Machine Learning-Based Prediction of Responsiveness to Neoadjuvant Chemoradiotheapy in Locally Advanced Rectal Cancer Patients from Endomicroscopy.

The protocol for treating locally advanced rectal cancer consists of the application of chemoradiotherapy (neoCRT) followed by surgical intervention. One issue for clinical oncologists is predicting the efficacy of neoCRT in order to adjust the dosage and avoid treatment toxicity in cases when surgery should be conducted promptly. Biomarkers may be used for this purpose along with in vivo cell-level images of the colorectal mucosa obtained by probe-based confocal laser endomicroscopy (pCLE) during colonoscopy. The aim of this article is to report our experience with Motiro, a computational framework that we developed for machine learning (ML) based analysis of pCLE videos for predicting neoCRT response in locally advanced rectal cancer patients. pCLE videos were collected from 47 patients who were diagnosed with locally advanced rectal cancer (T3/T4, or N+). The patients received neoCRT. Response to treatment by all patients was assessed by endoscopy along with biopsy and magnetic resonance imaging (MRI). Thirty-seven patients were classified as non-responsive to neoCRT because they presented a visible macroscopic neoplastic lesion, as confirmed by pCLE examination. Ten remaining patients were considered responsive to neoCRT because they presented lesions as a scar or small ulcer with negative biopsy, at post-treatment follow-up. Motiro was used for batch mode analysis of pCLE videos. It automatically characterized the tumoral region and its surroundings. That enabled classifying a patient as responsive or non-responsive to neoCRT based on pre-neoCRT pCLE videos. Motiro classified patients as responsive or non-responsive to neoCRT with an accuracy of ~ 0.62 when using images of the tumor. When using images of regions surrounding the tumor, it reached an accuracy of ~ 0.70. Feature analysis showed that spatial heterogeneity in fluorescence distribution within regions surrounding the tumor was the main contributor to predicting response to neoCRT. We developed a computational framework to predict response to neoCRT by locally advanced rectal cancer patients based on pCLE images acquired pre-neoCRT. We demonstrate that the analysis of the mucosa of the region surrounding the tumor provides stronger predictive power.

A Stochastic Binary Model for the Regulation of Gene Expression to Investigate Responses to Gene Therapy.

In this manuscript, we use an exactly solvable stochastic binary model for the regulation of gene expression to analyze the dynamics of response to a treatment aiming to modulate the number of transcripts of a master regulatory switching gene. The challenge is to combine multiple processes with different time scales to control the treatment response by a switching gene in an unavoidable noisy environment. To establish biologically relevant timescales for the parameters of the model, we select the RKIP gene and two non-specific drugs already known for changing RKIP levels in cancer cells. We demonstrate the usefulness of our method simulating three treatment scenarios aiming to reestablish RKIP gene expression dynamics toward a pre-cancerous state: (1) to increase the promoter's ON state duration; (2) to increase the mRNAs' synthesis rate; and (3) to increase both rates. We show that the pre-treatment kinetic rates of ON and OFF promoter switching speeds and mRNA synthesis and degradation will affect the heterogeneity and time for treatment response. Hence, we present a strategy for reaching increased average mRNA levels with diminished heterogeneity while reducing drug dosage by simultaneously targeting multiple kinetic rates that effectively represent the chemical processes underlying the regulation of gene expression. The decrease in heterogeneity of treatment response by a target gene helps to lower the chances of emergence of resistance. Our approach may be useful for inferring kinetic constants related to the expression of antimetastatic genes or oncogenes and for the design of multi-drug therapeutic strategies targeting the processes underpinning the expression of master regulatory genes.

Nonsurgical Embryo Recovery as a Feasible Tool for Supporting Embryo Biobanks of Locally Adapted Brazilian Sheep and Goats.

This study assessed the outcomes of nonsurgical embryo recovery (NSER) after superovulation (SOV) in five locally adapted Brazilian breeds of sheep and goats. The objective was to evaluate the feasibility and efficiency of using SOV combined with a less-invasive embryo collection technique for supplying the Brazilian animal gene bank with germplasm from specific genotypes of interest. Morada Nova (n = 20), Santa Inês (n = 20), and Somalis (n = 20) ewes received an intravaginal progesterone (330 mg) device for 9 days, while Canindé (n = 15) and Moxotó (n = 15) goats received an intravaginal medroxyprogesterone acetate (60 mg) device for 6 days. All females received 133 mg of porcine follicle-stimulating hormone (pFSH) administrated in six decreasing doses 12 hours apart, starting 60 hours before device removal, plus 37.5 µg of d-cloprostenol at the fifth and sixth pFSH dose. Donors in estrus were mated with fertile males. The corpora lutea (CL) number was assessed by ultrasonography 1 day before NSER. On day 6.5 or 7 after estrus, NSER was performed following hormonally induced cervical relaxation. A total of 97% of sheep and 90% of goats responded with estrus, and among those, 91% of sheep and 85% of goats presented a CL. In ewes, the numbers of CL were greater (p < 0.05) in the Santa Inês breed, while similar (p > 0.05) CL numbers were found among the goat breeds. All viable embryos were freezable (excellent and good quality) and the number per donor was 7.8 for sheep and 4.9 for goats. All parameters of NSER efficiency, embryo yield, and fertility post-NSER did not differ (p > 0.05) between breeds among each species. The SOV-NSER procedures applied for an embryo biobank supply of locally adapted Brazilian breeds of small ruminants were efficient regarding production of cryopreservable embryos, and preservation of donor fertility. Therefore, SOV followed by NSER is recommended for embryo biobank assembly in sheep and goats.

New strategies for targeting kinase networks in cancer.

Targeted strategies against specific driver molecules of cancer have brought about many advances in cancer treatment since the early success of the first small-molecule inhibitor Gleevec. Today, there are a multitude of targeted therapies approved by the Food and Drug Administration for the treatment of cancer. However, the initial efficacy of virtually every targeted treatment is often reversed by tumor resistance to the inhibitor through acquisition of new mutations in the target molecule, or reprogramming of the epigenome, transcriptome, or kinome of the tumor cells. At the core of this clinical problem lies the assumption that targeted treatments will only be efficacious if the inhibitors are used at their maximum tolerated doses. Such aggressive regimens create strong selective pressure on the evolutionary progression of the tumor, resulting in resistant cells. High-dose single agent treatments activate alternative mechanisms that bypass the inhibitor, while high-dose combinatorial treatments suffer from increased toxicity resulting in treatment cessation. Although there is an arsenal of targeted agents being tested clinically and preclinically, identifying the most effective combination treatment plan remains a challenge. In this review, we discuss novel targeted strategies with an emphasis on the recent cross-disciplinary studies demonstrating that it is possible to achieve antitumor efficacy without increasing toxicity by adopting low-dose multitarget approaches to treatment of cancer and metastasis.

Limited inhibition of multiple nodes in a driver network blocks metastasis.

Metastasis suppression by high-dose, multi-drug targeting is unsuccessful due to network heterogeneity and compensatory network activation. Here, we show that targeting driver network signaling capacity by limited inhibition of core pathways is a more effective anti-metastatic strategy. This principle underlies the action of a physiological metastasis suppressor, Raf Kinase Inhibitory Protein (RKIP), that moderately decreases stress-regulated MAP kinase network activity, reducing output to transcription factors such as pro-metastastic BACH1 and motility-related target genes. We developed a low-dose four-drug mimic that blocks metastatic colonization in mouse breast cancer models and increases survival. Experiments and network flow modeling show limited inhibition of multiple pathways is required to overcome variation in MAPK network topology and suppress signaling output across heterogeneous tumor cells. Restricting inhibition of individual kinases dissipates surplus signal, preventing threshold activation of compensatory kinase networks. This low-dose multi-drug approach to decrease signaling capacity of driver networks represents a transformative, clinically relevant strategy for anti-metastatic treatment.

Myokines in treatment-naïve patients with cancer-associated cachexia.

Cancer-associated cachexia is a complex metabolic syndrome characterized by weight loss and systemic inflammation. Muscle loss and fatty infiltration into muscle are associated with poor prognosis in cancer patients. Skeletal muscle secretes myokines, factors with autocrine, paracrine and/or endocrine action, which may be modified by or play a role in cachexia. This study examined myokine content in the plasma, skeletal muscle and tumor homogenates from treatment-naïve patients with gastric or colorectal stages I-IV cancer with cachexia (CC, N = 62), or not (weight stable cancer, WSC, N = 32). Myostatin, interleukin (IL) 15, follistatin-like protein 1 (FSTL-1), fatty acid binding protein 3 (FABP3), irisin and brain-derived neurotrophic factor (BDNF) protein content in samples was measured with Multiplex technology; body composition and muscle lipid infiltration were evaluated in computed tomography, and quantification of triacylglycerol (TAG) in the skeletal muscle. Cachectic patients presented lower muscle FSTL-1 expression (p = 0.047), higher FABP3 plasma content (p = 0.0301) and higher tumor tissue expression of FABP3 (p = 0.0182), IL-15 (p = 0.007) and irisin (p = 0.0110), compared to WSC. Neither muscle TAG content, nor muscle attenuation were different between weight stable and cachectic patients. Lumbar adipose tissue (AT) index, visceral AT index and subcutaneous AT index were lower in CC (p = 0.0149, p = 0.0455 and p = 0.0087, respectively), who also presented lower muscularity in the cohort (69.2% of patients; p = 0.0301), compared to WSC. The results indicate the myokine profile in skeletal muscle, plasma and tumor is impacted by cachexia. These findings show that myokines eventually affecting muscle wasting may not solely derive from the muscle itself (as the tumor also may contribute to the systemic scenario), and put forward new perspectives on cachexia treatment targeting myokines and associated receptors and pathways.

Binary Expression Enhances Reliability of Messaging in Gene Networks.

The promoter state of a gene and its expression levels are modulated by the amounts of transcription factors interacting with its regulatory regions. Hence, one may interpret a gene network as a communicating system in which the state of the promoter of a gene (the source) is communicated by the amounts of transcription factors that it expresses (the message) to modulate the state of the promoter and expression levels of another gene (the receptor). The reliability of the gene network dynamics can be quantified by Shannon's entropy of the message and the mutual information between the message and the promoter state. Here we consider a stochastic model for a binary gene and use its exact steady state solutions to calculate the entropy and mutual information. We show that a slow switching promoter with long and equally standing ON and OFF states maximizes the mutual information and reduces entropy. That is a binary gene expression regime generating a high variance message governed by a bimodal probability distribution with peaks of the same height. Our results indicate that Shannon's theory can be a powerful framework for understanding how bursty gene expression conciliates with the striking spatio-temporal precision exhibited in pattern formation of developing organisms.

A comparative analysis of noise properties of stochastic binary models for a self-repressing and for an externally regulating gene.

This manuscript presents a comparison of noise properties exhibited by two stochastic binary models for: (i) a self-repressing gene; (ii) a repressed or activated externally regulating one. The stochastic models describe the dynamics of probability distributions governing two random variables, namely, protein numbers and the gene state as ON or OFF. In a previous work, we quantify noise in protein numbers by means of its Fano factor and write this quantity as a function of the covariance between the two random variables. Then we show that distributions governing the number of gene products can be super-Fano, Fano or sub-Fano if the covariance is, respectively, positive, null or negative. The latter condition is exclusive for the self-repressing gene and our analysis shows the conditions for which the Fano factor is a sufficient classifier of fluctuations in gene expression. In this work, we present the conditions for which the noise on the number of gene products generated from a self-repressing gene or an externally regulating one are quantitatively similar. That is important for inference of gene regulation from noise in gene expression quantitative data. Our results contribute to a classification of noise function in biological systems by theoretically demonstrating the mechanisms underpinning the higher precision in expression of a self-repressing gene in comparison with an externally regulated one.

Physical implications of so(2, 1) symmetry in exact solutions for a self-repressing gene.

We chemically characterize the symmetries underlying the exact solutions of a stochastic negatively self-regulating gene. The breaking of symmetry at a low molecular number causes three effects. Two branches of the solution exist, having high and low switching rates, such that the low switching rate branch approaches deterministic behavior and the high switching rate branch exhibits sub-Fano behavior. The average protein number differs from the deterministically expected value. Bimodal probability distributions appear as the protein number becomes a readout of the ON/OFF state of the gene.

Peri/epicellular protein disulfide isomerase-A1 acts as an upstream organizer of cytoskeletal mechanoadaptation in vascular smooth muscle cells.

Although redox processes closely interplay with mechanoresponses to control vascular remodeling, redox pathways coupling mechanostimulation to cellular cytoskeletal organization remain unclear. The peri/epicellular pool of protein disulfide isomerase-A1 (pecPDIA1) supports postinjury vessel remodeling. Using distinct models, we investigated whether pecPDIA1 could work as a redox-dependent organizer of cytoskeletal mechanoresponses. In vascular smooth muscle cells (VSMCs), pecPDIA1 immunoneutralization impaired stress fiber assembly in response to equibiaxial stretch and, under uniaxial stretch, significantly perturbed cell repositioning perpendicularly to stretch orientation. During cyclic stretch, pecPDIA1 supported thiol oxidation of the known mechanosensor ß1-integrin and promoted polarized compartmentalization of sulfenylated proteins. Using traction force microscopy, we showed that pecPDIA1 organizes intracellular force distribution. The net contractile moment ratio of platelet-derived growth factor-exposed to basal VSMCs decreased from 0.90 ± 0.09 (IgG-exposed controls) to 0.70 ± 0.08 after pecPDI neutralization ( P < 0.05), together with an enhanced coefficient of variation for distribution of force modules, suggesting increased noise. Moreover, in a single cell model, pecPDIA1 neutralization impaired migration persistence without affecting total distance or velocity, whereas siRNA-mediated total PDIA1 silencing disabled all such variables of VSMC migration. Neither expression nor total activity of the master mechanotransmitter/regulator RhoA was affected by pecPDIA1 neutralization. However, cyclic stretch-induced focal distribution of membrane-bound RhoA was disrupted by pecPDI inhibition, which promoted a nonpolarized pattern of RhoA/caveolin-3 cluster colocalization. Accordingly, FRET biosensors showed that pecPDIA1 supports localized RhoA activity at cell protrusions versus perinuclear regions. Thus, pecPDI acts as a thiol redox-dependent organizer and noise reducer mechanism of cytoskeletal repositioning, oxidant generation, and localized RhoA activation during a variety of VSMC mechanoresponses. NEW & NOTEWORTHY Effects of a peri/epicellular pool of protein disulfide isomerase-A1 (pecPDIA1) during mechanoregulation in vascular smooth muscle cells (VSMCs) were highlighted using approaches such as equibiaxial and uniaxial stretch, random single cell migration, and traction force microscopy. pecPDIA1 regulates organization of the cytoskeleton and minimizes the noise of cell alignment, migration directionality, and persistence. pecPDIA1 mechanisms involve redox control of ß1-integrin and localized RhoA activation. pecPDIA1 acts as a novel organizer of mechanoadaptation responses in VSMCs.

Lessons and perspectives for applications of stochastic models in biological and cancer research.

The effects of randomness, an unavoidable feature of intracellular environments, are observed at higher hierarchical levels of living matter organization, such as cells, tissues, and organisms. Additionally, the many compounds interacting as a well-orchestrated network of reactions increase the difficulties of assessing these systems using only experiments. This limitation indicates that elucidation of the dynamics of biological systems is a complex task that will benefit from the establishment of principles to help describe, categorize, and predict the behavior of these systems. The theoretical machinery already available, or ones to be discovered to help solve biological problems, might play an important role in these processes. Here, we demonstrate the application of theoretical tools by discussing some biological problems that we have approached mathematically: fluctuations in gene expression and cell proliferation in the context of loss of contact inhibition. We discuss the methods that have been employed to provide the reader with a biologically motivated phenomenological perspective of the use of theoretical methods. Finally, we end this review with a discussion of new research perspectives motivated by our results.

Lessons and perspectives for applications of stochastic models in biological and cancer research

The effects of randomness, an unavoidable feature of intracellular environments, are observed at higher hierarchical levels of living matter organization, such as cells, tissues, and organisms. Additionally, the many compounds interacting as a well-orchestrated network of reactions increase the difficulties of assessing these systems using only experiments. This limitation indicates that elucidation of the dynamics of biological systems is a complex task that will benefit from the establishment of principles to help describe, categorize, and predict the behavior of these systems. The theoretical machinery already available, or ones to be discovered to help solve biological problems, might play an important role in these processes. Here, we demonstrate the application of theoretical tools by discussing some biological problems that we have approached mathematically: fluctuations in gene expression and cell proliferation in the context of loss of contact inhibition. We discuss the methods that have been employed to provide the reader with a biologically motivated phenomenological perspective of the use of theoretical methods. Finally, we end this review with a discussion of new research perspectives motivated by our results.

Synthetic enhancer design by in silico compensatory evolution reveals flexibility and constraint in cis-regulation.

BACKGROUND: Models that incorporate specific chemical mechanisms have been successful in describing the activity of Drosophila developmental enhancers as a function of underlying transcription factor binding motifs. Despite this, the minimum set of mechanisms required to reconstruct an enhancer from its constituent parts is not known. Synthetic biology offers the potential to test the sufficiency of known mechanisms to describe the activity of enhancers, as well as to uncover constraints on the number, order, and spacing of motifs. RESULTS: Using a functional model and in silico compensatory evolution, we generated putative synthetic even-skipped stripe 2 enhancers with varying degrees of similarity to the natural enhancer. These elements represent the evolutionary trajectories of the natural stripe 2 enhancer towards two synthetic enhancers designed ab initio. In the first trajectory, spatially regulated expression was maintained, even after more than a third of binding sites were lost. In the second, sequences with high similarity to the natural element did not drive expression, but a highly diverged sequence about half the length of the minimal stripe 2 enhancer drove ten times greater expression. Additionally, homotypic clusters of Zelda or Stat92E motifs, but not Bicoid, drove expression in developing embryos. CONCLUSIONS: Here, we present a functional model of gene regulation to test the degree to which the known transcription factors and their interactions explain the activity of the Drosophila even-skipped stripe 2 enhancer. Initial success in the first trajectory showed that the gene regulation model explains much of the function of the stripe 2 enhancer. Cases where expression deviated from prediction indicates that undescribed factors likely act to modulate expression. We also showed that activation driven Bicoid and Hunchback is highly sensitive to spatial organization of binding motifs. In contrast, Zelda and Stat92E drive expression from simple homotypic clusters, suggesting that activation driven by these factors is less constrained. Collectively, the 40 sequences generated in this work provides a powerful training set for building future models of gene regulation.

Stochastic model of contact inhibition and the proliferation of melanoma in situ.

Contact inhibition is a central feature orchestrating cell proliferation in culture experiments; its loss is associated with malignant transformation and tumorigenesis. We performed a co-culture experiment with human metastatic melanoma cell line (SKMEL- 147) and immortalized keratinocyte cells (HaCaT). After 8 days a spatial pattern was detected, characterized by the formation of clusters of melanoma cells surrounded by keratinocytes constraining their proliferation. In addition, we observed that the proportion of melanoma cells within the total population has increased. To explain our results we propose a spatial stochastic model (following a philosophy of the Widom-Rowlinson model from Statistical Physics and Molecular Chemistry) which considers cell proliferation, death, migration, and cell-to-cell interaction through contact inhibition. Our numerical simulations demonstrate that loss of contact inhibition is a sufficient mechanism, appropriate for an explanation of the increase in the proportion of tumor cells and generation of spatial patterns established in the conducted experiments.

Backward-stochastic-differential-equation approach to modeling of gene expression.

In this article, we introduce a backward method to model stochastic gene expression and protein-level dynamics. The protein amount is regarded as a diffusion process and is described by a backward stochastic differential equation (BSDE). Unlike many other SDE techniques proposed in the literature, the BSDE method is backward in time; that is, instead of initial conditions it requires the specification of end-point ("final") conditions, in addition to the model parametrization. To validate our approach we employ Gillespie's stochastic simulation algorithm (SSA) to generate (forward) benchmark data, according to predefined gene network models. Numerical simulations show that the BSDE method is able to correctly infer the protein-level distributions that preceded a known final condition, obtained originally from the forward SSA. This makes the BSDE method a powerful systems biology tool for time-reversed simulations, allowing, for example, the assessment of the biological conditions (e.g., protein concentrations) that preceded an experimentally measured event of interest (e.g., mitosis, apoptosis, etc.).

Embryo production in heifers with low or high dry matter intake submitted to superovulation.

This study investigated the influence of feed intake on superovulatory response and embryo production of Nelore heifers. Pubertal heifers were kept in a feedlot and were submitted to the same diets, but with different levels of feed consumption: High (1.7 M; n = 20) or Low (0.7 M; n = 19) feed intake. Heifers in the 1.7 M treatment consumed 170% (2.6% of body weight [BW] in dry matter) and the 0.7 M heifers ate 70% (1.1% of BW in dry matter) of a maintenance diet. After 7 wk on these diets, heifers were treated with eight decreasing doses of follicle-stimulating hormone (FSH) given every 12 h, totaling 133 mg Folltropin (Folltropin-V; Bioniche Animal Health, Canada) per heifer. Seven d after AI, heifers had their uteri flushed and embryos were recovered and graded according to the International Embryo Technology Society standards. Data were analyzed using the GLIMMIX procedure of SAS and results are presented as least-squares means ± SEM (P < 0.05). At the onset of the FSH treatment (Day 0 of the protocol), 1.7 M heifers had greater body condition score (BCS), BW and serum insulin concentrations than 0.7 M heifers (4.1 ± 0.1 vs. 3.0 ± 0.1; 462.5 ± 10.1 vs. 382.7 ± 10.4 kg; and 14.3 ± 1.7 vs. 3.5 ± 0.8 µIU/mL, respectively). The 0.7 M heifers had more follicles ≥6 mm at the time of the last FSH (Day 7; 47.9 ± 6.4 vs. 23.5 ± 4.3 follicles), related to a better follicle superstimulatory response to FSH. Similarly, 0.7 M heifers had more corpora lutea at the time of embryo collection (33.6 ± 1.4 vs. 15.7 ± 0.9) than the 1.7 M heifers, which resulted in greater number of recovered embryos and ova (9.9 ± 0.7 vs. 6.7 ± 0.6) and viable embryos (5.3 ± 0.5 vs. 3.8 ± 0.4), despite having similar proportions of viable embryos (∼62%). A negative correlation between circulating insulin and follicle superstimulatory response to FSH was observed (r = -0.68). Therefore, we conclude that high feed intake, for a long period of time, compromised the superovulatory response and embryo production potential of Bos indicus heifers possibly related to the elevation in circulating insulin.

Stochastic model for gene transcription on Drosophila melanogaster embryos.

We examine immunostaining experimental data for the formation of stripe 2 of even-skipped (eve) transcripts on D. melanogaster embryos. An estimate of the factor converting immunofluorescence intensity units into molecular numbers is given. The analysis of the eve dynamics at the region of stripe 2 suggests that the promoter site of the gene has two distinct regimes: an earlier phase when it is predominantly activated until a critical time when it becomes mainly repressed. That suggests proposing a stochastic binary model for gene transcription on D. melanogaster embryos. Our model has two random variables: the transcripts number and the state of the source of mRNAs given as active or repressed. We are able to reproduce available experimental data for the average number of transcripts. An analysis of the random fluctuations on the number of eves and their consequences on the spatial precision of stripe 2 is presented. We show that the position of the anterior or posterior borders fluctuate around their average position by ∼1% of the embryo length, which is similar to what is found experimentally. The fitting of data by such a simple model suggests that it can be useful to understand the functions of randomness during developmental processes.

Gene regulation and noise reduction by coupling of stochastic processes.

Here we characterize the low-noise regime of a stochastic model for a negative self-regulating binary gene. The model has two stochastic variables, the protein number and the state of the gene. Each state of the gene behaves as a protein source governed by a Poisson process. The coupling between the two gene states depends on protein number. This fact has a very important implication: There exist protein production regimes characterized by sub-Poissonian noise because of negative covariance between the two stochastic variables of the model. Hence the protein numbers obey a probability distribution that has a peak that is sharper than those of the two coupled Poisson processes that are combined to produce it. Biochemically, the noise reduction in protein number occurs when the switching of the genetic state is more rapid than protein synthesis or degradation. We consider the chemical reaction rates necessary for Poisson and sub-Poisson processes in prokaryotes and eucaryotes. Our results suggest that the coupling of multiple stochastic processes in a negative covariance regime might be a widespread mechanism for noise reduction.