Dark selection and ruxolitinib resistance in myeloid neoplasms

I am weathering the US election in Tampa, Florida. For this week, I am back at the Moffitt Cancer Center to participate in the 6th annual IMO Workshop. The 2016 theme is one of the biggest challenges to current cancer treatment: therapy resistance. All five teams participating this year are comfortable with the evolutionary view of cancer as a highly heterogeneous disease. And up to four of the teams are ready to embrace and refine a classic model of resistance. The classic model that supposes that:

  • treatment changes the selective pressure on the treatment-naive tumour.
  • This shifting pressure creates a proliferative or survival difference between sensitive cancer cells and either an existing or de novo mutant.
  • The resistant cells then outcompete the sensitive cells and — if further interventions (like drug holidays or new drugs or dosage changes) are not pursued — take over the tumour: returning it to a state dangerous to the patient.

Clinically this process of response and relapse is usually characterised by a (usually rapid) decrease in tumour burden, a transient period of low tumour burden, and finally a quick return of the disease.

But what if your cancer isn’t very heterogeneous? What if there is no proliferative or survival differences introduced by therapy among the tumour cells? And what if you don’t see the U curve of tumour burden? But resistance still emerges. This year, that is the paradox facing team orange as we look at chronic myelomonocytic leukemia (CMML) and other myeloid neoplasms.

CMML is a leukemia that usually occurs in the elderly and is the most frequent myeloproliferative neoplasm (Vardiman et al., 2009). It has a median survival of 30 months, with death coming from progression to AML in 1/3rd of cases and cytopenias in the others. In 2011, the dual JAK1/JAK2 inhibitor ruxolitinib was approved for treatment of the related cancer of myelofibrosis based on its ability to releave the symptoms of the disease. Recently, it has also started to see use for CMML.

When treating these cancers with ruxolitinib, Eric Padron — our clinical leader alongside David Basanta and Andriy Marusyk — sees the drastic reduction and then relapse in symptoms (most notably fatigue and spleen size) but none of the microdynamical signs of the classic model of resistance. We see the global properties of resistance, but not the evidence of selection. To make sense of this, our team has to illuminate the mechanism of an undetected — dark — selection. Once we classify this microdynamical mechanism, we can hope to refine existing therapies or design new therapies to adapt to it.

Heterogeneity in cancer is a difficult quantity to measure, yet — under most metrics — leukaemias are particularly homogenous. It is one of the reasons some other leukaemias have seen the development of targetted drugs to treat them like imatinib for chronic myeloid leukemia (CML). Unfortunately, CMML does not have such a wonder drug despite its homogeneity. To get an idea of the genetic homogeneity of CMML, it is worth looking at the number of somatic missense mutations per exome in the figure below.

Number of somatic mutations per exome by cancer. Box posts show 25% to 75% intervals for box and 5% to 95% for whiskers. Notice that CMML and JMML are in an inset at rates much lower than other cancers.

Number of somatic mutations per exome by cancer. Box posts show 25% to 75% intervals for box and 5% to 95% for whiskers. Figure 1a from Ball, List & Padron (2006) with data from Verstovsek et al. (2010), Stieglitz et al. (2015), & Merlevede et al. (2016).

Notice that CMML and its’ juvenile form — JMML — are in an inset with mutation numbers much lower than other cancers. In general, CMML is considered to be a genetically homogeneous cancer (Grossmann et al., 2010; Ball, List, & Padron, 2016), and although some mutations — like RUNX1, NRAS, and SETB1 — have prognostic relevance (Elena et al., 2016), the most common mutations — like TET2 and SRSF2 — are simply associated with the defining feature of monocytosis (Malcovati et al, 2014). However, the clinical effects of CMML and its treatment — including resistance — are far less homogeneous, suggesting that we need to look beyond genetic factors to epigenetic and cytokinetic effects (Ball, List, & Padron, 2016).

This point is reinforced by tracking the genotype of the tumour as it first responds to therapy and then relapses. Merlevede et al. (2016) used a combination of whole-exome & whole-genome sequencing to show that the mutation allele burden and clonal architecture of the bone marrow in CMML remained unchanged during treatment. This suggests that both the total tumour burden and proportional genetic composition remained static while resistance arose. Thus, it is unlikely that the genotype is acting as the unit of selection for ruxolitinib resistance in myeloid neoplasms.

Instead, team orange has to turn to other units of selection. Units that can propagate epigenetically against an unchanging genetic background. The most tempting candidates are in the cytokine network, which is known to be a factor in the symptoms of CMML. In particular, heterodimeric activation of the JAK-STAT pathway — when the second JAK2 at the cytokine receptor is replaced by a JAK1 or TYK2 (Koppikar et al., 2012) — seems like a promising candidate for the unit of this dark selection. I am looking forward to playing with models of this sort of cytogenetic resistance, and hopefully at the end of the week I will have good news to report on the progress of Team Orange.

References

Ball M, List AF, & Padron E (2016). When clinical heterogeneity exceeds genetic heterogeneity: thinking outside the genomic box in chronic myelomonocytic leukemia. Blood PMID: 27707735

Elena, C., Gallì, A., Such, E., Meggendorfer, M., Germing, U., Rizzo, E., … & Ambaglio, I. (2016). Integrating clinical features and genetic lesions in the risk assessment of patients with chronic myelomonocytic leukemia. Blood, 128(10): 1408-1417.

Grossmann, V., Kohlmann, A., Eder, C., Cross, N. C., Haferlach, C., Kern, W., … & Schnittger, S. (2010). Analyses of 81 chronic myelomonocytic leukemia (CMML) for EZH2, TET2, ASXL1, CBL, KRAS, NRAS, RUNX1, IDH1, IDH2, and NPM1 revealed mutations in 86.4% of all patients with TET2 and EZH2 being of high prognostic relevance. Blood, 116(21): 296-296.

Koppikar, P., Bhagwat, N., Kilpivaara, O., Manshouri, T., Adli, M., Hricik, T., … & Leung, L. (2012). Heterodimeric JAK-STAT activation as a mechanism of persistence to JAK2 inhibitor therapy. Nature, 489(7414), 155-159.

Malcovati, L., Papaemmanuil, E., Ambaglio, I., Elena, C., Gallì, A., Della Porta, M. G., … & Bono, E. (2014). Driver somatic mutations identify distinct disease entities within myeloid neoplasms with myelodysplasia. Blood, 124(9): 1513-1521.

Merlevede, J., Droin, N., Qin, T., Meldi, K., Yoshida, K., Morabito, M., … & Itzykson, R. (2016). Mutation allele burden remains unchanged in chronic myelomonocytic leukaemia responding to hypomethylating agents. Nature Communications, 7.

Stieglitz, E., Taylor-Weiner, A. N., Chang, T. Y., Gelston, L. C., Wang, Y. D., Mazor, T., … & Rosenberg, M. (2015). The genomic landscape of juvenile myelomonocytic leukemia. Nature Genetics.

Vardiman, J. W., Thiele, J., Arber, D. A., Brunning, R. D., Borowitz, M. J., Porwit, A., … & Bloomfield, C. D. (2009). The 2008 revision of the World Health Organization (WHO) classification of myeloid neoplasms and acute leukemia: rationale and important changes. Blood, 114(5): 937-951.

Verstovsek, S., Kantarjian, H., Mesa, R. A., Pardanani, A. D., Cortes-Franco, J., Thomas, D. A., … & Vaddi, K. (2010). Safety and efficacy of INCB018424, a JAK1 and JAK2 inhibitor, in myelofibrosis. New England Journal of Medicine, 363(12): 1117-1127.

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About Artem Kaznatcheev
From the Department of Computer Science at Oxford University and Department of Translational Hematology & Oncology Research at Cleveland Clinic, I marvel at the world through algorithmic lenses. My mind is drawn to evolutionary dynamics, theoretical computer science, mathematical oncology, computational learning theory, and philosophy of science. Previously I was at the Department of Integrated Mathematical Oncology at Moffitt Cancer Center, and the School of Computer Science and Department of Psychology at McGill University. In a past life, I worried about quantum queries at the Institute for Quantum Computing and Department of Combinatorics & Optimization at University of Waterloo and as a visitor to the Centre for Quantum Technologies at National University of Singapore. Meander with me on Google+ and Twitter.

6 Responses to Dark selection and ruxolitinib resistance in myeloid neoplasms

  1. Pingback: Dark selection in CMML – CancerEvo

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