Distribution of ETV6-NTRK3 translocations across neoplasms identified from the Mitelman Database.

2017 ◽  
Vol 35 (15_suppl) ◽  
pp. e23141-e23141
Author(s):  
Juan Carlos Malpartida ◽  
Eric Vick ◽  
Noah Hunter Richardson ◽  
Kruti Patel ◽  
Matthew K Stein ◽  
...  
Keyword(s):  
Ductal Carcinoma ◽  
Tumor Type ◽  
Invasive Adenocarcinoma ◽  
Data Set ◽  
Oncogenic Potential ◽  
Multiple Tumor ◽  
Cancer Types ◽  
Mitelman Database ◽  
Tumor Types ◽  
Tumor Profiling

e23141 Background: Discovered as a novel aberration in congenital fibrosarcoma (CF), the ETV6-NTRK3 translocation (EN) confers oncogenic potential and is inhibited by crizotinib. The present study aims to survey the scope of neoplasms that harbor EN across tumor types. Methods: Utilizing the National Cancer Institute’s Mitelman Database (MD) of Chromosome Aberrations and Gene Fusions patients (pts) were identified with EN and categorized based on tumor type, subtype and incidence. Cancer pts who received tumor profiling with Caris were also surveyed for EN. Results: 47 pts with EN across 12 cancer types were extracted from the MD and had median age of 0.17 years (7 unreported); 38% male; 51% acquired malignancies, 49% congenital; 62% cases were pediatric, 23% adult and 15% unknown. 0/204 pts with Caris tumor profiling were found to have an EN. Cancers with the highest number of EN were: 15 (31.9% EN data set) congenital mesoblastic nephromas (CMN), 10 (21.3%) CF, 7 (14.9%) breast carcinoma (BC; 6 secretory ductal carcinoma (SD) and 1 invasive adenocarcinoma (IA)) and 3 (6.4%) colorectal carcinoma (CRC). EN were found in 8 other malignancies (Table 1). Cancer types with the highest incidence of EN+ cases in the MD were gastrointestinal stromal tumor (GIST; 100%), CMN (75%) and CF (23.3%). Conclusions: These results further our understanding of the distribution of ETV6-NTRK3 translocations in multiple tumor types across the age spectrum and suggest that pts with CMN, CF, BC and CRC requiring high order therapy should be considered for NTRK3-based treatment. [Table: see text]

scholarly journals Predicting master transcription factors from pan-cancer expression data

2019 ◽  
Author(s):  
Jessica Reddy ◽  
Marcos A. S. Fonseca ◽  
Rosario I Corona ◽  
Robbin Nameki ◽  
Felipe Segato Dezem ◽  
...  
Keyword(s):  
Transcription Factor ◽  
Transcription Factors ◽  
Regulatory Elements ◽  
Ca 125 ◽  
Tumor Type ◽  
Expression Data ◽  
Primary Tumors ◽  
Cancer Types ◽  
Tumor Types ◽  
Pan Cancer

The function of critical developmental regulators can be subverted by cancer cells to control expression of oncogenic transcriptional programs. These "master transcription factors" (MTFs) are often essential for cancer cell survival and represent vulnerabilities that can be exploited therapeutically. The current approaches to identify candidate MTFs examine super-enhancer associated transcription factor-encoding genes with high connectivity in network models. This relies on chromatin immunoprecipitation-sequencing (ChIP-seq) data, which is technically challenging to obtain from primary tumors, and is currently unavailable for many cancer types and clinically relevant subtypes. In contrast, gene expression data are more widely available, especially for rare tumors and subtypes where MTFs have yet to be discovered. We have developed a predictive algorithm called CaCTS (Cancer Core Transcription factor Specificity) to identify candidate MTFs using pan-cancer RNA-sequencing data from The Cancer Genome Atlas. The algorithm identified 273 candidate MTFs across 34 tumor types and recovered known tumor MTFs. We also made novel predictions, including for cancer types and subtypes for which MTFs have not yet been characterized. Clustering based on MTF predictions reproduced anatomic groupings of tumors that share 1-2 lineage-specific candidates, but also dictated functional groupings, such as a squamous group that comprised five tumor subtypes sharing 3 common MTFs. PAX8, SOX17, and MECOM were candidate factors in high-grade serous ovarian cancer (HGSOC), an aggressive tumor type where the core regulatory circuit is currently uncharacterized. PAX8, SOX17, and MECOM are required for cell viability and lie proximal to super-enhancers in HGSOC cells. ChIP-seq revealed that these factors co-occupy HGSOC regulatory elements globally and co-bind at critical gene loci including MUC16 (CA-125). Addiction to these factors was confirmed in studies using THZ1 to inhibit transcription in HGSOC cells, suggesting early down-regulation of these genes may be responsible for cytotoxic effects of THZ1 on HGSOC models. Identification of MTFs across 34 tumor types and 140 subtypes, especially for those with limited understanding of transcriptional drivers paves the way to therapeutic targeting of MTFs in a broad spectrum of cancers.

Final results of a phase I study of oral belinostat (PXD101) in patients with solid tumors

2009 ◽  
Vol 27 (15_suppl) ◽  
pp. 3531-3531 ◽  
Author(s):  
W. K. Kelly ◽  
J. DeBono ◽  
G. Blumenschein ◽  
U. Lassen ◽  
J. Zain ◽  
...  
Keyword(s):  
Clinical Activity ◽  
Atypical Chest Pain ◽  
Dose Cohort ◽  
Multiple Tumor ◽  
Multiple Schedules ◽  
Deacetylase Inhibitor ◽  
Effect Of Food ◽  
Cancer Types ◽  
Elevated Creatinine ◽  
Tumor Types

3531 Background: Belinostat (Bel) is a histone deacetylase inhibitor with broad preclinical activity. IV Bel is well-tolerated with clinical activity at 1 g/m2 daily x5, q3w. Methods: Patients (pts) were treated with multiple schedules (see table) to assess safety, pharmacokinetics (PK) and efficacy. PK was done on day (d) 1 (fasting) and d7 (non-fasting) along with serial ECGs. Results: 92 pts, median age 60 (range 32–89) have been included. Major cancer types included colorectal (22%), prostate (17%), bladder (11%). Most frequent related adverse events (AEs), any grade (gr), were fatigue (53%), nausea (49%), anorexia (36%), vomiting (27%), diarrhea (25%). Only related gr 3/4 AE noted by more than 1 pt was fatigue. Hematological tox included gr 2: anemia (6 pts), leucopenia (2 pts), and thrombocytopenia (1 pt). Two events of gr 2 QTc prolongation were reported. Recommended dose (RD) for continuous dosing was determined as 250 mg, QD or BID, based on dose limiting toxicity (DLT; gr 3 if not indicated) seen in 2 pts in cohort 2A: dehydration and fatigue. Based on overall tolerability and DLTs (cohort 2C fatigue; 3C gr 2 nausea/vomiting/diarrhea; 4C atypical chest pain, elevated creatinine; 2D atrial fibrillation, hypokalemia, fatigue) the RD for d1–14 dosing was determined as 750 mg QD, with option for intra-pt dose escalation if limited tox. For d1–5 dosing, evaluation of the highest dose-cohort is not finalized; 1 pt had gr 3 psychosis, but also experienced same event 16d after treatment stopped. Exposure of Bel in plasma correlates with dose; PK on d1/d7 indicate a possible effect of food. To date, 33 pts (41%) have SD; 5 pts ≥6 months (d on treatment: 710 adenoidcystic, +488 bladder, 485 renal, 196 rectal, 182 prostate), and 12 pts 3–6 months. Conclusions: Oral Bel can be delivered safely with multiple schedules. The safety profile and long stabilizations in multiple tumor types makes Bel an interesting option for further evaluation as a monotherapy and in combination with chemotherapy. [Table: see text] [Table: see text]

scholarly journals A molecular portrait of microsatellite instability across multiple cancers

2016 ◽  
Author(s):  
Isidro Cortes-Ciriano ◽  
Sejoon Lee ◽  
Woong-Yang Park ◽  
Tae-Min Kim ◽  
Peter J. Park
Keyword(s):  
Microsatellite Instability ◽  
High Sensitivity ◽  
Tumor Type ◽  
Sequencing Data ◽  
Molecular Fingerprints ◽  
Dna Mismatch ◽  
Oncogenic Pathways ◽  
Cancer Types ◽  
Tumor Types ◽  
Pan Cancer

ABSTRACTMicrosatellite instability (MSI) refers to the hypermutability of the cancer genome due to impaired DNA mismatch repair. Although MSI has been studied for decades, the large amount of sequencing data now available allows us to examine the molecular fingerprints of MSI in greater detail. Here, we analyze ~8000 exome and ~1000 whole-genome pairs across 23 cancer types. Our pan-cancer analysis reveals that the prevalence of MSI events is highly variable within and across tumor types including some in which MSI is not typically examined. We also identify genes in DNA repair and oncogenic pathways recurrently subject to MSI and uncover non-coding loci that frequently display MSI events. Finally, we propose an exomebased predictive model for the MSI phenotype that achieves high sensitivity and specificity. These results advance our understanding of the genomic drivers and consequences of MSI, and a comprehensive catalog of tumor-type specific MSI loci we have generated enables efficient panel-based MSI testing to identify patients who are likely to benefit from immunotherapy.

scholarly journals 330 Integration of molecular cancer classification and next-generation sequencing to identify metastatic patients eligible for immune checkpoint inhibitors

2021 ◽  
Vol 9 (Suppl 3) ◽  
pp. A356-A356
Author(s):  
Daruka Mahadevan ◽  
Li Ma ◽  
Kai Treuner ◽  
Jenna Wong ◽  
Catherine Schnabel
Keyword(s):  
Immune Checkpoint ◽  
Immune Checkpoint Inhibitors ◽  
Checkpoint Inhibitors ◽  
Predictive Biomarkers ◽  
Tumor Type ◽  
Tumor Mutation Burden ◽  
Mutation Burden ◽  
Biomarker Testing ◽  
Cancer Types ◽  
Tumor Types

BackgroundImmune checkpoint inhibitors (ICIs) have improved patient outcomes and are a new standard of care for treating a variety of cancers. Eligibility for ICIs is established through determination of tumor type and use of predictive biomarkers. PD-L1, microsatellite instability (MSI), and tumor mutation burden (TMB) are FDA-approved predictive biomarkers for ICI therapies. However, the validity of these biomarkers remains controversial, as studies have shown a failure to predict ICI response in many cancer types.1 2 The 92-gene assay (CancerTYPE ID) is a validated gene expression classifier of 50 tumor types and subtypes for metastatic patients with ambiguous diagnoses. CancerTYPE ID provides critical cancer type identification to guide ICI treatment eligibility and selection. In the current study, analyses integrating tumor type with multimodal biomarker testing for PD-L1 and TMB were evaluated to identify patients for ICI eligibility.MethodsMOSAIC (Molecular Synergy to Advance Individualized Cancer Care) is an IRB-approved, de-identified database of CancerTYPE ID results from 2572 patients with tumor-specific multimodal biomarker testing by next-generation sequencing for TMB and immunohistochemistry for PD-L1. The Cochran-Mantel-Haenszel test was used to evaluate the relationship between PD-L1 and TMB across tumor types.ResultsTumor type was determined in 2377 of 2572 cases (92.4%), comprising 27 different tumor types including 14 tumor types with FDA-approved ICI therapies. Among the top 20 tumor types, PD-L1 was present in a larger proportion of tumors (weighted mean=78.9%, range=58.3%–100%) versus TMB (20.9%, 0%–72.7%) (figure 1). Varying expression levels of PD-L1 and TMB were noted across tumor types (Figure 1), and no relationship between PD-L1 and TMB (P=0.15) was observed. Prevalence of high TMB in melanoma (42.9%) and lung adenocarcinoma (38.9%), which are more likely to respond to ICI treatment, are consistent with published data; however, prevalence of high TMB in mesothelioma (20.0%), sarcoma (23.6%) and prostatic adenocarcinoma (33.3%), which are not likely to respond to ICI treatment, are higher than previously reported.3Abstract 330 Figure 1Prevalence of PD-L1 expression and high TMB in the 27 identified tumor typesConclusionsTumor type classification and cellular context are critical for ICI eligibility. CancerTYPE ID successfully differentiated 14 ICI-eligible tumor types from 13 non-ICI-eligible tumor types. Further, since there is no relationship between PD-L1 and TMB for different tumor types, accurate tumor type identification is necessary to select the most appropriate biomarker. This highlights the clinical utility of CancerTYPE ID combined with multimodal biomarker testing to guide ICI treatment and predict response based on tumor type identification, which may improve outcomes in patients with metastatic cancer.ReferencesMcGrail DJ, Pilié PG, Rashid NU, et al. High tumor mutation burden fails to predict immune checkpoint blockade response across all cancer types. Ann Oncol 2021;32(5):661–672.Gjoerup O, Brown CA, Ross JS, et al. Identification and utilization of biomarkers to predict response to immune checkpoint inhibitors. AAPS J 2020;22(6):132.Yarchoan M, Albacker LA, Hopkins AC, et al. PD-L1 expression and tumor mutational burden are independent biomarkers in most cancers. JCI Insight 2019;4(6):e126908.

scholarly journals A Tetra-Panel of Serum Circulating miRNAs for the Diagnosis of the Four Most Prevalent Tumor Types

2020 ◽  
Vol 21 (8) ◽  
pp. 2783
Author(s):  
Belén Pastor-Navarro ◽  
María García-Flores ◽  
Antonio Fernández-Serra ◽  
Salvador Blanch-Tormo ◽  
Fernando Martínez de Juan ◽  
...  
Keyword(s):  
Cancer Detection ◽  
Area Under The Curve ◽  
Roc Curves ◽  
Tumor Type ◽  
Circulating Mirnas ◽  
Detection Systems ◽  
Mirna Genes ◽  
Healthy Donors ◽  
Cancer Types ◽  
Tumor Types

The purpose of this study is to clinically validate a series of circulating miRNAs that distinguish between the 4 most prevalent tumor types (lung cancer (LC); breast cancer (BC); colorectal cancer (CRC); and prostate cancer (PCa)) and healthy donors (HDs). A total of 18 miRNAs and 3 housekeeping miRNA genes were evaluated by qRT-PCR on RNA extracted from serum of cancer patients, 44 LC, 45 BC, 27 CRC, and 40 PCa, and on 45 HDs. The cancer detection performance of the miRNA expression levels was evaluated by studying the area under the curve (AUC) of receiver operating characteristic (ROC) curves at univariate and multivariate levels. miR-21 was significantly overexpressed in all cancer types compared with HDs, with accuracy of 67.5% (p = 0.001) for all 4 tumor types and of 80.8% (p < 0.0001) when PCa cases were removed from the analysis. For each tumor type, a panel of miRNAs was defined that provided cancer-detection accuracies of 91%, 94%, 89%, and 77%, respectively. In conclusion, we have described a series of circulating miRNAs that define different tumor types with a very high diagnostic performance. These panels of miRNAs would constitute the basis of different approaches of cancer-detection systems for which clinical utility should be validated in prospective cohorts.

scholarly journals Cancer classification based on chromatin accessibility profiles with deep adversarial learning model

2020 ◽  
Vol 16 (11) ◽  
pp. e1008405
Author(s):  
Hai Yang ◽  
Qiang Wei ◽  
Dongdong Li ◽  
Zhe Wang
Keyword(s):  
Cancer Genomics ◽  
Molecular Classification ◽  
Chromatin Accessibility ◽  
High Dimensional ◽  
Tumor Type ◽  
Adversarial Learning ◽  
Coding Regions ◽  
Cancer Types ◽  
Tumor Types ◽  
Cancer Diagnosis And Therapy

Given the complexity and diversity of the cancer genomics profiles, it is challenging to identify distinct clusters from different cancer types. Numerous analyses have been conducted for this propose. Still, the methods they used always do not directly support the high-dimensional omics data across the whole genome (Such as ATAC-seq profiles). In this study, based on the deep adversarial learning, we present an end-to-end approach ClusterATAC to leverage high-dimensional features and explore the classification results. On the ATAC-seq dataset and RNA-seq dataset, ClusterATAC has achieved excellent performance. Since ATAC-seq data plays a crucial role in the study of the effects of non-coding regions on the molecular classification of cancers, we explore the clustering solution obtained by ClusterATAC on the pan-cancer ATAC dataset. In this solution, more than 70% of the clustering are single-tumor-type-dominant, and the vast majority of the remaining clusters are associated with similar tumor types. We explore the representative non-coding loci and their linked genes of each cluster and verify some results by the literature search. These results suggest that a large number of non-coding loci affect the development and progression of cancer through its linked genes, which can potentially advance cancer diagnosis and therapy.

Clinical activity and safety of the RET inhibitor pralsetinib in patients with RET fusion-positive solid tumors: Update from the ARROW trial.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. 3079-3079
Author(s):  
Vivek Subbiah ◽  
Philippe A. Cassier ◽  
Salvatore Siena ◽  
Guzman Alonso ◽  
Luis G. Paz-Ares ◽  
...  
Keyword(s):  
Thyroid Cancer ◽  
Solid Tumors ◽  
Phase 1 ◽  
Driver Mutations ◽  
Unknown Primary ◽  
Clinical Activity ◽  
Tumor Type ◽  
Multiple Tumor ◽  
Duration Of Response ◽  
Tumor Types

3079 Background: RET fusions are targetable oncogenic drivers in multiple solid tumor types. ARROW study (NCT03037385) data supported the US FDA approval of pralsetinib, a once-daily (QD) oral highly potent and selective RET inhibitor, for RET-altered metastatic non-small cell lung cancer (NSCLC) and advanced/metastatic thyroid cancer. Here we provide an update on the clinical activity of pralsetinib in patients (pts) with advanced RET fusion-positive solid tumors other than NSCLC and thyroid cancer (“other” RET fusion–positive solid tumors). Methods: The global ongoing ARROW study (84 sites in 13 countries) includes phase 1 dose-escalation (30–600 mg [QD or twice daily]) and phase 2 expansion cohorts (400 mg QD) defined by tumor type and RET alteration status. Primary objectives are overall response rate (ORR; blinded independent central review per RECIST v1.1) and safety. Results: Updated analyses were completed as of Nov 6, 2020 (data cut-off) for 21 pts with other RET fusion–positive solid tumors enrolled by May 22, 2020 (enrollment cut-off) (lung other than NSCLC, n = 4; pancreatic, n = 3; colon, n = 3; cholangiocarcinoma, n = 3; unknown primary [UP], n = 2; other, n = 6). Overall, 11 (52%) pts received ≥2 prior lines of therapy for metastatic disease. The most common RET fusion partners were CCDC6 and KIF5B (24% each), NCOA4 (19%), other (10%), and unknown (24%). Two pts with colon cancer were excluded from efficacy analyses due to other driver mutations ( KRAS, PIK3CB). In 19 evaluable pts, ORR was 53% (95% CI, 29–76) with 2 (11%) complete responses (CR) and 8 (42%) partial responses (PR). Responses occurred across multiple tumor types including 3/3 pts with pancreatic cancer (including a CR ongoing at 20.8 months on treatment), 2/2 pts with UP, 2/3 pts with cholangiocarcinoma, and in pts with mesenchymal, salivary duct, and lung carcinoid tumors. Median duration of response was 19.0 months (95% CI, 5.5–not estimable). Clinical benefit rate (proportion with CR, PR, or stable disease persisting ≥16 weeks) was 68% (95% CI, 43–87). Tumor shrinkage was observed in 89% of 18 evaluable pts with post-baseline tumor assessment. In all pts enrolled in ARROW who received pralsetinib 400 mg QD irrespective of tumor type (n = 471) the most common (≥25%) treatment-related adverse events (TRAEs) were increased aspartate aminotransferase (39%), anemia (35%), increased alanine aminotransferase (28%), constipation (26%), and hypertension (25%). Overall, 6% of pts discontinued treatment due to TRAEs. Conclusions: Pralsetinib showed robust, durable antitumor activity in patients with multiple RET fusion‒positive, heavily pre-treated, advanced solid tumors, and was well tolerated. These data highlight the need for broad RET testing to identify candidates who could benefit from treatment with pralsetinib. Enrollment of patients with other RET fusion–positive solid tumors in ARROW is ongoing. Clinical trial information: NCT03037385.

scholarly journals Inhibition of the CCL2 receptor, CCR2, enhances tumor response to immune checkpoint therapy

2020 ◽  
Vol 3 (1) ◽  
Author(s):  
Megan M. Tu ◽  
Hany A. Abdel-Hafiz ◽  
Robert T. Jones ◽  
Annie Jean ◽  
Katelyn J. Hoff ◽  
...  
Keyword(s):  
T Cell ◽  
Tumor Response ◽  
Multiple Cancer ◽  
Cell Recruitment ◽  
Multiple Tumor ◽  
Area Of Interest ◽  
Signaling Axis ◽  
Metastasis Models ◽  
Cancer Types ◽  
Tumor Types

AbstractImmunotherapies targeting the PD-1/PD-L1 axis are now a mainstay in the clinical management of multiple cancer types, however, many tumors still fail to respond. CCL2 is highly expressed in various cancer types and has been shown to be associated with poor prognosis. Inhibition or blockade of the CCL2/CCR2 signaling axis has thus been an area of interest for cancer therapy. Here we show across multiple murine tumor and metastasis models that CCR2 antagonism in combination with anti-PD-1 therapy leads to sensitization and enhanced tumor response over anti-PD-1 monotherapy. We show that enhanced treatment response correlates with enhanced CD8+ T cell recruitment and activation and a concomitant decrease in CD4+ regulatory T cell. These results provide strong preclinical rationale for further clinical exploration of combining CCR2 antagonism with PD-1/PD-L1-directed immunotherapies across multiple tumor types especially given the availability of small molecule CCR2 inhibitors and antibodies.

scholarly journals The human tumor microbiome is composed of tumor type–specific intracellular bacteria

Science ◽  
2020 ◽  
Vol 368 (6494) ◽  
pp. 973-980 ◽  
Author(s):  
Deborah Nejman ◽  
Ilana Livyatan ◽  
Garold Fuks ◽  
Nancy Gavert ◽  
Yaara Zwang ◽  
...  
Keyword(s):  
Smoking Status ◽  
Human Tumor ◽  
Comprehensive Analysis ◽  
Intracellular Bacteria ◽  
Tumor Type ◽  
Microbiome Composition ◽  
Normal Tissues ◽  
Cancer Types ◽  
Tumor Types

Bacteria were first detected in human tumors more than 100 years ago, but the characterization of the tumor microbiome has remained challenging because of its low biomass. We undertook a comprehensive analysis of the tumor microbiome, studying 1526 tumors and their adjacent normal tissues across seven cancer types, including breast, lung, ovary, pancreas, melanoma, bone, and brain tumors. We found that each tumor type has a distinct microbiome composition and that breast cancer has a particularly rich and diverse microbiome. The intratumor bacteria are mostly intracellular and are present in both cancer and immune cells. We also noted correlations between intratumor bacteria or their predicted functions with tumor types and subtypes, patients’ smoking status, and the response to immunotherapy.

scholarly journals Distinct signatures of codon and codon pair usage in 32 primary tumor types in the novel database CancerCoCoPUTs for cancer-specific codon usage

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Douglas Meyer ◽  
Jacob Kames ◽  
Haim Bar ◽  
Anton A. Komar ◽  
Aikaterini Alexaki ◽  
...  
Keyword(s):  
Gene Expression ◽  
Codon Usage ◽  
Solid Tumor ◽  
Synonymous Codon ◽  
Lobular Carcinoma ◽  
Tumor Type ◽  
Codon Preference ◽  
Codon Pair ◽  
Cancer Types ◽  
Tumor Types

Abstract Background Gene expression is highly variable across tissues of multi-cellular organisms, influencing the codon usage of the tissue-specific transcriptome. Cancer disrupts the gene expression pattern of healthy tissue resulting in altered codon usage preferences. The topic of codon usage changes as they relate to codon demand, and tRNA supply in cancer is of growing interest. Methods We analyzed transcriptome-weighted codon and codon pair usage based on The Cancer Genome Atlas (TCGA) RNA-seq data from 6427 solid tumor samples and 632 normal tissue samples. This dataset represents 32 cancer types affecting 11 distinct tissues. Our analysis focused on tissues that give rise to multiple solid tumor types and cancer types that are present in multiple tissues. Results We identified distinct patterns of synonymous codon usage changes for different cancer types affecting the same tissue. For example, a substantial increase in GGT-glycine was observed in invasive ductal carcinoma (IDC), invasive lobular carcinoma (ILC), and mixed invasive ductal and lobular carcinoma (IDLC) of the breast. Change in synonymous codon preference favoring GGT correlated with change in synonymous codon preference against GGC in IDC and IDLC, but not in ILC. Furthermore, we examined the codon usage changes between paired healthy/tumor tissue from the same patient. Using clinical data from TCGA, we conducted a survival analysis of patients based on the degree of change between healthy and tumor-specific codon usage, revealing an association between larger changes and increased mortality. We have also created a database that contains cancer-specific codon and codon pair usage data for cancer types derived from TCGA, which represents a comprehensive tool for codon-usage-oriented cancer research. Conclusions Based on data from TCGA, we have highlighted tumor type-specific signatures of codon and codon pair usage. Paired data revealed variable changes to codon usage patterns, which must be considered when designing personalized cancer treatments. The associated database, CancerCoCoPUTs, represents a comprehensive resource for codon and codon pair usage in cancer and is available at https://dnahive.fda.gov/review/cancercocoputs/. These findings are important to understand the relationship between tRNA supply and codon demand in cancer states and could help guide the development of new cancer therapeutics.

Sign in / Sign up

Export Citation Format

Share Document