80 Cancer testis antigen burden: A novel predictive biomarker for immunotherapy in solid tumors

BackgroundWhen expressed in cancer cells, cancer testis antigens (CTAs) are highly immunogenic and have the capacity to elicit cancer-specific immune responses in diverse malignancies. With their expression limited to tumor cells, CTAs have become a prime target of natural T cell response, immune cell-based therapy, and cancer vaccines. In this study, we investigated CTA burden in real-world clinical tumors spanning multiple histologies, revealing a novel prognostic gene expression-based biomarker.MethodsTargeted RNA-seq was performed on 5450 FFPE tumors representing 39 histologic types, predominantly composed of lung cancer (40.4%) followed by colorectal cancer (10.6%) and breast cancer (8.6%). Using an amplicon-based NGS approach, expression levels of 17 CTA genes were ranked against a reference population. Cancer Testis Antigen Burden (CTAB) was calculated as the sum of the gene expression rank for each CTA gene. The median CTAB of ≥171 was used as cutoff for CTAB High versus Low classification. We estimated Pearson’s correlation for all CTA genes to discover co-expression patterns between CTAs and histologies. Overall survival (OS) analysis was performed using CoxPh regression model whereas response analysis was performed using logistic regression model with p-values reported.ResultsWithin the tumor samples, CTAB values ranged from 0–1700, with kidney cancer demonstrating overall lowest mean CTAB (110) and melanoma the highest (550). NSCLC had an average CTAB of 283. In an immune checkpoint blockade treated retrospective cohort of 110 NSCLC patients, High CTAB showed better OS compared to Low CTA (HR: 0.55, p=0.07). Additionally, when combined with tumor inflammation and cell proliferation biomarkers, highly inflamed but poorly proliferative tumors with High CTAB had improved OS (HR: 0.27, p=0.05). No significant association with response was detectedConclusionsOur studies show that co-expression of multiple CTA genes occurs in many tumor types and can be reliably detected using a targeted RNA-seq approach. Utilization of this co-expression pattern to calculate CTAB reveals tumor-type associated signatures, which in a small NSCLC cohort is associated with the overall survival. The findings suggest that these immunogenic antigens expose the tumor cells to natural or immunotherapy augmented cell-based immune response, and that CTAB is a potential predictive marker for therapeutic response to checkpoint inhibitors. Further studies are needed to establish the predictive value in other tumor types, as well as the role of CTAB in immune cell therapies and vaccinations.

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IMMU-27. ANALYSIS OF IMMUNE SIGNATURES IN PEDIATRIC GLIOBLASTOMAS FOR PATIENT STRATIFICATION TO IMMUNOTHERAPY

Neuro-Oncology ◽

10.1093/neuonc/noaa222.381 ◽

2020 ◽

Vol 22 (Supplement_3) ◽

pp. iii365-iii365

Author(s):

Vidyalakshmi Chandramohan ◽

Tyler Evangelous ◽

Eric S Lipp ◽

Bhavna Hora ◽

Darell D Bigner ◽

...

Keyword(s):

Overall Survival ◽

Tumor Cells ◽

Immune Cells ◽

Median Overall Survival ◽

Immune Cell ◽

Cell Mass ◽

Cell Phenotype ◽

Patient Stratification ◽

Rna Seq ◽

Selection Of

Abstract BACKGROUND Pediatric glioblastoma (pGBM), despite being relatively rare (incidence rate: 0.5/100,000), are a leading cause of cancer deaths in children with a median overall survival of 9–15 months. In recent years, immunotherapy has emerged as one of the more promising advances in oncology, with impressive response rates reported in several malignancies. Effective application of immunotherapy in brain tumors depends upon a better understanding of the immune cell phenotype and mechanisms of immunosuppression in these tumors. This understanding will allow for the selection of patient population who are most likely to benefit from immunotherapeutic approaches. MATERIAL AND METHODS In order to determine the frequency, distribution, and phenotype of tumor-infiltrating immune cells in pGBMs, we undertook an immunohistochemical survey on 19 recurrent pGBMs for CD3, CD8, CD4, CD163, PD-1, PD-L1, and FoxP3; RNA-Seq was also performed on a subset of 9 cases. Distribution of lymphocytes (LYMPHS) was recorded as intratumoral (IT) or perivascular (PV). RESULTS The analysis indicates intratumoral CD3+ LYMPHS are commonly <5% of tumor cell mass; however, approximately half (10/19) of these recurrent pGBM have infiltrates that range from 5 to 30% CD3+ LYMPHS. Of these, 4/10 CD3+ tumors exhibit brisk CD8+ infiltrates that are associated with PD-L1+ tumor cells. These tumors with brisk CD3+/CD8+ LYMPHS and PD-L1+ tumor cells were associated with longer survivals. The data were confirmed by RNA-seq analysis. CONCLUSION PD-L1+ pGBMs associated with CD3+/CD8+ LYMPH infiltrates deserve further investigation as candidates for immunotherapy.

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282 Pan-tumor analysis of the association between PD-L1 combined positive score and response to pembrolizumab monotherapy

Journal for ImmunoTherapy of Cancer ◽

10.1136/jitc-2020-sitc2020.0282 ◽

2020 ◽

Vol 8 (Suppl 3) ◽

pp. A308-A308

Author(s):

Lingkang Huang ◽

Jared Lunceford ◽

Junshui Ma ◽

Kenneth Emancipator

Keyword(s):

Tumor Cells ◽

Immune Cells ◽

Roc Analysis ◽

Standard Of Care ◽

Tumor Type ◽

Operating Characteristics ◽

Positive Score ◽

Combined Positive Score ◽

Line Of Therapy ◽

Tumor Types

BackgroundPD-L1 is expressed on both tumor and immune cells; however, the mechanism by which PD-L1 modulates the adaptive immune response on tumor versus immune cells may differ. Additionally, the prevalence of PD-L1 expression and the partitioning between tumor and immune compartments varies by tumor type. While PD-L1 expression on tumor or immune cells can be scored separately, the PD-L1 combined positive score (CPS) captures both tumor and immune cell expression in one aggregate score. We performed a retrospective, exploratory analysis of the effectiveness of CPS as an enrichment biomarker across several studies of pembrolizumab monotherapy in patients with multiple tumor types.MethodsPD-L1 expression was assessed using PD-L1 IHC 22C3 pharmDx. Expression was measured using CPS (defined as the number of PD-L1–staining cells [tumor cells, lymphocytes, macrophages] divided by the total number of tumor cells, multiplied by 100) in tumor samples from single-arm (KEYNOTE-052 [UC], KEYNOTE-059 cohort 1 [G/GEJ], KEYNOTE-086 [TNBC], KEYNOTE-158 [cervical; SCLC], KEYNOTE-180 [EC], KEYNOTE-224 [HCC], KEYNOTE-427 [RCC]) and randomized (KEYNOTE-040 [HNSCC], KEYNOTE-045 [UC], KEYNOTE-061 [G/GEJ], KEYNOTE-119 [TNBC], KEYNOTE-240 [HCC]) pembrolizumab studies. Data were pooled across tumor types for pembrolizumab and for standard-of-care (in controlled studies), and then estimates of response rate, prevalence, and receiver operating characteristics (ROC) analysis were performed over various CPS cutpoints. CPS distribution by response, tumor type, and line of therapy were also assessed.ResultsThere were 3769 treated patients with available PD-L1 CPS (pembrolizumab, n=2678; standard-of-care, n=1091). The area under the ROC curve for ORR was 0.63 (95% CI, 0.61–0.66) for pembrolizumab and 0.48 (95% CI, 0.43–0.53) for standard-of-care when a positive association was evaluated between CPS and ORR (figure 1); individual cutpoints of 1, 10, 20, and 50 were examined (table 1). Figure 2 shows a boxplot of CPS distribution for response in pembrolizumab-treated patients.Abstract 282 Table 1Response Rates and Sensitivity at Individual CPS Cutpoints for Pembrolizumab-Treated PatientsAbstract 282 Figure 1ROC analysis of PD-L1 CPS for pembrolizumab versus standard-of-care therapyAbstract 282 Figure 2Boxplot of PD-L1 CPS distribution for responders versus nonresponders in pembrolizumab-treated patients by tumor type and line of therapy in order of descending median CPSConclusionsThis retrospective, exploratory pan-tumor analysis demonstrates that CPS is an effective scoring method for measuring PD-L1 expression and can be used as a predictive biomarker to identify patients likely to respond to pembrolizumab monotherapy. CPS demonstrated enrichment of response to pembrolizumab monotherapy across most, but not all, tumor types, including some tumor types for which efficacy favors pembrolizumab regardless of PD-L1 expression, and for which a companion diagnostic is therefore not needed. In the randomized studies, CPS did not show a consistent association with ORR for standard-of-care therapy.

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77 Prevalence of secondary immunotherapeutic targets in the absence of established immune biomarkers in solid tumors

Journal for ImmunoTherapy of Cancer ◽

10.1136/jitc-2021-sitc2021.077 ◽

2021 ◽

Vol 9 (Suppl 3) ◽

pp. A86-A86

Author(s):

Paul DePietro ◽

Mary Nesline ◽

Yong Hee Lee ◽

RJ Seager ◽

Erik Van Roey ◽

...

Keyword(s):

Gene Expression ◽

Lung Cancer ◽

Reference Population ◽

List Type ◽

Tumor Type ◽

Genomic Profiling ◽

Rna Seq ◽

Immune Biomarkers ◽

Cancer Types ◽

Immune Related Genes

BackgroundImmune checkpoint inhibitor-based therapies have achieved impressive success in the treatment of several cancer types. Predictive immune biomarkers, including PD-L1, MSI and TMB are well established as surrogate markers for immune evasion and tumor-specific neoantigens across many tumors. Positive detection across cancer types varies, but overall ~50% of patients test negative for these primary immune markers.1 In this study, we investigated the prevalence of secondary immune biomarkers outside of PD-L1, TMB and MSI.MethodsComprehensive genomic and immune profiling, including PD-L1 IHC, TMB, MSI and gene expression of 395 immune related genes was performed on 6078 FFPE tumors representing 34 cancer types, predominantly composed of lung cancer (36.7%), colorectal cancer (11.9%) and breast cancer (8.5%). Expression levels by RNA-seq of 36 genes targeted by immunotherapies in solid tumor clinical trials, identified as secondary immune biomarkers, were ranked against a reference population. Genes with a rank value ≥75th percentile were considered high and values were associated with PD-L1 (positive ≥1%), MSI (MSI-H or MSS) and TMB (high ≥10 Mut/Mb) status. Additionally, secondary immune biomarker status was segmented by tumor type and cancer immune cycle roles.ResultsIn total, 41.0% of cases were PD-L1+, 6.4% TMB+, and 0.1% MSI-H. 12.6% of cases were positive for >2 of these markers while 39.9% were triple negative (PD-L1-/TMB-/MSS). Of the PD-L1-/TMB-/MSS cases, 89.1% were high for at least one secondary immune biomarker, with 69.3% having ≥3 markers. PD-L1-/TMB-/MSS tumor types with ≥50% prevalence of high secondary immune biomarkers included brain, prostate, kidney, sarcoma, gallbladder, breast, colorectal, and liver cancer. High expression of cancer testis antigen secondary immune biomarkers (e.g., NY-ESO-1, LAGE-1A, MAGE-A4) was most commonly observed in bladder, ovarian, sarcoma, liver, and prostate cancer (≥15%). Tumors demonstrating T-cell priming (e.g., CD40, OX40, CD137), trafficking (e.g., TGFB1, TLR9, TNF) and/or recognition (e.g., CTLA4, LAG3, TIGIT) secondary immune biomarkers were most represented by kidney, gallbladder, and sarcoma (≥40%), with melanoma, esophageal, head & neck, cervical, stomach, and lung cancer least represented (≥15%).ConclusionsOur studies show comprehensive tumor profiling that includes gene expression can detect secondary immune biomarkers targeted by investigational therapies in ~90% of PD-L1-/TMB-/MSS cases. While genomic profiling could also provide therapeutic choices for a percentage of these patients, detection of secondary immune biomarkers by RNA-seq provides additional options for patients without a clear therapeutic path as determined by PD-L1 testing and genomic profiling alone.ReferenceHuang R S P, Haberberger J, Severson E, et al. A pan-cancer analysis of PD-L1 immunohistochemistry and gene amplification, tumor mutation burden and microsatellite instability in 48,782 cases. Mod Pathol 2021;34: 252–263.

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Expression of Apoptotic Protease Activating Factor-1 in adenoid cystic carcinoma of the salivary glands and its clinicopathological relevance

Vojnosanitetski pregled ◽

10.2298/vsp201026130d ◽

2020 ◽

pp. 130-130

Author(s):

Branko Dozic ◽

Boban Anicic ◽

Vladimir Sinobad ◽

Nikola Mikovic ◽

Srdjan Milanovic ◽

...

Keyword(s):

Overall Survival ◽

Salivary Glands ◽

Tumor Cells ◽

Statistical Significance ◽

Clinicopathological Parameters ◽

Tumor Type ◽

Immunohistochemical Expression ◽

Apoptosis Pathway ◽

Intrinsic Apoptosis Pathway ◽

Histological Tumor Type

Background/Aim. Apoptotic Protease Activating Factor-1 (Apaf-1) is a key molecule in the intrinsic or mitochondrial pathway of apoptosis. Some pathological conditions such as cancer, stroke, and neurodegenerative diseases, are the result of disregulation in the intrinsic apoptosis pathway. The aim of this study was to analyse the immunohistochemical expression of Apaf-1 in ACC tumor cells of the salivary glands and its correlation with clinicopathological parameters (gender, age, localization, histological type and overall survival). Methods. Formalin-fixed, paraffin-embedded tissues from 50 human ACC of the salivary glands, male and female, average age 58 years, were used for our present study. We used the technique of tissue microarray (TMA blocks). Sections from the TMA mould, 5?m thick, were stained with the streptavidin-biotin immunohistochemical technique using primary antibodies specific for Apaf-1 (Leica Biosystems, Newcastle, UK). Stained tissue sections were analyzed by the light microscope (Olympus type BH-2). Based on the data collected, the database was created in SPSS software v. 22.0 (SPSS Inc., Chicago, ILL, USA), which was used for a further statistical analysis. The statistical data analysis included methods of descriptive and analytical (inferential) statistics. Results. The results of the immunohistochemical analysis of Apaf-1 expression in the samples of patients with ACC of the salivary glands were compared with the clinicopathological parameters of these patients. The immunohistochemical expression of Apaf-1 showed no statistical significance with regard to the patients gender (p=0.552), age (p=0.106), histological tumor type (p=0.654) and localization of ACC in the salivary glands (p=0.486). There was no statistically significant correlation observed between overall survival of ACC patients and Apaf-1 expression in tumor cells (p=0.340,Long-Rank test). Conclusion. With regard to ACC, Apaf-1 expression is not in correlation with clinicopathological parameters (gender, age, localization, histological tumor type, outcome of the disease, and overall survival). Therefore, we believe Apaf-1 cannot be regarded as an independent prognostic factor.

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Integrated genomic and transcriptomic analysis revealed mutation patterns of de-differentiated liposarcoma and leiomyosarcoma

BMC Cancer ◽

10.1186/s12885-020-07456-2 ◽

2020 ◽

Vol 20 (1) ◽

Author(s):

Wenshuai Liu ◽

Hanxing Tong ◽

Chenlu Zhang ◽

Rongyuan Zhuang ◽

He Guo ◽

...

Keyword(s):

Gene Expression ◽

Sanger Sequencing ◽

Immune Cell ◽

Gene Copy Number ◽

Gene Copy ◽

Rna Seq ◽

Loss Of Function ◽

Rt Pcr ◽

The Difference ◽

Histologic Types

Abstract Background Treating patients with advanced sarcomas is challenging due to great histologic diversity among its subtypes. Leiomyosarcoma (LMS) and de-differentiated liposarcoma (DDLPS) are two common and aggressive subtypes of soft tissue sarcoma (STS). They differ significantly in histology and clinical behaviors. However, the molecular driving force behind the difference is unclear. Methods We collected 20 LMS and 12 DDLPS samples and performed whole exome sequencing (WES) to obtain their somatic mutation profiles. We also performed RNA-Seq to analyze the transcriptomes of 8 each of the LMS and DDLPS samples and obtained information about differential gene expression, pathway enrichment, immune cell infiltration in tumor microenvironment, and chromosomal rearrangement including gene fusions. Selected gene fusion events from the RNA-seq prediction were checked by RT-PCR in tandem with Sanger sequencing. Results We detected loss of function mutation and deletion of tumor suppressors mostly in LMS, and oncogene amplification mostly in DDLPS. A focal amplification affecting chromosome 12q13–15 region which encodes MDM2, CDK4 and HMGA2 is notable in DDLPS. Mutations in TP53, ATRX, PTEN, and RB1 are identified in LMS but not DDLPS, while mutation of HERC2 is only identified in DDLPS but not LMS. RNA-seq revealed overexpression of MDM2, CDK4 and HMGA2 in DDLPS and down-regulation of TP53 and RB1 in LMS. It also detected more fusion events in DDLPS than LMS (4.5 vs. 1, p = 0.0195), and the ones involving chromosome 12 in DDLPS stand out. RT-PCR and Sanger sequencing verified the majority of the fusion events in DDLPS but only one event in LMS selected to be tested. The tumor microenvironmental signatures are highly correlated with histologic types. DDLPS has more endothelial cells and fibroblasts content than LMS. Conclusions Our analysis revealed different recurrent genetic variations in LMS and DDLPS including simultaneous upregulation of gene expression and gene copy number amplification of MDM2 and CDK4. Up-regulation of tumor related genes is favored in DDLPS, while loss of suppressor function is favored in LMS. DDLPS harbors more frequent fusion events which can generate neoepitopes and potentially targeted by personalized immune treatment.

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Putative biomarkers for predicting tumor sample purity based on gene expression data

BMC Genomics ◽

10.1186/s12864-019-6412-8 ◽

2019 ◽

Vol 20 (1) ◽

Author(s):

Yuanyuan Li ◽

David M. Umbach ◽

Adrienna Bingham ◽

Qi-Jing Li ◽

Yuan Zhuang ◽

...

Keyword(s):

Gene Expression ◽

Gene Expression Data ◽

Supervised Machine Learning ◽

Tumor Type ◽

Expression Data ◽

Expression Levels ◽

Gene Set ◽

Tumor Purity ◽

Tumor Types ◽

Cancerous Cells

Abstract Background Tumor purity is the percent of cancer cells present in a sample of tumor tissue. The non-cancerous cells (immune cells, fibroblasts, etc.) have an important role in tumor biology. The ability to determine tumor purity is important to understand the roles of cancerous and non-cancerous cells in a tumor. Methods We applied a supervised machine learning method, XGBoost, to data from 33 TCGA tumor types to predict tumor purity using RNA-seq gene expression data. Results Across the 33 tumor types, the median correlation between observed and predicted tumor-purity ranged from 0.75 to 0.87 with small root mean square errors, suggesting that tumor purity can be accurately predicted υσινγ expression data. We further confirmed that expression levels of a ten-gene set (CSF2RB, RHOH, C1S, CCDC69, CCL22, CYTIP, POU2AF1, FGR, CCL21, and IL7R) were predictive of tumor purity regardless of tumor type. We tested whether our set of ten genes could accurately predict tumor purity of a TCGA-independent data set. We showed that expression levels from our set of ten genes were highly correlated (ρ = 0.88) with the actual observed tumor purity. Conclusions Our analyses suggested that the ten-gene set may serve as a biomarker for tumor purity prediction using gene expression data.

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Consensus gene expression analysis to identify key hallmarks of cancer in malignant melanoma.

Journal of Clinical Oncology ◽

10.1200/jco.2019.37.15_suppl.e21045 ◽

2019 ◽

Vol 37 (15_suppl) ◽

pp. e21045-e21045

Author(s):

Emma O'Connor ◽

Eileen E. Parkes ◽

Leeona Galligan ◽

James Bradford ◽

Shauna Lambe ◽

...

Keyword(s):

Gene Expression ◽

Overall Survival ◽

Gene Expression Analysis ◽

Epithelial Mesenchymal Transition ◽

Software Tool ◽

Rna Seq ◽

Combination Therapies ◽

Hallmarks Of Cancer ◽

Mesenchymal Transition ◽

Melanoma Patients

e21045 Background: Traditionally gene expression signatures (GES) are used individually to classify patients into subgroups. Signatures targeting the same biology are often developed independently and may not classify identically. We developed the claraT software tool that uses consensus between multiple published GES categorised by the Hallmarks of Cancer (Hanahan & Weinberg, 2011) to classify cancers. As metastatic melanoma represents poor prognostic disease (5-yr survival 15-20%), we applied claraT to the TCGA melanoma dataset to identify targetable biologies, validated in a cohort of melanoma patients treated with Ipilimumab. Methods: TCGA RNA-seq data ( n= 472) was analysed using the claraT platform including GES for immune ( n= 14), angiogenesis ( n= 9) and epithelial-mesenchymal transition (EMT) ( n= 12) Hallmarks. Samples were clustered for the combined and individual Hallmarks. Median progression-free (PFS) and overall-survival (OS) differences were analysed across identified subgroups. Analysis was validated in an Ipilimumab treated melanoma dataset ( n= 42) (Van Allen, 2015). Results: Clustering the combined Hallmarks identified 4 subgroups in the TCGA cohort: 1) Immune active, 2) Immune-EMT active, 3) EMT-Angiogenesis active, 4) All inactive. Groups 1&2 had significantly improved OS compared to Groups 3&4 (HR = 0.50, p< 0.0001). Clustering using single Hallmarks revealed that immune-positive tumours had significantly improved OS (HR = 0.53, p< 0.0001) compared to immune-negative tumours. Angiogenesis-negative tumours displayed improved PFS (HR = 0.73, p= 0.03) and OS (HR = 0.53, p <0.0001) compared to angiogenesis-negative tumours. Interestingly the EMT Hallmark was not found to be individually prognostic. When validated in the Ipilimumab treated dataset, patients classified as immune-positive had improved OS (HR = 0.357, p= 0.010) when compared to immune-negative. Similar trends were also observed for angiogenesis and EMT Hallmarks. Conclusions: This study demonstrates how simultaneous analysis of multiple GES ( n= 35 in this study) can identify robust biologies through consensus expression. This platform may have value in the identification of reliable biomarkers for clinical trials and could inform how combination therapies targeting key biologies may be used in cancer treatment.

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Signature Based on Immune-Related LncRNA Can Predict Overall Survival of Osteosarcoma Patients

10.21203/rs.3.rs-45370/v1 ◽

2020 ◽

Author(s):

Longqing Li ◽

Lianghao Zhang ◽

Manhas Adbul Khader ◽

Yan Zhang ◽

Xinchang Lu ◽

...

Keyword(s):

Gene Expression ◽

Overall Survival ◽

Immune Checkpoint ◽

High Throughput Sequencing ◽

Immune Cell ◽

Cox Regression ◽

Risk Group ◽

Low Risk ◽

Checkpoint Gene ◽

Cox Regression Analysis

Abstract Background: Osteosarcoma is a malignant bone tumor common in children and adolescents. Metastatic status remains the most important guideline for classifying patients and making clinical decisions. Despite many efforts, newly diagnosed patients receive the same therapy that patients have received over the last 4 decades. With the development of high-throughput sequencing technology and the rise of immunotherapy, it is necessary to deeply explore the immune molecular mechanism of osteosarcoma.Methods: We obtained RNA-seq data and clinical information of osteosarcoma patients from TCGA database and TARGET database. With the help of co-expression analysis we identified immune-related lncRNA and then by means of univariate Cox regression analysis prognostic-related lncRNA was screened out. And also by using least absolute shrinkage and selection operator regression method a model based on immune-related lncRNA was constructed. The differences in overall survival, immune infiltration, immune checkpoint gene expression, and tumor microenvironmental immunity type between the two groups were evaluated.Results: We constructed a signature consisting of 13 lncRNA. Our results show that signatures can reliably predict the overall survival of patients with osteosarcoma and can bring net clinical benefits. Further more, the signatures can be used for further risk stratification of the metastasis patients. Patients in the low-risk group had higher immune cell infiltration and immune checkpoint gene expression. The results from gene set variation analysis show that patients in low-risk group are closely related to immune-related pathways when compared with patients in high-risk group. Finally, patients in the low-risk group are more likely to be classified as TMIT I and hence more likely to benefit from immunotherapy.Conclusion: Our signature may be a reliable marker for predicting the overall survival of patients with osteosarcoma.

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Deep Learning Based Tumor Type Classification Using Gene Expression Data

10.1101/364323 ◽

2018 ◽

Cited By ~ 4

Author(s):

Boyu Lyu ◽

Anamul Haque

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Tumor Type ◽

Differential Analysis ◽

Rna Seq ◽

Heat Map ◽

Standard Practices ◽

Tumor Types ◽

Pan Cancer

ABSTRACTDifferential analysis occupies the most significant portion of the standard practices of RNA-Seq analysis. However, the conventional method is matching the tumor samples to the normal samples, which are both from the same tumor type. The output using such method would fail in differentiating tumor types because it lacks the knowledge from other tumor types. Pan-Cancer Atlas provides us with abundant information on 33 prevalent tumor types which could be used as prior knowledge to generate tumor-specific biomarkers. In this paper, we embedded the high dimensional RNA-Seq data into 2-D images and used a convolutional neural network to make classification of the 33 tumor types. The final accuracy we got was 95.59%, higher than another paper applying GA/KNN method on the same dataset. Based on the idea of Guided Grad Cam, as to each class, we generated significance heat-map for all the genes. By doing functional analysis on the genes with high intensities in the heat-maps, we validated that these top genes are related to tumor-specific pathways, and some of them have already been used as biomarkers, which proved the effectiveness of our method. As far as we know, we are the first to apply convolutional neural network on Pan-Cancer Atlas for classification, and we are also the first to match the significance of classification with the importance of genes. Our experiment results show that our method has a good performance and could also apply in other genomics data.

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Detection of Kita-Kyushu Lung Cancer Antigen-1, a Cancer/testis Antigen, in the stomach closer to the cancerous condition.

10.21203/rs.3.rs-87460/v1 ◽

2020 ◽

Author(s):

Takashi Fukuyama ◽

Toshikazu Otsuka ◽

Nobue Futawatari ◽

Kumiko Tahara ◽

Masaaki Watanabe ◽

...

Keyword(s):

Gene Expression ◽

Gastric Cancer ◽

Lung Cancer ◽

Helicobacter Pylori ◽

Cancer Testis Antigen ◽

Cancer Antigen ◽

Exact Test ◽

Cancer Prediction ◽

Precancerous Condition ◽

Cancer Testis

Abstract Background: Kita-Kyushu lung cancer antigen-1 (KK-LC-1) is a cancer/testis antigen (CTA) and is an attractive target for immunotherapy. An earlier study from our group demonstrated frequent KK-LC-1 expression in gastric cancers (GC) and non-tumor sites of the stomach carrying a tumor. Additionally, there was a correlation to Helicobacter pylori (Hp) infection. Currently it remains unclear whether KK-LC-1 is expressed in stomachs without gastric cancer.Methods: In the present study, we investigated differences in KK-LC-1 gene expression at non-tumor sites of stomachs with or without a tumor from 118 GC patients and 115 non-GC patients. Fisher’s exact test was used for the statistical analyses.Results: Our results show that KK-LC-1 gene expression was detected in 77% of non-tumor sites in stomachs with a tumor. Such findings were significantly higher than in stomachs without a tumor (7%, P <0.0001). All patients with KK-LC-1 expression at non-tumor sites of their stomachs without tumors carried Hp.Conclusions: KK-LC-1 appears to be detected in the stomach’s precancerous condition, but not in an atrophic stomach with Hp. KK-LC-1 may be a useful marker for gastric cancer prediction.

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