Disparities in Characteristics, Access to Care, and Oncologic Outcomes in Young-Onset Colorectal Cancer at a Safety-Net Hospital

Racial disparities in colorectal cancer clinicopathological and molecular tumor characteristics: a systematic review

PURPOSE

African Americans have the highest colorectal cancer (CRC) mortality of all racial groups in the USA, which may relate to differences in healthcare access or advanced stage at diagnosis. Recent evidence indicates that differences in tumor characteristics may also underlie disparities in mortality. To highlight recent findings and areas for investigation, we completed the first systematic review of racial disparities in CRC tumor prognostic markers, including clinicopathological markers, microsatellite instability (MSI), oncogene mutations, and novel markers, including cancer stem cells and immune markers.

METHODS

Relevant studies were identified via PubMed, limited to original research published within the last 10 years. Ninety-six articles were identified that compared the prevalence of mortality-related CRC tumor characteristics in African Americans (or other African ancestry populations) to White cases.

RESULTS

Tumors from African ancestry cases are approximately 10% more likely to contain mutations in KRAS, which confer elevated mortality and resistance to epidermal growth factor receptor inhibition. Conversely, African Americans have approximately 50% lower odds for BRAF-mutant tumors, which occur less frequently but have similar effects on mortality and therapeutic resistance. There is less consistent evidence supporting disparities in mutations for other oncogenes, including PIK3CA, TP53, APC, NRAS, HER2, and PTEN, although higher rates of PIK3CA mutations and lower prevalence of MSI status for African ancestry cases are supported by recent evidence. Although emerging evidence suggests that immune markers reflecting anti-tumor immunity in the tumor microenvironment may be lower for African American cases, there is insufficient evidence to evaluate disparities in other novel markers, cancer stem cells, microRNAs, and the consensus molecular subtypes.

CONCLUSION

Higher rates of KRAS-mutant tumors in in African Americans may contribute to disparities in CRC mortality. Additional work is required to understand whether emerging markers, including immune cells, underlie the elevated CRC mortality observed for African Americans.

Perioperative outcomes of robotic and laparoscopic surgery for colorectal cancer: a propensity score-matched analysis

BACKGROUND

Robotic colorectal surgery is becoming the preferred surgical approach for colorectal cancer (CRC). It offers several technical advantages over conventional laparoscopy that could improve patient outcomes. In this retrospective cohort study, we compared robotic and laparoscopic surgery for CRC using a national cohort of patients.

METHODS

Using the colectomy-targeted ACS-NSQIP database (2015-2020), colorectal procedures for malignant etiologies were identified by CPT codes for right colectomy (RC), left colectomy (LC), and low anterior resection (LAR). Optimal pair matching was performed. "Textbook outcome" was defined as the absence of 30-day complications, readmission, or mortality and a length of stay < 5 days.

RESULTS

We included 53,209 out of 139,759 patients screened for eligibility. Laparoscopic-to-robotic matching of 2:1 was performed for RC and LC, and 1:1 for LAR. The largest standardized mean difference was 0.048 after matching. Robotic surgery was associated with an increased rate of textbook outcomes compared to laparoscopy in RC and LC, but not in LAR (71% vs. 64% in RC, 75% vs. 68% in LC; p < 0.001). Robotic LAR was associated with increased major morbidity (7.1% vs. 5.8%; p = 0.012). For all three procedures, the mean conversion rate of robotic surgery was lower than laparoscopy (4.3% vs. 9.2%; p < 0.001), while the mean operative time was higher for robotic (225 min vs. 177 min; p < 0.001).

CONCLUSIONS

Robotic surgery for CRC offers an advantage over conventional laparoscopy by improving textbook outcomes in RC and LC. This advantage was not found in robotic LAR, which also showed an increased risk of serious complications. The associations highlighted in our study should be considered in the discussion of the surgical management of patients with colorectal cancer.

A Single Axial Slice of the Sternocleidomastoids and Paravertebral Muscles Associated with Worse Local Progression-Free Survival and Severe Toxicity in Sarcopenic Head and Neck Cancer Patients Undergoing Radiotherapy

Objective

The objective of this study is to contrast the predictive ability of targeted muscle groups as radiographic proxies of sarcopenia on computerized tomography (CT) with body mass index (BMI) in head and neck cancer patients (H&NCP) undergoing radiation at a safety net hospital, and to evaluate sarcopenia with survival, local progression, toxicities and treatment delays.

Methods

A retrospective review included 52 H&NCP treated between 2017-2019. The posterior neck muscles (PN), sternocleidomastoids (SCM), and their summed volume (AM) were contoured at C3 on patients’ pre-treatment CT scans, then normalized to obtain skeletal muscle index (MI) values. Pre-treatment BMI was also evaluated. Cutoffs for sarcopenia were determined by receiver operating characteristic curves. Overall survival and local recurrence-free survival were evaluated by Kaplan-Meier. Acute grade 3 or higher toxicities were evaluated by binomial logistic regression.

Results

Using all neck muscles (AM-MI) produced the best model for predicting outcomes, outperforming individual muscle groups and BMI. Local progression-free survival was worse in sarcopenic patients at 25.81 months versus 35.40 months (p=0.026). Acute grade 3 or higher toxicities were associated with sarcopenia (p=0.005).

Conclusions

In this small, retrospective single-institution experience at a safety net hospital, a single axial slice of the combined sternocleidomastoids and paravertebral muscles at C3 performed better than either muscle group alone or pre-treatment BMI at predicting oncologic outcomes.