Cancer biomarker

From Wikipedia, the free encyclopedia

https://en.wikipedia.org/wiki/Cancer_biomarker

 

Questions that can be answered by biomarkers

A cancer biomarker refers to a substance or process that is indicative of the presence of cancer in the body. A biomarker may be a molecule secreted by a tumor or a specific response of the body to the presence of cancer. Genetic, epigenetic, proteomic, glycomic, and imaging biomarkers can be used for cancer diagnosis, prognosis, and epidemiology. Ideally, such biomarkers can be assayed in non-invasively collected biofluids like blood or serum.

Cancer is a disease that affects society at a world-wide level. By testing for biomarkers, early diagnosis can be given to prevent deaths.

While numerous challenges exist in translating biomarker research into the clinical space; a number of gene and protein based biomarkers have already been used at some point in patient care; including, AFP (liver cancer), BCR-ABL (chronic myeloid leukemia), BRCA1 / BRCA2 (breast/ovarian cancer), BRAF V600E (melanoma/colorectal cancer), CA-125 (ovarian cancer), CA19.9 (pancreatic cancer), CEA (colorectal cancer), EGFR (Non-small-cell lung carcinoma), HER-2 (Breast Cancer), KIT (gastrointestinal stromal tumor), PSA (prostate specific antigen) (prostate cancer), S100 (melanoma), and many others. Mutant proteins themselves detected by selected reaction monitoring (SRM) have been reported to be the most specific biomarkers for cancers because they can only come from an existing tumor. About 40% of cancers can be cured if detected early through examinations.

Definitions of cancer biomarkers

Organizations and publications vary in their definition of biomarker. In many areas of medicine, biomarkers are limited to proteins identifiable or measurable in the blood or urine. However, the term is often used to cover any molecular, biochemical, physiological, or anatomical property that can be quantified or measured.

The National Cancer Institute (NCI), in particular, defines biomarker as a: “A biological molecule found in blood, other body fluids, or tissues that is a sign of a normal or abnormal process, or of a condition or disease. A biomarker may be used to see how well the body responds to a treatment for a disease or condition. Also called molecular marker and signature molecule."

In cancer research and medicine, biomarkers are used in three primary ways:

1. To help diagnose conditions, as in the case of identifying early stage cancers (diagnostic)
2. To forecast how aggressive a condition is, as in the case of determining a patient's ability to fare in the absence of treatment (prognostic)
3. To predict how well a patient will respond to treatment (predictive)

Role of biomarkers in cancer research and medicine

Uses of biomarkers in cancer medicine

Risk assessment

Cancer biomarkers, particular those associated with genetic mutations or epigenetic alterations, often offer a quantitative way to determine when individuals are predisposed to particular types of cancers. Notable examples of potentially predictive cancer biomarkers include mutations on genes KRAS, p53, EGFR, erbB2 for colorectal, esophageal, liver, and pancreatic cancer; mutations of genes BRCA1 and BRCA2 for breast and ovarian cancer; abnormal methylation of tumor suppressor genes p16, CDKN2B, and p14ARF for brain cancer; hypermethylation of MYOD1, CDH1, and CDH13 for cervical cancer; and hypermethylation of p16, p14, and RB1, for oral cancer.

Diagnosis

Cancer biomarkers can also be useful in establishing a specific diagnosis. This is particularly the case when there is a need to determine whether tumors are of primary or metastatic origin. To make this distinction, researchers can screen the chromosomal alterations found on cells located in the primary tumor site against those found in the secondary site. If the alterations match, the secondary tumor can be identified as metastatic; whereas if the alterations differ, the secondary tumor can be identified as a distinct primary tumor. For example, people with tumors have high levels of circulating tumor DNA (ctDNA) due to tumor cells that have gone through apoptosis. This tumor marker can be detected in the blood, saliva, or urine. The possibility of identifying an effective biomarker for early cancer diagnosis has recently been questioned, in light of the high molecular heterogeneity of tumors observed by next-generation sequencing studies.

Prognosis and treatment predictions

Another use of biomarkers in cancer medicine is for disease prognosis, which take place after an individual has been diagnosed with cancer. Here biomarkers can be useful in determining the aggressiveness of an identified cancer as well as its likelihood of responding to a given treatment. In part, this is because tumors exhibiting particular biomarkers may be responsive to treatments tied to that biomarker's expression or presence. Examples of such prognostic biomarkers include elevated levels of metallopeptidase inhibitor 1 (TIMP1), a marker associated with more aggressive forms of multiple myeloma, elevated estrogen receptor (ER) and/or progesterone receptor (PR) expression, markers associated with better overall survival in patients with breast cancer; HER2/neu gene amplification, a marker indicating a breast cancer will likely respond to trastuzumab treatment; a mutation in exon 11 of the proto-oncogene c-KIT, a marker indicating a gastrointestinal stromal tumor (GIST) will likely respond to imatinib treatment; and mutations in the tyrosine kinase domain of EGFR1, a marker indicating a patient's non-small-cell lung carcinoma (NSCLC) will likely respond to gefitinib or erlotinib treatment.

Pharmacodynamics and pharmacokinetics

Cancer biomarkers can also be used to determine the most effective treatment regime for a particular person's cancer. Because of differences in each person's genetic makeup, some people metabolize or change the chemical structure of drugs differently. In some cases, decreased metabolism of certain drugs can create dangerous conditions in which high levels of the drug accumulate in the body. As such, drug dosing decisions in particular cancer treatments can benefit from screening for such biomarkers. An example is the gene encoding the enzyme thiopurine methyl-transferase (TPMPT). Individuals with mutations in the TPMT gene are unable to metabolize large amounts of the leukemia drug, mercaptopurine, which potentially causes a fatal drop in white blood count for such patients. Patients with TPMT mutations are thus recommended to be given a lower dose of mercaptopurine for safety considerations.

Monitoring treatment response

Cancer biomarkers have also shown utility in monitoring how well a treatment is working over time. Much research is going into this particular area, since successful biomarkers have the potential of providing significant cost reduction in patient care, as the current image-based tests such as CT and MRI for monitoring tumor status are highly costly.

One notable biomarker garnering significant attention is the protein biomarker S100-beta in monitoring the response of malignant melanoma. In such melanomas, melanocytes, the cells that make pigment in our skin, produce the protein S100-beta in high concentrations dependent on the number of cancer cells. Response to treatment is thus associated with reduced levels of S100-beta in the blood of such individuals.

Similarly, additional laboratory research has shown that tumor cells undergoing apoptosis can release cellular components such as cytochrome c, nucleosomes, cleaved cytokeratin-18, and E-cadherin. Studies have found that these macromolecules and others can be found in circulation during cancer therapy, providing a potential source of clinical metrics for monitoring treatment.

Recurrence

Cancer biomarkers can also offer value in predicting or monitoring cancer recurrence. The Oncotype DX® breast cancer assay is one such test used to predict the likelihood of breast cancer recurrence. This test is intended for women with early-stage (Stage I or II), node-negative, estrogen receptor-positive (ER+) invasive breast cancer who will be treated with hormone therapy. Oncotype DX looks at a panel of 21 genes in cells taken during tumor biopsy. The results of the test are given in the form of a recurrence score that indicates likelihood of recurrence at 10 years.

Uses of biomarkers in cancer research

Developing drug targets

In addition to their use in cancer medicine, biomarkers are often used throughout the cancer drug discovery process. For instance, in the 1960s, researchers discovered the majority of patients with chronic myelogenous leukemia possessed a particular genetic abnormality on chromosomes 9 and 22 dubbed the Philadelphia chromosome. When these two chromosomes combine they create a cancer-causing gene known as BCR-ABL. In such patients, this gene acts as the principle initial point in all of the physiological manifestations of the leukemia. For many years, the BCR-ABL was simply used as a biomarker to stratify a certain subtype of leukemia. However, drug developers were eventually able to develop imatinib, a powerful drug that effectively inhibited this protein and significantly decreased production of cells containing the Philadelphia chromosome.

Surrogate endpoints

Another promising area of biomarker application is in the area of surrogate endpoints. In this application, biomarkers act as stand-ins for the effects of a drug on cancer progression and survival. Ideally, the use of validated biomarkers would prevent patients from having to undergo tumor biopsies and lengthy clinical trials to determine if a new drug worked. In the current standard of care, the metric for determining a drug's effectiveness is to check if it has decreased cancer progression in humans and ultimately whether it prolongs survival. However, successful biomarker surrogates could save substantial time, effort, and money if failing drugs could be eliminated from the development pipeline before being brought to clinical trials.

Some ideal characteristics of surrogate endpoint biomarkers include:

• Biomarker should be involved in process that causes the cancer
• Changes in biomarker should correlate with changes in the disease
• Levels of biomarkers should be high enough that they can be measured easily and reliably
• Levels or presence of biomarker should readily distinguish between normal, cancerous, and precancerous tissue
• Effective treatment of the cancer should change the level of the biomarker
• Level of the biomarker should not change spontaneously or in response to other factors not related to the successful treatment of the cancer

Two areas in particular that are receiving attention as surrogate markers include circulating tumor cells (CTCs) and circulating miRNAs. Both these markers are associated with the number of tumor cells present in the blood, and as such, are hoped to provide a surrogate for tumor progression and metastasis. However, significant barriers to their adoption include the difficulty of enriching, identifying, and measuring CTC and miRNA levels in blood. New technologies and research are likely necessary for their translation into clinical care.

Types of cancer biomarkers

Molecular cancer biomarkers

Tumor type	Biomarker
Breast	ER/PR (estrogen receptor/progesteron receptor)
	HER-2/neu
Colorectal	EGFR
	KRAS
	UGT1A1
Gastric	HER-2/neu
GIST	c-KIT
Leukemia/lymphoma	CD20
	CD30
	FIP1L1-PDGFRalpha
	PDGFR
	Philadelphia chromosome (BCR/ABL)
	PML/RAR-alpha
	TPMT
	UGT1A1
Lung	EML4/ALK
	EGFR
	KRAS
Melanoma	BRAF
Pancreas	Elevated levels of leucine, isoleucine and valine
Ovaries	CA-125

Other examples of biomarkers:

• Tumor suppressors lost in cancer
  • Examples: BRCA1, BRCA2
• RNA
  • Examples: mRNA, microRNA
• Proteins found in body fluids or tissue.
  • Examples: Prostate-specific antigen, and CA-125
• Antibodies to cancer antigens
  • Examples: Merkel cell polyomavirus
• DNA
  • Examples: Circulating Tumor DNA (ctDNA)

Cancer biomarkers without specificity

Not all cancer biomarkers have to be specific to types of cancer. Some biomarkers found in the circulatory system can be used to determine an abnormal growth of cells present in the body. All these types of biomarkers can be identified through diagnostic blood tests, which is one of the main reasons to get regularly health tested. By getting regularly tested, many health issues such as cancer can be discovered at an early stage, preventing many deaths.

The neutrophil-to-lymphocyte ratio has been shown to be a non-specific determinant for many cancers. This ratio focuses on the activity of two components of the immune system that are involved in inflammatory response which is shown to be higher in presence of malignant tumors. Additionally, basic fibroblast growth factor (bFGF) is a protein that is involved in the proliferation of cells. Unfortunately, it has been shown that in the presence of tumors it is highly active which has led to the conclusion that it may help malignant cells reproduce at faster rates. Research has shown that anti-bFGF antibodies can be used to help treat tumors from many origins. Moreover, insulin-like growth factor (IGF-R) is involved in cell proliferation and growth. It has is possible that it is involved in inhibiting apoptosis, programmed cell death due to some defect. Due to this, the levels of IGF-R can be increased when cancer such as breast, prostate, lung, and colorectum is present.

Biomarker	Description	Biosensor used
NLR (neutrophil-to-lymphocyte ratio)	Elevates with inflammation caused by cancer	No
Basic Fibroblast Growth Factor (bFGF)	This level increases when a tumor is present, helps with the fast reproduction of tumor cells	Electrochemical
Insulin-like Growth Factor (IGF-R)	High activity in cancer cells, help reproduction	Electrochemical Impedance Spectroscopy Sensor

Oncogenomics

From Wikipedia, the free encyclopedia

https://en.wikipedia.org/wiki/Oncogenomics

Oncogenomics is a sub-field of genomics that characterizes cancer-associated genes. It focuses on genomic, epigenomic and transcript alterations in cancer.

Cancer is a genetic disease caused by accumulation of DNA mutations and epigenetic alterations leading to unrestrained cell proliferation and neoplasm formation. The goal of oncogenomics is to identify new oncogenes or tumor suppressor genes that may provide new insights into cancer diagnosis, predicting clinical outcome of cancers and new targets for cancer therapies. The success of targeted cancer therapies such as Gleevec, Herceptin and Avastin raised the hope for oncogenomics to elucidate new targets for cancer treatment.

Overall goals of oncogenomics

Besides understanding the underlying genetic mechanisms that initiate or drive cancer progression, oncogenomics targets personalized cancer treatment. Cancer develops due to DNA mutations and epigenetic alterations that accumulate randomly. Identifying and targeting the mutations in an individual patient may lead to increased treatment efficacy.

The completion of the Human Genome Project facilitated the field of oncogenomics and increased the abilities of researchers to find oncogenes. Sequencing technologies and global methylation profiling techniques have been applied to the study of oncogenomics.

History

The genomics era began in the 1990s, with the generation of DNA sequences of many organisms. In the 21st century, the completion of the Human Genome Project enabled the study of functional genomics and examining tumor genomes. Cancer is a main focus.

The epigenomics era largely began more recently, about 2000. One major source of epigenetic change is altered methylation of CpG islands at the promoter region of genes (see DNA methylation in cancer). A number of recently devised methods can assess the DNA methylation status in cancers versus normal tissues. Some methods assess methylation of CpGs located in different classes of loci, including CpG islands, shores, and shelves as well as promoters, gene bodies, and intergenic regions. Cancer is also a major focus of epigenetic studies.

Access to whole cancer genome sequencing is important to cancer (or cancer genome) research because:

• Mutations are the immediate cause of cancer and define the tumor phenotype.
• Access to cancerous and normal tissue samples from the same patient and the fact that most cancer mutations represent somatic events, allow the identification of cancer-specific mutations.
• Cancer mutations are cumulative and sometimes are related to disease stage. Metastasis and drug resistance are distinguishable.

Access to methylation profiling is important to cancer research because:

• Epi-drivers, along with Mut-drivers, can act as immediate causes of cancers
• Cancer epimutations are cumulative and sometimes related to disease stage

Whole genome sequencing

The first cancer genome was sequenced in 2008. This study sequenced a typical acute myeloid leukaemia (AML) genome and its normal counterpart genome obtained from the same patient. The comparison revealed ten mutated genes. Two were already thought to contribute to tumor progression: an internal tandem duplication of the FLT3 receptor tyrosine kinase gene, which activates kinase signaling and is associated with a poor prognosis and a four base insertion in exon 12 of the NPM1 gene (NPMc). These mutations are found in 25-30% of AML tumors and are thought to contribute to disease progression rather than to cause it directly.

The remaining 8 were new mutations and all were single base changes: Four were in families that are strongly associated with cancer pathogenesis (PTPRT, CDH24, PCLKC and SLC15A1). The other four had no previous association with cancer pathogenesis. They did have potential functions in metabolic pathways that suggested mechanisms by which they could act to promote cancer (KNDC1, GPR124, EB12, GRINC1B)

These genes are involved in pathways known to contribute to cancer pathogenesis, but before this study most would not have been candidates for targeted gene therapy. This analysis validated the approach of whole cancer genome sequencing in identifying somatic mutations and the importance of parallel sequencing of normal and tumor cell genomes.

In 2011, the genome of an exceptional bladder cancer patient whose tumor had been eliminated by the drug everolimus was sequenced, revealing mutations in two genes, TSC1 and NF2. The mutations disregulated mTOR, the protein inhibited by everolimus, allowing it to reproduce without limit. As a result, in 2015, the Exceptional Responders Initiative was created at the National Cancer Institute. The initiative allows such exceptional patients (who have responded positively for at least six months to a cancer drug that usually fails) to have their genomes sequenced to identify the relevant mutations. Once identified, other patients could be screened for those mutations and then be given the drug. In 2016 To that end, a nationwide cancer drug trial began in 2015, involving up to twenty-four hundred centers. Patients with appropriate mutations are matched with one of more than forty drugs.

In 2014 the Center for Molecular Oncology rolled out the MSK-IMPACT test, a screening tool that looks for mutations in 341 cancer-associated genes. By 2015 more than five thousand patients had been screened. Patients with appropriate mutations are eligible to enroll in clinical trials that provide targeted therapy.

Technologies

Current technologies being used in Oncogenomics.

Genomics technologies include:

Genome sequencing

Further information: Cancer genome sequencing

• DNA sequencing: Pyrosequencing-based sequencers offer a relatively low-cost method to generate sequence data.
• Array Comparative Genome Hybridization: This technique measures the DNA copy number differences between normal and cancer genomes. It uses the fluorescence intensity from fluorescent-labeled samples, which are hybridized to known probes on a microarray.
• Representational oligonucleotide microarray analysis: Detects copy number variation using amplified restriction-digested genomic fragments that are hybridized to human oligonucleotides, achieving a resolution between 30 and 35 kbit/s.
• Digital Karyotyping: Detects copy number variation using genomics tags obtained via restriction enzyme digests. These tags are then linked to into ditags, concatenated, cloned, sequenced and mapped back to the reference genome to evaluate tag density.
• Bacterial Artificial Chromosome (BAC)-end sequencing (end-sequence profiling): Identifies chromosomal breakpoints by generating a BAC library from a cancer genome and sequencing their ends. The BAC clones that contain chromosome aberrations have end sequences that do not map to a similar region of the reference genome, thus identifying a chromosomal breakpoint.

Transcriptomes

Further information: Transcriptomics technologies

• Microarrays: Assess transcript abundance. Useful in classification, prognosis, raise the possibility of differential treatment approaches and aid identification of mutations in the proteins' coding regions. The relative abundance of alternative transcripts has become an important feature of cancer research. Particular alternative transcript forms correlate with specific cancer types.
• RNA-Seq

Bioinformatics and functional analysis of oncogenes

Bioinformatics technologies allow the statistical analysis of genomic data. The functional characteristics of oncogenes has yet to be established. Potential functions include their transformational capabilities relating to tumour formation and specific roles at each stage of cancer development.

After the detection of somatic cancer mutations across a cohort of cancer samples, bioinformatic computational analyses can be carried out to identify likely functional and likely driver mutations. There are three main approaches routinely used for this identification: mapping mutations, assessing the effect of mutation of the function of a protein or a regulatory element and finding signs of positive selection across a cohort of tumors. The approaches are not necessarily sequential however, there are important relationships of precedence between elements from the different approaches. Different tools are used at each step.

Operomics

Operomics aims to integrate genomics, transcriptomics and proteomics to understand the molecular mechanisms that underlie the cancer development.

Comparative oncogenomics

Comparative oncogenomics uses cross-species comparisons to identify oncogenes. This research involves studying cancer genomes, transcriptomes and proteomes in model organisms such as mice, identifying potential oncogenes and referring back to human cancer samples to see whether homologues of these oncogenes are important in causing human cancers. Genetic alterations in mouse models are similar to those found in human cancers. These models are generated by methods including retroviral insertion mutagenesis or graft transplantation of cancerous cells.

Source of cancer driver mutations, cancer mutagenesis

Mutations provide the raw material for natural selection in evolution and can be caused by errors of DNA replication, the action of exogenous mutagens or endogenous DNA damage. The machinery of replication and genome maintenance can be damaged by mutations, or altered by physiological conditions and differential levels of expression in cancer.

As pointed out by Gao et al., the stability and integrity of the human genome are maintained by the DNA-damage response (DDR) system. Un-repaired DNA damage is a major cause of mutations that drive carcinogenesis. If DNA repair is deficient, DNA damage tends to accumulate. Such excess DNA damage can increase mutational errors during DNA replication due to error-prone translesion synthesis. Excess DNA damage can also increase epigenetic alterations due to errors during DNA repair. Such mutations and epigenetic alterations can give rise to cancer. DDR genes are often repressed in human cancer by epigenetic mechanisms. Such repression may involve DNA methylation of promoter regions or repression of DDR genes by a microRNA. Epigenetic repression of DDR genes occurs more frequently than gene mutation in many types of cancer (see Cancer epigenetics). Thus, epigenetic repression often plays a more important role than mutation in reducing expression of DDR genes. This reduced expression of DDR genes is likely an important driver of carcinogenesis.

Nucleotide sequence context influences mutation probability and analysis of mutational (mutable) DNA motifs can be essential for understanding the mechanisms of mutagenesis in cancer. Such motifs represent the fingerprints of interactions between DNA and mutagens, between DNA and repair/replication/modification enzymes. Examples of motifs are the AID motif WRCY/RGYW (W = A or T, R = purine and Y = pyrimidine) with C to T/G/A mutations, and error-prone DNA pol η attributed AID-related mutations (A to G/C/G) in WA/TW motifs.

Another (agnostic) way to analyze the observed mutational spectra and DNA sequence context of mutations in tumors involves pooling all mutations of different types and contexts from cancer samples into a discrete distribution. If multiple cancer samples are available, their context-dependent mutations can be represented in the form of a nonnegative matrix. This matrix can be further decomposed into components (mutational signatures) which ideally should describe individual mutagenic factors. Several computational methods have been proposed for solving this decomposition problem. The first implementation of Non-negative Matrix Factorization (NMF) method is available in Sanger Institute Mutational Signature Framework in the form of a MATLAB package. On the other hand, if mutations from a single tumor sample are only available, the DeconstructSigs R package and MutaGene server may provide the identification of contributions of different mutational signatures for a single tumor sample. In addition, MutaGene server provides mutagen or cancer-specific mutational background models and signatures that can be applied to calculate expected DNA and protein site mutability to decouple relative contributions of mutagenesis and selection in carcinogenesis.

Synthetic lethality

Synthetic lethality arises when a combination of deficiencies in the expression of two or more genes leads to cell death, whereas a deficiency in only one of these genes does not. The deficiencies can arise through mutations, epigenetic alterations or inhibitors of one of the genes.

The therapeutic potential of synthetic lethality as an efficacious anti-cancer strategy is continually improving. Recently, the applicability of synthetic lethality to targeted cancer therapy has heightened due to the recent work of scientists including Ronald A. DePinho and colleagues, in what is termed 'collateral lethality'. Muller et al. found that passenger genes, with chromosomal proximity to tumor suppressor genes, are collaterally deleted in some cancers. Thus, the identification of collaterally deleted redundant genes carrying out an essential cellular function may be the untapped reservoir for then pursuing a synthetic lethality approach. Collateral lethality therefore holds great potential in identification of novel and selective therapeutic targets in oncology. In 2012, Muller et al. identified that homozygous deletion of redundant-essential glycolytic ENO1 gene in human glioblastoma (GBM) is the consequence of proximity to 1p36 tumor suppressor locus deletions and may hold potential for a synthetic lethality approach to GBM inhibition. ENO1 is one of three homologous genes (ENO2, ENO3) that encodes the mammalian alpha-enolase enzyme. ENO2, which encodes enolase 2, is mostly expressed in neural tissues, leading to the postulation that in ENO1-deleted GBM, ENO2 may be the ideal target as the redundant homologue of ENO1. Muller found that both genetic and pharmacological ENO2 inhibition in GBM cells with homozygous ENO1 deletion elicits a synthetic lethality outcome by selective killing of GBM cells. In 2016, Muller and colleagues discovered antibiotic SF2312 as a highly potent nanomolar-range enolase inhibitor which preferentially inhibits glioma cell proliferation and glycolytic flux in ENO1-deleted cells. SF2312 was shown to be more efficacious than pan-enolase inhibitor PhAH and have more specificity for ENO2 inhibition over ENO1. Subsequent work by the same team showed that the same approach could be applied to pancreatic cancer, whereby homozygously deleted SMAD4 results in the collateral deletion of mitochondrial malic enzyme 2 (ME2), an oxidative decarboxylase essential for redox homeostasis. Dey et al. show that ME2 genomic deletion in pancreatic ductal adenocarcinoma cells results in high endogenous reactive oxygen species, consistent with KRAS-driven pancreatic cancer, and essentially primes ME2-null cells for synthetic lethality by depletion of redundant NAD(P)+-dependent isoform ME3. The effects of ME3 depletion were found to be mediated by inhibition of de novo nucleotide synthesis resulting from AMPK activation and mitochondrial ROS-mediated apoptosis. Meanwhile, Oike et al. demonstrated the generalizability of the concept by targeting redundant essential-genes in process other than metabolism, namely the SMARCA4 and SMARCA2 subunits in the chromatin-remodeling SWI/SNF complex.

Some oncogenes are essential for survival of all cells (not only cancer cells). Thus, drugs that knock out these oncogenes (and thereby kill cancer cells) may also damage normal cells, inducing significant illness. However, other genes may be essential to cancer cells but not to healthy cells.

Treatments based on the principle of synthetic lethality have prolonged the survival of cancer patients, and show promise for future advances in reversal of carcinogenesis. A major type of synthetic lethality operates on the DNA repair defect that often initiates a cancer, and is still present in the tumor cells. Some examples are given here.

BRCA1 or BRCA2 expression is deficient in a majority of high-grade breast and ovarian cancers, usually due to epigenetic methylation of its promoter or epigenetic repression by an over-expressed microRNA (see articles BRCA1 and BRCA2). BRCA1 and BRCA2 are important components of the major pathway for homologous recombinational repair of double-strand breaks. If one or the other is deficient, it increases the risk of cancer, especially breast or ovarian cancer. A back-up DNA repair pathway, for some of the damages usually repaired by BRCA1 and BRCA2, depends on PARP1. Thus, many ovarian cancers respond to an FDA-approved treatment with a PARP inhibitor, causing synthetic lethality to cancer cells deficient in BRCA1 or BRCA2. This treatment is also being evaluated for breast cancer and numerous other cancers in Phase III clinical trials in 2016.

There are two pathways for homologous recombinational repair of double-strand breaks. The major pathway depends on BRCA1, PALB2 and BRCA2 while an alternative pathway depends on RAD52. Pre-clinical studies, involving epigenetically reduced or mutated BRCA-deficient cells (in culture or injected into mice), show that inhibition of RAD52 is synthetically lethal with BRCA-deficiency.

Mutations in genes employed in DNA mismatch repair (MMR) cause a high mutation rate. In tumors, such frequent subsequent mutations often generate “non-self” immunogenic antigens. A human Phase II clinical trial, with 41 patients, evaluated one synthetic lethal approach for tumors with or without MMR defects. The product of gene PD-1 ordinarily represses cytotoxic immune responses. Inhibition of this gene allows a greater immune response. When cancer patients with a defect in MMR in their tumors were exposed to an inhibitor of PD-1, 67% - 78% of patients experienced immune-related progression-free survival. In contrast, for patients without defective MMR, addition of PD-1 inhibitor generated only 11% of patients with immune-related progression-free survival. Thus inhibition of PD-1 is primarily synthetically lethal with MMR defects.

ARID1A, a chromatin modifier, is required for non-homologous end joining, a major pathway that repairs double-strand breaks in DNA, and also has transcription regulatory roles. ARID1A mutations are one of the 12 most common carcinogenic mutations. Mutation or epigenetically decreased expression of ARID1A has been found in 17 types of cancer. Pre-clinical studies in cells and in mice show that synthetic lethality for ARID1A deficiency occurs by either inhibition of the methyltransferase activity of EZH2, or with addition of the kinase inhibitor dasatinib.

Another approach is to individually knock out each gene in a genome and observe the effect on normal and cancerous cells. If the knockout of an otherwise nonessential gene has little or no effect on healthy cells, but is lethal to cancerous cells containing a mutated oncogene, then the system-wide suppression of the suppressed gene can destroy cancerous cells while leaving healthy ones relatively undamaged. The technique was used to identify PARP-1 inhibitors to treat BRCA1/BRCA2-associated cancers. In this case, the combined presence of PARP-1 inhibition and of the cancer-associated mutations in BRCA genes is lethal only to the cancerous cells.

Databases for cancer research

See also: Databases for oncogenomic research

The Cancer Genome Project is an initiative to map out all somatic mutations in cancer. The project systematically sequences the exons and flanking splice junctions of the genomes of primary tumors and cancerous cell lines. COSMIC software displays the data generated from these experiments. As of February 2008, the CGP had identified 4,746 genes and 2,985 mutations in 1,848 tumours.

The Cancer Genome Anatomy Project includes information of research on cancer genomes, transcriptomes and proteomes.

Progenetix is an oncogenomic reference database, presenting cytogenetic and molecular-cytogenetic tumor data.

Oncomine has compiled data from cancer transcriptome profiles.

The integrative oncogenomics database IntOGen and the Gitools datasets integrate multidimensional human oncogenomic data classified by tumor type. The first version of IntOGen focused on the role of deregulated gene expression and CNV in cancer. A later version emphasized mutational cancer driver genes across 28 tumor types. All releases of IntOGen data are made available at the IntOGen database.

The International Cancer Genome Consortium is the biggest project to collect human cancer genome data. The data is accessible through the ICGC website. The BioExpress® Oncology Suite contains gene expression data from primary, metastatic and benign tumor samples and normal samples, including matched adjacent controls. The suite includes hematological malignancy samples for many well-known cancers.

Specific databases for model animals include the Retrovirus Tagged Cancer Gene Database (RTCGD) that compiled research on retroviral and transposon insertional mutagenesis in mouse tumors.

Gene families

Mutational analysis of entire gene families revealed that genes of the same family have similar functions, as predicted by similar coding sequences and protein domains. Two such classes are the kinase family, involved in adding phosphate groups to proteins and the phosphatase family, involved with removing phosphate groups from proteins. These families were first examined because of their apparent role in transducing cellular signals of cell growth or death. In particular, more than 50% of colorectal cancers carry a mutation in a kinase or phosphatase gene. Phosphatidylinositold 3-kinases (PIK3CA) gene encodes for lipid kinases that commonly contain mutations in colorectal, breast, gastric, lung and various other cancers. Drug therapies can inhibit PIK3CA. Another example is the BRAF gene, one of the first to be implicated in melanomas. BRAF encodes a serine/threonine kinase that is involved in the RAS-RAF-MAPK growth signaling pathway. Mutations in BRAF cause constitutive phosphorylation and activity in 59% of melanomas. Before BRAF, the genetic mechanism of melanoma development was unknown and therefore prognosis for patients was poor.

Mitochondrial DNA

Mitochondrial DNA (mtDNA) mutations are linked the formation of tumors. Four types of mtDNA mutations have been identified:

Point mutations

Point mutations have been observed in the coding and non-coding region of the mtDNA contained in cancer cells. In individuals with bladder, head/neck and lung cancers, the point mutations within the coding region show signs of resembling each other. This suggests that when a healthy cell transforms into a tumor cell (a neoplastic transformation) the mitochondria seem to become homogenous. Abundant point mutations located within the non-coding region, D-loop, of the cancerous mitochondria suggest that mutations within this region might be an important characteristic in some cancers.

Deletions

This type of mutation is sporadically detected due to its small size ( < 1kb). The appearance of certain specific mtDNA mutations (264-bp deletion and 66-bp deletion in the complex 1 subunit gene ND1) in multiple types of cancer provide some evidence that small mtDNA deletions might appear at the beginning of tumorigenesis. It also suggests that the amount of mitochondria containing these deletions increases as the tumor progresses. An exception is a relatively large deletion that appears in many cancers (known as the "common deletion"), but more mtDNA large scale deletions have been found in normal cells compared to tumor cells. This may be due to a seemingly adaptive process of tumor cells to eliminate any mitochondria that contain these large scale deletions (the "common deletion" is > 4kb).

Insertions

Two small mtDNA insertions of ~260 and ~520 bp can be present in breast cancer, gastric cancer, hepatocellular carcinoma (HCC) and colon cancer and in normal cells. No correlation between these insertions and cancer are established.

Copy number mutations

The characterization of mtDNA via real-time polymerase chain reaction assays shows the presence of quantitative alteration of mtDNA copy number in many cancers. Increase in copy number is expected to occur because of oxidative stress. On the other hand, decrease is thought to be caused by somatic point mutations in the replication origin site of the H-strand and/or the D310 homopolymeric c-stretch in the D-loop region, mutations in the p53 (tumor suppressor gene) mediated pathway and/or inefficient enzyme activity due to POLG mutations. Any increase/decrease in copy number then remains constant within tumor cells. The fact that the amount of mtDNA is constant in tumor cells suggests that the amount of mtDNA is controlled by a much more complicated system in tumor cells, rather than simply altered as a consequence of abnormal cell proliferation. The role of mtDNA content in human cancers apparently varies for particular tumor types or sites.

Mutations in mitochondrial DNA in various cancers

Cancer Type	Location of Point mutations				Nucleotide Position of Deletions	Increase of mtDNA copy #	Decrease of mtDNA copy #
	D-Loop	mRNAs	tRNAs	rRNAs
Bladder	X	X		X	15,642-15,662
Breast	X	X	X	X	8470-13,447 and 8482-13459		X
Head and neck	X	X	X	X	8470-13,447 and 8482-13459	X
Oral	X	X			8470-13,447 and 8482-13459
Hepatocellular carcinoma (HCC)	X	X	X	X	306-556 and 3894-3960		X
Esophageal	X	X		X	8470-13,447 and 8482-13459	X
Gastric	X	X	X		298-348		X
Prostate	X			X	8470-13,447 and 8482-13459	X

57.7% (500/867) contained somatic point putations and of the 1172 mutations surveyed 37.8% (443/1127) were located in the D-loop control region, 13.1% (154/1172) were located in the tRNA or rRNA genes and 49.1% (575/1127) were found in the mRNA genes needed for producing complexes required for mitochondrial respiration.

Diagnostic applications

Some anticancer drugs target mtDNA and have shown positive results in killing tumor cells. Research has used mitochondrial mutations as biomarkers for cancer cell therapy. It is easier to target mutation within mitochondrial DNA versus nuclear DNA because the mitochondrial genome is much smaller and easier to screen for specific mutations. MtDNA content alterations found in blood samples might be able to serve as a screening marker for predicting future cancer susceptibility as well as tracking malignant tumor progression. Along with these potential helpful characteristics of mtDNA, it is not under the control of the cell cycle and is important for maintaining ATP generation and mitochondrial homeostasis. These characteristics make targeting mtDNA a practical therapeutic strategy.

Cancer biomarkers

Several biomarkers can be useful in cancer staging, prognosis and treatment. They can range from single-nucleotide polymorphisms (SNPs), chromosomal aberrations, changes in DNA copy number, microsatellite instability, promoter region methylation, or even high or low protein levels.

Cancer immunology

From Wikipedia, the free encyclopedia

https://en.wikipedia.org/wiki/Cancer_immunology 

 

Tumor-associated immune cells in the tumor microenvironment (TME) of breast cancer models

Cancer immunology is an interdisciplinary branch of biology that is concerned with understanding the role of the immune system in the progression and development of cancer; the most well known application is cancer immunotherapy, which utilises the immune system as a treatment for cancer. Cancer immunosurveillance and immunoediting are based on protection against development of tumors in animal systems and (ii) identification of targets for immune recognition of human cancer.

Definition

Cancer immunology is an interdisciplinary branch of biology concerned with the role of the immune system in the progression and development of cancer; the most well known application is cancer immunotherapy, where the immune system is used to treat cancer. Cancer immunosurveillance is a theory formulated in 1957 by Burnet and Thomas, who proposed that lymphocytes act as sentinels in recognizing and eliminating continuously arising, nascent transformed cells. Cancer immunosurveillance appears to be an important host protection process that decreases cancer rates through inhibition of carcinogenesis and maintaining of regular cellular homeostasis. It has also been suggested that immunosurveillance primarily functions as a component of a more general process of cancer immunoediting.

Tumor antigens

Main article: Tumor antigen

Tumors may express tumor antigens that are recognized by the immune system and may induce an immune response. These tumor antigens are either TSA (Tumor-specific antigen) or TAA (Tumor-associated antigen).

Tumor-specific

Tumor-specific antigens (TSA) are antigens that only occur in tumor cells. TSAs can be products of oncoviruses like E6 and E7 proteins of human papillomavirus, occurring in cervical carcinoma, or EBNA-1 protein of EBV, occurring in Burkitt's lymphoma cells. Another example of TSAs are abnormal products of mutated oncogenes (e.g. Ras protein) and anti-oncogenes (e.g. p53).

Tumor-associated antigens

Tumor-associated antigens (TAA) are present in healthy cells, but for some reason they also occur in tumor cells. However, they differ in quantity, place or time period of expression. Oncofetal antigens are tumor-associated antigens expressed by embryonic cells and by tumors. Examples of oncofetal antigens are AFP (α-fetoprotein), produced by hepatocellular carcinoma, or CEA (carcinoembryonic antigen), occurring in ovarian and colon cancer. More tumor-associated antigens are HER2/neu, EGFR or MAGE-1.

Immunoediting

Cancer immunoediting is a process in which immune system interacts with tumor cells. It consists of three phases: elimination, equilibrium and escape. These phases are often referred to as "the three Es" of cancer immunoediting. Both adaptive and innate immune system participate in immunoediting.

In the elimination phase, the immune response leads to destruction of tumor cells and therefore to tumor suppression. However, some tumor cells may gain more mutations, change their characteristics and evade the immune system. These cells might enter the equilibrium phase, in which the immune system does not recognise all tumor cells, but at the same time the tumor does not grow. This condition may lead to the phase of escape, in which the tumor gains dominance over immune system, starts growing and establishes immunosuppressive environment.

As a consequence of immunoediting, tumor cell clones less responsive to the immune system gain dominance in the tumor through time, as the recognized cells are eliminated. This process may be considered akin to Darwinian evolution, where cells containing pro-oncogenic or immunosuppressive mutations survive to pass on their mutations to daughter cells, which may themselves mutate and undergo further selective pressure. This results in the tumor consisting of cells with decreased immunogenicity and can hardly be eliminated. This phenomenon was proven to happen as a result of immunotherapies of cancer patients.

Tumor evasion mechanisms

Multiple factors determine whether tumor cells will be eliminated by the immune system or will escape detection. During the elimination phase immune effector cells such as CTL's and NK cells with the help of dendritic and CD4+ T-cells are able to recognize and eliminate tumor cells.

• CD8+ cytotoxic T cells are a fundamental element of anti-tumor immunity. Their TCR receptors recognise antigens presented by MHC class I and when bound, the Tc cell triggers its cytotoxic activity. MHC I are present on the surface of all nucleated cells. However, some cancer cells lower their MHC I expression and avoid being detected by the cytotoxic T cells. This can be done by mutation of MHC I gene or by lowering the sensitivity to IFN-γ (which influences the surface expression of MHC I). Tumor cells also have defects in antigen presentation pathway, what leads into down-regulation of tumor antigen presentations. Defects are for example in transporter associated with antigen processing (TAP) or tapasin. On the other hand, a complete loss of MHC I is a trigger for NK cells. Tumor cells therefore maintain a low expression of MHC I.
• Another way to escape cytotoxic T cells is to stop expressing molecules essential for co-stimulation of cytotoxic T cells, such as CD80 or CD86.
• Tumor cells express molecules to induce apoptosis or to inhibit T lymphocytes:
  • Expression of FasL on its surface, tumor cells may induce apoptosis of T lymphocytes by FasL-Fas interaction.
  • Expression of PD-L1 on the surface of tumor cells leads to suppression of T lymphocytes by PD1-PD-L1 interaction.
• Tumor cells have gained resistance to effector mechanisms of NK and cytotoxic CD8+ T cell:
  • by loss of gene expression or inhibition of apoptotic signal pathway molecules: APAF1, Caspase 8, Bcl-2-associated X protein (bax) and Bcl-2 homologous antagonist killer (bak).
  • by induction of expression or overexpression of antiapoptotic molecules: Bcl-2, IAP or XIAP.

Tumor microenvironment

Immune checkpoints of immunosuppressive actions associated with breast cancer

• Production of TGF-β by tumor cells and other cells (such as myeloid-derived suppressor cell) leads to conversion of CD4+ T cell into suppressive regulatory T cell (Treg) by a contact dependent or independent stimulation. In a healthy tissue, functioning Tregs are essential to maintain self-tolerance. In a tumor, however, Tregs form an immunosuppressive microenvironment.
• Tumor cells produce special cytokines (such as colony-stimulating factor) to produce myeloid-derived suppressor cell. These cells are heterogenous collection of cell types including precursors of dendritic cell, monocyte and neutrophil. MDSC have suppressive effects on T-lymphocytes, dendritic cells and macrophages. They produce immunosuppressive TGF-β and IL-10.
• Another producer of suppressive TGF-β and IL-10 are tumor-associated macrophages, these macrophages have mostly phenotype of alternatively activated M2 macrophages. Their activation is promoted by TH type 2 cytokines (such as IL-4 and IL-13). Their main effects are immunosuppression, promotion of tumor growth and angiogenesis.
• Tumor cells have non-classical MHC class I on their surface, for example HLA-G. HLA-G is inducer of Treg, MDSC, polarise macrophages into alternatively activated M2 and has other immunosuppressive effects on immune cells.

Immunomodulation methods

Immune system is the key player in fighting cancer. As described above in mechanisms of tumor evasion, the tumor cells are modulating the immune response in their profit. It is possible to improve the immune response in order to boost the immunity against tumor cells.

• monoclonal anti-CTLA4 and anti-PD-1 antibodies are called immune checkpoint inhibitors:
  • CTLA-4 is a receptor upregulated on the membrane of activated T lymphocytes, CTLA-4 CD80/86 interaction leads to switch off of T lymphocytes. By blocking this interaction with monoclonal anti CTLA-4 antibody we can increase the immune response. An example of approved drug is ipilimumab.
  • PD-1 is also an upregulated receptor on the surface of T lymphocytes after activation. Interaction PD-1 with PD-L1 leads to switching off or apoptosis. PD-L1 are molecules which can be produced by tumor cells. The monoclonal anti-PD-1 antibody is blocking this interaction thus leading to improvement of immune response in CD8+ T lymphocytes. An example of approved cancer drug is nivolumab.
  • Chimeric Antigen Receptor T cell
    • This CAR receptors are genetically engineered receptors with extracellular tumor specific binding sites and intracellular signalling domain that enables the T lymphocyte activation.
  • Cancer vaccine
    • Vaccine can be composed of killed tumor cells, recombinant tumor antigens, or dendritic cells incubated with tumor antigens (dendritic cell-based cancer vaccine) 

Relationship to chemotherapy

Main article: cancer immunotherapy

Obeid et al. investigated how inducing immunogenic cancer cell death ought to become a priority of cancer chemotherapy. He reasoned, the immune system would be able to play a factor via a 'bystander effect' in eradicating chemotherapy-resistant cancer cells. However, extensive research is still needed on how the immune response is triggered against dying tumour cells.

Professionals in the field have hypothesized that 'apoptotic cell death is poorly immunogenic whereas necrotic cell death is truly immunogenic'. This is perhaps because cancer cells being eradicated via a necrotic cell death pathway induce an immune response by triggering dendritic cells to mature, due to inflammatory response stimulation. On the other hand, apoptosis is connected to slight alterations within the plasma membrane causing the dying cells to be attractive to phagocytic cells. However, numerous animal studies have shown the superiority of vaccination with apoptotic cells, compared to necrotic cells, in eliciting anti-tumor immune responses.

Thus Obeid et al. propose that the way in which cancer cells die during chemotherapy is vital. Anthracyclins produce a beneficial immunogenic environment. The researchers report that when killing cancer cells with this agent uptake and presentation by antigen presenting dendritic cells is encouraged, thus allowing a T-cell response which can shrink tumours. Therefore, activating tumour-killing T-cells is crucial for immunotherapy success.

However, advanced cancer patients with immunosuppression have left researchers in a dilemma as to how to activate their T-cells. The way the host dendritic cells react and uptake tumour antigens to present to CD4⁺ and CD8⁺ T-cells is the key to success of the treatment.