Impact of consolidation chemotherapy after delayed complete surgery in advanced epithelial ovarian cancer: a propensity score analysis.

OBJECTIVES: This retrospective study aimed to assess the efficiency of consolidation chemotherapy after 6 cycles of neoadjuvant chemotherapy and delayed complete surgery on overall survival and progression-free survival among patients with advanced epithelial ovarian cancer. METHODS: This was a retrospective consecutive study with a propensity score to ensure balance for the baseline characteristics between the study groups. All patients treated for advanced ovarian cancer with 6 cycles of neoadjuvant chemotherapy followed by delayed complete surgery, without post-operative chemotherapy (group 1), or with post-operative chemotherapy (group 2), were included. We evaluated survival and the quality of cytoreductive surgery using the propensity score. RESULTS: From 2000 to 2017, 42 patients were included in group 1, and 59 in group 2. The median follow-up was 78 months (confidence interval (CI) 95% (60.1; not computable)). Neither progression-free survival nor overall survival were different between the two groups. The median progression-free survival was 10.2 months (CI 95% (8.8-17.0)) for group 1 and 10.4 months (CI 95% (7.9-12.8)) for group 2 (p=0.57). Five-year overall survival was 21.0% (CI 95% (10.4-42.3)) for group 1 and 26.1% (CI 95% (16.0-42.5)) for group 2 (p=0.73). CONCLUSIONS: Adding cycles of consolidation chemotherapy after delayed surgery following 6 cycles of neoadjuvant chemotherapy did not demonstrate any survival improvement in patients treated for advanced ovarian cancer not amenable to primary or interval surgery.

Whole genome copy number analysis in search of new prognostic biomarkers in first line treatment of mantle cell lymphoma. A study by the LYSA group.

Mantle cell lymphoma (MCL) is a lymphoproliferative disorder characterized by the t(11;14)(q13;q32) CCND1/IGH translocation. This lymphoma is however extremely heterogeneous in terms of molecular alterations. Moreover, the course of the disease can vary greatly between indolent forms with slow progression and aggressive conditions rapidly pejorative. The identification of early markers allowing to predict individual patients outcome has however been unsuccessful so far. The LyMa trial treated homogeneously a cohort of young MCL patients. This appeared as a good opportunity to search for biomarkers of response to therapy. DNA extracted from diagnostic paraffin-embedded lymph node biopsies from 100 patients with newly diagnosed MCL, homogeneously treated in this prospective clinical trial, were investigated for copy number alterations and copy neutral loss of heterozygosity using the Oncoscan SNP-array scanning the whole genome. An independent confirmatory cohort was used to strengthen the possibly relevant anomalies observed. Here we describe the recurrent anomalies identified with this technique. Deletions of 17p(TP53) and 9p(CDKN2A) were more frequent in refractory or early relapsing patients (10%), but had no significant impact in univariate analysis on progression-free (PFS) or overall survival (OS). Regardless of the presence of TP53 or CDKN2A deletions, gains in 7p22 (8,5%) were associated with better PFS in univariate but not in multivariate analysis including MCL International Prognostic Index and treatment. Gains of 11q(CCDN1), suggesting gains of the CCND1/IGH fusion, were associated with worse OS and PFS in univariate and multivariate analyses. This worse prognosis impact was confirmed by FISH in an independent confirmatory cohort. This work, using a whole genome approach, confirms the broad genomic landscape of MCL and shows that gains of the CCND1/IGH fusion can be considered as a new prognostic structural variant. Genomic abnormalities of prognostic impact could be useful to strengthen or de-escalate treatment schedules or choosing targeted therapies or CART-cells.

iTRAQ-Based Quantitative Proteomic Analysis Strengthens Transcriptomic Subtyping of Triple-Negative Breast Cancer Tumors.

Heterogeneity and lack of targeted therapies represent the two main impediments to precision treatment of triple-negative breast cancer (TNBC). Therefore, molecular subtyping and identification of therapeutic pathways are required to optimize medical care. The aim of the present study is to define robust TNBC subtypes with clinical relevance by means of proteomics and transcriptomics. As a first step, unsupervised analyses are conducted in parallel on proteomics and transcriptomics data of 83 TNBC tumors. Proteomics data unsupervised analysis did not permit separation of TNBC into different subtypes, whereas transcriptomics data are able to clearly and robustly identify three subtypes: molecular apocrine (C1), basal-like immune-suppressed (C2), and basal-like immune response (C3). Supervised analysis of proteomics data are then conducted based on transcriptomics subtyping. Thirty out of 62 proteins differentially expressed between C1, C2, and C3 belonged to biological categories which characterized these TNBC clusters: luminal and androgen-regulated proteins (C1), basal, invasion, and extracellular matrix (C2), and basal and immune response (interferon pathway and immunoglobulins) (C3). Although proteomics unsupervised analysis of TNBC tumors is unsuccessful at identifying clusters, the integrated approach is promising. Identification and measurement of 30 proteins strengthen subtyping of TNBC based on robust transcriptomics unsupervised analysis.

Identification of three subtypes of triple-negative breast cancer with potential therapeutic implications.

BACKGROUND: Heterogeneity and lack of targeted therapies represent the two main impediments to precision treatment of triple-negative breast cancer (TNBC), and therefore, molecular subtyping and identification of therapeutic pathways are required to optimize medical care. The aim of the present study was to define robust TNBC subtypes with clinical relevance. METHODS: Gene expression profiling by means of DNA chips was conducted in an internal TNBC cohort composed of 238 patients. In addition, external data (n = 257), obtained by using the same DNA chip, were used for validation. Fuzzy clustering was followed by functional annotation of the clusters. Immunohistochemistry was used to confirm transcriptomics results: CD138 and CD20 were used to test for plasma cell and B lymphocyte infiltrations, respectively; MECA79 and CD31 for tertiary lymphoid structures; and UCHL1/PGP9.5 and S100 for neurogenesis. RESULTS: We identified three molecular clusters within TNBC: one molecular apocrine (C1) and two basal-like-enriched (C2 and C3). C2 presented pro-tumorigenic immune response (immune suppressive), high neurogenesis (nerve infiltration), and high biological aggressiveness. In contrast, C3 exhibited adaptive immune response associated with complete B cell differentiation that occurs in tertiary lymphoid structures, and immune checkpoint upregulation. External cohort subtyping by means of the same approach proved the robustness of these results. Furthermore, plasma cell and B lymphocyte infiltrates, tertiary lymphoid structures, and neurogenesis were validated at the protein levels by means of histological evaluation and immunohistochemistry. CONCLUSION: Our work showed that TNBC can be subcategorized in three different subtypes characterized by marked biological features, some of which could be targeted by specific therapies.

Gene-expression molecular subtyping of triple-negative breast cancer tumours: importance of immune response.

INTRODUCTION: Triple-negative breast cancers need to be refined in order to identify therapeutic subgroups of patients. METHODS: We conducted an unsupervised analysis of microarray gene-expression profiles of 107 triple-negative breast cancer patients and undertook robust functional annotation of the molecular entities found by means of numerous approaches including immunohistochemistry and gene-expression signatures. A triple-negative external cohort (n=87) was used for validation. RESULTS: Fuzzy clustering separated triple-negative tumours into three clusters: C1 (22.4%), C2 (44.9%) and C3 (32.7%). C1 patients were older (mean=64.6 years) than C2 (mean=56.8 years; P=0.03) and C3 patients (mean=51.9 years; P=0.0004). Histological grade and Nottingham prognostic index were higher in C2 and C3 than in C1 (P<0.0001 for both comparisons). Significant event-free survival (P=0.03) was found according to cluster membership: patients belonging to C3 had a better outcome than patients in C1 (P=0.01) and C2 (P=0.02). Event-free survival analysis results were confirmed when our cohort was pooled with the external cohort (n=194; P=0.01). Functional annotation showed that 22% of triple-negative patients were not basal-like (C1). C1 was enriched in luminal subtypes and positive androgen receptor (luminal androgen receptor). C2 could be considered as an almost pure basal-like cluster. C3, enriched in basal-like subtypes but to a lesser extent, included 26% of claudin-low subtypes. Dissection of immune response showed that high immune response and low M2-like macrophages were a hallmark of C3, and that these patients had a better event-free survival than C2 patients, characterized by low immune response and high M2-like macrophages: P=0.02 for our cohort, and P=0.03 for pooled cohorts. CONCLUSIONS: We identified three subtypes of triple-negative patients: luminal androgen receptor (22%), basal-like with low immune response and high M2-like macrophages (45%), and basal-enriched with high immune response and low M2-like macrophages (33%). We noted out that macrophages and other immune effectors offer a variety of therapeutic targets in breast cancer, and particularly in triple-negative basal-like tumours. Furthermore, we showed that CK5 antibody was better suited than CK5/6 antibody to subtype triple-negative patients.

Prospective results for 5-year survival and toxicity of moderately hypofractionated radiotherapy with simultaneous integrated boost (SIB) in (very) high-risk prostate cancer.

Purpose: High-risk (HR) prostate cancer patients usually receive high-dose radiotherapy (RT) using a two-phase sequential technique, but data on a simultaneous integrated boost (SIB) technique are lacking. We prospectively evaluated the long-term results of urinary (GU) and digestive (GI) toxicity and survival data for high-dose RT using a SIB technique in HR and very high-risk (VHR) prostate cancer. Methods: Patients were treated using an SIB technique in 34 fractions, at a dose of 54.4 Gy to the pelvis and seminal vesicles and 74.8 Gy to the prostate, combined with 36 months of androgen-depriving therapy in a prospective multicenter study. Acute and late GU and GI toxicity data were collected. Overall survival (OS), biochemical-relapse-free survival (bRFS), loco-regional-relapse-free survival (LRRFS), metastasis-free-survival (MFS) and disease-free-survival (DFS) were assessed. Results: We recruited 114 patients. After a median follow-up of 62 months, very few patients experienced acute (M0-M3) (G3-4 GU = 3.7 %; G3-4 GI = 0.9 %) or late (M6-M60) severe toxicity (G3-4 GU = 5.6 %; G3-4 GI = 2.8 %). The occurrence of acute G2 + GU or GI toxicity was significantly related to the consequential late G2 + toxicity (p < 0.01 for both GU and GI). Medians of OS, bRFS, LRRFS, MFS and DFS were not reached. At 60 months, OS, bRFS, LRRFS, MFS and DFS were 88.2 % [82.1; 94.7], 86.0 % [79.4 %;93.2 %], 95.8 % [91.8 %;99.9 %], 87.2 % [80.9 %;94.0 %] and 84.1 % [77.2 %;91.6 %] respectively. Conclusion: SIB RT at a dose of 54.4 Gy to the pelvis and 74.8 Gy to the prostate is feasible, leading to satisfying tumor control and reasonable toxicity in HR and VHR prostate cancer.

How to Balance Prognostic Factors in Controlled Phase II Trials: Stratified Permuted Block Randomization or Minimization? An Analysis of Clinical Trials in Digestive Oncology.

In controlled phase II trials, major prognostic factors need to be well balanced between arms. The main procedures used are SPBR (Stratified Permuted Block Randomization) and minimization. First, we provide a systematic review of the treatment allocation procedure used in gastrointestinal oncology controlled phase II trials published in 2019. Second, we performed simulations using data from six phase II studies to measure the impacts of imbalances and bias on the efficacy estimations. From the 40 articles analyzed, all mentioned randomization in both the title and abstract, the median number of patients included was 109, and 77.5% were multicenter. Of the 27 studies that reported at least one stratification variable, 10 included the center as a stratification variable, 10 used minimization, 9 used SBR, and 8 were unspecified. In real data studies, the imbalance increased with the number of centers. The total and marginal imbalances were higher with SBR than with minimization, and the difference increased with the number of centers. The efficiency estimates per arm were close to the original trial estimate in both procedures. Minimization is often used in cases of numerous centers and guarantees better similarity between arms for stratification variables for total and marginal imbalances in phase II trials.

Tucatinib Combination Treatment After Trastuzumab-Deruxtecan in Patients With ERBB2-Positive Metastatic Breast Cancer.

Importance: Little is known regarding the outcomes associated with tucatinib combined with trastuzumab and capecitabine (TTC) after trastuzumab-deruxtecan exposure among patients with ERBB2 (previously HER2)-positive metastatic breast cancer (MBC). Objective: To investigate outcomes following TTC treatment in patients with ERBB2-positive MBC who had previously received trastuzumab-deruxtecan. Design, Setting, and Participants: This cohort study included all patients with MBC who were treated in 12 French comprehensive cancer centers between August 1, 2020, and December 31, 2022. Exposure: Tucatinib combined with trastuzumab and capecitabine administered at the recommended dose. Main Outcomes and Measures: Clinical end points included progression-free survival (PFS), time to next treatment (TTNT), overall survival (OS), and overall response rate (ORR). Results: A total of 101 patients with MBC were included (median age, 56 [range, 31-85] years). The median number of prior treatment lines for metastatic disease at TTC treatment initiation was 4 (range, 2-15), including 82 patients (81.2%) with previous trastuzumab and/or pertuzumab and 94 (93.1%) with previous ado-trastuzumab-emtansine) exposure. The median duration of trastuzumab-deruxtecan treatment was 8.9 (range, 1.4-25.8) months, and 82 patients (81.2%) had disease progression during trastuzumab-deruxtecan treatment, whereas 18 (17.8%) had stopped trastuzumab-deruxtecan for toxic effects and 1 (1.0%) for other reasons. Tucatinib combined with trastuzumab and capecitabine was provided as a third- or fourth-line treatment in 37 patients (36.6%) and was the immediate treatment after trastuzumab-deruxtecan in 86 (85.1%). With a median follow-up of 11.6 (95% CI, 10.5-13.4) months, 76 of 101 patients (75.2%) stopped TTC treatment due to disease progression. The median PFS was 4.7 (95% CI, 3.9-5.6) months; median TTNT, 5.2 (95% CI, 4.5-7.0) months; and median OS, 13.4 (95% CI, 11.1 to not reached [NR]) months. Patients who received TTC immediately after trastuzumab-deruxtecan had a median PFS of 5.0 (95% CI, 4.2-6.0) months; median TTNT of 5.5 (95% CI, 4.8-7.2) months, and median OS of 13.4 (95% CI, 11.9-NR) months. Those who received TTC due to trastuzumab-deruxtecan toxicity-related discontinuation had a median PFS of 7.3 (95% CI, 3.0-NR) months. Best ORR was 29 of 89 patients (32.6%). Sixteen patients with active brain metastasis had a median PFS of 4.7 (95% CI, 3.0-7.3) months, median TTNT of 5.6 (95% CI, 4.4 to NR), and median OS of 12.4 (95% CI, 8.3-NR) months. Conclusions and Relevance: In this study, TTC therapy was associated with clinically meaningful outcomes in patients with ERBB2-positive MBC after previous trastuzumab-deruxtecan treatment, including those with brain metastases. Prospective data on optimal drug sequencing in this rapidly changing therapeutic landscape are needed.

Exploring the impact of dexamethasone on gene regulation in myeloma cells.

Among glucocorticoids (GCs), dexamethasone (Dex) is widely used in treatment of multiple myelomas. However, despite a definite benefit, all patients relapse. Moreover, the molecular basis of glucocorticoid efficacy remains elusive. To determine genomic response to Dex in myeloma cells, we generated bulk and single-cell multi-omics data and high-resolution contact maps of active enhancers and target genes. We show that a minority of glucocorticoid receptor-binding sites are associated with enhancer activity gains, increased interaction loops, and transcriptional activity. We identified and characterized a predominant enhancer enriched in cohesin (RAD21) and more accessible upon Dex exposure. Analysis of four gene-specific networks revealed the importance of the CTCF-cohesin couple and the synchronization of regulatory sequence openings for efficient transcription in response to Dex. Notably, these epigenomic changes are associated with cell-to-cell transcriptional heterogeneity, in particular, lineage-specific genes. As consequences, BCL2L11-encoding BIM critical for Dex-induced apoptosis and CXCR4 protective from chemotherapy-induced apoptosis are rather up-regulated in different cells. In summary, our work provides new insights into the molecular mechanisms involved in Dex escape.

Validation of tumor-associated macrophage ferritin light chain as a prognostic biomarker in node-negative breast cancer tumors: A multicentric 2004 national PHRC study.

Novel prognostic biomarkers are imperatively needed to help direct treatment decisions by typing subgroups of node-negative breast cancer patients. Large screening of different biological compartments, such as the proteome, by means of high throughput techniques may greatly help scientists to find such markers. The present retrospective multicentric study included 268 node-negative breast cancer patients. We used a proteomic approach of SELDI-TOF-MS screening to identify differentially expressed cytosolic proteins with prognostic impact. The screening cohort was composed of 198 patients. Seventy supplementary patients were included for validation. Immunohistochemistry (IHC) and immunoassay (IA) were run to confirm the prognostic role of the marker identified by SELDI-TOF-MS screening. IHC was also used to explore links between selected marker and epithelial-mesenchymal transition (EMT)-like, proliferation and macrophage markers. Ferritin light chain (FTL) was identified as an independent prognostic marker (HR = 1.30-95% CI: 1.10-1.50, p = 0.001). Validation step by means of IHC and IA confirmed the prognostic value of FTL level. CD68 IHC showed that FTL was stored in tumor-associated macrophages (TAM), which exhibit an M2-like phenotype. We report here, first, the validation of FTL as a breast tumor prognostic biomarker in node-negative patients, and second, the fact that FTL is stored in TAM.

bc-GenExMiner: an easy-to-use online platform for gene prognostic analyses in breast cancer.

Gene prognostic meta-analyses should benefit from breast tumour genomic data obtained during the last decade. The aim was to develop a user-friendly, web-based application, based on DNA microarrays results, called "breast cancer Gene-Expression Miner" (bc-GenExMiner) to improve gene prognostic analysis performance by using the same bioinformatics process. bc-GenExMiner was developed as a web-based tool including a MySQL relational database. Survival analyses are performed with R statistical software and packages. Molecular subtyping was performed by means of three single sample predictors (SSPs) and three subtype clustering models (SCMs). Twenty-one public data sets have been included. Among the 3,414 recovered breast cancer patients, 1,209 experienced a pejorative event. Molecular subtyping by means of three SSPs and three SCMs was performed for 3,063 patients. Furthermore, three robust lists of stable subtyped patients were built to maximize reliability of molecular assignment. Gene prognostic analyses are done by means of univariate Cox proportional hazards model and may be conducted on cohorts split by nodal (N), oestrogen receptor (ER), or molecular subtype status. To evaluate independent prognostic impact of genes relative to Nottingham Prognostic Index and Adjuvant! Online, adjusted Cox proportional hazards models are performed. bc-GenExMiner allows researchers without specific computation skills to easily and quickly evaluate the in vivo prognostic role of genes in breast cancer by means of Cox proportional hazards model on large pooled cohorts, which may be split according to different prognostic parameters: N, ER, and molecular subtype. Prognostic analyses by molecular subtype may also be performed in three robust molecular subtype classifications.

Exclusive autologous fat grafting: Feasibility, satisfaction, and quality of life.

OBJECTIVE: The main objective of this study was to assess the feasibility of breast reconstruction by exclusive autologous fat grafting (AFG). The secondary objectives were to identify predictive factors for technique failure and to assess the satisfaction and quality of life of patients who have benefited from exclusive AFG breast reconstruction using satisfaction and WHOQOL-BREF surveys. METHOD: We carried out a monocentric retrospective study. We included 118 patients who achieved breast reconstruction that was initially planned as exclusive AFG reconstruction, between April 2015 and November 2020. RESULTS: The success rate was 72.88% (86 patients). The only risk factor for failure we objectified was irradiation (OR=2.90). A total of 85 percent of patients felt that the result met their expectations. However, 82.93% rated their quality of life as good or very good. CONCLUSION: The AFG technique is well described, easily reproducible, and serious complications are rare. It allows for a less invasive autologous reconstruction than free or pedicle flaps. This type of reconstruction should probably be reserved for motivated patients with a small breast size or agreeing to a contralateral breast reduction procedure.

Individual learning curve of cytoreductive surgery for peritoneal metastasis from colorectal cancer: A process with an impact on survival.

BACKGROUND: This study aimed to assess the learning curve (LC) of cytoredutive surgery (CRS) of peritoneal metastasis (PM) from colorectal cancer (CRC). Information about learning curves is important for developing teaching tools and well-structured training programs for the implementation of this complex procedure in new healthcare centers. The aim of this study was to estimate how many procedures an inexperienced surgeon must perform (the length of the learning period) in order to demonstrate an acceptably low rate of locoregional recurrence. METHODS: All consecutive 74 patients with CRS for CRC performed by a novice surgeon between 2012 and 2017 in a tertiary cancer center were included. The learning curve was calculated by a cumulative sum control chart (CUSUM) graph. Two groups were formed based on the length of the learning period and were compared on overall and disease free survival. RESULTS: The risk of locoregional recurrence decreased after surgeons had performed 19 cases, suggesting a learning period of this length. Overall survival and postoperative morbidity were not significantly different between learning and proficiency periods. Multiple linear regression analysis showed that the learning period and peritoneal cancer index are the only factors affecting disease free survival. A second learning period was observed in cases where patient care became more complex. CONCLUSIONS: This study confirms that learning period has negative impacts on disease-free survival. An initial experience supervised in specialized centers allow to have a short learning curve for CRS for peritoneal metastases for CRC.

bc-GenExMiner 4.5: new mining module computes breast cancer differential gene expression analyses.

'Breast cancer gene-expression miner' (bc-GenExMiner) is a breast cancer-associated web portal (http://bcgenex.ico.unicancer.fr). Here, we describe the development of a new statistical mining module, which permits several differential gene expression analyses, i.e. 'Expression' module. Sixty-two breast cancer cohorts and one healthy breast cohort with their corresponding clinicopathological information are included in bc-GenExMiner v4.5 version. Analyses are based on microarray or RNAseq transcriptomic data. Thirty-nine differential gene expression analyses, grouped into 13 categories, according to clinicopathological and molecular characteristics ('Targeted' and 'Exhaustive') and gene expression ('Customized'), have been developed. Output results are visualized in four forms of plots. This new statistical mining module offers, among other things, the possibility to compare gene expression in healthy (cancer-free), tumour-adjacent and tumour tissues at once and in three triple-negative breast cancer subtypes (i.e. C1: molecular apocrine tumours; C2: basal-like tumours infiltrated by immune suppressive cells and C3: basal-like tumours triggering an ineffective immune response). Several validation tests showed that bioinformatics process did not alter the pathobiological information contained in the source data. In this work, we developed and demonstrated that bc-GenExMiner 'Expression' module can be used for exploratory and validation purposes. Database URL: http://bcgenex.ico.unicancer.fr.

Development of an absolute assignment predictor for triple-negative breast cancer subtyping using machine learning approaches.

Triple-negative breast cancer (TNBC) heterogeneity represents one of the main obstacles to precision medicine for this disease. Recent concordant transcriptomics studies have shown that TNBC could be divided into at least three subtypes with potential therapeutic implications. Although a few studies have been conducted to predict TNBC subtype using transcriptomics data, the subtyping was partially sensitive and limited by batch effect and dependence on a given dataset, which may penalize the switch to routine diagnostic testing. Therefore, we sought to build an absolute predictor (i.e., intra-patient diagnosis) based on machine learning algorithms with a limited number of probes. To that end, we started by introducing probe binary comparison for each patient (indicators). We based the predictive analysis on this transformed data. Probe selection was first involved combining both filter and wrapper methods for variable selection using cross-validation. We tested three prediction models (random forest, gradient boosting [GB], and extreme gradient boosting) using this optimal subset of indicators as inputs. Nested cross-validation consistently allowed us to choose the best model. The results showed that the fifty selected indicators highlighted the biological characteristics associated with each TNBC subtype. The GB based on this subset of indicators performs better than other models.

The DNA methylation landscape of multiple myeloma shows extensive inter- and intrapatient heterogeneity that fuels transcriptomic variability.

BACKGROUND: Cancer evolution depends on epigenetic and genetic diversity. Historically, in multiple myeloma (MM), subclonal diversity and tumor evolution have been investigated mostly from a genetic perspective. METHODS: Here, we performed an analysis of 42 MM samples from 21 patients by using enhanced reduced representation bisulfite sequencing (eRRBS). We combined several metrics of epigenetic heterogeneity to analyze DNA methylation heterogeneity in MM patients. RESULTS: We show that MM is characterized by the continuous accumulation of stochastic methylation at the promoters of development-related genes. High combinatorial entropy change is associated with poor outcomes in our pilot study and depends predominantly on partially methylated domains (PMDs). These PMDs, which represent the major source of inter- and intrapatient DNA methylation heterogeneity in MM, are linked to other key epigenetic aberrations, such as CpG island (CGI)/transcription start site (TSS) hypermethylation and H3K27me3 redistribution as well as 3D organization alterations. In addition, transcriptome analysis revealed that intratumor methylation heterogeneity was associated with low-level expression and high variability. CONCLUSIONS: We propose that disrupted DNA methylation in MM is responsible for high epigenetic and transcriptomic instability allowing tumor cells to adapt to environmental changes by tapping into a pool of evolutionary trajectories.

Cost and Toxicity Comparisons of Two IMRT Techniques for Prostate Cancer: A Micro-Costing Study and Weighted Propensity Score Analysis Based on a Prospective Study.

BACKGROUND: Intensity modulated radiation therapy (IMRT) combined with androgen deprivation therapy (ADT) has become the standard treatment for patients with high-risk prostate cancer. Two techniques of rotational IMRT are commonly used in this indication: Volumetric Modulated Arc Therapy (VMAT) and helical tomotherapy (HT). To the best of our knowledge, no study has compared their related costs and clinical effectiveness and/or toxicity in prostate cancer. We aimed to assess differences in costs and toxicity between VMAT and HT in patients with high-risk prostate cancer with pelvic irradiation. MATERIAL AND METHODS: We used data from the "RCMI pelvis" prospective multicenter study (NCT01325961) including 155 patients. We used a micro-costing methodology to identify cost differences between VMAT and HT. To assess the effects of the two techniques on total actual costs per patient and on toxicity we used stabilized inverse probability of treatment weighting. RESULTS: The mean total cost for HT, €2019 3,069 (95% CI, 2,885-3,285) was significantly higher than the mean cost for VMAT €2019 2,544 (95% CI, 2,443-2,651) (p <.0001). The mean ± SD labor and accelerator cost for HT was €2880 (± 583) and €1978 (± 475) for VMAT, with 81 and 76% for accelerator, respectively. Acute GI and GU toxicity were more frequent in VMAT than in HT (p = .021 and p = .042, respectively). Late toxicity no longer differed between the two groups up to 24 months after completion of treatment. CONCLUSION: Use of VMAT was associated with lower costs for IMRT planning and treatment than HT. Similar stabilized long-term toxicity was reported in both groups after higher acute GI and GU toxicity in VMAT. The estimates provided can benefit future modeling work like cost-effectiveness analysis.

DNA hydroxymethylation is associated with disease severity and persists at enhancers of oncogenic regions in multiple myeloma.

BACKGROUND: Multiple myeloma (MM) is a heterogeneous plasma cell malignancy that remains challenging to cure. Global hypomethylation correlates with an aggressive phenotype of the disease, while hypermethylation is observed at particular regions of myeloma such as B cell-specific enhancers. The recently discovered active epigenetic mark 5-hydroxymethylCytosine (5hmC) may also play a role in tumor biology; however, little is known about its level and distribution in myeloma. In this study, we investigated the global level and the genomic localization of 5hmC in myeloma cells from 40 newly diagnosed patients, including paired relapses, and of control individuals. RESULTS: Compared to normal plasma cells, we found global 5hmC levels to be lower in myeloma (P < 0.001). Higher levels of 5hmC were found in lower grades of the International Staging System prognostic index (P < 0.05) and tend to associate with a longer overall survival (P < 0.1). From the hydroxymethylome data, we observed that the remaining 5hmC is organized in large domains overlapping with active chromatin marks and chromatin opening. We discovered that 5hmC strongly persists at key oncogenic genes such as CCND1, CCND2 and MMSET and characterized domains that are specifically hydroxymethylated in myeloma subgroups. Novel 5hmC-enriched domains were found at putative enhancers of CCND2 and MYC in newly diagnosed patients. CONCLUSIONS: 5hmC level is associated with clinical aspects of MM. Mapping 5hmC at a genome-wide level provides insights into the disease biology directly from genomic DNA, which makes it a potent mark to study epigenetics on large patient cohorts.

Logic programming reveals alteration of key transcription factors in multiple myeloma.

Innovative approaches combining regulatory networks (RN) and genomic data are needed to extract biological information for a better understanding of diseases, such as cancer, by improving the identification of entities and thereby leading to potential new therapeutic avenues. In this study, we confronted an automatically generated RN with gene expression profiles (GEP) from a cohort of multiple myeloma (MM) patients and normal individuals using global reasoning on the RN causality to identify key-nodes. We modeled each patient by his or her GEP, the RN and the possible automatically detected repairs needed to establish a coherent flow of the information that explains the logic of the GEP. These repairs could represent cancer mutations leading to GEP variability. With this reasoning, unmeasured protein states can be inferred, and we can simulate the impact of a protein perturbation on the RN behavior to identify therapeutic targets. We showed that JUN/FOS and FOXM1 activities are altered in almost all MM patients and identified two survival markers for MM patients. Our results suggest that JUN/FOS-activation has a strong impact on the RN in view of the whole GEP, whereas FOXM1-activation could be an interesting way to perturb an MM subgroup identified by our method.