Tracking colorectal cancer evolution in time and space

Temporal trends in age and stage-specific incidence of colorectal cancer in Saudi Arabia: A registry-based cohort study between 1997 and 2017

BACKGROUND

In Saudi Arabia, colorectal cancer (CRC) is the most common cancer in men and the third in women, posing a significant health burden. A comprehensive report of CRC incidence rates and trends in Saudi Arabia is lacking. This study aims to examine trends in CRC incidence among the Saudi population.

METHODS

We used data from the Saudi Cancer Registry to examine CRC age-specific incidence rates (ASIR) and age-standardized incidence rates (ASR) between 1997 and 2017. Joinpoint regression analysis was used to determine the magnitude and direction of observed trends stratified by age, sex, and CRC stage at diagnosis. Trends were measured using the annual percentage change (APC) and the average annual percentage change (AAPC) in CRC incidence rates.

RESULTS

In total, 19,463 new CRC cases were identified during the study period. Since 1997, ASR for CRC has steadily increased in men and women overall, irrespective of disease stages. The ASIR increased across all age groups and was more pronounced in older patients. Women aged 40-49 had a higher increase in incidence than men (AAPC= 5.3 % vs.4.7 %). Males aged 70-79 had an AAPC of 10.2 %, twice that of females (AAPC= 4.9 %). A consistent rise in ASIR was observed across all CRC stages and age groups in males and females. In recent years, males under 50 had a higher APC for distant CRC than females, while females aged 50-74 experienced a steeper increase in distant CRC than males.

CONCLUSION

We report a marked increase in the incidence of CRC over time in Saudi Arabia, affecting men and women across all age groups and disease stages at diagnosis. Our findings underscore the need to identify underlying risk factors and to develop and implement effective prevention policies and strategies, including screening programs to facilitate early detection and treatment.

Desorption electrospray ionization mass spectrometry imaging (DESI-MSI) in disease diagnosis: an overview

Tissue analysis, which is essential to histology and is considered the benchmark for the diagnosis and prognosis of many illnesses, including cancer, is significant. During surgery, the surgical margin of the tumor is assessed using the labor-intensive, challenging, and commonly subjective technique known as frozen section histopathology. In the biopsy section, large numbers of molecules can now be visualized at once (ion images) following recent developments in [MSI] mass spectrometry imaging under atmospheric conditions. This is vastly superior to and different from the single optical tissue image processing used in traditional histopathology. This review article will focus on the advancement of desorption electrospray ionization mass spectrometry imaging [DESI-MSI] technique, which is label-free and requires little to no sample preparation. Since the proportion of molecular species in normal and abnormal tissues is different, DESI-MSI can capture ion images of the distributions of lipids and metabolites on biopsy sections, which can provide rich diagnostic information. This is not a systematic review but a summary of well-known, cutting-edge and recent DESI-MSI applications in cancer research between 2018 and 2023.

Metabolically Active Zones Involving Fatty Acid Elongation Delineated by DESI-MSI Correlate with Pathological and Prognostic Features of Colorectal Cancer

Colorectal cancer (CRC) is the second leading cause of cancer deaths. Despite recent advances, five-year survival rates remain largely unchanged. Desorption electrospray ionization mass spectrometry imaging (DESI) is an emerging nondestructive metabolomics-based method that retains the spatial orientation of small-molecule profiles on tissue sections, which may be validated by ‘gold standard’ histopathology. In this study, CRC samples were analyzed by DESI from 10 patients undergoing surgery at Kingston Health Sciences Center. The spatial correlation of the mass spectral profiles was compared with histopathological annotations and prognostic biomarkers. Fresh frozen sections of representative colorectal cross sections and simulated endoscopic biopsy samples containing tumour and non-neoplastic mucosa for each patient were generated and analyzed by DESI in a blinded fashion. Sections were then hematoxylin and eosin (H and E) stained, annotated by two independent pathologists, and analyzed. Using PCA/LDA-based models, DESI profiles of the cross sections and biopsies achieved 97% and 75% accuracies in identifying the presence of adenocarcinoma, using leave-one-patient-out cross validation. Among the m/z ratios exhibiting the greatest differential abundance in adenocarcinoma were a series of eight long-chain or very-long-chain fatty acids, consistent with molecular and targeted metabolomics indicators of de novo lipogenesis in CRC tissue. Sample stratification based on the presence of lympovascular invasion (LVI), a poor CRC prognostic indicator, revealed the abundance of oxidized phospholipids, suggestive of pro-apoptotic mechanisms, was increased in LVI-negative compared to LVI-positive patients. This study provides evidence of the potential clinical utility of spatially-resolved DESI profiles to enhance the information available to clinicians for CRC diagnosis and prognosis.

Epistasis and evolutionary dependencies in human cancers

Cancer evolution is driven by the concerted action of multiple molecular alterations, which emerge and are selected during tumor progression. An alteration is selected when it provides an advantage to the tumor cell. However, the advantage provided by a specific alteration depends on the tumor lineage, cell epigenetic state, and presence of additional alterations. In this case, we say that an evolutionary dependency exists between an alteration and what influences its selection. Epistatic interactions between altered genes lead to evolutionary dependencies (EDs), by favoring or vetoing specific combinations of events. Large-scale cancer genomics studies have discovered examples of such dependencies, and showed that they influence tumor progression, disease phenotypes, and therapeutic response. In the past decade, several algorithmic approaches have been proposed to infer EDs from large-scale genomics datasets. These methods adopt diverse strategies to address common challenges and shed new light on cancer evolutionary trajectories. Here, we review these efforts starting from a simple conceptualization of the problem, presenting the tackled and still unmet needs in the field, and discussing the implications of EDs in cancer biology and precision oncology.

Evaluation of three advanced methodologies, COLD-PCR, microarray and ddPCR, for identifying the mutational status by liquid biopsies in metastatic colorectal cancer patients

A major effort has been focused on the detection of oncogenes' mutations in diverse types of clinical specimens including formalin-fixed and paraffin embedded tissues, presently the gold-standard samples, up to plasma, that constitute a noninvasive alternative source of tumor DNA. The reliable detection of mutations in circulating tumor DNA requires a high analytical sensitivity. Here, we applied three different highly sensitive methodologies (COLD-PCR, a microarray-based approach and the droplet digital PCR, ddPCR) to identify mutations in the plasma of 30 metastatic colorectal cancer patients previously genotyped on tissue biopsy. The methods showed a modest concordance rate with respect to the results obtained on tissue biopsies: 63.3% by ddPCR, 63% by microarray and 55.6% by COLD-PCR. This could be ascribed either to the different timing between tissue and liquid biopsy collection, which could reflect a different stage of disease progression or to the diverse sensitivity of the methodologies applied. Indeed, if we compare the results obtained on plasma samples, the concordance rates were higher especially by comparing ddPCR versus COLD-PCR (92.6%). Thus, we consider both methodologies as useful procedures easily transferable in a clinical setting. Notably, the ddPCR allows a quantitative assessment of the fractional abundance of the mutation.

Advances in Precision Medicine: Tailoring Individualized Therapies

The traditional bench-to-bedside pipeline involves using model systems and patient samples to provide insights into pathways deregulated in cancer. This discovery reveals new biomarkers and therapeutic targets, ultimately stratifying patients and informing cohort-based treatment options. Precision medicine (molecular profiling of individual tumors combined with established clinical-pathological parameters) reveals, in real-time, individual patient's diagnostic and prognostic risk profile, informing tailored and tumor-specific treatment plans. Here we discuss advances in precision medicine presented at the Irish Association for Cancer Research Annual Meeting, highlighting examples where personalized medicine approaches have led to precision discovery in individual tumors, informing customized treatment programs.