Tissue tumor mutational burden (tTMB) is calculated to aid in cancer treatment selection. High tTMB predicts a favorable response to immunotherapy in patients with non-small cell lung cancer. Blood TMB (bTMB) from circulating tumor DNA is reported to have similar predictive power and has been proposed as an alternative to tTMB. Across many studies not only are tTMB and bTMB not concordant but also as reported previously by our group predict conflicting outcomes. This implies that bTMB is not a substitute for tTMB, but rather a composite index that may encompass tumor heterogeneity. Here, we provide a thorough overview of the predictive power of TMB, discuss the use of tumor heterogeneity alongside TMB to predict treatment response and review several methods of tumor heterogeneity assessment. Furthermore, we propose a hypothetical method of estimating tumor heterogeneity and touch on its clinical implications.
e13545 Background: Genetic variants beyond FDA-approved drug targets are often identified in NSCLC patients. To address this challenge, in silico variant classification tools are available to determine whether specific variants contribute to disease pathogenicity or remain benign. Although the performance of in silico tools has been analyzed in previous studies, it has not been analyzed for actionable targets of FDA-approved therapies for NSCLC. The aim of this study is to compare the performance of commonly used in silico tools in classifying the pathogenicity of actionable variants in NSCLC. Methods: We evaluated the performance of several in silico tools: PolyPhen-2, Align-GVGD, and MutationTaster2. A curated set of targetable NSCLC missense variants (n = 179) was used. The dataset consisted of variants in the BRAF, EGFR, ERBB2, KRAS, MET, ALK, and ROS1 genes based on their indications as molecular targets in the NCCN Guidelines for NSCLC. Pathogenic variants (n = 80) were curated based on available literature and annotations according to the NCCN Guidelines, OncoKB, My Cancer Genome, and AACR Project GENIE. Benign variants (n = 99) were curated from the dbSNP database with the inclusion criteria of a benign or likely benign ClinVar assertion. The overall accuracy, sensitivity, specificity, and Matthews correlation coefficient (MCC) of each in silico tool were determined. The performance of each in silico tool in predicting pathogenicity for subsets of sensitizing (n = 18) and resistant (n = 57) variants was also evaluated. Results: PolyPhen-2 HumVar demonstrated the highest overall accuracy (0.80), specificity (0.69), and MCC (0.63) of the in silico tools analyzed. PolyPhen-2 HumDiv (0.75) and MutationTaster2 (0.69) had similar overall accuracies while Align-GVGD (0.50) had the lowest overall accuracy. Align-GVGD also had the lowest MCC (0.08), with the other in silico tools ranging from 0.50 to 0.63. All the in silico tools achieved high sensitivities, with MutationTaster2 performing the highest (1.00) and Align-GVGD performing the lowest (0.86). The specificities were remarkably low (0.20-0.69) for all the in silico tools, with the lowest specificity demonstrated by Align-GVGD. The overall accuracies when classifying the subsets of sensitizing and resistant variants were generally high, ranging from 0.84 to 1.00. Conclusions: These results suggest that the performance of the evaluated in silico tools to predict the pathogenicity of clinically actionable NSCLC missense variants is not fully reliable. The tools analyzed in this study could be acceptable to rule out pathogenicity in variants given their higher sensitivities, but are limited when it comes to identifying pathogenicity in variants due to low specificities.
Early recognition of immune-related adverse events (irAEs) of immunotherapy is important. Circulating proteome reflects host response to diseases and is being explored as a marker for response to immunotherapy. We used a serum-based proteomics test, Primary Immune Response (PIR), to explore the associations between developing irAEs and immunotherapy in non-small cell lung cancer (NSCLC) patients. Data of 38 consented NSCLC patients with baseline PIR test done within one week prior to the start of immunotherapy were collected. Samples were grouped into either sensitive or intermediate/resistant (not sensitive) by PIR classification. We analyzed the durations from the immunotherapy initiation to the first episode of irAE, each individual irAE, and each irAE above grade 1 using log-rank test. IrAEs were graded according to Common Terminology Criteria for Adverse Events (CTCAE) v5.0. Among the 38 NSCLC patients, 21 patients (55%) experienced one or more irAEs. The total number of irAEs was 33 with the majority classified as either grade 1 (n=18, 55%) or grade 2 (n=11, 33%) (Table 1). PIR-sensitive group showed longer irAE free period with the median ‘Time to first irAE' being 54 weeks compared to 9.5 weeks in PIR-not sensitive (p=0.22, HR=0.56, 95% CI=0.24-1.34). The median ‘Time to each irAE' were 45 weeks and 12 weeks in PIR-sensitive and PIR-not sensitive, respectively (p=0.1, HR=0.55, 95% CI=0.28-1.1). The median ‘Time to each irAE above grade 1' demonstrated similar results with less differences between the two groups with median values of 54 weeks and 30 weeks in PIR-sensitive and PIR-not sensitive, respectively (p=0.28, HR=0.57, 95% CI=0.21-1.56). Our results demonstrated a trend that PIR-sensitive patients are more likely to tolerate immunotherapy longer without developing irAEs. It implies the potential value of the baseline PIR test in predicting the development of irAEs and selecting subsets of patients who need close monitoring with immunotherapy. Distribution of irAEs by PIR classificationVariablesSensitiveNot-sensitiveTotal number of patients, n1325Patients without irAE, n (%)7 (54%)10 (40%)Patients with irAE, n (%)6 (46%)15 (60%)Total number of irAEs, n1023Grade 1, n (%)5 (50%)13 (57%)Grade 2, n (%)5 (50%)6 (26%)Grade 3, n (%)02 (8%)Grade 4, n (%)01 (4%)Grade 5, n (%)01 (4%) Citation Format: Myungwoo Nam, Leeseul Kim, William Cheng, William H. Bae, Jin Young Hwang, Yoonhee Choi, Yeun Ho Lee, Won Kyung Hur, Chan Mi Jung, Heayoon S. Cho, Young Kwang Chae. Potential role of serum proteome in predicting immune-related adverse events from immunotherapy in non-small cell lung cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 520.
Background: Apart from its role in ovarian cancer progression, epithelial-mesenchymal transition (EMT) can promote chemotherapy resistance. We aimed to analyze how the EMT score would affect the tumor microenvironment in ovarian cancer. Methods: cBioportal was queried to obtain The Cancer Genome Atlas (TCGA) data for the serous ovarian cancer (SOC) cohort (TCGA, 585 patients). The neoantigen prediction data was derived from the CloudNeo pipeline using TCGA mutation calling. EMT scores were calculated by subtracting the average RNA-seq z-scores of three epithelial marker genes from the average RNA-seq z-scores of 13 mesenchymal marker genes as described in the previous study. Patient samples were grouped as either EMT-high (highest 1/3) or EMT-low (lowest 1/3). CIBERSORT was applied to predict the tumor-infiltrating immune cells. Results: Among the 585 SOC patients, only 256 patients had mutation data available for our analysis. The EMT-low group had a significantly higher mutation count (p value=0.0004) and cytolytic score (p value=0.032) than the EMT-high group. In addition, the EMT-low samples were associated with improved overall survival in SOC patients (HR, 0.55; 95% CI, 0.39-0.78; P < 0.001). The median survival of EMT-low was 57.40, and EMT-high was 41.06 months. Neoantigen counts and PD-L1 express level tended to be higher in the EMT-high group although failed to show statistical significance. The immune cell infiltration rates were not different between both groups. Conclusions: Our study is the first to describe the association between the EMT potential, neoantigen counts, and cytolytic scores in SOC. In our analyses, tumors with low EMT potential had a significantly higher neoantigen burden and higher cytolytic scores, suggesting that tumors with low EMT potentials tend to be more immunogenic. Further studies are warranted to explore the utility of EMT scores as biomarkers to predict the treatment response to immunotherapy in SOC. The immunologic characteristics of EMT low and high SOCNumber of patientsNeoantigen countp-valueMutation countp-valueCytolytic scorep-valuePD-L1 expressionp-valueEMT low6088.190.183783.190.0004183.40.032190.0663EMT high5867.7472.7486.3515.52 Citation Format: Won Kyung Hur, Jin Young Hwang, Leeseul Kim, Myungwoo Nam, William H. Bae, Yoonhee Choi, Yeun Ho Lee, Heayoon S. Cho, Emma Yu, Chan Mi Jung, William Cheng, Eugene Kim, Christmann Low, Victor Wang, Jeff Chuang, Young Kwang Chae. The neoantigen and immune landscape of epithelial mesenchymal transition (EMT) low and high score serous ovarian cancers [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 626.
BackgroundTumors with high tumor mutational burden (TMB) or defects in mismatch repair (dMMR) respond well to immune checkpoint inhibitors (ICIs).1 2 TMB and DNA repair gene mutations including dMMR are closely related to the increase of neoantigens, which are recognized by immune cells to trigger an immune response.1 3 Although not a standard of care in thyroid cancer treatment, there are ongoing clinical trials for ICI use in differentiated thyroid carcinoma. However, not much has been explored concerning the neoantigen landscape and its association with immune traits in papillary thyroid cancer (PTC). We aim to analyze the immune landscape of PTC in association with neoantigen burden, TMB, and DNA repair gene mutations.MethodsWe used the PTC cohort data from The Cancer Genome Atlas (TCGA). The mutation counts and data for neoantigen prediction were acquired from TCGA mutation calling. CloudNeo pipeline was used for neoantigen prediction. TMB was calculated as the sum of missense and indel mutation counts per megabase pairs covered by whole-exome sequencing. Tumor-infiltrating immune cells were estimated using CIBERSORT.ResultsOut of the 496 PTC patients from cBioPortal, a subset of 400 patients with available mutation counts and predicted neoantigen burden was included in the study. Immune cell infiltration estimated by CIBERSORT showed macrophage M2 as the most abundant, followed by macrophage M0 and other T cells (figure 1). The TMB ranged from 0.03 to 2.05 with a median value of 0.2. Neoantigen burden ranged from 0 to 18 with a median value of 1, which is relatively low compared to the median value of 18 in non-small cell lung cancer (NSCLC)1 (figure 2). One or more DNA repair gene mutations were discovered in 32 patients (8%). The mutation status of repair genes was not related to TMB or neoantigen burden. TMB or neoantigen burden was not related to immune traits such as infiltration of CD8+ T cells or regulatory T cells, cytolytic activity score, and PD-L1 expression.Abstract 753 Figure 1Immune cell infiltration estimated by CIBERSORTAbstract 753 Figure 2Histogram of neoantigen burdenConclusionsThis is the first study to report the immune landscape of PTC in the context of neoantigen. The lack of association between TMB or neoantigen burden with immune traits may be due to the relatively low number of neoantigens in PTC compared to other immunogenic cancers such as NSCLC. Our results suggest that mutations in DNA repair genes or TMB are likely to have limited value in predicting response to ICI treatment in PTC.ReferencesChae YK, et al., Mutations in DNA repair genes are associated with increased neoantigen burden and a distinct immunophenotype in lung squamous cell carcinoma. Sci Rep 2019; 9:3235.Rizvi NA, et al., Cancer immunology. mutational landscape determines sensitivity to PD-1 blockade in non-small cell lung cancer. Science 2015; 348:124–128.Schumacher TN, Schreiber RD, Neoantigens in cancer immunotherapy. Science 2015; 348:69–74.
Standard–of–care treatment options provide an excellent prognosis for papillary thyroid cancers (PTCs); however, approximately 10% of cases are advanced PTCs, resulting in less than 50% 5–year survival rates. Understanding the tumor microenvironment is essential for understanding cancer progression and investigating potential biomarkers for treatment, such as immunotherapy. Our study focused on tumor–infiltrating lymphocytes (TILs), which are the main effectors of anti–tumor immunity and related to the mechanism of immunotherapy. Using an artificial intelligence model, we analyzed the density of intratumoral and peritumoral TILs in the pathologic slides of The Cancer Genome Atlas PTC cohort. Tumors were classified into three immune phenotypes (IPs) based on the spatial distribution of TILs: immune–desert (48%), immune–excluded (34%), and inflamed (18%). Immune–desert IP was mostly characterized by RAS mutations, high thyroid differentiation score, and low anti–tumor immune response. Immune–excluded IP predominantly consisted of BRAF V600E mutated tumors and had a higher rate of lymph node metastasis. Inflamed IP was characterized by a high anti–tumor immune response, as demonstrated by a high cytolytic score, immune–related cell infiltrations, expression of immunomodulatory molecules (including immunotherapy target molecules), and enrichment of immune–related pathways. This study is the first to investigate IP classification using TILs in PTC through a tissue–based approach. Each IP had unique immune and genomic profiles. Further studies are warranted to assess the predictive value of IP classification in advanced PTC patients treated with immunotherapy.
BackgroundTumor heterogeneity assessment may help predict response to immunotherapy. In melanoma mouse models, tumor heterogeneity impaired immune response.1 In addition, among lung cancer patients receiving immunotherapy, the high clonal neoantigen group had favorable survival and outcomes.2 Ideal methods of quantifying tumor heterogeneity are multiple biopsies or autopsy. However, these are not feasible in routine clinical practice. Circulating tumor DNA (ctDNA) is emerging as an alternative. Here, we reviewed the current state of tumor heterogeneity quantification from ctDNA. Furthermore, we propose a new tumor heterogeneity index(THI) based on our own scoring system, utilizing both ctDNA and tissue DNA.MethodsSystematic literature search on Pubmed was conducted up to August 18, 2020. A scoring system and THI were theoretically derived.ResultsTwo studies suggested their own methods of assessing tumor heterogeneity. One suggested clustering mutations with Pyclone,3 and the other suggested using the ratio of allele frequency (AF) to the maximum somatic allele frequency (MSAF).4 According to the former, the mutations in the highest cellular prevalence cluster can be defined as clonal mutations. According to the latter, the mutations with AF/MSAF<10% can be defined as subclonal mutations. To date, there have been no studies on utilizing both ctDNA and tissue DNA simultaneously to quantify tumor heterogeneity. We hypothesize that a mutation found in only one of either ctDNA or tissue DNA has a higher chance of being subclonal.We suggest a scoring system based on the previously mentioned methods to estimate the probability for a mutant allele to be subclonal. Adding up the points that correspond to the conditions results in a subclonality score (table 1). In a given ctDNA, the number of alleles with a subclonality score greater than or equal to 2 divided by the total number of alleles is defined as blood THI (bTHI) (figure 1). We can repeat the same calculation in a given tissue DNA for tissue THI (tTHI) (figure 2). Finally, we define composite THI (cTHI) as the mean of bTHI and tTHI.Abstract 18 Table 1Subclonality scoreAbstract 18 Figure 1Hypothetical distribution of all alleles found in ctDNA bTHI = the number of alleles with a subclonality score greater than or equal to 2/the total number of alleles found in ctDNA = 10/20 =50%Abstract 18 Figure 2Hypothetical distribution of all alleles found in tissue DNA tTHI= the number of alleles with a subclonality score greater than or equal to 2/the total number of alleles found in tissue DNA = 16/40 = 40% cTHI= (bTHI + tTHI)/2 = 45%ConclusionsTumor heterogeneity is becoming an important biomarker for predicting response to immunotherapy. Because autopsy and multiple biopsies are not feasible, utilizing both ctDNA and tissue DNA is the most comprehensive and practical approach. Therefore, we propose cTHI, for the first time, as a quantification measure of tumor heterogeneity.ReferencesWolf Y, Bartok O. UVB-Induced Tumor Heterogeneity Diminishes Immune Response in Melanoma. Cell 2019;179:219–235.McGranahan N, Swanton C. Clonal neoantigens elicit T cell immunoreactivity and sensitivity to immune checkpoint blockade. Science 2016;351:1463–1469.Ma F, Guan Y. Assessing tumor heterogeneity using ctDNA to predict and monitor therapeutic response in metastatic breast cancer. Int J Cancer 2020;146:1359–1368.Liu Z, Xie Z. Presence of allele frequency heterogeneity defined by ctDNA profiling predicts unfavorable overall survival of NSCLC. Transl Lung Cancer Res 2019;8:1045–1050.
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