TrendsTalk

Special Issue: Quantitative Cancer Biology

Integrating Quantitative Approaches in Cancer Research and Oncology

Deconvoluting the Complexity of Cancer Depends on Understanding the Dynamics of Dysregulated Information Flow across Biological Scales

Anna D. Barker, PhD

University of Southern California, Los Angeles, CA, USA and Arizona State University, Tempe, AZ, USA

Cancer comprises many ‘agents’ in a self-organizing complex adaptive system (CAS) that generally operates via simple rules at scale, exhibits redundancy, and operates far from equilibrium at the edge of chaos. Viewing and studying cancer as a CAS requires that the investigator understand that these elements may function somewhat independently or together to

Cancer Biology and Treatment: Is the Obvious Answer also Correct?

Robert Gatenby, MD

H. Lee Moffitt Cancer Center, Tampa, FL, USA

In 1756, Benjamin Franklin’s plan to view a lunar eclipse was disrupted when a violent nor’easter (i.e., a storm with winds coming from the north east) struck Philadelphia. Franklin, like all scientists of his time, assumed that the wind carried the storm so that his brother in Boston would similarly have missed the eclipse. He was shocked to learn the storm actually arrived in Boston after the eclipse, leading him to

Mathematical Models and Experimental Data: Chicken or Egg?

Stacey D. Finley, PhD

University of Southern California, Los Angeles, CA, USA

I work in the field of mathematical oncology, where my research group develops mechanistic mathematical models to understand cancer. These models not only establish correlative relationships between tumor characteristics and response to treatment, but also uncover why those relationships exist and how they can be exploited. Mathematical modeling is a much-needed approach to study cancer, given the

Where Is the EMT? Computer Vision at the Frontline of Precision Medicine

Susan E. Leggett, PhD and Celeste M. Nelson, PhD

Princeton University, Princeton, NJ, USA

In the era of new and rapidly evolving digital technologies, advances in image analysis have given new meaning to the adage, ‘A picture is worth a thousand words’. Computer vision enables the extraction of vast numbers of quantifiable metrics from digital images, which can be interpreted in high-dimensional space via advances in data science (dimensionality reduction, machine learning, etc.).

Forecasting Tumor Progression

Christina Curtis, PhD, MSc

Stanford University School of Medicine, Stanford, CA, USA

Cancers are the product of somatic evolutionary processes, fueled by the acquisition of mutations and mediated by cellular interactions within a structured tissue microenvironment.

Adaptation and evolution within such genetically and phenotypically heterogeneous populations limits therapeutic efficacy and cancer control. Mathematical and computational models are powerful tools to investigate the

Staying One Step Ahead: How Predictive Modeling of Tumor Progression Can Delay Therapeutic Resistance

Deepti Mathur, PhD and Joao B. Xavier, PhD

Memorial Sloan Kettering Cancer Center, New York, NY, USA

One of the greatest clinical challenges in cancer today is resistance to therapy. If a heterogeneous tumor contains even one resistant clone or the probability of generating a resistance mutation is nonzero, tumor recurrence is theoretically inevitable: the question becomes not if but when will a tumor come back resistant. Despite extraordinary efforts, new therapies for common

Advances in Computation Herald a New ‘Golden Era’ for Biology and Medicine

Andrea Califano, Dr

Columbia University Medical Center, New York, NY, USA

Many of today’s quantitative sciences, from economics to physics to meteorology, have gone through a ‘golden era’ during which they have experienced a profound transformation from a mostly empirical to an almost completely analytical formulation. The most revealing element of that transformation has been the ability to supplement the classical inductive processing based on experimental evidence with

Artificial Naturalism: Coevolving Pathology and Artificial Intelligence

Simon P. Castillo, PhD and Yinyin Yuan, PhD

The Institute of Cancer Research, London, UK

Our goal is to improve the treatment response and prognosis of patients with cancer; the trajectory to that goal might also be a goal, and it requires us all. At the Centre for Evolution and Cancer, we aim to understand how the evolutionary trajectory of cells within a tumor co-emerges with their diverse environmental contexts or local ecology. Quantitative tools following cell detection and

Cancer Is a Window on the Past

Paul Davies, PhD

Arizona State University, Tempe, AZ, USA

Cancer or cancer-like phenomena are found in almost all mammals, as well as birds, fish, insects, plants, fungi, and corals. The pervasive nature of cancer implies that it has deep evolutionary roots, stretching back to the dawn of multicellularity over 1 billion years ago. Unicellular organisms are effectively immortal in that they just replicate whenever they can. However, multicellular life outsources a vestige of