Prognostic Stratification of Diffuse Large B-cell Lymphoma Using Clinico-genomic Models: Validation and Improvement of the LymForest-25 Model.

Diffuse large B-cell lymphoma (DLBCL) is the most common type of non-Hodgkin lymphoma. Despite notable therapeutic advances in the last decades, 30%-40% of affected patients develop relapsed or refractory disease that frequently precludes an infamous outcome. With the advent of new therapeutic options, it becomes necessary to predict responses to the standard treatment based on rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP). In a recent communication, we presented a new machine learning model (LymForest-25) that was based on 25 clinical, biochemical, and gene expression variables. LymForest-25 achieved high survival prediction accuracy in patients with DLBCL treated with upfront immunochemotherapy. In this study, we aimed to evaluate the performance of the different features that compose LymForest-25 in a new UK-based cohort, which contained 481 patients treated with upfront R-CHOP for whom clinical, biochemical and gene expression information for 17 out of 19 transcripts were available. Additionally, we explored potential improvements based on the integration of other gene expression signatures and mutational clusters. The validity of the LymForest-25 gene expression signature was confirmed, and indeed it achieved a substantially greater precision in the estimation of mortality at 6 months and 1, 2, and 5 years compared with the cell-of-origin (COO) plus molecular high-grade (MHG) classification. Indeed, this signature was predictive of survival within the MHG and all COO subgroups, with a particularly high accuracy in the "unclassified" group. Integration of this signature with the International Prognostic Index (IPI) score provided the best survival predictions. However, the increased performance of molecular models with the IPI score was almost exclusively restricted to younger patients (<70 y). Finally, we observed a tendency towards an improved performance by combining LymForest-25 with the LymphGen mutation-based classification. In summary, we have validated the predictive capacity of LymForest-25 and expanded the potential for improvement with mutation-based prognostic classifications.

Detection of new drivers of frequent B-cell lymphoid neoplasms using an integrated analysis of whole genomes.

B-cell lymphoproliferative disorders exhibit a diverse spectrum of diagnostic entities with heterogeneous behaviour. Multiple efforts have focused on the determination of the genomic drivers of B-cell lymphoma subtypes. In the meantime, the aggregation of diverse tumors in pan-cancer genomic studies has become a useful tool to detect new driver genes, while enabling the comparison of mutational patterns across tumors. Here we present an integrated analysis of 354 B-cell lymphoid disorders. 112 recurrently mutated genes were discovered, of which KMT2D, CREBBP, IGLL5 and BCL2 were the most frequent, and 31 genes were putative new drivers. Mutations in CREBBP, TNFRSF14 and KMT2D predominated in follicular lymphoma, whereas those in BTG2, HTA-A and PIM1 were more frequent in diffuse large B-cell lymphoma. Additionally, we discovered 31 significantly mutated protein networks, reinforcing the role of genes such as CREBBP, EEF1A1, STAT6, GNA13 and TP53, but also pointing towards a myriad of infrequent players in lymphomagenesis. Finally, we report aberrant expression of oncogenes and tumor suppressors associated with novel noncoding mutations (DTX1 and S1PR2), and new recurrent copy number aberrations affecting immune check-point regulators (CD83, PVR) and B-cell specific genes (TNFRSF13C). Our analysis expands the number of mutational drivers of B-cell lymphoid neoplasms, and identifies several differential somatic events between disease subtypes.

Detection of Rare Germline Variants in the Genomes of Patients with B-Cell Neoplasms.

There is growing evidence indicating the implication of germline variation in cancer predisposition and prognostication. Here, we describe an analysis of likely disruptive rare variants across the genomes of 726 patients with B-cell lymphoid neoplasms. We discovered a significant enrichment for two genes in rare dysfunctional variants, both of which participate in the regulation of oxidative stress pathways (CHMP6 and GSTA4). Additionally, we detected 1675 likely disrupting variants in genes associated with cancer, of which 44.75% were novel events and 7.88% were protein-truncating variants. Among these, the most frequently affected genes were ATM, BIRC6, CLTCL1A, and TSC2. Homozygous or germline double-hit variants were detected in 28 cases, and coexisting somatic events were observed in 17 patients, some of which affected key lymphoma drivers such as ATM, KMT2D, and MYC. Finally, we observed that variants in six different genes were independently associated with shorter survival in CLL. Our study results support an important role for rare germline variation in the pathogenesis and prognosis of B-cell lymphoid neoplasms.

Personally Tailored Survival Prediction of Patients With Follicular Lymphoma Using Machine Learning Transcriptome-Based Models.

Follicular Lymphoma (FL) has a 10-year mortality rate of 20%, and this is mostly related to lymphoma progression and transformation to higher grades. In the era of personalized medicine it has become increasingly important to provide patients with an optimal prediction about their expected outcomes. The objective of this work was to apply machine learning (ML) tools on gene expression data in order to create individualized predictions about survival in patients with FL. Using data from two different studies, we were able to create a model which achieved good prediction accuracies in both cohorts (c-indexes of 0.793 and 0.662 in the training and test sets). Integration of this model with m7-FLIPI and age rendered high prediction accuracies in the test set (cox c-index 0.79), and a simplified approach identified 4 groups with remarkably different outcomes in terms of survival. Importantly, one of the groups comprised 27.35% of patients and had a median survival of 4.64 years. In summary, we have created a gene expression-based individualized predictor of overall survival in FL that can improve the predictions of the m7-FLIPI score.

Improved personalized survival prediction of patients with diffuse large B-cell Lymphoma using gene expression profiling.

BACKGROUND: Thirty to forty percent of patients with Diffuse Large B-cell Lymphoma (DLBCL) have an adverse clinical evolution. The increased understanding of DLBCL biology has shed light on the clinical evolution of this pathology, leading to the discovery of prognostic factors based on gene expression data, genomic rearrangements and mutational subgroups. Nevertheless, additional efforts are needed in order to enable survival predictions at the patient level. In this study we investigated new machine learning-based models of survival using transcriptomic and clinical data. METHODS: Gene expression profiling (GEP) of in 2 different publicly available retrospective DLBCL cohorts were analyzed. Cox regression and unsupervised clustering were performed in order to identify probes associated with overall survival on the largest cohort. Random forests were created to model survival using combinations of GEP data, COO classification and clinical information. Cross-validation was used to compare model results in the training set, and Harrel's concordance index (c-index) was used to assess model's predictability. Results were validated in an independent test set. RESULTS: Two hundred thirty-three and sixty-four patients were included in the training and test set, respectively. Initially we derived and validated a 4-gene expression clusterization that was independently associated with lower survival in 20% of patients. This pattern included the following genes: TNFRSF9, BIRC3, BCL2L1 and G3BP2. Thereafter, we applied machine-learning models to predict survival. A set of 102 genes was highly predictive of disease outcome, outperforming available clinical information and COO classification. The final best model integrated clinical information, COO classification, 4-gene-based clusterization and the expression levels of 50 individual genes (training set c-index, 0.8404, test set c-index, 0.7942). CONCLUSION: Our results indicate that DLBCL survival models based on the application of machine learning algorithms to gene expression and clinical data can largely outperform other important prognostic variables such as disease stage and COO. Head-to-head comparisons with other risk stratification models are needed to compare its usefulness.

Enfermedad de Castleman cervical pediátrica / Castleman's disease in the paediatric neck

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Unusual Forms of Adrenal and Extra-Adrenal Myelolipomas.

Myelolipomas are rare benign tumors of poorly understood tumorigenesis composed of mature hematopoietic tissue and fat. They mostly occur in the adrenal glands, but extra-adrenal myelolipomas have been reported in other locations such as the presacral region or retroperitoneum. It is not unusual that they are incidental findings revealed in the study of different diseases. We report 3 unusual examples of myelolipomas. The first is a multiple, unusually large, extra-adrenal myelolipoma, presented as an autopsy finding in an individual who had died suddenly from a central nervous system hemorrhage. The remaining 2 were incidental findings in patients studied for different reasons. Both were located within another neoplasm, namely an adrenal adenoma and a liver focal nodular hyperplasia. Moreover, the first showed infiltration by a non-Hodgkin lymphoma.

[Use of M-VAC in the adjuvant treatment of sarcomatoid carcinoma of the bladder]. / Empleo de M-VAC en el tratamiento adyuvante del carcinoma sarcomatoide de vejiga.

Sarcomatoid bladder carcinoma is a high-grade neoplasm and accounts for approximately 0,3% of all bladder malignancies. Sarcomatoid carcinoma originates from transitional cells of the bladder. Sarcomatoid carcinoma is charactericed by a epithelial component and a sarcomatoid component, consisting of spindle cells, that is only epithelial marker-positive. We report a 26-year-old woman diagnosed of stage III sarcomatoid bladder carcinoma (T3aN0M0) treated with partial cistectomy followed by 4 cycles of adjuvant chemotherapy with methotrexate, vinblastine, doxorubicin and cisplatin.

Empleo de M-VAC en el tratamiento adyuvante del carcinoma sarcomatoide de vejiga / Use of M-VAC in the adjuvant treatment of sarcomatoid carcinoma of the bladder

El carcinoma sarcomatoide de vejiga es una neoplasia de alto grado, que representa aproximadamente el 0,3% de todas las neoplasias vesicales y tiene su origen en las células transicionales de la vejiga. El carcinoma sarcomatoide se caracteriza por presentar un componente epitelial y un componente sarcomatoide, consistente en células fusiformes que solamente expresan marcadores de estirpe epitelial. Presentamos el caso de una mujer de 26 años diagnosticada de un carcinoma sarcomatoide de vejiga estadio III (T3aN0M0) tratada con cistectomía parcial seguida por 4 ciclos de quimioterapia adyuvante con metotrexato, vinblastina, adriamicina y cisplatino (AU)

Sarcomatoid bladder carcinoma is a high-grade neoplasm and accounts for approximately 0,3% of all bladder malignancies. Sarcomatoid carcinoma originates from transitional cells of the bladder. Sarcomatoid carcinoma is charactericed by a epithelial component and a sarcomatoid component, consisting of spindle cells, that is only epithelial marker-positive. We report a 26 year-old woman diagnosed of stage III sarcomatoid bladder carcinoma (T3aN0M0) treated with partial cistectomy followed by 4 cycles of adjuvant chemotherapy with methotrexate, vinblastine, doxorubicin and cisplatin (AU)