PURPOSE: Outcomes for patients with newly diagnosed multiple myeloma (NDMM) are heterogenous, with overall survival (OS) ranging from months to over 10 years. METHODS: To decipher and predict the molecular and clinical heterogeneity of NDMM, we assembled a series of 1,933 patients with available clinical, genomic, and therapeutic data. RESULTS: Leveraging a comprehensive catalog of genomic drivers, we identified 12 groups, expanding on previous gene expression–based molecular classifications. To build a model predicting individualized risk in NDMM (IRMMa), we integrated clinical, genomic, and treatment variables. To correct for time-dependent variables, including high-dose melphalan followed by autologous stem-cell transplantation (HDM-ASCT), and maintenance therapy, a multi-state model was designed. The IRMMa model accuracy was significantly higher than all comparator prognostic models, with a c-index for OS of 0.726, compared with International Staging System (ISS; 0.61), revised-ISS (0.572), and R2-ISS (0.625). Integral to model accuracy was 20 genomic features, including 1q21 gain/amp, del 1p, TP53 loss, NSD2 translocations, APOBEC mutational signatures, and copy-number signatures (reflecting the complex structural variant chromothripsis). IRMMa accuracy and superiority compared with other prognostic models were validated on 256 patients enrolled in the GMMG-HD6 (ClinicalTrials.gov identifier: NCT02495922 ) clinical trial. Individualized patient risks were significantly affected across the 12 genomic groups by different treatment strategies (ie, treatment variance), which was used to identify patients for whom HDM-ASCT is particularly effective versus patients for whom the impact is limited. CONCLUSION: Integrating clinical, demographic, genomic, and therapeutic data, to our knowledge, we have developed the first individualized risk-prediction model enabling personally tailored therapeutic decisions for patients with NDMM.

The deposition of immunoglobulin light chains (IgLCs) in the form of amorphous aggregates or amyloid fibrils in different tissues of patients can lead to severe and potentially fatal organ damage, requiring transplantation in some cases. There has been great interest in recent years to elucidate the origin of the very different in vivo solubilities of IgLCs, as well as the molecular determinants that drive either the formation of ordered amyloid fibrils or disordered amorphous aggregates. It is commonly thought that the reason of this differential aggregation behaviour is to be found in the amino acid sequences of the respective IgLCs, i.e. that some sequences display higher intrinsic tendencies to form amyloid fibrils. Here we perform in depth Thermodynamic and Aggregation Fingerprinting (ThAgg-Fip) of 9 multiple myeloma patient-derived IgLCs, the amino acid sequences of all of which we have solved by de novo protein sequencing with mass spectrometry. The latter technique was also used for one IgLc from a patient with AL amyloidosis. We find that all samples also contain proteases that fragment the proteins under physiologically relevant mildly acidic pH conditions, leading to amyloid fibril formation in all cases. Our results suggest that while every pathogenic IgLC has a unique ThAgg fingerprint, all sequences have comparable amyloidogenic potential. Therefore, extrinsic factors, in particular presence of, and susceptibility to, proteolytic cleavage is likely to be a strong determinant of in vivo aggregation behaviour. The important conclusion, which is corroborated by systematic analysis of our sequences, as well as many sequences of IgLCs from amyloidosis patients reported in the literature, challenges the current paradigm of the link between sequence and amyloid fibril formation of pathogenic light chains.

Ionizing radiotherapy (RT) is a widely used treatment strategy for malignancies. In solid tumors, RT-induced double-strand breaks lead to the accumulation of insertion-deletions (indels; ID), and their repair by nonhomologous end joining has been linked to the ID8 mutational signature in surviving cells. However, the extent of RT-induced mutagenesis in hematologic malignancies and its impact on their mutational profiles and interplay with commonly used chemotherapies has not yet been explored. In this study, we interrogated 580 whole-genome sequence (WGS) samples from patients with large B-cell lymphoma, multiple myeloma, and myeloid neoplasms and identified ID8 only in relapsed disease. Yet ID8 was detected after exposure to both RT and mutagenic chemotherapy (i.e., platinum and melphalan). Using WGS of single-cell colonies derived from treated lymphoma cells, we revealed a dose–response relationship between RT and platinum and ID8. Finally, using ID8 as a genomic barcode, we demonstrate that a single RT-surviving cell may seed distant relapse. Significance: RT and the ID8 indel signature are related, but their genomic impact on hematologic malignancies is unclear. Leveraging WGS, we linked ID8 to both RT and mutagenic chemotherapy and validated that platinum can induce ID8. We used ID8 as a genomic barcode to reveal that RT-resistant cells may seed systemic relapse.

Introduction Myelodysplastic syndromes (MDS) and acute myeloid leukemia (AML) comprise late complications following mutagenic treatment. Limited data is available on the outcome of patients (pts) developing therapy-related MDS and AML (tMDS, tAML) after treatment for multiple myeloma (MM). Methods From 1976 to 2011, 3814 pts were entered into the Düsseldorf MDS registry. We identified 200 pts with tMDS or tAML. Of those, 41 pts had also been diagnosed with multiple myeloma (mm-MDS/AML). We compared these 41 pts to pts with de novo MDS (n=3614) and to pts with tMDS with other underlying diseases (n=159, 55 pts with other hematological diseases (34.5%), 93 with solid tumors (58.5%) and 11 with other diseases (7%)). Patient characteristics Median time between MM diagnosis and the onset of MDS was 5.5 years (range 0-28.5 years). Median age at the time of diagnosis of mm-MDS/AML was 67.8 years (range 32.5-84.6 years). Of all 41 mm-MDS pts, 13 developed AML (32%). Median time to progression from MDS to AML was 5 months (range 0.5-68 months). According to the WHO classification of 2016, there were 7 MDS-SLD, 10 MDS-MLD, 1 MDS-RS SLD, 13 MDS-RS MLD, 7 MDS-EB I, 2 EB-2, 1 MDS del(5q). 58% of mm-MDS pts had a complex karyotype, mostly affecting chromosomes 5 (22%) and 7 (17%), less often affected were chromosomes 17 (13%), 20 (13%) and 21 (13%). At MDS diagnosis, 11 MM pts were in complete remission (22%), 29 pts showed partial remission (58%), and 10 pts a stable disease (20%). 84.4% of pts with mm-MDS/AML had received conventional chemotherapy, mostly anthracyclines and alkylating agents. 94.4% had received melphalan. 15% of pts had received novel agents including immunomodulatory drugs and proteasome inhibitors. Results Both mm-MDS pts and tMDS pts were significantly younger than de novo MDS pts, however, there was no age difference between mm-MDS and tMDS (mm-MDS: mean 67.8 years, range 32-85, tMDS: mean 64.3 years, range 21-85, p<0.05, de novo MDS: 71,9 years, range 18-105; p<0.05). Both mm-MDS pts and de novo MDS pts showed significantly more males than females (mm-MDS 67% male versus 33% female, de novo MDS 57% versus 43%, p<0.05) while tMDS pts showed an equal ratio (48% versus 52%). When we compared risk group distribution according to IPSS-R we found significantly fewer mm-MDS pts to be in the lower risk categories (p<0.05 for both mm-MDS versus t-MDS and mm-MDS versus de novo MDS). Both mm-MDS and tMDS pts had a significantly worse karyotype when compared to de novo MDS (p<0.05). More cell lineages were affected in mm-MDS and tMDS pts than in de novo MDS (p<0.05). 50% of mm-MDS pts were pancytopenic versus 26% of de novo pts (p<0.05). Hemoglobin levels were significantly lower in mm-MDS and tMDS pts than de novo MDS pts (p<0.05). mm-MDS pts showed significantly higher blast counts in the bone marrow than all tMDS (p<0.05). Progression to AML occurred significantly more often in mm-MDS pts. At 12 months we discovered 12% of de novo MDS pts to have transformed to AML, 19% of tMDS and 24% of mm-MDS. At 36 months, 20% of de novo MDS pts had transformed to AML, 34% of tMDS and 39% of mm-MDS (p0.05). Median survival of de novo MDS pts was 32 months (CI 29.940 - 34.192, range 1-345 months). In contrast, median overall survival of both mm-MDS and all other t-MDS was significantly shorter with 13 months in both groups (p0.05). Conclusion Pts developing a myeloid neoplasm after treatment for multiple myeloma present with biological characteristics similar to those seen in pts with other tMDS. However, both clinical and molecular features are more severe with higher bone marrow blast counts, worse karyotypes, a more unfavourable IPSS-R score, and a significantly higher rate of transformation to AML. Yet despite a more aggressive phenotype, prognosis is equally poor and independent of myeloma remission status in mm-MDS/AML pts suggesting secondary myeloid neoplasia to govern the stem cell niche independent of previous disease or treatment.

Figure S1: Overview of the Single-Cell Expansion Model. Figure S2: Mutational Profiles of Chemoradiotherapy-Treated Lymphoid Patients. Figure S3: ID83 signature relationships to SBS96 signatures in newly diagnosed MM and BCL. Figure S4: ID8-associated Peaks in ID8-negative Cases. Figure S5: Indel profiles of select ID8-positive subgroups. Figure S6: Similarity of ID8 Regardless of Disease Histology. Figure S7: Genomic Complexity has No Bearing on ID8 Contribution. Figure S8: De novo SBS96 and ID83 extractions for single-cell derived colonies. Figure S9: De-noised SBS96 profiles. Figure S10: De-noised ID83 Profiles. Figure S11: ID8 Fitting to a Melphalan-exposed Cell Line. Figure S12: Median profiles of sublethal-dose platinum and RT single-cell-derived colonies showing global contribution of ID8 in both exposure types. Figure S13: ID8 presence in t-MN.