Evolutionary Behavior in CLL: Implications for Personalized Therapy

Background on Previous Research

Prior studies by Dr. Landau and other groups, using a mixture of cells from the whole cancer population in each patient, demonstrated a genetic and epigenetic heterogeneity to CLL, reflecting its clinical heterogeneity. These studies showed that CLL evolves over the natural course of the disease as well as during therapeutic intervention. This so-called “clonal evolution” is a salient feature of CLL as well as other cancers. The ancestral cell (either the founding clone or the earliest cell in the lineage that can be identified), produces progeny (offspring) that may change over time. These changes result in the development of subclonal groups defined by different characteristics. Therefore, researchers can infer the origin of the ancestor by mapping out a “family tree” of the CLL-associated oncogenic driver mutations found in the population of CLL cells. Mutations that are found in the whole population have the oldest history and thus are found in the earliest CLL cell population that is still present in the patient; likewise, mutations that are found in only a portion of these cells, called subclonal mutations, cannot have been initial drivers of the disease (though they may be current oncogenic drivers in that subclonal population). The information that forms the family tree of these clones and subclones, derived from a sophisticated computational analysis of every mutation found in all of a patient’s cancer cells, has implications on the future clinical course of that patient’s disease as well as potential therapeutic options.

Sampling a particular patient’s cancer cells over time shows clonal evolution in action. The founding cancer clone will develop new mutations, creating new cancer cell subclones, with potentially different characteristics, with each new mutation. The resulting heterogeneity of the clonal population sometimes strengthens the disease, making it more difficult to treat, and it can also result in the dominance of a certain subclone (or group of subclones) at the expense of inferior subclones. In addition, a change in the cells’ environment may lead to epigenetic changes, called epimutations, in some of the cells, which result in an outgrowth of one or more subclones that are fitter than others, causing further variety in the clonal population, sometimes changing or perpetuating this clonal hierarchy. Environmental changes may sometimes be caused by therapeutic intervention. When this occurs, therapy may change the evolutionary trajectory of cancer cells, which would directly impact the response seen in that patient. CLL clones that have mutations allowing them to resist a particular therapy may make up a tiny fraction of the cancer cell population in early stages, but under therapeutic pressure, one or more of these subclones may come to become dominant and eventually make up a large portion of the population. These newly dominant clones may have other oncogenic drivers that are targetable by current drugs or, possibly, future drugs. Therefore, a more complete understanding of the genetic and epigenetic makeup of each patient’s cancer burden as well as its clonal evolution is critical to advancing personalized therapeutic approaches.

New Developments from the Landau Lab

Though prior studies have greatly increased our understanding of CLL genomics, epigenomics, and evolution in individual patients, most studies relied on sampling mixtures of cells from the whole cancer. Dr. Landau and an international group of colleagues sought to understand the history of CLL in individual patients at the single-cell level to get a more complete picture of CLL. This study was published online in Nature on May 15, 2019.  Dr. Landau is the senior author, and, reflecting the collaborative nature of this research, Drs. Federico Gaiti and Ronan Chaligne from Weill Cornell Medicine and Dr. Hongcang Gu of the Broad Institute are all credited with first authorship.

What do epimutations look like in CLL cells?

Dr. Landau and colleagues compared the rates of accumulation of epimutations (in this case, changes to the DNA called methylation) in single CLL cells to this rate in a close cell lineage control group of normal B cells from healthy individuals. They found that the epimutation rate is higher in CLL cells than in normal B cells. This shows that CLL cells have a higher capacity to generate epimutations. These studies also suggest that all the CLL cells in a given patient have a common origin.

The epimutations are not equally distributed in the genome. Since epimutations can increase or decrease each gene’s transcription, it was important to look at how genes are affected by epimutations. “Housekeeping genes” that are essential for cellular function have low epimutation rates. Similarly, genes likely essential for the CLL oncogenic characteristics also have low epimutation rates. However, other genes, including those that may facilitate the CLL cell’s survival if altered, have high epimutation rates. This indicates that a gene’s epimutation rate correlates to its role in the cell and demonstrates that genes that are nonessential, but potentially beneficial, to the CLL cell are subject to greater evolutionary pressures.

How do epimutations affect gene transcription?

To understand this, Dr. Landau’s group next looked at gene transcription by single-cell analysis. They showed that higher epimutation rates are associated with greater transcriptional entropy; that is, groups of cells with higher epimutation rates have greater dissimilarity when compared to other cells in that group. Generally, more methylation changes (a type of epimutation) close to a gene’s transcriptional regulatory region result in reduced transcription of that gene, and since Dr. Landau found that there is variability in DNA methylation when comparing different single CLL cells, there is variability in the transcription of these associated genes. Thus, differential methylation is associated with more randomness in transcription across the different CLL cells in a single patient.

What information can the epimutation rate provide about the history of the cells?

The epimutation rate also serves as an evolutionary molecular clock. Dr. Landau and colleagues used this concept to construct a family tree of all the CLL cells in a patient. The branching patterns of the family trees from multiple patients suggest a rapid evolutionary process after the emergence of the ancestral clone. The shape of these family trees also suggests that CLL does not likely have significant stem cell characteristics, which present a difficult therapeutic target in other cancers, and may therefore be generally easier to eradicate than cancers characterized by stem cells. Furthermore, the family tree shape reflects a longer proliferative history for each CLL cell, and this is correlated to an increased capacity for more epimutations.

One well-known oncogenic driver for CLL is SF3B1, which is thought to be mutated in subclones found later in the disease course, making it a logical tool for understanding the timing of mutant acquisition. Using known parameters of CLL proliferation as well as family tree construction, Dr. Landau was able to estimate that the acquisition of the SF3B1 mutation in one patient occurred 2,180 +/- 219 days after creation of the ancestral clone. The ability to understand the timing of oncogenic driver acquisition may one day improve the therapeutic choices available to treat each CLL patient.

Can CLL cell lineages also be affected by therapeutic intervention?

Since therapy may affect the clonal evolution of CLL cells, Dr. Landau and his group constructed family trees of patients undergoing treatment with the targeted therapy Imbruvica® (ibrutinib). CLL cells exist in both the blood and the lymph node, and ibrutinib treatment forces CLL cells in the lymph node to migrate to the blood. Blood samples from patients taken just before treatment started were compared to blood samples taken after one month of treatment. The cells taken after treatment have different family trees as well as different gene expression patterns. This is likely due to the recent introduction into the blood of CLL cells from the lymph node, which are more proliferative than CLL cells typically found in the blood. It is also possible that the CLL cells that migrated out of the lymph node differ from the CLL cells that remained in the lymph node, though that question needs to be answered with further experimentation.

Significance of the New Developments

The largely random nature of the acquisition of epimutations in CLL cells results in a heterogeneity of expression of potentially a large number of genes. The various CLL cells may be thought of as “sampling” their environment to find the fittest genetic/epigenetic patterns for that particular circumstance, resulting in the outgrowth of those clones that are best adapted to that environment. Since the environment may change, particularly with therapy, the fittest subclones in the heterogeneous population may then be able to withstand the therapy. This is the nature of cancer – as the patient-and-doctor team seek to kill the cancer cells, the cancer cells “seek” to survive.

Since the cancer cells continuously seek to evolve and adapt to their changing environment, researchers and physicians need to better understand how this happens. The genetic and epigenetic architecture of a patient’s cancer will, in future years, provide insight as to the best way to treat the patient and, potentially, to the problems the patient may encounter. For example, a comprehensive analysis of a patient’s cancer may indicate a tiny subclonal population containing mutations that are known to confer resistance to ibrutinib. Thus, though ibrutinib may initially kill the majority of the cancer cells, knowing that ibrutinib-resistant clones exist in the patient will allow doctors to anticipate the outgrowth of these clones with ibrutinib treatment.

In addition, Dr. Landau’s current study shows that other oncogenic drivers found in some subclones are increased in the blood samples from ibrutinib-treated patients. These drivers could be targetable in patients undergoing ibrutinib therapy, which may lead to better patient outcomes. Therefore, a comprehensive understanding of the subclonal characteristics in a given patient should result in better care for each patient and, perhaps, a cure.