BACKGROUND
Between 1999 and 2009 in the United Kingdom, 82,429 men between 50 and 69 years of age received a prostate-specific antigen (PSA) test. Localized prostate cancer was diagnosed in 2664 men. Of these men, 1643 were enrolled in a trial to evaluate the effectiveness of treatments, with 545 randomly assigned to receive active monitoring, 553 to undergo prostatectomy, and 545 to undergo radiotherapy.


METHODS
At a median follow-up of 15 years (range, 11 to 21), we compared the results in this population with respect to death from prostate cancer (the primary outcome) and death from any cause, metastases, disease progression, and initiation of long-term androgen-deprivation therapy (secondary outcomes).


RESULTS
Follow-up was complete for 1610 patients (98%). A risk-stratification analysis showed that more than one third of the men had intermediate or high-risk disease at diagnosis. Death from prostate cancer occurred in 45 men (2.7%): 17 (3.1%) in the active-monitoring group, 12 (2.2%) in the prostatectomy group, and 16 (2.9%) in the radiotherapy group (P = 0.53 for the overall comparison). Death from any cause occurred in 356 men (21.7%), with similar numbers in all three groups. Metastases developed in 51 men (9.4%) in the active-monitoring group, in 26 (4.7%) in the prostatectomy group, and in 27 (5.0%) in the radiotherapy group. Long-term androgen-deprivation therapy was initiated in 69 men (12.7%), 40 (7.2%), and 42 (7.7%), respectively; clinical progression occurred in 141 men (25.9%), 58 (10.5%), and 60 (11.0%), respectively. In the active-monitoring group, 133 men (24.4%) were alive without any prostate cancer treatment at the end of follow-up. No differential effects on cancer-specific mortality were noted in relation to the baseline PSA level, tumor stage or grade, or risk-stratification score. No treatment complications were reported after the 10-year analysis.


CONCLUSIONS
After 15 years of follow-up, prostate cancer-specific mortality was low regardless of the treatment assigned. Thus, the choice of therapy involves weighing trade-offs between benefits and harms associated with treatments for localized prostate cancer. (Funded by the National Institute for Health and Care Research; ProtecT Current Controlled Trials number, ISRCTN20141297; ClinicalTrials.gov number, NCT02044172.).

https://orca.cardiff.ac.uk/id/eprint/157779/3/STAFFURTH%2C%20JOHN%20-%20Fifteen-Year%20Outcomes%20after%20Monitoring%2C%20Surgery%2C%20or%20Radiotherapy%20for%20Prostate%20Cancer.pdf

Fifteen-Year Outcomes after Monitoring, Surgery, or Radiotherapy for Prostate Cancer.

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. 

/pdf/spatial-genomics-maps-the-structure-nature-and-evolution-of-27fcnnl7.pdf

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

Spatial biology of cancer evolution

Technology meets TILs: Deciphering T cell function in the -omics era.

Although advances in spatial transcriptomics (ST) enlarge to unveil spatial landscape of tissues, it remains challenging to delineate pathology-relevant and cellular localizations, and interactions exclusive to a spatial niche (e.g., tumor boundary). Here, we develop Cottrazm, integrating ST with hematoxylin and eosin histological image, and single-cell transcriptomics to delineate the tumor boundary connecting malignant and non-malignant cell spots in tumor tissues, deconvolute cell-type composition at spatial location, and reconstruct cell type-specific gene expression profiles at sub-spot level. We validate the performance of Cottrazm along the malignant-boundary-nonmalignant spatial axis. We identify specific macrophage and fibroblast subtypes localized around tumor boundary that interacted with tumor cells to generate a structural boundary, which limits T cell infiltration and promotes immune exclusion in tumor microenvironment. In this work, Cottrazm provides an integrated tool framework to dissect the tumor spatial microenvironment and facilitates the discovery of functional biological insights, thereby identifying therapeutic targets in oncologic ST datasets. 

/pdf/reconstruction-of-the-tumor-spatial-microenvironment-along-2fhivspa.pdf

Reconstruction of the tumor spatial microenvironment along the malignant-boundary-nonmalignant axis

Abstract Defining the transition from benign to malignant tissue is fundamental to improving early diagnosis of cancer 1 . Here we use a systematic approach to study spatial genome integrity in situ and describe previously unidentified clonal relationships. We used spatially resolved transcriptomics 2 to infer spatial copy number variations in &gt;120,000 regions across multiple organs, in benign and malignant tissues. We demonstrate that genome-wide copy number variation reveals distinct clonal patterns within tumours and in nearby benign tissue using an organ-wide approach focused on the prostate. Our results suggest a model for how genomic instability arises in histologically benign tissue that may represent early events in cancer evolution. We highlight the power of capturing the molecular and spatial continuums in a tissue context and challenge the rationale for treatment paradigms, including focal therapy. 

/pdf/spatially-resolved-clonal-copy-number-alterations-in-benign-2oj5tcl9.pdf

Spatially resolved clonal copy number alterations in benign and malignant tissue


 Defining the transition from benign to malignant tissue is fundamental to improve early diagnosis of cancer. We provide an unsupervised approach (SpatialInferCNV) to study spatial genome integrity, in situ, to gain molecular insight into clonal relationships. We employed spatially resolved transcriptomics (Visium, 10x Genomics) to infer spatial copy number variations in >120 000 spatial regions across multiple organs, tissues and tumors, including three whole axial prostates. We used this information to deduce clonal relationships between regions harboring 5-20 cells. We perform an in-depth spatial analysis of prostate cancer that includes an unprecedented interrogation of up to 50,000 tissue domains in a single patient. For these domains we inferred genome-wide information, which facilitated data driven clustering in a tissue wide fashion at high resolution. We observed that genome-wide somatic copy number alterations revealed distinct clonal patterns benign tissue and nearby tumours. In a tissue section from patient 1 (section H2_1), we observed that many CNVs occurred in clone C, most notably in chromosomes 8 and 10 encoding a MYC amplification and a PTEN deletion. This clone constituted a region of exclusively benign acinar cells branching off a duct lined by largely diploid cells in clones A and B. The unobserved ancestor to clone C then gave rise to a further unobserved clone, and then clones E, F and G. Our results suggest a model for how genomic instability arises in histologically benign tissue that may represent early events in prostate cancer evolution. Furthermore the spatial information allowed us to identify small clonal units not evident from morphology and hence would be overlooked by pathologists. We present the first comprehensive, large-scale, atlas of genomic evolution at high spatial resolution in prostate cancer. Our study adds an important new approach to the armamentarium of cancer molecular pathology. We highlight the power of an unsupervised approach to capture the molecular and spatial continuums in a tissue context and challenge the rationale for focal therapy in prostate cancer.
 Citation Format: Andrew Erickson, Mengxiao He, Emelie Berglund, Maja Marklund, Reza Mirzazadeh, Niklas Schultz, Linda Kvastad, Alma Andersson, Ludvig Bergenstråhle, Joseph Bergenstråhle, Ludvig Larsson, Leire Alonso Galicia, Alia Shamikh, Elisa Basmaci, Teresita Díaz De Ståhl, Timothy Rajakumar, Dimitrios Doultsinos, Kim Thrane, Andrew L. Ji, Paul A. Khavari, Firaz Tarish, Anna Tanoglidi, Jonas Maaskola, Richard Colling, Tuomas Mirtti, Freddie C. Hamdy, Dan J. Woodcock, Thomas Helleday, Ian G. Mills, Alastair D. Lamb, Joakim Lundeberg. The spatial landscape of clonal somatic copy number alterations in benign and malignant prostate epithelia [abstract]. In: Proceedings of the AACR Special Conference: Advances in Prostate Cancer Research; 2023 Mar 15-18; Denver, Colorado. Philadelphia (PA): AACR; Cancer Res 2023;83(11 Suppl):Abstract nr A051.

Abstract A051: The spatial landscape of clonal somatic copy number alterations in benign and malignant prostate epithelia

Epithelial cancers are typically heterogeneous with primary prostate cancer being a typical example of histological and genomic variation. Prostate cancer is the second most common male cancer in western industrialized countries. Prior studies of primary prostate cancer tumor genetics revealed extensive inter and intra-patient tumor heterogeneity. Recent advances have enabled extensive single-cell and spatial transcriptomic profiling of tissue specimens. The ability to resolve accurate prostate cancer tumor phylogenies at high spatial resolution would provide tools to address questions in tumorigenesis, disease progression, and metastasis. Recent advances in machine learning have enabled the inference of ground-truth genomic single-nucleotide and copy number variant status from transcript data. The inferred SNV and CNV states can be used to resolve clonal phylogenies, however, it is still unknown how faithfully transcript-based tumor phylogenies reconstruct ground truth DNA-based tumor phylogenies. We sought to study the accuracy of inferred-transcript to recapitulate DNA-based tumor phylogenies. We first performed in-silico comparisons of inferred and directly resolved SNV and CNV status, from single cancer cells, from three different cell lines. We found that inferred SNV phylogenies accurately recapitulate DNA phylogenies (entanglement = 0.097). We observed similar results in iCNV and CNV based phylogenies (entanglement = 0.11). Analysis of published prostate cancer DNA phylogenies and inferred CNV, SNV and transcript based phylogenies demonstrated phylogenetic concordance. Finally, a comparison of pseudo-bulked spatial transcriptomic data to adjacent sections with WGS data also demonstrated recapitulation of ground truth (entanglement = 0.35). These results suggest that transcript-based inferred phylogenies recapitulate conventional genomic phylogenies. Further work will need to be done to increase accuracy, genomic, and spatial resolution.

/pdf/clonal-phylogenies-inferred-from-bulk-single-cell-and-29w2s2c9.pdf

Timothy Rajakumar

Papers

Spatially resolved clonal copy number alterations in benign and malignant tissue

Abstract A051: The spatial landscape of clonal somatic copy number alterations in benign and malignant prostate epithelia

Clonal phylogenies inferred from bulk, single cell, and spatial transcriptomic analysis of cancer