Spatial biology of cancer evolution.

The natural history of cancers can be understood through the lens of evolution given that the driving forces of cancer development are mutation and selection of fitter clones. Cancer growth and progression are spatial processes that involve the breakdown of normal tissue organization, invasion and metastasis. For these reasons, spatial patterns are an integral part of histological tumour grading and staging as they measure the progression from normal to malignant disease. Furthermore, tumour cells are part of an ecosystem of tumour cells and their surrounding tumour microenvironment. A range of new spatial genomic, transcriptomic and proteomic technologies offers new avenues for the study of cancer evolution with great molecular and spatial detail. These methods enable precise characterizations of the tumour microenvironment, cellular interactions therein and micro-anatomical structures. In conjunction with spatial genomics, it emerges that tumours and microenvironments co-evolve, which helps explain observable patterns of heterogeneity and offers new routes for therapeutic interventions.

Spatial genomics maps the structure, nature and evolution of cancer clones.

Genome sequencing of cancers often reveals mosaics of different subclones present in the same tumour1-3. Although these are believed to arise according to the principles of somatic evolution, the exact spatial growth patterns and underlying mechanisms remain elusive4,5. Here, to address this need, we developed a workflow that generates detailed quantitative maps of genetic subclone composition across whole-tumour sections. These provide the basis for studying clonal growth patterns, and the histological characteristics, microanatomy and microenvironmental composition of each clone. The approach rests on whole-genome sequencing, followed by highly multiplexed base-specific in situ sequencing, single-cell resolved transcriptomics and dedicated algorithms to link these layers. Applying the base-specific in situ sequencing workflow to eight tissue sections from two multifocal primary breast cancers revealed intricate subclonal growth patterns that were validated by microdissection. In a case of ductal carcinoma in situ, polyclonal neoplastic expansions occurred at the macroscopic scale but segregated within microanatomical structures. Across the stages of ductal carcinoma in situ, invasive cancer and lymph node metastasis, subclone territories are shown to exhibit distinct transcriptional and histological features and cellular microenvironments. These results provide examples of the benefits afforded by spatial genomics for deciphering the mechanisms underlying cancer evolution and microenvironmental ecology.

Spatial transcriptomics.

Spatial transcriptomics, with other spatial technologies, has enabled scientists to dissect the organization and interaction of different cell types within the tumor microenvironment. We asked experts to discuss some aspects of this technology from revealing the tumor microenvironment and heterogeneity, to tracking tumor evolution, to guiding tumor therapy, to current technical challenges.

Mechanisms of breast cancer metastasis.

Invasive breast cancer tends to metastasize to lymph nodes and systemic sites. The management of metastasis has evolved by focusing on controlling the growth of the disease in the breast/chest wall, and at metastatic sites, initially by surgery alone, then by a combination of surgery with radiation, and later by adding systemic treatments in the form of chemotherapy, hormone manipulation, targeted therapy, immunotherapy and other treatments aimed at inhibiting the proliferation of cancer cells. It would be valuable for us to know how breast cancer metastasizes; such knowledge would likely encourage the development of therapies that focus on mechanisms of metastasis and might even allow us to avoid toxic therapies that are currently used for this disease. For example, if we had a drug that targeted a gene that is critical for metastasis, we might even be able to cure a vast majority of patients with breast cancer. By bringing together scientists with expertise in molecular aspects of breast cancer metastasis, and those with expertise in the mechanical aspects of metastasis, this paper probes interesting aspects of the metastasis cascade, further enlightening us in our efforts to improve the outcome from breast cancer treatments.

Pervasive chromosomal instability and karyotype order in tumour evolution.

Chromosomal instability in cancer consists of dynamic changes to the number and structure of chromosomes1,2. The resulting diversity in somatic copy number alterations (SCNAs) may provide the variation necessary for tumour evolution1,3,4. Here we use multi-sample phasing and SCNA analysis of 1,421 samples from 394 tumours across 22 tumour types to show that continuous chromosomal instability results in pervasive SCNA heterogeneity. Parallel evolutionary events, which cause disruption in the same genes (such as BCL9, MCL1, ARNT (also known as HIF1B), TERT and MYC) within separate subclones, were present in 37% of tumours. Most recurrent losses probably occurred before whole-genome doubling, that was found as a clonal event in 49% of tumours. However, loss of heterozygosity at the human leukocyte antigen (HLA) locus and loss of chromosome 8p to a single haploid copy recurred at substantial subclonal frequencies, even in tumours with whole-genome doubling, indicating ongoing karyotype remodelling. Focal amplifications that affected chromosomes 1q21 (which encompasses BCL9, MCL1 and ARNT), 5p15.33 (TERT), 11q13.3 (CCND1), 19q12 (CCNE1) and 8q24.1 (MYC) were frequently subclonal yet appeared to be clonal within single samples. Analysis of an independent series of 1,024 metastatic samples revealed that 13 focal SCNAs were enriched in metastatic samples, including gains in chromosome 8q24.1 (encompassing MYC) in clear cell renal cell carcinoma and chromosome 11q13.3 (encompassing CCND1) in HER2+ breast cancer. Chromosomal instability may enable the continuous selection of SCNAs, which are established as ordered events that often occur in parallel, throughout tumour evolution.

Interacting with Members of the Public to Discuss the Impact of Food Choices on Climate Change-Experiences from Two UK Public Engagement Events.

Food systems contribute to up to 37% of global greenhouse gas emissions, and emissions are increasing. Since the emissions vary greatly between different foods, citizens' choices can make a big difference to climate change. Public engagement events are opportunities to communicate these complex issues: to raise awareness about the impact of citizens' own food choices on climate change and to generate support for changes in all food system activities, the food environment and food policy. This article summarises findings from our 'Take a Bite Out of Climate Change' stand at two UK outreach activities during July 2019. We collected engagement information in three main ways: (1) individuals were invited to complete a qualitative evaluation questionnaire comprising of four questions that gauged the person's interests, perceptions of food choices and attitudes towards climate change; (2) an online multiple-choice questionnaire asking about eating habits and awareness/concerns; and (3) a token drop voting activity where visitors answered the question: 'Do you consider greenhouse gases when choosing food?' Our results indicate whether or not people learnt about the environmental impacts of food (effectiveness), how likely they are to move towards a more climate-friendly diet (behavioural change), and how to gather information more effectively at this type of event.

Pan-cancer computational histopathology reveals mutations, tumor composition and prognosis.

We use deep transfer learning to quantify histopathological patterns across 17,355 hematoxylin and eosin-stained histopathology slide images from 28 cancer types and correlate these with matched genomic, transcriptomic and survival data. This approach accurately classifies cancer types and provides spatially resolved tumor and normal tissue distinction. Automatically learned computational histopathological features correlate with a large range of recurrent genetic aberrations across cancer types. This includes whole-genome duplications, which display universal features across cancer types, individual chromosomal aneuploidies, focal amplifications and deletions, as well as driver gene mutations. There are widespread associations between bulk gene expression levels and histopathology, which reflect tumor composition and enable the localization of transcriptomically defined tumor-infiltrating lymphocytes. Computational histopathology augments prognosis based on histopathological subtyping and grading, and highlights prognostically relevant areas such as necrosis or lymphocytic aggregates. These findings show the remarkable potential of computer vision in characterizing the molecular basis of tumor histopathology.

Timing the Landmark Events in the Evolution of Clear Cell Renal Cell Cancer: TRACERx Renal.

Clear cell renal cell carcinoma (ccRCC) is characterized by near-universal loss of the short arm of chromosome 3, deleting several tumor suppressor genes. We analyzed whole genomes from 95 biopsies across 33 patients with clear cell renal cell carcinoma. We find hotspots of point mutations in the 5' UTR of TERT, targeting a MYC-MAX-MAD1 repressor associated with telomere lengthening. The most common structural abnormality generates simultaneous 3p loss and 5q gain (36% patients), typically through chromothripsis. This event occurs in childhood or adolescence, generally as the initiating event that precedes emergence of the tumor's most recent common ancestor by years to decades. Similar genomic changes drive inherited ccRCC. Modeling differences in age incidence between inherited and sporadic cancers suggests that the number of cells with 3p loss capable of initiating sporadic tumors is no more than a few hundred. Early development of ccRCC follows well-defined evolutionary trajectories, offering opportunity for early intervention.

Genomic Evolution of Breast Cancer Metastasis and Relapse.

Patterns of genomic evolution between primary and metastatic breast cancer have not been studied in large numbers, despite patients with metastatic breast cancer having dismal survival. We sequenced whole genomes or a panel of 365 genes on 299 samples from 170 patients with locally relapsed or metastatic breast cancer. Several lines of analysis indicate that clones seeding metastasis or relapse disseminate late from primary tumors, but continue to acquire mutations, mostly accessing the same mutational processes active in the primary tumor. Most distant metastases acquired driver mutations not seen in the primary tumor, drawing from a wider repertoire of cancer genes than early drivers. These include a number of clinically actionable alterations and mutations inactivating SWI-SNF and JAK2-STAT3 pathways.

Intratumoral heterogeneity and subclonal diversification of early breast cancer.

Heterogeneity has long been recognized as a feature of some primary breast cancers manifesting as mixed histopathological subtypes or variable expression of the therapeutic targets ER, PgR and HER2. The recent emergence of next generation sequencing (NGS) technologies has revolutionized our understanding of the extent and nature of subclonal diversification. Careful examination of primary breast cancers often reveals multiple genomically distinct subclones that may contain driver alterations that follow spatial patterns of segregation. Subclonality is of clinical relevance as it forms the substrate of selection and can give rise to aggressive clinical features such as invasiveness, metastasis and treatment resistance. However, spatial and temporal intra-tumoral heterogeneity pose fundamental challenges to representative sampling and consequently the feasibility of a personalized medicine approach. Fundamental clinical and biological questions are starting to be addressed by applying NGS to the study of intra-tumoral heterogeneity and the insights that it provides should be used to better inform the prospective design of clinico-genomics trials.