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Cancers often overexpress multiple clinically relevant oncogenes, but it is not known if combinations of oncogenes in cellular subpopulations within a cancer influence clinical outcomes. Using quantitative multispectral imaging of the prognostically relevant oncogenes MYC, BCL2, and BCL6 in diffuse large B-cell lymphoma (DLBCL), we show that the percentage of cells with a unique combination MYC+BCL2+BCL6− (M+2+6−) consistently predicts survival across four independent cohorts (n = 449), an effect not observed with other combinations including M+2+6+. We show that the M+2+6− percentage can be mathematically derived from quantitative measurements of the individual oncogenes and correlates with survival in IHC (n = 316) and gene expression (n = 2,521) datasets. Comparative bulk/single-cell transcriptomic analyses of DLBCL samples and MYC/BCL2/BCL6-transformed primary B cells identify molecular features, including cyclin D2 and PI3K/AKT as candidate regulators of M+2+6− unfavorable biology. Similar analyses evaluating oncogenic combinations at single-cell resolution in other cancers may facilitate an understanding of cancer evolution and therapy resistance. Significance: Using single-cell–resolved multiplexed imaging, we show that selected subpopulations of cells expressing specific combinations of oncogenes influence clinical outcomes in lymphoma. We describe a probabilistic metric for the estimation of cellular oncogenic coexpression from IHC or bulk transcriptomes, with possible implications for prognostication and therapeutic target discovery in cancer. This article is highlighted in the In This Issue feature, p. 1027

Supplementary methods. Supplementary Figure 1. Phenotyping of B-cells in non-malignant tissues. A, Quantitation of marker positivity across ten tonsil and two reactive lymph node samples (rLN). Analysis is spatially resolved between the GC and extra-GC zones. B, Spatial map of cellular coordinates based on cell segmentation of images in Figure 1B. Marker-positivity is indicated, and a total proportion of positive and negative cells is depicted as a pie chart. These maps were used to derive sub-population phenotypes depicted in Figure 1C. Scale bar is 100μm. C, Proliferation analysis (i.e., Ki67-positivity) among sub-populations in five tonsil samples. Median with interquartile range, whiskers denote 10th and 90th percentile. Supplementary figure 2. Example pseudo-colored mfIHC images for MYC, BCL2, BCL6 cases in DLBCL. Images of a range of mean fluorescent intensities are shown with equal scaling for reference. Supplementary figure 3. Global distribution of MYC, BCL2 and BCL6 sub-populations within DLBCL cohorts. Heat-maps displaying the percentage extent of individual markers and each sub-population within the DLBCL NUH, CMMC, SGH and MDA cohorts. Hierarchical k-means clustering of patients according to sub-population extent is applied. Positivity shading for single markers ranges between 0-100% positivity, whereas shading for sub-populations reflects 0-50% positivity and remains fully saturated until 100%. IPI Risk Group - International Prognostic Index Risk Group, FISH - fluorescence in situ hybridization. Supplementary figure 4. Intra-tumor heterogeneity of sub-populations. A, Correlation of sub-population extent quantification between two biopsies of the same patient for which at least two tissue microarray (TMA) biopsies are available. Correlation is shown separately for lymph node and extranodal biopsies. Spearman rho is indicated for each correlation. Axes are in exponential and equivalent in all panels. B, Sub-population percentage extent quantification across multiple TMA cores (columns) of the same patient (rows). Pie charts are ordered according to decreasing cell numbers evaluated per core. All patients from the NUH cohort with at least five cores are evaluated. A heterogenous cluster is highlighted by the red box. Supplementary figure 5. Spatial heterogeneity of sub-population interactions. A, Conceptual schematic of pair correlation function (PCF) plots depicting a clustered distribution (left, green) and a random distribution (right, grey). Representative counterpart spatial maps are above each plot. B, PCF analysis for sub-populations to investigate spatial clustering (top). Mean results for two independent cohorts (shading is cohort standard deviation). An example tissue microarray core is shown as physical distance reference for spatial analyses (bottom left). Absolute number of neighboring cells expected within a given radius (data from 3500 randomly selected cells across all images, mean with standard deviation) (bottom right). C, Actual spatial map of sub-populations of an example DLBCL case (top). Extent of all sub-populations within the sample is shown on the left. Simulated, hypothetical random distribution of cells for the same case (middle). PCF analysis for the shown sample and its matched simulated random distribution (bottom). Scale bars in B and C are 100µm. D, Mean deviations from expected neighbor abundance (Δ%) summarizing cell-cell interactions between sub-populations for the sample shown in (C). E, Sub-population interaction matrices from spatially distinct biopsies (cores in tissue microarray) for example DLBCL patients. Biopsies of stable, spatially homogenous, sub-population interaction profiles are grouped (top), whereas biopsies of a differing, heterogenous, interaction profile are grouped separately (bottom). Supplementary figure 6. Global deviations from expected spatial neighbor abundance (Δ%). Hierarchical clustering (minimum variance method) of measured Δ% for all cases in the SGH and MDA cohorts. Extents of sub-populations are indicated for reference (top). For the MDA cohort, multiple biopsies (n = 1-3) from the same patient were included in the analysis to determine spatial interaction similarity across spatially distinct regions (bottom). Supplementary figure 7. Correlation of predicted MYC, BCL2 and BCL6 sub-population percentage extent based on single oncogene positivity and observed percentage extent in DLBCL cohorts. Spearman rho, axes are equivalent in all panels. Supplementary figure 8. Variance of M+2+6- percentage extent in the context of positivity calling across a 15% cut-off. A, M+2+6- scoring variance across multiple pathological imaging fields. All whole-tissue DLBCL sections from University of Palermo (UP), and samples from the NUH TMA with at least four fields scored per patient and a mean M+2+6- score above 5% are shown. Mean with SD. Ordinates between 50-100% are compressed for clarity. Dashed line denotes M+2+6- 15% positivity. B, Stability of M+2+6- case positivity calling across scoring increasing number of imaging fields. All cases from panel A with at least five fields scored in this study are shown. Only one case is called M+2+6- Low (

Cancers often overexpress multiple clinically relevant oncogenes, but it is not known if combinations of oncogenes in cellular subpopulations within a cancer influence clinical outcomes. Using quantitative multispectral imaging of the prognostically relevant oncogenes MYC, BCL2, and BCL6 in diffuse large B-cell lymphoma (DLBCL), we show that the percentage of cells with a unique combination MYC+BCL2+BCL6− (M+2+6−) consistently predicts survival across four independent cohorts (n = 449), an effect not observed with other combinations including M+2+6+. We show that the M+2+6− percentage can be mathematically derived from quantitative measurements of the individual oncogenes and correlates with survival in IHC (n = 316) and gene expression (n = 2,521) datasets. Comparative bulk/single-cell transcriptomic analyses of DLBCL samples and MYC/BCL2/BCL6-transformed primary B cells identify molecular features, including cyclin D2 and PI3K/AKT as candidate regulators of M+2+6− unfavorable biology. Similar analyses evaluating oncogenic combinations at single-cell resolution in other cancers may facilitate an understanding of cancer evolution and therapy resistance.Significance:Using single-cell–resolved multiplexed imaging, we show that selected subpopulations of cells expressing specific combinations of oncogenes influence clinical outcomes in lymphoma. We describe a probabilistic metric for the estimation of cellular oncogenic coexpression from IHC or bulk transcriptomes, with possible implications for prognostication and therapeutic target discovery in cancer.This article is highlighted in the In This Issue feature, p. 1027

Quantitative single-cell analysis of MYC, BCL2, and BCL6 protein expression in B cells in nonmalignant tissues and diffuse large B-cell lymphoma. A, Schematic workflow of a quantitative digital pathology experiment. B, Spectrally unmixed multiplexed fluorescent images for CD20, MYC, BCL2, and BCL6 and nuclear counterstaining in tonsil tissue. The germinal center (GC) and extragerminal center (extra-GC) zones are indicated. C, Spatial map of MYC/BCL2/BCL6 subpopulations, i.e., possible permutations of MYC/BCL2/BCL6-positivity and -negativity within the CD20-positive cell population in a tonsil image. D, Quantitation of subpopulation extent within CD20-positive cells in tonsils and reactive lymph nodes resolved spatially between the GC and extra-GC zones. E, Example of pseudocolored MYC/BCL2/BCL6/CD20 mfIHC staining in diffuse large B-cell lymphoma (DLBCL; left). Cell segmentation and single oncogene positivity masks are shown within the CD20-positive cell population (right). F, Summary of percentage extent of subpopulations across patients from National University Hospital (NUH), Chi-Mei Medical Center (CMMC), MD Anderson (MDA), and Singapore General Hospital (SGH). Relevant clinicopathologic features are indicated; see also Supplementary Fig. S3. Patients were ordered arbitrarily according to extent of the triple-positive M+2+6+ subpopulation extent. IPI Risk Group, International Prognostic Index Risk Group; FISH, fluorescence in situ hybridization. G, Intrapatient spatial stability of subpopulations – proportion of subpopulations measured across four spatially distinct biopsies from the same patient (rows). Biopsy comparison overview is shown across 11 representative example DLBCL patients (columns). See also Supplementary Fig. S4A and S4B for a correlation analysis for all patients with multiple biopsies available. H, Proliferation analysis (i.e., Ki-67-positivity) among subpopulations in DLBCL samples. Proliferative BCL6-positive subpopulations are grouped. Median with interquartile range, whiskers denote 10th and 90th percentile. Mann–Whitney test (BCL6-positive vs. -negative subpopulations). All scale bars, 100 μm.

Transcriptomic analysis of M+2+6− high cases and potential role of CCND2. A, Correlation of observed M+2+6− percentage extent in the BCA cohort with the cell-of-origin (COO) DLBCL90-COO signature. Bonferroni corrected Kruskal–Wallis test for ABC vs. others. B, Correlation of M+2+6− metric in GEP cohorts with COO signatures. Mean M+2+6− metric value per group per cohort is shown. Bonferroni corrected paired-samples t test. C, Correlation of the M+2+6− percentage extent and metric evaluated by mfIHC and mRNA inference, respectively, with genetic subtypes (LymphGen classification). D, Sankey plot of M+2+6− dichotomized grouping matched with molecular features. E, Volcano plot of pooled direct correlation of gene mRNA expression and M+2+6− metric across seven GEP cohorts. Genes highly correlated with M+2+6− metric across datasets at absolute Spearman rho ≥0.2 and FDR≤0.001 are shown (see also Supplementary Table S11). The abscissa is scaled exponentially. F, Differential gene expression between primary GC B cells overexpressing M+2+ and M+2+6+ (see also Supplementary Table S12). Analysis is generated from 4 biological replicates from each condition, from cells of independent donors. G, Genes highly enriched in M+2+6− cells: correlation of results from E and F. H,CCDN2 gene expression in GEP cohorts in patients dichotomized by M+2+6− 15% metric (left) and in primary B cells (right). Paired t test (left); mean with standard deviation and FDR (FDR as per Supplementary Table S12) for t test (right). I, Single-cell RNA-seq of GC primary B cells transduced either with BCL2 and MYC (MYC-transduced) or BCL2 and BCL6 (BCL6-transduced). Untransduced GC primary B cells are also included. Expression of CCND2 is indicated in color. J, Proliferation analysis of M+2+6+ primary GC B cells overexpressing cyclin D2 (CCND2) compared with M+2+6+ primary GC B cells transduced with an empty vector (EV). Analysis performed with 3 biological replicates for each condition, using cells from 3 independent patients; mean with standard deviation; t test. UMAP, Uniform Manifold Approximation and Projection.

Multivariate analysis of continuous M+2+6− percentage extent at 5% increments as a predictor of OS in the NUH, SGH, and MDA cohorts of DLBCL (Cox proportional hazards model)

Supplementary table 1. Per-patient mfIHC MYC, BCL2 and BCL6 single oncogene and subpopulation scores for normal tonsil tissue and reactive lymph node tissue. Supplementary table 2. Per-patient mfIHC MYC, BCL2 and BCL6 single oncogene and subpopulation scores for DLBCL tissue (NUH, CMMC, SGH, MDA, BCA and UP). Supplementary table 6. Inferred percentage extents of MYC, BCL2, BCL6 and sub-population metrics in GEP cohorts. Supplementary table 11. Correlation of M+2+6- metric with gene expression in GEP cohorts. Supplementary table 12. Differential gene expression analysis of primary germinal center (GC) B-cells with M+2+ and M+2+6+ overexpression. Supplementary table 13. Differentially expressed genes between M+2+6- and all other malignant cells in scRNA-seq samples of DLBCL. Dichotomized non-parametric comparison, Wilcoxon rank sum test. Supplementary table 14. Analysis of positive enrichment of Wikipathways terms between M+2+6- and all other malignant cells in scRNA-seq samples of DLBCL by gprofiler2.

MYC, BCL2, and BCL6 protein coexpression in DLBCL can be inferred from individual marker data. A, Schematic of possible relationships between expression of three oncogenes in a population of cells. The distribution of these oncogenes can either reflect interdependent expression, independent/stochastic expression, or mutually exclusive expression. These relationships result in the percentage extent of oncogenes in the tumor being either strongly positively correlated, not correlated, or strongly negatively correlated, respectively. Created with BioRender.com. B, Correlation of MYC, BCL2, and BCL6 percentage extent across patients in DLBCL cohorts. Spearman correlation; axes are equivalent in all panels. C, Good correlation between probabilistically predicted subpopulation percentage extent based on single oncogene positivity and observed percentage extent in the NUH cohort. Cases of double-hit lymphoma (DHL, MYC+BCL2+ translocations or MYC+BCL6+ translocations) or triple-hit lymphoma (THL) are highlighted. Spearman rho for each correlation is shown; axes are equivalent in all panels. Correlation for other cohorts can be found in Supplementary Fig. S7. D, Prospective evaluation of an optimal dichotomization cutoff for M+2+6− percentage extent in the BCA cohort. Univariate Cox PH model at 1% extent positivity increment, HR for death with 95% CI. HR scale is exponential. Optimal dichotomization cutoff is highlighted in blue. E, Kaplan–Meier OS analysis of the chromogenic IHC BCA cohort evaluation stratified into M+2+6− metric high and low groups across an absolute value of 15% M+2+6−metric. Log-rank test, shading denotes 95% confidence interval. CMMC, Chi-Mei Medical Center.

Validation of prognostic significance of the M+2+6− subpopulation metric in gene-expression datasets. A, Distribution of single oncogene positivity in DLBCL cohorts as assessed by mfIHC (see Supplementary Fig. S9A and S9B). B, Impact of subpopulation metrics in GEP datasets on OS. Pooled univariate Cox PH model analysis; metric was used as a continuous variable in the model at 5% increments. HR per 5% increment with 95% CI is shown; CI are proportional on both tails but are capped at the graph's edges. Pooled P values were Bonferroni corrected to adjust for multiple testing and are shown for each subpopulation. C, Kaplan–Meier OS analysis of GEP cohorts stratified uniformly across absolute 15% M+2+6− metric into -high and -low groups. Log-rank test, shading denotes 95% CI. Total patient numbers in each group are shown. CMMC, Chi-Mei Medical Center.

Prognostic significance of subpopulations after R-CHOP therapy. A, Pooled univariate Cox PH model analysis for MYC, BCL2, and BCL6 single oncogene and subpopulations percentage extent as predictors of OS across multiple DLBCL cohorts. Percentage extent was used as a continuous variable in the model at 5% increments (see Survival Analysis) for an unbiased comparison between the variables. Pooled P values were Bonferroni corrected for single oncogenes and subpopulations independently to adjust for multiple testing and are shown for each variable. Hazard ratio (HR) with 95% confidence interval (CI) per 5%-positivity increment is shown (see also Supplementary Table S4). B, Kaplan–Meier OS analysis of dichotomized into M+2+6− high and low groups. Log-rank test, shading denotes 95% CI. An optimal dichotomization cutoff was used for stratification; total patient numbers in each group are shown.

BackgroundCancers often overexpress multiple clinically relevant oncogenes. However, it is not known if multiple oncogenes within a cancer combine uniquely in specific cellular sub-populations to influence clinical outcome. We studied this phenomenon using the prognostically relevant oncogenes MYC, BCL2 and BCL6 in Diffuse Large B-Cell Lymphoma (DLBCL).MethodsQuantitative multispectral imaging simultaneously measured oncogene co-expression at single-cell resolution in reactive lymphoid tissue (n=12) and four independent cohorts (n=409) of DLBCL. Mathematically derived co-expression phenotypes were evaluated in DLBCLs with immunohistochemistry (n=316) and eight DLBCL cohorts with gene expression data (n=4186). Bulk and single-cell RNA sequencing was performed on patient-derived B-cells with induced co-expression of MYC, BCL2 and BCL6.ResultsUnlike in non-malignant lymphoid tissue where the co-expression of MYC, BCL2 and BCL6 in a B-cell is limited, DLBCLs show multiple permutations of oncogenic co-expression in malignant B-cells. The percentage of cells with a unique combination MYC+BCL2+BCL6-(M+2+6-) consistently predicts survival in contrast to that of other combinations (including M+2+6+). An estimated percentage of M+2+6-cells can be derived from any quantitative measurement of the component individual oncogenes, and correlates with survival in immunohistochemistry and gene expression datasets. Comparative transcriptomic analysis of DLBCLs and transformed patient-derived B-cells identifies cyclin D2 (CCND2) as a potential BCL6-repressed regulator of proliferation in the M+2+6-population.ConclusionsUnique patterns of oncogene co-expression at single-cell resolution affect clinical outcomes in DLBCL. Similar analyses evaluating oncogenic combinations at the cellular level may impact diagnostics and target discovery in other cancers.

Objective: Barrett's oesophagus shows appearances described as ‘intestinal metaplasia’, in structures called ‘crypts’ but do not typically display crypt architecture. Here, we investigate their relationship to gastric glands.\ud \ud Methods: Cell proliferation and migration within Barrett's glands was assessed by Ki67 and iododeoxyuridine (IdU) labelling. Expression of mucin core proteins (MUC), trefoil family factor (TFF) peptides and LGR5 mRNA was determined by immunohistochemistry or by in situ hybridisation, and clonality was elucidated using mitochondrial DNA (mtDNA) mutations combined with mucin histochemistry.\ud \ud Results: Proliferation predominantly occurs in the middle of Barrett's glands, diminishing towards the surface and the base: IdU dynamics demonstrate bidirectional migration, similar to gastric glands. Distribution of MUC5AC, TFF1, MUC6 and TFF2 in Barrett's mirrors pyloric glands and is preserved in Barrett's dysplasia. MUC2-positive goblet cells are localised above the neck in Barrett's glands, and TFF3 is concentrated in the same region. LGR5 mRNA is detected in the middle of Barrett's glands suggesting a stem cell niche in this locale, similar to that in the gastric pylorus, and distinct from gastric intestinal metaplasia. Gastric and intestinal cell lineages within Barrett's glands are clonal, indicating derivation from a single stem cell.\ud \ud Conclusions: Barrett's shows the proliferative and stem cell architecture, and pattern of gene expression of pyloric gastric glands, maintained by stem cells showing gastric and intestinal differentiation: neutral drift may suggest that intestinal differentiation advances with time, a concept critical for the understanding of the origin and development of Barrett's oesophagus.

There is conflicting evidence for the role diet and lifestyle play in the development of mismatch repair (MMR)-deficient colorectal cancers (CRC). In this study, associations between MMR deficiency, clinicopathological characteristics, and dietary and lifestyle factors in sporadic CRC were investigated. Tumor samples from 185 individuals in the EPIC-Norfolk study were analyzed for MLH1 gene promoter methylation and microsatellite instability (MSI). Dietary and lifestyle data were collected prospectively using 7-day food diaries (7dd) and questionnaires. MMR-deficient tumor cases (MLH1 promoter methylation positive, MSI-H) were more likely to be female, older at diagnosis, early Dukes' stage (A/B), and proximal in location (MSI-H P = 0.03, 0.03, 0.02, and 0.001, respectively). Tumors with positive MLH1 promoter methylation (>20%) were associated with poor differentiation (P = 0.03). Low physical activity was associated with cases without MSI (P = 0.05). MMR deficiency was not significantly associated with cigarette smoking or alcohol, folate, fruit, vegetable, or meat consumption. We conclude that MMR-deficient tumors represent a distinct subset of sporadic CRC that are proximal in location, early Dukes' stage, and poorly differentiated, in cases that are female and older at diagnosis. There is no overall role for diet and lifestyle in MMR status in CRC, consistent with age-related susceptibility to MLH1 promoter methylation.

The tumour suppressor p53 is one of the most commonly altered genes in colorectal cancer (CRC) development. Genetic alterations in p53 may therefore be associated with postulated lifestyle risk factors for CRC, such as red meat consumption. In the European Prospective Investigation into Cancer and Nutrition-Norfolk study, we examined whether detailed estimates of dietary and lifestyle factors measured at baseline related to later development of p53 mutations in CRCs. After 10-year follow-up, there were 185 incident CRCs of which 34% had somatic p53 mutations (p53+). We observed significantly higher mean intakes of alcohol, total meat and red meat, in the group with p53 mutations and advanced Dukes' stage disease (daily alcohol intake was 7 and 12 g for p53- and p53 + cases, respectively, P = 0.04; daily total meat intake was 69 and 100 g for p53- and p53 + cases, respectively, P = 0.03 and daily red meat intake was 39 and 75 g for p53- and p53+ cases, respectively, P = 0.01). Each 50 g/day increment in total meat intake was associated with having p53 mutations in cases with advanced Dukes' stages [odds ratio (OR): 3.43, 95% confidence interval (CI): 1.47-7.96]. Similarly, each 50 g/day increment in red meat intake was also significantly associated with having consistent p53 mutations in cases with advanced Dukes' stages (OR: 2.42, 95 % CI: 1.18-4.96). These effects of total meat or red meat intake and advanced Dukes' stages were independent of age, sex, body mass index, smoking and alcohol intake. Furthermore, P values for interaction between daily total meat or red meat intake and Dukes' stages were statistically significant in multivariable models (Pinteraction < 0.001). Our results suggest that p53 mutations accelerate progression of CRC to advanced Dukes' stage in association with higher meat especially red meat intakes.

The genetic and morphological development of colorectal cancer is a paradigm for tumorigenesis. However, the dynamics of clonal evolution underpinning carcinogenesis remain poorly understood. Here we identify multipotential stem cells within human colorectal adenomas and use methylation patterns of nonexpressed genes to characterize clonal evolution. Numerous individual crypts from six colonic adenomas and a hyperplastic polyp were microdissected and characterized for genetic lesions. Clones deficient in cytochrome c oxidase (CCO(-)) were identified by histochemical staining followed by mtDNA sequencing. Topographical maps of clone locations were constructed using a combination of these data. Multilineage differentiation within clones was demonstrated by immunofluorescence. Methylation patterns of adenomatous crypts were determined by clonal bisulphite sequencing; methylation pattern diversity was compared with a mathematical model to infer to clonal dynamics. Individual adenomatous crypts were clonal for mtDNA mutations and contained both mucin-secreting and neuroendocrine cells, demonstrating that the crypt contained a multipotent stem cell. The intracrypt methylation pattern was consistent with the crypts containing multiple competing stem cells. Adenomas were epigenetically diverse populations, suggesting that they were relatively mitotically old populations. Intratumor clones typically showed less diversity in methylation pattern than the tumor as a whole. Mathematical modeling suggested that recent clonal sweeps encompassing the whole adenoma had not occurred. Adenomatous crypts within human tumors contain actively dividing stem cells. Adenomas appeared to be relatively mitotically old populations, pocketed with occasional newly generated subclones that were the result of recent rapid clonal expansion. Relative stasis and occasional rapid subclone growth may characterize colorectal tumorigenesis.

The tumour suppressor APC is the most commonly altered gene in colorectal cancer (CRC). Genetic and epigenetic alterations of APC may therefore be associated with dietary and lifestyle risk factors for CRC. Analysis of APC mutations in the extended mutation cluster region (codons 1276-1556) and APC promoter 1A methylation was performed on 185 archival CRC samples collected from participants of the European Prospective Investigation into Cancer (EPIC)-Norfolk study, with the aim of relating these to high-quality seven-day dietary and lifestyle data collected prospectively. Truncating APC mutations (APC(+) ) and promoter 1A methylation (PM(+) ) were identified in 43% and 23% of CRCs analysed, respectively. Distal CRCs were more likely than proximal CRCs to be APC(+) or PM(+) (p = 0.04). APC(+) CRCs were more likely to be moderately/well differentiated and microsatellite stable than APC(-) CRCs (p = 0.05 and 0.03). APC(+) CRC cases consumed more alcohol than their counterparts (p = 0.01) and PM(+) CRC cases consumed lower levels of folate and fibre (p = 0.01 and 0.004). APC(+) or PM(+) CRC cases consumed higher levels of processed meat and iron from red meat and red meat products (p = 0.007 and 0.006). Specifically, CRC cases harbouring GC-to-AT transition mutations consumed higher levels of processed meat (35 versus 24 g/day, p = 0.04) and iron from red meat and red meat products (0.8 versus 0.6 mg/day, p = 0.05). In a logistic regression model adjusted for age, sex and cigarette-smoking status, each 19 g/day (1SD) increment increase in processed meat consumption was associated with cases with GC-to-AT mutations (OR 1.68, 95% CI 1.03-2.75). In conclusion, APC(+) and PM(+) CRCs may be influenced by diet and GC-to-AT mutations in APC are associated with processed meat consumption, suggesting a mechanistic link with dietary alkylating agents, such as N-nitroso compounds.

Background The PTEN tumour suppressor gene and PIK3CA proto-oncogene encode proteins which contribute to regulation and propagation of signal transduction through the PI3K/AKT signalling pathway. This study investigates the prevalence of loss of PTEN expression and mutations in both PTEN and PIK3CA in colorectal cancers (CRC) and their associations with tumour clinicopathological features, lifestyle factors and dietary consumptions. Methods 186 adenocarcinomas and 16 adenomas from the EPIC Norfolk study were tested for PTEN and PIK3CA mutations by DNA sequencing and PTEN expression changes by immunohistochemistry. Dietary and lifestyle data were collected prospectively using seven day food diaries and lifestyle questionnaires. Results Mutations in exons 7 and 8 of PTEN were observed in 2.2% of CRC and PTEN loss of expression was identified in 34.9% CRC. Negative PTEN expression was associated with lower blood low-density lipoprotein concentrations (p = 0.05). PIK3CA mutations were observed in 7% of cancers and were more frequent in CRCs in females (p = 0.04). Analysis of dietary intakes demonstrated no link between PTEN expression status and any specific dietary factor. PTEN expression negative, proximal CRC were of more advanced Dukes' stage (p = 0.02) and poor differentiation (p < 0.01). Testing of the prevalence of PIK3CA mutations and loss of PTEN expression demonstrated that these two events were independent (p = 0.55). Conclusion These data demonstrated the frequent occurrence (34.9%) of PTEN loss of expression in colorectal cancers, for which gene mutations do not appear to be the main cause. Furthermore, dietary factors are not associated with loss of PTEN expression. PTEN expression negative CRC were not homogenous, as proximal cancers were associated with a more advanced Dukes' stage and poor differentiation, whereas distal cancers were associated with earlier Dukes' stage.

The National Institutes of Health Mammalian Gene Collection (MGC) Program is a multiinstitutional effort to identify and sequence a cDNA clone containing a complete ORF for each human and mouse gene. ESTs were generated from libraries enriched for full-length cDNAs and analyzed to identify candidate full-ORF clones, which then were sequenced to high accuracy. The MGC has currently sequenced and verified the full ORF for a nonredundant set of >9,000 human and >6,000 mouse genes. Candidate full-ORF clones for an additional 7,800 human and 3,500 mouse genes also have been identified. All MGC sequences and clones are available without restriction through public databases and clone distribution networks (see http://mgc.nci.nih.gov ).

Introduction Genetic heterogeneity within Barrett9s mucosa is significantly associated with the development of oesophageal adenocarcinoma (OA). Why genetic diversity promotes tumorigenesis is unclear. Pro-tumorigenic interaction between clones is a possible explanation. To look for evidence of clonal interaction within Barrett9s lesions, we have undertaken a high-resolution clonal-ordering analysis to produce phylogenetic trees and spatial maps that illustrate the spread of genetically distinct clones in endoscopic mucosal resection specimens (EMRs). Methods Entire EMR specimens were serially sectioned. Each crypt was classified according to histological grade. Aneuploidy was assessed using image cytometry. More than 50 individual crypts were then laser-capture microdissected from each EMR. The genetic mutation burden of individual crypts was assessed (K-ras, p53 and p16 point mutations and microsatellite loss of heterozygosity (LOH) data for up to16 loci on chromosomes 3p (FHIT), 5q (APC), 9p (p16), 17p (p53), 17q and 18q (SMAD4)). In each EMR, a point mutation in at least one gene was identified. The relative location of each analysed crypt was recorded and used to reconstruct a genetic and histological map of each EMR. Results EMRs were rarely genetically homogeneous. Typically, dysplastic crypts within individual lesions were clonal for a founding p53 or p16 point-mutated clone, whereas the adjacent metaplastic (non-dysplastic) crypts analysed did not contain the point mutation. Mosaic subclones with distinct patterns of LOH (often 18q and/or 3p) were present. In one EMR, the same p53 and p16 point mutations and 5q and 17p LOH were detected across the entire EMR. Conclusion Clonal expansion of mutated cells is frequently restricted to small numbers of crypts. Interactions between genetically distinct clones appears to be predominantly competitive: a crypt acquiring an additional pro-tumorigenic mutation can outcompete, and so clonally expand to the detriment of, the original crypts. Further investigation will reveal whether this progression is driven by mutation and selection or by additional mutations altering the behaviour of neighbouring pre-existing clones.