Oncologic outcomes following transanal total mesorectal excision: the United States experience

AIM

The benefits and short-term outcomes of transanal total mesorectal excision (taTME) for rectal cancer have been demonstrated previously, but questions remain regarding the oncologic outcomes following this challenging procedure. The purpose of this study was to analyze the oncologic outcomes following taTME at high-volume centers in the USA.

METHODS

This was a multicenter, retrospective observational study of 8 tertiary care centers. All consecutive taTME cases for primary rectal cancer performed between 2011 and 2020 were included. Clinical, histopathologic, and oncologic data were analyzed. Primary endpoints were rate of local recurrence, distal recurrence, 3-year disease recurrence, and 3-year overall survival. Secondary endpoints included perioperative complications and TME specimen quality.

RESULTS

A total of 391 patients were included in the study. The median age was 57 years (IQR: 49, 66), 68% of patients were male, and the median BMI was 27.4 (IQR: 24.1, 31.0). TME specimen was complete or near complete in 94.5% of cases and the rates of positive circumferential radial margin and distal resection margin were 2.0% and 0.3%, respectively. Median follow-up time was 30.7 months as calculated using reverse-KM estimator (CI 28.1-33.8) and there were 9 cases (2.5%) of local recurrence not accounting for competing risk. The 3-year estimated rate of disease recurrence was 19% (CI 15-25%) and the 3-year estimated overall survival was 90% (CI 87-94%).

CONCLUSION

This large multicenter study confirms the oncologic safety and perioperative benefits of taTME for rectal cancer when performed by experienced surgeons at experienced referral centers.

Abstract A47: Characterization of immune cell composition across cancer types in pan-cancer genomic cohorts

Introduction: Identifying immune cell signatures in individual tumors can help guide treatment selection for patients. Numerous deconvolution methods have been developed to estimate immune cell fractions from bulk gene expression data, but they have yet to be systematically applied and cross-validated in pan-cancer genomic cohorts. We undertook this study to cross-validate immune cell quantification methods across 25 cancer types spanning 11,011 samples and provide a public immuno-oncology resource. Methods: Using gene expression data from both The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC), we employed and compared six methods to estimate immune cell fractions in each cancer type (CIBERSORT, quanTIseq, EPIC, TIMER, MCP-counter, xCell; immunedeconv R package). We mapped these results to a common vocabulary of five broad cell categories for comparison: T cells, B cells, natural killer cells (NK cells), macrophages/monocytes, and myeloid dendritic cells (mDCs). In parallel, we computed immune cell proportions for seven cancer types using single-cell RNA-seq (scRNA-seq) data from a single-cell tumor immune atlas of 217 patients. We correlated the median immune cell fractions estimated from bulk deconvolution with scRNA-seq proportions for each cancer type. To demonstrate the application of this resource, we compared the immune cell fractions (1) in adjacent normal versus tumor tissues, and (2) in head and neck squamous cell carcinoma (HNSC) and colorectal adenocarcinoma (COAD) tumor subtypes using multivariable linear regression adjusted for age and sex. Results: Overall, 9,689 TCGA samples and 1,322 ICGC samples were analyzed, and correlations across six deconvolution tools were performed. Two well-performing cell estimation methods, EPIC and quanTIseq, demonstrated good correlation between median deconvoluted T cell enrichment score and single-cell cytotoxic CD8+ T cell populations (spearman coefficient; EPIC = 0.71, quanTIseq = 0.43). However, cross-cancer type correlations between scRNA-seq and bulk estimated fractions were not statistically significant for most cancer types. In multivariable regression comparing immune cell estimates across cancer subtypes, we found increased T cell (EPIC: OR=1.65, 95% CI = 1.23-2.21, quanTIseq: OR = 1.30, 95% CI = 1.20-1.40) and B cell (EPIC: OR = 2.92, 95% CI = 2.05-4.15, quanTIseq: OR = 1.48, 95% CI = 1.37-1.60) enrichment in HPV-positive compared to HPV-negative HNSC. In COAD, we found increased T cell enrichment in the microsatellite instability (MSI) subtype compared to the genome stable subtype (OR = 1.16, 95% CI = 1.01-1.33). Conclusion: Overall, our study provides a public resource of immune cell annotations for two widely used, pan-cancer genomics cohorts and can facilitate future studies that aim to characterize the tumor immune microenvironment. Furthermore, we validated the use of this resource by demonstrating enrichment of specific immune cell subsets in HNSC and COAD subtypes, consistent with prior reports of these well-characterized cancers.

Citation Format: Makda Getachew Zewde, Daniel Fulop, Alexander Tsankov, Kuan-lin Huang. Characterization of immune cell composition across cancer types in pan-cancer genomic cohorts [abstract]. In: Proceedings of the AACR Special Conference: Tumor Immunology and Immunotherapy; 2022 Oct 21-24; Boston, MA. Philadelphia (PA): AACR; Cancer Immunol Res 2022;10(12 Suppl):Abstract nr A47.

Methodological Considerations on COVID-19 Mortality in Cancer Patients: A Systematic Review and Meta-Analysis

Background

Patients with cancer are at risk for severe COVID-19. Previous studies examining mortality in cancer patients with COVID-19 have produced inconclusive results. Several published meta-analyses have aimed to estimate this association; however, because of methodological limitations in study selection and data aggregation, these studies do not reliably estimate the independent association between cancer and COVID-19 mortality. We conducted this systematic review and meta-analysis to determine whether cancer is an independent risk factor for COVID-19 mortality.

Methods

A literature search was performed in PubMed to identify studies that compared COVID-19 mortality in adult patients with and without cancer. Selection criteria included polymerase chain reaction–confirmed COVID-19, multivariate adjustment and/or matching for mortality risk estimates, and inclusion of hospitalized noncancer controls. Adjusted odds ratios and/or hazard ratios for mortality based on cancer status were extracted. Odds ratio and hazard ratio estimates were pooled using a random effects model.

Results

The analysis included 42 studies comprising 129 840 patients: 8612 cancer patients and 121 228 noncancer patients. Of these studies, 18 showed a null difference in survival between cancer and noncancer patients with COVID-19, and 24 studies showed statistically significantly worse survival in cancer patients with COVID-19. Meta-analysis revealed an increased risk of mortality in patients with cancer compared with noncancer patients with COVID-19 (odds ratio = 1.93, 95% confidence interval = 1.55 to 2.41; hazard ratio = 1.54, 95% confidence interval = 1.29 to 1.84).

Conclusion

We conclude that cancer is an independent risk factor for mortality in unvaccinated patients admitted for or diagnosed with COVID-19 during hospitalization.

Evaluation of Elafin as a Prognostic Biomarker in Acute Graft-versus-Host Disease

Acute graft-versus-host disease (GVHD) is a major cause of mortality in patients undergoing hematopoietic cell transplantation (HCT) for hematologic malignancies. The skin is the most commonly involved organ in GVHD. Elafin, a protease inhibitor overexpressed in inflamed epidermis, was previously identified as a diagnostic biomarker of skin GVHD; however, this finding was restricted to a subset of patients with isolated skin GVHD. The main driver of nonrelapse mortality (NRM) in HCT recipients is gastrointestinal (GI) GVHD. Two biomarkers, Regenerating islet-derived 3a (REG3α) and Suppressor of tumorigenesis 2 (ST2), have been validated as biomarkers of GI GVHD that predict long-term outcomes in patients treated for GVHD. We undertook this study to determine the utility of elafin as a prognostic biomarker in the general population of acute GVHD patients in whom GVHD may develop in multiple organs. We analyzed serum elafin concentrations as a predictive biomarker of acute GVHD outcomes and compared it with ST2 and REG3α in a large group of patients treated at multiple centers. A total of 526 patients from the Mount Sinai Acute GVHD International Consortium (MAGIC) who had received corticosteroid treatment for skin GVHD and who had not been previously studied were analyzed. Serum concentrations of elafin, ST2, and REG3α were measured by ELISA in all patients. The patients were divided at random into equal training and validation sets, and a competing-risk regression model was developed to model 6-month NRM using elafin concentration in the training set. Additional models were developed using concentrations of ST2 and REG3α or the combination of all 3 biomarkers as predictors. Receiver operating characteristic (ROC) curves were constructed using the validation set to evaluate the predictive accuracy of each model and to stratify patients into high- and low-risk biomarker groups. The cumulative incidence of 6-month NRM, overall survival (OS), and 4-week treatment response were compared between the risk groups. Unexpectedly, patients in the low-risk elafin group demonstrated a higher incidence of 6-month NRM, although the difference was not statistically significant (17% versus 11%; P = .19). OS at 6 months (68% versus 68%; P > .99) and 4-week response (78% versus 78%; P = .98) were similar in the low-risk and high-risk elafin groups. The area under the ROC curve (AUC) was 0.55 for elafin and 0.75 for the combination of ST2 and REG3α. The addition of elafin to the other 2 biomarkers did not improve the AUC. Our data indicate that serum elafin concentrations measured at the initiation of systemic treatment for acute GVHD did not predict 6-month NRM, OS, or treatment response in a multicenter population of patients treated systemically for acute GVHD. As seen in previous studies, serum concentrations of the GI GVHD biomarkers ST2 and REG3α were significant predictors of NRM, and the addition of elafin levels did not improve their accuracy. These results underscore the importance of GI disease in driving NRM in patients who develop acute GVHD.