Dynamics of breast-cancer relapse reveal late-recurring ER-positive genomic subgroups.

The rates and routes of lethal systemic spread in breast cancer are poorly understood owing to a lack of molecularly characterized patient cohorts with long-term, detailed follow-up data. Long-term follow-up is especially important for those with oestrogen-receptor (ER)-positive breast cancers, which can recur up to two decades after initial diagnosis1-6. It is therefore essential to identify patients who have a high risk of late relapse7-9. Here we present a statistical framework that models distinct disease stages (locoregional recurrence, distant recurrence, breast-cancer-related death and death from other causes) and competing risks of mortality from breast cancer, while yielding individual risk-of-recurrence predictions. We apply this model to 3,240 patients with breast cancer, including 1,980 for whom molecular data are available, and delineate spatiotemporal patterns of relapse across different categories of molecular information (namely immunohistochemical subtypes; PAM50 subtypes, which are based on gene-expression patterns10,11; and integrative or IntClust subtypes, which are based on patterns of genomic copy-number alterations and gene expression12,13). We identify four late-recurring integrative subtypes, comprising about one quarter (26%) of tumours that are both positive for ER and negative for human epidermal growth factor receptor 2, each with characteristic tumour-driving alterations in genomic copy number and a high risk of recurrence (mean 47-62%) up to 20 years after diagnosis. We also define a subgroup of triple-negative breast cancers in which cancer rarely recurs after five years, and a separate subgroup in which patients remain at risk. Use of the integrative subtypes improves the prediction of late, distant relapse beyond what is possible with clinical covariates (nodal status, tumour size, tumour grade and immunohistochemical subtype). These findings highlight opportunities for improved patient stratification and biomarker-driven clinical trials.Cancer Research UK (CRUK) travel grant (SWAH/047) 282 to visit Prof. Curtis’ Lab. C.R. is supported by award MTM2015-71217-R. Ca.C. is 283 supported by CRUK, ECMC and NIHR. C.C. is supported by the National Institutes 284 of Health through the NIH Director’s Pioneer Award (DP1-CA238296) and the Breast 285 Cancer Research Foundation

Idiopathic inflammatory myopathies and cancer : familial risk, genetics and consequences

Idiopathic inflammatory myopathies (IIMs) are a group of rare rheumatic inflammatory diseases (RIDs), characterised by a diverse range of clinical, serological and histopathological characteristics, with muscle weakness as a shared hallmark. While advancements in disease management have improved the survival rates of patients with IIM, the mortality rate among patients with IIM is still higher than the general population, mainly due to association with comorbidities such as cancer. The pathogenesis of IIM, the pathological link between IIM and cancer and the impact of cancer on the survival of patients with IIM remain a subject of uncertainty. The rarity and heterogeneity inherent in IIM pose significant challenges in filing these knowledge gaps. This thesis encompasses five studies, which aimed at addressing research questions concerning the genetic contribution to IIM and its link with other autoimmune diseases and cancer, as well as the disease burden in the context of cancer in a large representative population of patients with IIM. Study I was a population-based case-control family study including 7,615 first-degree relatives of 1,620 patients with IIM diagnosis between 1997 and 2016 and 37,309 first-degree relatives of 7,797 matched comparators without IIM. Patients with IIM were four times more likely to have at least one first-degree relative affected by IIM compared to matched comparators without IIM. The heritability of IIM, a proportion of the phenotypic variance that can be explained by additive genetic variance, was 22% in the Swedish population. Study II, with the same study population as in Study I, analysed the familial associations between IIM and a variety of autoimmune diseases under a causal framework. We found shared familial factors between IIM and other RIDs, inflammatory bowel diseases, autoimmune thyroid diseases and celiac disease. Study III, with a similar study population and analytical approach as in Study II, comprehensively investigated the familial co-aggregation of IIM and cancer. We did not observe a familial association between IIM and cancer overall but modification effect by sex was noted: there was a modest familial association (adjusted odds ratio=1.39) with cancer in male first-degree relatives of patients with IIM. We also found that offspring of patients with IIM were more likely to have a cancer diagnosis at age younger than 50 years compared to those of matched comparators without IIM. In the exploratory analysis by specific cancer types, findings suggest that IIM shared familial factors with myeloid malignancies and liver cancer. Study IV explored genetic correlation between IIM and B-cell lymphomas via a cross-trait secondary analysis using summary statistics from genome-wide associations studies of IIM and four common B-cell lymphoma subtypes including diffuse large B-cell lymphoma, follicular lymphoma, chronic lymphocytic leukaemia and marginal zone lymphoma. We detected a limited number of genomic loci, predominantly within the human leukocyte antigen region, demonstrating significant genetic correlations between IIM and common Bcell lymphoma subtypes. Study V, a cohort study, followed 1,826 patients to (first and second) cancer and death (overall and cause-specific death) events since IIM diagnosis for more than 20 years. Compared to patients with no cancer diagnosis after IIM, patients with a first cancer diagnosis after IIM faced a greater five-year mortality (22% versus 49%). This excessive risk was due to an increased risk of death from cancer. In patients with a first cancer diagnosis after IIM, the one-year risk of having a second primary cancer was 11% and having a second cancer diagnosis slightly increased the risk of death. We also reported several prognostic factors associated with increased risks of cancer and death (overall, from cancer and from other causes). This thesis offers useful insight into the role of genetics in IIM pathogenesis and its connections with other autoimmune diseases and cancer, as well as the impact of cancer on the survival of patients with IIM. The observed familial aggregation of IIM and familial associations between IIM and other autoimmune diseases suggest genetic involvement in the development of IIM. Family history of IIM, other RIDs, inflammatory bowel diseases, autoimmune thyroid diseases and celiac disease may serve as indicators pointing towards an IIM diagnosis. Missing heritability is suggested by the discrepancy between our family-based heritability and the SNP-based heritability, implying yet-to-be discovered genetic variants associated with IIM. The acquired knowledge of shared familial factors between IIM and other autoimmune diseases may inform future genetic studies aiming to uncover novel IIMassociated genetic variants. There is a limited shared familial/genetic susceptibility between IIM and cancer. The human leukocyte antigen region plays an important role in the limited shared genetic susceptibility between IIM and common B-cell lymphoma subtypes. IIM concomitant with cancer leads to a substantial increase in mortality, mainly due to cancer. Future research should focus on reducing cancer-related disease burden in patients with IIM

Pacific Symposium on Biocomputing 2023

The Pacific Symposium on Biocomputing (PSB) 2023 is an international, multidisciplinary conference for the presentation and discussion of current research in the theory and application of computational methods in problems of biological significance. Presentations are rigorously peer reviewed and are published in an archival proceedings volume. PSB 2023 will be held on January 3-7, 2023 in Kohala Coast, Hawaii. Tutorials and workshops will be offered prior to the start of the conference.PSB 2023 will bring together top researchers from the US, the Asian Pacific nations, and around the world to exchange research results and address open issues in all aspects of computational biology. It is a forum for the presentation of work in databases, algorithms, interfaces, visualization, modeling, and other computational methods, as applied to biological problems, with emphasis on applications in data-rich areas of molecular biology.The PSB has been designed to be responsive to the need for critical mass in sub-disciplines within biocomputing. For that reason, it is the only meeting whose sessions are defined dynamically each year in response to specific proposals. PSB sessions are organized by leaders of research in biocomputing's 'hot topics.' In this way, the meeting provides an early forum for serious examination of emerging methods and approaches in this rapidly changing field

Timely and reliable evaluation of the effects of interventions: a framework for adaptive meta-analysis (FAME)

Most systematic reviews are retrospective and use aggregate data AD) from publications, meaning they can be unreliable, lag behind therapeutic developments and fail to influence ongoing or new trials. Commonly, the potential influence of unpublished or ongoing trials is overlooked when interpreting results, or determining the value of updating the meta-analysis or need to collect individual participant data (IPD). Therefore, we developed a Framework for Adaptive Metaanalysis (FAME) to determine prospectively the earliest opportunity for reliable AD meta-analysis. We illustrate FAME using two systematic reviews in men with metastatic (M1) and non-metastatic (M0)hormone-sensitive prostate cancer (HSPC)