Maher Albitar

Cell‐free RNA (cfRNA) is emerging as a supplemental approach for liquid biopsy (LBx). Given that lymphoid cells and plasma cells express substantial quantities of immunoglobulin (Ig) and T‐cell receptor (TCR) RNA, we investigated the utility of using cfRNA for detecting B‐cell and T‐cell clonality in LBxs. Using next‐generation sequencing (NGS) of cfRNA, we clonotyped Igs and TCRs in LBx samples from patients with B‐cell neoplasm, T‐cell neoplasm, myeloid neoplasm and solid tumors alongside cancer‐free individuals. To establish a clonality cutoff, we clonotyped tissue RNA from patients with confirmed clonal B‐ or T‐cell and from polyclonal. Clonotype‐naïve testing of LBx samples demonstrated B‐cell clonality in 36% of B‐cell neoplasms, 7% of normal, 8% of T‐cell lymphomas, 13% of myeloid neoplasms, and 14% of solid tumors. T‐cell clonality using TCR beta or gamma demonstrated T‐cell clonality in 22% of T‐cell neoplasms, 3% of normal, 5% of myeloid neoplasms, 7% of solid tumors and 3% of B‐cell neoplasms. This confirms that Clonotype‐naïve cfRNA testing in LBx is a reliable approach for detecting B‐ and T‐cell clonality.

Background: Disease relapse is a primary cause of treatment failure after hematopoietic stem cell transplantation in the treatment of malignancy. Consolidation therapy early after transplantation may reduce this risk, but it is difficult to administer in the setting of various post-transplant complications. We proposed that testing donor cell chimerism and for persistent minimal residual disease (MRD) with next-generation sequencing (NGS) of plasma cell-free DNA (cfDNA) early after transplantation would identify those patients at higher risk of relapse who would possibly benefit from consolidation therapy. Methods: We enrolled 20 subjects with known tumor-associated somatic mutations into this prospective pilot study, testing plasma samples before and at 28, 56, and 84 days after transplantation. Pre- and post-transplant bone marrow samples were also analyzed. All samples were subjected to an agnostic, commercially available panel covering 302 genes. Results: Significantly more mutations (p < 0.0001) were detected in the plasma cfDNA than in the bone marrow cells in pre-transplant testing (92 versus 61 mutations, respectively), most likely reflecting sampling variation when bone marrow was used. Two subjects were negative for MRD in staging studies immediately before transplants. Most (19/20) subjects had intermittent or sustained MRD detected in post-transplant plasma cfDNA testing, albeit with much lower average variant allele frequencies (VAFs). Six out of 20 subjects suffered relapses within 12 months after transplantation, and all 6 could be identified by adverse-risk driver mutations that persisted after transplantation. No patients who cleared the adverse-risk mutations relapsed. Donor chimerism using cfDNA fell for all relapsed patients and contributed to the identification of patients at early risk for relapse. Conclusions: These data demonstrate that testing plasma cfDNA for persistent leukemia-associated somatic mutations and donor chimerism as early as 28 days after transplantation will identify a subset of patients with high-risk mutations who are at high risk of relapse. This early assessment of relapse risk may facilitate modifications to the treatment plan, reducing the risk of treatment failure.

Acute graft-versus-host disease (aGvHD) remains a major cause of morbidity and mortality after allogeneic hematopoietic stem cell transplantation (HSCT). We performed RNA analysis of 1408 candidate genes in bone marrow samples obtained from 167 patients undergoing HSCT. RNA expression data were used in a machine learning algorithm to predict the presence or absence of aGvHD using either random forest or extreme gradient boosting algorithms. Patients were randomly divided into training (2/3 of patients) and validation (1/3 of patients) sets. Using post-HSCT RNA data, the machine learning algorithm selected 92 genes for predicting aGvHD that appear to play a role in PI3/AKT, MAPK, and FOXO signaling, as well as microRNA. The algorithm selected 20 genes for predicting survival included genes involved in MAPK and chemokine signaling. Using pre-HSCT RNA data, the machine learning algorithm selected 400 genes and 700 genes predicting aGvHD and overall survival, but candidate signaling pathways could not be specified in this analysis. These data show that NGS analyses of RNA expression using machine learning algorithms may be useful biomarkers of aGvHD and overall survival for patients undergoing HSCT, allowing for the identification of major signaling pathways associated with HSCT outcomes and helping to dissect the complex steps involved in the development of aGvHD. The analysis of pre-HSCT bone marrow samples may lead to pre-HSCT interventions including choice of remission induction regimens and modifications in patient health before HSCT.

HER2 and hormone receptors are biomarkers for selecting breast cancer therapy and predicting outcomes. In the era of antibody‐drug conjugates (ADC), a relatively low HER2 expression level is adequate for targeting tumor cells. We explored the potential of RNA profiling, determined by next generation sequencing (NGS), to provide more flexible clinical biomarkers as compared with immunohistochemistry (IHC) or fluorescent in situ hybridization (FISH). Data from 57 breast cancers was used to study biomarker levels as detected by routine clinical transcriptomic tests. HER2 (ERBB2), estrogen receptor alpha (ESR1), and androgen receptor (AR) mRNA levels were compared with IHC and FISH results. There was a significant overlap in the levels of ERBB2 mRNA between cases scored by IHC as zero, 1+, and 2+. This variation correlated with progression‐free survival (PFS). Similarly, the ESR1 RNA accurately reflected estrogen receptor (ER) status. Patients with high AR mRNA had better PFS (p = 0.05). Patients expressing high ER and AR levels had better PFS than those expressing low ESR1 and AR (p = 0.03). These findings suggest that RNA analysis can be an alternative to IHC and FISH and provides continuous data that can better determine cut‐off points for predicting response to ADC.

Programmed death ligand-1 (PD-L1) immunohistochemistry (IHC) is routinely used to predict the clinical response to immune checkpoint inhibitors (ICIs); however, multiple assays and antibodies have been used. This study aimed to evaluate the potential of targeted transcriptome and artificial intelligence (AI) to determine PD-L1 RNA expression levels and predict the ICI response compared with traditional IHC. RNA from 396 solid tumors samples was sequenced using next-generation sequencing (NGS) with a targeted 1408-gene panel. RNA expression and PD-L1 IHC were assessed across a broad range of PD-L1 expression levels. AI was used to predict the PD-L1 status. PD-L1 RNA levels assessed by NGS demonstrated robust linearity across high and low expression ranges, and those assessed using NGS and IHC (tumor proportion score and tumor-infiltrating immune cells) had a similar pattern. RNA sequencing provided in-depth information on the tumor microenvironment and immune response, including CD19, CD22, CD8A, CTLA4, and PD-L2 expression status. Subanalyses showed a sustained correlation of mRNA expression with IHC (tumor proportion score and immune cells) across different solid tumor types. Machine learning showed high accuracy in predicting PD-L1 status, with the area under the curve varying between 0.83 and 0.91. Targeted transcriptome sequencing combined with AI is highly useful for predicting PD-L1 status. Measuring PD-L1 mRNA expression by NGS is comparable to measuring PD-L1 expression by IHC for predicting ICI response. RNA expression has the added advantages of being amenable to standardization and avoiding interpretation bias, along with an in-depth evaluation of the tumor microenvironment.