Novel recruitment approaches and operational results for a statewide population Cohort for cancer research: The Healthy Oregon Project.

Background: Cancer health research relies on large-scale cohorts to derive generalizable results for different populations. While traditional epidemiological cohorts often use costly random sampling or self-motivated, preselected groups, a shift toward health system-based cohorts has emerged. However, such cohorts depend on participants remaining within a single system. Recent consumer engagement models using smartphone-based communication, driving projects, and social media have begun to upend these paradigms. Methods: We initiated the Healthy Oregon Project (HOP) to support basic and clinical cancer research. HOP study employs a novel, cost-effective remote recruitment approach to effectively establish a large-scale cohort for population-based studies. The recruitment leverages the unique email account, the HOP website, and social media platforms to direct smartphone users to the study app, which facilitates saliva sample collection and survey administration. Monthly newsletters further facilitate engagement and outreach to broader communities. Results: By the end of 2022, the HOP has enrolled approximately 35,000 participants aged 18-100 years (median = 44.2 years), comprising more than 1% of the Oregon adult population. Among those who have app access, ∼87% provided consent to genetic screening. The HOP monthly email newsletters have an average open rate of 38%. Efforts continue to be made to improve survey response rates. Conclusion: This study underscores the efficacy of remote recruitment approaches in establishing large-scale cohorts for population-based cancer studies. The implementation of the study facilitates the collection of extensive survey and biological data into a repository that can be broadly shared and supports collaborative clinical and translational research.

Diurnal stability of cell-free DNA and cell-free RNA in human plasma samples.

Many emerging technologies are reliant on circulating cell-free DNA (cfDNA) and cell-free RNA (cfRNA) applications in the clinic. However, the impact of diurnal cycles or daily meals on circulating analytes are poorly understood and may be confounding factors when developing diagnostic platforms. To begin addressing this knowledge gap, we obtained plasma from four healthy donors serially sampled five times during 12 h in a single day. For all samples, we measured concentrations of cfDNA and cfRNA using both bulk measurements and gene-specific digital droplet PCR. We found no significant variation attributed to blood draw number for the cfDNA or cfRNA. This indicated that natural diurnal cycles and meal consumption do not appear to significantly affect abundance of total cfDNA, total cfRNA, or our two selected cfRNA transcripts. Conversely, we observed significant variation between individual donors for cfDNA and one of the cfRNA transcripts. The results of this work suggest that it will be important to consider patient-specific baselines when designing reliable circulating cfDNA or cfRNA clinical assays.

Large-Scale Characterization of Drug Responses of Clinically Relevant Proteins in Cancer Cell Lines.

Perturbation biology is a powerful approach to modeling quantitative cellular behaviors and understanding detailed disease mechanisms. However, large-scale protein response resources of cancer cell lines to perturbations are not available, resulting in a critical knowledge gap. Here we generated and compiled perturbed expression profiles of ∼210 clinically relevant proteins in >12,000 cancer cell line samples in response to ∼170 drug compounds using reverse-phase protein arrays. We show that integrating perturbed protein response signals provides mechanistic insights into drug resistance, increases the predictive power for drug sensitivity, and helps identify effective drug combinations. We build a systematic map of "protein-drug" connectivity and develop a user-friendly data portal for community use. Our study provides a rich resource to investigate the behaviors of cancer cells and the dependencies of treatment responses, thereby enabling a broad range of biomedical applications.

Circulating tumor DNA dynamics using patient-customized assays are associated with outcome in neoadjuvantly treated breast cancer.

Pathological complete response (pCR) is an accurate predictor of good outcome following neoadjuvant chemotherapy (NAC) for locally advanced breast cancer. The presence of circulating-tumor DNA (ctDNA) has recently been reported to be strongly predictive of poor outcome in similar patient groups. We monitored ctDNA levels from 10 women undergoing NAC for locally advanced breast cancer using a patient-specific, hybrid-capture sequencing technique sensitive to the level of one altered allele in 10,000. Plasma was collected prior to the start of NAC, prior to each infusion of NAC, and during follow-up for between 350 and 1150 d after the start of NAC. Prior to the start of NAC, ctDNA was detectable in 3/3 triple negative, 3/3 HER2+, and 2/4 HER2-, ER+ breast cancer patients. Total cell-free DNA levels were considerably higher when patients were on NAC than at other times. ctDNA dynamics during NAC showed that patients with pCR experienced rapid declines in ctDNA levels, whereas patients without pCR typically showed evidence of residual ctDNA after initiation of treatment. Intriguingly, two of three patients that showed marked increases in ctDNA while on NAC experienced rapid recurrences (<2 yr following start of NAC). The third patient that had increases in ctDNA levels while on NAC had low-grade ER+ disease and showed residual ctDNA after surgery, which became undetectable after local radiation. Taken together, these results demonstrate the ability of our approach to sensitively serially monitor ctDNA during NAC, and identifies a need to further investigate the possibility of stratifying patients who need additional treatment or identify therapies that are ineffective.

MHC class I loaded ligands from breast cancer cell lines: A potential HLA-I-typed antigen collection.

To build a catalog of peptides presented by breast cancer cells, we undertook systematic MHC class I immunoprecipitation followed by elution of MHC class I-loaded peptides in breast cancer cells. We determined the sequence of 3196 MHC class I ligands representing 1921 proteins from a panel of 20 breast cancer cell lines. After removing duplicate peptides, i.e., the same peptide eluted from more than one cell line, the total number of unique peptides was 2740. Of the unique peptides eluted, more than 1750 had been previously identified, and of these, sixteen have been shown to be immunogenic. Importantly, half of these immunogenic peptides were shared between different breast cancer cell lines. MHC class I binding probability was used to plot the distribution of the eluted peptides in accordance with the binding score for each breast cancer cell line. We also determined that the tested breast cancer cells presented 89 mutation-containing peptides and peptides derived from aberrantly translated genes, 7 of which were shared between four or two different cell lines. Overall, the high throughput identification of MHC class I-loaded peptides is an effective strategy for systematic characterization of cancer peptides, and could be employed for design of multi-peptide anticancer vaccines. SIGNIFICANCE: By employing proteomic analyses of eluted peptides from breast cancer cells, the current study has built an initial HLA-I-typed antigen collection for breast cancer research. It was also determined that immunogenic epitopes can be identified using established cell lines and that shared immunogenic peptides can be found in different cancer types such as breast cancer and leukemia. Importantly, out of 3196 eluted peptides that included duplicate peptides in different cells 89 peptides either contained mutation in their sequence or were derived from aberrant translation suggesting that mutation-containing epitopes are on the order of 2-3% in breast cancer cells. Finally, our results suggest that interfering with MHC class I function is one of the mechanisms of how tumor cells escape immune system attack.

Context Specificity in Causal Signaling Networks Revealed by Phosphoprotein Profiling.

Signaling networks downstream of receptor tyrosine kinases are among the most extensively studied biological networks, but new approaches are needed to elucidate causal relationships between network components and understand how such relationships are influenced by biological context and disease. Here, we investigate the context specificity of signaling networks within a causal conceptual framework using reverse-phase protein array time-course assays and network analysis approaches. We focus on a well-defined set of signaling proteins profiled under inhibition with five kinase inhibitors in 32 contexts: four breast cancer cell lines (MCF7, UACC812, BT20, and BT549) under eight stimulus conditions. The data, spanning multiple pathways and comprising ∼70,000 phosphoprotein and ∼260,000 protein measurements, provide a wealth of testable, context-specific hypotheses, several of which we experimentally validate. Furthermore, the data provide a unique resource for computational methods development, permitting empirical assessment of causal network learning in a complex, mammalian setting.