Clonal Heterogeneity and Tumor Evolution: Past, Present, and the Future

Intratumor heterogeneity, which fosters tumor evolution, is a key challenge in cancer medicine. Here, we review data and technologies that have revealed intra-tumor heterogeneity across cancer types and the dynamics, constraints, and contingencies inherent to tumor evolution. We emphasize the importance of macro-evolutionary leaps, often involving large-scale chromosomal alterations, in driving tumor evolution and metastasis and consider the role of the tumor microenvironment in engendering heterogeneity and drug resistance. We suggest that bold approaches to drug development, harnessing the adaptive properties of the immune-microenvironment while limiting those of the tumor, combined with advances in clinical trial-design, will improve patient outcome

Neoantigen quality, not quantity

Prioritizing expressed clonal neoantigens in genes required for cancer cell survival may reduce the likelihood of resistance to neoantigen therapies

Cancer Genome Evolutionary Trajectories in Metastasis

Metastatic cancer is a major cause of death and remains largely incurable. A better understanding of metastasis is therefore desperately needed to improve prognosis for late-stage disease. Here we survey the landscape of studies exploring the genomics of metastatic cancer. We consider evidence for genomic drivers of metastasis and explore studies investigating modes of metastatic spread

Genetic and non-genetic clonal diversity in cancer evolution

The observation and analysis of intra-tumour heterogeneity (ITH), particularly in genomic studies, has advanced our understanding of the evolutionary forces that shape cancer growth and development. However, only a subset of the variation observed in a single tumour will have an impact on cancer evolution, highlighting the need to distinguish between functional and non-functional ITH. Emerging studies highlight a role for the cancer epigenome, transcriptome and immune microenvironment in functional ITH. Here, we consider the importance of both genetic and non-genetic ITH and their role in tumour evolution, and present the rationale for a broad research focus beyond the cancer genome. Systems-biology analytical approaches will be necessary to outline the scale and importance of functional ITH. By allowing a deeper understanding of tumour evolution this will, in time, encourage development of novel therapies and improve outcomes for patients

Therapy sculpts the complex interplay between cancer and the immune system during tumour evolution

Cancer development is an evolutionary process. A key selection pressure is exerted by therapy, one of the few players in cancer evolution that can be controlled. As such, an understanding of how treatment acts to sculpt the tumour and its microenvironment and how this influences a tumour’s subsequent evolutionary trajectory is critical. In this review, we examine cancer evolution and intra-tumour heterogeneity in the context of therapy. We focus on how radiotherapy, chemotherapy and immunotherapy shape both tumour development and the environment in which tumours evolve and how resistance can develop or be selected for during treatment

SnapShot: Tumor evolution

Understanding how tumors grow and evolve over time is crucial to help shed light on the underlying reasons why treatments fail and tumors metastasize. This SnapShot provides a brief introduction into the main concepts of tumor evolution

Understanding the impact of immune-mediated selection on lung cancer evolution

Summary Understanding how a tumour evolves and avoids immune recognition is paramount to improving cancer immunotherapy and patient outcome. Here we examine our recent integration of multi-region genomic, transcriptomic, epigenomic, pathology, and clinical data, highlight the need for a systematic examination of immune escape mechanisms, and discuss implications for immunotherapy approaches

Germline ancestry influences the evolutionary disease course in lung adenocarcinomas

Evolutionary Applications published by John Wiley & Sons Ltd Precision medicine relies on targeting specific somatic alterations present in a patient's tumor. However, the extent to which germline ancestry may influence the somatic burden of disease has received little attention. We estimated the genetic ancestry of non-small-cell lung cancer (NSCLC) patients and performed an in-depth analysis of the influence of genetic ancestry on the evolutionary disease course. Compared with European Americans (EA), African Americans (AA) with lung adenocarcinoma (LUAD) were found to be significantly younger and smoke significantly less. However, LUADs from AAs exhibited a significantly higher somatic mutation burden, with a more pronounced tobacco carcinogen footprint and increased frequencies of alterations affecting cancer genes. Conversely, no significant differences were observed between lung squamous cell carcinomas (LUSC) from EAs and AAs. Our results suggest germline ancestry influences the somatic evolution of LUAD but not LUSC

deconstructSigs: delineating mutational processes in single tumors distinguishes DNA repair deficiencies and patterns of carcinoma evolution

BACKGROUND: Analysis of somatic mutations provides insight into the mutational processes that have shaped the cancer genome, but such analysis currently requires large cohorts. We develop deconstructSigs, which allows the identification of mutational signatures within a single tumor sample. RESULTS: Application of deconstructSigs identifies samples with DNA repair deficiencies and reveals distinct and dynamic mutational processes molding the cancer genome in esophageal adenocarcinoma compared to squamous cell carcinomas. CONCLUSIONS: deconstructSigs confers the ability to define mutational processes driven by environmental exposures, DNA repair abnormalities, and mutagenic processes in individual tumors with implications for precision cancer medicine

Multiple Instance Learning for Heterogeneous Images: Training a CNN for Histopathology

Multiple instance (MI) learning with a convolutional neural network enables end-to-end training in the presence of weak image-level labels. We propose a new method for aggregating predictions from smaller regions of the image into an image-level classification by using the quantile function. The quantile function provides a more complete description of the heterogeneity within each image, improving image-level classification. We also adapt image augmentation to the MI framework by randomly selecting cropped regions on which to apply MI aggregation during each epoch of training. This provides a mechanism to study the importance of MI learning. We validate our method on five different classification tasks for breast tumor histology and provide a visualization method for interpreting local image classifications that could lead to future insights into tumor heterogeneity