Footsteps of the Elephant

Most followers of cancer molecular biology will know a bit about epigenetics. It’s something of an elephant in the cancer room — the way in which DNA is modified by tags, small molecules attached to DNA or histone proteins, that modify local genome activity without changing DNA sequence. For now that’s all we need but if you want a bit more background, revisit Mapping the Methylome.

With a deal of perspiration and ingenuity a number of methods have been devised for mapping the methylation status  of the genome — i.e. epigenetic sequencing. Which is great but what you get is a bit like dots showing stations on a subway map. Get off an ‘N and R’ train at Broadway and clamber up to daylight and you’ll find yourself standing on — yes, Broadway. But so what? Your map tells you nothing about why you might want to visit Broadway. Or, in epigenetic terms, what the effect might be of the tags in that region of the genome.

A different view

Epigenetic changes are reversible and their effect is to regulate gene expression by altering chromatin accessibility — chromatin being the mixture of DNA and proteins that form chromosomes. And they’re important in the context of this blog because their effects on gene expression have been implicated in driving cancer initiation, progression and metastasis. Step forward Nadezhda Terekhanova, Li Ding and friends from Washington University in St. Louis with a rapid and sensitive method for profiling the epigenome (transposase-accessible chromatin using sequencing — ATAC-seq).

Analysis of tissue samples from 11 cancer types to reveal gene expression patterns in normal and tumour samples (primary and metastatic). ATAC-seq reveals the accessibility (to regulatory proteins)  of bits of the genome and this is compared with the gene expression patterns from single cells or single nuclei (sc/sn). From Terekhanova et al., 2023.

The clever bit about this approach is that they coupled single nucleus ATAC-seq with RNA sequencing. This amazing combination of technologies enables profiling of the epigenome and transcriptome in the same individual cell. Result: a map of how chromatin accessibility affects gene expression!

Representation of the results for 9 cancer types that identified 56,001 tissue- and cancer-cell-specific differentially accessible chromatin regions (DACRs) by comparing each cancer type to all others. The bubble size shows the percentage of cancer cells with accessible DACRs and the colour conveys the log₂ of the fold-change. The gene names below the dots (FOXS1, etc)  are the nearest gene of each DACR. Genes are grouped by those shared between cancers and those specific to cancer types. Cancer types: pancreatic ductal adenocarcinoma (PDAC), colorectal cancer (CRC), multiple myeloma (MM), breast cancer (BRCA), ovarian cancer (OV), uterine corpus endometrial carcinoma (UCEC), glioblastoma (GBM), clear-cell renal cell carcinoma (ccRCC) and skin cutaneous melanoma (SKCM). From Terekhanova et al., 2023.

Behold the elephant in the room!!

The numbers are amazing, as are the results. By screening over 1 million cells for both enrichment of accessible chromatin regions, transcription factor motifs and regulons, Terekhanova et al. identified epigenetic drivers associated with diverse cancers. As you might predict, some of these appeared in multiple cancers (regulatory regions of ABCC1 and VEGFA; GATA6 and FOX-family motifs). Others were cancer specific (regulatory regions of FGF19, ASAP2 and EN1, and the PBX3 motif).

Sometimes it’s more revealing to look at signalling pathways than individual genes and it emerged that among epigenetically altered pathways, TP53, hypoxia and TNF signalling were linked to cancer initiation, whereas oestrogen response, epithelial–mesenchymal transition (the EMT) and apical junctions (regulating cell-cell signalling) became prominent in metastatic transition.

A further important, though again perhaps unsurprising, finding was that correlations between enhancer accessibility and gene expression revealed cooperation between epigenetic and genetic drivers (i.e. mutations).

This paper really is an example of the best science: a novel approach to a problem that advances an important area of cancer research by combining stunning technologies.

References

Terekhanova, N.V., Karpova, A., Liang, WW. et al. Epigenetic regulation during cancer transitions across 11 tumour types. Nature (2023). https://doi.org/10.1038/s41586-023-06682-5

Seeing the Future

One of the major problems currently faced by the field of cancer therapy is the difficulty of predicting how an individual will respond to treatment. This is especially true of breast cancer when chemotherapy is used before surgery — called neoadjuvant therapy — with the aim of increasing survival rates after breast-conserving surgery.

As ever, there’s been no shortage of effort. A number of studies have taken molecular and pathology data, looked for anything that might predict overall response and failed to come up with clear indicators. Followers of these pages will not be surprised because they’ve contained many pieces describing the emerging complexity of both tumours and adjacent cells and tissues. We’ve seen that the mutation load rises as tumours go from early stages to metastatic growths (What’s New in Breast Cancers?, Family Tree of Breast Cancer). We’ve looked at snapshots of tissues that reveal the staggering complexity in terms of different types of cell coming and going in the tumour locale (the tumour microenvironment, TME in On The Slippery Slopes of Tumours). We’ve also seen how the tumour immune landscape can be perturbed in obesity by a metabolic tug of war between tumour and immune system cells (T cells) for circulating fats (Still Time for a Resolution).

Most arresting are the astonishing results of Keren et al. (Mosaic Masterpieces) who showed that in a large number of tissue sections of triple-negative breast cancers it was possible to pick out 36 different cell types — and that no two tumour slices showed the same make-up. Despite this bewildering variation they did manage to tease out some trends that related cell types in the TME to patient survival.

If at first …

Stephen-John Sammut, Carlos Caldas and colleagues from the Cancer Research UK Cambridge Institute have taken a further step by pulling together as much data as they could gather from 168 patient samples (this included DNA sequencing to reveal mutational features and profiling the patterns of immune cells, e.g., T lymphocytes). These patients were then either treated with chemotherapy against HER2 or not before surgery.

Overview of the study design. Pre-therapy breast tumours from 168 patients were profiled using DNA sequencing and RNA sequencing (RNA-seq) and digital pathology analysis. These data were integrated within machine learning models to predict responses. Responses were assessed using the Residual Cancer Burden classification — the amount of residual disease after neoadjuvant chemotherapy. sWGS = shallow whole-genome sequencing; WES = whole-exome sequencing. pCR = pathological complete response. RCB-I, II and III refer to increasing levels of residual disease. From Sammut et al., 2022.

The upshot of the mass of data is that the extent of residual disease after therapy was associated with pre-therapy features, including tumour mutational and copy number landscapes, tumour proliferation, immune infiltration and T cell dysfunction — notably exclusion from the TME. Thus higher immune cell infiltration (raised numbers of CD8 T cells) was associated with a complete response.

In addition, in HER-ve tumours the rate of proliferation is a key determinant of response to chemotherapy — usually associated with high mutation load and chromosome instability, as well as specific mutation in, e.g., TP53 and BRCA genes.

Unexpectedly, in HER2⁺ tumours treated with chemotherapy and HER2-targeted antibodies (Trastuzumab/Herceptin), responses appeared to be independent of proliferation. This is presumably connected with the fact that the most dangerous feature of cancer cells is when they become able to metastasize—migrate from their original sites and establish new tumours in other parts of the body. However for cells to become invasive they suspend cell division.

And the message

This work doesn’t solve the problem of uncertainty when treating tumours with drugs, either before surgery or more generally. But its considerable success in dissecting important factors that determine the outcome for a set of breast cancers is a major pointer to a future in which ineffective use of drugs can progressively be minimised.

Reference

Sammut, SJ., Crispin-Ortuzar, M., Chin, SF. et al. Multi-omic machine learning predictor of breast cancer therapy response. Nature 601, 623–629 (2022). https://doi.org/10.1038/s41586-021-04278-5