Researchers at\u00a0Moffitt Cancer Center\u00a0have developed a new way to predict how cancer cells evolve by gaining and losing whole chromosomes,\u00a0changes that help tumors grow,\u00a0adapt\u00a0and resist treatment.\u00a0<\/p>\n
In a study\u00a0published in\u00a0Nature Communications,\u00a0scientists\u00a0describe a computational approach called ALFA-K that uses longitudinal, single-cell data to reconstruct how cancer cells move through different chromosome states over time and\u00a0identify\u00a0which configurations are favored by evolution.\u00a0<\/p>\n
The findings show that cancer evolution is not random. Instead, tumors follow measurable rules shaped by chromosome configuration, evolutionary\u00a0dynamics\u00a0and treatment-related stress,\u00a0offering a new framework for\u00a0anticipating\u00a0how cancers change and respond to therapy.\u00a0<\/p>\n
Q&A with\u00a0Noemi Andor, Ph.D.,\u00a0corresponding\u00a0author and\u00a0associate member\u00a0in the\u00a0Integrated Mathematical Oncology Program\u00a0at Moffitt.\u00a0<\/p>\n
What problem were you trying to solve with this research, and why does it matter for understanding cancer?\u00a0<\/p>\n
Cancer evolves. As tumors grow, their cells constantly make mistakes when copying and dividing their DNA. Many of those mistakes involve gaining or losing whole chromosomes. This creates a mix of cancer cells with different chromosome combinations inside the same tumor.\u00a0<\/p>\n
The problem was that researchers had no reliable way to\u00a0determine\u00a0which of those combinations\u00a0help\u00a0cancer cells survive. The number of\u00a0possible chromosome\u00a0states is enormous, and most existing approaches could only capture snapshots in time or average behavior across many cells.\u00a0<\/p>\n
ALFA-K was developed to solve this problem by using longitudinal, single-cell data to reconstruct how cancer cells move through chromosome states over\u00a0time\u00a0and which states are favored by evolution. Without that understanding, cancer progression and treatment resistance can appear unpredictable. Our work\u00a0shows\u00a0they follow measurable rules.\u00a0<\/p>\n
Why are chromosome changes so important for tumor growth and treatment response?\u00a0<\/p>\n
Chromosomes\u00a0contain\u00a0hundreds or thousands of genes. When a cancer cell gains or loses a chromosome, it changes the dosage of many genes at once. This can\u00a0immediately\u00a0alter how the cell grows,\u00a0divides\u00a0or responds to stress.\u00a0<\/p>\n
These changes allow cancer cells to make large evolutionary jumps rather than small adjustments. They also create diversity within a tumor, which increases the chances that some cells survive treatment.\u00a0<\/p>\n
Importantly, the effects of these changes depend on a cell’s existing chromosome makeup. The same chromosome change can be helpful in one context and harmful in another. This context dependence helps explain why cancer evolution has been so difficult to predict.\u00a0<\/p>\n
How is ALFA-K different from\u00a0previous\u00a0tools, and what does it allow researchers to do?\u00a0<\/p>\n
Before ALFA-K, chromosome changes were often assumed to have fixed effects. Researchers sometimes\u00a0treated\u00a0gaining or losing a chromosome as always beneficial or always harmful. Real cancer evolution is more complex.\u00a0<\/p>\n
ALFA-K tracks thousands of individual cells over time,\u00a0accounting\u00a0for ongoing chromosome instability and reconstructs local fitness landscapes. These landscapes describe how\u00a0advantageous\u00a0or harmful a chromosome change is\u00a0given\u00a0a\u00a0cell’s current chromosome configuration.\u00a0<\/p>\n
The tool also shows that the rate of chromosome errors matters. When chemotherapy increases chromosome mis-segregation, cancer cells move across these landscapes more quickly. Depending on the landscape’s shape, this can push tumors toward chromosome states that are more tolerant of instability.\u00a0<\/p>\n
In this study, ALFA-K estimated the fitness of more than 270,000 distinct chromosome configurations. This made it possible to ask questions that were previously inaccessible.\u00a0<\/p>\n
What does whole-genome\u00a0doubling\u00a0mean, and why is it an important finding?\u00a0<\/p>\n
Whole-genome doubling occurs when a cell copies\u00a0all of\u00a0its chromosomes.\u00a0Previous\u00a0research showed that this can help cancer cells survive, but there was no way to measure how much protection it provides.\u00a0<\/p>\n
ALFA-K allows researchers to quantify that buffering effect. The method measures how much more tolerant genome-doubled cells are to chromosome mistakes compared with non-doubled cells.\u00a0<\/p>\n
This matters because buffering is not all or nothing. There is a threshold at which genome doubling becomes\u00a0advantageous. By quantifying that threshold, ALFA-K turns genome doubling from a descriptive observation into a predictable evolutionary event.\u00a0<\/p>\n
How could this research eventually help guide cancer treatment?\u00a0<\/p>\n
ALFA-K shifts cancer research from describing what tumors look like to predicting how they will evolve.\u00a0<\/p>\n
In the future, this approach could help doctors interpret repeat biopsies,\u00a0identify\u00a0when a tumor is approaching a dangerous evolutionary\u00a0transition\u00a0and choose treatments that limit cancer’s ability to explore harmful chromosome configurations.\u00a0<\/p>\n
The long-term goal is evolution-aware cancer therapy. This approach aims to\u00a0anticipate\u00a0how tumors will change rather than reacting after resistance has already\u00a0emerged.\u00a0<\/p>\n
This study was supported by\u00a0the National Cancer Institute (1R37CA266727-01A1,\u00a01R21CA269415-01A1,\u00a01R03CA259873-01A1).\u00a0<\/p>\n
Source:<\/p>\n
H. Lee Moffitt Cancer Center & Research Institute<\/a><\/p>\n Journal reference:<\/p>\n