PLUS: Predicting cancer metastasis potential based on positive and unlabeled learning

Identification of potential riboswitch elements in Homo sapiens mRNA 5'UTR sequences using positive-unlabeled machine learning

Riboswitches are a class of noncoding RNA structures that interact with target ligands to cause a conformational change that can then execute some regulatory purpose within the cell. Riboswitches are ubiquitous and well characterized in bacteria and prokaryotes, with additional examples also being found in fungi, plants, and yeast. To date, no purely RNA-small molecule riboswitch has been discovered in Homo Sapiens. Several analogous riboswitch-like mechanisms have been described within the H. Sapiens translatome within the past decade, prompting the question: Is there a H. Sapiens riboswitch dependent on only small molecule ligands? In this work, we set out to train positive unlabeled machine learning classifiers on known riboswitch sequences and apply the classifiers to H. Sapiens mRNA 5'UTR sequences found in the 5'UTR database, UTRdb, in the hope of identifying a set of mRNAs to investigate for riboswitch functionality. 67,683 riboswitch sequences were obtained from RNAcentral and sorted for ligand type and used as positive examples and 48,031 5'UTR sequences were used as unlabeled, unknown examples. Positive examples were sorted by ligand, and 20 positive-unlabeled classifiers were trained on sequence and secondary structure features while withholding one or two ligand classes. Cross validation was then performed on the withheld ligand sets to obtain a validation accuracy range of 75%-99%. The joint sets of 5'UTRs identified as potential riboswitches by the 20 classifiers were then analyzed. 1533 sequences were identified as a riboswitch by one or more classifier(s) and 436 of the H. Sapiens 5'UTRs were labeled as harboring potential riboswitch elements by all 20 classifiers. These 436 sequences were mapped back to the most similar riboswitches within the positive data and examined. An online database of identified and ranked 5'UTRs, their features, and their most similar matches to known riboswitches, is provided to guide future experimental efforts to identify H. Sapiens riboswitches.

Identification of potential riboswitch elements inHomo SapiensmRNA 5'UTR sequences using Positive-Unlabeled machine learning

Riboswitches are a class of noncoding RNA structures that interact with target ligands to cause a conformational change that can then execute some regulatory purpose within the cell. Riboswitches are ubiquitous and well characterized in bacteria and prokaryotes, with additional examples also being found in fungi, plants, and yeast. To date, no purely RNA-small molecule riboswitch has been discovered in Homo Sapiens. Several analogous riboswitch-like mechanisms have been described within the H. Sapiens translatome within the past decade, prompting the question: Is there a H. Sapiens riboswitch dependent on only small molecule ligands? In this work, we set out to train positive unlabeled machine learning classifiers on known riboswitch sequences and apply the classifiers to H. Sapiens mRNA 5'UTR sequences found in the 5'UTR database, UTRdb, in the hope of identifying a set of mRNAs to investigate for riboswitch functionality. 67,683 riboswitch sequences were obtained from RNAcentral and sorted for ligand type and used as positive examples and 48,031 5'UTR sequences were used as unlabeled, unknown examples. Positive examples were sorted by ligand, and 20 positive-unlabeled classifiers were trained on sequence and secondary structure features while withholding one or two ligand classes. Cross validation was then performed on the withheld ligand sets to obtain a validation accuracy range of 75%-99%. The joint sets of 5'UTRs identified as potential riboswitches by the 20 classifiers were then analyzed. 15333 sequences were identified as a riboswitch by one or more classifier(s) and 436 of the H. Sapiens 5'UTRs were labeled as harboring potential riboswitch elements by all 20 classifiers. These 436 sequences were mapped back to the most similar riboswitches within the positive data and examined. An online database of identified and ranked 5'UTRs, their features, and their most similar matches to known riboswitches, is provided to guide future experimental efforts to identify H. Sapiens riboswitches.

Positive-unlabeled learning to infer protection status and identify correlates in vaccine efficacy field trials

Correlates of protection (CoPs) are key guideposts that both support vaccine development and licensure as well as improve our understanding of the attributes of immune responses that may directly provide protection. Unfortunately, factors such as low rate of exposure and low efficacy can result in low power to discover correlates in field trials-making it difficult to identify these guideposts for the pathogens against which there is greatest need for further insights. To address this gap, we examine the ability of positive-unlabeled (PU) learning approaches to use immunogenicity data and infection status outcomes to accurately predict protection status. We report a combination of PU bagging and two-step reliable negative techniques that accurately classify the protection status of unlabeled (uninfected) samples from synthetic and real-world humoral immune response profiles in human trials and animal models and lead to the discovery of CoPs that are "missed" using conventional infection status case-control analysis.

Beta-blocker adjunct therapy as a prospective anti-metastatic with cardio-oncologic regulation

The prevailing treatment stratagem in cancer therapy still challenges the dilemma of a probable metastatic spread following an initial diagnosis. Including an anti-metastatic agent demands a significant focus to overrule the incidence of treatment failures. Adrenergic stimulation underlying the metastatic spread paved the way for beta blockers as a breakthrough in repurposing as an anti-metastatic agent. However, the current treatment approach fails to fully harness the versatile potential of the drug in inhibiting probable metastasis. The beta blockers were seen to show a myriad of grip over the pro-metastatic and prognostic parameters of the patient. Novel interventions in immune therapy, onco-hypertension, surgery-induced stress, induction of apoptosis and angiogenesis inhibition have been used as evidence to interpret our objective of discussing the potential adjuvant role of the drug in the existing anti-cancer regimens. Adding weight to the relative incidence of onco-hypertension as an unavoidable side effect from chemotherapy, the slot for an anti-hypertensive agent is necessitated, and we try to suggest beta-blockers to fill this position. However, pointing out the paucity in the clinical study, we aim to review the current status of beta blockers under this interest to state how the drug should be included as a drug of choice in every patient undergoing cancer treatment.

Harnessing immunotherapy to enhance the systemic anti-tumor effects of thermosensitive liposomes

Chemotherapy plays an important role in debulking tumors in advance of surgery and/or radiotherapy, tackling residual disease, and treating metastatic disease. In recent years many promising advanced drug delivery strategies have emerged that offer more targeted delivery approaches to chemotherapy treatment. For example, thermosensitive liposome-mediated drug delivery in combination with localized mild hyperthermia can increase local drug concentrations resulting in a reduction in systemic toxicity and an improvement in local disease control. However, the majority of solid tumor-associated deaths are due to metastatic spread. A therapeutic approach focused on a localized target area harbors the risk of overlooking and undertreating potential metastatic spread. Previous studies reported systemic, albeit limited, anti-tumor effects following treatment with thermosensitive liposomal chemotherapy and localized mild hyperthermia. This work explores the systemic treatment capabilities of a thermosensitive liposome formulation of the vinca alkaloid vinorelbine in combination with mild hyperthermia in an immunocompetent murine model of rhabdomyosarcoma. This treatment approach was found to be highly effective at heated, primary tumor sites. However, it demonstrated limited anti-tumor effects in secondary, distant tumors. As a result, the addition of immune checkpoint inhibition therapy was pursued to further enhance the systemic anti-tumor effect of this treatment approach. Once combined with immune checkpoint inhibition therapy, a significant improvement in systemic treatment capability was achieved. We believe this is one of the first studies to demonstrate that a triple combination of thermosensitive liposomes, localized mild hyperthermia, and immune checkpoint inhibition therapy can enhance the systemic treatment capabilities of thermosensitive liposomes.

Finding miRNA-RNA Network Biomarkers for Predicting Metastasis and Prognosis in Cancer

Despite remarkable progress in cancer research and treatment over the past decades, cancer ranks as a leading cause of death worldwide. In particular, metastasis is the major cause of cancer deaths. After an extensive analysis of miRNAs and RNAs in tumor tissue samples, we derived miRNA-RNA pairs with substantially different correlations from those in normal tissue samples. Using the differential miRNA-RNA correlations, we constructed models for predicting metastasis. A comparison of our model to other models with the same data sets of solid cancer showed that our model is much better than the others in both lymph node metastasis and distant metastasis. The miRNA-RNA correlations were also used in finding prognostic network biomarkers in cancer patients. The results of our study showed that miRNA-RNA correlations and networks consisting of miRNA-RNA pairs were more powerful in predicting prognosis as well as metastasis. Our method and the biomarkers obtained using the method will be useful for predicting metastasis and prognosis, which in turn will help select treatment options for cancer patients and targets of anti-cancer drug discovery.