Given a set of features, researchers are often interested in partitioning objects into homogeneous clusters. In health research, cancer research in particular, high-throughput data is collected with ...the aim of segmenting patients into sub-populations to aid in disease diagnosis, prognosis or response to therapy. Cluster analysis, a class of unsupervised learning techniques, is often used for class discovery. Cluster analysis suffers from some limitations, including the need to select up-front the algorithm to be used as well as the number of clusters to generate, in addition, there may exist several groupings consistent with the data, making it very difficult to validate a final solution. Ensemble clustering is a technique used to mitigate these limitations and facilitate the generalization and reproducibility of findings in new cohorts of patients.
We introduce diceR (diverse cluster ensemble in R), a software package available on CRAN: https://CRAN.R-project.org/package=diceR CONCLUSIONS: diceR is designed to provide a set of tools to guide researchers through a general cluster analysis process that relies on minimizing subjective decision-making. Although developed in a biological context, the tools in diceR are data-agnostic and thus can be applied in different contexts.
Some forms of chemotherapy can enhance antitumor immunity through immunogenic cell death, resulting in increased T-cell activation and tumor infiltration. Such effects could potentially sensitize ...tumors to immunotherapies, including checkpoint blockade. We investigated whether platinum- and taxane-based chemotherapy for ovarian cancer induces immunologic changes consistent with this possibility.
Matched pre- and post-neoadjuvant chemotherapy tumor samples from 26 high-grade serous carcinoma (HGSC) patients were analyzed by immunohistochemistry (IHC) for a large panel of immune cells and associated factors. The prognostic significance of post-chemotherapy TIL patterns was assessed in an expanded cohort (
= 90).
Neoadjuvant chemotherapy was associated with increased densities of CD3
, CD8
, CD8
TIA-1
, PD-1
and CD20
TIL. Other immune subsets and factors were unchanged, including CD79a
CD138
plasma cells, CD68
macrophages, and MHC class I on tumor cells. Immunosuppressive cell types were also unchanged, including FoxP3
PD-1
cells (putative regulatory T cells), IDO-1
cells, and PD-L1
cells (both macrophages and tumor cells). Hierarchical clustering revealed three response patterns: (i) TIL
tumors showed increases in multiple immune markers after chemotherapy; (ii) TIL
tumors underwent similar increases, achieving patterns indistinguishable from the first group; and (iii) TIL
cases generally remained negative. Despite the dramatic increases seen in the first two patterns, post-chemotherapy TIL showed limited prognostic significance.
Chemotherapy augments pre-existing TIL responses but fails to relieve major immune-suppressive mechanisms or confer significant prognostic benefit. Our findings provide rationale for multipronged approaches to immunotherapy tailored to the baseline features of the tumor microenvironment.
.
The increased pervasiveness of digital health technology is producing large amounts of person-generated health data (PGHD). These data can empower people to monitor their health to promote prevention ...and management of disease. Women make up one of the largest groups of consumers of digital self-tracking technology.
In this scoping review, we aimed to (1) identify the different areas of women's health monitored using PGHD from connected health devices, (2) explore personal metrics collected through these technologies, and (3) synthesize facilitators of and barriers to women's adoption and use of connected health devices.
Following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines for scoping reviews, we searched 5 databases for articles published between January 1, 2015, and February 29, 2020. Papers were included if they targeted women or female individuals and incorporated digital health tools that collected PGHD outside a clinical setting.
We included a total of 406 papers in this review. Articles on the use of PGHD for women steadily increased from 2015 to 2020. The health areas that the articles focused on spanned several topics, with pregnancy and the postpartum period being the most prevalent followed by cancer. Types of digital health used to collect PGHD included mobile apps, wearables, websites, the Internet of Things or smart devices, 2-way messaging, interactive voice response, and implantable devices. A thematic analysis of 41.4% (168/406) of the papers revealed 6 themes regarding facilitators of and barriers to women's use of digital health technology for collecting PGHD: (1) accessibility and connectivity, (2) design and functionality, (3) accuracy and credibility, (4) audience and adoption, (5) impact on community and health service, and (6) impact on health and behavior.
Leading up to the COVID-19 pandemic, the adoption of digital health tools to address women's health concerns was on a steady rise. The prominence of tools related to pregnancy and the postpartum period reflects the strong focus on reproductive health in women's health research and highlights opportunities for digital technology development in other women's health topics. Digital health technology was most acceptable when it was relevant to the target audience, was seen as user-friendly, and considered women's personalization preferences while also ensuring accuracy of measurements and credibility of information. The integration of digital technologies into clinical care will continue to evolve, and factors such as liability and health care provider workload need to be considered. While acknowledging the diversity of individual needs, the use of PGHD can positively impact the self-care management of numerous women's health journeys. The COVID-19 pandemic has ushered in increased adoption and acceptance of digital health technology. This study could serve as a baseline comparison for how this field has evolved as a result.
RR2-10.2196/26110.
Endometrioid ovarian carcinoma (ENOC) is generally associated with a more favorable prognosis compared with other ovarian carcinomas. Nonetheless, current patient treatment continues to follow a ..."one-size-fits-all" approach. Even though tumor staging offers stratification, personalized treatments remain elusive. As ENOC shares many clinical and molecular features with its endometrial counterpart, we sought to investigate The Cancer Genome Atlas-inspired endometrial carcinoma (EC) molecular subtyping in a cohort of ENOC.
IHC and mutation biomarkers were used to segregate 511 ENOC tumors into four EC-inspired molecular subtypes: low-risk
mutant (POLEmut), moderate-risk mismatch repair deficient (MMRd), high-risk p53 abnormal (p53abn), and moderate-risk with no specific molecular profile (NSMP). Survival analysis with established clinicopathologic and subtype-specific features was performed.
A total of 3.5% of cases were POLEmut, 13.7% MMRd, 9.6% p53abn, and 73.2% NSMP, each showing distinct outcomes (
< 0.001) and survival similar to observations in EC. Median OS was 18.1 years in NSMP, 12.3 years in MMRd, 4.7 years in p53abn, and not reached for POLEmut cases. Subtypes were independent of stage, grade, and residual disease in multivariate analysis.
EC-inspired molecular classification provides independent prognostic information in ENOC. Our findings support investigating molecular subtype-specific management recommendations for patients with ENOC; for example, subtypes may provide guidance when fertility-sparing treatment is desired. Similarities between ENOC and EC suggest that patients with ENOC may benefit from management strategies applied to EC and the opportunity to study those in umbrella trials.
Among women with epithelial ovarian cancer (EOC), histotype is one of the major prognostic factors. However, few data are available on histotype- specific survival and mortality estimates among these ...patients. We therefore examined survival and causes of death among women with EOC by histotype.
A population- based cohort including all ovarian cancer patients diagnosed in British Columbia (BC) between 1990 and 2014 was built using population-based administrative datasets. We compared causes of death within histotypes, by age at diagnosis, BRCA status, and time since diagnosis.
A total of 6975 women were identified as having been diagnosed with EOC between 1990 and 2014 in BC. The most common cause of death among these women was ovarian cancer until 10 years post diagnosis when other causes surpassed ovarian cancer as the leading cause of death. Among women with serous EOCs, ovarian cancer was the leading cause of death 12 years after diagnosis, whereas ovarian cancer was the leading cause of death for 8 years among women with non- serous EOCs. Among women with serous EOCs, ovarian cancer was the leading cause of death for 12 years among younger women (< 60 years of age) compared to 8 years among women > = 60 years of age, and those with BRCA mutations were more likely to die from ovarian cancer than those without a BRCA mutation.
Within 10 years from diagnosis, ovarian cancer is the leading cause of death among women diagnosed with EOC.
Abstract Endometrial cancer (EC) has four molecular subtypes with strong prognostic value and therapeutic implications. The most common subtype (NSMP; No Specific Molecular Profile) is assigned after ...exclusion of the defining features of the other three molecular subtypes and includes patients with heterogeneous clinical outcomes. In this study, we employ artificial intelligence (AI)-powered histopathology image analysis to differentiate between p53abn and NSMP EC subtypes and consequently identify a sub-group of NSMP EC patients that has markedly inferior progression-free and disease-specific survival (termed ‘p53abn-like NSMP’), in a discovery cohort of 368 patients and two independent validation cohorts of 290 and 614 from other centers. Shallow whole genome sequencing reveals a higher burden of copy number abnormalities in the ‘p53abn-like NSMP’ group compared to NSMP, suggesting that this group is biologically distinct compared to other NSMP ECs. Our work demonstrates the power of AI to detect prognostically different and otherwise unrecognizable subsets of EC where conventional and standard molecular or pathologic criteria fall short, refining image-based tumor classification. This study’s findings are applicable exclusively to females.
Abstract
Background
To drive translational medicine, modern day biobanks need to integrate with other sources of data (clinical, genomics) to support novel data-intensive research. Currently, vast ...amounts of research and clinical data remain in silos, held and managed by individual researchers, operating under different standards and governance structures; a framework that impedes sharing and effective use of data. In this article, we describe the journey of British Columbia’s Gynecological Cancer Research Program (OVCARE) in moving a traditional tumour biobank, outcomes unit, and a collection of data silos, into an integrated data commons to support data standardization and resource sharing under collaborative governance, as a means of providing the gynecologic cancer research community in British Columbia access to tissue samples and associated clinical and molecular data from thousands of patients.
Results
Through several engagements with stakeholders from various research institutions within our research community, we identified priorities and assessed infrastructure needs required to optimize and support data collections, storage and sharing, under three main research domains: (1) biospecimen collections, (2) molecular and genomics data, and (3) clinical data. We further built a governance model and a resource portal to implement protocols and standard operating procedures for seamless collections, management and governance of interoperable data, making genomic, and clinical data available to the broader research community.
Conclusions
Proper infrastructures for data collection, sharing and governance is a translational research imperative. We have consolidated our data holdings into a data commons, along with standardized operating procedures to meet research and ethics requirements of the gynecologic cancer community in British Columbia. The developed infrastructure brings together, diverse data, computing frameworks, as well as tools and applications for managing, analyzing, and sharing data. Our data commons bridges data access gaps and barriers to precision medicine and approaches for diagnostics, treatment and prevention of gynecological cancers, by providing access to large datasets required for data-intensive science.
Epithelial ovarian cancer (EOC) consists of 5 major histotypeshigh-grade serous carcinoma (HGSC), endometrioid carcinoma (EC), clear cell carcinoma (CCC), mucinous carcinoma (MC), and low-grade ...serous carcinoma (LGSC). Each can have a broad spectrum of morphologic appearances, and 1 histotype can closely mimic histopathologic features more typical of another. Historically, there has been a relatively high frequency of mixed, defined by 2 or more distinct histotypes present on the basis of routine histopathologic assessment, histotype carcinoma diagnoses (3% to 11%); however, recent immunohistochemical (IHC) studies identifying histotype-specific markers and allowing more refined histotype diagnoses suggest a much lower incidence. We reviewed hematoxylin and eosin–stained slides from 871 cases of EOC and found the frequency of mixed carcinomas to be 1.7% when modern diagnostic criteria are applied. Through international collaboration, we established a cohort totaling 22 mixed EOCs, consisting of 9 EC/CCC, 4 EC/LGSC, 3 HGSC/CCC, 2 CCC/MC, and 4 other combinations. We interrogated the molecular differences between the different components of each case using IHC, gene expression, and hotspot sequencing analyses. IHC data alone suggested that 9 of the 22 cases were not mixed tumors, as they presented a uniform immuno-phenotype throughout, and these cases most probably represent morphologic mimicry and variation within tumors of a single histotype. Synthesis of molecular data further reduces the incidence of mixed carcinomas. On the basis of these results, true mixed carcinomas with both morphologic and molecular support for the presence of >1 histotype within a given tumor represent <1% of EOCs.
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