%% Papers Published
@article{fds348757,
Author = {Ryser, MD and Mallo, D and Hall, A and Hardman, T and King, LM and Tatishchev, S and Sorribes, IC and Maley, CC and Marks, JR and Hwang,
ES and Shibata, D},
Title = {Minimal barriers to invasion during human colorectal tumor
growth.},
Journal = {Nature Communications},
Volume = {11},
Number = {1},
Pages = {1280},
Publisher = {Springer Science and Business Media LLC},
Year = {2020},
Month = {March},
url = {http://dx.doi.org/10.1038/s41467-020-14908-7},
Abstract = {Intra-tumoral heterogeneity (ITH) could represent clonal
evolution where subclones with greater fitness confer more
malignant phenotypes and invasion constitutes an
evolutionary bottleneck. Alternatively, ITH could represent
branching evolution with invasion of multiple subclones. The
two models respectively predict a hierarchy of subclones
arranged by phenotype, or multiple subclones with shared
phenotypes. We delineate these modes of invasion by merging
ancestral, topographic, and phenotypic information from 12
human colorectal tumors (11 carcinomas, 1 adenoma) obtained
through saturation microdissection of 325 small tumor
regions. The majority of subclones (29/46, 60%) share
superficial and invasive phenotypes. Of 11 carcinomas, 9
show evidence of multiclonal invasion, and invasive and
metastatic subclones arise early along the ancestral trees.
Early multiclonal invasion in the majority of these tumors
indicates the expansion of co-evolving subclones with
similar malignant potential in absence of late bottlenecks
and suggests that barriers to invasion are minimal during
colorectal cancer growth.},
Doi = {10.1038/s41467-020-14908-7},
Key = {fds348757}
}
@article{fds348344,
Author = {Sorribes, IC and Handelman, SK and Jain, HV},
Title = {Mitigating temozolomide resistance in glioblastoma via DNA
damage-repair inhibition.},
Journal = {Journal of the Royal Society, Interface},
Volume = {17},
Number = {162},
Pages = {20190722},
Publisher = {The Royal Society},
Year = {2020},
Month = {January},
url = {http://dx.doi.org/10.1098/rsif.2019.0722},
Abstract = {Glioblastomas are among the most lethal cancers, with a 5
year survival rate below 25%. Temozolomide is typically used
in glioblastoma treatment; however, the enzymes
alkylpurine-DNA-<i>N</i>-glycosylase (APNG) and
methylguanine-DNA-methyltransferase (MGMT) efficiently
mediate the repair of DNA damage caused by temozolomide,
reducing treatment efficacy. Consequently, APNG and MGMT
inhibition has been proposed as a way of overcoming
chemotherapy resistance. Here, we develop a mechanistic
mathematical model that explicitly incorporates the effects
of chemotherapy on tumour cells, including the processes of
DNA damage induction, cell arrest and DNA repair. Our model
is carefully parametrized and validated, and then used to
virtually recreate the response of heteroclonal
glioblastomas to dual treatment with temozolomide and
inhibitors of APNG/MGMT. Using our mechanistic model, we
identify four combination treatment strategies optimized by
tumour cell phenotype, and isolate the strategy most likely
to succeed in a pre-clinical and clinical setting. If
confirmed in clinical trials, these strategies have the
potential to offset chemotherapy resistance in patients with
glioblastoma and improve overall survival.},
Doi = {10.1098/rsif.2019.0722},
Key = {fds348344}
}
@article{fds345752,
Author = {Sorribes, IC and Moore, MNJ and Byrne, HM and Jain,
HV},
Title = {A Biomechanical Model of Tumor-Induced Intracranial Pressure
and Edema in Brain Tissue.},
Journal = {Biophysical Journal},
Volume = {116},
Number = {8},
Pages = {1560-1574},
Year = {2019},
Month = {April},
url = {http://dx.doi.org/10.1016/j.bpj.2019.02.030},
Abstract = {Brain tumor growth and tumor-induced edema result in
increased intracranial pressure (ICP), which, in turn, is
responsible for conditions as benign as headaches and
vomiting or as severe as seizures, neurological damage, or
even death. Therefore, it has been hypothesized that
tracking ICP dynamics may offer improved prognostic
potential in terms of early detection of brain cancer and
better delimitation of the tumor boundary. However,
translating such theory into clinical practice remains a
challenge, in part because of an incomplete understanding of
how ICP correlates with tumor grade. Here, we propose a
multiphase mixture model that describes the biomechanical
response of healthy brain tissue-in terms of changes in ICP
and edema-to a growing tumor. The model captures ICP
dynamics within the diseased brain and accounts for the
ability/inability of healthy tissue to compensate for this
pressure. We propose parameter regimes that distinguish
brain tumors by grade, thereby providing critical insight
into how ICP dynamics vary by severity of disease. In
particular, we offer an explanation for clinically observed
phenomena, such as a lack of symptoms in low-grade glioma
patients versus a rapid onset of symptoms in those with
malignant tumors. Our model also takes into account the
effects tumor-derived proteases may have on ICP levels and
the extent of tumor invasion. This work represents an
important first step toward understanding the mechanisms
that underlie the onset of edema and ICP in cancer-afflicted
brains. Continued modeling effort in this direction has the
potential to make an impact in the field of brain cancer
diagnostics.},
Doi = {10.1016/j.bpj.2019.02.030},
Key = {fds345752}
}
@article{fds348760,
Author = {Sorribes, IC and Basu, A and Brady, R and Enriquez-Navas, PM and Feng,
X and Kather, JN and Nerlakanti, N and Stephens, R and Strobl, M and Tavassoly, I and Vitos, N and Lemanne, D and Manley, B and O’Farrelly,
C and Enderling, H},
Title = {Harnessing patient-specific response dynamics to optimize
evolutionary therapies for metastatic clear cell renal cell
carcinoma – Learning to adapt},
Year = {2019},
Month = {February},
url = {http://dx.doi.org/10.1101/563130},
Abstract = {Renal cell carcinoma (RCC) is one of the ten most common and
lethal cancers in the United States. Tumor heterogeneity and
development of resistance to treatment suggest that
patient-specific evolutionary therapies may hold the key to
better patients prognosis. Mathematical models are a
powerful tool to help develop such strategies; however, they
depend on reliable biomarker information. In this paper, we
present a dynamic model of tumor-immune interactions, as
well as the treatment effect on tumor cells and the
tumor-immune environment. We hypothesize that the
neutrophil-to-lymphocyte ratio (NLR) is a powerful biomarker
that can be used to predict an individual patient’s
response to treatment. Using randomly sampled virtual
patients, we show that the model recapitulates patient
outcomes from clinical trials in RCC. Finally, we use in
silico patient data to recreate realistic tumor behaviors
and simulate various treatment strategies to find optimal
treatments for each virtual patient.},
Doi = {10.1101/563130},
Key = {fds348760}
}
@article{fds345753,
Author = {Poole, MI and Sorribes, I and Jain, HV},
Title = {Modeling hepatitis C virus protein and p53 interactions in
hepatocytes: Implications for carcinogenesis.},
Journal = {Mathematical Biosciences},
Volume = {306},
Pages = {186-196},
Year = {2018},
Month = {December},
url = {http://dx.doi.org/10.1016/j.mbs.2018.10.003},
Abstract = {Hepatitis C virus (HCV) infection has reached epidemic
proportions worldwide. Individuals with chronic HCV
infection and without access to treatment are at high risk
for developing hepatocellular carcinoma (HCC), a liver
cancer that is rapidly fatal after diagnosis. A number of
factors have been identified that contribute to HCV-driven
carcinogenesis such as scarring of the liver, and chronic
inflammation. Recent evidence indicates a direct role for
HCV-encoded proteins themselves in oncogenesis of infected
hepatocytes. The viral protein HCV core has been shown to
interact directly with the host tumor suppressor protein
p53, and to modulate p53-activity in a biphasic manner.
Here, biochemically-motivated mathematical models of HCV-p53
interactions are developed to elucidate the mechanisms
underlying this phenomenon. We show that by itself, direct
interaction between HCV core and p53 is insufficient to
recapitulate the experimental data. We postulate the
existence of an additional factor, activated by HCV core
that inhibits p53 function. We present experimental evidence
in support of this hypothesis. The model including this
additional factor reproduces the experimental results,
validating our assumptions. Finally, we investigate what
effect HCV core-p53 interactions could have on the capacity
of an infected hepatocyte to repair damage to its DNA.
Integrating our model with an existing model of the
oscillatory response of p53 to DNA damage predicts a
biphasic relationship between HCV core and the
transformative potential of infected hepatocytes. In
addition to providing mechanistic insights, these results
suggest a potential biomarker that could help in identifying
those HCV patients most at risk of progression to
HCC.},
Doi = {10.1016/j.mbs.2018.10.003},
Key = {fds345753}
}
%% Other
@misc{fds347385,
Author = {Sorribes Rodriguez and I},
Title = {Gliomas diagnosis, progress, and treatment: a mathematical
approach},
Editor = {Jain, H},
Year = {2019},
Month = {May},
Abstract = {The diagnosis and treatment of gliomas continue to pose a
significant challenge for oncologists who not only have to
contend with managing acute neurological symptoms, but also
the almost inevitable development of resistance to
treatment. Indeed, the last 25 years have produced minimal
advancements in treatment efficacy, even though significant
efforts and resources have been invested in the quest for
breakthroughs. This effort has not been restricted only to
clinicians or oncologists, with mathematical modeling also
playing an increasingly important role. A variety of models
aimed at providing new insights into glioma growth and
response to treatment have been proposed. Initially designed
to capture fundamental behavior of tumor cells, such as
growth and motility, these models quickly became
well-established and multiple extensions have since been
introduced. However, as increasing biological details of how
tumor cells respond to treatment at cellular and subcellular
levels are revealed, mathematical models need to include
this state of the art knowledge. The work presented in this
thesis seeks to do this by refocusing our attention back to
the most fundamental question: why are gliomas fatal?
Biologically, it is known that glioma lethality is driven by
a fast growth that increases intracranial pressure resulting
in lethal neurological damage, which current treatments fail
to prevent due to tumor cell resistance to treatments such
as chemotherapy. By creating mathematical models inspired by
these key elements of glioma malignancy, the work presented
here seeks to elucidate what drives resistance to
chemotherapy and how to overcome or mitigate it, as well as
how malignancy correlated with intracranial pressure
dynamics. Thus, the work comprises two main parts: (1) in
silico optimization of treatment strategies using
chemotherapy coupled with novel cell-repair inhibitors
currently in various stages of the clinical trial; and (2) a
study of tumor-induced intracranial pressure and edema in
gliomas of grade I-IV. A wide variety of mathematical
modeling techniques are used, that incorporate
biomechanical, biochemical, pharmacokinetics, and
pharmacodynamics aspects, and include a level of detail
hitherto unconsidered. The proposed models are validated and
analyzed by employing a diverse set of mathematical tools
that range from structural identifiability, parameter
estimation, to global and local sensitivity analysis. As a
result of this work, we propose a treatment strategy that
showed a 30% improvement in patient survival time over
conventional treatment when treating heterogeneous brain
tumors in silico. Moreover, the second part of this work
demonstrates how the spatio-temporal dynamics of
tumor-induced intracranial pressure correlate with cancer
grade, providing a better understanding of the mechanisms
that underlie increased intracranial pressure onset. Both
projects come together as a first step towards a better
understanding of the poor survival rates of patients
afflicted with gliomas. They raise new questions about what
characterizes the malignancy of primary brain tumors and how
clinicians can fight it. Continued modeling effort in these
directions has the potential to make an impact in the field
of brain cancer diagnostics and treatment.},
Key = {fds347385}
}
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Mathematics Department