A Mathematical Model to Estimate Chemotherapy Concentration at the Tumor-Site and Predict Therapy Response in Colorectal Cancer Patients with Liver Metastases

Tumour response following preoperative chemotherapy is affected by body mass index in patients with colorectal liver metastases

BACKGROUND

Colorectal cancer is the third most prevalent malignancy globally and ranks second in cancer-related mortality, with the liver being the primary organ of metastasis. Preoperative chemotherapy is widely recommended for initially or potentially resectable colorectal liver metastases (CRLMs). Tumour pathological response serves as the most important and intuitive indicator for assessing the efficacy of chemotherapy. However, the postoperative pathological results reveal that a considerable number of patients exhibit a poor response to preoperative chemotherapy. Body mass index (BMI) is one of the factors affecting the tumorigenesis and progression of colorectal cancer as well as prognosis after various antitumour therapies. Several studies have indicated that overweight and obese patients with metastatic colorectal cancer experience worse prognoses than those with normal weight, particularly when receiving first-line chemotherapy regimens in combination with bevacizumab.

AIM

To explore the predictive value of BMI regarding the pathologic response following preoperative chemotherapy for CRLMs.

METHODS

A retrospective analysis was performed in 126 consecutive patients with CRLM who underwent hepatectomy following preoperative chemotherapy at four different hospitals from October 2019 to July 2023. Univariate and multivariate logistic regression models were applied to analyse potential predictors of tumour pathological response. The Kaplan-Meier method with log rank test was used to compare progression-free survival (PFS) between patients with high and low BMI. BMI < 24.0 kg/m2 was defined as low BMI, and tumour regression grade 1-2 was defined as complete tumour response.

RESULTS

Low BMI was observed in 74 (58.7%) patients and complete tumour response was found in 27 (21.4%) patients. The rate of complete tumour response was significantly higher in patients with low BMI (29.7% vs 9.6%, P = 0.007). Multivariate analysis revealed that low BMI [odds ratio (OR) = 4.56, 95% confidence interval (CI): 1.42-14.63, P = 0.011], targeted therapy with bevacizumab (OR = 3.02, 95%CI: 1.10-8.33, P = 0.033), preoperative carcinoembryonic antigen level < 10 ng/mL (OR = 3.84, 95%CI: 1.19-12.44, P = 0.025) and severe sinusoidal dilatation (OR = 0.17, 95%CI: 0.03-0.90, P = 0.037) were independent predictive factors for complete tumour response. The low BMI group exhibited a significantly longer median PFS than the high BMI group (10.7 mo vs 4.7 mo, P = 0.011).

CONCLUSION

In CRLM patients receiving preoperative chemotherapy, a low BMI may be associated with better tumour response and longer PFS.

Realm of proteomics in breast cancer management and drug repurposing to alleviate intricacies of treatment

Breast cancer, a multi-networking heterogeneous disease, has emerged as a serious impediment to progress in clinical oncology. Although technological advancements and emerging cancer research studies have mitigated breast cancer lethality, a precision cancer-oriented solution has not been achieved. Thus, this review will persuade the acquiescence of proteomics-based diagnostic and therapeutic options in breast cancer management. Recently, the evidence of breast cancer health surveillance through imaging proteomics, single-cell proteomics, interactomics, and post-translational modification (PTM) tracking, to construct proteome maps and proteotyping for stage-specific and sample-specific cancer subtyping have outperformed conventional ways of dealing with breast cancer by increasing diagnostic efficiency, prognostic value, and predictive response. Additionally, the paradigm shift in applied proteomics for designing a chemotherapy regimen to identify novel drug targets with minor adverse effects has been elaborated. Finally, the potential of proteomics in alleviating the occurrence of chemoresistance and enhancing reprofiled drugs' effectiveness to combat therapeutic obstacles has been discussed. Owing to the enormous potential of proteomics techniques, the clinical recognition of proteomics in breast cancer management can be achievable and therapeutic intricacies can be surmountable.

A modeling-based approach to optimize COVID-19 vaccine dosing schedules for improved protection

While the development of different vaccines slowed the dissemination of SARS-CoV-2, the occurrence of breakthrough infections has continued to fuel the COVID-19 pandemic. To secure at least partial protection in the majority of the population through 1 dose of a COVID-19 vaccine, delayed administration of boosters has been implemented in many countries. However, waning immunity and emergence of new variants of SARS-CoV-2 suggest that such measures may induce breakthrough infections due to intermittent lapses in protection. Optimizing vaccine dosing schedules to ensure prolonged continuity in protection could thus help control the pandemic. We developed a mechanistic model of immune response to vaccines as an in silico tool for dosing schedule optimization. The model was calibrated with clinical data sets of acquired immunity to COVID-19 mRNA vaccines in healthy and immunocompromised participants and showed robust validation by accurately predicting neutralizing antibody kinetics in response to multiple doses of COVID-19 mRNA vaccines. Importantly, by estimating population vulnerability to breakthrough infections, we predicted tailored vaccination dosing schedules to minimize breakthrough infections, especially for immunocompromised individuals. We identified that the optimal vaccination schedules vary from CDC-recommended dosing, suggesting that the model is a valuable tool to optimize vaccine efficacy outcomes during future outbreaks.

Comparison of the immunomodulatory potential of platinum-based anti-cancer drugs and anthracyclins on human monocyte-derived cells

Macrophages and dendritic cells (DCs) are important contributors to anti-tumor immune responses. However, these highly plastic cells are also the primary targets of tumor manipulation, which may result in the development of tumor-promoting subtypes. The effect of chemotherapeutic agents on tumor cells is an area of intense study, but little is known about their effects on innate immune cells.We investigated the effects of four chemotherapeutic drugs (two platinum-based agents; oxaliplatin and cisplatin, and two anthracyclines; doxorubicin and epirubicin) on the differentiation, function, and viability of macrophages and DCs. Macrophages and DCs were differentiated from monocytes in the presence of these chemotherapeutic drugs and we compared their cell surface receptor expression, cytokine production, and chemotactic- and T-cell-polarizing ability.We have shown that differentiation in the presence of anthracyclines dose-dependently increases CTLA-4 expression in DCs. Antineoplastic agent-driven differentiation strongly modified the CCL2- or CCL5-induced chemotactic activity of both macrophages and DCs. DCs differentiated in the presence of high-dose cisplatin and a low dose of epirubicin promoted regulatory T-cell development, whereas oxaliplatin at specific doses induced both DCs and macrophages to enhance cytotoxic T-cell responses. Furthermore, we found that inflammatory macrophages are more sensitive to doxorubicin-induced cell death than their counterparts.In summary, our results confirm that chemotherapeutic agents acting on a similar basis may have different effects on the anti-tumor immune response. Treatment with optimal dose, combinations, and timing of chemotherapy may determine tumor immunity and the metastatic potential of tumors.

Personalized Immunotherapy in Colorectal Cancers: Where Do We Stand?

Colorectal cancer (CRC) is the second leading cause of cancer death in the world. Immunotherapy using monoclonal antibodies, immune-checkpoint inhibitors, adoptive cell therapy, and cancer vaccines has raised great hopes for treating poor prognosis metastatic CRCs that are resistant to the conventional therapies. However, high inter-tumor and intra-tumor heterogeneity hinder the success of immunotherapy in CRC. Patients with a similar tumor phenotype respond differently to the same immunotherapy regimen. Mutation-based classification, molecular subtyping, and immunoscoring of CRCs facilitated the multi-aspect grouping of CRC patients and improved immunotherapy. Personalized immunotherapy using tumor-specific neoantigens provides the opportunity to consider each patient as an independent group deserving of individualized immunotherapy. In the recent decade, the development of sequencing and multi-omics techniques has helped us classify patients more precisely. The expansion of such advanced techniques along with the neoantigen-based immunotherapy could herald a new era in treating heterogeneous tumors such as CRC. In this review article, we provided the latest findings in immunotherapy of CRC. We elaborated on the heterogeneity of CRC patients as a bottleneck of CRC immunotherapy and reviewed the latest advances in personalized immunotherapy to overcome CRC heterogeneity.

Early prediction of clinical response to checkpoint inhibitor therapy in human solid tumors through mathematical modeling

Background:

Checkpoint inhibitor therapy of cancer has led to markedly improved survival of a subset of patients in multiple solid malignant tumor types, yet the factors driving these clinical responses or lack thereof are not known. We have developed a mechanistic mathematical model for better understanding these factors and their relations in order to predict treatment outcome and optimize personal treatment strategies.

Methods:

Here, we present a translational mathematical model dependent on three key parameters for describing efficacy of checkpoint inhibitors in human cancer: tumor growth rate (α), tumor-immune infiltration (Λ), and immunotherapy-mediated amplification of anti-tumor response (µ). The model was calibrated by fitting it to a compiled clinical tumor response dataset (n = 189 patients) obtained from published anti-PD-1 and anti-PD-L1 clinical trials, and then validated on an additional validation cohort (n = 64 patients) obtained from our in-house clinical trials.

Results:

The derived parameters Λ and µ were both significantly different between responding versus nonresponding patients. Of note, our model appropriately classified response in 81.4% of patients by using only tumor volume measurements and within 2 months of treatment initiation in a retrospective analysis. The model reliably predicted clinical response to the PD-1/PD-L1 class of checkpoint inhibitors across multiple solid malignant tumor types. Comparison of model parameters to immunohistochemical measurement of PD-L1 and CD8+ T cells confirmed robust relationships between model parameters and their underlying biology.

Conclusions:

These results have demonstrated reliable methods to inform model parameters directly from biopsy samples, which are conveniently obtainable as early as the start of treatment. Together, these suggest that the model parameters may serve as early and robust biomarkers of the efficacy of checkpoint inhibitor therapy on an individualized per-patient basis.

Funding:

We gratefully acknowledge support from the Andrew Sabin Family Fellowship, Center for Radiation Oncology Research, Sheikh Ahmed Center for Pancreatic Cancer Research, GE Healthcare, Philips Healthcare, and institutional funds from the University of Texas M.D. Anderson Cancer Center. We have also received Cancer Center Support Grants from the National Cancer Institute (P30CA016672 to the University of Texas M.D. Anderson Cancer Center and P30CA072720 the Rutgers Cancer Institute of New Jersey). This research has also been supported in part by grants from the National Science Foundation Grant DMS-1930583 (ZW, VC), the National Institutes of Health (NIH) 1R01CA253865 (ZW, VC), 1U01CA196403 (ZW, VC), 1U01CA213759 (ZW, VC), 1R01CA226537 (ZW, RP, WA, VC), 1R01CA222007 (ZW, VC), U54CA210181 (ZW, VC), and the University of Texas System STARS Award (VC). BC acknowledges support through the SER Cymru II Programme, funded by the European Commission through the Horizon 2020 Marie Skłodowska-Curie Actions (MSCA) COFUND scheme and the Welsh European Funding Office (WEFO) under the European Regional Development Fund (ERDF). EK has also received support from the Project Purple, NIH (U54CA210181, U01CA200468, and U01CA196403), and the Pancreatic Cancer Action Network (16-65-SING). MF was supported through NIH/NCI center grant U54CA210181, R01CA222959, DoD Breast Cancer Research Breakthrough Level IV Award W81XWH-17-1-0389, and the Ernest Cockrell Jr. Presidential Distinguished Chair at Houston Methodist Research Institute. RP and WA received serial research awards from AngelWorks, the Gillson-Longenbaugh Foundation, and the Marcus Foundation. This work was also supported in part by grants from the National Cancer Institute to SHC (R01CA109322, R01CA127483, R01CA208703, and U54CA210181 CITO pilot grant) and to PYP (R01CA140243, R01CA188610, and U54CA210181 CITO pilot grant). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.