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This is a single-center, investigator-initiated, non-interventional study evaluating the role of the intestinal microbiome and autoimmune panels as a predictor for developing ≥ Grade 2 CTCAE v5.0 immune-related adverse event (irAE) and/or requiring systemic immunosuppression for irAEs in advanced solid tumor patients receiving immunooncology (IO) combinations at the Princess Margaret Cancer Centre. This is a minimal risk study involving the analysis of patient samples and does not involve therapeutic intervention.
The study will involve a prospective cohort of up to 120 patients and it is anticipated that patient accrual will be completed within 18 months.
Patients will receive IO combination as per their specific protocols from their other clinical trial or per their standard of care and samples will be collected at multiple time-points. No additional visits to the hospital will be needed for this study as safety assessments are already captured for all patients based on their participation in a clinical trial or per their standard of care.
Accumulating evidence supports that differential composition of fecal microbiome influences response to immunotherapy and development of colitis. Microbiome with different profiles are also associated with multiple diseases, including gastrointestinal (GI) or non-GI auto-immune pathologies. Little is known about the relationship between the microbiome composition or fecal calprotectin (fCal) and the development of non-colitis immune-related adverse events (irAEs) during treatment with IO combinations.
Autoimmune conditions and irAEs from immune checkpoint inhibitors (ICI) drugs both involve loss of tolerance to endogenous antigens and produce similar clinical presentations. ICI can increase humoral response. However, to date there is no evidence that autoimmune panels are correlated with the development of irAEs during IO combination therapy.
These findings suggest that analyzing the microbiome and autoimmune panels of patients treated with IO combinations at multiple time-points may be feasible. In addition, baseline, early shift and changes in microbiome and autoimmune panels at time of a serious irAE may be correlated with the development of serious irAEs and may change with appropriate immunosuppressive regimens.
We hypothesize that analysing the microbiome and autoimmune panels of patients treated with immunooncology (IO) combinations at multiple time-points is feasible. Additionally, we hypothesize that baseline, early shift and changes in microbiome and autoimmune panels at time of a serious immune-related adverse event (irAE) is correlated with the development of serious irAEs and will change with appropriate immunosuppressive regimens.
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| Measure | Description | Time Frame |
|---|---|---|
| Feasibility of evaluating intestinal microbiome composition and autoimmune panels in patients treated with immunooncology combinations through the analysis of stool and blood samples at multiple time-points. | To assess the feasibility, the endpoint will be deemed feasible if >50% of patients have biospecimens collected in at least 2 time-points. | 18 months |
| Measure | Description | Time Frame |
|---|---|---|
| Correlation between baseline intestinal microbiome composition to the development of ≥ Grade 2 CTCAE v5.0 irAEs and/or requiring systemic immunosuppression for irAEs. | Correlation between bacteria taxa obtained through 16S rRNA sequencing and irAEs. | 18 months |
| Correlation between baseline autoimmune panels to the development of ≥ Grade 2 CTCAE v5.0 irAEs and/or requiring systemic immunosuppression for irAEs. |
| Measure | Description | Time Frame |
|---|---|---|
| Evaluate the overall response rate (ORR) of the enrolled cohort measured by RECIST v1.1 and iRECIST. | ORR by RECIST v1.1 and iRECIST. | 18 months |
| Evaluate the median progression-free survival (PFS) of the enrolled cohort measured by RECIST v1.1 and iRECIST. |
Inclusion Criteria:
Exclusion Criteria:
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Advanced solid tumor patients receiving immunooncology combinations
| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Anna Spreafico, MD | Contact | 416-946-4501 | 3308 | tip@uhn.ca |
| Aileen Trang | Contact | 416-946-4501 | 7754 | aileen.trang@uhn.ca |
| Name | Affiliation | Role |
|---|---|---|
| Anna Spreafico, MD | Princess Margaret Cancer Centre | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Princess Margaret Cancer Centre | Recruiting | Toronto | Ontario | M5G 2M9 | Canada |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 37865776 | Derived | Genta S, Lajkosz K, Yee NR, Spiliopoulou P, Heirali A, Hansen AR, Siu LL, Saibil S, Stayner LA, Yanekina M, Sauder MB, Keshavarzi S, Salawu A, Vornicova O, Butler MO, Bedard PL, Razak ARA, Rottapel R, Chruscinski A, Coburn B, Spreafico A. Autoimmune PaneLs as PrEdictors of Toxicity in Patients TReated with Immune Checkpoint InhibiTors (ALERT). J Exp Clin Cancer Res. 2023 Oct 21;42(1):276. doi: 10.1186/s13046-023-02851-6. |
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Stool and blood samples at multiple time-points in patients undergoing treatment with immunooncology (IO) combination therapy.
Correlation between auto-antibodies detected through the 162 IgM and IgG antigen microarray with the development of ≥ Grade 2 CTCAE v5.0 irAEs and/or requiring systemic immunosuppression for irAEs. |
| 18 month |
| Correlation between baseline fecal calprotectin levels to the development of ≥ Grade 2 CTCAE v5.0 colitis and/or requiring systemic immunosuppression for colitis. | Correlation between fCal detection at baseline to development of ≥ Grade 2 CTCAE v5.0 irAEs and/or requiring systemic immunosuppression for irAEs. | 18 months |
| Correlation between the early changes in composition of intestinal microbiome and the development of ≥ Grade 2 CTCAE v5.0 irAEs and/or requiring systemic immunosuppression for irAEs. | Correlate changes in bacteria taxa composition through 16S rRNA sequencing from baseline samples to early time-point, with the development of ≥ Grade 2 CTCAE v5.0 irAEs and/or requiring systemic immunosuppression for irAEs | 18 months |
| Correlation between the early changes in composition of autoimmune panels and the development of ≥ Grade 2 CTCAE v5.0 irAEs and/or requiring systemic immunosuppression for irAEs. | Correlate the changes of auto-immune antibodies reactivity obtained through the 162 IgM and IgG antigen microarray from baseline to early time-point with the development of ≥ Grade 2 CTCAE v5.0 irAEs and/or requiring systemic immunosuppression for irAEs. | 18 months |
| Correlation between the early changes in fecal calprotectin levels to the development of ≥ Grade 2 CTCAE v5.0 colitis and/or requiring systemic immunosuppression for colitis. | Correlate the changes in fCal positivity from baseline to early time-point with the development of ≥ Grade 2 CTCAE v5.0 colitis and/or requiring systemic immunosuppression for colitis. | 18 months |
| Evaluate intestinal microbiome, autoimmune panel reactivity & fCal changes from baseline to development of ≥ Gr2 CTCAE v5.0 irAEs and/or requiring systemic immunosuppression for irAEs, & from event diagnosis to resolution to ≤ Gr1 CTCAE v5.0 irAEs | Monitor changes in bacteria taxa through 16S rRNA sequencing; reactivity of autoimmune antibodies through 162 IgM and IgG antigen microarray; and positivity of fCal at baseline, early time-point, at the time of event diagnosis, and at the time of event resolution | 18 months |
PFS by RECIST v1.1 and iRECIST.
| 18 months |
| Correlation between the baseline composition of the intestinal microbiome with PFS. | Microbiome composition through 16S rRNA sequencing with PFS by RECIST v1.1 and iRECIST. | 18 months |
| Correlation between the baseline composition of the intestinal microbiome with ORR. | Microbiome composition through 16S rRNA sequencing with ORR by RECIST v1.1 and iRECIST. | 18 months |
| Correlation between the baseline autoimmune panels with ORR. | Correlate the reactivity of baseline auto-antibodies detected through the 162 IgM and IgG antigen microarray with ORR per RECIST v1.1 and iRECIST. | 18 months |
| Correlation between the baseline autoimmune panels with PFS. | Correlate the reactivity of baseline auto-antibodies detected through the 162 IgM and IgG antigen microarray with PFS per RECIST v1.1 and iRECIST. | 18 months |
| Correlation between the change in composition of the intestinal microbiome at multiple time-points with ORR. | Change in microbiome composition as outlined above with ORR per RECIST v1.1 and iRECIST. | 18 months |
| Correlation between the change in composition of the intestinal microbiome at multiple time-points with PFS. | Change in microbiome composition as outlined above with PFS per RECIST v1.1 and iRECIST. | 18 months |
| Correlation between the change in autoimmune panels at multiple time-points with ORR. | Change in reactivity of auto-immune antibodies as outlined above with ORR per RECIST v1.1 and iRECIST. | 18 months |
| Correlation between the change in autoimmune panels at multiple time-points with PFS. | Change in reactivity of auto-immune antibodies as outlined above with PFS per RECIST v1.1 and iRECIST. | 18 months |
| Correlation between the composition of the intestinal microbiome at different time-points with radiomic analysis using voxels lying inside the tumor volume of interest. | Correlation of bacteria taxa composition through 16S rRNA sequencing with radiomic analysis. | 18 months |
| Correlation between the reactivity of autoimmune panels at different time-points with radiomic analysis using voxels lying inside the tumor volume of interest. | Correlation of auto-immune antibodies as outlined above with radiomic analysis | 18 months |