A deep learning system accurately classifies primary and metastatic cancers using passenger mutation patterns

In cancer, the primary tumour's organ of origin and histopathology are the strongest determinants of its clinical behaviour, but in 3% of cases a patient presents with a metastatic tumour and no obvious primary. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, we train a deep learning classifier to predict cancer type based on patterns of somatic passenger mutations detected in whole genome sequencing (WGS) of 2606 tumours representing 24 common cancer types produced by the PCAWG Consortium. Our classifier achieves an accuracy of 91% on held-out tumor samples and 88% and 83% respectively on independent primary and metastatic samples, roughly double the accuracy of trained pathologists when presented with a metastatic tumour without knowledge of the primary. Surprisingly, adding information on driver mutations reduced accuracy. Our results have clinical applicability, underscore how patterns of somatic passenger mutations encode the state of the cell of origin, and can inform future strategies to detect the source of circulating tumour DNA

A deep learning system accurately classifies primary and metastatic cancers using passenger mutation patterns.

In cancer, the primary tumour's organ of origin and histopathology are the strongest determinants of its clinical behaviour, but in 3% of cases a patient presents with a metastatic tumour and no obvious primary. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, we train a deep learning classifier to predict cancer type based on patterns of somatic passenger mutations detected in whole genome sequencing (WGS) of 2606 tumours representing 24 common cancer types produced by the PCAWG Consortium. Our classifier achieves an accuracy of 91% on held-out tumor samples and 88% and 83% respectively on independent primary and metastatic samples, roughly double the accuracy of trained pathologists when presented with a metastatic tumour without knowledge of the primary. Surprisingly, adding information on driver mutations reduced accuracy. Our results have clinical applicability, underscore how patterns of somatic passenger mutations encode the state of the cell of origin, and can inform future strategies to detect the source of circulating tumour DNA

Author Correction: Pan-cancer analysis of whole genomes

In the published version of this paper, the list of members of the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium and their affiliations contained minor errors in the affiliations. The original Article has been corrected to include the corrected list

A deep learning system accurately classifies primary and metastatic cancers using passenger mutation patterns

Abstract: In cancer, the primary tumour’s organ of origin and histopathology are the strongest determinants of its clinical behaviour, but in 3% of cases a patient presents with a metastatic tumour and no obvious primary. Here,as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, we train a deep learning classifier to predict cancer type based on patterns of somatic passenger mutations detected in whole genome sequencing (WGS) of 2606 tumours representing 24 common cancer types produced by the PCAWG Consortium. Our classifier achieves an accuracy of 91% on held-out tumor samples and 88% and 83% respectively on independent primary and metastatic samples, roughly double the accuracy of trained pathologists when presented with a metastatic tumour without knowledge of the primary. Surprisingly, adding information on driver mutations reduced accuracy. Our results have clinical applicability, underscore how patterns of somatic passenger mutations encode the state of the cell of origin, and can inform future strategies to detect the source of circulating tumour DNA

Sex differences in oncogenic mutational processes.

Sex differences have been observed in multiple facets of cancer epidemiology, treatment and biology, and in most cancers outside the sex organs. Efforts to link these clinical differences to specific molecular features have focused on somatic mutations within the coding regions of the genome. Here we report a pan-cancer analysis of sex differences in whole genomes of 1983 tumours of 28 subtypes as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium. We both confirm the results of exome studies, and also uncover previously undescribed sex differences. These include sex-biases in coding and non-coding cancer drivers, mutation prevalence and strikingly, in mutational signatures related to underlying mutational processes. These results underline the pervasiveness of molecular sex differences and strengthen the call for increased consideration of sex in molecular cancer research

Retrospective evaluation of whole exome and genome mutation calls in 746 cancer samples

Funder: NCI U24CA211006Abstract: The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) curated consensus somatic mutation calls using whole exome sequencing (WES) and whole genome sequencing (WGS), respectively. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2,658 cancers across 38 tumour types, we compare WES and WGS side-by-side from 746 TCGA samples, finding that ~80% of mutations overlap in covered exonic regions. We estimate that low variant allele fraction (VAF < 15%) and clonal heterogeneity contribute up to 68% of private WGS mutations and 71% of private WES mutations. We observe that ~30% of private WGS mutations trace to mutations identified by a single variant caller in WES consensus efforts. WGS captures both ~50% more variation in exonic regions and un-observed mutations in loci with variable GC-content. Together, our analysis highlights technological divergences between two reproducible somatic variant detection efforts

Automatic Tumour Typing based on Patterns of Somatic Passenger Mutations

In cancer, a tumour’s cell of origin is the strongest determinant of its clinical behaviour. While cell of origin is typically clear at the time of diagnosis, 3-5% of cancer patients present with a metastatic tumour and no obvious corresponding primary tumour. Despite advances in molecular testing, imaging, and pathology, the primary tumour site cannot be inferred in the majority of these cases. Recent large- scale analysis of cancer genomes has uncovered strong associations between cancer type and somatic mutations, prompting the use of somatic mutations as a tool for identifying cancer type. While existing approaches have attempted to use cancer-associated mutations, which may be more common in specific cancer types to infer the primary tumour type from the metastatic tissue, these methods have had only limited success. A more promising alternative is to use the association between patterns of somatic passenger mutations and cancer type, by exploiting the relationships between both regional mutation density and cancer type, and mutational processes and cancer type. Somatic point mutations accu- mulate in regions of closed chromatin, and so mutation density provides information about chromatin state, which in turn offers hints about the underlying cell type. As some mutational processes are highly cell-type specific, mutational processes also provide clues about cancer type. In this thesis, I describe a number of deep learning systems for automatic tumour typing based on patterns of somatic passenger mutations. I then address challenges for translating the classifier into clinical scenarios through the use of multiple algorithmic improvements. First, I make use of modern advancements in deep learning to extend the classifier to accurately discriminate between 29 cancer types. I then use a number of sta- tistical methods for assessing the uncertainty in the model’s predictions, and for improving uncertainty quantification. Finally, I make use of information theoretic metrics to use the model’s predictive uncer- tainty to automatically detect cancer samples that come from rare cancer types that the model was not trained to classify. These studies demonstrate the utility of passenger mutations as a tool for identifying cancer type, and address challenges for translating the deep learning classifier into clinical settings.Ph.D.2022-06-29 00:00:0

Impact of Plant Richness and Closeness to water on Insect Abundance

<p> In order to study the relationship between plant species diversity, distance from a water source and insect abundance, a study was done at the Grassland, East of Osgoode Hall, beside the Pond (York University) on October 14 and October 21, 2014. October 14, 2014 was sunny and 18°C and October 21st was 5°C with rain. Distance from the pond was measured using three discrete areas; the first area from the start of the pond to 20m away, the second from 20m to 40m and the final area from 40m to 60m away from the pond. 1m x 1m quadrats were used to measure plant species richness, and sweepnets were used to sweep for insects in order to determine insect abundance. Transects were used to mark out regions across the perimeter of the pond. Each transect was placed 5m away from the first transect, along the perimeter of Strong Pond.</p

Adult-Sapling Tree Data

<p>The physical characteristics of 20 trees were measured on September 23, 2014 at 3:00-4:00pm in the Saywell Woodslot (York University, 43° 46' 9.228"N, 79° 30' 29.556''W). Adult Maple Trees were identified, along with the nearest sapling to the adult tree. For each adult tree, the diameter at breast height, and canopy coverage was recorded, along with the distance from the adult to the closest sapling of the same species. For each sapling, diameter at breast height was measured and recorded.</p

Biodiversity of Insects found in the Woodslot and Grassland

<p>In order to study the abundance and diversity of insects in the grassland and woods habitats, a study was conducted in the Saywell Woodslot and Grassland, east of Strong College (York University), on September 30, 2014 at approximately 3:00pm. Insect abundance was observed using a quadrat. Each quadrat area was observed for 3 minutes, and data on the total number of insects (and snails), and the number of organisms from specific Classes was recorded. First quadrat was placed randomly in the habitat; each subsequent quadrat was placed 5 metres in front of the previous quadrat. 10 replications were done in the Woodslot and 10 in the Grassland.</p