A global transcriptional network connecting noncoding mutations to changes in tumor gene expression.

Although cancer genomes are replete with noncoding mutations, the effects of these mutations remain poorly characterized. Here we perform an integrative analysis of 930 tumor whole genomes and matched transcriptomes, identifying a network of 193 noncoding loci in which mutations disrupt target gene expression. These 'somatic eQTLs' (expression quantitative trait loci) are frequently mutated in specific cancer tissues, and the majority can be validated in an independent cohort of 3,382 tumors. Among these, we find that the effects of noncoding mutations on DAAM1, MTG2 and HYI transcription are recapitulated in multiple cancer cell lines and that increasing DAAM1 expression leads to invasive cell migration. Collectively, the noncoding loci converge on a set of core pathways, permitting a classification of tumors into pathway-based subtypes. The somatic eQTL network is disrupted in 88% of tumors, suggesting widespread impact of noncoding mutations in cancer

Combinatorial CRISPR-Cas9 screens for de novo mapping of genetic interactions.

We developed a systematic approach to map human genetic networks by combinatorial CRISPR-Cas9 perturbations coupled to robust analysis of growth kinetics. We targeted all pairs of 73 cancer genes with dual guide RNAs in three cell lines, comprising 141,912 tests of interaction. Numerous therapeutically relevant interactions were identified, and these patterns replicated with combinatorial drugs at 75% precision. From these results, we anticipate that cellular context will be critical to synthetic-lethal therapies

A platinum-resistant cancer subtype defined by a network of gene mutations

Despite many molecular studies showing a wide diversity of ovarian tumor types, ovarian cancer is still clinically treated as a single disease. Of five epithelial ovarian cancer subtypes, high grade serous ovarian cancer (HGSOC) has been identified as the most aggressive and constitutes roughly 60% of all malignant tumors. Aside from an almost universal loss of TP53 function, HGSOC is a highly heterogeneous disease. Several attempts to stratify this disease into more homogeneous cohorts have been made, but not applied in medical practice. Recently we developed Network-based Stratification (NBS), an unsupervised clustering method which uses somatic mutation profiles and known gene interaction networks, which successfully stratified HGSOC patients into four distinct and clinically relevant subtypes. Here we’ve developed a new supervised classifier, trained on the previously found HGSOC subtypes, that uses somatic mutation profiles of ovarian cancer to recover the ‘high-risk’ subtype and ‘standard-risk’ II-IV subtypes. We demonstrate the robustness of NBS across independent cohorts, by retrieving the NBS subtypes in a newly available HGSOC study from the International Cancer Genome Consortium (ICGC). To study the molecular characteristics of the ‘high-risk’ subtype, we’ve developed a supervised cell line classifier and classified the ovarian cell lines in the Cancer Cell Line Encyclopedia (CCLE) into ‘high-risk’ and ‘standard-risk’ subtypes. We established adequate cell line models of HGSOC and these subtypes and found the ‘high-risk’ subtype to be significantly resistant to cisplatin. Further exploration for the genes modulating response to cisplatin is underway in a whole-exome screen on a ‘high-risk’ HGSOC cell line

A platinum-resistant cancer subtype defined by a network of gene mutations

Despite many molecular studies showing a wide diversity of ovarian tumor types, ovarian cancer is still clinically treated as a single disease. Of five epithelial ovarian cancer subtypes, high grade serous ovarian cancer (HGSOC) has been identified as the most aggressive and constitutes roughly 60% of all malignant tumors. Aside from an almost universal loss of TP53 function, HGSOC is a highly heterogeneous disease. Several attempts to stratify this disease into more homogeneous cohorts have been made, but not applied in medical practice. Recently we developed Network-based Stratification (NBS), an unsupervised clustering method which uses somatic mutation profiles and known gene interaction networks, which successfully stratified HGSOC patients into four distinct and clinically relevant subtypes. Here we’ve developed a new supervised classifier, trained on the previously found HGSOC subtypes, that uses somatic mutation profiles of ovarian cancer to recover the ‘high-risk’ subtype and ‘standard-risk’ II-IV subtypes. We demonstrate the robustness of NBS across independent cohorts, by retrieving the NBS subtypes in a newly available HGSOC study from the International Cancer Genome Consortium (ICGC). To study the molecular characteristics of the ‘high-risk’ subtype, we’ve developed a supervised cell line classifier and classified the ovarian cell lines in the Cancer Cell Line Encyclopedia (CCLE) into ‘high-risk’ and ‘standard-risk’ subtypes. We established adequate cell line models of HGSOC and these subtypes and found the ‘high-risk’ subtype to be significantly resistant to cisplatin. Further exploration for the genes modulating response to cisplatin is underway in a whole-exome screen on a ‘high-risk’ HGSOC cell line

Coping with climate change: The role of climate related stressors in affecting the mental health of young people in Mexico.

Young people today are predicted to experience more climate change related stressors and harms than the previous generation, yet they are often excluded from climate research, policy, and advocacy. Increasingly, this exposure is associated with experience of common mental health disorders (CMD). The VoCes-19 study collected surveys from 168,407 young people across Mexico (ages 15-24 years) through an innovative online platform, collecting information on various characteristics including CMD and experience of recent climate harms. Logistic regression models were fit to explore characteristics associated with CMD. Structural equation models were fit to explore pathways between exposure, feeling of concern about climate change, and a sense of agency (meaning the respondent felt they could help address the climate crisis) and how these relate to CMD. Of the respondents, 42% (n = 50,682) were categorized as experiencing CMD, higher among those who experienced a climate stressor (51%, n = 4,808) vs those not experiencing climate stressors (41%, n = 43,872). Adjusting for key demographic characteristics, exposure to any climate event increased the odds of CMD by 50% (Odd Ratio = 1.57; 95% Confidence Interval (CI) 1.49, 1.64), highest for heatwaves. Specific climate impacts such as housing damage, loss of or inability to work, damage to family business, leaving school and physical health affected were adversely related to CMD, though for different climate hazards. More concern and less agency were related to CMD through different pathways, particularly for those exposed to recent events. Future research regarding the cumulative exposures to climate change, not just acute events but as an ongoing crisis, and various pathways that influence the mental health and well-being of young people must be clearly understood to develop programs and policies to protect the next generation

Mediation and moderation of the association between experiencing a climate event and CMD, by concern about climate change and perceived agency regarding climate change (SEM).

Mediation and moderation of the association between experiencing a climate event and CMD, by concern about climate change and perceived agency regarding climate change (SEM).</p

Logistic regression characteristics of factors associated with CMD, stratified by type of climate hazard experienced.

Logistic regression characteristics of factors associated with CMD, stratified by type of climate hazard experienced.</p

Descriptive characteristics of respondents comparing those who did and did not experience CMD.

Descriptive characteristics of respondents comparing those who did and did not experience CMD.</p

PLOS inclusivity indicates the inclusivity statement.

PLOS inclusivity indicates the inclusivity statement.</p