Independent Clinical Cohorts Research Articles

IGLV3-21*01 is an inherited risk factor for CLL through the acquisition of a single-point mutation enabling autonomous BCR signaling

The prognosis of chronic lymphocytic leukemia (CLL) depends on different markers, including cytogenetic aberrations, oncogenic mutations, and mutational status of the immunoglobulin (Ig) heavy-chain variable (IGHV) gene. The number of IGHV mutations distinguishes mutated (M) CLL with a markedly superior prognosis from unmutated (UM) CLL cases. In addition, B cell antigen receptor (BCR) stereotypes as defined by IGHV usage and complementarity-determining regions (CDRs) classify ∼30% of CLL cases into prognostically important subsets. Subset 2 expresses a BCR with the combination of IGHV3-21-derived heavy chains (HCs) with IGLV3-21-derived light chains (LCs), and is associated with an unfavorable prognosis. Importantly, the subset 2 LC carries a single-point mutation, termed R110, at the junction between the variable and constant LC regions. By analyzing 4 independent clinical cohorts through BCR sequencing and by immunophenotyping with antibodies specifically recognizing wild-type IGLV3-21 and R110-mutated IGLV3-21 (IGLV3-21R110), we show that IGLV3-21R110-expressing CLL represents a distinct subset with poor prognosis independent of IGHV mutations. Compared with other alleles, only IGLV3-21*01 facilitates effective homotypic BCR-BCR interaction that results in autonomous, oncogenic BCR signaling after acquiring R110 as a single-point mutation. Presumably, this mutation acts as a standalone driver that transforms IGLV3-21*01-expressing B cells to develop CLL. Thus, we propose to expand the conventional definition of CLL subset 2 to subset 2L by including all IGLV3-21R110-expressing CLL cases regardless of IGHV mutational status. Moreover, the generation of monoclonal antibodies recognizing IGLV3-21 or mutated IGLV3-21R110 facilitates the recognition of B cells carrying this mutation in CLL patients or healthy donors.

Penetrance Estimates Over Time to First and Second Primary Cancer Diagnosis in Families with Li-Fraumeni Syndrome: A Single Institution Perspective.

Li-Fraumeni syndrome (LFS) is a rare autosomal dominant disorder associated with TP53 germline mutations and an increased lifetime risk of multiple primary cancers (MPC). Penetrance estimation of time to first and second primary cancer within LFS remains challenging because of limited data and the difficulty of characterizing the effects of a primary cancer on the penetrance of a second primary cancer. Using a recurrent events survival modeling approach that incorporates a family-wise likelihood to efficiently integrate the pedigree structure, we estimated the penetrance for both first and second primary cancer diagnosis from a pediatric sarcoma cohort at MD Anderson Cancer Center [MDACC, Houston, TX; number of families = 189; single primary cancer (SPC) = 771; and MPC = 87]. Validation of the risk prediction performance was performed using an independent MDACC clinical cohort of TP53 tested individuals (SPC = 102 and MPC = 58). These findings showed that an individual diagnosed at a later age was more likely to be diagnosed with a second primary cancer. In addition, TP53 mutation carriers had a HR of 1.65 (95% confidence interval, 1.1-2.5) for developing a second primary cancer versus SPC. The area under the ROC (AUC) curve for predicting individual outcomes of MPC versus SPC was 0.77. In summary, we provide the first set of penetrance estimates for first and second primary cancer for TP53 germline mutation carriers and demonstrate its accuracy for cancer risk assessment. SIGNIFICANCE: These findings present an open-source R package LFSPRO that could be used for genetic counseling and health management of individuals with LFS as it estimates the risk of both first and second primary cancer diagnosis.See related article by Shin et al., p. 354.

Human sperm displays rapid responses to diet.

The global rise in obesity and steady decline in sperm quality are two alarming trends that have emerged during recent decades. In parallel, evidence from model organisms shows that paternal diet can affect offspring metabolic health in a process involving sperm tRNA-derived small RNA (tsRNA). Here, we report that human sperm are acutely sensitive to nutrient flux, both in terms of sperm motility and changes in sperm tsRNA. Over the course of a 2-week diet intervention, in which we first introduced a healthy diet followed by a diet rich in sugar, sperm motility increased and stabilized at high levels. Small RNA-seq on repeatedly sampled sperm from the same individuals revealed that tsRNAs were up-regulated by eating a high-sugar diet for just 1 week. Unsupervised clustering identified two independent pathways for the biogenesis of these tsRNAs: one involving a novel class of fragments with specific cleavage in the T-loop of mature nuclear tRNAs and the other exclusively involving mitochondrial tsRNAs. Mitochondrial involvement was further supported by a similar up-regulation of mitochondrial rRNA-derived small RNA (rsRNA). Notably, the changes in sugar-sensitive tsRNA were positively associated with simultaneous changes in sperm motility and negatively associated with obesity in an independent clinical cohort. This rapid response to a dietary intervention on tsRNA in human sperm is attuned with the paternal intergenerational metabolic responses found in model organisms. More importantly, our findings suggest shared diet-sensitive mechanisms between sperm motility and the biogenesis of tsRNA, which provide novel insights about the interplay between nutrition and male reproductive health.

Transcriptomic signatures related to the obesity paradox in patients with clear cell renal cell carcinoma: a cohort study.

Obesity is associated with an increased risk of developing clear cell renal cell carcinoma (RCC) but, paradoxically, obesity is also associated with improved oncological outcomes in this cancer. Because the biological mechanisms underlying this paradoxical association are poorly understood, we aimed to identify transcriptomic differences in primary tumour and peritumoral adipose tissue between obese patients and those at a normal weight. In this cohort study, we assessed data from five independent clinical cohorts of patients with clear cell RCC aged 18 years and older. Overweight patients were excluded from each cohort for our analysis. We assessed patients from the COMPARZ phase 3 clinical trial, a cohort from the Cancer Genome Atlas (TCGA), and a Memorial Sloan Kettering (MSK) observational immunotherapy cohort for their inclusion into our study. We assessed overall survival in obese patients (those with a body-mass index [BMI] ≥30 kg/m2) and in patients with a normal weight (BMI 18·5-24·9 kg/m2, as per WHO's BMI categories), defined as the time from treatment initiation (in the COMPARZ and MSK immunotherapy cohorts) or surgery (in the TCGA cohort) to the date of any-cause death or of censoring on the day of the last follow-up. We also evaluated and validated transcriptomic differences in the primary tumours of obese patients compared with those of a normal weight. We compared gene-expression differences in peritumoral adipose tissue and tumour tissue in an additional, prospectively collected cohort of patients with non-metastatic clear cell RCC (the MSK peritumoral adipose tissue cohort). We analysed differences in gene expression between obese patients and those at a normal weight in the COMPARZ, TCGA, and peritumoral adipose tissue cohorts. We also assessed the tumour immune microenvironment in a prospective cohort of patients who had nephrectomy for localised RCC at MSK. Of the 453 patients in the COMPARZ trial, 375 (83%) patients had available microarray data, pretreatment BMI measurements, and overall survival data for analyses, and we excluded 119 (26%) overweight patients, leaving a final cohort of 256 (68%) patients from this study for our analyses. From 332 patients in the TCGA cohort, we evaluated clinical and demographic data from 152 (46%) patients with advanced (ie, stages III and IV) clear cell RCC treated by nephrectomy; after exclusion of 59 (39%) overweight patients, our final cohort consisted of 93 (61%) patients. After exclusion of 74 (36%) overweight patients from the initial MSK immunotherapy study population of 203 participants, our final cohort for overall survival analysis comprised 129 (64%) participants. We found that overall survival was longer in obese patients than in those with normal weight in the TCGA cohort, after adjustment for stage or grade (adjusted HR 0·41, 95% CI 0·22-0·75), and in the COMPARZ clinical trial after adjustment for International Metastatic RCC Database (IMDC) risk score (0·68, 0·48-0·96). In the MSK immunotherapy cohort, the inverse association of BMI with mortality (HR 0·54, 95% CI 0·31-0·95) was not significant after adjustment for IMDC risk score (adjusted HR 0·72, 95% CI 0·40-1·30). Tumours of obese patients showed higher angiogenic scores on gene-set enrichment analysis-derived hallmark gene set angiogenesis signatures than did those of patients at a normal weight, but the degree of immune cell infiltration did not differ by BMI. We found increased peritumoral adipose tissue inflammation in obese patients relative to those at a normal weight, especially in peritumoral fat near the tumour. We found aspects of the tumour microenvironment that vary by BMI in the tumour and peritumoral adipose tissue, which might contribute to the apparent survival advantage in obese patients with clear cell RCC compared with patients at a normal weight. The complex interplay between the clear cell RCC tumour and peritumoral adipose tissue microenvironment might have clinical relevance and warrants further investigation. Ruth L Kirschstein Research Service Award, American Society of Clinical Oncology Young Investigator Award, MSK's Ludwig Center, Weiss Family Kidney Research Fund, Novartis, The Sidney Kimmel Center for Prostate and Urologic Cancers, and the National Institutes of Health (National Cancer Institute) Cancer Center Support Grant.

Genomewide Expression Profiling Identifies a Novel miRNA-based Signature for the Detection of Peritoneal Metastasis in Patients With Gastric Cancer.

This study aimed to conduct a genomewide transcriptomic profiling to develop a microRNA (miRNA)-based signature for the identification of peritoneal metastasis (PM) in patients with gastric cancer (GC). Even though PM in patients with GC has long been recognized to associate with poor prognosis, currently there is lack of availability of molecular biomarkers for its robust diagnosis. We performed a systematic biomarker discovery by analyzing miRNA expression profiles in primary tumors from GC patients with and without PM, and subsequently validated the expression of candidate miRNA biomarkers in 3 independent clinical cohorts of 354 patients with advanced GC. Five miRNAs (miR-30a-5p, -134-5p, -337-3p, -659-3p, and -3917) were identified during the initial discovery phase; three of which (miR-30a-5p, -659-3p, and -3917) were significantly overexpressed in the primary tumors from PM-positive patients in the testing cohort (P = 0.002, 0.04, and 0.007, respectively), and distinguished patients with versus without peritoneal metastasis with the value of area under the curve (AUC) of 0.82. Furthermore, high expression of these miRNAs also associated with poor prognosis (hazard ratio = 2.18, P = 0.04). The efficacy of the combination miRNA signature was subsequently validated in an independent validation cohort (AUC = 0.74). Finally, our miRNA signature when combined together with the macroscopic Borrmann's type score offered a much superior diagnostic in all 3 cohorts (AUC = 0.87, 0.76, and 0.79, respectively). We have established an miRNA-based signature that have a potential to identify peritoneal metastasis in GC patients.

530PD - Association between transit-amplifying signature and outcomes of patients treated with anti-epidermal growth factor receptor (EGFR) therapy in colorectal cancer

BackgroundIn CRC, the transit-amplifying (TA) subtype is enriched for tumours sensitive to anti-EGFR agents. At the same time, the presence of TA gene signature in tumours belonging to other subtypes may suggest potential anti-EGFR benefit. Hence, we evaluated the association between the TA signature (presence - class 1; absence - class 2) and outcomes in CRC patients (pts) treated with anti-EGFR therapy. MethodsFormalin-fixed, paraffin-embedded samples from a retrospective discovery cohort (D) and an independent clinical trial cohort (CO.20 study, validation (V)) were classified into the two TA classes using our published nCounter assay (NanoString Technologies). A subset of pts with extended RAS/BRAF wild-type (WT) tumours, cell lines, and pts-derived xenografts (PDXs) were also evaluated. Biomarker portability was evaluated in primary/metastatic samples and different platforms (microarrays). ResultsUp to 295 quality samples were included (D-84; V-121; 30 matched pts and control/treated PDXs). Class 1 pts had significantly longer progression-free survival [PFS; D – adjusted (adj) HR 1.95 (1.15-3.31) p<0.01; V – adj HR 1.59 (1.07-2.37) p<0.02] and higher disease control rate (DCR) [D – adj p<0.0001; V adj p<0.001]. In D cohort (unselected for RAS/BRAF) classes were significantly associated with sidedness (left 71% class 1; right 58% class 2). In 71WT pts, class 1 was associated with significantly higher DCR (83% vs. 39%, adj p: 0.003); and trend towards longer PFS and response. This association was more pronounced in a clinically relevant cohort of 51WT and left-sided pts (class 2 vs. class 1 median PFS: 2 vs. 5.62 months; HR: 1.88 (0.99-3.57) p: 0.049; response rate (class 2 vs. class 1, 8% vs. 33%, odds ratio 0.17(0.02-1.41) p: 0.09, adj (for age, gender) p: 0.14)). WT PDXs and cell lines showed similar response (Wilcoxon test; PDXs p-value: 0.04; cell lines p-value: 0.007). The TA classes were further validated using microarray data from metastatic samples (Khambata-Ford; PFS p<0.0001). ConclusionsThe detection of TA signature in primary or metastatic samples may further refine the selection of WT CRC pts for anti-EGFR therapy and may explain the differential benefit behind sidedness. Clinical trial identificationNCT00640471. Legal entity responsible for the studyThe authors. FundingNIHR Biomedical Research Centre at The Royal Marsden and the ICR; Cancer Research UK, MedTech SuperConnector. DisclosureD. Cunningham: Research grant / Funding (institution): Amgen; AstraZeneca; Bayer; Celgene; MedImmune; Merck Serono; Merrimack; Sanofi. N. Starling: Research grant / Funding (institution): AstraZeneca; Verastem. A. Sadanandam: Research grant / Funding (institution): Bristol-Myers Squibb; Licensing / Royalties: PCT/IB2013/060416. All other authors have declared no conflicts of interest.

Spatial UBE2N protein expression indicates genomic instability in colorectal cancers

BackgroundOne major hallmark of colorectal cancers (CRC) is genomic instability with its contribution to tumor heterogeneity and therapy resistance. To facilitate the investigation of intra-sample phenotypes and the de novo identification of tumor sub-populations, imaging mass spectrometry (IMS) provides a powerful technique to elucidate the spatial distribution patterns of peptides and proteins in tissue sections.MethodsIn the present study, we analyzed an in-house compiled tissue microarray (n = 60) comprising CRCs and control tissues by IMS. After obtaining protein profiles through direct analysis of tissue sections, two validation sets were used for immunohistochemical evaluation.ResultsA total of 28 m/z values in the mass range 800–3500 Da distinguished euploid from aneuploid CRCs (p < 0.001, ROC AUC values < 0.385 or > 0.635). After liquid chromatograph-mass spectrometry identification, UBE2N could be successfully validated by immunohistochemistry in the initial sample cohort (p = 0.0274, ROC AUC = 0.7937) and in an independent sample set of 90 clinical specimens (p = 0.0070, ROC AUC = 0.6957).ConclusionsThe results showed that FFPE protein expression profiling of surgically resected CRC tissue extracts by MALDI-TOF MS has potential value for improved molecular classification. Particularly, the protein expression of UBE2N was validated in an independent clinical cohort to distinguish euploid from aneuploid CRCs.

Use of the tau protein-to-peptide ratio in CSF to improve diagnostic classification of Alzheimer’s disease

Cerebrospinal fluid (CSF) tau and phospho-tau are well established biomarkers of Alzheimer’s disease. While these measures are conventionally referred to as ‘total tau’ (T-tau) and ‘phospho-tau’ (P-tau), several truncated and modified tau forms exist that may relay additional diagnostic information. We evaluated the diagnostic performance of an endogenous tau peptide in CSF, tau 175–190, in the phosphorylated and non-phosphorylated state. A liquid chromatography-mass spectrometry (LC-MS) method was established to measure these peptides in CSF and was used to analyze two independent clinical cohorts; the first cohort included patients with Alzheimer’s disease (AD, n = 15), Parkinson’s disease (PD, n = 15), progressive supranuclear palsy (PSP, n = 15), and healthy controls (n = 15), the second cohort included AD patients (n = 16), and healthy controls (n = 24). In both cohorts T-tau and P-tau concentrations were determined by immunoassay. While tau 175–190 and P-tau 175–190 did not differentiate the study groups, the separation of AD and controls by T-tau (area under the ROC Curve (AUC) = 95%) and P-tau (AUC = 92%) was improved when normalizing the ELISA measurements to the concentrations of the endogenous peptides: T-tau/tau 175–190 (AUC = 100%), P-tau/P-tau 175–190 (AUC = 95%). The separation between patients and controls by T-tau (AUC = 88%) and P-tau (AUC = 82%) was similarly improved in the second cohort by taking the ratios of T-tau/tau 175–190 (AUC = 97%) and P-tau/P-tau 175–190 (AUC = 98%). In conclusion, our results suggest that the performance of the AD biomarkers T-tau and P-tau could be improved by normalizing their measurements to the endogenous peptides tau 175–190 and P-tau 175–190, possibly because these endogenous tau peptides serve to normalize for physiological, and disease-independent, secretion of tau from neurons to the extracellular space and the CSF. Finally, the observations made here add to the general applicability of mass spectrometry as a tool for rapid identification and accurate quantification of biomarker candidates.

Abstract 2579: Genomewide transcriptomic profiling identifies a novel miRNA signature for predicting lymph node metastasis in patients with pancreatic ductal adenocarcinoma

Abstract Purpose: Pancreatic ductal adenocarcinoma (PDAC) is an extremely lethal cancer, and is projected to become the second leading cause of cancer-related deaths in the US by 2030. The lymph node (LN) status is an important predictor of survival outcomes in PDAC patients undergoing curative resection. The National Comprehensive Cancer Network guidelines recommend neoadjuvant therapy in patients with high-risk features such as large regional LNs, elevated CA-19-9 levels, large primary tumors, excessive weight loss, and extreme pain. Therefore, a pre-treatment diagnosis of LN metastasis (LNM) is critical in developing a more personalized treatment strategy in PDAC patients. Herein, for the first time, we performed a genomewide analysis to identify and develop a miRNA signature for the detection of LNM in PDAC patients. Methods: We analyzed a total of 518 PDAC patients, which included 269 patients from three genomewide miRNA-expression datasets (TCGA, GSE24279 and GSE35688) and 249 from two, independent clinical patient cohorts. During the biomarker discovery phase, we used rigorous computational and bioinformatic approaches to identify candidate miRNA biomarkers, which later were subjected to in-silico validations. The robustness and performance of this miRNA-signature was subsequently interrogated in two independent clinical cohorts (training cohort: n=150, validation cohort: n=99) using qRT-PCR assays. Furthermore, the clinical potential value of the miRNA signature was evaluated and compared to several clinicopathological factors using logistic regression analysis. Results: Through a comprehensive miRNA expression profiling analysis of LNM-positive and negative patients, we identified a panel of 7 miRNAs that significantly predicted LNM [the area under curve (AUC)=0.76, 0.75 and 0.92, respectively]. Using logistic regression analysis, we then optimized and trained a 6-miRNA risk-prediction model in the training cohort of patients, which robustly distinguished PDAC patients with LNM (AUC: 0.84, 95%CI: 0.76-0.89). The performance of this trained model was subsequently confirmed in an independent, large clinical validation cohort (AUC: 0.73, 95%CI: 0.64-0.82). Interestingly, multivariate analysis which incorporated several pre-operative clinical factors, revealed that our 6-miRNA signature was as an independent predictor of LNM in both patient cohorts (training cohort: OR=34.97; P&amp;lt;0.01, validation cohort: OR=10.11; P&amp;lt;0.01, respectively). Conclusion: We identified and developed a novel 6-miRNA signature that is highly robust in predicting presence of LNM in PDAC patients; highlighting the clinical impact of these findings in more appropriate patient selection and development of improved individualized treatment strategies for PDAC patients. Citation Format: Satoshi Nishiwada, Kensuke Yamamura, Raju Kandimalla, Takahiro Akahori, Kota Nakamura, Hideo Baba, Masayuki Sho, Ajay Goel. Genomewide transcriptomic profiling identifies a novel miRNA signature for predicting lymph node metastasis in patients with pancreatic ductal adenocarcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 2579.

Abstract 3148: Identification and establishment of an 18-gene redox signature for recurrence prediction in colorectal cancer

Abstract Purpose: Colorectal cancer (CRC) is the third most common cancers worldwide. The immediate clinical challenge is the identification of high-risk CRC patients, particularly stage II and III, who are genuine candidates for adjuvant chemotherapy, while sparing the low-risk patients from the toxicity and expense of such treatment modalities. According to ASCO guidelines, the high-risk stage II CRC patients are currently defined by specific clinicopathological risk factors. However, the prognosis of patients, even with similar clinicopathological features varies significantly; highlighting their inadequacy in identifying true high-risk CRC patients. Accumulating evidence shows that redox adaptation provides survival benefit to cancer cells and enhances resistance to conventional chemotherapy. Therefore, in this study we performed a comprehensive evaluation of the clinical potential of redox-related genes as recurrence prediction biomarkers in CRC patients. Experimental Design: We undertook a rigorous biomarker discovery effort (GSE33113, n=90), followed by validation of a redox gene signature for recurrence prediction in two datasets (GSE39582, n=461 and GSE14333, n=185) of stage II and III CRC patients. The 18-gene signature discovered from the genome wide discovery effort was subsequently validated in an independent in-house clinical cohort (n=128) by qRT-PCR assays. Univariate and multivariate analyses were performed using CoxPH regression by including various clinicopathological features. Results: A systematic analysis of 312 redox-related genes in three public datasets of 736 CRC patients led to the identification of an 18-gene panel for predicting recurrence free survival (RFS) in stage II and III CRC patients. This gene signature could successfully discriminate high vs. low risk patients in the discovery cohort (AUC=0.88, HR=17.0, p&amp;lt;0.0001), as well as two validation cohorts (GSE39582: AUC=0.70; HR=3.1, p&amp;lt;0.0001; GSE14333: AUC=0.63; HR=3.8, p&amp;lt;0.0001). Subsequent validation in an independent CRC clinical cohort (n=128) confirmed that this gene signature was significantly associated with RFS (AUC=0.81; HR=9.3, CI=3.8 to 22.5, p&amp;lt;0.0001). Univariate analysis identified tumor stage, tumor infiltration, and TNM stage as significant predictors of RFS, and multivariate analysis further confirmed that our gene signature was a significant predictor of RFS in stage II/III CRC patients (p&amp;lt;0.0001). A risk-assessment model by combining the 18-gene signature and the three clinical factors exhibited an even superior prediction for RFS (AUC = 0.877; HR=10.34, CI=4.2 to 25.6, p&amp;lt;0.0001). Conclusions: We identified and established a novel 18-gene redox signature for the prediction of tumor recurrence in high-risk stage II/III CRC patients, which has the potential for translation into the clinic for improved risk-assessment and stratification in CRC patients. Citation Format: Priyanka Sharma, Jasjit K. Banwait, Luis Bujanda, Ajay Goel. Identification and establishment of an 18-gene redox signature for recurrence prediction in colorectal cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 3148.

Abstract 2413: Genomewide expression profiling identifies a novel gene-expression signature for recurrence prediction in patients with early-onset colorectal cancer (EOCRC)

Abstract Purpose: Although the incidence of CRC has declined over the last decade, recent evidence indicates that the incidence of early onset CRC (EOCRC; patients younger than age 50) is actually on the rise. The disease presentation in patients with EOCRC is quite distinct, with often the disease diagnosis at more advanced stages, poorer histological grade, and higher local recurrence rates, compared to late onset CRC (LOCRC). Hence, availability of biomarkers that can predict recurrence in high-risk EOCRC patients is much needed as it will lead to improved prognosis, and tailoring personalized treatment regimens in this population. Herein, we have conducted a genomewide biomarker discovery approach, followed by robust bioinformatic analysis to establish a novel gene-based recurrence prediction classifier in patients with EOCRC. Methods: We undertook a comprehensive and systematic biomarker discovery effort by first analyzing six independent, genomewide expression datasets (GSE14333, GSE17538, GSE3311, GSE37892, GSE39084 and GSE39582) of EOCRC patients to identify (discovery dataset; n=81) and validate (validation dataset; n=75) a gene signature for predicting recurrence in EOCRC. Subsequently, the robustness of this signature was analytically evaluated in an in-house clinical training cohort (n=54). Using recursive feature elimination approach based on random forest classification, the signature was further refined and the gene signature was validated in another independent clinical cohort (n=54). Results: We initially identified a 25-gene signature from a total of 3591 mRNAs, which showed significant potential for predicting recurrence free survival (RFS) in both the in-silico discovery (HR, 32.83, 95% confidential interval (CI), 10.73 - 100.51, P&amp;lt;0.001) and validation datasets in the discovery phase (HR, 4.11, 95% CI, 1.77 - 9.56, P &amp;lt;0.001). Subsequently, based on our clinical training cohort, we developed a reduced and optimized 5-gene signature, which demonstrated robust prognostic power in both the clinical training cohort (HR, 3.67; 95% CI, 1.48-9.52; P=0.003) and an independent validation cohort (HR, 7.53; 95% CI, 1.73-51.3; P=0.007). In univariate and multivariate cox regression analysis, our 5-gene signature emerged as an independent predictor of worse RFS. Furthermore, a combination signature comprising of lymph node metastasis and our 5-gene signature demonstrated an even superior predictive performance compared to individual factors in both cohorts (training cohort: HR 4.56; 95% CI, 0.65-0.88; validation cohort: HR 8.25, 0.86; 95% CI, 0.74-0.94). Conclusion: Our novel 5-gene signature provides a robust prognostic tool for patients with EOCRC; which can be clinically transformative in guiding post-surgical decision-making to improve survival and management of young patients suffering from this malignancy. Citation Format: Kensuke Yamamura, Lina Zhu, Raju Kandimalla, Takatoshi Matsuyama, Yusuke Kinugasa, Francesc Balaguer, Hideo Baba, Xin Wang, Ajay Goel. Genomewide expression profiling identifies a novel gene-expression signature for recurrence prediction in patients with early-onset colorectal cancer (EOCRC) [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 2413.

Abstract 4935: Identification of GPR115 as a key G-protein coupled receptor as a key prognostic and therapeutic target in pancreatic ductal adenocarcinoma

Abstract Purpose: Pancreatic ductal adenocarcinoma (PDAC) remains the fourth leading cause of cancer-related deaths in the United States, and the incidence of this disease continues to rise. Although recent advances in treatment modalities for this malignancy has somewhat improved patient outcomes in the recent years, the overall prognosis of patients with PDAC remains dismal. From a biological viewpoint, while the G-protein-coupled receptor (GPCRs) proteins play a multitude of important functions in tumor pathogenesis, their clinical significance if any in human cancers, especially PDAC, remains unclear. Herein we aimed to perform a comprehensive profiling of all GPCRs, in an attempt to identify the most critical protein(s) that have important biological and clinical significance in PDAC. Methods: This study included systematic analysis of 796 PDAC patients, which included data analysis from 331 patients from public datasets (TCGA, ICGC and GSE57495) and 465 patients from two, independent, clinical patient cohorts. Computational and bioinformatic analysis of genomewide expression profiling datasets led to the selection of candidate GPCR genes during the discovery and validation steps. The clinical significance of candidate genes were subsequently investigated in two in-house patient cohorts (training cohort: n = 321, validation cohort: n = 144) using qRT-PCR assays. The data were analyzed by performing appropriate statistical analyses to determine patient prognosis. Finally, using a series of functional studies, we confirmed the biological function of the candidate GPCRs in PDAC. Results: Analysis of all 33 GPCRs, led to the identification of GPR115, as the only significantly up-regulated candidate, with a prognostic significance in all three public datasets (P=0.02, 0.04 and 0.05, respectively). The patients with high-GPR115 expression exhibited significantly poorer postoperative prognosis for OS and RFS, in two, large, independent clinical cohorts (training cohort: P&amp;lt;0.01, P&amp;lt;0.01, validation cohort: P=0.01 and 0.02, respectively). More importantly, multivariate analysis indicated that high GPR115 expression was an independent prognostic factor in both cohorts (HR=1.45; P=0.01, HR=2.25; P&amp;lt;0.01). Using Cox regression, we established a risk-prediction model by incorporating GPR115 expression together with various clinicopathological factors, which was highly accurate in predicting 5-year long-term survival following surgery in PDAC patients. In addition, siRNA-mediated gene silencing of GPR115 significantly inhibited the cell proliferation and migration in human PDAC cells. Conclusion: We for the first time have identified GPR115 as a key GPCR with important prognostic significance, as well as functional role in tumor progression; providing a rationale that this may be a potential target for therapeutic targeting in patients with PDAC. Citation Format: Satoshi Nishiwada, Tadanobu Shimura, Kensuke Yamamura, Fuminori Sonohara, Takahiro Akahori, Kota Nakamura, Naoya Ikeda, Yasuhiro Kodera, Masayuki Sho, Ajay Goel. Identification of GPR115 as a key G-protein coupled receptor as a key prognostic and therapeutic target in pancreatic ductal adenocarcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 4935.

Abstract 4907: Transcriptomic profiling identifies a novel mitochondrial gene expression signature that predicts recurrence in microsatellite stable stage II and III colorectal cancer

Abstract Purpose: Colorectal cancer (CRC) is the third most commonly diagnosed cancer in the United States. Current TNM (Tumor, Node, and Metastasis) classification approach is suboptimal in determining the prognosis of CRC patients. While there is a general consensus that stage II and III CRC patients with microsatellite instability (MSI) exhibit better outcomes, there is a lack of availability of biomarkers that can predict prognosis in microsatellite stable (MSS) patients. Given the emerging evidence that mitochondrial (mt) genomic instability is one of the hallmark features of cancer progression, herein, we systematically explored and established a novel mt-gene panel as a prognostic signature in CRC. Experimental design: Three genome wide mRNA expression profiling datasets were analyzed for the biomarker discovery (GSE33113, n=65) and validation (GSE14333, n=196; TCGA, n=62) of a mt-gene expression signature that predicts recurrence in stage II/III MSS CRC patients. Subsequently, a 14-gene signature was established, and independently tested (n=257) and validated (n=212) in two clinical cohorts, using qRT-PCR assays. The uni- and multi-variate Cox proportional hazard (CoxPH) regression analyses were performed to evaluate the performance of the mt-gene signature individually, and in combination with various clinicopathological factors (e.g. TNM staging, lymphatic invasion, and tumor infiltration) for predicting recurrence free survival (RFS). Results: The expression analysis of 568 mt-genes in CRC patients with and without recurrence, resulted in the identification of 32 significantly dysregulated genes (p&amp;lt;0.05). The univariate CoxPH analysis yielded a 14-gene signature that accurately predicted recurrence in the discovery (AUC = 0.89), and two independent validation cohorts (AUC = 0.75 and 0.79, respectively). This 14-gene signature demonstrated significant associations with poor RFS in independent testing and clinical validation cohorts of CRC patients (AUC = 0.87 and 0.73, respectively). Univariate analysis revealed that in addition to the mt-gene signature, TNM-stage (p = 0.007) and lymphatic invasion (p = 0.0004) were significant predictors of RFS; while multivariate analyses demonstrated that our mt-gene signature was an independent predictor (HR = 2.71; P&amp;lt;0.001) of tumor recurrence in stage II/III CRC patients. Intriguingly, a risk-stratification model comprising of the 14-gene signature together with TNM-stage and lymphatic invasion status achieved an even superior AUC of 0.89 and 0.78 in both clinical cohorts. Conclusions: We have identified a novel, 14-gene signature as an independent predictor of tumor recurrence; which in combination with TNM-stage and lymphatic invasion, offers an easy-to-translate and facile assay for the personalized risk-assessment in stage II/III MSS-CRC patients. Citation Format: SOUVIK GHATAK, Raju Kandimalla, Jasjit K. Banwait, Hiroyuki Uetake, Francesc Balaguer, Luis Bujanda, Ajay Goel. Transcriptomic profiling identifies a novel mitochondrial gene expression signature that predicts recurrence in microsatellite stable stage II and III colorectal cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 4907.

Abstract 4870: Dedicator of cytokinesis-x promotes chemo and radioresistance of gastric cancer by modulating WNT signaling and cancer stem cell traits

Abstract Prognosis of gastric cancer (GC) remains poor and the key players in its molecular pathogenesis are unknown at present. There is therefore a critical need to identify novel prognostic factors of GC. Dedicator of cytokinesis represents a protein family belonging to atypical Rho guanine nucleotide exchange factors (GEF) for Rac and/or Cdc42 GTPases. Dedicator of cytokinesis-x, a member of the Dock-C subfamily, exchanges GDP for GTP for Rac1 and Cdc42, both in vitro and in vivo. Here, we identified Dedicator of cytokinesis-x as an independent biomarker for GC prognosis. Elevated Dedicator of cytokinesis-x expression was associated with significantly shorter cumulative survival in both univariate and multivariate analyses. Moreover, higher dedicator of cytokinesis-x expression is positively correlated with tumor size, depth of invasion, lymph node metastasis, vascular invasion and pathological stage. To establish the downstream effectors of dedicator of cytokinesis-x, we analyzed the genes in relation to dedicator of cytokinesis-x expression using three independent clinical GC cohorts. Genes displaying a positive correlation with dedicator of cytokinesis-x were subsequently subject to gene ontology analysis and shown to be significantly involved in the WNT/β-catenin signaling pathway. Depletion of dedicator of cytokinesis-x suppressed cell proliferation, chemo- and radio-resistance, metastasis, oncosphere formation ability and tumorigenicity as well as expression of gastric cancer stem cell (CSC) markers. Dedicator of cytokinesis-x depletion was accompanied by a concomitant decrease in CD44, CD133 and nuclear β-catenin expressions. Moreover, interactions between dedicator of cytokinesis-x and β-catenin were observed in the nucleus of GC cells. This finding suggested that dedicator of cytokinesis-x may act as a tumor promoter through WNT/β-catenin activation. Citation Format: Hsiang-Cheng Chi, Kwang-Huei Lin. Dedicator of cytokinesis-x promotes chemo and radioresistance of gastric cancer by modulating WNT signaling and cancer stem cell traits [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 4870.

A Modular Cytokine Analysis Method Reveals Novel Associations With Clinical Phenotypes and Identifies Sets of Co-signaling Cytokines Across Influenza Natural Infection Cohorts and Healthy Controls.

Cytokines and chemokines are key signaling molecules of the immune system. Recent technological advances enable measurement of multiplexed cytokine profiles in biological samples. These profiles can then be used to identify potential biomarkers of a variety of clinical phenotypes. However, testing for such associations for each cytokine separately ignores the highly context-dependent covariation in cytokine secretion and decreases statistical power to detect associations due to multiple hypothesis testing. Here we present CytoMod—a novel data-driven approach for analysis of cytokine profiles that uses unsupervised clustering and regression to identify putative functional modules of co-signaling cytokines. Each module represents a biosignature of co-signaling cytokines. We applied this approach to three independent clinical cohorts of subjects naturally infected with influenza in which cytokine profiles and clinical phenotypes were collected. We found that in two out of three cohorts, cytokine modules were significantly associated with clinical phenotypes, and in many cases these associations were stronger than the associations of the individual cytokines within them. By comparing cytokine modules across datasets, we identified cytokine “cores”—specific subsets of co-expressed cytokines that clustered together across the three cohorts. Cytokine cores were also associated with clinical phenotypes. Interestingly, most of these cores were also co-expressed in a cohort of healthy controls, suggesting that in part, patterns of cytokine co-signaling may be generalizable. CytoMod can be readily applied to any cytokine profile dataset regardless of measurement technology, increases the statistical power to detect associations with clinical phenotypes and may help shed light on the complex co-signaling networks of cytokines in both health and infection.

A robust data-driven genomic signature for idiopathic pulmonary fibrosis with applications for translational model selection.

Idiopathic pulmonary fibrosis (IPF) is a chronic and progressive lung disease affecting ~5 million people globally. We have constructed an accurate model of IPF disease status using elastic net regularized regression on clinical gene expression data. Leveraging whole transcriptome microarray data from 230 IPF and 89 control samples from Yang et al. (2013), sourced from the Lung Tissue Research Consortium (LTRC) and National Jewish Health (NJH) cohorts, we identify an IPF gene expression signature. We performed optimal feature selection to reduce the number of transcripts required by our model to a parsimonious set of 15. This signature enables our model to accurately separate IPF patients from controls. Our model outperforms existing published models when tested with multiple independent clinical cohorts. Our study underscores the utility of elastic nets for gene signature/panel selection which can be used for the construction of a multianalyte biomarker of disease. We also filter the gene sets used for model input to construct a model reliant on secreted proteins. Using this approach, we identify the preclinical bleomycin rat model that is most congruent with human disease at day 21 post-bleomycin administration, contrasting with earlier timepoints suggested by other studies.

Integration of protein interaction and gene co-expression information for identification of melanoma candidate genes

Cutaneous melanoma is an aggressive form of skin cancer that causes death worldwide. Although much has been learned about the molecular basis of melanoma genesis and progression, there is also increasing appreciation for the continuing discovery of melanoma genes to improve the genetic understanding of this malignancy. In the present study, melanoma candidate genes were identified by analysis of the common network from cancer type-specific RNA-Seq co-expression data and protein-protein interaction profiles. Then, an integrated network containing the known melanoma-related genes represented as seed genes and the putative genes represented as linker genes was generated using the subnetwork extraction algorithm. According to the network topology property of the putative genes, we selected seven key genes (CREB1, XPO1, SP3, TNFRSF1B, CD40LG, UBR1, and ZNF484) as candidate genes of melanoma. Subsequent analysis showed that six of these genes are melanoma-associated genes and one (ZNF484) is a cancer-associated gene on the basis of the existing literature. A signature comprising these seven key genes was developed and an overall survival analysis of 461 cutaneous melanoma cases was carried out. This seven-gene signature can accurately determine the risk profile for cutaneous melanoma tumors (log-rank P=3.27E-05) and be validated on an independent clinical cohort (log-rank P=0.028). The presented seven genes might serve as candidates for studying the molecular mechanisms and help improve the prognostic risk assessment, which have clinical implications for melanoma patients.

Molecular stratification of high-grade unclassified renal cell carcinoma to improve prognostication and management strategy.

640 Background: Unclassified renal cell carcinoma (uRCC) constitutes a large portion of aggressive non-clear cell RCC with limited response to standard therapy. Clinicopathologic parameters or biomarkers to stratify this heterogeneous group of tumors are currently lacking. In a recently reported analysis of 62 high-grade primary uRCC [“discovery cohort (DC)”], we identified distinct molecular subsets. We aimed to validate this molecular schema in an independent clinical cohort and further delineate the clinicopathologic and molecular features that may refine prognostication and management. Methods: All cases was reviewed by experienced GU pathologists based on the current WHO criteria. Primary (n = 54) or metastatic (n = 21) tumor samples from 75 uRCC patients [“validation cohort (VC)”] were analyzed by a CLIA-approved targeted NGS platform for somatic alterations. 37 had germline testing results available. We performed integrative analysis of both VC and DC. Results: Somatic mutations found in VC were NF2 (24%), SETD2 (13%), SMARCB1 (9%), TP53 (9%), TSC1 (9%), FH (8%), TSC2 (5%), MTOR (5%), EP300 (5%), BAP1 (5%), PBRM1 (5%) and PIK3CA (5%), highly consistent with findings in DC. Germline alterations [ FH (11), SDHB (4), and SMARCB1 (1)] were detected in previously unsuspected patients. Integrative analysis supported the presence of NF2-loss (NF2), hyperactive mTOR-driven (MTOR), FH/SDH-deficient (FH/SDH), and chromatin/DNA damage response (Chrom/DDR) molecular subsets. Univariate analysis of combined DC and VC (n = 137, median f/u 26 mos, death 74%) showed a significantly higher risk associated with NF2 subset than the MTOR group (Table). Clonality analysis confirmed NF2 inactivating mutation as a main driver in the NF2 subset. Rare cases with alterations indicating sensitivity or resistance to immunotherapy were also identified. Conclusions: Molecular features of high-grade uRCC improve risk stratification and provide rationale for distinct management strategies. [Table: see text]

A genomewide transcriptomic approach identifies a novel gene expression signature for the detection of lymph node metastasis in patients with early stage gastric cancer.

BackgroundAlthough identification of lymph node (LN) metastasis is a well-recognized strategy for improving outcomes in patients with gastric cancer (GC), currently there is lack of availability of adequate molecular biomarkers that can identify such metastasis. Herein we have developed a robust gene-expression signature for detecting LN metastasis in early stage GC by using a transcriptome-wide biomarker discovery and subsequent validation in multiple clinical cohorts.MethodsA total of 532 patients with pathological T1 and T2 GC from 4 different cohorts were analyzed. Two independent datasets (n = 96, and n = 188) were used to establish a gene signature for the identification of LN metastasis in GC patients. The diagnostic performance of our gene-expression signature was subsequently assessed in two independent clinical cohorts using qRT-PCR assays (n = 101, and n = 147), and subsequently compared against conventional tumor markers and image-based diagnostics.FindingsWe established a 15-gene signature by analyzing multiple high throughput datasets, which robustly distinguished LN status in both training (AUC = 0.765, 95% CI 0.667–0.863) and validation cohorts (AUC = 0.742, 95% CI 0.630–0.852). Notably, the 15-gene signature was significantly superior compared to the conventional tumor markers, CEA (P = .04) and CA19–9 (P = .005), as well as computed tomography-based imaging (P = .04).InterpretationWe have established and validated a 15-gene signature for detecting LN metastasis in GC patients, which offers a robust diagnostic tool for potentially improving treatment outcomes in gastric cancer patients.FundNIH: CA72851, CA181572, CA14792, CA202797, CA187956; CPRIT: RP140784: Baylor Sammons Cancer Center polot grants (AG), VPRT: 9610337, CityU 21101115, 11102317, 11103718; JCYJ20170307091256048 (XW).

Genetic analysis suggests high misassignment rates in clinical Alzheimer's cases and controls

Genetic case-control association studies are often based on clinically ascertained cases and population or convenience controls. It is known that some of the controls will contain cases, as they are usually not screened for the disease of interest. However, even clinically assessed cases and controls can be misassigned. For Alzheimer's disease (AD), it is important to know the accuracy of the clinical assignment. The predictive accuracy of AD risk by polygenic risk score analysis has been reported in both clinical and pathologically confirmed cohorts. The genetic risk prediction can provide additional insights to inform classification of subjects to case and control sets at a preclinical stage. In this study, we take a mathematical approach and aim to assess the importance of a genetic component for the assignment of subjects to AD-positive and -negative groups, and provide an estimate of misassignment rates (MARs) in AD case/control cohorts accounting for genetic prediction modeling results. The derived formulae provide a tool to estimate MARs in any sample. This approach can also provide an estimate of the maximal and minimal MARs and therefore could be useful for statistical power estimation at the study design stage. We illustrate this approach in 2 independent clinical cohorts and estimate misdiagnosis rate up to 36% in controls unscreened for the APOE genotype, and up to 29% when E3 homozygous subjects are used as controls in clinical studies.