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.

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.

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.