Role of oncoproteomics in the personalized management of cancer

Oncoproteomics is the term used to describe the application of proteomic technologies in oncology and parallels the related field of oncogenomics. It is now contributing to the development of personalized management of cancer. Proteomic technologies are used for the identification of biomarkers in cancer, which will facilitate the integration of diagnosis and therapy of cancer. Molecular diagnostics, laser capture microdissection and protein biochips are among the technologies that are having an important impact on oncoproteomics. The discovery of protein patterns developed by the US Food and Drug Administration/National Cancer Institute Clinical Proteomics Program is capable of distinguishing cancer and disease-free states with high sensitivity and specificity and will also facilitate the development of personalized therapy of cancer. Examples of application are given for breast and prostate cancer and a selection of companies and their collaborations that are developing application of proteomics to personalized treatment of cancer are discussed. Continued refinement of techniques and methods to determine the abundance and status of proteins in vivo holds great promise for the future study of normal cells and the pathology of associated neoplasms. Personalized cancer therapy is expected to be in the clinic by the end of the first decade of the 21st century.     

Role of the CT scanner in the management of cancer.

Although the diagnostic scope of computed tomography has widened considerably in recent years, assessment of patients with suspected or known malignant disease remains the major reason for body CT referrals in the United Kingdom. This paper sets out to define important advantages and limitations of CT in cancer diagnosis, addressing the topics of primary diagnosis, staging, and patient follow up. There is relatively little information on the influence of CT on patient management in oncology but reported studies indicate that CT directly alters clinical decisions in 14-30% of patients. This aspect requires further evaluation and is of particular relevance when considering the appropriate use of high cost technology.     

Digital pathology in personalized cancer therapy

The development of small molecule inhibitors of growth factor receptors, and the discovery of somatic mutations of the tyrosine kinase domain, have resulted in new paradigms for cancer therapy. Digital microscopy is an important tool for surgical pathologists. The achievements in the digital pathology field have modified the workflow of pathomorphology labs, enhanced the pathologist's role in diagnostics, and increased their contribution to personalized targeted medicine. Digital image analysis is now available in a variety of platforms to improve quantification performance of diagnostic pathology. We here describe the state of digital microscopy as it applies to the field of quantitative immunohistochemistry of biomarkers related to the clinical personalized targeted therapy of breast cancer, non-small lung cancer and colorectal cancer: HER-2, EGFR, KRAS and BRAF genes. The information is derived from the experience of the authors and a review of the literature.     

Role of autophagy in cancer: management of metabolic stress

Human breast, ovarian, and prostate tumors display allelic loss of the essential autophagy gene beclin1 with high frequency, and an increase in the incidence of tumor formation is observed in beclin1(+/-) mutant mice. These findings suggest a role for beclin1 and autophagy in tumor suppression; however, the mechanism by which this occurs has been unclear. Autophagy is a bulk degradation process whereby organelles and cytoplasm are engulfed and targeted to lysosomes for proteolysis,(1,2) There is evidence that autophagy sustains cell survival during nutrient deprivation through catabolism, but also that autophagy is a means of achieving cell death when executed to completion. If or how either of these diametrically opposing functions proposed for autophagy may be related to tumor suppression is unknown. We found that metabolic stress is a potent trigger of apoptotic cell death, defects in which enable long-term survival that is dependent on autophagy both in vitro and in tumors in vivo.(3) These findings raise the conundrum whereby inactivation of a survival pathway (autophagy) promotes tumorigenesis. Interestingly, when cells with defects in apoptosis are denied autophagy, this creates the inability to tolerate metabolic stress, reduces cellular fitness, and activates a necrotic pathway to cell death. This necrosis in tumors is associated with inflammation and enhancement of tumor growth, due to the survival of a small population of surviving, but injured, cells in a microenvironment that favors oncogenesis. Thus, by sustaining metabolism through autophagy during periods of metabolic stress, cells can limit energy depletion, cellular damage, and cell death by necrosis, which may explain how autophagy can prevent cancer, and how loss of a survival function can be tumorigenic.     

Proteomic contributions to personalized cancer care

Cancer impacts each patient and family differently. Our current understanding of the disease is primarily limited to clinical hallmarks of cancer, but many specific molecular mechanisms remain elusive. Genetic markers can be used to determine predisposition to tumor development, but molecularly targeted treatment strategies that improve patient prognosis are not widely available for most cancers. Individualized care plans, also described as personalized medicine, still must be developed by understanding and implementing basic science research into clinical treatment. Proteomics holds great promise in contributing to the prevention and cure of cancer because it provides unique tools for discovery of biomarkers and therapeutic targets. As such, proteomics can help translate basic science discoveries into the clinical practice of personalized medicine. Here we describe how biological mass spectrometry and proteome analysis interact with other major patient care and research initiatives and present vignettes illustrating efforts in discovery of diagnostic biomarkers for ovarian cancer, development of treatment strategies in lung cancer, and monitoring prognosis and relapse in multiple myeloma patients.     

Finding neoepitopes in mouse models of personalized cancer immunotherapy

Background

Cancer immunotherapy uses one’s own immune system to fight cancerous cells. As immune system is hard-wired to distinguish self and non-self, cancer immunotherapy is predicted to target cancerous cells specifically, therefore is less toxic than chemotherapy and radiation therapy, two major treatments for cancer. Cancer immunologists have spent decades to search for the specific targets in cancerous cells.

Methods

Due to the recent advances in high throughput sequencing and bioinformatics, evidence has merged that the neoantigens in cancerous cells are probably the cancer-specific targets that lead to the destruction of cancer.We will review the transplantable murine tumor models for cancer immunotherapy and the bioinformatics tools used to navigate mouse genome to identify tumor-rejecting neoantigens.

Results

Several groups have independently identified point mutations that can be recognized by T cells of host immune system. It is consistent with the note that the formation of peptide-MHC I-TCR complex is critical to activate T cells. Both anchor residue and TCR-facing residue mutations have been reported. While TCR-facing residue mutations may directly activate specific T cells, anchor residue mutations improve the binding of peptides to MHC I molecules, which increases the presentation of peptides and the T cell activation indirectly.

Conclusions

Our work indicates that the affinity of neoepitopes for MHC I is not a predictor for anti-tumor immune responses in mice. Instead differential agretopic index (DAI), the numerical difference of epitope-MHC I affinities between the mutated and un-mutated sequences is a significant predictor. A similar bioinformatics pipeline has been developed to generate personalized vaccines to treat human ovarian cancer in a Phase I clinical trial.

     

Management of non-small cell lung cancer in the era of personalized medicine

Despite major advances in the molecular definition of the disease, screening and therapy, non-small cell lung cancer (NSCLC) is still characterized by a disappointing overall survival, depending on subtype and tumor stage. To address this challenge, in the last years the therapeutic algorithm of NSCLC has become much more complex and articulated, with different kinds of drugs, including chemotherapy, targeted-based agents, angiogenesis inhibitors and immunotherapy, and multiple lines of treatments, for patients with squamous and non-squamous hystology, EGFR mutation and ALK rearrangement. This short viewpoint describes the emerging strategies for the management of NSCLC, indicating how a personalized approach, characterized by a specific multidisciplinary involvement, implies a process that starts with early detection and includes surgery and systemic therapy.     

3D Bioprinted cancer models: Revolutionizing personalized cancer therapy

After cardiovascular disease, cancer is the leading cause of death worldwide with devastating health and economic consequences, particularly in developing countries. Inter-patient variations in anti-cancer drug responses further limit the success of therapeutic interventions. Therefore, personalized medicines approach is key for this patient group involving molecular and genetic screening and appropriate stratification of patients to treatment regimen that they will respond to. However, the knowledge related to adequate risk stratification methods identifying patients who will respond to specific anti-cancer agents is still lacking in many cancer types. Recent advancements in three-dimensional (3D) bioprinting technology, have been extensively used to generate representative bioengineered tumor in vitro models, which recapitulate the human tumor tissues and microenvironment for high-throughput drug screening. Bioprinting process involves the precise deposition of multiple layers of different cell types in combination with biomaterials capable of generating 3D bioengineered tissues based on a computer-aided design. Bioprinted cancer models containing patient-derived cancer and stromal cells together with genetic material, extracellular matrix proteins and growth factors, represent a promising approach for personalized cancer therapy screening. Both natural and synthetic biopolymers have been utilized to support the proliferation of cells and biological material within the personalized tumor models/implants. These models can provide a physiologically pertinent cell–cell and cell–matrix interactions by mimicking the 3D heterogeneity of real tumors. Here, we reviewed the potential applications of 3D bioprinted tumor constructs as personalized in vitro models in anticancer drug screening and in the establishment of precision treatment regimens.     

Engineering neoantigen vaccines to improve cancer personalized immunotherapy

Immunotherapy treatments harnessing the immune system herald a new era of personalized medicine, offering considerable benefits for cancer patients. Over the past years, tumor neoantigens emerged as a rising star in immunotherapy. Neoantigens are tumor-specific antigens arising from somatic mutations, which are proceeded and presented by the major histocompatibility complex on the cell surface. With the advancement of sequencing technology and bioinformatics engineering, the recognition of neoantigens has accelerated and is expected to be incorporated into the clinical routine. Currently, tumor vaccines against neoantigens mainly encompass peptides, DNA, RNA, and dendritic cells, which are extremely specific to individual patients. Due to the high immunogenicity of neoantigens, tumor vaccines could activate and expand antigen-specific CD4+ and CD8+ T cells to intensify anti-tumor immunity. Herein, we introduce the origin and prediction of neoantigens and compare the advantages and disadvantages of multiple types of neoantigen vaccines. Besides, we review the immunizations and the current clinical research status in neoantigen vaccines, and outline strategies for enhancing the efficacy of neoantigen vaccines. Finally, we present the challenges facing the application of neoantigens.     

Role of whole brain radiotherapy in the management of infratentorial metastases from lung and breast cancer

BackgroundBrain metastases (BM) occur in almost one third of patients with systemic malignancies. Only a small number of studies focus on infratentorial location and whole brain radiotherapy (WBRT) as the main non-surgical management. The aim of the study was to compare the prognosis of patients treated with WBRT among patients with supra- or infratentorial lesions.Materials and methodsAt a single center, 263 patients with either breast (BC) or lung (LC) cancer, that had developed BM and received treatment with WBRT, were analyzed during an 8-year period.ResultsA total of 152 patients with BC and 111 with LC were analyzed, median age at the time of BM was 50.7 years, systemic activity other than BM was detected in 91%. Newly diagnosed BM were supratentorial in 40%, infratentorial in 10% and 51% in both locations. Median overall survival was 13 months (95% CI: 11.1–14.8 months), without significant difference between supra- or infratentorial location. WBRT alone was administered in 79% of patients, whereas WBRT with chemtoreapy was provided for 21%.ConclusionIn patients with BM from LC or BC that were not candidates for surgical resection, palliative WBRT appears to be equally effective in those with supra- or infratentorial locations.     

Shifting from population-wide to personalized cancer prognosis with microarrays

The era of personalized medicine for cancer therapeutics has taken an important step forward in making accurate prognoses for individual patients with the adoption of high-throughput microarray technology. However, microarray technology in cancer diagnosis or prognosis has been primarily used for the statistical evaluation of patient populations, and thus excludes inter-individual variability and patient-specific predictions. Here we propose a metric called clinical confidence that serves as a measure of prognostic reliability to facilitate the shift from population-wide to personalized cancer prognosis using microarray-based predictive models. The performance of sample-based models predicted with different clinical confidences was evaluated and compared systematically using three large clinical datasets studying the following cancers: breast cancer, multiple myeloma, and neuroblastoma. Survival curves for patients, with different confidences, were also delineated. The results show that the clinical confidence metric separates patients with different prediction accuracies and survival times. Samples with high clinical confidence were likely to have accurate prognoses from predictive models. Moreover, patients with high clinical confidence would be expected to live for a notably longer or shorter time if their prognosis was good or grim based on the models, respectively. We conclude that clinical confidence could serve as a beneficial metric for personalized cancer prognosis prediction utilizing microarrays. Ascribing a confidence level to prognosis with the clinical confidence metric provides the clinician an objective, personalized basis for decisions, such as choosing the severity of the treatment.     

In silico drug combination discovery for personalized cancer therapy

Background

Drug combination therapy, which is considered as an alternative to single drug therapy, can potentially reduce resistance and toxicity, and have synergistic efficacy. As drug combination therapies are widely used in the clinic for hypertension, asthma, and AIDS, they have also been proposed for the treatment of cancer. However, it is difficult to select and experimentally evaluate effective combinations because not only is the number of cancer drug combinations extremely large but also the effectiveness of drug combinations varies depending on the genetic variation of cancer patients. A computational approach that prioritizes the best drug combinations considering the genetic information of a cancer patient is necessary to reduce the search space.

Results

We propose an in-silico method for personalized drug combination therapy discovery. We predict the synergy between two drugs and a cell line using genomic information, targets of drugs, and pharmacological information. We calculate and predict the synergy scores of 583 drug combinations for 31 cancer cell lines. For feature dimension reduction, we select the mutations or expression levels of the genes in cancer-related pathways. We also used various machine learning models. Extremely Randomized Trees (ERT), a tree-based ensemble model, achieved the best performance in the synergy score prediction regression task. The correlation coefficient between the synergy scores predicted by ERT and the actual observations is 0.738. To compare with an existing drug combination synergy classification model, we reformulate the problem as a binary classification problem by thresholding the synergy scores. ERT achieved an F1 score of 0.954 when synergy scores of 20 and -20 were used as the threshold, which is 8.7% higher than that obtained by the state-of-the-art baseline model. Moreover, the model correctly predicts the most synergistic combination, from approximately 100 candidate drug combinations, as the top choice for 15 out of the 31 cell lines. For 28 out of the 31 cell lines, the model predicts the most synergistic combination in the top 10 of approximately 100 candidate drug combinations. Finally, we analyze the results, generate synergistic rules using the features, and validate the rules through the literature survey.

Conclusion

Using various types of genomic information of cancer cell lines, targets of drugs, and pharmacological information, a drug combination synergy prediction pipeline is proposed. The pipeline regresses the synergy level between two drugs and a cell line as well as classifies if there exists synergy or antagonism between them. Discovering new drug combinations by our pipeline may improve personalized cancer therapy.

     

Predicting outcomes of prostate cancer immunotherapy by personalized mathematical models

Background

Therapeutic vaccination against disseminated prostate cancer (PCa) is partially effective in some PCa patients. We hypothesized that the efficacy of treatment will be enhanced by individualized vaccination regimens tailored by simple mathematical models.

Methodology/Principal Findings

We developed a general mathematical model encompassing the basic interactions of a vaccine, immune system and PCa cells, and validated it by the results of a clinical trial testing an allogeneic PCa whole-cell vaccine. For model validation in the absence of any other pertinent marker, we used the clinically measured changes in prostate-specific antigen (PSA) levels as a correlate of tumor burden. Up to 26 PSA levels measured per patient were divided into each patient''s training set and his validation set. The training set, used for model personalization, contained the patient''s initial sequence of PSA levels; the validation set contained his subsequent PSA data points. Personalized models were simulated to predict changes in tumor burden and PSA levels and predictions were compared to the validation set. The model accurately predicted PSA levels over the entire measured period in 12 of the 15 vaccination-responsive patients (the coefficient of determination between the predicted and observed PSA values was R ² = 0.972). The model could not account for the inconsistent changes in PSA levels in 3 of the 15 responsive patients at the end of treatment. Each validated personalized model was simulated under many hypothetical immunotherapy protocols to suggest alternative vaccination regimens. Personalized regimens predicted to enhance the effects of therapy differed among the patients.

Conclusions/Significance

Using a few initial measurements, we constructed robust patient-specific models of PCa immunotherapy, which were retrospectively validated by clinical trial results. Our results emphasize the potential value and feasibility of individualized model-suggested immunotherapy protocols.     

Application of oncoproteomics to aberrant signalling networks in changing the treatment paradigm in acute lymphoblastic leukaemia

Oncoproteomics is an important innovation in the early diagnosis, management and development of personalized treatment of acute lymphoblastic leukaemia (ALL). As inherent factors are not completely known – e.g. age or family history, radiation exposure, benzene chemical exposure, certain viral exposures such as infection with the human T‐cell lymphoma/leukaemia virus‐1, as well as some inherited syndromes may raise the risk of ALL – each ALL patient may modify the susceptibility of therapy. Indeed, we consider these unknown inherent factors could be explained via coupling cytogenetics plus proteomics, especially when proteins are the ones which play function within cells. Innovative proteomics to ALL therapy may help to understand the mechanism of drug resistance and toxicities, which in turn will provide some leads to improve ALL management. Most important of these are shotgun proteomic strategies to unravel ALL aberrant signalling networks. Some shotgun proteomic innovations and bioinformatic tools for ALL therapies will be discussed. As network proteins are distinctive characteristics for ALL patients, unrevealed by cytogenetics, those network proteins are currently an important source of novel therapeutic targets that emerge from shotgun proteomics. Indeed, ALL evolution can be studied for each individual patient via oncoproteomics.     

Individualizing breast cancer treatment—The dawn of personalized medicine

Identification of breast cancer not being a single disease but backed by multiple heterogeneous oncogenic subpopulations is of growing interest in developing personalized therapies to provide optimal outcomes. Through this review, we bring attention to evolution of tumor and microenvironment heterogeneity as a predominant challenge in stratifying therapies. Establishment of a ‘precancer niche’ serves as a prerequisite for genetically initiated cells to survive and promote neoplastic evolution towards clinically established cancer through development of tumor and its microenvironment. Additionally, continuous evolutionary interplay between tumor and recruited stromal cells along with many other components in the tumor microenvironment adds up to further complexity in developing targeted therapies. However, through continued excellence in developing high throughput technologies including the advent of single-nucleus sequencing, which makes it possible to sequence individual tumor cells, leads to improved abilities in decoding the heterogenic perturbations through reconstruction of tumor evolutionary lineages. Furthermore, simple liquid-biopsies in form of enumeration/characterization of circulating tumor cells and tumor microvesicles found in peripheral circulation, shed from distinct tumor lesions, show great promise as prospective biomarkers towards better prognosis in tailoring individualized therapies to breast cancer patients. Lastly, by means of network medicinal approaches, it is seemingly possible to develop a map of the cell's intricate wiring network, helping to identify appropriate interconnected protein networks through which the disease spreads, offering a more patient-specific outcome. Although these therapeutic interventions through designing personalized oncology-based trials are promising, owing to continuous tumor evolution, targeting genome instability survival pathways might become an economically viable alternative.     

DNA repair in personalized brain cancer therapy with temozolomide and nitrosoureas

Alkylating agents have been used since the 60ties in brain cancer chemotherapy. Their target is the DNA and, although the DNA of normal and cancer cells is damaged unselectively, they exert tumor-specific killing effects because of downregulation of some DNA repair activities in cancer cells. Agents exhibiting methylating properties (temozolomide, procarbazine, dacarbazine, streptozotocine) induce at least 12 different DNA lesions. These are repaired by damage reversal mechanisms involving the alkyltransferase MGMT and the alkB homologous protein ALKBH2, and through base excision repair (BER). There is a strong correlation between the MGMT expression level and therapeutic response in high-grade malignant glioma, supporting the notion that O⁶-methylguanine and, for nitrosoureas, O⁶-chloroethylguanine are the most relevant toxic damages at therapeutically relevant doses. Since MGMT has a significant impact on the outcome of anti-cancer therapy, it is a predictive marker of the effectiveness of methylating anticancer drugs, and clinical trials are underway aimed at assessing the influence of MGMT inhibition on the therapeutic success. Other DNA repair factors involved in methylating drug resistance are mismatch repair, DNA double-strand break (DSB) repair by homologous recombination (HR) and DSB signaling. Base excision repair and ALKBH2 might also contribute to alkylating drug resistance and their downregulation may have an impact on drug sensitivity notably in cells expressing a high amount of MGMT and at high doses of temozolomide, but the importance in a therapeutic setting remains to be shown. MGMT is frequently downregulated in cancer cells (up to 40% in glioblastomas), which is due to CpG promoter methylation. Astrocytoma (grade III) are frequently mutated in isocitrate dehydrogenase (IDH1). These tumors show a surprisingly good therapeutic response. IDH1 mutation has an impact on ALKBH2 activity thus influencing DNA repair. A master switch between survival and death is p53, which often retains transactivation activity (wildtype) in malignant glioma. The role of p53 in regulating survival via DNA repair and the routes of death are discussed and conclusions as to cancer therapeutic options were drawn.     

Mathematical model of a personalized neoantigen cancer vaccine and the human immune system

Cancer vaccines are an important component of the cancer immunotherapy toolkit enhancing immune response to malignant cells by activating CD4⁺ and CD8⁺ T cells. Multiple successful clinical applications of cancer vaccines have shown good safety and efficacy. Despite the notable progress, significant challenges remain in obtaining consistent immune responses across heterogeneous patient populations, as well as various cancers. We present a mechanistic mathematical model describing key interactions of a personalized neoantigen cancer vaccine with an individual patient’s immune system. Specifically, the model considers the vaccine concentration of tumor-specific antigen peptides and adjuvant, the patient’s major histocompatibility complexes I and II copy numbers, tumor size, T cells, and antigen presenting cells. We parametrized the model using patient-specific data from a clinical study in which individualized cancer vaccines were used to treat six melanoma patients. Model simulations predicted both immune responses, represented by T cell counts, to the vaccine as well as clinical outcome (determined as change of tumor size). This model, although complex, can be used to describe, simulate, and predict the behavior of the human immune system to a personalized cancer vaccine.     

Role of osteopontin in the pathophysiology of cancer

Osteopontin (OPN) is a multifunctional cytokine that impacts cell proliferation, survival, drug resistance, invasion, and stem like behavior. Due to its critical involvement in regulating cellular functions, its aberrant expression and/or splicing is functionally responsible for undesirable alterations in disease pathologies, specifically cancer. It is implicated in promoting invasive and metastatic progression of many carcinomas. Due to its autocrine and paracrine activities OPN has been shown to be a crucial mediator of cellular cross talk and an influential factor in the tumor microenvironment. OPN has been implicated as a prognostic and diagnostic marker for several cancer types. It has also been explored as a possible target for treatment. In this article we hope to provide a broad perspective on the importance of OPN in the pathophysiology of cancer.