14 research outputs found
A Three-Gene Expression Signature Identifies a Cluster of Patients with Short Survival in Chronic Lymphocytic Leukemia.
Chronic lymphocytic leukemia (CLL) is a lymphoproliferative disorder characterized by its heterogeneous clinical evolution. Despite the discovery of the most frequent cytogenomic drivers of disease during the last decade, new efforts are needed in order to improve prognostication. In this study, we used gene expression data of CLL samples in order to discover novel transcriptomic patterns associated with patient survival. We observed that a 3-gene expression signature composed of SCGB2A1, KLF4, and PPP1R14B differentiate a group of circa 5% of cases with short survival. This effect was independent of the main cytogenetic markers of adverse prognosis. Finally, this finding was reproduced in an independent retrospective cohort. We believe that this small gene expression pattern will be useful for CLL prognostication and its association with CLL response to novel drugs should be explored in the future
New Recurrent Structural Aberrations in the Genome of Chronic Lymphocytic Leukemia Based on Exome-Sequencing Data
Chronic lymphocytic leukemia (CLL) is the most frequent lymphoproliferative syndrome
in Western countries, and it is characterized by recurrent large genomic rearrangements.
During the last decades, array techniques have expanded our knowledge about CLL’s
karyotypic aberrations. The advent of large sequencing databases expanded our
knowledge cancer genomics to an unprecedented resolution and enabled the detection of
small-scale structural aberrations in the cancer genome. In this study, we have performed
exome-sequencing-based copy number aberration (CNA) and loss of heterozygosity
(LOH) analysis in order to detect new recurrent structural aberrations. We describe 54
recurrent focal CNAs enriched in cancer-related pathways, and their association with
gene expression and clinical evolution. Furthermore, we discovered recurrent large copy
number neutral LOH events affecting key driver genes, and we recapitulate most of the
large CNAs that characterize the CLL genome. These results provide “proof-of-concept”
evidence supporting the existence of new genes involved in the pathogenesis of CLL.S
Time to Treatment Prediction in Chronic Lymphocytic Leukemia Based on New Transcriptional Patterns
Chronic lymphocytic leukemia (CLL) is the most frequent lymphoproliferative syndrome in
western countries. CLL evolution is frequently indolent, and treatment is mostly reserved
for those patients with signs or symptoms of disease progression. In this work, we used
RNA sequencing data from the International Cancer Genome Consortium CLL cohort
to determine new gene expression patterns that correlate with clinical evolution.We
determined that a 290-gene expression signature, in addition to immunoglobulin heavy
chain variable region (IGHV) mutation status, stratifies patients into four groups with
notably different time to first treatment. This finding was confirmed in an independent
cohort. Similarly, we present a machine learning algorithm that predicts the need for
treatment within the first 5 years following diagnosis using expression data from 2,198
genes. This predictor achieved 90% precision and 89% accuracy when classifying
independent CLL cases. Our findings indicate that CLL progression risk largely correlates
with particular transcriptomic patterns and paves the way for the identification of high-risk
patients who might benefit from prompt therapy following diagnosis.S
The association of germline variants with chronic lymphocytic leukemia outcome suggests the implication of novel genes and pathways in clinical evolution
Background
Chronic Lymphocytic Leukemia (CLL) is the most frequent lymphoproliferative disorder in western countries and is characterized by a remarkable clinical heterogeneity. During the last decade, multiple genomic studies have identified a myriad of somatic events driving CLL proliferation and aggressivity. Nevertheless, and despite the mounting evidence of inherited risk for CLL development, the existence of germline variants associated with clinical outcomes has not been addressed in depth.
Methods
Exome sequencing data from control leukocytes of CLL patients involved in the International Cancer Genome Consortium (ICGC) was used for genotyping. Cox regression was used to detect variants associated with clinical outcomes. Gene and pathways level associations were also calculated.
Results
Single nucleotide polymorphisms in PPP4R2 and MAP3K4 were associated with earlier treatment need. A gene-level analysis evidenced a significant association of RIPK3 with both treatment need and survival. Furthermore, germline variability in pathways such as apoptosis, cell-cycle, pentose phosphate, GNα13 and Nitric oxide was associated with overall survival.
Conclusion
Our results support the existence of inherited conditionants of CLL evolution and points towards genes and pathways that may results useful as biomarkers of disease outcome. More research is needed to validate these findings.S
Survival prediction and treatment optimization of multiple myeloma patients using machine-learning models based on clinical and gene expression data
Multiple myeloma (MM) remains mostly an incurable disease with a heterogeneous clinical evolution. Despite the availability of several prognostic scores, substantial room for improvement still exists. Promising results have been obtained by integrating clinical and biochemical data with gene expression profiling (GEP). In this report, we applied machine learning algorithms to MM clinical and RNAseq data collected by the CoMMpass consortium. We created a 50-variable random forests model (IAC-50) that could predict overall survival with high concordance between both training and validation sets (c-indexes, 0.818 and 0.780). This model included the following covariates: patient age, ISS stage, serum B2-microglobulin, first-line treatment, and the expression of 46 genes. Survival predictions for each patient considering the first line of treatment evidenced that those individuals treated with the best-predicted drug combination were significantly less likely to die than patients treated with other schemes. This was particularly important among patients treated with a triplet combination including bortezomib, an immunomodulatory drug (ImiD), and dexamethasone. Finally, the model showed a trend to retain its predictive value in patients with high-risk cytogenetics. In conclusion, we report a predictive model for MM survival based on the integration of clinical, biochemical, and gene expression data with machine learning tools
VIII Encuentro de Docentes e Investigadores en Historia del Diseño, la Arquitectura y la Ciudad
Acta de congresoLa conmemoración de los cien años de la Reforma Universitaria de 1918 se presentó como una ocasión propicia para debatir el rol de la historia, la teoría y la crítica en la formación y en la práctica profesional de diseñadores, arquitectos y urbanistas.
En ese marco el VIII Encuentro de Docentes e Investigadores en Historia del Diseño, la Arquitectura y la Ciudad constituyó un espacio de intercambio y reflexión cuya realización ha sido posible gracias a la colaboración entre Facultades de Arquitectura, Urbanismo y Diseño de la Universidad Nacional y la Facultad de Arquitectura de la Universidad Católica de Córdoba, contando además con la activa participación de mayoría de las Facultades, Centros e Institutos de Historia de la Arquitectura del país y la región.
Orientado en su convocatoria tanto a docentes como a estudiantes de Arquitectura y Diseño Industrial de todos los niveles de la FAUD-UNC promovió el debate de ideas a partir de experiencias concretas en instancias tales como mesas temáticas de carácter interdisciplinario, que adoptaron la modalidad de presentación de ponencias, entre otras actividades.
En el ámbito de VIII Encuentro, desarrollado en la sede Ciudad Universitaria de Córdoba, se desplegaron numerosas posiciones sobre la enseñanza, la investigación y la formación en historia, teoría y crítica del diseño, la arquitectura y la ciudad; sumándose el aporte realizado a través de sus respectivas conferencias de Ana Clarisa Agüero, Bibiana Cicutti, Fernando Aliata y Alberto Petrina. El conjunto de ponencias que se publican en este Repositorio de la UNC son el resultado de dos intensas jornadas de exposiciones, cuyos contenidos han posibilitado actualizar viejos dilemas y promover nuevos debates.
El evento recibió el apoyo de las autoridades de la FAUD-UNC, en especial de la Secretaría de Investigación y de la Biblioteca de nuestra casa, como así también de la Facultad de Arquitectura de la UCC; va para todos ellos un especial agradecimiento
University teachers’ perceptions of the organizational processes and supervision of undergraduate dissertations
El
proceso
de
incorporación
del
Trabajo
Fin
de
Grado
(TFG)
en
el
plan
de
estudios
de
las
diferentes
titulaciones
de
grado
ha
propiciado
el
desarrollo
de
mecanismos
organizativos
y
de
coordinación
que
permiten
operativizar
su
implementación.
A
su
vez,
ha
implicado
para
la
mayoría
del
profesorado
un
importante
esfuerzo
en
el
seguimiento
y
tutorización
del
trabajo
del
alumnado.
Este
artículo
tiene
por
objeto
explorar
la
percepción
del
profesorado
de
la
Universidad
de
Santiago
de
Compostela
(USC)
sobre
los
componentes
organizativos
de
la
materia
de
TFG
y
el
proceso
de
tutorización.
Para
ello,
se
ha
desarrollado
un
estudio
de
corte
descriptivo
tipo
encuesta,
haciendo
uso
de
un
cuestionario
elaborado
ad-‐hoc
y
aplicado
en
formato
on-‐line
al
conjunto
de
profesorado.
En
total,
han
participado
en
el
estudio
282
docentes
de
diferentes
titulaciones
y
áreas
de
conocimiento,
con
representación
de
todos
los
centros
de
la
USC.
Los
resultados
obtenidos
muestran
escenarios
diferenciados
en
función
de
las
áreas,
fundamentalmente
en
lo
referente
a
la
carga
docente,
el
grado
de
integración
del
TFG
en
el
marco
de
los
planes
de
estudio
y
su
proyección
hacia
el
desempeño
profesional.The
process
of
incorporating
an
undergraduate
dissertation
(TFG
in
Spanish)
in
the
bachelor
degrees’
curriculum
has
implied
that
several
organisational
and
coordination
mechanisms
have
been
created
in
order
to
allow
for
it
to
be
implemented.
Moreover,
it
has
meant
a
considerable
effort
for
most
university
teachers
in
terms
of
supervising
and
monitoring
students’
dissertations.
This
article
aims
to
explore
the
teachers
in
the
University
of
Santiago
de
Compostela’s
perceptions
of
the
organisational
components
of
the
undergraduate
dissertation’s
course
as
well
as
its
supervision
process.
For
this
purpose,
a
qualitative
survey
study
has
been
carried
out,
based
on
an
ad-‐hoc
on-‐line
questionnaire
targeting
the
entire
teaching
community.
A
total
of
282
teachers
across
the
various
qualifications
and
knowledge
areas
covering
all
the
USC
centres
answered
the
questionnaire.
The
results
have
revealed
a
variety
of
concerns
affecting
different
areas,
in
particular
those
regarding
the
teaching
workload,
the
extent
to
which
the
undergraduate
dissertation
is
integrated
in
the
curriculum
and
its
relevance
in
the
students’
future
professional
career
Prognostic Stratification of Multiple Myeloma Using Clinicogenomic Models: Validation and Performance Analysis of the IAC-50 Model
A growing need to evaluate risk-adapted treatments in multiple myeloma (MM) exists. Several clinical and molecular scores have been developed in the last decades, which individually explain some of the variability in the heterogeneous clinical behavior of this neoplasm. Recently, we presented Iacobus-50 (IAC-50), which is a machine learning-based survival model based on clinical, biochemical, and genomic data capable of risk-stratifying newly diagnosed MM patients and predicting the optimal upfront treatment scheme. In the present study, we evaluated the prognostic value of the IAC-50 gene expression signature in an external cohort composed of patients from the Total Therapy trials 3, 4, and 5. The prognostic value of IAC-50 was validated, and additionally we observed a better performance in terms of progression-free survival and overall survival prediction compared with the UAMS70 gene expression signature. The combination of the IAC-50 gene expression signature with traditional prognostic variables (International Staging System [ISS] score, baseline B2-microglobulin, and age) improved the performance well above the predictability of the ISS score. IAC-50 emerges as a powerful risk stratification model which might be considered for risk stratification in newly diagnosed myeloma patients, in the context of clinical trials but also in real life