SUMMARY
Traditional phase I dose-finding strategies monitor drug response only for two weeks, based on the assumption
that it will suffice to observe how therapy affects doubling time of a homogeneous population over 2-4
generations. But with the paradigm shift that most cancers are heterogeneous comes an urgent need to consider
that therapy-induced shifts in population composition manifest over longer time frames. We previously coined
the “tip-over hypothesis of DNA damage therapy sensitivity”, proposing that cytotoxic therapy is effective if it pushes
a cell’s somatic copy number alteration (SCNA) load above a tipping point. Variable proximity of co-existing
tumor cells to this tipping point imply that dose-response relations need not be monotonic. Cytotoxic therapy can
drive one cell into apoptosis, while skyrocketing another cell into malignant proliferation. As the developers of
widely used computational and mathematical methods, with established research programs in tumor
metabolism, and with a broad record of modeling dynamic processes and integrating various omics- and
imaging platforms, our team brings complementary expertise to develop a personalized cytotoxic therapy strategy
that confines therapy-induced selection of resistant clones. We will test the potential of tumor cell DNA content and
dNTP substrate availability to predict a tumor’s vulnerability to increasing SCNA rate. Hereby, the
aforementioned tipping point is accounted for not by elevated SCNA load alone, but by an inability of the tissue
micro-environment (TME) to provide the necessary resources. Experiments are proposed in stomach and brain
tumors—two cancer types whose TME can “afford” vastly different amounts of DNA. Our preliminary studies
show that energetic costs of DNA content levels required for >75% SCNA load do not, in the absence of cytotoxic
therapy, justify the masking benefits they bring. In particular, we showed that limiting dNTP concentrations
amplify divergence in S-phase duration between high- and low-ploidy cells. Our hypothesis is that cytotoxic
therapy causes a net-increase in fitness of tumors that exceed the SCNA tipping point. This hypothesis is
founded on two unexpected recent findings: (i) integrated single-cell RNA- and DNA-sequencing analyses of
stomach cancer cells suggests that the risk of cell death immediately after an SCNA event, rather than just SCNA
rate, impacts clonal diversity. Aim 1 will integrate single cell sequencing with imaging and mathematical
modeling of heterogeneous populations that evolve through chromosome missegregations, to examine observed
SCNA landscapes and missegregation tolerances, and to predict effective cytotoxic therapy doses. (ii) Even
minimal changes in DNA content among co-existing clones within the same Glioblastoma can result in
significantly longer S-phases. Aim 2 will evaluate Oxygen, Phosphate and Glucose as rate-limiting substrates of
dNTP synthesis of co-evolving subpopulations in stomach and brain tissue environments. This is the first study
to investigate if and how clinical decisions can benefit from integrating a tumor environment’s energetic
provision with the energetic demands of cancer cells’ genomic makeup.