Molecular Heterogeneity Of Cancer Research Articles

Targeted and Self-Adjuvated Nanoglycovaccine Candidate for Cancer Immunotherapy.

Advanced-stage solid primary tumors and metastases often express mucin 16 (MUC16), carrying immature glycans such as the Tn antigen, resulting in specific glycoproteoforms not found in healthy human tissues. This presents a valuable approach for designing targeted therapeutics, including cancer glycovaccines, which could potentially promote antigen recognition and foster the immune response to control disease spread and prevent relapse. In this study, we describe an adjuvant-free poly(lactic-co-glycolic acid) (PLGA)-based nanoglycoantigen delivery approach that outperforms conventional methods by eliminating the need for protein carriers while exhibiting targeted and adjuvant properties. To achieve this, we synthesized a library of MUC16-Tn glycoepitopes through single-pot enzymatic glycosylation, which were then stably engrafted onto the surface of PLGA nanoparticles, generating multivalent constructs that better represent cancer molecular heterogeneity. These glycoconstructs demonstrated affinity for Macrophage Galactose-type Lectin (MGL) receptor, known to be highly expressed by immature antigen-presenting cells, enabling precise targeting of immune cells. Moreover, the glycopeptide-grafted nanovaccine candidate displayed minimal cytotoxicity and induced the activation of dendritic cells in vitro, even in the absence of an adjuvant. In vivo, the formulated nanovaccine candidate was also nontoxic and elicited the production of IgG specifically targeting MUC16 and MUC16-Tn glycoproteoforms in cancer cells and tumors, offering potential for precise cancer targeting, including targeted immunotherapies.

Cost-effective methylome sequencing of cell-free DNA for accurately detecting and locating cancer

Early cancer detection by cell-free DNA faces multiple challenges: low fraction of tumor cell-free DNA, molecular heterogeneity of cancer, and sample sizes that are not sufficient to reflect diverse patient populations. Here, we develop a cancer detection approach to address these challenges. It consists of an assay, cfMethyl-Seq, for cost-effective sequencing of the cell-free DNA methylome (with > 12-fold enrichment over whole genome bisulfite sequencing in CpG islands), and a computational method to extract methylation information and diagnose patients. Applying our approach to 408 colon, liver, lung, and stomach cancer patients and controls, at 97.9% specificity we achieve 80.7% and 74.5% sensitivity in detecting all-stage and early-stage cancer, and 89.1% and 85.0% accuracy for locating tissue-of-origin of all-stage and early-stage cancer, respectively. Our approach cost-effectively retains methylome profiles of cancer abnormalities, allowing us to learn new features and expand to other cancer types as training cohorts grow.

Spatial Transcriptomics for Tumor Heterogeneity Analysis.

The molecular heterogeneity of cancer is one of the major causes of drug resistance that leads to treatment failure. Thus, better understanding the heterogeneity of cancer will contribute to more precise diagnosis and improved patient outcomes. Although single-cell sequencing has become an important tool for investigating tumor heterogeneity recently, it lacks the spatial information of analyzed cells. In this regard, spatial transcriptomics holds great promise in deciphering the complex heterogeneity of cancer by providing localization-indexed gene expression information. This study reviews the applications of spatial transcriptomics in the study of tumor heterogeneity, discovery of novel spatial-dependent mechanisms, tumor immune microenvironment, and matrix microenvironment, as well as the pathological classification and prognosis of cancer. Finally, future challenges and opportunities for spatial transcriptomics technology’s applications in cancer are also discussed.

A hierarchical spike-and-slab model for pan-cancer survival using pan-omic data

BackgroundPan-omics, pan-cancer analysis has advanced our understanding of the molecular heterogeneity of cancer. However, such analyses have been limited in their ability to use information from multiple sources of data (e.g., omics platforms) and multiple sample sets (e.g., cancer types) to predict clinical outcomes. We address the issue of prediction across multiple high-dimensional sources of data and sample sets by using molecular patterns identified by BIDIFAC+, a method for integrative dimension reduction of bidimensionally-linked matrices, in a Bayesian hierarchical model. Our model performs variable selection through spike-and-slab priors that borrow information across clustered data. We use this model to predict overall patient survival from the Cancer Genome Atlas with data from 29 cancer types and 4 omics sources and use simulations to characterize the performance of the hierarchical spike-and-slab prior.ResultsWe found that molecular patterns shared across all or most cancers were largely not predictive of survival. However, our model selected patterns unique to subsets of cancers that differentiate clinical tumor subtypes with markedly different survival outcomes. Some of these subtypes were previously established, such as subtypes of uterine corpus endometrial carcinoma, while others may be novel, such as subtypes within a set of kidney carcinomas. Through simulations, we found that the hierarchical spike-and-slab prior performs best in terms of variable selection accuracy and predictive power when borrowing information is advantageous, but also offers competitive performance when it is not.ConclusionsWe address the issue of prediction across multiple sources of data by using results from BIDIFAC+ in a Bayesian hierarchical model for overall patient survival. By incorporating spike-and-slab priors that borrow information across cancers, we identified molecular patterns that distinguish clinical tumor subtypes within a single cancer and within a group of cancers. We also corroborate the flexibility and performance of using spike-and-slab priors as a Bayesian variable selection approach.

Abstract 24: Multi-feature ensemble learning on cell-free dna for accurately detecting and locating cancer

Abstract Early cancer detection by cell-free DNA (cfDNA) faces multiple challenges: the low fraction of tumor DNA in cfDNA, the molecular heterogeneity of cancer, and sample sizes that are too small to reflect the heterogeneous patient population. We have developed an integrated cancer detection system, CancerRadar, that addresses all three challenges. It consists of (1) a cost-effective experimental assay, cfMethyl-Seq, for genome-wide methylation profiling of cfDNA, which provides >12-fold enrichment over Whole Genome Bisulfite Sequencing (WGBS) in CpG islands; and (2) a computational platform to extract information from cfMethyl-Seq data and diagnose the patient. The platform derives cfDNA methylations, cfDNA fragment sizes, copy number variations (CNV), and microbial composition from the raw cfMethyl-Seq data, and performs multi-feature ensemble learning. We demonstrate the power of CancerRadar by detecting and locating cancer in a cohort of 275 colon, liver, lung, and stomach cancer patients and 204 non-cancer individuals. For cancer detection, we achieve a sensitivity of 85.6%± 6.7% across all stages and 80.6%±9.1% for early stages (I and II), with a specificity of 99% in both cases. These metrics are derived using leave-one-out cross-validation. During independent validation on a reserved subsample, it achieves a sensitivity of 89.1%±11.3% across all stages and 85.7%±14.2% for early stages, with a specificity of 97% (one false positive). For locating a tumor's tissue of origin (TOO), CancerRadar achieved an accuracy of 91.5%±5.0% for all stages and 89.1%±7.3% for early stages, on an independent subsample. This study is the first to integrate cfDNA methylation, cfDNA fragment size, CNV, and microbial composition analyses for cancer detection on the same patient cohort. cfDNA methylation was the most useful for detecting cancer, but including features from other categories significantly increased the performance, especially for early-stage cancer. In contrast, with respect to TOO prediction, methylation-derived features were overwhelmingly important while including other features did not further improve performance. To fully exploit the power of cfDNA methylation, we identified four types of methylation markers with different characteristics. We have also improved our previous read-level deconvolution algorithm to more accurately identify trace tumor signals. Finally, our data show that as training sample sizes increase, the detection power of CancerRadar continues to increase. Although all existing cancer detection studies are limited by training sample sizes, the CancerRadar system uniquely and cost-effectively retains the genome-wide epigenetic and genetic profiles of cancer abnormalities, thereby permitting the classification models to learn and exploit newly significant features as training cohorts grow, as well as expanding their scope to other cancer types. Citation Format: Mary Stackpole, Weihua Zeng, Shuo Li, Chun-Chi Liu, Yonggang Zhou, Shanshan He, Angela Yeh, Ziye Wang, Fengzhu Sun, Qingjiao Li, Zuyang Yuan, Asli Yildirim, Pin Jung Chen, Paul Winograd, Shize Li, Zorawar Noor, Edward Garon, Samuel French, Clara Magyar, Sarah Dry, Clara Lajonchere, Daniel Geschwind, Gina Choi, Sammy Saab, Frank Alber, Wing Hung Wong, Steven Dubinett, Denise Aberle, Vatche Agopian, Steven-Huy Han, Xiaohui Ni, Wenyuan Li, Xianghong Jasmine Zhou. Multi-feature ensemble learning on cell-free dna for accurately detecting and locating cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 24.

Major Histocompatibility Complex Genes as Therapeutic Opportunity for Immune Cold Molecular Cancer Subtypes.

Current immunotherapies are effective only in a subset of patients, likely due to several factors including defects in tumor cell antigen presentation, decreased response to immune effectors, and molecular heterogeneity of cancers. Recent molecular classifications enable the categorization of many tumor types. However, deregulation of major histocompatibility complex (MHC) gene expression is poorly characterized in the context of molecular cancer subtypes. To suppress the confounding effect of immune infiltrates on expression patterns of immunoregulators, we identified and removed genes with strong correlation to estimated immune compartment levels in each tumor type. Next, we reanalyzed a total of 13 TCGA cancer types encompassing 5651 tumors and 485 normal adjacent tissues by performing unsupervised clustering of 14 MHC genes. Subsequently, resultant clusters were statistically compared in terms of expression of other immune-related genes. Three MHC expression clusters were discovered by unsupervised clustering. We identified concordantly decreased expression of MHC genes (MHC-low) in 26 out of 55 molecular subtypes. Consequently, our study underlines the urgent need for designing strategies to enhance tumor MHC expression that could improve immune cold tumor rejection by cytotoxic T lymphocytes.

Clonal evaluation of prostate cancer molecular heterogeneity in biopsy samples by dual immunohistochemistry and dual RNA in situ hybridization.

Prostate cancer is frequently multifocal. Although there may be morphological variation, the genetic underpinnings of each tumor are not clearly understood. To assess the inter and intra tumor molecular heterogeneity in prostate biopsy samples, we developed a combined immunohistochemistry and RNA in situ hybridization method for the simultaneous evaluation of ERG, SPINK1, ETV1, and ETV4. Screening of 601 biopsy cores from 120 consecutive patients revealed multiple alterations in a mutually exclusive manner in 37% of patients, suggesting multifocal tumors with considerable genetic differences. Furthermore, the incidence of molecular heterogeneity was higher in African Americans patients compared with Caucasian American patients. About 47% of the biopsy cores with discontinuous tumor foci showed clonal differences with distinct molecular aberrations. ERG positivity occurred in low-grade cancer, whereas ETV4 expression was observed mostly in high-grade cancer. Further studies revealed correlation between the incidence of molecular markers and clinical and pathologic findings, suggesting potential implications for diagnostic pathology practice, such as defining dominant tumor nodules and discriminating juxtaposed but molecularly different tumors of different grade patterns.

A pan-cancer analysis of progression mechanisms and drug sensitivity in cancer cell lines.

Biomarker discovery involves identifying genetic abnormalities within a tumor. However, one of the main challenges in defining such therapeutic targets is accounting for the molecular heterogeneity of cancer. By integrating somatic mutation and gene expression data from hundreds of heterogeneous cell lines from the Cancer Cell Line Encyclopedia (CCLE), we identify sequences of genetic events that may help explain common patterns of oncogenesis across 22 tumor types, and evaluate the general effect of late-stage mutations on drug sensitivity and resistance mechanisms. Through gene enrichment analysis, we find several cancer-specific and immune pathways that are significantly enriched in each of our three proposed phases of cancer progression. By further analyzing the drug activity area associated with compounds that target the BRAF oncogene, a known predictor of drug sensitivity for several compounds used in cancer treatment, we verify that the acquisition of new driver mutations interferes with the targeted drug mechanism, meaning that cells without late-stage mutations generally respond better to drugs.

Abstract 2613: Predictive impact of low-frequency pretreatment T790M mutation in patients with EGFR-mutated non-small cell lung cancer treated with EGFR tyrosine kinase inhibitors

Abstract Background: T790M mutation in EGFR accounts for nearly 50% of the acquired mechanism of resistance to EGFR-tyrosine kinase inhibitors (TKIs). Previous studies suggested that EGFR T790M mutation was also detected in a considerable number of EGFR-TKI-naïve non-small-cell lung cancer (NSCLC) patients utilizing ultrasensitive detection methods such as droplet digital PCR (ddPCR). Here we investigated the significance of low-frequency pretreatment EGFR T790M mutation (preT790M) and its association with cancer molecular heterogeneity in the efficacy of EGFR-TKIs. Materials and methods: Fifty-two advanced NSCLC patients harboring activating EGFR mutations treated with first-line EGFR-TKIs at Osaka City University Hospital between August 2013 and July 2016 were enrolled in the study. DNAs from tumor biopsies at diagnosis were available from 44 patients for detecting preT790M by the cobas® EGFR Mutation Test v2 (cobas) and ddPCR (RainDance Technologies) and those from 33 were available for assessing the actionable mutations in 50 genes by next-generation sequencing (NGS). NGS was performed on the Ion PGM using Ion AmpliSeq Cancer Hotspot Panel v2 (Life Technologies). Paired biopsies before EGFR-TKIs treatment and at disease progression (PD) were obtained from 15 patients to assess the T790M mutation by cobas. Results: The overall detection rate of preT790M by ddPCR was 40.9% (18/44), while not detected in any case by cobas. The median progression free survival (mPFS) was 10.0 months for the patients with preT790M and 13.5 months for those without preT790M (p=0.288), respectively. When divided into 3 categories based on the frequency of preT790M such as high with T790M allele frequency (AF) >0.3%, low with AF ≤0.3% and undetected, 12 patients with high AF had a relatively shorter mPFS than 6 with low AF (p=0.090) and 26 without detectable preT790M (p=0.046) (7.7 vs 17.1 vs 13.5 months, respectively). NGS revealed 18 additional coexisting actionable mutations in 15 out of 33 patients: TP53 (n=7), PIK3CA (n=5), CTNNB1 (n=3) and uncommon EGFR (n=3). There was a trend for patients with higher preT790M AF to harbor less additional coexisting mutations (27% with high AF, 60% with low AF and 53% without preT790M, p=0.320). In 15 paired biopsies, T790M mutation was detected in 60% (9/15) at PD by cobas in the clinical setting; 67% (2/3) with high AF, 50% (1/2) with low AF and 60% (6/10) without detectable preT790M, suggesting T790M mutation after first-line EGFR-TKIs developed from both clonal selection and secondary acquisition models. Conclusion: Results of our study indicated that EGFR-mutated NSCLCs with higher AF preT790M >0.3% had significantly shorter duration of response to EGFR-TKIs. In addition, the AF of preT790M in EGFR-mutated NSCLC may be associated with coexisting actionable mutation load potentially affecting the efficacy of EGFR-TKIs. Citation Format: Yoshiya Matsumoto, Kenji Sawa, Jun Oyanagi, Mitsuru Fukui, Naoki Yoshimoto, Tomohiro Suzumura, Shigeki Mitsuoka, Kazuhisa Asai, Tatsuo Kimura, Nobuyuki Yamamoto, Tomoya Kawaguchi, Kazuto Hirata, Yasuhiro Koh. Predictive impact of low-frequency pretreatment T790M mutation in patients with EGFR-mutated non-small cell lung cancer treated with EGFR tyrosine kinase inhibitors [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 2613.

Abstract 1317: Multi-scale mapping of the physical and functional architecture of the cancer cell

Abstract Cancer is governed by modular systems of genes, the composition and organization of which remains poorly understood. Here, we integrate physical and functional networks from a wide range of molecular studies to assemble a comprehensive multi-scale map of human cancer cell biology. This map consists of a hierarchical catalog of protein complexes, signaling pathways and inter-pathway crosstalk implicated in cancer, and it suggests many uncharacterized functional modules as intriguing hypotheses for further validation. Analysis of the pattern of somatic mutations in The Cancer Genome Atlas (TCGA) reveals that these mutations target systems of varying scales above the level of individual genes. The map also provides a platform to integrate and interpret new 'omics data; we integrate new protein-protein interactions identified using AP-MS in multiple breast cancer cell lines, revealing how different functional modules are rewired in cancer cells. A general model browsing tool has been created to visualize and navigate these hierarchical cancer maps. This multi-scale mapping approach elucidates the molecular heterogeneity of cancer, connects tumor genotypes to phenotypes and, ultimately, enables a platform for cancer precision medicine. Citation Format: Fan Zheng, Michael K. Yu, Minkyu Kim, Keiichiro Ono, Mitchell Flagg, Jason F. Kreisberg, Nevan Krogan, Trey Ideker. Multi-scale mapping of the physical and functional architecture of the cancer cell [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 1317.

Abstract 3698: Unraveling triple-negative breast cancer molecular heterogeneity using an integrative multiomic analysis

Abstract Introduction : Triple negative breast cancers (TNBCs) represent about 10-20% of all breast cancers and show worse prognosis compared to other molecular subtypes with increased likelihood of early distant recurrence and death. Recent efforts of genome-wide gene expression profiling analyses have improved our understanding of the biological complexity and diversity of TNBCs reporting, at least 6 different molecular subtypes of TNBC namely Basal-like 1 (BL1), basal-like 2 (BL2), immunomodulatory (IM), mesenchymal (M), mesenchymal stem-like (MSL) and luminal androgen receptor (LAR). However, little is known regarding the potential driving molecular events within each subtype, their difference in survival and response to therapy. Further insight into the underlying genomic alterations is therefore needed. Aims : Here, we aimed to study the genomic aberrations that drive each of these TNBC subtypes by applying an integrated analysis of somatic mutation, copy number and gene expression of 550 TNBC derived from Molecular Taxonomy of Breast Cancer International (METABRIC) and The Cancer Genome Atlas (TCGA) consortia. Methods : The METABRIC & the TCGA datasets were used, with available data for copy-number aberrations, somatic mutations and gene expression. ER, HER2 and PR status were inferred based on the bimodality of their mRNA expression level. TNBC samples (n=550) were classified according to Lehmann's molecular subtypes using the TNBCtype online subtyping tool (http://cbc.mc.vanderbilt.edu/tnbc). Results : We were able to globally reproduce Lehmann's classification with Basal-like 1 (BL1), Immunomodulatory (IM), Androgen-Receptor-like (LAR), Mesenchymal-like (M) and Mesenchymal Stem Cell-like (MSL) being the most stable TNBC subtypes. Each subtype showed significant clinic-pathological characteristic differences. Using a multivariate model, the IM and LAR subtypes showed to be associated with a better prognosis (HR=0.68; CI=0.46-0.99; p=0.043) and a worst prognosis (HR=1.47; CI=1.0-2.14; p=0.046), respectively. BL1 subtype was found to be most genomically instable subtype with high TP53 mutation (92%) and copy-number deletion in genes involved in DNA repair mechanism (BRCA2, MDM2, PTEN, RB1 & TP53). LAR tumours were associated with higher mutational burden with significantly enriched mutations in PI3KCA (55%), AKT1 (13%) and CDH1 (13%) genes. M and MSL subtypes were associated with higher signature score for angiogenesis. IM showed high expression levels of immune signatures and check-point inhibitor genes such as PD1, PDL1 and CTLA4. Conclusions : Our findings highlight for the first time the substantial genomic heterogeneity that characterize TNBC molecular subtypes, allowing for a better understanding of the disease biology as well as the identification of several candidate targets paving novel approaches for the development of anti-cancer therapeutics for TNBC. Citation Format: Yacine Bareche, David Venet, Philippe Aftimos, Michail Ignatiadis, Martine Piccart, Francoise Rothe, Christos Sotiriou. Unraveling triple-negative breast cancer molecular heterogeneity using an integrative multiomic analysis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 3698.

MiRNA-135b Contributes to Triple Negative Breast Cancer Molecular Heterogeneity: Different Expression Profile in Basal-like Versus non-Basal-like Phenotypes.

The clinical and genetic heterogeneity of Triple Negative Breast Cancer (TNBC) and the lack of unambiguous molecular targets contribute to the inadequacy of current therapeutic options for these variants. MicroRNAs (miRNA) are a class of small highly conserved regulatory endogenous non-coding RNA, which can alter the expression of genes encoding proteins and may play a role in the dysregulation of cellular pathways. Our goal was to improve the knowledge of the molecular pathogenesis of TNBC subgroups analyzing the miRNA expression profile, and to identify new prognostic and predictive biomarkers.We conducted a human miRNome analysis by TaqMan Low Density Array comparing different TNBC subtypes, defined by immunohistochemical basal markers EGFR and CK5/6. RT-qPCR confirmed differential expression of microRNAs. To inspect the function of the selected targets we perform Gene Ontology and KEGG enrichment analysis.We identified a single miRNA signature given by miR-135b expression level, which was strictly related to TNBC with basal-like phenotype. miR-135b target analysis revealed a role in the TGF-beta, WNT and ERBB pathways. A significant positive correlation was identified between neoplastic proliferative index and miR-135b expression.These findings confirm the oncogenic roles of miR-135b in the pathogenesis of TNBC expressing basal markers. A potential negative prognostic role of miR-135b overexpression might be related to the positive correlation with high proliferative index. Our study implies potential clinical applications: miR-135b could be a potential therapeutic target in basal-like TNBCs.

POPDC1 is suppressed in human breast cancer tissues and is negatively regulated by EGFR in breast cancer cell lines

Breast cancer molecular heterogeneity has resulted in disparities in therapeutic response and targeting of molecular subtypes of breast cancer. This necessitates identification and validation of novel therapeutic targets for breast cancer treatment. Suppression of Popeye domain-containing (POPDC) proteins is hypothesized to promote malignant cell behaviour and poor clinical outcomes in various cancers. We aimed to determine whether POPDC proteins are suppressed in human ductal carcinoma tissues and if this correlates to clinical progression and Her2 status. We further assessed if the EGFR regulated POPDC1 in breast cancer. Here we show significant suppression of POPDC1 in malignant breast cancer tissues without correlation to clinical progression. Interestingly, POPDC2 and POPDC3 were highly expressed in malignant breast tissues. Furthermore, HER2+ status significantly correlated with high POPDC2 and POPDC3, but not POPDC1 expression. We further show for the first time that low POPDC1 correlates to high EGFR expression in breast cancer tissues and that EGFR negatively regulates POPDC1 expression in MCF7, MDA231 and SKBR3 breast cancer cells. Furthermore, overexpression of POPDC1 in MCF7, MDA231 and SKBR3 cells attenuated EGF-mediated cell migration and proliferation. These findings show that POPDC1 is suppressed in breast cancer and can potentially be targeted to inhibit EGFR-mediated cell migration and proliferation.

Novel workflow for combining Raman spectroscopy and MALDI-MSI for tissue based studies.

Molecular heterogeneity of cancer is a major obstacle in tumor diagnosis and treatment. To deal with this heterogeneity, a multidisciplinary combination of different analysis techniques is of urgent need because a combination enables the creation of a multimodal image of a tumor. Here, we develop a computational workflow in order to combine matrix-assisted laser desorption/ionization mass spectrometric (MALDI-MS) imaging and Raman microspectroscopic imaging for tissue based studies. The computational workflow can be used to confirm a spectral histopathology (SHP) based on one technique with another technique. In this contribution, we confirmed a Raman spectroscopic based SHP with MALDI-imaging. Owing to this combination, we could demonstrate, for a larynx carcinoma sample, that tissue types and different metabolic states could be extracted from the Raman spectra. Further investigations with the help of MALDI spectra yield a better characterization of variable epithelial differentiation and a better understanding of ongoing dysplastic alterations.

PD33-06 A RETROSPECTIVE COHORT STUDY OF RENAL CANCER MOLECULAR HETEROGENEITY AND DNA METHYLATION PROGNOSTIC MARKERS.

You have accessJournal of UrologyKidney Cancer: Basic Research III1 Apr 2015PD33-06 A RETROSPECTIVE COHORT STUDY OF RENAL CANCER MOLECULAR HETEROGENEITY AND DNA METHYLATION PROGNOSTIC MARKERS. Alexander Laird, Duncan Sproul, Grant Stewart, Fiach O'Mahony, Antony Riddick, Richard Meehan, and David Harrison Alexander LairdAlexander Laird More articles by this author , Duncan SproulDuncan Sproul More articles by this author , Grant StewartGrant Stewart More articles by this author , Fiach O'MahonyFiach O'Mahony More articles by this author , Antony RiddickAntony Riddick More articles by this author , Richard MeehanRichard Meehan More articles by this author , and David HarrisonDavid Harrison More articles by this author View All Author Informationhttps://doi.org/10.1016/j.juro.2015.02.2104AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail INTRODUCTION AND OBJECTIVES Predicting which renal cell cancer (RCC) patients recur after surgery remains difficult and there are currently no molecular prognostic markers in clinical use. Genetic Intra-Tumoural Heterogeneity (genetic ITH) has been described in RCC and may limit clinical translation of biomarkers. There has been no assessment of ITH at other molecular levels. We aimed to define and compare morphological, proteomic, transcriptomic and DNA methylation ITH in RCC and identify potential prognostic biomarkers. METHODS Tissue sampling was performed from multiple areas of primary tumours from 23 RCC patients (cohort 1) for intra-tumoural proteomic, transcrptomic and DNA methylation heterogeneity analysis. ITH was assessed using the ANOVA test and by comparison of unsupervised hierarchical clustering. Identification and validation of prognostic differentially methylated regions (DMR) was performed on 3 cohorts, 2 database and 1 experimental (cohort 2 TCGA, n=154; cohort 3 Edinburgh, n=46; cohort 4 CGP, n=194). RESULTS RCC exhibits protein, gene expression and methylation ITH. Protein ITH is greater than transcriptomic and methylation ITH for targets implicated in RCC pathogenesis (mean variance (MV [IQR]) 0.08[0.08], 0.07 [0.04], 0.07 [0.05] respectively; p<0.0001). Methylation ITH (MV 0.08 [0.05]) is significantly less than inter-patient variance of methylation (20.9 [8.5], p<0.0001). There were 322 DMRs between recurrent and non-recurrent patients in cohort 2 but only 5 DMRs were validated in cohort 3. NEFM gene promoter methylation was the only DMR associated with cancer specific survival (mean CSS; unmethylated-NEFM=107months, methylated-NEFM=49months; p<0.001), which was independent of TNM stage and nuclear grade on multivariate analysis (HR1.8 [1.1-3.0]; p=0.02). This was confirmed on cohort 4. CONCLUSIONS RCC intra-tumoural protein heterogeneity is significantly greater than methylation and gene-expression ITH. While methylation ITH exists, its occurrence is significantly less than inter-patient variance. This led us to investigate methylation associated prognostic markers in RCC and we found promoter methylation of NEFM is associated with poorer cancer specific survival independent of tumour stage, grade and node status. © 2015 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 193Issue 4SApril 2015Page: e711 Advertisement Copyright & Permissions© 2015 by American Urological Association Education and Research, Inc.MetricsAuthor Information Alexander Laird More articles by this author Duncan Sproul More articles by this author Grant Stewart More articles by this author Fiach O'Mahony More articles by this author Antony Riddick More articles by this author Richard Meehan More articles by this author David Harrison More articles by this author Expand All Advertisement Advertisement PDF downloadLoading ...

Molecular heterogeneity assessment by next-generation sequencing and response to gefitinib of EGFR mutant advanced lung adenocarcinoma.

Cancer molecular heterogeneity might explain the variable response of EGFR mutant lung adenocarcinomas to tyrosine kinase inhibitors (TKIs). We assessed the mutational status of 22 cancer genes by next-generation sequencing (NGS) in poor, intermediate or good responders to first-line gefitinib. Clinical outcome was correlated with Additional Coexisting Mutations (ACMs) and the EGFR Proportion of Mutated Alleles (PMA). Thirteen ACMs were found in 10/17 patients: TP53 (n=6), KRAS (n=2), CTNNB1 (n=2), PIK3CA, SMAD4 and MET (n=1 each). TP53 mutations were exclusive of poor/intermediate responders (66.7% versus 0, p=0.009). Presence of ACMs significantly affected both PFS (median 3.0 versus 12.3 months, p=0.03) and survival (3.6 months versus not reached, p=0.03). TP53 mutation was the strongest negative modifier (median PFS 4.0 versus 14.0 months). Higher EGFR PMA was present in good versus poor/intermediate responders. Median PFS and survival were longer in patients with EGFR PMA ≥0.36 (12.0 versus 4.0 months, p=0.31; not reached versus 18.0 months, p=0.59). Patients with an EGFR PMA ≥0.36 and no ACMs fared significantly better (p=0.03), with a trend towards increased survival (p=0.06). Our exploratory data suggest that a quantitative (PMA) and qualitative (ACMs) molecular heterogeneity assessment using NGS might be useful for a better selection of patients.

Abstract 4720: Evaluation of a genomic classifier (Decipher®) in subsets of primary tumors with common prostate cancer genomic alterations

Abstract Background ETS gene fusions and PTEN loss are the most common genomic alteration events in prostate cancer that stratify patients into molecularly distinct subtypes. Recently, a commercially available genomic classifier (GC, DecipherTM) has been validated to predict metastasis in patients with high-risk prostate cancer. In this study we evaluated the robustness of the GC model in the light of prostate cancer molecular heterogeneity. Methods: Analysis of PTEN expression loss, ERG overexpression and GC scores were performed on a previously reported genome-wide microarray dataset (Karnes et al., 2013). Microarray probesets mapping to exons of PTEN were benchmarked to identify a representative exon whose loss of expression was highly correlated to FISH analysis for PTEN deletion (PTEN-loss) performed on an independent dataset. ERG overexpression (ERG+) was determined similarly and the microarray probesets that best correlate with the FISH status (in independent dataset) were used to annotate the ERG status of the samples in this study. Receiver operating characteristic area under the curve (AUC), and multivariable regression analysis were used to assess GC performance for predicting metastasis in subsets of patient tumors with genomic alterations. Results Using the microarray method 42% of the tumors had ERG overexpression, 23% had PTEN-loss and 11% had both events. ERG+ and PTEN-loss were not on their own predictive of metastasis with AUCs of 0.52 (CI:0.44-0.6) and 0.47(CI 0.39-0.56), respectively. In ERG+ tumors GC had an AUC of 0.82 (p=4e-7) and high GC score ERG+ patients had a hazard ratio (HR) of 100 (p=6e-7) for metastasis. Similarly, in PTEN-loss tumors GC had an AUC of 0.83 (p=7.9e-5) and high GC score PTEN-loss patients had an HR of 62 (p=1.8e-4) for metastasis. When GC was evaluated in patient tumors with both ERG+ and PTEN-loss the AUC was 0.85 (p=2e-15) and high GC score patients had an HR of 95 (p=4e-4) for metastasis. In multivariable analysis, Decipher was the only significant risk factor with an HR of 55 (CI 12.2-249, p=1.8e-7) for metastasis after adjusting for ERG+, PTEN-loss, Gleason score, PSA, tumor stage, margins and adjuvant treatment. Conclusion The Decipher genomic classifier is a powerful prognostic marker that retains its significance for predicting metastasis in subsets of patients with tumors that harbor common genomic alterations. Patients with high GC scores and these alterations may represent an extremely high-risk subset of prostate cancer. Citation Format: Mohammed Alshalalfa, Ismael Vergara, Nicholas Erho, Elai Davicioni, Robert Jenkins, Kollmeyer Thomas. Evaluation of a genomic classifier (Decipher®) in subsets of primary tumors with common prostate cancer genomic alterations. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 4720. doi:10.1158/1538-7445.AM2014-4720

Cancer Outlier Analysis Based on Mixture Modeling of Gene Expression Data

Molecular heterogeneity of cancer, partially caused by various chromosomal aberrations or gene mutations, can yield substantial heterogeneity in gene expression profile in cancer samples. To detect cancer-related genes which are active only in a subset of cancer samples or cancer outliers, several methods have been proposed in the context of multiple testing. Such cancer outlier analyses will generally suffer from a serious lack of power, compared with the standard multiple testing setting where common activation of genes across all cancer samples is supposed. In this paper, we consider information sharing across genes and cancer samples, via a parametric normal mixture modeling of gene expression levels of cancer samples across genes after a standardization using the reference, normal sample data. A gene-based statistic for gene selection is developed on the basis of a posterior probability of cancer outlier for each cancer sample. Some efficiency improvement by using our method was demonstrated, even under settings with misspecified, heavy-tailed t-distributions. An application to a real dataset from hematologic malignancies is provided.

Classification OMS 2010 des tumeurs digestives : la quatrième édition

Gastric cancer is a major source of global cancer mortality with limited treatment options and poor patient survival. As our molecular understanding of gastric cancer improves, we are now beginning to recognize that these cancers are a heterogeneous group of diseases with incredibly unique pathogeneses and active oncogenic pathways. It is this molecular diversity and oftentimes lack of common oncogenic driver mutations that bestow the poor treatment responses that oncologists often face when treating gastric cancer. In this review, we will examine the treatments for gastric cancer including up-to-date molecularly targeted therapies and immunotherapies. We will then review the molecular subtypes of gastric cancer to highlight the diversity seen in this disease. We will then shift our discussion to basic science and gastric cancer mouse models as tools to study gastric cancer molecular heterogeneity. Furthermore, we will elaborate on a molecular process termed paligenosis and the cyclical hit model as key events during gastric cancer initiation that impart nondividing mature differentiated cells the ability to re-enter the cell cycle and accumulate disparate genomic mutations during years of chronic inflammation and injury. As our basic science understanding of gastric cancer advances, so too must our translational and clinical efforts. We will end with a discussion regarding single-cell molecular analyses and cancer organoid technologies as future translational avenues to advance our understanding of gastric cancer heterogeneity and to design precision-based gastric cancer treatments. Elucidation of interpatient and intratumor heterogeneity is the only way to advance future cancer prevention, diagnoses and treatment.

S5 Biomarkers for early detection and as surrogate endpoints in cancer prevention trials: issues and opportunities

In order to improve the early detection and diagnosis of cancer, give more accurate prognoses, stratify individuals by risk, predict response to treatment, and help the transition of basic research into clinical application, biomarkers are needed that accurately represent or predict clinical outcomes. To be useful in trials for chemopreventive agent development, biomarkers must be subject to modulation, easy to obtain and quantify, and have biological meaning, ideally representing steps in well-understood carcinogenic pathways. Though difficult to validate fully, wisely chosen biomarkers in early-phase trials can inform the prioritization of large-scale, long-term trials that measure clinical outcomes. When well-designed, smaller trials using biomarkers as surrogate endpoints should promote faster decisions regarding which targeted preventive agents to pursue, promising greater progress in the personalization of medicine. Biomarkers could become useful in distinguishing indolent from aggressive forms of ductal carcinoma in situ as well as localized invasive breast and prostate cancer, lesions that are often overtreated. Chemopreventive strategies that reduce the progression of early forms of premalignancy can benefit patients not only by reducing their risk of cancer and death from cancer but also by reducing their need for invasive interventions. Genomic and proteomic methods offer the possibility of revealing new potential markers, especially for diseases whose biology is complex or not well understood. Panels of markers may be used to accommodate the molecular heterogeneity of cancers. Biomarkers in phase 2 prevention trials of combinations of chemopreventive drugs have been used to demonstrate synergistic action of multiple agents, allowing use of lower doses, with less toxicity, a critical feature of interventions intended for cancer prevention.