Proteomics advances for precision therapy in ovarian cancer

ABSTRACT

Introduction: Due to the relatively low mutation rate and high frequency of copy number variation, finding actionable genetic drivers of high-grade serous carcinoma (HGSC) is a challenging task. Furthermore, emerging studies show that genetic alterations are frequently poorly represented at the protein level adding a layer of complexity. With improvements in large-scale proteomic technologies, proteomics studies have the potential to provide robust analysis of the pathways driving high HGSC behavior.

Areas covered: This review summarizes recent large-scale proteomics findings across adequately sized ovarian cancer sample sets. Key words combined with ‘ovarian cancer’ including ‘proteomics’, ‘proteogenomic’, ‘reverse-phase protein array’, ‘mass spectrometry’, and ‘adaptive response’, were used to search PubMed.

Expert opinion: Proteomics analysis of HGSC as well as their adaptive responses to therapy can uncover new therapeutic liabilities, which can reduce the emergence of drug resistance and potentially improve patient outcomes. There is a pressing need to better understand how the genomic and epigenomic heterogeneity intrinsic to ovarian cancer is reflected at the protein level and how this information could be used to improve patient outcomes.     

Climate change and the optimal flowering time of annual plants in seasonal environments

Long‐term phenology monitoring has documented numerous examples of changing flowering dates during the last century. A pivotal question is whether these phenological responses are adaptive or not under directionally changing climatic conditions. We use a classic dynamic growth model for annual plants, based on optimal control theory, to find the fitness‐maximizing flowering time, defined as the switching time from vegetative to reproductive growth. In a typical scenario of global warming, with advanced growing season and increased productivity, optimal flowering time advances less than the start of the growing season. Interestingly, increased temporal spread in production over the season may either advance or delay the optimal flowering time depending on overall productivity or season length. We identify situations where large phenological changes are necessary for flowering time to remain optimal. Such changes also indicate changed selection pressures. In other situations, the model predicts advanced phenology on a calendar scale, but no selection for early flowering in relation to the start of the season. We also show that the optimum is more sensitive to increased productivity when productivity is low than when productivity is high. All our results are derived using a general, graphical method to calculate the optimal flowering time applicable for a large range of shapes of the seasonal production curve. The model can thus explain apparent maladaptation in phenological responses in a multitude of scenarios of climate change. We conclude that taking energy allocation trade‐offs and appropriate time scales into account is critical when interpreting phenological patterns.     

Hypoxia-induced autophagy

A major challenge in formulating an effective immunotherapy is to overcome the mechanisms of tumor escape from immunosurveillance. We showed that hypoxia-induced autophagy impairs cytotoxic T-lymphocyte (CTL)-mediated tumor cell lysis by regulating phospho-STAT3 in target cells. Autophagy inhibition in hypoxic cells decreases phospho-STAT3 and restores CTL-mediated tumor cell killing by a mechanism involving the ubiquitin proteasome system and SQSTM1/p62. Simultaneously boosting the CTL-response, using a TRP-peptide vaccination strategy, and targeting autophagy in hypoxic tumors, improves the efficacy of cancer vaccines and promotes tumor regression in vivo. Overall, in addition to its immunosuppressive effect, the hypoxic microenvironment also contributes to immunoresistance and can be detrimental to antitumor effector cell functions.     

Lifespan Extension Via Dietary Restriction: Time to Reconsider the Evolutionary Mechanisms?

Dietary restriction (DR) is the most consistent environmental manipulation to extend lifespan. Originally thought to be caused by a reduction in caloric intake, recent evidence suggests that macronutrient intake underpins the effect of DR. The prevailing evolutionary explanations for the DR response are conceptualized under the caloric restriction paradigm, necessitating reconsideration of how or whether these evolutionary explanations fit this macronutrient perspective. In the authors’ opinion, none of the current evolutionary explanations of DR adequately explain the intricacies of observed results; instead a context-dependent combination of these theories is suggested which is likely to reflect reality. In reviewing the field, it is proposed that the ability to track the destination of different macronutrients within the body will be key to establishing the relative roles of the competing theories. Understanding the evolution of the DR response and its ecological relevance is critical to understanding variation in DR responses and their relevance outside laboratory environments.     

Adaptive seamless clinical trials using early outcomes for treatment or subgroup selection: Methods,simulation model and their implementation in R

Adaptive seamless designs combine confirmatory testing, a domain of phase III trials, with features such as treatment or subgroup selection, typically associated with phase II trials. They promise to increase the efficiency of development programmes of new drugs, for example, in terms of sample size and/or development time. It is well acknowledged that adaptive designs are more involved from a logistical perspective and require more upfront planning, often in the form of extensive simulation studies, than conventional approaches. Here, we present a framework for adaptive treatment and subgroup selection using the same notation, which links the somewhat disparate literature on treatment selection on one side and on subgroup selection on the other. Furthermore, we introduce a flexible and efficient simulation model that serves both designs. As primary endpoints often take a long time to observe, interim analyses are frequently informed by early outcomes. Therefore, all methods presented accommodate interim analyses informed by either the primary outcome or an early outcome. The R package asd , previously developed to simulate designs with treatment selection, was extended to include subgroup selection (so-called adaptive enrichment designs). Here, we describe the functionality of the R package asd and use it to present some worked-up examples motivated by clinical trials in chronic obstructive pulmonary disease and oncology. The examples both illustrate various features of the R package and provide insights into the operating characteristics of adaptive seamless studies.     

PA-CRM: A continuous reassessment method for pediatric phase I oncology trials with concurrent adult trials

Pediatric phase I trials are usually carried out after the adult trial testing the same agent has started, but not completed yet. As the pediatric trial progresses, in light of the accrued interim data from the concurrent adult trial, the pediatric protocol often is amended to modify the original pediatric dose escalation design. In practice, this is done frequently in an ad hoc way, interrupting patient accrual and slowing down the trial. We developed a pediatric-continuous reassessment method (PA-CRM) to streamline this process, providing a more efficient and rigorous method to find the maximum tolerated dose for pediatric phase I oncology trials. We use a discounted joint likelihood of the adult and pediatric data, with a discount parameter controlling information borrowing between pediatric and adult trials. According to the interim adult and pediatric data, the discount parameter is adaptively updated using the Bayesian model averaging method. Numerical study shows that the PA-CRM improves the efficiency and accuracy of the pediatric trial and is robust to various model assumptions.     

Sequential adaptive variables and subject selection for GEE methods

Modeling correlated or highly stratified multiple-response data is a common data analysis task in many applications, such as those in large epidemiological studies or multisite cohort studies. The generalized estimating equations method is a popular statistical method used to analyze these kinds of data, because it can manage many types of unmeasured dependence among outcomes. Collecting large amounts of highly stratified or correlated response data is time-consuming; thus, the use of a more aggressive sampling strategy that can accelerate this process—such as the active-learning methods found in the machine-learning literature—will always be beneficial. In this study, we integrate adaptive sampling and variable selection features into a sequential procedure for modeling correlated response data. Besides reporting the statistical properties of the proposed procedure, we also use both synthesized and real data sets to demonstrate the usefulness of our method.     

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Synthetic microbial communities have become a focus of biotechnological research since they can overcome several of the limitations of single-specie cultures. A paradigmatic example is Clostridium cellulovorans DSM 743B, which can decompose lignocellulose but cannot produce butanol. Clostridium beijerinckii NCIMB 8052 however, is unable to use lignocellulose but can produce high amounts of butanol from simple sugars. In our previous studies, both organisms were cocultured to produce butanol by consolidated bioprocessing. However, such consolidated bioprocessing implementation strongly depends on pH regulation. Since low pH (pH 4.5–5.5) is required for butanol fermentation, C. cellulovorans cannot grow well and saccharify sufficient lignocellulose to feed both strains at a pH below 6.4. To overcome this bottleneck, this study engineered C. cellulovorans by adaptive laboratory evolution, inactivating cell wall lyases genes (Clocel_0798 and Clocel_2169), and overexpressing agmatine deiminase genes (augA, encoded by Cbei_1922) from C. beijerinckii NCIMB 8052. The generated strain WZQ36: 743B*6.0*3△lyt0798△lyt2169-(pXY1-P_thl-augA) can tolerate a pH of 5.5. Finally, the alcohol aldehyde dehydrogenase gene adhE1 from Clostridium acetobutylicum ATCC 824 was introduced into the strain to enable butanol production at low pH, in coordination with solvent fermentation of C. beijerinckii in consortium. The engineered consortium produced 3.94 g/L butanol without pH control within 83 hr, which is more than 5-fold of the level achieved by wild consortia under the same conditions. This exploration represents a proof of concept on how to combine metabolic and evolutionary engineering to coordinate coculture of a synthetic microbial community.     

Adaptation of oysters to life on soft substrates

In spite of their sessile nature, oysters of the genera Crassostrea, Ostrea and their allies are adapted to live on soft muddy bottoms. To provide them with secure attachment and to keep the animal above the surface of the mud, these soft-bottom oysters employ two basic strategies. They tend to be gregarious in their settlement, thereby providing one another with surfaces for attachment, and they form lightweight shells. In addition to these common characteristics, these oysters have followed different morphological pathways to live successfully on soft muddy bottoms. There are at least four types of morphologies: (1) relay-type succession (shells pile up on one another over many generations); (2) konbo-type elongation (both valves grow vertically upwards, the dorsal hinge remaining at the base, near the umbo); (3) cone-type elongation (the attached valve grows upwards and the free valve acts as a lid); and (4) lightweight recliners (bulky shells with lightweight fabrics and cavities within them). Judging from the variety of taxa that have adopted it and the sizes of their populations, the relay-type is the most successful of these strategies. Recliners were particularly common during the Mesozoic. The relay-type appeared in the Late Jurassic, becoming the dominant adaptive strategy during the Cenozoic.