7514 Background: The TNFRSF17 gene, which encodes for the B cell maturation antigen (BCMA), is a small gene expressed mainly on the surface of plasma cells and some DLBCL cells. This gene is composed ...of three exons. Exon 1 is responsible for the extracellular domain, exon 2 for the transmembrane domain, and exon 3 for the TRAF binding domain. The BCMA is currently targeted by various types of immunotherapies as a major therapeutic approach for the treatment of multiple myeloma (MM). This includes CAR-T cells, bi-specific antibodies, and antibody-drug conjugates (ADC). However, emerging data indicates that alternative splicing in genes is an important mechanism for expression of different isoforms that may influence antibody-based therapies. We explored the potential of the presence of alternative splicing in BCMA transcripts via sequencing of BCMA RNA in patients with lymphoma or multiple myeloma. Methods: RNA was extracted from 587 fresh bone marrow samples with lymphoid/plasma cell neoplasms or from FFPE samples with lymphoma. In addition, cfRNA was extracted from 260 peripheral blood plasma samples from patients with lymphoma or multiple myeloma. RNA was sequenced using a hybrid capture-targeted RNA panel with analysis focused on TNFRSF17 (BCMA) gene transcript. Quantification of RNA transcript was done using Salmon algorithm. Results: Of the 587 lymphoma/plasma cell samples, 161 (27%) samples showed alternative splicing involving deletion of exon 2 (BCMA∆Ex2). Of the 260 cfRNA samples, 14 (6%) showed BCMA∆Ex2. The median percentage of BCMA∆Ex2 transcripts was 0.7% of total BCMA transcripts in cellular samples as compared with 9% in cfRNA samples. In cellular samples, there was a correlation between levels of BCMA and BCMA∆Ex2 (R=0.63). Cases with higher levels of BCMA had significantly higher levels of BCMA∆Ex2 (P<0.0001, Kruskal-Wallis). In contrast, cfRNA showed no correlation between levels of BCMA and levels of BCMA∆Ex2 (R=021, Spearman) and mildly higher level of BCMA∆Ex2 in cases with higher levels of BCMA (P=0.002, Kruskal-Wallis). Conclusions: BCMA exon 2 skipping is detected in a significant number of patients with multiple myeloma and lymphoma. The percentage of skipping transcripts is low in cells and relatively higher in cfRNA. Since exon 2 skipping deletes the transmembrane domain, this BCMA protein may remain in the cytoplasm or perhaps is secreted as cell-free BCMA protein. The demonstration of relatively higher levels of BCMA∆Ex2 in cfRNA is likely reflecting higher turnover of cells and raises the possibility that cells with this isoform of BCMA are more aggressive than cells without the expression of this isoform. Further studies are needed to explore the clinical relevance of the expression of such abnormal BCMA protein on treatment with the various forms of anti-body-based therapy or CAR-T.
3048 Background: Immune system plays a major role in the clinical course of cancer. Evaluating the local interaction between tumor cells and the immune cells (microenvironment) at the cancer site ...provides important information. However, currently little is known about the immune system as a whole in patients with cancer. Since cell-free circulating RNA (cfRNA) may reflect the entire body, we hypothesized that cfRNA might provide important information on the health and the status of the immune system. To simplify our approach, we evaluated only cellular biomarkers characteristic for lymphoid and myeloid cells using only the expression of 55 genes typically used in flow cytometry evaluation of hematologic cells. We compared findings between patients with cancer, patients with CHIP (clonal hematopoiesis of indeterminate potential), and normal individuals. Methods: cfRNA was extracted from plasma samples of 681 patients with various types of solid tumors, 113 patients with CHIP, and 34 normal individuals. cfRNA was sequenced via a hybrid capture-based panel targeting 55 genes reflecting immune cells including T-cells, B-cells, histiocytes, monocytes, and myeloid cells. The RNA was quantified using TPM (transcript per million). Results: There was significant difference (P<0.0001) between normal individuals and patients with cancers in the levels of circulating biomarkers specific for immune cells. Surprisingly, expression of B-cell, T-cell, monocytic/histiocytic genes were significantly lower in patients with solid tumors when compared to normal individuals. This included CD19, CD20, CD2, CD22, CD3D, CD3E, CD3G, CD4, CD52, CD7, CD79A, CD79B, CD8A, CD8B,CD33, FCER2(CD23), IL2RA(CD25), ITGAM(CD11B), and ITGAX(CD11C). In contrast, there was no significant difference between CHIP and normal for B- or T-cell markers. After adjusting for multiple testing, no deficiency in immune stimulatory markers was present in patients with CHIP. Patients with CHIP showed significantly (P<0.001) lower levels of CD38, CD58, CD16, CD15, CD25, and CD123 mRNA as compared with normal. Furthermore, using 35 immune cell biomarkers in a machine learning algorithm using 2/3 of samples for training and 1/3 for testing predicted the presence of cancer vs no cancer (AUC =0.820), and CHIP from cancer (AUC =0.0830) and CHIP vs normal (AUC =0.871). Conclusions: Immune related biomarkers using cfRNA provide important information on the immune system that can be used to monitor patients and to predict the presence of cancer or CHIP. The demonstration that patients with cancer have deficiency in overall systemic immune elements suggests that further studies are needed in monitoring the immune system and exploring means to boost systemic immunity to prevent the development of overt cancers.
Although numerous reports indicate that patients receiving autotransplants for lymphoma are at increased risk for myelodysplastic syndrome (MDS)/acute myeloid leukemia (AML), the separate ...contributions of pretransplantation- and transplantation-related therapy are not well characterized. We conducted a case-control study of 56 patients with MDS/AML and 168 matched controls within a cohort of 2 739 patients receiving autotransplants for Hodgkin disease or non-Hodgkin lymphoma at 12 institutions (1989-1995). Detailed abstraction of medical records was undertaken to determine all pre- and posttransplantation therapy, and transplantation-related procedures. In multivariate analyses, risks of MDS/AML significantly increased with the intensity of pretransplantation chemotherapy with mechlorethamine (relative risks RRs = 2.0 and 4.3 for cumulative doses < 50 mg/m2 and ≥ 50 mg/m,2respectively; trend over dose categories, P = .04) or chlorambucil (RRs = 3.8 and 8.4 for duration < 10 months or ≥ 10 months, respectively; trend, P = .009), compared with cyclophosphamide-based therapy. Transplantation-conditioning regimens including total-body irradiation (TBI) at doses 12 Gy or less did not appear to elevate leukemia risk (RR = 1.3; P = .48) compared with non-TBI regimens; however, a statistically significant increased risk was found for TBI doses of 13.2 Gy (RR = 4.6; P = .03). Peripheral blood stem cells were associated with a nonsignificant increased risk of MDS/AML (RR = 1.8; P = .12) compared with bone marrow grafts. Our data show that type and intensity of pretransplantation chemotherapy with alkylating agents are important risk factors of MDS/AML following autotransplantation. Transplantation-related factors may also modulate this risk; however, the apparent contribution of high-dose TBI requires confirmation.
Introduction: Lymphoma diagnosis and classification requires pathologist interpretation of morphology and large numbers of immunohistochemistry (IHC) stains of various CD markers. This process is ...subjective and requires a significant amount of tissue. In contrast, RNA quantification of the same CD markers used in IHC using next generation sequencing (NGS) requires little tissue and is less influenced by the antigen retrieval process used in IHC. However, IHC staining and microscopic examination allows evaluation of the expression in various subpopulations and makes diagnosis possible. In contrast, when total RNA is evaluated by NGS, distinguishing between subpopulations is lost. Machine learning algorithms are capable of multi-marker normalizing and compensate for the loss of subpopulation analysis. To confirm this, we explored the capability of using RNA quantification of 30 CD markers by NGS from FFPE tissue along with machine learning in the clinical diagnosis and classification of various types of lymphoma. Methods: Formalin-fixed paraffin-embedded (FFPE) tissue from 130 diffuse large B-cell lymphoma (DLBCL), 70 mantle cell lymphoma, 92 T-cell lymphoma, 48 follicular lymphoma, 36 Hodgkin lymphoma, and 52 marginal zone lymphoma samples were used for extracting mRNA. The studied samples were consecutive without selection and included mainly lymph node excisional biopsies or core biopsies. RNA sequencing was performed using a targeted hybrid capture panel that included CD1A, CD2, CD3D, CD3E, CD3G, CD4, CD5, CD7, CD8A, CD8B, CD10, CD14, CD19, CD20, CD22, CD33, CD34, CD38, CD40, CD44, CD47, CD68, CD70, CD74, CD79A, CD79B, CD81, CD138, CD200, and CD274 genes. Salmon v1.4.0 software was used for expression quantification (TPM). Random forest machine learning algorithm was used for predicting diagnosis. Randomly selected two thirds of samples were used for training and one third was used for testing. Results: In some cases, diagnosis can be made by simply inspecting the RNA levels of various CD markers. However, machine learning shows remarkably high sensitivity and specificity in the diagnosis of most lymphoma subclasses. Area under the curve (AUC) was at 1.00 (95% CI: 1.000-1.00) for DLBCL vs. T-cell lymphoma, Hodgkin vs. T-cell, Hodgkin vs. DLBCL, mantle vs. DLBCL, and Follicular lymphoma vs. marginal lymphoma with 100% sensitivity and specificity in the testing set. AUC was at 0.974 (95% CI: 0.920-1.000) for marginal lymphoma vs. mantle cell lymphoma with sensitivity of 88% and specificity of 100%. The AUC was at 0.887 (95% CI: 0.776-0.999) for follicular lymphoma vs. DLBCL with sensitivity of 81.3% and specificity of 83.7%. Conclusions: This data demonstrates that NGS quantification of RNA from 30 CD markers when combined with machine learning is adequate for reliable classification of various types of lymphoma. This approach can provide valuable information to distinguish between difficult diagnoses, and if trained adequately has the potential to expand to more borderline cases. More importantly, this technology can be automated and less susceptible to human errors. RNA quantification using NGS has the potential to replace the need for IHC and can be applied when samples are limited such as in needle aspiration or core biopsies.
Introduction: Human leukocyte antigen (HLA) plays a major role in the interaction between the immune system and oncogenic process in various types of tumors including lymphoma. Mono-allelic and ...bi-allelic loss of the HLA-I has been reported in diffuse large B-cell lymphoma (DLBCL) and other types of lymphoma. Poor outcome was also reported with the loss of the HLA-I system in lymphoma. Since HLA-I and HLA-II are both critical for healthy immune response and both can be expressed in the lymphoma cells as well as in the lymphoma responding microenvironment, analyzing both systems is crucial for deciphering the role of the HLA system in lymphoma. Toward this goal, we evaluated the expression level of various HLA-I and HLA-II genes and compared these levels between subtypes of lymphoma. We quantified RNA expression levels of 15 HLA-I and HLA-II genes using next generation sequencing (NGS) and used machine learning algorithm (random forest) to evaluate the depth of the significance in variation of the expression profiles between various types of lymphoma. Methods: RNA was extracted from formalin-fixed paraffin-embedded (FFPE) tissue from 99 diffuse large B-cell lymphoma (DLBCL), 79 T-cell lymphoma, 40 follicular lymphoma, 28 mantle cell lymphoma, and 14 Hodgkin lymphoma samples. RNA sequencing was performed using a targeted hybrid capture panel. The sequenced HLA-I genes were HLA-A, HLA-B, HLA-C, HLA-E, HLA-F, and HLA-H. The sequenced HLA-II genes were HLA-DPA1, HLA-DPB1, HLA-DQA1, HLA-DQB1, HLA-DQB2, HLA-DRA, HLA-DRB1, HLA-DRB5, and HLA-DRB6. Salmon v1.4.0 software was used for expression quantification (TPM). Random forest machine learning system was used for predicting subtypes. Two thirds of samples were used for training the random forest algorithm and one third was used for testing. Results: RNA levels of individual HLA-I and HLA-II genes varied mildly between various B-cell lymphoma subtypes without specific pattern, especially after adjusting for multiple testing. However, T-cell lymphoma showed overall significantly (P< 0.0001) higher HLA-I (B, C, and F) and lower HLA-II (DRB1 and DRB5) expression levels as compared with B-cell lymphoma. In contrast, using machine learning algorithm to evaluate combination of HLA-I and HLA-II expression profiles showed discrete and significant differences between various types of lymphoma. The levels of expression of HLA-I and HLA-II genes were adequate to distinguish between DLBCL and follicular lymphoma with AUC of 0.778 (95% CI: 0.46-1.00), and between DLBCL and T-cell lymphoma with AUC of 0.833 (95% CI: 0.624-1.00). The number of Hodgkin lymphoma and mantle cell cases was small but the random forest distinguished between DLBCL and Hodgkin lymphoma with AUC of 0.833 (95% CI: 0.294-1.00), and between DLBCL and mantle cell lymphoma with AUC of 0.778 (95% CI: 0.416-1.00). Conclusions: This data confirms the polygenic effects of the HLA-I and HLA-II on various types of lymphoma. The HLA-I and HLA-II combined expression profiles are significantly different between various types of lymphoma. More importantly, this data suggests that immune modulating therapy should consider the polygenic effects of both the HLA-I and HLA-II systems.
2634
Background: Poor response to immune checkpoint inhibitors (ICI) in colorectal cancer (CRC) is believed to be due to lack of immune suppressive tumor microenvironment (TME). In contrast, lung ...cancer TME is believed to be significantly more immunologically active and responsible for the relative success of ICI in lung cancer. We evaluated the TME in lung cancer and CRC using 43 immune biomarkers quantified using RNA sequencing and developed a model to classify TME into immunologically active (similar to lung cancer) vs inactive (similar to colorectal). These 43 immune biomarkers included B- and T-cell markers, cytokines and chemokines. Methods: RNA was extracted from FFPE samples from 707 patients with lung cancer, 227 patients with CRC, 131 patients with breast cancer, 111 patients with ovarian cancer, and 72 patients with pancreatic cancer. The expression levels of the 42 immunological markers were quantified using next generation sequencing (NGS) as a part of larger targeted RNA sequencing panel of 1408 genes. Using a machine learning algorithm, we first selected the relative genes that distinguish between two classes using two criteria: performance of each gene with K-fold cross-validation (K=12) and second based on stability measure using statistical significance tests. The selected genes were then used to predict one class from the other using random forest classifier. Samples were divided into a training set (67%) and testing set (33%). Results: A Bayesian-based algorithm selected the expression of 20 genes that were significantly relevant in differentiating between immunologically active and inactive TME. Using these 20 genes in Random Forest model, we can distinguish between lung and CRC with AUC of 0.997 (95% CI: 0.992-1.00) in the training set and AUC of 0.923 (95% CI: 0.880-0.966) in the testing set. Testing 131 breast cancer samples showed 23 (18%) with TME that can be classified as immunologically active. Of 111 ovarian samples 13 (12%) showed immunologically active TME and of 72 pancreatic samples, 17 (24%) showed microenvironment classified as active. The 20 genes that were adequate to distinguish between TME active vs inactive are: CD74, FCGBP, IL1R1, CD44, CD274, FCGR2B, IL21R, IL1RAP, IL7R, CD79A, CCL2, CYFIP2, CD19, IL2RA, CD8A, CD79B, ID1, CD22, FZD10, and IL1B, listed in order of their importance. Conclusions: This data shows that lung cancer TME is significantly different from that of CRC. Only 20 immune biomarkers adequate to distinguish between the two TME. The relevant biomarkers included CD274 (PD-L1) as well as one marker for T-cells (CD8A) but three markers for B-cells (CD19, CD22 and CD79B). This suggests that B-cells play a significant role in immunologically active TME and should be explored further as biomarkers for predicting response to ICI therapy.
3043
Background: Liquid biopsy is currently considered an important part of the clinical practice of oncology. However, proper interpretation of somatic mutations detected in liquid biopsy remains a ...challenge. Distinguishing cancer-related mutations from mutations resulting from clonal hematopoiesis of indeterminate potential (CHIP) can be difficult and a source of misinterpretation of liquid biopsy findings. We explored using cell-free RNA (cfRNA) profiling as a means for distinguishing between mutations detected as CHIP vs mutations detected as cancer. Methods: cfDNA and cfRNA from 102 patients with confirmed solid tumors, 93 patients with hematologic neoplasms and 40 patients with CHIP abnormalities were sequenced using a panel of 1501 gene for RNA and 284 genes for DNA. The solid tumors included lung, breast, ovarian, and colorectal. The hematologic neoplasms included lymphoid and myeloid neoplasms. Using a machine learning algorithm, we first selected the relative genes that distinguish between two classes using K-fold cross-validation (K=12). The selected genes were used to predict one class from the other using naïve Bayesian classifier, but we applied geometric mean naïve Bayesian (GMNB) to compensate for the expected underflow. Results: Using machine learning with cfRNA, we were able to distinguish between cases with confirmed cancer-related mutations and CHIP mutations with area under the curve (AUC) of 0.808 (95% CI: 0.720-0.895). Precision, recall and f1-score were 0.86, 0.422 and 0.566 for CHIP and 0.359, 0.825 and 0.500 for solid tumor, respectively. The machine learning required 70 genes for this classification. Leave-one-out (LOO) validation showed AUC of 0.766 (95% CI: 0.671-0.859). Distinguishing between hematologic neoplasms and CHIP was achievable using the expression of 30 genes. The AUC for this model was 0.787 (95% CI: 0.696-0.879). Precision, recall and f1-score were 0.906, 0.516, and 0.658 for CHIP and 0.438, 0.875, and 0.583 for hematologic neoplasms, respectively. Validation using LOO showed AUC of 0.736 (95% CI : 0.638-0.835). The top 10 genes that their expression was critical for distinguishing between CHIP and solid tumors were EIF4E, HNF1A, HOOK3, HIPK2, IL1RAP, BRAF, DNAJB1, GADD45B, H2AX, and HDAC6. The top 10 genes for distinguishing hematologic cancers from CHIP were PBX1, CAMTA1, PFDN5, PCBP1, SDC4, PRKACA, NT5C2, NBR1, RPS21, and AHCYL1. Conclusions: Analyzing cfRNA expression levels when used with machine learning may add another level of confidence to the ability of interpreting mutations detected in liquid biopsy testing, especially when these mutations are at low levels. This is particularly important in liquid biopsy testing for minimal residual disease (MRD) when a tissue baseline sample is not tested (tumor-agnostic).
7060
Background: Changes in the bone marrow microenvironment are believed to play a major role in the biology of Myelodysplastic syndrome (MDS) and acute myeloid leukemia (AML). To study the bone ...marrow microenvironment (BME) in MDS and AML and to compare it with normal BME, we studied the expression profile of 43 immune biomarkers and evaluated the differences in the BME between AML and MDS and compared it to that of normal BME. These 43 immune biomarkers included B- and T-cell markers, cytokines and chemokines. Methods: RNA was extracted from fresh bone marrow aspiration samples from 626 patients with AML, 564 patients with MDS, and 1449 individuals having bone marrow without any mutations or having low level mutations determined to be CHIP (clonal hematopoiesis of indeterminate potential) and considered normal. RNA levels of 42 immune biomarkers were quantified using next generation sequencing. Using a machine learning algorithm, we first selected the relative genes that distinguish between two classes using K-fold cross-validation (K = 12). The selected genes were used to predict one class from the other using random forest classifier. Samples were divided into a training set (67%) and testing set (33%) for each classification. Results: The random forest showed that MDS can easily be distinguished from normal using the expression of 15 genes (CYFIP2, CXCR4, IL1RAP, CD58, CD36, CD19, PAX5, CD79B, ID1, IL8, CD44, IL1R1, CD79A, IL21R, and CD74). The AUC for distinguishing MDS from normal was 0.996 in the training set and 0.931 (95% CI: 0.912-0.949) in the testing set. Distinguishing between AML and normal was also robust and achievable using the expression of only 10 genes (CYFIP2, IL1R1, CXCR4, IL8, IL21R, CD44, CD28, CD79A, and IL7R, and CD8A). The AUC for the training set was 0.994 and 0.972 (95% CI: 0.961-0.983) for the testing set. Eight of these 10 markers were shared with MDS algorithm. Only CD28 and IL7R were specifically needed for AML classification. Distinguishing between MDS And AML was achievable with high reliability with AUC of 0.994 (95% CI:0.992-0.997) in training set and 0.924 (95% CI: 0.896-0.952) in testing set). Only 10 biomarkers were used for distinguishing MDS from AML, nine of which (IL1R1, CYFIP2, CD44, IL1RAP, CXCR4, IL21R, CD74, IL8, and CD36) were used in distinguishing MDS from normal. The only unique biomarker was CD28. Comparing levels of these biomarkers, most of which showed highest level in normal BM, significantly lower level in MDS but the level was even significantly lower in AML (deeper reduction) than in MDS. Conclusions: This data suggests that the BME is significantly different in MDS from AML and both are different from normal. Few immune biomarkers play major role in defining each BME. However, relative increase or decrease between these immune biomarkers dictate the uniqueness of each microenvironment.
