An artificial intelligence model predicts the survival of solid tumour patients from imaging and clinical data.

BACKGROUND: The need for developing new biomarkers is increasing with the emergence of many targeted therapies. Artificial Intelligence (AI) algorithms have shown great promise in the medical imaging field to build predictive models. We developed a prognostic model for solid tumour patients using AI on multimodal data. PATIENTS AND METHODS: Our retrospective study included examinations of patients with seven different cancer types performed between 2003 and 2017 in 17 different hospitals. Radiologists annotated all metastases on baseline computed tomography (CT) and ultrasound (US) images. Imaging features were extracted using AI models and used along with the patients' and treatments' metadata. A Cox regression was fitted to predict prognosis. Performance was assessed on a left-out test set with 1000 bootstraps. RESULTS: The model was built on 436 patients and tested on 196 patients (mean age 59, IQR: 51-6, 411 men out of 616 patients). On the whole, 1147 US images were annotated with lesions delineation, and 632 thorax-abdomen-pelvis CTs (total of 301,975 slices) were fully annotated with a total of 9516 lesions. The developed model reaches an average concordance index of 0.71 (0.67-0.76, 95% CI). Using the median predicted risk as a threshold value, the model is able to significantly (log-rank test P value < 0.001) isolate high-risk patients from low-risk patients (respective median OS of 11 and 31 months) with a hazard ratio of 3.5 (2.4-5.2, 95% CI). CONCLUSION: AI was able to extract prognostic features from imaging data, and along with clinical data, allows an accurate stratification of patients' prognoses.

Molecular and Pharmacological Bladder Cancer Therapy Screening: Discovery of Clofarabine as a Highly Active Compound.

BACKGROUND: The heterogeneity of bladder cancers (BCs) is a major challenge for the development of novel therapies. However, given the high rates of recurrence and/or treatment failure, the identification of effective therapeutic strategies is an urgent clinical need. OBJECTIVE: We aimed to establish a model system for drug identification/repurposing in order to identify novel therapies for the treatment of BC. DESIGN, SETTING, AND PARTICIPANTS: A collection of commercially available BC cell lines (n = 32) was comprehensively characterized. A panel of 23 cell lines, representing a broad spectrum of BC, was selected to perform a high-throughput drug screen. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Positive hits were defined as compounds giving >50% inhibition in at least one BC cell line. RESULTS AND LIMITATIONS: Amongst >1700 tested chemical compounds, a total of 471 substances exhibited antineoplastic effects. Clofarabine, an antimetabolite drug used as third-line treatment for childhood acute lymphoblastic leukaemia, was amongst the limited number of drugs with inhibitory effects on cell lines of all intrinsic subtypes. We, thus, reassessed the substance and confirmed its inhibitory effects on commercially available cell lines and patient-derived cell cultures representing various disease stages, intrinsic subtypes, and histologic variants. To verify these effects in vivo, a patient-derived cell xenograft model for urothelial carcinoma (UC) was used. Well-tolerated doses of clofarabine induced complete remission in all treated animals (n = 12) suffering from both early- and late-stage disease. We further took advantage of another patient-derived cell xenograft model originating from the rare disease entity sarcomatoid carcinoma (SaC). Similarly to UC xenograft mice, clofarabine induced subcomplete to complete tumour remissions in all treated animals (n = 8). CONCLUSIONS: The potent effects of clofarabine in vitro and in vivo suggest that our findings may be of high clinical relevance. Clinical trials are needed to assess the value of clofarabine in improving BC patient care. PATIENT SUMMARY: We used commercially available cell lines for the identification of novel drugs for the treatment of bladder cancer. We confirmed the effects of one of these drugs, clofarabine, in patient-derived cell lines and two different mouse models, thereby demonstrating a potential clinical relevance of this substance in bladder cancer treatment.

A deep learning method for predicting knee osteoarthritis radiographic progression from MRI.

BACKGROUND: The identification of patients with knee osteoarthritis (OA) likely to progress rapidly in terms of structure is critical to facilitate the development of disease-modifying drugs. METHODS: Using 9280 knee magnetic resonance (MR) images (3268 patients) from the Osteoarthritis Initiative (OAI) database , we implemented a deep learning method to predict, from MR images and clinical variables including body mass index (BMI), further cartilage degradation measured by joint space narrowing at 12 months. RESULTS: Using COR IW TSE images, our classification model achieved a ROC AUC score of 65%. On a similar task, trained radiologists obtained a ROC AUC score of 58.7% highlighting the difficulty of the classification task. Additional analyses conducted in parallel to predict pain grade evaluated by the WOMAC pain index achieved a ROC AUC score of 72%. Attention maps provided evidence for distinct specific areas as being relevant in those two predictive models, including the medial joint space for JSN progression and the intra-articular space for pain prediction. CONCLUSIONS: This feasibility study demonstrates the interest of deep learning applied to OA, with a potential to support even trained radiologists in the challenging task of identifying patients with a high-risk of disease progression.

Integrating deep learning CT-scan model, biological and clinical variables to predict severity of COVID-19 patients.

The SARS-COV-2 pandemic has put pressure on intensive care units, so that identifying predictors of disease severity is a priority. We collect 58 clinical and biological variables, and chest CT scan data, from 1003 coronavirus-infected patients from two French hospitals. We train a deep learning model based on CT scans to predict severity. We then construct the multimodal AI-severity score that includes 5 clinical and biological variables (age, sex, oxygenation, urea, platelet) in addition to the deep learning model. We show that neural network analysis of CT-scans brings unique prognosis information, although it is correlated with other markers of severity (oxygenation, LDH, and CRP) explaining the measurable but limited 0.03 increase of AUC obtained when adding CT-scan information to clinical variables. Here, we show that when comparing AI-severity with 11 existing severity scores, we find significantly improved prognosis performance; AI-severity can therefore rapidly become a reference scoring approach.

A deep learning model to predict RNA-Seq expression of tumours from whole slide images.

Deep learning methods for digital pathology analysis are an effective way to address multiple clinical questions, from diagnosis to prediction of treatment outcomes. These methods have also been used to predict gene mutations from pathology images, but no comprehensive evaluation of their potential for extracting molecular features from histology slides has yet been performed. We show that HE2RNA, a model based on the integration of multiple data modes, can be trained to systematically predict RNA-Seq profiles from whole-slide images alone, without expert annotation. Through its interpretable design, HE2RNA provides virtual spatialization of gene expression, as validated by CD3- and CD20-staining on an independent dataset. The transcriptomic representation learned by HE2RNA can also be transferred on other datasets, even of small size, to increase prediction performance for specific molecular phenotypes. We illustrate the use of this approach in clinical diagnosis purposes such as the identification of tumors with microsatellite instability.

Comprehensive Molecular and Pathologic Evaluation of Transitional Mesothelioma Assisted by Deep Learning Approach: A Multi-Institutional Study of the International Mesothelioma Panel from the MESOPATH Reference Center.

INTRODUCTION: Histologic subtypes of malignant pleural mesothelioma are a major prognostic indicator and decision denominator for all therapeutic strategies. In an ambiguous case, a rare transitional mesothelioma (TM) pattern may be diagnosed by pathologists either as epithelioid mesothelioma (EM), biphasic mesothelioma (BM), or sarcomatoid mesothelioma (SM). This study aimed to better characterize the TM subtype from a histological, immunohistochemical, and molecular standpoint. Deep learning of pathologic slides was applied to this cohort. METHODS: A random selection of 49 representative digitalized sections from surgical biopsies of TM was reviewed by 16 panelists. We evaluated BAP1 expression and CDKN2A (p16) homozygous deletion. We conducted a comprehensive, integrated, transcriptomic analysis. An unsupervised deep learning algorithm was trained to classify tumors. RESULTS: The 16 panelists recorded 784 diagnoses on the 49 cases. Even though a Kappa value of 0.42 is moderate, the presence of a TM component was diagnosed in 51%. In 49% of the histological evaluation, the reviewers classified the lesion as EM in 53%, SM in 33%, or BM in 14%. Median survival was 6.7 months. Loss of BAP1 observed in 44% was less frequent in TM than in EM and BM. p16 homozygous deletion was higher in TM (73%), followed by BM (63%) and SM (46%). RNA sequencing unsupervised clustering analysis revealed that TM grouped together and were closer to SM than to EM. Deep learning analysis achieved 94% accuracy for TM identification. CONCLUSION: These results revealed that the TM pattern should be classified as non-EM or at minimum as a subgroup of the SM type.

Predicting Survival After Hepatocellular Carcinoma Resection Using Deep Learning on Histological Slides.

BACKGROUND AND AIMS: Standardized and robust risk-stratification systems for patients with hepatocellular carcinoma (HCC) are required to improve therapeutic strategies and investigate the benefits of adjuvant systemic therapies after curative resection/ablation. APPROACH AND RESULTS: In this study, we used two deep-learning algorithms based on whole-slide digitized histological slides (whole-slide imaging; WSI) to build models for predicting survival of patients with HCC treated by surgical resection. Two independent series were investigated: a discovery set (Henri Mondor Hospital, n = 194) used to develop our algorithms and an independent validation set (The Cancer Genome Atlas [TCGA], n = 328). WSIs were first divided into small squares ("tiles"), and features were extracted with a pretrained convolutional neural network (preprocessing step). The first deep-learning-based algorithm ("SCHMOWDER") uses an attention mechanism on tumoral areas annotated by a pathologist whereas the second ("CHOWDER") does not require human expertise. In the discovery set, c-indices for survival prediction of SCHMOWDER and CHOWDER reached 0.78 and 0.75, respectively. Both models outperformed a composite score incorporating all baseline variables associated with survival. Prognostic value of the models was further validated in the TCGA data set, and, as observed in the discovery series, both models had a higher discriminatory power than a score combining all baseline variables associated with survival. Pathological review showed that the tumoral areas most predictive of poor survival were characterized by vascular spaces, the macrotrabecular architectural pattern, and a lack of immune infiltration. CONCLUSIONS: This study shows that artificial intelligence can help refine the prediction of HCC prognosis. It highlights the importance of pathologist/machine interactions for the construction of deep-learning algorithms that benefit from expert knowledge and allow a biological understanding of their output.

Deep learning-based classification of mesothelioma improves prediction of patient outcome.

Malignant mesothelioma (MM) is an aggressive cancer primarily diagnosed on the basis of histological criteria1. The 2015 World Health Organization classification subdivides mesothelioma tumors into three histological types: epithelioid, biphasic and sarcomatoid MM. MM is a highly complex and heterogeneous disease, rendering its diagnosis and histological typing difficult and leading to suboptimal patient care and decisions regarding treatment modalities2. Here we have developed a new approach-based on deep convolutional neural networks-called MesoNet to accurately predict the overall survival of mesothelioma patients from whole-slide digitized images, without any pathologist-provided locally annotated regions. We validated MesoNet on both an internal validation cohort from the French MESOBANK and an independent cohort from The Cancer Genome Atlas (TCGA). We also demonstrated that the model was more accurate in predicting patient survival than using current pathology practices. Furthermore, unlike classical black-box deep learning methods, MesoNet identified regions contributing to patient outcome prediction. Strikingly, we found that these regions are mainly located in the stroma and are histological features associated with inflammation, cellular diversity and vacuolization. These findings suggest that deep learning models can identify new features predictive of patient survival and potentially lead to new biomarker discoveries.

External validation of the pathological nodal staging score in upper tract urothelial carcinoma: A population-based study.

OBJECTIVES: To externally validate our previously developed pathological nodal staging model (pNSS) that allows quantification of the likelihood that a patient with pathologic node-negative status has, indeed, no lymph node metastasis (LNM). PATIENTS AND METHODS: We analyzed data from 2,768 patients treated with radical nephroureterectomy (RNU) and lymph node dissection (LND) using the Surveillance, Epidemiology, and End Results database from 1988 to 2010. We estimated the sensitivity of pathologic nodal staging using a beta-binomial model and developed a new pNSS. Then, we compared these findings with those of the initial cohort. RESULTS: The mean and median numbers of lymph node (LN) removed were 5 and 2, respectively (interquartile range = 5) in the validation cohort, though 66.5% of the patients (n = 1814) were pN0. Similar to the development cohort, the probability of missing a LNM decreased as the number of nodes examined increased in the validation cohort. If only a single node was examined, 35% of patients would be misclassified as pN0 while harboring LNM. Even when 5 nodes were examined, 8% would be misclassified. The probability of having a positive node increased with advancing pathological T stage in both the cohorts. Patients with pT0-Ta-Tis-T1 disease in both cohorts would have more than a 95% chance of a correct pathologic nodal staging with 2 examined nodes. However, if a patient has pT3-T4 disease, more than 12 examined LNs are needed to reach 95% accuracy. CONCLUSIONS: We confirmed that the number of examined nodes needed for adequate staging depends on pT category. We externally validated our previous pNSS in a population-based database, which could help in the clinical decision-making regarding adjuvant chemotherapy administration.

An Epigenomic Approach to Improving Response to Neoadjuvant Cisplatin Chemotherapy in Bladder Cancer.

Bladder cancer is among the five most common cancers diagnosed in the Western world and causes significant mortality and morbidity rates in affected patients. Therapeutic options to treat the disease in advanced muscle-invasive bladder cancer (MIBC) include cystectomy and chemotherapy. Neoadjuvant cisplatin-based combination chemotherapy is effective in MIBC; however, it has not been widely adopted by the community. One reason is that many patients do not respond to neoadjuvant chemotherapy, and no biomarker currently exists to identify these patients. It is also not clear whether a strategy to sensitize chemoresistant patients may exist. We sought to identify cisplatin-resistance patterns in preclinical models of bladder cancer, and test whether treatment with the epigenetic modifier decitabine is able to sensitize cisplatin-resistant bladder cancer cell lines. Using a screening approach in cisplatin-resistant bladder cancer cell lines, we identified dysregulated genes by RNA sequencing (RNAseq) and DNA methylation assays. DNA methylation analysis of tumors from 18 patients receiving cisplatin-based chemotherapy was used to confirm in vitro results. Cisplatin-resistant bladder cancer cells were treated with decitabine to investigate epigenetic sensitization of resistant cell lines. Our results show that HOXA9 promoter methylation status is associated with response to cisplatin-based chemotherapy in bladder cancer cell lines and in metastatic bladder cancer. Bladder cancer cells resistant to cisplatin chemotherapy can be sensitized to cisplatin by the DNA methylation inhibitor decitabine. Our data suggest that HOXA9 promoter methylation could serve as potential predictive biomarker and decitabine might sensitize resistant tumors in patients receiving cisplatin-based chemotherapy.

Deep sequencing reveals clonal evolution patterns and mutation events associated with relapse in B-cell lymphomas.

BACKGROUND: Molecular mechanisms associated with frequent relapse of diffuse large B-cell lymphoma (DLBCL) are poorly defined. It is especially unclear how primary tumor clonal heterogeneity contributes to relapse. Here, we explore unique features of B-cell lymphomas - VDJ recombination and somatic hypermutation - to address this question. RESULTS: We performed high-throughput sequencing of rearranged VDJ junctions in 14 pairs of matched diagnosis-relapse tumors, among which 7 pairs were further characterized by exome sequencing. We identify two distinctive modes of clonal evolution of DLBCL relapse: an early-divergent mode in which clonally related diagnosis and relapse tumors diverged early and developed in parallel; and a late-divergent mode in which relapse tumors developed directly from diagnosis tumors with minor divergence. By examining mutation patterns in the context of phylogenetic information provided by VDJ junctions, we identified mutations in epigenetic modifiers such as KMT2D as potential early driving events in lymphomagenesis and immune escape alterations as relapse-associated events. CONCLUSIONS: Altogether, our study for the first time provides important evidence that DLBCL relapse may result from multiple, distinct tumor evolutionary mechanisms, providing rationale for therapies for each mechanism. Moreover, this study highlights the urgent need to understand the driving roles of epigenetic modifier mutations in lymphomagenesis, and immune surveillance factor genetic lesions in relapse.

Impact of ERBB2 mutations on in vitro sensitivity of bladder cancer to lapatinib.

Lapatinib, a dual tyrosine kinase inhibitor of ErbB1 and ErbB2, shows a clinical benefit in a subset of patients with advanced urothelial bladder cancer (UBC). We hypothesized that the corresponding gene, ERBB2, is affected by mutations in a subset of UBC and that these mutations impact ErbB2 function, signaling, UBC proliferation, gene expression, and predict response to lapatinib. We found ERBB2 mutations in 5 of 33 UBC cell lines (15%), all of which were derived from invasive or high grade tumors. Phosphorylation and activation of ErbB2 and its downstream pathways were markedly enhanced in mutated cell lines compared with the ERBB2 wild-type. In addition, the gene expression profile was distinct, specifically for genes encoding for proteins of the extracellular matrix. RT112 cells infected with ERBB2 mutants showed a particular growth pattern ("mini-foci"). Upon treatment with lapatinib, 93% of these "mini-foci" were reversed. The sensitivity to lapatinib was greatest among cell lines with ERBB2 mutations. In conclusion, ERBB2 mutations occur in a subset of UBC and impact proliferation, signaling, gene expression and predict a greater response to lapatinib. If confirmed in the clinical setting, this may lead the way toward personalized treatment of a subset of UBC.

Impact of distal ureter management on oncologic outcomes following radical nephroureterectomy for upper tract urothelial carcinoma.

BACKGROUND: There is a lack of consensus regarding the optimal approach to the bladder cuff during radical nephroureterectomy (RNU) for upper tract urothelial carcinoma (UTUC). OBJECTIVES: To compare the oncologic outcomes following RNU using three different methods of bladder cuff management. DESIGN, SETTING, AND PARTICIPANTS: Retrospective analysis of 2681 patients treated with RNU for UTUC at 24 international institutions from 1987 to 2007. INTERVENTION: Three methods of bladder cuff excision were performed: transvesical, extravesical, and endoscopic. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Univariable and multivariable models tested the effect of distal ureter management on intravesical recurrence, recurrence-free survival (RFS), cancer-specific survival (CSS), and overall survival (OS). RESULTS AND LIMITATIONS: Of the 2681 patients, 1811 (67.5%) underwent the transvesical approach; 785 (29.3%), the extravesical approach; and 85 (3.2%), the endoscopic approach. There was no difference in terms of RFS, CSS, and OS among the three distal ureteral management approaches. Patients who underwent the endoscopic approach were at significantly higher risk of intravesical recurrence compared with those who underwent the transvesical (p=0.02) or extravesical approaches (p=0.02); the latter two groups did not differ from each other (p=0.40). Actuarial intravesical RFS estimates at 2 and 5 yr after RNU were 69% and 58%, 69% and 51%, and 61% and 42% for the transvesical, extravesical, and endoscopic approaches, respectively. In multivariate analyses, distal ureteral management (p=0.01), surgical technique (open vs laparoscopic; p=0.02), previous bladder cancer (p<0.001), higher tumor stage (trend; p=0.01), concomitant carcinoma in situ (CIS) (p<0.001), and lymph node involvement (trend; p<0.001) were all associated with intravesical recurrence. Excluding patients with history of previous bladder cancer, all variables remained independent predictors of intravesical recurrence. CONCLUSIONS: The endoscopic approach was associated with higher intravesical recurrence rates. Interestingly, concomitant CIS in the upper tract is a strong predictor of intravesical recurrence after RNU. The association of laparoscopic RNU with intravesical recurrence needs to be further investigated.

Mechanism-based epigenetic chemosensitization therapy of diffuse large B-cell lymphoma.

UNLABELLED: Although aberrant DNA methylation patterning is a hallmark of cancer, the relevance of targeting DNA methyltransferases (DNMT) remains unclear for most tumors. In diffuse large B-cell lymphoma (DLBCL) we observed that chemoresistance is associated with aberrant DNA methylation programming. Prolonged exposure to low-dose DNMT inhibitors (DNMTI) reprogrammed chemoresistant cells to become doxorubicin sensitive without major toxicity in vivo. Nine genes were recurrently hypermethylated in chemoresistant DLBCL. Of these, SMAD1 was a critical contributor, and reactivation was required for chemosensitization. A phase I clinical study was conducted evaluating azacitidine priming followed by standard chemoimmunotherapy in high-risk patients newly diagnosed with DLBCL. The combination was well tolerated and yielded a high rate of complete remission. Pre- and post-azacitidine treatment biopsies confirmed SMAD1 demethylation and chemosensitization, delineating a personalized strategy for the clinical use of DNMTIs. SIGNIFICANCE: The problem of chemoresistant DLBCL remains the most urgent challenge in the clinical management of patients with this disease. We describe a mechanism-based approach toward the rational translation of DNMTIs for the treatment of high-risk DLBCL.

Impact of renal function on eligibility for chemotherapy and survival in patients who have undergone radical nephro-ureterectomy.

UNLABELLED: What's known on the subject? and what does the study add?: Radical nephroureterectomy (RNU), the standard of care treatment for high-risk urothelial carcinoma of the upper tract (UTUC), results in loss of a renal unit. Loss of renal function decreases eligibility for systemic chemotherapies and results in decreased overall survival in various malignancies. The study shows that only a small proportion of patients had a preoperative renal function that would allow cisplatin-based chemotherapy. Moreover, eGFR significantly decreased after RNU, thereby lowering the rate of cisplatin eligibility to only 16 and 52% of patients based on the thresholds of 60 and 45 mL/min/1.73 m(2) , respectively. Taken together with the rest of the literature, the findings of the study support the use of cisplatin-based chemotherapy, when indicated, in the neoadjuvant rather than adjuvant setting. OBJECTIVE: To report (i) the estimated glomerular filtration rate (eGFR) changes in patients undergoing radical nephro-ureterectomy (RNU) for upper tract urothelial carcinoma (UTUC); (ii) the rate of change in eGFR in patients eligible for cisplatin-based chemotherapy; and (iii) the association of preoperative, postoperative and rate of change of renal function variables with survival outcomes. PATIENT AND METHODS: We performed a retrospective analysis of 666 patients treated with RNU for UTUC at seven international institutions from 1994 to 2007. The eGFR was calculated at baseline and at 3-6 months (Modification of Diet in Renal Disease formula (MDRD) and Chronic Kidney Disease Epidemiology Collaboration formula (CKD-EP) equations). RESULTS: The median (interquartile range) eGFR decreased by 18.2 (8-12)% after RNU. A total of 37% of patients had a preoperative eGFR ≥ 60 mL/min/1.73 m(2) , which decreased to 16% after RNU (P < 0.001); 72% of patients had a preoperative eGFR ≥ 45 mL/min/1.73 m(2) , which decreased to 52% after RNU (P < 0.001). The distributions were similar when analyses were restricted to patients with locally advanced disease (pT3-pT4) and/or lymph node metastasis. Patients older than the median age of 70 years were more likely to have a decrease in eGFR after RNU (P < 0.001). None of the renal function variables was associated with clinical outcomes such as disease recurrence, cancer-specific and overall mortality; however, when analyses were restricted to patients who had no adjuvant chemotherapy and did not experience disease recurrence (n = 431), a preoperative eGFR ≥ 60 mL/min/1.73 m(2) (P = 0.03) and a postoperative eGFR ≥ 45 mL/min/1.73 m(2) (P = 0.04) were associated with better overall survival in univariable analyses. CONCLUSIONS: In patients who had UTUC, eGFR was low and furthermore, it significantly decreased after RNU. Renal function did not affect cancer-specific outcomes after RNU.

Predictors of survival in patients with soft tissue surgical margin involvement at radical cystectomy.

BACKGROUND: The presence of positive soft tissue surgical margins (STSM) at radical cystectomy (RC) is rare. Although some patients with STSM experience disease recurrence rapidly, some have long-term local disease control. We sought to describe the oncologic outcomes, identify predictors, and assess the impact of location and multifocality in patients with positive STSMs at RC. METHODS: We retrospectively collected the data of 4,335 patients treated with RC and pelvic lymphadenectomy at 11 academic centers from 1981 to 2008. STSM was defined as the presence of tumor at inked areas of soft tissue on the RC specimen. Univariate and multivariate Cox regression models addressed recurrence-free survival and cancer-specific survival after surgery. RESULTS: STSM were identified in 231 patients (5%). Actuarial recurrence-free survival estimates at 2 and 5 years after RC were 26 ± 3 and 21 ± 3%, respectively. Actuarial cancer-specific survival estimates at 2 and 5 years after RC were 33 ± 3 and 25 ± 4%, respectively. Higher body mass index (p = 0.050), higher tumor stage (p = 0.017), presence of grade 3 disease (p = 0.046), lymphovascular invasion (p = 0.003), and lymph node involvement (p = 0.003) were all independently associated with disease recurrence. Furthermore, higher tumor stage (p = 0.015), lymphovascular invasion (p = 0.006), and lymph node involvement (p = 0.006) were independently associated with cancer specific mortality. Location and multifocality of STSM were not associated with outcomes. CONCLUSIONS: Although most patients with STSM at RC had poor outcomes, more than one-fifth had durable cancer control. Pathologic features associated with disease recurrence in the general RC population also stratify patients with STSM into differential risk groups.

Clinical nodal staging scores for bladder cancer: a proposal for preoperative risk assessment.

BACKGROUND: Radical cystectomy (RC) with pelvic lymph node dissection (LND) is the standard of care for refractory non-muscle-invasive and muscle-invasive bladder cancer. Although consensus exists on the need for LND, its extent is still debated. OBJECTIVE: To develop a model that allows preoperative determination of the minimum number of lymph nodes (LNs) needed to be removed at RC to ensure true nodal status. DESIGN, SETTING, AND PARTICIPANTS: We analyzed data from 4335 patients treated with RC and pelvic LND without neoadjuvant chemotherapy at 12 academic centers located in the United States, Canada, and Europe. MEASUREMENTS: We estimated the sensitivity of pathologic nodal staging using a beta-binomial model and developed clinical (preoperative) nodal staging scores (cNSS), which represent the probability that a patient has LN metastasis as a function of the number of examined nodes. RESULTS AND LIMITATIONS: The probability of missing a positive LN decreased with an increasing number of nodes examined (52% if 3 nodes were examined, 40% if 5 were examined, and 26% if 10 were examined). A cNSS of 90% was achieved by examining 6 nodes for clinical Ta-Tis tumors, 9 nodes for cT1 tumors, and 25 nodes for cT2 tumors. In contrast, examination of 25 nodes provided only 77% cNSS for cT3-T4 tumors. The study is limited due to its retrospective design, its multicenter nature, and a lack of preoperative staging parameters. CONCLUSIONS: Every patient treated with RC for bladder cancer needs an LND to ensure accurate nodal staging. The minimum number of examined LNs for adequate staging depends preoperatively on the clinical T stage. Predictive tools can give a preoperative estimation of the likelihood of nodal metastasis and thereby allow tailored decision-making regarding the extent of LND at RC.

DNA methyltransferase 1 and DNA methylation patterning contribute to germinal center B-cell differentiation.

The phenotype of germinal center (GC) B cells includes the unique ability to tolerate rapid proliferation and the mutagenic actions of activation induced cytosine deaminase (AICDA). Given the importance of epigenetic patterning in determining cellular phenotypes, we examined DNA methylation and the role of DNA methyltransferases in the formation of GCs. DNA methylation profiling revealed a marked shift in DNA methylation patterning in GC B cells versus resting/naive B cells. This shift included significant differential methylation of 235 genes, with concordant inverse changes in gene expression affecting most notably genes of the NFkB and MAP kinase signaling pathways. GC B cells were predominantly hypomethylated compared with naive B cells and AICDA binding sites were highly overrepresented among hypomethylated loci. GC B cells also exhibited greater DNA methylation heterogeneity than naive B cells. Among DNA methyltransferases (DNMTs), only DNMT1 was significantly up-regulated in GC B cells. Dnmt1 hypomorphic mice displayed deficient GC formation and treatment of mice with the DNA methyltransferase inhibitor decitabine resulted in failure to form GCs after immune stimulation. Notably, the GC B cells of Dnmt1 hypomorphic animals showed evidence of increased DNA damage, suggesting dual roles for DNMT1 in DNA methylation and double strand DNA break repair.