Understanding Disease Biology and Informing the Management of Pancreas Cancer With Preclinical Model Systems

Whittle, Martin C.; Hingorani, Sunil R.

doi:10.1097/ppo.0000000000000289

Cited by 4 publications

(3 citation statements)

References 99 publications

(74 reference statements)

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“…Murine models of pancreatic cancer were chosen as they have proven useful for mechanistic investigations of pancreatic cancer progression, modeling well the human disease both genetically and phenotypically, particularly during the evolution of pre-cancerous lesions 8,9 . The murine models have produced an explosion of studies including pre-clinical drug tests and evaluation of additional genetic perturbations that expose tumor-suppressing and tumor-promoting disease modifiers [10][11][12] .…”

mentioning

confidence: 99%

VISTA: VIsual Semantic Tissue Analysis for pancreatic disease quantification in murine cohorts

Ternes¹,

Huang²,

Lanciault

et al. 2020

Sci Rep

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Mechanistic disease progression studies using animal models require objective and quantifiable assessment of tissue pathology. Currently quantification relies heavily on staining methods which can be expensive, labor/time-intensive, inconsistent across laboratories and batch, and produce uneven staining that is prone to misinterpretation and investigator bias. We developed an automated semantic segmentation tool utilizing deep learning for rapid and objective quantification of histologic features relying solely on hematoxylin and eosin stained pancreatic tissue sections. The tool segments normal acinar structures, the ductal phenotype of acinar-to-ductal metaplasia (ADM), and dysplasia with Dice coefficients of 0.79, 0.70, and 0.79, respectively. To deal with inaccurate pixelwise manual annotations, prediction accuracy was also evaluated against biological truth using immunostaining mean structural similarity indexes (SSIM) of 0.925 and 0.920 for amylase and pan-keratin respectively. Our tool’s disease area quantifications were correlated to the quantifications of immunostaining markers (DAPI, amylase, and cytokeratins; Spearman correlation score = 0.86, 0.97, and 0.92) in unseen dataset (n = 25). Moreover, our tool distinguishes ADM from dysplasia, which are not reliably distinguished with immunostaining, and demonstrates generalizability across murine cohorts with pancreatic disease. We quantified the changes in histologic feature abundance for murine cohorts with oncogenic Kras-driven disease, and the predictions fit biological expectations, showing stromal expansion, a reduction of normal acinar tissue, and an increase in both ADM and dysplasia as disease progresses. Our tool promises to accelerate and improve the quantification of pancreatic disease in animal studies and become a unifying quantification tool across laboratories.

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mentioning

confidence: 99%

VISTA: VIsual Semantic Tissue Analysis for pancreatic disease quantification in murine cohorts

Ternes¹,

Huang²,

Lanciault

et al. 2020

Sci Rep

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show abstract

“…These animals conditionally express endogenous mutant Kras and point mutant Trp53 alleles at physiologic levels and faithfully recapitulate the molecular progression and pathophysiology of the human disease from inception to invasion and metastasis. Animals are examined by abdominal palpation and serial high-resolution ultrasound (Vevo 2100; VisualSonics, Inc., Toronto, ON, Canada) beginning at 8–10 weeks of age and are enrolled in preclinical studies when the primary tumor diameter is 3.0–6.0 mm [5,23] (reviewed in [24]). A pegylated formulation of recombinant human hyaluronidase, PH20 (PEGPH20; Halozyme Therapeutics), was administered via bolus tail vein injection at 15 mg/kg.…”

Section: Methodsmentioning

confidence: 99%

Non-Invasive Monitoring of Stromal Biophysics with Targeted Depletion of Hyaluronan in Pancreatic Ductal Adenocarcinoma

Maloney

DuFort

Provenzano

et al. 2019

Cancers

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Pancreatic ductal adenocarcinoma (PDA) is characterized by a pronounced fibroinflammatory stromal reaction consisting of inordinate levels of hyaluronan (HA), collagen, immune cells, and activated fibroblasts that work in concert to generate a robust physical barrier to the perfusion and diffusion of small molecule therapeutics. The targeted depletion of hyaluronan with a PEGylated recombinant human hyaluronidase (PEGPH20) lowers interstitial gel–fluid pressures and re-expands collapsed intratumoral vasculature, improving the delivery of concurrently administered agents. Here we report a non-invasive means of assessing biophysical responses to stromal intervention with quantitative multiparametric magnetic resonance imaging (MRI) at 14 Tesla (T). We found that spin-spin relaxation time T2 values and glycosaminoglycan chemical exchange saturation transfer (GagCEST) values decreased at 24 h, reflecting depletion of intratumoral HA content, and that these parameters recovered at 7 days concurrent with replenishment of intratumoral HA. This was also reflected in an increase in low-b apparent diffusion coefficient (ADC) at 24 h, consistent with improved tumor perfusion that again normalized at 7 days after treatment. Phantom imaging suggests that the GagCEST signal is driven by changes in HA versus other glycosaminoglycans. Thus, multiparametric magnetic resonance imaging (MRI) can be used as a non-invasive tool to assess therapeutic responses to targeted stromal therapy in PDA and likely other stroma-rich solid tumors that have high levels of hyaluronan and collagen.

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“…Murine models of pancreatic cancer were chosen as they have proven useful for mechanistic investigations of pancreatic cancer progression, modeling well the human disease both genetically and phenotypically, particularly during the evolution of pre-cancerous lesions [8,9]. The murine models have produced an explosion of studies including pre-clinical drug tests and evaluation of additional genetic perturbations that expose tumor-suppressing and tumor-promoting disease modifiers [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

VISTA: Virtual ImmunoSTAining for pancreatic disease quantification in murine cohorts

Ternes¹,

Huang²,

Lanciault

et al. 2020

Preprint

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Mechanistic studies of pancreatic disease progression using animal models require objective and quantifiable assessment of tissue changes among animal cohorts. Disease state quantification, however, relies heavily on tissue immunostaining, which can be expensive, labor-and time-intensive, and all too often produces uneven staining that is prone to variable interpretation between experts and inaccurate quantification. Here we develop a fully automated semantic segmentation tool using deep learning for the rapid and objective quantification of histologic features using hematoxylin and eosin (H&E) stained pancreatic tissue sections acquired from murine pancreatic cancer models. The tool was successfully trained to segment and quantify multiple histopathologic features of pancreatic pre-cancer evolution, including normal acinar structures, the ductal phenotype of acinarto ductal metaplasia (ADM), dysplasia, and the expanding stromal compartment. Disease quantifications produced by our computational tool were highly correlated to the results obtained by immunostaining markers of normal and diseased tissue (DAPI, amylase, and cytokeratins; correlation score= 0.9, 0.95, and 0.91, respectively) and were able to accurately reproduce immunostain patterns. Moreover, our tool was able to distinguish ADM from dysplasia, which are not reliably distinguished by immunostaining, and avoid the pitfalls of uneven or poor-quality staining. Using this tool, we quantified the changes in histologic feature abundance for murine cohorts with oncogenic Kras-driven disease at 2 months and 5 months of age (n=12, n=13). The calculated changes in histologic feature abundance were consistent with biological expectations, showing an expansion of the stromal compartment, a reduction of normal acinar tissue, and an increase in both ADM and dysplasia as disease progresses (p= 2e-6, 6e-7, 4e-4, and 3e-5, respectively). These results demonstrate the tool's efficacy for accurate and rapid quantification of multiple histologic features using an objective and automated platform. Our tool promises to rapidly accelerate and improve the quantification of altered pancreatic disease progression in animal studies.

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Understanding Disease Biology and Informing the Management of Pancreas Cancer With Preclinical Model Systems

Cited by 4 publications

References 99 publications

VISTA: VIsual Semantic Tissue Analysis for pancreatic disease quantification in murine cohorts

VISTA: VIsual Semantic Tissue Analysis for pancreatic disease quantification in murine cohorts

Non-Invasive Monitoring of Stromal Biophysics with Targeted Depletion of Hyaluronan in Pancreatic Ductal Adenocarcinoma

VISTA: Virtual ImmunoSTAining for pancreatic disease quantification in murine cohorts

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