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Material and Methods, Figure legends

Phenotypic, transcriptomic and genetic validation of PDX models

List of the SNVs identified through RNA-sequencing

Differentially expressed genes in the human B-ALL cohort

Characteristics of the B-ALL cohort

Reagents and Resources

Purpose:Children with Down syndrome (constitutive trisomy 21) that develop acute lymphoblastic leukemia (DS-ALL) have a 3-fold increased likelihood of treatment-related mortality coupled with a higher cumulative incidence of relapse, compared with other children with B-cell acute lymphoblastic leukemia (B-ALL). This highlights the lack of suitable treatment for Down syndrome children with B-ALL.Experimental Design:To facilitate the translation of new therapeutic agents into clinical trials, we built the first preclinical cohort of patient-derived xenograft (PDX) models of DS-ALL, comprehensively characterized at the genetic and transcriptomic levels, and have proven its suitability for preclinical studies by assessing the efficacy of drug combination between the MEK inhibitor trametinib and conventional chemotherapy agents.Results:Whole-exome and RNA-sequencing experiments revealed a high incidence of somatic alterations leading to RAS/MAPK pathway activation in our cohort of DS-ALL, as well as in other pediatric B-ALL presenting somatic gain of the chromosome 21 (B-ALL+21). In murine and human B-cell precursors, activated KRASG12D functionally cooperates with trisomy 21 to deregulate transcriptional networks that promote increased proliferation and self renewal, as well as B-cell differentiation blockade. Moreover, we revealed that inhibition of RAS/MAPK pathway activation using the MEK1/2 inhibitor trametinib decreased leukemia burden in several PDX models of B-ALL+21, and enhanced survival of DS-ALL PDX in combination with conventional chemotherapy agents such as vincristine.Conclusions:Altogether, using novel and suitable PDX models, this study indicates that RAS/MAPK pathway inhibition represents a promising strategy to improve the outcome of Down syndrome children with B-cell precursor leukemia.

List of the SNVs identified through whole exome sequencing

List of fusion transcripts identified through RNA-sequencing

List of datasets enriched in murine B cell precursors (GSEA analyses)

Acute megakaryoblastic leukemia of Down syndrome (DS-AMKL) is a model of clonal evolution from a preleukemic transient myeloproliferative disorder requiring both a trisomy 21 (T21) and a GATA1s mutation to a leukemia driven by additional driver mutations. We modeled the megakaryocyte differentiation defect through stepwise gene editing of GATA1s, SMC3+/-, and MPLW515K, providing 20 different T21 or disomy 21 (D21) induced pluripotent stem cell (iPSC) clones. GATA1s profoundly reshaped iPSC-derived hematopoietic architecture with gradual myeloid-to-megakaryocyte shift and megakaryocyte differentiation alteration upon addition of SMC3 and MPL mutations. Transcriptional, chromatin accessibility, and GATA1-binding data showed alteration of essential megakaryocyte differentiation genes, including NFE2 downregulation that was associated with loss of GATA1s binding and functionally involved in megakaryocyte differentiation blockage. T21 enhanced the proliferative phenotype, reproducing the cellular and molecular abnormalities of DS-AMKL. Our study provides an array of human cell-based models revealing individual contributions of different mutations to DS-AMKL differentiation blockage, a major determinant of leukemic progression.

Tumors with microsatellite instability (MSI) represent a paradigm for the success of immune checkpoint inhibitor (ICI)-based immunotherapy, particularly in patients with metastatic colorectal cancer (mCRC). To date however, tools for predicting efficacy of these new therapies are lacking. Here we combined high-throughput DNA and RNA sequencing of tumors from 117 patients with MSI mCRC treated with anti-PD-1 +/- anti-CTLA-4 and enrolled into two cohorts, i.e., the NIPICOL clinical trial (NCT03350126) and the ImmunoMSI prospective cohort that were used as discovery and validation cohorts in this ancillary study, respectively. All analyses based on previously suggested DNA/RNA biomarkers of resistance failed to identify robust predictors of treatment response in patients, e.g., the level of MSI, mutational burden, the presence of specific somatic DNA variants as assessed by exome-sequencing and the activity of canonical signaling pathways or the presence of specific cellular contingents within the tumor bulk as estimated by RNA-sequencing. By contrast, resistance to ICI was found to depend both on a small subset of somatic DNA variants located in microsatellite-containing genes with very diverse biological functions and the expression of a stromal-oriented RNA component. Testing of these predictors in 5 large additional independent retrospective and prospective cohorts (IDEA and MOSAIC clinical trials) regrouping 446 patients with nonmetastatic or metastatic MSI CRC untreated with ICI allowed us to validate the specificity of the predictive nature of these indicators regarding ICI-treated MSI mCRC patients. The use of these DNA/RNA predictors will help to refine the ICI-based precision therapy of patients with metastatic MSI mCRC.

Background & Aims Next-generation sequencing (NGS) was recently approved by the United States Food and Drug Administration to detect microsatellite instability (MSI) arising from defective mismatch repair (dMMR) in patients with metastatic colorectal cancer (mCRC) before treatment with immune checkpoint inhibitors (ICI). In this study, we aimed to evaluate and improve the performance of NGS to identify MSI in CRC, especially dMMR mCRC treated with ICI. Methods CRC samples used in this post hoc study were reassessed centrally for MSI and dMMR status using the reference methods of pentaplex polymerase chain reaction and immunohistochemistry. Whole-exome sequencing (WES) was used to evaluate MSISensor, the Food and Drug Administration-approved and NGS-based method for assessment of MSI. This was performed in (1) a prospective, multicenter cohort of 102 patients with mCRC (C1; 25 dMMR/MSI, 24 treated with ICI) from clinical trials NCT02840604 and NCT033501260, (2) an independent retrospective, multicenter cohort of 113 patients (C2; 25 mCRC, 88 non-mCRC, all dMMR/MSI untreated with ICI), and (3) a publicly available series of 118 patients with CRC from The Cancer Genome Atlas (C3; 51 dMMR/MSI). A new NGS-based algorithm, namely MSICare, was developed. Its performance for assessment of MSI was compared with MSISensor in C1, C2, and C3 at the exome level or after downsampling sequencing data to the MSK-IMPACT gene panel. MSICare was validated in an additional retrospective, multicenter cohort (C4) of 152 patients with new CRC (137 dMMR/MSI) enriched in tumors deficient in MSH6 (n = 35) and PMS2 (n = 9) after targeted sequencing of samples with an optimized set of microsatellite markers (MSIDIAG). Results At the exome level, MSISensor was highly specific but failed to diagnose MSI in 16% of MSI/dMMR mCRC from C1 (4 of 25; sensitivity, 84%; 95% confidence interval [CI], 63.9%–95.5%), 32% of mCRC (8 of 25; sensitivity, 68%; 95% CI, 46.5%–85.1%), and 9.1% of non-mCRC from C2 (8 of 88; sensitivity, 90.9%; 95% CI, 82.9%–96%), and 9.8% of CRC from C3 (5 of 51; sensitivity, 90.2%; 95% CI, 78.6%–96.7%). Misdiagnosis included 4 mCRCs treated with ICI, of which 3 showed an overall response rate without progression at this date. At the exome level, reevaluation of the MSI genomic signal using MSICare detected 100% of cases with true MSI status among C1 and C2. Further validation of MSICare was obtained in CRC tumors from C3, with 96.1% concordance for MSI status. Whereas misdiagnosis with MSISensor even increased when analyzing downsampled WES data from C1 and C2 with microsatellite markers restricted to the MSK-IMPACT gene panel (sensitivity, 72.5%; 95% CI, 64.2%–79.7%), particularly in the MSH6-deficient setting, MSICare sensitivity and specificity remained optimal (100%). Similar results were obtained with MSICare after targeted NGS of tumors from C4 with the optimized microsatellite panel MSIDIAG (sensitivity, 99.3%; 95% CI, 96%–100%; specificity, 100%). Conclusions In contrast to MSISensor, the new MSICare test we propose performs at least as efficiently as the reference method, MSI polymerase chain reaction, to detect MSI in CRC regardless of the defective MMR protein under both WES and targeted NGS conditions. We suggest MSICare may rapidly become a reference method for NGS-based testing of MSI in CRC, especially in mCRC, where accurate MSI status is required before the prescription of ICI.