Clonal tracking in gene therapy patients reveals a diversity of human hematopoietic differentiation programs.

In gene therapy with human hematopoietic stem and progenitor cells (HSPCs), each gene-corrected cell and its progeny are marked in a unique way by the integrating vector. This feature enables lineages to be tracked by sampling blood cells and using DNA sequencing to identify the vector integration sites. Here, we studied 5 cell lineages (granulocytes, monocytes, T cells, B cells, and natural killer cells) in patients having undergone HSPC gene therapy for Wiskott-Aldrich syndrome or ß hemoglobinopathies. We found that the estimated minimum number of active, repopulating HSPCs (which ranged from 2000 to 50 000) was correlated with the number of HSPCs per kilogram infused. We sought to quantify the lineage output and dynamics of gene-modified clones; this is usually challenging because of sparse sampling of the various cell types during the analytical procedure, contamination during cell isolation, and different levels of vector marking in the various lineages. We therefore measured the residual contamination and corrected our statistical models accordingly to provide a rigorous analysis of the HSPC lineage output. A cluster analysis of the HSPC lineage output highlighted the existence of several stable, distinct differentiation programs, including myeloid-dominant, lymphoid-dominant, and balanced cell subsets. Our study evidenced the heterogeneous nature of the cell lineage output from HSPCs and provided methods for analyzing these complex data.

Binacox: automatic cut-point detection in high-dimensional Cox model with applications in genetics.

We introduce binacox, a prognostic method to deal with the problem of detecting multiple cut-points per feature in a multivariate setting where a large number of continuous features are available. The method is based on the Cox model and combines one-hot encoding with the binarsity penalty, which uses total-variation regularization together with an extra linear constraint, and enables feature selection. Original nonasymptotic oracle inequalities for prediction (in terms of Kullback-Leibler divergence) and estimation with a fast rate of convergence are established. The statistical performance of the method is examined in an extensive Monte Carlo simulation study, and then illustrated on three publicly available genetic cancer data sets. On these high-dimensional data sets, our proposed method outperforms state-of-the-art survival models regarding risk prediction in terms of the C-index, with a computing time orders of magnitude faster. In addition, it provides powerful interpretability from a clinical perspective by automatically pinpointing significant cut-points in relevant variables.

IDNetwork: A deep illness-death network based on multi-state event history process for disease prognostication.

Multi-state models can capture the different patterns of disease evolution. In particular, the illness-death model is used to follow disease progression from a healthy state to an intermediate state of the disease and to a death-related final state. We aim to use those models in order to adapt treatment decisions according to the evolution of the disease. In state-of-the art methods, the risks of transition between the states are modeled via (semi-) Markov processes and transition-specific Cox proportional hazard (P.H.) models. The Cox P.H. model assumes that each variable makes a linear contribution to the model, but the relationship between covariates and risks can be more complex in clinical situations. To address this challenge, we propose a neural network architecture called illness-death network (IDNetwork) that relaxes the linear Cox P.H. assumption within an illness-death process. IDNetwork employs a multi-task architecture and uses a set of fully connected subnetworks in order to learn the probabilities of transition. Through simulations, we explore different configurations of the architecture and demonstrate the added value of our model. IDNetwork significantly improves the predictive performance compared to state-of-the-art methods on a simulated data set, on two clinical trials for patients with colon cancer and on a real-world data set in breast cancer.

ConvSCCS: convolutional self-controlled case series model for lagged adverse event detection.

With the increased availability of large electronic health records databases comes the chance of enhancing health risks screening. Most post-marketing detection of adverse drug reaction (ADR) relies on physicians' spontaneous reports, leading to under-reporting. To take up this challenge, we develop a scalable model to estimate the effect of multiple longitudinal features (drug exposures) on a rare longitudinal outcome. Our procedure is based on a conditional Poisson regression model also known as self-controlled case series (SCCS). To overcome the need of precise risk periods specification, we model the intensity of outcomes using a convolution between exposures and step functions, which are penalized using a combination of group-Lasso and total-variation. Up to our knowledge, this is the first SCCS model with flexible intensity able to handle multiple longitudinal features in a single model. We show that this approach improves the state-of-the-art in terms of mean absolute error and computation time for the estimation of relative risks on simulated data. We apply this method on an ADR detection problem, using a cohort of diabetic patients extracted from the large French national health insurance database (SNIIRAM), a claims database containing medical reimbursements of more than 53 million people. This work has been done in the context of a research partnership between Ecole Polytechnique and CNAMTS (in charge of SNIIRAM).

Comparison of methods for early-readmission prediction in a high-dimensional heterogeneous covariates and time-to-event outcome framework.

BACKGROUND: Choosing the most performing method in terms of outcome prediction or variables selection is a recurring problem in prognosis studies, leading to many publications on methods comparison. But some aspects have received little attention. First, most comparison studies treat prediction performance and variable selection aspects separately. Second, methods are either compared within a binary outcome setting (where we want to predict whether the readmission will occur within an arbitrarily chosen delay or not) or within a survival analysis setting (where the outcomes are directly the censored times), but not both. In this paper, we propose a comparison methodology to weight up those different settings both in terms of prediction and variables selection, while incorporating advanced machine learning strategies. METHODS: Using a high-dimensional case study on a sickle-cell disease (SCD) cohort, we compare 8 statistical methods. In the binary outcome setting, we consider logistic regression (LR), support vector machine (SVM), random forest (RF), gradient boosting (GB) and neural network (NN); while on the survival analysis setting, we consider the Cox Proportional Hazards (PH), the CURE and the C-mix models. We also propose a method using Gaussian Processes to extract meaningfull structured covariates from longitudinal data. RESULTS: Among all assessed statistical methods, the survival analysis ones obtain the best results. In particular the C-mix model yields the better performances in both the two considered settings (AUC =0.94 in the binary outcome setting), as well as interesting interpretation aspects. There is some consistency in selected covariates across methods within a setting, but not much across the two settings. CONCLUSIONS: It appears that learning withing the survival analysis setting first (so using all the temporal information), and then going back to a binary prediction using the survival estimates gives significantly better prediction performances than the ones obtained by models trained "directly" within the binary outcome setting.

HSP110 T17 simplifies and improves the microsatellite instability testing in patients with colorectal cancer.

BACKGROUND: Every colorectal cancer (CRC) patient should be tested for microsatellite instability (MSI, a marker for defective DNA mismatch repair) as a first screen for Lynch syndrome (LS). In this study, we investigated whether it may be possible to improve the detection of MSI in CRC. We examined whether the HT17 DNA repeat (critical for correct splicing of the chaperone HSP110) might constitute a superior marker for diagnosis of the MSI phenotype in patients with CRC compared with the standard panel of markers (pentaplex). METHODS: The HT17 polymorphism was analysed in germline DNA from 1037 multi-ethnic individuals. We assessed its sensitivity and specificity for detecting MSI in a multicentre, population-based cohort of 685 patients with CRC and an additional series of 70 patients with CRC considered to be at-risk of LS. All cases were screened earlier for MSI using pentaplex markers. Cases showing discordant HT17/pentaplex results were further examined for the expression of mismatch repair proteins. RESULTS: HT17 status was analysed independently and blinded to previous results from pentaplex genotyping. HT17 showed no germline allelic variation outside a very narrow range. Compared with the pentaplex panel, HT17 showed better sensitivity (0.984 (95% CI 0.968 to 0.995) vs 0.951 (95% CI 0.925 to 0.972)) and similar specificity (0.997 (95% CI 0.989 to 1.000) for both) for the detection of MSI. Furthermore, HT17 alone correctly classified samples judged to be uncertain with the pentaplex panel and showed excellent ability to detect MSI in patients with LS. CONCLUSIONS: HT17 simplifies and improves the current standard molecular methods for detecting MSI in CRC.

Diagnosis of Constitutional Mismatch Repair-Deficiency Syndrome Based on Microsatellite Instability and Lymphocyte Tolerance to Methylating Agents.

BACKGROUND & AIMS: Patients with bi-allelic germline mutations in mismatch repair (MMR) genes (MLH1, MSH2, MSH6, or PMS2) develop a rare but severe variant of Lynch syndrome called constitutional MMR deficiency (CMMRD). This syndrome is characterized by early-onset colorectal cancers, lymphomas or leukemias, and brain tumors. There is no satisfactory method for diagnosis of CMMRD because screens for mutations in MMR genes are noninformative for 30% of patients. MMR-deficient cancer cells are resistant to genotoxic agents and have microsatellite instability (MSI), due to accumulation of errors in repetitive DNA sequences. We investigated whether these features could be used to identify patients with CMMRD. METHODS: We examined MSI by PCR analysis and tolerance to methylating or thiopurine agents (functional characteristics of MMR-deficient tumor cells) in lymphoblastoid cells (LCs) from 3 patients with CMMRD and 5 individuals with MMR-proficient LCs (controls). Using these assays, we defined experimental parameters that allowed discrimination of a series of 14 patients with CMMRD from 52 controls (training set). We then used the same parameters to assess 23 patients with clinical but not genetic features of CMMRD. RESULTS: In the training set, we identified parameters, based on MSI and LC tolerance to methylation, that detected patients with CMMRD vs controls with 100% sensitivity and 100% specificity. Among 23 patients suspected of having CMMRD, 6 had MSI and LC tolerance to methylation (CMMRD highly probable), 15 had neither MSI nor LC tolerance to methylation (unlikely to have CMMRD), and 2 were considered doubtful for CMMRD based on having only 1 of the 2 features. CONCLUSION: The presence of MSI and tolerance to methylation in LCs identified patients with CMMRD with 100% sensitivity and specificity. These features could be used in diagnosis of patients.

Patients with colorectal tumors with microsatellite instability and large deletions in HSP110 T17 have improved response to 5-fluorouracil­based chemotherapy.

BACKGROUND & AIMS: Patients with colorectal tumors with microsatellite instability (MSI) have better prognoses than patients with tumors without MSI, but have a poor response to 5-fluorouracil­based chemotherapy. A dominant-negative form of heat shock protein (HSP)110 (HSP110DE9) expressed by cancer cells with MSI, via exon skipping caused by somatic deletions in the T(17) intron repeat, sensitizes the cells to 5-fluorouracil and oxaliplatin.We investigated whether HSP110 T(17) could be used to identify patients with colorectal cancer who would benefit from adjuvant chemotherapy with 5-fluorouracil and oxaliplatin. METHODS: We characterized the interaction between HSP110 and HSP110DE9 using surface plasmon resonance. By using polymerase chain reaction and fragment analysis, we examined how the size of somatic allelic deletions in HSP110 T(17) affected the HSP110 protein expressed by tumor cells. We screened 329 consecutive patients with stage II­III colorectal tumors with MSI who underwent surgical resection at tertiary medical centers for HSP110 T(17). RESULTS: HSP110 and HSP110DE9 interacted in a1:1 ratio. Tumor cells with large deletions in T(17) had increased ratios of HSP110DE9:HSP110, owing to the loss of expression of full-length HSP110. Deletions in HSP110 T(17) were mostly biallelic in primary tumor samples with MSI. Patients with stage II­III cancer who received chemotherapy and had large HSP110 T(17) deletions (≥5 bp; 18 of 77 patients, 23.4%) had longer times of relapse-free survival than patients with small or no deletions (≤4 bp; 59 of 77 patients, 76.6%) in multivariate analysis (hazard ratio, 0.16; 95% confidence interval, 0.012­0.8; P = .03). We found a significant interaction between chemotherapy and T17 deletion (P =.009). CONCLUSIONS: About 25% of patients with stages II­III colorectal tumors with MSI have an excellent response to chemotherapy, due to large, biallelic deletions in the T(17) intron repeat of HSP110 in tumor DNA.

Estimation in a competing risks proportional hazards model under length-biased sampling with censoring.

What population does the sample represent? The answer to this question is of crucial importance when estimating a survivor function in duration studies. As is well-known, in a stationary population, survival data obtained from a cross-sectional sample taken from the population at time t(0) represents not the target density f (t) but its length-biased version proportional to t f (t), for t > 0. The problem of estimating survivor function from such length-biased samples becomes more complex, and interesting, in presence of competing risks and censoring. This paper lays out a sampling scheme related to a mixed Poisson process and develops nonparametric estimators of the survivor function of the target population assuming that the two independent competing risks have proportional hazards. Two cases are considered: with and without independent censoring before length biased sampling. In each case, the weak convergence of the process generated by the proposed estimator is proved. A well-known study of the duration in power for political leaders is used to illustrate our results. Finally, a simulation study is carried out in order to assess the finite sample behaviour of our estimators.

MS-CPFI: A model-agnostic Counterfactual Perturbation Feature Importance algorithm for interpreting black-box Multi-State models.

Multi-state processes (Webster, 2019) are commonly used to model the complex clinical evolution of diseases where patients progress through different states. In recent years, machine learning and deep learning algorithms have been proposed to improve the accuracy of these models' predictions (Wang et al., 2019). However, acceptability by patients and clinicians, as well as for regulatory compliance, require interpretability of these algorithms's predictions. Existing methods, such as the Permutation Feature Importance algorithm, have been adapted for interpreting predictions in black-box models for 2-state processes (corresponding to survival analysis). For generalizing these methods to multi-state models, we introduce a novel model-agnostic interpretability algorithm called Multi-State Counterfactual Perturbation Feature Importance (MS-CPFI) that computes feature importance scores for each transition of a general multi-state model, including survival, competing-risks, and illness-death models. MS-CPFI uses a new counterfactual perturbation method that allows interpreting feature effects while capturing the non-linear effects and potentially capturing time-dependent effects. Experimental results on simulations show that MS-CPFI increases model interpretability in the case of non-linear effects. Additionally, results on a real-world dataset for patients with breast cancer confirm that MS-CPFI can detect clinically important features and provide information on the disease progression by displaying features that are protective factors versus features that are risk factors for each stage of the disease. Overall, MS-CPFI is a promising model-agnostic interpretability algorithm for multi-state models, which can improve the interpretability of machine learning and deep learning algorithms in healthcare.

Severe hematopoietic stem cell inflammation compromises chronic granulomatous disease gene therapy.

X-linked chronic granulomatous disease (CGD) is associated with defective phagocytosis, life-threatening infections, and inflammatory complications. We performed a clinical trial of lentivirus-based gene therapy in four patients (NCT02757911). Two patients show stable engraftment and clinical benefits, whereas the other two have progressively lost gene-corrected cells. Single-cell transcriptomic analysis reveals a significantly lower frequency of hematopoietic stem cells (HSCs) in CGD patients, especially in the two patients with defective engraftment. These two present a profound change in HSC status, a high interferon score, and elevated myeloid progenitor frequency. We use elastic-net logistic regression to identify a set of 51 interferon genes and transcription factors that predict the failure of HSC engraftment. In one patient, an aberrant HSC state with elevated CEBPß expression drives HSC exhaustion, as demonstrated by low repopulation in a xenotransplantation model. Targeted treatments to protect HSCs, coupled to targeted gene expression screening, might improve clinical outcomes in CGD.

C-mix: A high-dimensional mixture model for censored durations, with applications to genetic data.

We introduce a supervised learning mixture model for censored durations (C-mix) to simultaneously detect subgroups of patients with different prognosis and order them based on their risk. Our method is applicable in a high-dimensional setting, i.e. with a large number of biomedical covariates. Indeed, we penalize the negative log-likelihood by the Elastic-Net, which leads to a sparse parameterization of the model and automatically pinpoints the relevant covariates for the survival prediction. Inference is achieved using an efficient Quasi-Newton Expectation Maximization algorithm, for which we provide convergence properties. The statistical performance of the method is examined on an extensive Monte Carlo simulation study and finally illustrated on three publicly available genetic cancer datasets with high-dimensional covariates. We show that our approach outperforms the state-of-the-art survival models in this context, namely both the CURE and Cox proportional hazards models penalized by the Elastic-Net, in terms of C-index, AUC( t) and survival prediction. Thus, we propose a powerful tool for personalized medicine in cancerology.

Association of Primary Resistance to Immune Checkpoint Inhibitors in Metastatic Colorectal Cancer With Misdiagnosis of Microsatellite Instability or Mismatch Repair Deficiency Status.

IMPORTANCE: Primary resistance to immune checkpoint inhibitors is observed in 10% to 40% of patients with metastatic colorectal cancer (mCRC) displaying microsatellite instability (MSI) or defective mismatch repair (dMMR). OBJECTIVE: To investigate possible mechanisms underlying primary resistance to immune checkpoint inhibitors of mCRC displaying MSI or dMMR. DESIGN, SETTING, AND PARTICIPANTS: This post hoc analysis of a single-center, prospective cohort included 38 patients with mCRC diagnosed as MSI or dMMR by local laboratories and entered into trials of immune checkpoint inhibitors between January 1, 2015, and December 31, 2016. The accuracy of MSI or dMMR status was also assessed in a retrospective cohort comprising 93 cases of mCRC that were diagnosed as MSI or dMMR between January 1, 1998, and December 31, 2016, in 6 French hospitals. Primary resistance of mCRC was defined as progressive disease according to Response Evaluation Criteria in Solid Tumors criteria, 6 to 8 weeks after initiation of immune checkpoint inhibitors, without pseudo-progression. All tumor samples were reassessed for dMMR status using immunohistochemistry with antibodies directed against MLH1, MSH2, MSH6, and PMS2, and for MSI using polymerase chain reaction with pentaplex markers and with the HSP110 T17 (HT17) repeat. MAIN OUTCOMES AND MEASURES: The primary outcome was positive predictive value. RESULTS: Among the 38 patients (15 women and 23 men; mean [SD] age, 55.6 [13.7] years) in the study with mCRC displaying MSI or dMMR, primary resistance to immune checkpoint inhibitors was observed in 5 individuals (13%). Reassessment of the status of MSI or dMMR revealed that 3 (60%) of these 5 resistant tumors were microsatellite stable or displayed proficient mismatch repair. The positive predictive value of MSI or dMMR status assessed by local laboratories was therefore 92.1% (95% CI, 78.5%-98.0%). In the retrospective cohort of 93 patients (44 women and 49 men; mean [SD] age, 56.8 [18.3] years) without immune checkpoint inhibitor treatment, misdiagnosis of the MSI or dMMR status by local assessment was 10% (n = 9), with a positive predictive value of 90.3% (95% CI, 82.4%-95.0%). Testing for MSI with the HT17 assay confirmed the MSI or dMMR status in 2 of 4 cases showing discrepant results between immunohistochemistry and pentaplex polymerase chain reaction (ie, dMMR but microsatellite stable). CONCLUSIONS AND RELEVANCE: Primary resistance of mCRC displaying MSI or dMMR to immune checkpoint inhibitors is due mainly to misdiagnosis of their MSI or dMMR status. Larger studies are required to confirm these findings. Microsatellite instability or dMMR status should be tested routinely using both immunohistochemistry and polymerase chain reaction methods prior to treatment with immune checkpoint inhibitors.

Identification of Positively and Negatively Selected Driver Gene Mutations Associated With Colorectal Cancer With Microsatellite Instability.

Background & Aims: Recent studies have shown that cancers arise as a result of the positive selection of driver somatic events in tumor DNA, with negative selection playing only a minor role, if any. However, these investigations were concerned with alterations at nonrepetitive sequences and did not take into account mutations in repetitive sequences that have very high pathophysiological relevance in the tumors showing microsatellite instability (MSI) resulting from mismatch repair deficiency investigated in the present study. Methods: We performed whole-exome sequencing of 47 MSI colorectal cancers (CRCs) and confirmed results in an independent cohort of 53 MSI CRCs. We used a probabilistic model of mutational events within microsatellites, while adapting pre-existing models to analyze nonrepetitive DNA sequences. Negatively selected coding alterations in MSI CRCs were investigated for their functional and clinical impact in CRC cell lines and in a third cohort of 164 MSI CRC patients. Results: Both positive and negative selection of somatic mutations in DNA repeats was observed, leading us to identify the expected true driver genes associated with the MSI-driven tumorigenic process. Several coding negatively selected MSI-related mutational events (n = 5) were shown to have deleterious effects on tumor cells. In the tumors in which deleterious MSI mutations were observed despite the negative selection, they were associated with worse survival in MSI CRC patients (hazard ratio, 3; 95% CI, 1.1-7.9; P = .03), suggesting their anticancer impact should be offset by other as yet unknown oncogenic processes that contribute to a poor prognosis. Conclusions: The present results identify the positive and negative driver somatic mutations acting in MSI-driven tumorigenesis, suggesting that genomic instability in MSI CRC plays a dual role in achieving tumor cell transformation. Exome sequencing data have been deposited in the European genome-phenome archive (accession: EGAS00001002477).

Nonsense-mediated mRNA decay impacts MSI-driven carcinogenesis and anti-tumor immunity in colorectal cancers.

Nonsense-mediated mRNA Decay (NMD) degrades mutant mRNAs containing premature termination codon (PTC-mRNAs). Here we evaluate the consequence of NMD activity in colorectal cancers (CRCs) showing microsatellite instability (MSI) whose progression is associated with the accumulation of PTC-mRNAs encoding immunogenic proteins due to frameshift mutations in coding repeat sequences. Inhibition of UPF1, one of the major NMD factors, was achieved by siRNA in the HCT116 MSI CRC cell line and the resulting changes in gene expression were studied using expression microarrays. The impact of NMD activity was also investigated in primary MSI CRCs by quantifying the expression of several mRNAs relative to their mutational status and to endogenous UPF1 and UPF2 expression. Host immunity developed against MSI cancer cells was appreciated by quantifying the number of CD3epsilon-positive tumor-infiltrating lymphocytes (TILs). UPF1 silencing led to the up-regulation of 1251 genes in HCT116, among which a proportion of them (i.e. 38%) significantly higher than expected by chance contained a coding microsatellite (P<2x10(-16)). In MSI primary CRCs, UPF1 was significantly over-expressed compared to normal adjacent mucosa (P<0.002). Our data provided evidence for differential decay of PTC-mRNAs compared to wild-type that was positively correlated to UPF1 endogenous expression level (P = 0.02). A negative effect of UPF1 and UPF2 expression on the host's anti-tumor response was observed (P<0.01). Overall, our results show that NMD deeply influences MSI-driven tumorigenesis at the molecular level and indicate a functional negative impact of this system on anti-tumor immunity whose intensity has been recurrently shown to be an independent factor of favorable outcome in CRCs.

Practical implications of imposing a new world standard population.

OBJECTIVE: The World Health Organization (WHO) has recently introduced a new world standard population for the production of age-standardized rates. In this study we compare cancer rates standardized to this population with those computed using reference populations in current practice, particularly the world standard of Segi (1960), in order to evaluate their adequacy as estimators of relative risk in diverse population groups and over time. METHODS: Incidence and mortality rates standardized using these reference populations were calculated and compared for various cancers. Standardized rate ratios were compared with more efficient methods of approximating relative risk, the Mantel & Haenszel and maximum-likelihood estimators. The differences were tested by taking a synthesis of the relative risks and by taking into account whether effects were homogeneous across age strata or not. RESULTS: There were no statistically significant differences between the relative risk estimates based on direct standardization and those obtained using Mantel & Haenszel (p < 0.99), or maximum-likelihood techniques (p > 0.99), regardless of whether the Segi or the WHO world population was used as the standard. CONCLUSIONS: Ratios of rates age-standardized using the world standard of Segi approximate relative risk as precisely as the WHO standard. For this, and important practical reasons, it is considered unnecessary to replace the Segi standard population for comparisons between cancer rates.

Incidence and mortality from stomach cancer in Japan, Slovenia and the USA.

The mortality and incidence from stomach cancer were compared in Japan (a country with a high incidence where there was full application of mass screening during this period) and 2 countries with no screening policy: the USA (with a very low incidence) and Slovenia (with an intermediate rate). The registered cases of stomach cancer were from the Osaka Cancer Registry, the Slovenian National Cancer Registry and the Surveillance, Epidemiology, and End Results (SEER) registries in the USA. In the period 1975-95, the age-adjusted incidence rate (/100,000) of stomach cancer declined in the 3 countries, as follows: Japan, from 76.0 to 53.0 in men and 38.4 to 21.3 in women; Slovenia, from 40.2 to 24.1 in men and 16.6 to 10.8 in women; and the USA, from 9.5 to 6.9 in men and 4.3 to 2.9 in women. During the same period, the age-adjusted mortality rate declined, as follows: Japan, from 60.2 to 34.2 in men and 30.5 to 14.1 in women; Slovenia, from 37.7 to 21.2 in men and 13.8 to 9.0 in women; the USA, from 5.6 to 4.7 in men and 2.5 to 2.3 in women. In the period studied, specific trends on incidence and mortality with a cohort effect occurred only in Japan: analysis by the age period-cohort model confirmed that the decline has occurred since the generations born in 1910. The trend therefore corresponds to unplanned prevention through changes in environmental factors occurring since the early 20th century. The study of stage-specific incidence rates confirmed the declining trend for regional cancer, whereas there was an increase in the incidence of localized cancer, associated with a period effect in 1975-95. This is attributed to the policy of early detection of stomach cancer, with the inclusion of intramucosal lesions of favorable prognosis and explains why mortality decreased faster than incidence during the period.