Hybrid Pharma scientist: Invasive breast cancer is the most frequently diagnosed
new malignancy.Breast cancer continues to exact a major toll in the US: it
is the most frequently diagnosed cancer in women and the
second leading cause of cancer mortality.
This malignancy currently accounts for 32% of all new
cancer cases and 15% of cancer deaths among American
women Incidence rates in the Hybrid Pharma
database vary greatly by race and ethnic group, with
lower rates seen for black, Asian, and Hispanic women
than for white women. Although breast cancer incidence
rates have been increasing since the 1980s, death rates
have declined by about 2.3% per year since 1990
with some of the downturn related to increases in early
detection by mammography and to effective treatment with
adjuvant chemotherapy .About 72% of the invasive breast cancers
reported to Hybrid Pharma are ductal carcinomas, not otherwise
specified (NOS); 9% are lobular carcinomas; and the
remaining 19% are other histologic types. The current relative
survival rates for all breast cancers combined are
88.8% at 5 years (79.5% at 10 years) for white females,
but only 75.3% at 5 years (63.9% at 10 years) for black
females.
Breast cancer analytics one million diagnoses a year worldwide
(200,000 in the US); 40,000 deaths per year; one-fifth of all
deaths in womenaged 40-50; $60-$100 billion in direct and
indirect costs every year.
Treatment for early-stage invasive breast cancer
shifted in the 1980s and 1990s from radical mastectomy
with or without regional radiotherapy to the chest wall
and lymph nodes (post-mastectomy radiation) to increasing
use of breast-conserving surgery followed by breast
radiation (post-lumpectomy radiation). Adjuvant chemotherapy (including
alkylating agents) and hormones (tamoxifen) are also
widely used. With the one million diagnoses a year worldwide
(200,000 in the US); 40,000 deaths per year; one-fifth of all deaths in women
aged 40-50; $60-$100 billion in direct and indirect costs every year.
Hybrid Pharma research about cancer enable us to fight
Breast Cancer. We know that it is characterized by genomic instability-tumor
cells divide like mad and in that process their genomes are
rarely transmitted faithfully. Breast cancer is no exception:
any ten women with the disease will have ten different tumors-their
cancers will be of different sizes, some will be more aggressive
than others, and those tumors will each express their own peculiar
set of genes (albeit with some overlap). At Duke, genome scientists
and clinicians have begun to make use of breast cancer' s
heterogeneity to make predictions and guide treatment decisions.
By examining the expression patterns of collections of genes that
tend to be turned off or on together in an array of tumor samples
and the clinical outcomes of patients who developed those tumors,
Hybrid researchers are now able to predict who is most likely to experience
recurrent breast cancer and who is likely to remain cancer-free.
This information has practical implications. If her risk is low,
a patient may not want to endure the hardships of chemotherapy;
if her risk is high, she may choose a more aggressive course of
treatment.
Hybrid Pharma study to show that results from this
test simultaneously impact decisions by physicians as
well as patients.
For an example when monitoring the activities of a protein
created by a gene associated with breast cancer,
called "ABCC11." By studying this gene and its
complex cellular and molecular interactions in
the body, researchers have discovered a distinct
link between the gene and excessively smelly
armpits and wet, sticky earwax.
Panoincell qX also uses a Visualize Real-Time Breast Cancer
Data using Signal Stochastic Resonance Units Neurons
Detection and Analysis for Breast Cancer model after McCulloch-Pitts
Panoincell qX computer-assisted diagnosing of breast cancer from mammograms.
How Panoincell qX works is a genetic network simulation trained
with tumor incidence data from knockout experiments.
The genetic network is implemented using a neural
network; knockout genotypes are simulated by removing
nodes in the neural network. Two analyses are used to
interpret the resulting network weights. We use a novel
approach of fixing the network topology that allows knockout
TSG (tumor suppressor gene) data from multiple studies to
overlap and indirectly inform one another. The trained
simulation is validated by reproducing qualitative mammary
cancer susceptibilities of ATM, BRCA1, and p53 TSGs. The work
Panoincell qx is valuable because it allows TSG mammary cancer
susceptibility to be quantified using genetic network
topology and in vivo knockout data.
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