequently mutated gene was CREPPB (12/18, 67%), in which the most prevalent mutation was p.K1497T (8/18, 44%). ARID1A was mutated in 10/18 cases (56%), followed by KMT2D (9/18, 50%). TET2, TP53, MEF2B & TNFAIP3 were all mutated in 7/18 cases (39%). SPEN & IRF2BP2 were mutated in 6/18 cases (33%). PIM1, NOTCH1, ATM, CARD11, DDX3X, IGLL5 were all mutated in 5/18 cases (28%) (Figure 2). Few of the mutations that are called in the top 15 recurrently mutated genes (8/180, 0.04%) lay close to 50% VAF, which might imply that these are SNPs not filtered out by the pipeline. Our SNV findings are comparable with previous literature, in which KMT2D, TET2 & TP53 are also amongst top-mutated genes (Menter et al., 2017), highlighting disruption of epigenetic regulation, NFkB pathway, and DNA damage response as drivers of PTLD. Conclusion: lcWGS in combination deep targeted sequencing allows for detection of CNV an SNV in diagnostic samples of patients with monomorphic PTLD. These data can potentially be used at screening, diagnosis and response monitoring, which is part of the ongoing prospective study NTR 7402. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

Determinants of Resistance to Engineered T-Cell Therapies Targeting CD19 in Large B-Cell Lymphomas

Background: Anti-CD19 chimeric antigen receptor (CAR19) T-cells have activity in patients with relapsed/refractory large B-cell lymphoma (rrLBCL), but over half of patients ultimately relapse. We originally applied cell-free DNA (cfDNA) analysis to a cohort (n=65) of patients receiving axicabtagene ciloleucel (axi-cel) to identify determinants of resistance and characterize molecular thresholds predictive of treatment failure. Here, we extend these analyses to an additional independent cohort (n=73) of patients to validate these thresholds and refine genomic alterations associated with resistance. Methods: We developed a novel hybrid capture strategy targeting 187 protein coding genes, all T-cell receptor (TCR) loci, and axi-cel retroviral vector. This approach allows for simultaneous evaluation of circulating tumor-derived DNA (ctDNA), CAR19-derived cfDNA (cfCAR) and T-cell receptor rearrangements (cfTCR) from blood plasma. We applied this platform to profile serial samples from discovery (n=65) and validation (n=73) cohorts of rrLBCL patients receiving CAR19 therapy (total n=128). Pretreatment and relapse tumor tissue was also profiled when available. A previously determined optimized pretreatment (Day 0) ctDNA threshold to stratify event free survival (EFS) was revalidated in the discovery cohort using bootstrap resampling (2.5 log10 haploid genome equivalents per milliliter). Results: The median follow-up was 36.4 months in the discovery cohort, and 12.5 months in the validation cohort. 55% (36/65) and 58% (42/73) of patients in the discovery and validation cohorts had progressed following CAR19, respectively. In both cohorts, patients with high pretreatment ctDNA demonstrated inferior EFS (Discovery: p<0.001, Validation, p=0.002). Similarly, a ~300-fold (2.5 log10) decrease in ctDNA levels at Day 28 (Major Molecular Response - MMR) post CAR19 infusion was identified as the optimal post-treatment change to stratify EFS, and patients in both cohorts who achieved MMR had significantly superior EFS (Discovery: p<0.001, Validation, p=0.028). When considering ctDNA, cfTCR and cfCAR as continuous variables in the discovery cohort, higher pre- and post-treatment ctDNA levels were prognostic of EFS at all assayed time-points, as were pre-treatment LDH, week 1 cfCAR19 and week 4 cfTCR levels (Fig. A). Forward stepwise multivariable regressions identified week 4 ctDNA level (HR=2.2, p<0.001) and week 1 cfCAR19 level (HR=0.6, p=0.017) as the optimal independent covariates for predicting outcomes (Fig. A). A Cox proportional hazards model incorporating these two variables was trained on the discovery cohort (CAR Outcome Score) and was validated to be prognostic for outcomes in the validation cohort (HR=2.7, p<0.001). Genomic alterations in several genes were significantly associated with inferior outcomes in both cohorts, including PAX5, IRF8, TMEM30A, PPM1D and TP53 (Fig. B). Additional, non-stereotyped, emergent alterations were noted in relapsing patients in other genes associated with immunological escape, including mutations in CD19 and amplifications in the PD-L1 locus. CIBERSORTx was used to characterize intratumoral cell type composition, which was found to be influenced by tumor genotypes. Further, CAR19 persistence in tumors at relapse was associated with specific changes in the tumor microenvironment, including regulatory T-cell expansion and B-cell depletion. Conclusions: Pre-treatment and dynamic ctDNA levels predict treatment failure of LBCL after CAR19 therapy, and a novel CAR outcome score incorporating week 4 ctDNA and week 1 cfCAR19 levels is strongly predictive of outcome. Genomic determinants of resistance to CAR19 therapy are diverse, including recurrent mutations in genes controlling B-cell identity (IRF8, PAX5), and those influencing the composition of the tumor microenvironment and heterotypic cell-cell interactions (TMEM30A). Additional immunological escape mechanisms arise under selective pressure from CAR19 cells. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

Genomic and transcriptomic analysis of a diffuse pleural mesothelioma patient-derived xenograft library

BackgroundDiffuse pleural mesothelioma (DPM) is an aggressive malignancy that, despite recent treatment advances, has unacceptably poor outcomes. Therapeutic research in DPM is inhibited by a paucity of preclinical models that faithfully recapitulate the human disease.MethodsWe established 22 patient-derived xenografts (PDX) from 22 patients with DPM and performed multi-omic analyses to deconvolute the mutational landscapes, global expression profiles, and molecular subtypes of these PDX models and compared features to those of the matched primary patient tumors. Targeted next-generation sequencing (NGS; MSK-IMPACT), immunohistochemistry, and histologic subtyping were performed on all available samples. RNA sequencing was performed on all available PDX samples. Clinical outcomes and treatment history were annotated for all patients. Platinum-doublet progression-free survival (PFS) was determined from the start of chemotherapy until radiographic/clinical progression and grouped into < or ≥ 6 months.ResultsPDX models were established from both treatment naïve and previously treated samples and were noted to closely resemble the histology, genomic landscape, and proteomic profiles of the parent tumor. After establishing the validity of the models, transcriptomic analyses demonstrated overexpression in WNT/β-catenin, hedgehog, and TGF-β signaling and a consistent suppression of immune-related signaling in PDXs derived from patients with worse clinical outcomes.ConclusionsThese data demonstrate that DPM PDX models closely resemble the genotype and phenotype of parental tumors, and identify pathways altered in DPM for future exploration in preclinical studies.

Genotyping and Minimal Residual Disease (MRD) Assessment in cfDNA By the Euroclonality-NGS DNA Capture (EC-NDC) Panel in Mantle Cell Lymphoma (MCL)

Background Modern concepts for the treatment of mantle cell lymphomas (MCL) increasingly integrate clinical and biological variables for risk-adapted treatment strategies. Novel next-generation sequencing (NGS) technologies are highly suited for tumor genotyping as well as disease monitoring by minimal residual disease (MRD) assessment, identifying patients (pts) with a high risk of relapse. Here, we investigated whether the EuroClonality (EC)-NGS DNA Capture (EC-NDC) assay is a) applicable for genotyping of MCL in peripheral blood (PB) and plasma cell-free (cf)DNA and b) suitable for MRD assessment in plasma as compared to the amplicon-based EuroClonality Immunoglobulin-based (IG-)NGS assay. Patients and methods A total of 109 PB samples (55 baseline and 54 interim staging) from a cohort of MCL treated within the TRIANGLE trial by the European MCL network (NCT02858258) were selected according to a PB infiltration ≥10% at diagnosis. From prospectively collected PB samples in national reference labs, DNA from the cellular blood compartment (PBC) and plasma cfDNA was extracted. For targeted capture, the EC-NDC assay (Univ8® Genomics, UK) was used to simultaneously detect immunoglobulin and T cell receptor (IG/TR) rearrangements, structural variants (SV) and single nucleotide variants (SNVs) in 72 genes (Stewart, Blood Adv, 2021). For diagnostic PBC and cfDNA samples, IG clonotypes were called with ≥6 unique reads and ≥5% of annotated IG reads, SV were called with ≥4 unique reads and SNV were called with ≥6 unique reads at ≥4% variant allele frequency (VAF). For amplicon-based MRD analysis the EuroClonality IG-NGS (IG-NGS) assay was used with IG clonotypes called with ≥5% of annotated IG reads and at ≥1 log higher abundance to the polyclonal background. MRD was analyzed by IG-NGS in PBC using 8 µg PBC DNA to reach a sensitivity of 10-6. For MRD analysis in cfDNA, we aimed to use ≥5000 genome equivalents (GE) for both IG-NGS and EC-NDC. Data were analyzed by ARResT/Interrogate (Bystry, Bioinformatics, 2017) with an adapted pipeline for targeted capture in cfDNA. MRD status was defined as positive by detecting a lymphoma-specific IG clonotype and/or SV at ≥1 read, or alternatively by retrieval of ≥1 disease-specific mutation at ≥3 reads. Results Total cfDNA amounts at baseline were high (mean 92.6 ng/mL plasma) and even increasing at interim staging (mean 123.8 ng/mL) while decreasing by 10-fold in 38 samples at end of treatment (EoT)(mean 11.45 ng/mL). At baseline, a tumor-specific IGH-VJ clonotype was detected by IG-NGS in all 55 PBC and cfDNA samples. Genotyping by EC-NDC showed fully concordant results in 55 PBC and cfDNA samples with respect to IG clonotypes, SV and SNV, except for 3 PBC samples with low usable reads covering IGH-VJ regions. The presence of a t(11;14)(q13;q32) translocation was confirmed in 54/55 pts in PBC and cfDNA, while one patient with unknown t(11;14) translocation status by FISH was negative by EC-NDC. EC-NDC identified 106 putative somatic SNVs (VAF 2.7-75%, median 30%) in PBC and cfDNA, and among those were genes described as drivers in MCL (ATM (25/55), TP53 (11/55), KMT2D (9/55), SAMHD1 (5/55), CCND1 (5/55) and 18 other genes (Fig. 1). Six pts with detectable t(11;14) and IG rearrangement did not show additional SNVs. On average, 6 MRD markers/pt were identified (range: 3-10). At interim staging, MRD was analyzed in 54 PBC and 44 cfDNA samples. Twenty-five (47%) pts were MRD+ in PBC by IG-NGS. Twenty (46%) pts were MRD+ in cfDNA by EC-NDC (8 cases with ≥2 markers, 4 with 2 markers, and 8 with 1 marker). From 10/44 (23%) cfDNA samples MRD+ by IG-NGS, 9 were also MRD+ by ED-NDC. From 44 pts with parallel analysis of PBC and cfDNA, 14 (32%) were MRD+ and 18 (41%) were MRD-, while 12 (27%) showed discordant results. Conclusion To the best of our knowledge, this is the first analysis comparing targeted capture and amplicon assays for application in PBC and cfDNA in MCL. The capture-based EC-NDC assay allows a comprehensive liquid biopsy-based genotyping of MCL. In addition, both IG-NGS and EC-NDC assays are suitable for MRD assessment in cfDNA with evidence of more sensitive MRD detection EC-NDC due to multiple MRD targets detected and traced by this assay. Clinical data are still needed to explore the potential prognostic impact of MRD assessment in plasma vs PBC. Updated MRD analysis at EoT and correlation with clinical data will be presented at the meeting. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

Disease Characterization and Response Prediction in Myeloma Patients Undergoing Conventional and Cellular Therapies from Circulating Tumor DNA

Background: Multiple myeloma (MM) is characterized by dramatic biological and clinical heterogeneity. Prognostic biomarkers including genomic alterations and bone marrow (BM) minimal residual disease can resolve some of this heterogeneity; however, these rely on invasive tumor sampling which limits their utility. Here we develop and apply plasma cell-free DNA liquid biopsies via Cancer Personalized Profiling by Deep Sequencing (CAPP-Seq) to characterize tumors and quantify treatment response in patients with MM and explore its utility during anti-BCMA CAR T-cell therapy. Methods: We developed a 480 kb MM-specific CAPP-Seq panel, which covers 86 recurrently mutated coding genes as well as other recurrently mutated regions, for disease detection and quantification from circulating tumor DNA (ctDNA). Using this hybrid-capture panel, we profiled 214 plasma, germline and tumor DNA samples from 35 patients including 15 patients undergoing idecabtagene vicleucel (ide-cel) therapy. Samples were sequenced to ultra-high unique molecular depth as previously described (Newman et al, Nat Biotechnol 2016) to quantify ctDNA. Copy number variants (CNVs) were identified using our recently developed method, CANARy (Chabon et al, Nature 2020). Tumor variants and ctDNA levels were identified prior to and following therapy to assess ctDNA dynamics, and were correlated with clinical indices and patient outcomes. Results: We applied CAPP-Seq to samples from 35 patients with newly diagnosed (ND, n=10) or relapsed/refractory (RR, n=25) plasma cell disorders. A median of 87 single-nucleotide variants (SNVs, range 2-283) were detected per case. Among these, a median of 5 mutations/patient were in coding genes such as KRAS (25% of patients), TP53 (25%) and NRAS (19%). Most other SNVs occurred in genomic regions targeted by activation induced deaminase (AID), such as IGH, IGK and IGL. To validate tumor genotyping from plasma, we compared SNVs observed in plasma to paired BM samples in 18 cases. Here, we observed 84% (594/703) of all mutations identified in BM were also present in plasma, including 90% (26/29) of all coding alterations. Interestingly, we detected an additional 259 SNVs in the plasma that were not present in the BM, including KRAS, NRAS, and FAM46C mutations, suggesting ctDNA may better capture spatial tumor heterogeneity. We next utilized CAPP-Seq to quantify disease burden prior to treatment. We detected ctDNA in 97% (31/32) of pretreatment plasma with a median allele frequency of 0.91% (range 0.006 to 36%) and 3.82 log haploid genome equivalents per mL of plasma (hGE/mL). Levels of ctDNA were higher in RRMM compared to NDMM patients (4.26 vs 3.08 log hGE/mL, p=0.02) and correlated with clinical risk factors including R-ISS (stage 1: 2.83 vs stage 2/3: 4.18 log hGE/mL, p=0.01) as well as disease burden measurements including M-spike (rho=0.53, p=0.02) and free light chain ratio (rho=0.40, p=0.03). The fold change in ctDNA level at a median of 36 days from treatment initiation correlated with clinical response (median 0.060 for non-progressors vs 7.0 for progressors, p=0.04). Further, we applied CAPP-Seq to serial samples from 15 patients with RRMM receiving ide-cel. Notably, patients with longer response (>90 days) had significantly lower ctDNA levels at day 28 compared to those with early (<90 days) progression (0.6 vs 4.1 log hGE/mL; p=0.009, Fig 1). Lower ctDNA levels at day 28 predicted longer time to progression (proportional hazard model, p=0.02, HR=1.6). Finally, we assessed clonal evolution upon relapse after ide-cel. In multiple patients we observed emergence of new CNVs in ctDNA, including a case with an emergent loss of chr16, where TNFRSF17 (BCMA) resides (Fig 2). This copy number loss was observed 36 days prior to clinical relapse, was validated through WGS of the relapsed tumor, and resulted in loss of BCMA by immunohistochemistry. Conclusions: Assessment of ctDNA has significant potential utility in MM, as it allows for mutational genotyping, quantification of disease burden, and identification of tumor clonal evolution. Lower levels of ctDNA during treatment and its clearance are associated with improved outcomes, while detection of emergent alterations after targeted therapies may inform subsequent treatment strategies. Use of ctDNA may improve response and relapse prediction and reveal mechanisms of therapy resistance. *SS and DMK are co-senior authors Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

Challenges and opportunities for advanced neuroimaging of glioblastoma.

Glioblastoma is the most aggressive of glial tumours in adults. On conventional magnetic resonance (MR) imaging, these tumours are observed as irregular enhancing lesions with areas of infiltrating tumour and cortical expansion. More advanced imaging techniques including diffusion-weighted MRI, perfusion-weighted MRI, MR spectroscopy and positron emission tomography (PET) imaging have found widespread application to diagnostic challenges in the setting of first diagnosis, treatment planning and follow-up. This review aims to educate readers with regard to the strengths and weaknesses of the clinical application of these imaging techniques. For example, this review shows that the (semi)quantitative analysis of the mentioned advanced imaging tools was found useful for assessing tumour aggressiveness and tumour extent, and aids in the differentiation of tumour progression from treatment-related effects. Although these techniques may aid in the diagnostic work-up and (post-)treatment phase of glioblastoma, so far no unequivocal imaging strategy is available. Furthermore, the use and further development of artificial intelligence (AI)-based tools could greatly enhance neuroradiological practice by automating labour-intensive tasks such as tumour measurements, and by providing additional diagnostic information such as prediction of tumour genotype. Nevertheless, due to the fact that advanced imaging and AI-diagnostics is not part of response assessment criteria, there is no harmonised guidance on their use, while at the same time the lack of standardisation severely hampers the definition of uniform guidelines.

EP08.02-110 Plasma-based Molecular Profiling to Guide Treatment Decisions in Patients with Advanced NSCLC and Limited Tissue Biopsy

Molecular profiling of tumor tissue is the gold standard for treatment decision making in advanced non-small cell lung cancer (NSCLC). However, tumor genotyping may be unavailable due to insufficient tissue samples. Plasma-based next-generation sequencing (NGS) is emerging as either complementary or alternative to standard tissue genotyping in advanced NSCLC. We present our experience using plasma-based NGS in advanced NSCLC.

ITHscore: comprehensive quantification of intra-tumor heterogeneity in NSCLC by multi-scale radiomic features.

To quantify intra-tumor heterogeneity (ITH) in non-small cell lung cancer (NSCLC) from computed tomography (CT) images. We developed a quantitative ITH measurement-ITHscore-by integrating local radiomic features and global pixel distribution patterns. The associations of ITHscore with tumor phenotypes, genotypes, and patient's prognosis were examined on six patient cohorts (n = 1399) to validate its effectiveness in characterizing ITH. For stage I NSCLC, ITHscore was consistent with tumor progression from stage IA1 to IA3 (p < 0.001) and captured key pathological change in terms of malignancy (p < 0.001). ITHscore distinguished the presence of lymphovascular invasion (p = 0.003) and pleural invasion (p = 0.001) in tumors. ITHscore also separated patient groups with different overall survival (p = 0.004) and disease-free survival conditions (p = 0.005). Radiogenomic analysis showed that the level of ITHscore in stage I and stage II NSCLC is correlated with heterogeneity-related pathways. In addition, ITHscore was proved to be a stable measurement and can be applied to ITH quantification in head-and-neck cancer (HNC). ITH in NSCLC can be quantified from CT images by ITHscore, which is an indicator for tumor phenotypes and patient's prognosis. • ITHscore provides a radiomic quantification of intra-tumor heterogeneity in NSCLC. • ITHscore is an indicator for tumor phenotypes and patient's prognosis. • ITHscore has the potential to be generalized to other cancer types such as HNC.

Making the Rounds: Exploring the Role of Circulating Tumor DNA (ctDNA) in Non-Small Cell Lung Cancer.

Advancements in the clinical practice of non-small cell lung cancer (NSCLC) are shifting treatment paradigms towards increasingly personalized approaches. Liquid biopsies using various circulating analytes provide minimally invasive methods of sampling the molecular content within tumor cells. Plasma-derived circulating tumor DNA (ctDNA), the tumor-derived component of cell-free DNA (cfDNA), is the most extensively studied analyte and has a growing list of applications in the clinical management of NSCLC. As an alternative to tumor genotyping, the assessment of oncogenic driver alterations by ctDNA has become an accepted companion diagnostic via both single-gene polymerase chain reactions (PCR) and next-generation sequencing (NGS) for advanced NSCLC. ctDNA technologies have also shown the ability to detect the emerging mechanisms of acquired resistance that evolve after targeted therapy. Furthermore, the detection of minimal residual disease (MRD) by ctDNA for patients with NSCLC after curative-intent treatment may serve as a prognostic and potentially predictive biomarker for recurrence and response to therapy, respectively. Finally, ctDNA analysis via mutational, methylation, and/or fragmentation multi-omic profiling offers the potential for improving early lung cancer detection. In this review, we discuss the role of ctDNA in each of these capacities, namely, for molecular profiling, treatment response monitoring, MRD detection, and early cancer detection of NSCLC.

LAMTOR3 is a prognostic biomarker in kidney renal clear cell carcinoma.

ObjectiveThe objective of the study was to investigate the expression of LAMTOR3 in kidney renal clear cell carcinoma (KIRC) and its clinical significance.MethodsThe expression of LAMTOR3 in KIRC and its relationship with clinical features were analyzed using the UALCAN online database. The results were verified using KIRC gene chip data and clinical specimens. The prognosis of KIRC patients was analyzed with the GEPIA2 database. GO, KEGG, and GSEA analyses were conducted to analyze the possible molecular mechanism of LAMTOR3 in KIRC. Immunohistochemical (IHC) and hematoxylin and eosin (H&E) staining were used for histopathological detection.ResultsUALCAN database analysis showed that LAMTOR3 expression in KIRC was significantly lower than in normal kidney tissues and correlated with the clinical stage, pathological grade, and tumor genotype (p < .05). GSE53757 dataset analysis consistently showed that the expression of LAMTOR3 in KIRC was significantly lower than in normal kidney tissues (p < .01). GEPIA2 database analysis indicated that patients with low LAMTOR3 expression had poor overall and disease‐free survival rates. GSEA analysis suggested that LAMTOR3 positively regulated the citrate cycle and drug metabolism cytochrome P450 and negatively regulated folate biosynthesis and olfactory transduction. The expression of LAMTOR3 in KIRC was also significantly correlated with immune cell infiltration. Finally, IHC showed that LAMTOR3 expression in the KIRC tissues was lower than in the adjacent normal tissues.ConclusionLAMTOR3 expression is significantly lower in KIRC. LAMTOR3 may be a potential marker for KIRC diagnosis and therapy.