Comparative Analysis of First-Line FOLFOX Treatment With and Without Anti-VEGF Therapy in Metastatic Colorectal Carcinoma: A Real-World Data Study.

BACKGROUND: FOLFOX (leucovorin calcium [folinic acid], fluorouracil, and oxaliplatin) combined with or without anti-VEGF therapy represents one of the primary first-line treatment options for metastatic colorectal carcinoma (mCRC). However, there is limited comparative data on the impact of anti-VEGF therapy on treatment effectiveness, survival outcomes, and tumor location. METHODS: This retrospective, comparative study utilized data from the AIM Cancer Care Quality Program and commercially insured patients treated at medical oncology clinics in the US. We analyzed 1652 mCRC patients who received FOLFOX, of which 1015 (61.4%) were also treated with anti-VEGF therapy (VEGF cohort). RESULTS: Patients in the VEGF cohort exhibited a higher frequency of lung (33% vs 23%; P < .001) and liver metastases (74% vs 62%; P < .001), underwent fewer liver surgeries prior to treatment (1.2% vs 3.6%; P = .002), and had a higher proportion of right-sided tumors (27% vs 18%; P = .001). Adjusted analysis revealed no significant difference in overall survival (OS) between patients treated with and without anti-VEGF (median survival: 25.4 vs 26.0 months; P = .4). FOLFOX-only treated patients experienced higher rates of post-treatment hospitalizations (22% vs 15%; P < .001). Notably, left-sided tumors treated with anti-VEGF showed a trend toward decreased OS (median survival: 26.8 vs 33 months; P = .09). CONCLUSION: Our real-world data analysis suggests that the addition of anti-VEGF to FOLFOX offers limited and short-lived benefits in the context of mCRC and may provide differential survival benefit based on tumor sidedness.

Polycomb-mediated methylation on Lys27 of histone H3 pre-marks genes for de novo methylation in cancer.

Many genes associated with CpG islands undergo de novo methylation in cancer. Studies have suggested that the pattern of this modification may be partially determined by an instructive mechanism that recognizes specifically marked regions of the genome. Using chromatin immunoprecipitation analysis, here we show that genes methylated in cancer cells are specifically packaged with nucleosomes containing histone H3 trimethylated on Lys27. This chromatin mark is established on these unmethylated CpG island genes early in development and then maintained in differentiated cell types by the presence of an EZH2-containing Polycomb complex. In cancer cells, as opposed to normal cells, the presence of this complex brings about the recruitment of DNA methyl transferases, leading to de novo methylation. These results suggest that tumor-specific targeting of de novo methylation is pre-programmed by an established epigenetic system that normally has a role in marking embryonic genes for repression.

Evidence for an instructive mechanism of de novo methylation in cancer cells.

DNA methylation has a role in the regulation of gene expression during normal mammalian development but can also mediate epigenetic silencing of CpG island genes in cancer and other diseases. Many individual genes (including tumor suppressors) have been shown to undergo de novo methylation in specific tumor types, but the biological logic inherent in this process is not understood. To decipher this mechanism, we have adopted a new approach for detecting CpG island DNA methylation that can be used together with microarray technology. Genome-wide analysis by this technique demonstrated that tumor-specific methylated genes belong to distinct functional categories, have common sequence motifs in their promoters and are found in clusters on chromosomes. In addition, many are already repressed in normal cells. These results are consistent with the hypothesis that cancer-related de novo methylation may come about through an instructive mechanism.

Comparison of FOLFIRINOX vs Gemcitabine Plus Nab-Paclitaxel as First-Line Chemotherapy for Metastatic Pancreatic Ductal Adenocarcinoma.

Importance: FOLFIRINOX (leucovorin calcium [folinic acid], fluorouracil, irinotecan hydrochloride, and oxaliplatin) and gemcitabine plus nab-paclitaxel are the 2 common first-line therapies for metastatic adenocarcinoma of the pancreas (mPC), but they have not been directly compared in a clinical trial, and comparative clinical data analyses on their effectiveness are limited. Objective: To compare the FOLFIRINOX and gemcitabine plus nab-paclitaxel treatments of mPC in clinical data and evaluate whether there are differences in overall survival and posttreatment complications between them. Design, Setting, and Participants: This retrospective, nonrandomized comparative effectiveness study used data from the AIM Specialty Health-Anthem Cancer Care Quality Program and from administrative claims of commercially insured patients, spanning 388 outpatient centers and clinics for medical oncology located in 44 states across the US. Effectiveness and safety of the treatments were analyzed by matching or adjusting for age, Charlson Comorbidity Index, ECOG performance status (PS) score, Social Deprivation Index (SDI), liver and lymph node metastasis, prior radiotherapy or surgical procedures, and year of treatment. Patients with mPC treated between January 1, 2016, and December 31, 2019, and followed up until June 30, 2020, were included in the analysis. Interventions: Initiation of treatment with FOLFIRINOX or gemcitabine plus nab-paclitaxel. Main Outcomes and Measures: Outcomes were overall survival and posttreatment costs and hospitalization. Median survival time was calculated using Kaplan-Meier estimates adjusted with inverse probability of treatment weighting and 1:1 matching. Results: Among the 1102 patients included in the analysis (618 men [56.1%]; median age, 60.0 [IQR, 55.5-63.7] years), those treated with FOLFIRINOX were younger (median age, 59.1 [IQR, 53.9-63.3] vs 61.2 [IQR, 57.2-64.3] years; P < .001), with better PS scores (226 [39.9%] with PS of 0 in the FOLFIRINOX group vs 176 [32.8%] in the gemcitabine plus nab-paclitaxel group; P = .02), fewer comorbidities (median Charlson Comorbidity Index, 0.0 [IQR, 0.0-1.0] vs 1.0 [IQR, 0.0-1.0]), and lower SDI (median, 36.0 [IQR, 16.2-61.0] vs 42.0 [IQR, 23.8-66.2]). After adjustments, the median overall survival was 9.27 (IQR, 8.74-9.76) and 6.87 (IQR, 6.41-7.66) months for patients treated with FOLFIRINOX and gemcitabine plus nab-paclitaxel, respectively (P < .001). This survival benefit was observed among all subgroups, including different ECOG PS scores, ages, SDIs, and metastatic sites. FOLFIRINOX-treated patients also had 17.3% fewer posttreatment hospitalizations (P = .03) and 20% lower posttreatment costs (P < .001). Conclusions and Relevance: In this comparative effectiveness cohort study, FOLFIRINOX was associated with improved survival of approximately 2 months compared with gemcitabine plus nab-paclitaxel and was also associated with fewer posttreatment complications. A randomized clinical trial comparing these first-line treatments is warranted to test the survival and posttreatment hospitalization (or complications) benefit of FOLFIRINOX compared with gemcitabine plus nab-paclitaxel.

Dynamic proteomics reveals bimodal protein dynamics of cancer cells in response to HSP90 inhibitor.

BACKGROUND: Drugs often kill some cancer cells while others survive. This stochastic outcome is seen even in clonal cells grown under the same conditions. Understanding the molecular reasons for this stochastic outcome is a current challenge, which requires studying the proteome at the single cell level over time. In a previous study we used dynamic proteomics to study the response of cancer cells to a DNA damaging drug, camptothecin. Several proteins showed bimodal dynamics: they rose in some cells and decreased in others, in a way that correlated with eventual cell fate: death or survival. Here we ask whether bimodality is a special case for camptothecin, or whether it occurs for other drugs as well. To address this, we tested a second drug with a different mechanism of action, an HSP90 inhibitor. We used dynamic proteomics to follow 100 proteins in space and time, endogenously tagged in their native chromosomal location in individual living human lung-cancer cells, following drug administration. RESULTS: We find bimodal dynamics for a quarter of the proteins. In some cells these proteins strongly rise in level about 12 h after treatment, but in other cells their level drops or remains constant. The proteins which rise in surviving cells included anti-apoptotic factors such as DDX5, and cell cycle regulators such as RFC1. The proteins that rise in cells that eventually die include pro-apoptotic factors such as APAF1. The two drugs shared some aspects in their single-cell response, including 7 of the bimodal proteins and translocation of oxidative response proteins to the nucleus, but differed in other aspects, with HSP90i showing more bimodal proteins. Moreover, the cell cycle phase at drug administration impacted the probability to die from HSP90i but not camptothecin. CONCLUSIONS: Single-cell dynamic proteomics reveals sub-populations of cells within a clonal cell line with different protein dynamics in response to a drug. These different dynamics correlate with cell survival or death. Bimodal proteins which correlate with cell fate may be potential drug targets to enhance the effects of therapy.

Deconvolving cell cycle expression data with complementary information.

MOTIVATION: In the study of many systems, cells are first synchronized so that a large population of cells exhibit similar behavior. While synchronization can usually be achieved for a short duration, after a while cells begin to lose their synchronization. Synchronization loss is a continuous process and so the observed value in a population of cells for a gene at time t is actually a convolution of its values in an interval around t. Deconvolving the observed values from a mixed population will allow us to obtain better models for these systems and to accurately detect the genes that participate in these systems. RESULTS: We present an algorithm which combines budding index and gene expression data to deconvolve expression profiles. Using the budding index data we first fit a synchronization loss model for the cell cycle system. Our deconvolution algorithm uses this loss model and can also use information from co-expressed genes, making it more robust against noise and missing values. Using expression and budding data for yeast we show that our algorithm is able to reconstruct a more accurate representation when compared with the observed values. In addition, using the deconvolved profiles we are able to correctly identify 15% more cycling genes when compared to a set identified using the observed values. AVAILABILITY: Matlab implementation can be downloaded from the supporting website http://www.cs.cmu.edu/~zivbj/decon/decon.html