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Cancer genetics and genomics

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Genome-wide view of cancer

Using our new understanding of cancer

Lecture overview

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The advent of next-generation sequencing allowed researchers to move from looking at changes in individual oncogenes and TS genes to genome-wide analyses of mutations.

A number of large international collaborative projects are using the tools of genomics to try to understand how cancer cells acquire all the capabilities.

Genome-wide view of cancer

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Six essential “hallmark” capabilities of cancer cells

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For example, the International Cancer Genome Consortium (https://icgc.org/) aims “to obtain a comprehensive description of genomic, transcriptomic and epigenomic changes in 50 different tumor types and/or subtypes which are of clinical and societal importance across the globe”.

Data release 27 (30th April 2018) comprised data from more than 20,000 cases involving 22 tumor sites collected through 84 projects in 17 countries.

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Multiplatform analyses describe changes at the chromosomal, DNA sequence, epigenetic, RNA, and protein levels

Cells of a tumor can be compared with the normal constitutional cells of the patient on many levels to provide an integrated picture of carcinogenesis.

Exome sequencing would reveal all coding sequence changes, while whole-genome sequencing could document structural variants and copy number changes, in addition to small changes in noncoding sequence.

To characterize the transcriptome, early studies used expression arrays, but RNA-Seq. is now preferred because it allows all transcripts to be identified, not just those featured on an expression array, and also has a greater dynamic range.

DNA methylation can be documented by whole-genome bisulfite sequencing.

Changes in the proteome could be followed by mass spectrometry or antibody-based methods.

The multiplatform approach used in one large collaborative study, The Cancer Genome Atlas (TCGA).

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Different genetic technologies used in the treatment of disease Multiple genomic analyses were used on 5074 tumor samples covering 12 types of cancer

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Even this battery of analyses cannot fully characterize a tumor.

To fully follow the mutational trajectory, it will be necessary to follow the development of a single tumor over time, to use single-cell technologies to document the differences between different subclones of cancer cells, and to identify the various roles of stromal cells.

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Circos plots and heat maps present the masses of genomic data in visually digestible forms

Genomic analysis of even a single tumor produces a huge volume and variety of data, which is not only a challenge to process and store, but also a challenge to present to a human reader.

Much ingenuity has been devoted to thinking up ways of presenting large volumes of data graphically so as to give scope for the natural pattern-detecting ability of the human eye and brain. One useful tool is the Circos plot.

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This is a way of giving an overall visual impression of the DNA-level changes in a single tumor or class of tumors.

Circos plots are useful for highlighting the relative importance of different types of variant in different tumors.

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A Circos plot

Circos plot shows variants in the SK-BR3 breast cancer cell line.

The outermost ring shows the chromosomes. Inner rings show the positions and numbers of single nucleotide variants (SNV), amplifications (Amp), and deletions (Del).

Connecting lines across the interior show chromosomal translocations (Transloc.).

Heat maps can be used to display various types of quantitative data but are particularly useful for comparing patterns of gene expression between different tumors.

Typically, data on expression levels of a hundred or so genes are shown in each of a large set of tumors, compared to the corresponding normal tissue.

Hierarchical clustering methods are used to display data from tumors with similar patterns side-by-side.

This allows our natural pattern-seeking ability to pick out sets of tumors that have similar overall patterns of disordered gene expression.

Heat Map A heat map showing hierarchical clustering of gene expression profiles in diffuse large B-cell lymphomas.

Each row shows aggregate data for one cDNA, and each column shows total data from mRNA from one tumor.

The scale at right shows the color-coding of hybridization intensity relative to a reference mRNA.

The tumors can be clearly seen to form two groups, shown by the orange and blue bars, and designated germinal center and activated B-like types, respectively. The distinction is biologically significant because 5-year survival in the two groups was 76% and 16%, respectively.

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Mutational signatures suggest the main mechanisms generating somatic mutations in a tumor

Different mutational processes produce characteristic signatures.

There are six possible single nucleotide substitutions (C>A, C>G, C>T, T>A, T>C, T>G, together with their complements on the opposite strand).

With four choices for each immediately upstream and downstream nucleotide, there are 96 possible single nucleotide changes affecting a triplet.

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Analysis of all point mutations (passengers as well as drivers) across panels of tumors can identify the signatures typical of a particular cancer type.

In some cases the mutagenic agent can be identified.

Hopefully, identifying the agents responsible for mutations in a tumor will aid in understanding the overall evolutionary process producing the tumor.

Mutational signatures For each of the changes shown across the top, there are 16 possible contexts, considering just the immediately upstream and immediately downstream nucleotides. Signature 1A seems to reflect random lifelong deamination of cytosine, and is seen in a wide variety of tumor types. Signature 2, also seen in a variety of tumors, is due to uncontrolled activity of the APOBEC family of cytidine deaminases. Signature 4 is smoking-related and seen in head and neck, liver, and lung cancer; signature 7 is restricted to melanomas and shows the mutagenic action of ultraviolet light. D indicates dinucleotide mutations are present.

Genomic data allow a new classification of tumors

Tumors are traditionally classified by their tissue of origin, histological appearance, and sometimes other features such as hormone dependence (B-cell lymphoma).

Two more-recent examples:

Breast cancers can be classified in a number of ways. A very common scheme is based on the presence or absence of estrogen (ER) and progesterone (PR) receptors and ERBB2 (HER2) amplification. Incorporating gene expression profiles shows that most tumors can be grouped into luminal A, luminal B, and ERBB2-amplified subtypes, but a set of “triple negative” (ER−, PR−, ERBB2−) tumors form a “basal” group with completely different biology.

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The different categories have different prognoses, and commercial kits (MammaPrint®, Oncotype DX®, and so on) are available to identify high-risk and low-risk expression signatures.

Pancreatic cancers have been divided by expression analysis into four subtypes: squamous, pancreatic progenitor, immunogenic, and aberrantly-differentiated endocrine exocrine (ADEX).

The subtypes correlate with histopathological characteristics, and tumors of the squamous subtype show significantly worse prognosis than the others.

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The multiplatform pan-cancer analysis identified 13 clusters, of which 11 had prognostic value.

Five of the clusters showed simple, near one-to-one relationships with the tissue of origin (the cancers labeled GBM, KIRC, LAML, OV, and UCEC), but other cancer types split between several clusters, and some clusters united cancers of different tissue origins.

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Different genetic technologies used in the treatment of disease Multiple genomic analyses were used on 5074 tumor samples covering 12 types of cancer

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Almost all breast cancers (BRCA) fell into either Cluster 3 or Cluster 4, and each of those clusters encompassed only breast cancers. All READ tumors fell into Cluster 7, but so did all COAD tumors.

On the other hand, bladder cancers (BLCA) split between Clusters 1, 2, and 8, of which only Cluster 8 was specific to bladder cancer.

Based on the study, one in ten (%10) cancer patients would be classified differently by this new molecular taxonomy versus the traditional tissue-of-origin tumor classification system.

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Pathways rather than individual mutations or genes reduces the complexity

Genes whose mutation frequency differs significantly between the clusters (Hoadley and colleagues, PMID 25109877).

The genes are from a list of 291 high-confidence cancer drivers. The overwhelming impression is of great heterogeneity.

In addition, there were many copy number variants that differed between and within clusters, and within each cluster there was also extensive heterogeneity of mutations.

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Orange, brown, and red squares indicate genes mutated to increasing extents above the frequency averaged across all clusters, as shown in the key. Yellow squares mark genes mutated at background frequency, and white squares mark genes not recorded as mutated in the sample.

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This analysis has already simplified the picture by considering mutated genes rather than individual mutations.

A further major simplification can be achieved by thinking in terms of altered pathways rather than altered genes.

Some of the considerable heterogeneity at the gene level of mutations in glioblastoma multiforme (GBM) tumors, all of which fall in Cluster 10.

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Regardless which of the 14 genes is mutated, the effect is on just two pathways: the p53-driven pathway producing cell senescence and apoptosis, and the RAS/PI(3)K pathway by which receptor tyrosine kinases affect cell proliferation and survival.

Two pathways targeted by frequent mutations in glioblastoma multiform

In different individual tumors the two pathways are compromised by mutations or copy number changes in a variety of different genes.

Some (shown in yellow) act as oncogenes, with activating changes; others, shown in blue, act as tumor suppressor genes with loss-of-function changes; but all effects converge on the two pathways. Frequently altered genes are shown by deeper shades of color.

(A) 87% of tumors have variants that affect the pathways through which p53 triggers senescence and apoptosis.

88% of tumors have genetic changes that affect the pathways by which receptor tyrosine kinases (EGFR, ERBB2, PDGFRA, and MET) acting though RAS and phosphoinositide 3-kinase [PI(3)K] control cell proliferation and survival.

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“Gene ontology” A systematic ascertainment of pathways affected in tumors

The Gene Ontology (GO) database (www.geneontology.org/) classifies all genes across a wide range of organisms in three ways: by molecular function, biological process, and cellular component.

The large datasets of mutations produced by whole-genome sequencing of tumors can be checked for enrichment of specific GO terms (http://geneontology.org/page/goenrichment-analysis).

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Given a set of genes that are up-regulated in a certain tumor type, an enrichment analysis will find which GO terms are over-represented (or under-represented) using annotations for that gene set. This provides an unbiased way of identifying target pathways in tumorigenesis.

Similar analyses can be performed using the Kyoto Encyclopedia of Genes and Genomes (http://www.genome.jp/kegg/), which focuses on molecular interaction networks.

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Twelve cell signaling pathways that are targets for oncogenes and tumor suppressor genes

Genome-wide approaches can identify unsuspected pathways

An unexpected finding from sequencing tumor genomes was the discovery of frequent mutations in IDH1.

Yet more than 70% of grade II and III astrocytomas and oligodendrogliomas, and the glioblastomas that develop from these lower-grade lesions, have IDH1 missense mutations, and evidence suggests these are early events in tumorigenesis.

The mutations are very specific: they always affect arginine 132, replacing it with histidine or sometimes serine (R132H/S).

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Tumors without mutations in IDH1 often have mutations affecting the homologous amino acid (R172H) of the IDH2 gene.

Mutations in R172 of IDH2 are also frequent in acute myelogenous leukemia (AML).

The high specificity of the mutations, always changing the same amino acid, and the fact that the effect is present in heterozygotes, show that this must be a gain-of-function effect. But what is the new function?

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The first clue emerged when it was shown that the mutant enzyme produced a novel metabolite, 2-hydroxyglutarate .

The likely pathogenic action of 2-hydroxyglutarate is interference with levels of DNA methylation.

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Some tumors show evidence of large-scale co-ordinated genetic changes

Although most tumors evolve gradually, slowly acquiring mutations and developing through successive histological stages, sometimes events move more quickly.

Occasionally a single event can generate large numbers of mutations. A number of different mechanisms can do this.

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Breakage–fusion–bridge cycles are a classic consequence of rearrangements that produce dicentric chromosomes.

In chromoplexy a tumor cell has multiple “chained” chromosomal rearrangements.

Kataegis produces large numbers of mutations clustered in kilobase- to megabase-sized regions in a single event.

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Chromothripsis is seen when a single chromosome shows tens to hundreds of rearrangements.

It involves fewer chromosomes than chromoplexy, but more rearrangements. Often there is a complete mix of deletions, duplications, and inversions. Sometimes the whole chromosome is affected, in other cases the changes are limited to a small region.

Chromothripsis is seen in 2–3% of most cancers.

Bone cancers show a particularly high frequency of chromothripsis.

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Each of these processes produces multiple random changes that most usually result in cell death.

Cells with a compromised p53 pathway may be better able to survive.

If, by chance, one of the events activates an oncogene or inactivates a tumor suppressor gene, the cell may obtain a growth advantage.

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The evolution of a tumor can be inferred from comparative analyses or directly by single cell studies

A resected colon from a patient with familial adenomatous polyposis (FAP) will typically contain many polyps showing, between them, all stages of development from normal but slightly hypertrophic epithelium through to full-blown carcinoma.

Many years ago Kinzler and Vogelstein (1996, PMID 8861899; see Further Reading) took advantage of this to explore the sequence of events underlying the tumorigenesis.

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Within a framework of overall heterogeneity, they were able to identify certain changes that were typical of the early stages and others that normally appeared later.

Using three-dimensional cultures of intestinal crypt stem cells (organoids) it has been shown that mutation of just four genes, APC, KRAS, SMAD4, and TP53, enabled the organoids to grow independently of all stem cell niche factors, and to produce invasive carcinomas.

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A model for the multistep development of colon cancer

In many tumors loss, activation, or mutation of certain genes is seen at particular histological stages. This is primarily a tool for thinking about how tumors develop, rather than a firm description.

Every colorectal cancer is likely to have developed through the same histological stages, but the underlying genetic changes are more varied.

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Comparing early-stage tumors with pre-cancerous lesions on the one hand and late-stage tumors on the other, can shed light on the general evolution of a class of tumors, but a deeper understanding requires the ability to characterize diverse cells within a single tumor.

It would be a great mistake to think of a tumor as a clonal colony of cells like a bacterial colony on a plate.

Genomic instability and the consequent high mutation rate are characteristic of the great majority of cancers, and so a single tumor will contain heterogeneous populations of cells related by branching mutational trajectories.

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Beyond simply documenting the heterogeneity of cells within a tumor, single-cell sequencing can be used to map the clonal evolution of cell populations within a tumor.

The heterogeneous cells can be assembled into phylogenetic lineages that identify the nature and sequence of driver mutations.

In leukemia, where the mutational landscape is much simpler than in most solid cancers, it is possible to reconstruct the evolution by studying the clonality of mutations in a single blood sample.

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If driver mutation A is found in all leukemic cells of a patient, but driver mutation B is present only in a subset of cells, it must follow that mutation A appeared first.

Metastasis, the formation of disseminated secondary tumors, is the process that kills cancer patients, but the biology of metastasis is poorly understood.

There do not seem to be specific mutations that act as general drivers of metastasis, which is unfortunate because they would be excellent targets for antimetastatic drugs.

Rare circulating tumor cells can be isolated from the blood of cancer patients, and some subset of these must be the agents of metastasis.

Characterizing these single cells and comparing them with both primary and secondary tumors will hopefully lead to a better understanding of metastasis.

After surgery to remove as much of a tumor as possible, traditional cancer treatment uses radiotherapy and/or chemotherapy.

In both cases the treatment is aimed at killing rapidly dividing cells.

Unfortunately, these include normal cells in the gastrointestinal tract, immune system, hair follicles, and so on, hence the long list of often severe side-effects including constipation and diarrhea, nausea, hair loss, tiredness, weakness, and immune suppression.

Using our new understanding of cancer

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Our new understanding of driver mutations in cancer has stimulated an intense effort to develop agents to target the specific molecules or pathways that drive tumor development.

There are three main approaches: Identify small molecules that inhibit a tumor-specific enzyme or signaling molecule or a pathway that is overactive in tumor cells.

Targeted anticancer therapies

Gefitinib and Erlotinib were developed as competitive inhibitors of the EGFR, which is overactive in many lung and other tumors. Many of the small-molecule drugs target specific mutations. Patients need to be genotyped to see whether or not their tumor carries the relevant mutation. The combination of a therapeutic agent and companion diagnostic may be the future of personalized medicine;

Develop monoclonal antibodies (MABs) against tumor-specific cell surface proteins. The effect may be to block the activity of a cell surface receptor or to trigger attack by the immune system. Sometimes the MAB is conjugated to a toxin or radioactive compound to kill targeted cells;

Engineer T lymphocytes to attack tumor-specific cell-surface antigens.

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The prototype targeted small-molecule drug was imatinib (Glivec®/Gleevec®).

Imatinib inhibits the tyrosine kinases encoded by the ABL1, KIT, and PDGFRA genes. It has a particular affinity for the chimeric BCR–ABL1 tyrosine kinase encoded by the 9;22 translocation Philadelphia chromosome in chronic myelogenous leukemia (CML).

Introduction of imatinib produced a step change in the prognosis of CML. It is also used for patients with gastrointestinal stromal tumors that have mutant KIT genes.

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A considerable number of other targeted drugs are marketed or in clinical trials.

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Olaparib, the PARP1 inhibitor, demonstrates the potential of synthetic lethality.

This describes the way a combination of two nonlethal deficiencies can lead to a lethal effect. Poly(ADP-ribose) polymerase is the key signaling molecule for activating the repair pathway of single-strand DNA breaks.

In the absence of PARP, single-strand gaps persist, replication forks collapse in S phase, and the damage has to be repaired by BRCA1/2-mediated homologous recombination.

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Cells with BRCA1/2 mutations are unable to do this and so are very vulnerable to inhibition of PARP.

In the absence of both PARP and BRCA1/2, the cell has to attempt to rescue the damage by nonhomologous end-joining. This introduces many errors that are likely to lead to cell death.

Thus, PARP inhibitors are very effective against tumors with BRCA1/2 mutations, but ineffective against other tumors.

Engineered T cells are the subject of intense technical development and hundreds of clinical trials.

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The initial results of treatment can be very positive, and although they are not free of side-effects, these drugs are generally better tolerated than conventional chemotherapy.

However, it is fair to note that there is a good deal of “hype” surrounding this new wave of targeted drugs. The concept is very beguiling, but the reality is usually more prosaic.

For most epithelial cancers, only a minority of patients have the genetic changes necessary to obtain benefit, and for those that do benefit, the result is not a cure but a temporary remission.

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“Liquid biopsies” allow the emergence of resistance to be followed

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It would be highly desirable to be able to monitor a tumor so as to detect any emerging resistant clone at the earliest possible stage, when hopefully some change of treatment could prevent it developing further.

With leukemias, serial blood tests allow this possibility. Solid tumors are much more difficult. The development of so-called “liquid biopsies” offers a possible solution.

Both circulating tumor cells (CTC) and cell-free circulating tumor DNA (ctDNA) are present in the peripheral circulation of patients with metastatic cancer.

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Comparison of liquid versus needle biopsies

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If they could be routinely and reliably characterized, they would offer several advantages over surgical biopsies.

Not only would they be more acceptable to patients, but they could also better capture the heterogeneity of tumor cells in an individual patient, both in different regions of the primary tumor and in metastases, and they could be repeated as often as desired to follow progression of the disease.

Liquid biopsies are particularly promising for guiding treatment by monitoring the emergence of resistant clones.

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Alternatively, exome sequencing or array-comparative genomic hybridization could be used for a less targeted test.

Circulating tumor cells are very rare. Typically, there might be one CTC among 107 white blood cells in 1 ml of peripheral blood.

Hopefully the cells could then be characterized by single-cell sequencing or single-cell transcriptomics. All these approaches push the available technology to its limits.

It is not yet part of routine clinical oncology.

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The new drugs give patients a few precious months of extra life, but they do not cure the cancer.

Sooner or later resistant clones emerge. They are also exceedingly expensive, especially the monoclonal antibodies.

The amount of money that can give one cancer patient 6 more months of life might confer far more benefit to more people if used, say, in mental health.

The future may lie with combination treatments

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As with cancer, physicians treating HIV patients face a highly mutable adversary that can quickly mutate to resistance against any individual antiviral drug.

However, combination treatments (highly-active antiretroviral therapy, HAART) are much more successful because it is very unlikely that one virus molecule could simultaneously mutate to become resistant to two or three drugs that attack different vulnerabilities in the virus.

In the same way, combinations of targeted drugs can have a much greater therapeutic effect than single drugs.

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