Clinical utility of pretreatment prediction of chemoradiotherapy response in rectal cancer: a review

Tumour budding as a prognostic biomarker in biopsies and resections of neoadjuvant-treated rectal adenocarcinoma

BACKGROUND

Tumour budding (TB) is a marker of tumour aggressiveness which, when measured in rectal cancer resection specimens, predicts worse outcomes and response to neoadjuvant therapy. We investigated the utility of TB assessment in the setting of neoadjuvant treatment.

METHODS AND RESULTS

A single-centre, retrospective cohort study was conducted. TB was assessed using the hot-spot International Tumour Budding Consortium (ITBCC) method and classified by the revised ITBCC criteria. Haematoxylin and eosin (H&E) and AE1/AE3 cytokeratin (CK) stains for ITB (intratumoural budding) in biopsies with PTB (peritumoural budding) and ITB (intratumoural budding) in resection specimens were compared. Logistic regression assessed budding as predictors of lymph node metastasis (LNM). Cox regression and Kaplan-Meier analyses investigated their utility as a predictor of disease-free (DFS) and overall (OS) survival. A total of 146 patients were included; 91 were male (62.3%). Thirty-seven cases (25.3%) had ITB on H&E and 79 (54.1%) had ITB on CK assessment of biopsy tissue. In univariable analysis, H&E ITB [odds (OR) = 2.709, 95% confidence interval (CI) = 1.261-5.822, P = 0.011] and CK ITB (OR = 2.165, 95% CI = 1.076-4.357, P = 0.030) predicted LNM. Biopsy-assessed H&E ITB (OR = 2.749, 95% CI = 1.258-6.528, P = 0.022) was an independent predictor of LNM. In Kaplan-Meier analysis, ITB identified on biopsy was associated with worse OS (H&E, P = 0.003, CK: P = 0.009) and DFS (H&E, P = 0.012; CK, P = 0.045). In resection specimens, CK PTB was associated with worse OS (P = 0.047), and both CK PTB and ITB with worse DFS (PTB, P = 0.014; ITB: P = 0.019). In multivariable analysis H&E ITB predicted OS (HR = 2.930, 95% CI = 1.261-6.809) and DFS (HR = 2.072, 95% CI = 1.031-4.164). CK PTB grading on resection also independently predicted OS (HR = 3.417, 95% CI = 1.45-8.053, P = 0.005).

CONCLUSION

Assessment of TB using H&E and CK may be feasible in rectal cancer biopsy and post-neoadjuvant therapy-treated resection specimens and is associated with LNM and worse survival outcomes. Future management strategies for rectal cancer might be tailored to incorporate these findings.

Prediction of Pathologic Complete Response for Rectal Cancer Based on Pretreatment Factors Using Machine Learning

BACKGROUND

Pathologic complete response after neoadjuvant therapy is an important prognostic indicator for locally advanced rectal cancer and may give insights into which patients might be treated nonoperatively in the future. Existing models for predicting pathologic complete response in the pretreatment setting are limited by small data sets and low accuracy.

OBJECTIVE

We sought to use machine learning to develop a more generalizable predictive model for pathologic complete response for locally advanced rectal cancer.

DESIGN

Patients with locally advanced rectal cancer who underwent neoadjuvant therapy followed by surgical resection were identified in the National Cancer Database from years 2010 to 2019 and were split into training, validation, and test sets. Machine learning techniques included random forest, gradient boosting, and artificial neural network. A logistic regression model was also created. Model performance was assessed using an area under the receiver operating characteristic curve.

SETTINGS

This study used a national, multicenter data set.

PATIENTS

Patients with locally advanced rectal cancer who underwent neoadjuvant therapy and proctectomy.

MAIN OUTCOME MEASURES

Pathologic complete response defined as T0/xN0/x.

RESULTS

The data set included 53,684 patients. Pathologic complete response was experienced by 22.9% of patients. Gradient boosting showed the best performance with an area under the receiver operating characteristic curve of 0.777 (95% CI, 0.773-0.781), compared with 0.684 (95% CI, 0.68-0.688) for logistic regression. The strongest predictors of pathologic complete response were no lymphovascular invasion, no perineural invasion, lower CEA, smaller size of tumor, and microsatellite stability. A concise model including the top 5 variables showed preserved performance.

LIMITATIONS

The models were not externally validated.

CONCLUSIONS

Machine learning techniques can be used to accurately predict pathologic complete response for locally advanced rectal cancer in the pretreatment setting. After fine-tuning a data set including patients treated nonoperatively, these models could help clinicians identify the appropriate candidates for a watch-and-wait strategy. See Video Abstract .

EL CNCER DE RECTO BASADA EN FACTORES PREVIOS AL TRATAMIENTO MEDIANTE EL APRENDIZAJE AUTOMTICO

ANTECEDENTES:La respuesta patológica completa después de la terapia neoadyuvante es un indicador pronóstico importante para el cáncer de recto localmente avanzado y puede dar información sobre qué pacientes podrían ser tratados de forma no quirúrgica en el futuro. Los modelos existentes para predecir la respuesta patológica completa en el entorno previo al tratamiento están limitados por conjuntos de datos pequeños y baja precisión.OBJETIVO:Intentamos utilizar el aprendizaje automático para desarrollar un modelo predictivo más generalizable para la respuesta patológica completa para el cáncer de recto localmente avanzado.DISEÑO:Los pacientes con cáncer de recto localmente avanzado que se sometieron a terapia neoadyuvante seguida de resección quirúrgica se identificaron en la Base de Datos Nacional del Cáncer de los años 2010 a 2019 y se dividieron en conjuntos de capacitación, validación y prueba. Las técnicas de aprendizaje automático incluyeron bosque aleatorio, aumento de gradiente y red neuronal artificial. También se creó un modelo de regresión logística. El rendimiento del modelo se evaluó utilizando el área bajo la curva característica operativa del receptor.ÁMBITO:Este estudio utilizó un conjunto de datos nacional multicéntrico.PACIENTES:Pacientes con cáncer de recto localmente avanzado sometidos a terapia neoadyuvante y proctectomía.PRINCIPALES MEDIDAS DE VALORACIÓN:Respuesta patológica completa definida como T0/xN0/x.RESULTADOS:El conjunto de datos incluyó 53.684 pacientes. El 22,9% de los pacientes experimentaron una respuesta patológica completa. El refuerzo de gradiente mostró el mejor rendimiento con un área bajo la curva característica operativa del receptor de 0,777 (IC del 95%: 0,773 - 0,781), en comparación con 0,684 (IC del 95%: 0,68 - 0,688) para la regresión logística. Los predictores más fuertes de respuesta patológica completa fueron la ausencia de invasión linfovascular, la ausencia de invasión perineural, un CEA más bajo, un tamaño más pequeño del tumor y la estabilidad de los microsatélites. Un modelo conciso que incluye las cinco variables principales mostró un rendimiento preservado.LIMITACIONES:Los modelos no fueron validados externamente.CONCLUSIONES:Las técnicas de aprendizaje automático se pueden utilizar para predecir con precisión la respuesta patológica completa para el cáncer de recto localmente avanzado en el entorno previo al tratamiento. Después de realizar ajustes en un conjunto de datos que incluye pacientes tratados de forma no quirúrgica, estos modelos podrían ayudar a los médicos a identificar a los candidatos adecuados para una estrategia de observar y esperar. (Traducción-Dr. Ingrid Melo ).

Prognostic Significance of MRE11 Overexpression in Colorectal Cancer Patients

Meiotic recombination 11 (MRE11) plays a critical role in the DNA damage response and maintenance of genome stability and is associated with the prognosis for numerous malignancies. Here, we explored the clinicopathological significance and prognostic value of MRE11 expression in colorectal cancer (CRC), a leading cause of cancer-related deaths worldwide. Samples from 408 patients who underwent surgery for colon and rectal cancer between 2006 and 2011, including a sub-cohort of 127 (31%) patients treated with adjuvant therapy, were analyzed. In Kaplan-Meier survival analyses, we found that high MRE11 expression in the tumor center (TC) was significantly associated with poor disease-free survival (DFS; p = 0.045) and overall survival (OS; p = 0.039). Intriguingly, high MRE11 expression in the TC was also significantly correlated with reduced DFS (p = 0.005) and OS (p = 0.010) in the subgroup with right-sided primary CRC. In multivariate analyses, high MRE11 expression (hazard ratio [HR] = 1.697, 95% confidence interval [CI]: 1.034-2.785; p = 0.036) and lymphovascular/perineural invasion (LVI/PNI; HR = 1.922, 95% CI 1.122-3.293; p = 0.017) showed significant association with worse OS in patients with right-sided tumors but not those with left-sided tumors. Moreover, in patients with right-sided tumors, high MRE11 was associated with worse OS for those with lymph node involvement (p = 0.006) and LVI/PNI (p = 0.049). Collectively, our results suggest that MRE11 may serve as an independent prognostic marker in those with right-sided severe CRC, with clinical value in the management of these patients.

A novel prediction model for pathological complete response based on clinical and blood parameters in locally advanced rectal cancer

Background

The aim of this study was to investigate whether clinical and blood parameters can be used for predicting pathological complete response (pCR) to neoadjuvant chemoradiotherapy (nCRT) in patients with locally advanced rectal cancer (LARC).

Methods

We retrospectively enrolled 226 patients with LARC [allocated in a 7:3 ratio to a training (n = 158) or validation (n = 68) cohort] who received nCRT before radical surgery. Backward stepwise logistic regression was performed to identify clinical and blood parameters associated with achieving pCR. Models based on clinical parameters (CP), blood parameters (BP), and clinical-blood parameters (CBP) were constructed for comparison with previously reported Tan's model. The performance of the four models was evaluated by receiver operating characteristic (ROC) curve analysis, calibration, and decision curve analysis (DCA) in both cohorts. A dynamic nomogram was constructed for the presentation of the best model.

Results

The CP and BP models based on multivariate logistic regression analysis showed that interval, Grade, CEA and fibrinogen-albumin ratio index (FARI), sodium-to-globulin ratio (SGR) were the independent clinical and blood predictors for achieving pCR, respectively. The area under the ROC curve of the CBP model achieved a score of 0.818 and 0.752 in both cohorts, better than CP (0.762 and 0.589), BP (0.695 and 0.718), Tan (0.738 and 0.552). CBP also showed better calibration and DCA than other models in both cohorts. Moreover, CBP revealed significant improvement compared with other models in training cohort (P < 0.05), and CBP showed significant improvement compared with CP and Tan's model in validation cohort (P < 0.05).

Conclusion

We demonstrated that CBP predicting model have potential in predicting pCR to nCRT in patient with LARC.

Prognostic Impact of TP53 Mutations and Tumor Mutational Load in Colorectal Cancer

The DNA damage response (DDR) is critical for maintaining genome stability, and abnormal DDR—resulting from mutations in DNA damage-sensing and repair proteins—is a hallmark of cancer. Here, we aimed to investigate the predictive power of DDR gene mutations and the tumor mutational load (TML) for survival outcomes in a cohort of 22 rectal cancer patients who received pre-operative neoadjuvant therapy. Univariate analysis revealed that TML-high and TP53 mutations were significantly associated with worse overall survival (OS) with TML-high retaining significance in multivariate analyses. Kaplan–Meier survival analyses further showed TML-high was associated with worse disease-free (p = 0.036) and OS (p = 0.024) results in our patient cohort. A total of 53 somatic mutations were identified in 22 samples with eight (36%) containing mutations in DDR genes, including ATM, ATR, CHEK2, MRE11A, RAD50, NBN, ERCC2 and TP53. TP53 was the most frequently mutated gene, and TP53 mutations were significantly associated with worse OS (p = 0.023) in Kaplan–Meier survival analyses. Thus, our data indicate that TML and TP53 mutations have prognostic value for rectal cancer patients and may be important independent biomarkers for patient management. This suggests that prognostic determination for rectal cancer patients receiving pre-operative neoadjuvant therapy should include consideration of the initial TML and tumor genetic status.

Gene-expression profiles of pretreatment biopsies predict complete response of rectal cancer patients to preoperative chemoradiotherapy

Purpose

Preoperative (neoadjuvant) chemoradiotherapy (CRT) and total mesorectal excision is the standard treatment for rectal cancer patients (UICC stage II/III). Up to one-third of patients treated with CRT achieve a pathological complete response (pCR). These patients could be spared from surgery and its associated morbidity and mortality, and assigned to a “watch and wait” strategy. However, reliably identifying pCR based on clinical or imaging parameters remains challenging.

Experimental design

We generated gene-expression profiles of 175 patients with locally advanced rectal cancer enrolled in the CAO/ARO/AIO-94 and -04 trials. One hundred and sixty-one samples were used for building, training and validating a predictor of pCR using a machine learning algorithm. The performance of the classifier was validated in three independent cohorts, comprising 76 patients from (i) the CAO/ARO/AIO-94 and -04 trials (n = 14), (ii) a publicly available dataset (n = 38) and (iii) in 24 prospectively collected samples from the TransValid A trial.

Results

A 21-transcript signature yielded the best classification of pCR in 161 patients (Sensitivity: 0.31; AUC: 0.81), when not allowing misclassification of non-complete-responders (False-positive rate = 0). The classifier remained robust when applied to three independent datasets (n = 76).

Conclusion

The classifier can identify >1/3 of rectal cancer patients with a pCR while never classifying patients with an incomplete response as having pCR. Importantly, we could validate this finding in three independent datasets, including a prospectively collected cohort. Therefore, this classifier could help select rectal cancer patients for a “watch and wait” strategy.

Translational relevance

Forgoing surgery with its associated side effects could be an option for rectal cancer patients if the prediction of a pathological complete response (pCR) after preoperative chemoradiotherapy would be possible. Based on gene-expression profiles of 161 patients a classifier was developed and validated in three independent datasets (n = 76), identifying over 1/3 of patients with pCR, while never misclassifying a non-complete-responder. Therefore, the classifier can identify patients suited for “watch and wait”.

Differential Expression of the Sphingolipid Pathway Is Associated with Sensitivity to the PP2A Activator FTY720 in Colorectal Cancer Cell Lines

Protein phosphatase 2A (PP2A) is a ubiquitously expressed intracellular serine/threonine phosphatase. Deregulation of PP2A is a common event associated with adenocarcinomas of the colon and rectum. We have previously shown that breast cancer cell lines are sensitive to the PP2A activator FTY720, and that sensitivity is predicted by high Aurora kinase A (AURKA) mRNA expression. In this study, we hypothesized that high relative AURKA expression could predict sensitivity to FTY720-induced apoptosis in colorectal cancer (CRC). The CRC cell lines NCI H716, COLO320DM, DLD-1, SW480, and HT-29 show a high relative AURKA expression as compared to LS411N, T84, HCT116, SW48, and LOVO. Following viability assays, LS411N, T84, HCT116, and SW480 were shown to be sensitive to FTY720, whereas DLD-1 and HT-29 were non-sensitive. Hence, AURKA mRNA expression does not predict sensitivity to FTY720 in CRC cell lines. Differentially expressed genes (DEGs) were obtained by comparing the sensitive CRC cell lines (LS411N and HCT116) against the non-sensitive (HT-29 and DLD-1). We found that 253 genes were significantly altered in expression, and upregulation of CERS4, PPP2R2C, GNAZ, PRKCG, BCL2, MAPK12, and MAPK11 suggests the involvement of the sphingolipid signaling pathway, known to be activated by phosphorylated-FTY720. In conclusion, although AURKA expression did not predict sensitivity to FTY720, it is evident that specific CRC cell lines are sensitive to 5 µM FTY720, potentially because of the differential expression of genes involved in the sphingolipid pathway.

Evaluation and Predictive Factors of Complete Response in Rectal Cancer after Neoadjuvant Chemoradiation Therapy

The response to neoadjuvant chemoradiation therapy is an important prognostic factor for locally advanced rectal cancer. Although the majority of the patients after neoadjuvant therapy are referred to following surgery, the clinical data show that complete clinical or pathological response is found in a significant proportion of the patients. Diagnostic accuracy of confirming the complete response has a crucial role in further management of a rectal cancer patient. As the rate of clinical complete response, unfortunately, is not always consistent with pathological complete response, accurate diagnostic parameters and predictive markers of tumor response may help to guide more personalized treatment strategies and identify potential candidates for nonoperative management more safely. The management of complete response demands interdisciplinary collaboration including oncologists, radiotherapists, radiologists, pathologists, endoscopists and surgeons, because the absence of a multidisciplinary approach may compromise the oncological outcome. Prediction and improvement of rectal cancer response to neoadjuvant therapy is still an active and challenging field of further research. This literature review is summarizing the main, currently known clinical information about the complete response that could be useful in case if encountering such condition in rectal cancer patients after neoadjuvant chemoradiation therapy, using as a source PubMed publications from 2010–2021 matching the search terms “rectal cancer”, “neoadjuvant therapy” and “response”.

Long non-coding RNA HOTAIR knockdown enhances radiosensitivity through regulating microRNA-93/ATG12 axis in colorectal cancer

Colorectal cancer (CRC) is a global healthcare problem. Radioresistance is a huge setback for CRC radiotherapy. In this text, the roles and molecular mechanisms of long non-coding RNA HOTAIR in CRC tumorigenesis and radioresistance were further investigated. ATG12 mRNA, HOTAIR, and microRNA-93 (miR-93) levels were measured by quantitative reverse transcription polymerase chain reaction (RT-qPCR) assay. Protein levels of LC3 I, LC3 II, p62, ATG12, cleaved caspase 3, Bax, and Bcl-2 were detected by western blotting assay in cells and were examined by immunohistochemistry (IHC) assay in tissues. Cell survival fractions, viability, and apoptotic rates were determined by clonogenic survival assay, CCK-8 assay, and flow cytometry analysis, respectively. The relationships of HOTAIR, miR-93, and ATG12 were tested by bioinformatics analysis and luciferase reporter assay. Mouse xenograft tumor models were established to investigate the influence of HOTAIR knockdown on CRC radioresistance in vivo. We found that HOTAIR expression was markedly upregulated in plasma from CRC patients after radiotherapy and CRC cells after irradiation. HOTAIR knockdown, miR-93 overexpression, or ATG12 silencing weakened cell viability, induced cell apoptosis, inhibited cell autophagy, and enhanced cell radiosensitivity in CRC. HOTAIR exerted its functions by downregulating miR-93. Moreover, HOTAIR functioned as a molecular sponge of miR-93 to regulate ATG12 expression. ATG12 protein expression was markedly upregulated and associated with miR-93 and HOTAIR expression in CRC tissues. Furthermore, HOTAIR knockdown enhanced radiosensitivity of CRC xenograft tumors by regulating miR-93/ATG12 axis. In conclusion, HOTAIR knockdown potentiated radiosensitivity through regulating miR-93/ATG12 axis in CRC, further elucidating the roles and molecular basis of HOTAIR in CRC radioresistance.

Aberrant Expression of RAD52, Its Prognostic Impact in Rectal Cancer and Association with Poor Survival of Patients

The DNA damage response enables cells to survive and maintain genome integrity. RAD52 is a DNA-binding protein involved in the homologous recombination in DNA repair, and is important for the maintenance of tumour genome integrity. We investigated possible correlations between RAD52 expression and cancer survival and response to preoperative radiotherapy. RAD52 expression was examined in tumour samples from 179 patients who underwent surgery for rectal cancer, including a sub-cohort of 40 patients who were treated with neoadjuvant therapy. A high score for RAD52 expression in the tumour centre was significantly associated with worse disease-free survival (DFS; p = 0.045). In contrast, reduced RAD52 expression in tumour centre samples from patients treated with neoadjuvant therapy (n = 40) significantly correlated with poor DFS (p = 0.025) and overall survival (OS; p = 0.048). Our results suggested that RAD52 may have clinical value as a prognostic marker of tumour response to neoadjuvant radiation and both disease-free status and overall survival in patients with rectal cancer.

Circulating Biomarkers for Response Prediction of Rectal Cancer to Neoadjuvant Chemoradiotherapy

Rectal cancer response to neoadjuvant Chemoradiotherapy (pCRT) is highly variable. In fact, it has been estimated that only about 21 % of patients show pathologic Complete Response (pCR) after therapy, while in most of the patients a partial or incomplete tumour regression is observed. Consequently, patients with a priori chemoradioresistant tumour should not receive the treatment, which is associated with substantial adverse effects and does not guarantee any clinical benefit. For Locally Advanced Rectal Cancer Patients (LARC), a standardized neoadjuvant treatment protocol is applied, the identification and the usefulness of prognostic or predictive biomarkers can improve the antitumoural treatment strategy, modifying the sequence, dose, and combination of radiotherapy, chemotherapy and surgical resection. For these reasons, a growing number of studies are actually focussed on the discovery and investigation of new predictive biomarkers of response to pCRT. In this review, we have selected the most recent literature (2012-2017) regarding the employment of blood-based biomarkers potentially predicting pCR in LARC patients and we have critically discussed them to highlight their real clinical benefit and the current limitations of the proposed methodological approaches.

Clinical multi-omics strategies for the effective cancer management

Cancer is a global health issue as a multi-factorial complex disease, and early detection and novel therapeutic strategies are required for more effective cancer management. With the development of systemic analytical -omics strategies, the therapeutic approach and study of the molecular mechanisms of carcinogenesis and cancer progression have moved from hypothesis-driven targeted investigations to data-driven untargeted investigations focusing on the integrated diagnosis, treatment, and prevention of cancer in individual patients. Predictive, preventive, and personalized medicine (PPPM) is a promising new approach to reduce the burden of cancer and facilitate more accurate prognosis, diagnosis, as well as effective treatment. Here we review the fundamentals of, and new developments in, -omics technologies, together with the key role of a variety of practical -omics strategies in PPPM for cancer treatment and diagnosis.

BIOLOGICAL SIGNIFICANCE

In this review, a comprehensive and critical overview of the systematic strategy for predictive, preventive, and personalized medicine (PPPM) for cancer disease was described in a view of cancer prognostic prediction, diagnostics, and prevention as well as cancer therapy and drug responses. We have discussed multi-dimensional data obtained from various resources and integration of multisciplinary -omics strategies with computational method which could contribute the more effective PPPM for cancer. This review has provided the novel insights of the current applications of each and combined -omics technologies, which showed their powerful potential for the establishment of PPPM for cancer.